Sea Water Desalination in Microgrids 3030966771, 9783030966775

Citation preview

Green Energy and Technology

Enrique Rosales-Asensio Francisco José García-Moya David Borge-Diez Antonio Colmenar-Santos

Sea Water Desalination in Microgrids

Green Energy and Technology

Climate change, environmental impact and the limited natural resources urge scientific research and novel technical solutions. The monograph series Green Energy and Technology serves as a publishing platform for scientific and technological approaches to “green”—i.e. environmentally friendly and sustainable—technologies. While a focus lies on energy and power supply, it also covers “green” solutions in industrial engineering and engineering design. Green Energy and Technology addresses researchers, advanced students, technical consultants as well as decision makers in industries and politics. Hence, the level of presentation spans from instructional to highly technical. **Indexed in Scopus**. **Indexed in Ei Compendex**.

More information about this series at https://link.springer.com/bookseries/8059

Enrique Rosales-Asensio · Francisco José García-Moya · David Borge-Diez · Antonio Colmenar-Santos

Sea Water Desalination in Microgrids

Enrique Rosales-Asensio Department of Electrical Engineering University of Las Palmas de Gran Canaria Las Palmas, Spain David Borge-Diez Department of Electrical, Systems and Automation Engineering University of Leon León, Spain

Francisco José García-Moya Department of Electrical, Systems and Automation Engineering University of Leon León, Spain Antonio Colmenar-Santos Department of Electrical, Electronic, Control, Telematic, and Applied Chemistry Engineering National Distance Education University (UNED) Madrid, Spain

ISSN 1865-3529 ISSN 1865-3537 (electronic) Green Energy and Technology ISBN 978-3-030-96677-5 ISBN 978-3-030-96678-2 (eBook) https://doi.org/10.1007/978-3-030-96678-2 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

The synergies between water, energy, and food sectors are obvious—although often planned independently—that is one of the identified catalysts for achieving the United Nation’s Sustainable Development Goals. This fact lowers the opportunity to maximize the positive impacts when developing joint solutions of companies or legislators. Climate change conditions are especially dramatical in islanded and water-scarce environments, where the restrictions of water, energy, and food provoke shortages and high costs. The book explored the feasibility of a dual microgrid based on energy electricity from renewable energies joint to a desalination plant to offer a sustainable-oriented solution to tackle water scarcity in isolated regions. This book proposes innovation approaches to improve the economic competitiveness of the designed microgrid in the liberalized market. The book explored its feasibility and analyzes how to improve desalination process and how to site and size the facilities to maximize the operation. It investigates the microgrid from an economic, environmental, legal, and technological point of view. The aim is not only to feed the desalination process, but also to provide the power grid with clean energy, taking advantage of the electricity production surplus. One of the main matters that the book deals with is to answer how a desalination microgrid scheme is economically and technologically sustainable in a water-scarce region. It developed a method to locate the facilities and used sustainability tools to describe synergies between systems. Alternative renewablebased energy plans affect a designed sustainability index that models how stressed is the related system—in this book, focused in a region at high water scarcity risk. The results presented have as an aim to be, itself, a technology transfer asset and to provide a potential economic and social benefit by launching to the market either a new product, a new process, or a new service, and opening a wide range of possibilities of new sustainable business models alongside the microgrid. In this sense, results obtained could be useful for companies with a TRL 6—technology demonstrated in relevant environment—by exploring and assessing the technical feasibility and commercial potential of a breakthrough innovation. Those could obtain public funds from SME phase II or similar to include some of the proposed ideas for a rapid

v

vi

Preface

business deployment. Besides, this book generates social value as it is an activity that benefits civil society and its interest groups. Las Palmas, Spain León, Spain León, Spain Madrid, Spain

Enrique Rosales-Asensio Francisco José García-Moya David Borge-Diez Antonio Colmenar-Santos

Acknowledgements The authors acknowledge the financial support provided by the Cabildo de Tenerife (through the Agustín de Betancourt Program) to conduct activities for the research project titled “Huertos comunitarios autosuficientes en zonas áridas: Reducción del coste normalizado de la energía y eliminación de la dependencia hídrica a través de energías renovables.” The authors confirm that this work has not spread any type of information that could undermine the knowledge protection generated in the aforementioned project.

Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1

2 Revision of Decision/Making Tools for Sustainable Planning and Proposal of a Novel Conceptual Framework for the Energy-Water-Food Nexus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Definition of Frameworks to Model Nexus Interactions . . . . . . . . . . 2.3 Review of Integrated Planning Tools . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Conceptual Framework Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

11 11 12 14 16 19 20

3 Comprehensive Assessment of Gran Canaria Food-Energy-Water Nexus with GIS-Based Tool . . . . . . . . . . . . . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Material and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Fuel and Electricity Price Volatility . . . . . . . . . . . . . . . . . . . . . 3.2.2 Sustainability Index Depiction . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.3 Alternative EEG-Plans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.4 Hydro-Pumping Supporting Strategy . . . . . . . . . . . . . . . . . . . . 3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 WEF Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Analysis and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

23 23 27 27 28 30 31 33 35 36 39 40

4 Review of Wind Energy Technology and Associated Market and Economic Conditions in Spain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Material and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

45 45 46 46 50

vii

viii

Contents

4.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

53 59 60

5 Photovoltaic Self-consumption and Net-Metering: Measures to Remove Economic Non-market Failure and Institutional Barriers that Restrict Their Use in Spain . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Material and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Analysis of the Regulatory Framework . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

63 63 67 68 73 79 80

6 Surrogate Optimization of Coupled Energy Sources in a Desalination Microgrid Based on Solar PV and Wind Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 Material and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.1 Software Framework for the Analysis . . . . . . . . . . . . . . . . . . . 6.5 General Description of the Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6 Black-Box Method and DYCORS Technique . . . . . . . . . . . . . . . . . . . 6.6.1 Desalination System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6.2 Electrochemical Storage Model . . . . . . . . . . . . . . . . . . . . . . . . 6.6.3 Costs, Benefits, Risks, Uncertainties, and Timeframes to Evaluate the Attributes of Energy Technologies . . . . . . . . 6.6.4 Expenditures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6.5 Revenues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6.6 Criteria Decision Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6.7 Description of the Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6.8 Desalination System and Reservoir Data . . . . . . . . . . . . . . . . 6.7 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

99 99 99 100 100 101 106 111 112

7 Reduction of Water Cost for an Existing Wind Energy-Based Desalination Scheme: A Preliminary Configuration . . . . . . . . . . . . . . . . 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Material and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Calculation of CCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.1 RO Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.2 Energy System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.3 Direct Capital Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.4 Indirect Capital Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

119 119 124 124 124 125 125 125

85 85 85 87 88 88 91 93 95 98

Contents

7.3.5 Total Capital Investment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4 Running Expenses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.1 IC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.2 Labor and Maintenance Costs . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.3 Contributions, Guarantee of Compensation for Specified Losses, and Reduction in the Value of Assets with the Passage of Time . . . . . . . . . . . . . . . . . . . . . 7.4.4 Total Fixed Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.5 VCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.6 Total Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.7 Cost of Water Representation . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

ix

125 126 126 126

127 127 127 127 128 128 130 131

8 Stress Mitigation of Conventional Water Resources in Water-Scarce Areas Through the Use of Renewable Energy Powered Desalination Plants: An Application to the Canary Islands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Material and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

137 137 140 144 149 150

9 Feasibility Analysis of Wind and Solar Powered Desalination Plants: An Application to Islands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Material and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.1 RO Desalination Plant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.2 The Aqua.Abib Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3 Analysis of Commercialization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4.1 Techno-economic Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

155 155 158 160 161 162 164 166 174 175

Appendix A: Review of Evaluated Nexus Tools . . . . . . . . . . . . . . . . . . . . . . . 179 Appendix B: Desalination Capacity, Technologies, and Location in Gran Canaria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187 Appendix C: Restrictions and Results of the Geo-morphological Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 Appendix D: Impacts of Energy Sources, Competitiveness of Food Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193

x

Contents

Appendix E: Locations of Proposed Sites . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197 Appendix F: Data Regarding Spanish Solar PV Sector . . . . . . . . . . . . . . . . 203 Appendix G: Project Cash Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 Appendix H: Parameters of Simulation, Characteristics of Grants, and Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219

Symbols, Abbreviations, and Acronyms

ρ t a AEU AI As ASAI ASR AWU BAT BBGSOTs BC BCS BEU BP BU BWU C(wind) C→E CAIDI CAPEX CCG CCs CCt CF CF CHEMAD CI CLEWS CO2 COE

Water density (kg/m3 ) Increase of time in hours Depreciation factor Acceptable emissions use Artificial intelligence Alternative scenario Average service availability system Acceptable stress resource Acceptable water use Best available technique Black box global stochastic optimization techniques Brine concentrate Blade control system Baseline emission use Booster pump Baseline use Baseline water use Interpolation function from the normalized power Cost per energy Customer average interruption duration Capital expenditure Combined cycle gas station Capital costs Capital costs Cartridge filter Conceptual framework Chemical addition Cost index Climate, land, energy, and water strategy Carbon Dioxide Energy cost xi

xii

coeur COW COW CR CRF CS DC DER DP dput DRES DS DWEER DYCORS E E→E EB EBITDA EC ED EEG EFSA EIB EIPF EP ER ERD ES ESC ESIF ESP ETI EU FB FCC FCI FE FIP FIT FM FP FWC G GA GC

Symbols, Abbreviations, and Acronyms

Euro cent Cost of water Water cost Country risk Cost recovery factor Compensation scheme Dechlorination Distributed energy resources Desalination plant Pumping units required for each available power value Distributed renewable energy source Deployment scenario Dual work exchanger energy recovery DYnamic COordinate search using Response Surface models Energy Emissions for energy Energy balance Earnings before interest, taxes, depreciation, and amortization European Council Energy demand Energy electricity generation European Food Standards Agency European Investment Bank Economic and industrial policy framework Entry point Energy recovery Energy recovery device Energy sector Energy storage capacity European Structural and Investment Fund Energy storage potential Education, training and information European Union Financial/budgetary Fuel consumption cost Fixed capital investment Food and energy Feed-in premium Feed-in tariff Financial market Feed pressure Feed water concentration Gravitational acceleration (9.8 m/s2 ) Genetic algorithm Geographical coordinate

Symbols, Abbreviations, and Acronyms

GCC GDP GF GHG GIS GVC GWh H Ha Hm3 HPP i IC ICT IDAE idx2 IEC Ieff IEU Inhab Irradiation IS IWU K kcal KPI kW kWh L→E LCOE LCW LEAP LGs LI M&S Me M2 M3 MB MED MENA MF MFE MINLP

xiii

Generation control center Gross domestic product General feed Greenhouse gas Geographic information systems Global value chain Gigawatts per hour Head (m) Hectare Cubic hectometer High pressure pump Each element of each water array Wideal Interest on capital Information and communications technology Institute for the Diversification and the Energy Savings Vector indexes of power mismatch International Electrotechnical Commission Effective investment rate Incremental emission use Inhabitant Solar irradiation profile Information system Incremental water use Incoming water flow/outgoing water flow ratio of the reverse osmosis process Kilocalories Key performance indicator Kilowatts Kilowatts per hour Earth for energy Levelized cost of electricity Levelized cost of water Long-range energy alternatives planning system Loan guarantees Land index Marshall and Swift Million euro Square meters Cubic meter Market-based Multiple-effect distillation Middle East and North Africa Media filter Market, financial and economic Mixed integer nonlinear programming

xiv

MPHS MRIT MS MSF MuSIASEM MW n n Ndesalination,t NDP NOH NORAM NOx NPV NQS NREAP NS NSC NT O&M OA OCs OIC OMEL P P&DI p.u. Paux,t Pavailable,t PF PGC PHES PIF Pnaux,t Pnominal Pnormalized,t Pnot_used,t PP ppp PSI Psolar PTC Punitary,t PV

Symbols, Abbreviations, and Acronyms

Micro Pumping Hydro Storage Maintenance, replacement, insurance and taxes Member states Multistage flash Multi-Scale Integrated Analysis and Ecosystem Metabolism Megawatts Number of years Plant life Number (integer) of pumping units National development plan Number of operating hours North American Nitrogen oxide Net present value Neutral quota scheme National Renewable Energy Action Plan Nexus sector National support scheme Water, energy, food nexus tools Operating and maintenance Optimization algorithm Operating costs Onshore installed capacity Iberian Energy Market Operator Pressure Policy and data input Per unit Power of auxiliary systems Vector of available power Project financing Power generation capacity Pumping Hydro Energy Storage Private investment fund Rated power of a pump Power that will be adjusted by the software optimization procedure (desalination plant) Normalized production Power surplus vector Planning phase Parts per million Policy support instrument Solar production Production tax credit Minimum unit power that can be processed Photovoltaic

Symbols, Abbreviations, and Acronyms

PW Pwind RBD REE RENS REP RES RO RODP ROS RPPs Rt SAIDI SAIFI SCM SEC SME SoC SS SWOT SWRO t TCI TDC TEC TER TFC TGCs TPP TRL TSD UN US V VC VC VSD VT W W&E W→E W→F WACC WB

xv

Potable water Production of electricity due to wind turbines Rotor blade diameter Red Eléctrica España S.A.U. (Spanish Transmission System Operator) Reference energy system Renewable energy production Renewable energy sources Reverse osmosis Reverse osmosis desalination plant Reverse osmosis system Renewable power plants Water ratio produced per power unit System average interruption duration index System average interruption frequency index Supply chain management Specific energy consumption Small and medium enterprise State of charge Single state Strengths, Weaknesses, Opportunities, Threats Sea water reverse osmosis Tones Total capital investment Total delivery cost Ecologic transition Technical, economic, and regulatory Total fixed cost for the annual operation Tradable green certificates Total permeate production Technology readiness level Total solids dissolved United Nations United States Volume to be stored (m3 ) Vapor compression Variable cost Variable speed drive Vapor turbine Watts Water and electricity Water for energy Water for food Weighted average cost of capital Water body

xvi

WC Wc WCC WDC WEAP WEF WEF Wflow WI Wideal,t WLES WP WPPs WR WSoc WT Ww

Symbols, Abbreviations, and Acronyms

Wind class Working capital Well construction cost Waste disposal cost Water evaluation and planning Water Energy Food Water, energy and food Water flow Water index Water production vector Water, land and energy system Wind power Wind power plants Water requirement State of charge of the deposit Wind turbine Water withdrawal

List of Figures

Fig. 1.1 Fig. 2.1

Fig. 2.2

Fig. 2.3

Fig. 3.1 Fig. 3.2 Fig. 3.3 Fig. 3.4 Fig. 3.5 Fig. 4.1 Fig. 4.2 Fig. 4.3 Fig. 4.4

Proposed design of a desalination microgrid. Source Own elaboration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Different levels of exhaustiveness in the analysis of the impact of the water, energy, and food nexus. Source Own elaboration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Estimation of the implications of water, land, emissions and cost of the energy policy evaluated. Source Own elaboration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Adoption of policy and data input to assess the water, land, emissions and cost likely consequences of the energy policy decisions examined for incorporation into a context-specific global index. Source Own elaboration . . . . . . . . . . . . . . . . . . . . . . WEF (water, energy, food) nexus. Source Own elaboration . . . . . Strategy for a renewable-PHES EP GIS analysis. Source Own elaboration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . WB, RO-DP, and fossil-fuel based power plants allocation. Source Own elaboration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Location sitting proposal. Source Own elaboration . . . . . . . . . . . Proposed control system of the WE nexus. Source Own elaboration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Progress of the portion of each IEC wind class by OIC. Source [14] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Evolution of wind power installed year by year in Spain (in MW). Source [21] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . New wind energy capacity installed in the EU-28 during 2015 (MW). Total 12,800.2 MW. Source [22] . . . . . . . . . Weights for the main components of the indicators. Source Own elaboration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5

13

18

18 24 32 33 35 38 48 49 50 51

xvii

xviii

Fig. 4.5

Fig. 4.6 Fig. 4.7

Fig. 5.1 Fig. 5.2

Fig. 5.3 Fig. 6.1 Fig. 6.2

Fig. 6.3 Fig. 6.4 Fig. 6.5 Fig. 6.6

Fig. 6.7 Fig. 6.8 Fig. 6.9

Fig. 6.10

Fig. 6.11

List of Figures

Weights for the main subcomponents of the indicators—“political and economic framework” (a); “markets structure and market regulation” (b); “grid infrastructure and grid regulation” (c); and “administrative procedures” (d). Source Own elaboration . . . . . . . . . . . . . . . . . . . Estimates of the WACC and dominant support schemes for wind energy onshore. Source Adapted from [36, 37] . . . . . . . Retrospective, retroactive changes and moratorium for renewable energies in Europe. Source Adapted from [47, 48] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Price difference between PV levelized electricity cost and household retail prices. Source Jäger-Waldau [28] . . . . . . . . a Annual PV installed capacity in Spain. Source Adapted from PVFINANCING [39]. b Solar PV capacity and additions, top 10 countries, 2015. Source Adapted from REN21 [41] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Scenarios of the European accumulated solar photovoltaic market in 2021. Source UNEF [47] . . . . . . . . . . . . . . . . . . . . . . . . Overview of the self-sufficient energy farm owned by the company Soslaires Canarias S.L. Source [11] . . . . . . . . . . Various tabs of the user interface containing the test information. a Program GUI 1: plant information; b program GUI 2. Solar metainformation; c program GUI 3: wind metainformation; d program GUI 4: storage metainformation; e program GUI 5: grid connection information. Source [31] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Simplified optimization logic. Source Own elaboration . . . . . . . . Schematic diagram of the evaluated microgrid by the DesalinationPlant software. Source Own elaboration . . . . Wind model speed (a) and capability (b) profiles. Source [31] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mean wind speeds by hours in a day of: a January; b April; c June; and d October in the case study. Source Own elaboration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mean monthly wind speeds recorded at the reference station for the case study. Source Own elaboration . . . . . . . . . . . . Synthetic solar radiation data. Source [31] . . . . . . . . . . . . . . . . . . Hourly solar irradiation data for the a January 17, 2016; b April 15, 2016; c June 10, 2016; and d October 19, 2016. Source Own elaboration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mean monthly solar irradiation (2016) recorded at the reference station for the case study. Source Own elaboration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Most effective use of resources evaluation. a Convergence. b Cluster plot or “color fish” plot . . . . . . . . . . . . . . . . . . . . . . . . . .

52 55

57 64

65 66 88

89 92 101 102

103 103 104

105

105 107

List of Figures

Fig. 6.12

Fig. 6.13

Fig. 6.14

Fig. 6.15

Fig. 7.1 Fig. 7.2 Fig. 7.3 Fig. 7.4

Fig. 7.5

Fig. 8.1 Fig. 8.2 Fig. 8.3

Fig. 9.1 Fig. 9.2 Fig. 9.3 Fig. 9.4 Fig. 9.5 Fig. 9.6 Fig. 9.7 Fig. 9.8

Monthly means of the a water produced by the system, b power used by the system, c power from RES and d battery SoC. Source Own elaboration . . . . . . . . . . . . . . . . . . . . . . Behaviour of the following variables during the last 15 days of January: a water produced by the system, b power used by the system, c power from RES and d battery SoC. Source Own elaboration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Behaviour of the following variables during the last 15 days of April: a water produced by the system, b power used by the system, c power from RES and d battery SoC. Source Own elaboration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Behaviour of the following variables during the last 15 days of June: a water produced by the system, b power used by the system, c power from RES and d battery SoC. Source Own elaboration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Typical reverse osmosis desalination scheme. Source Adapted from [26–28] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Typical cost structure for RO seawater desalination. Source [62] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Current scheme of the existing desalination plant in Soslaires Canarias S.L. Source Soslaires Canarias S.L . . . . . . Proposed refurbished scheme for the existing desalination plant in the company Soslaires Canarias S.L. Source Own elaboration and Soslaires Canarias S.L . . . . . . . . . . . . . . . . . . . . . Proposed refurbished scheme for the existing desalination plant in the company Soslaires Canarias S.L. (alternative). Source Own elaboration and Soslaires Canarias S.L . . . . . . . . . . Wind resource for Gran Canaria (left) and at the selected site, Arinaga (right) for 80 m height. Adapted from [34, 35] . . . . Solar resource for Gran Canaria (left) and at the selected site, Arinaga (right). Adapted from [34, 35] . . . . . . . . . . . . . . . . . Optimal results clustering (total installed capacity in kW vs. value of the optimization objective function). Source Own elaboration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Proposed placed (in line blue) for the allocation of the Plant [37] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Flux diagram of the commercialization analysis. Source Own elaboration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Net cash flow. Source Own elaboration . . . . . . . . . . . . . . . . . . . . . EEG installed power in Gran Canaria [47] . . . . . . . . . . . . . . . . . . RES % deployment comparison [15] . . . . . . . . . . . . . . . . . . . . . . . Wind resource of Gran Canaria (a), and the selected site (b) at 80 m [55] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Solar photovoltaic resource of the selected site [56] . . . . . . . . . . . Scenario I. RO plant (0% Granted) . . . . . . . . . . . . . . . . . . . . . . . .

xix

109

110

110

111 120 122 128

129

130 142 142

147 160 162 164 165 165 166 166 169

xx

Fig. 9.9 Fig. 9.10 Fig. 9.11 Fig. 9.12 Fig. 9.13 Fig. E.1 Fig. E.2 Fig. E.3 Fig. E.4 Fig. E.5 Fig. E.6 Fig. E.7 Fig. E.8

List of Figures

Scenario II. RO plant (50% Granted) . . . . . . . . . . . . . . . . . . . . . . . Scenario III. RO plant (100% Granted) . . . . . . . . . . . . . . . . . . . . . Scenario I. aqua.abib project (0% Granted) . . . . . . . . . . . . . . . . . Scenario II. aqua.abib project (50% Granted) . . . . . . . . . . . . . . . . Scenario III. aqua.abib project (100% Granted) . . . . . . . . . . . . . . Proposed sitting: Galdar. Source [30] . . . . . . . . . . . . . . . . . . . . . . Proposed sitting: Galdar (Botija). Source [30] . . . . . . . . . . . . . . . Proposed sitting: Galdar (del vino). Source [30] . . . . . . . . . . . . . . Proposed sitting: San Antonio. Source [30] . . . . . . . . . . . . . . . . . Proposed sitting: Agaete. Source [30] . . . . . . . . . . . . . . . . . . . . . . Proposed sitting: La aldea de San Nicolás. Source [30] . . . . . . . . Proposed sitting: Manantial. Source [30] . . . . . . . . . . . . . . . . . . . Proposed sitting: Santa Lucia de Tirajana. Source [30] . . . . . . . .

169 170 170 171 171 197 198 198 199 199 200 200 201

List of Tables

Table 2.1 Table 3.1 Table 3.2 Table 3.3 Table 3.4 Table 3.5 Table 3.6 Table 3.7 Table 4.1 Table 4.2 Table 5.1

Table 5.2

Table 5.3

Table 6.1 Table 6.2 Table 6.3 Table 6.4 Table 6.5

Selection criteria used in the review of the different tools . . . . . Global water footprint of food system . . . . . . . . . . . . . . . . . . . . . Life-cycle water withdrawal on energy sources . . . . . . . . . . . . . . Indexes associated with a renewable-based energy deployment strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . EEG structure, generated energy, and water withdrawal of the different scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Analysis steps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Short-term strategy location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Calculation of water and land indexes associated with a PHS scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wind turbine classes according to the IEC 61 400-1 standard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Relative weight of the components of each indicator . . . . . . . . . Internal rate of return from residential, commercial, and industrial markets under average conditions (irradiation, configuration, financial, etc.) for current settings of the Spanish regulatory framework . . . . . . . . . . . . . . . Most significant Spanish economic non-market failure and institutional barriers to the take-off solar photovoltaic technology and self-consumption . . . . . . . . . . . . . . . . . . . . . . . . . Measures proposed to address the economic non-market failure and institutional barriers that hamper the development of solar photovoltaic technology and self-consumption in Spain . . . . . . . . . . . . . . . . . . . . . . . . . . . Data of the desalination plant used for the simulation . . . . . . . . Reservoir data used for the simulation . . . . . . . . . . . . . . . . . . . . . Wind power plant data used for the simulation . . . . . . . . . . . . . . Solar power plant data used for the simulation . . . . . . . . . . . . . . Energy storage data used for the simulation . . . . . . . . . . . . . . . .

15 28 29 29 31 32 34 36 47 53

66

72

77 101 102 104 104 106

xxi

xxii

Table 6.6 Table 6.7 Table 7.1 Table 8.1 Table 8.2 Table 8.3 Table 8.4 Table 8.5 Table 8.6 Table 8.7 Table 9.1 Table 9.2 Table 9.3 Table 9.4 Table A.1 Table A.1 Table B.1 Table B.2 Table C.1 Table C.2 Table D.1 Table D.2 Table D.3 Table F.1 Table F.2 Table G.1 Table H.1 Table H.2 Table H.3

List of Tables

Investment costs, total costs and income, average benefit, and LCOE for the proposed scheme . . . . . . . . . . . . . . . . . . . . . . . Monthly means for different variables of the operation of the desalination plant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Direct capital costs as a percentage of the TDC. Source: [86, 94, 95] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Total annual volume of desalinated water in the Canary Islands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Number of desalination plants and total percentage of desalinated water by islands [16] . . . . . . . . . . . . . . . . . . . . . . . Population of Arinaga for the year 2018 [36] . . . . . . . . . . . . . . . Simulation parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results of the optimization process . . . . . . . . . . . . . . . . . . . . . . . Levelized costs of energy and water . . . . . . . . . . . . . . . . . . . . . . . Payback time periods comparison . . . . . . . . . . . . . . . . . . . . . . . . Financing card [47] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Key performance indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Techno-economic results of RO desalination plant . . . . . . . . . . . Techno-economic results of distillation plant . . . . . . . . . . . . . . . (a) Review of evaluated nexus tools . . . . . . . . . . . . . . . . . . . . . . . (b) Review of evaluated nexus tools . . . . . . . . . . . . . . . . . . . . . . . Installed capacity in Gran Canaria, and characteristics of desalination technologies [22] . . . . . . . . . . . . . . . . . . . . . . . . . Locations of the desalination installed capacity in Gran Canaria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . GIS restrictions for a short-term full renewable-PHS EP strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results of the geomorphological analysis . . . . . . . . . . . . . . . . . . Energy sources on emissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . SWOT analysis and economic competitiveness of the food subsector in Gran Canaria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Economic competitiveness of the food subsector in Gran Canaria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Key data regarding Spanish solar PV sector . . . . . . . . . . . . . . . . Comparison between Spanish and other self-consumption schemes around the world . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Project cash flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Parameters of simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Analysis of the characteristics of proposed grants . . . . . . . . . . . . Simulation results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

108 109 125 138 139 142 145 147 148 149 161 164 167 168 180 184 187 188 189 190 193 194 195 203 204 209 212 214 217

Chapter 1

Introduction

To meet the needs of the present without compromising the ability of future generations to meet theirs Brundtland Commission

Climate change conditions is especially dramatical in islanded environments, where these restrictions to water resources have conducted to an overexploitation of aquifers and wells that desalination plants have become essential. The synergies between Water, Energy, Food sectors are obvious—although often planned independently—is one of the identified catalysts for achieving the United Nation’s sustainable development goals. Isolated and water-scarce regions suffer high water and energy costs that itself results in high environmental costs. The book offers a sustainable approach to tackle with water scarcity for regions that need desalted water in isolated regions. Specially in those regions, sustainability must be a key feature that legal and market issues highlight as a parameter to be analyzed in order to improve the deployment of hybrid schemes. Among the problems that this book intends to help to solve, it highlights tackle the impact and costs of the RO desalination process, proposing improvements to reduce water global costs. The vast deployment of renewable energy technologies of electricity generation face barriers that stresses when it is combined with other sectors, technologies. This book proposes a methodology for locating and sizing the microgrid to maximize the benefits of the microgrid. The proposed microgrid uses desalted water to develop a water storage pumping scheme, that let the management of the energy surpluses in the system. The desalination microgrid takes advantage of this dual resource to conform rural microgrids that supply water and energy in the surrounding areas. Energy is used in the farms, desalination plant, citizens, and other elements which are part of the microgrid. Water is used in surrounding farms, by citizens, or other uses, of the microgrid. Specially in those regions, sustainability must be a key feature of any action taken that the legal issues highlight as a parameter. The results in this book provides solutions to increase the performance of RO desalination plants providing water and energy electricity at economic, and environmental affordable cost. The product includes the development of a microgrid © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 E. Rosales-Asensio et al., Sea Water Desalination in Microgrids, Green Energy and Technology, https://doi.org/10.1007/978-3-030-96678-2_1

1

2

1 Introduction

that included the planning of renewable and distributed energy electricity generation resources and a desalination plant, desalination microgrid, forecasting an amount of water that will let the development of the agri-food sector. Since it forecasted energy surpluses in the microgrid, it can be used to develop a sustainable way to charge electric vehicles in rural areas where the readiness of the grid does not offer car charging infrastructure. The book explores the feasibility of the microgrid from the inside, analyzing how to the improve desalination process, and how to size the facilities that power the system. It is also important to investigate it from an economic, environmental, legal, and technological point of view. One of the main matters that the books deals with is answer if in a water-scarce scenario, the deployment of renewable energy systems in a desalination microgrid is economically and technologically sustainable. In order to investigate the development of a microgrid it was necessary to investigate the market that it would be acting in. It proposed a method to locate the facilities in order to tackle water scarcity, and defines and uses sustainability tools to describe synergies between systems. It is a well-known fact that nowadays the energy sector is essential for the development of today’s societies. Indeed, energy is used in most of those activities in these societies. The energy-electricity sector contribution to energetic activity is increasing, that the contribution of renewable generation technologies pushes this trend. Two are the major problems of the electricity generated from renewable energy in isolated microgrids: intermittency and grid connection availability to share the generated energy. Not only energetic and technological problems must be faced, but also the economic, legal, social and environmental issues were managed in order to achieve the most from an overall point of view. For what water scarcity management in societies is concerned, isolated regions become a great subject to be analyzed, becoming extremely useful for transferring results to other regions. In order to let a sustainable development, the relationship between sectors is basic, that the study of the relationship between energy strategies and high energy consumption activities highlight in order to clarify the synergies and trade-offs between them. The impact assessment of energy strategies requires knowledge that may be estimated by quantitative means. The knowledge requirements review the means used to obtain a reference basic structure underlying a system to evaluate the way that a progressive development of inexhaustible energies in a particular geographical region can affect the demand of water and food. Alternative renewable-based energy plans affect to a designed water index model to know how stressed is the related system in a region at high scarcity risk. Through a geographic information system algorithm analysis, it was found locations for a two stages hybrid renewable-pumping energy storage strategy with desalted water from desalination plants in Gran Canaria that let store from 1144.15 MWh/0.1 Hm3 to 3076 MWh/0.1 Hm3 . Results showed that the deployment of increasing renewable energy resources let reduce the stress in the water subsystem from 8 to 20 Hm3 , fact that allowed a system analysis strategy to control the water index in order to support the decision-making strategies to control the water resource in arid regions.

1 Introduction

3

Climate change conditions have derived in accessing to drinkable water. This is especially dramatical in islanded environments, such as the Canary Islands in Spain, where these restrictions to water resources have conducted to an overexploitation of aquifers and wells that desalination plants have become essential. Optimized size hybrid wind and solar photovoltaic power plant is proposed to feed a desalination plant in Gran Canaria island. This study case not only feed the desalination process, but also provide the power grid with clean energy, taken advantage of the surplus electricity production. Considering the power dispatch in the island, the remuneration limits for power delivery and the remuneration for provided water, the optimal DRES associated with a desalination plant providing 5600 m3 /day of desalted water and a maximum annual electricity injection to the power grid of 5.88 GWh/year, an hybrid solar PV and wind generation installation with electrochemical storage is a feasible solution that makes the Levelized Cost of Electricity (LCOE) achieve a singular reduced value despite considering the additional costs of the desalination plant. Financial help must be provided to reduce the payback time in medium-sized hybrid desalination plants that its economic surveillance is a challenge. That is what is investigated in Chap. 9: carrying out of these initiatives in economies based on liberalized markets faces governments against the need for guaranteed profits. It was studied the influence of grants, investment rates, and energy and water sales on the commercialization of two desalination technologies through different scenarios. Specifically, a simulated reverse osmosis desalination plant has been compared with respect to an already granted novel pilot desalination plant showing the results a better fulfilment of the non-economic objectives, and economically profitable not only under certain conditions of conceded grants, and investor’s expected benefits, but also of sales of water-energy, that highlighted as a limiting factor. The Levelized Cost of Energy might be similar than the Spanish generation means, depending on the cost escalation rate of the loans, and conceded grants. It was found a reduction of 11 Coeur under the average price that could be achieved, for the standard scenario. The rapid deployment of renewable energy electricity generation systems in some regions suggest that it is the right moment for the implementation of the proposed microgrid design. In addition to the region that this book focuses in, there are increasing locations that suffer from water scarcity problems. To tackle with low rainfall scenarios, the implementation of automatically-controlled desalination plants with water intelligent management systems will become essential to mitigate these effects. This book elaborates on the assessment of the feasibility of hybrid systems that support the energetic system of desalination plants while providing a flux of water and energy that conform a microgrid. It is especially important in areas at hard scarcity risk, and without interconnection with other water, or energy systems like in islands. The subject this book presents is part of the research into the optimization of the management of renewable energy sources combined with desalination plants inside a microgrid. The deployment of desalination microgrids is expected to have beneficial effects at social level, fixing the population, and allowing the development of agriculture in arid regions. Those effects will have, itself, economic positive effects in the region. This book also investigated how the controlled management of resources allows

4

1 Introduction

the reduction of adverse effects on the environment, although to maximize them, they must face some challenges. It is analyzed the synergies between sectors, in order to increase the understanding of the trade-offs that will ease the design of the business model related to the desalination microgrid. Those related to renewable energy are often planned with public grants that try to boost markets regarding to the citizen’s needs. It is proposed the evaluation of the consequences of the deployment of alternative renewable energy plans. With the development of sustainability indexes, it was analyzed the water, energy, food subsystems. In order to develop a microgrid, both technological as well legislative issues are of vital importance, as long as they limit the deployment of renewable energy technologies of electricity generation in the energy electricity generation system. The proposed microgrid proposed a system for supporting the intermittency of RES through the smart management of the water from desalination plants, and the energy in the microgrid. Similarly, it must be studied the consequences of the implementation in regions that, on the one hand, have energy systems with a structure that can be greatly improved from the point of view of monetary, environmental and operating costs. The investigation of how to improve the reverse osmosis desalination process let increase the economic competitiveness of the business models in desalination microgrids. Based on the results, this book evaluates the feasibility of those hybrid systems analyzing their competitiveness from an environmental, corporate, and as from the nexus with other sectors point of view. To evaluate the feasibility was crucial to investigate some aspects that would led, or not, to the deployment of a specific business model. Among these, it was highlighted the manageability of this “combined resource” water-energy system, analyzing if there is a market that is able to absorb resource. In order to do this, it was necessary to review the technologies as well the market’s economic conditions, and the barriers that make it difficult these market’s development. As long as a desalination microgrid is originally designed for its integration in regions that lack of water resource, this book is referred expressly to a region that on the one hand suffers from water stress, and on the other shows high dependence on fossil fuels that additionally causes high costs in the energy electricity generation system. The island of Gran Canaria suffers the aforementioned problems as well as of a poorly organized agricultural sector. The desalination microgrid is developed by coupling a desalination plant to a pump-turbine water system to take advantage of water storage in different levels that let manage the intermittent nature of the primary renewable energy sources that mainly feed the microgrid. The scheme is able to generate two marketable products, water and energy, acting in markets under different rules that the microgrid must decide which resource to use, and when. In addition, the brine that appears in the desalination process could be used to generate other products, such as salt or others that increase the economic competitiveness of the plant. One of the main objectives is to establish a coherent decision framework to analyze how a scheme that combines the generation of electricity from renewable sources and the water resources provided

1 Introduction

5 Stored

Energy

Water

Pumping Water

Turbined Water

Energy to Grid

Fig. 1.1 Proposed design of a desalination microgrid. Source Own elaboration

by desalination plants can be used to compensate for their intermittent nature, while watering the surrounding areas that conform the defined microgrid. Figure 1.1 shows the proposed design of the desalination microgrid. Basically, it adapts the amount of water and energy generated to the needs of the interlinked subsystems with constrains like energy from renewable sources, or water needed in the system. It must be able to decide between the different energy management options that can be adopted. Ideally, the system should pump the water from a desalination plant to an upper water reservoir to store the surpluses of renewable energy that can be achieved in the microgrid. Once the energy is needed in the microgrid water is used in standard applications such as agriculture, or others. To design the desalination microgrid, it was necessary to investigate some of the better locations to develop the scheme. Using algorithms that locate the most favourable sites with a GIS software, the implementation of the dual energy storage/irrigation system in some of the locations of the islands were supported by a decision framework. Precisely, it is one of the challenges of this book: to develop a method that allowed these schemes to be physically located, based on a series of premises that these locations must fulfil. In order to develop a framework that fully maximizes the scheme, it must be done through the combined control of resources. To this end, it must be adjusted according to the amount of rainwater collected as well the amount of renewable resource in the region, and the needs for both water and energy in the microgrid. Optimisation of yields used is capable of incorporating self-learning systems that are able to adapt to the management of the water-energy-food system (based on multivariate control) to maximise the yield of the installation. This book proposes some improvements that lets a water costs reduction in the RO desalination process. Among the challenges faced it highlighted the investigation of the business model inside the designed microgrid.

6

1 Introduction

Focusing in how the microgrid would be economically sustainable investigated how to make the most of the benefits of a desalination microgrid. In order to do this, it needed a detailed analysis of the economic-financial viability of the microgrid. Developed in next chapters, it investigates the model in the microgrid regarding to which desalination technology would be the most profitable to implement the scheme. The most remarkable technological challenge faced is to find how to manage the resource in order to maximize the benefits of the scheme in the designed business model. It is done not only by investigating the synergies between sectors, with sustainability tools in exhaustive information systems, but also with forecasting, and prediction tools that let to size the microgrid in order to maximize the expected benefits of it. Despite the fact that they are very useful, weather statistical and probability models can be further developed, and improved. A better definition will allow maximizing the benefit of the scheme including citizen’s habits, or crop irrigation methodologies. Information models are equally useful when searching for the locations that show greater benefits either energetic or economic. Those aspects let the system to maintain safer operating conditions. As analyzed in depth in the following chapters, the optimization of the running time in these facilities will be a priority. For the economic viability of the proposed microgrid design, it is important to allow selling electricity in the electric system. With the proposed energy storage mean the recurrent peaks in consumption from the daily consumption curve could be cut, or generating energy in adjustment services. But also, the excess of energy electricity from renewable energies can be used either to charge power chemical batteries, produce hydrogen, etc., business models that are expected to grow in the years to come. Additionally, the development of the scheme might allow increasing the agricultural production capacity, fact that let to improve the business model. To this end, an intelligent energy-water management system for this double resource was implemented. Only by keeping the evolution of part of the water resource within the water-food-energy nexus controlled, the resources can be managed. The development of water management control system will make it possible to automatically control the desalination plant’s operating factor to supply water either to water crops, feed cattle food or water gardens. In addition, there is sufficient development in areas, especially in real time information management, that will allow the benefit of this system to be maximized. Parallel, this book also investigates how to reduce the high energy costs that generally present the most widespread desalination applications. It is investigated how to size the microgrid with renewable resources to power RO (Reverse Osmosis) desalination plants to lower the costs of the process. In addition, it was reviewed the RO process to find improvements that let reduce the costs associated to the process. In addition, it is especially important to investigate the legislative aspects that refers to the electricity generation process from renewables, as well other aspects regarding to: crossing of properties with pipelines, sites, environmental impact declarations, those aimed at modelling the configuration of a sustainable water-energy

1 Introduction

7

market, which will eventually and inevitably require public policies to promote sectors that are water users, etc. It is a well-known fact that you need to attract investment to make use of the water and the energy generated in such an innovative scheme. That is the reason why advertising, and information campaigns should be developed to explain how potential customers can benefit: from buying energy, from buying water, from tax authorities, from organizations that care for the environment. The feasibility of the deployment of the microgrid design that this book presents was analyzed from a technological, practical and economic point of view. Since it intended a balanced operation that maximizes the benefit that can be achieved from this scheme, whether at the energy, economic or environmental level. The analysis of the practical feasibility, started with a site-location analysis. It was necessary to locate the microgrid in a region that maximize the benefit of the scheme, and for an expected period of time to allow the monetary and environmental investment to be recovered. Therefore, since innovation is associated with the development of the agro-energy system, it should be easy for them to dispose of agricultural products, or energy, or products generated from it, increasing their business possibilities. Finally, the economic feasibility must be analyzed both in privately owned and in publicly owned desalination plants, as well the legislative and market barriers that must face the deployment of renewable energy-based power plants. It is a well-known fact that disruptive ideas normally need time until there is a sufficiently-developed market capable of absorbing the products to make the business model profitable. In this sense, economic support in the early ages is important to maintain the activity. Bearing these aspects in mind, the risks that the implementation of the innovative desalination microgrid described in this book would face, are mainly due to the inability to deploy the full potential of the scheme. Among these, it highlights a shortage of customers for the products generated by the type of business, or because of the deficient development of the various information systems associated with the innovation, or due to the legislative barriers that the deployment of the microgrid must face. Nor should we forget the regulatory changes in energy or environmental matters that can have a marked effect on an innovation that was originally designed to solve the problem of its intermittency under very controlled conditions. Additionally, legal aspects should be analyzed in order to test if the pipes can be installed without problems because of the properties they cross, as well as the roads, motorways, or other, although it should be noted that, as areas with more favourable slopes (wadis, etc.) are normally chosen, they will be less likely to suffer from these problems. In this sense it was assumed that the crossing of pipes by properties will not be a limitation to the scheme, and that water and electricity markets will be developed. This book proposed an integrated tool that provides corporations, whether public or private, a decision framework to analyze and decide under greater number of decision elements. With that tool, it is possible to compare and decide between alternatives related to the resource management problem. Due to their configuration, the markets that this book considers aims to reach, consist of all, present and future, users that need access to water and electricity. Although the market is initially designed for

8

1 Introduction

desalination plants that are located close to their “region of influence” and have a suitable orography for the implementation, it could also spread to irrigation communities that extract water from the subsoil for irrigation. This book presents an innovative vision to contextualize, understand and go deeper into the economic and environmental consequences of the implementation of hybrid renewable systems combined with desalination plants. The differential point that this study presents consists in maximizing the benefit of the water and renewable energy management by implementing a scheme that maximizes the benefit of the combined management. The synergies between the electricity and water markets were analyzed from the perspective of the systems theory. This point of view investigated how to design advanced pseudo-water markets that address the growing problem of water scarcity in regions particularly affected by drought problems. In addition, it aims to document energy and water to assess water consumption, as well as greenhouse gas emissions. The strategy that the commercialization of desalination microgrids must pursued consist of a series of steps that allow the deployment of the different products that the desalination microgrid offers. As will later be analyzed, to become profitable, the deployment of the model in the region under study would not be sufficient to achieve it, and the markets should be developed to enable the products to enter the market. Apart from the perceptive licenses given by administrations, be it environmental or others; these will be essential actors in boosting the target sectors through subsidies, or developing development plans and programs, as they have great influence on the profit model by being essential in the negotiation processes in the areas of agriculture and energy. As previously mentioned, the system can be easily scaled up by making small modifications to both the turbo-pumping units and the pipes, and by adjusting the size of the rafts to be sized to store the energy for which the hydraulic storage scheme was designed, fact that would allow extend the scheme to irrigation communities that use water from aquifers for irrigation. This fact will allow to extend the use of the resource management tools to monitor and control the resources (WEF) in the microgrid where it is implemented. Only focusing in the energy design of the scheme, it could be used with some slight modifications in industries, only changing the fluid, or combining hybridization of renewable technologies. Some waste management processes might take advantage of pumping, and turbine to recover, and adapt contaminated liquid while generating energy. In addition, schemes such as an electroliner in microgrid, or any installation that needs of electricity for the recharging of static batteries could benefit from the implementation of this innovation. In parallel with the development of technologies, markets would be developed to allow the potential of the scheme to be exploited. The indicators of success developed in the following chapters are classified into technological, practical, economic and market indicators that aim to answer questions about the deployment of desalination microgrids through the implementation of a hybrid energy system that supply small and medium-sized desalination plants.

1 Introduction

9

Among the technical aspects to be analyzed, it is highlighted the need to carry out an evaluation of the tools that will allow the problem of analyzing the relationship between the water, energy, and food subsectors (WEF). As far as this book focuses on a region, the study unequivocally reveals the relationship between WEF subsystems that facilitate the understanding of the scheme. In order to validate the chosen energy accumulation technology, an analysis of the morphological characteristics of the region of study is carried out, mainly energy and geo-morphological indicators. As long as RO-desalination process is the most widespread process to desalinate water it is revised in order to propose some improvements that let reduce the water costs related to the process itself. The key performance indicators used to analyze the behavior of a desalination microgrid include: amount of energy that can be generated? Is it capable of achieving a certain economic performance? Amount of CO2 emissions that can be avoided? Among the positive impacts can be expected, it highlights on society, through its economic and social impact, and on the environment, through its positive effects. Other impacts, that would allow regions that suffer from water scarcity to develop a sustainable business model, in addition to the positive effects on the income effect, the push and pull effect on related sectors, upstream or downstream of the established model, traditional, such as capital goods, steel, etc., or at an early stage of development such as: Big Data, Information Technologies, Artificial Intelligence, and real time information will certainly be key to maximizing the benefit of a microgrid that operates with water and energy. Equally important are the analysis of legislative and markets barriers regarding to renewable energy deployment. Both, technologies for generating electricity from renewable sources, and the activities in the first sector have the capacity to fix the population in rural areas. In addition, it is expected that the desalination microgrid as presented here, presents a potential for recovering the plant cover of arid lands. Focused in the island of Gran Canaria, the book analyzes the connection of the aforementioned sectors with different strategies for the development of the electricity generation system in the island that highlight the importance of renewable energy sources such as solar and wind, one of the most outstanding resources in the region. This region presents the problems associated with the scarcity of both, water resources and the percentage of renewable technologies in the electricity generation park, which is why it is particular for several energy systems that introduce, in addition to the implementation of renewables, the necessary systems for the manageability of the resource. This book has been divided into 9 chapters and their corresponding appendices, and has been written in such a way that the document is more readable and understandable, with each chapter retaining a certain independence in terms of content. This first introductory chapter aims to highlight and contextualize the problem of water scarcity and its connection to energy and food systems highly dependent on fossil-primary energy sources. It presents what would be the problems, as well the challenges to develop the desalination microgrids, the goals as well the benefits of developing such a scheme. The second chapter then reviews the tools dedicated to the analysis of the relationship between the water, and food sectors, in their connection

10

1 Introduction

with the energy generation system. They were reviewed from the point of view of their joint integration. The proposed tool was used in Chap. 3 to analyze the consequences of deploy a renewable-based energy plan combined with a pumping hydro energy storage strategy. It analyzed how much energy could be harvested when combined with the desalted water from the desalination plants. It was developed an algorithm to find the potential as the best locations to develop schemes of 8 MW, and 0.1 Hm3 . In Chap. 4, it is analyzed what is the state of art of the wind energy technology as well the market conditions of generation in Spain. Still on the subject of the legal aspects that point to the creation of microgrids, Chap. 5 refers to measures to remove the market barriers associated to renewable energy self-consumption. It also reviewed some of the most remarkable market failures regarding to renewable energy self-consumption that can slow its deployment. Chapter 6 then proposes an optimization method to size the renewable facility that feed the desalination microgrid in order to maximize benefits of its operation. Chapter 7 deals specifically with the RO desalination process. In it, it was proposed measures to improve the operation of the plant in a desalination microgrid, reducing the costs of the water. Chapter 8 continues with the optimized design of a hybrid solarwind system combined with a desalination plant in the specific region of Arinaga, on the island of Gran Canaria, which shows promising results in reducing energy and water costs when the scheme is installed in this region of particular characteristics that led to think that the scheme could be profitable. Chapter 9 focuses on an analysis of economic-financial survival by comparing desalination plants with two types of technology, reverse osmosis and a new distillation technology, which use renewable energies in their activity, under different assumptions of eligible activity and performance of their commercial activities. Finally, it was included part of the literature that have been seen during the redaction of this book, and that was not included in the chapters that conform the main body of the document. Finally, a section is reserved for the appendixes, where additional information will be shown that accompany the content of the book, for a better understanding of it.

