Hybrid Energy Systems for Offshore Applications 9780323898232, 0323898238

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Hybrid Energy Systems for Offshore Applications

HYBRID ENERGY SYSTEMS SERIES Series Editor: James Speight, PhD, DSc, PhD

ABOUT THE EDITOR: Dr. James G. Speight holds a PhD in Chemistry, a DSc in Geological Sciences, and a PhD in Petroleum Engineering. He has more than 50 years of experience in areas associated with (1) the properties, recovery, and refining of conventional crude oil, viscous crude oil, and tar sand bitumen, (2) the properties and refining of natural gas, and (3) the properties and refining of biomass, biofuels, biogas, and the generation of bioenergy as well as the production of energy from other sources. His work has also focused on environmental effects, environmental remediation, and safety issues associated with the production and use of energy. He is the author (and coauthor) of more than 90 books in petroleum science, petroleum engineering, biomass and biofuels, and environmental sciences.

VOLUMES IN THE SERIES: • Hybrid Nuclear Energy Systems by Malcolm Keller • Hybrid Energy Systems for Offshore Applications by Ibrahim Dincer, Valerio Cozzani and Anna Crivellari • Hybrid Technologies for Power Generation by Massimiliano Lo Faro

Hybrid Energy Systems for Offshore Applications

Ibrahim Dincer Ontario Tech. University, Canada

Valerio Cozzani University of Bologna, Italy

Anna Crivellari University of Bologna, Italy

Elsevier Radarweg 29, PO Box 211, 1000 AE Amsterdam, Netherlands The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States Copyright © 2021 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress ISBN: 978-0-323-89823-2 For Information on all Elsevier publications visit our website at https://www.elsevier.com/books-and-journals

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Contents Preface of the Series Editor......................................................................................xi Preface ................................................................................................................... xiii Nomenclature...........................................................................................................xv

CHAPTER 1 Introduction ................................................................... 1 1.1 Background.....................................................................................1 1.1.1 Sustainability concept ......................................................... 2 1.1.2 Inherent safety and environmental protection concepts ............................................................. 4 1.2 Closing remarks..............................................................................6

CHAPTER 2 Offshore renewable energy options ............................. 7 2.1 2.2 2.3 2.4 2.5 2.6

Offshore wind energy.....................................................................8 Solar energy....................................................................................9 Wave energy.................................................................................11 Tidal currents energy ...................................................................12 Challenges of offshore renewable energy sources ......................14 Opportunities for exploitation of offshore renewable energy sources ..............................................................................15 2.7 Closing remarks............................................................................18

CHAPTER 3 Innovative hybrid energy options............................... 19 3.1 General scheme of offshore hybrid energy systems....................20 3.2 Power to hydrogen .......................................................................22 3.2.1 Hydrogen production methods.......................................... 22 3.2.2 Seawater desalination methods......................................... 22 3.2.3 Gas grid injection end-use ................................................ 25 3.2.4 Industry and mobility sectors end-use.............................. 25 3.3 Power to synthetic natural gas .....................................................27 3.3.1 Synthetic natural gas production methods ....................... 27 3.3.2 Carbon dioxide supply methods ....................................... 29 3.3.3 Gas grid injection end-use ................................................ 31 3.4 Power to methanol .......................................................................31 3.4.1 Methanol production methods .......................................... 31 3.4.2 Industry and mobility sectors end-use.............................. 32 3.5 Gas to power ................................................................................33 3.5.1 Gas turbine technologies................................................... 33 3.5.2 Electrical grid end-use ...................................................... 35 3.6 Closing remarks............................................................................35

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CHAPTER 4 System modeling and analysis................................... 37 4.1 Energy analysis ............................................................................37 4.2 Exergy analysis ............................................................................39 4.3 Economic analysis........................................................................40 4.3.1 CAPEX and OPEX for electrolysis ................................ 41 4.3.2 CAPEX and OPEX for desalination............................... 41 4.3.3 CAPEX and OPEX for hydrogen compression.............. 41 4.3.4 CAPEX and OPEX for H2-enriched natural gas and synthetic natural gas transportation................... 41 4.3.5 CAPEX and OPEX for hydrogen and synthetic natural gas transportation................................................ 42 4.3.6 CAPEX and OPEX for hydrogen storage ...................... 42 4.3.7 CAPEX and OPEX for synthetic natural gas production........................................................................ 42 4.3.8 CAPEX and OPEX for carbon dioxide removal............ 43 4.3.9 CAPEX and OPEX for carbon dioxide transportation................................................................... 43 4.3.10 CAPEX and OPEX for carbon dioxide compression ....................................................... 43 4.3.11 CAPEX and OPEX for synthetic natural gas compression .............................................................. 44 4.3.12 CAPEX and OPEX for methanol production................. 44 4.3.13 CAPEX and OPEX for methanol storage ...................... 44 4.3.14 CAPEX and OPEX for methanol transportation............ 44 4.4 Exergoeconomic analysis.............................................................45 4.5 Environmental impact analysis ....................................................47 4.6 Inherent safety analysis................................................................47 4.7 SWOT analysis.............................................................................51 4.8 Closing remarks............................................................................54

CHAPTER 5 Sustainability index development .............................. 55 5.1 Sustainability assessment methodology for P2G and P2L systems...........................................................................56 5.1.1 Generalities ....................................................................... 56 5.1.2 Definition of offshore oil and gas site and renewable energy .............................................................. 58 5.1.3 Evaluation of alternative strategies and assessment of technology options ............................................................ 58 5.1.4 Definition of the reference process schemes and of the offshore renewable power plant............................. 59 5.1.5 Calculation of sustainability performance indicators....... 64

Contents

5.2

5.3

5.4

5.5 5.6

5.1.6 Calculation of profitability performance indicators ......... 75 5.1.7 Ranking of alternatives and sensitivity analysis .............. 76 Sustainability assessment methodology for G2P systems...........77 5.2.1 Generalities ....................................................................... 77 5.2.2 Definition of offshore oil and gas site and renewable energy ................................................................................ 78 5.2.3 Collection of renewable energy data ................................ 79 5.2.4 Selection of the converter and characterization of the power plant........................................................................ 82 5.2.5 Definition of the dispatching power plan......................... 87 5.2.6 Definition and management of the gas turbine park........ 89 5.2.7 Calculation of sustainability performance indicators....... 94 5.2.8 Ranking of alternatives and sensitivity analysis .............. 97 Inherent safety assessment methodology.....................................98 5.3.1 Generalities ....................................................................... 98 5.3.2 Definition of design options and characterization of targets ................................................................................ 98 5.3.3 Classification of units and identification of release modes .............................................................................. 101 5.3.4 Assignment of credit factors to release modes .............. 102 5.3.5 Characterization of accident scenarios ........................... 105 5.3.6 Calculation of damage parameters ................................. 107 5.3.7 Calculation of unit inherent safety KPIs ........................ 108 5.3.8 Calculation of facility inherent safety KPIs ................... 115 5.3.9 Ranking of alternatives and sensitivity analysis ............ 116 Integrated assessment methodology...........................................116 5.4.1 Generalities ..................................................................... 116 5.4.2 Definition of the reference process schemes.................. 118 5.4.3 Definition of the intensified process flowsheet.............. 118 5.4.4 Scale-up and preliminary design of equipment units..... 119 5.4.5 Calculation of the screening indicators .......................... 122 5.4.6 Ranking of alternatives and sensitivity analysis ............ 124 5.4.7 Application of detailed site-specific assessments .......... 124 Sensitivity analysis techniques...................................................125 Closing remarks..........................................................................125

CHAPTER 6 Case studies.............................................................. 127 6.1 Case study 1: OWT farm and P2G/P2L offshore hybrid energy systems .......................................................................................127 6.1.1 Definition of the offshore oil and gas site and evaluation of the options .................................................................. 127

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6.1.2 Definition of the offshore wind turbine farm and reference process schemes ....................................... 132 6.1.3 Assumptions made for the sustainability assessment ....................................................................... 132 6.1.4 Assumptions made for the profitability assessment ....................................................................... 139 6.1.5 Sustainability and profitability assessments results .............................................................................. 142 6.1.6 Sensitivity analysis results .............................................. 145 6.2 Case study 2: OWT farm and G2P offshore hybrid energy systems ...........................................................................150 6.2.1 Definition of the offshore oil and gas site and renewable power plant .................................................... 150 6.2.2 Definition of the dispatching power plan and sizing of the gas turbine park ......................................... 156 6.2.3 Assumptions made for the assessment ........................... 159 6.2.4 Preliminary comparison of the matching of power curves ................................................................... 165 6.2.5 Sustainability assessment results .................................... 168 6.2.6 Sensitivity analysis results .............................................. 198 6.3 Case study 3: Emerging methanol production routes for P2L offshore hybrid energy systems driven by wind and solar energies.......................................................................200 6.3.1 Definition of the reference process schemes................ 200 6.3.2 Definition of intensified process flowsheets ................ 201 6.3.3 Electrochemical reduction of CO2................................ 205 6.3.4 Homogeneous radical gas-phase reaction .................... 207 6.3.5 Low-temperature heterogeneous catalysis.................... 209 6.3.6 Homogeneous catalysis in solution .............................. 213 6.3.7 Membrane-based biocatalysis ....................................... 213 6.3.8 Plasma technology ........................................................ 217 6.3.9 Photocatalysis................................................................ 217 6.3.10 Supercritical water oxidation technology..................... 221 6.3.11 Fuel cells technology .................................................... 221 6.3.12 Electrosynthesis............................................................. 226 6.3.13 Screening of intensified flowsheets.............................. 228 6.3.14 Sustainability assessment results .................................. 248 6.3.15 Sensitivity analysis results ............................................ 253 6.3.16 Detailed site-specific assessment results ...................... 254 6.4 Closing remarks..........................................................................272

Contents

CHAPTER 7 Conclusions and future directions ........................... 277 7.1 Conclusions ................................................................................277 7.2 Future directions.........................................................................279 Bibliography ..........................................................................................................281 Index ......................................................................................................................301

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Preface of the Series Editor Hybrid energy systems are defined as the integration of several types of energy generation equipment such as electrical energy generators, electrical energy storage systems, and renewable energy sources.1 They represent a very promising sustainable solution for power generation in standalone applications. Technology will continue to evolve in the future, so that it will have wider applicability and lower costs. There will be more standardized designs, and it will be easier to select a system suited to particular applications. There will be increased communication between components, facilitating control, monitoring, and diagnosis. Finally, there will be increased use of power electric converters. Power electronic devices are already used in many hybrid systems, and as costs go down and reliability improves, they are expected to be used more and more. This series provides a medium for publishing up-to-date research and explaining the concepts behind the development of hybrid technology systems, including advances in theories, developments, principles, and bridges to practical case studies and applications in the overarching subjects related to advancing the energy mix. The intended audience are researchers, engineers, and managers in energy engineering, petroleum engineering, pipeline engineering, offshore engineering, nuclear engineering, and environmental engineering. My hope is that this series drives forward the energy transition needed to meet all of the world’s energy demands in a sustainable and economically viable way. JAMES G. SPEIGHT CD&W, Inc., Laramie, WY, United States

1

https://www.sciencedirect.com/topics/engineering/hybrid-energy-system (Accessed on January 13, 2021).

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Preface Due to rapidly increasing worldwide population and growing energy demands, the development of renewable energy technologies has become of primary importance in the effort to reduce greenhouse gas emissions. In addition, rapid increases in oil prices, coupled with concerns about the stability and security of fossil fuels extraction, have led to emphasized interest in the exploitation of offshore renewable energy sources, such as offshore wind, sunlight, waves, and tidal currents. However, it is often technically and economically infeasible to transport discontinuous renewable electricity for long distances to the shore. Another shortcoming of nonprogrammable renewable power is its integration into the onshore electrical network without affecting power quality, grid stability, and the dispatching process. On the other hand, the offshore oil and gas industry is striving to reduce the overall carbon footprint from onsite power generators and limiting large expenses associated with carrying electrical energy from the shore in the case of remote facilities. Furthermore, the increased complexity and expansion toward challenging areas of offshore hydrocarbon operations call for higher attention to safety and environmental protection issues against potential major accident hazards. The rise of offshore oil and gas assets approaching the end of their useful life requires a careful dealing with complex evaluation of the decommissioning options. Another multidimensional problem is the monetization of offshore natural gas reservoirs, particularly in the case of stranded and depleted gas fields close to the shore. Innovative hybrid energy systems, as Power to Gas (P2G), Power to Liquid (P2L), and Gas to Power (G2P) options, which appears to be potentially implemented at offshore locations, would offer the opportunity to overcome challenges of both the renewable and the oil and gas sectors by different strategies. The chemical conversion of renewable power into gas and liquid synthetic fuels (P2G and P2L) at offshore oil and gas facilities allows the easing of storage and transportation of renewable energy from remote areas and creating new opportunities for aging offshore structures. On the other hand, gas turbine energy balancing systems, coupled with renewable plants in G2P offshore projects, offer the advantages of improving the dispatchability of renewable power injected into the grid and of valorizing untapped gas resources. Despite the widespread experience of these concepts at the onshore context, no evidence has been found on offshore applications, and the existing literature studies are limited to feasibility assessments of the sole offshore P2G
hydrogen option. In this book, Chapter 1 introduces the concepts of sustainability and inherent safety and highlights the importance of quantitative metrics for the evaluation of alternative options in the offshore context. Chapter 2 presents various options for renewable power production from offshore renewable energy sources, including the main challenges related to the offshore renewable industry and opportunities for development through synergy

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with the offshore oil and gas sector. Chapter 3 contains the details of P2G, P2L, and G2P hybrid energy systems for the exploitation of offshore renewable sources at the given offshore oil and gas sites. Chapter 4 primarily concerns the analysis and modeling of the systems as described in Chapter 3 from the point of view of thermodynamic, economic, environmental impact, and inherent safety assessments. Chapter 5 describes a portfolio of novel methodologies based on multicriteria indicators for the sustainability and safety performance comparison of alternative P2G, P2L, and G2P offshore hybrid energy options. These methods can be used as decision-making tools supporting the choice of innovative hybrid energy systems for offshore green projects in the early design phases. Three case studies are defined in Chapter 6, covering different offshore scenarios of concern under various case studies, to provide an assessment of the effectiveness and value of the suite of tools developed. The outcomes of the case studies show that the supporting tools and novel metrics developed are able to capture criticalities of the analyzed offshore systems and to orient the choice of the best P2G/P2L/ G2P hybrid energy option from the sustainability and/or safety perspectives. Lastly, the book closes with Chapter 7, which aims summarizing the conclusions and addressing some recommendations for the further development and validation of systematic methodologies based on sustainability and inherent safety indicators for offshore hybrid energy systems. Ibrahim Dincer Valerio Cozzani Anna Crivellari

Nomenclature Aoil2sl Aoil2th Ap Arot Avuln AEP AHI API ASI AV B c cw C_ Cbm CGHG Cp Cprod Ctci Cimb2 cf CI CR d dp D e eGHG E EHI EPI ex exCOP _ Ex f F g Gsolar h Hm0 Hs Ht HHI

surface area of the oil slick (m2) surface area of the oil thick slick (m2) aperture area of single collector (m2) swept area of rotor of the wind turbine (m2) vulnerability area of a given target (m2) gross annual energy production (MW h/y) inherent hazard index addressing assets target (m2/y) potential hazard index addressing assets target (m2) aggregated sustainability index availability annual production of final product (MW h/y) average cost per unit of exergy ($/kW h) scale factor of Weibull distribution cost rate ($/h) bare-module cost from Guthrie method ($) cost due to GHG emissions (h) power coefficient of wind turbine total production cost ($/y) capital investment cost ($) cost due to negative power unbalance (h) credit factor (1/y) consistency index consistency ratio damage distance for human target (m) internal pipe diameter (inch or mm) diameter of rotor of the wind turbine (m) damage distance for assets target (m) greenhouse gas emissions (kgCO2eq/h) efficiency factor for pipeline design inherent hazard index addressing environment target (tonnes/y or tonnes d/y or km2/y or km2 d/y) potential hazard index addressing environment target (tonnes) specific exergy (kJ/kg) exergetic coefficient of performance exergy rate (kW) exergoeconomic factor total subindicators in each aspect of sustainability damage parameter for water column target (m) solar radiation (W/m2) specific enthalpy (kJ/kg) spectral significant wave height (m) wave significant height (m) total number of hours in the considered period inherent hazard index addressing the human target (m2/y)

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HHV HPI I In kw L Lp LCOP LCOE LGHG LHV LVOP LVOE mI _ m moil2rel moil2sl moil2th mw ncol N Nst Nt NPV p P pdown pup P0 Pd Pf Pr Probd PrIS _ Q r rn R Rsell Ru Rimb1 RI s S tlim T T0 T 02

higher heating value (kJ/kg) potential hazard index addressing the human target (m2) general indicator incentive (h/MW h) shape factor of Weibull distribution characteristic length of wave converter (m) pipeline length (miles or km) levelized cost of product (units of currency per MW h) levelized cost of product (units of currency per MW h) levelized greenhouse gas emissions (kgCO2eq/MW h) lower heating value (kJ/kg) levelized value of product (units of currency per MW h) levelized value of energy (units of currency per MW h) number of indicators in the evaluation matrix mass flowrate (kg/s or kg/h) released oil mass (tonnes) oil mass in the slick (tonnes) oil mass in the thick slick (tonnes) molecular weight (kg/kmol) number of solar collectors in the solar field number of units/technologies in the scheme number of stages of compression number of turbines in the wind farm net present value (units of currency) frequency occurrence power (W or W/m) downstream pressure (psia or bar) upstream pressure (psia or bar) reference environment pressure (Pa) dispatched power (W) forecast power (W) real power (W) probability of correct dispatching process intensification screening indicator heat rate (kW) discount rate nominal escalation factor universal gas constant (in kJ/mol K) revenue due to product/electricity selling (h) specific gas constant (J/kg K) revenue due to positive power unbalance (h) random index specific entropy (kJ/kg K) specific gravity of gas relative to air limit time imposed from simulation tool total number of years in the economic lifetime of the system reference environment temperature (K) mean zero-upcrossing period (s)

Nomenclature

T b;red Te Tp T prem Ts v Vst V_ s w _ W xc X z z0 zr Z Z_

reduced base tariff for incentive (h/MW h) energy period (s) peak wave period (s) premium tariff (h/MW h) absolute temperature of the boundary (K) wind speed (m/s) volume of storage tank (Nm3 or m3) volumetric gas flowrate at standard conditions (ft3/h) weight factor work rate (kW) molar fraction of compound normalized indicator height of hub of the turbine (m) roughness length (m) generic height (m) gas compressibility cost rate associated to capital and operating expenses ($/h)

Greek letters γ η ηGT λ λmax ξ ξd ρ τ ϕ Φ ψ

diatomic ratio of specific heats energy efficiency part-load efficiency of gas turbine scaling factor principal Eigen value absolute prediction error (W) absolute dispatching error (W) density (kg/m3) annual operational time of the system (h) maintenance factor net outranking flow exergy efficiency

Subscripts 0 act ass avail c ch col con conv d drv

dead state actual assets target available aspect of sustainability chemical collector converter conventional destruction driver

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eff el econ env f F fac gen h hum i in iso j k l max nom out ov P ph ref renew s t tech tot imb 1 imb 2

effective electrical economic category environment target/category subindicator in each aspect of sustainability fuel facility generator hour in the considered period of the year human target reference release mode inlet isoentropic accident scenario component/unit of the scheme assets category maximum nominal outlet overall product physical reference renewable sea state year of the economic lifetime technical category of sustainability total positive power imbalance negative power imbalance

Acronyms ADIOS AHP BC CAPEX CCS CCU CDF CELF CEPCI CF CI CRF CSP

automated data inquiry for oil spills analytic hierarchy process business case capital expenditure carbon capture and sequestration carbon capture and utilization cumulative density function constant-escalation levelization factor chemical engineering plant cost index capacity factor capital investment capital recovery factor concentrating solar power

Nomenclature

DNI EGR EHI ELECTRE EOR ETS FEED FIT G2P GHG GNOME GT HENG HI HIRA HNS HOMER HP HTF HVAC HVDC IDLH KPI LCA LNG LP MCDA MP MTFA O&M OPEX OSCAR OWM OWT P2G P2L P&ID PDF PEC PEM PFD PNEC PPI PrI PROMETHEE PTO PV QRA

direct normal irradiance (W/m2) enhanced gas recovery environmental hazard index elimination and et choice translating reality enhanced oil recovery emission trading scheme front-end engineering and design feed-in tariff gas-to-power greenhouse gas general NOAA operational modeling environment gas turbine hydrogen enriched natural gas inherent hazard index hazard identification and ranking hazardous and noxious substances hybrid optimization model for multiple energy resources high pressure heat thermal fluid high-voltage alternating current high-voltage direct current immediately dangerous to life and health key performance indicator life cycle analysis liquefied natural gas low pressure multicriteria decision analysis medium pressure methyl trifluoroacetate operation and maintenance operational expenditure oil spill contingency and response oil weathering model offshore wind turbine power-to-gas power-to-liquid piping and instrumentation diagram probability density function purchase equipment cost proton exchange membrane process flow diagram predicted no effect concentration producer price index process intensification preference ranking organization method for enrichment evaluation power takeoff photovoltaics quantitative risk assessment

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RHI RO ROC SAM SC SNG SPECO SWOT TCI TEC TEG TFA TRL TSO VCE WAM WEC WGM

renewable heat incentive renewable obligation renewable obligation certificate system advisor model scenario synthetic natural gas specific exergy costing method strengths, weaknesses, opportunities, threats total capital investment tidal energy converter triethylene glycol trifluoroacetic acid technology readiness level transmission system operator vapor cloud explosion weighted arithmetic mean wave energy converter weighted geometric mean

CHAPTER

Introduction

1

1.1 Background Humankind has historically faced critical energy challenges, primarily begun with wood and wood products, and these challenges have been driving forces for the search for shortcut solutions to manage daily needs and services. The search was relatively routine until the Industrial Revolution in the 1760s, which was a technological awakening for people. Although multiple tools and machines were specifically developed for the textile industry, the steam engine became a clear landmark in almost everything. Humankind began using coal excessively up until the 19th century, when oil became the major commodity in many sectors, ranging from industry to transportation and from petrochemical to residential. This is the so-called hydrocarbon era, which as is commonly known, has resulted in various insurmountable consequences, ranging from energetic issues to environmental challenges. Humanity today faces even more critical environmental problems spanning a growing range of pollutants, toxic materials, hazards, and ecosystem degradations over increasing areas. Among these problems, the most life-threatening include global climate change, stratospheric ozone depletion, and acid precipitation. The former stems from increasing atmospheric concentrations of greenhouse gases, which trap heat radiated from the earth’s surface. Global climate change raises the surface temperature of the earth and sea levels and is potentially the most important environmental problem relating to energy utilization. Energy is now recognized as a key element of the interactions primarily between nature and society and is treated as a fundamental factor in the environment and sustainable development. Environmental and sustainability challenges span a continuously growing range of pollutants, hazards, and ecosystem degradation factors that affect conditions at the local through regional to global levels. Some such crucial concerns arise from observable, chronic effects on, for instance, human health, while others stem from actual or perceived environmental risks such as possible accidental releases of hazardous materials. It is important to note that many environmental issues—for example, acid rain, stratospheric ozone depletion, and global climate change—are caused by or are related to the production, transformation, and use of energy. Due to a variety of recently developed potential solutions to current environmental problems associated with the harmful pollutant emissions, in order to be able to manage local and global transitions for Hybrid Energy Systems for Offshore Applications. DOI: https://doi.org/10.1016/B978-0-323-89823-2.00001-4 © 2021 Elsevier Inc. All rights reserved.

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a sustainable future, there is a strong need to deploy sustainable energy solutions covering renewable energy systems, carbon-free fuels (such as hydrogen and ammonia), energy conservation and efficient use, energy storage systems, hybrid and integrated energy systems for multiple useful outputs, cleaner systems and technologies for fossil fuels
based systems and applications, waste to energy systems and applications, and alternative fuels. In this book, the main focus is placed on hybrid energy systems for offshore applications where there is a unique opportunity to help achieve better energetic, environmental, and sustainability dimensions with unique solutions for better implementation practices.

1.1.1 Sustainability concept Sustainability consists in a comprehensive concept presenting different interpretations based on the approaches to and the goal of each work. No exact definition for sustainability exists [1]. However, the most frequently quoted definition is from Our Common Future (also known as the Brundtland Report) by the World Commission on Environment and Development [2]. This report introduced for the first time the concept of sustainable development as the “sustainability that satisfies the needs of the present without compromising the ability of future generations to meet their own needs”; thus sustainability can be seen as the final goal of balancing social and economic activities and the environment. In 1998 the Encyclopedia of Life Support Systems [3] presented an enhanced definition of sustainable development as “the wise use of resources through critical attention to policy, social, economic, technological and ecological management of natural and human engineered capital so as to promote innovations that assure a higher degree of human needs fulfilment, or life support, across all regions of the world, while at the same time ensuring intergenerational equity.” In the sustainability analysis of a system, three main pillars were commonly identified according to the triple bottom line framework [4]: environment, economy, and society. On the contrary, Dincer and Rosen [5] proposed four key requirements to reach sustainable development (Fig. 1.1): 1. 2. 3. 4.

Technological sustainability, for example, providing efficient technologies; Economic sustainability, for example, providing affordable technologies; Environmental sustainability, for example, minimizing environmental impact; Societal sustainability, for example, satisfying societal, ethical and safety standards.

These four domains of sustainability are commonly addressed in the literature on the evaluation and comparison of alternative energy systems [6
8]: a sustainable technology should balance energy consumption and production with minimal negative impacts on economics and environment, and meeting societal aspirations and needs.

1.1 Background

FIGURE 1.1 Four pillars of sustainability (Adapted from [5]).

Due to the multidisciplinary concept of sustainability, the use of multicriteria decision analysis (MCDA) methods have been widely applied to perform a broad evaluation of energy systems [9]. MCDA methodologies are popular techniques in sustainable energy management, providing solutions to complex problems involving conflicting and multiple domains [10]. The selection of the proper evaluation criteria or indicators quantifying the different aspects of sustainability plays a key role in the MCDA process for the resolution of the problem and thus the identification of the best alternative [11]. Clearly, it is not helpful to use too many indicators for sustainability performance assessment and decision making. The indicators should cover all aspects of sustainability without showing repeatability and overlap [12]. A set of desired properties of the indicators for an energy decision-making problem are defined as follows: • • • • • •

Exhaustivity: Criteria should reflect the essential characteristics and the whole performance of the system discriminating between the alternatives. Consistency: Indicators should be consistent with the decision-making objective. Independency: Indicators should not be functionally related at the same level. Measurability: Indicators should be measurable in quantitative value or qualitative terms. Simplicity: Indicators should be easily understandable and applicable. Limitation of measurement: Indicators should be small enough in number at each level in order to avoid communicating redundant and unneeded information.

During the last 20 years, several authors have directed their attention to the sustainability assessment of energy systems and hybrid energy systems proposing

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indicator-based methodologies [7,13]. Most of these methods define metrics addressing all four dimensions of sustainability (technical, economic, environmental, and societal) in order to assess the performance of alternative energy systems [6,8], even though they limit their application to the onshore context, disregarding the evaluation of systems implemented at offshore installations.

1.1.2 Inherent safety and environmental protection concepts Several new offshore projects have been developed in the Gulf of Mexico and in the North Sea, starting operations in challenging areas (e.g., with increased technological and operational complexity and/or in harsh environmental conditions), as well as implementing new design concepts (e.g., subsea production, advanced separation techniques) [14,15]. This requires greater attention to the potential for impacts on safety and environment. Lessons learned from past accidents [16,17], such as the Piper Alpha explosion in 1988, the Bombay High fire in 2005, and the Macondo blowout in 2010, tragically evidence the major accident hazards of offshore oil and gas operations affecting humankind, assets, environment, and reputation. Such potential threats inevitably increase in view of the progress of the offshore sector. The safety performance of an offshore oil and gas installation originates with decisions taken in the different stages of the project lifecycle [i.e., conceptual study, front-end engineering and design (FEED), detailed design, construction,

FIGURE 1.2 Key principles of the inherent safety philosophy (Adapted from [18]).

1.1 Background

commissioning, operation, decommissioning]. Procedural, passive, and active risk reduction strategies are often relied upon, but these have yet to achieve optimal risk reduction either due to inadequacies in procedures or due to the degradation of physical safety systems. A more robust way of achieving hazard management may be to take advantage of the inherent safety approach [18]. Moreover, it has been proven that an inherently safer design has a positive effect on all the pillars of sustainability [19]. Trevor Kletz [20] was the first to propose the inherent safety concept, consisting in eliminating hazards, where possible, or in drastically reducing them at their source rather than controlling the risk during the conceptual design and FEED phases, where the degrees of freedom available for system modification are higher, thus reducing design and management costs and simplifying the requirements for engineered safety devices and related procedures [21]. A well-known set of principles or guidewords were formalized to orient technology design toward inherent safety (Fig. 1.2): •

• • •

•

Intensification identifies the actions aimed at the minimization of the plant and equipment inventory, thus reducing the hazard level associated with the possible loss of containment. Moderation consists in the promotion of actions aimed at the reduction of the hazards due to operating conditions. Substitution classifies the actions aimed at the development of substances, process schemes, and equipment characterized by a higher inherent safety. Simplification concerns the design actions aimed at reducing the complexity of the process and/or of the plant, thus reducing the possibility of errors and likelihood of loss of containment. Limitation of effects consists in actions aimed at the design of a process and/or of a plant where the consequences of the possible loss of containment are effectively reduced and the possibility of escalation is minimized.

The inherent safety philosophy has been pointed out as a cost-effective strategy to address the underlying hazards in the early stages of a project in the process and chemical industry [22,23] and was suggested also for offshore oil and gas installations [24,25]. The key principles in Fig. 1.2 of minimizing the quantities of chemicals, substituting materials/reactions with less hazardous ones, moderating process conditions to reduce the impact in the case of release, and simplifying equipment layout are highly appropriate in the offshore oil and gas context due to specific features of offshore facilities, for example, high congestion and high process operating conditions. In the process and energy industry, the use of key performance indicators (KPIs) specifically developed to support an inherently safer design emerged during the last decade [26,27], as a result of an effort to overcome the difficulties related to selecting inherently safer alternative only by conceptual principles. However, Tang et al. [28] reported that no inherent safety indicators specific to the offshore sector had been proposed so far, although offshore oil and gas

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CHAPTER 1 Introduction

installations present distinctive features in terms of layouts and interaction with the environment that are not accounted for in the indicators developed for onshore applications. Accidental releases of hazardous substances at sea are a source of marine pollution that can result in both immediate and long-term environmental damage. Among hazardous substances that may be released offshore, oil spills are at the top, resulting in the most severe environmental consequences to all the marine compartments [29], even though accident databases record some events involving the so-called hazardous and noxious substances, which are chemicals soluble in water, as having caused extensive damage to the water column [30]. The consequences on the environment of an accident depend on the hazard of the spill, which represents the source of potential harm, and on the presence and the vulnerability of wildlife in the contaminated area, that is, on its intrinsic susceptibility to the hazard. The hazard of oil spills depends on multifaceted aspects related to the characteristics of the spill and to the environmental conditions during and after the spill event. The determination of the hazards associated with offshore oil spills is thus fundamental, in order to reduce their impact on the different marine compartments. KPIs developed for the chemical process industry have the potential to be also effective metrics for ranking the environmental hazard of spills identified and to support a prioritization of the spill that may be selected for a more detailed consequence and hazard assessment.

1.2 Closing remarks In this chapter, sustainability and inherent safety concepts were presented as a general background. Sustainability was conceptualized based on four domains (technical, economic, environmental, societal), and MCDA methodologies were proposed as widely used techniques for decision-making problems regarding the sustainability of energy systems. The inherent safety approach was considered as the most suitable method to identify and manage potential major accident hazards in the early design stages of the project and to deal with the complexities of offshore facilities (multiple targets, congested layout, etc.). The use of quantitative metrics to assess and compare the performance of alternative options was discussed from both the sustainability and the inherent safety viewpoints. Despite the availability of several sustainability and inherent safety indicators in the existing literature, examples of the application of indicator-based methodologies to the offshore process and energy systems assessment are currently limited, and there is a lack of a detailed and systematic procedure description to support the generalized application to a widespread analysis of offshore projects.

CHAPTER

Offshore renewable energy options

2

Since the advent of the Industrial Revolution, fossil fuels have been the main source of supply for the energy requirements to develop societies with better human health and welfare. According to the World Energy Outlook 2020 [31], in 2019 the global energy system continues to be dominated by fossil fuels, with oil accounting for 32% of energy supply worldwide, closely followed by natural gas and coal at 23% and 26%, respectively. In the Stated Policies Scenario, global energy demand was slated to return to its prepandemic level in early 2023, but this is delayed until 2025 in the event of a prolonged pandemic and deeper slump, as in the Delayed Recovery Scenario. Prior to the pandemic crisis in 2020, global energy consumption had been projected to increase by 12% over the period 2019
2030; growth over this period is set in the latest outlook as 9% in the Stated Policies Scenario and only 4% in the Delayed Recovery Scenario. Despite the growth of renewable energy worldwide, fossil fuels are expected to continue to meet most of the world’s energy demand. In recent years, concerns about pollution and climate change have raised public awareness that carbon dioxide (CO2) emissions associated with fossil fuel combustion represent the largest source of greenhouse gas emissions [32]. On the other hand, energy security has become complex, due to the combination of rising political issues in major energy-producing countries, resource competition, and record oil prices [33]. As a consequence, various internal frameworks and legally binding agreements have been released, emphasizing the urgent need for lowcarbon technologies, especially those from renewable energy sources [34]. In this framework, the European Union became a worldwide pioneer in promoting renewable energy exploitation with the aim to improve the supply security, competitiveness, and environmental sustainability of renewable sources [35]. The deployment of sustainable emission-free renewables plays an important role for decarbonizing the energy supply. To date, a wide range of onshore renewable energy resources has been promoted for large-scale exploitation, for example, hydro, solar, wind, geothermal, biofuels, and biomass [36]. On the other hand, a huge quantity of clean power can also be provided from offshore renewable energy sources, such as offshore wind, solar energy, and marine renewable energies in the form of surface waves and tidal streams [37]. This chapter presents the main information on offshore renewable energy sources and the classification of renewable energy converters. Moreover, the main Hybrid Energy Systems for Offshore Applications. DOI: https://doi.org/10.1016/B978-0-323-89823-2.00002-6 © 2021 Elsevier Inc. All rights reserved.

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CHAPTER 2 Offshore renewable energy options

challenges related to renewable power production and opportunities for largescale and more efficient exploitation of offshore renewables are described.

2.1 Offshore wind energy Offshore wind energy can be defined as the energy generated from the wind at sea. Wind is produced by uneven heating of the earth’s surface by the sun. A wind energy turbine can convert the kinetic energy of the wind into mechanical or electrical energy that can be harnessed for practical use: wind blows across the rotor blades, causing them to rotate and to drive a shaft that is connected to the rotor hub [38]. Over the last decade, offshore wind power has presented considerably increased capacity worldwide, reaching at the end of 2015 a quantity of 12.1 GW, of which 11 GW were developed in Europe [39]. Further growth led to a total installed offshore wind capacity in Europe of about 18 GW in 2018: United Kingdom made the largest contribution with 44% of all installations in megawatts, followed by Germany (34%), Denmark (7%), Belgium (6.4%), and the Netherlands (6%) [40]. The motivation for offshore development can be explained by several of its benefits compared to its onshore counterpart [41]: high available area to harvest wind energy since there are no limitations relative to urban buildings and human activities, stronger and more uniform wind speed with less turbulence, and limited visual and sound impact. However, these advantages are counterbalanced by some drawbacks [42]: higher costs of the permitting and engineering process, higher demand of raw materials, complex and expensive installation requiring specialized workers, the need for adequate port infrastructure for the movement and assembly operation of the components, expensive subsea cables for systems distant from the shore, and reduced reliability and availability with increasing distance of the system from the shore. Offshore wind turbines (OWTs) show similarities to onshore designs, although several modifications must be applied to deal specifically with aggressive marine environments. The main components of an OWT are foundation, substructure, tower, and blades
rotor
nacelle. Classification of OWTs can be based on the number of blades (two- and three-blades), energy extraction mechanism (lift and drag based), axis orientation (horizontal and vertical axes), method by which the power is regulated at high wind speeds (stall regulated and pitch regulated) [43]. Typical OWTs in operation are characterized by three-bladed, horizontal-axis, pitch-regulated, upwind rotors whose diameter can range from 65 to 130 m and whose capacity is between 1.5 and 5 MW. However, some vertical-axis wind turbine prototypes (e.g., Aerogenerator X, Deepwind) have been proposed due their simple structure, rotation regardless of wind direction, low maintenance costs, and potential for larger power production in deep waters [44]. Another classification of OWTs is based on the support structure, as illustrated in Fig. 2.1. Fixedgrounded monopile foundations are predominant for 3
4 MW OWTs up to a

2.2 Solar energy

FIGURE 2.1 Classification of OWTs based on substructure (Adapted from [47]). OWT, Offshore wind turbine.

water depth of 30 m. Different foundations (e.g., jacket, tripods, and tripiles) are employed for large sizes and intermediate water depth (up to 50 m) [45]. Currently, the development of OWTs has succeeded in providing different floating designs (tension leg platform, semisubmersible, Spar Buoy) that are able to operate at greater distances from the shore and in deeper waters, thus harnessing large energy potentials [46]. Several floating offshore projects have been fully commissioned and investigated around the world (e.g., Hywind, Sway, WindFloat, PelaStar, Winflo, Hexicon Energy Design) [40].

2.2 Solar energy Solar radiations, that is, electromagnetic radiations emitted by the sun, represent the most abundant natural, easily exploitable, clean, and reliable resource on Earth, and it can be exploited to produce solar power by means of solar photovoltaics (PV) and concentrating solar power (CSP) technologies [48]. Solar PV is one of the fastest growing renewable technologies, reaching a global installed capacity of 481 GW in 2018, while CSP technology accounted for around 5 GW in the same year [49]. Solar CSP plants have garnered increased interest in the solar energy sector due to its higher efficiency and lower costs [50]. Generally, CSP plants are composed of several components: solar concentrators, receiver, steam turbine and electrical generator, and thermal storage. Mirrors are used to concentrate solar rays and convert them into high-temperature heat; the heat is then channeled through a conventional generator to produce electricity. As illustrated in Fig. 2.2, CSP can be

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CHAPTER 2 Offshore renewable energy options

FIGURE 2.2 Classification of solar CSP technologies and related installed ratios in the technology mix (Adapted from [51]). CSP, Concentrating solar power.

classified into four main technologies. Among them, the parabolic trough collectors show the most advanced operational level, yielding up to 354 MW and operating at temperatures up to 500 C with high modularity and the best land use factor [51]. They are used to concentrate sunlight into receiver tubes placed in the trough’s focal line. A thermal transfer fluid such as a synthetic thermal oil is delivered along these tubes. After heated to approximately 400 C by the solar radiation, the oil is pumped through heat exchangers to supply heat for vapor generation or other thermal applications. By installing the solar parabolic trough plant at offshore areas, the requirements for the concentrator systems can be simplified due to the sun’s tracking along a vertical axis, and thermodynamic efficiency can increase due to the availability of huge quantities of cooling seawater [52]. Solar PV plants consist of multiple cells and mechanical and electrical connections, which allow the direct conversion of solar rays into electricity without any heat engine. Solar cells are the main components of the system, and they can be distinguished into three different generations based on materials (crystalline silicon wafer-based cells, thin-film cells, organic materials
based cells) [53]. The classification of solar PV technologies is shown in Fig. 2.3. In addition to the

2.3 Wave energy

FIGURE 2.3 Classification of solar PV technologies (Adapted from [55]). PV, Photovoltaics.

common grounded-mounted and rooftop solar PV arrays, emerging applications are represented by offshore and floating PV plants, which offer the benefits of higher solar reflectance and low visual impact in the open sea, as well as the availability of seawater for cooling [54]. Considering the same occupied area (e.g., at an offshore installation), it is reported that solar PV arrays at the commercial stage are more efficient in terms of electricity production compared to solar CSP technologies [48].

2.3 Wave energy Wave energy is generated by wind blowing over the sea surface, which is in turn created by the differential heating of the earth’s surface induced by solar energy. Several benefits support the emerging role of wave energy in the electricity mix compared to other resources [56]: Wave power density (2
3 kW m22) is greater than that of wind (0.4
0.6 kW m22) and solar (0.1
0.2 kW m22); wave energy offers smaller hourly and daily variability with greater predictability; waves can travel longer distances with minor energy losses and environmental interferences. On the other hand, the wave energy industry is a new and developing sector that requires research on basic components to overcome technological barriers, effective planning and consenting processes to deal with the non-technical issues, and innovative instruments to support demonstration projects [57]. In 2016, 21 precommercial and first-of-a-kind demonstration projects have started production in

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CHAPTER 2 Offshore renewable energy options

FIGURE 2.4 Classification of WEC technologies based on conversion principle. WEC, Wave energy converter (Adapted from [61]).

marine environments, of which 15 projects were located within European waters; maximum capacity has ranged from a few kW to 10 MW. A few of them are grid connected and have delivered electricity to the network [58]. The devices that capture the kinetic energy of waves and transform it into electricity are called wave energy converters (WECs). WECs consist of four main components: structure and prime mover, foundation or mooring, the power takeoff system that converts mechanical energy into electrical energy, and the control system. Classification of WEC devices can be according to installation location: at the shoreline, near the shore (within 10
25 m water depth), and offshore (in water depth greater than 40 m) [59]. WECs can be also classified based on their conversion principle, as illustrated in Fig. 2.4. According to a literature review [60], the following WECs were suggested for offshore installation due to their higher technological status: Pelamis (based on the attenuator principle), Powerbuoy (based on the point absorber principle), and Wave Dragon (based on the overtopping principle).

2.4 Tidal currents energy Tidal currents or tidal streams are water flows resulting from the rise and fall of the tides caused by the rotational and gravitational forces between Earth, Sun, and Moon [62]. Hence tidal current energy is more regular and predictable over a longer time scale compared to wind, wave, and solar energies and has the potential to provide a stable power output to the grid [63]. The tidal resource potential at a

2.4 Tidal currents energy

given site can be considered almost as reliable in a short time span [64]. In the wave energy sector, the tidal energy industry has not yet reached the full commercialization stage. However, significant progress toward commercialization was made in the period 2014
2016: 14 tidal energy projects were grid connected and operating by the end of 2016 with capacities ranging from few MW to a maximum of 14 MW. Several devices in the United Kingdom has delivered electricity to the network continuously for long periods [58]. Tidal energy converters (TECs) are turbines that utilize the energy of flowing water in tidal currents to generate electricity directly. Similar to OWT designs, TECs consist of a number of blades mounted on a hub, a gearbox, and a generator; these components are mounted on a support structure. Despite the similarities between the extraction methods of wind and tidal stream energies, the rotor diameter of the tidal stream turbine is expected to be about half that of an OWT of the same rated power since seawater is 800 times denser than air and water flow speed is generally slower. As a result, power outputs are comparable. On the contrary, TECs must withstand greater water loading forces and effects of blockage and free surfaces. Like conventional OWTs, TECs can be classified into horizontal- and vertical-axis technologies, which represent the first-generation tidal devices for bottom-mounted installation [65]. Moreover, the European Marine Energy Centre (EMEC) includes four other types of technologies to the classification of TECs, as illustrated in Fig. 2.5, thanks to the evolution of second- and third-generation devices. Another classification is made based on the TEC foundation, that is, the seabed mounted/gravity base, pile mounted, floating, hydrofoil

FIGURE 2.5 Classification of TEC technologies according to EMEC (Adapted from [67]). EMEC, European Marine Energy Centre; TEC, tidal energy converters.

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14

CHAPTER 2 Offshore renewable energy options

inducing downforce, which evolves with increasing distance from the shore, as in the case of OWTs’ substructures (Fig. 2.1) [66].

2.5 Challenges of offshore renewable energy sources Despite the large potential and ongoing technological development of offshore renewable power generation, the main problem with all renewable energy sources, including offshore resources, is generally their dependency on daily or seasonal patterns and on environmental conditions that vary from place to place [68]. It is often considered technically and economically infeasible to transport discontinuous renewable power over long distances [69]. High-voltage alternating current (HVAC) transmission lines are the preferred option to deliver large quantities of electricity from offshore areas due to the low cost of voltage transformation and more compact converter stations. However, HVAC cables have the disadvantage that energy losses become much larger over longer distances, in particular because of the capacitive phenomena related to the submarine cable requirement for dynamic-reactive power control. High-voltage direct current (HVDC) cables can minimize transmission losses and are suitable for large-capacity power transmission over long distances. Nevertheless, in DC systems, the most important energy losses are due to the power conversion that is independent of the cable length. HVDC is capital intensive and requires costly converter stations at either end of the transmission line [70]. A critical transmission distance beyond which HVDC technology becomes less efficient than HVAC is estimated at 55
80 km [43]. In addition, the penetration of large amounts of intermittent renewable power may cause difficulties in the operation of the onshore electric grid [71]. Connecting intermittent and uncertain renewable sources to the electrical grid introduces various technical challenges, such as power quality, protection, generation dispatch control, and reliability [72]. Usually grid operators evaluate the impact on the power quality of the local grid by means of dynamic models of the renewable power plant for use in a power system simulator. Such models are intended to demonstrate whether the actual power generation of the device and array of devices meets specific grid code requirements (e.g., frequency stability, voltage, power factor, harmonics) in order to guarantee a safe grid connection [73]. Currently in offshore wind power, there are different national codes and requirements, and different power system simulators are used by grid operators in this or that country, with scarce dialog between wind farm developers/producers and grid operators [38]. Similarly, international standards provide discrepancies about grid codes and requirements for connecting solar PV plants [74]. However, a great effort has been made to develop a harmonized grid code for wind and solar PV power integration in Europe [75,76]. From the perspective of wave and tidal current power, there are to date limited examples of operating devices delivering electricity to the grid, and no specific grid code requirements have yet been issued at both the national and the European levels [77,78].

2.6 Opportunities for exploitation of offshore renewable energy sources

2.6 Opportunities for exploitation of offshore renewable energy sources From a transition perspective, the oil and gas industry is striving to become more energy efficient and sustainable. In fact, offshore hydrocarbon installations often consist of energy-consuming facilities, and on-site electrical power is produced traditionally by low-efficiency, polluting gas turbines (GTs) and synchronous diesel generators to directly drive compressors and pumps, power control systems and cathodic protection, supply heating for living and recreation areas, etc. [79]. Electrification by means of grid-connected subsea cables represents a possible option to reduce the consumption of fossil fuels, but the cost is high, particularly for remote installations [80]. Furthermore, the increased development of the offshore industry to expand exploration and production in more challenging locations and to implement new technologies may complicate offshore operations and has the potential to create multi-faceted threats of major accidents with severe consequences for humans, assets, and the environment. On the other hand, offshore oil and gas production installations have a limited life cycle, and the production decline of several mature offshore fields is expected due to resource depletion. Thus the decommissioning of oil and gas assets is an unavoidable phase of offshore projects [81]. Among 6500 offshore oil and gas production installations worldwide, over 600 installations are designed to be decommissioned in the short term and a further 2000 structures by 2040 [82]. For example, in the North Sea, 349 fields across the UK, Norwegian, Danish, and Dutch continental shelves (2379 wells and 950,000 t of topsides) will enter the decommissioning phase over the period 2018
2027. In the UK continental shelf, including most of these infrastructures, about d15 billion is the forecasted cost of decommissioning by 2027 [83]. Moreover, in 80 gas production platforms installed in the northern Adriatic Sea, most are at the moment approaching the end of their lives or are not operating [38]. Apart from financial costs, the decommissioning of an offshore production facility is a complex process, characterized by technical feasibility, environmental protection, safety, public opinion, and legal challenges. Fam et al. [84] reviewed the international and national regulations relevant for decommissioning, arguing that regulations in experienced countries should guide countries with less experience in that field. A typical offshore oil and gas installation can be composed of a topside, a jacket that supports the topside, and pipeline for the export of hydrocarbons. Several options for the decommissioning of an obsolete offshore installation can be distinguished, as illustrated in Fig. 2.6. The selection among the alternatives should include the application of a systematic assessment accounting for multiple criteria (e.g., environmental, safety, etc.) [85,86]. Natural gas is a versatile form of low-polluting fuel, which is fast becoming the most promising of all the fossil resources since it is deposited more widely in the world than crude oil and coal [88]. Sources of natural gas may be not only unassociated gas reservoirs but also associated gas from oil reservoirs, which is

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CHAPTER 2 Offshore renewable energy options

FIGURE 2.6 Classification of decommissioning options for offshore oil and gas production installations (Adapted from [87]).

gas produced along with oil. Associated gas is generally considered an undesirable by-product, which is either reinjected, flared, or vented. To make natural gas and associated gas a major energy source coming after oil, the infrastructure needed to transport and distribute gas to the power market should be developed. Mokhatab et al. [70] reviewed the available technologies of gas transportation, such as compressed natural gas, natural gas hydrate, liquefied natural gas (LNG), pipeline natural gas, gas-to-liquid, with a wide range of possible products including clean fuels, plastic precursors, liquid hydrocarbons, and “gas to power” (G2P). The latter option consists in electrical power production at the producing field and the transportation of the electricity by cable to the grid market. The evaluation of gas monetization options has become a multi-dimensional problem requiring a systematic approach to select the optimal option. Bearing in mind the technicalities of gas transportation, the economically attractive gas transmission mode depends on a number of parameters: reserve base, production capacity, and distance between the gas source and the consumers, as illustrated in Fig. 2.7. In order to provide reliable and steady power to the consumer, the integration of multiple energy sources forming a hybrid power system has become very popular in recent years [90]. In general, the term “hybrid energy system” is used to define any power system that combines one or more renewable with non-renewable energy sources and that can be grid connected or off grid, depending upon its purpose and production method. A hybrid energy system includes invariably an electricity storage system to meet demand either when the demand is at peak load or when the renewable energy source is not available due to its intermittency. Therefore the basic components of the hybrid energy system mainly comprise renewable energy generators (e.g., OWTs, solar PV arrays, WECs, etc.), nonrenewable generators (e.g., GTs, diesel generators, etc.) to compensate for periods

2.6 Opportunities for exploitation of offshore renewable energy sources

FIGURE 2.7 Gas valorization options (Adapted from [89]).

of non-productivity, a power conditioning unit, a storage device, an electrical load, and sometimes an electrical grid [91]. Capitalizing on the strengths of both conventional and renewable energy sources, a suitable hybrid energy system could achieve reduction in the costs associated with the implementation and maintenance of the system, limited emission levels, and improvements in the reliability and performance of the overall system [69]. In addition, an appropriate electrical energy storage technology could enhance the smoothing of mismatches between the times and occurrences of peak loads and of maximum power generated, increasing system flexibility and reducing the costs and losses for electricity transmission [73]. Some electrical energy storage devices using various physical principles are pumped hydro storage, compressed air energy storage, batteries, capacitors, and flywheels. They vary broadly in terms of efficiency, storage capacity, cost, response time, as well as technical maturity, but they provide limited storage capacity and durations [92]. The synergy of offshore renewable production with hydrocarbon exploitation at offshore facilities could avoid or limit long and less stable transmission power links and also make the oil and gas operations more sustainable [37,93]. With respect to the decommissioning issues, leaving obsolete oil and gas infrastructure in place for alternative uses could offer great opportunities for prolonging their lifetime through energy, aquaculture, scientific, and multi-purpose applications (Fig. 2.6). In particular, the reuse of offshore structures for the exploitation of renewable energy sources would foster “green decommissioning” or the “blue economy,” which plays an important role in the energy transition landscape [94]. In recent years, the concept of the reconversion of aging offshore platforms for energetic uses (e.g., CO2 sequestration for enhanced oil recovery, offshore wind and wave energies exploitation) has been largely addressed in the literature [95
97]. Moreover, as shown in Fig. 2.7, G2P can be a more flexible, cheaper, less massive transport solution than pipeline or LNG technologies for stranded gas fields, that is, marginal gas fields and their associated gas with a flowrate between about 380 billion Nm3 and 38

17

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CHAPTER 2 Offshore renewable energy options

trillion Nm3 [98], and for depleted gas fields, that is, those whose remaining gas in the hydrocarbon reservoirs is no longer sufficient to cover production costs and so production has been stopped due to the progressive decrease in reservoir pressure. In addition, G2P appears the optimal option rather than gas-to-liquid if the gas field is located within a certain distance from the power market, for example, 2500 km. In a G2P system in the offshore context, gas can be processed and combusted on the offshore facility, and the produced electricity can then be sent either to the onshore grid or to other offshore platforms.

2.7 Closing remarks In this chapter, the main information on renewable power production from offshore renewable energy sources (offshore wind, solar, wave, and tidal currents energies) was provided, illustrating the energy conversion principle, the installed capacity and the energy conversion technologies on the market. The main challenges associated with the production and transportation of offshore discontinuous renewable power for onshore end-uses were described. A synergy with the offshore oil and gas production sector was proposed as a solution to overcome renewables issues. Reusing offshore oil and gas facilities that would be otherwise decommissioned and valorizing depleted gas fields through G2P technologies were presented as opportunities for the exploitation of offshore renewable energy sources. Suitable hybrid energy systems at offshore oil and gas sites need to be clearly identified and analyzed from both the sustainability and safety perspectives.

CHAPTER

Innovative hybrid energy options

3

This chapter presents innovative hybrid energy systems for offshore applications representing a promising solution for exploitation of offshore renewable energy sources through integration with oil and gas operations at offshore facilities. Chemical energy conversion based on hydrogen (H2) is a commonly recognized way to provide large storage capacity, with flexible storage durations from minutes to months, of surplus renewable electricity according to the peak-shaving technique [99]. Since hydrogen can easily be produced from water (H2O) electrolysis and valorized in a full range of sectors, H2 plays a fundamental role in the decarbonization and security of future energy systems [100]. H2 also appears to be the very first possible end product of the power to gas (P2G) hybrid energy system [101]. To provide a practically unlimited injection into the natural gas infrastructure or to be used directly as fuel, the second core block in the P2G system consists of methanation, where the generated H2 can further react with CO2 to form synthetic natural gas (SNG) [102]. Another peak-shaving solution than P2G systems is represented by power to liquid (P2L) hybrid energy systems [103] aiming at the production from renewable energy of synthetic liquid fuels as potential substitutes for liquid fossil fuels in an energy transition panorama. The production of renewable H2 can be coupled to downstream chemical synthesis involving different products, for example, methanol (CH3OH), dimethyl ether, ammonia, and Fischer-Tropsch fuel [104]. The opposite of peak-shaving is the valley-filling technique, which can be applied when the renewable energy is not enough to meet requirements. Valleyfilling techniques use conventional power generators to produce extra power and improve the dispatchability of renewable electricity into the electrical grid [105]. If gas turbines (GTs) burning natural gas are used as balancing technologies of renewable power, gas to power (G2P) hybrid energy systems are obtained. The details of P2G, P2L, and G2P hybrid energy systems for offshore applications are illustrated in the following discussion, including the general scheme, technology options for production methods, and end uses.

Hybrid Energy Systems for Offshore Applications. DOI: https://doi.org/10.1016/B978-0-323-89823-2.00003-8 © 2021 Elsevier Inc. All rights reserved.

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3.1 General scheme of offshore hybrid energy systems Converting electricity into gaseous fuels at the offshore facility and then delivering them to the onshore market can increase the valorization of the resource exploitation thanks to the direct use of energy without storage and reconversion into electricity [106]. Fig. 3.1 shows the schemes of offshore P2G (power to gas) hybrid energy systems involving H2 and SNG, including the main process stages. As shown from this figure, all the pathways start with H2 production (the first conversion step) by means of electrolysis after sea H2O desalination. A conditioning step (including storage and compression) and a second conversion step can then be added based on the final product of the pathway (H2 or SNG). All these steps are supposed to take place at the offshore oil and gas platform and to be powered by renewables and/or non-renewables (and related backup storage devices). The final product can then be transported to the onshore market by means of pipelines and sold to the market for specific end uses, that is, gas grid, industry, and mobility. According to the so-called methanol economy [107], CH3OH can be identified as a safer and easier-to-manage energy carrier than H2 and SNG, due to its higher volumetric energy density, minor transport/handling issues, and diverse applications [108]. Moreover, different green processes for CH3OH production were investigated instead of the traditional method via syngas [109], thus highlighting the promising role of this fuel for the future global energy transition. Fig. 3.2 illustrates the scheme of an offshore P2L hybrid energy system involving CH3OH, including the main process stages. As shown from this figure, the pathways can start with H2 production (first conversion step) by means of electrolysis after sea H2O desalination or directly with H2O without the need for electrolysis. A conditioning step (including storage and compression) and a second conversion step can then be added

FIGURE 3.1 Schematic diagram of offshore P2G hybrid energy systems.

3.1 General scheme of offshore hybrid energy systems

FIGURE 3.2 Schematic diagram of offshore P2L (CH3OH) hybrid energy system.

FIGURE 3.3 Schematic diagram of offshore G2P hybrid energy system.

to produce CH3OH and separate it from other by-products, such as P2L (power to liquid). Similarly to P2G systems in Fig. 3.1, all these steps may occur at the offshore oil and gas facility driven by renewable and/or non-renewable (and related backup storage devices) power. The final product can then be transported to the onshore market by means of ship and sold to the market for specific end uses, that is, industry and mobility. A G2P offshore hybrid energy system combines natural gas
fueled GT park installed at an offshore oil and gas facility and renewable power plant. Conventional power is produced to balance the renewable power and keep up the dispatched energy schedule for injection into the onshore electrical grid. Fig. 3.3 displays the scheme of the G2P (gas to power) system for offshore applications.

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3.2 Power to hydrogen 3.2.1 Hydrogen production methods Renewable H2 is mainly produced by H2O electrolysis, which is the most important H2O splitting method based on the generation of H2 and oxygen (O2) by means of direct electric current in an electrochemical device called an electrolyzer. The overall electrochemical reaction illustrated in Eq. (3.1) can be segmented into two reactions. At the negatively charged cathode, the reduction reaction occurs (Eq. [3.2]), while oxidation reaction takes place at the positively charged anode (Eq. [3.3]). H2 O-H2 1 1/ 2O2

(3.1)

H2 O 1 2e2 -H2 1 O2 2

(3.2)

1 O2 2 - / 2O2

2

1 2e

(3.3)

Electrolyzers can be classified preliminarily with respect to the state of the electrolyte (solid, liquid), the type of electrolyte (acid, alkaline, or ceramic), and the charge carrier (OH2, H3O1, O2 2 ) into three main types: alkaline H2O electrolysis with a liquid alkaline electrolyte, acidic proton exchange membrane (PEM) electrolysis with a proton conducting polymer electrolyte membrane, and solid oxide electrolysis with oxygen ions conduction. The main techno-economic and environmental parameters of H2O electrolysis technologies investigated are summarized in Table 3.1. For P2G applications, it is highly recommended that electrolyzers operate with high efficiency to avoid unnecessary energy losses, with highly dynamic behavior (small ramp-up time) to follow the fluctuant power input of renewables and with very low minimal load for flexible operation. Moreover, pressurized operation is advantageous to reduce or eliminate the cost of an external compressor and its associated additional equipment. Furthermore, space and weight are challenging constraints in the offshore context; thus a simple and compact layout is highly desirable. Also, a reliable and modular system may facilitate marine transportation and reduce the installation and maintenance time at offshore platforms. As a consequence, pressurized PEM electrolysis could be considered as the most feasible for the process stage of H2 production, since higher current densities, gas purity in the 99.99% range, design simplicity and reliability, and high compactness are enabled. Moreover, dynamic operation makes such a technology suitable for coupling with any intermittent renewable power. However, it is remarkable that strict requirements in terms of the inlet water have to be met to guarantee long-term performance: Ionic conductivity must be very low, with the number depending on the membrane characteristics.

3.2.2 Seawater desalination methods Among sea H2O desalination technologies, multistage flash distillation, multieffect distillation, mechanical vapor compression, reverse osmosis, electrodialysis, and membrane distillation can be evaluated. A comparison of the techno-economic and environmental parameters of these technologies is illustrated in Table 3.2.

Table 3.1 Comparison of H2O electrolysis technologies [99,110
115]. Alkaline electrolysis

Proton exchange membrane (PEM) electrolysis

Solid oxide electrolysis cell

Technological status

Commercial

Commercial

Research and development

System size range

0.25
760 Nm3 h21 H2 (1.8
5300 kWHHV)

0.01
240 Nm3 h21 H2 (0.2
1150 kWHHV)

—

Feed-in

Potassium lye (KOH)
H2O solution

Fresh H2O

Steam

H2O characteristics

H2O with an electrical conductivity of less than 5 μS cm21

Very pure H2O with low conductivity (,1 μS cm21)

Similar to PEM electrolysis

Electrolyte

Highly concentrated aqueous solution of KOH (25
30 wt.%)

Acid polymer membrane (proton conducting)

Solid oxides ceramic membrane (oxygen ions conducting)

Current density (A cm22)

0.3
0.5

0.6
2

0.3
0.6

Cell voltage (V)

1.8
2.4

1.8
2.2

0.9
1.3



Operating temperature ( C)

60
90

50
80

700
900

Operating pressure (bar)

10
30

20
50

1
15

Electrical consumption (kWh Nm23 H2)

4.5
7.0

4.5
7.5

2.5
3.5

Electrical consumption including auxiliaries (kWh Nm23 H2)

5.0
5.9

5.0
6.5

3.7
3.9

System efficiency (%HHV)

60
71

65%
83%

—

System lifetime (years)

10
20 years proven at 2%
4% annual degradation rate

5 years proven at 2%
4% annual degradation rate

1 year proven at 17% degradation rate

Stack lifetime (h)

60,000

40,000

—

Product purity (%)

99.5 (before purification); .99.999 (after deoxidizer and dryer)

99.95 (before purification); .99.9998 (after deoxidizer and dryer)

—

Investment costs (h2017/kWHHV)

873
2347

306
4748

—

Operational costs (% investment costs per year)

2
3

3
5

—

Minimum load factor (%)

20
40

0
10

—

Ramp-up time (%full load s21)

0.13
10

10
100

—

.4

, 0.03

—

2

Footprint (cell area, m ) GHG emissions

(kgCO2eq kg21H2)

0.5995
2.9975

Table 3.2 Comparison between sea H2O desalination technologies [116
120]. Multistage flash distillation

Multieffect distillation

Mechanical vapor compression

Sea H2O reverse osmosis

Electrodialysis

Technological status

Commercial

Commercial

Commercial

Commercial

Commercial

Research and development

System size range (m3 d21 H2O)

50,000
70,000

5000
15,000

100
3000

, 128,000

2
145,000

—

Feed-in

Sea H2O (any)

Sea H2O (any)

Sea H2O (any)

Sea H2O (35,000)

Brackish H2O (,5000)

Sea H2O (any)

Product quality (ppm)

10

10

10

400
500

150
500

—

Operating temperature ( C)

90
110

70

, 70

15
20

15
20

60
90

Energy use (kWh m23)

Electrical: 2.5
5; Thermal: 15.8
23.5

Electrical: 2
2.5; Thermal: 12.2
19.1

Electrical: 7
12

Electrical: 3
8 (with energy recovery)

Electrical: 0.8
5.5

Total electrical and thermal: 628

Investment costs (h2013/(m3 d21))

1596
3325

1197
2660

Operational costs (% investment costs per year)

1.5
2.5

GHG emissions (kgCO2eq m23H2O)

14.4
24

2.5
5.3

—

7.7
19.2

Membrane distillation

1197
3325

10.6
11.5

Investment costs in this table are estimated based on the assumption that US$1 5 h0.75.

4.8
8.6

3.2 Power to hydrogen

It is worth noting that reverse osmosis could be considered a reference technology for the sea H2O desalination stage in the offshore context, since it demonstrates high compatibility with renewable sources and good ability to produce relatively pure H2O required from PEM electrolysis. Moreover, reverse osmosis seems to be feasible for offshore applications due to modularity, minimum interruption time during maintenance, and low electrical energy needs.

3.2.3 Gas grid injection end-use Besides the on-site uses as fuel for ships and GTs, another end use of H2 can be in the onshore gas grid, as shown in Fig. 3.1. The injection of the produced H2 into an existing gas pipeline leads to a H2-enriched natural gas (HENG) blend, which offers several benefits in terms of emissions and efficiency compared to natural gas [121]. However, transportation of the HENG mixture depends first on the pipeline delivery pressure and on the maximum blending ratio tolerated by the existing infrastructure. Most transmission pipelines deliver natural gas along long distances at pressures from 60 bar to more than 125 bar, thus often requiring the operation of gas compressors at the offshore platform to ensure that the natural gas flowing through the pipeline maintains the desired pressure. Centrifugal compressors are used extensively due to their advantages of smooth operation, high reliability, and suitability for process fluctuations compared to other compressors. They range in size from pressure ratios of 1:3 per stage to as high as 13:1 on experimental models [122]. The range of 5%
20% by volume of H2 in natural gas can be considered feasible by taking into account the general behavior of the onshore gas grid and of the specific gas devices [123]. Upon arrival at the onshore terminal, the HENG blend may be injected into the natural gas grid, provided that acceptability standards on heating value and the Wobbe index, as required by national gas network regulations, are fulfilled [124].

3.2.4 Industry and mobility sectors end-use Possible onshore end uses for H2 other than gas grid injection can be the industry sector (refineries, chemical industry, light industry, etc.) and the mobility (fuel cell electric cars and buses) sector, as shown in Fig. 3.1. Thus relatively pure H2 can be delivered from the offshore facility to the onshore terminal, avoiding limitations in the admixture injection in the existing gas grid. As for the case of grid injection end use, H2 delivery through new pipeline requires a conditioning stage of compression at the offshore facility. As previously mentioned, centrifugal compressors are the preferred means for this purpose. The development of an offshore H2 transmission can be envisioned by adopting existing experience gained in the onshore context. As investigated in the European Roads2HyCom project [125], there are several H2 pipeline systems all around the world for industrial applications. These include networks in the Netherlands,

25

26

CHAPTER 3 Innovative hybrid energy options

Table 3.3 Example of diameters of pipeline delivering pure H2 for different throughputs. H2 volumetric flowrate (Nm3 h21)

Pipeline diameter (mm)

12,000

100
150

40,000

150
250

80,000

200
300

120,000

250
400

Northern France, Belgium, Germany (Ruhr and Leipzig areas), the United Kingdom (Teesside area), and North America (Gulf of Mexico, Texas
Louisiana). Overall, these pipelines transport pure and ultrapure H2, with an inner diameter of 100
300 mm and smaller extensions compared to the lengths of existing natural gas networks (about 1500 km in Western Europe and 900 km in the United States). They are mostly realized using low- to medium-strength steels, with maximum operating pressures of 100 bar [126]. An example of recommended diameters obtained from the application of the general flow equation based on Bernoulli law for a range of volumetric flowrate of H2 is reported in Table 3.3. These data, derived from a recent study [127], consider an upstream pressure from about 30
75 bar, a downstream pressure of 24 bar (gauge), and a pipeline length of 100 km. Once arrived at the onshore gas terminal, H2 can be addressed to the mentioned end-uses, after passing the required gas quality control measurements and fulfilling the specific market requirements. The design of new offshore pipeline is strictly related to pipeline flow capacity, which depends upon several factors, such as the desired mass flowrate at the destination and the required delivery pressure, as well as the pipe diameter, allowable pressure drops, viscosity, and molecular weight of the gas. The steady-state isothermal flow is a common approximation for a relatively long pipeline operating in stable conditions [128]. The Weymouth equation (Eq. 3.4) can be used for compressible fluid in turbulent flow, long pipelines pipe and pressure drop greater than 40% of the upstream pressure [129]: p2up 2p2down Ts V_ s 5 18:0625U UEU Ps SUZUTULp

!0:5 Udp 2:6667

(3.4)

where V_ s is the volumetric gas flowrate in ft3 h21 at standard conditions, E is the efficiency factor (equal to 1.0 for new pipe), pup is the upstream pressure in psia, pdown is the downstream pressure in psia, S is the specific gravity of gas relative to air, Z is the gas compressibility, T is the inlet temperature of the gas in  R, Ts, and Ps are the temperature and pressure at standard conditions (520 R, 14.73 psia), respectively, Lp is the pipeline length in miles, and dp is the internal pipe diameter in inch.

3.3 Power to synthetic natural gas

Once arrived at the onshore gas terminal, it can be supposed that H2 would be addressed to its primary value streams, that is, the industry and the emerging mobility sectors, after passing the required gas quality control measurements and fulfilling specific market requirements.

3.3 Power to synthetic natural gas 3.3.1 Synthetic natural gas production methods The combination of electrolysis and methanation allows the production of SNG, which can be easily delivered via existing gas pipeline without the need for new infrastructures or alternative systems. The current processes available to produce SNG are based on catalytic or biological three-step methanation involving H2 and CO2. Catalytic methanation is a thermochemical exothermic process that typically operates at high temperature (200 C
700 C) on a proper catalyst (usually based on nickel). SNG can be produced according to the Sabatier reaction (Eq. 3.5) [130]: CO2 1 4 H2 5 CH4 1 2 H2 O

(3.5)

From the thermodynamic point of view, the reaction yield is promoted by high pressures (up to 100 bar), low temperatures, stoichiometric ratio of reactants (i.e., H2 to CO2 molar ratio equal to 4:1), and removing the H2O produced in the reactors. Several steady-state reactor concepts were developed for the catalytic methanation, namely fixed-bed, fluidized-bed, three-phase, and structured reactors. Whichever reactor design is chosen, the generated heat of the methanation reaction needs to be removed continuously: One possibility is to use at least two adiabatic beds and dilute the feed through the recirculation of a part of the reactor’s cooled gas outlet, while another potential one is an isothermal operation by transferring the reaction heat to a cooling medium [131]. In biological methanation, methanogenic archaea work in complex cooperation with coenzymes as a catalyst for the synthesis of H2 and CO2. Although the conversion occurs with the same reaction as in chemical methanation, different temperature ranges for the reaction and response times are used. The method is currently moving from the research stage to the demonstration phase based on in situ digester and separate reactor. Some techno-economic and environmental parameters assessing the performance of methanation technologies are summarized in Table 3.4. Even though biological methanation appears more robust, highly reactive within seconds in its full power range, and less cost-intensive, catalytic methanation in an adiabatic fixed-bed reactor could be selected as the most feasible by taking advantage of higher maturity, smaller reactor size for the same feed gas flow (i.e., high gas hourly space velocity) and lower power input and maintenance time than other methanation technology. To operate the methanation reactor continuously, a H2 buffer storage needs to be installed, even though the correlation between the optimum capacity of the

27

Table 3.4 Comparison of methanation technologies [99,102,111,114,132]. Technological status

Catalytic methanation

Biological methanation

Commercial

Precommercial (pilot scale) 3

21

Up to 5.3 Nm3 h21 (,12 MWHHV)

System size range

Up to 1000 Nm h

Feed-in

Any mixture of CO2, H2, CH4, H2O with low tolerance for sulfur, O2, and vapor

Any mixture of CO2, H2, CH4, and H2O with high tolerance for sulfur and vapor but limited for O2

Catalyst

Ni, Ru, Rh, and Co

Methanogenic archaea

92
96

98
99

Operating temperature ( C)

200
750

20
60

Operating pressure (bar)

4
80

1
3

0.33

0.54

Efficiency excluding electrolysis (%HHV)

70
85

75
98

Catalyst lifetime (h)

24,000

—

Investment costs (h2013/kWHHV)

600
2750

100
800

Operational costs (% investment costs per year)

10 (including replacement of the catalysts)

5 (heating requirements), 5 (miscellaneous)

Minimum load factor (%)

0

0

Ramp-up time between 0% and 90% (min)

30
60

0.02
3

Response time from standby mode (min)

,5

,1

Footprint (gas hourly space velocity, h21)

500
5000

, 100

GHG emissions (kgCO2eq kg21SNG)

0.3
1.8

0.85

Product quality (conversion yield, %) 

21

Electrical consumption (kWh kg

SNG)

SNG (,500 MWHHV)

3.3 Power to synthetic natural gas

storage facility and methanation performance has yet to be clearly determined [111]. An estimation of the pressurized H2 storage can be based on the tank volume required to cover the inoperability period of the electrolyzers [133].

3.3.2 Carbon dioxide supply methods As previously described, apart from H2, CO2 is the second reactant for methanation. CO2 should be supplied with low costs and energy needs, ideally with high purity and suitable flowrate to balance fluctuating demand. Among the different CO2 sources, CO2 removal from raw natural gas or associated gas at the offshore facility represents a feasible option when the extracted hydrocarbons need to be purified to meet the requirements for transportation. CO2 is a naturally occurring diluent in oil and gas reservoirs and can react with H2S and H2O to form corrosive compounds that threaten steel pipelines; no more than 2%
3% concentration of CO2 in natural gas pipeline is usually recommended [70]. Chemical absorption with amine solutions and membrane permeation are two of the most mature technologies that may be employed for this purpose. A comparison based on relevant parameters between these separation techniques is reported in Table 3.5. A successful example of offshore carbon capture from natural gas by means of amine absorption is represented by the Sleipner Carbon Capture and Sequestration (CCS) project in the North Sea, where CO2 is separated from natural gas containing up to 9% CO2 at Sleipner T and transported to Sleipner A where it is injected into the Utsira formation.

Table 3.5 Comparison of CO2 removal technologies from natural gas [134
137]. Amine absorption

Membrane permeation

System size range

Gas flowrate .20 MMscfd

Gas flowrate ,20 MMscfd

Feed-in

Low CO2 concentration (2%
15% mol)

High CO2 concentration (15%
40% mol)

CO2 recovery (%)

82
91

33
81

Operating pressure (bar)

1
120

#4

Electrical consumption (kWh kg21CO2)

0.84
0.89

0.13
0.28

Investment costs (Mh2008)

2.1
12

1.2
9.6

Operational costs (% investment costs/year)

15

18

GHG emissions (kgCO2eq kg21CO2,recovered)

0.0828

0.1957

Investment costs in this table are estimated based on the assumption that US$1 5 h0.68.

