Energy Audits: Theoretical Examination and Modeling of Energy Audits (Sustainable Management, Wertschöpfung und Effizienz) 3658331666, 9783658331665

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Sustainable Management, Wertschöpfung und Effizienz

Michael Krutwig · Adrian Dumitru Tanțău

Energy Audits Theoretical Examination and Modeling of Energy Audits

Sustainable Management, ¨ pfung und Effizienz Wertscho Series Editors Gregor Weber, ecoistics.institute, Breunigweiler, Germany Markus Bodemann, Warburg, Germany René Schmidpeter, M3TRIX, Köln, Germany

In dieser Schriftenreihe stehen insbesondere empirische und praxisnahe Studien zu nachhaltigem Wirtschaften und Effizienz im Mittelpunkt. Energie-, Umwelt-, Nachhaltigkeits-, CSR-, Innovations-, Risiko- und integrierte Managementsysteme sind nur einige Beispiele, die Sie hier wiederfinden. Ein besonderer Fokus liegt dabei auf dem Nutzen, den solche Systeme für die Anwendung in der Praxis bieten, um zu helfen die globalen Nachhaltigkeitsziele (SDGs) umzusetzen. Publiziert werden nationale und internationale wissenschaftliche Arbeiten. Reihenherausgeber: Dr. Gregor Weber, ecoistics.institute Dr. Markus Bodemann Prof. Dr. René Schmidpeter, Center for Advanced Sustainable Management, Cologne Business School This series is focusing on empirical and practical research in the fields of sustainable management and efficiency. Management systems in the context of energy, environment, sustainability, CSR, innovation, risk as well as integrated management systems are just a few examples which can be found here. A special focus is on the value such systems can offer for the application in practice supporting the implementation of the global sustainable development goals, the SDGs. National and international scientific publications are published (English and German). Series Editors: Dr. Gregor Weber, ecoistics.institute Dr. Markus Bodemann Prof. Dr. René Schmidpeter, Center for Advanced Sustainable Management, Cologne Business School

More information about this series at http://www.springer.com/series/15909

Michael Krutwig · Adrian Dumitru Tant, a˘ u

Energy Audits Theoretical Examination and Modeling of Energy Audits

Michael Krutwig Karlsruhe, Germany

Adrian Dumitru Tant, a˘ u The Bucharest University of Economic Studies Bucharest, Romania

Prof. univ. Dr. Dr. Adrian Dumitru Tant, a˘ u; The Bucharest University of Economic Studies; Bucharest, Romania

ISSN 2523-8620 ISSN 2523-8639 (electronic) Sustainable Management, Wertschöpfung und Effizienz ISBN 978-3-658-33166-5 ISBN 978-3-658-33167-2 (eBook) https://doi.org/10.1007/978-3-658-33167-2 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2021 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. Responsible Editor: Anna Pietras This Springer Gabler imprint is published by the registered company Springer Fachmedien Wiesbaden GmbH part of Springer Nature. The registered company address is: Abraham-Lincoln-Str. 46, 65189 Wiesbaden, Germany

Abstract

Existing literature on energy audits consists almost exclusively of practical guides. This book looks at energy auditing from a scientific perspective. It discusses the nature of energy audits and provides a universally applicable data model as a basis for automatic processing of a large number of energy audits. Qualitative aspects of auditing are discussed in detail. The modeling enables an improved evaluation of subsidy programs for energy audits, but also a systematic and teamwork-oriented execution of energy audits. Keywords: Energy audit, Data model, ISO 50002, EED, Energy efficiency, Energy management

v

Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Introduction to the Research Field . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 Structure of the Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6 Conventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 1 2 4 8 14 17

2 Theoretical Consideration of Energy Audits . . . . . . . . . . . . . . . . . . . . . 2.1 Literature Research on General Audit Theory . . . . . . . . . . . . . . . . . 2.2 Investigations on Energy Audits . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Comparison between Financial Audit and Energy Audit . . . . . . . . 2.4 Research on the Political Significance of Energy Audits . . . . . . . . 2.5 Political Significance: A Comparison Study between Romania and Germany . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 Research on Quality Aspects of Energy Audits . . . . . . . . . . . . . . . 2.7 Non-energy Benefits (NEB) as a Quality Characteristic . . . . . . . . 2.8 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