Chapter 2

Revision of Decision/Making Tools for Sustainable Planning and Proposal of a Novel Conceptual Framework for the Energy-Water-Food Nexus

2.1 Introduction Often, political decisions are taken without the necessary coordination of different administrations and without considering the impact that a political decision in one activity may have on additional activities [1, 2]. A deficiency in coordination arises between different branches at the same hierarchy level, or between different levels, in the public administration. For instance, for a problem associated with a nation’s energy-related decisions, hydro-related resolutions are taken at a restricted area, without coordination between the two administration levels. Literature reflects the fact that a governments’ “isolated” way of dealing frequent results in policies that cannot be maintained at the current rate [3]. In this sense, there is a growing consensus about the importance of the water, energy, and food security nexus and the need to devise and implement policies and actions in an integrated manner [4]. Integrated resource management can be defined as the coordinated development and management of water, land, and related resources to maximize economic and social well-being in an equitable manner without compromising sustainability, and it has been so-called in recent times as “integrated water resource management” [5]. The first United Nations Water Conference, in Mar del Plata, Argentina (1977), recommended greater attention to an integrated resources management, emphasizing that water plans should not only consider economic aspects but also ensure the optimal social benefit of water resources as well as environmental protection [6]. The concept, popularized by the Dublin Declaration on Water and Sustainable Development (1992), promotes an integrated vision for resource management [5]. It stated that the management of water resources is an effective approach to address global challenges related to water management, restoration of degraded lands, adaptation to climate change, and the fight against hunger [7]. Some international bodies such as OECD [8] or the World Bank [9] already recommend integrated resource management through these related methodologies. Thus, this chapter is focused in the joint management of three fundamental resources: water, food and energy, but in this case,

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 E. Rosales-Asensio et al., Sea Water Desalination in Microgrids, Green Energy and Technology, https://doi.org/10.1007/978-3-030-96678-2_2

11

12

2 Revision of Decision/Making Tools for Sustainable Planning …

mainly focused on the energy as an entry point (EP) related with the other resources, in a single of two ways relationship. The adoption of management based on the water, energy and food nexus requires a more appropriate perception of the associated benefits and exposure to danger for each of the nexus sectors, as well as an analysis of interactions between sectors in order to facilitate integrated planning and decision making [10]. In this sense, analytical frameworks are used to assess the impact of policies on different sectors [11] and inform policies by quantifying resource exchanges and providing an assessment through which the potential and unexpected risks associated with the nexus are identified [11–13]. About 55 publications were reviewed for this chapter and, despite being a substantial and representative sample of the state-of-the-art literature, this number is not absolute. Even though some institutions and researchers, such as the FAO [14], World Bank [15], Pollit et al. [16] and Tol [17] have proposed such preliminary tools, they have been designed as frameworks for in-depth nexus analysis, not as simple, easyto-use tools for conducting basic evaluations. These wide-ranging tools are intensive in terms of the information, time, capacities, and funding needed. To our knowledge, there is no tool available in the scientific literature that, having energy as an EP and incorporating inputs relative to the specific explicit context, can be considered as simple. Therefore, there is a gap in scientific knowledge regarding tools that have energy as an EP—in order to address this gap, this chapter presents a conceptual framework. Section 2.2 states the objectives of the chapter and provides an adequate background. Section 2.3 describes the review criteria and selection criteria used to conduct the research. Section 2.4 proposes the conceptual framework for a tool that can conduct preliminary assessments of the basic impacts of the energy policy nexus. Finally, Sect. 2.5 explores the significance of the results and states the major outcomes.

2.2 Definition of Frameworks to Model Nexus Interactions The frameworks used to model nexus interactions can be based on both quantitative and qualitative methods [18, 19]. Although this chapter focuses on quantitative tools, qualitative tools can also provide important information. The methodology used by the FAO to assess the nexus combines both tools [14]; while other institutions such as the United Nations Economic Commission for Europe (UNECE) together with the KTH Royal Institute of Technology use other methodologies that are mainly qualitative [20]. The modeling tools for nexus integration can assist decision making and identify local objectives that are in line with broader sustainable development objectives [21]. Although fully integrated planning is preferable, assessing water requirements against an energy strategy could provide very useful initial information for other water end-uses. Some of the available methods used, such as the one proposed by

2.2 Definition of Frameworks to Model Nexus Interactions

13

Food and Agriculture Organization [22] or the one proposed by Mohtar and Daher [23], adopt food as an EP; variants, such as the one proposed by the United Nations Economic Commission for Europe and Royal Institute of Technology [20], adopt hydric resources as an EP; additional tools, like [24], employ the power derived from the utilization of physical or chemical resources as an EP. This chapter will, for the most part, be centered in an “energetic” view. The intersectoral nature of the nexus indicates the importance of perceiving the intended meaning resulting from the interrelationships among water, energy, and food through scenario simulation [22]. In this sense, the development of scenarios from quantitative tools is adopted to explain a number of several and probable events that will or are likely to happen in the future [22]. In fact, these scenarios constitute reasonable gradual developments to the present circumstances which, according to the way the elements of the nexus evolve and interact, may serve to assess the implications of certain policy decisions [22]. For instance, some policies advantageous for the energy and food sectors might exert excessive stress on the hydrological plans of countries with drought problems by promoting excessive use of water due to affordable pumping. In order to address these risks, several analytical frameworks have been developed within the context of the water, energy, and food nexus [14, 20, 25, 26]. The quantitative tools that analyze the impacts of energy policy on the nexus may vary in terms of their completeness, as shown in Fig. 2.1. The left side represents an “isolated” way of dealing with the situation, in which “political” and other data relevant to the energy sector (such as the resulting energy balance) are provided without considering the influence that it can have on the remaining elements of the nexus. Conversely, an alternate way of dealing with a larger content of scope (in the middle) would consist of a water, energy, food nexus tools with the essential facts serving as a receptacle for quantities relating to the energy sector but as too important data relating to water and food and land—in this case, providing outputs on the essential remaining nexus assets required for the policies analyzed.

Fig. 2.1 Different levels of exhaustiveness in the analysis of the impact of the water, energy, and food nexus. Source Own elaboration

14

2 Revision of Decision/Making Tools for Sustainable Planning …

Since the 2011 Bonn Conference [27], several frameworks have been developed and evolved [28–32]. The existence of different purposes has resulted in diverse boundaries for frameworks that have been developed at different levels -from a regional level [29, 31] to a global level [33, 34]. This variety of frames of reference with different inputs, outputs, and analytical characteristics has its origin in the complexity of the nexus [35]. The inputs are employed to characterize the schemes studied and their circumstances. With respect to the outputs, a number of the tools focus only on one component of the nexus; other tools describe additional components, and the rest represent additional elements, such as available land area, minerals or GHG. Finally, the fundamental analytical characteristics of the tools may, in addition, be dissimilar. Although the kind of information necessary can change depending on the tools used, most of them depend on a large amount of data (inputs) that are often not available on a large scale [36]. This is because much of the data needed for nexus evaluations are not centrally located under the authority of a single agency— practices related to data management of different agencies often differ between them [35].

2.3 Review of Integrated Planning Tools The review only includes tools that meet the following specific selection criteria: The tool deals with a minimum two out of three essential features of the water, energy and food nexus. The tool permits to conduct evaluations at a wide-state level. The tool is, to a large degree, accessible and available for use or is open access. The examination principle by which tools are to be judged have been arranged into three groups—required inputs, proportionate outputs (and, as a result, replied queries), and analytical aspects (see Table 2.1). It should be noted that the research developed in this chapter does not assess aspects such as in what way the tools can be employed to characterize scenarios and that an exhaustive analysis of the advantages and deficiencies of every tool is beyond its scope. The specific review criteria are described below: Entry requirements: Main entries: these entries constitute the principal data in the examination, either with regard to facts and statistics collected for reference or analysis. These entries would, as a rule, have to be supplied by the user of the tool. Examples could be the amounts and types of energy available for the studied nation, the quality of being able to be used or distinct kinds of water being obtainable, access to land for cultivation, or the expenses associated to distinct energy or water technologies. Exits/inquires replied to: The standard of examination inside this category gives facts about what exits might be predicted from the tool and, as a consequence, what queries could possibly be answered with it.

2.3 Review of Integrated Planning Tools

15

Table 2.1 Selection criteria used in the review of the different tools Revised standard

Selection criteria

Entry requirements 1.(a) Main entries

–

Departures/questions answered 2.(a) The tool considers the element “energy”

At least two of the three

2.(b) The tool considers the element “water”

At least two of the three

2.(c) The tool considers the element “food”

At least two of the three

2.(d) The tool takes greenhouse gas emissions into account

–

2.(e) The tool yields financial indexes, in particular, the expenditures of the scenario

–

2.(f) The tool considers the requirements of the required ground

–

Analytical typical features 3.(a) The tool is extensively approachable, available for use or open access

Yes

3.(b) The tool allows different analyses to be carried out at a At least at the national level wide country degree 3.(c) It is possible to be used in dissimilar sites (i.e., diverse nations)

–

3.(d) The tool presents to difficulty and supplies useful exploratory evaluations

–

Source Own elaboration

(a) (b) (c) (d)

(e)

The tool considers the element “energy”. The tool considers the element “water”. The tool considers the element “food”. The tool takes GHG into account: Policies on energy, water, or food and land use are not possible to be carried out separately from climate change for the reason that the impacts are clear and two-way; each of the three nexus subdivisions produce and discharge a considerable amount of GHG and will be influenced by climate change. Even though considering the impact of climate change on the nexus’ essential features is beyond the outlook of this chapter, a number of the revised tools consider the impacts of the policies evaluated or the schemes on GHG. A detailed nexus tool might allow the evaluation of the give-and-take that might happen among carbon dioxide emissions and the essential parts of the nexus. The tool yields financial indexes, in particular, the expenses of the scheme: Because financial examination is essential to political decision-making, a number of tools are capable of supplying an assessment of the financial consequences of the schemes analyzed (in particular, the incurred costs). The addition of non-financial expenses (i.e., consequences of an industrial or commercial activity which affects other stakeholders without this being reflected in market

16

(f)

2 Revision of Decision/Making Tools for Sustainable Planning …

prices) might be important to consider the financial worth of ecosystem services (for example, maintaining areas with a large number of trees, land not used could lead to the capture of some of the CO2 discharges and thus make the externalities connected with them smaller). The tool considers the requirements of the required ground: Some countries do not have a restriction on the surface of the land to be used, whereas other countries do so. A number of the revised tools are capable of supplying relevant information on the previously mentioned aspects.

Analytical characteristics of the tools: The standard of examination inside this arrangement describes a number of analytical characteristics of the tools that are treated as significant for this research. (a) (b) (c) (d)

The tool is widely accessible, available for use, or open access. The tool allows different analyses to be carried out at the national level. It is able to be used at various sites (i.e., several nations). The tool is easily understood and has useful basic estimations.

Built on the revised principles listed before, Table A.1 in Appendix A presents an examination of the tools, showing the various principles included in the evaluated tools. Table A.1 has been outlined to supply an imaged explanation of the problems associated with each tool; if a criterion has not been assessed for a particular tool, the corresponding box within Table A.1 is left blank, if a criterion has been assessed by the tool, information is included within the box. The following section introduces the conceptual framework (“CF”) for a tool to help fill this issue—it is based on [23], that has food as an EP and permits an evaluation of the effects of the nexus in addition to the exchanges between each of the elements.

2.4 Conceptual Framework Definition This section puts forward a CF for a tool that can carry out exploratory evaluations of the impact of the water, energy, and food nexus on energy policy. This frame of reference aims to address some of the gaps previously identified—its main output being the assessment of the basic requirements of a given resource (such as the volumes of water in addition to land surfaces) combined by a number of particular actions for the power derived from the utilization of physical or chemical resources. The presented tool can (i) accommodate inputs that are particular to a given context; (ii) yield results in a useful and convenient layout; (iii) be uncomplicated from an analytical point of view, while also supplying a basic view of the situation. The proposed conceptual framework is based on an approach based on situations in which the nation’s energy balances (EB) is paramount for all the schemes. This allows the user to build a number of schemes by altering the EB connected to various energy policies (such as, for example, a higher adoption of inexhaustible energies)

2.4 Conceptual Framework Definition

17

in addition to examining the rising effects on the water, energy, and food nexus. Despite the fact that this chapter focuses on renewables, the proposed tool deals with the full EB as, among other factors, a further development of inexhaustible resources would typically impact the remaining components of the nexus in consequence of the replacement of different kinds of energy that would otherwise be required. In order to depict these replacements, it is necessary to consider the complete energy balance. The majority of nations collect the information of their EB as a division of their country-level data; while the International Energy Agency (IEA) does the same through a standardized structure for the processing, storage, and the display of data [37], which represents an essential benefit for the CF put forward. The first step in using the proposed tool would be to provide an energy balance corresponding to a base scheme. Such an EB might depict either a current or a prospective energy scenario built on forecasts. The next step would be to supply a substitute EB representing the energy policy scenario to be examined from the view of the nexus (for example, by placing more attention on inexhaustible energies). Such an EB should reflect changes in the use of technologies and be consistent with energy policies that remain unchanged (the proposed tool estimates the accumulative EB simply by deducting the substitute and reference energy balances). The accumulative EB would depict the alterations in the energy circumstances as a result of the policy examined. A following phase of the arranged tool would be to assess the implications of the incremental energy balance in terms of water, land, emissions, and cost. The tool would multiply the accumulative EB by the matrix data they describe. In this case, with regard to every kind of energy (energy balance vertical arrangements) and for each energy supply chain scenario (energy balance rows), it would result in (i) the quantity of water, (ii) land needed per unit of energy, (iii) the quantity of discharges expelled in each of these scenarios, or (iv) the unit expenses provoked. This way of proceeding is depicted in Fig. 2.2, in which every of the above-mentioned matrices has been named, correspondingly (i) W → E, (ii) L → E, (iii) E → E, and (iv) C → E. These data matrices are external data and are, generally, nation specific. The outcome of this phase is the elemental accumulative employment of water and land assets (such as capacity of water or earth surface), accumulative expenses or accumulative discharges originated by the energy policy examined. The proposed tool yields data around the meaning of the examined policy nexus, not about the way the policy should be planned to curtail those connotations of the nexus. As an illustration, the tool may supply data about the earth area necessary to meet a particular solar energy target without taking into consideration the area that is actually ready for use. On the other hand, and as mentioned before, this perspective exclusively supplies data around the essential resources needed, not the characteristics, allocation, or opposing employment of these resources (this would serve as a possible future improvement of the analysis). For example, photovoltaic solar energy may require a fewer amount of water in comparison to what is required by fission energy though the kind of water it requires is distinct (such as fresh or salt water)—a circumstance that the tool may not reach.

18

2 Revision of Decision/Making Tools for Sustainable Planning …

Fig. 2.2 Estimation of the implications of water, land, emissions and cost of the energy policy evaluated. Source Own elaboration

The final step in the suggested CF would be to evaluate whether an accumulative employment of assets or discharges is tolerable. As discussed above, a given principle of action adopted may have the same performance in two different contexts but can be acceptable in only one of them [14]. As shown in Fig. 2.3, contrasting these tolerable levels with those of the EB available as another possibility (base scenario + accumulative) would generate four fragmentary indices (WI, LI, CI, and EI, see Fig. 2.3), each of which are indicated

Water increase (m3)

+

( (

Acceptable water (m3)

=

( (

emis-

sions (tCO2)

2)

2

)

Political implications of the land

( . .)

=

Global index (

+ ( Acceptable emissions (tCO2)

=

Acceptable Cost

Legislator's entry

2)

Political implications of emissions

2)

( . .)

+

Cost increase ($)

Political implications of water (%)

Acceptable land (m2)

Increase in

)

( . .)

+ Increase in land (m2)

3

3)

($) =

($) ( . .)

X

Policy implications of costs (%)

Legislator's entry

Fig. 2.3 Adoption of policy and data input to assess the water, land, emissions and cost likely consequences of the energy policy decisions examined for incorporation into a context-specific global index. Source Own elaboration

2.4 Conceptual Framework Definition

19

in p.u. values. Should some of these indices show an index higher than the unit, it would signify that the satisfactory maximum has been surpassed; the contrary would occur if the indicator is less than the unit. Lastly, the fourth fractional index shall be added to a general index according to the political significance that each feature has in the nation to be evaluated—in countries where a resource is considered critical, legislators may decide that its corresponding index is of greater importance. The outputs resulting from the suggested CF might provide a basis for comprehensive qualitative and quantitative examinations. Although a particular number of qualitative features are satisfied by way of the data supplied by end-users in various phases of the suggested tool, comprehensive evaluation is necessary.

2.5 Conclusions Today, most policy decisions with potential consequences for the water, energy, and food nexus are made through different institutions (such as distinct government departments or distinct positions in the public hierarchy of the government in power) without the necessary degree of coordination. The challenges facing the water, energy, and food nexus are, in part, a consequence of this “fragmented” policy applied to interrelated resources. With these circumstances, a completely coordinated dealing with the situation to asset outlining would be desirable. Although such a fully integrated approach represents a challenge, a practical initial position would be to examine in what way conclusions have an effect on those remaining elements that constitute the nexus. In particular, and taking a policy centered in the power derived from the utilization of physical or chemical resources perspective, it would suggest a perception of the consequences of water and food for energy decisions. In this sense, analytical frameworks might, in a high degree, be used in several ways. In this sense, this chapter evaluated a number of available tools. The most significant information discovered as the result of the investigation has been the following: Access to data is a key challenge. In order to carry out a proper assessment of the water, energy, and food nexus, it is necessary to have access to both data from each of these sectors and data able to express the quantity of their mutual connections. The compilation of standardized data might serve to succeed in dealing with issues in existence at the current time related to consistency, comparability, and scale in addition to the absence of statistical information that are collected, observed, or recorded at regular time intervals. Most of the tools able to be used or obtained for people responsible for or involved in formulating policies nowadays are comprehensive and complex and require a significant amount of data, human resources, time, and economic sufficiency. Similarly, this chapter has identified the necessity for exploratory tools able to yield extremely useful basic estimations that could provide a basis for further (more complex) developments. The frame of reference put forward in this chapter aims to propose an exploratory tool that has energy as an EP, which in turn provides a starting point that can ultimately

20

2 Revision of Decision/Making Tools for Sustainable Planning …

support the integration of energy within the so-called nexus of water, energy, and food. The CF could present “snapshots” of the impact of renewable energy development (in addition to alternative approaches) on related resources such as water and land occupation. For the proposed tool, every outcome constitutes a group of different energy policy decisions in which EB is accepted as key information. In this case, the tool put forward could roughly calculate or judge the value of the water, land, emissions, and cost involvements of every outcome in order to “combine” them into a global indicator that specifically regards the principle of actions proposed by the governments’ choices for each particular circumstance. The “product” presented by the tool put forward in this chapter might comprise an early step in the direction of a further exhaustive examination of the impact of the development of inexhaustible energies on the water, energy, and food nexus for several circumstances. Then, the proposed conceptual framework constitutes a novel approach for energy policy makers which only consider partial impacts of the energy management. By considering the nexus of energy, water and food, energy management policies may be redefined and differences with current policies must be investigated.

References 1. Medema W, Furber A, Adamowski J, Zhou Q, Mayer I (2016) Exploring the potential impact of serious games on social learning and stakeholder collaborations for transboundary watershed management of the St. Lawrence River Basin. Water 8(5):1–24 2. Eddington N, Eddington I (2011) Reconceptualising vocational education and training systems in broader policy domains: monitoring and evaluation. Res Comp Int Educ 6(3):255–272 3. Weitz N (2015) Cross-sectoral integration in the sustainable development goals: a nexus approach. Stockholm Environment Institute, Stockholm 4. Policy Recommendations from the Bonn2011 Nexus-Conference (2011) In: Bonn2011 conference: the water, energy and food security nexus—solutions for a green economy, 16–18 Nov 2011. https://www.water-energy-food.org/fileadmin/user_upload/files/documents/bonn2011_ policyrecommendations.pdf. Accessed 1 Apr 2019 5. Roy D, Barr J, Venema HD. Ecosystem approaches in integrated water resources management (IWRM) a review of transboundary river basins. International Institute for Sustainable Development (IISD), Winnipeg 6. Chéné JM (2009) Integrated water resources management: theory vs practice. Nat Res Forum 33(1):2–5 7. FAO (2017) Watershed management in action: lessons learned from FAO field projects. Food and Agriculture Organization of the United Nations, Rome 8. OECD. DAC guidelines on aid and environment. OECD, Paris. http://www.oecd.org/dac/env ironment-development/1887756.pdf. Accessed 1 Apr 2019 9. Dhaka Water & Sanitation Authority Government of Bangladesh (2008) Dhaka water supply and sanitation project: environmental management framework. World Bank, Washington 10. Al-Zubari WK. The water-energy-food nexus in the Arab region: understanding the nexus and associated risks. Water, Energy & Food Security Resource Platform, Bonn. https://www.water-energy-food.org/fileadmin/user_upload/files/documents/giz/nexusregional-dialogues/mena/Policy_Briefs/Policy_Briefs_1_English.pdf. Accessed 2 Apr 2019

References

21

11. ECOSOC (2016) Good practices and policies for intersectoral synergies to deploy renewable energy: the water-energy-food ecosystems nexus approach to support the sustainable development goals. United Nations Economic and Social Council, New York 12. De Strasser L, Lipponen A, Howells M, Stec S, Bréthaut C (2016) A methodology to assess the water energy food ecosystems nexus in transboundary river basins. Water 8(2):1–28 13. Savage R, Spooner S, Kravva V, McMahon A, Parker J, Ross P (2016) TOPIC GUIDE: managing the water, energy, food and land nexus in the context of climate change and food security. Department for International Development, London 14. Flammini A, Puri M, Pluschke L, Dubois O (2014) Walking the nexus talk: assessing the water-energy-food nexus in the context of the sustainable energy for all initiative. Food and Agriculture Organization of the United Nations, Rome 15. World Bank (2013) Thirsty energy: securing energy in a water-constrained world. World Bank, Washington, DC. www.worldbank.org/en/topic/sustainabledevelopment/brief/water-ene rgy-nexus 16. Pollit H, Barker A, Barton J, Pirgmaier E, Polzin C, Lutter S et al (2010) A scoping study on the macroeconomic view of sustainability: final report for the European Commission, DG Environment. Sustainable Europe Research Institute, Cambridge 17. Tol RSJ (2006) Integrated assessment modelling. Hamburg University and Centre for Marine and Atmospheric Science, Hamburg. https://core.ac.uk/download/pdf/7079921.pdf 18. Bieber N, Ho Ker J, Wanga X, Triantafyllidis C, van Dam KH, Koppelaar RHEM et al (2018) Sustainable planning of the energy-water-food nexus using decision making tools. Energy Policy 113:584–607 19. Endo A, Kumazawa T, Kimura M, Yamada M, Kato T, Kozaki K (2018) Describing and visualizing a water–energy–food nexus system. Water 10(9):1245 20. UNECE (United Nations Economic Commission for Europe), KTH (Kungliga Tekniska Högskolan) (2014) Water-food-energy-ecosystems nexus: reconciling different uses in transboundary river basins. https://www.unece.org/fileadmin/DAM/env/documents/2014/WAT/09S ept_8-9_Geneva/Methodology_1Sept2014_clean_forWeb.pdf 21. Bleischwitz R, Hoff H, Spataru C, van der Voet E, VanDeveer SD (2014) Routledge handbook of the resource nexus. Routledge, Oxford 22. FAO (2014) The water-energy-food nexus at FAO: concept note. The Food and Agriculture Organization of the United Nations, Rome. http://www.ourenergypolicy.org/wp-content/upl oads/2014/06/FAO.pdf 23. Mohtar R, Daher B (2014) Water-energy-food nexus: a basis for strategic resource planning. https://agrilifecdn.tamu.edu/wefnexus/files/2017/01/DaherMohtar_Jour_Date_WEFNexusA BasisResPlan.pdf 24. Loulou R, Goldstein G, Kanudia A, Lettila A, Remme U (2016) Documentation for the TIMES Model PART I. IEA, Paris. https://iea-etsap.org/docs/Documentation_for_the_TIMES_ModelPart-I_July-2016.pdf 25. Rosemarin A, Hoff H, Kalberg L, Fodge M, Kuylenstierna J, Granit J (2013) Unpacking the water-energy-food nexus: tools for assessment and cooperation along a continuum. In: Jägerskog A et al (eds) Cooperation for a water wise world—partnerships for sustainable development. Report No. 32. Stockholm International Water Institute, Stockholm, pp 45–50 26. Bazilian M, Rogner H, Howells M, Hermann S, Arent D, Gielen D et al (2011) Considering the energy, water and food nexus: towards an integrated modelling approach. Energy Policy 39(12):7896–7906 27. Hoff H (2011) Understanding the nexus. In: Background paper for the Bonn2011 nexus conference: the water, energy and food security nexus. Stockholm Environment Institute, Stockholm, pp 1–52 28. Andrews-Speed P, Bleischwitz R, Boersma T, Johnson C, Kemp G, VanDeveer SD (2012) The global resource nexus. The struggles for land, energy, food, water, and minerals. Transatlantic Academy, Washington, DC

22

2 Revision of Decision/Making Tools for Sustainable Planning …

29. Bizikova L, Roy D, Swanson D, Venema HD, McCandless M (2013) The water-energyfood security nexus: towards a practical planning and decision-support framework for landscape investment and risk management. International Institute for Sustainable Development, Winnipeg 30. European Report Development (2012) Confronting scarcity: managing water, energy and land for inclusive and sustainable growth. In: The 2011/2012 European report on development 31. ICIMOD (2015) Contribution of Himalayan ecosystems to water, energy, and food security in South Asia: a nexus approach. International Centre for Integrated Mountain Development, Kathmandu 32. UNECE (2014) Water-food-energy-ecosystems nexus: reconciling different uses in transboundary river basins UNECE water convention. UNECE, Geneva 33. WBCSD (2014) Co-optimizing solutions: water and energy for food, feed and fiber. World Business Council for Sustainable Development, Geneva. https://docs.wbcsd.org/2014/05/WBCSD_ Co-op_Report.pdf. Accessed 30 Apr 2019 34. World Economic Forum (2011) Global risks 2011, 6th edn. World Economic Forum, Geneva. http://www3.weforum.org/docs/WEF_Global_Risks_Report_2011.pdf. Accessed 30 Apr 2019 35. McGrane SC, Acuto M, Artioli F, Chen PY, Comber R, Cottee J. Scaling the nexus: towards integrated frameworks for analysing water, energy and food. Geogr J 36. McCarl BA, Yang Y, Srinivasan R, Pistikopolous EN, Mohtar RH (2017) Data for WEF nexus analysis: a review of issues. Curr Sustain/Renew Energy Rep 4:137–143 37. IEA (2014) Energy balances of non-OECD countries. IEA/OECD, Paris. ISBN 978-92-6421708-9

Chapter 3

Comprehensive Assessment of Gran Canaria Food-Energy-Water Nexus with GIS-Based Tool

3.1 Introduction Over the last centuries resource consumption has increased in such a way that compromises the planet’s ability to provide them in a sustainable manner [1]. Activities related to economic development have led to impacts in the environment. These include stress and contamination of water, land, fishing, as well as the GHG emissions related to the economic activity. Manufacturing, growing vegetables, feeding animals, or international trade are some of the most resources–taking activities related to societies. As life becomes more inwardly realized, there is growing needs of energy for the welfare of homes, for its construction, and decommissioning. This led international organizations to face this growing problem. In this sense, Brundtland Commission [2] stated in 1973 that sustainable development consists in securing water, energy and food supplies for current and future generations, while maintaining a healthy and unharmed environment. Not only UN constantly alerts and gives indications such as sustainable development goals [3], but also researchers that investigate the nexus among the water, energy, and food systems for a better understanding of the synergies, are intended to provide a decision framework for the decision makers. Food production requires land, water and energy; transporting and treating water requires energy; and energy production requires water and land as well, and even food [4], and other resources. Water is needed for irrigation as well as for human consumption, but also in industry or for energy electricity generation. In addition, energy, in its multiple forms, is required for food production processes, including tree felling, harvest, fertilizer production, and transport. Figure 3.1 describes how in a closed system these components enter and leave and how they are related through the components of which they are composed. Furthermore, it must be taken into account the CO2 that is generated during these activities. It stands out that, at the current rate of population growth, the agricultural sector is challenged with doubling food production by 2050 [5]. It is highlighted that 71% of current world water withdrawals are attributed to the agricultural sector [6]. By 2010 the energy sector consumed around 15% of the global water withdrawals [7] and © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 E. Rosales-Asensio et al., Sea Water Desalination in Microgrids, Green Energy and Technology, https://doi.org/10.1007/978-3-030-96678-2_3

23

24

3 Comprehensive Assessment of Gran Canaria Food-Energy-Water …

CO2

Water

Energy

Food

Fig. 3.1 WEF (water, energy, food) nexus. Source Own elaboration

contributed two-thirds of global GHG emissions [8]. Overall, the EU produces and supplies more food than its population needs—producing 3416 kcal/inhabitant/day. This is far more than the 2000–2600 kcal/inhabitant/day average daily energy intake requirement set by the EFSA (European Food Standard Agency) [9]. To enhance synergies between WEF systems and increase environmental sustainability, some institutions encourage innovation projects that optimize the resource management process. European Commission proposed the SME-Phase II [10] in order to provide funding for innovation projects that get involved in the process of sustainability, and green economy. Borge-Diez et al. [11] analyzed techno-economic issues of those facilities finding relationship between grants and water and energy sales. As long as strategies and policies are not aligned, and are carried out individually, it will be more difficult to meet the growing demand of resources. To deal with this situation, it is developed a comprehensive nexus approach [12] to sustain the decision-making process of the resource management strategies. There are several frameworks that investigate the trade-offs between these systems. Among the tools that have been proposed, it highlights: WEAP (Water Evaluation and Planning) [13] for addressing specific aspects of water resource planning. LEAP (Long-range Energy Alternatives Planning System) [14] developed for energy policy analysis and climate change mitigation assessment. MuSIASEM (Multi-Scale Integrated Analysis of Societal and Ecosystem Metabolism) [15] who proposed a method to characterize flows within society. Agrawal et al. [16] who analyzed different scenarios with LEAP and WEAP to forecast the water and energy supply system of the power sector. And Liu et al. [17], who developed an approach to conduct an input–output analysis of the WEF system. Due to their isolated nature, the water scarcity problem is more acute in small islands. As a consequence, islands are a promising candidate to investigate the

3.1 Introduction

25

sustainability of WEF systems. The research conducted in this chapter uses a nexus analysis tool to evaluate a system where alternative methods for providing water resource is needed. It analyzed the consequences of the deployment of a long-term renewable-EEG (Electricity Energy Generation) strategy combined with a Pumping Hydro Storage scheme to support the manageability of renewable EEG plants. Zhao et al. [18] described the problems of EEG systems that a renewable energy deployment might comprise, and highlighted frequency deviations in cases of sudden changes in supply or demand. PHES is a feasible option for large-scale storage in Europe, accounting for an installed capacity of around 42.6 GW in the EU members [19]. Jawahar and Michael [20] reviewed the most suitable turbines, and found that they mainly depend on the flux and height. Notton et al. [21] highlighted this system for the management of renewable energy. Increasing the manageability of the electricity system is especially important in isolated systems due to their generally lower interconnection ratio, and fuel supply difficulties. As long it was combined with small and medium sized desalination plants, this research assumed a micro-pumping energy system storage deployment combined with the desalted water from desalination plants. The PHES schemes are easily scheduled for production, and allow accounting the stored energy. The high efficiency of the energy conversion process, often in excess of 90%, makes it a sustainable and efficient mean to support the intermittent nature of renewable energies. Additionally, the energy that is stored becomes renewable when the energy used for pumping comes from renewable sources. Cabrera et al. [22] proposed this energy storage option to achieve a 70% renewable EEG system in Gran Canaria. This chapter analyzed the 50 most representative bodies of water on the island of Gran Canaria to investigate the amount of energy that could be stored in the PHS strategy that makes use of water from desalination plants. The aim was to provide energy storage for the alternative energy plan based on renewable energies. Notton et al. [23] studied the electricity peak-shaving capacity of such a scheme to schedule the stored energy in high-demand hours. Rosales-Asensio et al. [24] proposed a method to optimize the size of renewable powered desalination plants. This chapter proposed locations and investigated the feasibility and implications on the WEF system. The region was analyzed with a GIS (Geographical Information System) tool to find out the energy storage capacity. In the scientific literature Fitzgerald et al. [25] implemented a method to find the potential to support small-scale renewable energy plans, and to locate the best sites for the upper reservoir. Those included a topographical as well as a constrains analysis. Among these, it highlights distance between reservoirs, minimum head, and distance to the electricity transmission grid. Note that one of the outstanding differences between fossil-fuel and renewable energy sources consists in its transport, provoking the deployment of renewable technologies problems, like grid stability in the region where the resource is higher. This chapter did not assume those constrains as a grid stability study would be out of the scope for the chapter here presented. Soha et al. [26] followed similar restrictions to evaluate the potential in a region of Hungary. Ghorbani et al. [27] estimated the potential of a region from four different topologies to evaluate the candidate sites for implementing

26

3 Comprehensive Assessment of Gran Canaria Food-Energy-Water …

a feasible PHS strategy. Lacal et al. [28] proposed a methodology regarding to a regional approach, with constrains focused in the conditions that must fulfil a site to be candidate. Most of the techniques to provide water from non-conventional sources require a high energy consumption (see Table A.1). It implies increasing water costs and, consequently, higher agricultural production costs. Other less energy-intensive techniques, but less able to deliver high desalination fluxes, and highly dependent in weather conditions, also stand out [29, 30]. In Gran Canaria, RO (Reverse Osmosis), MSF (Multi Stage Flash), and VC (Vapor Compression) technologies account for an installed desalination capacity of 138,000 m3 /day [31]. To reduce their high operating costs, it is common the self-consumption, and sales of electricity from renewable energy sources, but it brings a variety of problems, including intermittency, or optimization of the available resource these strategies must deal with [32]. In the scientific literature Liu and Fernandez [33, 34] proposed strategies for the integration of renewable-based technologies in the techno-economical system. To undertake the WEF nexus analysis, several analytical frameworks have been developed. Some focused in the WE nexus, analysing the design and operation of these systems [35–38]. The variety of frameworks and approach, with different inputs, outputs, and viewpoints has its origin in the complexity of the nexus [39]. Variants, such as the one proposed by the United Nations Economic Commission for Europe and the Royal Institute of Technology adopted hydric resources as an entry point [40, 41]. To analyze the WEF system, the proposed tool needed input data, starting with the water consumption baseline scenario. At the time of writing this chapter, the WEF system in Gran Canaria had particular conditions of water consumption. The consumption in the energy, human consumption, and food system made up the baseline scenario to develop the analysis. Since these tools measure the sustainability of the system, a consumption parameter must be given to compare different scenarios. This chapter analyzed the water index from alternative renewable-based energy strategies combined with the PHS scheme linked to desalination plants. Within the proposed scheme, the energy is stored by pumping water from the desalination plant lower reservoir to an upper reservoir; fact that enabled the linkage to the water-food subsystem. This additional water storage capacity affects to the WEF indexes as well. In the materials and methods section, it is introduced the tool used to develop the WEF analysis, as well the initial conditions in the system. Furthermore, it presents the alternative renewable energy plan to be analyzed, as well the GIS analysis to find locations for the pumping energy storage strategy. The result’s section, presented the WEF analysis of three strategies to reach in the long-term the objective of 1 GW of renewable energy. It also presents the results of the GIS analysis that support that plan highlighting the most promising locations. The analysis and discussion section discusses the implications for the subsystems of the nexus, and it is proposed a system to control the water production system through the control of the desalination plant’s operating factor.

3.2 Material and Methods

27

3.2 Material and Methods A monitoring framework should assess the resource costs of energy policy plans [42, 43]. This work analyzed the consequences of deploying various alternative energy plans with a resource monitoring tool. In this tool, the energy balance is given as an exogenous input, independently of its feasibility. This section shows the baseline energy balance, that corresponded to the EEG in Gran Canaria, and proposed some alternative energy balances—based on assumptions made to boost renewable energy systems—to investigate the consequences on a designed water index. This chapter used a tool that estimated the water (land, emissions) costs that an alternative energy plan might cause. The output of the tool is an index that evaluates the costs of water (land, emissions) associated to the deployment of the proposed energy plan. The baseline water scenario used was: water for human consumption, crop irrigation, cattle feed, and water consumption from different power generation technologies. The analysis left apart subsystems such as industry or gardening due to the difficulty in accounting for the water costs of these systems. Given the low share of the industry in the island’s economy [7], it will not have a major effect in the index depiction.

3.2.1 Fuel and Electricity Price Volatility To conduct the sustainability analysis of the system, this section reviews the water and emission costs of the food, as well of the EEG system in Gran Canaria. The human average water consumption per inhabitant in the Canary Islands is assumed to be 160 l per day [44]. Provided that the inhabitants of Gran Canaria are 845,000 [45], it implies a total water consumption of 49.35 Hm3 /year. Furthermore, it was accounted the water costs associated to the food production subsystem of the island. The water costs depend on the type of product grown, and in the type of production. Gerbens-Leenes et al. studied the water footprint of different agrarian products [46]. In Gran Canaria, 37% (11,884 ha) of the agricultural land is cultivated, and the total production in 2006 reached a maximum, and lowered until 309,600 t in 2011 [47]. Table 3.1 presents the global water, and emission footprint as well the baseline scenario in the studied food system. A correction factor of 1.2 was applied to account for system losses. The impacts that the EEG system has on the elements of the nexus (water, land, emissions) includes the impacts generated during the following operations and phases: fuel cycle, extraction, processing and transport, as well as the components associated. Also, those related to the construction of the plant, its operation, and its decommissioning. For what PV is concerned, the key drivers of the lifecycle GHG emissions from PV systems include: location, system lifetime, mounting type, upstream electricity fuel mix, performance ratio, and the efficiency of the PV cell. A lot of factors interfere in the process. For example, lifetime, radiation level, and

28

3 Comprehensive Assessment of Gran Canaria Food-Energy-Water …

Table 3.1 Global water footprint of food system Product

Global water footprint (l/kg)

Baseline scenario (Hm3 )

Global emissions Baseline scenario footprint (CO2 /kg) (t CO2 )

Tomatoes

180

30.6

1.4

238

Potatoes

250

7

0.5

14

23.3

7

33.98

Pork

4800

Chicken

3900

9.98

6

15.35

Apple/pear

700

70

0.4

40

Banana

800

68

0.7

59.5

Milk

250

22

3

264

Human consumption

49.34

Total consumption

280.22

664.83

Source [48–52]

efficiency degrading rate. Besides, the impacts of operating PV, and wind power stations is negligible. On the other hand, technologies such as coal, nuclear, natural gas cycles might cause higher impacts due to the impact during the operation. Table 3.2 shows the water withdrawals, and the CO2 equivalent emissions associated to the most remarkable EEG, including the baseline scenario related to the system.

3.2.2 Sustainability Index Depiction To facilitate the process of estimating the consequences of choosing between alternative energy schemes, different indexes, such as water, land, emissions, or costs index can be analyzed. This chapter focused in the water, and emissions index. Table 3.3 shows the baseline scenario of water consumption for the studied system as well the actual rates of CO2 equivalent emissions. Equation (3.1) depicted the parameters for the calculation of the sustainable index of the water, land, and emissions system. Likewise, the baseline scenario shows the initial conditions of the water (land, emissions) system related to the WEF system. While AU (Acceptable Use) refers to an accepted external entry (hypothetically to maintain the sustainability of the water, land, emissions system), IU (Incremental Use) reflects the consequences that the deployment of different energy plans had on those indexes. As RosalesAsensio et al. [58] indicated, the acceptable scenario corresponded to a legislator entry. Equation (3.2) divides the qualitative indicator into various terms that would help to estimate the legislator entry. To calculate the (BU) baseline use scenario this chapter included the EEG subsystem, water consumption in agriculture, livestock farming, and water consumption of the region’s inhabitants. From this baseline

3.2 Material and Methods

29

Table 3.2 Life-cycle water withdrawal on energy sources Technology

Water impacts (m3 /MWh)

Baseline scenario (Hm3 )

Emissions impacts (gCO2 /kWhe)

Baseline scenario (t CO2 )

Biomass Wet cooling might 1.89–2.271 be saved with dry 0.378 cooling, reduced 151.4–378.5 for dedicated energy crops

650

Coal Thermal power station

15.16

888

Fuel Thermal power station

14.13

21.37

780

1,243,200

Nuclear Nuclear power station

41.69

24.2

Natural gas Simple circuit

15.918

Combined cycle

8.34

12.21

250

499 352,500

Photovoltaic

3.79

0.22

300

17,250

CSP

34.11 0.245

123.7

2013

Solar

Wind Wind powered power plant (onshore)

0.985

Total

34.04

1,614,963

Source Adapted from [51–57] Table 3.3 Indexes associated with a renewable-based energy deployment strategy Index Water index Emissions index Source Own elaboration

Incremental use

Baseline use

m3 )

BWU

(Hm3 )

Alternative EP

313.8

IWU (Mill

Acceptable use AWU (Hm3 ) 321.8

IEU (t of CO2 )

BEU (t of CO2 )

AEU (t of CO2 )

Alternative EP

1,616,253.23

Legislator’s entry

30

3 Comprehensive Assessment of Gran Canaria Food-Energy-Water …

scenario, the incremental use implications of the alternative energy plans presented in the following section were analyzed. IU (Incremental Use) corresponded to the incremental use regarding to the deployment of those alternative EEG-energy plans. BU + I U = Sustainabilit y I ndex ( p.u) AU

AWU =

n 

Raingauges values +

i=0 n 

+

n 

(3.1)

Desalted water × C.F

i=0

Capacit y o f r eser voir s

(3.2)

i=0

As from the analysis of the WB of the island, Gran Canaria harbours more than 69 big reservoirs with a total storage capacity of 78 Hm3 , and more than 110 small basins [59]. The average yearly rainfall in Gran Canaria approximates to 300 mm, the evapotranspiration 195 mm, the water infiltration 57 mm, and the water runoff 43 mm. The water subsystem is complemented by a total desalination capacity on the island of 138,000 m3 /day [31]. The predominant desalination system on the island is RO, with a total of 80% of the installed capacity (see Table B.2). With this capacity the RO plants must work 45.28 days to fill the storage system. According to the presented data, the AWU (acceptable water use) term comprises, the total capacity of reservoirs, 78 Hm3 of water, plus the rainfall in the cultivated land of the island, the 37% of the surface, it is 173.16 Hm3 , and water infiltration in the rest of the island’s surface, it is 43.19 Hm3 . This system is complemented with the RO desalination plants working at a 30% of operation factor (14.49 Hm3 ), and the proposed extra storage system of desalted water (5 Hm3 ) which implies an increase in the operating factor of the desalination plants of 12.3%. Those accounted for a total AWU of 313.8 Hm3 .

3.2.3 Alternative EEG-Plans As from the total of EEG installed power of Gran Canaria island, the thermal groups involve 1024 MW [60]. The proposed renewable energy plan intends to reach in the long-term 1 GW of installed power from renewable sources [61] supported by the PHS scheme. The analysis was conducted in two steps to reach the proposed energy objective. The short-term plan intended to reach half the power proposed for the long-term strategy. As can be seen in Table 3.4, the majority of the energy generated on the island comes from VT (Vapor Turbine), and CCG (Combined Cycle gas). It also presents the water costs associated with the seven alternative scenarios (AS) proposed. These

3.2 Material and Methods

31

Table 3.4 EEG structure, generated energy, and water withdrawal of the different scenarios Technology

Energy (GWh)

AS (I) Ww (Hm3 )

AS (II) Ww (Hm3 )

AS (III) Ww (Hm3 )

AS (IV) Ww (Hm3 )

AS V) Ww (Hm3 )

AS (VI) Ww (Hm3 )

AS (VII) Ww (Hm3 )

Vapor turbine

1409.83

21.37

21.37

21.37

21.37

21.37

21.37

0

Combined cycle

1464.89

6.1

6.1

6.1

0

0

0

10.81

Wind power 248.97

0.77

0.35

0.125

1.44

0.69

0.245

1.38

Solar power 57.53

0.22

1.33

2.77

0.22

2.67

5.55

0.0002

Total

28.46

29.15

30.36

23.03

24.73

27.16

12.19

3181.2

Source Adapted from [56–62]

scenarios are related to the deployment of a full wind renewable energy plan (I and IV), a half solar-wind energy plan (II and V), and a full-solar energy plan (III and VI). These six alternative plans virtually replaced CCG power stations in the EEG system of the island. Finally, the alternative scenario (VII) virtually replaced VT power stations with wind power stations, for the long-term strategy. It also shows the initial conditions of power generation in the Gran Canaria system.

3.2.4 Hydro-Pumping Supporting Strategy The energy storage potential of the 50 proposed sites was evaluated per 0.1 Hm3 for the long term (see Table C.1) proposed for this alternative energy plan. Despite the fact that the turbine selection to develop this method of energy storage depends on the altitude as well on the work-flux, this chapter analyzed a crossflow turbine [63]. This turbine might provide a flow between 0.03 and 16 m3 /s, at 85% of efficiency, and a power of 7 MW. The deployment of the PHES strategy was assessed through a geo-morphological analysis of the 50 Gran Canaria’s water bodies [59]. The analysis is developed with QGIS, a GIS software [64] that analyzed the energy capacity, as well the constraints of the short-term strategy that must fulfil the proposed locations. QGIS is a software that used data downloaded from information providers [65] to conform the model, and applied physical and energetic constraints to find the places that fulfil them. In the scientific literature, Bocin-Dumitriu [66] distinguished constraints related to the regional approach, and a wider analysis. Provided that those analysis try find out the energy storage potential, energetic constraints like head, and distance between reservoirs were used. Further restrictions (see Table B.1) were imposed to identify the better sites that support the short-term strategy of the proposed energy plan. Table 3.5 shows the steps given to develop the geo-morphological analysis that locate and find the maximum potential energy that could be achieved.