29

30

CHAPTER 3 Innovative hybrid energy options

Given the strict constraints on modularity and the footprint and equipment weight of most offshore oil and gas platforms, membrane permeation offers several advantages with respect to amine absorption. Moreover, it seems to better perform with gas streams having a relatively high CO2 concentration and moderate gas flowrates, thanks to its simpler process flow scheme. Therefore membrane permeation could be selected as the most suitable technology for the CO2 capture stage even though further gas treatment and significant compression may be required before methanation. It must be remarked that further gas treatment and significant compression may be required before supplying relatively pure CO2 to the methanation reactor. The onshore CO2 capture and transport to the offshore installation can be an alternative to deal with the problems with on-site CO2 separation, as well as valorizing offshore enhanced oil recovery (EOR) or enhanced gas recovery (EGR) activities [138]. Over 40 years, several projects worldwide have been developed and applied successfully, with CO2-EOR technology offering two major advantages: additional hydrocarbon recovery that extend the producing life of the depleted oil and gas fields and CO2 storage to reduce GHG emissions. Production from natural gas reservoirs can also benefit from CO2-EGR applications, which is a recent technique providing pressure support in natural gas reservoirs to prevent subsidence and H2O intrusion via both displacement and repressurization on the remaining natural gas. Two main modes may be applied to transport CO2 to the offshore site: offshore pipeline and shipping [139]. Shipping transport requires liquefaction, cryogenic buffer storage, and on-ship conditioning. Concerning the pipeline option, existing offshore transmissions can be reused for CO2 transportation under specific design constraints. Carbon steel pipelines seem to be metallurgically suitable, provided that a proper verification of moisture content is performed. However, the main constraint of existing infrastructure is the design pressure, which may be lower than the range of 200
300 bar of optimal pressure required for new pipes delivering CO2; thus CO2 transportation capacity may be reduced. Another limitation is the age of the existing pipeline, requiring integrity analysis to be performed to evaluate its use during its remaining service life [140]. Examples of primary candidates of North Sea pipelines for reuse in CO2 transportation can be found in the literature [141]. Knoope et al. [142] pointed out that gaseous CO2 transportation in the range 15
30 bar is a cost-effective solution if the required pressure at the offshore facility is lower than 80 bar (i.e., the minimum allowable level for the safe transportation of liquid-phase CO2) and that the design pressure of existing infrastructure is between 90 and 150 bar. Moreover, gas phase transportation is recommended for relatively small CO2 mass flowrates, for example, less than 100 kg s21. It is expected that, considering the stoichiometric SNG reaction, an amount of 3 kg s21 of CO2 flowrate is at the maximum needed for methanation at the offshore platform if about 20,000 Sm3 h21 of H2 are supplied to the reactor.

3.4 Power to methanol

3.3.3 Gas grid injection end-use Besides the on-site uses as fuel for ships and GTs, the main onshore end use of SNG is gas grid injection, as shown in Fig. 3.1. Since CH4 is the main component of conventional natural gas, SNG produced at the offshore facility can be delivered as existing natural gas without any limit in terms of maximum concentration, given the required compression to reach the desired delivery pressure. Once arrived at the onshore gas terminal, SNG can be directly injected into the onshore natural gas network, provided that the constraints on heating value and Wobbe index imposed from the local grid code are met [124].

3.4 Power to methanol 3.4.1 Methanol production methods Among the CO2-to-CH3OH production processes promoting the carbon capture and utilization concept, the catalytic hydrogenation and electrochemical reduction of CO2 have been widely investigated in the literature [143,144]. Catalytic hydrogenation of CO2 can occur by means of the following reaction: CO2 1 3 H2 5 CH3 OH 1 H2 O

(3.6)

As proved in the ICI, Lurgi, and Mitsubishi processes, typical ranges of temperature and pressure for this reaction are 250 C
300 C and 50
100 bar, respectively, over a Cu-based catalyst in a multitubular reactor requiring a strong need for H2O and heat removal [145]. On the other hand, the electrochemical conversion of CO2 involves the reduction of CO2 and oxidation of H2O in an electrochemical cell at mild operating conditions, according to the overall global reaction: CO2 1 2 H2 O 5 CH3 OH 1 1:5 O2

(3.7)

A comparison based on some techno-economic and environmental parameters of these two production methods is reported in Table 3.6. It is worth noting that the thermochemical route based on catalytic hydrogenation could be selected as the most suitable technology because of its higher maturity level. Electrochemical reduction of CO2 is still far from industrial application due to its lower efficiencies and product yields, while the status of catalytic hydrogenation is proved by the CRI’s George Olah plant in Svartsengi (Iceland) where about 4 ton/year of renewable CH3OH are produced. The same considerations discussed for CO2 supply to methanation can be applied to offshore CH3OH synthesis. It should be noted that catalytic hydrogenation of CO2 may require that both H2 and CO2 are compressed to higher levels than those required for catalytic methanation, thus adding further compression

31

32

CHAPTER 3 Innovative hybrid energy options

Table 3.6 Comparison of CH3OH synthesis technologies using CO2 source [143,144,146,147]. Catalytic hydrogenation

Electrochemical reduction

Technological status

Commercial/precommercial (pilot scale)

Research and development

System size range

0.05
3000 ton/year

—

Feed-in

CO2, H2

CO2, H2O

Catalyst/electrode

Cu-based

Cu-based

Product quality (CH3OH selectivity %)

30
99.5

2
40 (faradic efficiency)

Operating temperature ( C)

250
300

15
25

Operating pressure (bar)

50
100

1
1.5

Energy efficiency excluding electrolysis (%)

70
85

5
60

Investment costs (Mh2014/(tCH3OH/d))

0.18
0.22

—

Operational costs (% investment costs per year)

5

—

Greenhouse gas emissions (kgCO2eq kg21CH3OH)

0.2
3.8

—

Investment costs in this table are estimated based on the assumption that US$1 5 h0.75.

steps in the pathway producing CH3OH. In particular, it would be convenient to precompress CO2 to the same pressure as H2 before being mixed and then to compress them together to the operating pressure of reactor dedicated to CH3OH synthesis [148].

3.4.2 Industry and mobility sectors end-use Differently from H2 and SNG, CH3OH can be obtained from the thermocatalytic plant in its liquid phase in ambient conditions. Therefore it can be stored at the offshore platform in storage tanks commonly used for gasoline storage, provided a proper protection from ignition sources in a dedicated location, and then delivered to the onshore market using the sealed cargo in tanker ships similar to those adopted for marine transportation of hydrocarbons [149]. Despite the several applications of CH3OH as a basic building block for a wide variety of chemical products, the most promising end use of renewable CH3OH can be considered the mobility sector due to its suitability for a flex-fuel mixture with gasoline in conventional internal combustion engine
driven road vehicles [150,151].

3.5 Gas to power

3.5 Gas to power 3.5.1 Gas turbine technologies The most common method to generate power from natural gas uses GT generators, which are a type of internal combustion engine comprised of three sections (compressor, combustion chamber and turbine), mounted on the same shaft. Compressed air is mixed with fuel injected through nozzles; the air
fuel mixture ignites under constant pressure conditions, and the hot combustion gases are routed to spin a turbine driving a generator that converts the energy into electricity [152]. Therefore the process of producing electricity involves combustion, compression, heat transfer, and spinning, resulting in the need for equipment consuming a great deal of fuel and requiring considerable operation and maintenance efforts and inevitably producing GHG emissions. The thermodynamic process used in GTs is the Brayton cycle, which is characterized by the firing temperature and pressure ratio. The pressure ratio is the compressor discharge pressure divided by the compressor inlet pressure, while the firing temperature is defined as the highest temperature in the cycle. The fuel to power efficiency and the resulting emissions of the engine can be optimized by increasing these two parameters, depending on the design of GTs [153]. Commercially, GTs are classified into industrial (heavy frame), aeroderivative designs, and micro-GTs characterized by different capacity ranges that can fulfill the high requirements of a wide spectrum of applications in terms of efficiency, reliability, flexibility, and environmental compatibility. Table 3.7 summarizes the comparison of these types of GT technologies in a simple cycle. GTs come in either in simple-cycle or combined-cycle configurations. Simplecycle power plants use GTs without heat recovery, while combined-cycle power plants use GTs and recover the waste heat from their exhaust gas streams with heat recovery steam generators to allow steam to run steam turbine generators, thus producing additional power. A typical simple-cycle GT can convert 30%
40% of the fuel input into shaft output. Compared to simple-cycle installations, combined-cycle installations show higher power plant efficiency (55%
60%) and thus less environmental emissions, but they are characterized by longer start-up time, purge needs, and ramp-up to full load. GTs are common technologies at offshore oil and gas platforms producing their own power. Due to weight and space constraints in offshore installations, GT simple-cycle configurations are the preferred method for power generation [157]. Aeroderivative GTs are typically the most prevalent in offshore facilities because of their compactness, lighter-weight designs, and higher power density than comparable GTs [152]. Their high efficiency and fast-start capabilities mean that aeroderivative GTs also perform well in decentralized power generation applications. Moreover, micro-GTs allow the valorization of the associated gas with high tolerances on H2S, CO2, and N2, thus requiring minimal fuel pretreatment and no exhaust after treatment [158]. An example of a large-scale offshore application of the G2P technology is the advanced Sevan 700 MW power plant

33

Table 3.7 Comparison of commercial GT (gas turbine) technologies in simple cycle [105,153
156]. Heavy frame GTs

Aeroderivative GTs

Micro-GTs

Output power range

Up to 340 MW

Up to 66 MW

30
800 kW

Nominal frequency

50/60 Hz

50/60 Hz

50/60 Hz

Pressure ratio

18:1

30:1

3
4.5:1

Fuel-to-power efficiency

28%
34%

37%
42%

26%
33%

Internal design

Single shaft fixed to generator speed, multiple variable compressor vanes to control airflow

Multiple independent shafts to run at optimal speed with secondary turbine matched to generator speed

Air bearing technology, one moving part, no coolants, oils, or grease

Operational flexibility

Single high-power unit

Multiple lower-power units

Multiple lower-power units

Starting

No fast start capability without adverse impact to cyclic life, only about 350 kW required

10 min depending upon configuration, without impact to cyclic life, helper motors required

About 2 min, helper motors required

Ramp-up time (acceleration to load)

Slow (10
15 min)

Rapid (idle to full load in 2 min)

—

Maintenance location

On-site maintenance requiring larger space

Either on-site or at off-site facility

—

Maintenance downtime

110
140 d (,99% availability)

Up to 40 d (99% availability)

—

Annual inspections

6
15 d

30 h (heavy lift equipment not necessary)

—

Combustion inspection

2
5 d

Every 4000
8000 h

8000
40,000 h

Skid dimension

2

50 m

37 m

2

22 m2

Driver skid weight

120 tonnes

60 tonnes

12
14 tonnes

Investment costs (h2013/ kW)

620

900

1158

Fixed operational costs

7 h2013/kW year

11 h2013/kW year

0.034
0.038 h2013/kWh

Greenhouse gas emissions (kgCO2 eq kWh21)

202

202

185

Investment costs in this table are estimated based on the assumption that US$1 5 h0.75.

3.6 Closing remarks

concept, developed by Sevan Marine in cooperation with Siemens Oil and Gas, which aims to serve as a power supply hub for offshore oil and gas operations and to transmit power from marginal or remote gas fields to shore over distances of 70
100 km. The concept consists of a cylindrical platform equipped by eight combined-cycle GTs powered by imported and regasified LNG or by gas from a local (stranded) gas field and an amine-based CO2 capture system for injection into the subseabed reservoir facilitating EOR initiatives [159].

3.5.2 Electrical grid end-use For the purpose of integrating non-programmable renewable power into the onshore electrical grid, the producer is usually asked by the local energy authorities, e.g. the transmission system operator (TSO), to declare the forecast power to be injected into the grid before the real injection. Thus, the local TSO can perform some activities for good dispatching, for example, planning reserve power generation required to offset unforeseen deviations between supply and demand, as well as quantifying balancing capacity reserves needed from utilities when supply is variable in order to cover unforeseen deficits (i.e., forecasting lower than injection) and surpluses (i.e., forecasting greater than injection) [160]. Based on local regulations put in place to deal with grid integration and imbalances of renewable power, the producer may receive some incentives for participating on the market (i.e., when power injection is greater than the declared one) but may pay economic penalties in case of negative power unbalances (i.e., when power injection is lower than declared) [105].

3.6 Closing remarks In this chapter, P2G, P2L, and G2P hybrid energy systems for offshore applications were presented as innovative strategies to overcome the challenges of both the renewable and oil and gas sectors. The chemical conversion of renewable power into gas and liquid synthetic fuels at offshore oil and gas facilities (P2G and P2L hybrid energy options) allows the easing of the storage and transportation of offshore renewable energy from remote areas and creates new opportunities for obsolete offshore structures. On the other hand, GT energy balancing systems, coupled with renewable plants in G2P offshore projects (G2P hybrid energy option), allow for improvement in the dispatchability of offshore renewable power injected into the onshore electrical grid and the valorization of depleted gas fields. Each hybrid energy system was defined in terms of main process stages up to delivery to the onshore market. Technology options for raw material supply, production, compression, and storage of the pathways were described and compared. Suitable technologies for offshore applications were addressed. Finally, main enduses at the onshore context were illustrated, including the industry and mobility sectors, the gas grid, and the electrical grid.

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CHAPTER

System modeling and analysis

4

This chapter presents the analysis and modeling of hybrid energy systems for offshore applications from the point of view of sustainability and safety assessments. Thermodynamic analysis based on both energy and exergy approaches is described and utilized. In addition, economic and exergoeconomic analyses are provided for further assessments. The environmental impact assessment is also presented to address the clean environment requirements for better sustainability. Finally, the modeling approach in the inherent safety analysis is presented and discussed in detail. Furthermore, at the end of the chapter, the SWOT (strengths, weaknesses, opportunities, and threats) technique is applied to the different offshore hybrid energy options.

4.1 Energy analysis As a part of the energy analysis based on the first law of thermodynamics, the mass and energy balance equations are used in order to estimate the thermal loads of the system components, as well as the energy efficiency of the main subsystems and of the overall system. Several general assumptions can be considered in the analysis of process components, as in the following list. Appropriate assumptions made for specific components, including the renewable energy systems, may be introduced. • • • • • •

All the units, including throttling valve, pumps, compressors, fans, flash drums, and separators, are adiabatic. The absorption, desorption, and distillation columns are adiabatic. The columns for absorption, regeneration, and distillation are adiabatic. The column and related top condenser and bottom reboiler are considered as one unit for the distillation and desorption processes. The heat exchangers, columns, and reactors are isobaric. The steady-state and steady-flow conditions exist. The general mass and energy equations for the analysis of a system are [5]: X

X m_ in 5 m_ out X X m_ in hin 1 Q_ in 1 W_ in 5 m_ out hout 1 Q_ out 1 W_ out

Hybrid Energy Systems for Offshore Applications. DOI: https://doi.org/10.1016/B978-0-323-89823-2.00004-X © 2021 Elsevier Inc. All rights reserved.

(4.1) (4.2)

37

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CHAPTER 4 System modeling and analysis

where m_ is the mass flowrate in inlet or outlet from the component of the system, Q_ is the heat rate required or released from the component, W_ is the work rate required or produced from the component, and h is the specific enthalpy associated to the inlet and outlet streams of the component. In the following, literature data on the specific electrical consumptions associated with some process components of power to gas (P2G), power to liquid (P2L), and gas to power (G2P) hybrid energy options are reported. This information can be used to estimate the electrical power W_ of the component needed in the energy balance equation. Given the considerations reported for the different technologies in Chapter 3, Innovative Hybrid Energy Options, electrical energy requirements are presented for the most suitable technologies for offshore applications [i.e., proton exchange membrane (PEM) for electrolysis, reverse osmosis for desalination, membrane separation for CO2 removal from natural gas, catalytic methanation in an adiabatic fixed-bed reactor for synthetic natural gas (SNG) production, catalytic hydrogenation for CH3OH production]. The specific electrical power of the commercial Siemens Silyzer 200 stack associated with the electrolyzer (5.25 kWh Nm23) and with the utilities and losses by rectification (0.60 kWh Nm23) [112] can be used to estimate the electrical power required for electrolysis. To convert the specific power from kWh Nm23 into kWh kg21, the H2 density at normal temperature and pressure equal to 0.08375 kg m23 can be considered. For reverse osmosis desalination, a value of 6.7 kWh m23H2O can be used as the specific electrical power, as suggested in the literature [119]. The electrical power required for H2 compression can be evaluated by considering the adiabatic (isoentropic) compression, that is, assuming that the process takes place without any heat exchange between the compressed gas and the environment and with no variations of entropy. The fluid power can be divided by the isoentropic efficiency of the compressor in order to obtain the shaft power and by the driver efficiency to account for the electrical motors driving the compressor [161]: m_ Nst Uγ UZUTURu U U W_ comp 5 3600U1000 γ21

!   γ21   pdown Nst Uγ 2 1 = ηiso ηdrv pup

(4.3)

where m_ is the mass flowrate of the fluid in kg h21, Z is the compressibility factor, T is the inlet temperature of the compressor in K, Ru is the specific gas constant in J kg21 K21, Nst is the number of stages, γ is the diatomic ratio of specific heats (usually equal to 1.41 for H2), pup is the upstream pressure of the compressor in bar, pdown is the downstream pressure of the compressor in bar, ηiso and ηdrv are the isoentropic and driver efficiencies, which can be considered in the case of H2 as 75% and 95%, respectively [162]. Eq. (4.3) can also be applied in the case of SNG compression, with the values of the isoentropic and driver efficiencies previously reported. For CO2 compression, the isoentropic and driver efficiencies in Eq. (4.3) can be considered equal to 80% and 99%, respectively [142].

4.2 Exergy analysis

For CO2 capture from raw gas, the literature values for a membrane separation unit producing 6.24 3 104 kg h21 of CO2 in the permeate stream (i.e., 7.53 3 103 kW and 7.80 3 103 kW for compression and capture duty, respectively [135]) can be rescaled based on the actual CO2 flowrate produced in the methanation reactor through stoichiometric reaction. A specific electrical power value of 0.33 kWh kg21SNG, reported in the literature for methanation driven by wind energy [114], can be adopted. For the CH3OH synthesis driven by wind energy, a value of 0.402 kWh kg21CH3OH [146] can be considered for the calculation of the required electrical power.

4.2 Exergy analysis The exergy-based approach based on the second law of thermodynamics offers more illuminating insights into the process performance of a system than does energy analysis. The general assumptions described in energy analysis can be considered also for this assessment. In addition, the changes in the kinetic and potential exergies can be disregarded. The reference environment conditions (dead state) can be assumed as a temperature T0 equal to 298.15K and a pressure P0 equal to 100 kPa. For a given control volume, beside the mass balance equation in Eq. (4.1) and the energy balance equation in Eq. (4.2), the exergy balance equation in Eq. (4.4) is used in the exergy analysis [5]: X X _ Q_ Q_ W_ 1 _ in _ out _ W _ d _ in 1 Ex m_ in exin 5 Ex 1 Ex m_ out exout 1 Ex Ex out 1

(4.4)

where ex is the specific exergy associated with the inlet and outlet streams of the _ Q_ is the exergy rate due to heat transfer, Ex _ W_ is the component of the system, Ex _ exergy rate due to mechanical or electrical work, and Exd is the exergy destruction rate due to irreversibility. _ W_ in Eq. (4.4) can be evaluated as follows: Ex _ W_ 5 W_ Ex

(4.5)

where W_ is the electrical power required associated with the component defined _ Q_ expressed in Eq. (4.4) is calculated as: in Eq. (4.2). On the other hand, Ex   _ Q_ 5 Q_ 1 2 T0 =Ts  Ex

(4.6)

where Q_ is the heat duty associated with the component defined in Eq. (4.2), Ts is the absolute temperature of the boundary where the heat rate crosses, and T0 is the temperature of the reference environment. The specific exergy associated to each stream in Eq. (4.4) is defined as the sum of the two main parts: ex 5 exph 1 exch

(4.7)

39

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CHAPTER 4 System modeling and analysis

where exph is the physical exergy rate per mass flow, that is, the maximum work obtainable from the system as it reaches the thermal and mechanical equilibrium with the environment. The physical exergy exph is calculated as: exph 5 h 2 h0 2 T0 ðs 2 s0 Þ

(4.8)

where h and s are the specific enthalpy and entropy of the stream, respectively, at operating conditions of the stream and at reference environment conditions; T0 is the reference environment temperature. On the other hand, the chemical exergy exch is the chemical exergy rate per mass flow, that is, the maximum work obtainable from the system as it moves from the environmental state to the reference state, which is defined as [5]: exch 5

 X 1 X 0 U x x ex 1 R T ln ð x Þ c 0 c c ch;c c mw

(4.9)

where xc is the molar fraction of compound c in the stream, ex0ch is the standard specific chemical exergy of the compound at T0 and P0 in kJ mol21 units, R is the universal gas constant (in kJ mol21 K21), T0 is the reference environment temperature, and mw is the molecular weight of the stream. Clearly enough, exch is the same as ex0ch of the final product if the outlet stream from the reference process scheme can be considered relatively pure.

4.3 Economic analysis Common metrics used for the economic analysis of a system are estimates of capital expenditure (CAPEX) and operational expenditure (OPEX). Typically, CAPEX includes development and project management costs, (i.e., development and consenting services, environmental surveys, resource and met-ocean assessment, geological and hydrographical surveys, engineering and consulting), all equipment costs, balance of plant costs (including electronics, instrumentation, and controls), installation and commissioning costs (including installation of equipment and balance of plant, site work, logistics, development insurance, construction project management, and spent contingency), and sometimes decommissioning costs. OPEX commonly comprises fixed and variable operation and maintenance (O&M) costs including costs associated with operations related to the management of the assets in terms of health and safety, logistics, monitoring and to maintenance and service activities for equipment and the balance of plant [163]. In the following, CAPEX and OPEX estimations from the literature are reported for some process components of P2G, P2L, and G2P hybrid energy options. Given the considerations reported for the electrolysis technologies in Chapter 3, Innovative Hybrid Energy Options, costs estimations are presented here for the most suitable option for offshore applications (i.e., PEM electrolysis, reverse osmosis for desalination, catalytic methanation in an adiabatic fixed-bed reactor for SNG production, membrane separation for CO2 removal, catalytic hydrogenation for CH3OH production).

4.3 Economic analysis

4.3.1 CAPEX and OPEX for electrolysis CAPEX of electrolysis can be calculated for each component by using the following cost function [148]:  0:85 CAPEX 5 1; 500; 000U W_

(4.10)

where W_ represents the installed power of electrolyzers in kW. The cost is in h referred to the year 2015. The annual OPEX of electrolysis takes into account the O&M costs, which can be estimated as 3.5% of CAPEX, and also the costs for replacement of the stack, which can be considered to be approximately 35% of CAPEX for every 40,000 h of operations [151], for a total contribution of 11% of CAPEX.

4.3.2 CAPEX and OPEX for desalination The CAPEX associated with reverse osmosis can be estimated by multiplying the cost of a single integrated system, which includes pretreatment installation, cartridge filtration, high-pressure pump, and the LennRO module (h31,000 referred to the year 2017) [164] for the number of components required based on the H2O needs of the electrolyzers. In addition, the costs for further operations, such as a second desalination process to remove salts toward 300 ppm by means of a Lennetch module for brackish H2O and a posttreatment through ion exchange polishing to ensure that the H2O quality reaches values lower than 1 μS cm21, can be considered in total as h17,000 referred to year 2017 per each component. The annual OPEX associated with reverse osmosis can be accounted for 2% of CAPEX [117].

4.3.3 CAPEX and OPEX for hydrogen compression CAPEX of the compression stage can be estimated as follows [165]:  0:9 W_ CAPEX 5 15; 000U 10

(4.11)

where W_ is the electrical power required for compression in kW. The cost value is in $ referred to the year 2006. Annual OPEX associated to compression can be evaluated as 4% of CAPEX [165].

4.3.4 CAPEX and OPEX for H2-enriched natural gas and synthetic natural gas transportation If the H2-enriched natural gas (HENG) mixture transportation can be supposed to occur via an existing gas pipeline to the onshore gas terminal, the CAPEX calculation can be disregarded. The OPEX can be considered approximately equal to

41

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CHAPTER 4 System modeling and analysis

the costs for the international offshore natural gas pipeline according to the ENGIE’s energy overview [166], that is, 0.0008 $/(km MMBTu) referred to the year 2014, where MMBTu stands for millions British thermal units. To calculate the OPEX value, the volumetric flowrate of the fluid in MMBtu h21 can be used based on the conversion 1 S m3 h21 5 0.03955 MMBTu h21. Considering that an existing gas pipeline can be used also for SNG transportation, the CAPEX associated with the transportation is negligible. The OPEX per unit of pipeline length can be calculated by applying the cost values and assumptions previously reported for HENG transportation.

4.3.5 CAPEX and OPEX for hydrogen and synthetic natural gas transportation Concerning the CAPEX of a dedicated H2 pipeline, Andre´ et al. [161] developed a method for designing and dimensioning an onshore H2 pipeline that may be adopted in order to approximate investment costs per unit of length for offshore transmissions since it disregards licensing costs. Under this assumption, the CAPEX per unit of pipeline length can be calculated as follows: CAPEX 5 418; 869 1 762:8Udp 1 2:306Udp2

(4.12)

where dp is the inlet pipe diameter in mm. The cost value is in $ km21 referred to the year 2004. It should be noted that this cost does not include the installation of specific coatings and/or other protections that may be required in offshore environment, thus representing a relatively rough estimation. The annual OPEX associated with H2 transportation can be considered as 2% of CAPEX [161].

4.3.6 CAPEX and OPEX for hydrogen storage The CAPEX of H2 storage can be calculated by using the following cost function [133]:  0:75 CAPEX 5 80 3 2500U Vst =2500

(4.13)

3

where Vst is the storage volume in the N m required to store the produced H2 in tanks. The cost is in h referred to the year 2006. The annual OPEX associated to H2 storage can be estimated as 2% of CAPEX [133].

4.3.7 CAPEX and OPEX for synthetic natural gas production The CAPEX associated with SNG production includes the reactor cost, assumed as 300 $ kW21 referred to the year 2016, and the balance of the plant costs (heat exchanger, separators, etc.), assumed as 350 $ kW21 referred to the year 2016 [167]. The heating value of SNG can be used to convert the produced SNG mass

4.3 Economic analysis

flowrate produced in the reactor through stoichiometric reaction into its corresponding energy content for the calculation of the total CAPEX costs. The OPEX calculation can be performed by considering a contribution of 6.25% of CAPEX associated with the methanation reactor and of 6.5% of the CAPEX associated with the balance of the plant [167], thus leading to an overall fraction of 9.75% of total CAPEX.

4.3.8 CAPEX and OPEX for carbon dioxide removal The CAPEX of the membrane separation technology can be approximated by rescaling the literature cost of $1.41 3 107 (referred to the year 2008) for a unit producing 6.24 3 104 kg h21 of CO2 in the permeate stream [135] based on the CO2 flowrate required for the considered methanation reaction. A fraction of 18% of the CAPEX can be adopted for the OPEX associated with membrane separation, as suggested in [136].

4.3.9 CAPEX and OPEX for carbon dioxide transportation Considering that CO2 is delivered from the landfall terminal to the offshore facility via an existing gas pipeline, the CAPEX for a CO2 pipeline can be disregarded. On the other hand, the annual OPEX associated with offshore CO2 transportation can be calculated by converting the offshore pipeline OPEX proposed for the survey vessel, repair vessel, and intelligent pigging by IEA [168]:  OPEX 5

     14; 000 98; 000 2:5 ULp 1 70; 000U19:8ULp 1 ULp 1 700U ULp 1 120; 000 6 10 10 (4.14)

where Lp is the pipeline length in km. The assumptions for the use of Eq. (4.14) are that the survey vessel operates once a year [169], the repair vessel once every 10 years, and intelligent pigging once every 4 years; the survey vessel speed is equal to 6 km d21 [168]. The cost is in $/km/year referred to the year 2000.

4.3.10 CAPEX and OPEX for carbon dioxide compression The CAPEX of the CO2 compressor can be calculated as follows [142]:  23 0:67 _ WU10 CAPEX 5 14; 800; 000U 13

(4.15)

where W_ is the power required for compression in kW. The cost value is in $ referred to the year 2002. W_ can be evaluated by approximating adiabatic (isoentropic) compression and by applying Eq. (4.3). The annual OPEX associated with compression can be evaluated as 5% of CAPEX [147].

43

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CHAPTER 4 System modeling and analysis

4.3.11 CAPEX and OPEX for synthetic natural gas compression For SNG compression, the following cost function can be used [170]: CAPEX 5 267; 000U

 23 0:67 _ WU10 445

(4.16)

where W_ is the power required for compression in kW. The cost value is in h referred to the year 2012. W_ can be evaluated by approximating adiabatic (isoentropic) compression and by applying Eq. (4.3). The annual OPEX associated with compression can be evaluated as 6% of CAPEX [151].

4.3.12 CAPEX and OPEX for methanol production The CAPEX of CH3OH production can be calculated by using the following cost function [148]:  0:65 _ CAPEX 5 14; 200; 000U m=54; 000

(4.17)

where m_ is the CH3OH mass flowrate in kg h21 produced in the reactor through stoichiometric reaction. The cost is in h referred to the year 2015. Annual OPEX associated to CH3OH production can be estimated as 1.04% of CAPEX [148].

4.3.13 CAPEX and OPEX for methanol storage For CH3OH storage, the CAPEX of storage tanks can be estimated by using the following cost function [171]: CAPEX 5 250; 000 1 94:2UVst

(4.18)

where Vst is the volume of the tank in m3. Eq. (4.18) can be applied for volumes between 2000 and 50,000 m3. Otherwise, for smaller tanks, CAPEX can be calculated as follows: CAPEX 5 65; 000 1 158:7UVst

(4.19)

The cost values proposed in Eqs. (4.18) and (4.19) are in $ referred to the year 2008. Annual OPEX associated to CH3OH storage can be considered as 2% of CAPEX [171].

4.3.14 CAPEX and OPEX for methanol transportation Considering that the produced CH3OH can be transported via ship to the onshore terminal, platform supply vessels can be used as they commonly support offshore activities, transporting cargo, equipment, and personnel. A main cost driver of

4.4 Exergoeconomic analysis

such vessels is the cargo capacity, expressed in terms of clear deck area (i.e., the space available to place cargo on the deck) and deadweight tonnage (i.e., the cargo carrying capacity). Daily rates reported for a vessel with deck areas between 300 and 600 m2 and tonnage capacity between 1000 and 2000 tonnes are from $6000 to $12,000 referred to the year 2015 [172]. According to the time charter contract method used for oil and gas operations, the vessel owner responsibilities cover the vessel, crewing, maintenance, and insurance, and the charterer is responsible for port charges, cargo loading and discharge, and bunkers. Moreover, the payment is calculated based on the daily hire rate. The service speed of the vessel can be considered as 80% of the average speed of a platform supply vessel (typically between 14 and 16 knots [173]) and can be used to estimate the number of days required for the total round trip along a given distance between the harbor and the offshore platform. Round trip time is commonly divided into sailing time (40% of round trip time), loading time at the harbor (25% of round trip time), and unloading time at the platform (35% of the round trip time) [173]. Sailing time can be first estimated as twice the distance between the harbor and the platform divided by the assumed vessel speed; then loading and unloading times can be derived by applying the mentioned proportions. By supposing regular voyages of the supply vessel per year, the annual costs for CH3OH transportation can be obtained based on the distance between the onshore harbor and the offshore platform.

4.4 Exergoeconomic analysis Exergoeconomics combining exergy-based thermodynamic assessment and economic principles at the system components level provides useful information about the costeffectiveness of the analyzed systems [174]. In the following, the specific exergy costing method [175] is presented as a way to determine and compare the economic performance of a process scheme by using results from the exergy analysis. Once the thermodynamic data are collected, the procedure involves the calculation of the product and fuel costs for each component, both expressed in terms of exergy. Definitions of the concepts of fuel and product can be retrieved in [176]. Then a general cost balance equation is applied to each component: X

X W_ Q_ W_ Q_ C_ in 1 C_ in 1 C_ in 1 Z_ 5 C_ out 1 C_ out 1 C_ out C_ F 1 Z_ 5 C_ P

(4.20) (4.21)

where C_ is the total cost rates of the exergy streams across a specific component in the system, C_ Q_ is the cost rate associated with the exergy due to heat transfer, C_ W_ is the cost rate associated with exergy due to mechanical or electrical work, Z_ is the cost rate associated with capital and operating expenses, and C_ F and C_ P are the cost rates associated with fuel and product, respectively.

45

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CHAPTER 4 System modeling and analysis

The C_ associated with each inlet or outlet of the component in Eq. (4.20) can be calculated as: C_ 5 c m_ ex

(4.22)

where c is the average cost per unit of exergy associated to inlet and outlet streams of the component, m_ is the mass flowrate in inlet or outlet from the component, ex is the specific exergy associated with the inlet and outlet streams of the component described in the preceding exergy analysis. Similarly, C_ Q_ and C_ W_ illustrated in Eq. (4.20) are defined as: _ Q_ C_ Q_ 5 cQ_ Ex

(4.23)

_ W_ C_ W_ 5 cW_ Ex

(4.24)

where cQ_ and cW_ are the average costs per unit of exergy associated with heat _ W_ and Ex _ Q_ are the exergy rates associated with work and work, respectively; Ex and heat defined in Eq. (4.4) and Eq. (4.5). Z_ in Eq. (4.20) and Eq. (4.21) is the annual levelized total cost defined as: Z_ 5 Z_CI 1 Z_O&M

(4.25)

where Z_CI is the capital investment (CI) cost rate, and Z_O&M is the O&M cost rate. These terms can be calculated for the k-th component of the option by means of the following equations: TCIUCRF PECk UP Z_CI;k 5 τ PECk

(4.26)

ϕUTCIUCELF PECk Z_O&M;k 5 UP PECk τ

(4.27)

where TCI is the total capital investment, CRF is the capital recovery factor, τ is the annual operational time of the system, PEC is the purchase equipment cost of the component, ϕ is the maintenance factor, and CELF is the constant-escalation levelization factor. TCI can be approximated to 4.16 times the total PEC of the plant [176]. CRF in Eq. (4.26) and CELF in Eq. (4.27) can be determined as: r ð11r ÞT ð11r ÞT 2 1   K 1 2 K T CRF CELF 5 ð1 2 K Þ CRF 5

(4.28)

(4.29)

where r is the effective discount rate reflecting the relation between the future and present values, T is the economic lifetime of the system, and K is a constant defined as follows: K5

ð1 1 rn Þ ð1 1 r Þ

(4.30)

4.6 Inherent safety analysis

where rn is the nominal escalation factor representing the impact of resource depletion, new technologies, and increased demand in addition to inflation.

4.5 Environmental impact analysis Environmental impact assessment is a tool used to evaluate whether the analyzed system or process may cause adverse environmental impacts. The analysis consists in assessing the level of impact associated with each component and provides recommendations to minimize such impacts [5]. A common metric used to estimate the environmental impact is GHG emission expressed as the mass of CO2 equivalents (CO2eq) per year emitted from the components of the system. In the following, literature data are reported on the specific GHG emissions associated with some process components of P2G, P2L, and G2P hybrid energy options. This information can be used to estimate the GHG emissions needed in the environmental impact analysis of the systems. Given the considerations reported for the different technologies in Chapter 3, Innovative Hybrid Energy Options, specific GHG emissions are presented in the following for the most suitable technologies for offshore applications (i.e., PEM for electrolysis, reverse osmosis for desalination, catalytic methanation in an adiabatic fixed-bed reactor for methanation, catalytic hydrogenation for CH3OH production). A value of 0.60 kgCO2eq/kgH2 [114] can be used for the calculation of GHG emissions associated to electrolysis. For reverse osmosis, a value of 0.024 kgCO2eq/kWh can be adopted, as suggested in Lai et al. [119]. In the case of methanation, the literature value of 0.30 kgCO2eq/kgSNG [114] can be adopted. On the other hand, specific GHG emissions associated with CH3OH production can be estimated as 0.269 kgCO2eq/kgCH3OH [146].

4.6 Inherent safety analysis Inherent safety analysis may be based on KPIs, thus requiring the analysis of the consequences of the potential accident scenarios associated with each unit of the reference process scheme of the system under analysis. The consequence analysis is strictly related to the critical targets of concern. In the offshore context, three main target categories can be considered: humans, assets, and the marine environment. Human targets in the offshore context are present both on the decks of the installation (e.g., personnel involved in processing and/or maintenance operations) and on board marine vessels approaching the facility. Asset targets can be divided into three main subcategories. The first includes process and utility equipment containing hazardous material, whose damage may trigger secondary scenarios with potential accident escalation (i.e., domino accidents)

47

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CHAPTER 4 System modeling and analysis

[17,177]. The second asset subcategory contains all the structural elements of the installation (e.g., legs, columns, beams, and other critical components) whose damage may result in the partial or total collapse of the structure, while in the third subcategory are the hulls of supply vessels. Environmental targets include marine organisms that may be damaged in the different environmental compartments (sea surface, water column, seafloor, shoreline) based on the type of fluid released (oil, diesel fuel, and chemicals such as glycol and CH3OH). The air compartment of the environment is typically not of concern in the assessment of offshore accidental releases, due to the rapid dilution of the vapor and gas phases into the atmosphere. Oil spills can cause a damage to all four compartments, as proved by past accidents. However, in case the loss of containment event occurs at open sea, the sea surface and the water column are the first to be affected by oil [178]. As soon as the oil is released, it starts immediately to spread over the sea surface, creating a slick that causes damage to the organisms living in the proximity of the sea surface, particularly in the case of high-viscosity and low-volatility oils (i.e., persistent slicks). In the water column, oil enters the upper layers of the sea in the form of suspended droplets, as well as of dissolved oil compounds. The environmental damage of this compartment tends to be attributed only to the dissolved oil fraction and is usually limited due to the positive effect of the currents on the dilution of the extremely small fraction of hydrosoluble oil molecules that dissolve in water. The contamination of the seafloor mainly occurs when oil droplets dispersed in the water column interact with the particles suspended in the water, thus leading to aggregates that sink slowly down to the seabed. Sedimentation mainly occurs along coastal zones with shallow waters where particulate is abundant and water is subjected to intense mixing. As the oil slick and droplets in the contaminated water column are moved simultaneously by wind and currents, the spilled oil has the potential to reach the shore and the low-depth seabed in front of it [179]. Therefore the seafloor and shoreline pollution can be considered secondary targets in the case of open sea spills. Overall, the sea surface in the present methodology is considered the main environmental compartment for the quantification of the consequences of oil spills because this is the first compartment to be impacted by the oil and the one from which the contamination of the other compartments begins. On the other hand, as demonstrated by the analysis of past chemical spills, the water column is considered the sole environmental compartment that may be polluted by spills of completely hydrosoluble chemicals. As a result, two subcategories of targets can be considered the environmental targets: the sea surface compartment polluted by oil spills and the water column compartment damaged by the release of soluble chemicals. In view of the accidents’ consequences modeling, a reference damage threshold should be defined for each critical target in order to characterize the extent of the effects of potential accident scenarios. Table 4.1 shows examples of threshold values that may be adopted. The thresholds for human targets are based on technical documents as representing 1% probability of irreversible effects on individuals exposed to fire, explosion, and toxic release [180
183].

Table 4.1 Threshold values of accident scenarios for each offshore target. Target Accident scenario and related effect Flash fire
transient radiation Fireball
transient radiation Jet fire
stationary radiation Pool fire
stationary radiation Vapor cloud explosion (VCE)
overpressure Physical/mechanical explosion
overpressure Toxic cloud in atmosphere
toxic concentration Ecotoxic dissolved volume (hydrosoluble chemicals)
ecotoxic concentration Ecotoxic floating slick (oil)
slick thickness

Assets (process and utility equipment)

Assets (facility structures)

Assets (marine structures)

Marine environment (sea surface)

Marine environment (water column)

1 /2 lower flammability limit, vol% 7 kW m22 7 kW m22

n.c.

n.c.

n.c.

—

—

15a
50b kW m22 15a
50b kW m22

100 kW m22 100 kW m22

100 kW m22 100 kW m22

— —

— —

7 kW m22

15a
50b kW m22

100 kW m22

100 kW m22

—

—

0.14 barg

0.16b
0.22a barg

0.50 barg

0.30 barg

—

—

0.14 barg

0.16b
0.22a barg

0.50 barg

0.30 barg

—

—

Immediately dangerous to life and health, ppm —

—

—

—

—

—

—

—

—

—

Predicted no effect concentration in marine water, mg L21

—

—

—

—

Thickness for lethal dose to organisms, μm

Humans

Apex “a”: atmospheric target equipment; apex “b” pressurized target equipment. n.c. stands for not applicable.

50

CHAPTER 4 System modeling and analysis

Domino escalation thresholds for offshore process and utility equipment are based on previous studies by Cozzani et al. [184,185]. For other asset categories, the heat load and blast overpressure proposed for the design of offshore structures can be considered on the basis of relevant technical reports and standards [180,186]. Toxic effects are not of concern for this target category. With respect to the pollution of the marine environment, the damage to the ecosystems may be caused by different environmental stress mechanisms (e.g., chemical stress, physical stress, particulate stress of droplets, burial stress, etc.) on the relevant environmental compartment. Chemical stressors (ecotoxicity) are closely related to accidental releases and can affect all the compartments. In the case of the pollution of the sea surface due to oil spills, the threshold value can be assumed to be the thickness of the floating slick causing lethal damage to seabirds, dolphins, sea otters, sea bears, and other mammals living on the sea surface [187]. The most obvious impact of oil on these organisms is the fouling of their plumage or fur or skin, causing the loss of the insulation properties of their outer protective layer and thus their death by hypothermia [188]. On the other hand, the predicted no effect concentration of a chemical compound on sea birds, fishes, and sea plants during long-term or short-term exposure [189,190] can be conservatively assumed as the limit value for environmental pollution of the water column due to chemical dissolution. Consequence analysis with respect to human and asset targets can be performed by using suitable consequence models described in detail in the literature [191
194]. Several models and commercial software tools are available and may be used for this purpose; for example, the process hazard analysis software (PHAST) by DNV GL and the ALOHA hazard modeling program for the CAMEO software suite by EPA are commonly adopted for consequence analysis of atmospheric releases. Concerning the sea surface compartment impacted by possible surface oil spills, the simulation tools for predicting fates and effects of oil spills into the sea are the freeware software by NOAA, that is, Automated Data Inquiry for Oil Spills (ADIOS) and General NOAA Operational Modeling Environment (GNOME), and the licensed tools by SINTEF, that is, Oil Spill Contingency and Response (OSCAR) and Oil Weathering Model (OWM). With respect to these oil fate simulation tools, GNOME is not able to take into account any value of mentioned temperatures, and the ADIOS and OWM tools consider the thermal equilibrium between air, water, and release, thus requiring the users to specify only one value. OSCAR distinguishes among the three temperatures, and thus all the three values are needed as an input. Concerning the environmental data, ADIOS, OWM, and GNOME evaluate only the wind speed for modeling, whereas OSCAR also evaluates the current’s speed. Except for ADIOS, which has a limit in the released mass in input (between 320 kg and 79,415 tonnes), the tools are able to simulate all the possible values of oil mass. For accidental spills with a relatively short duration (instantaneous or continuous spills with a duration of about 10 minutes), it is advised to simulate the releases as instantaneous with ADIOS, GNOME, and OWM, while setting a minimum duration time with OSCAR (e.g., 1 h) since it requires this parameter in addition to the released mass without allowing the instantaneous spill simulation.

4.7 SWOT analysis

Furthermore, all the tools can provide the oil budget (i.e., oil mass balance) over time. GNOME, ADIOS, and OWM have a time limit on the simulation (3 days for GNOME, 5 days for the others), but OSCAR does not pose limits on simulation time (i.e., a limit time cannot be identified). It is worth mentioning that an oil spill on the sea surface leads to a slick characterized by first an expansion phase until a maximum condition is reached and then by a reduction phase until it is exhausted by the end of its lifetime. Only the OSCAR tool allows users to simulate both phases, whereas ADIOS, GNOME, and OWM consider solely the sole expansion phase and output results until the end of this phase (if the expansion phase duration is lower than the limit time imposed by the software) or until their limit time (if the expansion phase duration is greater than the limit time imposed by the software). Moreover, ADIOS, GNOME, and OWM model the slick as a homogeneous entity, that is, with variable thickness over time but spatially uniform at each point of the slick. OSCAR considers a variable slick thickness over space and time, that is, a realistic representation of the slick. As a result, ADIOS, GNOME, and OWM allow users to estimate the oil mass in the slick beyond the simulation time limit. OSCAR provides the oil mass in the ecotoxic thick slick over the entire lifetime of the thick slick (i.e., the time when the slick shows a thickness equal to or greater than the threshold value somewhere).

4.7 SWOT analysis To raise full awareness about all the factors involving the feasibility of innovative offshore hybrid energy options, SWOT analysis [195], which is a tool frequently used in the field of business management, can be adopted to highlight the SWOT of the options under analysis. Figs. 4.1, 4.2, 4.3 and 4.4 illustrate the results from

FIGURE 4.1 SWOT analysis for offshore P2G-H2 hybrid energy systems. SWOT, strengths, weaknesses, opportunities, and threats; P2G, power to gas.

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FIGURE 4.2 SWOT analysis for offshore P2G-SNG hybrid energy systems. SWOT, strengths, weaknesses, opportunities, and threats; P2G, power to gas; SNG, synthetic natural gas.

FIGURE 4.3 SWOT analysis for offshore P2L
CH3OH hybrid energy systems. SWOT, strengths, weaknesses, opportunities, and threats; P2L, power to liquid.

the application of SWOT analysis to P2G-H2, P2G-SNG, P2L-CH3OH, and G2P offshore hybrid energy systems, respectively. As shown from Figs. 4.1 and 4.2, a common opportunity of P2G systems for offshore applications is the reconversion of aging offshore platforms for energetic uses (e.g., CO2 sequestration for EOR, offshore wind and wave energies exploitation), thus fostering so-called green decommissioning or blue economy. Both types of systems can be adopted to exploit remote fields without subsea cables, thus avoiding long and less stable transmission power links thanks to the chemical conversion of electricity into renewable H2 or SNG.

4.7 SWOT analysis

FIGURE 4.4 SWOT analysis for offshore G2P hybrid energy systems. SWOT, strengths, weaknesses, opportunities, and threats; G2P, gas to power.

P2G-H2 systems show the advantage of a relatively simple conversion chain. P2G
SNG systems can guarantee unlimited compatibility with existing gas infrastructures, thus avoiding the need for feasibility studies of H2 injection into the gas flows or design/installation issues for dedicated H2 pipelines. However, P2G
SNG systems require a CO2 supply in the input to the conversion chain. CO2 source options may depend on the infrastructure in place, available space at the facility to host onsite separation of CO2 from extracted gas, and the required flowrate and purity for methanation. If CO2-EOR or CO2-EGR activities exist at the offshore oil and gas site, they represent a further chance to supply CO2 for chemical synthesis without the need for new carbon capture operations. From Fig. 4.3, P2L
CH3OH systems exhibit the opportunity for green decommissioning of aging facilities and the exploitation of remote fields without a subsea cable, as in the case of P2G systems. Furthermore, P2L
CH3OH systems can allow regular ship voyages between the offshore facility and onshore port to transport liquid CH3OH in tankers or supply vessels. In addition, the presence of CO2-EOR or CO2-EGR activities at the oil and gas field offers the chance to supply CO2 for chemical synthesis, as described for P2G-SNG systems. On the other hand, a more complex conversion chain is required compared to P2G systems, including reaction, heat management, and separation, to obtain a liquid synthetic fuel at ambient conditions. Unlike P2G and P2L systems, G2P systems may be adopted when the offshore facility is located in stranded or depleted gas fields, relatively close to the onshore electrical network and linked to it by means of subsea electrical cables. As illustrated in Fig. 4.4, G2P systems offer dual opportunities: (1) compensating renewable power output fluctuations for grid integration and (2) monetizing stranded and depleted gas resources. However, these systems require a suitable gas turbine (GT) park design and management depending upon the size and entity of renewable

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power to be integrated into the grid. In addition, forecast analysis needs to be carried out based on the local legislative framework on renewable integration into the grid and power unbalance mechanism.

4.8 Closing remarks Innovative P2G, P2L, and G2P hybrid energy systems have to be analyzed and modeled for a thorough multi-criteria evaluation and screening. In this chapter, system modeling and analysis were described, addressing both the aspects of sustainability and inherent safety. Thermodynamic analysis based on the energy and exergy approaches was presented to assist the assessment of the technical aspect of sustainability. Literature data on specific electrical power required by some components of P2G and P2L hybrid energy systems were also detailed. Economic analysis based on cost estimation was defined for the evaluation of economic sustainability. Cost information from the technical literature was reported for some components of P2G and P2L hybrid energy systems. An exergoeconomic method was described as an alternative to analyze the economic performance of hybrid energy systems using results from the exergy assessment. Environmental impact analysis based on GHG emissions estimation was proposed to cover the environmental pillar of sustainability. Literature data on specific GHG emissions associated with some components of P2G and P2L hybrid energy systems were also detailed. For the inherent safety analysis, modeling of the consequences of accident scenarios associated with the hybrid energy system under analysis was presented with respect to specific targets of the offshore context, that is, humans, assets and marine environment. These targets were defined and characterized in terms of threshold values required for damage estimation during consequence analysis. Possible consequence simulation tools were also detailed. Finally, SWOT analysis was provided to highlight the SWOT of P2G, P2L, and G2P hybrid energy systems, thus addressing the preliminary feasibility evaluation of these systems in different offshore situations.

CHAPTER

Sustainability index development

5

This chapter presents the indicator-based methodologies developed for the performance assessment of the offshore hybrid energy options described in Chapter 3, Innovative Hybrid Energy Options. These methodologies are further developed by applying the system modeling and analysis approaches as reported in Chapter 4, which is specifically about System Modeling and Analysis. Here, four assessment methodologies are described, based on a schematic procedure similar to the multi-criteria decision analysis (MCDA) approach, but they are tailored to capture specific features associated with the analyzed hybrid energy options and/or performance aspects. These quantitative methodologies represent detailed decisionmaking support tools in problems regarding the choice of the optimal offshore system in decommissioning projects and the valorization of depleted reservoirs. Fig. 5.1 displays the general assessment procedure based on the MCDA approach. As shown in the figure, different alternatives are first formulated, defining a common reference basis and boundaries for the analysis and collection of the required input data. After that, proper indicators are defined to evaluate the performance of the alternatives. Optionally, indicators scales are transformed into commensurable units in the normalized step; then weights are assigned to the indicators to reflect their importance in the weight elicitation step. Next, a mathematical algorithm (MCDA method) is applied for the aggregation of the indicators. Finally, alternatives are ranked based on the single and aggregated indicators. Sensitivity analysis may be performed to test the robustness of the results.

FIGURE 5.1 General approach for the assessment methodologies of offshore hybrid energy systems.

Hybrid Energy Systems for Offshore Applications. DOI: https://doi.org/10.1016/B978-0-323-89823-2.00005-1 © 2021 Elsevier Inc. All rights reserved.

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The different characteristics of P2G/P2L systems and G2P systems ask for the development of separate methodologies able to capture the peculiarities of the hybrid energy options even though presenting the same model framework and a similar set of sustainability indicators. Therefore two assessment methodologies for the comparison of the sustainability performance are defined for the P2G and P2L options in Section 5.1 and for the G2P options in Section 5.2, respectively. The complexity of the threats of concern in the offshore context requires the development of an array of inherent safety indicators to completely address the different targets of the potential hazards (people, environment, assets, etc.). A multi-target methodology assessing the inherent safety profile of alternative offshore design options at early design stages is thus described in Section 5.3. In order to provide an integrated analysis of sustainability and safety performance, a systematic multi-criteria methodology is presented in Section 5.4, based on the idea of process intensification, for the conceptual design of emerging chemical production processes. Finally, Section 5.5 describes some sensitivity analysis techniques that can be used to verify the robustness of the results obtained through assessment methodologies.

5.1 Sustainability assessment methodology for P2G and P2L systems 5.1.1 Generalities A sustainability assessment methodology is described to provide a systematic comparison of alternative P2G and P2L options for offshore renewable energy conversion at given offshore oil and gas sites. A limited yet exhaustive number of indicators addressing the technical, economic, environmental, and societal pillars of sustainability are defined to capture specific features of the strategies including production at the offshore oil and gas installations, transportation to the land, and end use at the onshore market. The methodology is intended to be a decision-making tool to assess the feasibility of P2G and P2L projects at offshore oil and gas production facilities located at areas that are remote and distant from the onshore electrical grid, and that may be at the end of their production lifecycle. Projects for renewable power exploitation have been initiated or are under investigation for the energetic valorization of such sites. The approach has a general applicability to any type of renewable energy source, even though resources for which technology development has succeeded in providing renewable generators able to operate at higher distances from shore and in deeper waters (e.g., offshore wind power) are currently the most reasonable to be considered in the analysis. Moreover, different P2G and P2L systems may be compared, even though systems producing H2, SNG, CH3OH for

5.1 Sustainability assessment methodology for P2G and P2L systems

FIGURE 5.2 Flowchart of the sustainability assessment methodology for P2G and P2L offshore hybrid energy systems.

multiple applications in the onshore market are specifically considered in the description of the methodology. The flowchart of the methodology is illustrated in Fig. 5.2, including references to the steps of the general MCDA approach displayed in Fig. 5.1. Novel aspects of the methodology are summarized as follows: 1. In the formulation of the alternatives, a step is introduced to identify the suitable field, offering opportunity for P2G/P2L conversion, and a step is included to identify suitable P2G/P2L strategies and define them from the production stage to onshore end use. 2. In the evaluation of performance indicators, proper sustainability indicators are defined to avoid double-counting and to quantify separately important aspects (e.g., exergy, costs, revenues), and a profitability indicator is proposed as a further important measure. 3. In the normalization of indicators, the reference values needed for the normalization are proposed for determining the performance of the best available process for the alternatives under analysis. 4. In the aggregation of indicators, a consolidated approach based on appropriate criteria of sustainability and perspectives of the decision makers is adopted to estimate the key weights among indicators.

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5.1.2 Definition of offshore oil and gas site and renewable energy As shown in Fig. 5.2, a preliminary step (step 0) is necessary, divided into two stages. The first stage (step 0.1) concerns the definition of the offshore oil and gas site, providing input data about the field and infrastructures. Table 5.1 summarizes this information. In the second stage (step 0.2), the offshore renewable energy to be exploited is preliminarily evaluated by taking into account existing projects and/or feasibility studies on resource potential assessment in the selected area.

5.1.3 Evaluation of alternative strategies and assessment of technology options In step 1 of the procedure in Fig. 5.2, a preliminary evaluation of the possible P2G and P2L strategies that may be considered for application of the methodology to the selected site is performed based on the information collected in step 0.1 about the field and infrastructures. The hybrid energy options to be considered need first to be identified and characterized in terms of process stages from input supply to end use. Process stages can be input supply, first conversion, first conditioning (including storage and compression), second conversion, second conditioning (including storage and compression), and transportation. A further specification should be made about the location of each process stage, that is, onshore or offshore areas. Clearly enough, if gas pipelines from the offshore platform to the onshore gas terminal and/or to other platforms are not present at the site under analysis, options producing H2 and SNG for the gas grid should be excluded from the analysis, as well as options providing input CO2 via pipeline. If CO2 is present in a relatively small amount in the raw gas processed at the offshore platform, onsite Table 5.1 Details of the offshore site required in input to the sustainability assessment methodology. Data about the field Entity of gas reservoirs (type, production, CO2 presence and concentration) Water depth Distance from site to the closest onshore grid, to onshore shipyard, to port Data about the infrastructures Characteristics of the offshore structures (type, remaining lifetime) Preliminary dimensions and estimated free space of the offshore structure, if available Features of gas pipelines to the grid and/or to other platforms, if present (delivery pressure, length, capacity) Features of electrical subsea cables to the grid and/or other platforms, if present (voltage, length, diameter) Number of regular voyages of supply vessels every year, if present

5.1 Sustainability assessment methodology for P2G and P2L systems

separation of CO2 may be unfeasible and should not be taken into account in the alternative options to be assessed. Once the P2G and P2L strategies and associated process stages are evaluated, a technology option needs to be selected for each process stage. A review of alternative technologies for the different process stages illustrated was reported in Chapter 3. Table 5.2 summarizes some recommendations that may be used to address technology selection in this step of the procedure, also indicating the technology proposed as the most suitable according to the criteria proposed. However, there is no doubt that other options may be chosen based on the status of knowledge and data availability about the technologies selected for the analysis.

5.1.4 Definition of the reference process schemes and of the offshore renewable power plant Step 2 of the methodology consists in the definition, characterization, and collection of quantitative data for the comparison of the strategies identified in step 1. The comparison of different options requires first the definition of a common reference basis and of common boundaries to be considered in the analysis. When different P2G and P2L strategies are characterized by the same starting process, for example, H2 production, a common reference basis may be introduced by considering an equal production capacity of the electrolyzers. Such a capacity can be expressed in terms of the ratio of the rated size of electrolyzers to the size of the renewable energy plant that is assumed to supply power to electrolyzers. Therefore the choice of the optimal ratio may be based on the typical actual energy production of the renewable plant with respect to its theoretical maximum at the site (i.e., capacity factor). Moreover, the capacity of electrolyzers may be chosen by considering their design features (i.e., dimensions, distance between stacks, weight), provided that space and weight requirements of the offshore platform are met. An example of such analyses is reported in the literature [164]. Clearly enough, the final decision may be addressed by taking into account the impact of such a capacity on the economics of the conversion processes. The boundaries of the alternative strategies need to be defined consistently among the options. The goal of the assessment is to analyze the performance of pathways converting energy supplied by the same renewable power plant into chemicals at a given offshore location and delivering it to the onshore market for a given end use. Thus boundaries should limit the process stages identified in step 1, leaving outside on-site utilities (e.g., boilers for steam production, nitrogen separation facilities, wastewater treatments, etc.) since they may be already in place or may have been designed independently for other uses at the offshore platform. The performance and costs of the renewable plant and its connection with the oil and gas platform are important in assessing the overall profitability of P2G and P2L strategies but are neutral with respect to the selection of the technological alternatives for electric energy conversion if powered by renewable electricity.

59

Table 5.2 Recommendations for the assessment of technology options of some process stages of P2G and P2L strategies. Process stage

Technology options

Recommendations for the choice

Proposed technology

H2 production

Alkaline electrolysis, PEM electrolysis, solid oxide electrolysis cell

Proton exchange membrane (PEM) electrolysis

Sea H2O desalination

Multi-stage flash distillation, multi-effect distillation, mechanical vapor compression, reverse osmosis, electrodialysis, membrane distillation

H2, SNG, CO2 compression H2 new pipeline

Centrifugal compressor, reciprocating compressor

For P2G and P2L applications, it is recommended that electrolyzers operate with high efficiency to avoid unnecessary energy losses, with highly dynamic behavior (small ramp-up time) to follow the fluctuant power input of renewables and with very low minimal load for flexible operation. Pressurized operation is advantageous to reduce or eliminate the cost of an external compressor and its associated additional equipment. A simple and compact layout is highly desired in offshore context. A reliable and modular system may facilitate marine transportation and reduce installation and maintenance time at the offshore platform. For sea H2O desalination driven by renewable energies, it is suggested that the technology option demonstrate high compatibility with renewable sources and good ability to produce water at a suitable purity as required for input from the electrolysis technology. A technology with high modularity, minimum interruption time during maintenance and low electrical energy needs is more suitable for offshore applications. Smooth operation, high reliability, and suitability for process fluctuations may be better features. The design of new offshore pipeline is strictly related to the pipeline flow capacity, which depends on several factors, such as the desired mass flowrate at the destination and required delivery pressure, as well as the pipe diameter, allowable pressure drops, viscosity, and molecular weight of the gas. The steady-state isothermal flow may be a good approximation for relatively long pipeline operating in stable conditions [128].

Use of the Weymouth equation applicable to compressible fluid in turbulent flow, long pipelines, and pressure drop greater than 40% of the upstream pressure [129]

Low- to medium-strength steels, with maximum operating pressures of 100 bar

Reverse osmosis

Centrifugal compressor

(Continued)

Table 5.2 Recommendations for the assessment of technology options of some process stages of P2G and P2L strategies. Continued Process stage

Technology options

Recommendations for the choice

Proposed technology

SNG production

Catalytic methanation, biological methanation

Catalytic methanation

CO2 capture from raw gas

Amine absorption, membrane permeation

CO2 delivery

Liquid transportation, gas transportation in pipeline

CH3OH production CH3OH delivery

Catalytic hydrogenation, electrochemical reduction Time charter or voyage charter party contract for ship hire

Offshore methanation may take advantages from highermaturity technology, smaller reactor size per equal feed gas (i.e., higher gas hourly space velocity), lower power input and maintenance time. Given the strict constraints of offshore oil and gas platform, modularity, footprint, and equipment weight may be the more important criteria. Relatively higher concentration of CO2 in moderate gas flowrates may require a simpler process scheme. Gaseous CO2 transportation in the range of 15
30 bar may be a cost-effective solution if the required pressure at the offshore facility is lower than 80 bar (i.e., the minimum allowable level for safe transportation of liquid phase CO2) and the design pressure of existing infrastructure is between 90 and 150 bar. For P2G and P2L applications at offshore platforms, the need for a relatively small CO2 flowrate (i.e., below 10 kg s21) is expected. Higher maturity, higher conversion of CO2, higher selectivity of CH3OH are better characteristics. A contract method mostly used for supply vessels in oil and gas operations and based on a daily hire rate is more suitable.

Membrane permeation

Gas transportation

Catalytic hydrogenation Time charter party contract

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Therefore the boundaries for the calculation of the technical, economic, environmental, and societal indicators, as well as of the aggregated indicator for each alternative, may be limited to the conversion process, thus excluding renewable energy conversion and transmission to the offshore platform. Nevertheless, these steps should be included in the analysis only for profitability assessment, in order to investigate effectively the viability of the offshore projects. As a consequence, step 2 is divided in two stages, as illustrated in Fig. 5.2: the first one (step 2.1) is dedicated to the definition of the reference process schemes, and the second one (step 2.2) is the definition of the renewable power plant. In step 2.1, for each reference process scheme, components should be selected for the technology option identified in step 1. An example of quantitative data that should be collected for each component is summarized in Table 5.3. If the electrolyzer’s capacity is set as the reference basis, H2 production in the scheme can be first calculated and then inputs (e.g., sea H2O and CO2) and further outputs (e.g., SNG and CH3OH) of the process schemes can be estimated based on the collected technical features of components. It should be noted that in case of use of an existing gas pipeline in the field, CAPEX associated with the transportation of gas in the strategy may be disregarded. Concerning transportation via ship, the service speed of the supply vessel may be defined in order to estimate the number of days required for the total round trip and total OPEX associated with ship hire based on the distance between the closest harbor and the offshore platform and number of ship voyages (collected in step 0.1). Decommissioning costs may be excluded from the analysis if the site can be energetically reused in the future, taking advantage of the groundwork and construction already carried out. Table 5.3 Input data for the process schemes definition in the sustainability assessment methodology. Technical data Unit capacity, electrical and mechanical efficiency, and pressure ratio per stage in case of machinery, reaction conversion, and stochiometric molar ratio in case of reactor Materials (substances, composition, properties, e.g., enthalpy and entropy), operating conditions (pressure, temperature), nominal flowrates, inventories Specific electrical power required, specific heat duty (in kWh kg21 or kWh Nm23) Economic data CAPEX (in units of currency) OPEX (in units of currency per year) Cost price of input material, for example, CO2 supply from onshore market, if present (in units of currency per mass) Gray market price (excluding financial incentive), green market price (including financial incentive) of the final products (in units of currency per mass) Environmental data GHG emission factor (in kgCO2eq kWh21 or kgCO2eq kg21 or kgCO2eq Nm23)

5.1 Sustainability assessment methodology for P2G and P2L systems

Market prices of the final products of the hybrid energy options strictly depend on the end use defined for the strategy and the economic scenario where the strategy is going to operate. In order to investigate the economic feasibility of renewable P2G and P2L products from a possible investor’s view, two different prices can be considered in the comparative assessment, as illustrated in Table 5.3: gray price and green market prices. Gray prices represent the conventional prices of the products without any consideration of their renewable energy content; they may be estimated based on the statistical data of the local market for the gas grid, industry, and/or mobility sectors. Based on the policy support instruments implemented in the regulations for promotion of renewable energy use, green prices can be defined as the prices of the products generated by 100% renewable energy, which include a specific green premium in addition to gray prices or are directly green tariffs established by local governments. Possible extensions of current incentives for biogas and biofuels, as well as the introduction of carbon emissions allowances and/or taxes at the national level, may be considered for the estimation of these green prices of the strategies. In step 2.2, the renewable power plant intended to be linked to the offshore oil and gas platform hosting the P2G or P2L conversion processes is characterized by means of the preliminary technical and economic data summarized in Table 5.4. The size may be chosen based on information about the capacity of existing offshore plants operating at full scale in distant zones and/or the availability of technoeconomic investigations in the literature. It is worth noting that the levels of access [i.e., the percentage of time that a device can be accessed for operations and maintenance (O&M)] and of availability (i.e., the time that the device is able to produce power) play an important role in the associated OPEX calculation of the renewable plant. Furthermore, total CAPEX usually includes a CAPEX-associated offshore sub-station (i.e., alternating current switchgear, transformers, converter electronics, and filter, used to increase the voltage prior to its use) and electrical connection to shore and onshore sub-station. Therefore cost data related to the offshore sub-station may be disregarded if the substation is assumed to be located at the offshore oil and gas platform linked to the renewable plant, taking advantage of the infrastructure’s sharing of the hybrid energy system. Moreover, the cost associated with the export cable to shore and the onshore sub-station may be included in the analysis if further business scenarios are Table 5.4 Input data for the renewable plant definition in the methodology. Technical data Size, preliminary layout, net energy production Distance to the offshore platform, to the closest port Economic data CAPEX including electrical transmission (in units of currency) OPEX including electrical transmission (in units of currency per year) Market price for renewable electricity into the onshore grid (in units of currency per kWh)

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considered, for example, zero integration of wind energy into the offshore oil and gas operations and the sole selling of renewable electricity to the grid, P2G and P2L conversion of the excess wind power that is otherwise curtailed according to grid agreements. In these latter situations, the market price associated with the selling of renewable electricity to the grid needs to be estimated (as shown in Table 5.4), taking into account the incentive mechanism implemented at the national level for renewable power integration (e.g., feed-in tariff, feed-in-premiums, quota-based tradable green certificates, investment subsidies, or tax cuts). Clearly, eligibility requirements to receive support should be verified case by case based on local regulations. It is worth mentioning that the methodology does not pose any restriction on the approach used to obtain the required input data or on the databases and tools to support their collection. The uncertainty of input data may be verified in the last step of the procedure through sensitivity analysis.

5.1.5 Calculation of sustainability performance indicators In the third step of the methodology, the sustainability assessment of the defined reference process schemes for P2G and P2L strategies is performed by applying the battery limits and using information described in step 2. A set of indicators addressing the technical, economic, environmental, and societal aspects of sustainability is calculated for each reference process scheme. It must be remarked that the proposed set is a result of an optimization aimed to capture specific features of P2G and P2L offshore hybrid energy options. Clearly enough, the set is open to the addition of further indicators in view of an improved assessment.

5.1.5.1 Technical performance assessment In order to address both the quantity and the quality of energy, two metrics are presented to evaluate the technical performance of the P2G and P2L strategies, namely global energy efficiency based on the first law of thermodynamics and the global exergy efficiency based on the second law of thermodynamics. For steady-state processes in systems, the energy and exergy efficiencies are defined as the ratio of energy or exergy in product outputs with respect to energy or exergy in inputs [5]. The energy efficiency or first-law efficiency of each reference process scheme can be derived from the application of the energy analysis described in Chapter 4, System Modeling and Analysis and defined as: LHVUm_ η 5 PN   _ _ k51 W k 1 Qk

(5.1)

where η is the global energy efficiency indicator; m_ is the mass flowrate of the useful product (i.e., H2, SNG, CH3OH) in the outlet from the reference process scheme; LHV is the lower heating value on a mass basis of the useful product; W_ and Q_ are the electrical power required and heat duty, respectively, of the k-th

5.1 Sustainability assessment methodology for P2G and P2L systems

Table 5.5 Heating values and standard chemical exergies of some P2G and P2L final products. LHV, Lower heating value; HHV, higher heating value. Chemical energy carrier

LHV (kJkg21) [196]

HHV (kJkg21) [196]

0 exch (kJkmol21) [197]

H2 SNG CH3OH

120,000 50,050 19,920

141,800 55,530 22,660

236,090 831,200 720,000

component of the reference process scheme; and N is the total number of process stages in the reference process scheme. It is worth noting that the higher heating value (HHV) may be used in Eq. (5.1) as alternative to LHV. W_ and Q_ can be calculated by combining the specific electrical power required and heat duty of the component with information about the unit capacity. Literature data reported in Chapter 4, System Modeling and Analysis (energy analysis description) for the specific electrical power of some components of P2G and P2L systems may be used for W_ estimation. Values of HHV and LHV of the final products of the P2G and P2L hybrid energy options analyzed in Chapter 3, Innovative Hybrid Energy Options, are summarized in Table 5.5. Similarly, the exergy efficiency or second-law efficiency of each reference process scheme can be derived from the application of the exergy analysis described in Chapter 4, System Modeling and Analysis, and defined as: ψ5

exout Um_ out   _ Q_ W _ _ k51 Exk 1 Exk

PN

(5.2)

where ψ is the global exergy efficiency indicator; ex is the specific total exergy _ W_ and Ex _ Q_ are the on mass basis of the useful product defined in Eq. (4.7); Ex exergy rate due to electrical work and heat duty, respectively, of the k-th component of the reference process scheme defined in Eqs. (4.5) and (4.6); and N is the total number of process stages in the reference process scheme. For the calculation of ex, the values of ex0ch;c of the final products of the P2G and P2L hybrid energy options analyzed in Chapter 3, Innovative Hybrid Energy Options, are summarized in Table 5.5. Given the definitions of η and ψ, the higher their values, the higher technical performance of the hybrid energy system will be.

5.1.5.2 Economic performance assessment Two metrics are proposed to assess the economic performance of the different alternatives and to identify the most suitable solution from a possible investor’s perspective, namely the levelized cost of product (LCOP) and the levelized value

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of product (LVOP), thus accounting separately for the effect of costs and revenues associated with each strategy. Both these parameters are expressed in units of currency per MWh of energy content in the final product of the option. LCOP represents a measure of the present costs (discounted to present value) divided by the production of the final product in terms of energy content throughout the economic lifetime of the project: PN LCOP 5

k51

 P P  k;t CAPEXk 1 Tt51 Nk51 OPEX ð11r Þt PT  Bt  t51

(5.3)

ð11r Þt

where LCOP is the proposed levelized cost of product indicator; CAPEX in units of currency is the capital expenditure associated with the k-th component of the reference scheme; OPEX, in units of currency per year, is the operating expenditure associated with the k-th component of the reference scheme at the t-th year of the lifecycle; N is the total number of process stages considered in the reference scheme; T is the total number of years in the economic lifetime of the strategy; B is the annual production of the final product at the t-th year of the economic lifetime; and r is the discount rate referred to the t-th year used to discount OPEX and B values. B in Eq. (5.3) can be estimated by multiplying the annual mass flowrate of the final product of each reference scheme for the corresponding heating value. Cost estimations reported in Chapter 4, System Modeling and Analysis (economic analysis description) for some components of P2G and P2L systems may be used for CAPEX and OPEX estimation. In accordance with the levelized cost concept, LVOP is proposed as an economic parameter quantifying the annualized total revenue (discounted to present value) derived from selling the final product to a given market with regard to the production in terms of energy content (discounted to present value) during the economic lifetime of the project: PT  Rsell;t  t51

ð11r Þt

t51

Bt ð11r Þt

LVOP 5 P  T



(5.4)

where LVOP is the proposed levelized value of product indicator, Rsell is the revenue gained at the t-th year from the product selling to the corresponding market in units of currency per year, T is the total number of years in the project lifespan of the strategy, B is the annual production of the final product at the t-th year of the economic lifetime defined in Eq. (5.3), and r is the discount rate referred to the t-th year used to discount Rsell and B values. Rsell in Eq. (5.4) can be calculated by multiplying the produced flowrate of the final product of each pathway for the corresponding market price. It is worth noting that two different values of LVOPs can be calculated using in Eq. (5.4) the grey and green market prices of the products, respectively. The first LVOP value does not consider any financial incentive for selling renewable chemicals, while the second accounts for support schemes for promoting renewable-based P2G and P2L products in the market.

5.1 Sustainability assessment methodology for P2G and P2L systems

Based on the definitions of the proposed indicators, the lower the value of LCOP and the higher the value of LVOP, the higher is the economic performance of the hybrid energy option.

5.1.5.3 Exergoeconomic performance assessment The exergoeconomic assessment of systems can be performed by using the several exergoeconomic indicators defined for each component and sub-system [198]. Concerning the SPECO method described in Chapter 4, System Modeling and Analysis (exergoeconomic analysis description), the cost rate associated with _ d can be obtained from the combination of the exergy and exergoeconomic balEx ances on each component, expressed as follows: _ d C_ d 5 cF Ex

(5.5)

where C_ d is the cost rate of exergy destruction; cF is the average cost per unit exergy of fuel calculated as the ratio of the cost rate associated with fuel C_ F and _ F , which is derived by using the exergy rates of states included its exergy rate Ex _ d is the exergy destruction rate defined in in the fuel of each component; and Ex Eq. (4.4). Furthermore, another parameter is evaluated for each component, that is, the exergoeconomic factor (f), expressed as the share of nonexergy-related costs to the overall costs of the component:     f 5 Z_ = Z_ 1 C_ d

(5.6)

where f is the exergoeconomic factor, Z_ is the CI and O&M cost rate, and C_ d is the cost rate given in Eq. (5.5). It should be noted that the calculation of this parameter can be performed only for components whose Z_ value is available. Knowing the relative importance of Z_ and C_ d cost sources can be important in evaluating how to enhance the improvement of the component performance. The summation of C_ d and Z_ of each component gives the total cost rate C_ total of the components, which can be selected as a significant exergoeconomic indicator in the comparative assessment of alternative options. It is worth noting that the higher the C_ total , the lower the exergoeconomic performance of the component will be. Thus by calculating and comparing C_ total values, the components with the highest cost impact can be identified. Similarly, the calculation of total C_ d and total Z_ of all the components of the scheme and then the application of Eq. (5.6) lead to the overall f, which can be used as another significant indicator in the exergoeconomic assessment of the options.