19 19 23 32 33

3 Research on Energy Balances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Approaches to Definition and Typification of Energy Balances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.1 Energy Balance of a National Economy . . . . . . . . . . . . . . . 3.1.2 Complete Energy Balance of an Organization . . . . . . . . . . 3.1.3 Partial Energy Balance of an Organization . . . . . . . . . . . . . 3.2 Literature Review on Energy Baseline and Energy Performance Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

55

38 43 46 50

55 56 58 61 61

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Contents

3.3 Research on Quality in Energy Balancing . . . . . . . . . . . . . . . . . . . . 3.4 Measurements for More Accurate Energy Balances . . . . . . . . . . . . 3.4.1 Emerging Technologies for Energy Measurements . . . . . . 3.4.2 Using LoRaWAN and OPC UA for Measurement—A Case Study . . . . . . . . . . . . . . . . . . . . . 3.5 Findings for the Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

63 65 66

4 Studies on Business Aspects of Energy Efficiency Measures . . . . . . . 4.1 Assessing Economic Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.1 Net Present Value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.2 Internal Rate of Return . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.3 Return on Investment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.4 Payback Period . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.5 Annuity Method and Equivalent Annual Cost . . . . . . . . . . 4.1.6 Life-cycle Cost Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Quality Consideration in EEM Calculation . . . . . . . . . . . . . . . . . . . 4.2.1 Challenges in Calculating Energy Savings . . . . . . . . . . . . . 4.2.2 Requirements for Energy Audits in Germany . . . . . . . . . . . 4.2.3 A Proposal to Better Detect Non-energy Benefits . . . . . . . 4.3 Findings for the Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

87 88 88 89 90 90 91 92 93 94 95 96 98

5 Research on Multiple Energy Audits Processing . . . . . . . . . . . . . . . . . . 5.1 Cross-audit Evaluations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.1 Literature Review on Cross-audit Evaluations . . . . . . . . . . 5.1.2 Evaluation of an SME Program for Energy Audits in Germany . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Conducting Multiple Energy Audits . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Aspects of Collaboration on Energy Audits . . . . . . . . . . . . . . . . . . .

101 102 102

6 Design of an Universal Data Model for Energy Audits . . . . . . . . . . . . 6.1 Methods of Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.1 Methodical Approach for the Development of the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.2 Methodology for the Model Description . . . . . . . . . . . . . . . 6.2 The Overarching Model for the SaaS and Companies . . . . . . . . . . 6.3 The Energy Audit Data Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 The Energy Balance Data Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5 The EEM Data Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6 The Non-energy Benefit Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

79 84

105 109 111 113 114 114 115 116 121 125 129 132

Contents

ix

7 Validation of the Model with Audit Data . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Data Extraction and Cleansing before Validation . . . . . . . . . . . . . . 7.2 Results of the Energy Balance Model Validation . . . . . . . . . . . . . . 7.2.1 Analysis by Audit Type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.2 Analysis by Business Area . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Validation of the EEM Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.1 Descriptive Statistics of the Samples . . . . . . . . . . . . . . . . . . 7.3.2 Examination of the EEM Input Data . . . . . . . . . . . . . . . . . . 7.3.3 Examination of the Calculated EEM Values . . . . . . . . . . . .

135 135 140 141 144 146 148 150 155

8 Main Contributions and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Summary of the Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Contributions to Current Research . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3 Discussion and Assessment of the Results . . . . . . . . . . . . . . . . . . . . 8.4 Prospects for Further Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