32

3 Comprehensive Assessment of Gran Canaria Food-Energy-Water …

Table 3.5 Analysis steps Step

Action

1

Include DEM (digital elevation model) of Gran Canaria

2

Locations of WB (green), fossil-fuel power stations (purple), and desalination plants (orange) (Fig. 3.3)

3

Analysis of the maximum potential energy. Distance–head between WB and sea level (see Table C.1)

4

GIS analysis of the locations. See Fig. 3.2

Source Adapted from [67]

NO

SITES OF ¿Capacity?

DESALINATION PLANTS

Buffer 5 km ¿Σ Capacity?

WATER BODIES

YES SITES PSS, WB

GIS MODEL

DEM

S-T Strategy

DATA BASE

Fig. 3.2 Strategy for a renewable-PHES EP GIS analysis. Source Own elaboration

The energy storage capacity that those schemes may achieve was solved with the general statement given in Eq. (3.3) [68]. E SC × 3600 = ρ × g × V × h × 10−6 (MWh)

(3.3)

As previously mentioned, the energy plan proposes a total of 1 GW of EEG from renewable sources in the long term. It should be taken into account the already installed power from renewable energy sources [54, 60], and the power from the aforementioned microturbine in the 50 WB. In this sense, the proposed EEG plan must require 454.2 MW of new renewable installed capacity. Additionally, it was developed in two stages. Figure 3.2 proposes the algorithm to perform the GIS analysis as from the 50 WB, the sites of the desalination plants, and thermal power stations.

3.3 Results

33 Gáldar

Arucas-Moya

Jinámar

Barranco La Aldea

Airport Region

Arinaga Puerto Rico San Bartolomé de Tirajana

Water Bodies R.O. Desalination Plants Thermal Power Stations

Fig. 3.3 WB, RO-DP, and fossil-fuel based power plants allocation. Source Own elaboration

3.3 Results This section presents the results of the short-term strategy analysis for implementing the proposed PHES scheme that supported the full renewable energy plan deployment. Figure 3.3 shows the locations according to the analysis depicted in Table 3.5. It shows WB (Water Bodies) as Green points, RO Desalination Plants, as points in orange, and Thermal Power Stations as purple points. Those locations were manually georeferenced and added to the GIS model to develop the GIS analysis. The DEM (Digital Elevation Model) was used to analyze the total energy as well the paths from the WB to the desalination plants. The maximum energy that could be obtained from the reserve system under study was, according to Eq. (3.3), 3076 MWh (see Table C.1). For what desalination capacity is concerned, the locations that fulfilled the desalination capacity restriction were: Gáldar, Arucas-Moya, Telde, Jinámar, San Bartolomé de Tirajana, Puerto Rico, and Barranco la Aldea (see Table A.2). As can

34

3 Comprehensive Assessment of Gran Canaria Food-Energy-Water …

be seen in Table 3.6 the energy storage potential reached 1144.15 MWh/0.1 Hm3 in 15 locations. With the proposed turbine placement, it would mean 105 MW of extra power. The days needed to fill the upper reservoirs to develop this scheme depended on the desalination capacity of the surrounding desalination plants, varying from 8 to 44 days. Figure 3.4 shows some locations that fulfilled the restrictions imposed in Table B.1. Those showed better energetic characteristics to implement the proposed scheme. In this sense they could be a strong candidate for the SME’s innovation project accelerator of the EU. A complete list of candidate sites with land registry information can be consulted in Appendix F. Table 3.6 Short-term strategy location Location

ESP (MWh/0.1 Hm3 )

GC

PHES power (MW)

Actual desalination installed capacity (m3 /day)

Days to fill the system

Gáldar

222.95

(433, 311) (436, 311) (434, 310) (436, 311) (437, 311)

35

13,000

38.46 (11.1%)

Barranco la Aldea

226.625

(428, 309) (430, 309) (431, 396) (432, 309)

28

5000

80 (22.8%)

Arucas-Moya

75.95

(445, 311) (446, 311)

14

4500

44.4 (12.6%)

San Bartolomé de Tirajana

98

(441, 307) (450, 307) (444, 308)

21

15,000

20 (5.71%)

Jinámar

24.5

(454, 308)

7

11,500

8.69 (2.4%)

Source Own elaboration

3.3 Results

35

Fig. 3.4 Location sitting proposal. Source Own elaboration

3.3.1 WEF Index This section presents the results that the proposed alternative energy plans provoked on the other nexus’ systems, in the short, as well in the long term. As from the previously presented baseline scenario (BWU), and AWU, this section reviewed the consequences of the deployment of the alternative energy plans (I–VII) on the index (see Eq. 3.1). Results in Table 3.7 showed that the higher reduction was achieved for the full wind deployment scenario. In the long term, the water consumption could be lowered from 8.11 to 20 m3 depending on the scenario. It should be underlined that the largest GHG emission reductions were achieved through the virtual closure of steam turbine power plants. In addition, the virtual closure of these plants implementing the Rankine thermodynamic cycle caused the biggest reduction in the results of the water index. The CO2 emissions could be converted into e by applying an exchange ratio. It should be noted that since 2017 there has been a sharp increase in the cost of emission allowances from 5.65 to 20 e/t in 2020 [69]. Taking this into account, the proposed scheme would allow savings of more than 25 Me, as well as making progress towards the fulfilment of the objectives for GHG emission reduction internationally defined [70]. From this point of view, it would be more advisable to reduce fuel-fired power

36

3 Comprehensive Assessment of Gran Canaria Food-Energy-Water …

Table 3.7 Calculation of water and land indexes associated with a PHS scheme Energy Incremental Long-term plan water use water index (Mill m3 )

(I) and −10.71 (IV) (II) and (V)

−8.81

(III) and (VI)

−8.11

(VII)

−10.71

Short-term Long-term L-T Short-term S-T WI change WI GHG EI change GHG (%) change emission (%) emission (%) reduction reduction (t of (t of CO2 ) CO2 )

−10.71+313.8 321.66

= −2.65

−3.4

1120.89

−0.78

560.44

0.9422 −8.81+313.8 321.66

=

−2.3

−2.8

960

−0.647

480

=

−2.3

−2.6

686.8

−0.601

343.4

−3.1

−6.2

2750.35

−1.43

1385

0.948 −8.11+313.8 321.66

0.950 −20+313.8 321.66

=

0.913 Source Own elaboration

plants, as there is a greater reduction in emissions than in combined cycle power plants.

3.4 Analysis and Discussion To ease the decision-making process that affects interrelated systems for a long period of years, the increase of the internal knowledge, as well of trade-offs between the systems might ease the analysis of the consequences of the decisions taken. RosalesAsensio et al. [71] proposed measures to overcome the barriers that the deployment of renewable energies found in Spain, and reviewed the wind technologies, and economic conditions in the Spanish market [72]. The analysis here developed tested different EEG plans, allowing the decision makers to estimate the consequences on other elements of the nexus. For what the GIS analysis is concerned, around the South-East region of Gran Canaria were located reservoirs with high energy potential, but they did not fulfil the restriction of distance to densely-populated areas. Parallel, by the time of writing of this chapter, a PHS facility of 220 MW of power [73] was under construction in the South-west of Gran Canaria, and due to this fact, it was left out of the analysis in this chapter. Both the northwest and southeast regions of the island stand out for their energy potential to develop the scheme. That region houses several small reservoirs, and lower population density. Some of the better locations that become candidate sites to develop the scheme were shown in Appendix F. A total of 3.076 GWh/0.1 Hm3 of energy storage could be achieved from the site system to sea level in the long term,

3.4 Analysis and Discussion

37

about 30% of the daily generated energy in Gran Canaria (see Table C.1). Said that, it must be highlighted that, to maximize the synergies between water-energy system, the water should be delivered at different heights to take advantage of bioclimatic floors [74]. Note that the desalination capacity could not afford enough desalted water to fill the system in a day and, since it did not meet the constraints of the short-term strategy, it cannot be easily implemented. As from the daily desalination capacity of the island, it will take 36 days to pump water to the upper reservoirs system. However, extra water from surrounding aquifers could be added to the desalted water upper storage site, lowering the number of days to fulfil the system. Additionally, as can be seen in Figs. 3.3 and 3.4, some WB were located too close and would take more days to pump water. To carry out a more exhaustive analysis, the paths should be further investigated to find the most favourable one in terms of energy efficiency, and benefits of the scheme. According to the considerations of the proposed energy plan, in the long term it comprised an installed power of 454.2 MW. For Gran Canaria, it was expected a wind capacity factor of 0.32 [75], it was predicted a generation of energy of 1220.89 GWh/year in the scenarios IV and VII, it is the 83% of the generated energy in the baseline scenario. In the long term, the storing scheme must work 1.05 h/day to supply the 17% that the deployed energy plan could not provide. Besides, for a solar P–V capacity factor of 0.18, it was expected 960 GWh (scenario V), and 686.8 GWh (scenario VI), fact that imply the storing reservoirs system should work 1.21 h, and 1.7 h respectively. Secondly, the short-term strategy found 15 locations that comprised 105 MW of installed power for the proposed scheme. It was expected 610.44 GWh of energy for the full wind deployment, 41.5% of the energy generated in the thermal power station of the system, 481.91 GWh in the half wind-solar deployment, and 343.37 GWh for the full solar deployment. In the short-term energy strategy, the PHES option allowed the addition of a maximum storage capacity of 1144.15 MWh/0.1 Hm3 . It must be taken into account that seawater desalination consumes approximately 9% of total electricity demand on Gran Canaria island and, this is in spite of the fact that energy consumption of the desalination process evolved from 22 to 2 kWh/m3 in 2010 [76]. To face this high energy cost, it is common to build desalination plants governed with renewable energy. Rosales-Asensio et al. [77] analyzed the consequences on the LCOE, as well in the LCW (levelized cost of water) of a hybrid desalination plant and found that an appropriate location allowed both indicators to be reduced. The scheme here proposed is likely to further improve these indicators, although it should be accounted the energy needed in the pumping process and other costs. Both desalination capacity of the island (controlled by the operating factor), and the rainfall data are inputs of the proposed system. The system allows to face scenarios of increasing water demand, especially in a region like the studied here, that suffer from water stress, lack of rainfall, and high tourism rates [78]. Figure 3.5 proposes a retro-feed control system that models the water index. This model incorporates data of water footprint of the sectors studied in this chapter, but it would also allow for the effect of adding other sectors such as industry, or water consumption patterns or climate prediction. The transfer function should deal at least

38

3 Comprehensive Assessment of Gran Canaria Food-Energy-Water …

EEG Infrastructure. ENERGY

Petrol imports

Energy Consumption

FOOD

Food,

Internal production,

Industry

DPI, DPE

Rainfall, Desalted Water, & Reservoirs

GardeningÖ

Water Index

Feedback

Legislator Entry

Fig. 3.5 Proposed control system of the WE nexus. Source Own elaboration

with rainfall data, water storage monitoring information systems, and consumption data from the system. The output of this model is a global water index that can be controlled just increasing or decreasing the operation index of the desalination plants. Especially on islands, where desalination is essential, a strategy such as the one presented here will maximize the synergies between desalination plants, the power generation system and the agricultural sector. The better modelled the system, the easier planning the development of the food subsector. Additionally, the more exhaustive are the water withdrawals (BWU) accounted, the better modelled the water control system. This will lead to enhance the management of the index that help to face the environmental and social consequences of the water footprint [79]. Since agriculture accounts for 2% of the economy of Gran Canaria island (see Table E.2), it would be desirable to boost this economic sector [74]. The water index system control would provide scope for enabling water for agriculture in case of lowprecipitation scenarios. Optimization models for sustainability analysis often take into account constraints like feed demand or planetary boundaries [80]. Increasing, or decreasing the operating factor of desalination plants might allow to maintain the water index near to a fixed sustainability index in a region, or closed system. Once the water is used to generate electricity in high-demand hours, it would be used to irrigate crops, to feed animals in the surrounding areas, and pumped from the locations of the desalination plant taking advantage of the surpluses of renewable energy during the valley of the daily energy consumption curve. The proposed waterenergy storage strategy would allow to control energy as well water to maintain a defined sustainability index in an interconnected WEF system. Furthermore, as long

3.4 Analysis and Discussion

39

as it is suited for small microgrids, the proposed scheme gives a perspective to manage systems that would lead to social inequities such as energy or water transport [81]. In this sense, the better the transfer function is defined, the better will be the development of the water index control system. The development of the system should include data management services because these would improve the water-energy system. In this process, weather forecasting IS (Information Systems), or AI (Artificial Intelligence) systems will play an important role through analysis of data for a further development and scheduling of water and energy resource.

3.5 Conclusions This study analyzed the consequences on a sustainability index of different renewable-based energy plans. It proposed a hydro storage energy strategy to increase the manageability of renewable energy sources, and support them. This chapter proposed 50 WB of Gran Canaria to locate the PHES scheme, and developed a decision-making process to find the best energetic and technological locations for the short, as well for the long term. Within the applied constraints of the short-term strategy, it was found a total energy storage potential of 1.144 GWh/0.1 Hm3 in the 15 candidate sites that could be easily developed as innovation projects. For the long-term strategy, it was found 2.87 GWh/0.1 Hm3 of energy storage capacity in the given system. For the capacity factor of the proposed renewable energy sources, the long-term energy plan was not able to generate the same energy that the power plants it aimed to replace. Although, only working 1 h/day, the PHS scheme was able to support the renewable energy plan. The system of reservoirs needed more than 10 working hours a day to generate the same energy that was generated in thermal power plants. Once established the initial conditions in Gran Canaria island, the sustainability tool evaluated the consequences of the deployment of renewablebased energy plans, helping in the decision-making problem of choosing between different energy strategies. The index analysis showed that water consumption could be lowered from 8.11 to 20 m3 depending on the renewable strategy. The most favourable alternative scenario (VII) let reduce the water index more than 6% from the actual water withdrawals. These savings allowed the decision-making process to investigate the deployment of rural micro-grid boost strategies with greenhouses, or crops in a closed system. The proposed control system allowed the control of desalination plants, linked to the energy strategy, developing an integrated control. Given the dual water-energy use of the resource, the indexes could be lowered letting the proposed energy plan to reduce the installed power from renewable sources.

40

3 Comprehensive Assessment of Gran Canaria Food-Energy-Water …

References 1. Daher BT, Mohtar RH (2015) Water–energy–food (WEF) nexus tool 2.0: guiding integrative resource planning and decision-making. Water Int 40(5–6):748–771. https://doi.org/10.1080/ 02508060.2015.1074148 2. World Commission on Environment and Development (1973) Brundtland commission. In: Our common future, from one earth to one world. The General Assembly of the United Nation 3. United Nations (2020) Sustainable development goals. https://www.un.org/sustainabledevelop ment/sustainable-development-goals/2020. Accessed 8 May 2020 4. Mohtar RH, Daher B (2012) Water, energy, and food: the ultimate nexus 5. Organisation for Economic Co-operation and Development (OECD-IEA) (2010) Sustainability in the water energy food nexus. In: Bhaduri A, Ringler C, Dombrowsky I, Mohtar R, Scheumann W (eds). OECD-IEA 6. Charting Our Water Future (2009) https://www.mckinsey.com/~/media/mckinsey/dotcom/cli ent_service/sustainability/pdfs/charting%20our%20water%20future/charting_our_water_fut ure_full_report_.ashx. Accessed 9 June 2020 7. International Energy Agency (IEA) (2012) World energy outlook. https://www.iea.org/reports/ world-energy-outlook-2012. Accessed 9 June 2020 8. International Energy Agency (IEA) (2020) CO2 emissions from fuel combustion overview. https://www.iea.org/reports/co2-emissions-from-fuel-combustion-overview. Accessed 10 Sept 2020 9. European Union (2015) Resource efficiency scoreboard. https://ec.europa.eu/environment/res ource_efficiency/targets_indicators/scoreboard/pdf/EU%20Resource%20Efficiency%20Scor eboard%202015.pdf. Accessed 10 May 2020 10. European Commission (2019) H2020 funding guide. https://ec.europa.eu/research/partic ipants/docs/h2020-funding-guide/cross-cutting-issues/sme_en.htm#phase2. Accessed 10 Dec 2019 11. Borge-Diez D, García-Moya F-J, Cabrera-Santana P, Rosales-Asensio E (2020) Feasibility analysis of wind and solar powered desalination plants: an application to islands. Sci Total Environ 142878. https://doi.org/10.1016/j.scitotenv.2020.142878. ISSN 0048-9697 12. Bonn 2014 Nexus Conference (2014) What policy-makers and scientists can do for water, energy and food. https://www.water-energy-food.org/news/bonn-2014-nexus-confer ence-what-policy-makers-and-scientists-can-do-for-water-energy-and-food/. Accessed 10 Oct 2020 13. Stockholm Environment Institute (2014) Projects and tools. https://www.sei.org/projects-andtools/tools/weap/. Accessed 20 Aug 2019 14. Stockholm Environment Institute (2013) Projects and tools. https://www.sei.org/projects-andtools/tools/leap-long-range-energy-alternatives-planning-system/. Accessed 20 June 2020 15. Food and Agriculture Organization of the United Nations (2018) An innovative accounting framework for the food-energy-water nexus. Application of the MuSIASEM approach to three case studies. http://www.fao.org/3/i3468e/i3468e.pdf. Accessed 20 Nov 2019 16. Agrawal N, Ahiduzzaman M, Kumar A (2018) The development of an integrated model for the assessment of water and GHG footprints for the power generation sector. Appl Energy 216:558–575. https://doi.org/10.1016/j.apenergy.2018.02.116 17. Liu SY, Wang ZY, Han MY, Wang GD, Hayat T, Chen GQ (2021) Energy-water nexus in seawater desalination project: a typical water production system in China. J Clean Prod 279. https://doi.org/10.1016/j.jclepro.2020.123412 18. Zhao H, Wu Q, Hu S, Xu H, Rasmussen CN (2015) Review of energy storage system for wind power integration support. Appl Energy 137:545–553. https://doi.org/10.1016/j.apenergy.2014. 04.103 19. Gimeno-Gutiérrez M, Lacal-Arántegui R (2015) Assessment of the European potential for pumped hydropower energy storage based on two existing reservoirs. Renew Energy 75:856– 868

References

41

20. Jawahar CP, Michael PA (2017) A review on turbines for micro hydro power plant. Renew Sustain Energy Rev 72:882–887. https://doi.org/10.1016/j.rser.2017.01.133 21. Notton G et al (2018) Intermittent and stochastic character of renewable energy sources: consequences, cost of intermittence and benefit of forecasting. Renew Sustain Energy Rep 87:96–105 22. Cabrera P, Lund H, Carta JA (2018) Smart renewable energy penetration strategies on islands: the case of Gran Canaria. Energy. https://doi.org/10.1016/j.energy.2018.08.02 23. Notton G, Mistrushi D, Stoyanov L (2017) Operation of a photovoltaic-wind plant with a hydro pumping-storage for electricity peak-shaving in an island context. Sol Energy 157:20–34 24. Rosales-Asensio E, Rosales A-E, Colmenar-Santos A (2021) Surrogate optimization of coupled energy sources in a desalination microgrid based on solar PV and wind energy. Desalination 500. https://doi.org/10.1016/j.desal.2020.114882 25. Fitzgerald N, Lacal R, Mckeogh E, Leahy P (2012) A GIS-based model to calculate the potential for transforming conventional hydropower schemes and non-hydro reservoirs to pumped hydropower schemes. Energy 41:483–490 26. Soha T, Munkácsy B, Harmat A et al (2017) GIS-based assessment of the opportunities for small-scale pumped hydro energy storage in middle-mountain areas focusing on artificial landscape features. Energy 141:1363–1373. https://doi.org/10.1016/j.energy.2017.11.051 27. Ghorbani N, Makian H, Breyer C (2019) A GIS based method to identify potential sites for pumped hydro energy storage—case of Iran. Energy 169:854–867 28. Lacal Arántegui R, Bocin-Dumitriu A et al (2012) SETIS expert workshop on the assessment of the potential of pumped hydropower storage. Joint Research Centre of the European Commission. JRC71928 29. Aqua.abib (2020) The whole distillation system. https://www.aqua-abib.com/technology-2/. Accessed 15 May 2020 30. Paton C, Davies P (2004) The seawater greenhouse for arid lands. www.seawatergreenhouse. com. Accessed 15 May 2020 31. Athidrotecnia (2018, 2020) Acercamiento a la Hidrología de Gran Canaria. http://www.athidr otecnia.com/agua_edu/es_agua-5__890.html. Accessed 15 May 2020 32. Van Meerwijk AJH, Benders RMJ, Davila-Martinez A et al (2016) Swiss pumped hydro storage potential for Germany’s electricity system under high penetration of intermittent renewable energy. J Mod Power Syst Clean Energy 4:542–553. https://doi.org/10.1007/s40565-0160239-y 33. Liu J, Mei C, Wang H et al (2018) Powering an island system by renewable energy, a feasibility analysis in the Maldives. Appl Energy 227:18–27. https://doi.org/10.1016/j.a 34. Fernandez Prieto L, Rodriguez Rodriguez G, Schallenberg Rodriguez J (2019) Wave energy to power a desalination plant in the north of Gran Canaria island: wave resource, socioeconomic and environmental assessment. J Environ Manage 231:546–551. https://doi.org/10.1016/j.jen vman.2018.10.071 35. Endo A, Tsurita I, Burnett K, Orencio PM (2017) A review of the current state of research on the water, energy, and food nexus. J Hydrol 11:20–30. https://doi.org/10.1016/j.ejrh.2015. 11.010 36. Simpson Gareth B, Jewitt Graham PW (2019) The development of the water-energy-food nexus as a framework for achieving resource security: a review. Front Environ Sci 7. https://doi.org/ 10.3389/fenvs.2019.00008 37. Mimoune Hamiche A, Boudghene Stambouli A, Flazi S (2016) A review of the water-energy nexus. Renew Sustain Energy Rev 65:319–331. https://doi.org/10.1016/j.rser.2016.07.020 38. Zhang X, Vesselinov VV (2016) Energy-water nexus: balancing the tradeoffs between two-level decision makers. Appl Energy 183:77–87. https://doi.org/10.1016/j.apenergy.2016.08.156 39. Goldstein J, Hazy JK, Lichtenstein BB (2010) Complexity and the nexus of leadership. In: Leveraging nonlinear science to create ecologies of innovation. Palgrave Macmillan 40. United Nations Economic Commission for Europe (UNECE) (2017) Deployment of renewable energy: the water-energy-food-ecosystem nexus approach to support the sustainable development goals. http://www.unece.org/fileadmin/DAM/env/water/publications/WAT_NONE_7_

42

41. 42.

43.

44. 45. 46.

47.

48.

49.

50. 51.

52.

53. 54.

55. 56. 57.

58.

3 Comprehensive Assessment of Gran Canaria Food-Energy-Water … Deployment/060617_v3_FINAL_Deployment_of_renewable_energy-_The_water-energyfood-ecosystems_nexus_approach_to_support_the_SDGs_complete_LR_map-manually-cor rected.pdf. Accessed 25 July 2020 United Nations (2020) Water food and energy. https://www.unwater.org/water-facts/waterfood-and-energy/. Accessed 12 July 2020 United Nations (2015) The agenda for sustainable development. https://www.un.org/ga/search/ view_doc.asp?symbol=A/RES/70/1&Lang=E. Resolution adopted by the General Assembly on 25 September 2015. Accessed 25 July 2019 European Commission (2015) Resource efficiency scoreboard. https://ec.europa.eu/enviro nment/resource_efficiency/targets_indicators/scoreboard/pdf/EU%20Resource%20Efficie ncy%20Scoreboard%202015.pdf. Accessed 25 July 2020 Cabildo de Tenerife. Ahorrar agua para un hogar+sostenible, https://www.tenerifesolidario. org/sites/default/files/Miniguia-Agua-H%2BS.pdf. Accessed 15 June 2020 Canary Islands Institute of Statistics (ISTAC) (2020) http://www.gobiernodecanarias.org/istac/ jaxi-istac/tabla.do. Accessed 25 July 2020 UNESCO-IHE Institute for Water Education (2018) Business water footprint accounting; a tool to assess how production of goods and services impacts on freshwater resources worldwide. https://www.waterfootprint.org/media/downloads/Report27-BusinessWaterFootp rint.pdf. Accessed 20 May 2020 Canary Islands Institute of Statistics (ISTAC) (2017) http://www.gobiernodecanarias.org/ istac/jaxi-istac/menu.do?uripub=urn:uuid:ef5f2e5c-e2c4-4c1d-b5ed-c20fe946ce6f. Accessed 14 May 2020 Heinonen J, Ottelin J, Ala-Mantila S, Wiedmann T, Clarke J, Junnila S (2020) Spatial consumption-based carbon footprint assessments—a review of recent developments in the field. J Clean Prod 256. https://doi.org/10.1016/j.jclepro.2020.120335 Fry J, Lenzen M, Jin Y, Wakiyama T, Baynes T, Wiedmann T, Malik A, Chen G, Wang Y, Geschke A, Schandl H (2018) Assessing carbon footprints of cities under limited information. J Clean Prod 176:1254–1270. https://doi.org/10.1016/j.jclepro.2017.11.073. ISSN 0959-6526 Berndes G (2010) Future biomass energy supply: the consumptive water use perspective. Int J Water Resour Dev 24:235–245. https://doi.org/10.1080/07900620701723489 Manomet Center for Conservation Sciences (2010) Biomass sustainability and carbon policy study: report to the commonwealth of Massachusetts Department of Energy Resources. In: Walker T (ed) Natural capital initiative report NCI-2010-03 Bryngelsson D, Wirsenius S, Hedenus F, Sonesson U (2016) How can the EU climate targets be met? A combined analysis of technological and demand-side changes in food and agriculture. Food Policy 59:152–164. https://doi.org/10.1016/j.foodpol.2015.12.012 Whitaker M et al (2012) Lifecycle greenhouse gas emissions of coal-fired electricity generation: systematic review and harmonization. J Ind Ecol 16 Shires TM et al (2009) Compendium of greenhouse gas emissions estimation methodologies for the oil and natural gas industry. Prepared by URS Corporation for the American Petroleum Institute. http://www.api.org/ehs/climate/new/upload/2009_GHG_COMPENDIUM.pdf. Accessed 12 Jan 2020 Frankl P et al (2005) Final report on technical data, costs and life cycle inventories of PV applications. In: New energy externalities developments for sustainability. Project No. 502687 FitchRatings (2012) Construction and operational solar project issues: a review of siting, sun, technology, and regulation. Special report, 26 June 2012 Vattenfall wind power certified environmental product declaration EPD of electricity from Vattenfall’s wind farms (2010) http://www.vattenfall.com/en/file/Certified_Environme ntal_Product_Declaration_of_Electricty_from_Vattenfall_s_Windfarms_2010_12180312. pdf. Accessed 08 Apr 2020 Rosales-Asensio E, De La Puente-Gil A, García-Moya F-J, Blanes-Peiró J, De Simón Martin M (2020) Decision-making tools for sustainable planning and conceptual framework for the energy-water-food nexus. Energy Rep

References

43

59. Insular council of waters of Gran Canaria (2020) http://www.aguasgrancanaria.com/. Accessed 1 Apr 2020 60. Gobierno de Canarias. Anuario energético de Canarias. http://www.gobiernodecanarias. org/ceic/energia/doc/Publicaciones/AnuarioEnergeticoCanarias/ANUARIO-ENERGETICOCANARIAS-2016.pdf. Accessed 25 July 2020 61. Hernández MJ (2018) Canarias aspira a ser un 80% renovable. La Provincia. http://www.lap rovincia.es/sociedad/2011/06/22/canarias-aspira-80-renovable/382041.html. Accessed 10 Apr 2020 62. Red Eléctrica España (2018) Series estadísticas nacionales. https://www.ree.es/es/datos/public aciones/series-estadisticas-nacionales. Accessed 20 Sept 2020 63. Cink Hydro-Energy (2020) 2-cell-crossflow-turbine. https://www.cink-hydro-energy.com/en/ 2-cell-crossflow-turbine/. Accessed 15 Apr 2020 64. QGIS license of operation. https://docs.qgis.org/2.8/en/docs/gentle_gis_introduction/gnu_ free_documentation_license.html. Accessed 1 Apr 2019 65. Geospatialdata (2020) https://datarade.ai/data-categories/geospatial-data. Accessed 20 July 19 66. Bocin-Dumitriu A (2012) SETIS expert workshop on the assessment of the potential of pumped hydropower storage, no. Apr, pp 1–29 67. Spanish service of cartography website (2020) http://www.ign.es/csw-inspire/srv/eng/main. home. Accessed 9 Nov 2020 68. Portero U, Velázquez S, Carta JA (2015) Sizing of a wind-hydro system using a reversible hydraulic facility with seawater. A case study in the Canary Islands. Energy Convers Manag 106:1251–1263. https://doi.org/10.1016/j.enconman.2015.10.054 69. European Central Bank (2020) The implications of fiscal measures to address climate change. https://www.ecb.europa.eu/pub/economic-bulletin/focus/2020/html/ecb.ebb ox202002_04~a7d137cb35.en.html. Accessed 15 June 2020 70. The Paris agreement (2015) https://unfccc.int/process-and-meetings/the-paris-agreement/theparis-agreement 71. Rosales-Asensio E, Simón-Martín M, Borge-Diez D, Pérez-Hoyos A, Comenar Santos A (2019) An expert judgement approach to determine measures to remove institutional barriers and economic non-market failures that restrict photovoltaic self-consumption deployment in Spain. Sol Energy 180:307–323. https://doi.org/10.1016/j.solener.2019.01.031. ISSN 0038-092X 72. Rosales-Asensio E, Borge-Diez D, Blanes-Peiró JJ, Pérez-Hoyos A, Comenar-Santos A (2019) Review of wind energy technology and associated market and economic conditions in Spain. Renew Sustain Energy Rev 101:415–427. https://doi.org/10.1016/j.rser.2018.11.029. ISSN 1364-0321 73. Chira-Soria System (2019) https://www.ree.es/en/press-office/monographs/2019/05/chirasoria-key-making-energy-transition-canary-islands-possible. Accessed 19 July 2020 74. Council of Gran Canaria (2018) Strategic plan of the primary sector. http://descargas.granca naria.com/sectorprimario/Plan%20EstrategicoGC_def.pdf. Accessed 24 Mar 2020 75. Canary Islands Government (2019) The Canary Islands business territorial system—IDECanarias. https://visor.grafcan.es/visorweb/default.php?svc=svcRecursoEolico. Accessed 25 Oct 2019 76. Berenguel-Felices F, Lara-Galera A, Muñoz-Medina MB (2020) Requirements for the construction of new desalination plants into a framework of sustainability. Sustainability 77. Rosales-Asensio E, García-Moya FJ, González-Martínez A, Borge-Diaz D, de Simón-Martín M (2019) Stress mitigation of conventional water resources in water-scarce areas through the use of renewable energy powered desalination plants: an application to the Canary Islands. Energy Rep 6:124–135. https://doi.org/10.1016/j.egyr.2019.10.031 78. Tourism Board of Gran Canaria Island Council of Gran Canaria (2018) Tourism statistics. http://www.grancanaria.com/turismo/es/area-profesional/informes-y-estadisticas/estadi sticas/. Accessed 8 June 2020 79. Gerbens-Leenes PW, Hoekstra AY (2008) Business water footprint accounting: a tool to assess how production of goods and services impacts on freshwater resources worldwide. Value of water research report series no. 27. UNESCO-IHE, Delft

44

3 Comprehensive Assessment of Gran Canaria Food-Energy-Water …

80. Chamas Z, Abou Najm M, Al-Hindi M, Yassine A, Khattar R (2021) Sustainable resource optimization under water-energy-food-carbon nexus. J Clean Prod 278. https://doi.org/10.1016/ j.jclepro.2020.123894 81. Hall D, Lobina E (2004) Private and public interests in water and energy. Nat Res Forum 28:268–277. https://doi.org/10.1111/j.1477-8947.2004.00100.x

Chapter 4

Review of Wind Energy Technology and Associated Market and Economic Conditions in Spain

4.1 Introduction The year 2015 was a year in which more than a forty percent of the electrical energy generated by Denmark were from wind energy resources (the highest amount ever registered in the world) [1], whereas Ireland, Portugal and Spain produced approximately 20% of its electricity from wind power [2], and 11 of the 28 Member States of the EU had a wind energy penetration rate above 10% [3]. To meet the target of 20% of energy from renewable resources, the European Commission projects that by 2020, 34% of electricity will come from renewable resources and that the wind will contribute 12% of all electricity consumed in the European Union [4]. The research conducted in this chapter provides evidence of state-of-the-art and economic aspects that currently exist in the Spanish wind power sector. During the last 30 years, wind energy has become a major player in electricity generation worldwide [5], rising from just 1.3 GW in 1986 to roughly 490 GW (worldwide aggregated) by 2016 (during this period worldwide electricity consumption has increased from 8978 TWh [6] to 21,191 TWh [7]), of which approximately 140 GW correspond to the European Union [2]. Although this chapter centers on Spain, the market clearly has a pan-European and global scale, some parts of the research has a global focus. Section 4.2 of this chapter sets out the methodology used to carry out the research presented. In Sect. 4.3, a theoretical framework for the technological situation is presented, also showing an analysis of the market situation; Sect. 4.4 shows the weights attributed by the experts consulted in the Spanish wind sector to the main indicators (and the weights for the components of each indicator). Section 4.5 discusses the results obtained in Sect. 4.4, the economic aspects of wind projects and the implications of the current regulatory situation in Spain. Finally, Sect. 4.6 presents the main conclusions resulting from the research.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 E. Rosales-Asensio et al., Sea Water Desalination in Microgrids, Green Energy and Technology, https://doi.org/10.1007/978-3-030-96678-2_4

45

46

4 Review of Wind Energy Technology and Associated Market …

4.2 Material and Methods The assessment of the constraints on the sector development requires a good understanding of the perspectives of the stakeholders; in particular, that of the promoters and investors of wind projects. To this end, a number of stakeholders from the Spanish wind sector were contacted through interviews. First, we identified the main factors that influence the diffusion of wind energy in Spain, selecting appropriate indicators to represent each of them. With this step, the conceptual framework of the prospective indicator was developed. It was then necessary to carry out an evaluation of the relative relevance (weight) of each of the factors through a consultation with experts from the Spanish wind sector. For this step, contributions from more than 20 stakeholders were collected through questionnaires (see Appendix A and Acknowledgments). The quantification of the indicator. Data were collected from various data sources, including interviews (in-depth) of experts from the Spanish wind sector. A total of 21 interviews were conducted (see section Acknowledgments); collecting additional information for the quantification of individual components of normative and legal documents and other public data sources. Once the factors and data of the indicators were obtained, it was essential to evaluate the relative weight of each component (see Appendix A) to obtain the final indicator score. The aggregation of the different factors and subfactors is a particularly critical issue, because the weighting of subfactors can strongly influence the overall score and hence the message provided by the indicator. Therefore, the weighting of the indicators presented is based on empirical results of a comprehensive process of stakeholder consultation. This was done through questionnaires (see Appendix A) to Spanish wind industry players (mainly project promoters and other Spanish wind industry players, see Acknowledgments). To this end, interested parties were asked to indicate the relevance of the indicators at a scale between 10 (“extremely relevant”) and 0 (“not relevant at all”)—see Appendix A. More than 20 sets of data were collected from relevant stakeholders in the wind energy sector, as shown in the Results section.

4.3 Theory Windmills start to perform work at a wind gust of approximately 3 m/s (11 km/h) [8] and its power continues to increase until it reaches the nominal power of mechanical power [9], typically reaching its maximum power at 15 m/s [10] and generally cutting with a gust velocity of approximately 25 m/s [10, 11]. In the early 1980s, windmills that dominated the market (with a nominal power of less than 200 kW) were the fixed-pitch three-bladed Danish induction wind turbines [12, 13]. Since then, the state-of-the-art of the sector has been dramatically developed and virtually every new production windmill is currently supplied with both a VSD

4.3 Theory

47

and a BCS and their nominal power and rotor dimensions have been continuously increasing [14]. At present, windmills that have a power rating from 1.5 to 3 MW, a distance from the turbine platform to the rotor of an installed windmill from 90 to 110 m; and a rotor diameter of 97 to 117 m are generally installed in land projects [14]. According to Standard 61 400-1 of the International Electrotechnical Commission (IEC), wind turbines are organized in different classes according to their average annual velocity, turbulence, and extreme wind gusts during the last 50 years [15]. A summary of wind turbine classes is shown in Table 4.1. Besides the specific manufacturing constraints linked to every windmill class (which are intended to ensure the integrity of the wind turbine), those wind turbines located in locations where a low average wind speed exists are usually equipped with larger rotors, higher towers and have a moderate nominal power to seek a balance between equipment costs and output power [14]. In general, wind turbines installed at sites with poor wind resources involve greater expenses (by nominal power) than those installed at sites with a high wind potential [14]. However, these higher expenses are to some extent offset with lower power units, electronic devices for conversion purposes and gearcases (where pertinent), which allows windmills to be designed to be economically advantageous when used in locations where the environmental resources are poor [14]. The industrialization of the manufacturing of windmill components has received a growing acceptance in the sector. As a global approach, it allows the manufacturing larger amounts of standardized components that are broadly analogous in other windmills. It both accelerates the time to launch a new product and helps to reduce the assembly costs thanks to a better machinery usage for the SCM. This approach might be used for windmills in production and for windmills under development. A usual Table 4.1 Wind turbine classes according to the IEC 61 400-1 standard

WC

Annual average wind speed

Turbulence level

Extreme 50-year gust

IA

High wind speed—10 m/s

High turbulence—18%

High gust—70 m/s

IB

High wind speed—10 m/s

Low turbulence—18%

High gust—70 m/s

IIA

Medium wind speed—8.5 m/s

High turbulence—18%

Medium gust—59.5 m/s

IIB

Medium wind speed—8.5 m/s

Low turbulence—18%

Medium gust—59.5 m/s

IIIA

Low wind speed—7.5 m/s

High turbulence—18%

Low gust—52.5 m/s

IIIB

Low wind speed—7.5 m/s

High turbulence—18%

Low gust—52.5 m/s

IV

6.0 m/s

–

42 m/s

Source [15]

48

4 Review of Wind Energy Technology and Associated Market …

Installed capacity (%)

procedure generally adopts the same platform for a number of “sibling” windmills, having both identical nominal powers and rotor lengths to optimize performance for a number of WCs. An alternative approach would be to employ a number of windmill platforms and use at the same time an indistinctive RBD for different power ratings [16].

Europe

100% 80% 60% 40% 20% 0% 2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

Year IEC I

IEC Ia

IEC Ib

IEC I/II

IEC II

IEC II a

IEC II b

IEC II/III

IEC III

IEC III a

IEC III b

IEC S

Installed capacity (%)

North America 100% 80% 60% 40% 20% 0% 2005

2007

2008

2009

2010

2011

2012

2013

2014

Year IEC I

Installed capacity (%)

2006

IEC Ia

IEC Ib

IEC I/II

IEC II

IEC II a

IEC II b

IEC II/III

IEC III

IEC III a

IEC III b

IEC S

Asia

100% 80% 60% 40% 20% 0% 2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

Year IEC I

IEC Ia

IEC Ib

IEC I/II

IEC II

IEC II a

IEC II b

IEC II/III

IEC III

IEC III a

IEC III b

IEC S

Installed capacity (%)

Rest of the world 100% 80% 60% 40% 20% 0% 2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

Year IEC I

IEC Ia

IEC Ib

IEC I/II

IEC II

IEC II a

IEC II b

IEC II/III

IEC III

IEC III a

Fig. 4.1 Progress of the portion of each IEC wind class by OIC. Source [14]

IEC III b

IEC S

4.3 Theory

49

Figure 4.1 presents the OIC progress taking into account to the WC breakdown. From this figure, it is possible to conclude that in Europe, class I windmills are steadily having a lower importance in favor of class II and class III windmills [14]. Class III windmills are more noticeable in Asia, primarily owing to the typical poor resources in much of this continent [14]. In NORAM, the most used windmills are Class II (for average resources [14]). A similar scenario can be observed globally, where class II windmills prevail as a consequence of the lower importance of class I because sites with high wind potential have generally been occupied during the first decade of the XXI century [14]. 2015 was an unprecedented year for the wind industry because for the first time in history, the annual installed capacity exceeded 60 GW (the last “record” was set in 2014 with 51.7 GW of annual installed capacity worldwide) [17]. At the beginning of 2016, the global installed wind power capacity was 432.9 GW (approximately seven percent of the worldwide PGC, 420 GW onshore, 12 GW offshore [1]), representing a cumulative market growth of more than 17% [17]. In recent years, the largest increase in wind energy installation has been in China, Germany, the United States and Brazil [18]. In Spain, 2016 was the third consecutive year in which there has been no new wind facility (although Spain has installed approximately 23 GW of wind energy capacity since 2012, the sector has stagnated owing to cuts imposed to subsidies for energy sources of renewable origin) [19, 20]. This circumstance is shown in Fig. 4.2, corresponds to Germany and Denmark. For the most part, this is a consequence of their renewable energy policy foreseeability, which assures a profitability to the venture capitalists who embark on future wind energy projects and favors investments in this sector [22]. On the contrary, in the particular case of Spain (in which the wind industry was a very strong market until a few years ago), the lack of political predictability and effective regulations has meant EvoluƟon of wind power installed year by year in Spain (in MW) Wind power capacity (MW)

25000 20000 15000 10000 5000 0 1998199920002001200220032004200520062007200820092010201120122013201420152016

Year Installed wind power capacity (MW)

Total annual wind power capacity addiƟons (MW)

Fig. 4.2 Evolution of wind power installed year by year in Spain (in MW). Source [21]

50

4 Review of Wind Energy Technology and Associated Market …

New wind energy capacity installed in the EU-28 during 2015 (MW) 1

11

172 76 132 145

23

217 224

274 323

295 379

586 615 975

6013

1073 1266

Greece

Denmark

Ireland

Belgium

Italy

Austria

Finland

Netherlands

Sweden

UK

France

Poland

Germany

Estonia

Cyprus

Romania

CroaƟa

Portugal

Lithuania

Fig. 4.3 New wind energy capacity installed in the EU-28 during 2015 (MW). Total 12,800.2 MW. Source [22]

that practically not a single MW of wind power [22] (see Fig. 4.2). It is therefore found that a stable regulatory framework and high predictability remain essential to provide investors with the necessary security [22]. The only wind farm built in 2014 in continental Spain was the Cordal de Montouto wind farm (barely 14 MW), which operates strictly without support in the wholesale electricity market [23] and became the first Spanish wind farm without receiving any subsidy [24]. Given the reduction in terms of wind generation in recent years, it is necessary to propose new actions and upgrading legislation to get over the current obstacles and guarantee a proper development of the Spanish wind sector. This will be something that will be proposed in the following sections (Fig. 4.3).

4.4 Results Figure 4.4 shows the weights attributed by Spanish wind energy stakeholders to the main indicators. The boxes represent ranges that cover 50% of the values (upper and lower quartiles separated by the median) whereas the boundaries (extreme edges) show the minimum and maximum marks obtained. Figure 4.5 shows the results presented in Fig. 4.4 by presenting the weights for the components of the indicator—“political and economic framework” (a); “market structure and market regulation” (b); “network infrastructure and network regulation” (c); and “administrative procedures” (d).

4.4 Results

51

Fig. 4.4 Weights for the main components of the indicators. Source Own elaboration

As shown in Fig. 4.4, taking into account the opinions of the 21 experts consulted on wind energy, the economic and political issues is the most remarkable factor, with an average relevance of 9 out of 10. This indicated that the most repeated mark by the experts of the Spanish wind sector to this factor was 10 points (“extremely relevant”) and half of them attributed at least 9 points to it. This underlines the relevance of this aspect in wind projects (which are investment intensive). Factors such as grid regulation, market and administration issues are deemed with a lower importance than political and economic issues, with an average mark ranging from seven to eight. This underlines how important a foreseeable political modus operandi is to obtain a sustainable spread of wind technology. The overall energy planning and a solid scheme to support non-exhaustive energy sources are a very remarkable element (median = 9). Interestingly, its importance ranks above that of remunerative issues (average = 8). Income risk and access to finance are also weighted high (median = 8)—however the responses show a greater variety in scores. One of the most noteworthy aspects of the results is that some stakeholders regarded access to finance as “not at all relevant”. In general, marks to this element have a great deal of variation among stakeholders. The existence and reliability of the renewable energy policy framework is considered to be highly relevant by wind energy experts. This could be explained by the high capital requirements related to wind projects, which pose a high risk to the investor in case of unforeseen (policy) changes. With regard to access to finance, the picture is less clear. The experts in wind energy attributed a relevance to this question with a median = 7. For the final weight of each sub-indicator, the median marks derived from the survey are used. The median provides a better representation of the overall trend of a data set as it is less susceptible to extreme values and the potential asymmetry of the results. The final values, i.e., the weights used to weight the components are presented in Table 4.2. The weights are derived from a sample comprising 21 data sets. A weight of 0.9 refers to an average relevance of 9 points out of 10. Weights are used to scale each sub-indicator.