5.1.5.4 Environmental performance assessment An indicator called levelized GHG emissions (LGHG) is proposed as a measure of the environmental impact of the proposed strategies, in accordance with the LCOP and LVOP indicators described above. LGHG can be defined as the

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emissions from relevant components of the scheme divided by the production of the final product in terms of energy content: PN LGHG 5

k51 eGHG;k LHVUm_

(5.7)

where LGHG in kgCO2eqMWh21 is the proposed environmental performance indicator for the P2G and P2L strategy, eGHG is the mass flowrate of GHG emissions from the k-th component of the scheme, N is the total number of process stages considered in the scheme, m_ is the mass flowrate of the useful product in outlet from the reference process scheme, and LHV is the lower heating value on the mass basis of the useful product. The value of eGHG can be calculated by combining the specific GHG emissions of the component with information about the unit capacity. Literature data reported in Chapter 4, System Modeling and Analysis (environmental impact analysis description) for the specific GHG emissions of some components of P2G and P2L systems may be used for eGHG estimation. It is worth noting that the HHV may be used in Eq. (5.7) as an alternative to LHV. Given the definition of the LGHG indicator, the lower results its value, the higher is the environmental performance of the hybrid energy option.

5.1.5.5 Societal performance assessment The societal dimension of sustainability is preliminarily disregarded in the description of this methodology. Throughout the present chapter (Section 5.3), some safety-related indicators that may be used to address this specific aspect of sustainability are defined.

5.1.5.6 Aggregated performance assessment The calculation of the above described indicators allows us to assess the expected performance of each reference process scheme with respect to specific issues. For the purpose of an easy and clear communication of the overall sustainability profile of the alternative strategies, a procedure is required to compare and combine the indicators addressing different aspects into a single-value indicator. The construction of a composite indicator in general requires the application of the normalization, weighting, and aggregation steps, even though the approach to adopt in each step is based on the type of multi-criteria assessment applied. In MCDA method, normalization or scaling refers to any procedure where diverse-unit cardinal scores are converted into a dimensionless numerical value with a common direction [12]. Normalization appears a necessary precursor to weighting and aggregation procedures when applying compensatory MCDA methodologies, even though it is not essential in noncompensatory MCDA techniques, which usually rank the alternatives based on their relative performance. Compensability refers to the possibility of offsetting a bad performance of an alternative on one indicator by a sufficiently good performance of the same alternative on another indicator. Moreover, in compensatory approaches, weights are substitution rates represent the capacity for a tradeoff between the indicators.

5.1 Sustainability assessment methodology for P2G and P2L systems

Thus, on the one hand variation in one of them leads accordingly to a change in the others. On the other hand, weights used in noncompensatory methods are considered importance coefficients indicating the voting power of the indicators [199]. With regard to decision context involving sustainability, a “weak” sustainability perspective enables the substitution of different forms of capital (e.g., financial, ecological, human), while “strong” sustainability perspective believes that some types of natural capital are highly important and cannot be substituted by human-made capital [9]. According to these concepts, noncompensatory MCDA methods allow the adoption of the strong sustainability perspective by eliminating or limiting the need for compensation between aspects of sustainability, whereas compensatory approaches only make sense from the weak sustainability concept [1]. In the following discussion, a partial compensatory aggregation approach is described for use in the present methodology by revising a consolidated procedure from the existing literature [8,200]. As shown in Fig. 5.2, the approach is composed of three main steps: the technical, economic, environmental, and societal indicators calculated for each reference process scheme are first (1) normalized and then combined into an overall aggregated indicator by means of appropriate weighting (2) and aggregation (3) techniques. Concerning normalization, a nondimensional indicator can be determined between zero (undesired) and 1 (desired) by comparing the actual indicator (Iact) with respect to a given target value (Itarget). When the aim is to minimize the value of the indicator to increase performance (e.g., minimizing the environmental emissions, minimizing levelized costs, minimizing the hazard level for society) the nondimensional indicator (X) is calculated for each option as follows: X5

Itarget Iact

(5.8)

If Iact is lower than Itarget, X in Eq. (5.8) is set equal to 1. On the other hand, when the goal is to maximize the indicator to increase performance (e.g., maximizing efficiencies, maximizing levelized revenues), the following equation is applied to calculate X: X5

Imax 2 Itarget Imax 2 Iact

(5.9)

where Imax is the maximum theoretical limit (e.g., 1 in the case of efficiency indicators). If Iact is greater than Itarget, X in Eq. (5.9) is set equal to 1. The selection of Itarget is an important yet critical phase that may strongly influence the results of the assessment. In this methodology, external normalization is proposed, consisting in the use of reference values representing performance of the best available process for the considered alternative. When the most suitable technology options are selected for the process stages of the strategy, target performance may be referred to these technologies and considered as the expected performance in the near future due to improvement in the process.

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It must be remarked that complete flexibility is given to data sources from the literature used to collect the target values, even though the proper selection of data is fundamental to obtaining significant results. The normalized indicator associated with η and ψ, that is, Xη and Xψ, can be calculated by means of Eq. (5.9). The final technical normalized indicator (Xtech) should be obtained as a suitable aggregation of Xη and Xψ. Concerning the economic aspects, since LCOP and LVOP have opposite directions, the normalized indicator associated with LCOP, that is, XLCOP, is calculated by applying Eq. (5.8), while the normalized indicator associated with LVOP is derived through Eq. (5.9). It is reasonable to expect a decrease in LCOP due to cost reductions related to technology improvement and/or supply chain improvement, while an increase in LVOP may be due to higher incentives promoted by regulations for the production of renewable chemicals. The final economic normalized indicator (Xecon) should be calculated by means of the proper aggregation of XLCOP and XLVOP. From the environmental perspective, as for LCOP, the normalized indicator associated with LGHG, that is, XLGHG, can be calculated by applying Eq. (5.8). Since one sole indicator is proposed within the environmental dimension in the present methodology, XLGHG corresponds to the final environmental normalized indicator (Xenv). Weighting is a relatively controversial issue of the entire aggregation approach, which can imply intrinsic subjectivity and application of social, environmental, or economic policy objectives, and may strongly influence the outcomes [12]. Tradeoff weights are needed to obtain a composite indicator by combining normalized indicators. A common method to extract tradeoffs between indicators is the analytic hierarchy process (AHP) method [201], which makes use of pair-wise comparisons to evaluate performance of the alternatives on indicators (scoring) and indicators among themselves (weighting). To limit the intrinsic subjectivity of this process, the application of a literature procedure for deriving importance coefficients between indicators based on appropriate criteria and the perspectives of decision makers is adopted in this methodology for the elicitation of tradeoff weights, as described next. The evaluation of the relative importance of indicators is performed based on three main criteria: time, space, and receptor. The time and space aspects of an indicator are related to the intergenerational and intra-generational equity, respectively, of sustainability, while the receptor criterion is associated with the level of impact of an indicator on human and ecosystem targets. The assessment is performed by using a Likert rating scale on five levels (i.e., 1: very unimportant, 2: unimportant, 3: neutral, 4: important, 5: very important) as a function of the perspective of the decision makers. Three different archetypes are defined to categorize abstractly the possible decision makers: the individualist, egalitarian, and hierarchist schemes. Equal weighting can be further added to these schemes. The individualist perspective is self-centered and unconcerned about inter- and intra-generational equity. Moreover, its viewpoint can be considered resilient,

5.1 Sustainability assessment methodology for P2G and P2L systems

focusing on human rather than ecosystem targets. Thus, according to the individualist scheme, indicators are evaluated on a short-term horizon and local perception and are based on concerns with respect to the human receptor. The egalitarian archetype is interested in inter- and intra-generational equity and shows a precautionary thinking characterized by a long-term and global viewpoint. In addition, its perspective is susceptible, which can be translated into concerns addressing the ecosystem rather than human receptors. The hierarchist scheme has a more tolerant approach to decision making, based on negotiation and compromise. It exhibits a medium-term horizon and a regional perspective, as well as giving importance to both the receptors impartially. To have an example of the procedure, Table 5.6 may be considered. For each perspective, a score in the range between 1 to 5 is attributed to each of three indicators, Y1, Y2, and Y3, with respect to given criteria. Y1, Y2, and Y3 may be sub-indicators in a category (e.g., energy efficiency and exergy efficiency) or category indicators aggregated in the overall indicator (e.g., technical, economic, and environmental aspects). The overall score associated with each indicator is the sum of the scores given for three criteria. The relative importance of Y1, Y2, and Y3 is determined as the ratio of the associated overall score to the sum of overall scores, that is, 0.22, 0.38, and 0.41, respectively. After that, the overall scores identified in Table 5.6 are used to derive tradeoffs between indicators by means of pair-wise comparisons. The score associated with the pair-wise comparison Y1-Y2 is the overall score associated with Y1 (i.e., 8 in Table 5.6) minus the overall score associated with Y2 (i.e., 14 in Table 5.6), i.e., 26. Similarly, the score associated with the pair-wise comparison Y2-Y3 is 21 and Y1-Y3 is 27. Thus, the tradeoffs associated with these scores can be derived by using Table 5.7, which shows results for the pair-wise comparison between two general indicators A and B. Once the tradeoff for each pair-wise comparison is determined, a pair-wise comparison matrix can be drawn for a given perspective to summarize the estimated tradeoffs. Table 5.8 illustrates the comparison matrix associated with the data in Table 5.6 by using Table 5.7. Also, the sum of the entries in the column are reported in this table for each indicator. It should be noted that for a group of three indicators only three comparisons are needed to complete the matrix, since Table 5.6 Example of scoring based on time, space, receptor criteria for a given perspective. Criteria

Indicator Y1

Indicator Y2

Indicator Y3

Time Space Receptor Overall score (sum) Relative importance weight

3 3 2 8 0.22

5 4 5 14 0.38

5 5 5 15 0.41

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Table 5.7 Tradeoff of indicator A with respect to indicator B associated with pair-wise comparison A-B. Pair-wise comparison A-B

Tradeoff

Pair-wise comparison A-B

Tradeoff

212 211 210 29 28 27 26 25 24 23 22 21 0

0.077 0.083 0.091 0.100 0.111 0.125 0.143 0.167 0.200 0.250 0.330 0.500 1.000

11 12 13 14 15 16 17 18 19 110 111 112

2.000 3.000 4.000 5.000 6.000 7.000 8.000 9.000 10.000 11.000 12.000 13.000

Table 5.8 Example of pair-wise comparison matrix by using scores in Table 5.6 and data in Table 5.7. Criteria

Indicator Y1

Indicator Y2

Indicator Y3

Indicator Y1 Indicator Y2 Indicator Y3 Sum

1.000 6.993 8.000 15.993

0.143 1.000 2.000 3.143

0.125 0.500 1.000 1.625

values on the diagonal cells are always 1, and values in the remaining cells are equal to the reciprocal of their counterpart. Finally, an evaluation matrix can be created (Table 5.9): Values in the columns associated with X, Y, and Z are first obtained by dividing each entry in Table 5.8 by the sum of the entries in the column; then the tradeoff weights between indicators are estimated through the common maximal eigenvector method (i.e., averaging values across the rows). As shown in Table 5.9, the tradeoff weights associated with X, Y, and Z are 0.062, 0.354, and 0.584. To the check consistency of the evaluations, a consistency index is estimated by the following equation [201]: CI 5 ðλmax 2 mI Þ=ðmI 2 1Þ

(5.10)

where CI is the consistency index, λmax is the principal eigenvalue, and mI is the number of indicators in the evaluation (i.e., the size of the evaluation matrix). λmax can be estimated by multiplying each row of the pair-wise comparison

5.1 Sustainability assessment methodology for P2G and P2L systems

Table 5.9 Example of evaluation matrix associated with the pair-wise comparison matrix in Table 5.8 and tradeoff weights. Criteria

Indicator X

Indicator Y

Indicator Z

Tradeoff weight

Indicator X Indicator Y Indicator Z

0.063 0.437 0.500

0.045 0.318 0.636

0.077 0.308 0.615

0.062 0.354 0.584

Table 5.10 Values of random index to check consistency of pair-wise comparisons [201]. Size of comparison m

2

3

4

5

6

7

8

9

10

Random index

0

0.58

0.90

1.12

1.24

1.32

1.41

1.45

1.51

matrix in Table 5.8 for the tradeoff vector, thus obtaining a vector of three elements (0.185, 1.077, 1.786). Then, the latter vector is divided by the corresponding element in the tradeoff vector, assuming the mean value, that is equal to 3.035, in the example described. A CI value of 0.0175 is then derived by applying Eq. (5.10). A perfectly consistent decision maker should always obtain a CI equal to 0. However, small inconsistencies may be tolerated. Therefore, a random index (RI) is estimated by using Table 5.10 (if the number of indicators is less than 11), then a consistency ratio is calculated as follows [201]: CR 5 CI=RI

(5.11)

where CR is the consistency ratio, CI is the consistency index, and RI is the random index. A value of CR lower than 0.1 is suggested for tolerable inconsistencies and reliable results from the process, otherwise the judgments are untrustworthy [201]. For the example, the illustrative RI of 0.58 is applied from Table 5.10, and the CR is estimated as 0.030, thus verifying the consistency of the evaluations. Once the weights between sub-indicators within each category and weights between category indicators are identified, a two-stage aggregation process is performed to obtain a single-value composite indicator. Among the different aggregation rules, the weighted arithmetic mean (WAM) or the weighted geometric mean (WGM) can be used for each category indicator and then for the overall indicator. Next, normalized sub-indicators (e.g., Xη, Xψ, XLCOP, XLVOP, XLGHG) are aggregated into a category indicator associated with each aspect of sustainability (e.g., Xtech, Xecon, Xenv) by means of Eq. (5.12) in case of WAM and of Eq. (5.13) in case of WGM: Xc 5

XF f 51

wf ;c UXf ;c

w F  Xc 5 Lf 51 Xf ;c f ;c

XF f 51

XF f 51

wf ;c 5 1

wf ;c 5 1

(5.12) (5.13)

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where the pedix “c” refers to the aspect of sustainability, pedix “f” refers to the sub-indicator in the category c, F is the total number of sub-indicators in category c, and w is the weight associated with sub-indicator f in category c. The sum of the intra-category weights is equal to 1 irrespective of the aggregation procedure. Overall indicator aggregating the category indicators of sustainability is called in this methodology the aggregated sustainability index (ASI), which is determined by using WAM in Eq. (5.14) or WGM in Eq. (5.15): ASI 5

XC c51 C

wc UXc

ASI 5 Lc51 ðXc Þwc

XC c51

XC c51

wc 5 1

wc 5 1

(5.14) (5.15)

where pedix “c” refers to the aspect of sustainability, C is the total number of aspects (categories) considered in the sustainability assessment, and w is the weight associated with category c. The sum of the category weights is equal to 1 irrespective of the aggregation procedure. It should be noted that WAM is characterized by full substitutability and compensability, thus implying that the overall aggregated indicator may be indifferent to extreme values of indicators. On the other hand, WGM can assume only partial compensability and substitutability [202], reflecting appropriately bad performance in any category or indicator in the overall aggregated indicator. As a consequence, the larger difference between indicators is penalized through a lower aggregated indicator [203]. Moreover, despite the larger adoption of WAM to obtain comprehensive metrics, it is more susceptible to double-counting issues when indicators are not independent [12]. Thus, in this latter case, WGM is the preferred method. It must be remarked that the MCDA method just described should be applied when the process alternatives producing the same final product are compared in order to use consistent process-related target values among the options. In the case of a comparison of process schemes producing different products, a noncompensatory method that is able to deal with heterogeneous scales of indicators and to maintain their original concrete verbal meaning seems to be more suitable. In this case, the calculated technical, economic, environmental, and societal indicators are aggregated into a single-value metrics without the need for the normalization stage. Some examples of noncompensatory MCDA methods are preference-based or outranking methods, for example, ELECTRE (ELimination and Et Choice Translating REality) [204] and PROMETHEE (Preference Ranking Organization METHod for Enrichment Evaluation) [205], which are widely used to assess sustainability-related problems. The basic principle of these methods is the construction of outranking relations—for example, a binary relation (S) defined on a set of alternatives such that, for each pair a and b, alternative a is at least as good as (i.e., it outranks) alternative b (aSb)—and then the exploitation of the relations to allow the ranking of alternatives. Both ELECTRE and PROMETHEE perform a pair-wise comparison of alternatives in order to rank them with respect to a number of criteria (i.e., indicators). To analyze the outranking relations, they

5.1 Sustainability assessment methodology for P2G and P2L systems

make use of threshold values accounting for indifference and preference when two alternatives are compared (three types are required with ELECTRE and two types with PROMETHEE), as well as weights between indicators in terms of importance coefficients. Among the different methods in the ELECTRE and PROMETHEE families, ELECTRE II, III, IV and PROMETHEE II allow the users to obtain a final ranking of alternatives, based on a dimensionless indicator with PROMETHEE II (i.e., the net outranking flow) [206,207]. Different approaches may be applied to determine the proper threshold values and criteria weights [208,209]. The approach based on time-space-receptor criteria and individualist-egalitarian-hierarchist archetypes proposed in this methodology can be useful for weights elicitation. For example, relative importance weights illustrated in Table 5.6 may be adopted for the ranking of alternatives based on indicators Y1, Y2, and Y3 with the ELECTRE and PROMETHEE methods.

5.1.6 Calculation of profitability performance indicators In step 4 of the procedure illustrated in Fig. 5.2, a profitability analysis among the alternative strategies is performed in order to check the effective viability of the alternative projects. This can provide another important measure of the performance of the strategies, in addition to the technical, economic, environmental, and societal performance assessments carried out in the previous step. To be a worthwhile investment, a venture for a new energy conversion process must be profitable. It is not enough that the venture makes a large net profit: the profit over its lifetime must be greater than the original capital investment for the venture. Among the possible profitability measures [163], the widely used net present value (NPV) is proposed in the methodology separately from LCOP and LVOP, in order to avoid any possible double-counting problems and aggregation issues into an overall sustainability indicator requiring the definition of common battery limits for the calculation of the indicators: NPV 5

XT t51

Rsell;t 2 ð11rÞt

PN k51

OPEXk;t 1 OPEXrenew;t ð11r Þt

! 2

XN k51

CAPEXk 2 CAPEXrenew

(5.16)

where Rsell is the revenue at t-th year defined in Eq. (5.4), CAPEX and OPEX are the costs parameters associated with the k-th unit and to the renewable power plant, N is the total number of the process stages considered in the reference scheme, T is the total number of years in the economic lifetime of the strategy, r is the discount rate referred to the t-th year used to discount Rsell and OPEX values. A positive value of NPV implies that the revenues are greater than the total costs over the analyzed period; thus the investor can make a profit. Otherwise, the project is still not viable. An NPV equal to 0 means no loss or gain. As discussed in the description of step 2, NPV may be calculated for different business scenarios besides the situation considered in the formulation of

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Eq. (5.16), in order to investigate the attractiveness of the projects by adding or changing the revenues gained from the onshore market. Examples can be the baseline situation of zero integration of renewable power into the chemical processes; thus only the electrical grid connection between the offshore wind farm and onshore sub-station, as well as revenues from the selling electricity to suppliers, may be conceptualized. Another situation may be the inclusion of an electrical export cable to shore in addition to the connection to the platform; thus a part of the offshore renewable power may be directly routed to the land, and a double revenue may be obtained from selling both electricity and the final product of the pathway. In the case of these latter business scenarios, the market price associated with selling renewable electricity to the grid needs to be included, as well as costs for electrical cables and onshore sub-station.

5.1.7 Ranking of alternatives and sensitivity analysis The calculation of the sustainability performance indicators in step 3 of the procedure in Fig. 5.2 allows us to compare and rank the reference process schemes of the strategies from various viewpoints and the overall fingerprinting of sustainability. However, the construction of aggregated indicators may result in issues of uncertainty associated with erroneous input data, normalization, weighting, and aggregation methods. The use of sensitivity analysis can assist in identifying gaps and verifying the robustness of the ranking of the process schemes based on the aggregated indicator. In particular, if the compensatory aggregation approach proposed in the methodology is adopted, the normalized approach based on target values defined in the normalization of the indicators is unavoidably affected by a level of uncertainty, since target values are derived from estimates about future projections. On the other hand, it is recognized that a number of parameters, for example preference functions and threshold values, should be determined for the use of outranking methods, thus resulting in a potential source of uncertainty which needs to be taken into account. Concerning weight elicitation, the adoption of an approach based on time-spacereceptor criteria and individualist-egalitarian-hierarchist perspectives may be a way to reduce the uncertainty associated with this stage. However, sensitivity analysis needs to be applied in order to check the influence of weights variation on the aggregated indicators. The calculation of the profitability indicator provides a ranking of the strategies under different business scenarios, as well as an identification of viable projects. This can be considered as a further important measure, in addition to the integrated performance assessment performed in the previous step. In this case, NPV values may be affected by uncertainty related to the estimation of input data, which are the economic parameters associated with renewable power plants and reference process schemes. Therefore in the final step of the procedure, sensitivity analysis may be applied to assess the robustness of the relative ranking of

5.2 Sustainability assessment methodology for G2P systems

the alternatives based on NPV by identifying the most critical (uncertain) input parameters and varying them in the given ranges. Some sensitivity analysis techniques that may be used for the sake of these verifications are described in Section 5.5.

5.2 Sustainability assessment methodology for G2P systems 5.2.1 Generalities A sustainability assessment methodology is presented to compare the sustainability performance of offshore renewable plants coupled with a gas turbine (GT) energy balancing system installed at existing offshore oil and gas installations for G2P applications. The set of sustainability indicators defined in the assessment model described in Section 5.1 is properly adapted to capture the peculiarities of concern. Renewable-based target values are introduced for normalization of the proposed indicators in view of single-value metrics, which eases the ranking of the alternative systems. The methodology is intended to be a support tool to evaluate the feasibility of offshore G2P projects at offshore oil and gas production facilities in nonassociated gas reserves classified as stranded or depleted fields, which are located relatively close to the onshore electrical grid and for which projects on renewable power exploitation have been initiated or are under investigation for the energetic valorization of the site. The approach has a general applicability to every type of offshore renewable energy source for which technology development and incentive schemes have promoted integration into the onshore network, even though offshore wind and wave energy sources are specifically considered for the description of the methodology. Finally, the method can be adopted to both assess an alternative G2P offshore hybrid energy system at a specific offshore site and compare different offshore sites for the same type of hybrid solution. The flowchart of the methodology is illustrated in Fig. 5.3, that includes a reference to the steps of the general MCDA approach displayed in Fig. 5.1. The novel aspects of the methodology are summarized as follows: 1. In the formulation of the alternatives, a step is introduced to identify the suitable field offering the opportunity for G2P conversion, some steps are included to define the dispatching power plan to declare to the grid operator, and some steps are introduced to design the gas turbine park and manage it under a given control strategy. 2. In the evaluation of performance indicators, the set of technical, economic, and environmental indicators defined in the P2G/P2L sustainability methodology (Section 5.1) is revised to account for the peculiarities of G2P systems (hourly basis, electricity oriented, unbalance power terms).

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3. In the normalization of indicators, reference values for normalization are proposed as measures of the expected performance of the renewable energy plant in the future.

5.2.2 Definition of offshore oil and gas site and renewable energy As shown in Fig. 5.3, a preliminary step (step 0), divided into two stages, is necessary. The first stage (step 0.1) concerns the definition of the offshore oil and gas sites providing input data about the field and infrastructures, as summarized in Table 5.11. In the second stage (step 0.2), the offshore renewable energy to be exploited is evaluated by taking into account existing projects and/or feasibility studies on renewable potential assessment and power integration into the grid in the selected areas. It should be noted that in the case of more than one offshore

FIGURE 5.3 Flowchart of the sustainability assessment methodology for G2P offshore hybrid energy systems.

5.2 Sustainability assessment methodology for G2P systems

Table 5.11 Details of the offshore site required in input to the sustainability assessment methodology. Data about the field Entity gas reserves (type, production, properties, e.g., heating value) Water depth, bathymetry Distance from site to the closest onshore grid, to onshore shipyard, to port Data about the infrastructures Characteristics of the offshore structures (type, remaining lifetime) Preliminary dimensions, elevation of decks, estimated free space of the offshore structure Features of electrical subsea cables to the grid and/or to other platforms, if present (voltage, length, diameter)

site to analyze, the same type of renewable source should be considered for a consistent comparison.

5.2.3 Collection of renewable energy data In the first step of the procedure illustrated in Fig. 5.3, specific data about the renewable energy potential need to be collected for each offshore site. This consists of characteristic meteo-marine parameters associated with each type of renewable source and needed for the estimation of the available theoretical renewable power at a given site. In the case of offshore wind energy, the wind force is converted into a turning force by acting on the rotor blades of a wind turbine; theoretically, the power of an air mass that the wind can transfer to the rotor is [38]: Pwind;avail ðvwind Þ 5

1 Uρ UArot Uv3wind ðzÞ 2 air

(5.17)

where Pwind,avail is the available kinetic wind power, ρair is the density of air (assumed equal to 1.225 kg m23 at normal atmospheric pressure and 15 C), Arot is the swept area of the wind turbine, and vwind is the mean wind speed at turbine hub height z. However, the power that can be extracted by the rotor is not equal to the kinetic power of the flow; according to the Betz’s limit, the mechanical energy that is theoretically extractable from the wind is the maximum power coefficient Cp,Betz equal to 0.593 [43]. Therefore the average hourly wind speed is the environmental parameter required for the application of the proposed methodology when offshore wind energy is selected as a renewable source. Independently of whether the location corresponds to deep or shallow water, the wave energy flux corresponding to the power content per unit of surface of the crest length is [210]: 2 Pwave;avail 5 0:577UHm0 UT02

(5.18)

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where Pwave,avail is the available wave power in kW m21; Hm0 is the spectral significant wave height, which can be approximated to the significant wave height Hs (i.e., the average height of the highest third of waves during a certain period, typically 30 minutes); and T02 denotes the mean zero-upcrossing period, which can be approximated to Te0.49/0.577 or to Tp/1.5, where Te is the energy period and Tp is the peak wave period (i.e., the wave period with the highest energy). Therefore the average hourly values of Hs, Te, or Tp are considered input data to the present methodology in the case of selection of a wave energy source. For the purpose of G2P hybrid energy options integrated into the electrical network, two different types of parameters need to be retrieved independently from the considered renewable source in a given period: real data and forecast data. The choice of the period may be based on the time interval during which this information is available. It is worth noting that in case of more than one offshore site to analyze, the same period should be considered for a consistent comparison. Real data are in situ measurements derived from specific devices located at the offshore site or near it. An anemometer is the device for measuring wind speed (and often wind direction and air temperature), which can be classified into cup anemometer, hot wire anemometer, laser Doppler anemometer, and sonic anemometer. If wind measurements are not available at the height at which the turbine is to be installed, wind speed values can be adjusted to the turbine hub height z by means the following wind shear logarithmic law [211]:   ln

z z0

vwind ðzÞ 5 vwind;r U   ln zz0r

(5.19)

where vwind(z) is the wind speed at hub height z, vwind,r is the wind speed at generic height zr (e.g., measured wind from anemometer), z is the turbine hub height, zr is the generic height at which vwind,r is obtained (e.g., anemometer height), and z0 is the roughness length in the current wind direction (commonly 0.0002 m for water areas in open sea [212]). The available wind speed profile at the site can be assessed by taking into account the minimum threshold value of 6 m s21 at hub height for the exploitation of offshore wind at a given site [213]. Once collected, wind data are usually modeled by means of a distribution describing the frequency of various wind speeds over the selected period. Different statistical distributions can be used to represent the nature of the wind [214]; among them the most widely used is the Weibull probability density function: pðvwind ; kw ; cw Þ 5

   kw kw vwind kw 21 2 vwind e cw cw cw

(5.20)

where vwind is the random wind speed, kw is the shape factor, and cw is the scale factor. Calculation of these latter parameters can be performed according to methods in the literature [215]. In situ recording of wave data can be performed by means of different devices: wavestaff, pressure recorder, accelerometer buoy or waverider buoy, or shipborne

5.2 Sustainability assessment methodology for G2P systems

wave recorder. Based on the type of instrument and recording technique used, there are different ways to obtain statistics on wave climate. Among them, the average values of wave parameters previously described (and also mean wave direction) are commonly provided for short-term statistics. Bivariate distributions of occurrences corresponding to different combinations of Hs and Te (or Tp ) can be derived, that is, scatter diagrams. Besides a scatter diagram, a more condensed way to describe wave conditions is to group some bins of scatter diagrams into a limited number of zones, referred to as sea states, according to the recommendations given in [216]. The monthly and annual available power per unit front can be also calculated by means of Eq. (5.18); a value of 10 kW m21 was suggested as the possible minimum threshold for wave exploitation at a given site [94]. It should be noted that records from measuring devices may be provided at fixed intervals, which can vary from a few minutes to 10
30 minutes, and in some cases, they may be missing over the desired period. Therefore a proper approach should be applied to convert the available values of parameters into hourly averages for the application of this methodology, for example, arithmetic mean. If an entire month’s data are lacking, the missing hourly values may be created using linear interpolation from the same month in previous or subsequent years. It is worth noting that the use of time series that are as complete as possible is recommended in order to achieve consistent results, particularly in the comparison of different offshore sites. On the other hand, forecast data can be obtained by means of a proper forecasting approach that depends first on the renewable source considered and forecast horizon. Wind speed forecasting techniques can be divided into four main groups according to the time horizon required [217]: ultralow short-term forecasting (from a few minutes to 1 h ahead), for example, the persistence method; short-term forecasting (from 1 to 6 h ahead), for example, the statistical methods including auto regressive (AR), auto regressive moving average (ARMA), and auto regressive integrated moving average (ARIMA); medium-term forecasting (from several hours to 1 day ahead); and long-term forecasting (from 1 day to 1 week or more ahead), for example, the physical approach based on numerical weather prediction (NWP) using weather forecast data. Around a 6 h forecast horizon can be identified as the limit between the use of statistical and physical methods [218]. Concerning wave forecasting, two major groups can be distinguished: physicsbased and time series models [219]. Physics models, such as the WAVEWATCH III, the Wave Model (WAM) of the European Commission for Medium-range Weather Forecasts (ECMWF) and Simulating WAves Near shore (SWAN), use the energy balance equation, which solves the wave action balance as a function of source and sink terms. They can include wind-induced forces, nonlinear wave
wave interactions, and dissipation by white capping in deep water, while they can consider shoaling and bottom friction in shallow water. On the other hand, time series methods include regressions and neural networks, even though newer techniques such as genetic programming algorithms and artificial intelligence, are used. From

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a comparison between physics and time series models in the literature [220], it emerged that statistical methods are more accurate than physic models over a horizon of 1
4 h, while for longer forecasts physic-based methods tend to have better forecasting features, provided that the convergence point between the two groups of techniques at which comparable accurate results may be achieved is around the 6 h forecast horizon. This latter finding is thus similar to those for wind forecasting. In order to provide an optimal integration of renewable energy generation into the electrical network through energy balancing systems, it is important to forecast renewable data over a time horizon consistent with the operation of electricity grids and markets. The requirements regarding renewable power predictions rely on the specific market and are usually driven by market operation constraints rather than by technical or physical issues. These horizons can be about 6 h in the United States, Canada, and United Kingdom [221], while in Europe forecasts of 4
5 h in advance are required for real-time unit commitments (e.g., time required for switching on alternate sources), and forecasts in the order of 2
3 days ahead are used to determine the available reserves for the day-ahead market [219]. Overall, a time horizon of up to 6 h ahead allows users to react to varying production and regulating capacities at the system operators’ disposal; thus a selection of a given value of the forecast horizon between 3 and 6 h is proposed in the present methodology, as shown in Fig. 5.3. In accordance with the retrieved real data, forecast renewable parameters should be collected on an hourly basis over the same period. Based on the forecast data set used, some information may be missing within the interval; thus averages between the available hourly values may be performed to fill the gaps for the sake of simplicity.

5.2.4 Selection of the converter and characterization of the power plant 5.2.4.1 Selection of renewable energy converter The procedure for the selection of the suitable renewable energy converter is different depending on the type of renewable source being considered. The choice of the offshore wind turbine (OWT) can be based on the water depth of the offshore site, according to the classification of the types of structures and foundations reported in Chapter 2, Offshore Renewable Energy Options. Moreover, an extreme 50-year gust and turbulence intensity, commonly used in the definition of the wind turbine International Electrotechnical Commission (IEC) class, can be determined according to the wind conditions at the offshore site and adopted as criterion for the converter selection [222]. In order to gain more accurate results, a turbine with a height as equal as possible to the height of the anemometer can be also considered in the turbine choice. Furthermore, the power curve of the wind turbine representing the relationship between the produced output power and hub height wind speed, as well the associated trend of the power coefficient

5.2 Sustainability assessment methodology for G2P systems

Cp (also called the efficiency of the aerogenerator), is given by the manufacturers and can be used to model the performance of the OWT. The selection of a wind turbine characterized by a power curve that better matches the wind regime of the site allows us to optimize the efficiency of the wind energy plant. The gross annual energy production (AEP) can be estimated as the key factor of the selected offshore wind turbine [223]: AEPwind 5

ð vcut2off

pwind ðvÞUPOWT ðvÞUnyear UAVOWT dv

(5.21)

vcut2in

where AEPwind is the predicted amount of wind power produced over a year (in kWh per year or MWh per year) based on the turbine power curve excluding losses (e.g., downtime, wake, electrical and other losses); pwind is the wind speed frequency distribution calculated, for example, by means of Eq. (5.20); POWT is the electrical power for each wind speed derived from the power curve; nyear is the number of hours per year; and AV is the OWT availability, that is, the amount of time the device is able to produce power based on device reliability and access level for maintenance (typically 90%
97% [224]). Another parameter that can be used to evaluate the chosen offshore wind turbine is the capacity factor (CF) representing the ratio of annual energy production to maximum energy production if the turbine runs at its rated power all year. Therefore the higher the AEP and CF are, the higher the suitability will be of the turbine for the offshore site in terms of production. Once the proper device is selected, the actual electrical power produced by OWT can be calculated for each wind speed over the period under analysis: POWT ðvwind Þ 5 Pwind;avail ðvwind ÞUCp ðvwind Þ

(5.22)

where POWT is the produced power from the OWT; Pwind;avail is the available wind power illustrated in Eq. (5.17); Cp is the power coefficient of the OWT, which usually accounts for the aerodynamic efficiency (describing blades performance), mechanical efficiency (i.e., efficiency of the drivetrain and electrical efficiency (i.e., efficiency of generator and power electronics); and vwind is the average wind speed within the operating limits of the turbine (cut-in and cutoff). It should be noted that Cp varies with wind speed and is commonly provided by the manufacturer in addition to the power curve. On the other hand, the selection of the suitable wave energy converter (WEC) can be first based on the sea characteristics at the site of interest, that is, the water depth and the distance from shore, as described in the classification of WECs in Chapter 2, Offshore Renewable Energy Options. Also, the technological development level, that is, the availability of real tests performed at different scales in terms of reliability, power production, and designs, as well as connection to electrical network, can be considered. In particular, a conversion matrix relating the produced power Pwave to Hs and Te (or Tp ) for a characteristic length of WEC installed at a given water depth is a useful way to model performance of the device and evaluate the suitability of the wave climate at the selected area. Conversion matrices for other devices are available in the literature, with different bin resolutions [219,225]. It is

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worth noting that the power matrix may refer to WEC electrical power; otherwise it corresponds to the absorbed power and power take-off, and generator efficiencies should be considered for electrical power generation. Similar to the offshore wind energy, AEP can be estimated by means of the WEC [216]: AEPwave 5

X

P Up ULUηWEC;s Uenwave;s UηPTO Uηgen Unyear UAVWEC s wave;avail;s wave;s

(5.23)

where s is the sea state over the period of interest characterized by proper combination of Hs and Te (or Tp ), Pwave;avail is the available wave power following Eq. (5.18), pwave and enwave are the probabilities of occurrence and wave energy contributions of the sea state defined by the proper combinations of Hs and Te (or Tp ), L is the characteristic or active length of the device along which the machine absorbs the incoming wave energy (e.g., width of the ramp for an overtopping floating WEC, floater diameter in case of point absorber fixed WEC, length of WEC in case of attenuator, chamber width in case of oscillating water column), ηWEC is the WEC efficiency accounting for the primary conversion in the device and representing the ratio between the relative amount of energy absorbed and available wave energy to the device (or capture width ratio), ηPTO is the power take-off (PTO) efficiency used for conversion of absorbed power into rotating mechanical power, and ηgen is the efficiency of generator used for conversion of rotating mechanical power into electrical power (e.g., frequency converters and filters). ηWEC values are often determined based on experimental tests or numerical simulations as a function of Hs and Te (or Tp ) for the given operating ranges based on the working principle of the device, while ηPTO and ηgen have typical values based on the type of PTO; nyear is the number of hours per year, AV is the WEC availability, that is, the amount of time the device is able to produce power based on device reliability and access level for maintenance (typically lower than 90% [224]). In the case of the lack of sea trials data for the calculation of ηWEC , the WEC conversion matrix provided in the literature can be used to convert the wave height and period series into power series ðPwave Þ by means of interpolations in computer programming codes. In the case of a power matrix referring to the absorbed power, AEP by means of WEC can be calculated as follows: AEPwave 5

X

P Up Uη Uη UAVWEC Unyear s wave;s wave;s PTO gen

(5.24)

Otherwise, if the power refers to electrical power, ηPTO and ηgen can be excluded from Eq. (5.24). Similar to the case of offshore wind power, CF associated with wave energy production from WEC can be calculated. Higher values of AEP and CF at a given site can address the selection of the suitable WEC. The simple way to estimate the electricity production of a WEC at a specific site over the desired time interval is to convert the wave data into power data by using the power matrix associated with the WEC as previously described. Therefore the electrical power produced from WEC for each sea state is: PWEC ðsÞ 5 Pwave ðsÞUηPTO Uηgen

(5.25)

5.2 Sustainability assessment methodology for G2P systems

where PWEC is the WEC electrical power, s is the sea state over the selected period characterized by a combination of Hs and Te (or Tp ), Pwave is the power derived from the conversion of wave data at sth state by using the power matrix of WEC, and ηPTO and ηgen are the power take-off and generator efficiencies, respectively. Eqs. (5.22) and (5.25) can be applied by using both the real and forecast data collected for the offshore site in step 1; thus real and forecast power curves on an hourly basis are obtained accordingly for use in the following step of the procedure. It should be noted that the selection of different forecast horizons in step 1 results in different forecast power curves.

5.2.4.2 Characterization of the renewable power plant Once the renewable energy device for the analysis is selected, the size of the renewable power system needs to be defined in this step of the procedure. Information about the capacity of the existing plants already connected to the network or previously studied for future grid integration should be taken into account for a proper decision making. It is worth noting that the same type of renewable energy device and the same size of the plant should be adopted to ensure a consistent comparison between different offshore sites, provided that suitability to environmental conditions (e.g., water depth, average wind speed, wave energy flux per unit of crest length, etc.) of both the locations is verified. In the case of offshore sites characterized by worse environmental features than those required for the adoption of productive and mature energy converters, proper similarity laws should be applied in order to scale down the reference devices to ones that are more suitable for the analyzed site. This could be verified more likely in case of the marine energy sector since it is not at as advanced a stage of development as the wind energy and solar energy industries. Various simplified scaling laws for wind turbines are available in the technical literature [226], which use the expression of POWT in Eq. (5.22) and incorporate variations in rotational speed, variation of rotor radius, and variation of the incidence of the blades (pitch setting) with respect to the rotor plane in the case of a stall-regulated rotor. In wind turbine dimensional analysis, examples of dimensionless parameters are power coefficient Cp , thrust coefficient, moment coefficient, etc. If the scaling factor λ is based on the rotor diameter (D), the theory of geometric similarity between model and prototype devices can be applied, keeping constant the tip speed ratio (i.e., the ratio of circumferential speed at the blade tip and wind speed upstream from the rotor) by maintaining the same blade profile and number of blades and materials, as well as by adjusting proportionally all other dimensions (radius, profile chord, etc.) [227]. The main rules are summarized in Table 5.12. Therefore by varying D with respect to the value selected for the reference OWT, Pwind varies, and thus the power curve may better match the wind speed distribution of less productive sites leading to an increase in the associated AEP and CF. These relations are approximations that are valid under specific assumptions, among which weight does not represent an issue for wind

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Table 5.12 Example of main scaling rules based on scaling factor λ for OWTs and WECs. OWT, Offshore wind turbine; WEC, wave energy converter. OWT (λ 5 D1/D2)

WEC (λ 5 L1/L2) Scale dependence

Parameter Wind power Pwind

Relation Pwind;1 =Pwind;2

Scale dependence

Rotational speed Ω

Ω1 =Ω2

λ21

Weight W

W1 =W2

λ3

Wave power Pwave

Pwave;1 =Pwave;2

Significant wave height Hs Wave period Te or Tp

Hs;1 =Hs;2

λ7=2 λ

Te;1 =Te;2 or Tp;1 =Tp;2

Same value as reference machine

λ2

turbine blades and design parameters do not lead to Reynolds number, that is, the ratio between inertial force and viscous force, of less than 200,000. In the case of primary conversion in WEC, gravity and inertial forces are dominant, and the effect of the remaining forces such as kinematic viscosity is small; thus mechanical similarity is achieved by the Froude’s scaling law between model and prototype devices [228]. The scaling factor λ based on Froude similitude can be defined as the ratio between the characteristic length L of the device along which the machine absorbs the incoming wave energy (e.g., width of the ramp for an overtopping floating WEC, floater diameter in the case of absorber fixed WEC, length of WEC in case of attenuator, chamber width in case of oscillating water column), which derives from the fact that by varying the capture width the output power changes, the device’s response keeps constant with the spread over the frequency range similar. Then Pwave and Hs parameters change according to the scale dependence illustrated in Table 5.12, while Te (or Tp ) remains the same as that of the reference machine. By using these rules, the original conversion matrix of the WEC design selected for the reference site can be scaled down according to the procedure described in [210] to assess the different ratings of the device at different wave climates. Once the nominal power of the downscaled devices is identified, it is important to determine the number of converters needed to produce the benchmark nameplate capacity of the renewable plant, following technical and economic considerations for the suitable layout. Having calculated the real power and forecast power on an hourly basis, the curves can be matched over the selected period. It should be noted that the matching is specific for each offshore site, as well as for each forecast horizon selected in step 1. Clearly enough, a deviation between the real and forecast powers may appear in some hours of the analyzed period. Having characterized the renewable plant from the technical point of view (power production), some economic parameters need to be collected for the sake

5.2 Sustainability assessment methodology for G2P systems

of the economic performance assessment in this methodology. The required information consists in preliminary estimates of CAPEX and OPEX to the renewable plant, as described in Section 5.1. It should be noted that CAPEX and OPEX for grid connection of the renewable plant are usually shared in a different way among the producer and transmission system operator (TSO) based on the cost approach applied at the transmission grid level in a given country. According to the shallow cost approach, the producer bears the cost to connect the plant to the closest connection point of the onshore grid. In the deep cost approach, the producer bears the costs of all connections and grid reinforcement due to integration [45]. Another important economic parameter to be collected for the application of this methodology is the electricity market price for renewable power, that depends on the pull mechanism adopted at the national level for the promotion of renewable energy integration, as described in Section 5.1. Finally, for the sake of integration of renewable electricity into the onshore grid, prices established by the local TSO for positive and negative power imbalances need to be collected. Such prices, commonly expressed in units of currency per MWh of power imbalance, are based on the local legislative framework on renewable integration into the grid and power imbalance mechanism. Clearly enough, eligibility requirements to these schemes should be verified case by case based on local regulations.

5.2.5 Definition of the dispatching power plan In step 3 of the procedure in Fig. 5.3, power prediction errors are analyzed and used to define the dispatching plan to declare to the TSO in view of the integration of renewable power into the grid. In order to limit the possible penalties imposed by the TSO associated with incorrect declarations, the prediction error between the real injected power and forecast power should be estimated and minimized as accurately as possible. To reduce the risk related to the randomness of renewable energy source and to foster the improvement of power predictions, a common principle is imposing on producers a certain probability of being able to correctly produce the declared power (i.e., probability of correct dispatching, Probd ), thus penalizing them only for injections that incur an error higher than the allowable one with respect to forecast injections. It is worth mentioning that power injections into the grid do not correspond to real power production at the offshore site due to different energy losses, among which interarray losses (i.e., losses related to wake effects) and electrical losses (i.e., ohmic losses dissipated as heat in interarray cables, export cables to shore, and HVAC sub-station). Electrical losses vary with power plant layout, voltage levels, cable length, and type of sub-station, and can fall under the responsibility of the TSO or the producer based on the country’s policy [229]. However, array losses can be generally reduced by optimizing the layout of the renewable plant, for example, by means of spacing between the devices. Moreover, HVAC cables (with maximum ratings of about 200 MW per three-phase cable at a voltage level

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of 150
170 kV and a maximum distance of around 200 km), for which power losses increase significantly with cable length, give relatively small losses if the distance between the offshore site and injection point is limited, that is, 20
50 km, as in the case of G2P offshore hybrid energy solutions. Therefore in the present methodology, a simplified assumption is made: the produced power derived from real weather data in step 2 is approximated to the power injected into the grid. Under this hypothesis, the prediction error is calculated in step 3 of the procedure as follows: ξ 5 Pr 2 Pf

(5.26)

where ξ is the absolute error between the real power and forecast power corresponding to the same hour over the period under analysis, Pr and Pf are the real and forecast powers, respectively, calculated in step 2 of the procedure for the renewable plant at a given site. As a matter of fact, ξ has a negative value in the case of production lower than the forecast quantity; otherwise it shows a positive value. It is worth noting that every set of ξ values is estimated for a given forecast horizon, given the definition of the forecast power. Once estimated, prediction errors are statistically analyzed in order to quantify the power corresponding to a Probd lower than 100%, that is, the dispatched power (Pd ), and match it with the corresponding Pr and Pf curves in a dispatching plan. A proper distribution can be selected to approximate the sample of ξ values for different intervals over the analyzed period, that is, for each month if the period of the analysis is one year. Commercial statistical tools can be used to analyze easy data errors, provide the histogram of the errors, and fit a large number of distributions. The choice of the best fitting is based on the analysis of specific accuracy parameters provided from the tool for the different distributions. Moreover, probability density function (PFD) and cumulative distribution function (CDF) of the sample of data can be obtained from the statistical analysis for each distribution. In order to define the dispatching plan, a given value of Probd lower than 100% needs to be selected, as shown in Fig. 5.3. Assuming a Probd of 80% means that for 20% of the time, the renewable energy source is not enough to produce the forecast power for injection into the grid. Thus Pd declared for injection into the grid is necessarily lower than the forecast power Pf , and the available power between Pd and Pf is considered to be lost. The reduction of Pd with respect to Pf is called in this methodology the dispatching error and is defined as follows: ξ d 5 Pd 2 Pf

(5.27)

where ξd is the dispatching absolute error between Pd and Pf at the same hour over the selected interval whose value should be negative, and Pd and Pf are the dispatched and forecast powers, respectively. In the proposed method, ξd is calculated as the error corresponding to a cumulative probability of incorrect dispatching equal to 100% minus Probd , that is, the probability that ξ is lower than or

5.2 Sustainability assessment methodology for G2P systems

equal to the allowable ξd . Thus the best CDF curve selected by means of the statistical tool for each sample of ξ is used to determine ξd for a given Probd . For example, if Probd is set as 80% and a given CDF is adopted for ξ data, the allowable ξd is the negative power value corresponding to a cumulative probability of incorrect dispatching of 20%, that is, CDF equal to 20%. If ξd and Pf are known, the hourly Pd values can be estimated by applying Eq. (5.27), provided that the smallest possible value for Pd is 0. It is worth noting that a given value of ξd is obtained for the CDF curve of ξ values in a given time interval (e.g., one month); thus different ξd values correspond to different intervals of the period under analysis. Moreover, ξ d depends strictly on the assumed Probd ; thus Pd changes by varying Probd while keeping the same forecast power Pf .

5.2.6 Definition and management of the gas turbine park 5.2.6.1 Definition of the gas turbine park In step 4 of the procedure in Fig. 5.3, the size of the GT park is estimated by considering the maximum power that could be provided from the turbomachines PGT;max in the analyzed period, that is, when Probd is equal to 100%, and thus Pf corresponds to Pd at each hour. Given the function of GTs, PGT;max can be evaluated only during the hours at which Pr is lower than Pf as the difference between the hourly Pf and Pr at the same hour. Fig. 5.4 shows an example of matching between the real power and the forecast power for WEC, calculated for an interval of few days in a month: Hourly PGT;max is represented by means of the yellow bars in the figure, while grays bars in the figure are the hourly power surpluses when Pr is greater than Pf , and GTs are not intended to work.

FIGURE 5.4 Example of estimation of maximum power that could be provided from gas turbines coupled with WEC for a given interval.

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It is recommended to calculate PGT;max for the same intervals at which ξ d values are estimated in step 3. Then the final size of the GT park is conservatively assumed in the present methodology as the highest PGT;max occurring among the values calculated for different intervals, that is, the maximum PGT;max over the entire period of the analysis. It is worth noting that the size of the GT park is strictly related to the forecast horizon selected for Pf ; thus different capacities may be derived by varying the horizon. Moreover, when different offshore sites are evaluated at the same the interval, the different sizes of the GT park may be obtained according to the different trend of both the real and the forecast power curves. Given the concept of the G2P hybrid energy option, the available quantity of gas at the offshore depleted field should be enough to supply the required fuel to the GT park. Conservatively, the gross valley filling energy that may be required by the GT park can be estimated from PGT;max and checked that it is lower than the energy derived from the available gas. Once the size of the GT park has been identified, it is necessary to select the equipment model to install at the offshore site by taking into account the nominal power and footprints of single machines. When different offshore sites are evaluated, the same GT model should be selected for a consistent comparison. Among the GTs typologies described in Chapter 3, Innovative Hybrid Energy Options, compact and lightweight aeroderivative GTs are ideally suited for power generation at the offshore platform in the low- to medium-range (4
66 MW), while micro-GTs are the common option for small-capacity (lower than 1 MW). After selection, the nominal power at full load (PGT;nom ), the nominal efficiency at full load (ηGT;nom ), and the dimensions of the machine are known. The total number of GTs (NGT;tot ) needed to reach the identified size of the GT park with their single nominal capacity is calculated, and then the total footprint of the park is derived. This latter parameter can be checked to see whether it is lower than the free space in the offshore platform.

5.2.6.2 Management of the gas turbine park In step 5 of the procedure in Fig. 5.3, the suitable part-load control strategy for the GT park designed in the previous step needs to be defined. When a certain number of machines are operating at part load, the electrical efficiency of the park tends to decrease with respect to the efficiency at full load (ηGT;nom ) leading to the so-called part-load efficiency (ηGT ). The efficiency reduction at part-load can be expressed as function of the part-load ratio depending on the category of GT. For aeroderivative GTs, the literature correlation is [105]: ηGT ηGT;nom

 5 0:7035U

PGT

PGT;nom

3

 2   PGT PGT 1 0:1481 2 1:91151U 1 2:0642U PGT;nom PGT;nom (5.28)

For micro-GTs, a proper correlation can be derived by regressing the part load efficiency curve of commercial micro-GTs by Capstone Turbine Corporation

5.2 Sustainability assessment methodology for G2P systems

(C800 and C330 models) [154,230], expressed as follows: ηGT ηGT;nom

 5 2:1812U

PGT

PGT;nom

3

 2   PGT PGT 1 0:0584 2 4:6655U 1 3:4475U PGT;nom PGT;nom (5.29)

In both Eqs. (5.28) and (5.29), ηGT is the part-load efficiency, ηGT;nom is the efficiency of at full load (i.e., the nominal efficiency), PGT is the power produced at part-load, and PGT;nom is the power produced at full load (i.e., the nominal power). The average efficiency of the entire park at part-load relies on the number of GTs in operation and their part-load, provided that keeping in operation the minimum number of turbines to produce the required power may result in the highest average load (i.e., highest average efficiency) and the lowest number of maintenance hours and costs. Among the possible control strategies proposed in the literature for GTs [105,231,232], the approach that consists in managing each GT of the park in parallel at the same part-load is applied in this methodology. Starting from the fullload condition of the park, that is, when every machine is operating at its nominal power (PGT;1;nom ), if the load decreases, all the machines reduce their load equally up to the condition at which one machine can be switched off and all remaining machines return to operation with their PGT;1;nom . After a further decrease of the load, these machines reduce again their load up to the condition at which a second machine can be switched off and the remaining machines return to operation at full load. This procedure proceeds until one machine is working in the system before reaching its minimum technical load. It is worth noting that a minimum allowable load is set for GTs, that is, 50% of the nominal load in order to meet the environmental limits on CO and NOx commonly imposed in the technical specifications. In the following discussion, an example of the application of such an approach to some hours of a day is described, using Table 5.13 as supporting material. Since a Probd less than 100% has been defined in step 2, the effective power that should be provided by the GT park to satisfy the defined dispatching plan (PGT;eff ) for a given hour is lower than the corresponding PGT;max identified in step 4. PGT;eff can be estimated only during the hours at that Pr is less than Pd as the difference between the hourly Pd and Pr at the same hour. As shown in Table 5.13, PGT;max is required at all the hours displayed because Pr is lower than Pf , while PGT;eff shows a value only in case of 5 out of 7 h (i.e., when Pr is lower than Pd ). Having estimated the need for a GT park with a total capacity of 52.2 MW and NGT;tot equal to nine aeroderivative turbines (each one with single nominal capacity PGT;1;nom of 5.8 MW and nominal efficiency ηGT;nom of 32.2%), it is possible to define the power corresponding to the switch-off point including the number of remaining turbines in operation and their part-load power range, according to the above described approach. Table 5.14 summarizes these data. As shown in Table 5.14, the GT park can operate from its nominal capacity PGT;nom of 52.2 MW to the minimum allowable load limit equal to 50% of PGT;1;nom (i.e., 2.9 MW). At 52.2 MW, all nine machines operate at full load. For power lower than 52.2 MW, the nine machines operate in parallel at the same

91

Table 5.13 Example of estimation of hourly part-load efficiency of the gas turbine park. Hour

Pr (MW)

Pf - 6 h horizon (MW)

PGT;max (MW)

Pd
80% Probd (MW)

PGT;eff (MW)

NGT

PGT (MW)

PGT =PGT;nom (%)

ηGT =ηGT ;nom (%)

ηGT (%)

16 17 18 19 20 21 22

8.3 16.2 30.6 35.4 43.3 42.7 37.3

47.2 49.3 49.8 50.0 50.2 50.3 50.2

38.9 33.0 19.2 14.6 6.9 7.6 12.9

36.8 38.8 39.3 39.5 39.8 39.8 39.5

28.5 22.6 8.7 4.1 — — 2.5

5 4 2 1 — — —

28.5 22.6 8.7 4.1 — — —

55 43 17 8 — — —

82 74 44 30 — — —

26 24 14 10 — — —

5.2 Sustainability assessment methodology for G2P systems

Table 5.14 Example of power ranges for operating gas turbines in the part-load control strategy. Number of operating turbines NGT

Power for turbine switch-off (MW)

Power range for operating turbines before the next switch-off (MW)

9 8 7 6 5 4 3 2 1 0

52.2 46.4 40.6 34.8 29.0 23.2 17.4 11.6 5.8 2.9

46.4 , PGT # 52.2 40.6 , PGT # 46.4 34.8 , PGT # 40.6 29.0 , PGT # 34.8 23.2 , PGT # 29.0 17.4 , PGT # 23.2 11.6 , PGT # 17.4 5.8 , PGT # 11.6 2.9 , PGT # 5.8 —

part-load until switching off one of them and operating the other eight machines at full load for a total power of 46.4 MW, which is the power corresponding to the first switch-off. Thus, the power range for the operation of nine machines at the same part-load before the switch-off is 46.4
52.2 MW. This procedure is applied to determine other PGT ranges of operating turbines, whose values are illustrated in the table. From these data, the number of operating turbines NGT required to supply PGT;eff at the hours of interest can be estimated, as shown in Table 5.13. The hourly powers supplied by the operating turbines (PGT ) are then reported. No turbine operates during hour 22:00 since PGT;eff is below the minimum load limit for the park of 2.9 MW; thus PGT is zero at this time, and the quantity corresponds to the negative unbalanced power for which the producer may pay an economic penalty to the TSO. After that, the hourly ratio PGT =PGT;nom can be calculated, and the corresponding hourly ratio ηGT =ηGT;nom can be derived by the application of Eq. (5.28). Given ηGT;nom , the hourly ηGT values of the GT park are then evaluated. In view of the technical performance assessment in step 6 of the procedure, in addition to the power produced from the GT park during the operating hours, the hourly fuel input power (Pfuel ) is required, resulting from the fuel (natural gas) combustion in GTs. Pfuel can be calculated based on the hourly PGT and ηGT values. Table 5.15 summarizes the results obtained, based on the preceding example. The hourly Pfuel can be obtained as the ratio of PGT to ηGT at each hour. Thus, fuel input power occurs only during the hours at which GTs effectively operates under the defined control strategy. Moreover, for the sake of the environmental assessment, GHG emissions from the GT park are required, provided that emissions from the renewable power plant can be neglected, as mentioned in the description of step 2. Hourly emissions (eGHG;GT ), commonly expressed in units of CO2eq for a given period, can be

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Table 5.15 Example of estimation of hourly fuel consumption and GHG emissions of the gas turbine park. Hour

PGT (MW)

ηGT (%)

Pfuel (MW)

eGHG;GT (kgCO2eq)

16:00 17:00 18:00 19:00 20:00 21:00 22:00

28.5 22.6 8.7 4.1 — — —

26 24 14 10 — — —

107.9 94.7 61.1 42.8 — — —

21,796 19,130 12,350 8639 — — —

evaluated from the Pfuel values estimated at the same hours by assuming a typical emission factor per unit of fuel (natural gas) power. For example, the typical emission factor values are 202 kg MWhfuel21 in the case of aeroderivative GTs [105] and 185 kg MWhfuel21 in the case of micro-GTs [233,234]. Referring to the preceding example, eGHG;GT values from aeroderivative turbines in the considered period are reported in Table 5.15. The next step of the procedure is the economic evaluation of the CAPEX and OPEX associated with the GT plant. Examples of costs data for aeroderivative and micro-GTs that may be used are available in the literature [122,233,235]. Furthermore, in view of the integration of a hybrid energy system into the grid, the price associated with the selling of conventional electricity to the grid is needed for the analysis. It should be noted that they rely upon the time interval and local market considered in the analysis. The wholesale market electricity prices, commonly expressed in units of currency per MWh, may be retrieved from available statistical trends at the national level over the desired period and associated with conventional electrical power produced from the GT park. Finally, the last information required for the economic analysis is the price associated with GHG emissions from the GT plant, which may be a carbon allowance of total direct GHG emissions from specific sectors in a cap-and-trade system [e.g., emission trading scheme (ETS)] or a predefined carbon tax on GHG emissions based on the policy adopted by local government to reduce carbon emissions [236]. Information about the regional, national, and subnational carbon pricing initiatives implemented, scheduled, and under considerations, including the associated prices, is annually published by the World Bank [237].

5.2.7 Calculation of sustainability performance indicators In step 6 of the methodology, the sustainability performance of the G2P hybrid energy system at the offshore site is assessed by using information derived from the previous steps and calculating a set of indicators addressing technical, economic,

5.2 Sustainability assessment methodology for G2P systems

environmental, and societal aspects. The set is adapted from the indicators defined in the sustainability assessment model for P2G and P2L hybrid energy options presented in Section 5.1, with the aim to capture in a concise yet representative way specific features of the G2P offshore hybrid energy systems. Clearly enough, the set is open to the addition of further indicators in view of an improved assessment.

5.2.7.1 Technical performance assessment Compared to the technical indicators defined in the sustainability model in Section 5.1, one indicator is proposed in this methodology for assessing the technical performance of the G2P hybrid energy system:  PT PHt  t51 h51 Pr;h 1 PGT;h ηel 5 PT P   Ht h51 Prenew;avail;h 1 Pfuel;h t51

(5.30)

where ηel is the electrical efficiency of the offshore hybrid energy system, Pr is the hourly real power produced from the renewable plant estimated in step 2, PGT is the hourly power supplied by the GT park estimated in step 5, Prenew;avail is the hourly available renewable power estimated in step 1, Pfuel is the hourly fuel consumption of the GT park calculated in step 5, “h” in pedix is the hour at which parameters are evaluated over the total number of hours in the entire year (Ht ), and “t” in pedix is the year at which parameters are evaluated over the entire period (T). It is worth mentioning that energy losses occurring in interarray cables and export cable are neglected for the calculation of Eq. (5.30). PGT and Pfuel give a contribution only during the hours at which the GT park operates under the control strategy defined in step 5. Given the definition of the proposed indicator, the higher ηel , the higher technical performance of the hybrid energy system will be.

5.2.7.2 Economic performance assessment Similarly to the economic indicators defined in the sustainability model in Section 5.1, two indicators are proposed in the present methodology, that is, the levelized cost of energy (LCOE) and the levelized value of energy (LVOE), by revising the definition of such standard economic metrics for power generation systems given in the literature [238]. All these parameters are expressed in units of currency per MWh of total electrical power produced from the hybrid energy system and intended for grid injection. LCOE is defined for the hybrid energy system: CAPEXrenew 1 CAPEXGT 1 LCOE 5

PT PHt

PT PHt t51

h51

t51

! OPEXrenew;h 1 OPEXGT;h



h51

! Pr;h 1 PGT;h



11Hrt

Ht Ut

11Hrt

Ht Ut

(5.31)

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where CAPEX in units of currency is the capital expenditure associated with the renewable plant and GT park, OPEX is the operational expenditure in units of currency per hour evaluated at h-th hour over the analyzed interval for the renewable plant and GT park, Pr and PGT are the hourly powers defined in Eq. (5.30), “h” and “t” pedices are defined in Eq. (5.30), Ht is the total number of hours in the year, T is the economic lifetime of the system, and r is the discount rate referred to the period T. As opposed to ηel , the lower LCOE, the higher economic performance of the hybrid energy system will be from the costs viewpoint. On the other hand, LVOE is calculated as follows: !

PT PHt t51

LVOE 5

Rsell;h 1 Rimb1;h 2 Cimb2;h 2 CGHG;h



h51

PT PHt t51

h51

11Hrt

Ht Ut

!

(5.32)

Pr;h 1 PGT;h



11Hrt

Ht Ut

where Rsell is the hourly revenue gained from selling electrical power to the grid; Rimb1 is the hourly revenue gained due to the positive imbalance of the produced power Pr with respect to the declared Pd in the dispatching plan; Cimb2 is the hourly cost paid due to the negative imbalance of the produced power Pr with respect to declared Pd , which are not covered by the GT plant; and CGHG is the hourly cost associated with GHG emissions from the GT park. Rsell at h-th hour in Eq. (5.32) can be defined: Rsell;h 5 Priceel;conv UPGT;h 1 Priceel;renew UPr;h

(5.33)

where Priceel;conv is the price in units of currency per MWh of conventional electrical power, Pr and PGT are the hourly powers defined in Eq. (5.30), and Priceel;renew is the price in units of currency per MWh of renewable electrical power based on the national pull mechanism. Hourly Rimb1 and Cimb2 in Eq. (5.33) are calculated as: Rimb1;h 5 Priceimb1 UPimb1;h

(5.34)

Cimb2;h 5 Priceimb2 UPimb2;h

(5.35)

where Priceimb1 and Priceimb2 are the prices in units of currency per MWh of positive and negative imbalances, respectively, collected in step 2; Pimb1 is the hourly positive imbalance occurring when Pr is greater than Pd , which is estimated once the dispatching plan is defined in step 3; Pimb2 is hourly negative imbalance occurring when Pr is lower than Pd and GTs cannot operate due to the technical minimum load limit, which can be estimated once the management of the GT park is defined in step 5. CGHG at h-th hour in Eq. (5.32) is estimated as: CGHG;h 5 PriceGHG UeGHG;GT;h

(5.36)

where PriceGHG is the price in units of currency per mass of CO2eq emissions, and eGHG;GT are the hourly GHG emissions from the GT park.

5.2 Sustainability assessment methodology for G2P systems

5.2.7.3 Environmental performance assessment As the environmental indicator defined in the sustainability model in Section 5.1, the LGHG quantifying GHG emissions from the GT park divided by the total energy production over the analyzed period is proposed in this methodology: PT PHt eGHG;GT;h LGHG 5 PT t51   PHt h51 P r;h 1 PGT;h h51 t51

(5.37)

where eGHG;GT are the hourly GHG emissions from the GT park calculated in step 5, Pr and PGT are the hourly powers defined in Eq. (5.30), “h” and “t” pedices are defined in Eq. (5.30), Ht is the total number of hours in the year, and T is the economic lifetime of the system. Similar to LCOE, the lower LGHG, the higher the environmental performance of the hybrid energy system.

5.2.7.4 Societal performance assessment As discussed in the sustainability model in Section 5.1, the societal dimension of sustainability is preliminarily disregarded also in this methodology. Throughout the present chapter (Section 5.3), some safety-related indicators that may be used to address this aspect of sustainability are defined.

5.2.7.5 Aggregated performance assessment The indicators defined above allow the assessment of different aspects of sustainability. In order to quantify and communicate the overall performance of the system, the aggregation of the indicators into a single-value metric is recommended. The compensatory aggregation approach presented in the sustainability assessment methodology in Section 5.1 is proposed for application, since alternative G2P offshore hybrid energy systems producing the same product, that is, renewable and conventional electricity for grid applications, are compared. Therefore, the normalization based on proper references target values, weighting-based time-space-receptor criteria and individualist-egalitarian-hierarchist perspectives, and the aggregation based on WAM and WGM methods are applied in order to calculate the ASI indicator defined in Eqs. (5.14) or (5.15) for each system under analysis. The details of these stages and the related considerations are reported in Section 5.1.5.6. For the sake of the assessment methodology for G2P hybrid energy options, the target values for the normalization of the disaggregated indicators are intended to be reference values for the power generation technology, for example, measures of the expected performance of the renewable energy plant in the near- or long-term future provided from projections available in the relevant literature.

5.2.8 Ranking of alternatives and sensitivity analysis The calculation of the indicators in the previous step allows the comparison and ranking of different G2P offshore hybrid energy systems at a given site or of the same G2P offshore hybrid energy system at different locations. The ranking can be

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performed from the viewpoint of different aspects (e.g., technical, economic, environmental) and the overall profile of sustainability. The considerations presented for sensitivity analysis in Section 5.1.7 can be applied also in this methodology.

5.3 Inherent safety assessment methodology 5.3.1 Generalities A systematic methodology is defined to assess the inherent safety performance of offshore facilities where hazardous materials (chemicals, oil, natural gas) are present. The assessment is performed by means of a specific set of inherent safety and environmental protection key performance indicators (KPIs) based on the consequences of potential accident scenarios with respect to different targets of concern in offshore oil and gas production installations, that is, humans, assets, and marine environment. The proposed multi-target methodology can be used to compare the inherent safety performance of alternative designs of P2G/P2L/G2P offshore hybrid energy options at offshore oil and gas production sites. It is intended to be an efficient support tool to orient inherent safety-oriented choices by ranking the units of the option and of the entire scheme for further detailed assessment (risk assessment and management of change). With its safety focus, the method is suitable to complement the technical, economic, and environmental considerations of sustainability, thus providing some metrics to address the societal aspect of the assessment methodologies described in Sections 5.1 and 5.2. The flowchart of the methodology is illustrated in Fig. 5.5. The figure also includes a reference to the steps of the general MCDA approach displayed in Fig. 5.1. The novel aspects of the methodology are summarized as follows: 1. In the formulation of the alternatives, some steps are introduced to classify equipment into functional units, some to assign the loss of containment and credibility of their releases, and some to identify the accident scenarios and estimate damage parameters using consequence simulation models in offshore structures. 2. In the evaluation of performance indicators, a set of inherent safety indicators is defined addressing multiple offshore targets (humans, assets, marine environment) for the single units and the design option. 3. In the normalization of indicators, the reference values for normalization are proposed as the characteristic impact area of accident scenarios causing damage to each target of concern.

5.3.2 Definition of design options and characterization of targets As shown in Fig. 5.5, a preliminary step (step 0) encompasses the definition of the design options and characterization of the potential targets of interest. The

5.3 Inherent safety assessment methodology

FIGURE 5.5 Flowchart of the inherent safety assessment methodology for P2G/P2L/G2P offshore hybrid energy systems.

definition of process design options to be analyzed is performed by collecting the specific input data summarized in Table 5.16. These data consist of preliminary information about process equipment and utilities, general layout of the installation, and average environmental conditions, in view of a simplified assessment coherent with the use of KPIs and different from a detailed analysis, such as a quantitative risk assessment (QRA), where more operational details (e.g., related to personnel dislocation) are commonly needed. In order to complete step 0, the potential targets of a major accident occurring on an offshore installation are characterized. As described in Chapter 4, System Modeling and Analysis (inherent safety analysis procedure), the following target categories are considered: humans, assets (process and utilities equipment, facility structures, marine structures) and the environment (sea surface compartment polluted by oil spills and water column compartment damaged by releases of soluble chemicals). Once the targets have been defined, a reference damage threshold should be assumed for each critical target in order to characterize the extent of the effects of potential accident scenarios in the following step of the procedure. Table 4.1 reports an example of threshold values of accident scenarios for each target of concern proposed for the application of the present approach. Clearly enough,

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Table 5.16 Input data required for the inherent safety assessment methodology (Adapted from [239]). Data about the process, utilities, and equipment Process flow diagram (PFD), materials (substances, composition, dangerous properties, physical properties, that is, API in case of oil), operating conditions (pressure, temperature), nominal flowrates General specifications (preliminary diameter of the main process lines, inventory of the main equipment units) Piping and instrumentation diagram (P&ID), if present Data about the installation Characteristics of the offshore structure (fixed/floating, manned/unmanned) Preliminary layout, topside dimensions and deck elevation Data about the environment Average meteo-marine parameters (yearly average air temperature, yearly average wind speed, yearly average sea surface temperature, water depth) Ships approaching the installation (supply vessel, shuttle tanker, work vessel, authority ship)

Table 5.17 Reference vulnerability areas for each target of concern (Adapted from [239]).

Target

Humans

Area of the vulnerability zone (m2)

Plan view area of the topside

Assets (process and utility equipment) Plan view area of the topside

Assets (facility structures)

Assets (marine structures)

Marine environment

Plan view area of the topside

Circle area of the safety zone

Circle area of the safety zone

different thresholds may be considered depending on the framework of application and on the presence of specific requirements deriving, for example, from applicable technical standards or legislation. As the different targets can be present in different spatial zones around the facility, it is not possible to directly compare the KPIs related to different target categories. Thus a normalization based on the characterization of the spatial zones where the targets are present (i.e., vulnerability zones for a target) will be used in steps 5 and 6 of the procedure illustrated in Fig. 5.5. The plan view area of the vulnerability zone will be used as normalization factor, which is the “yardstick” for defining the relative magnitude of the potential accident scenarios in the normalization process. Table 5.17 reports examples of these areas for each target of

5.3 Inherent safety assessment methodology

concern. The plan view area of the topside of the platform is proposed for human targets, process and utility equipment and facility structures. On the other hand, for marine assets structures and marine environment targets, the radius of the safety zone around the installation established by the authorities for ship/installation collision avoidance is suggested as the impact area. It must be remarked that in case of the sea surface compartment polluted by oil spills, the impact area may be covered several orders of magnitudes. As a consequence, the estimation of normalization factor seems to be more complex than for the other targets and cannot ignore the definition of case studies. Nevertheless, the circle area of the safety zone (or at most multiples of it) can be preliminarily assumed as a first try for the purpose of normalization.

5.3.3 Classification of units and identification of release modes As shown in Fig. 5.5, the starting point for the application of the procedure is the identification of equipment units and assignment of release modes for each offshore design option selected for the analysis. A classification of equipment based on its function was developed. A suitable scheme of eight general categories is proposed in Table 5.18 based on an in-depth analysis of the most common process and utility equipment used in offshore oil and gas facilities. From each main category, one or more subcategories are derived specifying some equipment features.

Table 5.18 Functional categorization for equipment of offshore production oil and gas facilities (Adapted from [239]). General categories

Subcategories

Code

Process/storage vessel

Atmospheric vessel (storage tank, degasser, column, cryogenic tank, etc.) Pressurized vessel (separator, column, knockout drum, scrubber, etc.) Filter (cartridge, basket, plate screen, etc.) Shell & tube, plate, air cooler HP/LP vent, HP/LP flare Sealine Riser (steel fixed, flexible), umbilical Process piping, manifold, header Pump (centrifugal, reciprocating) Compressor (centrifugal, reciprocating) Surface, subsea Launchers, receivers Purge burner, reactor, etc.

Eq. (1.1)

Heat exchanger Flare/vent system Pipe

Pressure change equipment Wellhead Pig trap Others

HP, High pressure; LP, low pressure.

Eq. (1.2) Eq. Eq. Eq. Eq. Eq. Eq. Eq. Eq. Eq. Eq. Eq.

(1.3) (2.1) (3.1) (4.1) (4.2) (4.3) (5.1) (5.2) (6.1) (7.1) (8.1)

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Table 5.19 Proposed set of reference release modes associated with equipment units (Adapted from [239]). Reference release mode

Code

Small leak, continuous release from a 10 mm equivalent diameter hole

R1

Medium leak, continuous release from a 50 mm equivalent diameter hole

R2

Catastrophic rupture

Instantaneous release of the inventory

R3a

Continuous release from a full-bore rupture of the main pip connected to the equipment

R3b

Reference release modes for possible offshore critical events are associated with each category of equipment. Critical events in offshore production oil and gas facilities are linked to the loss of containment of hydrocarbons and chemicals from process equipment and pipework [17,240]. The definition of reference release modes is used to characterize the loss of containment events in terms of release geometry, duration, entity, or conditions [241,242]. On the other hand, the wide availability and variability of techniques for the identification of appropriate release categories may create inconsistencies when applied to different types of equipment. For this reason, in the methodology, a set of four reference release modes are applied (Table 5.19). Table 5.20 matches the proposed release modes with the equipment categories proposed in Table 5.18. (Figs. 5.6 and 5.7).

5.3.4 Assignment of credit factors to release modes Credit factors are used in the methodology in order to account for the different credibilities of the possible loss of containment events associated with each equipment category. They actually include causes as the potential origin of the consequences of the release scenarios assessed. Further details about the concept of credit factors are reported in previous studies [26]. As a matter of fact, equipment units, due to the inherent characteristics of their design and operation mode (multiple connections, moving parts, safety margins, pressure cycles, etc.) have different likelihoods of occurrence in given release modes. Credit factors are based on expected equipment leak frequency data. In step 2 of the methodology, credit factors are calculated as the frequency of the reference release modes defined in Table 5.19 to occur for the equipment unit of interest. The reference frequencies reported in the technical literature (e.g., by DNV, OREDA, IOGP, HSE) can be used as credit factors for standard technologies. For each unit, the credit factor of a release mode shall account for both the main equipment body (e.g., the process vessel) and the expected number of

5.3 Inherent safety assessment methodology

Table 5.20 Association of reference event tree codes to release modes of equipment units (Adapted from [239]).