159 159 161 163 168

Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

171

Abbreviations

AGEB AI AMQP ANRE BAFA CAEX CCE CEM CPES CPPS CPS CS CSC CSV CT D-BPSK DIN EB EBM EDL-G EEAP EED EEI EEM

Arbeitsgemeinschaft Energiebilanzen e. V. Artificial Intelligence Advanced Message Queuing Protocol Autoritatea Nationala de Reglementare în Domeniul Energiei Bundesamt für Wirtschaft und Ausfuhrkontrolle Computer Aided Engineering Exchange Cost of Conserved Energy Coarsened Exact Matching Cyber-physical Energy Systems Cyber-physical Production Systems Cyber-physical Systems Companion Specification (OPC UA) Conservation Supply Curve Comma-separated values Current Transformers Differential Binary Phase-Shift Keying Deutsches Institut für Normung Energy Baseline Energieberatung im Mittelstand Gesetz über Energiedienstleistungen und andere Energieeffizienzmaßnahmen Australian Enterprise Energy Audit Program European Energy Efficiency Directive Energy Efficiency Indicators Energy Efficiency Measures

xi

xii

EEO EIB EMAS EnMS EnPIs ESD ESO GAAP GAE GAGAS GDP GFSK GHG GIC HVAC IAASB IAC ICT IFAC IFRS IIoT IoT IRES ISM ISO ktoe kWh LoRa LoRaWAN LPWAN LTE m2m MQTT MS MWh NaaS NB-IoT NEB

Abbreviations

Energy Efficiency Obligations, also: Energy Efficiency Opportunities (= EEM) European Investment Bank Eco-management and Audit Scheme Energy Management System Energy Performance Indicators EU Energy Service Directive Energy Saving Opportunity Generally Accepted Accounting Principles Gross Available Energy Generally Accepted Government Auditing Standards Gross Domestic Product Gaussian Frequency-Shift Keying Greenhouse Gases Gross Inland Energy Consumption Heating, Ventilation and Air Conditioning International Auditing and Assurance Standards Board Industrial Assessment Centers (an US subsidy program) Information and Communication Technology International Federation of Accountants International Financial Reporting Standard Industrial Internet of Things Internet of Things International Recommendations for Energy Statistics Industrial, Scientific and Medical (radio band) International Organization for Standardization kilo tonne of oil equivalent kilowatts per hour Long Range Long Range Wide Area Network Low Power Wide Area Networks Long Term Evolution Machine-to-Machine Message Queue Telemetry Transport (protocol) Member State(s) (of the European Union) Megawatts per hour Network as a Service Narrowband IoT Non-energy benefit(s)

Abbreviations

NEEAP NS OCR OFDMA OLE OPC OPC UA PBP PDCA POET RPMA SaaS SBC SC-FDMA SDR SEAP SF SME SQL TFC toe TSN UDP VPN WSN WTP

xiii

National Energy Efficiency Action Plan Network Server (a LoRaWAN component) Optical Character Recognition Orthogonal Frequency-Division Multiple Access Object Linking and Embedding OLE for Process Control OPC Unified Architecture Payback Period (also PBT, Payback Time) Plan-Do-Check-Act (P)erformance, (O)peration, (E)quipment and (T)echnology Random Phase Multiple Access Software as a Service Single Board Computer Single Carrier Frequency Division Multiplex Access Short Range Device (radio band) Swedish Energy Audit Program Spreading Factor (in LoRaWAN networks) Small and Medium sized Enterprises Structured Query Language Total Final Energy Consumption tonne of oil equivalent Time Sensitive Networks User Datagram Protocol Virtual Private Network Wireless Sensor Networks Willingness To Pay (assessment method)