52

4 Review of Wind Energy Technology and Associated Market …

Fig. 4.5 Weights for the main subcomponents of the indicators—“political and economic framework” (a); “markets structure and market regulation” (b); “grid infrastructure and grid regulation” (c); and “administrative procedures” (d). Source Own elaboration

4.5 Discussion

53

Table 4.2 Relative weight of the components of each indicator Indicator

Weight

Energy strategy and a support scheme foreseeability

0.9

Level of economic compensation

0.8

Income uncertainty

0.8

Ease to financing

0.7

Electricity sector independency

0.8

Non-discriminatory markets

0.65

Contracts reliability

0.9

Expenses to connect the electricity grid

0.7

Waiting time for access to the electricity grid of renewable energies

0.7

Connection transparency to the electricity grid

0.7

Treatment of electrical dispatch (of renewable energies)

0.8

Development of the transparent and predictable electricity grid

0.7

Administrative costs

0.5

Administrative time length

0.8

Administrative intricacy

0.8

Integrating wind energy into spatial and environmental planning

0.8

Source Own elaboration

4.5 Discussion The current EU 2020 reference framework sets a 20% goal for energy coming from non-exhaustive resources [25]. In general, it can be said that the national action plans for RES and the biennial control provided for in Directive 2009/28/EC on the promotion of the use of energy from renewable sources have been adequate in encouraging clarity for venture capitalists and other stakeholders and thus have favored a rapid development of renewable energies from 10.4% in 2007 to 17% in 2015 [25]. With the transposition of the directive by all MS before the time limit of 5 December 2010 and the acceptance of the NREAP, the grounds for a resolute EU-28 pan-European plan in the field of renewable energies were laid [26]. To guarantee that every MS adapts the Europe 2020 plan of action to its specific circumstances, the Commission recommends that the EU’s objectives be adapted to every MS objectives [27]. Despite the ambitious objectives of many of the MSs, the quantitative objectives of many forecasts have been revised downwards, hardening the circumstances for the establishment of windmills in order to favor locations with the most efficient conditions of production [28]. As far as renewable energies are concerned, successful business models usually consist of a combination of several models that simultaneously face several barriers [29]. To assist those models, a merger of administrative actions or administrative/political packages is necessary [29]. The policies of MS to promote renewable

54

4 Review of Wind Energy Technology and Associated Market …

energy in the past come from two counter-alternatives: fixed and specific FITs for a given energy resource solution and NQS (independent of the technology involved) joined with the TGCs [30]. Every one of these options emphasizes two different policy objectives: (i) fixed and specific feed-in tariffs (FITs) for a given technology, which focuses on minimizing the risk of investment and avoiding excessive remuneration for less expensive technologies, whereas (ii) neutral quota systems (independent of the technology concerned) focuses on maximizing market harmony and the selection of energy resource solutions with the minimum costs in the short term [30]. The current trend clearly shows that, although FIT and FIP are the most popular support schemes, auction schemes have gained popularity in recent years [31]. A number of institutions (including the European Commission) are increasingly recommending to limit the use of FITs, and to promote support instruments that expose market price signals, such as FIPs [32]. As stated in the Communication from the Commission “Guidelines on State aid for environmental protection and energy 2014–2020 (2014/C 200/01)” [33] with a view to encouraging the integration of RES into the electricity market, it is essential that beneficiaries sell their electricity directly in the market and are subject to market obligations. That Communication sets out the next additive requirements applied from 1 January 2016 to all new aid schemes and measures [33]. The aid is granted through an additional premium on the market price where generators sell their electricity directly in the market. Unless there is no liquid intraday market, beneficiaries are subject to the obligation for generators to match their forecasted electricity generation in real time (balancing responsibilities). Measures are in place to ensure that generators do not have incentives to generate electricity at negative prices. Finally, with regard to new trends in the renewable energy sector, in accordance with the European guidelines on environmental and energy aid, from 1 January 2017, any State aid granted by a MS must be granted “in a bidding process based on clear, transparent and non-discriminatory criteria” [34, 35]. Therefore, it is clear that tenders will become, in the short or medium term, the key to benefiting from any support mechanism in the renewable energy sector, especially in the solar and wind sectors [35]. Figure 4.6 shows the relationship between the WACC and the support schemes for the promotion of wind energy on land. The first impression from this figure is that there is no a clear connection between the selection of a specific assistance regime and a high or low weighted average cost of capital rate: markets with a quota system such as Belgium are capable of reaching a small weighted average cost of capital amount; and in a number of markets with a FIT, the total cost needed to bring a project to a commercially operable status might be remarkably important. Nonetheless, it is relevant to take into account two aspects: The initial one would be the particular arrangement of the assistance scheme. As an illustration, Belgium grants a bottom price favorable for TGCs, so several liabilities are offset; Another aspect is the particular uncertainty of each state. A number of markets continue to struggle with the consequences of recent economic pressures. With the

4.5 Discussion

55

WACC ASSESSMENT AND ASSISTANCE SCHEMES

below 6.0 6.0-6.9 7.0-7.9 8.0-8.9 9.0-9.9 10.0-10.9 above 11.0

Feed-in tariff Premium tariff Quota Tender Other

Fig. 4.6 Estimates of the WACC and dominant support schemes for wind energy onshore. Source Adapted from [36, 37]

aforementioned circumstances, the liabilities of the country suggest to be of a higher importance than the risks of administrative arrangements and the NSC. Subsequently, correlations among assistance regimes are just significant if the global state liability is akin. In this sense, it is interesting to compare homogenous markets such as the Nordic countries. Even though these states enjoy quite a small CR, the global weighted average cost of capital in Denmark and Finland is considerably lower than the one existing in Sweden. The Spanish feed-in-premium system (which was the first European state to use this support scheme in 1998 [38]) was halted in 2012 and eventually discarded in 2013 [38].1 Under this decree, generators were allowed to, on a yearly basis, choose between the flat rate and the premium [38]. With this law, wind farms had the possibility of selling the electricity generated to the distributor [38]. Under the RD436 premium option, renewable energy generators could trade their electricity in the market, controlled by the Spanish market operator (OMEL) or straightforwardly to clients through two-sided arrangements [39]. 1

With Royal Decree-Law 1/2012 (27 January 2012), all financial stimulus for the production of electricity were abolished for those installations that were not registered as of 28 January 2012 [38].

56

4 Review of Wind Energy Technology and Associated Market …

Global compensation was expressed by the market tariff of electricity (or the agreed premium) and the supplementary factors of the tariff, made up of a prime and a stimulus for market share [40, 41]. Many renewable plants chose the premium option (99.8% of WPPs in 2008 [38, 39]) because of the significantly greater compensation with this alternative. The rise in electricity prices and the unexpected benefits of RPPs forced the establishment of ceilings and bottom tariffs in 2007 [38]. In the case of a cap-and-floor premium feed-in scheme, there are 4 scenarios that lead to divergent remuneration degree: When the aggregate of the market tariff of electricity and the prime is lower than the minimal limit (floor), the global compensation degree is equivalent to the minimal. The eventual prime is determined as the deviation between the minimal level and the market price of electricity. In this case, the global compensation degree is invariable, whereas the actual prime is adapted taking into account the market price of electricity. If the aggregate of the market price of electricity and the reference prime fluctuates between the minimal limit and the highest limit, the reference premium is paid in addition to the market price of the electricity. Thus, the general level of remuneration increases, whereas the actual prime remains invariable. Before the electricity tariff surpasses the maximum tariff, the global degree of remuneration coincides with the maximum price and the actual remuneration is determined as the inequality between the maximum price and the electricity tariff. Global compensation stays invariable and the actual remuneration decreases. If the price of electricity in the market is superior to the maximum price, no remuneration is compensated and the global compensation is identical to the market tariff of electricity. The estimation procedure described above guaranteed the renewable energy producer a minimum income, which, on the one hand, provided investment certainty for renewable energy projects and, on the other hand, diminished the unexpected gains that have occurred because of the increase in the prices of electricity without an increased cost of technology. Although the European Commission has stated that, in the case of Spain, the wind power benefits for electricity consumers in terms of reduction of total sales prices exceeded the costs of subsidies [42]; in October 2015, a support regime was established that ultimately proved to be counterproductive for the advancement of wind energy (Specific Remuneration Regime), which granted a specific compensation regime for wind farms [43]. The allocation of the specific remuneration scheme has been made through public tendering [43]. There are a number of Member States (such as Spain and the Czech Republic [44]), which, for a number of reasons, presently do not offer any type of aid scheme to the wind power sector [44]. In 2012, the Spanish Government acknowledged that its feed-in-tariff scheme was unsustainable and therefore had to intervene (taking actions of dubious legality) to curtail the expenses of the aid offered [45]. In July 2013, the Spanish Government approved a Royal Decree-Law that established immediate actions to ensure the budgeting sustainability of the electricity system [46]. Some of these measures included a new remuneration scheme for renewable energy and

4.5 Discussion

57

retroactively changed the existing system [46]. In December 2013, the new Electricity Law radically changed the remuneration of renewable energy [46]. Venture capitalists disapproved what they felt was a brusque administration adjustment and argued that the policy assistance scheme was not trustworthy [45]. Although the Spanish feedin-tariff scheme was successful in stimulating investment, it was at the cost of a high toll for taxpayers [45]; what was especially detrimental to venture capitalist certainty was the existence of retrospective adjustments in salaries and the duration of aid [45]. In recent years, a number of EU Member States have reduced support for renewables, having as a result a large number of allegations that these administration adjustments have retrospectively stressed current ventures [46]. Although the Czech Republic, Bulgaria, Poland, Romania and Italy have also brought in amendments with harmful results for their renewable energy resources sectors, Spain has attracted attention as a result of both the magnitude of the cutbacks and the considerable number of assets involved [46]. As shown in Fig. 4.7, although various retrospective measures for renewable energies have been taken throughout the European Union, Spain, together with the Czech Republic, Bulgaria and Romania, are the only countries where retroactive measures were taken for the wind power sector (this chapter differentiates the concept of “retrospective” from “retroactive”) by limiting to 7% the return on investment of renewable energy projects [47]. The criticisms have gone so far that official letters were sent by different European commissioners to the Governments of Spain and the Czech Republic [48].

Retrospective Retroactive Moratorium

Fig. 4.7 Retrospective, retroactive changes and moratorium for renewable energies in Europe. Source Adapted from [47, 48]

58

4 Review of Wind Energy Technology and Associated Market …

Having refrained from making harmful or retroactive changes to energy policy, Austria, Denmark, Germany, Ireland, the United Kingdom and Portugal are positive examples of countries that are on track to meet and even exceed their 2020 targets, while at the same time committing themselves openly with sustainable support for renewable energies [46]. This is in contrast to the Spanish case, where investors have taken measures (denouncing the retroactive nature of the measures taken) to national courts and international arbitration tribunals [46]. As anticipated in the Results section, issues related to policy design and the sudden change of policies are the most relevant barriers to the diffusion of wind energy in Spain. Table B.1 (Appendix B) shows a summary of these barriers ordered in order of severity. It may seem surprising that, in mature wind power markets such as the Spanish one, regulatory and market risks are the most prominent (see Results section). However, this reflects what is happening in many mature markets: the limits of policies that have been conceived to encourage the deployment of wind energy have been exceeded. At the present, an unfamiliar point starts, which may demand unconventional requirements, and as a result distinctive political, regulatory and market proposals to be able to continue integrating wind energy into the electrical system. It is not the aim of this chapter to positioning for or against in relation to the current litigation between investors and the Spanish Government (this should be done by the national and European courts); this chapter is restricted to, after conducting a large number of interviews with experts from the Spanish wind sector (see Acknowledgments) and with the methodology set out in the Materials and Methods section, to proposing the following political actions to achieve a new effective development of the Spanish wind sector (which has been stagnant in recent years), without compromising the country’s economic stability: As shown in Fig. 4.6 (adapted from [36, 37]) a valuable criterion that indicates the financing environment in a territory is the WACC. What is most striking from Fig. 4.6 is the extremely large disparity between EU MSs. From a venture capitalist’s point of view, Germany offers a reduced liability atmosphere for investments in wind energy, allowing ventures with comparatively small financial assets. On the contrary side would be Spain, where conditions are less favorable, exhibiting weighted average cost of capital rates that are double the values of Germany. This extremely large weighted average cost of capital discrepancy can be interpreted by the matter that Germany, with a relatively low risk environment, is able to allow a greater proportion of (reduced) obligations in the weighted average cost of capital. A further substantial motivation is the intense rivalry between German financial institutions (something that does not happen in Spain), which, significantly reduces the cost of debt. In Germany, many banks see wind energy projects as safe investments. Subsequently, German project investors deal with considerably curtailed debt amounts than investors in less competitive states. It is difficult to assess whether, in Spain, the difficulty of accessing finance for wind energy projects by banks is because of particular renewable energy policies (e.g., the level of assistance per kWh), as a result of the financial crisis of recent years; or a result of the absence

4.5 Discussion

59

of rivalry between Spanish financial institutions. In any case, this serves to demonstrate the (growing) energy policy gap between energy reference countries such as Germany and Spain. The impacts of these large weighted average cost of capital rates are noteworthy, particularly considering that CAPEX is the predominant component in wind power ventures. Large CAPEX straightforwardly leads to higher electricity costs for investors in wind power projects, which require higher premiums to have a viable business. Consequently, in the MSs with the highest liabilities, identical installed capacity will result in greater expenses when confronted by a market with curtailed liabilities and hence reduced CAPEX. The contrast also shows the importance of wind resources for the financial evaluation. Markets with comparatively poor wind resources (such as Germany) can be commercially a great deal more appealing than markets with eminently better wind resources (such as Spain). This demonstrates that environmental conditions are just some of the elements among others in the venture determination and that other circumstances, such as policy arrangement liabilities or State liabilities, have an impact on the WACC and thus on the feasibility of the wind power projects. This chapter as a whole and the fourteen (14) measures presented in Table C.1 (Appendix C) aim to highlight these circumstances and counter the current negative trend of the Spanish wind sector by proposing political actions to achieve a re-launch of the same without compromising the country’s economic stability.

4.6 Conclusions In 2013 Spain retroactively modified the remuneration system for wind energy (Royal Decree 9/2013). As a result, the rate of return of wind energy was reduced to seven percent, having as a result a severe contraction in the compensation of wind power facilities. This chapter considers the most polemical feature of this compensation scheme is its retroactive condition, as it affects both new and under exploitation facilities. This governmental arrangement has jeopardized investor certainty in the wind energy sector administrative framework. The existing wind energy compensation scheme in Spain comprises 3 three elements: (i) the market price supplemented by a support estimated supposing a modest return, (ii) a regulated yearly compensation for capacity expenditures (pertinent when market tariff does not grant a recapture of capacity expenses) and (iii) a yearly supervised compensation for the wind park exploitation (pertinent if operating expenses surpass predicted power selling revenue). The current evolution and adjustments for the Spanish wind power sector have triggered non-compliance with the European Commission’s Guidelines on State Aid for the Protection of the Environment and Energy [33] as these developments do not generate an increase in the exposure of wind turbines to markets. When wind turbines are exposed to market signals, two essential aims are pursued: (i) to control the reduction of technological costs and (ii) to avoid potential distortions in the electricity market by sources indifferent to market signals. Spain has put

60

4 Review of Wind Energy Technology and Associated Market …

into action brusque adjustments and retroactive actions that hinder investor confidence and jeopardize the expansion of Spanish wind energy sector and, therefore, the possibility of reaching the objectives set for 2020. If Spanish support policies do not offer support, existing schemes operating in medium wind locations will not earn sufficient earning to be exploited over their useful life, which will prevent the development of a sector as advanced as the wind sector in Spain and will jeopardize the adequate energy planning of the country.

References 1. 2. 3. 4.

5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16.

17. 18.

19. 20. 21.

World Energy Council (2017) World energy resources 2016. World Energy Council, London Wind IEA (2016) IEA wind TCP 2015 annual report. International Energy Agency, Paris WindEurope (2017) Wind in power: 2016 European statistics. WindEurope, Brussels Morales PJ (2015) Electrical energy generation in Europe: the current situation and perspectives in the use of renewable energy sources and nuclear power for regional electricity generation. Springer International Publishing, Cham Lantz E, Wiser R, Hand M (2012) IEA wind task 26 WP2: the past and future cost of wind energy. National Renewable Energy Laboratory, Golden Kilgore WC (1996) International energy annual, 1995. Energy Information Administration, Washington Enerdata. Global energy statistical yearbook 2017. Enerdata, Grenoble. https://yearbook.ene rdata.net/electricity/electricity-domestic-consumption-data.html. Accessed 25 Oct 2017 Pollis H, Vidal JFG, Pinto L, Martínez J (2013) Elementos de eficiência energética e fomento à geração sustentável de energia eólica, no contexto da mudança do clima. MMA, Brasília Johnson GL (2001) Wind energy systems. Prentice Hall, Upper Saddle River IRENA (2012) Wind power: volume 1, power sector issue 5/5—renewable energy technologies: cost analysis series. IRENA, Masdar City Al-Bahadly I (2011) Wind turbines. InTech, Rijeka Giorgio P (2013) Wind energy systems: notes for students. Università degli Studi di Padova, Padova Wittholz H, Pan D (2004) Supply-chain capabilities in the Canadian wind power industry. Industry Canada, Ottawa Serrano-González J, Lacal-Arántegui R (2016) Technological evolution of onshore wind turbines—a market-based analysis. Wind Energy 19(12):2135–2348 Venkata Yaramasu BW (2017) Model predictive control of wind energy conversion systems. Wiley, Chichester de Vries E (2013) Platform sharing becoming norm for turbine manufacturers, http://www. windpowermonthly.com/article/1179386/platform-sharing-becoming-norm-turbine-manufa cturers. Accessed 15 July 2017 GWEC (2016) Global wind report: annual market update 2015. Global Wind Energy Council, Brussels Janssen K, Faulstich S, Hahn B, Hirsch J, Neuschäfer M, Pfaffel S et al (2015) Wind energy report: Germany 2014. Fraunhofer Institute for Wind Energy and Energy System Technology, Kassel Windpower Monthly (2016) Spain announces 3GW auction. http://www.windpowermonthly. com/article/1418943/spain-announces-3gw-auction. Accessed 13 July 2017 El País (2017) Spain’s use of renewable energy sources stagnates. https://elpais.com/elpais/ 2017/03/15/inenglish/1489563590_304234.html. Accessed 13 July 2017 Asociación Empresarial Eólica (2017) Potencia instalada. https://www.aeeolica.org/es/sobrela-eolica/la-eolica-en-espana/potencia-instalada/. Accessed 18 July 2017

References

61

22. EWEA (2016) Wind in power 2015 European statistics. The European Wind Energy Association, Brussels 23. REM (2015) Renewable energy monitor—issue 447, 26 Feb 2015, week 08. https://www.iam ericas.org/documents/REM_Week_08.pdf. Accessed 18 July 2017 24. Scotland against spin (2015) Apr 2015, Issue 26. https://www.wind-watch.org/alerts/wp-con tent/uploads/2015/04/SAS-Newsletter-26.pdf. Accessed 18 July 2017 25. European Commission (2017) Proposal for a DIRECTIVE OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL on the promotion of the use of energy from renewable sources (recast)-COM(2016) 767 final/2. European Commission, Brussels 26. Amariei D, Bachler J (2013) Transnational sustainable energy strategy. CERE—Center of Excellence for Renewable Energy, Energy Efficiency and Environment, Vienna 27. Mercier-Laurent E (2011) Innovation ecosystems. Wiley, Hoboken 28. EurObserv’ER (2014) Wind energy barometer—Feb 2014. EurObserv’ER, Paris 29. IEA-RETD (2013) Business models for renewable energy in the built environment. Routledge, Oxon 30. Held A, Ragwitz M, de Visser E, Klessmann C (2014) Design features of support schemes for renewable electricity. Ecofys Netherlands B.V., Utrech 31. Lucas H, Ferroukhi R, Hawila D (2013) Renewable energy auctions in developing countries. The International Renewable Energy Agency, Abu Dhabi 32. European Commission (2013) European Commission guidance for the design of renewables support schemes—accompanying the document communication from the commission delivering the internal market in electricity and making the most of public intervention. European Commission, Brussels 33. European Commission (2014) Communication from the commission: guidelines on state aid for environmental protection and energy 2014–2020 (2014/C 200/01). Official Journal of the European Union, Luxembourg 34. Mäntysaari P (2015) EU electricity trade law: the legal tools of electricity producers in the internal electricity market. Springer International Publishing AG, Cham 35. Solar and wind energies in France: from feed-in-tariffs to feed-in-premium. Taylor Wessing, Paris. https://www.lexology.com/library/detail.aspx?g=9a5c03fb-e0bf-4130-b3224fcc4073d526. Accessed 11 Sept 2017 36. Goswami Y, Kreith F (2015) Energy efficiency and renewable energy handbook, 2nd edn. CRC Press, Boca Raton 37. Angelopoulos D, Brückmann R, Jirous F, Konstantinaviciute I, Noothout P, Psarras J et al (2016) Risks and cost of capital for onshore wind energy investments in EU countries. Energy Environ 27(1):82–104 38. Held A, Ragwitz M, Gephart M, Kleßmann C, de Visser E (2014) Best practice design features for RESE support schemes and best practice methodologies to determine remuneration levels. Dia-Core Project, Karlsruhe 39. Ragwitz M, Winkler J, Klessmann C, Gephart M, Resch G (2012) Recent developments of feed-in systems in the EU—a research paper for the International Feed-In Cooperation. ISI Fraunhofer, Karlsruhe 40. Ministerio de Industria y Energía (1998) Real Decreto 2818/1998, de 23 de diciembre, sobre producción de energía eléctrica por instalaciones abastecidas por recursos o fuentes de energía renovables, residuos y cogeneración. Ministerio de Industria y Energía, Madrid 41. Ministerio de Economía (2004) Real Decreto 436/2004, de 12 de marzo, por el que se establece la metodología para la actualización y sistematización del régimen jurídico y económico de la actividad de producción de energía eléctrica en régimen especial. Ministerio de Economía, Madrid 42. European Commission. Energy prices and costs report. European Commission, Brussels. http:// ec.europa.eu/energy/sites/ener/files/documents/20140122_swd_prices.pdf. Accessed 11 Sept 2017 43. Jimeno M (2015) Renewable energy policy database and support—RES-LEGAL EUROPE national profile: Spain. RES LEGAL EUROPE, Berlin

62

4 Review of Wind Energy Technology and Associated Market …

44. APEE. What are retrospective and retroactive changes to support schemes? Association of Producers of Ecological Energy, Varna. http://apee.bg/documents/legislation/. Accessed 12 Sept 2017 45. Mahalingam A, Reiner DM (2016) Energy subsidies at times of economic crisis: a comparative study and scenario analysis of Italy and Spain. University of Cambridge, Cambridge 46. Egenhofer C, Alessi M, Núñez-Ferrer J, Hassel A. Why the future of European renewables policy may be decided in Washington and not in Brussels. CEPS, Brussels. https://www.ceps.eu/publications/why-future-european-renewables-policy-may-bedecided-washington-and-not-brussels. Accessed 12 Sept 2017. 47. Westerhof J (2014) 4th policy briefing Keep on Track! May 2014. National policy update. Keep on Track!, Brussels 48. Pelisson M. Chain reaction—the spread of retrospective measures on support for renewable energy in the EU MS. European Renewable Energies Federation, EREF Policy Adviser, Brussels. https://www.goiener.com/wp-content/uploads/2013/12/Maelle_Pelisson_041213_APPA. ppsx. Accessed 12 Sept 2017

Chapter 5

Photovoltaic Self-consumption and Net-Metering: Measures to Remove Economic Non-market Failure and Institutional Barriers that Restrict Their Use in Spain

5.1 Introduction The countries that participated in the United Nations Framework Convention on Climate Change, in the Agreement of Paris on 12 December 2015, recognized the seriousness of the global climate change problem and agreed to take measures to cope with it so that the increase in the global average temperature remains well below 2 °C with respect to pre-industrial levels [38]. The Intergovernmental Panel on Climate Change in its Fifth Assessment Report, published in 2014, identifies the generation of electricity as one of the main causes of the increase in the global emissions of greenhouse gasses [27]. Furthermore, Article 45 of the Spanish Constitution recognizes “the right to enjoy an adequate environment for the development of the person and the duty to preserve it”; and imposes “a mandate on public authorities to ensure the rational use of natural resources to protect and improve the quality of life and defend and restore the environment” [14]. Renewable electricity self-consumption is one of the most appropriate instruments to reduce the environmental impact of electricity generation [24, 37, 42]. It is expected that in the medium term (2030) it would not imply a higher levelized cost of electricity than that of the current electricity mix (mainly based on fossil and nuclear energy) [5, 23, 31, 35, 49]. Recent outstanding technological developments [20] linked to high solar irradiation levels in Spain allow installation of technologies such as PV, which, even now, are funded directly by savings in the electricity bill, without the need for any financial aid (Fig. 5.1) [16, 28]. Besides the aforementioned, self-consumption of electricity strengthens the energy independence of Spain and favors the reduction of imported fossil fuels, allowing a match in the trade balance [1, 15, 30]—in 2015 Spain avoided the purchase of 1422 tonnes of oil equivalent (toe), which represented a saving of 357.1 million euros [47]. Directive 2009/28/EC established the need to promote a change in the energy model towards a decentralized energy production [21] because it has many advantages such as greater security of local energy supply, a shorter distance from the generation source to consumer, which implies lower losses, and the promotion © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 E. Rosales-Asensio et al., Sea Water Desalination in Microgrids, Green Energy and Technology, https://doi.org/10.1007/978-3-030-96678-2_5

63

64

Title Suppressed Due to Excessive Length

Fig. 5.1 Price difference between PV levelized electricity cost and household retail prices. Source Jäger-Waldau [28]

of the development and cohesion of the local community by providing sources of income and creating employment at the local level [26]. In 2015, electricity generated from grid-connected PV systems contributed 8.3 TWh or 3.2% of the Spanish electricity production [32]. These figures are in contrast with those of much of the rest of Europe (in countries such as Italy, Greece or Germany) where this contribution reaches approximately 8% [25]. Despite the aforementioned advantages and the fact that Spain drove the global solar PV market in 2008, the truth is that, as the Renewable Energy Policy Network for the 21st Century points out, Spain has “virtually disappeared” from the solar PV picture (falling from the largest market in 2008 to a modest fifth place in 2015 in Europe with regard to the total installed capacity [32]) owing to retroactive policy changes and a new tax on self-consumption [41]. As shown in Fig. 5.2a, changes to the regulatory framework (from 2008 onwards) have had a detrimental effect on the annual PV capacity, and the addition to the solar PV capacity for the year 2015 was by far the lowest of the top 10 countries (Fig. 5.2b). Furthermore, even though there is a global and European growing trend (see Fig. 5.3), Spain is not even foreseen to

5.1 Introduction

65

Annual PV installed capacity in Spain 3500 3000

MW

2500 2000 1500 1000 500 0 2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

0)

(6.11)

Pused,t = Pideal

(6.12)

and creates,

Next, DesalinationPlant replaces the values of the indices found, and reservoir constraints are considered: Pused,id x2 = Pideal.id x2 − Pmismatch.id x2 to reach to

(6.13)

6.6 Black-Box Method and DYCORS Technique

Wideal,t = Pused,t × rt

97

∀t ∈ Periods

(6.14)

Then, DesalinationPlant calculates the quantity of seawater required. To do this, earliest in order, DesalinationPlant calculates a ratio of water flow incoming/water flow outgoing (k). k = nominal_unit_ f lowin /nominal_unit_ f low_out Wintake,t = Wideal,t × k

∀t ∈ Periods

(6.15) (6.16)

Eventually, Pnot used,t at this point would be: Pnot used,t = Pavailable,t − Pused,t

∀t ∈ Periods

(6.17)

In order to provide simplicity to the notation, the equations presented represent intelligent vector operations. Reservoir DesalinationPlant models a reservoir in such a way that it can effectively take into account the mismatches in water demand. This circumstance can be seen in (6.9). Reservoir equations are given in an algorithmic way. First, the assumptions of the initial state of the deposit must be adjusted in DesalinationPlant. DesalinationPlant evaluates which is the increase of time by using t. DesalinationPlant finds a tentative amount of water: w = Wt−1 + Wideal,t−1 × t − Wdemand,t−1 ∀t ∈ (1 . . . Periods)

(6.18)

DesalinationPlant corrects the provisional value of water: If w > capacity Wt = capacity W mismatch, t = w − capacit y Else if w < 0 Wt = 0 Wmismatch,t = W Else: Wt = 0 Wmismatch,t = W By using W, DesalinationPlant assesses reservoir WSOC and the water flow Wflow : W soc, t =

Wt capacit y

∀t ∈ Periods

(6.19)

98

6 Surrogate Optimization of Coupled Energy Sources …

W f low, t =

dW t dt

∀t ∈ Periods

W f low, t =

dW t dt

∀t ∈ Periods

(6.20)

Wind power plant model DesalinationPlant uses a wind profile of the site as a wind model. Pwind is obtained in the following way: The power curve of the wind turbine is normalized in such a way that the power is between 0 and 1. The wind speed has m/s as units. This normalized power curve of the wind turbine is converted into the C(wind) interpolation function. The normalized production is achieved by passing the wind speed profile to C(wind) function, obtaining:   Pnormalized, t = C windprofile

∀t ∈ Periods

(6.21)

The final output Pwind is obtained by multiplying Pnormalized by the nominal power of the wind farm, obtained from the optimizer. This is: Pwind, t = Pnormalized, t · Pnominal

∀t ∈ Periods

(6.22)

Solar plant model Psolar is calculated, taking into account a radiation profile of the location, as follows: Psolar, t = Irradiation ·

Pnominal 1000

∀t ∈ Periods

(6.23)

Pnominal it is a value that will be adjusted by the optimization procedure, obtaining next (taking into account the irradiation profile, irradiation) the solar production.

6.6.2 Electrochemical Storage Model For the DesalinationPlant development, a determination in the modelling was made to include the possibility of assessing the performance of electrochemical storage in any potential microgrid. This storage shall not be understood as repetitious or excessive even though a reservoir were available. This is due to the fact that power available to desalinate water and the storage capacity of water are not both infinite. This circumstance does not occur. The benefit of the electrochemical storage is the possibility of storing all the surplus electricity to provide electricity to the plant when

6.6 Black-Box Method and DYCORS Technique

99

renewable resources are not available. Said that, the need to use energy storage system (Li-ion batteries) usually is not only to cover the power needs of the desalination unit when RES are not available but also to stabilize the power from this variable source (especially wind energy). Moreover, is a way to secure the operation of the RO unit, regarding the availability of power, during the flushing process/es.

6.6.3 Costs, Benefits, Risks, Uncertainties, and Timeframes to Evaluate the Attributes of Energy Technologies The methods for calculating expenditures and revenues, and taking decisions about determining the optimum use of resources are described below.

6.6.4 Expenditures To assess scheme expenditures, it is considered, for every technology, the following financial criterion: Unit expenditures: Minimum cost for buying any standard unit. Conservation expenses: Cost incurred (referred to the cost of the device) to carry out an annual maintenance. Useful lifetime: Number of years that a determined device is estimated will be in operation. Investment after the useful lifetime: Unitary cost incurred (referred to the installation cost of the device) to replace a certain device. The aforementioned amounts allow to evaluate the expenses incurred for each technology and period. These costs will indicate the expenses for the first 365days, in addition to the maintenance expenditures plus reconditioning in following 365-days periods. Failure in meeting water demands at any time would mean a “penalty” equivalent to the quantity of unmatched water demanded multiplied by the water tariff. The potential energy required to restore electrical energy in the electrochemical storage by connecting it to a power supply is likewise considered as an expense. This expense is evaluated as the quantity of energy required multiplied by the unit energy cost at that time.

6.6.5 Revenues System revenues are provided through sales of water and electricity surpluses.

100

6 Surrogate Optimization of Coupled Energy Sources …

Water revenues are evaluated as the volume of water provided to the farmer multiplied by the cost of water. Energy revenues are evaluated as the quantity of electricity delivered to the electricity network multiplied by its cost.

6.6.6 Criteria Decision Making A systematic approach to determine the optimum use of scarce resources has been conducted. This procedure involves a comparison of two or more alternatives in achieving a specific objective, under the given assumptions and constraints. To do so, all the profits for the extent of time evaluated will be taken into account. Unit expenditures and conservation expenses for every technology used have been included in the model. Costs related to (i) land purchases; (ii) cost, interest, and other charges involved in the borrowing of money to build or purchase assets; or (iii) total of all employee wages plus the cost of benefits and payroll taxes paid by an employer; have not been included in the cost calculations. Be that as it may, the costs that are taken into account will produce a linear combination of the optimal value found because the costs that will not be considered will simply add costs to the cash flow.

6.6.7 Description of the Case Study This section describes the real case study used to applied the method presented in this research. The installation used is located in Playa Vargas, a sea village on the island of Gran Canaria (Canary Islands, Spain). Gran Canaria is a European island situated in the Atlantic Ocean, close to the northwest coast of Africa. With 851,231 inhabitants [56] and 4.04 million tourist visitors in 2019 [57], the island has a high population density (545.60 people/km2 [56]) and it has maintained an important activity of agriculture. All these factors produce an important stress for the limited water and energy resources on the island [58]. In this context, Soslaires Canarias S.L. was created as a pioneer utility providing renewable energy [10] and its own desalinated water to cultivate about 650,000 m2 [11]. It should be noted that Soslaires Canarias S.L. supplies water to 735 farmers [12] and is one of the few companies worldwide that specifically aims to transform seawater into drinking water (using desalination plants) and to distribute electrical energy through the use of wind energy. For these reason Soslaires Canarias S.L. has awakened interest in researchers from other countries [13]. A complete representation of the scheme used as case study is presented in Fig. 6.4.

6.6 Black-Box Method and DYCORS Technique

101

Fig. 6.4 Schematic diagram of the evaluated microgrid by the DesalinationPlant software. Source Own elaboration

6.6.8 Desalination System and Reservoir Data The model of the desalination system was adjusted based on experimental information collected from an actual running scheme of Soslaires Canarias S.L. installations, located in Playa de Vargas (Canary Islands). This fact, far from being a disadvantage, was a businesslike choice to design DesalinationPlant (Tables 6.1 and 6.2). Table 6.1 Data of the desalination plant used for the simulation Membranes

Sea pumps

Number of units (in total)

6

Number of pumps

8

Power of auxiliaries

41.16 kW

Power of the auxiliaries

110 kW

Water flow inlet

590.8 m3

Nominal flow

115 m3 /h

Water flow out

236.3

Cost per kW

2000 e [60]

Membrane unitary cost

600 e/m3 [59]

Maintenance cost as a percentage of the installation cost

26% [61]

Maintenance cost as a percentage of the investment cost

27% [62]

Equipment life

15 years [63]

Life span

10 years [64]

After life investment

100%

After life investment

100%

m3

Source Data provided by Soslaires Canarias S.L. and [59]

102 Table 6.2 Reservoir data used for the simulation

6 Surrogate Optimization of Coupled Energy Sources … Water deposit Head

190 m

Storage capacity

180,000 m3

Water demand profile

50 m3

Water price

0.60 e/m3 [65]

Investment cost

962,000 e [65]

Maintenance cost as a percentage of the investment cost

1%

Life span

50 years [66]

After life investment

100%

Source Data provided by Soslaires Canarias S.L. and [59]

• Wind energy data Figure 6.5a shows the wind profile measured in the site of the case study. The results obtained in Figs. 6.6 and 6.7 indicate that, in the area studied, wind direction and speed present significant variations, both seasonal and monthly as well as daily. As far as speed is concerned, the strongest winds occur in summer (June), while during winter (January), the speed decreases. This characteristic is accentuated if only winds above 10 m/s are considered, since in the month of June these winds

Fig. 6.5 Wind model speed (a) and capability (b) profiles. Source [31]

6.6 Black-Box Method and DYCORS Technique

103

Fig. 6.6 Mean wind speeds by hours in a day of: a January; b April; c June; and d October in the case study. Source Own elaboration

Fig. 6.7 Mean monthly wind speeds recorded at the reference station for the case study. Source Own elaboration

account for the majority of all hourly records; while in January the frequency is greatly reduced. On the other hand, it has also been observed that the daily wind regime presents changes: the speed is maximum during the central hours of the day (14:00–18:00 h), while it decreases significantly during the night (Fig. 6.8). • Wind energy data See Table 6.3. • Solar energy data See Table 6.4.

104

6 Surrogate Optimization of Coupled Energy Sources …

Fig. 6.8 Synthetic solar radiation data. Source [31]

Table 6.3 Wind power plant data used for the simulation

Wind power plant Cost per kW

900 e/kW [67]

Maintenance cost as a percentage of the investment cost

4% [67]

Life span

25 years [67]

After life investment

80%

Maximum power size considered for the optimization

1925 kW

Source Data provided by Soslaires Canarias S.L. and [67]

Table 6.4 Solar power plant data used for the simulation

Solar power plant Panel size

2.0 m2 [68]

Efficiency

17.5% [68]

Cost per kW

1700 e/kW [68]

Maintenance cost as a percentage of the investment cost

8% [69]

Life span

25 years [69]

After life investment

80%

Maximum power size considered for the optimization

2900 kW

Source Data provided by Soslaires Canarias S.L. and [68]

As can be seen in the values of the graph corresponding to the year 2016, photovoltaic generation will reach its maximum in the area studied between the months of May and October, coinciding with the months of greatest solar radiation (Fig. 6.10). As expected, the maximum radiation values are recorded during the summer months and, more specifically, in the case of hourly radiation, during the central hours of the day (Fig. 6.9).

6.6 Black-Box Method and DYCORS Technique

105

Fig. 6.9 Hourly solar irradiation data for the a January 17, 2016; b April 15, 2016; c June 10, 2016; and d October 19, 2016. Source Own elaboration

Fig. 6.10 Mean monthly solar irradiation (2016) recorded at the reference station for the case study. Source Own elaboration

• Energy storage data See Table 6.5. In order to carry out the selection of the DER (wind energy and photovoltaic solar energy), it has been assumed that, at most, 50% of the energy is produced by the DER

106 Table 6.5 Energy storage data used for the simulation

6 Surrogate Optimization of Coupled Energy Sources … Energy storage Charge/discharge efficiency

98% [70]

Unitary cost

300 e/kWh [71]

Minimum and maximum state of charge

30–100 [71]

Maintenance cost as a percentage of the installation cost

1% [71–73]

Life span

7 years [71–73]

After life investment

100%

Maximum power size considered for the optimization

1527 kWh

Source Data provided by Soslaires Canarias S.L. and [59]

along the year. For this, a consumption of the installation of 3,624,771 kWh/year has been considered [65]; a wind capacity factor of a 43.0% [74]; and a solar capacity factor of 28.5%. To smooth out the volatility and make a wind turbine/solar PV act like a conventional generator [75], it has been supposed that the maximum battery size will be that one would be capable of charging or discharging up to 1018 kW (the microgrid loads [65]) for 90 min.

6.7 Results and Discussion Main outcomes achieved after applying the method presented in Sect. 6.2 are exposed in this section. Figure 6.11 contains two graphs in which the objective function (benefit) is represented on a negative scale, in such a way that the reduction of the benefits to the smallest possible amount or degree actually means that the profit is made as large or great as possible. Figure 6.11a provides the convergence boundary along with every outcome obtained in the simulation conducted. It is possible to observe how the optimization method is consistently able to find an optimal order of magnitude earlier that two hundred assessments. Other depiction of the outcomes is presented in Fig. 6.11b, which shows local minima and an overall optimum found from the DYCORS method. Table 6.6 shows investment costs, total costs and income, average benefit, and LCOE for the proposed scheme. As can be seen, for the selected solution the electrochemical storage is about 9 kWh, with the size of the resources distributed relative to the wind turbines and the photovoltaic solar panels of 1922 kW and 243 kW respectively. Due to the high installation cost of electrochemical storage [76–79] and the inherent economic benefits of operating in a grid-connected mode [80–82] (as is the case with the micro-network evaluated in the research presented in this chapter), of Table 6.6 it can be seen how the optimal solution found by DesalinationPlant does

6.7 Results and Discussion

107

Fig. 6.11 Most effective use of resources evaluation. a Convergence. b Cluster plot or “color fish” plot

108 Table 6.6 Investment costs, total costs and income, average benefit, and LCOE for the proposed scheme

6 Surrogate Optimization of Coupled Energy Sources … Parameter

Amount and unit

Solar farm size

243.00 kW

Wind farm size

1922.14 kW

Storage size

8.89 kWh

Desalination plant costs

981,600.00 e

Solar farm costs

413,100.00 e

Wind farm costs

1,729,928.53 e

Storage costs

2666.63 e

Total investment costs

3,127,295.16 e

Total costs

611,225.10 e

Total income

10,623,079.19 e

Average benefit

173,302.08 e/year

Source Own elaboration

not contemplate (or marginally contemplates) the use of batteries. A circumstance to some extent “surprising” is the fact that, with equal costs per kW installed of photovoltaic solar energy and wind power, DesalinationPlant would have left out the energy mix of the microgrid to wind energy. This is due to the regular solar resource in Pozo Izquierdo (Canary Islands), while generation due to wind energy is less regular. Once results obtained have been studied it should be remarked that even though there are some crucial research papers in the field of RO desalination powered by renewable energy [83–86] (in particular in the research optimization of renewablepowered desalination and challenges regarding membrane fouling) to our knowledge this is the first approach that evaluates the applicability of the DYCORS method to specifically optimize wind, solar, and pumped-storage hydroelectricity resources for a microgrid with desalination purposes. Figure 6.12 and Table 6.7 show the monthly means for (a) the water produced by the system, (b) the power used by the from the grid, (c) the power generated by the RES optimally selected by the algorithm and (d) the state of the charge (SoC) in battery. In Fig. 6.12, it can be seen how the water produced by the system increases in the summer months coinciding with a greater availability of wind and solar resources (Fig. 6.12c). Figure 6.12b shows the power imported from the conventional electricity grid to the micro grid. Of all the months of the year, January is the month that shows the greatest import of electrical energy. This is due to two main reasons. Firstly, this is due to the fact that the availability of the renewable resource is lower during that month. Secondly, this circumstance is due to the fact that DesalinationPlant starts the simulation for January 1st considering that the water reservoir is at half of its total capacity. As expected, the batteries present a lower SoC in the winter months due to the lower wind and solar resources (Fig. 6.12d).

6.7 Results and Discussion

109

Fig. 6.12 Monthly means of the a water produced by the system, b power used by the system, c power from RES and d battery SoC. Source Own elaboration Table 6.7 Monthly means for different variables of the operation of the desalination plant Month

Water produced (m3 )

Power used by the system (kW)

Power from RES (kW)

Battery SoC (p.u.)

January

407.65

110.93

403.73

0.76

February

447.86

32.40

535.26

0.88

March

733.77

33.41

897.65

0.90

April

840.20

32.40

1109.60

0.95

May

942.92

32.40

1199.12

0.95

June

1114.01

32.40

1496.03

0.98

July

1215.73

32.40

1703.70

0.98

August

1166.50

32.40

1530.98

0.96

September

1143.49

31.35

1444.24

0.99

October

549.38

33.41

593.86

0.86

November

540.15

31.35

629.00

0.95

December

369.58

32.40

471.60

0.90

Year

789.27

38.94

1001.23

0.92

Source Own elaboration

110

6 Surrogate Optimization of Coupled Energy Sources …

Fig. 6.13 Behaviour of the following variables during the last 15 days of January: a water produced by the system, b power used by the system, c power from RES and d battery SoC. Source Own elaboration

Fig. 6.14 Behaviour of the following variables during the last 15 days of April: a water produced by the system, b power used by the system, c power from RES and d battery SoC. Source Own elaboration

6.7 Results and Discussion

111

Fig. 6.15 Behaviour of the following variables during the last 15 days of June: a water produced by the system, b power used by the system, c power from RES and d battery SoC. Source Own elaboration

Figures 6.13, 6.14, and 6.15 show respectively and for the months of January, April, June and October, the hourly behaviour for (a) the water produced by the system, (b) the power used from the grid, (c) the power generated by the RES optimally selected by the algorithm and (d) the state of the charge (SoC) in battery. Once the results obtained have been evaluated, it can be seen how (i) the greater the resource of renewable origin, the greater the amount of water produced by the system; (ii) the power obtained from the conventional electricity grid will be the greater the fewer the resources of renewable origin; (iii) the SoC will be the more constant and higher the greater the resource of renewable origin available.

6.8 Conclusions Subrogated optimization models used in this chapter have provided a robust method for sizing renewable micro-grids that have water desalination systems as a load by using a computer software, DeslinationPlant, which has been developed ad hoc and validated from experimental data. Through DesalinationPlant it is possible to model the renewable microgrid with a degree of detail that would have been impossible to achieve through a linear program. In general, linear and non-linear programming impose a series of restrictions on modelling in search of a global optimum. However, from the engineering point of view it is necessary to find a point of equilibrium between the modelling of the system and accuracy of the mathematical solution.

112

6 Surrogate Optimization of Coupled Energy Sources …

Other methods which use black box optimization, such as genetic algorithms, are able to provide a flexible reference frame to carry out the modelling; however, they require a large number of evaluations of the objective function. The objective function was modeled for a simulation over a year, using time profiles, in addition to a complete subsequent simulation for a specified number of years. This objective function is evaluated for the sizing of distributed resources (decision variables). Given a decision variable, an economic parameter is generated and optimized. The method used (DYCORS) provides both the aforementioned flexible frame of reference and at the same time is able to find significant optimal values in just 100 iterations. To carry out these optimizations, it was necessary to develop an ad hoc software (DesalinationPlant). This software was programmed in Python with a graphical user interface that uses Qt. The software has been licensed using the GPL3 license to comply with the libraries. The existence of this software in this way has been demonstrated as an excellent disseminator of the developed knowledge since this one can be easily extended by experts (and used by non-experts). The DesalinationPlant software has been licensed as an open source scheme in such a way that it can be used and extended by others, constituting in itself an exceptional platform for the transfer of knowledge.