Equipment equations Eq. (1.1) Atmospheric vessel

Eq. (1.2) Pressurized vessel

Eq. (1.3) Filter

Eq. (2.1) Heat exchanger

Eq. (3.1) Flare/vent system Eq. (4.1) Sealine

Eq. (4.2) Riser, umbilical

Eq. (4.3) Manifold, header

Eq. (5.1) Pump Eq. (5.2) Compressor Eq. (6.1) Wellhead

Eq. (7.1) Pig trap

Postrelease substance state Liquid Gas Liquid gas Liquid Gas Liquid gas Liquid Gas Liquid gas Liquid Gas Liquid gas Gas Liquid Gas Liquid gas Liquid Gas Liquid gas Gas Liquid gas Liquid Liquid gas Gas Gas Liquid gas Liquid Gas Liquid gas

R1

R2

R3a

R3b

(a) (d) (g) (a) (d) (g) (a) (d) (g) (a) (d) (g) (d) (l) (m) (n) (a) / (l) (d) / (m) (g) / (n) (d) / (m) (f) / (n) (a) (g) (d) (d) / (m) (g) / (n) (a) (d) (g)

(a) (d) (g) (a) (d) (g) (a) (d) (g) (a) (d) (g) (d) (l) (m) (n) (a) / (l) (d) / (m) (g) / (n) (d) / (m) (f) / (n) (a) (g) (d) (d) / (m) (g) / (n) (a) (d) (g)

(b) (e) (h) (c) (f) (i) (b) (f) (i) (b) (f) (i) (f)






(a) (d) (g) (a) (d) (g) (a) (d) (g) (a) (d) (g) (d) (l) (m) (n) (a)/(l) (d)/ (m) (g)/(n) (d)/ (m) (f)/(n) (a) (g) (d) (d)/ (m) (g)/(n) (a) (d) (g)









(c) (f) (i)

R1, R2, R3a, and R3b are the reference release modes defined in Table 5.19. Letters (a) to (l) identify the reference event tree corresponding to the release, as reported in Figs. 5.6 and 5.7. The codes marked with star ( ) refer to a release below the water level.

auxiliary items (i.e., valves, instruments, filters) that are known to be potential leakage points. The effect of engineered barriers (active and procedural) on frequency reduction is neglected in the assessment.

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FIGURE 5.6 Set of event trees for releases above the water level. VCE, Vapour cloud explosion; BLEVE, boiling liquid expanding vapour explosion (Adapted from [239]).

5.3 Inherent safety assessment methodology

FIGURE 5.7 Set of event trees for releases below the water level (Adapted from [239]). VCE, Vapour cloud explosion.

If P&IDs of the equipment are not available as input data, generic data can be selected from a reference table of credit factors for different equipment classes, as illustrated in Table 5.21, which is developed from a survey of several P&IDs of offshore oil and gas facilities. The specific value to be used in the proposed ranges in Table 5.21 should be selected depending on the level of complexity of the equipment (e.g., many flanged connections, instruments, and valves correspond to the upper bound of the range) and age/maintenance status of the plant. Furthermore, modifications of the adopted credit factors may be applied to equipment not in permanent service (e.g., test separators). In this case, the credit factor may be scaled by a utilization factor equal to the ratio of the working hours of the equipment to the yearly service hours of the plant.

5.3.5 Characterization of accident scenarios Step 3 of the methodology links the release modes to major accident scenarios associated with each unit, for example, pool fire, fireball, toxic dispersion, etc. Coherently with established consequence analysis methods, postrelease event trees are used for this task. While case-specific event trees can be built with a number of consolidated techniques [193,241,242], the standardized characteristics of the equipment and operations in the offshore oil and gas industry have allowed the development of a customized set of reference event trees applicable to the present assessment. Figs. 5.6 and 5.7 report the proposed set of generic event trees for releases above and below the water level, respectively. Event trees are associated with each release mode on the basis of the equipment category (Table 5.18) and the physical state of the substance after the release (liquid phase, gas phase, gas/liquid mixture) according to the scheme reported in Table 5.20. The event trees from Figs. 5.6 and 5.7 must be pruned accounting for the specific characteristics of the released material (e.g., if nontoxic materials are

105

Table 5.21 Example of ranges of credit factors for releases from offshore oil and gas equipment. Equipment equations

R1

R2

R3a

Eq. (1.1)

2.2  1023 4 1.1  1022

7.6  1024 4 3.7  1023

2.7  1025 4 2.4  1024

Storage vessels (chemicals, diesel tanks)

23

22

23

23

R3b

23

n.a.

23

1.1  1023 4 1.6  1023

Process vessels (oily drains tanks, oil
water degasser/separators)

4.0  10

Production separators

7.1  1023 4 2.0  1022

1.3  1023 4 1.7  1022

3.0  1024 4 1.0  1023

3.0  1024 4 1.0  1023

Other vessels (knockout drum, coalescer, scrubber)

6.8  1023 4 1.8  1022

1.1  1023 4 9.7  1023

2.5  1024 4 4.8  1024

2.5  1024 4 4.8  1024

Eq. (1.3)

Filter

3.7  1023 4 1.6  1022

6.7  1024 4 2.8  1023

2.3  1024 4 3.9  1024

2.3  1024 4 3.9  1024

Eq. (2.1)

Shell & tube (HC shell/tube side)

4.4  1023 4 2.3  1022

6.2  1024 4 3.2  1023

3.0  1024 4 1.5  1023

3.0  1024 4 1.5  1023

Eq. (3.1)

Flare/vent system

Case specific

Eq. (4.1)

Sealine

4.9  1024 4 1.2  1023

7.6  1025 4 2.2  1024

n.a.

3.9  1025 4 1.0  1024

Eq. (4.2)

Steel riser, umbilical

4.3  1024 4 1.1  1023

7.5  1025 4 3.4  1024

n.a.

3.9  1025 4 1.6  1024

Eq. (4.3)

Manifold, header

1.0  1023 4 4.1  1023

1.5  1024 4 6.9  1024

n.a.

1.4  1025 4 2.8  1024

n.a.

8.0  1024

n.a.

1.3  1024 4 5.1  1024

n.a.

1.0  1023 4 3.0  1023

Eq. (1.2)

Eq. (5.1)

Eq. (5.2)

Eq. (6.1)

23

4 1.2  10

22

Reciprocating pump

5.1  10

Centrifugal pump

6.9  1023 4 1.7  1022 22

4 1.4  10

22

23

1.2  10

4 2.3  10

23

4 3.1  10

7.2  1024 4 2.3  1023 23

Centrifugal compressor

1.2  1022 4 1.6  1022

1.4  1023 4 1.9  1023

n.a.

3.1  1024 4 3.9  1024

Surface wellhead

7.5  1026 4 1.3  1025

7.2  1026 4 1.3  1025

n.a.

4.4  1026 4 3.1  1025

n.a.

6.3  1027 4 2.3  1025

8.1  1024 4 1.1  1023

8.1  1024 4 1.1  1023

Subsea wellhead

1.1  10

4 1.9  10

Eq. (7.1)

Launchers

7.2  1023 4 1.2  1022

Eq. (8.1)

Others

Case specific

26

1.0  10

4 9.0  10

4 1.6  10

5.0  10

25

6.0  10

23

1.1  10

Reciprocating compressor

26

4 8.0  10

1.1  10

26

4 1.8  10

1.6  1023 4 2.4  1023

n.a. stands for not applicable. R1, R2, R3a, and R3b are the reference release modes defined in Table 5.19.

5.3 Inherent safety assessment methodology

released, the branches related to the “toxic cloud” are neglected). The event trees may be customized to include the specific results of bow-tie analysis or of other hazard identification techniques and, in particular, to include the action of safety systems for the mitigation of release consequences.

5.3.6 Calculation of damage parameters In step 4 of the procedure illustrated in Fig. 5.5, the consequence analysis of each accident scenario following the release mode of each unit is carried out in order to calculate the damage parameter addressing each of the targets of concern (human, assets, sea surface, water column). The damage parameters for human and asset targets are defined as the maximum horizontal distance from the unit where the effect associated with the fire/ explosion/toxic scenarios reaches the threshold value defined in Table 4.1 and the target may be present. The calculation of the physical effects of an accidental scenario can be performed by using suitable consequence models reported in Chapter 4, System Modeling and Analysis (inherent safety analysis description). Concerning the sea surface compartment impacted by possible surface oil spills, different damage parameters may be estimated in this methodology with different levels of detail, based on the desired complexity of the analysis as well as availability of simulation tools for predicting fates and effects of oil spills into the sea. The tools that may be used are described in Chapter 4, System Modeling and Analysis. The proposed damage parameters are briefly described in the following, including some features for their estimation. The simplest damage parameter for sea surface pollution (i.e., level 1 of detail) is considered the oil mass spilled into the sea, which can be estimated by using the release categories in Table 5.19 and the release source models available in the literature, without the need for any simulation tool and threshold value. Higher-level damage parameters quantifying the impact of oil spills on the sea surface (i.e., level 2 of detail) are the trend over time of the oil mass in the slick or the trend over time of the oil mass in the thick slick (i.e., with a thickness equal to or greater than the threshold value in Table 4.1) and the persistence of the oil mass in the slick over a given time. The calculation of these parameters depends on the simulation tool adopted for consequence analysis (e.g., OSCAR, ADIOS, GNOME, OWM) and, in particular, on the ability to represent the slick as realistically as possible, the simulation time, the ability to consider oil release temperature, air and sea surface temperatures, the ability to account for the wind and currents fields in the site, as discussed in Chapter 4, System Modeling and Analysis. The most sophisticated damage parameters for environmental targets over the sea surface (i.e., level 3 of detail) are considered related to the geometric features of the oil slick, that is, surface area of the slick or of the thick slick (with a thickness equal to or greater than the threshold value in Table 5.17) and its persistence over a given time. These parameters can be estimated by means of a limited number of tools (e.g., OSCAR and ADIOS) with differences imposed by the

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characteristics (and limits) of the software, as explained in Chapter 4, System Modeling and Analysis. Lastly, a sole damage parameter is proposed to quantify the water column pollution due to possible surface spills of completely soluble chemicals: This is the polluted volume of water derived from the ratio of the spilled mass of the chemical compound to the corresponding threshold value reported in Table 4.1. For the sake of simplicity and scarce availability of suitable simulation tools, such a parameter can be estimated as radius of a hypothetical cylinder with volume equal to the calculated polluted volume and height equal to a water depth of 50 m. The latter value is assumed as the maximum limit in depth for water column contamination, also based on the results of simulations involving different chemical compounds in the OSCAR tool. Offshore installations are characterized by a multi-level layout. The basic features of the preliminary layout (e.g., release point location, congested volumes) should be taken into account in the accident scenario simulation. Particular attention should be given to the simulation of liquid releases and subsea releases. The typical use of grated surfaces in offshore facilities results in liquid pools formed at sea level rather than at the elevation of the unit originating the release. Simulation of releases from equipment that is partially or totally submerged, for example, risers or subsea items, must take into account the dispersion phenomena both below and above the sea surface. In order to obtain the worst-case consequences (maximum values of damage parameters), release orientation, target elevation, atmospheric conditions, and marine conditions shall be appropriately selected among credible values in order to obtain the worst credible cases. However, the method allows considering any other release/target characteristic and meteorological condition, when relevant.

5.3.7 Calculation of unit inherent safety KPIs For each unit of the design option, in step 5 of the procedure, a set of two inherent safety KPIs are calculated, addressing each target of interest: Both indicators provide a quantification of the inherent safety performance of the units but focus on specific aspects of interest for the early design stages. The first KPI is called the potential hazard index (PI), which captures the worstcase accident scenario in terms of the highest damage parameters within the release categories of the unit. Being independent from the credit factors, they rely upon design choices, for example, operative conditions, unit inventories, and equipment locations. The second KPI, named the inherent hazard index (HI), is calculated by weighting the damage parameters with credit factors associated with releases of the unit. Therefore this latter metric introduces the role of the safety score of the equipment unit used in the operation, anticipating the safety performance assessment typically defined only in the later detailed design stages of a project. By ranking the hazard level of potentially and credibly critical units, the proposed KPIs allow the limitation of time and costs for the offshore project and

5.3 Inherent safety assessment methodology

thus the ability to address inherently safer solutions during the early design phase. It is worth noting that the proposed indicators both show lower values as the inherent safety performance of the unit increases. The procedure for the calculation of these KPIs is described in the following for each target of concern.

5.3.7.1 Performance assessment for humans For humans, the unit inherent safety KPIs are defined as follows:   2 HPIk 5 πmaxi maxj di;j;k

(5.38)

 X 2 HHIk 5 π i cfi;k Umaxj di;j;k

(5.39)

where HPI is the human potential hazard index (i.e., addressing the human target) for the k-th unit (in m2), HHI is the human inherent hazard index (i.e., addressing the human target) for the k-th unit (in m2/y), d is the damage parameter for the human target (in m) associated with the j-th accident scenario following the i-th release mode of the k-th unit, cf is the credit factor (in 1/y) assigned to the i-th release mode of the k-th unit. The distance d is estimated by means of consequence analysis as described in step 4, while cf is attributed to equipment release as described in step 3. As a matter of facts, HPI represents the maximum impact area derived from the worst-case accident scenario affecting humans among those originated from the unit reference release modes, while HHI is the credible damage area from the unit releases.

5.3.7.2 Performance assessment for assets Concerning the assets, the unit inherent safety KPIs are defined for each category (process and utility equipment, facility structures, marine structures) as follows:   APIl;k 5 πmaxi maxj e2i;j;k;l

(5.40)

 X AHIl;k 5 π i cfi;k Umaxj e2i;j;k;l

(5.41)

where API is the assets potential hazard index (i.e., addressing the l-th assets category) for the k-th unit (in m2), AHI is the assets inherent hazard index (i.e., addressing the l-th assets category) for the k-th unit (in m2/y), e is the damage parameter addressing l-th assets category (in m) defined in step 5 for the jth accident scenario following the ith release mode of the k-th unit, and cf is the credit factor defined in Eq. (5.39). The distance e is estimated by means of consequence analysis as described in step 4, while cf is attributed to equipment release as described in step 3. Given similar equations, API and AHI have the same meaning and unit of measure reported above for KPIs addressing human target. A set of three potential KPIs and of three inherent KPIs may be the most obtained for the assets target.

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5.3.7.3 Performance assessment for marine environment For the environmental damage of the sea surface and water column compartments, a different calculation of the unit inherent safety KPIs is proposed with respect to human and asset targets. As previously explained, different types of damage parameters are considered in addressing the vulnerable targets living along water column impacted by chemical spills and the vulnerable targets on the sea surface polluted by oil spills. Therefore a different definition of KPIs associated with chemical releases and oil spills into the sea is provided in this methodology. The potential and inherent hazard KPIs associated with chemical releases are defined: EPIchem;k 5 πmaxi g2i;k  X EHIchem;k 5 π i cfi;k Ug2i;k

(5.42) (5.43)

where EPIchem (in m2) and EHIchem (in m2/y) are the environment potential hazard index and inherent hazard index, respectively, (i.e., addressing the water column environmental target) associated to chemical spills derived from the k-th unit; g is the damage parameter for water column environmental target (in m) following the i-th release mode of the k-th unit; and cf is the credit factor defined in Eq. (5.39). The distance g is estimated from the polluted water column as described in step 4, while cf is attributed to equipment release as described in step 3. Concerning the KPIs related to oil spills, three levels of KPIs are defined, with an increasing level of detail from the first to the third, corresponding to the three levels of damage parameters described in step 4 of the method. It should be noted that such KPIs are alternatives for the quantification of the hazard level of oil leakages. The selection of suitable KPIs is dependent upon the availability of data for the analysis and simulation tools for oil fate modeling. The level-1 KPIs for oil spill hazard quantification are associated with the oil mass spilled into the sea and can be calculated as follows: EPIoil1:1;k 5 maxi moil2rel;i;k X  cfi;k Umoil2rel;i;k EHIoil1:1;k 5 i

(5.44) (5.45)

where EPIoil1:1 is the environment potential hazard indicator of level 1 addressing the environmental target on the sea surface associated to oil spills derived from the k-th unit (in units of tonnes), EHIoil1:1 is the environment inherent hazard indicator of level 1 addressing the environmental target on the sea surface due to oil spills derived from the k-th unit (in tonnes/y), moil2rel is the oil mass spill following the i-th release mode from the k-th unit (in units of tonnes),and cf is the credit factor defined in Eq. (5.39). moil2rel is estimated as described in step 4, while cf is attributed to equipment release as described in step 3. Clearly enough, avoiding the use of the simulation tools for consequences of oil fate, the proposed two KPIs represent a preliminary estimation of the potential and credible hazard associated with the oil spill from the unit.

5.3 Inherent safety assessment methodology

The level-2 KPIs for the quantification of an oil leak hazard can be divided into two sub-level indicators: level-2.1 KPIs are based on the sole oil mass in the slick, while level-2.2 KPIs are based on both oil mass in the slick (or thick slick) and its persistence over a limit time (or thick slick lifetime). It must be remarked that the expressions of these KPIs may be different according to the simulation software used, given unavoidable differences (and limits) of the tools. KPIs of level 2.1 can be calculated as follows:   EPIoil2:1;k 5 mint504tlim maxi moil2sl;i;k ðtÞ X   cfi;k Umoil2sl;i;k ðtÞ EHIoil2:1;k 5 mint504tlim i

(5.46) (5.47)

where EPIoil2:1 is the environment potential hazard indicator of level 2.1 addressing the environmental target on the sea surface associated to oil spills derived from the k-th unit (in units of tonnes), EHIoil2:1 is the environment inherent hazard indicator of level 2.1 addressing the environmental target on the sea surface associated to oil spills derived from the k-th unit (in units of tonnes/y), moil2sl is the oil mass in the slick following the i-th release mode from the k-th unit as function of time t (in units of tonnes), tlim is the limit time imposed by the simulation software (e.g., ADIOS, GNOME, OWM tools), and cf is the credit factor defined in Eq. (5.39). moil2sl and tlim are estimated as described in step 5 for the given simulation tools. Given the considerations reported for the consequence tools in Chapter 4, System Modeling and Analysis, it should be remarked that Eqs. (5.46) and (5.47) can be calculated only by using the ADIOS, GNOME, and OWM tools. In case of the OSCAR software, as the thick slick extinguishes at a given time, the oil mass in the thick slick becomes null; thus the level-2.1 KPI would lose its content. KPIs of level 2.2 are defined as follows: EPIoil2:2;k 5

ð tlim



 maxi moil2sl;i;k ðtÞ dt

(5.48)

0

EHIoil2:2;k 5

ð tlim X 0

i



cfi;k Umoil2sl;i;k ðtÞ



dt

(5.49)

where EPIoil2:2 is the environment potential hazard indicator of level 2.2 addressing the environmental target on the sea surface associated to oil spills derived from the k-th unit (in units of tonnes  days), EHIoil2:2 is the environment inherent hazard indicator of level 2.2 addressing the environmental target on the sea surface associated to oil spills derived from the k-th unit (in units of tonnes  days /year), moil2sl is the oil mass in the slick defined in Eq. (5.46), tlim is the time limit imposed by the simulation software (e.g., ADIOS, GNOME, OWM), and cf is the credit factor defined in Eq. (5.39). Given the considerations reported for the consequence tools in Chapter 4, System Modeling and Analysis, it is worth noting that Eqs. (5.48) and (5.49) can be calculated by using the ADIOS, GNOME, and OWM tools. In the case of the OSCAR software, the equations

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to be applied are: EPIoil2:2;k 5

ð~



 maxi moil2th;i;k ðtÞ dt

(5.50)

0

EHIoil2:2;k 5

ð ~ X   cfi;k Umoil2th;i;k ðtÞ dt i

(5.51)

0

where moil2th is the oil mass of the thick slick with a thickness equal or greater a threshold value in Table 4.1 following the ith release mode from the k-th unit as function of time (in tonnes), and N represents the concept that OSCAR does not pose limits on simulation time; thus the calculation can be made over the lifetime of the thick slick. Clearly enough, KPIs of level 2.2 represent the potential and credible oil mass slick exposures. The level-3 KPIs consider the geometric features of the thick slick, that is, the surface of the slick; thus they are based on the most accurate modeling approach, since the thick area is the key parameter causing sea surface pollution. At this level, two sub-level indicators are defined: The KPIs of level 3.1 are a function of the sole area of the slick (or thick slick), while level-3.2 KPIs represent the slick surface exposure, as previously mentioned. Similarly to level 2 KPIs, expressions of these KPIs may be different according to the simulation software used. Level 3.1 KPIs are calculated as follows:   EPIoil3:1;k 5 maxt504tlim maxi Aoil2sl;i;k ðtÞ X   cf UA ð t Þ EHIoil3:1;k 5 maxt504tlim i;k oil2sl;i;k i

(5.52) (5.53)

where EPIoil3:1 is the environment potential hazard indicator of level 3.1 addressing the environmental target on the sea surface associated to oil spills derived from the k-th unit (in units of km2), EHIoil3:1 is the environment inherent hazard indicator of level 3.1 addressing the environmental target on the sea surface associated to oil spills derived from the k-th unit (in units of km2/y), Aoil2sl is the oil surface area of the slick following the i-th release mode from the k-th unit as a function of time t (in km2), tlim is the limit time defined in Eq. (5.46), and cf is the credit factor defined in Eq. (5.39). It should be noted that Eqs. (5.52) and (5.53) should be applied only in the case of simulations with ADIOS among the tools reported in Chapter 4, System Modeling and Analysis. For modeling by the OSCAR tool, the following expressions can be used:   EPIoil3:1;k 5 maxt504 ~ maxi Aoil2th;i;k ðtÞ X   cf UA ð t Þ EHIoil3:1;k 5 maxt504N i;k oil2th;i;k i

(5.54) (5.55)

where Aoil2th is the surface of the thick slick with a thickness equal to or greater than a threshold value in Table 4.1 following the i-th release mode from the k-th unit as function of time (in km2), and N represents the concept that OSCAR does not pose limits on simulation time; thus the calculation can be made over the lifetime of the thick slick.

5.3 Inherent safety assessment methodology

The KPIs of level 3.2 calculated with tools limiting the simulation time and dismissing the slick thickness concept, for example, ADIOS tool, are defined as follows: EPIoil3:2;k 5

ð tlim



 maxi Aoil2sl;i;k ðtÞ dt

(5.56)

0

EHIoil3:2;k 5

ð tlim X



i

0

 cfi;k UAoil2sl;i;k ðtÞ dt

(5.57)

where EPIoil3:2 is the environment potential hazard indicator of level 3.2 addressing the environmental target on the sea surface associated to oil spills derived from the k-th unit (in units of km2  days), EHIoil3:2 is the environment inherent hazard indicator of level 3.2 addressing the environmental target on the sea surface associated to oil spills derived from the k-th unit (in units of km2  days/year), Aoil2sl is the oil surface area of the slick defined in Eq. (5.52), tlim is the time limit defined in Eq. (5.46), and cf is the credit factor defined in Eq. (5.39). Otherwise, if more detailed software tools are available for use (e.g., the OSCAR tool), the following equations can be applied for the calculation of level-3.2 KPIs: EPIoil3:2;k 5

ðN

  maxi Aoil2th;i;k ðtÞ dt

(5.58)

0

EHIoil3:2;k 5

ð ~ X  0

i

 cfi;k UAoil2th;i;k ðtÞ dt

(5.59)

where Aoil2th and N are defined in Eqs. (5.54) and (5.55).

5.3.7.4 Multi-target performance assessment For a concise yet representative inherent safety performance comparison between different units of the design option under analysis, an aggregated indicator addressing all the targets of the potential hazards in a single value is more suitable than an array of single indicators. In the present methodology, the KPIs calculated for single targets are combined in a two-stage compensatory MCDA approach, which comprises normalization of the indicators calculated for each category of targets and then aggregation into one multi-target indicator by applying proper tradeoff weights. A site-specific external normalization approach based on spatial dimensions influenced by the impacts associated with each category [243] is adapted in this methodology to obtain normalized indicators within each category of targets for further aggregation. The normalization factors are intended in this methodology as the “yardstick” for defining the relative magnitude of the potential accident scenarios affecting humans, assets, and the environment. Thus the characteristic zones where the targets of concern are present (i.e., vulnerability zones for a target) are considered, and the footprint area of such a vulnerability zone (i.e., the area of the footprint of the vulnerability zone on the horizontal plane view) defined for each target of

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concern in step 0 is used as the normalization factor, in order to normalize the unit KPIs addressing the specific targets. Each normalized indicator can be obtained by applying the following general formula: X5

Iact Avuln

(5.60)

where X is the normalized indicator associated with Iact ; Iact is the actual indicator addressing a specific target of concern among humans, assets, and environment; and Avuln is the area of the vulnerability zone defined for the specific target in step 0 of the methodology. In the case of the sea surface environment polluted by oil spills, different level KPIs are defined in terms of meaning and units of measure. Thus the normalization illustrated in Eq. (5.60) and further aggregation can be applied only when KPIs of level 3.1, based on the oil slick surface and defined in Eqs. (5.54) and (5.55), are used as the KPIs addressing this sub-category of the environment target. For all the other KPIs previously defined, there are no issues about direct application of Eq. (5.60). It should be noted that in case of asset and environment targets, more than one sub-category of targets may exist, that is, process and utility equipment, facility structures, and marine structures. The maximum value of the normalized indicators addressing the sub-categories of the target is considered conservatively for the further aggregation steps. The same procedure can be applied in the case of KPIs addressing environmental targets, considering the maximum normalized value among those addressing the sea surface compartment due to oil spills and the water column compartment due to chemical spills. It is worth noting that only one KPI among the different level KPIs defined for sea surface pollution due to oil leaks should be selected for the aggregation procedure. The application of such normalization procedure is performed for both potential and inherent KPIs separately, thus leading to two sets of normalized indicators addressing the three main categories of targets for each unit of the design option: The first set includes the potential hazard KPIs, that is, PIhum , PIass , PIenv for humans, assets, and the environment, respectively; the second set comprises the inherent hazard KPIs, that is, HIhum , HIass , HIenv . Finally, the WAM or WGM method is used to obtain single-value multi-target indicators for each unit of the design option, as described in the sustainability assessment methodology in Section 5.1.5.6. Eq. (5.14) can be adapted to apply the WAM method in this methodology as follows: PIov;k 5 whum UPIhum;k 1 wass UPIass;k 1 wenv UPIenv;k

(5.61)

HIov;k 5 whum UHIhum;k 1 wass UHIass;k 1 wenv UHIenv;k

(5.62)

where PIov and HIov are the overall potential and inherent hazard KPIs, respectively, addressing multiple targets for the k-th unit, PIhum ; PIass and PIenv are the normalized potential hazard KPIs addressing the human, assets, and environment targets,

5.3 Inherent safety assessment methodology

respectively, for the k-th unit; HIhum , HIass , and HIenv are the normalized inherent hazard KPIs addressing humans, assets, and environment, respectively, for the k-th unit; and whum , wass , and wenv are the weights associated with human, assets, and environment target categories, respectively, reflecting the tradeoffs or substitution rates that can be accepted among the categories of targets (their summation closes to one). The unit aggregated KPIs using the WGM technique are obtained adapting Eq. (5.15): ass env hum PIov;k 5 PIwhum;k UPIwass;k UPIwenv;k

(5.63)

ass env hum HIov;k 5 HIwhum;k UHIwass;k UHIwenv;k

(5.64)

where the nomenclature is defined in Eqs. (5.61) and (5.62). The selection of weight factors may be controversial in the context of the inherent safety of offshore oil and gas facilities: They should be independent from the assessed alternatives but may depend on the local conditions and policy implemented by the oil and gas company. Equal priority can be assigned, that is, weight factors equal to one-third if the main categories of targets considered for the analysis are three, at least in a preliminary step, provided that proper sensitivity analysis is performed to investigate the influence of weights on the KPIs results.

5.3.8 Calculation of facility inherent safety KPIs Having calculated the single-target and multi-target KPIs for the units, the inherent safety performance for each facility design option is estimated in step 6 of the procedure. As shown in Fig. 5.5, the normalized single-target indicators are first calculated and then combined to obtain the aggregated multi-target KPIs for the facility. Clearly enough, this procedure is applied to both potential and inherent KPIs, as performed for the single units. The normalized single-target indicators are derived by the summation of indicators estimated for the units of the design option in step 6. For example, for human targets, the facility KPIs can be calculated as follows: PIhum;fac 5 HIhum;fac 5

XN k51

PIhum;k

(5.65)

k51

HIhum;k

(5.66)

XN

where PIhum;fac and HIhum;fac are the potential and inherent hazard KPIs addressing humans, respectively, for the facility design option; k is the unit of the option; and N is the total number of units analyzed for the option. Similar expressions can be applied to the other categories of targets. From these indicators, the multi-target KPIs indicating the overall hazard level of the entire facility are derived by applying similar equations for the single units according to the method adopted for aggregation. In these equations, weights among the KPIs addressing single target categories are the same as those considered for the single units.

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5.3.9 Ranking of alternatives and sensitivity analysis The aggregated KPIs calculated in step 6 allow the assessment and ranking of the expected inherent safety performance of the units of each design option, based either on a direct assessment of the potentially worst-case accident scenarios (potential hazard KPIs) or on the likely safety performance of the unit by accounting for the fragility of equipment (inherent hazard KPIs), with respect to both specific categories of targets and as a whole. It is worth noting that the higher the values of the indicators, the higher the criticality of the unit will be. From the calculation of facility KPIs in step 6, the global inherent safety performance of alternative design options can be compared and ranked in terms of either the hazard level of process operations (potential hazard KPIs) or the hazard level weighted by credit factors of equipment (inherent hazard KPIs), addressing specific categories of targets and as a whole. It should be remarked that the lower the values of indicators, the higher inherent safety profile of the option will be. For both units and facility, the ranking based on single-target indicators allows highlighting the different contributors to the safety profile, while ranking based on multi-target indicators provides information about the overall inherent safety fingerprinting of the design options. Quite obviously, the results obtained are influenced by the weights used in the aggregation procedure, which may be considered less accurate measures than other parameters used in the assessment. Therefore sensitivity analysis is worth performing in order to check the influence of variation in the values of weights on the aggregated KPIs and rankings of the alternatives. Some sensitivity analysis techniques that may be used for these verifications are described in Section 5.5.

5.4 Integrated assessment methodology 5.4.1 Generalities The goal of this methodology is to provide a systematic approach for analyzing the feasibility of chemical production processes at the early maturity level (i.e., experimental proof of concept, technology validated in the lab, technology validated/demonstrated at the industrial site or pilot plant) based on the concept of process intensification (PrI). The desired chemical product may be one of the promising chemical energy carriers to be used in P2G or P2L offshore hybrid energy options described in Chapter 3, Innovative Hybrid Energy Options (i.e., H2, SNG, CH3OH). The process routes may be novel production methods classified in the existing literature as alternatives to the conventional production process in order to make the synthesis more renewable based and sustainable. A set of screening indicators addressing the technical, economic, environmental, and societal aspects is proposed to rank the process intensification level of the alternative process schemes. Therefore this method represents an attempt to cover all the four dimensions of sustainability.

5.4 Integrated assessment methodology

The methodology is intended to be a support tool orienting the choice of relatively new yet promising chemical processes for their implementation in P2G and P2L offshore hybrid energy projects. In this way, the proposed approach represents a precursor for further detailed site-specific assessment of the renewablebased integrated systems at a given offshore site. The flowchart of the methodology is illustrated in Fig. 5.8, and references the steps of the general MCDA approach displayed in Fig. 5.1. The novel aspects of the methodology are summarized as follows: 1. In the formulation of the alternatives, a step is introduced to define process flowsheets of emerging chemical production routes, and a step is included to test the feasibility of the flowsheets using conceptual scale-up and design of the simulated equipment. 2. In the evaluation of performance indicators, an integrated set of sustainability and inherent safety indicators from the methodologies described in Sections 5.1 and 5.3 are proposed as screening metrics addressing all four pillars of sustainability.

FIGURE 5.8 Overview of the integrated assessment methodology for P2G and P2L offshore hybrid energy systems (Adapted from [244]).

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5.4.2 Definition of the reference process schemes As shown in Fig. 5.8, a preliminary step (step 0) consists in the definition of the reference process schemes for each alternative production route. This requires (1) a review of the state-of-the-art technologies for each alternative process applying the production route considered; and (2) the selection of the most suitable process, based on the yield (overall conversion and selectivity in the desired product) and on the mildest operating conditions, that is, mild temperature and pressure, continuous operations etc., according to the inherent safety principles. The summary of the information used for the selection of the reference process schemes is reported in Table 5.22. It has to be remarked that most of the information about processes still under development are available in the literature from laboratory or, more rarely, from pilot scale tests. In order to perform a consistent assessment between different technologies for which information is available at different scales, a common reference basis should be defined, for example, the production potentiality and the purity of the desired product. Toward this aim, a reference industrial scale production rate may be used as a benchmark, along with the purity specification of the product, based on current market requirements. Moreover, the boundaries for the analysis of the alternative process schemes should be defined consistently among the options, for example, limits of the main production process (i.e., the core process from the raw material to the desired product at the reference basis). At the end of step 0, for each defined reference scheme, a flowsheet representing the process steps with their interconnections is created, selecting suitable basic unit operations and their combination in an early process design framework [245,246].

5.4.3 Definition of the intensified process flowsheet In step 1 of the methodology, reference process schemes should be obtained by a process optimization procedure based on the PrI concept, which is applied to the Table 5.22 Input data required for the selection of the reference process scheme (Adapted from [244]). Stoichiometry of all reactions, range of temperatures and pressures, phase Molar conversion of reactants, selectivity or yield of products Physical state and method of regeneration of catalyst, if present Physical state, purity, flowrate of raw materials Physical state, purity, flowrate of the desired product Operating mode (continuous, batch, semicontinuous) Preliminary technology readiness level Process constraints, if present (e.g., processing conditions where some material can decompose or become unstable, a substrate that can be damaged, or a liquid medium that can be inactive)

5.4 Integrated assessment methodology

process flowsheets obtained in step 0. Different definitions of PrI can be found in the literature [247]. The approach applied herein consists in process integration in terms of material recovery (i.e., recycling reactants to the reaction operation and purging one or more undesirable compounds from the process) and/or heat and power recovery (i.e., high-temperature streams may be cooled and/or condensed in order to heat and/or vaporize cold streams in the process, and/or power produced by turbines and heat engines may be supplied to compressors and pumps) [248]. Task integration (i.e., combining multiple unit operations into single process units in order to increase unit performance in terms of product quality, compactness, environmental impact, and energy use) can be then carried out. In order to facilitate the definition of the intensified process flowsheet, process simulators (e.g., Aspen PLUS and Aspen HYSYS) may be used for the simulation of steady-state processes. The creation of case studies and the use of built-in tools (e.g., Adjust, Recycle, etc.) allows for a less time-consuming sensitivity analysis of the main process parameters and, consequently, for an easier selection of the intensified set of process conditions. Simplified models and shortcut procedures offered are useful to approximate reaction and separation operations, with limited available information, in order to obtain an estimate of their performance. PrI activities may be simulated using the recycle operator and heat exchanger models. Given the intensified process flowsheets for each scheme, a preliminary screening can be performed based on the operating conditions of unit operations. As shown in Fig. 5.8, a reference process scheme may not proceed to the following step if it does not allow achieving the required production rate and purity of the product. The screening thus allows identifying the technologies still under development that are not yet mature enough for the scale-up and that will not be further considered.

5.4.4 Scale-up and preliminary design of equipment units In step 2 of the procedure in Fig. 5.8, a conceptual scale-up and design of all equipment is carried out for all process schemes still considered after completing step 1. The results of the mass and energy balances provided by process simulations are used to carry out these activities. Standard approaches for a preliminary design of the process and utility equipment for heat and mass transfer [246,249,250] can be adopted in order to estimate the main geometric data of the equipment and to evaluate the required units for a given operation. Common heuristics or rules of thumb available in the literature [163,251] may be applied to address proper design choices. It is important to underline that the conceptual design of process routes at the low maturity level implies dealing with scale-up issues of equipment used at the laboratory scale. Scale-up of chemical process units is usually based on the use of dimensionless groups [252]. Table 5.23 summarizes examples of scale-up approaches proposed in the literature for some reactor concepts. The number of equipment items required for each operation is thus quantified, and a further preliminary screening of the alternative process schemes is carried out. As shown in Fig. 5.8, a reference process scheme may not proceed to step 3 if the

119

Table 5.23 Example of scale-up approaches for different concepts of reactors employed in alternative chemical production processes (Adapted from [244]). Equipment unit

Scale-up approach

Isothermal tubular reactor

1. Increasing the tube diameter (up to a given diameter dtube, full2scale) while keeping constant tube length (up to a given length Ltube,full2scale) 2. Increasing the number of tubes (up to a given number ntube, full2scale per reactor) while keeping constant inlet flowrate per unit of tube area 1. Fixing the interelectrode gap while increasing the superficial area of individual cells (up to a given area Acell,full2scale) 2. Stacking individual cells in multi-cell reactors (up to a given number of cells ncell,full2scale per reactor) 1. Fixing the specific input energy while increasing discharge power (up to a given value Pdbd,full2scale) 2. Stacking individual reactors based on the related power supplied by generator (up to a given Pgen,dbd,full2scale per stack) 1. Increasing the outside diameter of the lamp (up to a given diameter dlamp,full2scale) and working volume in reactive lamp (up to a given ratio εlamp,full2scale while keeping constant reaction performance 2. Increasing the volume of reactor (up to a given volume Vimm, full2scale) while keeping constant produced flowrate per unit of internal volume

Electrochemical reactor

Nonthermal plasma dielectric barrier discharge reactor Photocatalytic reactor (immersion-type with lamp)

Reference source

Example of full-scale parameter

[253]

dtube,full2scale 5 60 mm Ltube,full2scale 5 6 m ntube,full2scale 5 6000

[254]

Acell,full2scale 5 16 m2, ncell, full2scale 5 200

[255]

Pdbd,full2scale 5 28 W (frequency 50 Hz, voltage 20 kV)Pgen,dbd, full2scale 5 300 W

[256]

dlamp,full2scale 5 10 mm εlamp,full2scale 5 90% Vimm,full2scale 5 1000 m3

5.4 Integrated assessment methodology

number of units derived from the scale-up and design approach is not realistic. Table 5.24 summarizes the working assumptions that may be introduced to carry out this screening, in order to discard the processes with too high a number of units, Table 5.24 Assumptions for preliminary design of equipment in alternative chemical production routes (Adapted from [244]). Equipment unit Distillation column (sieve trays)

Heat exchanger (shell and tubes)

Flash drum (vertical)

Pervaporation unit (organophilic membrane)

Multi-tubular reactor

Electrochemical reactor

Plasma reactor

Design approach Determination of internal diameter and height of the column, number of sieve trays, weight of the shell based on the design pressure and internal diameter Determination of the geometric area by selecting overall heat transfer coefficients from the literature Determination of diameter of the vessel with a typical design parameter, weight of the shell based on the design pressure and internal diameter Determination of feed flowrate to the membrane of the module, number of membranes Determination of diameter, length and number of tubes (see Table 5.23) Determination of number of cells (see Table 5.23) Determination of power provided from generator (see Table 5.23)

Criteria for single equipment design

Assumed maximum number of units

0.6
4.5 m diameter, 8
50 m height, number of trays , 25, weight 4
1134 t

Maximum number of columns in largescale plant: 20

14
1114 m2 geometric area

Maximum number of heat transfer equipment in largescale plant: 40

0.5
6 m diameter, 454 kg
417 t weight

Maximum number of heat transfer equipment in largescale plant: 40

1
6429 membranes per module, 1
50 modules per unit, maximum surface area of the unit 37.5 m2 30
60 mm tube diameter, 2
6 m tube length, 1
6000 tubes 1
200 cells

Maximum total surface area in large-scale plant: 0.3 hectares

1
300 W power supply

Maximum total surface area in large-scale plant: 0.3 hectares Maximum total surface area in large-scale plant: 0.3 hectares Maximum power input to large-scale plant: 463 MW(8 GJ t21 per 5000 t d21 product)

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which clearly affects the process economics according to current industrial practice [163,250,257
259].

5.4.5 Calculation of the screening indicators The effect on the design of the gradual intensification of the process scheme is assessed in step 3 of the procedure through the evaluation of screening indicators addressing the different objectives of the PrI concept. The original objective of PrI is to propose a sustainable process design aiming to be efficient (large reduction of energy consumption), economical (large reduction of processing costs), and environmental friendly (reduction of carbon emissions and waste). The extension of the motivations of PrI to safety-related societal issues, that is, reduction of the size of equipment units, offers the opportunity to significantly reduce the inventory of dangerous substances in the process and to limit the consequences of potential hazards, thus realizing one of the principles of inherent safety, conceptualized by Kletz [260], that is, minimization. Therefore in the present methodology, some of the technical, economic, and environmental indicators defined in the sustainability performance assessment model in Section 5.1 are complemented with some safety metrics addressing specific targets of potential hazards presented in the inherent safety assessment method in Section 5.3. Among the technical indicators introduced in Section 5.1.5, the global energy efficiency of the reference process scheme, η, defined in Eq. (5.1) can be selected to address the energetic objective of the PrI concept in this methodology. To cover the economic aspect of PrI, the levelized cost of the product (LCOP) indicator, defined in Eq. (5.3), can be chosen for the application of this method. The LGHG indicator defined in Eq. (5.7) can be used to assess the environmental performance of the intensified process scheme in this methodology. Table 5.3 summarizes the input data required for the calculation of these indicators. Most of this information can be easily collected by using the outputs of the process simulator adopted in step 2. Since the goal of this step is to perform a screening analysis of the alternatives production routes independently from the location of the plant, simplified approaches may be adopted to estimate the cost data associated with the process scheme, for example, applying general assumptions commonly recommended in the literature for estimation of the total capital investment and annual costs of a new onshore chemical process plant [163]. Concerning the inherent safety metrics, the most relevant target among humans, assets, and environment should be selected for the purpose of the present screening analysis based on the hazard properties of the desired product of the production processes. For example, humans can be considered the critical targets when H2, SNG, and CH3OH are evaluated in the process schemes due to their flammability and toxicity (in case of CH3OH) properties. Among the potential and inherent KPIs described in the performance assessment for human targets in Section 5.3.7, the HHI defined in Eq. (5.39) for each unit of the process scheme can be selected in this methodology because it represents a more effective

5.4 Integrated assessment methodology

measure of the unit hazard level weighting damage area of accident scenarios and safety scores of equipment. The HHI associated with the entire process scheme can be obtained by summing up the single units’ HHIs. The required input data for calculation of this indicator are reported in Table 5.16. By applying the same considerations previously made for the other metrics, only information about the process and utility equipment may be collected, disregarding facility and environment conditions. The assumptions proposed for the consequence analysis of accident scenarios in onshore plants, e.g., to consider conservative environmental conditions and/or height of release [261], may be applied for estimation of damage distances required in HHI. In order to provide a more concise assessment of the PrI level of the intensified process designs, an overall indicator can be calculated from the aggregation of the technical, economic, environmental, and societal metrics previously evaluated. The compensatory MCDA approach presented in the sustainability assessment methodology in Section 5.1 can be adopted in the present method, since alternative process schemes producing the same final chemical product are compared. Therefore normalization based on process-target values, weighting-based time-space-receptor criteria and individualist-egalitarian-individualist perspectives, and aggregation based on WAM or WGM are applied in order to obtain a single-value indicator for the intensified process design, that is, the Process Intensification Screening (PrIS) indicator. The details of these stages and related considerations are described in Section 5.1.5.6. Specific differences applied in this methodology are described next. Since the aim of the integrated assessment methodology is to provide a screening of emerging chemical routes for further assessment, only one indicator is defined for each dimension of the PrI concept; thus a single-step aggregation procedure is needed after the normalization of the indicators. Moreover, since different production schemes that have not already reached the commercial status are compared, target values for the normalization of disaggregated indicators may be considered as performance measures associated with the actual large-scale production process of the chemical compound under analysis. Once collected, the target values from the technical literature, Xη can be calculated by means of Eq. (5.9), while XLCOP , XLGHG , and XHHI through Eq. (5.8). Finally, the PrIS indicator can be calculated for each intensified process scheme by applying WAM or WGM method: PrIS 5 wη UXη 1 wLCOP UXLCOP 1 wLGHG UXLGHG 1 wHHI UXHHI

(5.67)

wLCOP wLGHG wHHI UXLGHG UXHHI PrIS 5 Xηwη UXLCOP

(5.68)

where PrIS is the process intensification screening indicator proposed as the aggregated indicator for each scheme; Xη , XLCOP , XLGHG , and XHHI are the normalized indicators associated with the η, LCOP, LGHG, HHI indicators, respectively; and wη , wLCOP , wLGHG , and wHHI are the weights attributed to η, LCOP, LGHG, HHI indicators, respectively, estimated based on the time-space- criteria and individualist-egalitarian-hierarchist perspectives of decision makers.

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5.4.6 Ranking of alternatives and sensitivity analysis The calculation of the screening indicators allows for a comparing and ranking of the reference process schemes still under consideration. The ranking can be performed from different viewpoints (i.e., technical, economic, environmental, inherent safety) and overall fingerprinting of the PrI concept. As described in Section 5.1, the adoption of the proposed compensatory aggregation approach can introduce some uncertainties related to the target values used in the normalization of indicators and weights for the final aggregation. Sensitivity analysis is worth performing in order to check the influence of variation in the target values on the PrIS indicator of the process scheme and rankings of the alternatives. Techniques described in Section 5.5 may be applied to test the stability of the results.

5.4.7 Application of detailed site-specific assessments In the last step of the procedure, P2G and P2L offshore hybrid energy options can be conceptualized and properly simulated integrating the best intensified process schemes that emerged in step 4 with renewable plants at a given offshore oil and gas site. Detailed site-specific assessments are then carried out to analyze and compare the assessment of such advanced integrated systems. As shown in Fig. 5.8, the application of detailed assessment methods requires to input not only the information related to the process scheme from the previous steps of the procedure, but also the information associated with the oil and gas site considered for the analysis in terms of offshore oil and gas infrastructures in order to host the conversion process and meteo-marine conditions and evaluate renewable energy exploitation. Data that may be needed are summarized in Table 5.1. As in the approach previously described, the sustainability and safety aspects of the alternative systems can then be evaluated based on a detailed set of technical, economic, environmental, and inherent safety indicators. The inherent safety methodology described in Section 5.3 can be applied for a thorough assessment of the inherent safety fingerprinting of offshore alternative design options based on multi-target inherent safety KPIs. On the other hand, exergy has several qualities that make it suitable as the confluence of energy, economic viability, and environment; thus exergy-based methods can be identified as promising tools for quantifying and improving the sustainability performance of innovative system designs. As described in Chapter 4, System Modeling and Analysis, exergy analysis allows us to quantify the exergy efficiencies of the integrated system and its components as a true measure of approach to ideality, as well as the thermodynamic imperfections of the integrated system and its components in terms of exergy destructions representing losses in energy quality. Therefore it gives a more illuminating insight into process performance than energy analysis does. Moreover, the connection between exergy and economics appears fundamental for a thorough feasibility evaluation of emerging integrated systems at early design stage. As discussed in Chapter 4,

5.6 Closing remarks

System Modeling and Analysis, exergoeconomics, combining exergy-based thermodynamic assessment and economic principles at the level of system components provides useful information about the cost-effectiveness of the analyzed system and its components. By combining exergy analysis with a comprehensive environmental assessment method (e.g., the LCA method), exergoenvironmental analysis can also reveal the environmental impact assigned to the exergy streams, identifying the system components with the highest environmental impact and the options for possible improvements [262]. A new ranking of alternatives can be produced from a detailed site-specific evaluation. A further sensitivity analysis investigating the effect of varying some key parameters on the final results may then be applied.

5.5 Sensitivity analysis techniques In practical applications regarding the comparison of different alternatives based on aggregated performance indicators, it is important to understand how uncertainty may affect the ranking of the alternatives and to test how robust such a ranking may be considered. Among the different techniques available in the literature to perform sensitivity analysis [263], discernibility analysis, generated from the combination of comparative analysis and uncertainty analysis, allows us to verify the effect of variation in influential parameters on the relative performance of the output indicators among couples of the alternatives rather than on their absolute values [264]. Based on the idea of discernibility analysis, a confidence interval for each uncertain parameter can be identified; then the propagation of uncertainties in the parameters can be investigated through the Monte Carlo simulation method [265], attributing a proper distribution of probabilities of the values of the parameters in the selected range. By Monte Carlo runs, cumulative probability distributions of differences between selected couples of alternative options are provided. The analysis of such distributions highlights possible inversions in the ranking of the alternatives as well as their probabilities. Clearly enough, the lower the probability for a possible inversion in the ranking, the higher the robustness of these results will be. When several input parameters are considered in the analysis, a simple one-at-a-time sensitivity can ease the identification of the most influential variables for further stochastic variation by means of the Monte Carlo approach.

5.6 Closing remarks In this chapter, novel quantitative assessment methodologies were presented to support the selection of the more appropriate P2G, P2L, or G2P hybrid energy system for offshore applications from the sustainability and safety viewpoints, as

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well as considering challenging situations that may arise in the future, that is, end-of-life oil and gas infrastructures, operations in harsh environment and remote areas, depleted gas reservoirs. These methodologies are useful for the analysis of the conceptual and front-end engineering design phases of offshore green projects when modifications and/or improvements arising from sustainability and safety drivers are considered. The proposed methods are based on a common systematic MCDA procedure but present specific peculiarities related to the type of offshore hybrid energy system analyzed and the type of evaluation. Multi-criteria performance indicators addressing the technical, economic, and environmental aspects of sustainability and/or inherent safety aspects were defined within the methodologies in order to provide a clear yet comprehensive communication of the performance of innovative offshore systems. Furthermore, approaches for aggregating indicators into a synthetic metric were described taking into account the criteria and perspectives of sustainability, multiple targets of offshore major accident hazards, and the process intensification concept of emerging production routes. Finally, sensitivity analysis techniques were described to prove the robustness of the results derived from the application of the developed methodologies.

CHAPTER

Case studies

6

In this chapter, the assessment methodologies presented in Chapter 5, Sustainability Index Development, are applied to three case studies. The case studies introduce different challenging situations, from offshore remote fields to depleted gas reservoirs and aging installations, thus offering opportunities for the development of power to gas (P2G), power to liquid (P2L) and gas to power (G2P) offshore hybrid energy options, and serve as examples of emerging chemical process routes requiring a preliminary investigation for further application in P2G/P2L offshore hybrid energy options. The analysis of the case studies aims to demonstrate the ability of the developed portfolio of assessment methodologies to capture in a flexible way the different issues related to the offshore context to assess the alternatives by means of a comprehensive multicriteria decision analysis (MCDA) approach and to orient the most suitable solution for each problem. In the following sections, the case studies are presented in detail, including proper assumptions made for the analysis, results, and discussion.

6.1 Case study 1: OWT farm and P2G/P2L offshore hybrid energy systems This case study consists of a gas production platform located in a remote area of the North Sea where exploitation of offshore wind energy is being considered for the energetic valorization of the site. Therefore such a case study provides the opportunity for the conversion of renewable electricity produced from an offshore wind turbine (OWT) farm into alternative chemical energy carriers at the offshore facility, that is, H2, synthetic natural gas (SNG), and CH3OH. The goal of the analysis is to demonstrate the potential of the sustainable assessment methodology presented in Section 5.1 to compare and rank alternative P2G and P2L hybrid energy options at the defined offshore site.

6.1.1 Definition of the offshore oil and gas site and evaluation of the options A remote area located in the North Sea is considered as the location of an offshore oil and gas platform hosting the process units of the alternative P2G and P2L options for conversion of renewable energy. Offshore wind energy is Hybrid Energy Systems for Offshore Applications. DOI: https://doi.org/10.1016/B978-0-323-89823-2.00006-3 © 2021 Elsevier Inc. All rights reserved.

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considered a renewable source to be exploited at the offshore site and converted into chemical energy carriers at the offshore facility, given previous feasibility analyses on large-scale offshore wind
H2 platforms in the North Sea [164,266]. As required from step 0 of the procedure illustrated in Fig. 5.2, the offshore oil and gas site is defined providing input data about the field and infrastructures. The platform is supposed to be an off-grid gas production platform in the central part of the North Sea, where gas fields are actually exploited and linked to the gas market in the United Kingdom, Norway, Germany, France, and Belgium by means of sea lines. Most of these gas pipelines are considered for reuse in CO2 transportation in a previous investigation [141]. The Sleipner Vest field in the Sleipner area (blocks 15/6 and 15/9 in the North Sea) is taken as a reference, where the water depth is 100 m, and natural gas with 4%
9% CO2 is extracted and treated by using amine absorption to remove CO2 [267]. The United Kingdom is selected as the reference market for the end uses of the final products of the conversion processes, as well as for the possible supply of required input materials. A gas sea line linking the gas platform to a UK gas terminal is considered, with a length of 500 km, a delivery pressure of 155 bar, a capacity of 72  106 Sm3 day21, which are features similar to those of the existing Langeled pipeline from Sleipner to Easington (United Kingdom) [268]. Furthermore, another sea line 400 km long is considered for transportation of gases other than natural gas (e.g., CO2) from the shore, inspired by the Central Area Transmission System natural gas transportation and processing system delivering gas from the Central North Sea to Teesside terminal [141]. Moreover, an onshore harbor located at a distance of around 200 km from the offshore area (approximately the distance of the Aberdeen port from the Sleipner area) is considered for the docking of supply vessels to and from the offshore platform. About 12 voyages per year are reasonably estimated for supply vessels. Having collected the input data summarized in Table 5.1, the possible conversion strategies are evaluated according to step 1 of the procedure. Given the characteristics of the selected oil and gas site, six hybrid energy options are considered for analysis in this case study, illustrated in Fig. 6.1. As shown from this figure, beside the baseline option, consisting in direct delivery of the offshore electrical power generated to the onshore grid (i.e., zero integration), Options 1 and 2 produce H2, Options 3a and 3b have SNG as final product, and Options 4a and 4b aim at CH3OH production. Two different options are considered for the CO2 feed stream needed for the options producing SNG and CH3OH, that is, CO2 separation from raw gas at the offshore facility (in Options 3a and 4a) or CO2 purchase from the onshore market and delivery to the offshore site (in Options 3b and 4b). The electrical power required for the process operations at the offshore facility is considered to be completely supplied by an OWT farm located in proximity to the gas platform and linked to it by means of an electrical grid connection. The 500 km pipeline can be adopted to deliver a H2
natural gas mixture and SNG, while the 400 km pipeline can be used to supply gaseous CO2 from the shore. Regarding the transportation of pure H2, the maximum operating pressure

6.1 Case study 1

FIGURE 6.1 Simplified block diagram of the P2G and P2L routes for offshore renewable energy conversion considered in the analysis (Adapted from [269]).

is set as 100 bar, bearing in mind the experience gained from H2 pipelines mostly realized around the world [126]. For each alternative, the details of the technologies adopted for the process stages, components, and their operating conditions are reported in Table 6.1. Table 6.1 summarizes the process stages and related technologies considered in the reference process scheme of Option 1 based on the state-of-art technologies reviewed in Chapter 2, Offshore Renewable Energy Options, and recommendations reported in Table 5.2. This table also reports the components assumed for each technology, including their main features and operating conditions. Except for the transportation stage, the components of the other process stages considered for Option 2 can be found in Table 6.1, including their features and operating conditions. The components and related features and operating conditions considered for the process stages of Options 3a and 3b illustrated are shown in Table 6.2, except H2 production and sea H2O desalination, whose details are summarized in Table 6.1. The components and related features and operating conditions considered for the process stages of Options 4a and 4b illustrated in Fig. 6.1 are shown in Table 6.3, except the details of H2 production and sea H2O desalination summarized in Table 6.1 and the information of CO2 input supply and H2 storage and CO2 compression reported in Table 6.2.

129

Table 6.1 Features and operating conditions of components of Option 1 (Adapted from [269]). Step

Process stage

Technology

Components

Main features

Input supply

Desalination of sea H2O

Reverse osmosis

Lenntech LennRO desalination unit

2000 L h21 stack capacity, 45% recovery, 95% salt rejection at 25 C

First conversion

H2 production

PEM electrolysis

Siemens Silyzer 200 electrolyzer

First conditioning Transportation

H2 compression

Centrifugal compression Subsea gas pipeline

Compressor

H2 delivery in natural gas mixture

Existing gas pipeline

21 1.25 MW stacked capacity, 225 Nm3 hH2 23 under nominal load, 1.5 L NmH2 fresh H2O demand, 80,000 h lifetime 75% isoentropic efficiency, 95% driver efficiency, pressure ratio 5:1 per stage 10% by volume of H2 in natural gas mixture, 500 km length

Operating conditions 15 C, 75 bar (permeate stream) 80 C, 30 bar

80 C, 30
150 bar 150 bar (delivery pressure)

Table 6.2 Features and operating conditions of some components of Options 3a and 3b (Adapted from [269]). Step

Process stage

Input supply (Option 3a)

Input supply (Option 3b) First conditioning

Second conversion Second conditioning Transport

Operating conditions

Technology

Components

Main features

CO2 capture from raw gas

Membrane separation

Two-stage membrane cascade with retentate recycle

CO2 delivery from onshore market H2 storage

Gas transportation

Existing gas pipeline

9.5% CO2 by volume in feed stream (60 C, 90 C, 7  105), 2% CO2 by volume in retentate stream of the first stage, 90% CO2 by volume in permeate stream of second stage, 81% CO2 recovery 400 km length

Pressurized tank Centrifugal compression Catalytic methanation Centrifugal compression Gas transportation

Tank

48 h storage capacity

30 bar

Compressor

80% isoentropic efficiency, 99% driver efficiency, pressure ratio 2.04:1 per stage 85% energy efficiency (MWhHH of outlet SNG per MWhHHV of inlet H2), molar H2:CO2 ratio 5 4:1 75% isoentropic efficiency, 95% driver efficiency, pressure ratio 5:1 per stage 500 km length

4 C
50 C, 1
30 bar 250 C, 30 bar

CO2 compression SNG production SNG compression SNG delivery

SNG, Synthetic natural gas.

Fixed bed reactor Compressor Existing gas pipeline

50 C, 1 bar (permeate stream of the second stage) 4 C and 26 bar (delivery conditions)

250 C, 30
150 bar 150 bar (delivery pressure)

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CHAPTER 6 Case studies

6.1.2 Definition of the offshore wind turbine farm and reference process schemes According to step 2 of the procedure in Fig. 5.2, the OWT farm and reference process schemes of the alternative hybrid energy options are defined. Given the water depth of the Sleipner area, an offshore floating wind farm is considered for renewable power conversion. Among the possible floating concepts, the Hywind II technology by Equinor consisting of a Spar-Buoy foundation moored with three catenary mooring lines to the seabed, stabilized using ballast, designed to carry one upwind turbine in the region of 2.3
6 MW in water depths greater than 100 m, is considered. Technical and economic data of the renewable plant are taken from a previous study investigating the levelized cost of energy (LCOE) of different floating concepts at a generic Northern European site [270]. The OWT farm is assumed to be composed of 100 wind turbines of 5 MW nominal capacity each, thus leading to total size of 500 MW. The main technical data associated to the plant are summarized in Table 6.4. Table 6.5 reports economic information of the plant. Given the size of the OWT farm, a common reference basis for the analysis of the alternative hybrid energy options is set as the maximum power feed fraction of the wind farm output to the electrolyzers, that is, 10% in this case study. Therefore the total electrolyzers’ capacity is 50 MW for all the alternatives, which corresponds to 753.8 kg h21 of H2 production based on the features of the technology considered in this process stage shown in Table 6.1. Specific battery limits are considered in the assessment. The performance and costs of the OWT farm and its connection with the oil and gas platform are considered in the overall profitability of P2G and P2L strategies but are excluded in the sustainability assessment due to neutrality with respect to the selection of the technological alternatives for electricity conversion. Thus the boundaries for the calculation of the sustainability indicators for each alternative are limited to process stages.

6.1.3 Assumptions made for the sustainability assessment Technical, economic, and environmental dimensions of sustainability are investigated in this case study, thus leaving the societal aspect out of the scope of the analysis. To quantify the technical aspect of the sustainability performance of the alternative process schemes, among the indicators defined in the methodology description, the energy efficiency indicator, η, is selected in this case study. Higher heating values (HHVs) summarized in Table 5.5 are assumed in Eq. (5.1). The mass flowrate m_ of the final product and electrical power required W_ associated with the components of each strategy are estimated based on the data reported in Chapter 4, System Modeling and Analysis (energy analysis description). W_ values are summarized in Table 6.6.

Table 6.3 Features and operating conditions of some components of Options 4a and 4b (Adapted from [269]). Step

Process stage

Technology

Components

Main features

First conditioning

H2 1 CO2 compression

Centrifugal compression

Compressor

Second conversion Second conditioning

CH3OH production CH3OH storage

Catalytic hydrogenation Atmospheric tank

Multi-tubular reactor Tank

75% isoentropic efficiency, 95% driver efficiency, maximum pressure ratio 5:1 per stage 96% conversion efficiency, molar H2:CO2 ratio 5 3:1 4 weeks storage capacity

Transport

CH3OH delivery

Liquid transportation

Supply vessel

200 km voyage

Operating conditions 4 C
50 C, 30
80 bar 240 C, 80 bar Ambient conditions Ambient conditions

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CHAPTER 6 Case studies

Table 6.4 Technical data of the offshore wind turbine farm considered in the analysis (Adapted from [269]). Value/properties Size of offshore wind farm Layout Distance offshore
onshore substations Distance to construction and operations port Offshore converter substation Interarray cable

Export cable Theoretical production Capacity factor Wind farm availability Aerodynamic array losses Electrical array losses Other losses Net load factor Net energy production

500 MW (100 turbines of 5 MW capacity) Square formation (10 3 10) with inner distance between each turbine of 1 km 200 km 200 km 500 MW voltage source converter Alternating current, 33 kV voltage, copper, 300 mm2 area, 191.6 km total length, 224 MW average power transmitted within the farm, 0.3% average power loss Direct current, 320 kV voltage, extruded, 1500 mm2 area, 200 km length, 0.5% average power loss 360 MWh MW21 year21 45.8% 94% 7% 1.8% (interarray, offshore substation, export cable), 1.3% (interarray, offshore substation) 3% 38.1% (interarray, offshore substation, export cable), 38.3% (interarray, offshore substation) 3340.9 MWh MW21 year21 (interarray, offshore substation, export cable), 3357.9 MWh MW21 year21 (interarray, offshore substation)

Regarding the economic performance assessment, both the levelized cost of product (LCOP) and the levelized value of product (LVOP) proposed in the assessment model in Section 5.1.5.2 are calculated for the reference process schemes. The capital expenditure (CAPEX) and operational expenditure (OPEX) required in Eq. (5.3) are calculated as detailed in Chapter 4, System Modeling and Analysis (economic analysis description). The reference currency and year for the cost analysis are euros referred to the year 2018. Thus, if required, cost adjustments are performed by considering conversion rates and total industrial producer price index (PPI) [271] summarized in Table 6.7. The final CAPEX and OPEX values considered for calculation of the economic indicators are summarized in Table 6.6 for the different process stages of the alternatives. The calculation of LVOP in Eq. (5.4) is performed considering both gray and green prices of the final products. The gray prices for H2 and SNG intended for

6.1 Case study 1

Table 6.5 Economic data of the offshore wind turbine farm considered in the analysis (Adapted from [269]). Cost

Cost segment

Value

CAPEX production and acquisition

Turbine (excluding tower) Substructure and tower Mooring system Interarray cable Offshore substation (excluding platform) Export cable Onshore substation Turbine Mooring system Interarray cable Export cable Material Labor Equipment (mother vessel, port facilities, two maintenance vessels) Other equipment

6.41 3.74 4.61 5.38 1.27 8.86 7.15 7.86 1.67 3.77 1.18 7.10 4.80 4.01

CAPEX installation and commissioning

OPEX O&M

OPEX operation phase insurance

             

106 h 106 h 105 h 107 h 105 h 107 h 105 h 105 h 107 h 107 h 108 h 106 h year21 106 h year21 106 h year21

4.04  107 h year21 1.75  104 h year21

All economic values in table are referred to year 2013. CAPEX, Capital expenditure; OPEX, operational expenditure.

the grid (Options 1, 3a, and 3b) are based on the UK 2018 wholesale price of natural gas (nonhousehold consumers) equal to 30 h MWh21 [274] by applying the corresponding HHVs (i.e., 39.4 kWh kg21 for H2, 14.5 kWh kg21 for SNG). For Option 2, the H2 selling price is obtained as the average value between European selling prices for light industry (3.8
9.4 h kg21) and for car and buses (4
7 h kg21) [275]. In the case of CH3OH, the market price for Options 4a/4b is estimated based on the Methanex European posted contract price referred to December 2018 [276]. The values used in this case study are summarized in Table 6.8. The nondomestic renewable heat incentive (RHI) established by the UK Office of Gas and Electricity Markets (OfGem) for the first 40,000 MWh of injected biomethane into the grid between May 22 and December 31, 2018 [277], that is, 57.2 d MWh21, is considered to calculate the equivalent the green prices of H2 in the natural gas mixture in the case of Option 1 and of SNG in the case of Options 3a and 3b. Conversion from d MWh21 to h kg21 is performed by using the 2018 currency conversion rate in Table 6.7 and the HHV value of 39.4 kWh kg21 for H2 and of 14.5 kWh kg21 for SNG. The UK emission trading 21 scheme (ETS) price of 16.37 $ tCO2 referred to year 2018 [278] (i.e.,

135

Table 6.6 Technical, economic, and environmental data of process stages of the alternatives (Adapted from [269]). _ (kW) W

CAPEX (h)

Annual OPEX (h year21)

GHG emissions (kgCO2 eq year21)

9.05  10

3.31  105

6.62  103

1.90  104

5.27  104

4.35  107

4.56  106

3.96  106

 105  106  106

3.17  8.79  2.26  5.68  1.24  7.91  2.20  1.31  8.60  6.87  8.21  1.30  2.21  1.65  9.59  1.02 

104 105 104 106 105 104 105 106 104 106 103 104 105 105 104 105





1.73  107

3.97  106




2.31  107



1.55  103

1.39  10 5.14  102

3.65  106

1.99  105 1.90  106

3.80  3.15  6.87  9.95  9.52 

104 105 106 103 104

9.10  106









4.11  105


8.23  103 2.07  105

Process stage

Option

Desalination

1, 2, 3a, 3b, 4a, 4b 1, 2, 3a, 3b, 4a, 4b 1 1 2 2 3a 3a 3a, 3b, 4a, 4b 3a, 3b 3b 3b 3b 3a, 3b 3a, 3b 4a 4a 4a

9.01
6.17
1.02 3.06
4.99

1.04 3.22
1.36 4.08 5.67

4a, 4b 4b 4b 4b 4b 4a, 4b 4a, 4b

H2 production H2 compression H2 transportation H2 compression H2 transportation CO2 capture CO2 compression H2 storage SNG production CO2 purchase CO2 transportation CO2 compression SNG compression SNG transportation CO2 capture CO2 compression H2 1 CO2 compression CH3OH production CO2 purchase CO2 transportation CO2 compression H2 1 CO2 compression CH3OH storage CH3OH transportation

 102  102  103  102  102

 10  102  103  102  102

7.94
5.64 2.48 6.89 1.58 1.10 1.35

1.64 2.17
9.19 1.92 2.03

 105      

105 108 105 106 107 107

 105  105

CAPEX, Capital expenditure; GHG, greenhouse gas; OPEX, operational expenditure; SNG, synthetic natural gas.





6.1 Case study 1

Table 6.7 Values of currency conversion rates and price indices considered in the cost adjustments. Year

Exchange rate $-h [272]

Exchange rate d-h [272]

PPI index (total market) [273]

2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000

0.85 0.89 0.90 0.90 0.75 0.75 0.78 0.72 0.75 0.72 0.68 0.73 0.80 0.80 0.80 0.88 1.06 1.12 1.09

1.13 1.14 1.22 1.38 1.24 1.18 1.23 1.15 1.17 1.12 1.26 1.46 1.47 1.46 1.47 1.44 1.59 1.61 1.64

104.2 102.0 99.1 100.0 101.6 103.0 103.1 100.8 96.2 93.4 96.3 92.6 90.5 88.1 85.7 83.9 83.8 83.9 83.0

PPI, Producer price index.

Table 6.8 Gray and green market prices of the final products considered for the alternatives (Adapted from [269]). Final product and end use Gray market price (h kg21) Green market price (h kg21)

Option 1

Option 2 (H2)

Option 3a/3b

Option 4a/4b

H2 admixture for grid injection 1.18

H2 for industry and mobility sectors 6.00

SNG for grid injection 0.44

CH3OH for mobility sector 0.43

2.55

6.14

0.94

0.46

SNG, Synthetic natural gas. 21 13.91 h tCO2 by applying the 2018 currency conversion rate in Table 6.7) is assumed to calculate the cost savings of the CO2 emissions allowance thanks to the production of renewable H2 and CH3OH with respect to the fossil counterparts. Considering greenhouse gas (GHG) emissions for H2 produced by means of steam reforming (with CH4 in input) equal to 72.4 gCO2eq MJ21 (i.e.,

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about 10.3 kgCO2eq kgH221) and for H2 produced from electrolysis equal to 0 gCO2eq MJ21 (i.e., 0 kgCO2eq kgH221) [279], the green premium in Option 2 is estimated as the difference between these two GHG emissions multiplied by the considered ETS price, i.e., 0.14 h kg21. The same approach is applied to estimate the savings in the case of Options 4a and 4b by assuming GHG emissions for diesel equal to 95.10 gCO2eq MJ21 (i.e., about 3.80 kgCO2eq kgdiesel21) and for Sunflower biodiesel equal to 32 gCO2eq MJ21 (i.e., about 1.28 kgCO2eq kgdiesel21). The added value for CH3OH as fuel in the mobility sector is 0.035 h kg21. The financial incentive calculated for Options 2, 4a, and 4b are then added to the gray prices to obtain the green selling prices of the products. Table 6.8 summarizes the values of the green selling prices adopted for the alternative strategies. For the economic analysis, a lifetime of 10 years is conservatively assumed for all the alternatives, without any further investment, considering the lifetime of the electrolyzers, which are expected to be the most expensive components of the conversion processes. The CAPEX of each component of the process stage occurs at the beginning of the project, while the OPEX, production, and revenues are discounted each year of the project lifespan. The discount rate r is considered constant along the economic lifetime period and is equal to 8%, in agreement with previous studies [151,167]. Neither income taxes nor depreciation are considered, for the sake of simplicity. The levelized GHG (LGHG) indicator defined in Eq. (5.7) is used to quantify the environmental performance of the alternatives. GHG emissions associated with each reference process scheme are calculated as detailed in Chapter 4, System Modeling and Analysis (environmental impact analysis). The values obtained for the relevant process stages of the six alternatives are summarized in Table 6.6. To provide a concise yet representative comparison between the overall performance of the alternatives based on different aspects, the previously defined indicators are aggregated into a global indicator. Among the approaches for aggregated performance assessment described in Section 5.1.5.6, a noncompensatory MCDA method is applied in this case study since alternatives producing different products (H2, SNG, CH3OH) are compared, thus avoiding the need for normalization of indicators based on process-related target values. The PROMETHEE (Preference Ranking Organization METHod for Enrichment Evaluation) II method is selected to obtain a complete ranking of the alternatives based on the net outranking flow (Φ) ranging between 21 and 1. The calculation of the aggregated scores of the six alternative conversion technologies is performed by using Visual Promethee 1.4 Academic Edition software [209], where PROMETHEE II is implemented. The values of the indicators estimated by applying Eqs. (5.1), (5.3), (5.4), and (5.7) are provided as an input, using an evaluation performance matrix. The maximization is then selected for η and LVOP, while the other two indicators are minimized. Based on the performance data provided in input to the software, the recommendations provided from the tool about the most suitable preference function and related preference thresholds are applied.

6.1 Case study 1

The evaluation of relative importance weights among indicators is performed by applying the approach described in Section 5.1.5.6 based on time-spacereceptor criteria and individualist-egalitarian-hierarchist perspectives. Equal weighting is further added to the archetypes of decision makers. Before scoring and weighting, the indicators selected for the assessment of the case study are classified in terms of time, space, and receptor based on their definitions. Being a measure of resource use, η is considered important for a long-term perspective where resource scarcity may require more efficient methods. It is relevant on a global scale since improvements may lead to better resource utilization, lower emissions, and reduced costs. The ecosystem is evaluated as the main receptor by this indicator, but also humans may be influenced since resource use and costs are human-related areas. LCOP exhibits mainly short-term, local/regional, and anthropocentric perspectives. However, it can be considered unimportant over time since externalities (e.g., available resources, sociopolitical variations and other local/regional factors) are internalized into the cost assessment. As opposed to LCOP, LVOP can influence both humans and ecosystem receptors at a regional scale because financial incentives on market prices of the products may be implemented due to policy adopted by the local government as a response to environmental issues and resource depletion. With respect to time, it shows the same features of LCOP. Finally, LGHG is considered a very important long-term and global-scale concern, even though its effect may be short-term and at a local scale based on the incidence of weather. Thus it is evaluated as neutral on time and space criteria. Both humans and ecosystems are sensitive receptors. For each archetype, the given scores are assigned to indicators based on the three criteria. The overall score to each indicator is estimated as the sum of the scores given with respect to each criterion. The relative importance of the indicators is determined as the ratio of the associated overall score to the sum of overall scores. All the values of assigned scores and weights are reported in Table 6.9. Fig. 6.2 illustrates the comparison of weights based on the different perspectives previously mentioned to be used in the aggregated sustainability assessment. As shown in this figure, the individualist archetype prioritized LCOP, LGHG, and LVOP because of its selfish and resilient viewpoint, while the egalitarian method gave higher priority to η besides LGHG. On the other hand, slight variations in prioritizing the indicators appeared in the case of the hierarchist perspective, thus leading to similar findings to those of the equal weighting scheme (25% to each indicator).

6.1.4 Assumptions made for the profitability assessment For the purpose of the profitability assessment based on net present value (NPV) in Eq. (5.16), three business cases (BCs) are analyzed and compared with one another: BC1, BC2, and BC3. BC1 represents the baseline situation of zero

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Table 6.9 Scores and importance weights assigned to indicators based on different perspectives (Adapted from [269]). Indicators for sustainability performance assessment η

LCOP

LVOP

LGHG

Schemes

Criteria

Score 1
5

Score 1
5

Score 1
5

Score 1
5

Individualist

Time Space Receptor Sum Importance weight Time Space Receptor Sum Importance weight Time Space Receptor Sum Importance weight

2 3 2 7 0.179 5 5 4 14 0.292 3.5 4 3 10.5 0.273

3 4 4 11 0.282 3 3 1 7 0.146 3 3.5 2.5 9 0.260

3 2 5 10 0.256 3 4 5 12 0.250 3 3 5 11 0.234

3 3 5 11 0.282 5 5 5 15 0.313 4 4 5 13 0.234

Egalitarian

Hierarchist

LCOP, Levelized cost of product; LGHG, levelized greenhouse gas; LVOP, levelized value of product.