List of Figures

Figure Figure Figure Figure Figure Figure Figure Figure Figure Figure

1.1 1.2 1.3 1.4 1.5 2.1 2.2 2.3 2.4 2.5

Figure 2.6 Figure 3.1 Figure Figure Figure Figure

3.2 3.3 3.4 3.5

Figure 3.6 Figure 3.7 Figure 3.8

Figure 3.9

Objectives and sub-objectives of this work . . . . . . . . . . . . . . Methodology of this work . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methodology of the iterative model development . . . . . . . . Screenshots of the analyzing tool . . . . . . . . . . . . . . . . . . . . . . Structure diagram of the book . . . . . . . . . . . . . . . . . . . . . . . . The energy audit procedure according to EN 16247-1 . . . . PDCA-cycle of ISO 50001 . . . . . . . . . . . . . . . . . . . . . . . . . . . EU governance analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Policy cycle of Article 8 EED . . . . . . . . . . . . . . . . . . . . . . . . Transposition of Article 8 EED in Germany and Romania . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . An EEM classification scheme . . . . . . . . . . . . . . . . . . . . . . . . Energy Flow Chart for the Federal Republic of Germany in 2019 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . LoRa system components . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dissolving the automation pyramid with OPC UA . . . . . . . The OPC UA architecture stack . . . . . . . . . . . . . . . . . . . . . . . The mounted camera module on the analog energy meter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Measurement setup with current transformer [c], ct bridge [a] and LoRaWAN base server [b] . . . . . . . . . . . . . . . Configuration of the OPC UA client in the EnMS . . . . . . . . Screenshot: Visualization of the total consumption of all phases in the course of the day (15-minute intervals) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . OPC UA case study setup with low-cost hardware . . . . . . .

5 8 10 14 15 24 29 35 38 41 52 57 69 73 74 78 80 81

82 83

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List of Figures

Figure Figure Figure Figure Figure Figure Figure

4.1 5.1 6.1 6.2 6.3 6.4 6.5

Figure Figure Figure Figure Figure Figure

6.6 6.7 7.1 7.2 7.3 7.4

Figure Figure Figure Figure Figure Figure Figure Figure Figure

7.5 7.6 7.7 7.8 7.9 7.10 7.11 7.12 7.13

Proposal for an NEB classification scheme . . . . . . . . . . . . . . Histograms of the cross-audit EEM evaluation . . . . . . . . . . . The audit model in the overall context of the SaaS . . . . . . . The company data model . . . . . . . . . . . . . . . . . . . . . . . . . . . . The energy audit data model . . . . . . . . . . . . . . . . . . . . . . . . . The energy balance data model . . . . . . . . . . . . . . . . . . . . . . . Automatically generated Sankey diagram of a consumer structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The EEM data model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The NEB characteristics model . . . . . . . . . . . . . . . . . . . . . . . The process of data cleansing, filtering and extraction . . . . Filter selection in the analysis tool (screenshot) . . . . . . . . . . Balances by audit type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mean values of consumption, procurement and energy use by audit type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Number of balances by main branch . . . . . . . . . . . . . . . . . . . Number of employees in Germany in 2018 by sector . . . . . EEM frequencies by main branch . . . . . . . . . . . . . . . . . . . . . EEM frequencies by consumer type . . . . . . . . . . . . . . . . . . . EEM frequencies by energy carrier . . . . . . . . . . . . . . . . . . . . Histogram of the annual saving values . . . . . . . . . . . . . . . . . Histogram of net present values (NPV) values . . . . . . . . . . . Histogram of payback period values . . . . . . . . . . . . . . . . . . . Histogram of internal rate of return (IRR) values . . . . . . . .

97 108 117 118 121 126 128 130 133 137 139 142 143 144 145 151 152 153 154 155 156 157

List of Tables

Table 2.1 Table 2.2 Table 3.1 Table Table Table Table Table

3.2 3.3 5.1 5.2 5.3

Table Table Table Table Table

7.1 7.2 7.3 7.4 7.5

Comparison between energy audits and energy management systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Key energy figures and targets from 2018 for Germany and Romania . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Scheme for an energy consumption balance of organizations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Scheme for an energy use balance of organizations . . . . . . . . Common standards for local sensor networks . . . . . . . . . . . . . Cross-audit evaluation of a German SME program . . . . . . . . . Cross-audit EEM evaluation of a German SME program . . . . Cross-audit energy balances evaluation of a German SME program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary of all energy balances (after cleansing) . . . . . . . . . . Balances (all years) by audit type per year . . . . . . . . . . . . . . . . Record of the extracted EEM data . . . . . . . . . . . . . . . . . . . . . . . Descriptive statistics of the EEM data (part 1/2) . . . . . . . . . . . Descriptive statistics of the EEM data (part 2/2) . . . . . . . . . . .

31 39 59 60 68 106 107 108 140 141 147 149 149

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1

Introduction

This chapter provides a brief introduction to the thematic environment of this book and illustrates the motivation behind this work. This chapter begins by presenting the hypothesis and then lays out the objectives and sub-objectives of this study. This is followed by a discussion of the scientific methodology used to pursue these objectives.