References 1. Tzen E, Rossis K, González J, Cabrera P, Peñate B, Subiela V (2017) Wind technology design and reverse osmosis systems for off-grid and grid-connected applications. In: Mahmoudi H, Ghaffour N, Goosen MFA, Bundschuh J (eds) Renewable energy technologies for water desalination, 1st edn. CRC Press, London. https://doi.org/10.1201/9781315643915 2. Qasim M, Badrelzaman M, Darwish NN, Darwish NA, Hilal N (2019) Reverse osmosis desalination: a state-of-the-art review. Desalination 459:59–104. https://doi.org/10.1016/j.desal. 2019.02.008 3. Carta JA, González J, Cabrera P, Subiela VJ (2015) Preliminary experimental analysis of a small-scale prototype SWRO desalination plant, designed for continuous adjustment of its energy consumption to the widely varying power generated by a stand-alone wind turbine. Appl Energy 137:222–239. https://doi.org/10.1016/j.apenergy.2014.09.093 4. González J, Cabrera P, Carta JA (2019) Wind energy powered desalination systems. In: Desalination: water from water, 2nd edn. Wiley, Hoboken, NJ, pp 567–646. https://doi.org/10.1002/ 9781119407874.ch14 5. Ghaffour N, Mujtaba IM (2018) Desalination using renewable energy. Desalination 435:1–2. https://doi.org/10.1016/j.desal.2018.01.029 6. Schäfer AI, Remy C, Richards BS (2004) Performance of a small solar-powered hybrid membrane system for remote communities under varying feedwater salinities. Water Sci Technol Water Supply 233–243. https://doi.org/10.2166/ws.2004.0113 7. Cai YH, Schäfer AI (2020) Renewable energy powered membrane technology: Impact of solar irradiance fluctuation on direct osmotic backwash. J Memb Sci 598:117666. https://doi.org/ 10.1016/j.memsci.2019.117666

References

113

8. Subiela V, Carta JA, González J, Vicente C, Sublela J, Carta JA, Gonzalez J (2004) The SDAWES project: lessons learnt from an innovative project. Desalination 168:39–47. https:// doi.org/10.1016/j.desal.2004.06.167 9. Carta JA, González J, Subiela V (2004) The SDAWES project: an ambitious R&D prototype for wind-powered desalination. Desalination 161:33–48. https://doi.org/10.1016/S0011-916 4(04)90038-0 10. Cabrera P, Carta JA, González J, Melián G (2018) Wind-driven SWRO desalination prototype with and without batteries: a performance simulation using machine learning models. Desalination 435:77–96. https://doi.org/10.1016/j.desal.2017.11.044 11. Khan MAM, Rehman S, Al-Sulaiman FA (2018) A hybrid renewable energy system as a potential energy source for water desalination using reverse osmosis: a review. Renew Sustain Energy Rev 97:456–477. https://doi.org/10.1016/j.rser.2018.08.049 12. Karavas CS, Arvanitis KG, Papadakis G (2019) Optimal technical and economic configuration of photovoltaic powered reverse osmosis desalination systems operating in autonomous mode. Desalination 466:97–106. https://doi.org/10.1016/j.desal.2019.05.007 13. Delgado-Torres AM, García-Rodríguez L, del Moral MJ (2020) Preliminary assessment of innovative seawater reverse osmosis (SWRO) desalination powered by a hybrid solar photovoltaic (PV)—tidal range energy system. Desalination 477:114247. https://doi.org/10.1016/j. desal.2019.114247 14. Prathapaneni DR, Detroja K (2020) Optimal design of energy sources and reverse osmosis desalination plant with demand side management for cost-effective freshwater production. Desalination 496:114741. https://doi.org/10.1016/j.desal.2020.114741 15. Maleki A (2018) Design and optimization of autonomous solar-wind-reverse osmosis desalination systems coupling battery and hydrogen energy storage by an improved bee algorithm. Desalination 435:221–234. https://doi.org/10.1016/j.desal.2017.05.034 16. Garcia RS, Weisser D (2006) A wind-diesel system with hydrogen storage: joint optimisation of design and dispatch. Renew Energy 31:2296–2320. https://doi.org/10.1016/j.renene.2005. 11.003 17. Rubio-Maya C, Uche-Marcuello J, Martínez-Gracia A, Bayod-Rújula AA (2011) Design optimization of a polygeneration plant fuelled by natural gas and renewable energy sources. Appl Energy 88:449–457. https://doi.org/10.1016/j.apenergy.2010.07.009 18. Schäfer AI, Broeckmann A, Richards BS (2007) Renewable energy powered membrane technology. 1. Development and characterization of a photovoltaic hybrid membrane system. Environ Sci Technol 41:998–1003. https://doi.org/10.1021/es061166o 19. Li S, Cai YH, Schäfer AI, Richards BS (2019) Renewable energy powered membrane technology: a review of the reliability of photovoltaic-powered membrane system components for brackish water desalination. Appl Energy 253:113524. https://doi.org/10.1016/j.apenergy. 2019.113524 20. Cabrera P, Carta JA (2019) Computational intelligence in the desalination industry. Springer, Cham, pp 105–131. https://doi.org/10.1007/978-3-030-25446-9_5 21. González J, Cabrera P, Carta JA (2018) Desalination: water from water. Wiley, pp 567–646 22. Cabrera P, Carta JA, González J, Melián G (2017) Artificial neural networks applied to manage the variable operation of a simple seawater reverse osmosis plant. Desalination 416:140–156. https://doi.org/10.1016/j.desal.2017.04.032 23. Freire-Gormaly M, Bilton A (2017) An experimental system for characterization of membrane fouling of solar photovoltaic reverse osmosis systems under intermittent operation. Desalin Water Treat 73:54–63. https://doi.org/10.5004/dwt.2017.20391 24. Freire-Gormaly M, Bilton AM (2018) Experimental quantification of the effect of intermittent operation on membrane performance of solar powered reverse osmosis desalination systems. Desalination 435:188–197. https://doi.org/10.1016/j.desal.2017.09.013

114

6 Surrogate Optimization of Coupled Energy Sources …

25. Freire-Gormaly M, Bilton A (2019) Impact of intermittent operation on reverse osmosis membrane fouling for brackish groundwater desalination systems. J Memb Sci 583:220–230 26. Freire-Gormaly M, Bilton AM (2019) Design of solar powered reverse osmosis desalination systems considering membrane fouling caused by intermittent operation. Renew Energy 135:108–121 27. Richards BS, Capao DPS, Schafer AI (2008) Renewable energy powered membrane technology. 2. The effect of energy fluctuations on performance of a photovoltaic hybrid membrane system. Environ Sci Technol 42:4563–4569 28. Bourouni K, Ben M’Barek T, Al Taee A (2011) Design and optimization of desalination reverse osmosis plants driven by renewable energies using genetic algorithms. Renew Energy 36:936–950. https://doi.org/10.1016/j.renene.2010.08.039 29. Müller J, Shoemaker CA, Piché R (2014) SO-I: a surrogate model algorithm for expensive nonlinear integer programming problems including global optimization applications. J Glob Optim 59:865–889. https://doi.org/10.1007/s10898-013-0101-y 30. Regis RG, Shoemaker CA (2013) Combining radial basis function surrogates and dynamic coordinate search in high-dimensional expensive black-box optimization. Eng Optim 45:529– 555. https://doi.org/10.1080/0305215X.2012.687731 31. Peñate-Vera S (2018) GitHub—SanPen/DesalinationPlant: software written and used in the production of desalination plant: optimization of energy sources. https://github.com/SanPen/ DesalinationPlant. Accessed 20 Sept 2020 32. Razavi S, Tolson BA, Burn DH (2012) Review of surrogate modeling in water resources. Water Resour Res 48. https://doi.org/10.1029/2011WR011527 33. Golinska-Dawson P (ed) (2014) Logistics operations, supply chain management and sustainability, 1st edn. Springer International Publishing. https://doi.org/10.1007/978-3-319-072 87-6 34. Mousavi SM, Vahdani B, Tavakkoli-Moghaddam R, Hashemi H (2014) Location of crossdocking centers and vehicle routing scheduling under uncertainty: a fuzzy possibilistic– stochastic programming model. Appl Math Model 38:2249–2264. https://doi.org/10.1016/j. apm.2013.10.029 35. Cisty M (2010) Hybrid genetic algorithm and linear programming method for least-cost design of water distribution systems. Water Resour Manag 24:1–24. https://doi.org/10.1007/s11269009-9434-1 36. Corradi L, Zavelani A (1974) A linear programming approach to shakedown analysis of structures. Comput Methods Appl Mech Eng 3:37–53. https://doi.org/10.1016/0045-7825(74)900 41-3 37. Jha MK (ed) (2010) Natural and anthropogenic disasters: vulnerability, preparedness and mitigation, 1st edn. Springer Netherlands. https://doi.org/10.1007/978-90-481-2498-5 38. Im J, Park J (2013) Stochastic structural optimization using particle swarm optimization, surrogate models and Bayesian statistics. Chin J Aeronaut 26:112–121. https://doi.org/10.1016/j. cja.2012.12.022 39. Fernández C, Pantano N, Godoy S, Serrano E, Scaglia G (2019) Parameters optimization applying Monte Carlo methods and evolutionary algorithms. Enforcement to a trajectory tracking controller in non-linear systems. Rev Iberoam Autom Inform Ind 16:89–99. https:// doi.org/10.4995/riai.2018.8796 40. Regis RG (2013) An initialization strategy for high-dimensional surrogate-based expensive black-box optimization. Springer, New York, NY, pp 51–85. https://doi.org/10.1007/978-14614-8987-0_3 41. Müller J (2016) MISO: mixed-integer surrogate optimization framework. Optim Eng 17:177– 203. https://doi.org/10.1007/s11081-015-9281-2

References

115

42. Lubis Y (2017) Non-linear programming approach for optimization of construction project’s control system. Int J Appl Eng Res 12:12934–12939 43. Dahl G (2014) A mini-introduction to convexity. Oslo University, Oslo. https://www.uio.no/ studier/emner/matnat/math/MAT-INF3100/v14/convmat-inf3100.pdf. Accessed 20 Sept 2020 44. Munier N (2011) A strategy for using multicriteria analysis in decision-making. Springer Netherlands. https://doi.org/10.1007/978-94-007-1512-7 45. Antipova E, Boer D, Cabeza LF, Guillén-Gosálbez G, Jiménez L (2013) Multi-objective design of reverse osmosis plants integrated with solar Rankine cycles and thermal energy storage. Appl Energy 102:1137–1147. https://doi.org/10.1016/j.apenergy.2012.06.038 46. Tsiakis P, Papageorgiou LG (2005) Optimal design of an electrodialysis brackish water desalination plant. Desalination 173:173–186. https://doi.org/10.1016/j.desal.2004.08.031 47. Al-Nory M, El-Beltagy M (2014) An energy management approach for renewable energy integration with power generation and water desalination. Renew Energy 72:377–385. https:// doi.org/10.1016/j.renene.2014.07.032 48. Voros NG, Kiranoudis CT, Maroulis ZB (1998) Solar energy exploitation for reverse osmosis desalination plants. Desalination 115:83–101. https://doi.org/10.1016/S0011-9164(98)000 29-0 49. Black-box optimization competition (n.d.) https://www.ini.rub.de/PEOPLE/glasmtbl/projects/ bbcomp/. Accessed 21 Sept 2020 50. Guria C, Bhattacharya PK, Gupta SK (2005) Multi-objective optimization of reverse osmosis desalination units using different adaptations of the non-dominated sorting genetic algorithm (NSGA). Comput Chem Eng 29:1977–1995. https://doi.org/10.1016/j.compchemeng.2005. 05.002 51. Katsigiannis YA, Georgilakis PS, Karapidakis ES (2010) Multiobjective genetic algorithm solution to the optimum economic and environmental performance problem of small autonomous hybrid power systems with renewables. IET Renew Power Gener 4:404–419. https://doi.org/ 10.1049/iet-rpg.2009.0076 52. Ruiz-García A, Nuez I (2016) Long-term performance decline in a brackish water reverse osmosis desalination plant. Predictive model for the water permeability coefficient. Desalination 397:101–107. https://doi.org/10.1016/j.desal.2016.06.027 53. Hernández JA, Bassam A, Siqueiros J, Juárez-Romero D (2009) Optimum operating conditions for a water purification process integrated to a heat transformer with energy recycling using neural network inverse. Renew Energy 34:1084–1091. https://doi.org/10.1016/j.renene.2008. 07.004 54. Uehara H, Miyara A, Ikegami Y, Nakaoka T (1996) Performance analysis of an OTEC plant and a desalination plant using an integrated hybrid cycle. J Sol Energy Eng Trans ASME 118:115–122. https://doi.org/10.1115/1.2847976 55. Afshar A, Shojaei N, Sagharjooghifarahani M (2013) Multiobjective calibration of reservoir water quality modeling using multiobjective particle swarm optimization (MOPSO). Water Resour Manag 27:1931–1947. https://doi.org/10.1007/s11269-013-0263-x 56. Canary Islands Institute of Statistics (ISTAC) (n.d.) http://www.gobiernodecanarias.org/istac/. Accessed 20 Aug 2020 57. Tourism Statistics (n.d.) Tourism board of Gran Canaria Island Council of Gran Canaria. https://www.grancanaria.com/turismo/es/area-profesional/informes-y-estadisticas/estadisti cas/. Accessed 20 Aug 2020 58. Cabrera P, Lund H, Carta JA (2018) Smart renewable energy penetration strategies on islands: the case of Gran Canaria. Energy 162:421–443. https://doi.org/10.1016/j.energy.2018.08.020 59. Toray (n.d.) Toray membranes. http://www.toraymembranes.com/ 60. Gülich JF (2010) Centrifugal pumps, 2nd edn. Springer Berlin Heidelberg. https://doi.org/10. 1007/978-3-642-12824-0

116

6 Surrogate Optimization of Coupled Energy Sources …

61. Xylem Water Solutions (n.d.) Life cycle costs (LCC) for wastewater pumping systems 62. American Membrane Technology Association (n.d.) Membrane desalination costs. https:// www.amtaorg.com/wp-content/uploads/6_MembraneDesalinationCosts.pdf 63. Hunter Water Corporation (n.d.) Operating and maintenance cost estimating guideline. https://www.hunterwater.com.au/Resources/Documents/Drawings_Plans_Specs/Guidel ine---Water-and-Sewer-Cost-Estimating.pdf 64. Pinnau I (2008) Membranes for water treatment: properties and characterization. Adv Membr Technol. https://web.stanford.edu/group/ees/rows/presentations/Pinnau.pdf 65. Lozano-Ruano JB (2010) Soslaires Canarias S.L. http://www.islhagua.itccanarias.org/c/doc ument_library/get_file?p_l_id=22263&folderId=22087&name=DLFE-925.pdf. Accessed 20 Sept 2020 66. Zheng JJ, Zhao C (2008) Dam safety in China and the life span evaluation of old concrete dams. In: Walraven J, Stoelhorst D (eds) Tailor made concrete structures. CRC Press, pp 9–14 67. Stehly T, Beiter P, Heimiller D, Scott G (2017) 2017 cost of wind energy review. www.nrel. gov/publications. Accessed 24 Sept 2020 68. SUNPOWER (n.d.) SunPower® performance series | P17. https://us.sunpower.com/solar-res ources/sunpower®-p-series-commercial-p17-360-com/. Accessed 20 Sept 2020 69. Fu R, Feldman D, Margolis R, Woodhouse M, Ardani K (2009) U.S. solar photovoltaic system cost benchmark: Q1 2017. www.nrel.gov/publications. Accessed 24 Sept 2020 70. CD Technologies (n.d.) Featherweight lithium ion battery LI TEL 48-170 C. http://www.cdt echno.com/pdf/lit/12_1093_1214.pdf. Accessed 20 Sept 2020 71. Diorio N, Dobos A, Janzou S (2015) Economic analysis case studies of battery energy storage with SAM. www.nrel.gov/publications. Accessed 24 Sept 2020 72. Ultralife (n.d.) Li-ion vs. lead acid. http://www.beck-elektronik.de/uploads/media/lithium-ionvs-lead-acid.pdf. Accessed 20 Sept 2020 73. Zakeri B, Syri S (2015) Electrical energy storage systems: a comparative life cycle cost analysis. Renew Sustain Energy Rev 42:569–596. https://doi.org/10.1016/j.rser.2014.10.011 74. Gobierno de Canarias (n.d.) Gran Canaria: horas equivalentes. http://www.gobiernodecanar ias.org/ceic/doc/industriayenergia/energia/eolica/horas_gc.pdf. Accessed 20 Sept 2020 75. Jansen R (n.d.) Smoothing it out: improving wind turbine reliability with energy storage. https://www.src.sk.ca/blog/smoothing-it-out-improving-wind-turbine-reliability-ene rgy-storage. Accessed 20 Sept 2020 76. Menconi ME, dell’Anna S, Scarlato A, Grohmann D (2016) Energy sovereignty in Italian inner areas: off-grid renewable solutions for isolated systems and rural buildings. Renew Energy 93:14–26. https://doi.org/10.1016/j.renene.2016.02.034 77. Vieira FM, Moura PS, de Almeida AT (2017) Energy storage system for self-consumption of photovoltaic energy in residential zero energy buildings. Renew Energy 103:308–320. https:// doi.org/10.1016/j.renene.2016.11.048 78. Jülch V (2016) Comparison of electricity storage options using levelized cost of storage (LCOS) method. Appl Energy 183:1594–1606. https://doi.org/10.1016/j.apenergy.2016.08.165 79. Gioutsos DM, Blok K, van Velzen L, Moorman S (2018) Cost-optimal electricity systems with increasing renewable energy penetration for islands across the globe. Appl Energy 226:437– 449. https://doi.org/10.1016/j.apenergy.2018.05.108 80. Gallardo-Calles JM, Colmenar-Santos A, Ontañon-Ruiz J, Castro-Gil M (2013) Wind control centres: state of the art. Renew Energy 51:93–100. https://doi.org/10.1016/j.renene.2012. 09.012 81. Maleki A et al (2016) Design of a cost-effective wind/photovoltaic/hydrogen energy system for supplying a desalination unit by a heuristic approach. Sol Energy 139:666–675 82. Peng W et al (2018) Optimization of a hybrid system for solar-wind-based water desalination by reverse osmosis: comparison of approaches. Desalination 442:16–31

References

117

83. Wu B et al (2018) Optimal design of stand-alone reverse osmosis desalination driven by a photovoltaic and diesel generator hybrid system. Sol Energy 163:91–103 84. Maleki A et al (2016) Weather forecasting for optimization of a hybrid solar-wind-powered reverse osmosis water desalination system using a novel optimizer approach. Energy 114:1120– 1134 85. Zhang G et al (2018) Simulated annealing-chaotic search algorithm based optimization of reverse osmosis hybrid desalination system driven by wind and solar energies. Sol Energy 173:964–975 86. Mortazavi SM et al (2019) A review of solar compound parabolic collector in water desalination systems. Int J Model Simul

Chapter 7

Reduction of Water Cost for an Existing Wind Energy-Based Desalination Scheme: A Preliminary Configuration

7.1 Introduction Due to the increasing population and global industrial sector development in recent decades there has been a rapid increase in global water requirements [1]. Locations where, until relatively recently, their availability was not a problem right now is a priority [2]. In the Mediterranean region, factors such as tourism have led to very high water consumption in summer, and new instruments have had to be sought to ensure the needs of the population [3, 4]. As energy and water demand and environmental needs grow, recycling will play a more important role, so that, along with their conservation and efficiency, they become the most important aspects for sustainable resource management [5]. Management of unconventional water resources is a crucial problem, especially in water scarce areas [6]. Unused water resources through the purification of non-potable water resources is considered critical to meet future needs [7]. Any water containing total dissolved solids (TDS) of less than 1000 ppm is considered to be fresh water [8]. For their part, brackish water is associated with TDS values of between 1000 and 10,000 ppm [9]. The aim of desalination is to achieve total concentrations of dissolved solids less than 500 ppm as they are typically acceptable for drinking water [10]. In general, the two main types of technologies used worldwide for desalination can be classified as thermal processes or as membranes [11]. Desalination based on thermal processes involves a phase change or the use of distillation techniques to reduce the total dissolved solids content in non-potable water [12]. More mature technologies are based on simple thermal processes using techniques such as multiple-effect distillation (MED) or multistage flash (MSF) [13, 14]. MSF and MED processes are particularly suitable when it is possible to take advantage of the low-pressure steam provided by a nearby power plant [15]. As a consequence, the economic viability of these technologies will depend directly on the volume of water produced [12] and the cost of energy to produce the steam [16].

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 E. Rosales-Asensio et al., Sea Water Desalination in Microgrids, Green Energy and Technology, https://doi.org/10.1007/978-3-030-96678-2_7

119

120

7 Reduction of Water Cost for an Existing Wind …

Thermal processes consume a lot of energy, and are rarely used in public water supply applications in Europe, the USA and Australia as they are generally not profitable [17, 18]: membrane-based (nanofiltration and reverse osmosis) desalination is primarily used. Within the electrically driven membrane processes, electrodialysis is the most widely used [19]. In electrodialysis, the sign of the water transfer coefficient is positive (cation exchange membranes) or negative (anion exchange membranes), depending on the direction the water takes along with the species carrying the charge [19]. For a given range of salt contents, electrodialysis has a clear economic advantage over other desalination processes, being used mainly in small-sized plants fed with brackish water [20]. For TDS higher than brackish water (TDS between 1000 and 10,000 ppm) it is considered that technologies such as reverse osmosis are economically more advantageous than electrodialysis [21]. Reverse osmosis is a process of membranes based on a pressure potential [22] in which water, from a pressurized salt solution, is separated from salts dissolved therein through a permeable membrane [23]. The amount of energy (hydraulic pressure) required to pass water through the membrane depends on the material and thickness of the membrane as well as the osmotic pressure of the feed [24]. Therefore, energy in the form of hydraulic pressure is required to overcome both the physical resistance of the membrane itself and the osmotic pressure of the system [24]. Because this pressure is applied to force the water against the natural osmotic gradient to produce a smaller amount of saline water from the concentrated water, the process is called “reverse osmosis” [24]. The required quality of the water product is achieved through additional steps such as gravel filtration and activated carbon filtration as well as membrane filtration [25] (Fig. 7.1). The initial stage of a reverse osmosis system must incorporate a suitable pretreatment to prevent filter fouling [29]. Subsequently and to finish the pretreatment stage the suspended solids are eliminated through sand filters or other methods [30]. Normally the pretreatment consists of a fine filtration and the addition of acids or

CHEMAD ER

DC

BC

Feed Wa-

PW HPP Pretreat-

MF

ROS

CF

Fig. 7.1 Typical reverse osmosis desalination scheme. Source Adapted from [26–28]

7.1 Introduction

121

other chemical components to inhibit the precipitation and the growth of microorganisms [31]. Usually, the manufacturer provides operating guidelines and limits for the membrane, such as operating pressure, pH range or feed water quality [32]. For optimal membrane performance, feed water must meet these parameters [32]. The next step in the reverse osmosis process is to increase the feed water pressure and pump it through closed vessels [33–35]—the overall system performance can be determined by the performance of a single vessel [36, 37]. Permeate decreases due to membrane fouling and the polarization resistance on the surface of the membrane [38]. The same vessel is responsible for collecting both drinking water (often called permeate) and the concentrate [39–42]. Current desalination systems comprise of a stage where a high-pressure energy is recovered through energy recovery devices (ERDs) [43, 44]. The use of desalination has become widespread due to the advances made in this technology (both in thermal and membrane-based desalination), which has caused capital costs for systems and components to be reduced and performance characteristics improved [45]. To compare the costs associated with each technology, what is usually done is to evaluate the energy used by each cubic meter of drinking water to carry out the desalination of water [46]. For sea water, thermal systems typically have a consumption of between 3.5 and 4.5 kWh to produce 1 m3 of water [47], which is higher than that of reverse osmosis systems where the consumption is normally between 3.0 and 4.5 kWh/m3 [48]. In desalination of seawater, unlike thermal distillation technologies (which are a mature technology [49]), the efficiency of reverse osmosis technology has improved considerably over the last two decades [50]. From the outset, reverse osmosis desalination has focused on the enhancement and development of membrane elements, which are considered the heart of the process [51]. Because the cost of energy is the most important factor in the cost of a desalination system (generally 20% to 30% of the total water cost), this is an obvious objective for cost reduction and for obtaining water at the lowest possible cost [52]. One of the main disadvantages of thermal systems is the significant heat needed (in the form of steam) that they need at the input [53], which makes it not always possible to install them in non-centralized systems [54]. This is not the case with desalination through reverse osmosis where only electricity (available through the electric grid) is required as energy input [21], which has led to an increase in the acceptance of this technology as an appropriate technology [55]. Historically, some end-uses of certain renewable resources have been underutilized, which has meant that they have been exploited at rates below their maximum sustainable yield [56]. Desalination has the potential to make use of unusable and inexhaustible water [57], greatly increasing the amount of water for consumption by populations and for agriculture [16], and would contribute to alleviating the global shortage of water [58]. However, high capital costs have often led to the desalination process not being competitive [59] with the commonly used energy sources. Although reverse osmosis is the most energy efficient desalination method [60], energy consumption remains an important part of the total cost of water desalination, accounting for as much as 45% of the total cost of production of the permeate [61] (Fig. 7.2). In certain isolated regions of the world, such as the Canary Islands,

122

7 Reduction of Water Cost for an Existing Wind …

Typical cost structure for RO seawater desalination 3% 4% 5%

7% 44%

37%

Electric power

Capital cost

Membrane Replacement

Labor

Maintenance and Parts

Consumables

Fig. 7.2 Typical cost structure for RO seawater desalination. Source [62]

high levelized costs of energy (LCOE) represent a problem for existing desalination systems. Therefore, alternative solutions for producing water must be found. Taking into account that about 90% of the costs of a reverse osmosis desalination system correspond to the costs of electrical energy, capital costs and maintenance costs (see Fig. 7.2), a likely candidate to replace traditional reverse osmosis desalination is reverse osmosis plant powered by inexhaustible resources [63]. Desalination systems based on renewable energies have as advantages the following: The part of the levelized cost of energy (LCOE) relative to the transmission and distribution of electric energy is directly eliminated, so potentially the final LCOE might be lower. Although the 13 ce/kWh of the LCOE corresponding to photovoltaic solar energy [64] means its cost is still high, in the case of wind energy, the same figure is about 6 ce/kWh [64], which makes it competitive with other traditional energy sources in certain locations (as in the Canary Islands, for example). The installed cost of a wind power project is dominated by the initial cost of capital for wind turbines (including towers and installations), which typically represents around 80% of the total installed costs [64]. Operation and maintenance costs are typically around 20% of the total LCOE of wind energy systems [65]. This change is positive because, on the one hand, capital costs can be amortized over the useful life of the facility [66] (much higher than that of fuels) and on the other hand it reduces the sensitivity to market prices of fuels [67]. A real challenge for renewable energy-based desalination systems would be the optimal technological design of combined plants that increase the efficiency as well as the volume of the desalination plant while lowering costs [68]. Renewable energy sources by their very nature tend to be intermittent and of variable intensity [69, 70]. Desalination processes (especially those based on membrane processes [71]) are designed for continuous steady-state operation [72]. The addition of renewable energy resources to the grid influences the voltage of the grid

7.1 Introduction

123

during normal operation (steady state) and in the voltage response during abnormal (transient) operation [73]. If a more or less direct coupling of the wind turbine(s) and the desalination system is desired, it will be affected by variations in power and interruptions caused by the energy resource (the wind) [74]. Because these variations will have a negative effect on the performance and useful life of certain components of the desalination system equipment, backup systems, such as batteries [74], diesel generators, or flywheels could be integrated into the system [75]. However, energy storage generally increases capital costs, thus affecting freshwater costs [74]. In the recent scientific literature, it is possible to find novel systems of desalination that use renewable energies as energy resources. As an example, Lai et al. [76] propose the use of a Clark pump as a workflow converter device that works within a series of desalination columns to first recover the energy from the high-pressure brine to later convert it into potential gravitational energy of water in a higher tank. Peñate et al. [77] evaluated the most suitable design for SWRO desalination using autonomous wind energy systems. A design based on a variable capacity with a nominal output of 1000 m3 /d in the study was compared to a fixed capacity desalination plant. The results show that due to the intermittent nature of wind resources, the gradual capacity desalination plant is able to adjust the available energy and maximize annual water production. Cendoya et al. [78] propose a modular system comprising a wind turbine, an energy fluctuation compensator, a reverse osmosis desalination system and an alkaline electrolyzer. All these modules are interconnected through electronic power converters by a local AC bus, which in turn is linked to a pre-existing weak electricity network. Cendoya et al. [78] shows how the use of synchronous permanent magnet generators in wind power conversion systems, and supercapacitors to compensate for their fluctuations, is a very promising alternative for renewable energy generation systems with good energy quality. Although these results are promising, there are still problems of economic feasibility, performance and commercialization. Eventually the final efficiency of the reverse osmosis system fueled by renewable energies is to be evaluated taking into account its COW. Therefore, the final objective of this chapter is to research how to integrate inexhaustible resources and reverse osmosis desalination systems to minimize the cost of water production. To do this, it will use exclusive data collected for more than 15 years by the company Soslaires Canarias S.L. (to our knowledge, the only one in the world that has a scheme in operation similar to the scheme proposed in this chapter) located on the island of Gran Canaria (Spain). There is a collaboration project with Soslaires Canarias S.L. (unique for the reasons explained above), this research being part of it. Section 7.2 presents a first approach to today’s most noteworthy desalination technologies and exposes the potential applicability of the use of renewable resources to carry out these processes in an economic way. For its part, Sect. 7.3 will present the methodology used to conduct the research carried out in this chapter and reduce the water cost of the scheme currently in operation by Soslaires Canarias S.L. Section 7.4 presents the basis for carrying out the calculations, while Sect. 7.5 is reserved for discussion and shows the results of the water cost analysis for the reverse osmosis desalination system powered by renewable energies. Section 7.6 shows conclusions,

124

7 Reduction of Water Cost for an Existing Wind …

which will present the implications (constraints and benefits) of a generalization of the scheme and actions proposed here.

7.2 Material and Methods The cost of a renewable energy-based RO scheme has 2 main components: CCs intrinsic in the purchase of the equipment and the installation of the required facilities; plus OCs to desalinate the water (fresh water). CCs and OCs corresponds to those for 2017 and are referenced for e/m3 of product water. In those cases where reference costs correspond to years prior to 2017, costs have been recalculated by using M&S cost indexes [79]. The cost of the scheme is evaluated assuming a combined use of a reverse osmosis desalination plant and wind energy, using for this purpose (exclusive) information of the scheme used by Soslaires Canarias S.L. [80] for more than 15 years and research [81] (which is also the result of the aforementioned project). Soslaires’ current scheme comprises four Gamesa G47/660 [82] wind generators which have a capacity factor of 38.5% for their location (Southeastern Gran Canaria, Spain) [83].

7.3 Calculation of CCs 7.3.1 RO Scheme Total capital investment (TCI) includes all costs necessary to acquire the equipment required for the control system (costs of equipment purchased), labor costs and materials to install such equipment (direct costs of installation), costs of site preparation and buildings, and other costs (indirect installation costs) [84]. The costs associated with chemical storage tanks, permeate tanks for the product, and those related to filters are obtained through those provided by Matches [85]. Pumps costs have been determined from correlation curves proposed by Peters et al. [86] and the pump sizing software developed by Grundfos [87]. The costs associated with the reverse osmosis plant and the required civil works are determined directly from the information provided by Dietrich et al. [88] and the WinFlows software [89]. For their part, the costs of the ERD are determined from information provided by the manufacturer FlowServe Corporation [90]. All these costs have been supervised and corroborated by Soslaires Canarias S.L. (which has a scheme similar to that presented in this chapter). Once transport and taxes (estimated at 2% of the cost of purchasing the equipment) and miscellaneous costs (5% of the cost of purchasing the equipment) are added to the capital costs for the reverse osmosis plant [86], the total costs of delivery of the reverse osmosis system are obtained.

7.3 Calculation of CCs

125

7.3.2 Energy System The costs related to wind turbines correspond to those provided by the manufacturer for four Gamesa G47/660 wind turbines [82].

7.3.3 Direct Capital Costs They are estimated taking into account existing interrelations in the industrial sector, utilizing as a basis all expenses related to the equipment delivered [86]. Wells costs are estimated supposing a feed water flow of 500 m3 /d for each well with a depth of about 50 m [91]. The costs of engineering, supervision and construction correspond, each separately, to around 25% of total direct costs [92]. All these costs have been supervised and corroborated by Soslaires Canarias S.L.

7.3.4 Indirect Capital Costs For their part, indirect capital costs are normally calculated as a percentage of direct capital costs, averaging 33% [92].

7.3.5 Total Capital Investment Total capital investment (TCI) includes fixed capital investment (FCI) (see Table 7.1), composed of direct costs and indirect costs, and by the working capital (Wc ) [93]. It is obtained by putting together a provision to cover unforeseeable expenses the project may incur due to project setbacks, prices modifications, etc. (about 10% of costs both directly and not directly attributable to the scheme). Working capital is Table 7.1 Direct capital costs as a percentage of the TDC. Source: [86, 94, 95]

Spending related to direct capital costs

TDC percentage

Main equipment (wind turbines, desalination 0.40 plant, …) Instrumentation and control

0.20

Installation of pipes and shutoffs

0.30

Electricity

0.20

Constructions

0.10

Land improvement

0.20

Auxiliary installations

0.05

126

7 Reduction of Water Cost for an Existing Wind …

the amount of capital required to start up the plant and finance ordinarily amounts to the production cost before revenues from the process start. This represents 10% of TCI [86], and is put together with the FCI to produce the TCI—which is the money needed to project, purchase, place in position ready for use and build the proposed scheme.

7.4 Running Expenses The most important running expenses comprise plant overheads, contributions, guarantees of compensation for specified losses, reductions in the value of assets with the passage of time, and monitoring and scheme preservation costs [96]. Further important running costs are variable costs involving waste materials, energy and water, among others [96].

7.4.1 IC IC take into account a (7.1) [91]. Following similar studies the interest rate Ieff is assumed to be 8% [92], having been taken as a useful life for the plant for a period n of 20 years. a=

I e f f · (1 + I e f f )n (1 + I e f f )n − 1

(7.1)

7.4.2 Labor and Maintenance Costs Labor costs are determined on the basis of the costs per m3 of water for the desalination industry [91]. It has been supposed that the useful life of the membranes is 5 years [92] and that of the wind turbine is 20 years. Regular basic conservation costs have been provided by Soslaires Canarias S.L. (which has been using a scheme similar to that studied in this chapter for more than 15 years).

7.4 Running Expenses

127

7.4.3 Contributions, Guarantee of Compensation for Specified Losses, and Reduction in the Value of Assets with the Passage of Time This part stands for 2% [97] of the TCI, assuming that the reduction in the value of assets with the passage of time is 10% of the TCI [98, 99].

7.4.4 Total Fixed Costs Summing up the entire costs related to work, MRIT, depreciation plus interest, gives TFC [84, 96]. As they occur regardless level of generation of the reverse osmosis system, they are called fixed costs [84, 96].

7.4.5 VCs VC are costs that pivot on the amount of water generation in the short term [100]. These costs include waste materials, energy and water among others [96]. TPP is estimated taking into account both NOH of the plant and water flow rate [101], which is used to determine steady state water costs [102]. The costs of raw materials are based on the use of various sources [89, 103–106], which show the costs of chemicals for pre-treatment and post-treatment of water. Use of acid was determined through the software TorayDS2 (Version 2.0.8.129) [107] depending on the input and the pH of the desired product water. All these costs have been supervised and corroborated by Soslaires Canarias S.L. Although the cost of the brine removal phase varies depending on the needs of each location, it has been assumed that it represents 5% of the total cost of the scheme [108]. Although not desirable because the plant must be stopped, to avoid irreversible damage to the membrane periodic cleaning is necessary [109]. The scheme will use electricity from the electricity grid only if it is strictly necessary, in this case being used as the price of energy that corresponds to the type of user in question.

7.4.6 Total Costs The total costs are obtained in e/m3 by adding all fixed costs to all variable costs [110] per cubic meter of permeate.

128

7 Reduction of Water Cost for an Existing Wind …

7.4.7 Cost of Water Representation Although this chapter is based on the actual scheme that has been used for more than 15 years by Soslaires Canarias S.L., the methodology developed in it might be used in similar schemes and represents itself a frame of reference to define a desalination scheme based on wind energy.

7.5 Results and Discussion Through data provided by Soslaires Canarias S.L. we know that at present the desalination plant is in operation for 350 days a year and has a permeate production of 5211 m3 /d, which gives an annual production of 1.82 × 106 m3 /year. Summing up all CCs and OCs for this amount of production is that at present the cost of producing water from the wind energy-based desalination system is 0.804 e/m3 . This cost can vary depending on the location of each scheme, costs of construction of the wells, type of feed water, and waste disposal costs. The current scheme in Soslaires Canarias S.L. is shown in Fig. 7.3. With the configuration that the desalination scheme currently has and because it is a SS lacks from a BP between stages. It implies that the scheme will need a dual work exchanger energy recovery (DWEER) booster pump due to the lower outlet pressure of the brine

Fig. 7.3 Current scheme of the existing desalination plant in Soslaires Canarias S.L. Source Soslaires Canarias S.L

7.5 Results and Discussion

129

in comparison to the FP. The nominal values of operation of the reverse osmosis system are presented in Fig. 7.3. In accordance with the design of Fig. 7.3, an overall cost of water is estimated as discussed in Sect. 7.3 “Calculation”. This scheme comprises a SS so CCs relative to the membranes are restrained. A summary of the costs incurred in the scheme are presented in Table F.1. Following information provided by Soslaires Canarias S.L. it has been assumed that the desalination plant is in operation 350 days/year, has a permeate flow of 5211 m3 /day and an annual production of 1.82 × 106 m3 /year. As can be seen from Table F.1, adding together all CCs and OCs of production has a COW of 0.804 e/m3 for the steady state. This cost may vary depending on location, WCC, FWC and WDC. To reduce water costs currently incurred by Soslaires Canarias S.L. this chapter proposes an alternative configuration that, based on a slight increase in the capital cost of the scheme, is expected to significantly reduce the cost of water production. For this, a two-stage reverse osmosis desalination system is proposed, with energy recovery and the option of an intermediate pump in the second stage. The proposed design is presented in Fig. 7.4. As can be seen in Fig. 7.4, there is a booster pump for water (from the input to the reverse osmosis system) which, although it has already undergone an energy recovery from the dual work exchanger energy recovery, continues to have a smaller P in comparison to the GF of the reverse osmosis unit. Based on the design of Fig. 7.4, COW is determined on the basis of capital and operating costs. It is assumed a 350 days of operation per year, with a permeate flow of 5671 m3 /day, which gives an annual production of 2 × 106 m3 /year. Adding all CCs and OCs for this amount of production has a steady state cost of water of about 0.782 e/m3 . This cost can vary depending on the location, costs of construction of

Fig. 7.4 Proposed refurbished scheme for the existing desalination plant in the company Soslaires Canarias S.L. Source Own elaboration and Soslaires Canarias S.L

130

7 Reduction of Water Cost for an Existing Wind …

Fig. 7.5 Proposed refurbished scheme for the existing desalination plant in the company Soslaires Canarias S.L. (alternative). Source Own elaboration and Soslaires Canarias S.L

the wells, composition of the feed water and waste disposal costs. A summary of the costs for the proposed configuration can be seen in Table F.2. An alternative solution to the scheme proposed in Fig. 7.4 would be to use an inter-stage booster pump, which would make the use of a booster pump at the output of the dual work exchanger energy recovery unnecessary because the pressure at the output of the SS is sufficient to meet the energy requirements of the dual work exchanger energy recovery—consequently the supply P of the DWEER equals the main supply pressure. Because the only real differences between the scheme of Fig. 7.4 and that of Fig. 7.5 is the location and size of the BP, CCs and OCs are very similar. In the case of the scheme of Fig. 7.5 the second stage will operate with more P and a larger amount of permeate. However, due to incoming energy restrictions, flow acceptable by the membranes and the feed pressure, there is a permeate amount limitation, so there are no significant differences in either the design or in the cost of the reverse osmosis schemes of Figs. 7.4 and 7.5. As a consequence, only the cost for one of these schemes will be evaluated since the costs of both can be considered similar without incurring major errors (see Table F.2).

7.6 Conclusions The main technological challenge to integrate a desalination plant based on reverse osmosis with energy sources of renewable origin is due to an interplay of a technology defined by its inherent temporal variations with a plant commonly arranged to operate under a constant regime that has a high number of working restrictions. In this chapter

7.6 Conclusions

131

an evaluation of the economic feasibility of several configurations of desalination plants based on reverse osmosis and wind energy has been carried out, considering the cost of water (COW) as the central element of the evaluation. An exploratory expenses assessment was introduced taking into account alternative configurations to the currently available in the company Soslaires Canarias S.L. (industrial partner with which there is a collaboration agreement), evidencing the results that the two alternative schemes proposed to the one currently in existence have the potential to be advantageous from the point of view of the cost of water (COW). These configurations were defined considering that wind turbines with a nominal power of 2.64 MW are the ones that provide the necessary energy. The results showed that in case of carrying out the actions suggested in this chapter, the scheme of the company Soslaires Canarias S.L. (the industrial partner through which the investigations presented here are being carried out) would be able to reduce its cost of water (COW) by about 0.022 EUR per cubic meter (0.804–0.782 e/m3 ) for the current LCOE of wind turbine technology in the study area (about 6 ce/kWh for the southeast of the island of Gran Canaria, Spain). This would mean that, with an additional total capital investment cost of 196,000 e (from Soslaires Canarias S.L. current scheme), and supposing an average membrane life expectancy of 10 years for current membranes state-of-the-art [111], a net present value of 74,360.95 e; a profitability index of 1.3794; and an internal rate of return of 224.4881% would be achieved.

References 1. UNESCO (2012) Managing water report under uncertainty and risk—The United Nations World Water Development Report 4: Volume 1. Paris: United Nations Educational, Scientific and Cultural Organization 2. Wilmoth A (2016) Focus on Oklahoma earthquakes boosts water recycling efforts. News OK. 29 Sept 2016 3. European Parliament (2003) Parliamentary questions 19 September 2003: Answer given by Mrs Wallström on behalf of the Commission, http://www.europarl.europa.eu/sides/getAllAns wers.do?reference=E-2003-2274&language=EN. Accessed 05.05.17 4. Lipponen A, Bonvoisin N (2015) Water for a sustainable world—the United Nations World Water Development Report 2015. United Nations Educational, Scientific and Cultural Organization, Paris 5. Water recycling and reuse (2012) The environmental benefits. https://www3.epa.gov/region9/ water/recycling/. Accessed 05.05.17 6. Liu S, Gikas P, Papageorgiou LG (2012) A two-step optimisation approach for integrated water resources management. In: Bogle IDL, Fairweather M (eds) 22nd European symposium on computer aided process engineering (ESCAPE); 17–20 June 2012, Springer, London, pp 96–100 7. South East Water (2013) Draft Water Resources Management Plan 2014. South East Water, Snodland 8. Ahuja S, Larsen MC, Eimers JE, Patterson CL, Sengupta S (2014) Comprehensive water quality and purification, 1st edn. Elsevier, Waltham 9. Whitworth TM, Lee R (2003) Desalting of saline waters—applications to New Mexico. New Mex Geol 25(1):16–20

132

7 Reduction of Water Cost for an Existing Wind …

10. Cooley H, Gleick PH, Wolff G (2006) Desalination, with a grain of salt: a California perspective. Pacific Institute for Studies in Development, Environment, and Security, Oakland 11. Krishna HJ (2004) Introduction to desalination technologies. Texas Water Development Board, Austin 12. Shatat M, Riffat SB (2014) Water desalination technologies utilizing conventional and renewable energy sources. Int J Low-Carbon Technol 9(1):1–19 13. Guillén-Burrieza E, Alarcón-Padilla DC, Palenzuela P, Zaragoza G (2015) Techno-economic assessment of a pilot-scale plant for solar desalination based on existing plate and frame MD technology. Desalination 374:70–80 14. Khawajia AD, Kutubkhanaha IK, Wie JM (2008) Advances in seawater desalination technologies. Desalination 221:47–69 15. Lypiridis C, Katsandi C (2011) No added salt: opportunities and technologies for desalination. Bloomberg New Energy Finance, New York 16. Committee on Advancing Desalination Technology (2008) Desalination: a national perspective. The National Academy Press, Washington 17. Water Research Foundation (2010) Guidelines for implementing Seawater and Brackish Water Desalination Facilities. Las Cruces: WERC (A Consortium for Environmental Education and Technology Development) 18. Singh R (2014) Membrane technology and engineering for water purification, 2nd edn. Butterworth-Heinemann, Oxford 19. Zlotorowicza A, Stranda RV, Burheimb OS, Wilhelmsenb Ø, Kjelstrup S (2017) The permselectivity and water transference number of ion exchange membranes in reverse electrodialysis. J Membr Sci 523:402–408 20. Fernandez-Gonzalez C, Dominguez-Ramos A, Ibañez R, Irabien A (2015) Sustainability assessment of electrodialysis powered by photovoltaic solar energy for freshwater production. Renew Sustain Energy Rev 47:604–615 21. Al-Karaghouli A, Kazmerski LL (2013) Energy consumption and water production cost of conventional and renewable-energy-powered desalination processes. Renew Sustain Energy Rev 24:343–356 22. Van der Brugger B, Vandecasteele C, Van Gestel T, Doyen W, Leysen R (2004) A review of pressure-driven membrane processes in wastewater treatment and drinking water. Environ Prog 22(1):46–56 23. Ribeiro dos Anjos NF (1999) Source book of alternative technologies for freshwater augmentation in Latin America and the Caribbean. United Nations Environment Programme, Nairobi 24. Bergman R (2007) Reverse osmosis and nanofiltration, 2nd edn. Denver: American Water Works Association 25. Chriwa Wasseraufbereitungstechnik GmbH (2013) SBR technology for aerobic treatment of bevarage production waste water, http://www.chriwa.de/en/know-how/63-englisch/techno logy/references. Accessed 16.05.17 26. General Electric Company (2017) Water for the world: 2017 product catalog. GE Water & Process Technologies, Philadelphia 27. NRS Engineering Water Solutions (2008) FINAL Pilot Study Report Texas Seawater Desalination Demonstration Project. Harlingen: NRS Engineering Water Solutions 28. Bhabha Atomic Research Centre (2016) Residual detail engineering, procurement, supply, inspection & testing, installation & commissioning of 3x1.5 MLD Two Pass Sea Water Reverse Osmosis Desalination Plant with Ultrafiltration(UF) pretreatment at OSCOM, IREL, Matikhalo, Dist: Ganjam, Odisha on LSTK basis. Mumbai: Bhabha Atomic Research Centre 29. AECOM (2010) Engineering Feasibility Study on the Costs of Treatment Options for Reducing Disinfection Byproducts in Public Drinking Water Systems: Volume I. Montpelier: Vermont Department of Environmental Conservation (Water Supply Division) 30. Kable Intelligence Ltd. (2015) Tuas Seawater Desalination Plant—Seawater Reverse Osmosis (SWRO), Singapore. Kable Intelligence Ltd., London

References

133

31. Rizzuti L, Ettouney HM, Cipollina A (2007) Solar desalination for the 21st Century: a review of modern technologies and researches on desalination coupled to renewable energies. Springer, Berlin 32. Koskela T (2016) Removal of hardness from groundwater with nanofiltration—case study: Meri-Lapin Vesi Oy. Helsinki Metropolia University of Applied Sciences, Helsinki 33. Kucera J (2010) Reverse osmosis: design, processes, and applications for engineers. Wiley, Chichester 34. Sobana S, Panda RC (2011) Identification, modelling, and control of continuous reverse osmosis desalination system: a Review. Sep Sci Technol 46(4):551–560 35. Hydranautics (2001) Flow configuration. Nitto Denko Corporation, Tokyo 36. Forstmeiera M, Mannerheim F, D’Amato F, Shah M, Liu Y, Baldea M, Stell A (2007) Feasibility study on wind-powered desalination. Desalination 203:463–470 37. EPA (2005) Membrane filtration guidance manual. United States Environmental Protection Agency, Washington, DC 38. Abdel-Rahman AK, Abbara AAA, Bayoumi MR (2010) Modeling of the permeate flux decline during MF and UF crossflow filtration of sugarcane juice. J Eng Sci 38(1):157–176 39. Guirguis MJ (2011) Energy recovery devices in seawater reverse osmosis desalination plants with emphasis on efficiency and economical analysis of isobaric versus centrifugal devices. University of South Florida, Tampa 40. WABAG (2014) Desalination technologies: reverse osmosis & thermal desalination for drinking water, industrial water & water recycling. VA TECH WABAG LIMITED, Chennai 41. RWO (2016) SRO-COM Compact Seawater Reverse Osmosis Desalination. Bremen: RWO GmbH 42. Mohsen MS (2010) Potential for wind-powered desalination systems in Jordan. Int J Therm Environ Eng 1(2):109–123 43. Bhutani N, Purohit A, Mantravadi VK, Mekapati S, Subbaiah S (2013) Energy efficiency solutions for RO desalination plant. Desalin Water Treat 51(25–27):5049–5055 44. Sirkar KK (2014) Separation of molecules, macromolecules and particles: principles, phenomena and processes. Cambridge Series in Chemical Engineering, Cambridge 45. IAEA (2000) Examining the economics of seawater desalination using the DEEP code. International Atomic Energy Agency, Vienna 46. Shanghai Jiao Tong University (2015) Sea vs brackish water. Shanghai Jiao Tong University, Shangai, http://2015.igem.org/Team:SJTU-BioX-Shanghai/Human_Pra ctice/Intake. Accessed 15.05.17 47. Watersolutions (2012) New thermal desalination process claims 1 kWh/m3 energy use. Watersolutions AG, Buchs, http://www.waterworld.com/articles/2012/10/new-thermal-desali nation-process-claims-1kwh-m3-energy-use.html. Accessed 15.05.17 48. Fritzmann C, Löwenberg J, Wintgens T, Melin T (2007) State-of-the-art of reverse osmosis desalination. Desalination 216:1–76 49. Nada T (2014) Characterisation of nanofiltration membranes for sulphate rejection. University of Glasgow, Glasgow 50. Elimelech M, Phillip WA (2011) The future of seawater desalination: energy, technology, and the environment. Science 333:712–717 51. AQUALYNG (2009) The recuperator. Aqualyng Global, Dubai, http://www.aqualyng.com/ en/recuperator/TheRecuperator.aspx. Accessed 23.05.17 52. FILMTEC Membranes (2016) How FilmTec’s new high-rejection, low-energy seawater element can reduce your desalination costs. Lenntech BV, Delft 53. Dincer I, Rosen MA (2007) Exergy: energy, environment and sustainable development. Elsevier, Amsterdam 54. Varbanets M, Pronk W (2006) Point-of-use membrane systems: place in the world of water supply. Techneau, Marigny 55. Thomson M, Miranda M, Gwillim J, Rowbottom A, Draisey I (2001) Batteryless photovoltaic reverse-osmosis desalination system. Harwell Laboratory, Energy Technology Support Unit, Oxford