FIGURE 6.2 Weights among the indicators based on different perspectives for the aggregated sustainability assessment (Adapted from [269]).

6.1 Case study 1

integration of wind energy into the offshore oil and gas operations; thus only electrical grid connection between the offshore wind farm and the onshore substation, as well as revenues from the electricity selling to suppliers, is conceptualized. BC2 represents the situation described in Fig. 6.1, which considers the electrical connection between the wind farm and the platform but only the gas grid or ship travel to shore; thus the entire offshore wind power is used for the processes at the platform, and only revenues coming from the selling of final products to the onshore markets can be gained. Finally, BC3 aims to conceptualize the situation of an offshore hybrid energy system integrated into the electrical network that uses electrolysis as an energybalancing system to limit wind curtailments according to the grid connection agreements to handle large electrical power fluctuations of the wind power plant and absorbing the unpredicted excess of power production, otherwise curtailed, for the synthesis of valuable chemicals. As a result, an electrical export cable to shore is present in addition to the connection mentioned in BC2; thus a part of offshore wind power is directly routed to the land, and the other part is converted into the chemical energy carrier at the offshore platform. In such a situation, a double revenue can be obtained coming from the selling of both electricity and the final product of the pathway. To perform the profitability analysis for the different BCs previously described, economic data related to the offshore wind farm in Table 6.5 are adjusted by applying the cost indices in Table 6.7. The assumptions on the economic lifetime and discount rate mentioned for the LCOP and LVOP calculation are applied also for the calculation of NPV. Moreover, the following further specific assumptions are made. The offshore substation, including switchgear, transformers, converter electronics, and filter, and used to increase the voltage prior to its use, is supposed to be located at the offshore gas platform considered in the analysis; thus the platform costs of the offshore substation are disregarded in the calculation of the CAPEX, as illustrated in Table 6.5. The electrical connection between the OWT farm and the gas platform (where the offshore substation is located) is considered for all BCs, while the export subsea cable between the offshore substation and onshore substations is evaluated only in the cases of BC1 and BC3. According to these two situations, different values for electrical losses, load factor, and net energy production are reported in Table 6.4. Moreover, the CAPEX associated with export cable and its installation, as well as the CAPEX associated with the onshore substation reported in Table 6.5 are excluded from the profitability analysis of BC2 but are included in the assessment of BC1 and BC3. For the assessment of BC1 and BC3, market prices of renewable electricity delivered to the grid are estimated based on the current policy implemented in the reference country. The United Kingdom supports large-scale renewable electricity projects, including offshore wind farms, under the renewables obligation (RO) mechanism [280]. According to RO, once the generating plant has been

141

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accredited, generators can be issued renewables obligation certificates (ROCs) by the UK OfGem, based on the net renewable electricity generated each month by the plant, and then sell them directly or indirectly to electricity suppliers, providing an additional income on top of selling electricity at the wholesale price. Suppliers are asked to purchase ROCs amounting to a “headroom” figure for each financial year, involving a forecasting renewable generation and adding 10%. Suppliers can then redeem the purchased ROCs to show their compliance with the obligation, paying a penalty in the form of the buyout price for every MWh below the obligation. The buyout payments made are put into a buyout fund, which is redistributed among the suppliers who presented correct number of ROCs at the end of the period. In this case study, it is supposed that eligibility requirements for full accreditation under the RO scheme are met by the OWT farm, being an offshore wind generating station, already commissioned and located within the territorial waters of the United Kingdom and connected to a transmission network in Great Britain, and that then accreditation is granted after the application approval. As a consequence, revenues derived from the electricity market (at the wholesale market price) and ROCs market (at the ROCs recycle value reported each year by OfGem) awarded for eligible output are included in the NPV calculation of BC1 and BC3. For installed capacity after 2016, the level of support for the OWT farm is established as 1.8 ROCs per MWh of eligible electricity produced each month. The UK wholesale electricity market of 64.3 d MWh21 referred to December 2018 [281] and a recycle rate per ROC of 5.85 d/ROC referred to year 2018 [280] are assumed for the calculations. To convert these prices into euros, the corresponding exchange rate reported in Table 6.7 is applied. The net energy production from the OWT farm reported in Table 6.4 is used to calculate the eligible monthly renewable output for the analysis of BC1 and BC3, assuming, for the sake of simplicity, 500 and 440 MW capacities, respectively. Therefore in BC3, about 60 MW of the available wind power is considered for conversion into H2, SNG, CH3OH at the offshore platform, avoiding curtailments. The final annual revenues from renewable electricity selling to the UK market are calculated equal to 1.41  108 h year21 for BC1 and 1.08  108 h year21 for BC3.

6.1.5 Sustainability and profitability assessments results Table 6.10 summarizes the results obtained for the technical, economic, and environmental performance indicators calculated for the case study, based on the data summarized in Table 6.7. The ranking of the alternatives with respect to each indicator is also reported. As shown in Table 6.10, Options 1 and 2 are the most efficient, with values of η around 60%. Option 2 appears slightly more efficient than Option 1 due to the lower delivery pressure assumed for the H2 pipeline, leading to lower electrical power required for compression (Table 6.6). In the other options, the additional process stages of conversion and conditioning needed to produce SNG and

Table 6.10 Technical, economic, and environmental indicators for the alternatives and associated ranking (Adapted from [269]). η

Option Option Option Option Option Option

1 2 3a 3b 4a 4b

LCOP

LVOP (with incentive)

LVOP (no incentive)

LGHG

Value (%)

Rank

Value (h MWh21)

Rank

Value (h MWh21)

Rank

Value (h MWh21)

Rank

Value (kgCO2eq MWh21)

Rank

59.39 59.71 39.92 40.90 43.39 44.82

2 1 6 5 4 3

43.4 212.0 89.0 125.0 123.0 177.0

1 6 2 4 3 5

28.0 142 30 30 120 120

4 1 3 3 2 2

60.2 145.0 64.6 64.6 129.0 129.0

4 1 3 3 2 2

14.3 14.3 132.0 41.4 299.0 108.0

1 1 5 3 6 4

LCOP, Levelized cost of product; LGHG, levelized greenhouse gas; LVOP, levelized value of product.

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CHAPTER 6 Case studies

CH3OH implies a decrease in the expected technical performance. For these processes, η results lower for Options 3a and 3b (around 40%) than for Options 4a and 4b (around 44%). Concerning the ranking of the alternatives based on LCOP, Option 1 shows the best economic performance (i.e., lowest LCOP value) in addition to the best technical performance based on η, while Option 2 demonstrates the highest value of LCOP. This can be attributed to the higher transportation costs associated with this option, as shown in Table 6.6. The two sets of LVOP values calculated, based respectively on gray and green market prices of the final products (Table 6.8), are both summarized in Table 6.10 to evaluate the impact of the financial incentives on the levelized revenues. In both cases, the highest LVOP is associated with the H2 intended for use in industry and mobility (Option 2), followed by CH3OH for mobility (Options 4a/4b). As a consequence, the industry and mobility sectors are the most advantageous end markets for P2G and P2L products. Another finding gained in the case study is that the upgrading from gray to green market prices appears highly beneficial in case of Options 1, 3a, and 3b, for which gas grid injection is the end use of the product. These options show approximately a doubling of the LVOP values when incentives are considered. On the other hand, the highest environmental performance, corresponding to the lowest LGHG value, is obtained for Options 1 and 2, thus confirming the ranking based on η and LCOP. The highest values calculated for Options 3b and 4b are attributed to the larger environmental impact associated with onsite CO2 capture, as reported in Table 6.6. Table 6.11 summarizes the values of the overall aggregated indicators, Φ, derived from the application of the PROMETHEE II method to the technical, economic, and environmental indicators of the six conversion processes considered in the case study. The table also reports the ranking of the alternatives based on different sets of weight coefficients, corresponding to different archetypes of decision makers illustrated in Fig. 6.2. When analyzing the results, Option 1 shows the best aggregated performance according to the individualist, hierarchist, and equal weighting perspectives. Option 2 is the most performant based on the egalitarian scheme, thus confirming the ranking previously identified based on indicators addressing the single aspect of sustainability. The NPV of the alternatives is calculated for the different BCs previously described by applying Eq. (5.16). The results are displayed in Fig. 5.3. As shown in this figure, the baseline situation considered in BC1 results in the most profitable investment with an NPV of about 226 Mh. All the P2G and P2L pathways in BC2 show negative values of NPV, thus indicating that the sole revenues from selling the products are not sufficient to cover the total costs under the assumed input data. In this case, the break-even market prices of the products to reach NPV equal to 0 are estimated as 13.3, 20.4, 6.8, 7.5, 2.4, and 2.9 h kg21 for Options 1, 2, 3a, 3b, 4a, and 4b, respectively. Therefore, with respect to the market prices in Table 5.8, to reach the break-even point, prices should increase about

6.1 Case study 1

Table 6.11 Ranking of the alternatives based on the overall aggregated sustainability indicator for different perspectives (Adapted from [269]). Individualist scheme

Option Option Option Option Option Option

1 2 3a 3b 4a 4b

Egalitarian scheme

Hierarchist scheme

Equal weighting

Φ Value

Rank

Φ Value

Rank

Φ Value

Rank

Φ Value

Rank

0.317 0.213 20.161 20.123 20.184 20.063

1 2 4 5 3 6

0.313 0.410 20.273 20.167 20.258 20.26

2 1 3 5 4 6

0.358 20.262 20.195 20.170 20.176 20.080

1 2 3 5 4 6

0.335 0.273 20.200 20.163 20.181 20.064

1 2 3 5 4 6

three times for Option 2, five times for Options 1 and 4a, six times for Option 4b, seven times for Option 3a, and eight times for Option 3b. When additional revenues deriving from selling electricity to suppliers are considered in BC3, more favorable results are obtained: The NPV values of all the alternatives increased proportionally with BC2, becoming less negative as in Options 2, 3a, 3b, 4b, and even positive in the case of Option 1 (37 Mh) and Option 4a (10 Mh).

6.1.6 Sensitivity analysis results In this case study, the adoption of the approach based on time-space-receptor criteria and different archetypes of decision makers for weights elicitation in the sustainability assessment is considered as an alternative way to reduce uncertainty associated with this stage; thus sensitivity analysis is not applied to verify the robustness of the Φ value obtained for the alternatives. In the case of profitability analysis, the sensitivity analysis is applied to the most profitable P2G and P2L alternatives emerging in the NPV assessment in Fig. 6.3, that is, Option 1 and Option 4a of BC3. As described in Section 5.5, it is possible to take advantage of a one-at-a-time fixed variation with respect to the baseline value of cost factors involved in the NPV calculation to identify the most influential parameters. The effects on NPV of the CAPEX and OPEX associated with the technologies of the process stages, the CAPEX and OPEX of the wind farm, green market prices of the product, wholesale electricity price, and discount rate are investigated by varying their base value within a 6 20% range, one at a time. Fig. 6.4 shows the tornado charts obtained for the selected alternatives. As shown in Fig. 6.4, all the input factors drive a linear direct variation in NPV, except for the market prices of the products and wholesale electricity price, which varied inversely. For both Options 1 and Option 4a, the wholesale electricity price is identified as the most critical parameter, followed by the OPEX and CAPEX of the wind farm. The next ones are the discount rate and

145

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FIGURE 6.3 Comparison of NPV of the alternatives in the BCs considered for profitability analysis. BC, Business case; NPV, net present value (Adapted from [269]).

FIGURE 6.4 Tornado charts from sensitivity analysis on NPV of (A) Option 1 and (B) Option 4a of BC3. BC, Business case; NPV, net present value (Adapted from [269]).

market prices of the products. Finally, the CAPEX and OPEX associated with electrolyzers are other relevant factors derived from the analysis of both alternatives. These eight input parameters are thus considered as the most uncertain factors in the analysis. To study uncertainty propagation from the data set for the results, the Monte Carlo approach is adopted by assuming a probability distribution for each uncertain parameter and then repeating the calculation for a reasonably high number of times, randomly varying it within proper ranges of values. Triangular distribution

6.1 Case study 1

is adopted in this case study for all the key parameters, since it is often used in estimating cost risks because the math is simple, and it nearly approximates a lognormal distribution [283]. The uncertainty ranges are determined based on specific considerations discussed here. Table 6.12 summarizes the minimum and maximum values of the ranges adopted for the Monte Carlo simulations. The maximum values of the ranges for the CAPEX and annual OPEX of the wind farm are derived from the CAPEX and annual OPEX estimates reported for commercial floating wind deployments [284], that is, d2.7  106 and d0.09  106/year referred to the year 2015, respectively. On the other hand, the minimum values of the ranges associated with these parameters are evaluated based on the cost prospects for the offshore wind farm in the North Sea after 2030 [285]: A value of d2.84  108 referred to the year 2018 is obtained for the CAPEX by summation of the contributions of its components, while a value of d4.6  107/year referred to year 2018 is estimated for annual OPEX. To convert the cost values into euros referred to year 2018, the average exchange rate and price indices reported in Table 6.7 are used. The limits for the CAPEX and annual OPEX of the electrolyzers are derived from statistical data [275]. Information about the PEM electrolyzer operating at 30 bar and 1 MW at year 2017 is used for the maximum values of the ranges: CAPEX of 1500 h kW21 and, O&M costs equal to 4% of CAPEX year21 and stack replacement costs of 600 h kW21 for every 40,000 h of operation (i.e., 40% of CAPEX year21). On the other hand, for minimum values of the ranges, data about the PEM electrolyzer operating at 60 bar and 20 MW capacity at year 2025 are considered, that is, the CAPEX of 700 h kW21, and O&M costs are equal to 2% of CAPEX year21, and stack replacement costs of 210 h kW21 every 50,000 h of operation (i.e., 30% of CAPEX year21). By multiplying for capacity considered for electrolyzers in the case study, the final values of the limits can be obtained. The minimum value of the range for the H2 market price in Option 1 is considered equal to the H2 injection price forecast in 2025 in the United Kingdom [275], under the assumptions that injection tariffs will decrease as a carbon tax emerges in the near future, leading to an increase in the wholesale natural gas price. On the contrary, the maximum value of the range associated with this variable is estimated by assuming the nondomestic RHI established by OfGem for the first 40,000 MWh of injected biomethane first published before 2018 [277], that is, d65.1/MWh between January 1 and March 31, 2016. From this information, the price in h kg21 is obtained by applying the 2016 currency conversion rate in Table 6.7 and HHV value of 39.4 kWh kg21 for H2. Concerning the market price of CH3OH in Option 4a, minimum value of the range is assumed equal to the gray market price (Table 6.8). The maximum limit of the range is estimated by considering the forecast ETS price of the CO2 emis21 sions allowance in 2025 [275], equal to 28.1 h tCO2 and by calculating the added value of renewable CH3OH due to cost savings according to the procedure described for green market price estimation.

147

Table 6.12 Ranges assumed for the key uncertain parameters in the Monte Carlo simulation (Adapted from [269]).

Minimum value of the range Maximum value of the range

Wind farm CAPEX (h)

Wind farm OPEX (h)

Electrolysis CAPEX (h)

Electrolysis OPEX including O&M and stack replacement (% CAPEX year21)

H2 price in Option 1 (h kg21)

CH3OH price in Option 4a (h kg21)

Electricity price (h MWh21)

Discount rate (%)

3.21  108

5.20  107

3.50  107

7

1.400

0.428

59.9

2

1.94  109

6.47  107

7.50  107

12

3.129

0.499

75.2

14

CAPEX, Capital expenditure; OPEX, operational expenditure.

6.1 Case study 1

The UK wholesale electricity price attributed to wind power sold to the grid for the year 2017 [275] is assumed as the minimum limit of the range in the sensitivity analysis, while the price value forecast for year 2025 is considered the maximum limit. Discount rate is instead varied as suggested in a previous study [286]. Fig. 6.5 displays the results of the analysis in terms of distribution of NPV differences between couples of the selective alternatives (Option 1 and Option 4a in BC3) with respect to the baseline situation BC1, calculated by means 106 Monte Carlo runs. As shown in Fig. 6.5, the ranking of both the options with respect to BC1 is affected by the variability in the input parameters. In particular, Option 1 has about a 40% probability of being more profitable than BC1, while Option 4a shows a higher NPV for an approximately 30% probability. Therefore the results of the analysis confirm the profitability ranking illustrated in Fig. 6.4 over a moderate number of simulations. It is verified that the increase in the number of runs up to 107 simulations does not affect the findings illustrated in Fig. 6.5.

FIGURE 6.5 Cumulative probability of the NPV differences of Options 1 and 4a in BC3 with respect to BC1. BC, Business case; NPV, net present value (Adapted from [269]).

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CHAPTER 6 Case studies

6.2 Case study 2: OWT farm and G2P offshore hybrid energy systems This case study concerns a gas production installation located in a depleted field in the Adriatic Sea relatively close to the shore, where the feasibility of an OWT farm is being investigated for the integration of renewable power into the onshore electrical network. As a result, this case study provides the opportunity to improve the dispatchability of offshore wind energy and valorize the untapped gas resources using gas turbine (GT) technologies at the offshore facility. The aim of the analysis is to prove the capacity of the sustainability assessment methodology presented in Section 5.2 to compare and rank alternative G2P hybrid energy options at the defined offshore site.

6.2.1 Definition of the offshore oil and gas site and renewable power plant An offshore depleted gas field located in the Adriatic Sea and relatively close to the onshore grid is being considered as the location for the development of the G2P offshore hybrid energy options. Offshore wind energy is selected as a renewable source to be exploited for energetic valorization of the site and integrated into the onshore electrical network, given a previous study investigating the feasibility of an offshore wind farm in the northern Adriatic Sea, off the coast of Rimini (Italy) [97]. As required from step 0 of the procedure illustrated in Fig. 5.3, the offshore oil and gas site is defined providing input data about the field and infrastructures. The oil and gas platform is supposed to be one of the gas production platforms in the A.C 1.AG. block in the northern Adriatic Sea, where water depth is about 25 m. Natural gas is actually extracted by means of 18 wells out of a total of 58 drilled wells at relatively low pressure and sent via pipeline to the Casalborsetti onshore gas gathering and treatment plant (Ravenna). Average gas production at the field is estimated at the end of 2016 as 54,809,296 Sm3 year21. The platform at the field is a multileg fixed structure with topside area of 48 3 26 m2, aging about 25 years and located around 20 km from the coast [287]. No subsea cables to the grid or other platforms are installed in the area. The closest onshore transmission grid (alternating current, 380 kV voltage) operated by the Italian TSO TERNA [288] is located in Ravenna, about 25 km away from the gas field. According to step 1 of the methodology in Fig. 5.3, data about wind speed and frequencies are retrieved from an experimental campaign launched in the area between November 2013 and November 2016 through an anemometer installed at the Garibaldi A platform at 50 m above the sea level. The average 10 min wind speeds collected in the year 2015
16 are used to assess the wind energy potential in this case study.

6.2 Case study 2

The observed probability density function of the wind speed at 50 m a.s.l. is shown in Fig. 6.6. The average observed wind speed yields 3.79 m s21. Different methods suggested in the literature [215,289] are used to determine the parameters of the Weibull fitted distribution, that is, the shape factor k and the scale factor c in Eq. (5.20). The parameters identified with the empirical method (k and c equal to 1.54 and 4.21, respectively) are found to best fit with a coefficient of determination R2 of 0.949, as shown in Fig. 6.6. The arithmetic mean is used for the sake of simplicity to convert measured wind speeds into a series of hourly values in the analyzed period. The ERA5 reanalysis data set for global climate and weather by the European Centre for Medium-Range Weather Forecasts (ECMWF) [290] is adopted in this case study to retrieve short-term forecast data about wind speed in the selected site. ERA5, with free access, is developed by means of the Copernicus Climate Change Service and provides hourly values of several terrestrial and oceanic weather variables from 1979 to 2
3 months before the present, with a global horizontal coverage and horizontal resolution 0.25 3 0.25 . Among the available parameters, the reduced resolution 10-member ensemble (EDA) subdaily data related to horizontal and vertical wind speeds at 10 m above sea level are downloaded for the analysis. The forecast can be downloaded at two different initialization times in the day (06:00 and 18:00) for four different forecast horizons or steps (3, 6, 9, 12 h). Therefore, in case of instantaneous parameters, the horizontal and vertical wind speeds from the forecast at time 06:00 and step 3 h represent

FIGURE 6.6 Data about the wind speed at the selected offshore site (experimental probability density and Weibull distribution fitted with different methods).

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the wind speeds at 06:00 1 3 h, that is, at 09:00. Similarly, the horizontal and vertical wind speeds from the forecast at time 06:00 and step 6 h represent the wind speeds at 06:00 1 6 h, that is, at 12:00. Data from ERA5 for the forecast steps 3 and 6 h are considered in this case study: Data related to forecast 3 h represent wind speeds at 09:00 and 21:00 in the day, while data related to forecast 6 h represent wind speeds at 12:00 and 0:00 of the next day. Having downloaded the data for the period of interest, the resulting hourly wind speed to be used for wind power calculation is obtained by combining the horizontal and vertical components of wind speeds from ERA5. Figs. 6.7 and 6.8 illustrate the trend of the resulting wind speeds at 09:00 and 21:00, respectively, related to the forecast horizon 3 h over the analyzed period. Figs. 6.9 and 6.10 show the trend of the resulting wind speeds at 12:00 and 00:00 (next day), respectively, related to forecast horizon 6 h over the analyzed period. To obtain the wind speeds for each hour of the day in the period of interest for a given forecast horizon, a simplified approach based on arithmetic mean between the available values is applied, thus filling the gaps in the wind speed series related to the two forecast steps from ERA5. To take an example, with reference to the forecast horizon 3 h, the wind speed value at 15:00 is first calculated as the arithmetic mean between the available values from ERA5 at 09:00 and 21:00; then the wind speed value at 12:00 is the arithmetic mean between the value at 09:00 (available in ERA5) and the value at 15:00 (previously calculated), and so forth to obtain the hourly values in the year. The same approach is applied to the series related to the forecast horizon 6 h. According to step 2 of the procedure in Fig. 5.3, the OWT model is chosen to calculate the real and forecast wind power curves from the wind speed data. Given the average wind speed of 3.8 m s21 and the water depth of 25 m at the offshore site, the commercially available Nordex N90/2500 Offshore by the German Nordex SE characterized by the cut-in speed of 3 m s21 and monopile

FIGURE 6.7 Forecast wind speeds (horizon 3 h) at time 09:00.

6.2 Case study 2

FIGURE 6.8 Forecast wind speeds (horizon 3 h) at time 21:00.

FIGURE 6.9 Forecast wind speeds (horizon 6 h) at time 12:00.

FIGURE 6.10 Forecast wind speeds (horizon 6 h) at time 00:00 of next day.

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steel substructure is considered in this case study. The technical data of the OWT are summarized in Table 6.13. The power curve of Nordex N90/2500 Offshore and related power coefficient Cp trend are illustrated in Fig. 6.11. The annual energy production (AEP) at the offshore site by means of the selected OWT is estimated as described in the methodology description. Before this calculation, the wind speeds measured from the anemometer are adjusted to the hub height of the turbine (Table 6.13) according to Eq. (5.19). Average wind speed in the adjusted sample of data is 4.04 m s21. The parameters of a Weibull fitted distribution based on the empirical method are k and c equal to 1.54 and 4.46, respectively, with coefficient of determination R2 of 0.960. Power values are derived by using the power curve in Fig. 6.11. The number of hours in the year is considered as 8784 and the availability of OWT as 0.97. By applying Eq. (5.21), the AEP obtained is equal to 2052 MWh year21. Capacity factor CF of the selected OWT over the analyzed period is derived (9.35%). The nominal size of the OWT farm is fixed in this case study as 50 MW, as similarly considered in a previous feasibility study in the northern Adriatic Sea [97], thus 20 NORDEX N90/2500 Offshore turbines are considered. The farm is supposed to be placed in proximity of the gas production platform, linked via subsea cables to the platform where an offshore substation increasing the voltage (e.g., up to 132 kV) is installed. An export high-voltage alternating current transmission line of about 20 km is assumed between the platform and the coast. The available wind power from OWT farm, Pwind;avail , on an hourly basis is calculated by using real wind speed data and Arot of the selected turbine in Eq. (5.17) and then multiplying for the number of turbines in the farm. The actual electrical power produced by the OWT farm on an hourly basis is determined by using the real and forecast wind speeds data and the power curve in Fig. 6.11 for a single OWT and then multiplying for the number of turbines in the farm. Adjustments of the measured and forecast wind speeds to the hub height are applied by means of Eq. (5.19). For a renewable nonprogrammable power plant with size greater than 10 MVA linked to the electrical grid, the Italian dispatching system operates Table 6.13 Main technical data of Nordex N90/2500 Offshore wind turbine [291]. Parameter

Value

Nameplate capacity Rotor diameter Swept area Hub height Cut-in speed Rated speed Cut-off speed Wind class

2500 kW 90 m 6.362 m2 104 m 3.5 m s21 12 m s21 25 m s21 IEC IIa

6.2 Case study 2

FIGURE 6.11 Power curve for NORDEX N90/2500 Offshore [292].

under the single pricing mechanism; thus the economic penalization or gain associated with the plant depends only on the global sign of the zonal imbalance (i.e., the imbalance associated with the macro area where the dispatching point under analysis is located) [293]. According to this model, the producer should pay TERNA, in the case of negative imbalance, and obtain a remuneration by TERNA in the case of positive imbalance from the power plant: If the power imbalance of the plant has an opposite sign than the zonal imbalance during the same relevant period in the macro area where the dispatching point is located, such an imbalance is not fully penalized but may receive some benefits, for example, producer may gain more than the actual electricity market price for the positive imbalance or pay TSO a lower price than the actual electricity market price for the negative imbalance. Table 6.14 summarizes the principles of the single pricing mechanism. In this table, PriceMGP represents the price of the accepted supply offers in the day-ahead market, while PriceMGPk and PriceMGPm are the average prices of the bids and supply offers, respectively, accepted in the market of ancillary services in real-time balance, weighted by volume. Considering the incentive scheme for the promotion of renewable energy sources other than solar PV, the Italian legislation [294] establishes that a new renewable plant or hybrid energy plant is eligible to ask Gestore Servizi Energetici for a feed-in tariff (FIT) based on the renewable type and size of the plant. In the case of offshore wind power, small-scale plants ranging between 1 and 60 kW of nominal power can directly request permission for FIT after starting the production, while large-scale plants (nominal power greater than 5 MW) should participate in public unique bid auction to determine the incentive level

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Table 6.14 Summary of the single pricing mechanism for unbalance remuneration or penalization.

Positive zonal unbalance Negative zonal unbalance

Positive imbalance of power plant

Negative imbalance of power plant

Producer receives by   TSO min PriceMGP ; PriceMBk Producer receives by  TSO max PriceMGP ; PriceMBm

Producer pays TSOmin  PriceMGP ; PriceMBk Producer pays TSOmax  PriceMGP ; PriceMBm

MB, Mercato del bilanciamento (balancing market); MGP, Mercato del giorno prima (day-ahead market).

for the produced power and, if entered in a useful position, ask for the FIT after realizing the plant and starting the production. For plants with nominal power greater than 500 kW, FIT is an incentive for the net renewable power injected into the grid defined as follows: InOWT 5 Tb;red 1 Tprem 2 PriceMGP

(6.1)

where InOWT in h MWh21 is the incentive for offshore wind power produced from the plant and injected into the grid available for a maximum of 25 years, Tb;red is the base tariff (165 h MWh21) reduced by a given percentage assigned during the auction (between 2% and 40%), PriceMGP is the zonal electricity market price in the day-ahead market, Tprem is the premium tariff (40 h MWh21) when the producer covers the costs for the connection of the plant to the grid.

6.2.2 Definition of the dispatching power plan and sizing of the gas turbine park EasyFit software [295] is adopted in this case study to perform the statistical analysis of the prediction errors (ξ) calculated on an hourly basis by means of Eq. (5.26) for the forecast horizons 3 and 6 h. For each month of the analyzed year, a histogram of errors is obtained, and the best fitted distribution is properly selected. Tables 6.15 and 6.16 summarize the distributions and related parameters adopted for each monthly error sample based on forecast horizons of 3 and 6 h, respectively. Two different probabilities of correct dispatching, Probd , are considered in this case study, that is, 80% and 90%. The dispatching error, ξ d, corresponding to these two probabilities is calculated for each monthly error sample based on forecast horizons 3 and 6 h. Cumulative distribution function (CDF) of the fitted distribution of prediction errors is set equal to 20% to estimate ξ d values corresponding to Probd of 80%, while a CDF of 10% is used to estimate ξd values corresponding to Probd of 90%. ξd findings for the monthly data based on forecast horizon 3 h are reported in Table 6.15, while Table 6.16 summarizes the results

6.2 Case study 2

Table 6.15 Probability distributions of prediction error based on forecast horizon 3 h and dispatching error corresponding to 80% and 90% probability of correct dispatching. Month of the period 1 2

3 4 5 6 7 8 9 10

11 12

Fitted distribution of the prediction errors ξ (and related parameters)

Dispatching error ξd (MW) corresponding to Probd of 80% or CDF 5 20%

Dispatching error ξd (MW) corresponding to Probd of 90% or CDF 5 10%

Cauchy (σ 5 0.55, μ 5 0) Log-logistic 3P (σ 5 11.6, β 5 27.4, γ 5 226.5) Cauchy (σ 5 1.733, μ 5 0) Cauchy (σ 5 4.655, μ 5 0) Cauchy (σ 5 1.620, μ 5 20.249) Cauchy (σ 5 3.014, μ 5 20.695) Laplace (λ 5 0.148, σ 5 20.823) Cauchy (σ 5 1.428, μ 5 0) Cauchy (σ 5 1.341, μ 5 0) Log-logistic 3P (σ 5 6.47108, β 5 3.32109, γ 5 23.32109) Cauchy (σ 5 1.537, μ 5 0) Cauchy (σ 5 3.066, μ 5 0)

20.757

21.693

22.158

23.800

22.386

25.335

26.407

214.327

22.479

25.235

24.484

29.971

27.024

211.716

21.967

24.395

21.845

24.126

27.123

211.295

22.115

24.730

24.220

29.436

CDF, Cumulative distribution function.

of ξd for monthly data based on the forecast horizon 6 h. By applying Eq. (6.27), the hourly dispatched power series (Pd) series is derived for the different forecast horizons and Probd , bearing in mind that the smallest possible value for Pd is 0. The size of the GT park coupled with the OWT farm is estimated based on the differences between the hourly Pf and Pr values during the hours at which Pr is lower than Pf, also indicated as PGT;max . Table 6.17 summarizes the monthly PGT;max values obtained for the two different forecast horizons considered in the analysis. From Table 6.17 the highest PGT;max for forecast horizon 3 h is 49.2 MW associated with month 5, while 50.0 MW in month 6 is the greatest PGT;max for

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Table 6.16 Probability distributions of prediction error based on forecast horizon 6 h and dispatching error corresponding to 80% and 90% probability of correct dispatching. Month of the period 1 2 3 4 5 6 7 8 9 10 11 12

Fitted distribution of the prediction errors ξ (and related parameters)

Dispatching error ξd (MW) corresponding to Probd of 80% or CDF 5 20%

Dispatching error ξd (MW) corresponding to Probd of 90% or CDF 5 10%

Cauchy (σ 5 0.90, μ 5 0) Cauchy (σ 5 0.34, μ 5 0) Cauchy (σ 5 2.17, μ 5 0) Laplace (λ 5 0.09, σ 5 20.44) Cauchy (σ 5 1.786, μ 5 20.271) Cauchy (σ 5 3.247, μ 5 20.946) Cauchy (σ 5 2.429, μ 5 20.682) Cauchy (σ 5 2.329, μ 5 20.609) Cauchy (σ 5 2.464, μ 5 20.868) Cauchy (σ 5 2.086, μ 5 20.550) Cauchy (σ 5 2.547, μ 5 0) Cauchy (σ 5 4.097, μ 5 0)

21.245

22.784

20.470

21.050

22.984

26.672

210.477

218.067

22.723

25.766

25.415

210.939

24.025

28.157

23.814

27.775

24.259

28.450

23.422

26.971

23.501

27.839

25.640

212.611

CDF, Cumulative distribution function.

forecast horizon 6 h. As a matter of fact, the nameplate size of the GT park is approximated to 50 MW in this case study. Given this capacity, aeroderivative GTs are evaluated suitable for the park. The SGT-A05 KB7HE model by Siemens is selected for the GT park. Technical data of this turbomachine is the nominal power at full-load (PGT;1;nom ) of 5.8 MW, electrical efficiency at full-load (ηGT;nom ) of 32.2%, footprint 9 3 27 m2. Therefore the GT park in this case study is considered to be composed of 9 machines for a total nominal power (PGT;nom ) of 52.2 MW, total electrical efficiency at full-load (ηGT;nom ) of 32.2%, total footprint 219 m2. This latter value is considered reasonable, given the topside dimensions of the offshore gas platform where the GT is supposed to be installed.

6.2 Case study 2

Table 6.17 Maximum power which could be provided from gas turbines (GTs) for the two forecast horizons. Month

PGT;max (MW) for forecast horizon 3 h

PGT;max (MW) for forecast horizon 6 h

1 2 3 4 5 6 7 8 9 10 11 12

49.1 22.1 48.9 39.7 49.2 41.1 27.8 31.8 48.5 47.2 25.3 49.0

49.9 38.1 50.3 47.1 48.7 50.0 40.4 21.9 48.7 49.7 41.7 49.9

GHG emissions from a conventional power plant are legislated in Italy by the European ETS scheme based on the cap and trade principle. The ETS legislation creates allowances that represent essentially rights to emit GHG emissions. Each year, some allowances are given for free to certain participants, while the remaining are sold via auctions. At the end of the year, participants return allowances based on the actual emissions in that year. A participant that has insufficient allowances either reduces the emissions or buys more allowances on the market.

6.2.3 Assumptions made for the assessment Four different scenarios (SCs) are compared in this case study to evaluate the influence of different forecast horizons (3 and 6 h) and different probabilities of correct dispatching (80% and 90%) on the sustainability performance of alternative G2P offshore hybrid energy options at the defined offshore site. A reference SC (SC1) is considered consisting of forecast power data based on time horizon 3 h and dispatched power data defined with Probd equal to 80%. From SC1, other three SCs are then introduced with respect to this reference scenario: SC2 is derived by increasing Probd to 90% and considering the same forecast horizon as SC1. SC3 considers an increased forecast horizon of 6 h but the same Probd as SC1. SC4 is based on both a higher forecast horizon and Probd than SC1 (6 h and 90%, respectively). Given the definition of the OWT farm (50 MW nominal size) and the GT park (52.2 MW nominal size); in this case study, the comparative assessment of the defined SCs based on sustainability indicators is focused on a more restricted yet

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representative interval than the entire analyzed year to better demonstrate the potential of the approach. Three days between February 26 and 28, 2016 (month 4 of the period under analysis) are selected. To quantify the technical aspect of sustainability performance of the G2P hybrid energy system in the different SCs, the electrical efficiency indicator, ηel , defined in Eq. (5.30) is adopted. Pwind;avail values are estimated by using measured the wind speeds and swept area of the rotor in Eq. (5.17). PGT and Pfuel are calculated by applying the control strategy according to step 5 of the methodology. The estimation of the part-load efficiency is performed by applying Eq. (5.28) for aeroderivative GTs. The part-load power ranges illustrated in Table 5.14 are used to determine the number of operating turbines before the next switch-off. To compare the environmental performance of the G2P offshore hybrid energy options, the LGHG defined in Eq. (5.37) is used. An emission factor of 21 202 kg MWhfuel suggested for aeroderivative GTs [105] is assumed to estimate the hourly GHG emissions from the operating machines of the park. Regarding the economic performance assessment, both LCOE and levelized value of energy (LVOE) defined in Eqs. (5.31) and (5.32), respectively, are calculated for the different SCs. The CAPEX and OPEX of the GTs required in Eq. (5.31) are derived from the literature costs of aeroderivative GTs in a 50 MW power plant [156]. The reference currency and year for the cost analysis are euros referred to 2016. Thus cost adjustments are performed by considering conversion rates from dollars to euros and PPI values from the years 2013 to 2016, summarized in Table 6.7. Literature investment and O&M costs for a 54 MW OWT farm composed of 15 turbines in the Northern Adriatic Sea [97] are approximated in this case study to the CAPEX and OPEX of the renewable plant in Eq. (5.31), due to the similarity in features of the plant and location. No cost adjustments are applied to these data. Moreover, decommissioning costs are disregarded in the analysis. CAPEX and OPEX assumptions and values for the OWT farm and GTs in the case study are summarized in Table 6.18. It is worth noting that the OPEX associated to GTs is the only parameter varying among the SCs since it relies on the number of operating turbines employed every hour to fulfill the defined dispatching power plan. Concerning the revenues and costs for the positive and negative unbalances required for calculation of LVOE in Eq. (5.32), the single pricing model is supposed to be applied to energy imbalances generated by the difference between the dispatched power Pd declared to the grid operator and the total produced from the OWT farm and GTs intended for grid injection. The dispatching point is considered to be located in Ravenna; thus the macro area for imbalance prices estimation is assumed to be North Italy. Moreover, in this case study, it is supposed that eligibility requirements for access to unique bid auctions for renewable incentives are met by the OWT farm, being a new offshore wind generating station with a nominal capacity greater than 5 MW connected to the Italian electrical grid, and then accreditation of the incentive is granted after the request approval. InOWT in Eq. (6.1) is calculated by making the following assumptions: The net energy is

6.2 Case study 2

Table 6.18 Economic data of the offshore wind turbine (OWT) farm and gas turbines (GTs) considered in the hybrid energy system. Cost

Cost segment

CAPEX of Onshore yard OWT farm

Literature assumption

Value

Estimate of the activity cost at the port of Ravenna (Italy) 4.32  103 kh per turbine

4.59  106 h

Wind turbine structure (nacelle, tower, generator) Transition piece and 1.4  103 h t21 of steel, 467.682 t per support foundation structure (monopile) Export cable to shore 1.3  103 h m21 Transformer and intra2.39  102 h kW21 array cables Wind turbine installation 6 days/turbine Foundation installation 6 days/support structure Contingency 2.45  102 h kW21 Project authorization 3.17  102 h kW21 O&M 1.44  102 h kW21 year21

OPEX of OWT farm CAPEX of Capital cost GTs OPEX of Fixed O&M GTs

1.51  103 $2013 kW21

8.64  107 h 1.31  107 h 2.60  104 h 1.20  107 h 6.48 8.30 1.22 1.59 8.22

 106 h  105 h  107 h  107 h  102 h h21

6.83  107 h

2.9  10 $2013 kW21 year21 2.68  1023 h kWh21

CAPEX, Capital expenditure; OPEX, operational expenditure.

equal to the produced renewable electricity disregarding the losses along the transmission and utility services due to unavailability of data, Tb;red is the base tariff reduced by the average value between the minimum and maximum percentages (i.e., 21%), the costs for the transmissions are covered by the producer according to the shallow cost approach, thus Tprem of 40 h MWh21 is considered. Regarding costs for GHG emissions required in Eq. (5.32), The producer of the hybrid energy plant is assumed to participate in the European ETS cap and trade system buying allowances from auctions or other participants for GHG emissions from the GT park. The carbon allowance price is fixed in this case study equal to 21 15 h tCO2 , as suggested in a previous study [105]. CAPEX values are supposed to occur at the beginning of the project, while OPEX, power production revenues, and costs are discounted at each hour of the selected interval. Referring to Eqs. (5.31) and (5.32), the discount rate r is considered constant over the selected interval and equal to 7%, in agreement with a previous study [105], while the time interval for evaluation (Ht) is set as 72 h. Neither income taxes nor depreciation are considered, for the sake of simplicity.

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To rank the SCs based on the aggregated sustainability performance, the aggregated sustainability index (ASI) indicator is calculated by applying the compensatory aggregation approach described in the methodology description. Table 6.19 reports the target values used in this case study for normalization of the disaggregated indicators related to technical upper limits of wind energy conversion and performance of offshore wind power in the near future. For each target value, a brief description and assumption are included. The evaluation of the relative importance weights among indicators is performed by applying the approach described in the methodology description based on the time-space-receptor criteria and individualist-egalitarian-hierarchist perspectives. Equal weighting is further added to the archetypes of decision makers. Before scoring and weighting, the indicators selected for the assessment of the case study are classified in terms of time, space, and receptor based on their definitions. The same considerations introduced for η, LCOP, LVOP, and LGHG in case study 1 are applied to the corresponding performance indicators in this case study. Since the economic aspect is quantified by LCOE and LVOE, the relative importance weights among the two indicators are first identified (Table 6.20) and

Table 6.19 Target values assumed for normalization of disaggregated indicators. Target

Value

Description and assumption

ηel;target

52.9%

LCOEtarget

92.4 h2016 MWh21

LVOEtarget

34.2 h2016 MWh21

LGHGtarget

12 kgCO2 eq MWh21

Betz limit for wind energy conversion through a turbine. Estimated LCOE for new generation technologies (offshore wind) entering in services in 2022/2023. The literature value of 108 $2018 MWh21 is converted into h2016 MWh21 using exchange rates and PPI values in Table 6.7. Estimated levelized avoided cost of electricity (i.e., proxy measure for potential revenues from sales of electricity generated from the plant) for new generation technologies (offshore wind) entering in service in 2023. The literature value of 40 $2018 MWh21 is converted into h2016 MWh21 using exchange rates and PPI values in Table 6.7. Harmonized mean of published CO2eq emissions estimates for offshore wind power.

LCOE, Levelized cost of energy; PPI, producer price index.

Literature source [43] [296,297]

[297]

[298]

6.2 Case study 2

then used to derive the evaluation matrix and the trade-off weights associated to them (Table 6.21). The same procedure is then applied to determine the trade-off weights among the category indicators. Relative importance weights are determined by using scores attributed to η and LGHG in Table 6.9 and assuming averages of the scores assigned to LCOE and LVOE in Table 6.20 for the different archetypes of decision makers. Table 6.22 summarizes these values, while Fig. 6.12 illustrates the comparison of trade-off weights based on the different perspectives. It is verified that all the evaluations are consistent according to the consistency ratio index in Eq. (5.11). From Fig. 6.12, it clearly appears that there are variations among the different archetypes in prioritizing some specific indicators over others. The individualist archetype gives higher priority to LGHG with a trade-off weight of about 50%, followed by the economic aspect indicators (40% weight). Similarly, the egalitarian method prioritizes LGHG (60% weight) but prefers more η (35% weight) than economic indicators. The hierarchist archetype exhibits a similar priority to all the indicators as the egalitarian archetype. No archetype approximates the equal weighting scheme.

Table 6.20 Scores and weights assigned for aggregation of subindicators quantifying the technical aspects based on different perspectives. Indicators for economic aspect LCOE

LVOE

Schemes

Criteria

Score 1
5

Score 1
5

Individualist

Time Space Receptor Sum Relative importance weight Time Space Receptor Sum Relative importance weight Time Space Receptor Sum Relative importance weight

3 4 4 11 0.524 3 3 1 7 0.368 3 3.5 2.5 9 0.450

3 2 5 10 0.476 3 4 5 12 0.632 3 3 5 11 0.550

Egalitarian

Hierarchist

LCOE, Levelized cost of energy; LVOE, levelized value of energy.

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Table 6.21 Evaluation matrix and trade-off weights among economic indicators based on different perspectives. Individualist Egalitarian Hierarchist Equal weighting

LCOE LVOE LCOE LVOE LCOE LVOE LCOE LVOE

LCOE

LVOE

Trade-off weight

0.667 0.333 0.143 0.857 0.248 0.752


0.667 0.333 0.143 0.857 0.248 0.752


0.667 0.333 0.143 0.857 0.248 0.752 0.500 0.500

LCOE, Levelized cost of energy; LVOE, levelized value of energy.

Table 6.22 Scores and weights assigned to the category indicators based on different perspectives. Technical aspect

Economic aspect

Environmental aspect

ηel

LCOE, LVOE

LGHG

Schemes

Criteria

Score 1
5

Score 1
5

Score 1
5

Individualist

Time Space Receptor Sum Relative importance weight Time Space Receptor Sum Relative importance weight Time Space Receptor Sum Relative importance weight

2 3 2 7 0.246

3 3 5 10.5 0.368

3 3 5 11 0.386

5 5 4 14 0.364

3 4 3 9.5 0.247

5 5 5 15 0.390

3.5 4 3 10.5 0.313

3 3 4 10 0.299

4 4 5 13 0.388

Egalitarian

Hierarchist

LCOE, Levelized cost of energy; LCOP, levelized cost of product; LGHG, levelized greenhouse gas; LVOE, levelized value of energy.

6.2 Case study 2

FIGURE 6.12 Weights among the category indicators based on different perspectives for the sustainability assessment.

6.2.4 Preliminary comparison of the matching of power curves A preliminary investigation of the expected performance of the G2P hybrid energy system in the different SCs is carried out by comparing the matching of real power (Pr), forecast power (Pf), and dispatched power (Pd) curves over the selected interval. Figs. 6.13, 6.14, 6.15, and 6.16 illustrate the matching of the power curves calculated for SC1, SC2, SC3, SC4, respectively. In all these figures, the Pr curves are the same, the orange bars represent the hourly effective power that should be provided by the GT park to satisfy the defined dispatching plan (PGT;eff ), while the gray bars consist in the positive imbalance power during the hours where the operation of the GT park is not required. As shown in Fig. 6.13, Pd is lower than Pf for a quantity that represents the dispatching error ξd estimated in month 4 of the period for forecast horizon of 3 h and Probd equal to 80%, that is, 26.4 MW (Table 6.15), provided that the smallest possible Pd is 0. By summing up the orange bars illustrated in the figure, the total PGT;eff over the selected interval is 189.2 MW. Quite obviously, this value is lower than the maximum power that could be provided from the turbomachines (PGT;max ), which is estimated in this SC as 389.9 MW. Therefore, considering the defined dispatching plan based on Probd of 80%, it is emerged a decrease of 51.5% in PGT;eff occurs with respect to PGT;max . By summing up the gray contributions in the figure, the total positive imbalance in power results equal to 627.8 MW. From Fig. 6.14 it appears evident that the Pf curve is the same as SC1, given the equal forecast horizon assumed in SC2. Therefore PGT;max is estimated equal to the value reported in SC1 (389.9 MW). A different Pd curve is displayed

165

166

CHAPTER 6 Case studies

FIGURE 6.13 Matching of Pr, Pf, and Pd curves over the selected interval for SC1.

FIGURE 6.14 Matching of Pr, Pf, and Pd curves over the selected interval for SC2.

compared to SC1, given the increased Probd . As shown in Table 6.11, the dispatching error ξ d of month 4 is identified as 214.3 MW, that is, more than double of ξd in SC1. As a result, the total PGT;eff over the selected interval is estimated as 56 MW against 189.2 MW of SC1, and the reduction in PGT;eff compared to PGT;max is 85.6%. Therefore the higher Probd is, the lower the power that can be provided from the GTs with respect to the maximum power. Furthermore, the number of hours related to a positive power imbalance (gray bars) is clearly larger than that in Fig. 6.13. The total positive power imbalance for SC2 is calculated as 836.8 MW, which is higher than that obtained in SC1.

6.2 Case study 2

FIGURE 6.15 Matching of Pr, Pf, and Pd curves over the selected interval for SC3.

FIGURE 6.16 Matching of Pr, Pf, and Pd curves over the selected interval for SC4.

From the preliminary comparison of SC2 with respect to SC1, the performance of the G2P hybrid energy system is expected to be characterized by lower costs for negative imbalances due to higher Probd , higher revenues for positive imbalances due to higher total positive imbalance power, lower fuel consumption of GTs, as well as lower GHG emissions and related costs due to lower PGT;eff . On the other hand, lower revenues from electricity selling to the grid may be derived because of lower PGT;eff . Fig. 6.15 shows a different trend in Pf curve compared to Fig. 6.13 due to the higher forecast horizon considered in SC3 than the reference scenario. By summing up the orange bars illustrated in the figure, the total PGT;max over the selected interval is 918.5 MW, which is significantly higher than that in SC1

167

168

CHAPTER 6 Case studies

(389.9 MW). As a result, a slightly higher dispatching error ξ d of month 4 is obtained in this case, that is, 210.5 MW (Table 6.12), compared to SC1 (26.4 MW). However, the decrease in PGT;eff with respect to PGT;max is 57.8%, which is barely greater than that in SC1, as for ξd finding. On the other hand, a lower total positive power imbalance is calculated, that is, 564.6 MW, compared to SC1. Overall, compared to the reference scenario, for SC2, higher costs for negative imbalances are expected due to higher PGT;max and PGT;eff , lower revenues from positive imbalances due to lower total positive power imbalance, and higher fuel consumption of GTs as well as GHG emissions and related costs. For the revenues due to electricity selling, it is not possible to predict an increase or decrease due to the different trend in Pf, which implies a different number of hours where the GT park could be operative. As expected from the adoption of both the higher forecast horizon and Probd , a different Pf curve (as in SC3) as well as higher ξd (as in SC2) emerge in Fig. 6.16 compared to Fig. 6.13. Quite obviously, the total PGT;max in SC4 over the selected interval is the same as that estimated in SC3, that is, 918.5 MW. Given a ξ d value for month 4 of 218.1 MW (Table 5.12), PGT;eff in SC4 is calculated as 144.2 MW, which is slightly lower than that in SC1 (189.2 MW) but higher than that in SC2 (56 MW) where Probd is the same. However, the decrease in PGT;eff compared to PGT;max is 84.3%, that is, higher than that in SC1 but about the same as that in SC2. Concerning the positive imbalance power (gray bars in Fig. 6.16), a total value of 719.6 MW is obtained, which is greater than that of SC1 (627.8 MW) but lower than SC2 (836.8 MW) when Probd is the same. Compared to the other SCs, in case of SC4 it appears difficult to predict the performance of the G2P hybrid energy system due to the combined effects of the higher forecast horizon and Probd .

6.2.5 Sustainability assessment results Tables 6.23
6.26 illustrate the technical and environmental data obtained for SC1, SC2, SC3, and SC4, respectively, over the interval of 3 days selected for the analysis. Each table contains the hourly available wind power (Pwind;avail ) and the electrical power produced (Pr) from the OWT farm. Moreover, they include the hourly power that should be provided by the GT park to satisfy the defined dispatching plan (PGT;eff ), the number of operating GTs (NGT ) at the considered hour estimated, the resulting hourly electrical power produced from the GTs (PGT ), and the efficiency of the park at part-load obtained by applying Eq. (5.28). The tables report also the hourly fuel input power (Pfuel ) resulting from the fuel (natural gas) combustion in GTs, estimated by means of the PGT and ηGT values, and the associated hourly GHG emissions (eGHG;GT ) evaluated from the Pfuel values, estimated at the same hours by assuming a typical emission factor per unit of fuel (natural gas) power (i.e., 202 kg MWhfuel21 [105]).

Table 6.23 Hourly power data of offshore wind turbine farm, and hourly power, fuel consumption, greenhouse gas emissions of gas turbine park for SC1. ηGT (%)

PGT;eff (MW)

NGT

PGT (MW)

ηGT (%)

Pfuel (MW)

eGHG;GT (kgCO2 eq)

10.48

0.0




0.0




0.0

0.0

4.78

4.1

1

4.1

10

42.8

8640.3

0.00

9.8

2

9.8

15

64.5

13,027.3

0.09

0.00

10.3

2

10.3

16

65.8

13,293.1

16.00

0.30

0.00

10.8

2

10.8

16

67.1

13,552.0

27/02

17.00

0.72

0.00

11.9

3

11.9

17

70.0

14,134.6

0.0

27/02

18.00

9.11

3.56

8.9

2

8.9

14

61.7

12,469.3

36.0

7278.7

27/02

19.00

17.44

8.30

4.8

1

4.8

10

46.1

9306.0

55.0

11,109.9

27/02

20.00

34.54

16.23

0.0




0.0




0.0

0.0

14

61.0

12,329.0

27/02

21.00

68.59

30.61

0.0




0.0




0.0

0.0

13

57.6

11,629.4

27/02

22.00

82.30

35.36

0.0




0.0




0.0

0.0

3.9

9

41.8

8437.0

27/02

23.00

110.74

43.29

0.0




0.0




0.0

0.0

0

0.0




0.0

0.0

28/02

0.00

108.15

42.68

0.0




0.0




0.0

0.0

2.8

0

0.0




0.0

0.0

28/02

1.00

88.44

37.30

0.0




0.0




0.0

0.0

0.70

2.7

0

0.0




0.0

0.0

28/02

2.00

75.24

33.01

0.0




0.0




0.0

0.0

6.22

1.92

0.8

0

0.0




0.0

0.0

28/02

3.00

63.06

28.47

0.0




0.0




0.0

0.0

16.00

7.10

2.47

0.0




0.0




0.0

0.0

28/02

4.00

82.30

35.36

0.0




0.0




0.0

0.0

17.00

6.86

2.32

0.0




0.0




0.0

0.0

28/02

5.00

74.43

32.74

0.0




0.0




0.0

0.0

Date

Time

Pwind;avail (MW)

Pr (MW)

PGT;eff (MW)

26/02

0.00

12.76

5.82

0.0




0.0




0.0

0.0

27/02

12.00

21.88

26/02

1.00

6.69

2.22

0.0




0.0




0.0

0.0

27/02

13.00

11.00

26/02

2.00

8.06

3.01

0.0




0.0




0.0

0.0

27/02

14.00

0.39

26/02

3.00

7.88

2.91

0.0




0.0




0.0

0.0

27/02

15.00

26/02

4.00

13.01

5.96

0.0




0.0




0.0

0.0

27/02

26/02

5.00

8.06

3.01

0.0




0.0




0.0

0.0

26/02

6.00

7.97

2.96

0.0




0.0




0.0

26/02

7.00

4.02

0.68

3.0

1

3.0

8

26/02

8.00

0.24

0.00

5.24

2

6.9

13

26/02

9.00

0.65

0.00

8.7

2

8.7

26/02

10.00

2.63

0.25

7.6

2

7.6

26/02

11.00

6.77

2.27

3.9

1

26/02

12.00

9.92

4.09

1.3

26/02

13.00

6.22

1.92

26/02

14.00

4.08

26/02

15.00

26/02 26/02

NGT

PGT (MW)

Pfuel (MW)

eGHG;GT (kgCO2 eq)

Date

Time

Pwind;avail (MW)

Pr (MW)

26/02

18.00

5.08

1.13

0.0




0.0




0.0

0.0

28/02

6.00

81.02

34.95

0.0




0.0




0.0

0.0

26/02

19.00

6.00

1.77

0.0




0.0




0.0

0.0

28/02

7.00

57.50

26.19

0.0




0.0




0.0

0.0

26/02

20.00

10.45

4.44

0.0




0.0




0.0

0.0

28/02

8.00

42.25

19.68

0.0




0.0




0.0

0.0

26/02

21.00

14.98

7.00

0.0




0.0




0.0

0.0

28/02

9.00

36.49

17.03

0.0




0.0




0.0

0.0

26/02

22.00

27.21

12.93

0.0




0.0




0.0

0.0

28/02

10.00

74.43

32.74

0.0




0.0




0.0

0.0

26/02

23.00

29.96

14.23

0.0




0.0




0.0

0.0

28/02

11.00

57.84

26.34

0.0




0.0




0.0

0.0

27/02

0.00

29.10

13.83

0.0




0.0




0.0

0.0

28/02

12.00

65.24

29.33

0.0




0.0




0.0

0.0

27/02

1.00

38.52

17.90

0.0




0.0




0.0

0.0

28/02

13.00

87.99

37.16

0.0




0.0




0.0

0.0

(Continued)

Table 6.23 Hourly power data of offshore wind turbine farm, and hourly power, fuel consumption, greenhouse gas emissions of gas turbine park for SC1. Continued PGT;eff (MW)

ηGT (%)

eGHG;GT (kgCO2 eq)

Date

Time

Pwind;avail (MW)

Pr (MW)

27/02

2.00

21.35

10.24

0.0




0.0




0.0

0.0

28/02

27/02

3.00

18.06

8.62

0.0




0.0




0.0

0.0

28/02

NGT

PGT (MW)

Pfuel (MW)

Date

ηGT (%)

Pwind;avail (MW)

Pr (MW)

PGT;eff (MW)

14.00

81.02

34.95

0.0




0.0




0.0

0.0

15.00

118.20

45.02

0.0




0.0




0.0

0.0

Time

NGT

PGT (MW)

Pfuel (MW)

eGHG;GT (kgCO2 eq)

27/02

4.00

12.28

5.55

0.0




0.0




0.0

0.0

28/02

16.00

55.18

25.20

16.2

3

16.2

20

80.5

16,266.2

27/02

5.00

31.07

14.73

0.0




0.0




0.0

0.0

28/02

17.00

44.20

20.57

22.8

4

22.8

24

95.3

19,244.7

27/02

6.00

11.57

5.13

0.0




0.0




0.0

0.0

28/02

18.00

71.28

31.60

12.0

3

12.0

17

70.3

14,193.0

27/02

7.00

17.14

8.14

0.0




0.0




0.0

0.0

28/02

19.00

72.45

32.03

11.7

3

11.7

17

69.6

14,054.6

27/02

8.00

35.26

16.53

0.0




0.0




0.0

0.0

28/02

20.00

137.10

47.64

0.0




0.0




0.0

0.0

27/02

9.00

85.33

36.33

0.0




0.0




0.0

0.0

28/02

21.00

223.49

49.95

0.0




0.0




0.0

0.0

27/02

10.00

41.43

19.30

0.0




0.0




0.0

0.0

28/02

22.00

76.05

33.29

10.6

2

10.6

16

66.5

13,442.7

27/02

11.00

39.30

18.28

0.0




0.0




0.0

0.0

28/02

23.00

58.18

26.48

17.3

3

17.3

21

82.9

16,738.5

Table 6.24 Hourly power data of offshore wind turbine farm, and hourly power, fuel consumption, greenhouse gas emissions of gas turbine park for SC2. Date

Time

Pwind;avail (MW)

Pr (MW)

PGT;eff (MW)

NGT

PGT (MW)

ηGT

Pfuel (MW)

eGHG;GT (kgCO2 eq)

Date

Time

Pwind;avail (MW)

Pr (MW)

PGT;eff (MW)

NGT

PGT (MW)

ηGT (%)

Pfuel (MW)

eGHG;GT (kgCO2 eq)

26/02

0.00

12.76

5.82

0.0




0.0




0.0

0.0

27/02

12.00

21.88

10.48

0.0




0.0




0.0

0.0

26/02

1.00

6.69

2.22

0.0




0.0




0.0

0.0

27/02

13.00

11.00

4.78

0.0




0.0




0.0

0.0

26/02

2.00

8.06

3.01

0.0




0.0




0.0

0.0

27/02

14.00

0.39

0.00

1.9




0.0




0.0

0.0

26/02

3.00

7.88

2.91

0.0




0.0




0.0

0.0

27/02

15.00

0.09

0.00

2.4




0.0




0.0

0.0

26/02

4.00

13.01

5.96

0.0




0.0




0.0

0.0

27/02

16.00

0.30

0.00

2.9




0.0




0.0

0.0

26/02

5.00

8.06

3.01

0.0




0.0




0.0

0.0

27/02

17.00

0.72

0.00

4.0

1

4.0

10

41.8

8444.3

26/02

6.00

7.97

2.96

0.0




0.0




0.0

0.0

27/02

18.00

9.11

3.56

1.0




0.0




0.0

0.0

26/02

7.00

4.02

0.68

0.0




0.0




0.0

0.0

27/02

19.00

17.44

8.30

0.0




0.0




0.0

0.0

26/02

8.00

0.24

0.00

0.0




0.0




0.0

0.0

27/02

20.00

34.54

16.23

0.0




0.0




0.0

0.0

26/02

9.00

0.65

0.00

0.8

0

0.0




0.0

0.0

27/02

21.00

68.59

30.61

0.0




0.0




0.0

0.0

26/02

10.00

2.63

0.25

0.0




0.0




0.0

0.0

27/02

22.00

82.30

35.36

0.0




0.0




0.0

0.0

26/02

11.00

6.77

2.27

0.0




0.0




0.0

0.0

27/02

23.00

110.74

43.29

0.0




0.0




0.0

0.0

26/02

12.00

9.92

4.09

0.0




0.0




0.0

0.0

28/02

0.00

108.15

42.68

0.0




0.0




0.0

0.0

26/02

13.00

6.22

1.92

0.0




0.0




0.0

0.0

28/02

1.00

88.44

37.30

0.0




0.0




0.0

0.0

26/02

14.00

4.08

0.70

0.0




0.0




0.0

0.0

28/02

2.00

75.24

33.01

0.0




0.0




0.0

0.0

26/02

15.00

6.22

1.92

0.0




0.0




0.0

0.0

28/02

3.00

63.06

28.47

0.0




0.0




0.0

0.0

26/02

16.00

7.10

2.47

0.0




0.0




0.0

0.0

28/02

4.00

82.30

35.36

0.0




0.0




0.0

0.0

26/02

17.00

6.86

2.32

0.0




0.0




0.0

0.0

28/02

5.00

74.43

32.74

0.0




0.0




0.0

0.0

26/02

18.00

5.08

1.13

0.0




0.0




0.0

0.0

28/02

6.00

81.02

34.95

0.0




0.0




0.0

0.0

26/02

19.00

6.00

1.77

0.0




0.0




0.0

0.0

28/02

7.00

57.50

26.19

0.0




0.0




0.0

0.0

26/02

20.00

10.45

4.44

0.0




0.0




0.0

0.0

28/02

8.00

42.25

19.68

0.0




0.0




0.0

0.0

26/02

21.00

14.98

7.00

0.0




0.0




0.0

0.0

28/02

9.00

36.49

17.03

0.0




0.0




0.0

0.0

26/02

22.00

27.21

12.93

0.0




0.0




0.0

0.0

28/02

10.00

74.43

32.74

0.0




0.0




0.0

0.0

26/02

23.00

29.96

14.23

0.0




0.0




0.0

0.0

28/02

11.00

57.84

26.34

0.0




0.0




0.0

0.0

27/02

0.00

29.10

13.83

0.0




0.0




0.0

0.0

28/02

12.00

65.24

29.33

0.0




0.0




0.0

0.0

27/02

1.00

38.52

17.90

0.0




0.0




0.0

0.0

28/02

13.00

87.99

37.16

0.0




0.0




0.0

0.0

(Continued)

Table 6.24 Hourly power data of offshore wind turbine farm, and hourly power, fuel consumption, greenhouse gas emissions of gas turbine park for SC2. Continued Date

Time

Pwind;avail (MW)

Pr (MW)

PGT;eff (MW)

NGT

PGT (MW)

ηGT

Pfuel (MW)

eGHG;GT (kgCO2 eq)

Date

Time

Pwind;avail (MW)

Pr (MW)

PGT;eff (MW)

NGT

PGT (MW)

ηGT (%)

Pfuel (MW)

eGHG;GT (kgCO2 eq)

27/02

2.00

21.35

10.24

0.0




0.0




0.0

0.0

28/02

14.00

81.02

34.95

0.0




0.0




0.0

0.0

27/02

3.00

18.06

8.62

0.0




0.0




0.0

0.0

28/02

15.00

118.20

45.02

0.0




0.0




0.0

0.0

27/02

4.00

12.28

5.55

0.0




0.0




0.0

0.0

28/02

16.00

55.18

25.20

8.3

2

8.3

14

59.9

12,104.2

27/02

5.00

31.07

14.73

0.0




0.0




0.0

0.0

28/02

17.00

44.20

20.57

14.9

3

14.9

19

77.4

15,635.0

27/02

6.00

11.57

5.13

0.0




0.0




0.0

0.0

28/02

18.00

71.28

31.60

4.1

1

4.1

10

42.4

8572.2

27/02

7.00

17.14

8.14

0.0




0.0




0.0

0.0

28/02

19.00

72.45

32.03

3.8

1

3.8

9

40.9

8265.2

27/02

8.00

35.26

16.53

0.0




0.0




0.0

0.0

28/02

20.00

137.10

47.64

0.0




0.0




0.0

0.0

27/02

9.00

85.33

36.33

0.0




0.0




0.0

0.0

28/02

21.00

223.49

49.95

0.0




0.0




0.0

0.0

27/02

10.00

41.43

19.30

0.0




0.0




0.0

0.0

28/02

22.00

76.05

33.29

2.7




0.0




0.0

0.0

27/02

11.00

39.30

18.28

0.0




0.0




0.0

0.0

28/02

23.00

58.18

26.48

9.4

2

9.4

15

63.1

12,736.8

Table 6.25 Hourly power data of offshore wind turbine farm, and hourly power, fuel consumption, greenhouse gas emissions of gas turbine park for SC3. Date

Time

Pwind;avail (MW)

Pr (MW)

PGT;eff (MW)

NGT

PGT (MW)

ηGT (%)

Pfuel (MW)

eGHG;GT (kgCO2 eq)

Date

Time

Pwind;avail (MW)

Pr (MW)

PGT;eff (MW)

NGT

PGT (MW)

ηGT (%)

Pfuel (MW)

eGHG;GT (kgCO2 eq)

26/02

0.00

12.76

5.82

0.0




0.0




0.0

0.0

27/02

12.00

21.88

10.48

0.0




0.0




0.0

0.0

26/02

1.00

6.69

2.22

0.5

0

0.0




0.0

0.0

27/02

13.00

11.00

4.78

0.0




0.0




0.0

0.0

26/02

2.00

8.06

3.01

0.8

0

0.0




0.0

0.0

27/02

14.00

0.39

0.00

9.9

2

9.9

15

64.6

13,050.3

26/02

3.00

7.88

2.91

1.4

0

0.0




0.0

0.0

27/02

15.00

0.09

0.00

14.2

3

14.2

19

75.8

15,315.8

26/02

4.00

13.01

5.96

0.0




0.0




0.0

0.0

27/02

16.00

0.30

0.00

19.1

4

19.1

22

87.0

17,573.0

26/02

5.00

8.06

3.01

3.0

1

3.0

8

35.6

7181.8

27/02

17.00

0.72

0.00

28.7

5

28.7

26

108.3

21,877.6

26/02

6.00

7.97

2.96

3.6

1

3.6

9

39.5

7980.2

27/02

18.00

9.11

3.56

29.4

6

29.4

27

110.1

22,232.2

26/02

7.00

4.02

0.68

6.4

2

6.4

12

53.2

10,744.9

27/02

19.00

17.44

8.30

28.5

5

28.5

26

107.9

21,795.5

26/02

8.00

0.24

0.00

8.5

2

8.5

14

60.4

12,197.6

27/02

20.00

34.54

16.23

22.6

4

22.6

24

94.7

19,130.0

26/02

9.00

0.65

0.00

9.2

2

9.2

15

62.5

12,627.7

27/02

21.00

68.59

30.61

8.7

2

8.7

14

61.1

12,350.0

26/02

10.00

2.63

0.25

9.6

2

9.6

15

63.8

12,893.0

27/02

22.00

82.30

35.36

4.1

1

4.1

10

42.8

8638.7

26/02

11.00

6.77

2.27

9.0

2

9.0

15

62.0

12,515.6

27/02

23.00

110.74

43.29

0.0




0.0




0.0

0.0

26/02

12.00

9.92

4.09

7.9

2

7.9

13

58.4

11,796.3

28/02

0.00

108.15

42.68

0.0




0.0




0.0

0.0

26/02

13.00

6.22

1.92

10.3

2

10.3

16

65.9

13,303.5

28/02

1.00

88.44

37.30

2.5

0

0.0




0.0

0.0

26/02

14.00

4.08

0.70

12.2

3

12.2

17

70.7

14,288.5

28/02

2.00

75.24

33.01

6.5

2

6.5

12

53.6

10,823.8

26/02

15.00

6.22

1.92

11.3

2

11.3

16

68.3

13,806.5

28/02

3.00

63.06

28.47

10.9

2

10.9

16

67.5

13,632.9

26/02

16.00

7.10

2.47

11.0

2

11.0

16

67.7

13,677.9

28/02

4.00

82.30

35.36

3.6

1

3.6

9

39.8

8032.8

26/02

17.00

6.86

2.32

11.8

3

11.8

17

69.7

14,082.7

28/02

5.00

74.43

32.74

4.6

1

4.6

10

45.3

9140.9

26/02

18.00

5.08

1.13

13.3

3

13.3

18

73.6

14,860.3

28/02

6.00

81.02

34.95

0.3

0

0.0




0.0

0.0

26/02

19.00

6.00

1.77

13.0

3

13.0

18

72.8

14,710.6

28/02

7.00

57.50

26.19

5.2

1

5.2

11

48.0

9692.8

26/02

20.00

10.45

4.44

11.0

2

11.0

16

67.8

13,686.6

28/02

8.00

42.25

19.68

3.1

1

3.1

8

36.2

7313.1

26/02

21.00

14.98

7.00

8.8

2

8.8

14

61.4

12,406.3

28/02

9.00

36.49

17.03

1.2

0

0.0




0.0

0.0

26/02

22.00

27.21

12.93

3.2

1

3.2

9

37.3

7536.7

28/02

10.00

74.43

32.74

0.0




0.0




0.0

0.0

26/02

23.00

29.96

14.23

2.6

0

0.0




0.0

0.0

28/02

11.00

57.84

26.34

0.0




0.0




0.0

0.0

27/02

0.00

29.10

13.83

3.4

1

3.4

9

38.2

7712.3

28/02

12.00

65.24

29.33

0.0




0.0




0.0

0.0

27/02

1.00

38.52

17.90

0.0




0.0




0.0

0.0

28/02

13.00

87.99

37.16

0.0




0.0




0.0

0.0

(Continued)

Table 6.25 Hourly power data of offshore wind turbine farm, and hourly power, fuel consumption, greenhouse gas emissions of gas turbine park for SC3. Continued Date

Time

Pwind;avail (MW)

Pr (MW)

PGT;eff (MW)

NGT

ηGT

PGT (MW)

(%)

Pfuel (MW)

eGHG;GT (kgCO2 eq)

Date

Time

Pwind;avail (MW)

Pr (MW)

PGT;eff (MW)

NGT

PGT (MW)

ηGT (%)

Pfuel (MW)

eGHG;GT (kgCO2 eq)

27/02

2.00

21.35

10.24

2.9

0

0.0




0.0

0.0

28/02

14.00

81.02

34.95

0.0




0.0




0.0

0.0

27/02

3.00

18.06

8.62

3.2

1

3.2

9

37.4

7548.5

28/02

15.00

118.20

45.02

0.0




0.0




0.0

0.0 0.0

27/02

4.00

12.28

5.55

5.1

1

5.1

11

47.5

9595.8

28/02

16.00

55.18

25.20

0.0




0.0




0.0

27/02

5.00

31.07

14.73

0.0




0.0




0.0

0.0

28/02

17.00

44.20

20.57

0.0




0.0




0.0

0.0

27/02

6.00

11.57

5.13

1.8

0

0.0




0.0

0.0

28/02

18.00

71.28

31.60

0.0




0.0




0.0

0.0

27/02

7.00

17.14

8.14

0.0




0.0




0.0

0.0

28/02

19.00

72.45

32.03

0.0




0.0




0.0

0.0

27/02

8.00

35.26

16.53

0.0




0.0




0.0

0.0

28/02

20.00

137.10

47.64

0.0




0.0




0.0

0.0

27/02

9.00

85.33

36.33

0.0




0.0




0.0

0.0

28/02

21.00

223.49

49.95

0.0




0.0




0.0

0.0

27/02

10.00

41.43

19.30

0.0




0.0




0.0

0.0

28/02

22.00

76.05

33.29

0.0




0.0




0.0

0.0

27/02

11.00

39.30

18.28

0.0




0.0




0.0

0.0

28/02

23.00

58.18

26.48

0.0




0.0




0.0

0.0

Table 6.26 Hourly power data of offshore wind turbine farm, and hourly power, fuel consumption, greenhouse gas emissions of gas turbine park for SC4. ηGT

Time

Pwind;avail (MW)

Pr (MW)

PGT;eff (MW)

26/02

0.00

12.76

5.82

0.0




0.0




0.0

0.0

27/02

12.00

21.88

10.48

0.0




0.0




0.0

0.0

26/02

1.00

6.69

2.22

0.0




0.0




0.0

0.0

27/02

13.00

11.00

4.78

0.0




0.0




0.0

0.0

NGT

PGT (MW)

(%)

Pfuel (MW)

eGHG;GT (kgCO2 eq)

Date

Time

Pwind;avail (MW)

Pr (MW)

PGT;eff (MW)

ηGT

Date

NGT

PGT (MW)

(%)

Pfuel (MW)

eGHG;GT (kgCO2 eq)

26/02

2.00

8.06

3.01

0.0




0.0




0.0

0.0

27/02

14.00

0.39

0.00

2.3

0

0.0




0.0

0.0

26/02

3.00

7.88

2.91

0.0




0.0




0.0

0.0

27/02

15.00

0.09

0.00

6.6

2

6.6

12

54.1

10,933.9

26/02

4.00

13.01

5.96

0.0




0.0




0.0

0.0

27/02

16.00

0.30

0.00

11.5

2

11.5

17

69.0

13,946.0

26/02

5.00

8.06

3.01

0.0




0.0




0.0

0.0

27/02

17.00

0.72

0.00

21.1

4

21.1

23

91.4

18,453.2

26/02

6.00

7.97

2.96

0.0




0.0




0.0

0.0

27/02

18.00

9.11

3.56

21.8

4

21.8

23

93.1

18,801.1

26/02

7.00

4.02

0.68

0.0




0.0




0.0

0.0

27/02

19.00

17.44

8.30

20.9

4

20.9

23

91.0

18,372.4

26/02

8.00

0.24

0.00

0.9

0

0.0




0.0

0.0

27/02

20.00

34.54

16.23

15.0

3

15.0

19

77.6

15,670.4

26/02

9.00

0.65

0.00

1.6

0

0.0




0.0

0.0

27/02

21.00

68.59

30.61

1.1

0

0.0




0.0

0.0

26/02

10.00

2.63

0.25

2.0

0

0.0




0.0

0.0

27/02

22.00

82.30

35.36

0.0




0.0




0.0

0.0

26/02

11.00

6.77

2.27

1.4

0

0.0




0.0

0.0

27/02

23.00

110.74

43.29

0.0




0.0




0.0

0.0

26/02

12.00

9.92

4.09

0.3

0

0.0




0.0

0.0

28/02

0.00

108.15

42.68

0.0




0.0




0.0

0.0

26/02

13.00

6.22

1.92

2.7

0

0.0




0.0

0.0

28/02

1.00

88.44

37.30

0.0




0.0




0.0

0.0

26/02

14.00

4.08

0.70

4.6

1

4.6

10

45.2

9124.6

28/02

2.00

75.24

33.01

0.0




0.0




0.0

0.0

26/02

15.00

6.22

1.92

3.7

1

3.7

9

40.1

8097.4

28/02

3.00

63.06

28.47

3.3

1

3.3

9

38.1

7689.1

26/02

16.00

7.10

2.47

3.4

1

3.4

9

38.6

7797.2

28/02

4.00

82.30

35.36

0.0




0.0




0.0

0.0

26/02

17.00

6.86

2.32

4.2

1

4.2

10

43.1

8703.6

28/02

5.00

74.43

32.74

0.0




0.0




0.0

0.0

26/02

18.00

5.08

1.13

5.7

1

5.7

11

50.4

10,183.4

28/02

6.00

81.02

34.95

0.0




0.0




0.0

0.0

26/02

19.00

6.00

1.77

5.4

1

5.4

11

49.1

9920.2

28/02

7.00

57.50

26.19

0.0




0.0




0.0

0.0

26/02

20.00

10.45

4.44

3.4

1

3.4

11

38.7

7817.8

28/02

8.00

42.25

19.68

0.0




0.0




0.0

0.0

26/02

21.00

14.98

7.00

1.2

0

0.0

9

0.0

0.0

28/02

9.00

36.49

17.03

0.0




0.0




0.0

0.0

26/02

22.00

27.21

12.93

0.0




0.0




0.0

0.0

28/02

10.00

74.43

32.74

0.0




0.0




0.0

0.0 0.0

26/02

23.00

29.96

14.23

0.0




0.0




0.0

0.0

28/02

11.00

57.84

26.34

0.0




0.0




0.0

27/02

0.00

29.10

13.83

0.0




0.0




0.0

0.0

28/02

12.00

65.24

29.33

0.0




0.0




0.0

0.0

27/02

1.00

38.52

17.90

0.0




0.0




0.0

0.0

28/02

13.00

87.99

37.16

0.0




0.0




0.0

0.0

(Continued)

Table 6.26 Hourly power data of offshore wind turbine farm, and hourly power, fuel consumption, greenhouse gas emissions of gas turbine park for SC4. Continued PGT;eff (MW)

ηGT

eGHG;GT (kgCO2 eq)

Date

Time

Pwind;avail (MW)

Pr (MW)

27/02

2.00

21.35

10.24

0.0




0.0




0.0

0.0

28/02

27/02

3.00

18.06

8.62

0.0




0.0




0.0

0.0

28/02

NGT

PGT (MW)

(%)

Pfuel (MW)

Date

ηGT

Pwind;avail (MW)

Pr (MW)

PGT;eff (MW)

14.00

81.02

34.95

0.0




0.0




0.0

0.0

15.00

118.20

45.02

0.0




0.0




0.0

0.0

Time

NGT

PGT (MW)

(%)

Pfuel (MW)

eGHG;GT (kgCO2 eq)

27/02

4.00

12.28

5.55

0.0




0.0




0.0

0.0

28/02

16.00

55.18

25.20

0.0




0.0




0.0

0.0

27/02

5.00

31.07

14.73

0.0




0.0




0.0

0.0

28/02

17.00

44.20

20.57

0.0




0.0




0.0

0.0

27/02

6.00

11.57

5.13

0.0

0

0.0




0.0

0.0

28/02

18.00

71.28

31.60

0.0




0.0




0.0

0.0

27/02

7.00

17.14

8.14

0.0




0.0




0.0

0.0

28/02

19.00

72.45

32.03

0.0




0.0




0.0

0.0 0.0

27/02

8.00

35.26

16.53

0.0




0.0




0.0

0.0

28/02

20.00

137.10

47.64

0.0




0.0




0.0

27/02

9.00

85.33

36.33

0.0




0.0




0.0

0.0

28/02

21.00

223.49

49.95

0.0




0.0




0.0

0.0

27/02

10.00

41.43

19.30

0.0




0.0




0.0

0.0

28/02

22.00

76.05

33.29

0.0




0.0




0.0

0.0

27/02

11.00

39.30

18.28

0.0




0.0




0.0

0.0

28/02

23.00

58.18

26.48

0.0




0.0




0.0

0.0

6.2 Case study 2

Some economic performance data associated to the power imbalances with respect to the declared power to grid operator are summarized in Tables 6.27, 6.28, 6.29, and 6.30 for SC1, SC2, SC3, and SC4, respectively, over the interval of 3 days selected for the analysis. Each table includes the hourly positive power imbalance (Pimb1 ) occurring when real power Pr is greater than the dispatched power Pd estimated once defined the dispatching plan, the hourly negative power imbalance (Pimb2 ) occurring when Pr is lower than Pd , and GTs cannot operate due to the technical minimum load limit. Moreover, the following information published by the Italian TSO TERNA and GME is summarized: the sign of the hourly aggregated zonal imbalance associated the macro area where the point of dispatching is located (North Italy) [299], the hourly prices of the accepted supply offers (PriceMGP ) in the day-ahead market (MGP) in the macro area where the point of dispatching is located (North Italy) [300], the average prices of the bids (PriceMBk ) and supply offers (PriceMBm ) accepted in the market of ancillary services in real-time balance (MB), weighted by volume, in the macro area where the dispatching point belongs (North Italy) [301]. These data are used to estimate revenues received by TERNA for positive imbalances (Rimb1 ) and costs paid to TERNA for negative imbalances (Cimb2 ), according to the single pricing mechanism defined in Table 6.14. Hourly Rimb1 and Cimb2 results are reported in Tables 6.27
6.30. Tables 6.31, 6.32, 6.33, and 6.34 show the details of the economic data about revenues due to electricity selling to the grid obtained for SC1, SC2, SC3, and SC4, respectively, over the interval of 3 days selected for the analysis in this case study. The hourly MGP prices for conventional electricity (PriceMGP ) by GME, as well as the hourly financial incentives for offshore wind power integrated into the grid (InOWT ), are reported in the tables. The revenues from the selling of conventional electricity produced from GTs (Rsell;GT ) are derived by combining MGP prices with PGT values (in Tables 6.23
6.26), while the revenues from the selling of renewable electricity produced from OWTs (Rsell;OWT ) are estimated by combining the sum of MGP prices and InOWT with Pr values (in Tables 6.23
6.26). The total revenues (Rsell ) are then the summation of Rsell;GT and Rsell;OWT . All the results are summarized in Table 6.31
6.34. Other information reported in these tables is the cost associated with GHG emissions from the system (CGHG ) from the combination of eGHG;GT (in Tables 6.23
6.26) and the European ETS carbon allowance price of 15 h tCO2 21, as well as the discounted OPEXGT values estimated based on the data shown in Table 6.18 and economic assumptions (8% interest rate, 72 h economic period). Table 6.35 summarizes the technical, economic, and environmental parameters calculated for the four SCs related to the entire interval selected for the analysis. As shown in this table, the GT park in SC2 operates for the lowest number of hours among the considered scenarios, requires the smallest amount of fuel (Pfuel ) and OPEX, and produces the smallest power to satisfy the defined dispatching plan (PGT ) and GHG emissions (eGHG;GT ). Moreover, the hybrid energy system in SC2 shows the highest revenues due to positive power imbalance (Rimb1 ) and the

177

Table 6.27 Hourly positive and negative power imbalances and associated revenues and costs for SC1. Date

Time

Pimb1 (MW)

Pimb2 (MW)

Zonal imbalance

PriceMGP (h MWh21)

PriceMBk PriceMBm (h MWh21) (h MWh21)

Rimb1 (h)

Cimb2 (h)

Date

Time

Pimb1 (MW)

Pimb2 (MW)

Zonal imbalance

PriceMBk PriceMGP (h MWh21) (h MWh21)

PriceMBm (h MWh21)

Rimb1 (h)

Cimb2 (h)

26/02 0.00

5.8




Sign 2

34.86

21.23




203




27/02 12.00 2.0




Sign 1

33

72.1

67




26/02 1.00

2.2




Sign 2

32

15.73




71




27/02 13.00





Sign 1

30.72

72.97







26/02 2.00

3.0




Sign 2

27.85

19.28

68

205




27/02 14.00





Sign 1

28.89

25

70.1







26/02 3.00

2.9




Sign 2

26.25

19.28

70.94

207




27/02 15.00





Sign 1

28.55

24.9

70.04







26/02 4.00

6.0




Sign 2

26.15

19.43

70.9

423




27/02 16.00





Sign 1

29.65

25

70.04







26/02 5.00

2.2




Sign 2

26.25

20

66.42

144




27/02 17.00





Sign 2

33

23.83










26/02 6.00

0.7




Sign 1

30.5

19.51

75.5

14




27/02 18.00





Sign 2

38.2

24.64










26/02 7.00







Sign 1

26

22.27

75.5







27/02 19.00





Sign 2

46.45

24.26










26/02 8.00







Sign 1

38.97




76.39







27/02 20.00 2.0




Sign 2

46

25.93




2




26/02 9.00







Sign 1

45

30.18

76.7







27/02 21.00 2.0




Sign 2

40.01

24.45




16




26/02 10.00





Sign 1

45.5

32.65

79.23







27/02 22.00 15.8




Sign 2

35

22.82




22





26/02 11.00





Sign 1

43.56

34.4

85.11







27/02 23.00 21.8




Sign 2

31.39

20.07




32

26/02 12.00


1.3

Sign 1

39




76.32




52

28/02 0.00

32.3




Sign 2

25

2.38

66.26

33




26/02 13.00


2.8

Sign 1

36

24

75.92




68

28/02 1.00

32.8




Sign 2

26.17

2.27

75.24

28




26/02 14.00


2.7

Sign 1

36

27.38

75.5




75

28/02 2.00

28.4




Sign 2

21.9




61.25

26




26/02 15.00


0.8

Sign 1

37.42




77.35




32

28/02 3.00

26.2




Sign 2

19.15




61.25

23




26/02 16.00 0.4




Sign 1

37.55




76.12

14




28/02 4.00

22.7




Sign 2

17.73




66.74

30




26/02 17.00 1.5




Sign 2

37.5




76.58

115




28/02 5.00

30.5




Sign 2

17.31




61.25

30

26/02 18.00 0.9




Sign 2

43.8

28.99

75.5

66




28/02 6.00

29.5




Sign 1

17.46




61.25

33





26/02 19.00 1.8




Sign 2

49.51

28.09

75.5

134




28/02 7.00

32.6




Sign 1

20.4




51.99

25





26/02 20.00 4.4




Sign 2

46.44




80.12

356




28/02 8.00

24.7




Sign 1

20.27




55.5

20

26/02 21.00 7.0




Sign 2

44

35

76.75

538




28/02 9.00

19.6




Sign 2

24.65

0.52

52.37

17




26/02 22.00 12.9




Sign 2

41.08

30.55




531




28/02 10.00 17.0




Sign 1

27.38

4.11




30




26/02 23.00 14.2




Sign 2

35.55




67.84

965




28/02 11.00 30.3




Sign 2

36

7.49




16




27/02 0.00

13.8




Sign 2

28.16

17.37

74.46

1030




28/02 12.00 16.2




Sign 1

37

8.67




14




27/02 1.00

17.5




Sign 2

33.78

16.33

99.95

1753




28/02 13.00 14.0




Sign 1

34.38

4.74

82.32

16




27/02 2.00

8.9




Sign 2

31.52

15.23




280




28/02 14.00 16.2




Sign 1

30.55

5.12




2




27/02 3.00

6.7




Sign 2

30.4

17.99




204




28/02 15.00 2.1




Sign 1

30.4

4.4

82.32

7




27/02 4.00

3.1




Sign 2

31.36

13.59




96




28/02 16.00





Sign 2

30.86

3.44

84.75







27/02 5.00

11.1




Sign 2

31.31

13.59




349




28/02 17.00





Sign 2

30

0.31

81.8







27/02 6.00

1.0




Sign 1

31.53

12.72




12




28/02 18.00





Sign 2

32

8.4

84.75







27/02 7.00

3.4




Sign 1

33

15.55




53




28/02 19.00





Sign 2

36.68

11.6










27/02 8.00

10.6




Sign 1

33.53

16.53




176




28/02 20.00 3.8




Sign 2

37.5

11.13




141




27/02 9.00

29.8




Sign 1

36

20.62




614




28/02 21.00 6.1




Sign 2

36.68

11.08

73.34

46




27/02 10.00 12.3




Sign 1

34




82.32

417




28/02 22.00





Sign 2

34.83

7.61










27/02 11.00 10.3




Sign 1

34.99







361




28/02 23.00





Sign 2

30.4

6.95










Table 6.28 Hourly positive and negative power imbalances and associated revenues and costs for SC2. Date

Time

Pimb1 (MW)

Pimb2 (MW)

Zonal imbalance

PriceMGP (h MWh21)

PriceMPk PriceMPm (h MWh21) (h MWh21)

Rimb1 (h)

Cimb2 (h)

Date

Time

Pimb1 (MW)

Pimb2 (MW)

Zonal imbalance

PriceMPk PriceMGP (h MWh21) (h MWh21)

PriceMPm (h MWh21)

Rimb1 (h)

Cimb2 (h)

26/02 0.00

5.8




Sign 2

34.86

21.23




203




27/02 12.00 10.0




Sign 1

33

72.1

329




26/02 1.00

2.2




Sign 2

32

15.73




71




27/02 13.00 3.8




Sign 1

30.72

72.97

116


48

26/02 2.00

3.0




Sign 2

27.85

19.28

68

205




27/02 14.00


1.9

Sign 1

28.89

25

70.1




26/02 3.00

2.9




Sign 2

26.25

19.28

70.94

207




27/02 15.00


2.4

Sign 1

28.55

24.9

70.04




60

26/02 4.00

6.0




Sign 2

26.15

19.43

70.9

423




27/02 16.00


2.9

Sign 1

29.65

25

70.04




72

26/02 5.00

3.0




Sign 2

26.25

20

66.42

200




27/02 17.00





Sign 2

33

23.83










26/02 6.00

3.0




Sign 1

30.5

19.51

75.5

58




27/02 18.00


1.0

Sign 2

38.2

24.64







38

26/02 7.00

0.7




Sign 1

26

22.27

75.5

15




27/02 19.00 3.2




Sign 2

46.45

24.26




147




26/02 8.00







Sign 1

38.97




76.39







27/02 20.00 9.9




Sign 2

46

25.93




455




26/02 9.00




0.8

Sign 1

45

30.18

76.7




23

27/02 21.00 23.7




Sign 2

40.01

24.45




948




26/02 10.00 0.2




Sign 1

45.5

32.65

79.23

8




27/02 22.00 29.8




Sign 2

35

22.82




1042




26/02 11.00 2.3




Sign 1

43.56

34.4

85.11

78




27/02 23.00 40.3




Sign 2

31.39

20.07




1264




26/02 12.00 4.1




Sign 1

39




76.32

160




28/02 0.00

40.7




Sign 2

25

2.38

66.26

2696




26/02 13.00 1.9




Sign 1

36

24

75.92

46




28/02 1.00

36.3




Sign 2

26.17

2.27

75.24

2735




26/02 14.00 0.7




Sign 1

36

27.38

75.5

19




28/02 2.00

33.0




Sign 2

21.9




61.25

2022




26/02 15.00 1.9




Sign 1

37.42




77.35

72




28/02 3.00

28.5




Sign 2

19.15




61.25

1744




26/02 16.00 2.5




Sign 1

37.55




76.12

93




28/02 4.00

35.4




Sign 2

17.73




66.74

2360




26/02 17.00 2.3




Sign 2

37.5




76.58

178




28/02 5.00

32.7




Sign 2

17.31




61.25

2005

26/02 18.00 1.1




Sign 2

43.8

28.99

75.5

85




28/02 6.00

34.9




Sign 1

17.46




61.25

610





26/02 19.00 1.8




Sign 2

49.51

28.09

75.5

134




28/02 7.00

26.2




Sign 1

20.4




51.99

534





26/02 20.00 4.4




Sign 2

46.44




80.12

356




28/02 8.00

19.7




Sign 1

20.27




55.5

399

26/02 21.00 7.0




Sign 2

44

35

76.75

538




28/02 9.00

17.0




Sign 2

24.65

0.52

52.37

892




26/02 22.00 12.9




Sign 2

41.08

30.55




531




28/02 10.00 32.7




Sign 1

27.38

4.11




135




26/02 23.00 14.2




Sign 2

35.55




67.84

965




28/02 11.00 24.1




Sign 2

36

7.49




867




27/02 0.00

13.8




Sign 2

28.16

17.37

74.46

1030




28/02 12.00 21.9




Sign 1

37

8.67




190




27/02 1.00

17.9




Sign 2

33.78

16.33

99.95

1789




28/02 13.00 24.1




Sign 1

34.38

4.74

82.32

114




27/02 2.00

10.2




Sign 2

31.52

15.23




323




28/02 14.00 10.0




Sign 1

30.55

5.12




51




27/02 3.00

8.6




Sign 2

30.4

17.99




262




28/02 15.00 14.7




Sign 1

30.4

4.4

82.32

65




27/02 4.00

5.5




Sign 2

31.36

13.59




174




28/02 16.00





Sign 2

30.86

3.44

84.75







27/02 5.00

14.7




Sign 2

31.31

13.59




461




28/02 17.00





Sign 2

30

0.31

81.8







27/02 6.00

5.1




Sign 1

31.53

12.72




65




28/02 18.00





Sign 2

32

8.4

84.75







27/02 7.00

8.1




Sign 1

33

15.55




127




28/02 19.00





Sign 2

36.68

11.6










27/02 8.00

16.5




Sign 1

33.53

16.53




273




28/02 20.00 11.7




Sign 2

37.5

11.13




438




27/02 9.00

36.3




Sign 1

36

20.62




749




28/02 21.00 14.0




Sign 2

36.68

11.08

73.34

1027




27/02 10.00 19.3




Sign 1

34




82.32

656




28/02 22.00


2.7

Sign 2

34.83

7.61







92

27/02 11.00 18.2




Sign 1

34.99







638




28/02 23.00





Sign 2

30.4

6.95










Table 6.29 Hourly positive and negative power imbalances and associated revenues and costs for SC3. Date

Time

Pimb1 (MW)

Pimb2 (MW)

Zonal imbalance

PriceMGP (h MWh21)

PriceMPk PriceMPm (h MWh21) (h MWh21)

Rimb1 (h)

Cimb2 (h)

Date

Time

Pimb1 (MW)

Pimb2 (MW)

Zonal imbalance

PriceMPk PriceMGP (h MWh21) (h MWh21)

PriceMPm (h MWh21)

Rimb1 (h)

Cimb2 (h)

26/02 0.00

3.7




Sign 2

34.86

21.23




128




27/02 12.00 10.5




Sign 1

33

72.1

346




26/02 1.00




0.5

Sign 2

32

15.73







15

27/02 13.00 2.3




Sign 1

30.72

72.97

72




26/02 2.00




0.8

Sign 2

27.85

19.28

68




53

27/02 14.00





Sign 1

28.89

25

70.1







26/02 3.00




1.4

Sign 2

26.25

19.28

70.94




101

27/02 15.00





Sign 1

28.55

24.9

70.04







26/02 4.00

1.1




Sign 2

26.15

19.43

70.9

76




27/02 16.00





Sign 1

29.65

25

70.04







26/02 5.00







Sign 2

26.25

20

66.42







27/02 17.00





Sign 2

33

23.83










26/02 6.00







Sign 1

30.5

19.51

75.5







27/02 18.00





Sign 2

38.2

24.64










26/02 7.00







Sign 1

26

22.27

75.5







27/02 19.00





Sign 2

46.45

24.26










26/02 8.00







Sign 1

38.97




76.39







27/02 20.00





Sign 2

46

25.93










26/02 9.00







Sign 1

45

30.18

76.7







27/02 21.00





Sign 2

40.01

24.45










26/02 10.00





Sign 1

45.5

32.65

79.23







27/02 22.00





Sign 2

35

22.82










26/02 11.00





Sign 1

43.56

34.4

85.11







27/02 23.00 3.5




Sign 2

31.39

20.07




111




26/02 12.00





Sign 1

39




76.32







28/02 0.00

2.9




Sign 2

25

2.38

66.26

190




26/02 13.00





Sign 1

36

24

75.92







28/02 1.00




2.5

Sign 2

26.17

2.27

75.24




185

26/02 14.00





Sign 1

36

27.38

75.5







28/02 2.00







Sign 2

21.9




61.25







26/02 15.00





Sign 1

37.42




77.35







28/02 3.00







Sign 2

19.15




61.25







26/02 16.00





Sign 1

37.55




76.12







28/02 4.00







Sign 2

17.73




66.74







26/02 17.00





Sign 2

37.5




76.58







28/02 5.00







Sign 2

17.31




61.25







26/02 18.00





Sign 2

43.8

28.99

75.5







28/02 6.00




0.3

Sign 1

17.46




61.25







26/02 19.00





Sign 2

49.51

28.09

75.5







28/02 7.00







Sign 1

20.4




51.99







26/02 20.00





Sign 2

46.44




80.12







28/02 8.00







Sign 1

20.27




55.5







26/02 21.00





Sign 2

44

35

76.75







28/02 9.00




1.2

Sign 2

24.65

0.52

52.37




63

26/02 22.00





Sign 2

41.08

30.55










28/02 10.00 20.4




Sign 1

27.38

4.11




78




26/02 23.00


2.6

Sign 2

35.55




67.84




177

28/02 11.00 26.6




Sign 2

36

7.49




734










Sign 2

28.16

17.37

74.46







28/02 12.00 35.8




Sign 1

37

8.67




230




27/02 1.00

2.1




Sign 2

33.78

16.33

99.95

212




28/02 13.00 34.9




Sign 1

34.38

4.74

82.32

170




27/02 2.00




2.9

Sign 2

31.52

15.23







90

28/02 14.00 45.0




Sign 1

30.55

5.12




179




27/02 3.00







Sign 2

30.4

17.99










28/02 15.00 25.2




Sign 1

30.4

4.4

82.32

198




27/02 4.00







Sign 2

31.36

13.59










28/02 16.00 20.6




Sign 2

30.86

3.44

84.75

2136




27/02 5.00

6.6




Sign 2

31.31

13.59




207




28/02 17.00 31.6




Sign 2

30

0.31

81.8

1682




27/02 6.00




1.8

Sign 1

31.53

12.72







23

28/02 18.00 32.0




Sign 2

32

8.4

84.75

2678




27/02 7.00

2.2




Sign 1

33

15.55




34




28/02 19.00 47.6




Sign 2

36.68

11.6




1175




27/02 0.00

27/02 8.00

12.6




Sign 1

33.53

16.53




208




28/02 20.00 50.0




Sign 2

37.5

11.13




1787




27/02 9.00

33.4




Sign 1

36

20.62




688




28/02 21.00 33.3




Sign 2

36.68

11.08

73.34

3664




27/02 10.00 17.3




Sign 1

34




82.32

589




28/02 22.00 26.5




Sign 2

34.83

7.61




1159

92

27/02 11.00 18.0




Sign 1

34.99







629




28/02 23.00 20.4




Sign 2

30.4

6.95




805




Table 6.30 Hourly positive and negative power imbalances and associated revenues and costs for SC4. Date

Time

Pimb1 (MW)

Pimb2 (MW)

Zonal imbalance

PriceMGP (h MWh21)

PriceMPk PriceMPm (h MWh21) (h MWh21)

Rimb1 (h)

Cimb2 (h)

Date

Time

Pimb1 (MW)

Pimb2 (MW)

Zonal imbalance

PriceMPk PriceMGP (h MWh21) (h MWh21)

PriceMPm (h MWh21)

Rimb1 (h)

Cimb2 (h)

26/02 0.00

5.8




Sign 2

34.86

21.23




203




27/02 12.00 10.5




Sign 1

33

72.1

346




26/02 1.00

2.2




Sign 2

32

15.73




71




27/02 13.00 4.8




Sign 1

30.72

72.97

147


57

26/02 2.00

3.0




Sign 2

27.85

19.28

68

205




27/02 14.00


2.3

Sign 1

28.89

25

70.1




26/02 3.00

2.9




Sign 2

26.25

19.28

70.94

207




27/02 15.00





Sign 1

28.55

24.9

70.04







26/02 4.00

6.0




Sign 2

26.15

19.43

70.9

423




27/02 16.00





Sign 1

29.65

25

70.04







26/02 5.00

3.0




Sign 2

26.25

20

66.42

200




27/02 17.00





Sign 2

33

23.83










26/02 6.00

3.0




Sign 1

30.5

19.51

75.5

58




27/02 18.00





Sign 2

38.2

24.64










26/02 7.00

0.7




Sign 1

26

22.27

75.5

15




27/02 19.00





Sign 2

46.45

24.26










26/02 8.00




0.9

Sign 1

38.97




76.39




35

27/02 20.00





Sign 2

46

25.93










26/02 9.00




1.6

Sign 1

45

30.18

76.7




48

27/02 21.00


1.1

Sign 2

40.01

24.45







45

26/02 10.00


2.0

Sign 1

45.5

32.65

79.23




66

27/02 22.00 3.5




Sign 2

35

22.82




121




26/02 11.00


1.4

Sign 1

43.56

34.4

85.11




48

27/02 23.00 11.1




Sign 2

31.39

20.07




349




26/02 12.00


0.3

Sign 1

39




76.32




11

28/02 0.00

10.5




Sign 2

25

2.38

66.26

693




26/02 13.00


2.7

Sign 1

36

24

75.92




66

28/02 1.00

5.1




Sign 2

26.17

2.27

75.24

386




26/02 14.00





Sign 1

36

27.38

75.5







28/02 2.00

1.1




Sign 2

21.9




61.25

66




26/02 15.00





Sign 1

37.42




77.35







28/02 3.00







Sign 2

19.15




61.25







26/02 16.00





Sign 1

37.55




76.12







28/02 4.00

4.0




Sign 2

17.73




66.74

266




26/02 17.00





Sign 2

37.5




76.58







28/02 5.00

3.0




Sign 2

17.31




61.25

183




26/02 18.00





Sign 2

43.8

28.99

75.5







28/02 6.00

7.3




Sign 1

17.46




61.25

127





26/02 19.00





Sign 2

49.51

28.09

75.5







28/02 7.00

2.4




Sign 1

20.4




51.99

49

26/02 20.00





Sign 2

46.44




80.12







28/02 8.00

4.5




Sign 1

20.27




55.5

92




26/02 21.00


1.2

Sign 2

44

35

76.75




93

28/02 9.00

6.4




Sign 2

24.65

0.52

52.37

335




26/02 22.00 4.4




Sign 2

41.08

30.55




179




28/02 10.00 26.6




Sign 1

27.38

4.11




109




26/02 23.00 5.0




Sign 2

35.55




67.84

338




28/02 11.00 26.3




Sign 2

36

7.49




948




27/02 0.00

4.2




Sign 2

28.16

17.37

74.46

315




28/02 12.00 29.3




Sign 1

37

8.67




254




27/02 1.00

9.7




Sign 2

33.78

16.33

99.95

970




28/02 13.00 37.2




Sign 1

34.38

4.74

82.32

176




27/02 2.00

4.7




Sign 2

31.52

15.23




149




28/02 14.00 34.9




Sign 1

30.55

5.12




179




27/02 3.00

4.4




Sign 2

30.4

17.99




132




28/02 15.00 45.0




Sign 1

30.4

4.4

82.32

198




27/02 4.00

2.5




Sign 2

31.36

13.59




79




28/02 16.00 25.2




Sign 2

30.86

3.44

84.75

2136




27/02 5.00

14.2




Sign 2

31.31

13.59




445




28/02 17.00 20.6




Sign 2

30

0.31

81.8

1682




27/02 6.00

5.1




Sign 1

31.53

12.72




65




28/02 18.00 31.6




Sign 2

32

8.4

84.75

2678




27/02 7.00

8.1




Sign 1

33

15.55




127




28/02 19.00 32.0




Sign 2

36.68

11.6




1175




27/02 8.00

16.5




Sign 1

33.53

16.53




273




28/02 20.00 47.6




Sign 2

37.5

11.13




1787




27/02 9.00

36.3




Sign 1

36

20.62




749




28/02 21.00 50.0




Sign 2

36.68

11.08

73.34

3664




27/02 10.00 19.3




Sign 1

34




82.32

656




28/02 22.00 33.3




Sign 2

34.83

7.61




1159




27/02 11.00 18.3




Sign 1

34.99







640




28/02 23.00 26.5




Sign 2

30.4

6.95




805




Table 6.31 Hourly revenues from electricity selling, greenhouse gas emissions costs and operational expenditure (OPEX) of the gas turbine park for SC1. Date

Time

PriceMGP (h MWh21)

InOWT (h MWh21)

Rsell;GT (h)

Rsell;OWT (h)

Rsell (h)

CGHG (h)

OPEXGT (h)

Date

Time

PriceMGP (h MWh21)

InOWT (h MWh21)

Rsell;GT (h)

Rsell;OWT (h)

Rsell (h)

CGHG (h)

OPEXGT (h)


26/02

0.00

34.9

135.5

0

992

992







27/02

12.00

33.0

137.4

0

1786

1786




26/02

1.00

32.0

138.4

0

378

378







27/02

13.00

30.7

139.6

127

815

942

130

11

26/02

2.00

27.9

142.5

0

513

513







27/02

14.00

28.9

141.5

284

0

284

195

26

26/02

3.00

26.3

144.1

0

497

497







27/02

15.00

28.6

141.8

294

0

294

199

27

26/02

4.00

26.2

144.2

0

1016

1016







27/02

16.00

29.7

140.7

320

0

320

203

29

26/02

5.00

26.3

144.1

0

513

513







27/02

17.00

33.0

137.4

392

0

392

212

32

26/02

6.00

30.5

139.9

0

505

505







27/02

18.00

38.2

132.2

340

606

947

187

24

26/02

7.00

36.0

134.4

109

116

225

109

8

27/02

19.00

46.5

123.9

221

1414

1635

140

13

26/02

8.00

39.0

131.4

268

0

268

167

18

27/02

20.00

46.0

124.4

0

2764

2764







26/02

9.00

45.0

125.4

391

0

391

185

23

27/02

21.00

40.0

130.3

0

5214

5214







26/02

10.00

45.5

124.9

346

42

389

174

20

27/02

22.00

35.0

135.4

0

6024

6024







26/02

11.00

43.6

126.8

172

387

559

127

11

27/02

23.00

31.4

139.0

0

7375

7375







26/02

12.00

39.0

131.4

0

697

697

109

8

28/02

0.00

25.0

145.4

0

7270

7270







26/02

13.00

36.0

134.4

0

328

328







28/02

1.00

26.2

144.2

0

6354

6354







26/02

14.00

36.0

134.4

0

119

119







28/02

2.00

21.9

148.5

0

5624

5624







26/02

15.00

37.4

132.9

0

328

328







28/02

3.00

19.2

151.2

0

4850

4850







26/02

16.00

37.6

132.8

0

420

420







28/02

4.00

17.7

152.6

0

6024

6024







26/02

17.00

37.5

132.9

0

395

395







28/02

5.00

17.3

153.0

0

5577

5577







26/02

18.00

43.8

126.6

0

192

192







28/02

6.00

17.5

152.9

0

5954

5954







26/02

19.00

49.5

120.8

0

302

302







28/02

7.00

20.4

150.0

0

4462

4462







26/02

20.00

46.4

123.9

0

756

756







28/02

8.00

20.3

150.1

0

3352

3352







26/02

21.00

44.0

126.4

0

1193

1193







28/02

9.00

24.7

145.7

0

2900

2900







26/02

22.00

41.1

129.3

0

2202

2202







28/02

10.00

27.4

143.0

0

5577

5577







26/02

23.00

35.6

134.8

0

2424

2424







28/02

11.00

36.0

134.4

0

4486

4486







27/02

0.00

28.2

142.2

0

2356

2356







28/02

12.00

37.0

133.4

0

4996

4996







27/02

1.00

33.8

136.6

0

3049

3049







28/02

13.00

34.4

136.0

0

6331

6331







27/02

2.00

31.5

138.8

0

1744

1744







28/02

14.00

30.6

139.8

0

5954

5954







27/02

3.00

30.4

140.0

0

1469

1469







28/02

15.00

30.4

140.0

0

7669

7669







27/02

4.00

31.4

139.0

0

945

945







28/02

16.00

30.9

139.5

501

4293

4794

244

43

27/02

5.00

31.3

139.0

0

2509

2509







28/02

17.00

30.0

140.4

685

3503

4188

289

61

27/02

6.00

31.5

138.8

0

874

874







28/02

18.00

32.0

138.4

384

5384

5767

213

32

27/02

7.00

33.0

137.4

0

1386

1386







28/02

19.00

36.7

133.7

430

5456

5886

211

31

27/02

8.00

33.5

136.8

0

2815

2815







28/02

20.00

37.5

132.9

0

8116

8116







27/02

9.00

36.0

134.4

0

6189

6189







28/02

21.00

36.7

133.7

0

8510

8510







27/02

10.00

34.0

136.4

0

3287

3287







28/02

22.00

34.8

135.5

369

5671

6039

202

28

27/02

11.00

35.0

135.4

0

3114

3114







28/02

23.00

30.4

140.0

525

4511

5036

251

46

Table 6.32 Hourly revenues from electricity selling, greenhouse gas emissions costs and operational expenditure (OPEX) of the gas turbine park for SC2. Date

Time

Rsell;GT PriceMGP InOWT (h MWh21) (h MWh21) (h)

Rsell;OWT (h)

Rsell (h)

CGHG (h)

OPEXGT (h)

Date

Time

Rsell;GT PriceMGP InOWT (h MWh21) (h MWh21) (h)

Rsell;OWT (h)

Rsell (h)

CGHG (h)

26/02 26/02 26/02 26/02 26/02 26/02 26/02 26/02 26/02 26/02 26/02 26/02 26/02 26/02 26/02 26/02 26/02

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00 11.00 12.00 13.00 14.00 15.00 16.00

34.9 32.0 27.9 26.3 26.2 26.3 30.5 36.0 39.0 45.0 45.5 43.6 39.0 36.0 36.0 37.4 37.6

992 378 513 497 1016 513 505 116 0 0 42 387 697 328 119 328 420

992 378 513 497 1016 513 505 116 0 0 42 387 697 328 119 328 420







































27/02 27/02 27/02 27/02 27/02 27/02 27/02 27/02 27/02 27/02 27/02 27/02 28/02 28/02 28/02 28/02 28/02

12.00 13.00 14.00 15.00 16.00 17.00 18.00 19.00 20.00 21.00 22.00 23.00 0.00 1.00 2.00 3.00 4.00

33.0 30.7 28.9 28.6 29.7 33.0 38.2 46.5 46.0 40.0 35.0 31.4 25.0 26.2 21.9 19.2 17.7

1786 815 0 0 0 0 606 1414 2764 5214 6024 7375 7270 6354 5624 4850 6024

1786 815


131 606 1414 2764 5214 6024 7375 7270 6354 5624 4850 6024






127












135.5 138.4 142.5 144.1 144.2 144.1 139.9 134.4 131.4 125.4 124.9 126.8 131.4 134.4 134.4 132.9 132.8

0 0 0 0 0 0 0 0 0 0 0 0 0 0 165 137 129

137.4 139.6 141.5 141.8 140.7 137.4 132.2 123.9 124.4 130.3 135.4 139.0 145.4 144.2 148.5 151.2 152.6

0 0 0 190 341 695 834 970 689 0 0 0 0 0 0 64 0

OPEXGT (h)

11

26/02 26/02 26/02 26/02 26/02 26/02 26/02 27/02 27/02 27/02 27/02 27/02 27/02 27/02 27/02 27/02 27/02 27/02 27/02

17.00 18.00 19.00 20.00 21.00 22.00 23.00 0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00 11.00

37.5 43.8 49.5 46.4 44.0 41.1 35.6 28.2 33.8 31.5 30.4 31.4 31.3 31.5 33.0 33.5 36.0 34.0 35.0

132.9 126.6 120.8 123.9 126.4 129.3 134.8 142.2 136.6 138.8 140.0 139.0 139.0 138.8 137.4 136.8 134.4 136.4 135.4

157 250 268 160 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

395 192 302 756 1193 2202 2424 2356 3049 1744 1469 945 2509 874 1386 2815 6189 3287 3114

395 192 302 756 1193 2202 2424 2356 3049 1744 1469 945 2509 874 1386 2815 6189 3287 3114











































28/02 28/02 28/02 28/02 28/02 28/02 28/02 28/02 28/02 28/02 28/02 28/02 28/02 28/02 28/02 28/02 28/02 28/02 28/02

5.00 6.00 7.00 8.00 9.00 10.00 11.00 12.00 13.00 14.00 15.00 16.00 17.00 18.00 19.00 20.00 21.00 22.00 23.00

17.3 17.5 20.4 20.3 24.7 27.4 36.0 37.0 34.4 30.6 30.4 30.9 30.0 32.0 36.7 37.5 36.7 34.8 30.4

153.0 152.9 150.0 150.1 145.7 143.0 134.4 133.4 136.0 139.8 140.0 139.5 140.4 138.4 133.7 132.9 133.7 135.5 140.0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

5577 5954 4462 3352 2900 5577 4486 4996 6331 5954 7669 4293 3503 5384 5456 8116 8510 5671 4511

5577 5954 4462 3352 2900 5577 4486 4996 6331 5954 7669 4550 3951 5514 5596 8116 8510 5671 4795












182 235 129 124


191

22 40 11 10

25

Table 6.33 Hourly revenues from electricity selling, greenhouse gas emissions costs and operational expenditure (OPEX) of the gas turbine park for SC3. Date

Time

PriceMGP (h MWh21)

InOWT (h MWh21)

Rsell;GT (h)

Rsell;OWT (h)

Rsell (h)

CGHG (h)

OPEXGT (h)

Date

Time

PriceMGP (h MWh21)

InOWT (h MWh21)

Rsell;GT (h)

Rsell;OWT (h)

Rsell (h)

CGHG (h)

OPEXGT (h)

26/02

0.00

34.9

135.5

0

992

992







27/02

12.00

33.0

137.4

0

1786

1786







26/02

1.00

32.0

138.4

0

378

378







27/02

13.00

30.7

139.6

0

815

815







26/02

2.00

27.9

142.5

0

513

513







27/02

14.00

28.9

141.5

286

0

286

196

26

26/02

3.00

26.3

144.1

0

497

497







27/02

15.00

28.6

141.8

407

0

407

230

38

26/02

4.00

26.2

144.2

0

1016

1016







27/02

16.00

29.7

140.7

566

0

566

264

51

26/02

5.00

26.3

144.1

78

513

591

108

8

27/02

17.00

33.0

137.4

945

0

945

328

76

26/02

6.00

30.5

139.9

109

505

614

120

10

27/02

18.00

38.2

132.2

1124

606

1730

333

78

26/02

7.00

36.0

134.4

231

116

347

161

17

27/02

19.00

46.5

123.9

1322

1414

2736

327

76

26/02

8.00

39.0

131.4

330

0

330

183

23

27/02

20.00

46.0

124.4

1038

2764

3802

287

60

26/02

9.00

45.0

125.4

413

0

413

189

24

27/02

21.00

40.0

130.3

349

5214

5563

185

23

26/02

10.00

45.5

124.9

438

42

480

193

26

27/02

22.00

35.0

135.4

144

6024

6169

130

11

26/02

11.00

43.6

126.8

391

387

778

188

24

27/02

23.00

31.4

139.0

0

7375

7375







26/02

12.00

39.0

131.4

307

697

1003

177

21

28/02

0.00

25.0

145.4

0

7270

7270







26/02

13.00

36.0

134.4

372

328

700

200

28

28/02

1.00

26.2

144.2

0

6354

6354







26/02

14.00

36.0

134.4

438

119

557

214

32

28/02

2.00

21.9

148.5

142

5624

5766

162

17

26/02

15.00

37.4

132.9

421

328

749

207

30

28/02

3.00

19.2

151.2

209

4850

5060

204

29

26/02

16.00

37.6

132.8

414

420

834

205

29

28/02

4.00

17.7

152.6

64

6024

6088

120

10

26/02

17.00

37.5

132.9

442

395

837

211

31

28/02

5.00

17.3

153.0

80

5577

5656

137

12

26/02

18.00

43.8

126.6

583

192

775

223

35

28/02

6.00

17.5

152.9

0

5954

5954




0

26/02

19.00

49.5

120.8

644

302

946

221

35

28/02

7.00

20.4

150.0

105

4462

4568

145

14

26/02

20.00

46.4

123.9

512

756

1268

205

29

28/02

8.00

20.3

150.1

62

3352

3414

110

8

26/02

21.00

44.0

126.4

388

1193

1581

186

23

28/02

9.00

24.7

145.7

0

2900

2900







26/02

22.00

41.1

129.3

133

2202

2335

113

9

28/02

10.00

27.4

143.0

0

5577

5577







26/02

23.00

35.6

134.8

0

2424

2424







28/02

11.00

36.0

134.4

0

4486

4486







27/02

0.00

28.2

142.2

95

2356

2450

116




28/02

12.00

37.0

133.4

0

4996

4996







27/02

1.00

33.8

136.6

0

3049

3049







28/02

13.00

34.4

136.0

0

6331

6331







27/02

2.00

31.5

138.8

0

1744

1744







28/02

14.00

30.6

139.8

0

5954

5954







27/02

3.00

30.4

140.0

98

1469

1567

113




28/02

15.00

30.4

140.0

0

7669

7669







27/02

4.00

31.4

139.0

159

945

1104

144

13

28/02

16.00

30.9

139.5

0

4293

4293







27/02

5.00

31.3

139.0

0

2509

2509







28/02

17.00

30.0

140.4

0

3503

3503







27/02

6.00

31.5

138.8

0

874

874







28/02

18.00

32.0

138.4

0

5384

5384







27/02

7.00

33.0

137.4

0

1386

1386







28/02

19.00

36.7

133.7

0

5456

5456







27/02

8.00

33.5

136.8

0

2815

2815







28/02

20.00

37.5

132.9

0

8116

8116







27/02

9.00

36.0

134.4

0

6189

6189







28/02

21.00

36.7

133.7

0

8510

8510







27/02

10.00

34.0

136.4

0

3287

3287







28/02

22.00

34.8

135.5

0

5671

5671







27/02

11.00

35.0

135.4

0

3114

3114







28/02

23.00

30.4

140.0

0

4511

4795







Table 6.34 Hourly revenues from electricity selling, greenhouse gas emissions costs and operational expenditure (OPEX) of the gas turbine park for SC4. Date

Time

PriceMGP (h MWh21)

InOWT (h MWh21)

Rsell;GT (h)

Rsell;OWT (h)

Rsell (h)

CGHG (h)

OPEXGT (h)

Date

Time

PriceMGP (h MWh21)

InOWT (h MWh21)

Rsell;GT (h)

Rsell;OWT (h)

Rsell (h)

CGHG (h)

OPEXGT (h)

26/02

0.00

34.9

135.5

0

992

992







27/02

12.00

33.0

137.4

0

1786

1786







26/02

1.00

32.0

138.4

0

378

378







27/02

13.00

30.7

139.6

0

815

815








26/02

2.00

27.9

142.5

0

513

513







27/02

14.00

28.9

141.5

0

0

0




26/02

3.00

26.3

144.1

0

497

497







27/02

15.00

28.6

141.8

190

0

190

164

18

26/02

4.00

26.2

144.2

0

1016

1016







27/02

16.00

29.7

140.7

341

0

341

209

31

26/02

5.00

26.3

144.1

0

513

513







27/02

17.00

33.0

137.4

695

0

695

277

56

26/02

6.00

30.5

139.9

0

505

505







27/02

18.00

38.2

132.2

834

606

1440

282

58

26/02

7.00

36.0

134.4

0

116

116







27/02

19.00

46.5

123.9

970

1414

2384

276

56

26/02

8.00

39.0

131.4

0

0

0







27/02

20.00

46.0

124.4

689

2764

3453

235

40

26/02

9.00

45.0

125.4

0

0

0







27/02

21.00

40.0

130.3

0

5214

5214







26/02

10.00

45.5

124.9

0

42

42







27/02

22.00

35.0

135.4

0

6024

6024







26/02

11.00

43.6

126.8

0

387

387







27/02

23.00

31.4

139.0

0

7375

7375







26/02

12.00

39.0

131.4

0

697

697







28/02

0.00

25.0

145.4

0

7270

7270







26/02

13.00

36.0

134.4

0

328

328







28/02

1.00

26.2

144.2

0

6354

6354







26/02

14.00

36.0

134.4

165

119

284

137

12

28/02

2.00

21.9

148.5

0

5624

5624







26/02

15.00

37.4

132.9

137

328

465

121

10

28/02

3.00

19.2

151.2

64

4850

4914

115

9

26/02

16.00

37.6

132.8

129

420

549

117

9

28/02

4.00

17.7

152.6

0

6024

6024







26/02

17.00

37.5

132.9

157

395

552

131

11

28/02

5.00

17.3

153.0

0

5577

5577







26/02

18.00

43.8

126.6

250

192

443

153

15

28/02

6.00

17.5

152.9

0

5954

5954







26/02

19.00

49.5

120.8

268

302

570

149

14

28/02

7.00

20.4

150.0

0

4462

4462







26/02

20.00

46.4

123.9

160

756

916

117

9

28/02

8.00

20.3

150.1

0

3352

3352







26/02

21.00

44.0

126.4

0

1193

1193







28/02

9.00

24.7

145.7

0

2900

2900







26/02

22.00

41.1

129.3

0

2202

2202







28/02

10.00

27.4

143.0

0

5577

5577







26/02

23.00

35.6

134.8

0

2424

2424







28/02

11.00

36.0

134.4

0

4486

4486







27/02

0.00

28.2

142.2

0

2356

2356







28/02

12.00

37.0

133.4

0

4996

4996







27/02

1.00

33.8

136.6

0

3049

3049







28/02

13.00

34.4

136.0

0

6331

6331







27/02

2.00

31.5

138.8

0

1744

1744







28/02

14.00

30.6

139.8

0

5954

5954







27/02

3.00

30.4

140.0

0

1469

1469







28/02

15.00

30.4

140.0

0

7669

7669







27/02

4.00

31.4

139.0

0

945

945







28/02

16.00

30.9

139.5

0

4293

4293







27/02

5.00

31.3

139.0

0

2509

2509







28/02

17.00

30.0

140.4

0

3503

3503







27/02

6.00

31.5

138.8

0

874

874







28/02

18.00

32.0

138.4

0

5384

5384







27/02

7.00

33.0

137.4

0

1386

1386







28/02

19.00

36.7

133.7

0

5456

5456







27/02

8.00

33.5

136.8

0

2815

2815







28/02

20.00

37.5

132.9

0

8116

8116







27/02

9.00

36.0

134.4

0

6189

6189







28/02

21.00

36.7

133.7

0

8510

8510







27/02

10.00

34.0

136.4

0

3287

3287







28/02

22.00

34.8

135.5

0

5671

5671







27/02

11.00

35.0

135.4

0

3114

3114







28/02

23.00

30.4

140.0

0

4511

4511







194

CHAPTER 6 Case studies

Table 6.35 Technical, economic, environmental parameters in the four SCs. Parameter

SC1

SC2

SC3

SC4

Number of operating hours for GT park Total Pwind;avail (MWh) Total Pfuel (MWh) Total Pr (MWh) Total PGT (MWh) Total eGHG;GT (kgCO2 eq) Discounted total power (MWh) CAPEXOWT (h) CAPEXGT (h) Discounted total OPEXOWT (h) Discounted total OPEXGT (h) Total Rsell (h) Total Rimb1 (h) Total Cimb2 (h) Total CGHG (h)

18

6

36

14

2881.86 1134.39 1190.92 181.42 229,146.24 1279.66

2881.86 325.53 1190.92 44.41 65,757.83 1151.90

2881.86 2256.20 1190.92 373.69 455,752.80 1458.96

2881.86 819.36 1190.92 130.69 165,510.35 1232.35

151,472,096 68,253,730 55,165.43

151,472,096 68,253,730 55,165.43

151,472,096 68,253,730 55,165.43

151,472,096 68,253,730 55,165.43

483.63

118.38

996.19

348.38

209,032.78 10,404.10 227.32 3437.19

204,262.25 34,373.76 333.00 986.37

216,711.28 20,163.94 714.10 6836.29

207,922.76 26,609.79 468.52 2482.66

CAPEX, Capital expenditure; GT, gas turbine; OPEX, operational expenditure.

lowest costs associated to GHG emissions (CGHG ) and negative power imbalance (Cimb2 ). Clearly enough, the higher Probd and lower forecast horizon, the better performance on these parameters, thus confirming the findings obtained in the preliminary comparison of the power curves matching. On the other hand, the largest revenues due to electricity selling to the grid (Rsell ) is obtained when lower Probd and higher forecast horizon are considered (SC3) because of greater PGT produced over many operating hours of the GT park. From the data in Table 6.35 the technical, economic, and environmental indicators are calculated for each SC. Fig. 6.17 illustrates the comparison of these indicators among the four SCs. With respect to the technical performance in Fig. 6.17A, the highest electrical efficiency, ηel, of the hybrid energy system is obtained in SC2, as a result of the lower number of operating hours of the GT park and fuel consumption shown in Table 6.35. It is worth noting that the ranking of SCs based on ηel is inversely proportional to these two parameters. As a consequence, it can be concluded that the higher the Probd and the lower the forecast horizon selected for the analysis, the better technical performance of the hybrid energy system will be. Looking at the environmental indicator results in Fig. 6.17B, SCs are ranked on LGHG in the same order as emerged in ηel, that is, SC2 provides the best performance (i.e., the lowest LGHG value) followed by SC4, SC1, and finally SC3

6.2 Case study 2

FIGURE 6.17 Comparison of technical, economic, and environmental indicators among the four SCs: (A) electrical efficiency, (B) LGHG, levelized greenhouse gases, (C) LCOE, levelized cost of energy, (D) LVOE, levelized value of energy.

with the highest LGHG. This is a direct consequence of diminishing eGHG;GT while decreasing the number of operating hours of the GT park, as illustrated in Table 6.35. The same conclusions previously described about the influence of Probd and forecast horizon on ηel can be drawn in the case of LGHG. Concerning the LCOE findings in Fig. 6.17C, the lowest value of this indicator is calculated for SC3, while the highest one is for SC2. Therefore, referring to Eq. (6.31), the effect of the discounted total power produced from the hybrid system prevails on LCOE rather than total costs: The highest power generated in SC3 leads to the best economic performance even though the greatest OPEXGT is associated with the GT park, as shown in Table 6.35. As a matter of fact, the lower the Probd and the higher the forecast horizon selected for the analysis, the better the LCOE indicator will be, differently from the ηel and LGHG conclusions. From Fig. 6.16D, SC2 exhibits the scenario characterized by the highest LVOE value, while SC3 is the worst performant, thus confirming the ranking based on ηel and LGHG. This highlights that the results on the Rimb1 , CGHG , and Cimb2 parameters play an important role in the estimation of LVOE rather than Rsell values. Therefore it can be concluded that the higher the Probd and the lower the forecast horizon selected for the analysis, the better the economic performance on LVOE of the hybrid energy system will be.

195

196

CHAPTER 6 Case studies

By using target values in Table 6.19 for the normalization of indicators and applying weight trade-offs in Tables 6.21 and 6.22 to the normalized indicators, ASI values for four SCs are calculated based on the different perspectives of decision makers and weighted mean method. Fig. 6.18 displays the results. Looking at the results obtained through the weighted arithmetic mean (WAM) method in Fig. 6.18A, SC2 is identified as the scenario with the best performance (i.e., highest ASI) based on all the schemes of decision makers, thus confirming the findings obtained from ηel, LGHG, and LVOE comparison. For this SC, comparable ASI values are obtained according to egalitarian, hierarchist, and equal weighting methods, ranging from 0.42 to 0.46, while the individualist approach yields a lower ASI (i.e., 0.3). SC4 and SC1 are the second and third ranked with similar values of ASI based on the same perspective of decision makers. On the other hand, SC3 results in the most penalized scenario with respect to all perspectives. Therefore forecast data based on a lower time horizon and a dispatching power plan defined on higher Probd can be considered optimal parameters to enhance the overall sustainability fingerprinting of the G2P hybrid energy system. The same ranking of the SCs appears focused on Fig. 6.18B when the weighted geometric mean (WGM) method is used for the calculation of ASI. However, it should be noted that, being an equal archetype, the egalitarian method provides the highest ASI values of the schemes. Moreover, the lower values of ASI are obtained for all the SCs compared to Fig. 6.18A, as a consequence of the large differences between normalized indicators. A further investigation is performed with the aim to evaluate, in each of the four defined SCs, the potential benefits of the G2P offshore hybrid energy system compared to the sole offshore renewable plant linked to the onshore electrical grid (i.e., without the energy-balancing GTs). In this latter case, costs associated with the negative imbalance (Cimb2 ) are quantified based on all the hourly negative differences between Pr and Pd ; GHG emissions and related costs are not considered but are attributed only to the renewable power receiving the financial incentive (InOWT ); ηel and Rsell are evaluated with the sole terms associated with

FIGURE 6.18 Comparison of ASI indicators for the four SCs based on different perspectives and (A) WAM and (B) WGM methods. ASI, Aggregated sustainability index; WAM, weighted arithmetic mean; WGM, weighted geometric mean.

6.2 Case study 2

the wind power; the CAPEX and OPEX of GTs are not estimated. Table 6.36 summarizes the main results obtained for the different SCs excluding the energybalancing system (SCno-G2P). Fig. 6.19 illustrates the relative difference in the technical and economic performance indicators of the G2P hybrid energy system with respect to the sole renewable plant in the four SCs. Quite obviously, LGHG comparison is neglected due to the assumption of zero emissions from the OWT farm.

Table 6.36 Technical and economic parameters calculated for the four SCs excluding the gas turbines. Parameter

SC1no-G2P

SC2no-G2P

SC3no-G2P

SC4no-G2P

Total Pwind;avail (MWh) Total Pr (MWh) Discounted total power (MWh) CAPEXOWT (h) Discounted total OPEXOWT (h) Total Rsell (h) Total Rimb1 (h) Total Cimb2 (h)

2881.86 1190.92 1279.66

2881.86 1190.92 1151.90

2881.86 1190.92 1458.96

2881.86 1190.92 1232.35

151,472,096 55,165.43

151,472,096 55,165.43

151,472,096 55,165.43

151,472,096 55,165.43

20,287 10,404.10 7867.82

202,873.65 34,373.76 3157.05

202,873.65 20,163.94 16,924.58

202,873.65 26,609.79 6142.36

CAPEX, Capital expenditure; OPEX, operational expenditure.

FIGURE 6.19 Relative difference in the performance of G2P hybrid energy system with respect to the sole OWT farm for the four SCs. OWT, Offshore wind turbine.

197

198

CHAPTER 6 Case studies

As shown in Table 6.36, all the parameters remain unchanged compared to the SCs evaluated in the hybrid energy systems (Table 6.35), except the Cimb2 values, which are significantly higher due to the lack of GT park. Among the scenarios, the greatest Cimb2 is identified in SC3no-G2P (about 17 kh), while the largest increase in Cimb2 with respect to the data in Table 6.35 is attributed to SC1no-G2P (almost 33%). Despite these results, from Fig. 6.19 it appears evident that the adoption of the G2P hybrid energy system with respect to the sole renewable plant is not advantageous over the selected time interval. The negative percentages illustrated in the figure mean that the hybrid energy option produces worse indicators than the OWT farm in each SC, that is, lower η, higher LCOP, and lower LVOE. However, it should be noted that SC2 shows greater potential for enhancement in η and LVOE compared to other scenarios, with relative differences between the hybrid energy system and the sole OWT farm lower than 7%. This confirms the outcomes from the previous analyses on disaggregated and aggregated indicators about the beneficial effect of lower forecast horizons and higher Probd on sustainability performance.

6.2.6 Sensitivity analysis results The stability of the ranking of the four SCs based on ASI indicators is investigated by applying the sensitivity analysis technique described in Section 5.5. The Monte Carlo method is adopted to investigate the influence of the random variation of target values used for the normalization of disaggregated indicators on the relative differences of ASI of SC2, SC3, and SC4 with respect to the reference SC1. The analysis of the effect of varying weights among indicators is reasonably disregarded due to the approach adopted for weights elicitation, as discussed in case study 1. All the target values are equally varied between 6 20% the baseline value reported in Table 6.19. The uniform distribution is conservatively adopted for all the indicators to ensure more conservative results and to avoid assumptions about the distributions of the reference indicators. In all the simulations, 106 runs are performed. The results of the analysis are illustrated in Fig. 6.20 based on different perspectives of decision makers and weighted mean methods. Fig. 6.20 clearly shows that SC2 and SC4 are always the best options with respect to SC1 for all the perspectives of decision makers and for both aggregation methods because of the positive ASI differences obtained with 106 simulations. On the other hand, a negative difference appears in the case of SC3 in all the situations and runs. Therefore it can be concluded that the ranking of the scenarios depicted in Fig. 6.18 is completely proved. Equal findings are obtained by increasing the variation range of target values up to 6 50%, as well as running up the Monte Carlo simulations to 107.

FIGURE 6.20 Cumulative probability of the ASI differences of SC2, SC3, and SC4 with respect to SC1 using (A) individualist, (B) egalitarian, (C) hierarchist, (D) equal weighting schemes. ASI, Aggregated sustainability index; WAM, weighted arithmetic mean; WGM, weighted geometric mean.

200

CHAPTER 6 Case studies

6.3 Case study 3: Emerging methanol production routes for P2L offshore hybrid energy systems driven by wind and solar energies This case study consists of alternative process routes for renewable CH3OH production at low maturity level that need to be investigated comprehensively, based on different performance aspects, for a suitable adoption in P2L offshore hybrid energy options. Thus the integrated assessment methodology described in Section 5.4 is applied to this case study to check the viability of the alternative processes integrating both sustainable and inherent safety analyses and to address the most feasible process schemes for a detailed assessment of P2L hybrid energy systems powered by wind and solar energies and implemented at an offshore oil and gas facility in the Atlantic Ocean.

6.3.1 Definition of the reference process schemes Other than the conventional route, potentially more efficient and green pathways for the CH3OH production have been proposed according to Olah’s concept of the Methanol Economy: These routes can be based on the direct partial oxidation of CH4 valorizing the exploitation of natural gas, or they can use CO2 as an input source to promote the CCU schemes [109]. Eleven production technologies reviewed in the technical literature Zakaria and Kamarudin [143,302] are considered in this case study and displayed in Fig. 6.21. According to step 0 of the procedure in Fig. 5.8, the reference process schemes of the selected technologies are defined, based on the technical literature. Table 6.37 shows the main features of the process schemes considered. As evidenced in Table 6.37, the most mature process results are the catalytic hydrogenation of CO2, which shows the highest technology readiness level, given the operational CRI George Olah pilot plant where about 4000 t year21 (i.e., 500 kg h21) of renewable CH3OH is produced by recycling flue CO2. This process also shows the highest yield, combined with a potentially high flowrate and a suitable purity of the product. The other 10 processes considered are only demonstrated at laboratory scale. Most of the proposed schemes operate in a continuous mode, except for biocatalysis, photocatalysis, and electrosynthesis. In case of homogeneous catalysis in solution, no information is reported about the yield in CH3OH, since the process produces CF3COOCH3 [methyl trifluoroacetate (MTFA)], from which CH3OH may be derived by further reaction steps. As a consequence, the performance of the catalytic hydrogenation of CO2 is assumed as a reference in this case study. The process specifications used to quantify the flowsheets and to carry out the scale-up are commercial purity of CH3OH (99.7
99.8 wt.%) and production potential equal to 500 kg h21, based on the largest plant for renewable CH3OH via catalytic hydrogenation of CO2 (George Olah facility). The physical state and conditions of gaseous feed streams

6.3 Case study 3

FIGURE 6.21 Overview of the existing alternative routes for CH3OH production starting from CH4 or CO2 (Adapted from [244]).

are assumed equal to those considered for a typical gas storage (i.e., gaseous CH4 at 25 C and 138 bar, O2, N2, He, and Ar at 25 C and 156 bar, gaseous CO2 at 25 C and 57 bar, air at 25 C and 8 bar). Liquid solutions are considered to be supplied at ambient temperature and pressure.

6.3.2 Definition of intensified process flowsheets As required from step 1 of the procedure in Fig. 5.8, an intensified process flowsheet is defined for each reference process scheme applying the PrI (process intensification) activities described in the methodology section. Aspen HYSYS v10 [313] is used as process simulator to facilitate the simulation of the intensified process flowsheets. For most of the unit operations, Aspen components are used: shell and tubes exchanger for heat transfer; pump, compressor, and control valve for pressure change; two-phase separator and distillation column for separation; conversion reactor for CH3OH synthesis reaction; continuous stirred tank reactor, followed by distillation column for reactive distillation. The main boundary conditions and assumptions made for the simulation of these components are that the pumps and compressors have an adiabatic efficiency of 75%; the maximum outlet temperature of fluid in compressor is 250 C; the minimum difference between the outlet temperatures of the fluids in shell and tubes exchanger is 15 C
30 C; the filling factor of the separators is 50%; the valve operating characteristics is linear with 50% opening; the distillate rate is fixed in the distillation column equal to the benchmark CH3OH flowrate, while reflux ratio and number

201

Table 6.37 Main features of the 11 processes considered for CH3OH production (Adapted from [244]). CH3OH conc. in outlet stream

Operating conditions

Operating mode

CO2/CH4 conversion

CH3OH yield

CH3OH rate (t h21)

CH3OH phase

235 C, 50 bar, gas 25 C, 1 bar, gas
liquid 451 C, 50 bar, gas

Continuous

47%

98.9%

4.15

Continuous

0.246%

0.25%

3.66  1029

Liquid (25 C, 1 bar) Liquid

Continuous

9.5%

72.2%

1.62  1027

Gas

4

50 C, 20 bar, gas
liquid

Continuous

0.5%

46.1%

6.81  1028

Liquid

0.26 mol% (0.46 wt.%)

[306]

4

Continuous

0.181%













[307]

4

10.36%

8.50  1028

Liquid

4

Batch (2 batches per day) Continuous

10.36%

[308]

23%

8.28%

3.36  1028

Gas

Photocatalysis

[309]

4

0.20%

2.16  10210

Gas

[310]

4

Semibatch (4 batches per day) Continuous

0.348%

Supercritical water technology

4.5%

1.3%

1.94  1027

Gas

0.09 mol% (0.16 wt.%) 0.69 mol% (0.58 wt.%) 0.00025 mol% (0.00041 wt.%) 0.009 mol% (0.016 wt.%)

Fuel cell technology

[311]

4

Continuous

0.69%

0.61%

1.22  1029

Gas

Electrosynthesis

[312]

4

85 C, 83 bar, gas
liquid 28 C, 1 bar, gas
liquid 25 C, 1 bar, gas 55 C, 1 bar, gas 410 C, 250 bar, gas
liquid 100 C, 1 bar, gas 25 C, 1 bar, gas
liquid

Semibatch (4 batches per day)

62.19%

60.66%

7.43  1029

Liquid

Literature source

TRL

CO2 catalytic hydrogenation CO2 electroreduction

[146]

6
7

[303]

4

Homogeneous radical gas-phase reaction Low-temperature heterogeneous catalysis Homogeneous catalysis in solution Membrane-based biocatalysis Plasma technology

[304]

4

[305]

Process scheme

TRL, Technology readiness level.

99.5 mol% (99.7 wt.%) 0.00017 mol% (0.0003 wt.%) 6.57 mol% (11.51 wt.%)

0.06 mol% (0.05 wt.%) 0.017 mol% (0.030 wt.%)

6.3 Case study 3

of stages are set based on conventional column design procedure; the stoichiometric reactions and associated conversion of the main reactant are set in the conversion reactor; kinetic information about the reaction of MTFA into CH3OH is fixed in a continuous stirred tank reactor. Moreover, the Aspen component splitter is adopted to model the membrane unit of the pervaporation plant, assuming experimental data provided in the literature for PERVAP 4060 membrane at 70 C [257]. The Peng Robinson package available in Aspen HYSIS is used for the thermodynamic properties of the compounds involved in the processes, except aqueous solutions for which the UNIQUAC package for liquid is adopted. The intensified process flowsheets obtained in step 1 are reported in the following, with associated details on the material streams, energy streams of the components, and overall performance obtained after the PrI actions. Aspen HYSYS simulations are used to obtain part of the data.

6.3.2.1 Catalytic hydrogenation of CO2 Fig. 6.22 illustrates the intensified process flowsheet produced with Aspen HYSYS simulation for catalytic hydrogenation of CO2. Data about the material streams in the flowsheet are summarized in Table 6.38, while Table 6.39 reports information on the energy streams and utilities related to the relevant components in the flowsheet. The performance of the CH3OH production plant obtained with the proposed intensified process flowsheet is relatively pure liquid CH3OH in the distillate with the benchmark flowrate (stream 14) at 63 C and 1.01 bar, overall conversion of CO2 of 99.9%, and overall yield of CH3OH with respect to input CO2 of 55.8%.

FIGURE 6.22 Intensified process flowsheet for catalytic hydrogenation of CO2 (Adapted from [244]).

203

Table 6.38 Material streams of the intensified process flowsheet illustrated in Fig. 6.22 (Adapted from [244]).

Stream

Vapor phase fraction

Temperature ( C)

Pressure (kPa)

Molar flowrate (kmol h21)

Molar fraction CO2

Molar fraction H2

Molar fraction H2O

Molar fraction CH3OH

1 2 3 4 5 6 7 8 9 90 10 11 12 13 14 15

1.00 1.00 0.98 1.00 1.00 0.58 1.00 1.00 1.00 1.00 0.00 0.14 1.00 0.00 0.00 0.00

25 25 73 235 235 120 120 120 120 120 120 77 77 77 63 94

5700 15,600 5000 5000 5000 5000 5000 5000 5000 5000 5000 101.3 101.3 101.3 101.3 101.3

28.00 80.00 181.98 181.98 129.02 129.02 74.77 0.07 74.70 73.98 54.25 54.25 7.55 46.69 15.64 31.05

1.0000 0.0000 0.3096 0.3096 0.2314 0.2314 0.3829 0.3829 0.3829 0.3831 0.0226 0.0226 0.1608 0.0002 0.0007 0.0000

0.0000 1.0000 0.6515 0.6515 0.3032 0.3032 0.5213 0.5213 0.5213 0.5212 0.0026 0.0026 0.0189 0.0000 0.0000 0.0000

0.0000 0.0000 0.0097 0.0097 0.2190 0.2190 0.0239 0.0239 0.0239 0.0240 0.4878 0.4878 0.1939 0.5354 0.0003 0.8049

0.0000 0.0000 0.0292 0.0292 0.2464 0.2464 0.0719 0.0719 0.0719 0.0718 0.4870 0.4870 0.6264 0.4644 0.9990 0.1951

6.3 Case study 3

Table 6.39 Energy streams associated with components of the intensified process flowsheet in Fig. 6.22 (Adapted from [244]). Heat flow Stream (kW)

Utility

2462.40 MP steam generation Q_HE1 691.89 Cooling water 1 Q_CD1 365.63 Cooling water 1 Q_RB1 379.07 MP steam Q_HX1 317.61 HP steam Q_R1

Inlet Outlet Pressure temperature temperature (kPa) ( C) ( C)

Utility mass flowrate (kg h21)

889.9

174

175

688.70

250

30

40

57,737.86

250

30

40

30,511.92

889.9 3913

175 250

174 249

688.70 671.36

HP, High pressure; MP, medium pressure.

FIGURE 6.23 Intensified process flowsheet for electroreduction of CO2 (Adapted from [244]).

6.3.3 Electrochemical reduction of CO2 Fig. 6.23 illustrates the intensified process flowsheet produced with Aspen HYSYS simulation for electroreduction of CO2. Data about the material streams in the flowsheet are summarized in Table 6.40, while Table 6.41 reports information on the energy streams and utilities related to relevant components in the flowsheet. Even though incomplete, the performance of the CH3OH production plant obtained with the proposed intensified process flowsheet is highly diluted

205

Table 6.40 Material streams of the intensified process flowsheet illustrated in Fig. 6.23 (Adapted from [244]). Stream

Vapor phase fraction

Temperature ( C)

Pressure (kPa)

Molar flowrate (kmol h21)

Molar fraction CO2

Molar fraction CH3OH

Molar fraction H2O

Molar fraction O2

1 2 3 4 5 6 60 7 8 9 10

0.000 1.000 1.000 1.000 1.000 1.000 1.000 0.000 0.006 1.000 0.000

20.00 25.00 24.85 25.00 25.00 25.00 25.00 25.00 99.80 99.80 99.80

101.325 5700 101.325 101.325 101.325 101.325 101.325 101.325 101.325 101.325 101.325

2,825,000.00 100.00 27,729.67 27,629.25 27.63 27,601.63 27,629.67 2,825,097.93 2,825,097.93 17,873.03 2,807,224.90

0.000 1.000 0.073 0.070 0.070 0.070 0.070 0.000 0.000 0.005 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0000018

1.000 0.000 0.031 0.031 0.031 0.031 0.031 1.000 1.000 0.994 0.9999973

0.000 0.000 0.896 0.899 0.899 0.899 0.899 0.000 0.000 0.000 0.000

6.3 Case study 3

Table 6.41 Energy streams associated to components of the intensified process flowsheet in Fig. 6.23 (Adapted from [244]). Stream

Heat flow (kW)

Q_R1 Q_HX1

305,915 4,791,928

FIGURE 6.24 Intensified process flowsheet for homogeneous radical gas-phase reaction (Adapted from [244]).

CH3OH in H2O in the liquid stream from D1 (stream 10) at 99.8 C and 1.01 bar, overall conversion of CO2 of 98.1%, and overall yield of CH3OH with respect to input CO2 of 4.9%.

6.3.4 Homogeneous radical gas-phase reaction Fig. 6.24 illustrates the intensified process flowsheet produced with Aspen HYSYS simulation for homogeneous radical gas-phase reaction. Data about the material streams in the flowsheet are summarized in Table 6.42, while Table 6.43 reports information on energy streams and utilities related to relevant components. The performance of the CH3OH production plant obtained with the proposed intensified process flowsheet is relatively pure liquid CH3OH in the distillate with the benchmark flowrate (stream 21) at 58 C and 1.01 bar, overall conversion of CH4 of 98.3%, and overall yield of CH3OH with respect to input CH4 of 26.0%.

207

Table 6.42 Material streams of the intensified process flowsheet illustrated in Fig. 6.24 (Adapted from [244]).

Stream

Vapor phase fraction

Temperature ( C)

Pressure (kPa)

Molar flowrate (kmol h21)

Molar fraction CH4

Molar fraction N2

Molar fraction CO

Molar fraction CO2

Molar fraction CH3OH

Molar fraction H2O

Molar fraction O2

1 2 3 4 5 6 7 8 9 10 11 12 13 14 140 15 16 17 18 19 20 21 22

1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.997 0.995 1.000 1.000 1.000 1.000 0.000 0.100 0.173 1.000 0.000 1.000 0.000 0.000

25.00 25.00 25.00 239.85 199.34 235.00 451.00 451.00 210.76 25.00 240.00 240.00 240.00 240.00 240.00 240.00 95.00 66.56 66.56 66.56 58.27 58.27 99.20

13,800 15,600 15,600 5000 5000 5000 5000 5000 5000 5000 5000 5000 5000 5000 5000 5000 5000 101.3 101.3 101.3 101.3 101.3 101.3

60.00 84.00 1.00 23,698.23 23,698.23 23,698.23 23,698.23 23,698.24 23,698.24 23,698.24 23,698.24 23,579.42 23.58 23,555.84 23,553.23 118.82 118.82 118.82 20.62 98.21 0.04 15.65 82.52

1.0000 0.0000 0.0000 0.0465 0.0465 0.0465 0.0465 0.0439 0.0439 0.0439 0.0439 0.0441 0.0441 0.0441 0.0443 0.0002 0.0002 0.0002 0.0010 0.0000 0.0000 0.0000 0.0000

0.0000 0.0000 1.0000 0.0009 0.0009 0.0009 0.0009 0.0009 0.0009 0.0009 0.0009 0.0009 0.0009 0.0009 0.0009 0.0000 0.0000 0.0000 0.0001 0.0000 0.0000 0.0000 0.0000

0.0000 0.0000 0.0000 0.6666 0.6666 0.6666 0.6666 0.6675 0.6675 0.6675 0.6675 0.6708 0.6708 0.6708 0.6707 0.0017 0.0017 0.0017 0.0096 0.0000 0.0003 0.0000 0.0000

0.0000 0.0000 0.0000 0.2824 0.2824 0.2824 0.2824 0.2833 0.2833 0.2833 0.2833 0.2841 0.2841 0.2841 0.2841 0.1144 0.1144 0.1144 0.6577 0.0004 0.2945 0.0017 0.0000

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0009 0.0009 0.0009 0.0009 0.0000 0.0000 0.0000 0.0000 0.1767 0.1767 0.1767 0.1250 0.1876 0.7052 0.9981 0.0337

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0036 0.0036 0.0036 0.0036 0.0000 0.0000 0.0000 0.0000 0.7070 0.7070 0.7070 0.2067 0.8120 0.0000 0.0001 0.9663

0.0000 1.0000 0.0000 0.0035 0.0035 0.0035 0.0035 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

6.3 Case study 3

Table 6.43 Energy streams associated to components of the intensified process flowsheet in Fig. 6.24 (Adapted from [244]). Heat flow Stream (kW) Q_HE2 Q_CD1 Q_RB1 Q_HX4 Q_R1 Q_HX3 Q_HX2 Q_HE1

Utility

25,820.9 Ammonia refrigerant 999.7 Cooling water 1 1079.0 LP steam 411.0 LP steam 28444.5 HP steam generation 52,755.6 Molten salt 8444.4 HP steam 36,864.7 Cooling water 1

Pressure (kPa)

Inlet temperature ( C)

Outlet temperature ( C)

Utility mass flowrate (kg h21)

40

250

250

74,113.2

250

30

40

83,438.3

232 232 3913

125 125 249

124 124 250

1768.0 673.6 17,849.9

700

530

470

2,079,501.9

3913 250

250 30

249 40

17,370.0 3,076,330.7

HP, High pressure; LP, low pressure.

FIGURE 6.25 Intensified process flowsheet for low-temperature heterogeneous catalysis from Aspen HYSYS (Adapted from [244]).