134

7 Reduction of Water Cost for an Existing Wind …

56. U.S. Congress–Office of Technology Assessment (1987) Integrated renewable resource management for U.S. insular areas. U.S. Government Printing Office, Washington, DC 57. Robins N (2012) Bonds and climate change: the state of the market in 2012. HSBC, London 58. DHV Water BV, BRL Ingénierie (2004) Seawater and brackish water desalination in the Middle East, North Africa and Central Asia: a review of key issues and experience in six countries—final report. World Bank, Washington 59. Trubyanov MM, Vorotyntsev IV, Drozdov PN, Vorotyntsev VM (2016) Novel membrane column concepts for high purity gases production. Prague: International Conference on Membrane Processes; 2016 May 15–19; Czech Republic, Prague 60. AQUASURE (2016) Energy efficiency: Renewable energy. Aquasure, Dalyston, https://www. aquasure.com.au/energy-efficiency 61. Zhu A, Christofides PD, Cohen Y (2009) Energy consumption optimization of reverse osmosis membrane water desalination subject to feed salinity fluctuation. Ind Eng Chem Res 48:9581– 9589 62. Eltawil MA, Zhengming Z, Yuan L (2009) A review of renewable energy technologies integrated with desalination systems. Renew Sustain Energy Rev 13:2245–2262 63. Jose D, Jeyaprabha SB (2013) Design and simulation of wind turbine system to power RO desalination plant. Int J Recent Technol Eng 2(1):102–105 64. IRENA (2012) Renewable energy technologies: cost analysis series, vol 1: Power Sector, Issue 5/5, Wind Power. International Renewable Energy Agency, Abu Dhabi 65. IRENA (2016) The power to change: solar and wind cost reduction potential to 2025. International Renewable Energy Agency, Bonn 66. Bueché M (2003) Fleet management (vehicles services) focused review final report 2003. Mercury Fleet Management Consulting, Rockville 67. Vanderheyden W (2012) Development of an agent-based model for electric vehicle penetration of the European fleet over the next decennium. Universiteit Hasselt, Hasselt 68. Eltawil MA, Zhengming A, Yuan L (2008) Renewable energy powered desalination systems: technologies and economics-state of the art. Twelfth International Water Technology Conference, Alexandria; 27–30 Mar, Egypt, Alexandria 69. Baltas P, Perrakis K, Tzen E (1996) European network to integrate renewable energy into water production. Mediterranean Conference on Renewable Energy Sources for Water Production, Santorino. 10–12 June, Greece, Santorini 70. Ribeiro J, Epp J, Tondi G (2002) Potential use of PV for water desalination. PV in Europe— from PV technology to energy solutions, Rome; 7–11 Oct, Italy, Rome 71. Drioli E, Giorno L (2009) Membrane operations: Innovative separations and transformations. Wiley, Chichester 72. ProDes project (2010) Example of project development opportunities in Greece, Italy, Spain and Portugal: Deliverable 5.2. Munich: WIP Renewable Energies 73. Omole A (2010) Voltage stability impact of grid-tied photovoltaic systems utilizing dynamic reactive power control. University of South Florida, Tampa 74. Gude VG (2015) Energy storage for desalination processes powered by renewable energy and waste heat sources. Appl Energy 137:877–898 75. Bundschuh J, Hoinkis J (2012) Renewable energy applications for freshwater production. CRC Press, Boca Raton 76. Lai W, Ma Q, Lu H, Weng S, Fan J, Fang H (2016) Effects of wind intermittence and fluctuation on reverse osmosis desalination process and solution strategies. Desalination 395:17–27 77. Peñate B, Castellano F, Bello A, García-Rodríguez L (2011) Assessment of a stand-alone gradual capacity reverse osmosis desalination plant to adapt to wind power availability: a case study. Energy 36:4372–4384 78. Cendoya MG, Toccaceli GM, Battaiotto PE (2014) Wind generation applied to water desalination and H2 production in remote areas with weak networks. Int J Hydrogen Energy 39:8827–8832 79. Florida Department of Revenue (2017) Equipment index factors 2017 (national average). Tallahassee: Florida Department of Revenue, http://floridarevenue.com/dor/property/tpp/pdf/ factor.pdf. Accessed 31.05.17

References

135

80. Lozano Ruano JB. Soslaires Canarias, S.L (2010) http://www.islhagua.org/c/document_lib rary/get_file?p_l_id=22263&folderId=22087&name=DLFE-925.pdf. Accessed 31.05.17 81. Colmenar Santos A, Peñate Vera S, Rosales Asensio E (2017) Sizing of wind, solar and storage facilities associated to a desalination plant using stochastic optimization. Computational Methods in Systems and Software 2017, Prague. 12–14 Sept, Czech Republic, Prague 82. The Wind Power (2017) G47/660 (Gamesa), http://www.thewindpower.net/scripts/fpdf181/ turbine.php?id=166. Accessed 31.05.17 83. J. Pons Ingenieros S.L. (2007) Parque eólico Casa Santa Palos, http://www.gobcan.es/indust ria/eolica/V-07_GC-307.pdf. Accessed 31.05.17 84. Mussatti DC, Vatavuk WM (2002) EPA air pollution control cost manual (Chapter 2: Cost Estimation—Concepts and Methodology). United States Protection Agency, Washington, DC 85. Matches (2014) Index of Process Equipment, http://www.matche.com/equipcost/Equipment Index.html. Accessed 31.05.17 86. Peters MS, Timmerhaus KD, West RE (2003) Plant design and economics for chemical engineers, 2nd edn. Mc Graw Hill, New York 87. Grundfos (2017) Dimensionar prod. por aplicación, http://product-selection.grundfos.com/ sizing-by-application.html?qcid=232961715. Accessed 01.06.17 88. Wateruse Association (2012) Seawater desalination costs: White paper. Alexandria: Wateruse Association 89. General Electric Company (2017) Winflows 3.3.2: Membrane System Design Software. Boston: General Electric Company, https://www.gewater.com/resources/winflows. Accessed 01.06.17 90. FLOWSERVE (2017) Energy recovery device—DWEER, https://www.flowserve.com/en/ products/pumps/specialty-products/energy-recovery-device/energy-recovery-device-dweer. Accessed 01.06.17 91. Lamei A (2009) A technical economic model for integrated water resources management in tourism dependent arid coastal regions: the case of Sharm El Sheikh, Egypt. CRC Press, Leiden 92. Atab MS, Smallbone AJ, Roskilly AP (2016) An operational and economic study of a reverse osmosis desalination system for potable water and land irrigation. Desalination 397:174–184 93. Barbosa-Cánovas GV, Pastore G, Cando˘gan K, Medina Meza IG, Caetano Da Silva Lannes S, Buckle K et al (2017) Global food security and wellness. Springer Nature, New York 94. Bagajewicz MJ, Savelski MJ (2017) Lectures: comments and supplemental material chemical engineering design I ChE 4253, http://www.ou.edu/class/che-design/design1/cost-est. htm. Accessed 01.06.17 95. Bejan A, Tsatsaronis G, Moran M (1996) Thermal design and optimization. Wiley, Chichester 96. Environmental considerations of selected energy conserving manufacturing process options: Vol. II. Industry Priority Report. Ohio: Industrial Environmental Research Laboratory, U.S. Environmental Protection Agency (1976) 97. Vatavuk WM (1990) Estimating costs of air pollution control. Lewis Publishers, Michigan 98. Mehta VK, Mehta R (2005) Principles of power system. S. Chand, New Delhi 99. State of Arizona (2016) Arizona form 140–2016 Resident Personal Income Tax Return, https://www.taxformfinder.org/forms/2016/2016-arizona-form-140-instructions.pdf. Accessed 31.05.17 100. Veseth M (2014) Introductory microeconomics. Academic Press, Massachusetts 101. Oklejas E, Leachman LM, Kitzmiller RT, Seisan A, Kadaj E (2007) A Novel equipment centralization schema reduces the cost of permeate. IDA World Congress, Maspalomas. 21–26 Oct; Spain, Maspalomas 102. Lubis MR (2009) Desalination using vapor-compression distillation. Texas A&M University, College Station 103. ICIS (2017) ICIS pricing: Petrochemical, energy and fertilizer market intelligence, https:// www.icis.com/about/price-reports/. Accessed 10.06.17 104. Pacific Genuity (2008) Product, http://pacificgenuity.com/product.html. Accessed 10.06.17

136

7 Reduction of Water Cost for an Existing Wind …

105. Wingert JL (2011) Final pH adjustment, http://www.jlwingert.com/pdfspecs/ap005.pdf. Accessed 10.06.17 106. Goldie I, Aziz M, Abozrida AH, Sanderson RD (2008) Development of improved local antifouling spiral-wrap membrane systems. Pretoria: Water Research Commission 107. Toray (2017) TorayDS(DS/DS2)—new RO Design software, https://ap3.toray.co.jp/torayw ater/webapp/client/torayDsDetail.do. Accessed 10.06.17 108. Hafez A, El-Manharawy S (2002) Economics of seawater RO desalination in the Red Sea region, Egypt: Part 1—a case study. Desalination 153:335–347 109. Latterman S (2010) Development of an environmental impact assessment and decision support system for seawater desalination plants. CRC Press, Boca Raton 110. AECOM (2015) Water reuse and environmental conservation project: Aqaba water reuse financial planning assistance June 2015. Washington D.C.: United States Agency for International Development 111. Cedel BV (2017) Water purification Powered by solar energy. Assen: Cedel B.V, http://cedel. nl/wp-content/uploads/2017/09/Information-brochure-small.pdf. Accessed 10.01.18

Chapter 8

Stress Mitigation of Conventional Water Resources in Water-Scarce Areas Through the Use of Renewable Energy Powered Desalination Plants: An Application to the Canary Islands

Securing water in a water-scarce environment is a key objective for the society development. Once it is analyzed how a specific energy plan at large-scale deployed might provide a sustainable way to face and palliate the water stress through a smart WEF nexus management, it become interesting to investigate how this approach affects to a small region. Focusing in a region with the mentioned problems concerning to water scarcity, electricity generation characteristics, and specific characteristics of population, consumption patterns.

8.1 Introduction Broadly speaking, it can be said that the climate of the Canary Islands is characterized by very scarce and irregular rainfall [1], which, together with the intensification of agriculture, the increase in population and the development of tourism [2], has led to overexploitation of the aquifer [3, 4]. Note the fact that, according to Falkenmark and Lindh [5] a consumption of 20% of total renewable water resources is considered as the limit of over-exploitation of a system. According to this criterion, and taking the average annual contributions as total resources, the basins of the Canary Islands (with a consumption/spending ratio greater than 60% [6]) clearly exceed the limit of overexploitation. In this sense, desalination has made possible in the last fifty years the settlement of populations, the growth of tourism and the development of arid geographical areas [7]. The Canary Islands currently have a population of about 2,188,519 inhabitants, with Tenerife (906,854 inhabitants), Gran Canaria (845,676 inhabitants) and Lanzarote (141,437 inhabitants) being the most populated. The least populated island is El Hierro with 10 960 inhabitants [8]. In this sense, it is predicted that, if current trends continue, the Canary Islands will have a population increase of 17.2% in the next 15 years, being the second Spanish autonomous community where the population is expected to increase the most during this period [9]. In terms of tourist arrivals, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 E. Rosales-Asensio et al., Sea Water Desalination in Microgrids, Green Energy and Technology, https://doi.org/10.1007/978-3-030-96678-2_8

137

138

8 Stress Mitigation of Conventional Water Resources …

there has also been a rapid increase in recent years, from 10,432,046 tourists in 2010 to 15,975,507 in 2017 [10]. In this sense, the desalination of water in the Canary Islands is necessary due to (i) some natural limitations and (ii) others of a strictly economic nature. The former derives from the hydrogeological balance itself, which determines, in simplistic terms, that the increase in extraction above natural recharge leads to a systematic decrease in groundwater levels and, at the same time, in the yield of the exploitations [11]. The immediate consequence is the need to divert capital towards refilling works that do not generate new resources and towards the search for water in aquiferous areas of inferior quality [11]. Furthermore, once a certain maximum density of exploitations has been reached, the new ones will divert flows from the existing ones rather than from untapped deposits [11]. In economic terms, on the other hand, the effects are translated into a systematic reduction in the profitability of the uses and thus, into an increase in the price of the water that these uses extract [11]. In some cases, this decrease in profitability also stems from the worsening of water quality, which makes water use more limited [11]. The research presented in this chapter will focus on the area of Arinaga, belonging to the municipality of Agüimes (island of Gran Canaria). This population is currently insufficiently supplied (with an average net supply of 95 l/h–d) and it is supplied with groundwater (losses in the network are in the order of 30%) [12]. Due to the natural, economic and population increase limitations mentioned above, a seawater desalination plant will be needed in this area to supply the increased consumption of drinking water and to give a rest to the (over)exploited water resources. Due to the abundance of wind and solar resources in the area [13], a possible technology to be used to power the facility would be solar and/or wind energy-in this sense, the right combination of a renewable energy source with a desalination technology may be the key to meeting the demand for energy and water in an economic, efficient and environmentally friendly manner [14]. Table 8.1 shows the total volume of Table 8.1 Total annual volume of desalinated water in the Canary Islands Hm3 /year

Lanzarote

Fuerteventura

Public

34.24

23.95

Private

Gran Canaria

Tenerife

La Gomera

El Hierro

Total

62.01

24.17

0.00

1.50

145.87

9.02

13.02

59.95

13.09

0.73

0.49

96.30

FCCA 10.74 estimates (not officially inventoried)

17.34

59.95

32.23

0.73

0.49

121.48

Total (official) 43.25

36.97

121.96

37.26

0.73

1.99

242.16

Total according to FCCA estimates

41.29

121.96

56.40

0.73

1.99

267.34

Source [15]

44.98

8.1 Introduction

139

Table 8.2 Number of desalination plants and total percentage of desalinated water by islands [16] Concepts

Lanzarote

Fuerteventura

Gran Canaria

Tenerife

La Gomera

El Hierro

La Palma

Total number 49 of desalination plants

66

135

46

0

2

1

% of the total 99% volume of water consumed on the island

86%

52%

9%

0%

19%

0%

desalinated water in the Canary Islands. On the other hand, Table 8.2 represents the number of desalination plants and the total percentage of desalinated water by islands. As it can be seen, the total water consumed in Lanzarote is now desalinated, also in Fuerteventura (where the first desalination plant began operating in 1970) [17]. In Tenerife, an island with more water resources, they are increasingly dependent on desalination: it accounts for 47% of household consumption, with an annual increase in desalination of more than 16% since 2000 [17]. In Gran Canaria, 86% of water for human consumption is desalinated, and 52% of the total supplied is desalinated [17]. There are currently 319 desalination plants in the Canary Islands with a drinking water production capacity greater than 66,000 m3 /day [18] of which more than 100 are located on the island of Gran Canaria [19]. Due to these circumstances, and as far as the Canary Islands are concerned, Gran Canaria is the most suitable island for the assessment of the proposed investigation. Gran Canaria’s estimated actual production is about 40% of all desalinated water in the Canary Islands, which exceeds the desalinated water forecasts contained in the island’s hydrological plan [20]. On this island, desalination plants are atomized and distributed along the entire coastline [20]. This is due to several factors: that the investment has been made in a staggered manner, that the orography of the island is quite rugged, that a dispersed model has been preferred in the face of potential contamination of the coastline and that distribution costs are saved [20]. On the contrary, the costs resulting from the scale increase [20]. In the scientific literature, it is possible to find a large number of research papers that have mapped water needs and renewable energy sources as a strategic tool for planning new desalination systems. Among the most notable may be those carried out by Shatat and Worall [21] who presented an economic and comparative evaluation study for a small scale solar powered water desalination system; Padrón and Ávila [22] who modeled hybrid systems with base in the renewable energy to compare many different design options based on their technical and economic merits; Mahmoudi and Spahis [23] who evaluated a brackish water greenhouse desalination unit powered by wind energy for desalting groundwater for irrigation purposes; Poompavai and Kowsalya [24] who presented various control strategies carried out

140

8 Stress Mitigation of Conventional Water Resources …

in solar PV and wind energy-based water pumping systems; Mokheimer and Sahin [25] who modeled and simulated a hybrid wind/solar powered reverse osmosis desalination system; Gude and Nirmalakhandan [26] who presented a sustainable phasechange desalination process driven solely by solar energy without any reliance on grid power; Abdelshafy and Hassan [27] who presented a grid-connected hybrid renewable energy integrated with a reverse osmosis desalination plant to provide fresh water for a residential community; Ismail and Azab [28] who presented a theoretical investigation of the performance of two sequential desalination systems, multi-effect distillation and mechanical vapor compression: Zhag and Maleki [29] who developed a novel framework for optimization of hybrid systems in remote areas; Li and Loy-Benitez [30] who designed a sustainable and reliable hybrid renewable energy system coupled with a desalination system, considering different operational scenarios with fluctuating renewable energy supply, and changeable water demand; ´ c [31] who evaluated the impact of desalination in a combination Novosel and Cosi´ with pump storage that utilizes the produced brine on the penetration of intermittent renewable energy sources in an energy system; Cherif and Belhadj [32] who elaborated an energy and water production estimation on a large-scale time from Photovoltaic–Wind hybrid system coupled to a reverse osmosis desalination unit; or the investigation conducted by Ali and Turki [33], who presented a specific class of standalone battery-less PV/Wind-reverse osmosis desalination system. However, the proposal of sites in arid zones, currently not supplied through desalination plants, which can potentially see their water stress reduced through the use of desalination plants powered by renewable energy have not been given the same attention, so a study that addresses them is necessary. From a deep survey of grey literature and updated literature related to the topic addressed here, it was possible to find that—even though there are plenty deal of different approaches—this chapter undoubtedly contributes to the pool of existing knowledge by giving the aforementioned perspective (to our knowledge, so far not addressed). By performing this thorough literature review, we ensure the originality of the idea here presented (so far not explicitly shown to our knowledge in any scientific paper). This first section briefly discussed the possibilities of renewable energy-driven desalination technologies; in the second section, the method used to conduct the research will be exposed; in the third section, a case study on the implementation of the proposed technology will be presented. The fourth section is reserved for conclusions, where the economic and environmental consequences resulting from the implementation of the outlined project in the third sections are presented.

8.2 Material and Methods This section will focus on the dimensioning of a desalination plant and its consumption, for which a wind system with the necessary capacity to feed the plant will also be dimensioned. In addition, a study will also be carried out on the excess electrical energy generated by the photovoltaic wind/solar system.

8.2 Material and Methods

141

Thus, the sections of this chapter will be those listed below, separated into two large groups. First, the sections of the desalination plant, which are: • Location of the desalination facilities. • Calculation of the daily production required to supply the target populations (plant capacity). • Calculation of the specific consumption of the desalination plant. Second, the sections of the wind/photovoltaic solar system, which are as follows: • Location of the wind turbines. • Calculation of the photovoltaic wind/solar potential at the location of the wind turbines and solar panels. • Study of energy production and evaluation of energy use, evaluating the excess of wind/solar PV energy. As a design condition, it has been assumed that the annual excess of energy cannot be higher than the energy demanded by the desalination plant. In terms of location, the following conditions have been established: • The desalination plant will have to be located at a point close to the sea, in order to avoid high costs in the collection system. • The wind energy system must be located at a point with a high wind potential that is close to the desalination plant. • The photovoltaic solar energy system must be located at a point with a high solar resource that is close to the desalination plant. In order to avoid interference of the desalination plant activities with the daily life of the inhabitants close to the plant site, the facilities shall be located in an area dedicated to industrial activity. The population to be supplied must not have current access to the desalinated water supply. The soil must be suitable for the construction of this type of facility. Within the Canary Islands, the islands most dependent on desalinated water are Gran Canaria, Lanzarote and Fuerteventura. Due to the fact that (i) these last two islands use practically all of the desalinated water and (ii) the extension of the existing desalination plants is relatively simple, there is no real need for a new seawater desalination plant. Due to this, they were rejected, and the island of Gran Canaria was chosen for the investigation presented. Based on the conditions established in the Materials and Methods section, the Arinaga Industrial Estate has been selected as the site for the seawater desalination plant. This site is industrial land, close to the sea, without nearby desalination plants and has several towns nearby to supply. Figures 8.1 and 8.2 represent, respectively, from the Territorial Information System of the Canary Islands—IDECanarias [34], the wind resource and photovoltaic potential (in kWh/kWp) for the island of Gran Canaria. As can be seen, the coastal areas close to the Arinaga industrial estate (see Figs. 8.1 and 8.2) have, respectively, one of the best wind resources at 80 m and solar for the island of Gran Canaria. Let’s notice in both figures the proposed tentative location of the desalination plant.

142

8 Stress Mitigation of Conventional Water Resources …

Tentative desalination plant loWind speed

Arinaga

Fig. 8.1 Wind resource for Gran Canaria (left) and at the selected site, Arinaga (right) for 80 m height. Adapted from [34, 35]

irradiation 2

salination Arinaga

Fig. 8.2 Solar resource for Gran Canaria (left) and at the selected site, Arinaga (right). Adapted from [34, 35]

The next step will be the study of the population nuclei near the Arinaga Industrial Estate, and how many people live in the area. Table 8.3 shows that, by 2018, the towns near the Arinaga industrial estate had a total of 21,214 inhabitants. Once the location was decided, the next steps would be the dimensioning of the production capacity of the seawater desalination plant. In order to determine the definitive capacity of the plant, the number of inhabitants of the areas to be supplied will be studied (Cruce, Polígono and Playa de Arinaga). The objective is for the seawater desalination plant to be able to supply the inhabitants of these areas completely for a period of 10 years (until 2029), so as to be able to give rest to the local water resources currently exploited for this purpose. The first step in this section is the search for the numbers of inhabitants in the nuclei that we wish to supply, for which we consulted the data from the National Statistics Institute. From these data, it is concluded that the populations studied had, Table 8.3 Population of Arinaga for the year 2018 [36] Playa de Arinaga

Cruce de Arinaga

Polígono residencial

Polígono industrial

Total

9721

10,384

1068

41

21,214

8.2 Material and Methods

143

in 2017, a total of 21,214 (see Table 8.3). Considering the forecasts for the coming years and taking as a reference the 10 years prior to the economic crisis (1997–2007), it will be assumed that the annual population growth rate for the evaluated area will be 5% per year. As a result of the assumption of this 5% annual increase, it will be assumed that the populations studied will have, by 2019, a population of 34,555 inhabitants. For water consumption, the average consumption per inhabitant for the Canary Islands will be used, which will be assumed to be invariable and situated at 160 L per inhabitant per day [37]. Thus, assuming that for the area studied, for the year 2029 there will be a total of 34,555 inhabitants who will consume 160 L of water per day on average, it will be possible to calculate the daily capacity of the seawater desalination plant through Eq. (8.1). Capacity = Consumption · inhabitants = 0.160 m3 /day · 32, 944 = 5528.8 m3 /day

(8.1)

As a consequence, in order to cover the foreseeable demand for water for human consumption in the areas to be supplied, by 2029 a capacity of approximately 5600 m3 /day will be required for the proposed seawater desalination station. It should be borne in mind, however, that since more water is usually consumed during the summer months, it is good practice to design the desalination plant in excess. Once the capacity of the necessary desalination plant is known, it is necessary to dimension the energy supply system of the seawater desalination plant, specifically by means of wind turbines and/or photovoltaic solar panels. To this end, the wind and solar resource of the area in which the wind/photovoltaic solar system will be implemented must be analyzed, as it has been shown in Figs. 4.1 and 4.3 respectively. Once the location of the wind turbine/photovoltaic solar panels was selected, the wind and solar resources were studied at this point. For this purpose, the resources available through the website of the Canary Islands Territorial Information System— IDECanarias [34] and the data included in the DesalinationPlant software (which uses data from this area as a default) [38] will be used. This software, specifically developed for this type of installations, will be used to carry out a techno-economic evaluation of the proposed installation. In order to do this, the first thing you need to know is your annual energy consumption. In this case, assuming, according to [39], that the consumption of the reverse osmosis desalination plant can be considered 3 kWh/m3 , the annual energy consumption over a year will be: Annual consumption = 5600 m3 /day · 3.0 kWh · 350 days/year = 5.88 GWh/year

(8.2)

Note that, instead of 365 days a year, 350 days have been assumed because this is the typical number of operating days a year for a desalination plant of this type [40].

144

8 Stress Mitigation of Conventional Water Resources …

As a result of the above calculations it can be concluded that the proposed seawater desalination plant will have an annual consumption of 5.88 GWh/year. In this case, and considering the design conditions set out in the section on materials and methods (where it was indicated that the annual excess of energy could not exceed the energy demanded by the desalination plant), the annual energy produced by RES-based power station that supply the Desalination Plant may not exceed 11.76 GWh/year. This fact is due to some restrictions applied by Spanish´s energy regulation (in order to avoid these facilities sell energy electricity surpluses). They are allowed to sell energy to lower their energy bills, but only some of the energy produced from the RES-based power station that they have installed to run the facility with. This restriction was the first to handle with. As we have previously mentioned, in this chapter the software desalination plant is used to size the facility. This software [38] uses a “black stochastic optimization box” to optimize the amount of RES (wind and solar) and electrochemical storage involved in the desalination plant operation in order to obtain, in this case, the maximum benefit for the operation of the plant. Among the options that the software uses to optimize we can name: the desalination plant, the solar and wind farm, as well as the energy storage option. Considering a capacity factor of 0.32 for wind energy and 0.18 for solarphotovoltaic energy [41], an amount energy of 11.76 kWh/year implies that, for the aforementioned capacity factor, a maximum of 4.2 MW must be used for sizing the wind-farm in the software in order to run the optimization. Similarly, the solar farm must be sized in the range of 0 to 7.4 MW to fulfil this restriction [42]. The energy storage device must provide energy as long as the RES-based power station is not able to provide enough. Particularly, to enable the program to size, for the annual excess of energy of 5.88 GWh/year at the most as well as the energy for the facility to operate, this option must be sized between 0 and 1340 kW, capacity enough to cover the aforementioned energy restriction. Table 8.4 shows all considered parameters for the conducted optimization. In addition, data of hourly wind speed (for a hub height of 80 m), solar global hourly irradiation and electricity spot market prices were also provided.

8.3 Results and Discussion The optimum result of the developed simulation is shown in Table 8.5, as well as in Fig. 8.3, but results from different configurations carried out during the optimization process can be evaluated from every and each iteration that the program developed, highlighting the optimum result for the proposed scheme and given parameters, maximizing the benefit for the proposed problem. Despite the wind profile is less consistent than the irradiation profile, the optimization results showed a wind-based configuration for the RES-based power station in this problem; perhaps due to the higher energetic benefit that can be achieved from this resource. In addition, the battery storage system is basically not used, as the

41.16

6 [43]

180,000

190

110

8

Efficiency (%)

17

PV panel size (m2)

2 [51]

Solar photovoltaic power plant

Nominal Power (kW)

50 [46]

0.60

1050 [52]

Installation cost (e/kWp)

115

962,000

8

Maintenance costs (%)

2000 [48]

1

25

Equipment life (years)

26 [49]

50 [47]

100

80%

1/7400 (continued)

After life investment Min./Max. power (%) for sizing (kW)

15 [50]

100

After life investment (%)

100

After life investment (%)

After life investment (%)

Equipment life (years)

10 [45]

Equipment life

Equipment life (years)

Maintenance cost (%)

27 [44]

Maintenance cost (%)

Maintenance cost (%)

Investment cost (e)

600

Cost/installed (e/kW)

Cost/installed kW (e/kW)

Water price (e/m3 )

233

Water flow out (m3 /h)

Nominal Flow (m3)

Water demand (m3 )

368

Water flow in (m3 /h)

Number of pumps

Desalination pumps

Storage Capacity (m3 )

Relative Head (m)

Water storage

Auxiliares Power (kW)

Membrane Pack

Desalination plant

Table 8.4 Simulation parameters

8.3 Results and Discussion 145

20/95

98/98

Source Own elaboration

Min./Max. SoC (%)

Charging/discharging efficiency (%)

25

900

10

Maintenance costs (%)

Equipment life (years)

Installation cost (e/kW)

4

1000

Energy storage [54]

Maintenance costs (%)

Installation cost (e/kW)

Wind farm [53]

Desalination plant

Table 8.4 (continued)

10

Equipment life (years)

80

1/4200

Min./Max. power for sizing (kW)

80

1/1340

After life investment Min./Max. power (%) for sizing (kWh)

After life investment (%)

146 8 Stress Mitigation of Conventional Water Resources …

8.3 Results and Discussion

147

Table 8.5 Results of the optimization process Parameter

Optimal result

Parameter

Optimal result

Solar PV size (kW)

866

Storage cost (e)

871.70

Wind farm size (kW)

4100

Investment costs (e)

5,991,109

Storage size (kWh)

1

Total cost (e)

12,783,098

Desalination plant cost (e)

981,600

Total income (e)

19,366,879

Solar PV installation cost (e)

909,300

Average yearly benefit (e/year)

253,222

Wind farm installation cost (e)

4,099,337

Costs-incomes ratio (–)

0.795

Source Own elaboration

Fig. 8.3 Optimal results clustering (total installed capacity in kW vs. value of the optimization objective function). Source Own elaboration

algorithm found more profitable to obtain the electricity not provided by RES from the power grid and sell the surplus rather than storage it in expensive Li-ion batteries. From the associated RES-based power station, it would be provided by the electric system, or a micro grid. As it can be seen from the results shown in Table 8.5 and with the aforementioned capacity factors, the maximum energy produced by the RES-based power station scarcely surpass the energy restriction of annual excess of energy (maximum energy that can be injected in the power grid) that must be lower than 5.88 GWh/year. It must be highlighted the value of the costs-incomes ratio, used as optimization parameter, which in this result means that each euro of income has a cost of e0.795. This value must not be compared with the LCOE. Different approaches for LCOE can be seen in Table 8.6. Typical LCOE, which has been calculated considering only the power generation facility achieves the value of 7.04 e/MWh, which represents an 82.23% better value respect the cost of the power energy from the grid. On the other hand, if we consider the complete process, i.e. including the water desalination costs and incomes, the LCOE achieves 7.34 e/MWh. The obtained value for LCOE

148

8 Stress Mitigation of Conventional Water Resources …

Table 8.6 Levelized costs of energy and water Parameter

Results

Observations

Classic LCOE (e/MWh)

7.04

Only power generation facilities costs are included without considering water sales

Global LCOE (e/MWh)

7.34

All costs are included but also incomes from water sales

Classic LCOE injected electricity to power grid (e/MWh)

29.64

Only power generation facilities costs are included without considering water sales

Global LCOE injected electricity to power grid (e/MWh)

30.88

All costs are included but also incomes from water sales

LCW without considering electricity sales (e/m3 )

12.87

Power generation facilities costs are included but not electricity sales

LCW considering electricity sales (e/m3 )

−19.75

Costs are reduced by incomes from electricity sales

LCW without RES (e/m3 )

428.63

Equivalent desalination plant fed only by electricity from the power grid

Source Own elaboration

must be evaluated considering that it can vary by technology, country and project, based on the renewable energy resource, capital and operating costs, as well as the efficiency of the technology. It must be considered the associated costs with electricity consumption (see Appendix F); and the costs of the associated desalination plant. In addition, it has been included the costs of the water storage deposit as well as the pumps. On the other hand, we should also consider that the costs of solar photovoltaic and wind energy technology have fallen during lasts years [41]. Table 8.6 also shows the Levelized Cost of Water (LCW) which achieves 12.87 e/m3 in classical terms (not considering the extra incomes from the electricity selling). This value means that producing water by this method is a 46.16% more expensive than the expected remuneration for it (process not economically feasible by itself without financial support), but if we consider the whole process and we include the extra incomes from the electricity selling, this value changes drastically up to −19.75 e/m3 , which means that each m3 of produced desalted water makes a net profit). These values must be considered in the context that the production of desalted water in an equivalent plant without renewable energy sources would conduct to a LCW greater than 428 e/m3 . From the results shown in Table 8.5, the payback is known to be met in more than 11 years, due to the high investment costs of the project, affecting the viability of these facilities. In accordance with the provisions of Article 10 of Law 24/2013, of 26 December, on the Electricity Sector, the electricity systems of non-mainland territories are subject to a singular regulation that considers the specificities deriving from their territorial location [54]. The peculiarities of electricity systems in non-mainland territories with respect to the mainland system, deriving fundamentally from their isolated nature and small size, make it more difficult to integrate electricity production from renewable energy

8.3 Results and Discussion Table 8.7 Payback time periods comparison

149 Parameter

Results

Considering TEC 1380/2018

Wind farm cost (e)

4,099,337

2,307,337

Investment cost (e)

5,991,109

4,199,109

Payback time (years)

>11

8.3

Source Own elaboration

sources due to their characteristics. While in the peninsular system the percentage of electricity production from renewable energy sources stood at 40.2% in 2016, in non-mainland territories this percentage stood at around 6% [55]. In order to make progress in meeting the binding objectives established in Directive 2009/28/EC [56] and its revision (European Union; Directive (EU) 2018/2001 of the European Parliament and of the Council of 11 December 2018 on the promotion of the use of energy from renewable sources) it is considered advisable to set up wind and photovoltaic power station that contribute to the diversification of primary energy sources, as well as the reduction of energy dependence and the reduction of CO2 emissions [57]. On the other hand, despite the fact that the activity of electricity production from renewable energy sources generates income from the sale of energy on the market, these are not sufficient to recover their investment costs and avoid a financing deficit, which makes it necessary to grant public aid. Based on the subsidiarity principle, established in the Lisbon treaty, Article 5 [58], are the EU members that must fulfil this task. Specifically, for this region, to ensure the implementation of electrical energy production facilities with wind and photovoltaic technology, the first calls for aid are established (for electrical energy production facilities with wind technology located in the Canary Islands and for investment in electrical energy production facilities with solar photovoltaic technology located in the Balearic Islands.). Now on, a comparison is made in Table 8.7 in order to clarify the feasibility of the project. The TEC Order 1380/2018 [59] is a thoroughly lost grant that is given in advance in order to help the investment of financeable projects. The projects that this aid will cover must fulfil some terms: first, the wind resource where the wind farm will be placed must be higher than 5.2 m/s. Second, the minimum amount of money to bankroll per project will be 500 ke. And, finally, the unitary investment price must be lower than 1.2 Me/MW. As of results shown in Table 8.5 can be observed the designed facility fulfil these requirements.

8.4 Conclusions The water resources of the Canary Islands have suffered a serious deterioration throughout several decades, trying to supply the water consumption of the population and tourism, both in an unprecedented growth. Thus, the technology of seawater

150

8 Stress Mitigation of Conventional Water Resources …

desalination has acquired a certain prominence in the economic development of the region, to the point of being responsible for a large part of the total energy consumption of the Canary archipelago, usually from fossil fuels, that have a negative impact on the environment. Thus, the combination of desalination by reverse osmosis with renewable energies, in this case, wind and solar photovoltaic, is a step towards sustainability and the reduction of dependence on fossil fuels. The capacity of the proposed plant, 5600 m3 /day, aims to supply the total population of Arinaga until 2029, which corresponds to the 10 assumed years of the expected life of the desalination plant membranes. During this time, the stress of the currently exploited conventional water resources will be mitigated, in this case obtaining drinking water for human consumption without supposing extra demand for the power grid, since its operation is based on self-sufficiency.

References 1. Gran Enciclopedia Virtual Islas Canarias. Características del clima canario, https://www.gevic. net/info/contenidos/mostrar_contenidos.php?idcat=22&idcap=92&idcon=532. Accessed 16.06.19 2. Proyecto Clima Impacto. Hidrología y recursos hídricos: Recursos hídricos, http://climaimpa cto.eu/wp-content/uploads/2012/10/22_CALIDAD_DEL_AGUA_20102012.pdf. Accessed 16.06.19 3. Gobierno de Canarias. Servicios de salud: sanidad ambiental: aguas de consumo, https://www3. gobiernodecanarias.org/sanidad/scs/scs/1/plansalud/psc02/psc02_3m.htm. Accessed 16.06.19 4. Santamarta Cerezal JC (2013) Hidrología y recursos hídricos en islas y terrenos volcánicos: Métodos, Técnicas y Experiencias en las Islas Canarias. Colegio de Ingenieros de Montes, Madrid 5. Falkenmark M, Lindh G (1976) Water for a starving world. Westview Press, Boulder 6. Iglesias A, Estrela T, Gallart F, Impactos sobre los recursos hídricos, http://hispagua.cedex.es/ sites/default/files/hispagua_documento/07_recursos_hidricos_2_tcm7-12423.pdf 7. iAGUA, Canarias y la desalación: 50 años de experiencia que permiten superar los retos del presente, https://www.iagua.es/blogs/baltasar-penate-suarez/canarias-y-desalacion-aguas50-anos-experiencia-que-permiten-superar 8. ISLáGUA, Descripción del proyecto, http://islhagua.itccanarias.org/web/guest/canarias. Accessed 20.06.19 9. Canarias 7. La población canaria subirá un 17,2% en 15 años por los foráneos, https://www. canarias7.es/sociedad/la-poblacion-canaria-subira-un-17-2-en-15-anos-por-los-foraneosCY5670260. Accessed 20.06.19 10. Hosteltur. Canarias: las cifras del ciclo expansivo y los interrogantes para 2018, https://www. hosteltur.com/126503_canarias-cifras-ciclo-expansivo-interrogantes-2018.html. Accessed 20.06.19 11. Consejo Insular de Aguas de Tenerife. Producción industrial de agua, https://www.aguastene rife.org/images/pdf/planhidrologico/memoria06.pdf. Accessed 20.06.19 12. Instituto Tecnológico Geominero de España. Estudio Hidrogeológico de la Zona de ArinagaTirajana, Gran Canaria (Sistema 83. Subsistema S.1.B. Tomo I. Memoria), http://info.igme.es/ SidPDF/038000/277/Tomo%20I%20Memoria/38277_0001.pdf. Accessed 20.06.19 13. Grupotecmared, La energía eólica, motor de desarrollo sostenible en la Comarca del Sureste de Gran Canaria, https://www.smartgridsinfo.es/2018/09/27/energia-eolica-motor-desarrollosostenible-comarca-sureste-gran-canaria. Accessed 20.06.19

References

151

14. IEA-ETSAP, Water Desalination Using Renewable Energy: Insights for Policymakers, https://iea-etsap.org/E-TechDS/PDF/I12IR_Desalin_MI_Jan2013_final_GSOK.pdf. Accessed 20.06.19 15. Centro Canario del Agua, Producción de agua desalada en Canarias, http://fcca.es/docume ntos/08_agua_en_canarias/Desalacion%20en%20Canarias/Listado%20de%20Desaladoras% 20de%20Canarias%20(Oficial).xlsx. Accessed 15.06.19 16. Centro Canario del Agua, Datos estadísticos sobre el agua en Canarias, http://fcca.es/doc umentos/08_agua_en_canarias/Estadisticas%20y%20datos%20de%20Canarias/Estadisticas_ sobre_el_agua_en_Canarias.xls. Accessed 15.06.19 17. La Provincia, Canarias y el agua: referente mundial, https://www.laprovincia.es/provincia-esp eciales/2018/05/30/canarias-agua-referente-mundial/1063344.html. Accessed 20.06.19 18. Canarias 7. Canarias cuenta con 319 desaladoras que producen 660.000 m3 diarios, https:// www.canarias7.es/hemeroteca/canarias_cuenta_con_319_desaladoras_que_producen_660 000_m3_diarios-KGCSN315690. Accessed 20.06.19 19. Gobierno de Canarias. Desaladora, https://www3.gobiernodecanarias.org/medusa/wiki/index. php?title=Desaladora. Accessed 20.06.19 20. Moreno-Deus E, Guerra-Marrero JL, La gestión del agua en Gran Canaria: Una política hidráulica en condiciones extremas, https://www.camarazaragoza.com/medioambiente/docs/ bibliocamara/bibliocamara26.pdf. Accessed 20.06.19 21. Shatat M, Worall M, Riffat S (2013) Economic study for an affordable small-scale solar water desalination system in remote and semi-arid region. Renew Sustain Energy Rev 25:543–551 22. Padrón I, Avila D, Marichal GN, Rodríguez JA (2019) Assessment of hybrid renewable energy systems to supplied energy to autonomous desalination systems in two islands of the canary archipelago. Renew Sustain Energy Rev 101:221–230 23. Mahmoudi H, Spahis N, Goosen MF, Sablanid S, Abdul-wahab SA, Ghaffour N (2009) Assessment of wind energy to power solar brackish water greenhouse desalination units: a case study from Algeria. Renew Sustain Energy Rev 13(8):2149–2155 24. Poompavai T, Kowsalya M (2019) Control and energy management strategies applied for solar photovoltaic and wind energy fed water pumping system: A review. Renew Sustain Energy Rev 107:108–122 25. Mokheimer EMA, Sahin EZ, Al-Sharafi A, Ali AI (2013) Modeling and optimization of hybrid wind–solar-powered reverse osmosis water desalination system in Saudi Arabia. Energy Convers Manage 75:86–97 26. Gude VG, Nirmalakhandan N (2010) Sustainable desalination using solar energy. Energy Convers Manage 51(11):2245–2251 27. Abdelshafy AM, Hassan H, Jurasz J (2018) Optimal design of a grid-connected desalination plant powered by renewable energy resources using a hybrid PSO–GWO approach. Energy Convers Manage 173:331–347 28. Ismail TM, Azab AK, Elkady MA, Elnasr MMA (2016) Theoretical investigation of the performance of integrated seawater desalination plant utilizing renewable energy. Energy Convers Manage 126:811–825 29. Zhangab W, Maleki A, Rosen MA, Liu J (2019) Sizing a stand-alone solar-wind-hydrogen energy system using weather forecasting and a hybrid search optimization algorithm. Energy Convers Manage 180:609–621 30. Li Q, Loy-Benitez J, Nam K, Hwangbo S, Rashidi J, Yoo C (2019) Sustainable and reliable design of reverse osmosis desalination with hybrid renewable energy systems through supply chain forecasting using recurrent neural networks. Energy 178:277–292 ´ c B, Krajaˇci´c G, Dui´c N, Pukšec T, Mohsenb MS et al (2014) The influence of 31. Novosel T, Cosi´ reverse osmosis desalination in a combination with pump storage on the penetration of wind and PV energy: a case study for Jordan. Energy 76:73–81 32. Cherif H, Belhadj J (2011) Large-scale time evaluation for energy estimation of standalone hybrid photovoltaic–wind system feeding a reverse osmosis desalination unit. Energy 36(10):6058–6067

152

8 Stress Mitigation of Conventional Water Resources …

33. Ali IB, Turki M, Belhadj J, Roboam X (2018) Optimized fuzzy rule-based energy management for a battery-less PV/wind-BWRO desalination system. Energy 159:216–228 34. Sistema de Información Territorial de Canarias – IDECanarias, https://visor.grafcan.es/vis orweb/default.php?svc=svcRecursoEolico. Accessed 25.06.19 35. Sede Electrónica del Catrasto, https://www1.sedecatastro.gob.es/. Accessed 25.06.19 36. INE. Nomenclátor: Población del Padrón Continuo por unidad poblacional, http://www.ine. es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736177010&menu=result ados&secc=1254736195526&idp=1254734710990. Accessed 15.06.19 37. Cabildo de Tenerife. Ahorrar agua para un hogar+sostenible, https://www.tenerifesolidario. org/sites/default/files/Miniguia-Agua-H%2BS.pdf. Accessed 15.06.19 38. Desalinationplant, https://github.com/SanPen/DesalinationPlant. Accessed 25.06.19 39. Fritzmann C, Lowenberg J, Wintgens T, Melin T (2007) State-of-the-art of reverse osmosis desalination. Desalination 216:1–76 40. Rosales-Asensio E, Borge-Diez D, Pérez-Hoyos A, Colmenar-Santos A (2019) Reduction of water cost for an existing wind-energy-based desalination scheme: a preliminary configuration. Energy 167:548–560 41. International Renewable Energy Agency. Renewable Power Generation Costs in 2017 [Internet]. IRENA; 2018 [cited 19 July 2019]. Available from https://doi.org/10.1007/Spring erReference_7300 42. Lazard. Lazards-Levelized-Cost-of-Storage-Analysis-10, (November) [Internet]. Lazard; 2015 [cited 2019 July 20]. Available from https://www.lazard.com/media/2391/lazards-levelizedcost-of-storage-analysis-10.pdf 43. Toray Membranes.com [Internet]. Wilmington, NC. Eastern Reverse Osmosis Systems [cited 2019 July 8]. Available from http://www.toraymembranes.com/ 44. American Membrane Technology Association. Membrane desalination costs [Internet]. AMPA; 2007 [cited 2019 July 8]. Available from https://www.amtaorg.com/wp-content/upl oads/06_Membrane_Desalination_Costs.pdf 45. Pinnau I. Membranes for Water Treatment: Properties and Characterization [Internet]. Stanford University; 2008. Accessed 19.03.19. Available from https://web.stanford.edu/group/ees/rows/ presentations/Pinnau.pdf 46. Gülich JF (2008) Centrifugal pumps, 3rd edn. Springer, Berlin 47. Xylem Water Solutions. Life Cycle Costs (LCC) for wastewater pumping systems [Internet], https://www.xylem.com/siteassets/support/white-papers/white-papers-pdf/life-cycle-costslcc-for-wastewater-pumping-systems.pdf. Accessed 13.4.19 48. Hunter Water Corporation. Operating and maintenance cost estimating guideline. Available from https://www.hunterwater.com.au/Resources/Documents/Drawings_Plans_Specs/Guidel ine---Water-and-Sewer-Cost-Estimating.pdf. Accessed 15.5.19 49. Lozano-Ruano JB. Soslaires Canarias SL (2010) Available from http://www.islhagua.itccan arias.org/c/document_library/get_file?p_l_id=22263&folderId=22087&name=DLFE-925. pdf. Accessed 15.1.19 50. Jia J, Zheng C, Zhao C (2008) Dam safety in China and the life span evaluation of old concrete dams. In: Walraven JC, Stoelhorst D (eds) Tailor made concrete structures. CRC Press, Boca Raton, pp 9–14 51. SUNPOWER. SunPower® Performance Series | P17, https://us.sunpower.com/solar-resources/ sunpower%C2%AE-p-series-commercial-p17-360-com/. Accessed 6.09.18 52. Fu R, Feldman R, Margolis R, Woodhouse M, Ardani K (2017) U.S. Solar Photovoltaic System Cost Benchmark: Q12017. Golden: National Renewable Energy Laboratory 53. Stehly T, Heimiller D, Scott G (2017) 2016 Cost of wind energy review. National Renewable Energy Laboratory, Golden 54. Ley 24/2013, de 26 de diciembre, del Sector Eléctrico. Boletín Oficial del Estado No. 310; de 27 de diciembre de 2013. BOE-A-2013-13645, http://www.boe.es/boe/dias/2013/12/27/pdfs/ BOE-A-2013-13645.pdf. Accessed 15.06.18 55. REE (2018) Informes del sistema eléctrico, https://www.ree.es/sites/default/files/11_PUBLIC ACIONES/Documentos/InformesSistemaElectrico/2019/Avance_ISE_2018.pdf. Accessed 20.06.19

References

153

56. European Parliament and the Council (2009) Directive 2009/28/EC of the European Parliament and of the Council of 23 April 2009 on the promotion of the use of energy from renewable sources 57. European Parliament and the Council (2018) Directive (EU) 2018/2001 of the European Parliament and of the Council of 11 December 2018 on the promotion of the use of energy from renewable sources (Text with EEA relevance) 58. European Union. Treaty of Lisbon amending the Treaty on European Union and the Treaty establishing the European Community, signed at Lisbon, 13 Dec 2007 59. Instituto para la Diversificación y Ahorro de Energía (2019) Ayudas a la inversión en instalaciones de producción de energía eléctrica con tecnologías eólica y fotovoltaica situadas en los territorios no peninsulares cofinanciadas con fondos comunitarios FEDER, https://www.idae. es/en/node/13481. Accessed 15 July 19

Chapter 9

Feasibility Analysis of Wind and Solar Powered Desalination Plants: An Application to Islands

Despite the fact that a sustainable energy production is highly recommended from different points of view (use of indigenous renewable energy resources that are cheaper, less polluting resources among others) it is important to analyze what is the capacity of high investment projects to achieve a certain economic competitiveness. It is specially important for desalination plants piloted with renewable energy that need very high investment. From a nexus approach it is done for a better understanding of the synergies between energy from renewable energy sources, water desalination techniques, and markets.