6.3.5 Low-temperature heterogeneous catalysis Fig. 6.25 illustrates the intensified process flowsheet produced with Aspen HYSYS simulation for low-temperature heterogeneous catalysis. Data about the material streams in the flowsheet are summarized in Table 6.44, while Table 6.45

209

Table 6.44 Material streams of the intensified process flowsheet illustrated in Fig. 6.25 (Adapted from [244]).

Stream

Vapor phase

Temperature ( C)

Pressure (kPa)

Molar flowrate (kmol h21)

Molar fraction CH4

Molar fraction CO2

Molar fraction CH3OH

Molar fraction H2O

Molar fraction H2O2

1 2 3 4 5 6 7 8 80 9 10 11 12 13 14 15 16 17

1 0 0 0 1 1 1 1 1 0 0 0 1.00 0.00 0.00 0.00 0 0

25.0 25.0 50.0 50.2 49.7 50.0 50.0 50.0 50.0 50.0 68.6 100.0 77 77 63 94 70.0 70.0

13,800 101.3 101.3 2000 2000 2000 2000 2000 2000 2000 2000 2000 101.3 101.3 101.3 101.3 101.3 101.3

15.8 4735.8 4735.8 4735.8 2796.1 2782.9 2.8 2780.1 2780.3 4751.2 4751.2 4751.2 7.55 46.69 15.64 31.05 4732.9 162,198,719,127

1.000 0.000 0.000 0.000 0.957 0.957 0.957 0.957 0.957 0.000 0.000 0.000 0.1608 0.0002 0.0007 0.0000 0.000 0.000

0.000 0.000 0.000 0.000 0.036 0.036 0.036 0.036 0.036 0.000 0.000 0.000 0.0189 0.0000 0.0000 0.0000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.003 0.003 0.003 0.1939 0.5354 0.0003 0.8049 0.003 0.003

0.000 0.996 0.996 0.996 0.007 0.007 0.007 0.007 0.007 0.997 0.997 0.997 0.6264 0.4644 0.9990 0.1951 0.997 0.997

0.000 0.004 0.004 0.004 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

18 19 20 21 22 23 24 25 26 27 28 280 29 30 31 32 33 34 35

0 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0

70.0 70.0 25.0 210.3 210.0 9.3 70.0 70.0 70.0 70.0 70.0 70.0 70.0 21.0 210.5 210.2 10.5 66.9 99.7

101.3 0.267 0.267 0.267 101.3 101.3 101.3 101.3 101.3 101.3 101.3 101.3 0.267 0.267 0.267 101.3 101.3 101.3 101.3

162,192,655,283 6,063,843.5 6,063,843.5 6,063,843.5 6,063,843.5 6,063,843.5 6,063,843.5 6,063,560.9 162,198,718,844 4055.0 162,198,714,789 162,198,714,394 282.6 282.6 282.6 282.6 282.6 15.6 267.0

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.003 0.017 0.017 0.017 0.017 0.017 0.017 0.017 0.003 0.003 0.003 0.003 0.067 0.067 0.067 0.067 0.067 1.000 0.012

0.997 0.983 0.983 0.983 0.983 0.983 0.983 0.983 0.997 0.997 0.997 0.997 0.933 0.933 0.933 0.933 0.933 0.000 0.988

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Table 6.45 Energy streams associated to components of the intensified process flowsheet in Fig. 6.25 (Adapted from [244]).

Stream

Heat/power flow (kW)

Q_R1 Q_HX3 Q_HE1 Q_HE3 W_P2 Q_HX4 W_P3 Q_HE5 Q_CD1

21301 3263 1032 79,600,703 4038 8,049,077 0.20 3724 3141

Q_RB1 W_P1 Q_HX1

3726.58 60.80 2558.73

LP, Low pressure.

Utility Cooling water 1 LP steam Cooling water 1 Ammonia refrigerant
LP steam
Ammonia refrigerant Cooling water 1 for 882 kW (Q_HX1 for remaining kW) LP steam
Process fluid (H2O2/H2O)

Pressure (kPa)

Inlet temperature ( C)

Outlet temperature ( C)

Utility mass flowrate (kg h21)

250 232 250 151.8 3913 232
151.8 250

30 125 30 225 250 125
225 30

40 124 40 225 249 124
225 40

108,607 5348 86,135 272,238,972 671.36 13,192,805
12,738 73,599

232
1.01

125
25

124
50

6108
85,600

6.3 Case study 3

FIGURE 6.26 Intensified process flowsheet for homogeneous catalysis in solution (Adapted from [244]).

reports information on energy streams and utilities related to the relevant components. The performance of the CH3OH production plant obtained with the proposed intensified process flowsheet is relatively pure liquid CH3OH in the distillate with the benchmark flowrate (stream 34) at 66.9 C and 1.01 bar, overall conversion of CH4 of 83.1%, and overall yield of CH3OH with respect to input CH4 of 98.99%.

6.3.6 Homogeneous catalysis in solution Fig. 6.26 illustrates the intensified process flowsheet produced with Aspen HYSYS simulation for homogeneous catalysis in solution. Data about the material streams in the flowsheet are summarized in Table 6.46, while Table 6.47 reports information on energy streams and utilities related to the relevant components. The performance of the CH3OH production plant obtained with the proposed intensified process flowsheet is relatively pure liquid CH3OH in the distillate with the benchmark flowrate (stream 36) at 24.1 C and 1.01 bar, overall conversion of CH4 of 80.7%, and overall yield of CH3OH with respect to input CH4 of 2.6%.

6.3.7 Membrane-based biocatalysis Fig. 6.27 illustrates the intensified process flowsheet produced with Aspen HYSYS simulation for membrane-based biocatalysis. Data about the material

213

Table 6.46 Material streams of the intensified process flowsheet illustrated in Fig. 6.26 (Adapted from [244]).

Stream

Vapor phase fraction

Temperature ( C)

Pressure (kPa)

Molar flowrate (kmol h21)

Molar fraction CH4

Molar fraction CH3OH

Molar fraction CO2

Molar fraction CO

Molar fraction H2O

Molar fraction TFA

Molar fraction MTFA

Molar fraction O2

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

1 1 1 1 1 0 0 0 0.99 1 1 1 0 0.15 1 1 1 1 1 1

25.0 25.0 25.0 25.0 85.0 25.0 28.2 85.0 102.6 85.0 85.0 85.0 85.0 60.5 60.5 60.5 60.5 250.0 133.7 250.0

13,800 15,000 15,600 13,800 13,800 101.3 8300 8300 8300 8300 8300 8300 8300 101.3 101.3 101.3 101.3 1353 1353 5937

600.00 430.00 399.50 1429.50 1429.50 154,000 154,000 154,000 510,450 483,313 483.31 482,830 180,756 180,756 26,211 26.21 26,186 26,186 26,186 26,186

1.000 0.000 0.000 0.420 0.420 0.000 0.000 0.000 0.412 0.428 0.428 0.428 0.018 0.018 0.122 0.122 0.122 0.122 0.122 0.122

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.413 0.412 0.412 0.412 0.067 0.067 0.458 0.458 0.458 0.458 0.458 0.458

0.000 1.000 0.000 0.301 0.301 0.000 0.000 0.000 0.096 0.100 0.100 0.100 0.001 0.001 0.010 0.010 0.010 0.010 0.010 0.010

0.000 0.000 0.000 0.000 0.000 0.585 0.585 0.585 0.011 0.004 0.004 0.004 0.520 0.520 0.133 0.133 0.133 0.133 0.133 0.133

0.000 0.000 0.000 0.000 0.000 0.415 0.415 0.415 0.022 0.009 0.009 0.009 0.390 0.390 0.269 0.269 0.269 0.269 0.269 0.269

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003

0.000 0.000 1.000 0.279 0.279 0.000 0.000 0.000 0.045 0.047 0.047 0.047 0.001 0.001 0.005 0.005 0.005 0.005 0.005 0.005

21 22 23 230 24 25 26 26-2 27 27-2 28 29 30 30-2 31 32 33 33-2 34 35 36 37

1 1 0.99 0.99 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0

TFA, Trifluoroacetic acid.

193.8 221.4 102.6 102.6 60.5 15.0 50.0 50.0 61.2 61.2 99.2 26.5 26.5 26.5 72.7 24.2 24.2 24.2 72.7 24.1 24.1 71.9

5937 8300 8300 8300 101.3 101.3 101.3 101.3 101.3 101.3 101.3 101.3 101.3 101.3 101.3 101.3 101.3 101.3 101.3 101.3 101.3 101.3

26,186 26,186 509,016 509,021 154,543 154,543 154,543 346.17 143.00 286.00 203.17 0.70 19.30 77.20 266.00 0.78 13.87 55.48 62.55 0.32 15.64 39.52

0.122 0.122 0.412 0.412 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.055 0.000 0.000 0.000 0.023 0.000 0.000 0.000 0.008 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.002 0.002 0.002 0.006 0.006 0.000 0.011 0.071 0.071 0.001 0.057 0.382 0.382 0.003 0.159 0.998 0.140

0.458 0.458 0.415 0.415 0.001 0.001 0.001 0.001 0.003 0.003 0.000 0.824 0.010 0.010 0.000 0.858 0.005 0.005 0.000 0.832 0.001 0.000

0.010 0.010 0.096 0.096 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.133 0.133 0.011 0.011 0.586 0.583 0.583 0.583 0.001 0.001 0.993 0.000 0.000 0.000 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.269 0.269 0.022 0.022 0.411 0.413 0.413 0.413 0.991 0.991 0.007 0.107 0.918 0.918 0.998 0.061 0.609 0.609 0.997 0.000 0.001 0.854

0.003 0.003 0.000 0.000 0.002 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.001 0.000 0.001 0.004 0.004 0.000 0.000 0.000 0.006

0.005 0.005 0.044 0.044 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Table 6.47 Energy streams associated to components of the intensified process flowsheet in Fig. 6.26 (Adapted from [244]).

Stream

Heat/power flow (kW)

Q_R2 Q_R1 Q_CD1 Q_RB1 Q_CD2 Q_RB2 W_P1 Q_HX2

2228,047 2251,069 5507 5800 26,152 26,279 19,460 291,570

Q_HX1 W_K1 Q_HE1 W_K2 Q_HE2 W_K3 Q_CD3 Q_RB3 Q_CD4 Q_RB4 Q_HX3

777 84,333 53,866 53,589 26,916 12,850 11,239 11,386 3898 3988 176,758

Utility Cooling water 1 Cooling water 2 Cooling water 1 LP steam Cooling water 2 LP steam
LP steam for 225,363 kW (hot gases in HE1 and HE2) LP steam
Process fluid (TFA/H2O) in HX2
Process fluid (TFA/H2O) in HX2
Cooling water 2 LP steam Cooling water 2 LP steam LP steam

LP, Low pressure; TFA, trifluoroacetic acid.

Pressure (kPa)

Inlet temperature ( C)

Outlet temperature ( C)

Utility mass flowrate (kg h21)

250 250 250 232 250 232
232

30 5 30 125 5 125
125

40 15 40 124 15 124
124

20,951,548 19,030,352 459,555 9506 2,180,336 43,072
369,380

232
8300
8300
250 232 250 232 232

125
28.2
28.2
5 125 5 125 125

124
85
85
15 124 15 124 124

1274
1,645,097
822,124
937,059 18,662 324,996 6537 289,715

6.3 Case study 3

FIGURE 6.27 Intensified process flowsheet for biocatalysis (Adapted from [244]).

streams in the flowsheet are summarized in Table 6.48, while Table 6.49 reports information on the energy streams and utilities related to the relevant components in the flowsheet. The performance of the CH3OH production plant obtained with the proposed intensified process flowsheet is relatively pure liquid CH3OH in the distillate with the benchmark flowrate (stream 30) at 66.9 C and 1.01 bar, overall conversion of CH4 of 99.1%, and overall yield of CH3OH with respect to input CH4 of 94.7%.

6.3.8 Plasma technology Fig. 6.28 illustrates the intensified process flowsheet produced with Aspen HYSYS simulation for plasma technology. Data about the material streams in the flowsheet are summarized in Table 6.50, while Table 6.51 reports information on the energy streams and utilities related to the relevant components in the flowsheet. The performance of the CH3OH production plant obtained with the proposed intensified process flowsheet is relatively pure liquid CH3OH as the bottom product with the benchmark flowrate (stream 35) at 67.7 C and 1.01 bar, overall conversion of CH4 of 88.7%, and overall yield of CH3OH with respect to input CH4 of 48.1%.

6.3.9 Photocatalysis Fig. 6.29 illustrates the intensified process flowsheet produced with Aspen HYSYS simulation for photocatalysis. Data about the material streams in the

217

Table 6.48 Material streams of the intensified process flowsheet illustrated in Fig. 6.27 (Adapted from [244]).

Stream

Vapor phase fraction

Temperature ( C)

Pressure (kPa)

Molar flowrate (kmol h21)

Molar fraction CH4

Molar fraction CH3OH

Molar fraction H2O

Molar fraction N2

Molar fraction O2

1 2 3 4 5 6 7 8 9 90 10 11 12 13 14 15 16

1 1 1 0 0 1 1 1 1 1 0 0 0 0 0 1 1

25.0 25.0 25.0 25.0 28.0 27.7 28.0 28.0 28.0 28.0 28.0 43.0 70.0 70.0 70.0 70.0 25.0

13,800 800 800 101.3 101.3 101.3 101.3 101.3 101.3 101.3 101.3 101.3 101.3 101.3 101.3 0.267 0.267

16.5 1.8 8.6 18,040.4 18,040.4 1956.1 1931.5 2.1 1929.4 1929.3 18,056.8 18,056.8 18,056.8 331,884,266,383 331,872,390,796 11,875,587.4 11,875,587.4

1.0000 0.0000 0.0000 0.0000 0.0000 0.0806 0.0731 0.0731 0.0731 0.0731 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0009 0.0009 0.0009 0.0009 0.0009 0.0063 0.0063

0.0000 0.0000 0.0000 1.0000 1.0000 0.0366 0.0371 0.0371 0.0371 0.0371 0.9991 0.9991 0.9991 0.9991 0.9991 0.9937 0.9937

0.0000 1.0000 0.0000 0.0000 0.0000 0.7035 0.7123 0.7123 0.7123 0.7123 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

0.0000 0.0000 1.0000 0.0000 0.0000 0.1793 0.1774 0.1774 0.1774 0.1774 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

17 18 19 20 21 22 23 230 24 25 26 27 28 29 30 31 32 33

0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0

210.3 210.1 9.4 70.0 70.0 70.0 70.0 70.0 70.0 70.0 21.0 210.3 210.1 10.8 66.9 99.9 70.8 55.0

0.267 101.3 101.3 101.3 101.3 101.3 101.3 101.3 101.3 0.267 0.267 0.267 101.3 101.3 101.3 101.3 101.3 101.3

11,875,587.4 11,875,587.4 11,875,587.4 11,875,587.4 11,875,123.0 331,884,265,919 331,884,249,324 331,884,248,326 16,594.2 464.4 464.4 464.4 464.4 464.4 15.6 448.7 17,043.0 17,043.0

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

0.0063 0.0063 0.0063 0.0063 0.0063 0.0009 0.0009 0.0009 0.0009 0.0366 0.0366 0.0366 0.0366 0.0366 0.9987 0.0031 0.0010 0.0010

0.9937 0.9937 0.9937 0.9937 0.9937 0.9991 0.9991 0.9991 0.9991 0.9634 0.9634 0.9634 0.9634 0.9634 0.0013 0.9969 0.9990 0.9990

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Table 6.49 Energy streams associated to components of the intensified process flowsheet in Fig. 6.27 (Adapted from [244]).

Stream

Heat/power flow (kW)

Q_R1 Q_HX4 W_P1 Q_HE4 W_P2 Q_CD1

2787.2 15,644,478 7808.8 6096.7 0.3 6647.1

Q_RB1 Q_HE2 Q_HX3 Q_HX1

7584.3 155,658,775 10,565.2 1168.2

LP, Low pressure.

Utility Cooling water 2 LP steam
Ammonia refrigerant
Cooling water 1 for 5479 kW (Q_HX1 for remaining kW) LP steam Ammonia refrigerant LP steam Process fluid (H2O)

Pressure (kPa)

Inlet temperature ( C)

Outlet temperature ( C)

Utility mass flowrate (kg h21)

250 232
151.8
250

5 125
225
30

15 124
225
40

65,632 25,644,178
20,850
457,200

232 151.8 232 1.01

125 225 125 25

124 225 124 28

12,431 532,319,329 73,599 325,000

6.3 Case study 3

FIGURE 6.28 Intensified process flowsheet for plasma technology (Adapted from [244]).

flowsheet are summarized in Table 6.52, while Table 6.53 reports information on the energy streams related to the relevant components in the flowsheet. Even though incomplete, the performance of the CH3OH production plant obtained with the proposed intensified process flowsheet is highly diluted CH3OH in H2O in the liquid stream from D1 (stream 14) at 99.8 C and 1.01 bar, overall conversion of CH4 of 77.8%, and overall yield of CH3OH with respect to input CH4 of 45.3%.

6.3.10 Supercritical water oxidation technology Fig. 6.30 illustrates the intensified process flowsheet produced with Aspen HYSYS simulation for supercritical water technology. Data about the material streams in the flowsheet are summarized in Table 6.54, while Table 6.55 reports information on energy streams related to the relevant components. Even though incomplete, the performance of the CH3OH production plant obtained with the proposed intensified process flowsheet is highly diluted CH3OH in H2O in the liquid stream from D2 (stream 23) at 89.5 C and 1.01 bar, overall conversion of CH4 of 70.2%, and overall yield of CH3OH with respect to input CH4 of 20.4%.

6.3.11 Fuel cells technology Fig. 6.31 illustrates the intensified process flowsheet produced with Aspen HYSYS simulation for photocatalysis. Data about the material streams in the

221

Table 6.50 Material streams of the intensified process flowsheet illustrated in Fig. 6.28 (Adapted from [244]).

Stream

Temperature ( C)

Pressure (kPa)

Molar rate (kmol h21)

Molar fraction CH4

Molar fraction CH3OH

Molar fraction H2

Molar fraction N2

Molar fraction N2O

Molar fraction Ar

Molar fraction CO

Molar fraction CO2

Molar fraction CH2O

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

25.0 25.0 50.0 10.1 25.0 24.0 25.0 191.0 100.0 25.0 129.1 95.5 92.5 25.0 250.8 181.9

13,000 15,600 5700 5700 5700 101.3 101.3 301.6 301.6 301.6 621.8 621.8 622.8 621.8 2557 2557

32.5 46.0 29.1 107.6 107.6 4579 4591 4591 4591 4591 4591 4591 4591 4591 4591 4591

1.000 0.000 0.000 0.302 0.302 0.046 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040

0.000 0.000 0.000 0.000 0.000 0.000 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004

0.000 0.000 0.000 0.000 0.000 0.123 0.125 0.125 0.125 0.125 0.125 0.125 0.125 0.125 0.125 0.125

0.000 0.000 0.000 0.000 0.000 0.291 0.297 0.297 0.297 0.297 0.297 0.297 0.297 0.297 0.297 0.297

0.000 0.000 1.000 0.270 0.270 0.009 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003

0.000 1.000 0.000 0.428 0.428 0.491 0.490 0.490 0.490 0.490 0.490 0.490 0.490 0.490 0.490 0.490

0.000 0.000 0.000 0.000 0.000 0.007 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008

0.000 0.000 0.000 0.000 0.000 0.019 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020

0.000 0.000 0.000 0.000 0.000 0.012 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014

17 18 19 20 21 210 22 23 24 25 26 27 28 29 30 31 32 33 34 35

25.0 230.0 230.0 230.0 230.0 230.0 60.9 95.0 230.0 59.0 132.6 25.0 230.0 82.0 39.6 39.6 39.6 24.4 24.4 67.7

2557 2557 2557 2557 2557 2557 2557 2557 2557 390.0 390.0 101.3 2557 2557 101.3 101.3 101.3 101.3 101.3 101.3

4591 4591 4568 91.4 4477 4471 4471 4471 4471 4471 4471 4471 23.0 23.0 23.0 6.1 16.9 1.0 0.2 15.7

0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.040 0.001 0.001 0.001 0.005 0.000 0.000 0.000 0.000

0.004 0.004 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.755 0.755 0.755 0.239 0.941 0.093 0.880 0.999

0.125 0.125 0.126 0.126 0.126 0.126 0.126 0.126 0.126 0.126 0.126 0.126 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.297 0.297 0.298 0.298 0.298 0.298 0.298 0.298 0.298 0.298 0.298 0.298 0.003 0.003 0.003 0.011 0.000 0.000 0.000 0.000

0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.010 0.000 0.002 0.000 0.000

0.490 0.490 0.492 0.492 0.492 0.493 0.493 0.493 0.493 0.493 0.493 0.493 0.008 0.008 0.008 0.029 0.000 0.000 0.000 0.000

0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.020 0.012 0.012 0.012 0.044 0.000 0.005 0.000 0.000

0.014 0.014 0.013 0.013 0.013 0.013 0.013 0.013 0.013 0.013 0.013 0.013 0.218 0.218 0.218 0.661 0.058 0.900 0.119 0.001

224

CHAPTER 6 Case studies

Table 6.51 Energy streams associated to components of the intensified process flowsheet in Fig. 6.28 (Adapted from [244]). Heat/ power flow Stream (kW) Q_HE5 Q_HX2 W_K3 Q_HE7 Q_R1 Q_HX1 W_EX1 Q_HE8 W_EX2 Q_HE2 Q_CD1 Q_RB1

Utility

2241.0

Cooling water 2 4469.1 HP steam 7544.8
5365.2 Cooling water 2 21500.7 Cooling water 2 16.9 LP steam 5508.7
2172.6 Refrigerant 1 3432.0
2477.3 Cooling water 2 9.9 Cooling water 2 35.8 LP steam

Pressure (kPa)

Inlet temperature ( C)

Outlet temperature ( C)

Utility mass flowrate (kg h21)

250

5

15

186,835

3913
250

250
5

249
15

9447
447,304

250

5

15

125,113

232
105.3

125
239

124
240

28
42,755


250


5


15


206,540

250

5

15

824.8

232

125

124

59

HP, High pressure; LP, low pressure.

FIGURE 6.29 Intensified process flowsheet for photocatalysis (Adapted from [244]).

Table 6.52 Material streams of the intensified process flowsheet illustrated in Fig. 6.29 (Adapted from [244]).

Stream

Vapor phase

Temperature ( C)

Pressure (kPa)

Molar flowrate (kmol h21)

Molar fraction CH4

Molar fraction CO2

Molar fraction CH3OH

Molar fraction H2O

Molar fraction H2

Molar fraction C2H6

Molar fraction He

1 2 3 4 5 6 7 8 9 10 100 11 12 13 14

1 1 1 1 0 0 1 1 1 1 1 0 0 1 0

25.0 55.0 25.0 55.0 25.0 55.0 55.0 55.0 55.0 55.0 55.0 55.0 99.8 99.8 99.8

13,800 13,800 15,600 15,600 101.3 101.3 101.3 101.3 101.3 101.3 101.3 101.3 101.3 101.3 101.3

50 50 86 86 7,000,000 7,000,000 154,429.3 154,445.1 154.4451 154,290.6 154,293.3 7,000,013 7,000,013 11,323.01 6,988,690

1 1 0 0 0 0 0.072 0.072 0.072 0.072 0.072 0.000 0.000 0.000 0.000

0 0 0 0 0 0 0.007 0.007 0.007 0.007 0.007 0.000 0.000 0.001 0.000

0 0 0 0 0 0 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0 0 0 0 1 1 0.155 0.155 0.155 0.155 0.155 1.000 1.000 0.994 1.000

0 0 0 0 0 0 0.476 0.476 0.476 0.476 0.476 0.000 0.000 0.001 0.000

0 0 0 0 0 0 0.005 0.005 0.005 0.005 0.005 0.000 0.000 0.000 0.000

0 0 1 1 0 0 0.285 0.285 0.285 0.285 0.285 0.000 0.000 0.004 0.000

226

CHAPTER 6 Case studies

Table 6.53 Energy streams associated to components of the intensified process flowsheet in Fig. 6.29 (Adapted from [244]). Stream

Heat flow (kW)

Q_R1 Q_HX4 Q_HX3 Q_HX1 Q_HX2

2308.5 6,963,042.8 4,536,603.5 21.1 15.0

FIGURE 6.30 Intensified process flowsheet for supercritical water technology (Adapted from [244]).

flowsheet are summarized in Table 6.56, while Table 6.57 reports information on the energy streams related to the relevant components in the flowsheet. The performance of the CH3OH production plant obtained with the proposed intensified process flowsheet is relatively pure liquid CH3OH in the distillate with the benchmark flowrate (stream 24) at 62.7 C and 1.01 bar, overall conversion of CH4 of 87.4%, and overall yield of CH3OH with respect to input CH4 of 55.2%.

6.3.12 Electrosynthesis Fig. 6.32 illustrates the intensified process flowsheet produced with Aspen HYSYS simulation for photocatalysis. Data about the material streams in the flowsheet are summarized in Table 6.58, while Table 6.59 reports information on the energy streams related to the relevant components in the flowsheet. The performance of the CH3OH production plant obtained with the proposed intensified

Table 6.54 Material streams of the intensified process flowsheet illustrated in Fig. 6.30 (Adapted from [244]).

Stream

Vapor phase fraction

Temperature ( C)

Pressure (kPa)

Molar flowrate (kmol h21)

Molar fraction CH4

Molar fraction CO2

Mol. Fraction CH3OH

Molar fraction H2O

Molar fraction H2

Molar fraction CO

Molar fraction O2

Molar fraction N2

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 180 19 20 21 22 23

1 1 1 1 1 1 1 1 0 0 1 1 1 1 0.026 1 1 1 1 0 0 0.002 1 0

25.0 78.0 25.0 25.0 25.0 250.0 25.0 286.8 25.0 27.2 410.0 0.3 410.0 410.0 210.0 210.0 210.0 210.0 210.0 210.0 85.0 89.5 89.5 89.5

13,800 25,000 800 800 800 4031 4023 25,000 101.3 25,000 25,000 25,000 25,000 25,000 25,000 25,000 25,000 25,000 25,000 25,000 25,000 101.3 101.3 101.3

3000 3000 4450 1500 5950 5950 5950 5950 6,690,000 6,690,000 6,690,000 182,268 182,268 6,872,655 6,872,655 176,906 3538 173,368 173,318 6,695,749 6,695,749 6,695,749 10,197 6,685,552

1.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.257 0.257 0.007 0.007 0.253 0.253 0.253 0.253 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.002 0.002 0.000 0.000 0.002 0.002 0.002 0.002 0.000 0.000 0.000 0.121 0.000

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001 0.0001 0.0000 0.0000 0.0000 0.0000 0.0001 0.0001 0.0001 0.0001 0.0001

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 1.000 1.000 0.000 0.000 0.974 0.974 0.000 0.000 0.000 0.000 0.999 0.999 0.999 0.680 1.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.294 0.294 0.008 0.008 0.309 0.309 0.309 0.309 0.000 0.000 0.000 0.018 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.022 0.022 0.001 0.001 0.023 0.023 0.023 0.023 0.000 0.000 0.000 0.002 0.000

0.000 0.000 1.000 0.000 0.748 0.748 0.748 0.748 0.000 0.000 0.000 0.374 0.374 0.010 0.010 0.368 0.368 0.368 0.367 0.000 0.000 0.000 0.042 0.000

0.000 0.000 0.000 1.000 0.252 0.252 0.252 0.252 0.000 0.000 0.000 0.051 0.051 0.001 0.001 0.045 0.045 0.045 0.045 0.000 0.000 0.000 0.137 0.000

228

CHAPTER 6 Case studies

Table 6.55 Energy streams associated to components of the intensified process flowsheet in Fig. 6.30 (Adapted from [244]). Stream

Heat/power flow (kW)

Q_R1 Q_HE2 Q_HX3 Q_HX2 W_P1 Q_HX1 W_K1 W_K2 Q_HE1 W_K3

1,351,174 95,436,720 13,682,072 776,678 1,103,318 87,653,871 1499 11,181 11,642 13,237

FIGURE 6.31 Intensified process flowsheet for fuel cell technology (Adapted from [244]).

process flowsheet is relatively pure liquid CH3OH in the distillate with the benchmark flowrate (stream 34) at 66.9 C and 1.01 bar, overall conversion of CH4 of 100%, and overall yield of CH3OH with respect to input CH4 of 93%.

6.3.13 Screening of intensified flowsheets According to step 1 of the flowchart in Fig. 5.8, a preliminary screening of the alternative process technologies is carried out based on the features of outlet streams after the synthesis section. Table 6.60 summarizes the main findings. As shown in this table, the mass fractions of CH3OH in the liquid stream after the synthesis section is less than the lower limit assumed in the case of CO2 electroreduction, photocatalysis, and supercritical water technology. These three process

Table 6.56 Material streams of the intensified process flowsheet illustrated in Fig. 6.31 (Adapted from [244]).

Stream

Vapor phase fraction

Temperature ( C)

Pressure (kPa)

Molar rate (kmol h21)

Molar fraction CH4

Molar fraction CH3OH

Molar fraction H2

Molar fraction N2

Molar fraction N2O

Molar fraction Ar

Molar fraction CO

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 160 17 18

1 1 1 1 1 1 1 0.998 1 1 1 1 0.995 1 1 1 1 0 0

25 25 120 97.1 25 25 25.0 26.4 100 100 258.5 55 210.0 210.0 210.0 210.0 210.0 210.0 36.6

13,800 15,600 101.3 101.3 800 800 800 101.3 101.3 101.3 250 250 250 250 250 250 250 250 250

28.3 45 340 413.3 34 32 66 74,954 74,954 74,943 74,943 74,943 74,943 74,576 75 74,501 74,475 368 368

1.000 0.000 0.000 0.069 0.000 0.000 0.000 0.048 0.048 0.047 0.047 0.047 0.047 0.048 0.048 0.048 0.048 0.000 0.000

0.000 0.000 1.000 0.823 0.000 0.000 0.000 0.006 0.006 0.006 0.006 0.006 0.006 0.001 0.001 0.001 0.001 0.941 0.941

0.000 1.000 0.000 0.109 0.000 0.000 0.000 0.435 0.435 0.435 0.435 0.435 0.435 0.437 0.437 0.437 0.437 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.059 0.059

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.037 0.037 0.037 0.037 0.037 0.037 0.037 0.037 0.037 0.037 0.000 0.000

0.000 0.000 0.000 0.000 0.000 1.000 0.485 0.376 0.376 0.376 0.376 0.376 0.376 0.378 0.378 0.378 0.378 0.000 0.000

0.000 0.000 0.000 0.000 1.000 0.000 0.515 0.098 0.098 0.098 0.098 0.098 0.098 0.099 0.099 0.099 0.099 0.000 0.000 (Continued)

Table 6.56 Material streams of the intensified process flowsheet illustrated in Fig. 6.31 (Adapted from [244]). Continued

Stream

Vapor phase fraction

Temperature ( C)

Pressure (kPa)

Molar rate (kmol h21)

Molar fraction CH4

Molar fraction CH3OH

Molar fraction H2

Molar fraction N2

Molar fraction N2O

Molar fraction Ar

Molar fraction CO

19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35

0 0 1 0 1 0 0 0 1 0 0 0.27 1 0 1 0 0

85.0 85.0 85.0 85.0 62.7 62.7 99.6 50.0 25.0 230.0 82.0 39.6 39.6 39.6 24.4 24.4 67.7

250 101.3 101.3 101.3 101.3 101.3 101.3 101.3 101.3 2557 2557 101.3 101.3 101.3 101.3 101.3 101.3

368 368 0 367 0 16 352 352 4471.2 23.0 23.0 23.0 6.1 16.9 1.0 0.2 15.7

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.040 0.001 0.001 0.001 0.005 0.000 0.000 0.000 0.000

0.941 0.941 0.536 0.941 0.000 0.000 0.983 0.983 0.000 0.755 0.755 0.755 0.239 0.941 0.093 0.880 0.999

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.126 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.059 0.059 0.066 0.059 0.847 0.999 0.017 0.017 0.298 0.003 0.003 0.003 0.011 0.000 0.000 0.000 0.000

0.000 0.000 0.278 0.000 0.148 0.001 0.000 0.000 0.003 0.003 0.003 0.003 0.010 0.000 0.002 0.000 0.000

0.000 0.000 0.119 0.000 0.005 0.000 0.000 0.000 0.493 0.008 0.008 0.008 0.029 0.000 0.000 0.000 0.000

0.000 0.000 0.002 0.000 0.000 0.000 0.000 0.000 0.008 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

6.3 Case study 3

Table 6.57 Energy streams associated to components of the intensified process flowsheet in Fig. 6.31 (Adapted from [244]). Heat/ power Stream flow (kW) Utility Q_HX3 Q_R1 Q_CD1 Q_RB1 W_K1 Q_HE2 Q_HE1 Q_HX1

402.9 21410.1

LP steam Cooling water 1 42,66.5 Cooling water 1 4400.4 LP steam 89,708.5
40,356.4 LP steam 114,855.3 Cooling water 1 60,365.8 Refrigerant 1

Pressure (kPa)

Inlet temperature ( C)

Outlet temperature ( C)

Utility mass flowrate (kg h21)

232 250

125 30

124 40

660 117,676

250

30

40

355,995

232
232 250

125
125 30

124
124 40

7213
129,807 9,584,588

105.3

239

240

98,942

LP, Low pressure.

FIGURE 6.32 Intensified process flowsheet for electrosynthesis (Adapted from [244]).

schemes are not further considered. For all the other processes, step 2 of the methodology is applied to carry out a further screening. According to step 2, the separation stages are designed to increase the product concentration to the fixed benchmark for the processes succeeding in step 1. In the case of three process schemes (low-temperature heterogeneous catalysis, biocatalysis, and electrosynthesis), two or more stages of pervaporation are first

231

Table 6.58 Material streams of the intensified process flowsheet illustrated in Fig. 6.32 (Adapted from [244]).

Stream

Vapor phase fraction

Temperature ( C)

Pressure (kPa)

Molar flowrate (kmol h21)

Molar fraction CH4

Molar fraction CH2O

Molar fraction CH3OH

Molar fraction H2

Molar fraction H2O

1 2 3 4 5 6 60 7 8 9 10 11 12 13 14 15 16 17 18

1 0 1 1 1 1 1 0 0 0 0 0 1 1 0 0 0 0 0

25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 40.0 70.0 70.0 70.0 70.0 25.0 210.4 210.1 9.3 70.0 70.0

101.3 101.3 101.3 101.3 101.3 101.3 101.3 101.3 101.3 101.3 101.3 101.3 0.267 0.267 0.267 101.3 101.3 101.3 101.3

185,218 54,300 185,217 185,218 0 185,217 185,200 54,299 54,299 54,299 5,319,901,265,251,550 5,319,713,993,394,860 187,271,856,691 187,271,856,691 187,271,856,691 187,271,856,691 187,271,856,691 187,271,856,691 187,265,066,050

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.014 0.000 0.014 0.014 0.014 0.014 0.014 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.002 0.002 0.002 0.002 0.002 0.002 0.002

0.956 0.000 0.956 0.956 0.956 0.956 0.956 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.031 1.000 0.031 0.031 0.031 0.031 0.031 1.000 1.000 1.000 1.000 1.000 0.998 0.998 0.998 0.998 0.998 0.998 0.998

19 20 21 22 23 24 25 26 27 28 280 29 30 31 32 33 34 35 36 37

1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0

70.0 25.0 210.3 210.1 9.3 70.0 70.0 70.0 70.0 70.0 70.0 70.0 25.0 210.5 210.2 8.8 66.9 99.7 70.1 54.2

0.267 0.267 0.267 101.3 101.3 101.3 101.3 101.3 101.3 101.3 101.3 0.267 0.267 0.267 101.3 101.3 101.3 101.3 101.3 101.3

6,790,641 6,790,641 6,790,641 6,790,641 6,790,641 6,790,641 6,790,356 5,319,901,265,251,270 50,539 5,319,901,265,200,730 5,319,901,265,197,250 286 286 286 286 286 16 270 50,809 50,809

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.015 0.015 0.015 0.015 0.015 0.015 0.015 0.000 0.000 0.000 0.000 0.067 0.067 0.067 0.067 0.067 0.999 0.013 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.985 0.985 0.985 0.985 0.985 0.985 0.985 1.000 1.000 1.000 1.000 0.933 0.933 0.933 0.933 0.933 0.001 0.987 1.000 1.000

Table 6.59 Energy streams associated to components of the intensified process flowsheet in Fig. 6.32 (Adapted from [244]). Stream

Heat/power flow (kW)

Utility

Q_R1 Q_HX2 Q_HX3 Q_HE2 W_P1 Q_HX4 Q_HE4 W_P2 Q_HE6 Q_P3 Q_CD1 Q_RB1

544 35,275 246,063,926,956 2,453,239,531,199 122,518,000 9,012,486 89,129,187 4511 3775 0 3106 3708

Cooling water 1 LP steam LP steam Ammonia refrigerant
LP steam Ammonia refrigerant
Ammonia refrigerant
Cooling water 1 LP steam

LP, Low pressure.

Pressure (kPa)

Inlet temperature ( C)

Outlet temperature ( C)

Utility mass flowrate (kg h21)

40 232 232 151.8
232 151.8
151.8

30 125 125 225
125 225
225

124 124 124 225
124 225
225

45,373 57,818 403,310,024,150 8,652,635,120,233
14,771,876 314,382,995
13,314

30 232

40 125

124

259,156 6078

6.3 Case study 3

applied to provide a suitable CH3OH concentration in the stream, followed by a distillation train. For the remaining five process schemes, distillation without any pervaporation stage is selected. It is worth noting that, in case of the homogeneous catalysis in solution process, pervaporation cannot be applied due to the significant amount of trifluoroacetic acid present in the postsynthesis stream. To complete the application of step 2, the number of units required for product purification, the number of reactors, and the number of heat transfer equipment items are estimated by applying the approaches and criteria for scale-up and preliminary design of process and utility equipment, summarized in Tables 5.23 and 5.24. According to the approach in Table 5.24, the number of pervaporation units is estimated by using the technical data of the experimental pervaporation membrane considered in Ref. [257] and feed flowrate of each pervaporation unit simulated in Aspen HYSYS. The estimation of the geometric area of the heat transfer equipment is made by combining the heat duties derived from simulations in Aspen HYSYS with tabulated design overall heat transfer coefficient appropriate to the fluids and equipment. The information provided by the shortcut method in Aspen HYSYS facilitates the application of the standard design approach for the determination of the diameter and height of distillation column. Then the total number of these items of equipment is calculated according to the criteria illustrated in Table 5.24. The scale-up rules proposed in Table 5.23 are applied to estimate the main design parameters of tubular reactors (i.e., diameter, length, number of tubes), electrochemical reactors (i.e., number of cells), plasma reactors (i.e., power

Table 6.60 Preliminary screening of the alternative CH3OH process routes from application of step 1 (Adapted from [244]).

Process scheme

Mass fraction of CH3OH in the postsynthesis liquid stream

CO2 catalytic hydrogenation CO2 electroreduction Homogeneous radical gas-phase reaction Low-temperature heterogeneous catalysis Homogeneous catalysis in solution Membrane-based biocatalysis Plasma technology Photocatalysis Supercritical water technology Fuel cell technology Electrosynthesis

60.65% 0.00032% 18.76% 0.46% 0.14% 0.16% 94.47% 0.00058% 0.02% 10.08% 0.05%

Rejected for further analysis in step 2 X

X X

235

236

CHAPTER 6 Case studies

supply to the reactor). Then the number of equipment items required is calculated following the criteria in Table 5.24. The results of the scale-up and design of equipment are compared with the reasonable limits for a real chemical process plant reported in Table 5.24, leading to a further screening of the process schemes. The outcomes are summarized in Table 6.61. As shown in this table, the total surface area of pervaporation units exceeds the reasonable limits assumed in Table 5.24 in case of low-temperature heterogeneous catalysis, biocatalysis, and electrosynthesis. The homogeneous catalysis in solution requires a number of distillation columns exceeding the limits defined in Table 5.24. The process scheme based on plasma technology needs a significantly higher power supply to the plasma reactors compared to the limit reported in Table 5.24. The process using fuel cell electrochemical reactors does not comply with the limits set for a reasonable number of transfer equipment items. As a consequence, these six process schemes are not further considered in step 3. On other hand, catalytic hydrogenation of CO2 and homogeneous radical gasphase reaction are the only intensified process schemes that fulfill the feasibility criteria defined in Table 5.24. Thus having succeeded in the screening of step 2, these schemes are suitable for application of step 3 of the methodology. Fig. 6.33 shows the final intensified process flowsheets obtained for the two schemes, including the actual number of equipment items estimated in the design. In the following, the reference process scheme based on catalytic hydrogenation of CO2 is indicated as Scheme A, while the reference process scheme based on the homogeneous radical gas-phase reaction from CH4 is indicated as Scheme B. Tables 6.38 and 6.39 previously summarized the information about the material streams and energy streams, respectively, of Scheme A illustrated in Fig. 6.33A. These type of data associated with Scheme B shown in Fig. 6.33B are reported in Tables 6.62 and 6.63. For each component in Fig. 6.33, Table 6.64 (for Scheme A) and Table 6.65 (for Scheme B) report the main geometric information derived from the conceptual scale-up and design of process equipment, main factors considered for equipment costs, purchased cost, and the final bare-module cost (Cbm ) according to the Guthrie method [163]. Cost adjustments to h2019 are performed by considering currency conversion rates in Table 6.7 and Chemical Engineering Plant Cost Index (CEPCI) values [314]. Table 6.66 (for Scheme A) and Table 6.67 (for Scheme B) summarize the cost segments of capital investment (Ctci ) and annual total production cost (Cprod ) considered in the economic assessment of the process schemes. A plant operating factor of 0.9 (i.e., 328.5 days over the year) is assumed. The input data for the calculation of the human inherent hazard index (HHI) are information on material streams from simulation in Aspen HYSYS (Table 6.38 for Scheme A and Table 6.62 for Scheme B) and equipment data from preliminary design (Table 6.64 for Scheme A and Table 6.65 for Scheme B). Reference event trees considered in this study for each release mode on the

Table 6.61 Preliminary screening of the alternative CH3OH process routes from application of step 2 (Adapted from [244]). Process scheme succeeding in step 1 (see Table 6.60)

Total surface area of pervaporation units

Number of distillation columns

Number of heat transfer equipment

Surface area/power need for reactor





1 1

4 16

0.00006 ha (1 multi-tubular reactor) 0.007 ha (1 multi-tubular reactor)

Rejected for further analysis in step 3

CO2 catalytic hydrogenation Homogeneous radical gas-phase reaction Low-temperature heterogeneous catalysis Homogeneous catalysis in solution Membrane-based biocatalysis Plasma technology

193 ha (51,537 units)

1

n.c.

n.c.

X


395 ha (105,211 units)


53 1 1

n.c. n.c. n.c.

X X X

Fuel cell technology




1

64

Electrosynthesis

6,319,304 ha (1,685,147,841 units)

1

n.c.

n.c. n.c. 2100 MW (6,641,584 plasma reactors) 0.21 ha (10 electrochemical reactors) n.c.

ha, Hectare (1 ha 5 10,000 m2); n.c., not considered.

X X

238

CHAPTER 6 Case studies

FIGURE 6.33 (A) Scheme A: intensified process flowsheet for catalytic hydrogenation of CO2, (B) Scheme B: intensified process flowsheet for homogeneous radical gas-phase reaction (Adapted from [244]).

basis of the physical state of substance after the release (liquid phase, gas phase, gas/liquid mixture) are reported in Figs. 5.6 and 5.7. General atmospheric conditions for an onshore industrial site are assumed for consequence simulation analysis: 2 m s21 wind speed and Pasquill class F (conservative parameters), 25 C ambient temperature, 70% relative humidity, 0.17 surface roughness, 29.85 C surface temperature. For the estimation of damage distances for human targets, well-known consequences analysis models reported in the TNO’s Yellow Book [319] and in other relevant publications [194], both implemented in the PHAST tool, are used. Threshold values of accident scenarios reported in a previous study [320] are adopted in the simulations. Damage distance for flash fire and toxic cloud are evaluated at 1 m height above the ground. Moreover, due to lack of information about the layout of the plant, a charge strength of 5 and obstructed volume of 2500 m3 are assumed for the TNO Multi-Energy model. Credit factors are assigned to each release mode considering the failure frequencies of process equipment and reactors reported in the technical literature [321,322]. The credit factor for each release mode accounted for both the main equipment body (e.g., process vessel) but also for the expected number of auxiliary items and connections (valves, instruments, etc.) belonging to the unit.

Table 6.62 Material streams of Scheme B illustrated in Fig. 6.33B (Adapted from [244]). Stream

Temperature ( C)

Pressure (kPa)

Molar flowrate (kmol h21)

Stream in Fig. 6.24 for vapor fraction and composition

b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11 b12 b13 b14 b15 b16 b17 b18 b19 b20 b21 b22 b23 b24 b25 b26 b27 b28

25 25 25 239.9 239.9 199.3 239.9 199.3 239.9 199.3 199.3 199.3 234 199.3 234 234 234 451 234 451 234 451 451 451 451 210.8 451 210.8

13,800 15,600 15,600 5000 5000 5000 5000 5000 5000 5000 5000 5000 5000 5000 5000 5000 5000 5000 5000 5000 5000 5000 5000 5000 5000 5000 5000 5000

60 83.5 0.05 23,701 7900 7900 7900 7900 7900 7900 23,701 11,851 11,851 11,851 11,851 23,701 7900.3 7900.3 7900.3 7900.3 7900.3 7900.3 23,701 23,701 7899.6 7899.6 7899.6 7899.6

1 2 3 4 4 5 4 5 4 5 5 5 6 5 6 6 6 7 6 7 6 7 7 8 8 9 8 9

Stream

Temperature ( C)

Pressure (kPa)

Molar flowrate (kmol h21)

Stream in Fig. 6.24 for vapor fraction and composition

b29 b30 b31 b32 b33 b34 b35 b36 b37 b38 b39 b40 b41 b42 b43 b44 b45 b46 b460 b47 b480 b49 b50 b51 b52 b53 b54

451 210.8 210.8 210.8 55 210.8 55 210.8 55 55 55 240 240 240 240 240 240 240 240 240 95 66.4 66.4 66.4 58.3 58.3 99.2

5000 5000 5000 5000 5000 5000 5000 5000 5000 5000 5000 5000 5000 5000 5000 5000 5000 5000 5000 5000 5000 101.3 101.3 101.3 101.3 101.3 101.3

7899.6 7902.0 23,701 7900.3 7900.3 7900.3 7900.3 7900.3 7900.3 23,701 11,851 11,851 11,851 11,851 23,701 23,583 23.58 23,557 23,559 118.4 118.4 118.4 20.76 97.60 0.039 15.64 81.91

8 9 9 9 10 9 10 9 10 10 10 11 10 11 11 12 13 14 140 15 16 17 18 19 20 21 22

Table 6.63 Energy streams associated with components of Scheme B in Fig. 6.33B (Adapted from [244]). Stream

Heat flow (kW)

Utility

Pressure (kPa)

Inlet temperature ( C)

Outlet temperature ( C)

Utility mass flowrate (kg h21)

Q_HE5 Q_HE6 Q_CD2 Q_RB2 Q_HX10 Q_R2 Q_HX7 Q_HX8 Q_HX9 Q_HX5 Q_HX6 Q_HE2 Q_HE3 Q_HE4

12,907 12,907 999.7 1079.0 411.0 28444.5 17,662.8 17,662.8 17,662.8 4108.4 4108.4 12,289.3 1289.3 12,289.3

Ammonia refrigerant Ammonia refrigerant Cooling water 1 LP steam LP steam HP steam generation Molten salt Molten salt Molten salt HP steam HP steam Cooling water 1 Cooling water 1 Cooling water 1

40 40 250 232 232 3913 700 700 700 3913 3913 250 250 250

250 250 30 125 125 249 530 530 530 250 250 30 30 30

250 250 40 124 124 250 470 470 470 249 249 40 40 40

37,056.6 37,056.6 83,438.3 1768.0 673.6 17,849.9 693,167.3 693,167.3 693,167.3 8685 8685 37,056.6 37,056.6 37,056.6

HP, High pressure; LP, low pressure.

Table 6.64 Geometric and economic data of the units of Scheme A in Fig. 6.33A (Adapted from [244]).

Unit HX1

R1 HE1

D1

V1 D2 CO1

CD1

RB1

Main geometric information Horizontal, carbon steel, 60 tubes, 365 mm shell diameter, 9.42 m2 geometrical area 4 tubes (57 mm external diameter, 2 m length) Horizontal, carbon steel, 42 tubes, 316 mm shell diameter, 6.6 m2 geometrical area Vertical, carbon steel, 508 mm diameter, 3.05 m height

Design temperature and pressure 

Size factor for cost function

M/L/P factors for cost function

Main costs ($2006)

Purchased costs ($2006)

Baremodule factor

Baremodule cost Cbm (h2019)

532 F, 1066 psig

Area:150 ft

M: 1, L: 1.25, P: 1.37

Base cost: 7379

1.26  104

3.17

4.38  104




Inlet gas flowrate: 2920.6 kg h21 Area: 150 ft2







2.50  106

4.30

1.18  107

M: 1, L: 1.25, P: 1.37

Base cost: 7379

1.26  104

3.17

4.38  104

Weight: 4200 lb, diameter: 3 ft, height: 12 ft
Diameter: 3 ft, height: 12 ft Weight: 9000 lb, diameter: 3 ft, height: 27 ft, no. of trays: 12

M: 1

Vessel: 25,209, platform and ladders: 4722
Platform and ladders: 4722 Vessel: 51,121, platform and ladders: 8471, installed trays: 10,996 Base cost: 7379

2.99  104

4.30

1.41  105


4.72  103


4.30


2.23  104

7.04  104

3.17

3.33  105

9.06  103

3.17

3.15  104

Base cost: 7379

9.06  103

3.17

3.15  104

505 F, 1066 psig 298 F, 804 psig

2


Vertical, carbon steel, 508 mm diameter, 3.05 m height Vertical, diameter 390 mm, height 8 m, weight 243 kg, 12 trays, carbon steel


221 F, 10 psig 177 F, 10 psig

Horizontal, carbon steel, 80 tubes, 365 mm shell diameter, 12.6 m2 geometrical area Vertical, carbon steel, 36 tubes, 291 mm shell diameter, 7.2 m2 geometrical area

196 F, 10 psig

Area: 150 ft2

M: 1, L: 1.25, P: 0.98

307 F, 10 psig

Area: 150 ft2

M: 1, L: 1.25, P: 0.98

L, Tube-length; M, material; P, shell-side pressure.



M: 1

Table 6.65 Geometric and economic data of the units of Scheme B in Fig. 6.33B (Adapted from [244]).

Unit HX2
HX3
HX4

HX5
HX6

HX7
HX8
HX9

R2

HE2
HE3
HE4

HE5-HE6

Main geometric information Horizontal, carbon steel, 548 tubes,1260 mm shell diameter, 394 m2 geometrical area Horizontal, carbon steel, 257 tubes, 1020 mm shell diameter, 205 m2 geometrical area Horizontal, carbon steel, 332 tubes, 993 mm shell diameter, 238 m2 geometrical area 1218 tubes (42.4 mm external diameter, 6 m length) Horizontal, carbon steel, 1648 tubes, 1239 mm shell diameter, 357 m2 geometrical area Vertical, carbon steel, 500 tubes, 993 mm shell diameter, 100 m2 geometrical area

Design temperature pressure

Size factor for cost function

M/L/P factor for cost function

Main costs ($2006)

Purchased costs ($2006)

Baremodule factor

Baremodule cost Cbm (h2019)

894 F, 1066 psig

Area: 4236 ft2

M: 1, L: 1, P: 1.37

Base cost: 27,598

3.77  104

3.17

1.31  105

532 F, 1066 psig

Area: 2210 ft2

M: 1, L: 1, P: 1.37

Base cost: 17,961

2.57  104

3.17

8.95  104

1036 F, 1066 psig

Heat rate: 60,003,188 BTU h21







1.17  106

3.17

4.07  106




Inlet gas flowrate: 758,020 kg h21







9.26  107

4.30

4.36  108

461 F, 1066 psig

Area: 3845 ft2

M: 1, L: 1.12, P: 1.37

Base cost: 25,753

3.94  104

3.17

1.37  105

181 F, 1066 psig

Area: 1081 ft2

M: 1, L: 1.25, P: 1.37

Base cost: 12,336

2.11  104

3.17

7.31  104

D3

HX10

V2 D4

CO2

CD2

RB2

Vertical, carbon steel, 508 mm diameter, 3.05 m height Horizontal, carbon steel, 36 tubes, 297 mm shell diameter, 8.4 m2 geometrical area
Vertical, carbon steel, 508 mm diameter, 3.05 m height Vertical, 620 mm diameter, 11 m height, 526 kg weight, 18 trays, carbon steel

10 F, 804 psig

Horizontal, carbon steel, 224 tubes, 573 mm shell diameter, 52.8 m2 geometrical area Vertical, carbon steel, 102 tubes, 473 mm shell diameter, 20.5 m2 geometrical area

L, Tube-length; M, material; P, shell-side pressure.

Vessel: 32,773, platform and ladders: 4722 Base cost: 6419

3.75  104

4.30

1.77  105

9.82  103

3.17

3.41  104






Platform and ladders: 4722


4.72  103


4.30


2.23  104

Weight: 9000 lb, diameter: 3 ft, height: 36 ft, no. of trays: 18

M: 1

7.48  104

4.30

3.53  105

196 F, 10 psig

Area: 568 ft2

M: 1, L: 1.25, P: 0.98

Vessel: 51,121, ladders: 10,687, Installed trays: 13,007 Base cost: 9588

1.18  104

3.17

4.09  104

307 F, 10 psig

Area: 221 ft2

M: 1, L: 1.25, P: 0.98

Base cost: 7680

9.43  103

3.17

3.28  104

Weight: 4200 lb, diameter: 3 ft, height: 12 ft Area: 150 ft2

M: 1


202 F, 10 psig


Diameter: 3 ft, height: 12 ft

230 F, 10 psig

307 F, 1066 psig

M: 1, L: 1.12, P: 1.37

Table 6.66 Cost segments of total capital investment and annual production costs for Scheme A (Adapted from [244]). Cost segment

Calculation

Assumption

Cost value

Total bare-module for process equipment (Ctbm ) Site (Csite ) Building (Cbuild )

Sum of Cbm values in Table 6.64




1.24  107 h2019

15% Ctbm 30% Ctbm

Grassroots onshore plant Process and nonprocess buildings in a grassroots plant Sizesteam (HX1): 2000 lb h21 Sizewater (HE1 1 CD1): 1000 gpm Share of 3% contractor’s fee and 15% contingency Preliminary estimate Relatively new process
Market price [275] Market price [315] Typical cost [163] Typical cost [163] Typical cost [163] 3 sections (reaction, vapor recovery, liquid separation), 1 operator per section, continuous operations fluids processing in medium plant




1.86  106 h2019 3.72  106 h2019

Offsite facilities (Call ) Total depreciable capital (Ctdc ) Royalties (Croy ) Start-up (Cstart ) Total permanent investment (Cdpi ) Raw material (Craw ) Utility (Cutil )

Direct wages and benefits (Cdw&b )

Directs salaries and benefits (Csal;o ) Operating supplies (Csuppl ) Technical assistance (Cassist )

820 $  (Sizesteam)0.81 1000 $  (Sizewater)0.68 1.18  ðCtbm 1 Call 1 Cbuild 1 Csite Þ 2% Ctdc 30% Ctdc Ctdc 1 Croy 1 Cstart H2: 7 h kg21 CO2: 0.025 h kg21 HP steam: 0.0145 $ kg21 H2O for MP steam generation: 0.5 $ m23 Cooling water: 0.02 $ m23 5 shifts  operators/ shift  2080 h year21  35 $ year21

15% Cdw&b 6% Cdw&b $60,000  operators/shift

2.50  106 h2019 1.20  105 h2019 2.46  107 h2019 4.92 7.38 3.25 6.80 2.43 6.86 2.55 1.24 9.76

        

105 h2019 106 h2019 107 h2019 107 h2019 year21 105 h2019 year21 104 h2019 year21 103 h2019 year21 104 h2019 year21 105 h2019 year21

1.46  105 h2019 year21 5.85  104 h2019 year21 1.61  105 h2019 year21

Control laboratory (Clab ) Labor-related operations (CO ) Maintenance wages and benefits (Cmw&b ) Salaries and benefits (Csal;m ) Material and services (Cserv ) Maintenance overhead (Cover;m ) Maintenance (CM ) Operating overhead (Cover;o ) Property tax and insurance (Ctax )

$65,000  operators/shift Cdw&b 1 Csal;o 1 Csuppl 1 Cassist 1 Clab 3.5% Ctdc 25% Cmw&b 100% Cmw&b 5% Cmw&b Cmw&b 1 Csal;m 1 Cserv 1 Cover;m 0.228    Cdw&b 1 Csal;o 1 Cmw&b 1 Csal;m 2% Ctdc

General expenses (Cgen ) Total production cost (Cprod ) Working capital cost (Cwc )

8% (Ctdc 2 1.18 Call) 6% (1.18 Call) CH3OH: 0.350 h kg21 2% S Craw 1 Cutil 1 CO 1 CM 1 Cover;o 1 Ctax 1 Cd;dir 1 Cd;all 1 Clic 11.55% S CCOM 1 Cprod 18.58% S 1 8.33% Craw

Total capital investment (Ctci )

Cdpi 1 Cwc

Direct plant depreciation (Cd;dir ) Allocated plant depreciation (Cd;all ) Sales (S) Licensing fee Cost of manufacture (CCOM )

1.74 1.52 8.61 2.15 8.61 4.31 1.98 5.01

Process of low risk located away from a heavily populated area

Market price [276] Preliminary estimate


4.92  105 h2019 year21



30 d for cash reserves, 7 d for liquid inventory, 30 d for accounts receivable and payable


1.70 2.20 1.38 4.15 7.48

       

105 h2019 year21 106 h2019 year21 105 h2019 year21 105 h2019 year21 105 h2019 year21 104 h2019 year21 106 h2019 year21 105 h2019 year21



Fluid handling process





    

106 h2019 year21 105 h2019 year21 106 h2019 year21 104 h2019 year21 107 h2019 year21

1.60  105 h2019 year21 7.49  107 h2019 year21 1.21  107 h2019 4.45  107 h2019

Table 6.67 Cost segments of total capital investment and annual production costs in Fig. 6.33B (Adapted from [244]). Cost segment

Calculation

Assumption

Cost value

Total bare-module for process equipment (Ctbm ) Site (Csite ) Building (Cbuild )

Sum of Cbm values in Table 6.65




4.50  108 h2019

15% Ctbm 30% Ctbm

6.75  107 h2019 1.35  108 h2019

Offsite facilities (Call )

820 $  (Sizesteam)0.81 1000 $  (Sizewater)0.68

Grassroots onshore plant Process and nonprocess buildings in a grassroots plant Sizesteam (HX10 1 RB2): 5384 lb h21 Sizewater (HE2 1 HE3 1 HE4 1 CD2): 13,928 gpm Sizerefrig (HE5 1 HE6): 7342 t Share of 3% contractor’s fee and 15% contingency Preliminary estimate Relatively new process
Market price [316] Market price [317] Market price [317] Typical cost [163] Typical cost [163] Typical cost [163] Typical cost [163] Market price [318] 3 sections (reaction, vapor recovery, liquid separation), 1 operator per section, continuous operations fluids processing in medium plant


Total depreciable capital (Ctdc ) Royalties (Croy ) Start-up (Cstart ) Total permanent investment (Cdpi ) Raw material (Craw )

Utility (Cutil )

Direct wages and benefits (Cdw&b )

Directs salaries and benefits (Csal;o )

Sizerefrig/1000  $11,000  (1000)0.77 1.18  ðCtbm 1 Call 1 Cbuild 1 Csite Þ 2% Ctdc 30% Ctdc Ctdc 1 Croy 1 Cstart CH4: 0.89 $ L21 O2: 0.54 $ L21 N2: 0.62 $ L21 LP steam: 0.0066 $ kg21 H2O for HP steam generation: 0.5 $ m23 Refrigeration 230 F: 2.4 $ t21 day21 Cooling water: 0.02 $ m23 Molten salt: 0.93 $ kg21 5 shifts  operators/ shift  2080 h year21  35 $ year21

15% Cdw&b

9.46  105 h2019 7.21  105 h2019 1.81  107 h2019 8.20  108 h2019 1.64 2.46 1.08 5.43 6.03 4.59 1.14 7.71 5.17 4.46 1.36 9.76

           

107 h2019 108 h2019 109 h2019 107 h2019 year21 107 h2019 year21 104 h2019 year21 105 h2019 year21 104 h2019 year21 106 h2019 year21 105 h2019 year21 1010 h2019 year21 105 h2019 year21

1.46  105 h2019 year21

Operating supplies (Csuppl ) Technical assistance (Cassist ) Control laboratory (Clab ) Labor-related operations (CO ) Maintenance wages and benefits (Cmw&b ) Salaries and benefits (Csal;m ) Material and services (Cserv ) Maintenance overhead (Cover;m ) Maintenance (CM ) Operating overhead (Cover;o ) Property tax and insurance (Ctax )

6% Cdw&b $60,000  operators/shift $65,000  operators/shift Cdw&b 1 Csal;o 1 Csuppl 1 Cassist 1 Clab 3.5% Ctdc 25% Cmw&b 100% Cmw&b 5% Cmw&b Cmw&b 1 Csal;m 1 Cserv  1 Cover;m  Cdw&b 1 Csal;o 0.228  1 Cmw&b 1 Csal;m 2% Ctdc

General expenses (Cgen ) Total production cost (Cprod ) Working capital cost (Cwc )

8% (Ctdc 2 1.18 Call) 6% (1.18 Call) CH3OH: 0.350 h kg21 2% S Craw 1 Cutil 1 CO 1 CM 1 Cover;o 1 Ctax 1 Cd;dir 1 Cd;all 1 Clic 11.55% S CCOM 1 Cprod 18.58% S 1 8.33% Craw

Total capital investment (Ctci )

Cdpi 1 Cwc

Direct plant depreciation (Cd;dir ) Allocated plant depreciation (Cd;all ) Sales (S) Licensing fee Cost of manufacture (CCOM )







5.85 1.61 1.74 1.52

   

104 h2019 year21 105 h2019 year21 105 h2019 year21 106 h2019 year21

Fluid handling process





2.87 7.18 2.87 1.44 6.60

    

107 h2019 year21 106 h2019 year21 107 h2019 year21 106 h2019 year21 107 h2019 year21


Process of low risk located away from a heavily populated area

Market price [276] Preliminary estimate


8.44  106 h2019 year21 1.64  107 h2019 year21



30 days for cash reserves, 7 days for liquid inventory, 30 days for accounts receivable and payable


6.16 2.99 1.38 4.15 7.48

    

107 h2019 year21 106 h2019 year21 106 h2019 year21 104 h2019 year21 107 h2019 year21

1.60  105 h2019 year21 1.39  1010 h2019 year21 1.17  109 h2019 2.25  109 h2019

248

CHAPTER 6 Case studies

6.3.14 Sustainability assessment results Table 6.68 summarizes the technical, economic, and environmental data used for the estimation of the indicators, while Table 6.69 shows the credit factors, event trees, damage distances of the final accident scenarios, and HHI values used or calculated for each relevant equipment item (i.e., containing dangerous material) of the two schemes. These tables are derived from the data and assumptions previously reported. Fig. 6.34 shows the technical, economic, environmental, and inherent safety indicators calculated for the two process schemes considered. As shown from Fig. 6.34A, when the energy efficiency indicator is considered, Scheme A results in the most efficient, with values of η around 48%. A value as low as 4% is obtained for Scheme B, due to the significant energy input required to preheat the gas mixture for CH3OH synthesis to 451 C. Concerning economic performance, the LCOP values in Fig. 6.34B are the higher by about two orders of magnitude for Scheme A. The higher the number of process equipment and the larger the entity of specific utilities required (e.g., refrigeration, molten salt) are the main cause for the considerably higher costs of Scheme B. Fig. 6.34C shows that the highest environmental performance (i.e., the lowest LGHG value) is obtained for Scheme A. The higher value of LGHG estimated for Scheme B can be attributed to the larger environmental impact of the waste gas streams produced (Table 6.68). When inherent safety indicators are compared in Fig. 6.34D, a higher safety performance of Scheme A (i.e., a lower value of HHI) is evident with respect to Table 6.68 Technical, economic, and environmental data of Scheme A in Fig. 6.33A and Scheme B in Fig. 6.33B (Adapted from [246]). Parameter

Scheme A

Scheme B

Input energy

_ a2 : 5376.0 kW, Q _ HX1 : 317.6 kW Q

Output energy

_ a14 : 2773.3 kW Q _ a8 : 1.3 kgCO2eq h21, Q _ a12 : 53.5 kgCO2eq h21 Q

_ HX7 : _ b1 : 13,382.4 kW, Q Q _ HX8 : 1785.2 kW, 1785.2 kW, Q _ HX10 : 411.0 kW, _ HX9 : 1785.2 kW, Q Q _ QRB2 : 1078.9 kW _ b14 : 2775.6 kW Q

GHG emissions

Total capital investment cost Ctci Total annual production cost Cprod GHG, Greenhouse gas.

4.45  107 h

_ b50 : _ b45 : 708.7 kgCO2eq h21, Q Q _ b52 : 611.0 kgCO2eq h21, Q 0.5 kgCO2eq h21 2.25  109 h

7.49  107 h year21

1.39  1010 h year21

Table 6.69 Credit factors, event trees, damage distances, and HHI for each unit of Scheme A in Fig. 6.33A and Scheme B in Fig. 6.33B (Adapted from [244]). Equipment unit (scheme)

Key substance or mixture

Release mode

Credit factor (1/year)

HX1 (Scheme A)

H2 1 CO2 (gas)

R1 (Scheme A)

H2 1 CO2 (gas), H2 1 CH3OH (gas)

HE1 (Scheme A)

H2 1 CH3OH (gas)

D1 (Scheme A)

H2 1 CO2 (gas), CH3OH (liquid)

D2 (Scheme A)

CH3OH (gas), CH3OH (liquid)

CO1
CD1 (Scheme A)

CH3OH (gas), CH3OH (liquid)

HX2
HX3
HX4 (Scheme B)

CO 1 CH4 (gas)

HX5
HX6 (Scheme B)

CO 1 CH4 (gas)

HX7
HX8
HX9 (Scheme B)

CO 1 CH4 (gas)

R1 R2 R3a/R3b R1 R2 R3a/R3b R1 R2 R3a/R3b R1 R2 R3a/R3b R1 R2 R3a/R3b R1 R2 R3a/R3b R1 R2 R3a/R3b R1 R2 R3a/R3b R1 R2 R3a/R3b

9.8 1.4 5.6 1.0 5.0 1.3 9.8 1.4 5.6 9.6 2.2 3.7 5.8 1.6 6.5 1.1 3.7 2.4 9.8 1.4 5.6 9.8 1.4 5.6 9.8 1.4 5.6

 1023  1023  1024  1025  1026  1025  1023  1023  1024  1023  1023  1024  1023  1023  1025  1022  1023  1024  1023  1023  1024  1023  1023  1024  1023  1023  1024

Event tree

Final accident scenario

Damage distance (m)

HHI (m2 year21)

(d) (d) (f) 1 (d) (d) (d) (f) 1 (d) (d) (d) (f) 1 (d) (a)/(d) (a)/(d) (c)/(f) 1 (a)/(d) (a)/(d) (a)/(d) (b)/(e) 1 (a)/(d) (a)/(d) (a)/(d) (b)/(e) 1 (a)/(d) (d) (d) (f) 1 (d) (d) (d) (f) 1 (d) (d) (d) (f) 1 (d)

VCE VCE VCE VCE VCE VCE VCE VCE VCE VCE VCE Toxic cloud Pool fire Jet fire Toxic cloud Pool fire Pool fire VCE Toxic cloud Toxic cloud Toxic cloud Toxic cloud Toxic cloud Jet fire Toxic cloud Toxic cloud Jet fire

13.0 9.3 9.3 23.9 23.9 23.9 23.9 23.9 23.9 14.4 14.4 54.8 6.1 8.1 9.8 10.9 10.9 15.6 74.9 194.6 190.0 66.1 161.4 168.6 42.4 93.5 141.3

5.71

5.02  1022

2.11  10

1.12  10

1.03

5.86

4.05  102

3.02  102

1.29  102

(Continued)

Table 6.69 Credit factors, event trees, damage distances, and HHI for each unit of Scheme A in Fig. 6.33A and Scheme B in Fig. 6.33B (Adapted from [244]). Continued Equipment unit (scheme)

Key substance or mixture

Release mode

Credit factor (1/year)

R2 (Scheme B)

CO 1 CH4 (gas)

HE2
HE3
HE4 (Scheme B)

CO 1 CH4 (gas), ammonia (gas)

HE5
HE6 (Scheme B)

CO 1 CH4 (gas)

D3 (Scheme B)

CO 1 CH4 (gas), CH3OH (liquid)

HX10 (Scheme B)

CH3OH (liquid)

D4 (Scheme B)

CO2 (gas), CH3OH (liquid)

CO2
CD2 (Scheme B)

CH3OH (gas), CH3OH (liquid)

R1 R2 R3a/R3b R1 R2 R3a/R3b R1 R2 R3a/R3b R1 R2 R3a/R3b R1 R2 R3a/R3b R1 R2 R3a/R3b R1 R2 R3a/R3b

1.0 5.0 1.3 9.8 1.4 5.6 6.7 7.4 2.1 9.6 2.2 3.7 9.8 1.4 5.6 5.8 1.6 6.5 1.1 3.7 2.4

VCE, Vapor cloud explosion.

 1025  1026  1025  1023  1023  1024  1022  1023  1023  1023  1023  1024  1023  1023  1024  1023  1023  1025  1022  1023  1024

Event tree

Final accident scenario

Damage distance (m)

HHI (m2 year21)

(d) (d) (f) 1 (d) (d) (d) (f) 1 (d) (d) (d) (f) 1 (d) (a)/(d) (a)/(d) (c)/(f) 1 (a)/(d) (a) (a) (b) 1 (a) (a)/(d) (a)/(d) (b)/(e) 1 (a)/(d) (a)/(d) (a)/(d) (b)/(e) 1 (a)/(d)

Toxic cloud Toxic cloud Jet fire Toxic cloud Toxic cloud Jet fire Toxic cloud Toxic cloud Jet fire Toxic cloud Toxic cloud Toxic cloud VCE VCE VCE Pool fire Pool fire Pool fire Pool fire Pool fire Jet fire

42.4 94.0 227.9 69.0 186.8 141.0 136.5 789.2 168.4 127.5 843.0 1070.7 7.3 7.3 7.3 5.6 5.6 5.6 12.3 12.3 26.1

2.32

3.38  102

1.85  104

6.72  103

1.98

7.34  1021

7.69

6.3 Case study 3

FIGURE 6.34 Values of the disaggregated screening indicators calculated for Schemes A and B: (A) energy efficiency, (B) LCOP, levelized cost of product, (C) LGHG, levelized greenhouse gases, (D) HHI, human inherent hazard index (Adapted from [244]).

Scheme B. The most critical units in both processes are the coolers and pressurized flash drums after the synthesis reactors, as shown in Table 6.69. Toxic clouds due to CO releases result in more severe consequences than the vapor cloud explosion due to H2 releases (Table 6.69), thus penalizing the performance of Scheme B. To rank the overall performance of the alternative process schemes, the aggregated indicator PrI screening (PrIS) is calculated for both process schemes by the procedure illustrated in the methodology description. Target values assumed for the normalization of disaggregated indicators are related to the performance of the actual reference process for large-scale CH3OH production (i.e., steam reforming from natural gas) and reported in Table 6.70. The values of the weights, calculated for the four alternative perspectives of decision makers, are reported in Table 6.71. From this table, it appears evident that there are variations between the different archetypes in prioritizing specific indicators over the others. The individualist archetype prioritizes the HHI indicator with a trade-off weight of almost 63% and neglects the energy efficiency indicator (trade-off of 5%). On the other hand, the egalitarian method gives the highest importance to LGHG (53%) followed by η (36%) and the lowest priority to HHI (a mere 3% of weight). Similarly, the hierarchist method exhibits a similar preference to the egalitarian method with the highest trade-off to LGHG (58%). No archetype approximates the equal weighting scheme.

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Fig. 6.35 displays the aggregated indicator PrIS for the two process schemes obtained considering the different perspectives of decision makers. As shown in this figure, for both Schemes A and B, the egalitarian method gives the highest values of the aggregated indicator calculated with the WAM method in Fig. 6.35A and the WGM method in Fig. 6.35B, compared to the other archetypes. When considering the same archetype and weighting method, relatively lower values of the PrIS (i.e., worse aggregated performance) are obtained for Scheme B if compared to Scheme A, as a direct consequence of the worst values of all the disaggregated indicators, evident from Fig. 6.34. It may be noted that the values of PrIS indicators calculated with the WAM method in Fig. 6.35A are more penalizing for Scheme B with respect to Scheme A than when the WGM method is used in Fig. 6.35B. Therefore the WGM method tends to further penalize the less performant Scheme B.

Table 6.70 Target values assumed for the normalization of disaggregated indicators (Adapted from [244]). Target

Value

Description/assumption

ηtarget

52.3%

LCOPtarget

62.9 h2019 MWh21

LGHGtarget

63.3 kgCO2eq MWh21

HHItarget

6 m2 year21

Ratio of total energy in CH3OH based on LHV (1163 MW) to total energy in input (1760 MW) for the conventional large-scale plant (5000 t day21) without CO2 capture source. LCOP in 25 year economic lifetime and 8% interest rate for the conventional large-scale plant (5000 t day21) without CO2 capture source. The literature value of 275.1 $2014 t21 is converted into h2019 MWh21 using CEPCI values of 576.1 (for 2014) and 613.3 (for 2019) and exchange rate in Table 6.7. GHG emissions for the conventional large-scale plant (5000 t day21) without CO2 capture source. The literature value of 0.3533 tCO2eq t21 is converted into kgCO2eq MWh21 by using LHV of CH3OH. Average risk to human life from major chemical industrial accidents.

Literature source [323]

[323]

[323]

[244]

CEPCI, Chemical engineering plant cost index; GHG, greenhouse gas; LCOP, levelized cost of product; LHV, lower heating value.