9.1 Introduction Among the 17 UN sustainable development goals [1] to achieve a better and more sustainable future, we found the goal of reaching affordable and clean energy next to clean water and sanitation. Water scarcity affects more than 40 per cent of the global population. Energy is central to nearly every major challenge and opportunity the world faces today. Be it for jobs, security, climate change, food production or increasing incomes. Additionally, among the European objectives included in the Lisbon Treaty [2], it highlights the Article 194: to improve competitiveness, achieve security of supply of energy, sustainability in order to increase the installed capacity of primary renewable energy-based technologies. Different scenarios developed by IEA [3] suggest that energy demand could expand by 30% worldwide between today and 2040, where the convergence of cheaper renewable energy technologies, digital applications and the rising role of electricity is a crucial vector for change. So, EEG is supposed to increase significantly, putting affordable, universally available and cleaner electricity at the centre of strategies for economic development. Without decisive action, energy-related GHG emissions could more than double by 2050, and soil erosion and water scarcity will

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 E. Rosales-Asensio et al., Sea Water Desalination in Microgrids, Green Energy and Technology, https://doi.org/10.1007/978-3-030-96678-2_9

155

156

9 Feasibility Analysis of Wind and Solar Powered Desalination …

speed up all over the world. Due to this fact, Governments face the challenge of revert, palliate, or more properly be prepared to tackle these events. We cannot forget that worldwide, fossil fuels involve the vast majority of its energy consumption, and globally around 80% of global primary energy use still comes from fossil fuel [4]. Specifically, to fulfil their 2020 climate and energy package [5], the EU institutions set three key targets: 20% cut in GHG emissions, 20% of EU final energy from renewables, 20% of improvement in energy efficiency, among other initiatives. Those will be a set of binding legislation. Moreover, in 2016 European Commission presented the package: “Clean Energy for all Europeans” [6] in which several agreements have been made in order to move along this strategy, as the agreeing on setting in 32% the final energy consumption from renewable technologies by 2030 for the whole European Union with rising expectative. To boost this strategy new legislation has already come [7] to enhance the development of renewable-based projects, that will help society to grow and thrive while lowering energy dependence, as well as the reduction of GHG emissions. Despite the fact that 70% of earth’s surface is covered by water, and 97% of it is in liquid phase water availability for safe consumption and agriculture has become a relevant problem around the world. For that reason, from the second half of the 20th-century, water desalination technologies were developed for areas under water scarcity risk. According to the World Resource Institute, water stress in 2040 would become extremely high in Spain, Greece, as well as in the MENA region. Gómez et al. analyzed the most used technologies of desalination in the Canary Islands. Among these, distillation methods such as multistage flash, multiple-effect distillation, vapour compression, and membrane processes such as electrodialysis reversal, membrane distillation, and reverse RO. Van der Brugger et al. [8] investigated the energy-intensive process that RO desalination comprises, focusing on the rate operation and the high pressure in this process. This produces -as long as fossil fuels are used for EEG—an associated environmental impact, and increasing prices of the desalted water resource, in regions that lack them, causing loss of competitiveness for agriculture, industry, or human usage. Provided that EEG costs in islands are higher due to its isolated nature, these issues become even more acute. The efforts focus on how to improve the efficiency of the desalination process. In fact, more than 80% investment costs of a RO desalination plant involve capital and electricity costs. Water desalination in Gran Canaria entails an energy electricity consumption of around 350 GWh/year, around a 10% of the total final energy consumption of the island. Energy costs are nearly 52% of operating and maintenance costs, and 39% of total cost [9]. In the Canary Islands, by 2012, the installed desalination capacity was 273 084 m3 /day, entailing 122 Hm3 /year. Especially in Gran Canaria, 86% of water for human consumption is desalinated, as well as the 52% of total supply [10]. 62 Hm3 out of them were desalinated in public desalination plants, and 60 Hm3 from private property. Ghalavand et al. [11] analyzed costs related to the involved technologies in the desalination process as well as costs of energy in the region where the desalination plant is located. Their isolated nature provokes a higher energy bill for desalting those 122 Hm3 . In order to improve the competitiveness of the process, to reduce costs,

9.1 Introduction

157

a dedicated RES-based power station has become an extended option to run these facilities to overcome electricity prices, lowering operational costs, while reducing associated GHG associated emissions. Rosales et al. [12] analysed this scheme, and found that for a 6 ce/ kWh of energy costs, the costs of water might be reduced by investing in the internal process of a private property RO Desalination plant [13], while economic analysis advised the investment. Other proposed technology that comes from innovation process in desalination technologies is the thermo-solar water distillation [14], a technology that harvest sunlight with a 70% of efficiency heating the air between two plastic layers and the hot air naturally rises until a vortex of a pyramid surpassing 100 °C, instantly evaporating sprayed seawater separating it due to its weight difference. The benefit of this project, apart from the fact that it offers goods as water, salt, is that it proposes low energy consumption. By the time of the redaction of this chapter was being developed the first pilot plant. On the one hand, Governments have to face the issue of the integration of renewable technologies, trying to fulfil agreements made to face climate change, while solving the associated problem concerning the reduction of the competitiveness of the economy that itself comprises multiple actors. According to Spanish National Grid [15], Gran Canaria, a region where just 10% of generated electricity comes from RES provokes a high rate of GHG emissions, existing concern about the economic competitiveness of these facilities, as well as the environmental impact of the energy used in the process. However, RES-desalination solutions appeared becoming feasible alternatives. González et al. [16] focused on wind powered desalination systems. Colmenar et al. [17] investigated actions to face these problems in the region of study, and years that came grants and benefits were highlighted. Rukh et al. [18] reviewed measures adopted by Governments in order to boost RES-based technologies of EEG, and how they affect the commercialization of these disruptive technologies. In order to ensure the implementation of RES-based power stations—specially wind and photovoltaic technology—in the non-mainland territories of Spain, last years, calls for aid have been established in the Canary Islands, and for investment in EEG facilities with solar photovoltaic technology located in the Balearic Islands. Developed by TEC/1380/2018 order [19], it is incompatible with others proposed from European Union, or others, as well as, it is incompatible with the specific retributive regime [20–22], given in order to reduce the extra costs of the facilities in the business of generation of electricity from RES. Spanish authorities, in order to lower the high costs of desalted water, let sales of surpluses of the generated electricity when operating those desalination plants, but with a maximum of twice the energy needed to run the facility. European authorities proposed the SME grant Phase 2 [23]: tool bound to innovation projects underpinned by a strategic business plan and feasibility assessment that focuses on the industrial, economic, and social problem to overcome, or the business opportunity of projects involving renewable energy technologies. The research conducted in this chapter virtually granted two water desalination technologies following them and this scheme as a guide, and developed a techno-economic evaluation of these projects in order to decide if investment or not in a given project would be advisable. For evaluating projects, it is common using

158

9 Feasibility Analysis of Wind and Solar Powered Desalination …

market and technical studies, economic evaluation, or the analysis and administration of risk. The aim of this chapter is to find out what was the influence of parameters (grants, investment rates, W&E sales) over KPI for the investment decision regarding the commercialization of novel projects associated with water desalination. In the scientific literature it is common to find reviews using the software HOMER to size desalination plants minimizing, or optimizing parameters. Khan Meer et al. [24] studied the deployment of solar PV and wind energy sources to run a RO facility, and found a total water production cost between 0.45 and 0.89 e/m3 . Similarly, Eltawil et al. [25] found those costs to be between 0.59 and 2.81 e/m3 depending on the technology, the size of the facility, and the market that was acting in. Javed et al. [26] conducted a techno-economic study of a hybrid solar-wind-battery for a standalone system, and investigated the influence of a low loss of power supply probability, finding that it had an enormous influence in direct, and O&M costs. Shallenberg et al. [27] were interested in investigating the relationship between energy and desalination technologies, and focusing on the Canary Islands showed the energy and monetary costs of desalination. In contrast, it is observed a lack of studies concerning the comparison of real investment scenarios of projects to develop an analysis of the investment decision. In the first stage, the characteristics of proposed grants were analyzed: objectives, ambition, expected impacts, implementation, or measures to maximize impact stand out as of them. Secondly, it was analyzed if the proposed technologies fulfilled the desired characteristics in order to apply for the proposed grants, facilities dimensioning, and what is its influence in the investment decision for its commercialization through a sensitive analysis that investigated if this business model would be profitable enough for private investors.

9.2 Material and Methods This section describes the two analyzed RES-desalination systems and describes the method to find out what is the influence of grants, investment rates, W&E sales for the investment decision regarding the commercialization of novel projects associated with water desalination. This section analysed the solar and wind profiles of the region with geographic information tools in order to find the RES potential of the region, letting explore the scalability/replicability. The Canary Islands are a Spanish archipelago located in the Atlantic Ocean, in a region known as Macaronesia, around 100 km off the northwest coast of Africa and about 1350 km from Europe [28]. Despite its proximity to Africa, the Canarian archipelago is economically and politically European, as it is part of the European Union. The archipelago has seven main islands, with a population of around 2.1 million inhabitants and a density of 287.39 inhabitants per km2 [29]. The Canary Islands is the eighth most populous administrative region Spain and its population is mostly concentrated in the two capital islands (Tenerife and Gran Canaria) [29]. The economy in the islands is based primarily on tourism, which represents around 30% of the Gross Domestic Product (GDP) in the

9.2 Material and Methods

159

islands. However, construction sector and tropical agriculture have been also drivers in the islands economy. Canary Islands have suffered an important lack of water resources along the history [30]. Water resources in the islands have been set in two systems of water collection, based on the location and climate of the involved islands [30]. Western islands contain high mountains and forests which retain the clouds in the north face of the islands. For this reason, the water resources of these islands are mainly the groundwater. Easter islands, by contrast, does not contains high mountains and forests and they are more arid. In these islands the desalination is the main source of water [30]. Since all economic sectors in the islands are intensive in the use of water, the water resources in the islands are overexploited [30]. This has motivated several initiatives from the governments to promote the desalination development on the islands and to control the water use [31]. In term of electricity and grid interconnection there are six islands electrically independent each other. Only two of the eastern islands (Lanzarote and Fuerteventura) are interconnected by a underwater connection. This insularity in electricity terms avoid synergies between isolated energy systems and generate additional costs which are proportionally spread out in the electricity bills throughout the country, to allow citizens in the islands to pay the same price per kWh as those who live on mainland Spain [32]. Total installed electrical power in the islands was 3308.6 MW, at the end of 2018, with 18.5% as renewable energy (612.3 MW) [33]. These renewable energy sources (RES) are mainly wind (397.3 MW) and solar photovoltaic (186.5 MW). However, another RES as mini-hydraulic, hydropower and biogas are participating with 2.0 MW, 22.8 MW and 3.7 MW respectively [33]. In the Canary Islands there is a lot of potential for the exploitation of RES, mainly wind and solar and one of the priorities for the islands governments is to increase the level of RES in the electrical systems [27]. Canary Islands is specially considered as an outermost region in the UE [34]. Remoteness supposes to local manufacturers a number of constraints which push up the cost prices of their products, thereby making them uncompetitive with products from elsewhere (especially mainland Spain and the other EU Member States) [34]. For these reasons, it has implemented specific measures, which, by means of tax exemptions or reductions for local products, are designed to: (i) encourage productive industrial activity, (ii) safeguard their competitiveness with outside products, and (iii) thus increase the proportion of the Canaries’ GDP accounted for by industrial activity [34]. In this sense, for instance, the harmonised rules on VAT do not apply to the Canary Islands [34]. However, there is another local consumer tax known as the IGIC (Impuesto General Indirecto de Canarias—Canaries General Indirect Tax) applied at several different rates [34]. Additionally, another consumer tax known as the AIEM is applied to a limited list of locally manufactured products specified in the Decision 377/2014/EU [35]. Focused in Gran Canaria. Cabrera et al. [28] noted that in the south east of that island, where it is located the “Barranco de Tirajana” thermal power station suffer all the aforementioned problems: water scarcity, high environmental impact [36] on population due to the high GHG emissions and loss of competitiveness. The location of the proposed site for the facility is shown in Fig. 9.1. Now on, the technologies

160

9 Feasibility Analysis of Wind and Solar Powered Desalination …

Fig. 9.1 Proposed placed (in line blue) for the allocation of the Plant [37]

that were proposed as candidates to ask for grants were analyzed and dimensioned for a further analysis of its commercialization performance evaluation.

9.2.1 RO Desalination Plant The first analyzed technology is a RES-powered RO desalination plant, sized with the software Desalination Plant [38]. Colmenar et al. [39] analyzed that only by optimizing three variables (wind energy, solar energy and energy storage) is able to provide a minimum operational cost for a RO plant, and for a given desalination capacity. Due to the fact that simulation of the desalination plant is coupled to a hybrid distributed energy resource is itself a complex task, Desalination Plant implements a quite complex economic metric. As Im and Park [40] reviewed to obtain the variables, stochastic optimization algorithms have their own mechanisms to find the global optimum. They require a large number of evaluations to reach it, and to optimize the system, it is necessary to provide the wind and solar profiles of both, speed and irradiation data from the region of Gran Canaria [29, 33]. Provided that the maximum amount of funding the SME instrument would provide was e2.5 million per project, and it involved 70% of the total investment, e3.5 million was the total budget for the design of the RES-powered desalination facility. According to RosalesAsensio et al. [41], for a standard desalination plant it might be assumed to cost e1 million [9] in order to perform the simulation. Similarly, it was assumed a total cost of 1000 e per installed kW of wind power [42], fact that it implied that the wind installed power must be of 2.5 MW for dimensioning this facility within the Software.

9.2 Material and Methods

161

Similarly, for solar photovoltaic power it was assumed 1050 e per installed kW [43] it is: 2.33 MW. According to Spanish legislation, these facilities can sell a restricted amount of energy: the same amount that they are able to generate with the installed capacity to run the desalination plant [20]. Taking into account a capacity factor of 0.32 for wind energy, and 0.18 [44] for solar-photovoltaic energy the expected EEG capacity to size the wind powered desalination plant could be calculated. Expected energy capacity = 2500 kW × 2803 h/year = 7,008,000 kWh/year. Expected capacity (water) = 3,504,000 kWh/year × 1 m3 /3 kWh × 1 year/350 days = 3337 m3 /day. The specific energy consumption (SEC) considered in this study for the water treatment (3 kWh/m3 ) was determined as the average value of different RO desalination plant installed in The Canary Islands [45]. From these results, the expected capacity for sizing the associated desalination plant resulted in 139 m3 /hour of capacity in the associated desalination plant. The rest of the necessary data to develop the simulation can be consulted in Appendix H (Table H.1).

9.2.2 The Aqua.Abib Project This second facility to be analysed is a novel distillation facility joint to a RES-based Power Station. program (phase II) of the European Commission. As we previously mentioned, incomes consisted in W&E sales for the desalination plant, and water, electricity and salt sales for the aqua.abib project. The cost of this project was set at 2.4 million e [46], and as can be seen in Table 9.1 funding will be provided by different sources. The system offers a flux of 25,000 m3 /year, enough water for 1200 people (71.41 m3 /day). In addition, it is able to generate 875 mTon of NaCl. The website of the project [47] shows that European Commission granted around 50% of the total cost, it is said: 1.4 million e. Provided that the deployment of wind power was mandatory to opt for the grant, this research assumed it for the associated renewable plant of the aqua.abib pilot plant, for the same region. The necessary amount to fulfil the 3.5 million e condition was proposed as wind deployment to the desalination plant. Provided that this pilot plant is in its early stages of technological development, the 0–50–100% analysis of the grant departed from the already 1.4 million e granted. Table 9.1 Financing card [47]

Funding source

Amount

European commission

1.4 million e

ENISA (Spanish public administration)

0.3 million e

Bank loan

0.2 million e

Private capital

1.6 million e

162

9 Feasibility Analysis of Wind and Solar Powered Desalination …

9.3 Analysis of Commercialization As from the analysis of the characteristics, and conditions that proposed grants demanded highlight: feasibility of the project, summarizing measures to maximize impact, or uses (research, commercial, social, or environmental investment). Once the facility perceives the grant, it is demanded an extra accounting system to record all dealings (transactions). Variable costs related to the increasing administrative tasks imply increasing costs, which additionally would demand more agile structures of information management systems (see Table G.2 in Appendix G). Furthermore, these facilities must subscribe to insurance, and an economic guarantee of 20,000 e per installed MW, extra-costs that were accounted as 1% of the total project costs [48]. On the other hand, taxes, workforce, environmental impact studies, and studies to find out the wind resource, geotechnical studies, security and health plans, as well as promotion costs will not be financeable. In order to boost wind-powered technologies, TEC order 1380/2018 [19], for instance, demanded more than 800 kW of installed wind power. Furthermore, the region to locate the wind power station must have a wind resource speed higher than 40% of the maximum resource for this region, namely 13 m/s at 60 m high. In addition, it demanded requirements of state air safety agency, nearness node of connection to the grid, with a minimum voltage value. This chapter conducted a commercialization analysis following a flux diagram (Fig. 9.2): it varied the amount granted, the Ieff of the rest of the amount invested, and calculated parameters that were taken into account in order to explore its commercialization performance. The amount of conceded grant to build the facility varied

Fig. 9.2 Flux diagram of the commercialization analysis. Source Own elaboration

9.3 Analysis of Commercialization

163

from 0 to 100%, and the effective investment rate (Ieff) of the cost recovery factor (CRF) between 3.5% and 24%. In order to understand how the profit-earning capacity was affected, it was distinguished between the EEG facility and the desalination plant because of the importance of the annual capital costs. These vary depending on the amount and characteristics of the loans conceded to build the facility, and how are they yearly capitalized. Among the most used ways, the research conducted in this chapter opted for the same amount of yearly money payment as shown in (2) [48], under the assumption all investment came from loans at a specific Ieff. Among the most widely used parameters that indicate the performance competitiveness of these facilities we can name the levelized cost of energy (LCOE) and cost of water (COW). LCOE is an economic assessment of the average total cost; including: capital, operating and maintenance, as well as fuel costs to build and operate a facility over its lifetime divided by the total energy output over the lifetime of the facility under a specific Ieff. Gökςek and Gökςek [49] predicted (for a windpowered RO desalination plant, connected to the grid) a LCOE from 0.068 e/kWh to 0.14 e/kWh, and a COW from 0.7 to 2.46 e/ m3 . These variations depended on the size of the desalination plant, the region where it is located, and on the technology involved in the process. Feo et al. [50] studied the region where this chapter is focused in, and gave an average price for water of 0.6 e/ m3 . Karunathilake et al. [51] used a fuzzy logic-based optimization process to discover the optimal system capacities and energy mix. This technique is also used by other authors in chemical process for another purposes [52]. n LC O E =

CCt +O MCt+FCt (1+Ie f f )n n Electricit y i=1 (1+Ie f f )n

(9.1)

Ie f f (1 + Ie f f )n (1 + Ie f f )n − 1

(9.2)

i=1

C R F = C.C

The project was analysed for 20 years of return of investment, varying the amount of sold electricity, as well water sales from 70 to 200% in order to investigate the capacity of both facilities to achieve the economic KPI, and for a better understanding of its commercialization performance. Specifically, to achieve this task, this research calculated the net cash flow of each year (net utility) with data from Tables G2 and G3, in Appendix G. The general statements (see Fig. 9.3) consisted in developing a balance of all the costs and incomes generated during a whole year [48], starting with the marginal utility, or predicted earnings before interests, taxes, depreciation and amortization (EBITDA). On the other hand: capital, operating, taxes, and administrative costs as shown in Appendix G (Table G.3). In the calculus of the NPV, it was used 6% as inflation rate (company earnings are limited to maintain its real purchasing value) instead of calculating the profitability of the facility as a whole with an investor choice, provided that it was accounted in the CRF.

164

9 Feasibility Analysis of Wind and Solar Powered Desalination …

Fig. 9.3 Net cash flow. Source Own elaboration

Table 9.2 Key performance indicators Objective

Success metrics

Establishment of an optimal energy mix for a Microgrid to make an abandoned farm profitable

Software validation with assimilable experiences

Economic analysis

Design a micro grid scheme of about 2 MW resulting in an annual positive net cash flow of 200,000 e

Social analysis

Job creation (direct or indirect)

Impact from the environmental point of view

Reduction of emission of 5000 tons of CO2 to the atmosphere through the scheme

Reduction of water production costs in current To propose a configuration that reduces water desalination plants production costs by 4 coeur/m3 compared to previous assimilable experiences using the BATs Source Own elaboration

Table 9.2 shows the key KPI related to this research, and to provide a success metric. Among these, this research focused on economic analysis. It is the energy and GHG emissions, water costs and related jobs.

9.4 Results and Discussion The deployment of renewable technologies for EEG in Gran Canaria is far from the mentioned EU objectives of renewable deployment. With a population of approximately 850,000 inhabitants [29], the ratio wind power/inhabitant in the island was lower than 100 W/inhab, far from the mean of Spain, 495.8 W/inhab, and far from the EU members mean: 301.1 W/inhab [15], and this without taking into account that during 2019, more than 4.5 million tourists visited the island [53]. Similarly,

9.4 Results and Discussion

165

the photovoltaic installed power ratio of 85.9 W/inhab in Canary Islands versus the Spanish mean of photovoltaic capacity per inhabitant of 103.4 W/inhab, even lower than in EU: 197.8 W/inhab [54] (Figs. 9.4 and 9.5). The percentage of uploaded energy from RES-based technologies varies within the year, finding maximum rates of 21% in July, and minimum of 5.4% in October [33]. On the other hand, the rainfall in this region was found to be lower than 0.150 m3 /year [31], and as a consequence it could be considered as a desert climate. Due to fact, this location was found to be a “laboratory of research” for other places with difficult agricultural, economic, and climatic conditions, making it a suitable candidate to opt for the grant through the proposal of an integrated solution that might contribute to improve the economic, societal, and environmental situation of regions suffering from draught problems. Fig. 9.4 EEG installed power in Gran Canaria [47]

Fig. 9.5 RES % deployment comparison [15]

RES Deployment comparison (%)

Wind Energy UE

Photovoltaic Spain

Gran Canaria

166

9 Feasibility Analysis of Wind and Solar Powered Desalination …

Fig. 9.6 Wind resource of Gran Canaria (a), and the selected site (b) at 80 m [55]

Fig. 9.7 Solar photovoltaic resource of the selected site [56]

As we have previously mentioned, the wind resource must be higher than 40% of the maximum resource for this region, namely 13 m/s at 60 m high [55], so that, higher than 5.3 m/s. The proposed region (see Figs. 9.6 and 9.7) fulfilled the requirements for wind resource as well as the potential of solar photovoltaic energy for this region: 1500–1600 kWh (see Fig. 9.7) [56] that grants demand.

9.4.1 Techno-economic Results The simulation results for sizing the RES powered RO desalination plant used the costs-incomes ratio as optimization parameter, obtaining as a result, a profitability index of 0.784 for the operation of the facility as a whole. Moreover, results showed

9.4 Results and Discussion

167

an energy deployment where PV and battery storage were almost null, while the wind farm reached the maximum sizing power (2500 kW). The rest of simulation results can be consulted in Appendix G, including annual costs to run the facility, as well as the incomes of simulated operations during one year. (In a standard decision-making problem costs of operating the solar photovoltaic plant, and the storage device that the simulation showed would have been left apart because of its small size). These showed that average year wages from W&E sales were 407,132 e/year, and the global O&M costs reached 123,737 e/year, resulting in an EBITDA of 297,595 e. On the other hand, for the operation of the distillation plant as well as for those related to the wind farm were assumed proportional O&M costs. The revenues came from water, salt, and electricity sales with the same simulated pricing of the desalination plant, resulting in an EBITDA of 100,551 e (Appendix G). The results of the analysis depicted in Fig. 9.2 are shown in Tables 9.3 and 9.4, respectively for both facilities, and for the aforementioned grant conditions. Firstly, the CRF of the necessary loan to build the facility, obtained as previously explained (see Eq. 9.2). It also included the performance parameters LCOE, and the COW of both schemes. As can be observed, the distillation plant showed positive net utility only when half grant was conceded (1.4 + 0.55 million e) (see Table 9.1), and under advantageous Table 9.3 Techno-economic results of RO desalination plant Ieff (%)

3.5

5.5

7.5

12

24

CRF (e)

175,123

209,398

245,423

334,736

608,429

LCOE (coeur/kWh)

5.7

6.5

7.4

9.6

16.3

Net utility

19,660

−28,057

−78,220

−202,598 −583,659

20.12

31.74

Never

Never

−18,336

X

X

X

X

CRF (e)

87,623

104,765

122,798

167,486

304,430

LCOE (coeur/kWh)

3.6

4.0

4.4

5.5

8.8

Net utility

107,160

76,576

44,405

−35,348

−279,660

10.26

11.88

22.57

Never

1,072,781 691,401

290,231

X

X

CRF (e)

68,712

96,292

131,357

238,725

COW (e)

0.44

0.48

0.52

0.62

0.91

Net utility

194,783

181,341

167,203

132,138

24,770

3.84

4.05

4.59

7.78

NO grant

Discounted payback period (years) 15.82 NPV Half grant

Discounted payback period (years) 9.11 NPV Full grant

Discounted payback period (years) 3.66 NPV Source Own elaboration

82,154

2,165,433 1,997,813 1,821,513 1,384,138 45,385

168

9 Feasibility Analysis of Wind and Solar Powered Desalination …

Table 9.4 Techno-economic results of distillation plant Ieff (%)

3.5

5.5

7.5

12

24 32,408

NO grant CRF (e)

41,615

62,640

86,400

144,720

LCOE (coeur/kWh)

5.7

6.5

7.4

9.6

16.3

Net utility

−27,479

−66,104

−105,715

−203,332

−560,097

Discounted payback period (years)

26.5

33.74

Never

Never

Never

NPV

X

X

X

X

X 165,204

Half grant CRF

20,807

31,320

43,200

72,360

LCOE (coeur/kWh)

3.6

4.0

4.4

5.5

8.8

Net Utility

29,471

−3079

−34,079

−106,929

−299,129

Discounted payback period (years)

18.17

155

Never

Never

Never

NPV

X

X

X

X

X

CRF

41,615

62,640

86,400

144,720

321,408

COW

0.7

1.4

3.4

7.9

13.3

Net utility

49,271

28,271

7571

−38,729

−162,729

Discounted payback period (years)

15.3

19.34

29.26

Never

Never

NPV

211,905

19,021

X

X

X

Full grant

Ieff conditions. On the other hand, when the RO plant perceived half grant (1.25 million e) it showed a positive net utility up to 10% of Ieff condition. The sensitive analysis included the NPV and the payback period calculation, for different investment rates, and for the different options of conceded grants. As can be observed, most scenarios showed a negative NPV, marked with X when it is tremendously negative, advising against the investment. The best NPV result advised the investment in the aqua.abib project only when whole grant (2.5 million e) was conceded, but for up to 6% of Ieff, (note that money came from different institutions, probably under different investment rates conditions), although the discounted payback period remained higher than 15 years in this case. Note that despite more profitability margin appeared for the RO plant, the NPV became null even at an Ieff lower than 3.5%, for a not-conceded grant scenario, setting the limit of the investment decision. Also, when half grant was conceded, it became negative above 12% Ieff. Finally, for a whole conceded grant scenario the Ieff that set this limit was found to be above 24%. In addition, it highlights that only when half grant was conceded, and for an Ieff of 7.5% a reduction of 11 coeur under the average 0.6 e/m3 was achieved, while the aqua.abib project showed a price higher than 3 e/m3 . To deepen in the understanding of the economic performance, Fig. 9.8 showed the influence that the variation of the incomes derived from W&E sales from 75 to 200% of expected incomes, had for achieving the net utility KPI objective (200 000

9.4 Results and Discussion

169

200 000 €

Fig. 9.8 Scenario I. RO plant (0% Granted)

e) in both facilities. It was done through the deployment of 3 scenarios: 0, 50, and 100% of conceded grant, and varying the Ieff of the non-granted amount between 3.5% and 24%. As from their analysis can be observed that: First, in scenario (I) (Figs. 9.8 and 9.11) that no grant was conceded, the RO plant reached the economic objective only for a very low Ieff. This happened only when W&E sales increased over the 140% of capacity. Despite the fact that it started with 1400 ke granted, the distillation plant did not achieve the economic KPI objective of 200,000 e of net utility. Secondly, scenario (II) (Figs. 9.9 and 9.12) analyzed the influence of half grant on the net utility of the plants. As can be seen, only the RO plant achieved the net utility KPI stated objective when W&E sales increased. In the light of the results the RO-plant would be able to provide an Ieff of 20% when 200% of W&E were sold. On the other hand, the distillation plant did not reach the objective (Fig. 9.12).

200 000 €

Fig. 9.9 Scenario II. RO plant (50% Granted)

(%) Ieff

9 Feasibility Analysis of Wind and Solar Powered Desalination …

3.5 8.5 13.5 18.5 24

170

200 000 €

Fig. 9.10 Scenario III. RO plant (100% Granted)

Finally, scenario (III) (Figs. 9.10 and 9.13) compared both facilities with similar amounts of conceded grants. As can be observed the RO plant would provide benefits from 3.5 to 24% of Ieff, as W&E sales increased. This was the only scenario where the distillation plant reached the economic objective (Fig. 9.13), but only for 200% of increasing sales, and for specific conditions of low Ieff. Among the cases that reached the economic objective, in scenario I (Figs. 9.8 and 9.11), the RO plant achieved it only for a very low Ieff, and only when W&E sales increased over 140% of capacity. Note that the RO plant simulation showed a near-zero battery deployment, and low electricity costs results, meaning that the plant operated at 35% of its nominal capacity (see capacity factor of wind energy). As can be seen in the scenario (II) (Figs. 9.9 and 9.12), only the RO plant achieved the objective from 3.5 up to 20%, as W&E sales increased. Only in the scenario (III) the distillation plant reached the economic objective (Fig. 9.13), but only for 200% of

200 000 €

Fig. 9.11 Scenario I. aqua.abib project (0% Granted)

9.4 Results and Discussion

171

200 000 €

Fig. 9.12 Scenario II. aqua.abib project (50% Granted)

200 000 €

Fig. 9.13 Scenario III. aqua.abib project (100% Granted)

increasing sales, although it must be remarked that due to its working configuration this plant could not increase its capacity of selling water, so in essence it probably will not be achieved. As from these results, it did not advise the investment in the Distillation Plant due to its higher investment needs as well as its lower business capacity related to W&E sales. Although, for a scenario of low W&E demand, the difference among the profitability of these two facilities came near. Despite the better performance of the RO plant, both projects needed help to pay the loans to capitalize the project, if being attractive for private investment was expected. That the facility reached the objective for particular conditions might

172

9 Feasibility Analysis of Wind and Solar Powered Desalination …

imply for policy makers that a specific water-energy deployment would let those facilities reach benefits allowing extra electricity sales. Analyzing data of the RO plant performance it is highlighted that the smaller the size of the RO desalination plant the lower the competitiveness of its performance, and precisely it proportionally happened to the distillation plant that additionally would imply benefits to the environment. Precisely, in the region of study it can be found the Barranco de Tirajana thermal power station [57] that consumed 233 713.47 tons of fuel oil, and 278,341.4 of non-automotive gas-oil in 2018 (last year of available data). For what GHG emission was concerned, it stressed: the methane, with total emissions of 16.82 Ton/year; CO: 224.341 tons/year, NOx emissions of 2112.28 tons, and CO2 , with 1 620 654 tons/year, among others. The reduction of GHG emissions that this scheme achieved, accounted as 0.521 kg CO2 /kWh (see Appendix D) was of 3651 tons/year for a 100% scenario of the RO plant, under the proposed objective, that only could be achieved when more electricity generation from this scheme were sold. Although the activity generated incomes, most scenarios showed that these did not let recover their investment costs in a reasonable period of time for the aqua.abib project while providing profitability enough to avoid financing deficit, fact that force to grant public aid, or increase W&E sales in these facilities. In some of them, loans ought to be returned in less than 10 years. For a grant scenario that the RO plant perceived half grant (1.25 million e), results demonstrated that was able to achieve a discounted payback period of 10.26 years versus the aqua.abib project, that granted (1.4 million e) showed 33.74 at the same Ieff (7.5%) (Table 9.2). Scenarios showed that the profitability could be achieved increasing the amount of demanded water, that was highly interconnected to activities such as cattle farming, agriculture, and others that eventually might demand increasing water, and therefore the economic activity itself. Market should be analyzed in order to know if proposed scenarios were capable of absorbing such an amount of resource, specially in sectors like food industry, and energy management. These would provide resources to the market for society to grow and thrive, incomes that were not accounted in the economic balance. Provided that the price of electricity generation from conventional sources in areas, like in islands, is higher, the gap between the costs of the deployment of these disruptive technologies and the traditional ones may be closed up with lesser economic impact. As Soshinskaya et al. [58] indicated, costs of energy electricity (LCOE) vary within the year, and the region where the facility is located. Provided that this chapter assumed a year for the period calculation and the Canaria region for the calculus, in essence the market that is acting in might differ from others with different RES characteristics. In addition, other non-accounted costs are the hidden costs of these projects highlighting: water impacts, land impacts, and GHG emissions costs associated with the deployment of different energy strategies. Similarly, NREL analyzed [9] the real competitiveness of hybrid powered RO desalination plants, predicting a COW around 1 e/m3 for a 1.5 MW powered seawater desalination plant, in a grid-isolated topology, and a cost of 0.62 e/m3 for a gridconnected topology with energy prices of 0.11 e/kWh. The developed analysis showed a lower COW up to 12% of Ieff, but only when half grant was conceded,

9.4 Results and Discussion

173

a scheme that might lower the energy prices while providing benefits to investors. Eltawil et al. [25] found a COW between 0.66 and 0.75 e/m3 for RO desalination plants of similar capacity (2843–3720 m3 /day). Results demonstrated that even for high Ieff, the economic, and KPI COW reduction objective might also be achieved, although it must be noted [59] that water desalination depends on parameters such as salinity, or conductivity that could affect these costs as well. In comparison, the COW in the aqua.abib project was proven to be much higher, despite that it was calculated for 100% of plant capacity. Globally, in Gran Canaria only 11% of EEG comes from renewable sources yet [60], and that an optimal energy mix with a renewable energy penetration of 65% would be obtained without jeopardising grid reliability [61]. Despite the different costs, both schemes (it is expected to range between 750 kW and 5.0 MW) can be widely used to reach the 1.2 GW installed power from renewable energy sources needed to reach the optimal energy mix of this island while providing water at a lower cost. This chapter highlighted the analysis of the economic objective in an investment decision, but variable weights could be given to each KPI (or others) of Table 9.2, depending on the demanded objectives. Notice that the profitability of the facility derived fundamentally from electricity sales, although it is important to note that due to the isolated nature, and the small size of the system [62] might occur problems in the operation of the system, specially to maintain the necessary reliability indices (SAIFI, SAIDI, CAIDI and ASAI), although, specially for these facilities there is also the possibility of storing the energy surpluses. The energy electricity prices can be lowered from the current price to a considerably lower amount (note that the cost of EEG in the rest of Spain is 59 e/MWh) [63]. As long as more business opportunities related to these facilities are to come, specially linked to the electrification of the economy. For example, Dalton et al. [64] presented Blue Growth, new related investment opportunities that would increase the profitability as further stages and activities that improve and commercialize the innovation appear. At this point, it would be expected that grants could be progressively retired. Provided that generating electricity in non-mainland regions—specifically in Spain—is a global “burden” for the economy that represents an extra cost in the Spanish case of e900 million per year [63], increasing the deployment of those systems will not lower the global competitiveness of the sector. Due to its outstanding wind resource and to the possibility of obtaining second-to-none capacity factors, (the highest wind capacity factor ever reported, a 52%, corresponded to a wind farm located in Gran Canaria) [65, 66] it is expected that those projects has as an outcome in the production of electricity from wind at a cost of roughly 7 coeur per kWh [67]; and a LCOE for the whole scheme of 10 coeur per kWh—producing electricity in Gran Canaria has a cost of 190.7 e/MWh [68]. The analysis of the competitiveness of facilities here developed offered a step forward in order to clarify the integration of combined-businesses models of renewable energy deployment. Results of the research conducted in this chapter would allow decision makers (politics/ investors) take a deeper view of the performance of facilities offering an extra decision point specially when interlinked with water systems.

174

9 Feasibility Analysis of Wind and Solar Powered Desalination …

9.5 Conclusions In the years to come, as water scarcity increases, water supply will become a critical issue, so research such as the one presented here are of high importance to investigate how a sustainable way to provide water-energy might become economically attractive for investors not only in regions threatened with droughts, as the MENA region, but also others. This chapter provided a better understanding of the issue that the commercialization of these disruptive technologies involved, and demonstrated that despite that both schemes fulfilled the demanded characteristics of the grants, different economic scenarios appeared, although they were conditioned to the market conditions, and still to economic benefits from public institutions. In conclusion, apart from the cost-effective scenario that paves the way for an economically-efficient investment, this chapter contributes by proposing the use of an optimal renewable energy mix to conduct the desalination needed to provide fresh water to orchards located in arid zones. Moreover, the possibility of selling surplus energy to the electric grid is suggested to allow the orchard’s owner to achieve possible profitability. Additionally, this chapter demonstrated that the profitability of this scheme can be scheduled only letting more / less sales from this scheme, assessing a novel water operation strategy to take full advantage of the potential that can be reached from its use. As have been demonstrated, some scenarios achieved economic objectives, especially under conditions of more energy sales. Besides, the LCOE was found lower than the mean of EEG costs in Gran Canaria. Among the expected impacts of this scheme are some remarkable social-related effects. Firstly, important and improved availability, acceptance and use by local communities of innovative models of water management in difficult climate conditions and arid areas can be enabled. Secondly, reduced pressure on the environment, improved ecosystem services, water yields and sustainable food production are predictable. Thirdly, an enhanced innovation in water-energy nexus can be gotten, particularly supporting the sustainable use of water with less and affordable energy cost. This initiative can increase the water management capacity of local communities in rural, remote and arid areas. Additionally, the development of effective models of knowledge transfer in sustainable water management is reached. And, finally, an increase of socio-economic growth and stability of targeted areas could be attained. In overall terms, it was demonstrated that water costs can be reduced as well. Parallel, the benefits on the environment boost the green economy that will imply social benefits as well. The economic surveillance of these projects enables the penetration of renewable energies into the electricity network of Canary Islands to reach quotas that vary between 40 and 60%, depending on the amount of sold electricity, while a reduction of GHG emissions was achieved. Specifically, this chapter found a reduction of GHG emissions of 0.22% of the annual equivalent emissions of Tirajana Thermal Power Station where it was located. In the investment decisionmaking problem of disruptive technologies projects, grants resulted indispensable to develop those business-combined models.

References

175

References 1. United Nations (2020) Sustainable Development Goals, https://www.un.org/sustainabledeve lopment/sustainable-development-goals/20200. Accessed 20 Jan 2020 2. Treaty of Lisbon amending the Treaty on European Union and the Treaty establishing the European Community, signed at Lisbon, 13 December 2007. European Union 2007 3. International Energy Agency (IEA) (2018) World Energy Outlook 4. Strategic Energy Technologies Information System (2018) The Strategic Energy Technology (SET) Plan. At the heart of Energy Research & Innovation in Europe, https://setis.ec.europa.eu/ strategic-energy-technology-set-plan-heart-of-energy-research-innovation-europe. Accessed 9 June 2020 5. Directive 2012/27/EU of the European Parliament and of the Council on Energy Efficiency Amending Directives 2009/125/EC and 2010/30/EU and Repealing Directives 2004/8/EC and 2006/32/EC. Official Journal of the European Union 14/11/2012; OJ L315:1, 25 Oct 2012 6. Communication from the commission to the European Parliament, the Council, the European Economic and Social Committee, the Committee of the Regions and the European Investment Bank. Clean Energy for all Europeans. COM/2016/0860 final, 2016 7. Directive (EU) 2018/2001 of the European Parliament and of the Council on the promotion of the use of energy from renewable sources, 11 Dec 2018 8. Van der Brugger B, Vandecasteele C, Van Gestel T, Doyen W, Leysen R (2004) A review of pressure-driven membrane processes in wastewater treatment and drinking water. Environ Prog 22(1):46e56 9. National Renewable Energy Laboratory (2005) Integrated Wind Energy/Desalination Systems, https://www.nrel.gov/docs/fy07osti/39485.pdf. Accessed 10 Nov 2019 10. La Provincia (2019) Canarias y el agua: referente mundial, https://www.laprovincia.es/provin cia-especiales/2018/05/30/canarias-agua-referente-mundial/1063344.html. Accessed 20 Nov 2019 11. Ghalavand Y, Hatamipour, M, Rahimi A (2014) A review on energy consumption of desalination processes. Desalin Water Treatm 54. https://doi.org/10.1080/19443994.2014. 892837 12. Rosales-Asensio E, Borge-Diez D, Pérez-Hoyos A, Colmenar-Santos A (2019) Reduction of water cost for an existing wind-energy-based desalination scheme: a preliminary configuration. Energy 167:548–560. https://doi.org/10.1016/j.energy.2018.11.004 13. Lozano-Ruano JB, Soslaires Canarias SL (2019) http://www.islhagua.itccanarias.org/c/doc ument_library/get_file?p_l_id=22263&folderId=22087&name=DLFE-925.pdf. Accessed 15 Dec 2019 14. Aqua.abib (2020) The whole distillation system, https://www.aqua-abib.com/value-2/. Accessed 15 May 2020 15. Spanish Power Transmission Company (2018) Report on the Spanish electricity system, https:// www.ree.es/es/datos/publicaciones/series-estadisticas-nacionales. Accessed 20 Oct 19 16. González J, Cabrera P, Carta JA (2019) Wind energy powered desalination systems. Desalin. Water from water, 2nd edn. Wiley, New York 17. Colmenar-Santos A, Borge-Diez D, Rosales-Asensio E, López-Rey Á (2017) Strategies and regulations for renewable energy development in Islands. Energy Plan: Approach Assessm 131–166 18. Rukh S Shah, Takala J, Zhu L (2017) Commercialization of renewable energy technologies: a ladder building approach. Renew Sustain Energy Rev 78:855–867. https://doi.org/10.1016/j. rser.2017.05.005 19. Institute for the Energy Diversification and Saving (IDAE) (2019) Investment aids for energy electricity power stations from wind and PV solar technologies located in non-mainland territories, and co-financed by Community funds from the European Regional Development Fund, https://www.idae.es/en/node/13481. Accessed 15 Nov 2019

176

9 Feasibility Analysis of Wind and Solar Powered Desalination …

20. IET order 1045/2014 approval of retributive parameters for EEG facilities from renewable energy sources. Official Bulletin of the Spanish State No. 150, BOE-A-2014-6495, 26 Dec 2014 21. Royal Decree 413/2014, regulating the activity for generating electricity from renewable energy sources co-generation, and waste plants. Official Bulletin of the Spanish State No. 140. BOEA-2014-6123, 6 June 2014 22. Royal Decree 738/2015 regulating the activity for generating energy electricity and clearance proceeding in non-mainland electric systems. Official Bulletin of the Spanish State No. 183. BOE-A-2015-8646, 31 June 2015 23. European Commission (2019) H2020 funding guide, https://ec.europa.eu/research/partic ipants/docs/h2020-funding-guide/cross-cutting-issues/sme_en.htm#phase2. Accessed 10 Dec 2019 24. Khan Meer AM, Rehman S, Al-Sulaiman Fahad A (2018) A hybrid renewable energy system as a potential source for water desalination using reverse osmosis: a review. Renew Sustain Energy Rev 97:456–477. https://doi.org/10.1016/j.rser.2017.05.005 25. Eltawil MA, Zhengming Z, Yuan L (2009) A review of renewable energy technologies integrated with desalination plants. Renew Sustain Energy Rev 13:2245–2262. https://doi.org/10. 1016/j.rser.2009.06.011 26. Javed SJ, Song MA, Ma T (2019) Techno-economic assessment of a stand-alone hybrid solarwind-battery system for a remote island using genetic algorithms. Energy 176:704–717. https:// doi.org/10.1016/j.energy.2019.03.131 27. Shallenberg J, Veza JM, Blanco A (2014) Energy efficiency and desalination in Canary Islands. Renew Sustain Energy Rev 40:741–748. https://doi.org/10.1016/j.rser.2014.07.213 28. Cabrera P, Lund H, Carta JA (2018) Smart renewable energy penetration strategies on islands: the case of Gran Canaria. Energy 162:421–443 29. Canary Islands Institute of Statistics (2020) Statistical Series, http://www.gobiernodecanarias. org/istac/. Accessed 27 Sept 2020 30. Santamarta JC, Rodríguez-Martín J (2013) Introduction to water problems in Canary Islands. In: Environmental security, geological hazards and management. https://doi.org/10.13140/RG. 2.1.4474.0729 31. Gómez-Gotor A, Del Río-Gamero B, Prieto Prado I, Casañas A (2018) The history of desalination in the Canary Islands. Desalination 428:86–107. https://doi.org/10.1016/j.desal.2017. 10.051 32. Nuez I, Osorio J (2019) Calculation of tourist sector electricity consumption and its cost in subsidised insular electrical systems: the case of the Canary Islands, Spain. Energy Policy 132:839–853. https://doi.org/10.1016/j.enpol.2019.06.032 33. Canary Islands Government (2018) Anuario Energético Canarias, http://www.gobiernod ecanarias.org/istac/jaxi-istac/menu.do?uripub=urn:uuid:131cf873-66a9-408d-8cfa-537d6b e05067. Accessed 5 Dec 2019 34. European Commission (2020) Turnover taxes in the Canary Islands. Available https://ec.eur opa.eu/taxation_customs/business/other-taxes/turnover-taxes-canary-islands_en. Accessed 27 Sept 2020 35. European Commission (2020) EUR-Lex. Decision 377/2014/EU. Available https://eurlex.europa.eu/legal-content/EN/TXT/?qid=1436779587434&uri=CELEX%3A32014D0377. Accessed 27 Sept 2020 36. El Diario (2019) Centrales térmicas contaminantes, https://www.eldiario.es/canariasahora/soc iedad/centrales-termicas-canarias-contaminantes-Espana_0_885412500.html. Accessed 12 Sept 2019 37. Land Registry website (2019) https://www1.sedecatastro.gob.es/. Accessed 25 Sept 2019 38. Desalinationplant (2018) https://github.com/SanPen/DesalinationPlant. Accessed 25 July 2019 39. Colmenar-Santos A, Vera SP, Asensio ER (2018) Sizing of wind, solar and storage facilities associated to a desalination plant using stochastic optimization. Adv Intell Syst Comput 661:172–183. https://doi.org/10.1007/978-3-319-67618-0_16

References

177

40. Im J, Park J (2013) Stochastic structural optimization using particle swarm optimization, surrogate models and Bayesian statistics. Chin J Aeronaut 26(1):112–121 41. Rosales-Asensio E, García-Moya FJ, González-Martínez A, Borge-Diaz D, de Simón-Martín M (2019) Stress mitigation of conventional water resources in water-scarce areas through the use of renewable energy powered desalination plants: an application to the Canary Islands. Energy Rep 6:124–135. https://doi.org/10.1016/j.egyr.2019.10.031 42. Stehly T, Heimiller D, Scott G (2017) Cost of wind energy review. National Renewable Energy Laboratory, Golden 43. Fu R, Feldman R, Margolis R, Woodhouse M, Ardani K (2017) U.S. solar photovoltaic system cost benchmark: Q12017. National Renewable Energy Laboratory, Golden 44. International Renewable Energy Agency (IRENA) (2018) Renewable Power Generation Costs in 2017. IRENA. https://doi.org/10.1007/SpringerReference_7300 45. Arenas-Urrea S, Díaz-Reyes F, Peñate-Suárez B, de la Fuente-Bencomo JA (2019) Technical review, evaluation and efficiency of energy recovery devices installed in the Canary Islands desalination plants. Desalination 450:54–63. https://doi.org/10.1016/j.desal.2018.07.013 46. European Commission (2020) Investment Project EIPP-20170333, https://ec.europa.eu/eipp/ desktop/en/projects/project-8281.html. Accessed 26 Apr 2020 47. Aqua.abib (2020) SunAqua 18 Project, https://www.aqua-abib.com/technology-2/. Accessed 15 Apr 2020 48. Baca Urbina G (2015) Projects evaluation, 7th edn. McGraw Hill 49. Gökςek M, Gökςek ÖB (2016) Technical and economic evaluation of fresh water production from a wind-powered small scale seawater reverse osmosis system. Desalination 381:47–57. https://doi.org/10.1016/j.desal.2015.12.004 50. Feo García J, Fecha Sadhwani AJ, Álvarez Álvarez J (2013) Costs optimization in production lines for reverse osmosis desalination plants in the Canary Islands [Thesis Doctoral]. Las Palmas University 51. Karunathilake H, Hewage k, Prabatha T, Ruparathana R, Sadiq R (2020) Project deployment strategies for community renewable energy: a dynamic multi-period planning approach. Renew Energy 152:237–258. https://doi.org/10.1016/j.desal.2015.12.004 52. Palacio-Morales JA, Isaza-Hurtado JA, Tobón-Mejía AF, Herrera-Cuartas JA (2019) Methodology for NMPC tuning with Takagi Sugeno fuzzy inference system and multidimensional fuzzy sets for applications in non-linear chemical processes. Revista Iberoamericana de Automática e Informática Industrial 16(1):100–113. https://doi.org/10.4995/riai.2018.9773 53. Tourism board of Gran Canaria Island Council of Gran Canaria (2018) Tourism Statistics, http://www.grancanaria.com/turismo/es/area-profesional/informes-y-estadisticas/estadi sticas/. Accessed 8 Aug 2019 54. EurobservER (2017) Photovoltaic Barometer, https://www.eurobserv-er.org/photovoltaic-bar ometer-2017/. Accessed 9 Nov 2019 55. Canary Islands Government (2019) The Canary Islands Business Territorial System—IDECanarias, https://visor.grafcan.es/visorweb/default.php?svc=svcRecursoEolico. Accessed 25 Oct 2019 56. Canary Islands Government (2019) The Canary Islands Business Territorial System—IDECanarias, https://visor.grafcan.es/visorweb/default.php?svc=svcRecursoFotovoltaico. Accessed 25 Oct 2019 57. Spanish Ministry of Environment (2020) Spanish Register of Emissions and Pollutant Sources, PRTR-España: Complex Sheet, http://www.prtr-es.es/informes/fichacomplejo.aspx?Id_Com plejo=1795. Accessed 9 Jan 2020 58. Soshinskaya M, Crijns-Graus Wina HJ, Van der Meer J, Guerrero JM (2014) Application of a microgrid with renewables for a wáter treatment plant. Appl Energy 134:20–34. https://doi. org/10.1016/j.apenergy.2014.07.097 59. Hispagua (2007) RO seawater desalination energy consumption: current situation and outlook, http://hispagua.cedex.es/sites/default/files/hispagua_articulo/Ingcivil/2007_148_ 113.pdf. Accessed 13 Nov 2019

178

9 Feasibility Analysis of Wind and Solar Powered Desalination …

60. El Diario (2019) La luz de las renovables tardará en alumbrar en Canarias, http://www.eld iario.es/canariasahora/premium_en_abierto/luz-renovables-tardara-alumbrar-Canarias_0_4 52154846.html. Accessed 1 Mar 2020 61. Wind Energy and Electric Vehicles Magazine (WEEV) (2013) A report confirms that the highest possible renewable energies penetration will allow to create new job opportunities in Gran Canaria, http://www.evwind.com/2013/01/03/un-informe-confirma-que-la-maxima-pen etracion-de-las-energias-renovables-al-minimo-coste-permitira-crear-empleo-en-canarias/. Accessed 1 Mar 2020 62. Uche-Soria M, Rodriguez MC (2018) Special regulation of isolated power systems: the Canary Islands, Spain. Sustainability 10(7):2572. https://doi.org/10.1016/j.rser.2018.02.007 63. Spanish Ministry of Environment (2019) Motion for resolution of the Directorate General for Energy Policy and Mines that approves the level of generation costs settlement of the facilities under the specific remuneration regime, generation costs settlement of the facilities under the additional remuneration regime, https://www.boe.es/diario_boe/txt.php?id=BOE-B-201934033. Accessed 4 Apr 2020 64. Dalton G, Bardócz T, Blach M, Campbell D, Lawrence G et al (2019) Feasibility of investment in Blue Growth multiple-use of space and multi-use platform projects; results of a novel assessment approach and case studies. Renew Energy Rev 107:338–359. https://doi.org/10.1016/j. rser.2019.01.060 65. Adwen (2015) Adwen´s 5MW wind turbine reaches a yearly output of 22,8 GWh, https://w3. windfair.net/wind-energy/news/18885-adwen-s-5-mw-wind-turbine-reaches-a-yearly-outputof-22-8-gwh. Accessed 1 Mar 2020 66. La Provincia (2011) Canarias aspira a ser un 80% renovable, http://www.laprovincia.es/soc iedad/2011/06/22/canarias-aspira-80-renovable/382041.html. Accessed 1 Mar 2020 67. International Energy Agency (IEA) (2012) IEA Wind 2012 Annual Report, https:// www.ieawind.org/annual_reports_PDF/2012/2012%20IEA%20Wind%20AR_smallPDF. pdf. Accessed 3 Mar 2020 68. Official Bulletin of the Canary Islands (BOC) (2008) 20 February order: approving the changes in the charges for the provision of water supply at the request of the undertaking Canaragua S.A. for its implementation in the municipal district of Santa Lucia (Gran Canaria).- Expte. P 17/2007-LP, http://www.gobiernodecanarias.org/boc/2008/047/004.html. Accessed 1 Mar 2020

Appendix A

Review of Evaluated Nexus Tools

See Tables A.1a and A.1b.

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 E. Rosales-Asensio et al., Sea Water Desalination in Microgrids, Green Energy and Technology, https://doi.org/10.1007/978-3-030-96678-2

179

Large amount of data required Technical and economic parameters of thermal power plants, agricultural machinery, water supply chain, desalination terminals, fertilizer production, etc.

Local information and features of WLES Local production of WEF (by technology) Policy data taking into account particular circumstances

WEF 2.0: guiding integrative resource planning and decision-making [2]

1(a) Fundamental entries

Standard of review

CLEWs [1]

Tool and reference

Consequences of food production on the commercialization of energy Energy used in water (pumping, treatment, desalination) Energy used for food (tillage, fertilizer production, distribution and harvesting)

Energy balance, including electricity generation and refining Energy for food Foreign energy (virtual)

2(a) Energy

Table A.1 (a) Review of evaluated nexus tools

Consequences of nutritious substances manufacturing on a restricted particular area

Water balance Water supply and desalination Water Pumping W→F W→F (hydroelectric power, generating station refrigeration, fuel derived immediately from living matter cultivated plants)

2(b) Water

Degree of regional production of various kind of nutritious substances

Irrigation Technologies Use of fertilizers Use of agricultural machinery

2(c) Food

Selected economic indicators

2(e) Economy

(continued)

Consequences of Expenses of nutritious substances nutritious substances manufactirung on manufacturing GHG discharges

Restricted to both a particular area and foreign emissions Accumulated GHG discharges

2(d) Greenhouse discharges

180 Appendix A: Review of Evaluated Nexus Tools

Standard of review

Requires a large amount of data Techno-economic information on energy equipment

Evaluation of the country studied in order to classify it by typology

Tool and reference

SEI (Stockholm Environment Institute) Modeling water and energy [3]

Food and Agriculture Organization of the United Nations tool [4]

Table A.1 (continued)

Specific to each type of intervention

Area or region / drained by a river, river system, or other body of water devising Simulation of water requirements and provisions Water held underground in the soil or in pores and crevices in rock evaluation Water characteristics evaluation Storage and hydroelectricity evaluation

Specific to each Specific to each type type of intervention of intervention

Comprehensive examination of ED and its transformations EB

/

GHG discharges from the energy sector

(continued)

Specific to each type of intervention

Includes a financial module

Appendix A: Review of Evaluated Nexus Tools 181

Standard of review

Description of the distinctive nature or features of the energy sector Diagrammatic representations of areas of land based on GIS Characterization of the water needed for FE Data on the required workforce in addition to machine’s availability

Requires a large amount of data Socio-economic indicators, including workforce evolution Land Availability Climate change impact assessment

Tool and reference

WBCSD nexus tool [5]

MuSIASEM [6]

Table A.1 (continued)

Assessment of energy flows in society

Energy needed for water Energy needed for food (for irrigation, fertilizer manufacturing or devices for performing work)

Assessment of water flows in society

Water needed for power generation Water needed for food production

/

Assessment of food Implications of all flows in society flows on emissions

Food production

(continued)

Added costs and values

/

182 Appendix A: Review of Evaluated Nexus Tools

Complete set of data needed to characterize local supply of water to land or crops to help growth and hydroelectric power entreprises

Requires a large amount of data Techno-economic information on energy technologies Characterization of the RENS

Diagnostic Tools for Investment (DTI) in water for agriculture and energy [7]

MARKAL/TIMES [8–21]

Source Own elaboration

Standard of review

Tool and reference

Table A.1 (continued) Water administration Use of water for (i) farming, including cultivation of the soil for the growing of crops and the rearing of animals to provide food, wool, and other products; and (ii) to the generation of power derived from the utilization of physical or chemical resources

Energy outlining Water use in the with a large degree energy sector of technical specific aspects EB Effectiveness of energy policy

Effect of hydroelectric power entreprises on bettering quality of life Percentage of people in a given area that have relatively simple, stable access to electricity

/

Availability of food and individuals’ accessibility to it, where accessibility includes affordability and agricultural manufacturing

Emissions from the energy sector

Effect of supply of water to land or crops to help growth, typically by means of channels and hydroelectric power on GHG discharges

Overall expenses of the ES, containing the necessary water provision

Agriculture’s contribution to gross domestic product and profit production Expenditure requirements Effect of supply of water to land or crops to help growth enterprises on improving local quality of life

Appendix A: Review of Evaluated Nexus Tools 183

It is possible that some developer works on the tool

Food and Agriculture Organization of the United Nations tool [4]

Specific to each type of intervention

It is possible that some developer works on the tool Without charge for developing nations

SEI (Stockholm / Environment Institute) Modeling water & energy [3]

It is conceivable that some developer works on the tool

Land for food

WEF 2.0: guiding integrative resource planning and decision-making [2]

3(a) Accessibility

Biofuel crops It is possible that some Types of land according to developer works on the the circumstances tool

2(f) Earth

Standard of review

CLEWs [1]

Tool and reference

Table A.1 (b) Review of evaluated nexus tools

National Subnational

National Global

National

National Global

3(b) National geographical level

Through the use of different typologies can be used in different geographies

Can be used to different geographies

Can be applied to dissimilar geographies

It might be applied to dissimilar geographies. However, it is resource-intensive

3(c) Applicable to dissimilar geographies

(continued)

A quick assessment of the nexus is uncomplicated and depends on the ready for use indexes The adoption of nation categorization and the proposition indexes for every kind of intervention facilitates their use

/

Simple reference frame Includes policies of importance for a sustainability index

/

3(d) Uncomplicated although capable of providing a preliminary assessment, including explicit policy entries

184 Appendix A: Review of Evaluated Nexus Tools

Land use

Land use

Cultivated land

/

WBCSD nexus tool [5]

MuSIASEM [6]

Diagnostic Tools for Investment (DTI) in water for agriculture and energy [7]

MARKAL/TIMES [8–21]

Source Own elaboration

Standard of review

Tool and reference

Table A.1 (continued)

Applicable to any country

It is possible that some developer works on the tool It might be used to dissimilar nations

It is possible that some developer works on the tool

It is possible that some developer works on the tool

National Global Regional Local

National

National

National Global Regional Local

It can be used to dissimilar geographies. However, it is resource-intensive

It might be applied to dissimilar geographies. However, it is resource-intensive

It might be applied to dissimilar geographies. However, it is resource-intensive

/

/

/

Can be used to dissimilar / geographies

Appendix A: Review of Evaluated Nexus Tools 185

Appendix B

Desalination Capacity, Technologies, and Location in Gran Canaria

See Tables B.1 and B.2.

Table B.1 Installed capacity in Gran Canaria, and characteristics of desalination technologies [22] Technology

Power consumption

Brine production

SHAMS-Titanium MED desalination

Low

AD and MED + AD TEchnology

Inner area use

Investment cost (m3 )

Operational costs

Yes

No

High

Moderate

Low

Yes

No

High

Moderate

TNO Netherlands (Kuipers et al.)

Low

Yes

No

High

Moderate

Dutch Pyramic/Seawater seawater Greenhouse

Low

Yes

Yes

High

Moderate

Aqua. abib water solutions

Low

No

Yes

High

Moderate

Multi stage flash

High

Yes

No

Moderate

Low

Multi effect distillation

High

Yes

No

Moderate

Low

Thermal vapour High compression MED

Yes

No

Low

Low

Mechanical vapour High compression

Yes

No

Low

Low

Reverse osmosis

Yes

No

Moderate

Moderate

High

Installed capacity (m3 /day)

38,000

5200

94,800

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 E. Rosales-Asensio et al., Sea Water Desalination in Microgrids, Green Energy and Technology, https://doi.org/10.1007/978-3-030-96678-2

187

188

Appendix B: Desalination Capacity, Technologies, and Location in Gran Canaria

Table B.2 Locations of the desalination installed capacity in Gran Canaria

Desalination Plant

Location

System

m3 /day

Gáldar-Agaete I

Gáldar

OI

3000

Agragua

Gáldar

OI

10,000

Guía I

Guía

VC

1500

Arucas-Moya I

Arucas

OI

4000

Granja Ag. Experimental

Arucas

CV

500

Las Palmas I

Jinámar

MEF

20,000

Las Palmas II

Jinámar

MEF

18,000

Las Palmas III

Jinámar

OI

36,000

Unelco

Jinámar PS

CV

1000

Salinetas, S.A

Salinetas

OI

600

Airport I

Airport G.C

OI

1000

Airport II

Airport

OI

500

Mando Aéreo de Canarias

Gando

OI

1000

Sureste I

Santa Lucía

OI

10,000

Bonny

Juan Grande

OI

8000

Elmasa II

Las Burras

OI

7500

Elmasa III

Las Burras

OI

7500

Unelco

Bco. Tirajana

CV

500

Anfi del Mar

Bco. La Verga

OI

200

Puerto Rico I

Puerto Rico

CV

1200

Puerto Rico II

Puerto Rico

CV

1000

Coagrisan

Bco. La Aldea

OI

5000

Source [22]

Appendix C

Restrictions and Results of the Geo-morphological Analysis

See Tables C.1 and C.2.

Table C.1 GIS restrictions for a short-term full renewable-PHS EP strategy Restriction proposed

Reason

Restriction imposed

WB in Gran Canaria

Storage capacity

Most representative WB of Gran Canaria

Distance to desalination plants

Problems. Load losses, crossing roads, and properties

10 km at the most

Proximity Thermal power stations as restriction

Despite it can be transported, the grid is better prepared in that region

Distance is fixed in 20 km, because electricity can be transported through the grid

Proximity to grid

Works to develop Grid

10 km

Amount of energy to be stored

To easily schedule the operation of the PHS scheme

The scheme must provide at least 2 h of uninterrupted operation at maximum capacity

Law restriction

In order to fulfil the requirements of laws relating to: spatial planning and the environment, air safety

Fulfil laws that might provoke disputes related to the deployment of the wind park and the PHS system

Proximity to densely populated areas

Difficulty for developing the scheme in a short period of time

Path

Difficulty for developing the Cannot contain peaks higher scheme in a short period of time than the starting height, neither cross populated areas

Source Own elaboration

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 E. Rosales-Asensio et al., Sea Water Desalination in Microgrids, Green Energy and Technology, https://doi.org/10.1007/978-3-030-96678-2

189

190

Appendix C: Restrictions and Results of the Geo-morphological Analysis

Table C.2 Results of the geomorphological analysis

Geographic Coordinates

Profile Tool

Energy (MWh)

437,058.3 3,087,042.37

232.75

434,468.76 3,087,023.62

147

436,249.07 3,077,169.51

53.9

436,891.72 3,072,192.99

44.1

437,135.89 3,077,624.02

53.9

436,906.09 3,086,941.75

208.25

437,377.2 3,115,500.81

22.05

436,780.24 3,114,874.68

29.4

436,340.58 307,325,654

29.4

436,253.31 3,077,209.02

98

436,096.48 3,111,267.15

49

435,994.63 3,086,900.52

49

435,972.44 3,073,649.11

49

435,994.53 3,086,883.19

232.75

435,034.87 3,104,167.94

98

434,487.99 3,072,114.42

29.4

434,544.43 3,087,059.04

122.5

(continued)

Appendix C: Restrictions and Results of the Geo-morphological Analysis Table C.2 (continued)

Geographic Coordinates

Profile Tool

191 Energy (MWh)

434,524.22 310,896.46

98

433,570.51 3,113,684.32

24.5

433,094.42 3,098,816.71

24.5

431,390.95 3,081,329.34

98

431,339.2 3,096,481.69

49

430,110.42 3,094,797.34

91.875

428,348.42 3,095,300.53

61.25

459,344.57 3,091,543.76

31.85

454,762.63 3,083,927.05

24.5

451,910.28 3,104,898.76

66.15

451,925.62 3,105,303

66

451,622.32 3,105,641.37

53.9

451,640.12 3,103,133.09

98

451,352.61 3,103,470.95

110.25

451,055.21 3,102,963.45

147

45,109,635 3,105,445.3

73.5

(continued)

192 Table C.2 (continued)

Appendix C: Restrictions and Results of the Geo-morphological Analysis Geographic Coordinates

Profile Tool

Energy (MWh)

449,591.06 3,074,889.37

22.05

449,707.91 3,107,718.15

53.9

449,475.63 3,107,522.01

66.15

449,372.69 3,106,512.67

98

449,303.65 3,106,290.65

73.5

449,303.65 3,106,290.65

68.6

446,270.55 3,111,028.68

46.55

444,418.76 3,097,757.1

24.5

444,167.81 3,083,682.02

100.45

442,797.43 3,090,237.22

24.5

441,631.91 3,073,386.37

24.5

441,498.29 3,093,332.3

39.2

Total energy (MWh)

3076.22

Source Own elaboration

Appendix D

Impacts of Energy Sources, Competitiveness of Food Sector

See Tables D.1, D.2 and D.3.

Table D.1 Energy sources on emissions

Technology

Land impacts

Biomass

T/MWh

CO2 emissions

1.5

Coal

T/MWh

CO2 emissions SO2 Emissions NOx PM

1.01 0.0005–0.014 0.0003–0.003 0.0001–0.003

Nuclear

T/MWh

GHG (CO2 -eq)

0.0037–0.1

Natural gas

T/MWh

CO2 from CCCTs CO2 from CTs

0.35–0.4 0.55–0.68

Solar

T/MWh

CO2 equivalent emissions

0.02–0.06

Wind

T/MWh

CO2 equivalent emissions

0.014

Source Adapted from [23–29]

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 E. Rosales-Asensio et al., Sea Water Desalination in Microgrids, Green Energy and Technology, https://doi.org/10.1007/978-3-030-96678-2

193

194

Appendix D: Impacts of Energy Sources, Competitiveness of Food Sector

Table D.2 SWOT analysis and economic competitiveness of the food subsector in Gran Canaria Weakness

Vulnerability to weather conditions, morphological characteristics of terrain that difficulties the mechanization, low exports diversification quotes and vulnerability to market changing conditions. Higher prices vs competitors. Saturation of market due to: no planning of types of production Imports of: cattle food, seeds, feed, packaging, low diversity and volume of the product range limits competitiveness, funding dependency because of the high production costs

Threats

Liberalization implies competition, lower public funding, increasing rustic land for tourism, local regulation, loss of coordination between administrations. High negotiation power of agents, lack of production coordination strategies, excessive dependency of fossil fuel imports that increases the prices of water supply (due to its interlinkage). And agriculture: fragile ecosystem facing plagues, land erosion,

Strengths

Good climate that let cultivate along the year, Added value of local fresh products, prestige and high quality of some products, the more tourism, the more promotion of the products, enhancement of the profitability of farms, with products and activities. Existence of different markets for the commercialization. Transgenic-free region

Opportunities Organization of exports markets, added value and better cost-effectiveness in diversified crops, bioclimatic floors that allow different types of crops, the lower mechanization the lower impacts, outermost region. UE funding. New tendencies, image enhancement, recover the agricultural landscape with the related benefits, promotion of association. Production planning to sustain prices lowering the negative market effects; exploitation of Canarias trademark. New improvement methods of water managing water resource Source Own elaboration

223.8

Gross Value Added at market prices

Source Own elaboration

744 000

Workers 1.6

237.88

729 000

613

581

Agriculture, livestock and fishery

1.4

2010 Mill e

GDP (gross domestic product)

%

2015

Mill e

Year

Table D.3 Economic competitiveness of the food subsector in Gran Canaria

1.6

1.5

%

2008

240.8

740 900

614.5

Mill e

1.62

1.44

%

2005

227.66

771 800

571.5

Mill e

1.75

1.55

%

2000

211.99

792 500

455.2

Mill e

2.12

1.7

%

Appendix D: Impacts of Energy Sources, Competitiveness of Food Sector 195

Appendix E

Locations of Proposed Sites

See Figs. E.1, E.2, E.3, E.4, E.5, E.6, E.7 and E.8.

Fig. E.1 Proposed sitting: Galdar. Source [30]

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 E. Rosales-Asensio et al., Sea Water Desalination in Microgrids, Green Energy and Technology, https://doi.org/10.1007/978-3-030-96678-2

197

198

Appendix E: Locations of Proposed Sites

Fig. E.2 Proposed sitting: Galdar (Botija). Source [30]

Fig. E.3 Proposed sitting: Galdar (del vino). Source [30]

Appendix E: Locations of Proposed Sites

Fig. E.4 Proposed sitting: San Antonio. Source [30]

Fig. E.5 Proposed sitting: Agaete. Source [30]

199

200

Appendix E: Locations of Proposed Sites

Fig. E.6 Proposed sitting: La aldea de San Nicolás. Source [30]

Fig. E.7 Proposed sitting: Manantial. Source [30]

Appendix E: Locations of Proposed Sites

Fig. E.8 Proposed sitting: Santa Lucia de Tirajana. Source [30]

201

Appendix F

Data Regarding Spanish Solar PV Sector

See Tables F.1 and F.2.

Table F.1 Key data regarding Spanish solar PV sector Installed capacity Electricity (MW) generation (GWh)

Number of installations

Total support (Me)

Average support (cente/kWh)

2016

4675

5794

61,404

2764

31.8

2015

4663

8211

61,338

2863

34.9

2014

4646

8170

61,096

2805

34.3

2013

4637

8261

60,984

3265

39.5

2012

4510

7994

59,883

2855

35.7

2011

4247

7248

57,710

2665

35.9

2010

3839

6400

54,920

2897

45.2

2009

3630

6073

52,100

2868

46.2

2008

3463

2503

51,310

1155

45.3

2007

690

473

20,284

215

43.3

2006

146

99

9874

45

42.7

2005

47

38

5391

16

39.9

2004

23

17

3266

6

36.7

Source Adapted from CNE (2011, 2017)

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 E. Rosales-Asensio et al., Sea Water Desalination in Microgrids, Green Energy and Technology, https://doi.org/10.1007/978-3-030-96678-2

203

Excess PV electricity

Yes (“solar tax”)

Savings on the electricity bill

Yes

Revenues from None excess electricity

Charges to finance transportation and distribution

3

4

Revenues from self-consumed PV

Right to self-consume

2

PV 1 Self-consumption

Below 100 kW

Spain

None

Savings on the electricity bill

Yes

Wholesale Feed-in tariff market price (FiT) or feed-in minus taxes premium (FiP)

Yes (“solar tax”)

Savings on the electricity bill

Yes

Above 100 kW

Germany

Yes

Israel

Yes

China

Yes

Australia

Retail electricity prices (full net-metering)

In specific states

None

Retail Market electricity price + prices (full bonus net-metering)

None

(continued)

Feed-in tariff

Tariff structure changes in some states

Savings on the Savings on Savings on Savings on the electricity bill the electricity the electricity bill bill electricity bill + bonus

Yes

United States (varies by state)

Table F.2 Comparison between Spanish and other self-consumption schemes around the world

204 Appendix F: Data Regarding Spanish Solar PV Sector

Other system characteristics

Regulatory scheme duration

Third party ownership accepted

8

Geographical compensation

6

7

Maximum timeframe for compensation

5

Table F.2 (continued)

None

Unlimited

None

Real-time

Below 100 kW

Spain

Yes

Unlimited

None

Real-time

Above 100 kW

All

20 years Feed-in Tariff (FiT)

On site only

Real-time

Germany

Yes

Unlimited

On-site

Vary by state

United States (varies by state)

Real-time

China

30 min

Australia

Yes

Unlimited

None

20 years

(continued)

Yes (e.g. solar leasing)

Unlimited but FiT are revised annually

Credits can On site only On site only be transferred to other consumers (but without transmission and distribution costs)

2 years

Israel

Appendix F: Data Regarding Spanish Solar PV Sector 205

100 kW but below or equal to capacity contracted

Below or equal to capacity contracted

Yes (except the Canary and Baleares islands)

11 PV system size limitations

Above 10 kW (except the Canary and Baleares islands)

Above 100 kW

None

Grid codes and additional taxes/fees

Below 100 kW

Spain

10 Other enables of None self-consumption

9

Table F.2 (continued)

Time of use tariff in some states

Vary by state. E.g. in Massachusetts, net energy metering is calculated monthly with a minimum bill. Arizona utilities have implemented fixed charges to account for grid costs to account for grid costs

United States (varies by state)

None

System costs—grid, back-up and balancing costs

Israel

Minimum 10% Yes, but 5 MW of depends on the self-consumption state: from 10 kW to 10 MW (or no limit)

Battery storage incentives

Grid codes compliance and partial EEG-surcharge

Germany

20 MW – 35 kV

None

None

China

None

None

(continued)

Yes (injection control/ramp-rate control/no DC-injection)

Australia

206 Appendix F: Data Regarding Spanish Solar PV Sector

Source Adapted from CNE (2011, 2017)

Taxes on batteries

Renewable energy sources act (EEG) levy must be paid anyway by the prosumer (above 10 kW)

Taxes on batteries

13 Additional features

Above 100 kW

Germany

Distributor’s Distributor’s 52 GW of PV license license installations

Below 100 kW

Spain

12 Electricity system limitations

Table F.2 (continued)

Multiple other policies depending on the state or at federal level

In some states

United States (varies by state)

None

No, but costs are linked to PV penetration

Israel

Australia

None

None

7 GW for None (except distributed additional grid PV codes) installations

China

Appendix F: Data Regarding Spanish Solar PV Sector 207

Appendix G

Project Cash Flow

See Table G.1.

Table G.1 Project cash flow Year Desalination Battery Wind cost plant cost cost (e) (e) (e)

Solar Electricity Water cost (e) cost (e) sales (e)

Electricity sales (e)

0

981,600

871.71

4,099,337.15 909,300 0

0

1

14,752

87.171

163,973.49

90,930

953.11

49,312.78 725,362.37

0

2

14,752

87.171

163,973.49

90,930

953.11

49,312.78 725,362.37

3

14,752

87.171

163,973.49

90,930

953.11

49,312.78 725,362.37

…

…

…

…

…

…

…

23

14,752

87.171

163,973.49

90,930

953.11

49,312.78 725,362.37

24

14,752

87.171

163,973.49

90,930

953.11

49,312.78 725,362.37

25

14,752

87.171

163,973.49

90,930

953.11

49,312.78 725,362.37

…

Source Own elaboration It has been adopted as hypothesis that components replacement costs are included in the yearly operation and maintenance costs

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 E. Rosales-Asensio et al., Sea Water Desalination in Microgrids, Green Energy and Technology, https://doi.org/10.1007/978-3-030-96678-2

209

Appendix H

Parameters of Simulation, Characteristics of Grants, and Simulation Results

See Tables H.1, H.2 and H.3.

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 E. Rosales-Asensio et al., Sea Water Desalination in Microgrids, Green Energy and Technology, https://doi.org/10.1007/978-3-030-96678-2

211

41.16

6 [43]

180,000

190

110

8

Installation cost (e/kW)

1050 [52]

8

Maintenance costs (%)

2000 [48]

Cost /installed kW (e/kW)

962,000

1

25

80%

1 / 7400

Min./Max. power for sizing (kW)

100

After life investment (%)

100

After life investment (%)

100

After life investment (%)

(continued)

Min. / Max. power for sizing (kW)

After life investment (%)

15 [50]

After life investment (%)

Equipment life (years)

26 [49]

50 [47]

Equipment life (years)

10 [45]

Equipment life

Equipment life (years)

Maintenance cost (%)

27 [44]

Maintenance cost (%)

Maintenance cost (%)

Investment cost (e)

600

Cost/installed (e/kW)

Equipment life (years)

Installation cost (e/kWp)

115

Nominal Flow (m3)

0.60

Water price (e/m3 )

233

Water flow out (m3 /h)

Maintenance costs (%)

17

2 [51]

Wind farm [53]

Efficiency (%)

PV panel size (m2)

Solar photovoltaic power plant

Nominal Power (kW)

50 [46]

Water demand (m3 )

368

Water flow in (m3 /h)

Number of pumps

Desalination pumps

Storage capacity (m3 )

Relative head (m)

Water storage

Auxiliares power (kW)

Membrane Pack

Desalination plant

Table H.1 Parameters of simulation

212 Appendix H: Parameters of Simulation, Characteristics of Grants, and Simulation …

Min./Max. SoC (%)

20/95

98/98

25

900

10

10

80

After life investment (%)

1/1340

Min./Max. power for sizing (kWh)

1 / 4200

Equipment life

After life investment (%)

Maintenance cost (%)

Equipment life (years)

80

Cost/installed (e/kW)

Maintenance costs (%)

Water flow out (m3 /h)

Installation cost (e/kW)

Water flow in (m3 /h)

Charging/discharging efficiency (%)

4

Auxiliares power (kW)

Energy storage [54]

1000

Membrane Pack

Desalination plant

Table H.1 (continued)

Appendix H: Parameters of Simulation, Characteristics of Grants, and Simulation … 213

214

Appendix H: Parameters of Simulation, Characteristics of Grants, and Simulation …

Table H.2 Analysis of the characteristics of proposed grants Grant

SME Phase 2 [24]

IDAE TEC/1380/2018[20]

1. Objectives

Clear, measurable, realistic and achievable within the duration of the project. Brief presentation of the overall structure of the work plan. Gantt chart + Pert chart + KPI

– Installed Power higher than 800 kW – Budget allocation: 80 M e – Incompatible with other grants – Ambit: aids proposed for investment in projects involving RES based technologies (PV-WD) for generating electricity in non-mainland territories – The real installed power must be calculated according to definition from the Spanish regulation: article 3 of: Royal Decree 413/2014, June the sixth

2. Relation to the work program

Explain the current stage of development of the business innovation project. Material scope:

– Material scope: – Geographic scope: – Available until December, the thirty-first

3. Ambition

• Describe the identified – Duties: customer pain point? What is – Enterprises who fulfil related the business need, requirements plus the technological challenge or 24/2013 law – Extra accounting system, to market opportunity? record all transactions related • What is your innovation? to each operation to be • What is the market’s co-funded state-of-the-art? How would your innovation compare with available solutions, practices or products (e.g. performance, costs, ease-of-use, gender dimension, climate change or environmental aspects, benefits to society)? (continued)

Appendix H: Parameters of Simulation, Characteristics of Grants, and Simulation …

215

Table H.2 (continued) Grant

SME Phase 2 [24]

IDAE TEC/1380/2018[20]

1. Expected impacts:

Discussion Entering the market • Who are the targeted users and/or customers and why will they want to buy the product/service (unique selling point)? Are they new or already part of your customer base? • What is the market in terms of type (e.g. niche, /high volume, new/mature, growth rate), size (e.g. volume, value, geographical scope) and growth? • Who are the main direct and indirect competitors to the proposed invention? (Competitors, substitutes and alternatives) • Which are the barriers to entry? How do you intend to overcome them?

Expected results, expected in POPE: – C030: extra capacity of electricity generation from RES (MW) – Reduction of GHG emissions in tons of equivalent CO2 /year – The Conversion factor to CO2 emissions from Non-Renewable energy to be used must be: 0.521 kg CO2 /kWh of final energy. The final energy will be evaluated by lowering the generated energy in the node by 4%

2. Business model

• Outline your business model, including the revenue model and your commercialization plan with an approximate time-to-market or deployment • Why is your model scalable? How do you intend to scale-up and reach European and/or global markets? Financing • Indicate the estimated funding requirements and the timeline to reach the commercialization stage of your innovation. How do you intend to finance the 30% of co financing rate? Outline your plans to ensure the subsequent financing of your innovation (next rounds, top-up financing, etc.)

Implementation Work plan

Brief presentation of the overall – Show to IDAE the economic structure of the work plan. Gantt information as well as the chart + Pert chart + Milestones financial path of the project (continued)

216

Appendix H: Parameters of Simulation, Characteristics of Grants, and Simulation …

Table H.2 (continued) Grant

SME Phase 2 [24]

IDAE TEC/1380/2018[20]

Management structure and procedures

Describe the organizational structure and the decision-making Explain why the organizational structure and decision-making mechanism are appropriate to the complexity and scale of the project Describe, where relevant, how effective innovation management will be addressed in the management structure

– Financeable budget must be presented

Resources to be committed

Information in this section matches the costs as stated in the budget • What is unique in your approach, compared to those of other companies? • Why now? Explain the historical evolution of your category and define recent trends that make your solution possible • What is the current development stage of your innovation? Refer to Technology Readiness Levels (TRL) or something analogous for non-technological innovations • Which milestones led to the current development stage (e.g. proof of concept completed, early field trials under way)? Describe the results obtained on the technological, practical and economic feasibility of the innovation • What are the further stages and activities needed to commercialize your innovation?

Within the hiring processes related to bankable expenses: – Provide at least 3 different offers of different suppliers previously to the hiring commitment for the facility execution, as well as maintain its documentation, including the justification of the selected offer – All contracted services must be demonstrable – Demonstrate the accomplishment of the activity, and undergo to verification and control – Match the costs as stated in the budget, letting the organism control the development – Fulfil the requirements of publicity and diffusion according to Law 38/2003 (arts. 18.4) – Preserve documents that justify the project, including management and control procedures that guarantee a good use of the proposed grants, as well as provide the documents ex-post

Source Own elaboration

Appendix H: Parameters of Simulation, Characteristics of Grants, and Simulation …

217

Table H.3 Simulation results Parameter

Simulation results (kW)

Solar farm

1

Desalination plant

Investment (e)

O&M (e)

1050

84

981,600

73,487

Wind farm

2499.96

2,499,967.68

32 000

Battery

0.83

750.22

75

Electricity costs

3891

Source Own elaboration

Water

21,408

Electricity

385,724 −20,831

Taxes on electricity (7%) Total

Sales (e)

3,483,367.8

109,537

386,301

Bibliography

1. Alsayegh O, Saker N, Alqattan A (2018) Integrating sustainable energy strategy with the second development plan of Kuwait. Renew Sustain Energy Rev 82(3):3430–3440 2. American Membrane Technology Association. Membrane desalination costs, https://www.amt aorg.com/wp-content/uploads/06_Membrane_Desalination_Costs.pdf (2008). Accessed 3 Jan 2020 3. Anindito Y, Haas J, Olivares M, Nowak W, Kern J (2019) A new solution to mitigate hydropeaking? Batteries versus re-regulation reservoirs. J Clean Prod 210:477–489. https:// doi.org/10.1016/j.jclepro.2018.11.040 4. Babonneau F, Caramanis M, Haurie A (2016) A linear programming model for power distribution with demand response and variable renewable energy. Appl Energy 181:83–95 5. Bhowmik C, Bhowmik S, Ray A, Pandey KM (2017) Optimal green energy planning for sustainable development: a review. Renew Sustain Energy Rev 71:796–813 6. Bosch AB, Moncho I, Lopez E (2019) Sustainable water desalination using solar energy. In: International conference on water and sustainability. Barcelona, June 2019 7. Daher BT, Mohtar RH (2015) Water–energy–food (WEF) Nexus Tool 2.0: guiding integrative resource planning and decision-making. Water Int 40(5–6):748–771 8. European Commission (2019) H2020 funding guide, https://ec.europa.eu/research/participa nts/docs/h2020-funding-guide/cross-cutting-issues/sme_en.htm#phase2 (2019). Accessed 10 Dec 2019 9. FitchRatings (2012) Construction and operational solar project issues: a review of siting, sun, technology, and regulation. Special Report, 26 June 2012 10. Flammini A, Puri M, Pluschke L, Dubois O (2014) Walking the nexus talk: assessing the water-energy-food nexus in the context of the sustainable energy for all initiative. Food and Agriculture Organization of the United Nations, Rome 11. Fortes P, Simoes SG, Gouveia JP, Seixas J (2019) Electricity, the silver bullet for the deep decarbonization of the energy system? Cost-effectiveness analysis for Portugal. Appl Energy 237:292–303 12. Frankl P et al (2005) Final report on technical data, costs and life cycle inventories of PV applications. New Energy Externalities Developments for Sustainability. Project No. 502687 13. Fthenakis VM et al (2008) Emissions from photovoltaic life cycles. Environ Sci Technol 426:2168–2174 14. Giampietro M, Mayumi K, Ramos-Martin J (2009) Multi-scale integrated analysis of societal and ecosystem metabolism (MuSIASEM): theoretical concepts and basic rationale. Energy 34(3):313–322

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 E. Rosales-Asensio et al., Sea Water Desalination in Microgrids, Green Energy and Technology, https://doi.org/10.1007/978-3-030-96678-2

219

220

Bibliography

15. Gülich JF (2008) Centrifugal pumps, 3rd edn. Springer, Berlin. Xylem Water Solutions (2019) Life Cycle Costs (LCC) for wastewater pumping systems, https://www.xylem.com/siteassets/ support/white-papers/white-papers-pdf/life-cycle-costs-lcc-for-wastewater-pumping-systems. pdf. Accessed 10 Jan 2020 16. Hall D, Lobina E (2004) Private and public interests in water and energy. Nat Res Forum 28:268–277. https://doi.org/10.1111/j.1477-8947.2004.00100.x 17. Howells M, Hermann S, Welsch M, Bazilian M, Segerström R, Alfstad T et al (2013) Integrated analysis of climate change, land-use, energy and water strategies. Nat Clim Chang 3:621–626 18. Hunter Water Corporation (2019) Operating and maintenance cost estimating guideline, https://www.hunterwater.com.au/Resources/Documents/Drawings_Plans_Specs/Guidel ine---Water-and-Sewer-Cost-Estimating.pdf. Accessed 15 Dec 2019 19. Institute for the Energy Diversification and Saving (IDAE) (2019) Investment aids for energy electricity power stations from wind and PV solar technologies located in non-mainland territories, and co-financed by Community funds from the European Regional Development Fund. https://www.idae.es/en/node/13481. Accessed 15 Nov 2019 20. Jia J, Zheng C, Zhao C (2008) Dam safety in China and the life span evaluation of old concrete dams. In: Walraven JC, Stoelhorst D (eds) Tailor made concrete structures. CRC Press, Boca Raton, pp 9–14 21. Khan Z, Linares P, García-González J (2017) Integrating water and energy models for policy driven applications. A review of contemporary work and recommendations for future developments. Renew Sustain Energy Rev 67:1123–1138 22. Lazard (2015) Lazards-Levelized-Cost-of-Storage-Analysis-10, https://www.lazard.com/ media/2391/lazards-levelized-cost-of-storage-analysis-10.pdf. Accessed 21 Nov 2019 23. Loulou R, Goldstein G, Kanudia A, Lettila A, Remme U (2016) Documentation for the TIMES Model PART I. Paris: IEA, https://iea-etsap.org/docs/Documentation_for_the_TIMES_ModelPart-I_July-2016.pdf 24. Hemmati R (2017) Technical and economic analysis of home energy management system incorporating small-scale wind turbine and battery energy storage system. J Clean Prod 159:106–118. https://doi.org/10.1016/j.jclepro.2017.04.174 25. Ma W, Xue X, Liu G (2018) Techno-economic evaluation for hybrid renewable energy system: application and merits. Energy 159:385–409 26. Manomet Center for Conservation Sciences (2010) Biomass sustainability and carbon policy study: report to the Commonwealth of Massachusetts Department of Energy Resources. Walker T (ed) Natural Capital Initiative Report NCI-2010-03 27. McCarty NA, Bryden KM (2016) An integrated systems model for energy services in rural developing communities. Energy 113:536–557 28. Mirjat NH, Uqaili MA, Harijan K, Walasai GD, Mondal MAHM, Sahin H (2018) Long-term electricity demand forecast and supply side scenarios for Pakistan (2015–2050): a LEAP model application for policy analysis. Energy 165:512–526 29. Mondal MAH, Bryan E, Ringler C, Rosegrant M (2017) Ethiopian power sector development: renewable based universal electricity access and export strategies. Renew Sustain Energy Rev 75:11–20 30. Prasadab RD, Bansal RC, Raturi A (2014) Multi-faceted energy planning: a review. Renew Sustain Energy Rev 38:686–699 31. Salman M (2013) Diagnostic, financial, and institutional tool for investment in water for agriculture. FAO, Rome 32. SEI (Stockholm Environment Institute) (2019) Modeling water & energy (LEAP/WEAP), https://www.weap21.org/WebHelp/Link_to_LEAP.htm. Accessed 5 May 2019 33. Shires TM et al (2009) Compendium of greenhouse gas emissions estimation methodologies for the oil and natural gas industry. Prepared by URS Corporation for the American Petroleum Institute, http://www.api.org/ehs/climate/new/upload/2009_GHG_COMPENDIUM.pdf. Accessed 12 Jan 2020 34. Stanford University (2008) Membranes for water treatment: properties and characterization, https://web.stanford.edu/group/ees/rows/presentations/Pinnau.pdf. Accessed 19 Mar 2020

Bibliography

221

35. Stehly T, Heimiller D, Scott G (2017) Cost of wind energy review. National Renewable Energy Laboratory, Golden 36. SUNPOWER (2019) SunPower® Performance Series P17, https://us.sunpower.com/solar-res ources/sunpower%C2%AE-p-series-commercial-p17-360.com. Accessed 20 Nov 2019 37. Tokimatsu K, Höök M, McLellan B, Wachtmeister H, Murakami S, Yasuoka R, Nishio M (2018) Energy modeling approach to the global energy-mineral nexus: exploring metal requirements and the well-below 2 °C target with 100 percent renewable energy. Appl Energy 225:1159–1175 38. Toray Membranes, Wilmington NC (2009) Eastern reverse osmosis systems, http://www.tor aymembranes.com/. Accessed 8 Dec 2019 39. Vaillancourt K, Bahn O. Roy PO, Patreau V (2018) Is there a future for new hydrocarbon projects in a decarbonizing energy system? A case study for Quebec (Canada). Appl Energy 218:114–130 40. Vattenfall (2010) Wind power certified environmental product declaration EPD of electricity from Vattenfall’s wind farms, http://www.vattenfall.com/en/file/Certified_Environme ntal_Product_Declaration_of_Electricty_from_Vattenfall_s_Windfarms_2010_12180312. pdf. Accessed 08 Apr 2020 41. WBCSD (2014) Co-optimizing solutions: water and energy for food, feed and fiber. World Business Council for Sustainable Development. World Business Council for Sustainable Development, Geneva. Retrieved from https://docs.wbcsd.org/2014/05/WBCSD_Co-op_Report.pdf. Accessed 30 Apr 2019 42. Weitzel T, Schneider M, Glocka CH, Löber F, Rinderknecht S (2018) Operating a storageaugmented hybrid microgrid considering battery aging costs. J Clean Prod 188:638–654. https:// doi.org/10.1016/j.jclepro.2018.03.296 43. Whitaker M et al (2012) Lifecycle greenhouse gas emissions of coal-fired electricity generation: systematic review and harmonization. J Ind Ecol 16 44. Zhou N, Zhang J, Khanna N, Fridley D, Jiang S, Liu X (2019) Intertwined impacts of water, energy development, and carbon emissions in China. Appl Energy 238:79–91