6.3 Case study 3

Table 6.71 Weights among the indicators based on different perspectives of decision makers (Adapted from [244]). Indicators for integrated assessment Perspective of decision maker

η (%)

LCOP (%)

LGHG (%)

HHI (%)

Individualist Egalitarian Hierarchist

5 36 22

17 8 10

14 53 58

63 3 10

LCOP, Levelized cost of product; LGHG, levelized greenhouse gas.

FIGURE 6.35 PrIS calculated for Scheme A and Scheme B with (A) aggregation by the WAM method and (B) aggregation by the WGM method. PrIS, Process intensification screening; WAM, weighted arithmetic mean; WGM, weighted geometric mean (Adapted from [244]).

6.3.15 Sensitivity analysis results Fig. 6.36 illustrates the results of sensitivity analysis, showing the cumulative probability distributions of the differences of PrIS in Scheme B with respect to Scheme A. Sensitivity analysis is carried out by varying randomly the target values selected for normalization of disaggregated metrics according to the Monte Carlo method described in Section 5.5. The adoption of the approach based on time-spacereceptor criteria and different archetypes of decision makers for weights elicitation in the integrated assessment is considered as an alternative procedure to reduce the uncertainty associated to this stage. Thus sensitivity analysis is not applied to verify the effect of stochastic variation in weights on the PrIS indicators. All the target values are equally varied between 6 30% the baseline value reported in Table 6.70. The uniform distribution is conservatively adopted for all the indicators, thus avoiding any assumption about the distributions of the reference indicators. It is verified that 106 simulations are sufficient to reach the convergence and that a higher number of simulations do not modify the results.

253

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FIGURE 6.36 Cumulative probability of the difference in the PrIS value of Scheme B with respect to Scheme A using (A) aggregation by WAM method and (B) aggregation by WGM method. PrIS, Process intensification screening; WAM, weighted arithmetic mean; WGM, weighted geometric mean (Adapted from [244]).

From Fig. 6.36, it clearly appears that Scheme B is invariably outranked by Scheme A with the assumed uncertainty ranges of the target values, independently from the perspective and aggregation method used, since the PrIS differences are always negative. This confirms the robustness of the ranking shown in Fig. 6.35.

6.3.16 Detailed site-specific assessment results A detailed site-specific assessment is carried out to further analyze the performance of P2L offshore hybrid energy options composed of the Schemes A and B driven by offshore wind and solar thermal energies at a remote offshore oil and gas location. The exergy and exergoeconomics analyses described in Chapter 4, System Modeling and Analysis, are selected for the thorough feasibility evaluation. Both Schemes A and B are assumed to be installed at an offshore gas production platform located in the Thebaud gas field of the Offshore Energy Sable Project [324], which has started the first activity of the decommissioning phase since 2017. The field is approximately 225 km offshore Nova Scotia (Canada), in the Atlantic Ocean, in water depths ranging between 20 m and 80 m. Hydrocarbons produced at the satellite platforms in the field are transported to the Thebaud facility and mixed with wellstreams extracted from the Thebaud wellheads. The resulting mixture is separated and dehydrated on-site using triethylene glycol (TEG) solvent. The produced H2O is treated and discharged into the sea, while gas and condensate are recombined and delivered to an onshore gas plant in Nova Scotia.

6.3 Case study 3

In this detailed assessment, Scheme A makes use only of infrastructures of the offshore facility without any integration with its oil and gas operations. H2 is produced by means of desalination and electrolysis of sea H2O, while CO2 is delivered from the onshore market via ship. The schematic of the P2L offshore hybrid energy option based on Scheme A (Option 1) is shown in Fig. 6.37, where five subsystems are indicated with different colors: H2 production (red), CH3OH synthesis (green), CH3OH separation (light blue), solar
thermal plant (orange), wind
diesel plant (blue). The useful product is relatively pure liquid CH3OH to be used as a fuel for onshore energy production, industrial, and mobile applications. On the other hand, since CH4 is required as a raw material in Scheme B, the coupling of the process with the oil and gas operations occurring at the selected offshore facility is considered. Fig. 6.38 shows the schematic of the P2L offshore hybrid energy option based on scheme B (Option 2), where 11 subsystems are represented with different colors: hydrocarbons separation (magenta), gas dehydration (dark green), gas treatment (ocher), O2 production (red), CH3OH synthesis (green), CH3OH separation (light blue), solar
thermal plant (orange), wind
diesel plant (blue), ammonia refrigeration cycles (brown), propane refrigeration cycle (gray). In addition to the main product (liquid CH3OH), oil and export gas to be delivered to onshore treatment plants, as well as H2 to be employed as fuel at the offshore facility, are secondary useful products. In both options, offshore wind farm and solar parabolic solar collectors are considered to supply electrical and heat power, respectively, meeting the energy demand of the process schemes for CH3OH production. Even though these

FIGURE 6.37 Flow diagram of P2L offshore hybrid energy option based on Scheme A for CH3OH production (Option 1) (Adapted from [325]).

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FIGURE 6.38 Flow diagram of P2L offshore hybrid energy option based on Scheme B for CH3OH production (Option 2) (Adapted from [325]).

resources have several advantages, wind or solar generators in a stand-alone system cannot continuously supply the load due to their fluctuating nature. Therefore diesel generators are integrated with the wind farm, while solar thermal energy storage are combined with the parabolic trough technology, as shown in Figs. 6.37 and 6.38. The renewable power plants integrated with Schemes A and B are designed adopting two commercial tools based on environmental conditions at the given offshore location and energy needs of the process. The Hybrid Optimization Model for Multiple Energy Resources (Homer) Pro version 3.12.3 (Academic) [326] was used as the design and simulation tool for the hybrid system composed of an offshore wind farm, diesel generators, and converter linked to a given electrical load. The wind resource input for Homer is the monthly average wind speed data at the selected offshore site retrieved from the NASA Surface Meteorology and Solar Energy database. The total electrical consumption required in input to each process scheme is obtained through Aspen HYSIS simulation and set as the capacity of the primary load in the hybrid renewable system. The design of a solar
thermal plant is performed by means of the open source System Advisor Model (SAM) version 2017.9.5 [327]. A modified version of the physical trough model [328] is used to simulate the solar parabolic trough system for thermal applications. The solar resource data at the considered offshore field retrieved from the NREL National Solar Radiation Database are given as input to SAM, including a design value of direct normal irradiance (DNI). Obtained from Aspen HYSYS, the heat rates required in the heat exchanger HX2 of Option 1 (in Fig. 6.37), heat exchangers HX9, HX10-HX11, and vaporizer VP4 of Option 2 (in Fig. 6.38), their values are considered as the design heat sink power in SAM

6.3 Case study 3

Table 6.72 Key parameters and assumptions for the design of the wind
diesel and solar
thermal plants. Renewable plant

Key operating parameter

Wind
diesel plant

Average wind speed of 8.52 m s21

Solar parabolicthermal plant

Average hourly DNI of 625 W m22

Assumptions The maximum capacity for offshore wind farm is set as 80 turbines for a total capacity of around 606 MW. The lowest number of diesel generators for suitable dispatch control of the wind turbines is selected. The number of storage hours under nominal conditions are selected as 11, given the night time from 7 p.m. to 6 a.m., when solar collectors do not run. The maximum dimensions of thermal storage tanks are considered as 3 m diameter and 17 m height. A target solar multiple (i.e., solar field aperture area multiple of the heat sink power rated capacity) greater than 1 is set to obtain a solar field that operates at its design point for more hours of the year and generates more thermal output. The most compact collector is chosen from the SAM library, that is, Luz LS-2 collector with an aperture area of 235 m2. A row spacing of 5 m is fixed to obtain zero shading effect between solar collectors [329]. The Schott PTR80 receiver is chosen from the SAM database keeping all the features provided.

DNI, Direct normal irradiance; SAM, system advisor model.

modeling. The key parameter and assumptions applied to the design of the wind
diesel plant in the Homer tool and the solar
thermal plant in the SAM software are summarized in Table 6.72.

6.3.16.1 Exergy analysis results The exergy approach described in Chapter 4, System Modeling and Analysis, is applied to assess and compare the exergy efficiencies (ψ) and exergy destruction _ d ) of the integrated systems and associated rates due to the irreversibility (Ex subsystems. Most of the process operations of the two options are simulated by means of Aspen HYSYS v10 [313]: heat transfer by using Aspen shell and tubes exchanger and air cooler; pressure change by adopting Aspen pump, compressor, and control valve; separation through Aspen two-phase separator, three-phase separator, distillation column; CH3OH synthesis reaction by means of Aspen conversion reactor; TEG absorption-regeneration by using Aspen natural gas dehydration flowsheet.

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Moreover, the Aspen recycle operator is used to recycle streams, while the Aspen component splitter is adopted to simply model some specific components of the desalination plants (i.e., pressure exchanger and reverse osmosis unit) and the electrolyzers by assuming technical data provided in the literature. The main assumptions made for the modeling are that pumps and compressors have an adiabatic efficiency of 75%, that a maximum outlet temperature of fluid in compressor is 250 C, that the minimum difference between the outlet temperatures of the fluids in shell & tubes exchanger is 15 C
30 C, that the air inlet conditions to the air cooler is 10 C and 1.02 bar, that the minimum allowable correction factor is 80%, that the filling factor of separators is 50%, that the valve operating characteristics are linear with 50% opening, that the distillate rate is fixed in the CH3OHH2O distillation column equal to the benchmark CH3OH flowrate, while the reflux ratio and number of stages are set based on conventional column design procedure [259], the parameters of the Aspen natural gas dehydration flowsheet are kept at default, and the stoichiometric global reaction and overall conversion of the base reactant are set in a conversion reactor. The stoichiometry in reactor R1 in Fig. 6.37 is 22, 26, 12, 12 for CO2, H2, H2O, CH3OH, respectively; the overall conversion of CO2 is 47%. The stoichiometry in reactor R2 in Fig. 6.38 is 23, 24, 11, 11, 11, 14 for CH4, O2, CH3OH, CO, CO2, H2O, respectively; the overall molar conversion of CH4 is 9.5%. The reflux ratio in column CO1 in Fig. 6.37 is estimated at 1.619, and the number of stages is 9, while the reflux ratio in column CO4 in Fig. 6.38 is 7.09, and the number of stages is 20. The Aspen recycle operator is used to recycle streams by setting the tolerance multipliers for vapor fraction, temperature, pressure, flow, enthalpy, and composition between 1 and 10 (forward transfer direction). In addition, the Aspen component splitter is adopted to simply model some specific components of the desalination plants (i.e., pressure exchanger and reverse osmosis unit) and of the electrolyzers by assuming technical data provided in the literature. The large package for sea H2O reverse osmosis desalination commercialized by Lenntech [330] is considered: Proper reverse osmosis units with 45% recovery and 95% salt rejection are selected based on required volumetric permeate flow for processes. Information about associated pumps and pressure exchangers is derived based on mathematical models proposed by Lenntech for the typical desalination process with energy recovery. The commercial Silyzer 200 PEM electrolyzer [331] is selected for EL1
EL26 in the two options: For each unit, the electrical consumption is 1.25 MW, the nominal energy efficiency is 75%, the nominal capacity of each stack is 225 Nm3 h21 H2 (i.e., 18.8 kg h21), and the desalinated H2O needed in input is equal to 1.5 L Nm23 of H2 (i.e., 342.6 kg h21). By applying the overall reaction of electrolysis, the produced O2 flowrate from each stack is considered as 113 Nm3 h21 O2 (i.e., 149.7 kg h21). The monthly production data of the Thebaud platform related to September 2018 is assumed for the raw gas and produced H2O mixture (state 41 in

6.3 Case study 3

Fig. 6.38) fed to the chemical process in Option 2. The composition given in input to Aspen HYSYS is reported in Table 6.73. The exergy destruction rates and exergy efficiencies of the process subsystems of Option 1 are presented in Table 6.74. Table 6.75 summarizes the exergy performance equations for the process subsystems of Option 2. For refrigeration cycles, the exergetic coefficient of performance (exCOP) is reported. In both Options 1 and 2, the input exergy of the wind
diesel subsystem _ wind ) corresponds to the exergy associated with wind power (equal to the (Ex actual power generated by the wind turbines W_ wt calculated through Eq. 5.22) and the chemical exergy exdiesel of the diesel fuel with mass flowrate m_ diesel , while the exergy in output is the exergy associated to the electrical load W_ load linked to wind turbines, diesel generators, and converter in the hybrid system simulated in Homer. The average wind speed reported in Table 6.72 is used to calculate W_ wt . The exergy destruction rate of the wind
diesel plant is: _ wind 1 m_ diesel exdiesel 2 W_ load _ d;wind2diesel 5 Ex Ex

(6.2)

Table 6.73 Composition of input raw gas of Option 2 (state 41 in Fig. 6.38). Compound

Molar composition

CH3OH H2O N2 O2 CO CO2 H2 CH4 Ethane Propane i-Butane n-Butane i-Pentane n-Pentane n-Hexane C6
C10 C10
C15 C15
C20 C20
C25 C25
C30 C30
C35 C35
C40 C40 1


0.0597 0.0003

0.0005
0.6470 0.0788 0.0679 0.0197 0.0296 0.0296 0.0020 0.0020 0.0246 0.0148 0.0099 0.0049 0.0030 0.0030 0.0010 0.0020

259

Table 6.74 Exergy destruction rates and exergy efficiency equations of subsystems of Option 1 in Fig. 6.37. Subsystem

Exergy destruction rate equation

Exergy efficiency equation

H2 production

_ in;P1 1 W _ in;P2 1 W _ in;EL12EL10 _ d 5W Ex _ Q _ _ 1 ex1 2 m _ 16 ex16 2 m _ 34 ex34 1 Ex 1m

CH3OH synthesis

_ in;K1 1 W _ in;P3 _ d 5m _ 17 ex17 1 m _ 11 ex11 1 W Ex _ _ _ _ Q 2m _ Q 2 Ex _ Q _ ex 2 Ex 1 Ex

_ _ Q _ 16 ex16 1 m _ 34 ex34 2 Ex _ _ Ψ 5 ðm out;EL12EL10 2 m15 ex15 2 m8 ex8 Þ= _ Q _ in;P2 1 W _ in;EL12EL9 1 Ex _ _ in;P1 1 W _ ðW in;HX1 1 m1 ex1 Þ _ _ _ Q 1 Ex _ Q _ 29 ex29 2 m _ 24 ex24 2 m _ 28 ex28 1 Ex Ψ 5 ðm out;R1 out;HE1 Þ= _ _ _ _ Q _ ex 1 W _ ex 1 m 1W 1 Ex Þ ðm

in;HX1

in;HX2

CH3OH separation

_ d 5m _ 29 ex29 Ex

29

29

_ _ Q 1 Ex

in;VP1

out;R1

_ 30 ex30 2m

out;HE1

_ _ Q 2 Ex

out;CD1

17

17

11

11

_ 30 ex30 2 m _ 31 ex31 Ψ 5 ðm

in;K1

in;P3

_ _ Q 1 Ex

in;HX2

_

out;CD1 Þ=ðm29 ex29

_ _ Q 1 Ex in;VP1 Þ

6.3 Case study 3

The exergy efficiency of the wind
diesel subsystem is considered as:     _ wind 1 m_ diesel exdiesel ψwind2diesel 5 W_ load = Ex

(6.3)

Concerning the solar
thermal plant, the exergy in input is considered the _ col ), which can be calculated by multiplying exergy rate of solar collectors (Ex the solar irradiance and the total aperture area of collectors for the Petela
Landsberg
Press factor [332]:        _ col 5 Aap ncol Gsolar 1 1 1=3 T0 =Tsolar 4
4=3 T0 =Tsolar Ex

(6.4)

where Aap is the aperture area of single collector, ncol is the number of solar collectors in the solar field, Gsolar is the solar radiation assumed equal to the design DNI reported in Table 6.72, T0 is the reference environment temperature, and Tsolar is the sun temperature equal to 6000K. The exergy destruction rate of the subsystem is: _ col 2 Ex _ Q_ solar;u _ d;solar2thermal 5 Ex Ex

(6.5)

_ Q_ solar;u is the exergy associated with useful thermal output from the colwhere Ex _ _ Q lectors (Q_ solar;u ). The exergy rate associated with total heat losses (Ex out;solar ) from the thermal storage tanks, receiver, and field piping is considered as part of _ d;solar2thermal in Eq. (6.5). Ex The exergy efficiency of the solar
thermal subsystem is:     Q_ _ col _ Q_ solar;u 2 Ex _ out;solar Ex ψsolar2thermal 5 Ex

(6.6)

_ d of each option corresponds to the summation of Ex _ d calcuOverall, the Ex lated for the related subsystems. Overall ψ of Option 1 is defined as follows:

  _ Q_ _ out;EL12EL10 _ Q ψ 5 m_ 30 ex30 2 m_ 8 ex8 2 m_ 15 ex15 2 m_ 24 ex24 2 m_ 28 ex28 2 Ex 2 Ex out;solar (6.7)   _ col;HX2 1 m_ 1 ex1 1 m_ 11 ex11 _ wind 1 m_ diesel exdiesel 1 Ex Ex

Overall, ψ of Option 2 is calculated as:

 ψ 5 m_ 135 ex135 1 m_ 48 ex48 1 m_ 78 ex78 1 m_ 101 ex101 2 m_ 44 ex44 2 m_ 68 ex68 2 m_ 76 ex76 2 m_ 82 ex82 2 m_ 86 ex86 2 m_ 96 ex96 2 m_ 126 ex126 2 m_ 130 ex130 2 m_ 133 ex133 2 m_ 136 ex136 _ _ _ _ _ _ _ _ Q _ Q _ Q _ Q _ Q _ Q _ Q 2 Ex out;EL112EL26 2 Exout;CD2 2 Exout;HE11 2 Exout;CD4 1 Exout;CD5 2 Exout;CD6 2 Exout;CD7  Q_ Q_ Q_ Q_ Q_ Q_ _ out;CD8 _ out;CD9 _ out;CD10 _ out;solar;HX9 _ out;solar;HX102HX11 _ out;solar;VP4 2 Ex 2 Ex 2 Ex 2 Ex 2 Ex 2 Ex  _ wind 1 m_ diesel exdiesel 1 Ex _ col;HX9 1 Ex _ col;HX102HX11 1 Ex _ col;VP4 1 m_ 41 ex41 1 m_ 89 ex89 Ex  1 m_ 183 ex183 1 m_ 190 ex190 1 m_ 207 ex207 1 m_ 210 ex210 1 m_ 213 ex213 1 m_ 216 ex216 1 m_ 219 ex219

(6.8)

261

Table 6.75 Exergy destruction rates and exergy efficiency equations of subsystems of Option 2 in Fig. 6.38. Subsystem

Exergy destruction rate equation

Hydrocarbons separation

_ _ in;K2 1 W _ in;P4 1 W _ in;P5 1 W _ in;K3 1 Ex _ Q _ d 5m _ 41 ex41 1 W Ex in;HX3 _ _ _ _ Q _ Q _ Q _ 64 ex64 2 m _ 48 ex48 2 Ex 2m out;HE2 2 Exout;HE3 2 Exout;HE4

Exergy efficiency equation   _ _ _ _ Q _ Q _ Q _ 64 ex64 1 m _ 48 ex48 2 m _ 44 ex44 1 Ex Ψ5 m out;HE3 1 Exout;HE4 1 Exout;HE5   _ _ _ _ _ _ Q _ ex 1 W m 1W 1W 1W 1 Ex

Gas dehydration

_ _ in;P6 1 Ex _ Q _ d 5m _ 64 ex64 1 W _ Ex in;VP2 2 m75 ex75

    _ _ _ Q _ in;P6 1 Ex _ Q _ 75 ex75 2 m _ 68 ex68 2 Ex _ 64 ex64 1 W Ψ5 m m in;VP2 out;CD2

Gas treatment

_ _ _ Q _ Q _ d 5m _ 75 ex75 1 2 m _ 78 ex78 2 m _ 87 ex87 2 Ex Ex out;HE7 2 Exout;HE8

O2 production

_ _ d 5m _ in;P7 1 W _ in;P8 1 W _ in;EL112EL26 1 Ex _ Q _ 89 ex89 1 W Ex in;HX4 _ 101 ex101 2 m _ 102 ex102 2 m _ 137 ex137 2m

CH3OH synthesis

_ in;K4 1 W _ in;K5 1 W _ in;K6 1 W _ in;K7 _ d 5m _ 102 ex102 1 m _ 87 ex87 1 W Ex _ _ _ _ _ _ Q Q Q Q Q _ _ _ _ _ _ Q 1 Ex in;HX8 1 Exin;HX9 1 Exin;HX10 1 Exin;HX11 1 Exin;HX12 1 Exin;VP4 _ _ _ _ _ Q 2 Ex _ Q _ Q _ Q _ 128 ex128 2 Ex 2m out;R2 out;HE9 2 Exout;HE10 2 Exin;HE12

41

41

in;K2

Ammonia cycle 1

_ d 5m _ 134 ex134 Ex

_ _ Q 1 Ex

in;VP3

_ 135 ex135 2m

_ d 5m _ in;F1 1 W _ in;K8 _ 183 ex183 1 W Ex

out;CD3

out;HE7

_ _ Q 2 Ex out;CD4

Ammonia cycle 2

_ d 5m _ 207 ex207 1 m _ 210 ex210 1 m _ 213 ex213 1 m _ 216 ex216 1 m _ 219 ex219 Ex _ _ in;F3 1 W _ in;F4 1 W _ in;F5 1 W _ in;F6 1 W _ in;F7 1 W _ in;K10 1 Ex _ Q 1W out;HE12 _ _ _ _ _ _ Q _ Q _ Q _ Q _ Q 2 Ex 2 Ex 2 Ex 2 Ex 2 Ex

Propane cycle

_ _ _ d 5m _ in;F2 1 W _ in;K9 1 Ex _ Q _ Q _ 190 ex190 1 W Ex out;HE8 2 Exout;CD5

out;CD6

out;CD7

out;CD8

out;CD9

in;K3

in;HX3

in;HX9

in;HX10

in;HX11

in;HX12

in;VP4

    _ _ _ Q _ Q _ 135 ex135 1 Ex _ _ 134 ex134 1 Ex Ψ5 m m in;VP3 out;CD3 2 m136 ex136

_ _ Q 2 Ex

_ _ Q 1 Ex

in;P5

  _ _ _ Q _ Q _ 87 ex87 1 m _ 78 ex78 1 Ex _ _ _ Ψ5 m 1 Ex 2 m ex 2 m ex 2 m ex 76 76 82 82 86 86 out;HE7 out;HE8   _ m75 ex75   _ _ Q _ 101 ex101 1 m _ 102 ex102 1 m _ 137 ex137 2 m _ 96 ex96 2 Ex Ψ5 m out;EL112EL26   _ _ in;P7 1 W _ in;P8 1 W _ in;EL112EL26 1 Ex _ Q _ 89 ex89 1 W m in;HX4  _ _ _ _ _ _ Q _ Q _ Q _ Q _ Q _ 134 ex134 1 Ex _ Ψ5 m out;R2 1 Exout;HE9 1 Exout;HE10 1 Exout;HE12 2 m126 ex126 2 Exout;HE11   _ in;K4 1 W _ in;K5 1 W _ in;K6 _ 87 ex87 1 m _ 130 ex130 2 m _ 133 ex133 _ 102 ex102 1 W m 2m  _ _ _ _ _ _ _ in;K7 1 Ex _ Q _ Q _ Q _ Q _ Q _ Q 1W 1 Ex 1 Ex 1 Ex 1 Ex 1 Ex in;HX8

CH3OH separation

in;P4

out;CD10

    _ _ Q _ in;F1 1 W _ in;K8 W exCOP 5 Ex out;HE7     _ _ Q _ in;F3 1 W _ in;F4 1 W _ in;F5 1 W _ in;F6 1 W _ in;F7 1 W _ in;K10 W exCOP 5 Ex out;HE12     _ _ Q _ in;F2 1 W _ in;K9 W exCOP 5 Ex out;HE8

6.3 Case study 3

_ d of the subsystems of the two options. Fig. 6.39 displays the findings on Ex _ d in By examining this figure, the wind
diesel plant subsystem has the highest Ex both options, even with a difference of one order of magnitude between them due to the different size of these plants in the two options. The second critical subsystem in the irreversibility contribution analysis is the H2 production subsystem in Option 1 (Fig. 6.39A) and the gas treatment subsystem in Option 2 (Fig. 6.39B). Electrolyzers in Option 1 and the flash drums in Option 2 are actually found as _ d. the most critical components with the greatest Ex Fig. 6.40 shows ψ values of different subsystems in the two options, except the refrigeration cycles for which exCOP is illustrated. According to Fig. 6.40A, the CH3OH synthesis subsystem with ψ of 60% is the most efficient one in Option 1, while the hydrocarbons separation and gas dehydration subsystems give the highest ψ in Option 2 (Fig. 6.40B) with values close to 100%. On the other hand, for both options, the wind
diesel plant shows the worst ψ (almost 20%), in _ d subsystems analysis. full accordance with Ex Fig. 6.41 illustrates the comparison of overall exergy parameters between the _ d belongs to Option 2 two options. From this figure, the highest amount of Ex 5 with a value of 3.210 kW, which is an order of magnitude higher than that calculated for Option 1. This is due to the higher number of OWTs considered in the wind
diesel subsystem, which is responsible for the highest fraction of the total exergy destruction rate in both options (Fig. 6.39). From the ψ point of view, Option 2 gives the best performance with a value of 87%, which is significantly greater than that of Option 1. This outcome is attributed to the integration of the process scheme for CH3OH production with oil and gas operations, which provides, as input, hydrocarbons and yields multiple coproducts with relatively higher chemical exergy in addition to CH3OH. As a matter of fact, the highest ψ are calculated for the hydrocarbons separation, gas dehydration, and gas treatment subsystems in Option 2 (Fig. 6.40).

6.3.16.2 Exergoeconomics analysis results The SPECO method described in Chapter 4, System Modeling and Analysis, is selected as the exergoeconomic approach to determine and compare the economic performance of the two options by using results from the exergy analysis. Exergoeconomic indicators described in Chapter 5, Sustainability Index Development, are selected for the evaluation in this case study: the total cost rate (C_ total ), that is, cost rate associated with capital investment and O&M expenses _ plus the cost rate associated with Ex _ d (C_ d ), and the exergoeconomic factor (f). (Z) The main cost parameters assumed in the analysis are shown in Table 6.76. For each process component of the two options, purchase equipment cost (PEC) is obtained by using the proper equipment cost function reported in the literature based on specific size parameters: input power in the case of pump and pressure exchanger [336]; permeate flowrate in the case of reverse osmosis unit [337]; input power in the case of the compressor; geometric area in the case of the cooler/condenser/heater/reboiler; weight, diameter, and height in the case of

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CHAPTER 6 Case studies

FIGURE 6.39 Exergy destruction rate of subsystems of (A) Option 1 and (B) Option 2 (Adapted from [325]).

the vertical vessel/column [163]; input electrical power in the case of electrolyzer; and produced CH3OH flowrate in the case of reactor [148]. It is worth noting that components of the separation and dehydration subsystems of Option 2 are excluded from PEC evaluation since they are already installed at the facility. Moreover, the PEC of all the throttling valves (V1 in Fig. 6.37 and V5
V7 in Fig. 6.38), booster pumps (P2 in Fig. 6.37 and P7 in Fig. 6.38), and CO2 pump (P3 in Fig. 6.37) are not considered due to their relatively small cost compared to the others. Cost balance equations and fuel and product and related auxiliary equations of the process components of Options 1 and 2 are defined through the application of Eqs. (4.20) and (4.21) in Chapter 4, System Modeling and Analysis. The cost per unit of exergy c is assumed to be 0 for the seawater source, the rejected brine streams, CO2, and air. On the other hand, a given value is assumed for the average cost per unit of exergy associated with the natural gas in input to Option 2, as reported in Table 6.76.

6.3 Case study 3

FIGURE 6.40 Exergy efficiency of subsystems of (A) Option 1 and (B) Option 2 (Adapted from [325]).

To complete the linear system of equations defined for each option, the cost balance equations of the wind
diesel plant and solar
thermal storage plants integrated into the processes are also considered. The cost balance equation for the wind
diesel subsystem is considered as: W_ C_ wind 1 C_ diesel 1 Z_wt 1 Z_diesel 1 Z_conv 5 C_ load

(6.9)

where C_ wind is the cost rate associated with wind energy in input to the wind turbines; C_ diesel is the cost rate of the diesel source of the conventional power generators; Z_wt , Z_diesel , and Z_conv are the costs associated with the CI and O&M of W_ the wind farm, diesel generators, and converter, respectively, C_ load is the cost rate associated with the electrical load provided to the process of each option. The PEC associated with the wind turbines (i.e., turbine rotor and tower, control system, wiring, installation) is retrieved from the Homer database: PECwind 5 Nt U11;112;280

(6.10)

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FIGURE 6.41 Comparison of overall exergy parameters between the two options (Adapted from [325]).

Table 6.76 Main cost parameters assumed in the exergoeconomic analysis. Parameter

Value

Economic data reference year Economic system lifetime T (year) Annual operational hours τ Effective discount rate r Maintenance factor γ Nominal escalation rate rn Diesel average cost per unit of exergy Natural gas average cost per unit of exergy

2018 15 8000 0.10 0.06 0.03 0.102 $ kWh21 (28.23 $ GJ21) 0.00658 $ kWh21 (1.83 $ GJ21)

Literature source

[148] [333] [334] [333] Diesel fuel price derived from Homer software equal to 1 $ L21 [335]

where Nt is the number of turbines in the wind farm. The PEC of the diesel generator and converter in Homer are: PECdiesel 5 300 W_ diesel

(6.11)

PECcon 5 300 W_ con

(6.12)

where W_ diesel is the power produced by diesel generators whenever there is insufficient wind energy to supply the electrical load, which is calculated from Homer by setting the constraints in Table 6.72; W_ con is the power converted from the alternating current to direct current provided from Homer once the electrical load to be supplied is selected. In the present study, C_ wind is assumed to be 0 since no average cost per unit of exergy is associated with wind energy, while C_ diesel is calculated according to Eq. (4.24) by assuming the average cost per unit of exergy reported in Table 6.76.

6.3 Case study 3

W_ By solving Eq. (6.10), C_ load can be obtained and thus the average cost per unit of W_ can be estimated according to Eq. (4.24). This is considered as equal to exergy cload _ the average cost per unit of exergy cW associated with the electrical consumption of relevant components powered from the wind
diesel plant in each option. On the other hand, the cost balance equation for the solar trough parabolic collectors
thermal storage subsystem is [334]: Q_ C_ HTF;in 1 C_ solar 1 Z_col 1 Z_th:storage 5 C_ HTF;out 1 C_ out

(6.13)

where C_ solar is the cost rate associated with solar power in input to the solar collectors; C_ HTF is the cost rate of the heat thermal fluid source in input to and output from the receivers, which corresponds to the cost rate of the outlet and inlet service fluid, respectively, used for HX2 in Option 1, and for HX9, HX10
HX11, and VP4 in Option 2; Z_col , and Z_th:storage are the costs associated with CI and O&M of the solar collectors and thermal storage tanks, respectively; Q_ and C_ out is the cost rate associated with thermal losses calculated by means of Eq. (4.23). The PEC associated with the solar collectors and thermal storage tanks can be calculated as follows [334]: PECcol 5 355 Aap

(6.14)

PECth:storage 5 27 Q_ storage

(6.15)

where Aap is the total aperture area of the collectors; Q_ storage is the total heat (in thermal kWh) stored in the cold/hot pairs of tanks, both derived from SAM modeling. In the present study, C_ solar is assumed to be 0 since no average cost per Q_ unit of exergy is associated with renewable energy. Similarly, C_ out is considered null due to heat loss into the environment. Since the average cost per exergy unit of the heating thermal fluid is the same between the inlet and outlet of the receiver, Eq. (6.13) can be solved in combination with the cost balance equation of the related exchanger in the chemical process of the two options. The exergoeconomic analysis of the process subsystems defined for the two options is performed by summing up the values of C_ d for the components constituting each subsystem to obtain the overall C_ total . Equally, the cost rate associated with CI and O&M of each subsystem is derived from the summation of Z_ calculated for the related components. f of each subsystem is then calculated. With a similar procedure, overall exergoeconomic performance of the two options are estimated by considering C_ d and Z_ of all process components and of the wind
diesel and solar
thermal subsystems, thus obtaining the overall C_ total and overall f. Fig. 6.42 illustrates the contribution of each subsystem to overall cost rate C_ total of the two options. In case of Option 1 (Fig. 6.42A), the largest percentage

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FIGURE 6.42 Percentage of total cost rate for the subsystems of (A) Option 1 and (B) Option 2 (Adapted from [325]).

(44.5%) is represented by the wind
diesel plant, followed by the H2 production subsystem with 27% portion. However, as shown in Fig. 6.42B, the CH3OH synthesis subsystem gives the highest share of C_ total in Option 2, showing a percentage of about 47%, while the wind
diesel plant contributes 30%. The exergoeconomic factors calculated for the subsystems of each option are shown in Fig. 6.43. As appears evident, the wind
diesel plant is identified in both options as the subsystem with the maximum f, that is, 100% in the case of Option 1 (Fig. 6.43A) and 91% in the case of Option 2 (Fig. 6.43B), thus indicating that the nonexergy costs associated with capital investment and O&M expenses are dominant and that replacing the wind turbines and diesel generators with more affordable ones is suggested to improve the subsystem economic performance. Conversely, for the other critical subsystems in the total cost rate analysis, f values lower than 10% are obtained, thus highlighting that the exergy_ d , are much higher than nonexergyrelated costs, that is, costs associated with Ex related costs. It is thus recommended to reduce irreversibility by increasing capital investment and O&M costs and changing out inefficient components with ones having a better exergetic performance, as highlighted in the f analysis performed for single components. Fig. 6.44 illustrates the comparison of overall exergoeconomic parameters between the two options. From this figure, Option 2 results in the more expensive scheme with C_ total equal to 1.4105 $ h21 because of the higher number of expensive components for CH3OH synthesis in addition to the larger offshore wind farm size compared to Option 1. On the other hand, Option 2 is characterized by a lower f, that is, about 31% against 48% of Option 1, thus indicating that about _ d . Consequently, in general, 70% of total costs of Option 2 are related to Ex Option 2 could offer greater opportunity for improving exergoeconomic

6.3 Case study 3

FIGURE 6.43 Exergoeconomic factor for the subsystems of (A) Option 1 and (B) Option 2 (Adapted from [325]).

performance by means of choosing more expensive components, while in Option 1 nonexergy-related and exergy-related costs should be optimized simultaneously through appropriate trade-offs.

6.3.16.3 Sensitivity analysis results A parametric study is carried out to investigate the influence of some operating variables that are common to the two options on exergy and exergoeconomic performance parameters. Given the integration of renewable plants into CH3OH production processes of the two options, wind speed and DNI are selected as variables for the sensitivity analysis.

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The sensitivity range for wind speed is chosen between 5.5 and 10 m s21, as suggested from available information about the average weather in the proximity of the selected site. By varying the value of the wind speed in input to Homer software while keeping constant the electrical load required for the CH3OH process, a change in the number of wind turbines N (and eventually of diesel generators) is expected, leading to variation of electrical power from the turbines W_ wt and diesel flowrate m_ diesel . On other hand, the variation range for DNI is suggested from the SAM tool based on the collected annual solar resource data in its database, that is, between 400 and 925 W m22. By varying the design DNI in input to the SAM tool while keeping constant the useful thermal output Q_ solar;u required for the CH3OH process and operating conditions of the thermal fluid, changes in number of collectors ncol , aperture area Aap , and thermal losses are _ col . expected, leading to variation of the exergy of solar collectors Ex Figs. 6.45 and 6.46 show the results of the sensitivity analysis on wind speed _ d of the wind
diesel plant of Option 1 variation. As illustrated in Fig. 6.45A, Ex shows a rapid drop of one order of magnitude with an increase in the wind speed from 5.5 to 8 m s21 due to the significant reduction in the number of turbines Nt from 26 to 8 (with a fixed number of diesel generators of 1), while ψ of the wind
diesel plant increases with a similar yet opposite trend from 6% to 20%. Even _ d of Option 1 follows the trend of Ex _ d with slightly higher values, the overall Ex of the wind
diesel plant, as a confirmation of the highest contribution of this subsystem to the total cost rate (evidenced in Fig. 6.42A). However, a small increase in overall ψ is found. From Fig. 6.45B, similar conclusions can be drawn _ d of the wind
diesel plant and overall scheme of Option 2, even about the Ex though Nt decreases from 70 to 27 (with a fixed number of diesel generators of 4) _ d is higher. As in and the trade-off between overall and wind
diesel plant Ex Option 1, a steady variation of ψ of the wind
diesel plant from 9% to 20% but an insignificant increase in overall ψ occur with an increase in wind speed. For both Option 1 in Fig. 6.46A and Option 2 in Fig. 6.46B, C_ total of the wind
diesel plant and overall scheme has the same increasing profile of corresponding _ d displayed in Fig. 6.45, thus indicating that the exergy-related costs largely Ex dominate over nonexergy-related costs with increased wind speed. On the other hand, f of Option 1 remains essentially unvaried while a slight decrease in f of Option 2 is observed. Figs. 6.47 and 6.48 illustrate the outcomes of the sensitivity analysis derived _ d of the solar
thermal plant from DNI change. With an increase in DNI, Ex linked to HX2 in Fig. 6.47A fluctuates over three DNI ranges, while C_ total in Fig. 6.48A progressively decreases by showing approximately constant value within the same DNI ranges. This can be explained by considering that ncol and Aap tend to gradually decrease from 8 to 6 and then to 4 in these ranges. Accordingly, the ψ of the solar
thermal plant in Fig. 6.47C slightly improves from 33% to 35%, while f in Fig. 6.48C rises to a small extent. Concerning the entire scheme, no visible effects of varying DNI emerge on both overall exergy and exergoeconomic parameters.

6.3 Case study 3

FIGURE 6.44 Comparison of overall exergoeconomic parameters between the two options (Adapted from [325]).

FIGURE 6.45 Effect of varying wind speed variation on E_ xd and ψ of the wind
diesel plant and overall scheme of (A) Option 1 and (B) Option 2.

_ d of the most relevant solar
thermal plant in Option 2 From Fig. 6.47B, Ex (i.e., that linked to HX10-HX11) decreases monotonously because of the continuous decrease in ncol and Aap over the considered DNI sensitivity range. As a result, C_ total in Fig. 6.48B decreases considerably, ψ in Fig. 6.47D improves from 44% to 46%, and f in Fig. 6.48D remains essentially unvaried. On the other hand, differently from Option 1, overall exergy and exergoeconomic performance of Option 2 appears slightly more affected by the DNI variation: C_ total clearly decreases while f increases. A critical issue related to operations at offshore areas concerns higher O&M costs compared to the onshore counterpart. Therefore since an offshore oil and gas facility is proposed as the location in the present study, another sensitivity

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FIGURE 6.46 Effect of varying wind speed on C_ total and f of the wind
diesel plant and overall scheme of (A) Option 1 and (B) Option 2.

analysis is performed that aims at investigating the influence of varying the maintenance factor on the exergoeconomic analysis results. The base value of 0.06 assumed in the assessment (Table 6.76) increases to 0.18, and the effect of this _ C_ d , C_ total , and f of the two options is analyzed. The results variation on overall Z, are shown in Fig. 6.49. As appears evident from this figure, the maintenance factor has a negative impact on the cost rates of both options leading to a linear increase of these parameters. In the case of Option 1 in Fig. 6.49A, Z_ and C_ d increase in parallel with relatively similar amounts at different maintenance factor values Despite a similar increase in rate, a trade-off of about one order of magnitude separates the values of Z_ and C_ d of Option 2 in Fig. 6.49B, over the entire maintenance factor range, with C_ d values greater than Z_ values. For both options, f barely rises with increasing maintenance factor.

6.4 Closing remarks In this chapter, three case studies were presented to prove the applicability of the novel assessment methodologies described in Chapter 5, Sustainability Index Development. Case study 1 was introduced to demonstrate the potential of the sustainability assessment methodology described in Section 5.1 for the conversion of offshore wind energy at the facility and the selling of the P2G and P2L products to the onshore market. Options 1 and 2 showed the best performance based on technical, economic, and environmental indicators and the aggregated sustainability indicator. This was also confirmed by applying different criteria and perspectives of decision makers to the elicitation of weights among indicators. A situation where P2G and P2L strategies are used to limit wind power curtailments due to grid integration appeared more favorable in the NPV assessment than the sole chemical production. Selling H2 admixture to the grid operator and CH3OH to the industry and mobility sectors, in addition to electricity to the network, gave

FIGURE 6.47 Effect of varying DNI on the E_ xd of solar plant and overall scheme of (A) Option 1 and (B) Option 2, and on the ψ of solar plant and overall scheme of (C) Option 1 and (D) Option 2. DNI, Direct normal irradiance.

FIGURE 6.48 Effect of varying DNI on C_ total of solar plant and overall scheme of (A) Option 1 and (B) Option 2, and on C_ total and f of solar plant and overall scheme of (C) Option 1 and (D) Option 2. DNI, Direct normal irradiance.

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FIGURE 6.49 Effect of increasing the maintenance factor on overall cost rates and f of (A) Option 1 and (B) Option 2.

positive NPV values. Sensitivity analysis through Monte Carlo simulations proved the robustness of these findings. Case study 2 was presented to test the sustainability assessment methodology described in Section 5.2 on alternative G2P hybrid energy options defined based on different short-term forecast horizons and Probd . In the selected time interval (3 days), the hybrid energy system in SC2, characterized by a lower forecast horizon (3 h) and higher Probd (90%), showed the best performance based on ηel, LVOE and LGHG, as well as the aggregated indicator ASI. Despite the fact that the hybrid energy system in SC3, based on a higher forecast horizon (6 h) and lower Probd (80%), exhibited the lowest LCOP indicator among the alternatives, such a system gave the worst ASI value. This was also confirmed by applying different criteria and perspectives of decision makers to the elicitation of weights among indicators, as well as using WAM and WGM methods for aggregation. Sensitivity analysis performed by varying randomly the target values used in the normalization procedure revealed a high degree of confidence in the ranking of the scenarios based on ASI. Even though the G2P offshore hybrid energy system allowed a significant reduction in the negative imbalance costs of the offshore wind power production with respect to the declared dispatching plan, the sustainability performance of the OWT farm, excluding the GT energy-balancing technologies, appeared more favorable over the analyzed period. Case study 3 was considered to prove the capability of the integrated assessment methodology described in Section 5.4, thus investigating comprehensively the sustainability and inherent safety performance of emerging process schemes for CH3OH production and addressing the most feasible ones to detailed exergy and exergoeconomic performance assessment. For a given CH3OH productivity of 500 kg h21, catalytic hydrogenation of CO2 (Scheme A) and homogeneous radical gas-phase reaction of CH4 (Scheme B) resulted in the most feasible intensified process schemes, succeeding in the preliminary screenings based on appropriate features of separation operations and a reasonable number of equipment items in

6.4 Closing remarks

the plant. The other nine schemes, still immature for the selected CH3OH benchmark, were thus excluded from the integrated assessment. Scheme A showed the best performance based on disaggregated technical, economic, environmental, and inherent safety indicators, as well as on the aggregated PrIS indicator. This was also confirmed by the application of different criteria and perspectives of decision makers to the elicitation of weights among indicators, as well as different weighting techniques. The results of the sensitivity analysis investigating the effect of random variations in target values used for the normalization of indicators on PrIS of the two schemes confirmed that the overall higher PrI level of the intensified Scheme A over Scheme B is not affected by data uncertainty. In case study 3, exergy and exergoeconomic site-specific analyses were performed integrating the two schemes with offshore renewable energy sources into P2L offshore hybrid energy options at a remote gas production platform in the Atlantic Ocean. These analyses allowed identifying that the option based on Scheme B is the most expensive system with the highest portion of thermodynamic irreversibility. However, these drawbacks were compensated for by the use of CH4 from offshore oil and gas operations at the facility, which yields multiple fuels, thus leading to improved exergy efficiency and cost savings compared to the option based on Scheme A. The effect of varying the wind speed demonstrated that the number of wind turbines drops for both options with an increase _ d and C_ total significantly decrease, while overall ψ in wind speed, thus overall Ex and f change imperceptibly. No evident influence of varying DNI emerged on both overall exergy and exergoeconomic parameters of the option based on Scheme A, while a clear decrease in C_ total appeared for the option based on Scheme B where three solar
thermal plants are linked to CH3OH process. The change in maintenance factor showed a negative impact on all the cost rates of the two options, which linearly increase, while overall f clearly increased in the case of the option based on Scheme B.

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CHAPTER

Conclusions and future directions

7

7.1 Conclusions This book covered numerous aspects of innovative power to gas (P2G), power to liquid (P2L), and gas to power (G2P) hybrid energy systems for offshore applications derived from the synergetic integration between the renewable and the oil and gas sectors: fundamentals, system modeling and analysis, multi-criteria performance assessment, and comparative studies. Four new decision-making assessment methodologies were presented to support the choice of the suitable P2G, P2L, and G2P hybrid energy systems for offshore applications in the early design phases from the sustainability and inherent safety viewpoints. The sustainability assessment methodology developed for P2G and P2L offshore hybrid energy options allows a comparison of alternative strategies at remote oil and gas production facilities in the decommissioning phase, including renewable production of H2, synthetic natural gas (SNG), CH3OH at the offshore installation, transportation to the shore, and multiple end uses at the onshore market. The sustainability assessment methodology developed for G2P offshore hybrid energy options allows assessing alternative systems composed of offshore renewable plants coupled with gas turbine (GT) technologies exploiting untapped gas resources at the offshore facility and producing electricity for injection into the close onshore grid. Both methods provide a similar concise yet representative set of indicators capturing the technical, economic, environmental, and societal aspects of sustainability. The aggregated indicator (ASI) communicating the overall sustainability profile of the strategies is defined based on process-related or renewable source-related target values for the normalization of indicators (if the compensatory approach is adopted), time-spacereceptor criteria and individualist-egalitarian-hierarchist perspectives of decision makers for weights elicitation, and two weighted mean methods for the aggregation. The inherent safety assessment methodology allows the ranking of the hazard levels of process units and of the overall facility, showing the specific safety and environmental concerns of alternative design options. The method consists of a systematic procedure that introduces innovative criteria concerning the assessment of damage to the three main target categories of potential offshore hazards (i.e., personnel, structural assets, and marine environment polluted by oil and chemical spills) and that accounts for the specific peculiarities of the offshore structures in accident modeling (i.e., high congestion, multi-level layout) and for the credibility of loss of Hybrid Energy Systems for Offshore Applications. DOI: https://doi.org/10.1016/B978-0-323-89823-2.00007-5 © 2021 Elsevier Inc. All rights reserved.

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containment from oil and gas equipment. Aggregated multi-target key performance indicators (KPIs) quantifying the overall safety fingerprinting of the alternatives are defined based on the vulnerability zones of the target of concern for the normalization of indicators and two weighted average methods for the aggregation. Some of the output KPIs can be used for both the stand-alone inherent safety performance analysis and the integration into the sustainability model developed for P2G, P2L, and G2P offshore hybrid energy options to address the societal dimension. The integrated assessment methodology combines sustainability and inherent safety analysis based on the idea of process intensification in order to investigate comprehensively the feasibility of emerging chemical process routes for the synthesis of P2G/P2L products and to orient the choice of suitable processes into P2G and P2L projects at remote oil and gas areas. The method consists of a detailed procedure to test the feasibility of intensified process flowsheets and designs through preliminary screenings and to rank the PrI level of the most feasible ones based on a set of technical, economic, environmental, and inherent safety metrics selected from the indicators defined in the sustainability and inherent safety methodologies described in this book. The aggregated indicator (PrIS) quantifying the overall performance of the options is calculated according to the approach described in the sustainability assessment methodologies. The analysis based on these metrics is the precursor to the detailed site-specific assessment of the integrated systems driven by renewable energy sources at remote offshore oil and gas sites. Three case studies are introduced to proof the effectiveness and value of the new decision-making methodologies, which offer opportunities for the valorization of offshore wind power at remote oil and gas field (case study 1) and at a depleted gas field close to the shore (case study 2), and which introduce emerging chemical production routes to be investigated for implementation in oil and gas processes at remote areas (case study 3). For all the case studies, the applied methods are able to meet the expectations depicted “a priori” in the description of the methodologies. They succeed in identifying critical alternatives from sustainability and/or safety perspectives, orienting the choice of the optimal system given the scenario under analysis. Furthermore, the applicability and flexibility of the methods are proved, since the proposed tools allow us to guarantee a sound and auditable assessment of all the different systems analyzed, capturing the peculiarities of concern and requiring the input information available at early design stages of innovative projects. When applied, sensitivity analysis based on the Monte Carlo simulation approach confirms the ability of the methods to yield robust and meaningful results. Overall, the results obtained pave the way to consolidate informed strategies for the sustainable and inherently safe exploitation of offshore renewable energies. The developed methodologies are effective in assessing and comparing alternative process schemes integrating offshore renewable energies at oil and gas fields from the perspective of sustainability and inherent safety. They represent valid support tools in orienting choices during the early design phases of the projects, allowing us to identify critical components of the analyzed schemes and also critical steps of emerging processes (e.g., reaction yield, product purity, separation, heat transfer, etc.).

7.2 Future directions

7.2 Future directions Even though the methodologies presented in this book provide systematic decision support tools based on sustainability and inherent safety indicators, some recommendations for further work are highlighted. A limited yet relevant number of indicators are defined for the sustainability assessment of alternative offshore hybrid energy options. However, the set is actually open to the addition of further indicators in view of an improved assessment. In particular, some indicators, such as “job creation” and “social acceptance,” may be investigated for their inclusion in the societal aspect, in addition to the safety-related metrics proposed in this book. Furthermore, other indicators may be considered to address the environmental domain of sustainability, for example, based on life-cycle assessment (LCA) or exergoenvironmental analysis. The panel method, composed of academic and industrial experts in the field of sustainability, may be incorporated for weights elicitation in the multi-criteria decision analysis (MCDA) aggregation approach to provide a more realistic perspective of decision makers. Similar recommendations may be applied in the case of weights among inherent safety multi-target KPIs. The sustainability assessment methodologies for P2G, P2L, and G2P hybrid energy options may be further validated by considering other offshore renewable energy sources (e.g., waves, tidal currents, solar radiations) and/or a combination of them, as well as comparing a given hybrid energy system at different locations to investigate the influence of case-specific parameters. The full application of the inherent safety multi-target KPIs methodology to alternative P2G/P2L/G2P hybrid energy options at different offshore oil and gas sites may be performed to prove the effectiveness of the proposed indicators in capturing hazards of the alternative design schemes. Process modeling using commercial simulators in the integrated assessment methodology may be further improved, by reducing the simplified assumptions made and improving the reaction/separation models adopted. The optimization procedure may further be applied to the case studies to determine the optimal values of the parameters that can maximize the overall aggregated performance metrics of the system, for example, maximizing ASI in the case of the sustainability models, minimizing inherent safety multi-target KPIs of the entire design option, maximizing PrIS in the case of the integrated assessment methodology. Finally, more advanced sensitivity techniques than the Monte Carlo
based discernibility analysis proposed in this book may be adopted to verify the uncertainty of input data and test the robustness of the results obtained in the case studies.

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Index Note: Page numbers followed by “f” and “t” refer to figures and tables, respectively.

A Accident scenarios characterization, 105
107 consequences, 47, 98 threshold values, 49t, 99
100, 236
238 Acid precipitation, 1 Active risk reduction strategies, 4
5 Aeroderivative GTs, 33
35, 90, 93
94, 157
158, 160 Aggregated performance assessment, 68
75, 97 Aggregated sustainability index (ASI), 74, 162, 196f cumulative probability, 199f values, 196 Alkaline H2O electrolysis, 22 ALOHA hazard modeling program, 50 Ammonia, 19 Analytic hierarchy process method (AHP method), 70 Anemometer, 80, 82
83, 150, 154 Annual energy production (AEP), 83
84, 154 Aspen HYSYS, 119, 256
257 simulation, 203 v10, 201
203 Aspen PLUS, 119 Aspen recycle operator, 257
258 Auto regressive (AR), 81 Auto regressive integrated moving average (ARIMA), 81 Auto regressive moving average (ARMA), 81 Automated Data Inquiry for Oil Spills (ADIOS), 50, 112
113

B Blue economy, 17
18, 52 Bombay High fire (2005), 4 Brayton cycle, 33 Brundtland Report. See Our Common Future Business cases (BCs), 139
141 Buyout fund, 141
142

C CAMEO software, 50 Capacity factor (CF), 59, 83, 154 Capital expenditure (CAPEX), 40, 75, 134, 138, 145
147. See also Operational expenditure (OPEX)

CAPEX-associated offshore sub-station, 63
64 for carbon dioxide compression, 43 removal, 43 transportation, 43 for desalination, 41 for electrolysis, 41 for hydrogen compression, 41 H2-enriched natural gas and SNG transportation, 41
42 and SNG transportation, 42 storage, 42 for methanol production, 44 for SNG compression, 44 production, 42
43 Carbon dioxide (CO2) CAPEX and OPEX for carbon dioxide compression, 43 carbon dioxide removal, 43 carbon dioxide transportation, 43 emissions, 7 supply methods, 29
30 Carbon-free fuels, 1
2 Catalytic hydrogenation of CO2, 200
201, 203
204, 203f Charge carrier, 22 Chemical Engineering Plant Cost Index (CEPCI), 236 Chemical stressors, 50 Climate change, 7 Compressed natural gas, 16 Concentrating solar power (CSP), 9, 10f Consistency, 3 Consistency index (CI), 72
73 Consistency ratio (CR), 72
73, 163 Cost balance equation, 45 for solar trough parabolic collectors
thermal storage subsystem, 267 for wind
diesel subsystem, 265 Credit factors, 102, 110, 248 assignment to release modes, 102
105 ranges, 106t Cumulative distribution function (CDF), 88, 156
157

301

302

Index

D Damage parameters calculation, 107
108 levels, 110 Decision-making methodologies, 278 Delayed Recovery Scenario, 7 Desalination CAPEX and OPEX for, 41 reverse osmosis, 38 sea H2O, 20, 22 Dimethyl ether, 19 Direct normal irradiance (DNI), 256
257 Discernibility analysis, 125 Dispatched power, 88, 159, 165, 177 Dispatching error, 88
89, 156
157, 167
168 Dispatching power plan, 87
89, 156
159 maximum power, 159t probability distributions of prediction error, 157t, 158t DNV GL program, 50 Domino escalation thresholds, 50

E Economic analysis, 37, 40
45 CAPEX and OPEX for carbon dioxide compression, 43 for carbon dioxide removal, 43 for carbon dioxide transportation, 43 for desalination, 41 for electrolysis, 41 for H2-enriched natural gas and SNG transportation, 41
42 for hydrogen and SNG transportation, 42 for hydrogen compression, 41 for hydrogen storage, 42 for methanol production, 44 for methanol storage, 44 for methanol transportation, 44
45 for SNG production, 42
43 for synthetic natural gas compression, 44 Economic performance assessment, 65
67, 95
96 Economic sustainability, 2 Ecotoxicity, 50 Electrical grid end-use, 35 Electrification, 15 Electrochemical reduction of CO2, 205
207 Electrodialysis, 22, 60t Electrolysis, 20
21 CAPEX and OPEX for, 41 of GHG emissions, 47 water, 19, 22 Electrolyte, 22 Electrolyzers, 22, 41

CAPEX and OPEX, 147 production capacity, 59 Electrosynthesis, 226
228 ELimination and Et Choice Translating REality (ELECTRE), 74
75 Emission trading scheme (ETS), 94, 135
138 Encyclopedia of Life Support Systems, 2 Energy, 1
2 analysis, 37
39 balance equation, 81
82 extraction mechanism, 8
9 security, 7 Enhanced gas recovery (EGR), 30 Enhanced oil recovery (EOR), 30 Environmental compartments, 48 Environmental impact analysis, 47 Environmental performance assessment, 67
68, 97 Environmental sustainability, 2 European Commission/Centre for Medium-range Weather Forecasts (ECMWF), 81
82, 151
152 European Marine Energy Centre (EMEC), 13
14, 13f Event trees, 105 for releases above water level, 104f for releases below water level, 105f Exergetic coefficient of performance (ExCOP), 259, 263 Exergoeconomic analysis, 37, 45
47 Exergoeconomic factor, 67, 263, 268, 269f Exergoeconomic performance assessment, 67 Exergoeconomics analysis results, 263
269, 266t Exergy analysis, 39
40 results, 257
263 efficiency of solar
thermal subsystem, 261 of wind
diesel subsystem, 261 rate of solar collectors, 261 Exhaustivity, 3

F Feed-in tariff (FIT), 155
156 Fischer-Tropsch fuel, 19 Fluid, 48 compressible, 26 maximum outlet temperature, 258 power, 38 thermal transfer, 9
10 Forecast power, 35, 85
87, 90, 159, 165 Fossil fuels, 1
2, 7, 15, 19 Front-end engineering and design (FEED), 4
5

Index

Froude similitude, 86 Fuel(s), 45 carbon-free, 1
2 cells technology, 221
226 fossil, 1
2, 7, 15, 19

Green decommissioning, 17
18, 52 Green processes for CH3OH production, 20
21 Greenhouse gas emissions (GHG emissions), 47, 135
138, 159, 177
194 Guthrie method, 236

G

H

Gas grid injection end-use, 31 power to hydrogen, 25 power to synthetic natural gas, 31 Gas to power (G2P), 16, 38, 40 dispatching power plan, 87
89 electrical grid end-use, 35 gas turbine park, 89
94 technologies, 33
35 generalities, 77
78 hybrid energy system, 277, 279 offshore hybrid energy systems, 21, 150
199, 197f offshore oil and gas site and renewable energy, 78
79 offshore site, 79t ranking of alternatives and sensitivity analysis, 97
98 renewable energy data, 79
82 selection of converter and characterization of power plant, 82
87 characterization of renewable power plant, 85
87 selection of renewable energy converter, 82
85 sustainability assessment methodology for G2P systems, 77
98 sustainability performance indicators, 94
97 Gas transportation, 16 Gas turbines (GTs), 15, 19, 77 comparison of commercial GT technologies in simple cycle, 34t park, 89
94 estimation of hourly fuel consumption and GHG emissions, 94t management, 90
94 power ranges for operating gas turbines, 93t sizing of, 156
159 technologies, 33
35, 150 economic data, 161t technical and economic parameters, 197t General NOAA Operational Modeling Environment (GNOME), 50
51, 111
112 Global climate change, 1 Global energy demand, 7 Global energy system, 7

Hazard index (HI), 108, 110 Hazardous substances, 6 High-voltage alternating current transmission (HVAC transmission), 14 Higher heating values (HHVs), 64
65, 132 Homogeneous catalysis in solution, 213, 213f Homogeneous radical gas-phase reaction, 207
208 Hybrid energy systems, 16
17, 37 Hybrid Optimization Model for Multiple Energy Resources (Homer), 256, 265
267 Hydrocarbon era, 1 Hydrogen (H2), 19 CAPEX and OPEX for hydrogen compression, 41 hydrogen storage, 42 gas grid injection end-use, 25 comparison between sea H2O desalination technologies, 24t comparison of H2O electrolysis technologies, 23t example of diameters of pipeline delivering pure H2, 26t power to hydrogen, 22
27 hydrogen production methods, 22 seawater desalination methods, 22
25 Hydrogen-enriched natural gas (HENG), 25 CAPEX and OPEX for HENG transportation, 41
42

I Independency, 3 Indicator-based methodologies, 55 Industrial Revolution, 1, 7 Inherent safety analysis, 47
51 threshold values of accident scenarios for each offshore target, 49t assessment methodology, 98
116 accident scenarios, 105
107 assignment of credit factors to release modes, 102
105, 106t classification of units and identification of release modes, 101
102 damage parameters, 107
108

303

304

Index

Inherent safety (Continued) design options and characterization of targets, 98
101 functional categorization for equipment of offshore production oil and gas facilities, 101t generalities, 98 input data, 100t ranking of alternatives and sensitivity analysis, 116 reference vulnerability areas, 100t unit inherent safety KPIs, 108
115 and environmental protection concepts, 4
6, 4f Innovative hybrid energy options. See also Offshore renewable energy resources gas to power, 33
35 general scheme of offshore hybrid energy systems, 20
21 power to hydrogen, 22
27 power to methanol, 31
32 power to synthetic natural gas, 27
31 Integrated assessment methodology, 116
125, 278 application of detailed site-specific assessments, 124
125 generalities, 116
117 intensified process flowsheet, 118
119 ranking of alternatives and sensitivity analysis, 124 reference process schemes, 118 scale-up and preliminary design of equipment units, 119
122, 120t screening indicators, 122
123 Intensification, 5 Intensified process flowsheets, 118
119, 201
204 for biocatalysis, 217f catalytic hydrogenation of CO2, 203
204, 203f for electroreduction of CO2, 205f for electrosynthesis, 231f energy streams, 205t, 207t, 209t, 212t, 216t, 220t, 224t, 226t, 228t, 231t, 234t, 240t for fuel cell technology, 228f homogeneous catalysis in solution, 213f for homogeneous radical gas-phase reaction, 207f for low-temperature heterogeneous catalysis, 209f material streams, 204t, 206t, 208t, 210t, 214t, 218t, 222t, 225t, 227t, 229t, 232t, 239t for plasma technology, 221f for supercritical water technology, 226f International Electrotechnical Commission (IEC), 82
83

J Job creation, 279

K Key performance indicators (KPIs), 5
6

L LennRO module, 41 Levelized cost of energy (LCOE), 95
96, 132 Levelized cost of product (LCOP), 65
66, 70, 122, 134, 139 Levelized GHG emissions (LGHG), 67
68, 138 Levelized value of energy (LVOE), 95
96, 160 Levelized value of product (LVOP), 65
66, 70, 134
135, 139, 144 Life-threatening, 1 Liquefied natural gas (LNG), 16. See also Synthetic natural gas (SNG) Low-temperature heterogeneous catalysis, 209
213

M Macondo blowout (2010), 4 Matching of power curves, 165
168 Measurability, 3 Mechanical vapor compression, 22 Membrane distillation, 22 Membrane-based biocatalysis, 213
217 Methanol (CH3OH), 19, 200, 202t, 203, 207, 213
217 CAPEX and OPEX for methanol production, 44 methanol storage, 44 methanol transportation, 44
45 economy, 20
21 power to methanol industry and mobility sectors end-use, 32 methanol production methods, 31
32 preliminary screening, 235t, 237t production routes for P2L offshore hybrid energy systems driven by wind and solar energies, 200
272 cost segments of total capital investment and annual production costs, 244t, 246t credit factors, event trees, damage distances, and HHI, 249t electrochemical reduction of CO2, 205
207 electrosynthesis, 226
228 fuel cells technology, 221
226 geometric and economic data of units, 242t homogeneous catalysis in solution, 213

Index

homogeneous radical gas-phase reaction, 207
208 intensified process flowsheets, 201
204 low-temperature heterogeneous catalysis, 209
213 membrane-based biocatalysis, 213
217 photocatalysis, 217
221 plasma technology, 217 reference process schemes, 200
201 screening of intensified flowsheets, 228
247 supercritical water oxidation technology, 221 target values assumed for normalization of disaggregated indicators, 252t technical, economic, and environmental data, 248t values of disaggregated screening indicators, 251f weights among indicators, 253t Methyl trifluoroacetate (MTFA), 200 Moderation, 5 Monte Carlo simulation approach, 125, 146
147, 198, 278 Monte Carlo
based discernibility analysis, 279 ranges assumed for key uncertain parameters, 148t Multi-criteria decision analysis (MCDA), 3, 55, 74, 127 Multieffect distillation, 22 Multistage flash distillation, 22 Multi-target methodology, 98 Multi-target performance assessment, 113
115

N Natural gas hydrate, 16 reservoirs, 30 sources, 15
16 Net present value (NPV), 75, 139
141 alternatives, 144
145, 146f cumulative probability, 149f Tornado charts from sensitivity analysis, 146f Nondimensional indicator, 69 Nordex N90/2500 offshore wind turbine, 154 power curve and Cp trend for, 155f technical data of, 154t Normalization, 68
69 of disaggregated indicators, 123 procedure, 114 Noxious substances, 6

O Office of Gas and Electricity Markets (OfGem), 135
138 Offshore hybrid energy systems, 20
21

Offshore oil and gas site and evaluation of options, 127
131 features and operating conditions of components, 130t, 131t, 133t P2G and P2L routes for, 129f Offshore oil and gas site and renewable energy, 58 Offshore oil and gas site and renewable power plant, 150
156 forecast wind speeds, 152f, 153f single pricing mechanism, 156t wind speed, 151f Offshore P2G hybrid energy systems, 20, 20f, 21f Offshore P2L hybrid energy system, 20
21 Offshore renewable energy resources, 7
8 challenges of, 14 offshore wind energy, 8
9 opportunities for exploitation of, 15
18 gas valorization options, 17f solar energy, 9
11 tidal currents energy, 12
14 wave energy, 11
12 Offshore renewable power plant, 59
64 Offshore wind energy, 8
9 Offshore wind turbines (OWTs), 8
9, 9f economic data, 135t, 161t farm, 127
199 hourly power data of, 169t, 171t, 173t, 175t technical data, 134t OfGem. See Office of Gas and Electricity Markets (OfGem) Oil Spill Contingency and Response (OSCAR), 50 Oil spills, 6, 48 chemical releases and, 110 consequences, 48 hazard, 6 Oil Weathering Model (OWM), 50 Onshore renewable energy resources, 7 Operation and maintenance cost (O&M cost), 40 Operational expenditure (OPEX), 40, 75, 134, 138, 145
147. See also Capital expenditure (CAPEX) for carbon dioxide compression, 43 removal, 43 transportation, 43 for desalination, 41 for electrolysis, 41 for H2-enriched natural gas and synthetic natural gas transportation, 41
42 for hydrogen compression, 41 storage, 42 and synthetic natural gas transportation, 42 for methanol production, 44

305

306

Index

Operational expenditure (OPEX) (Continued) for synthetic natural gas compression, 44 production, 42
43 Our Common Future, 2

P Panel method, 279 Parametric study, 269 Part-load efficiency, 90, 92t Partial compensatory aggregation approach, 69 Passive risk reduction strategies, 4
5 Pelamis, 12 Petela
Landsberg
Press factor, 261 Photocatalysis, 217
221 Photovoltaics (PV), 9 Physical exergy, 39
40 Piper Alpha explosion (1988), 4 Plasma technology, 217 Pollution, 7 marine, 6 seafloor and shoreline, 48 Potential hazard index (PI), 108
109 Power gas to power, 33
35 power to hydrogen, 22
27 power to methanol, 31
32 power to synthetic natural gas, 27
31 Power to gas (P2G), 19, 38, 40 hybrid energy system, 19, 277
279 offshore hybrid energy systems, 127
149 P2G-H2 systems, 51f, 53 P2G
SNG systems, 52f, 53 profitability performance indicators, 75
76 ranking of alternatives and sensitivity analysis, 76
77 recommendations for assessment of technology options of process stages, 60t sustainability assessment methodology for, 56
77, 57f evaluation of alternative strategies and assessment of technology options, 58
59 generalities, 56
57 input data for process schemes definition in, 62t input data for renewable plant definition, 63t offshore oil and gas site and renewable energy, 58 reference process schemes and offshore renewable power plant, 59
64 sustainability performance indicators, 64
75 aggregated performance assessment, 68
75 economic performance assessment, 65
67

environmental performance assessment, 67
68 evaluation matrix, 73t exergoeconomic performance assessment, 67 heating values and standard chemical exergies, 65t pair-wise comparison matrix, 72t scoring based on time, space, receptor criteria, 71t societal performance assessment, 68 technical performance assessment, 64
65 tradeoff of indicator, 72t values of random index, 73t Power to liquid (P2L), 19, 38, 40 flow diagram, 255f, 256f hybrid energy system, 277
279 offshore hybrid energy systems, 127
149 P2L
CH3OH systems, 53 profitability performance indicators, 75
76 ranking of alternatives and sensitivity analysis, 76
77 recommendations for assessment of technology options of process stages, 60t sustainability assessment methodology for, 56
77, 57f evaluation of alternative strategies and assessment of technology options, 58
59 generalities, 56
57 input data for process schemes definition in, 62t input data for renewable plant definition, 63t offshore oil and gas site and renewable energy, 58 reference process schemes and offshore renewable power plant, 59
64 sustainability performance indicators, 64
75 aggregated performance assessment, 68
75 economic performance assessment, 65
67 environmental performance assessment, 67
68 evaluation matrix, 73t exergoeconomic performance assessment, 67 heating values and standard chemical exergies, 65t pair-wise comparison matrix, 72t scoring based on time, space, receptor criteria, 71t societal performance assessment, 68 technical performance assessment, 64
65 tradeoff of indicator, 72t values of random index, 73t Powerbuoy, 12 Prediction errors, 88, 156

Index

Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE), 74
75 PROMETHEE II method, 144 Pressurized PEM electrolysis, 22 Probability density function (PFD), 88, 151 Procedural strategies, 4
5 Process hazard analysis software (PHAST), 50 Process intensification (PrI), 116, 118
119, 201
203 Process intensification screening (PrIS), 251, 253f cumulative probability of difference, 254f indicator, 123 Producer price index (PPI), 134 Profitability assessment assumptions for, 139
142 ranking of alternatives, 145t results, 142
145 technical, economic, and environmental indicators, 143t performance indicators, 75
76 Project lifecycle, 4
5 Proton exchange membrane (PEM), 22, 38 Purchase equipment cost (PEC), 263
264

Q Quantitative risk assessment (QRA), 98
99

R Random index (RI), 72
73 Ranking of alternatives and sensitivity analysis, 76
77, 97
98, 116, 124 Real power, 86, 165 Reference process schemes, 59
64, 118, 132, 200
201 Renewable energy exploitation, 7 generators, 16
17 selection of renewable energy converter, 82
85 systems, 1
2 Renewable H2, 22 Renewable heat incentive (RHI), 135
138 Renewables obligation (RO), 141
142 Renewables obligation certificates (ROCs), 141
142 Reverse osmosis, 22, 47 desalination, 38 units, 258

S Sabatier reaction, 27 Safety assessment methodology, 277
278

Scaling, 68
69 factor, 85
86 laws, 85
86 Screening indicators, 122
123 of intensified flowsheets, 228
247 Sea states, 80
81 Seawater desalination methods, 22
25 Sedimentation, 48 Sensitivity analysis, 278 results, 145
149, 198
199, 253
254, 269
272 techniques, 125 Siemens Silyzer 200 stack, 38 Silyzer 200 PEM electrolyzer, 258 Simplicity, 3, 151 Simplification, 5 Simulating WAves Near shore (SWAN), 81
82 Single-value metrics, 74, 77 SINTEF, 50 Site-specific assessment results, 254
272 composition of input raw gas, 259t exergoeconomics analysis results, 263
269, 266t exergy analysis results, 257
263, 260t sensitivity analysis results, 269
272 Social acceptance, 279 Societal performance assessment, 68, 97 Societal sustainability, 2 Solar cells, 10
11 Solar CSP plants, 9
10, 10f Solar energy, 9
11 Solar radiations, 9 Solar
thermal plants, 256
257, 257t Sole damage parameter, 108 Stated Policies Scenario, 7 Stratospheric ozone depletion, 1 Strengths, weaknesses, opportunities, and threats technique (SWOT technique), 37, 51
54 for offshore G2P hybrid energy systems, 52f for offshore P2G-H2 hybrid energy systems, 51f for offshore P2G-SNG hybrid energy systems, 52f Substitution, 5 Supercritical water oxidation technology, 221 Sustainability, 2
4, 3f Sustainability assessment methodology, 277, 279. See also Integrated assessment methodology assumptions for, 132
139 gray and green market prices, 137t scores and importance weights, 140t technical, economic, and environmental data of process stages, 136t

307

308

Index

Sustainability assessment methodology (Continued) values of currency conversion rates and price indices, 137t weights among indicators, 140f for G2P systems, 77
98 for P2G and P2L systems, 56
77, 57f results, 142
145, 168
198, 248
252 hourly positive and negative power imbalances and revenue and costs, 178t, 180t, 182t, 184t hourly revenues from electricity selling, greenhouse gas emissions costs and OPEX of gas turbine park, 186t, 188t, 190t, 192t technical, economic, environmental parameters, 194t, 195f Sustainability index development assessment methodologies of offshore hybrid energy systems, 55f inherent safety assessment methodology, 98
116 integrated assessment methodology, 116
125 sensitivity analysis techniques, 125 sustainability assessment methodology for G2P systems, 77
98 sustainability assessment methodology for P2G and P2L systems, 56
77 Sustainability performance indicators aggregated performance assessment, 97 calculation of, 94
97 economic performance assessment, 95
96 environmental performance assessment, 97 societal performance assessment, 97 technical performance assessment, 95 Sustainable emission-free renewables, 7 Sustainable technology, 2 Synergy of offshore renewable production, 17
18 Synthetic natural gas (SNG), 19, 27, 38, 127 CAPEX and OPEX for SNG compression, 44 SNG production, 42
43 power to SNG, 27
31 carbon dioxide supply methods, 29
30 comparison of methanation technologies, 28t gas grid injection end-use, 31 synthetic natural gas production methods, 27
29 transportation CAPEX and OPEX for HENG and, 41
42 CAPEX and OPEX for hydrogen and, 42 System Advisor Model (SAM), 256
257 System modeling and analysis economic analysis, 40
45

energy analysis, 37
39 environmental impact analysis, 47 exergoeconomic analysis, 45
47 exergy analysis, 39
40 inherent safety analysis, 47
51 SWOT analysis, 51
54 Systematic methodology, 98

T Target values for normalization of disaggregated indicators, 162, 162t Technical performance assessment, 64
65, 95 Technological sustainability, 2 Thermal transfer fluid, 9
10 Thermodynamic analysis, 37 Tidal currents energy, 12
14 Tidal energy converters (TECs), 13
14, 13f Tradeoff weights, 70 Transmission system operator (TSO), 35 Triethylene glycol (TEG), 254

U Unit inherent safety KPIs, 108
115 calculation of facility inherent safety KPIs, 115 multi-target performance assessment, 113
115 performance assessment for assets, 109 humans, 109 marine environment, 110
113

V Valley-filling techniques, 19 Vertical-axis wind turbine prototypes, 8
9

W Water (H2O), 19 Wave Dragon, 12 Wave energy converters (WECs), 12, 12f Wave energy sector, 12
13 Wave Model (WAM), 81
82 WAVEWATCH III, 81
82 Weibull probability density function, 80 Weighted arithmetic mean (WAM), 73, 196 Weighted geometric mean method (WGM method), 73, 196 Weighting, 70 Weymouth equation, 26 Wind, 8 shear logarithmic law, 80 wind
diesel plants, 256
257, 257t