1.1

Introduction to the Research Field

An energy audit is a process used to examine and describe the energetic situation of an organization. It can be carried out for corporations, companies, business processes, technical facilities, buildings, private households, appliances, and for all other objects in which energy flows are converted. The most common form of energy flow conversion is called “consumption,” during which the supplied energy is converted into kinetic energy, temperature change, light, chemical reactions, or other processes. In this case, the energy is no longer usable as energy itself. The aim of an energy audit is to achieve a more efficient use of energy and thus save energy. During an energy audit, the input and output energy flows and all energy consumption within a system are documented and examined for their optimization potential through measurements, calculations, and estimations. As they form a subset of the larger umbrella of energy efficiency, energy audits are interdisciplinary. Scientific articles on energy audits can be found in numerous Electronic supplementary material The online version of this chapter (https://doi.org/10.1007/978-3-658-33167-2_1) contains supplementary material, which is available to authorized users.

© The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2021 M. Krutwig und A. Dumitru Tan¸ta˘ u, Energy Audits, Sustainable Management, Wertschöpfung und Effizienz, https://doi.org/10.1007/978-3-658-33167-2_1

1

2

1

Introduction

thematic journals on energy, sustainability, energy economics, energy policy, economics, sensor technology, and computer science, as well as in numerous sectorand process-specific publications. For example, a case study of an energy audit in a wastewater treatment plant is most likely to be found in a journal on water treatment, while an evaluation of subsidy programs for energy audits is more likely to be found in policy-oriented publications. The present paper discusses energy audits in relation to several factors. The topic of this book can be assigned to the energy sector, but it also concerns business administration and deals with aspects of energy policy and applies methods of computer science and sensor technology. Energy audits have taken place for more than 50 years, but they became popular in Europe with the establishment of the European Energy Efficiency Directive (EED) (European Parliament 2018) in 2012. The EED led to the development of numerous subsidy programs for energy audits in all member states and made energy audits obligatory for large companies, a policy that has been in place since 2015. Since this year, a sharp increase in demand for energy audits has kept researchers occupied evaluating audits conducted in the past to examine the impact of existing policies and to make recommendations for future policy decisions. Scientific publications on individual energy audits are usually more focused on physics. These publications feature methods and case studies that provide new insights for the analysis of processes and the improvement of their efficiency. A select number of scientific papers also cover the modeling of energy audits or parts of audits, such as energy efficiency measures. The present work supplements these studies with a holistic data model for energy audits. This book is not about a single audit, but rather a number of audits. The research questions revolve around the processing of multiple energy audits. This work investigates how a multitude of energy audits can be controlled more efficiently by homogenization. The term “control” refers to both the structured execution and documentation of an energy audit as well as the evaluation across a number of audits. Although there exist international standards for energy audits, such as ISO 50002 and EN 16247, these are overly general for homogenization. A data model for energy audits is required to close this gap. On the one hand, this data model should be universal enough to represent all typical energy audits conducted for companies. On the other hand, it must also be specific enough so that the quality of energy audits is not compromised.

1.2

Motivation

This work is driven by the increasing importance of energy audits in Europe over the last few years due to the EED and the national legislation that resulted from

1.2 Motivation

3

this directive. The EED is a political instrument implemented by the European Parliament to increase energy efficiency, thereby reducing the use of limited energy resources and decreasing dependence on energy imports from just a few regions of the world. This directive establishes a common framework for all member states to promote energy efficiency and achieve several defined goals. In the first version of the EED of 2012, one of these targets was achieving energy savings of 20% by 2020. In addition to defining these targets, the EED also prescribes corresponding measures such as mandatory annual energy savings for member states, a renovation rate for buildings, and other mandatory and promotional measures. Another central measure of this directive is the obligation for large companies to regularly conduct energy audits. The mandatory energy audits established by the EED have resulted in a completely new type of audit that no longer aligns with the rules of well-known standards such as ISO 50002. These standards assume that a company has an interest in energy efficiency. The obligation for companies to carry out audits has resulted in a situation of changed interest. Now that the government is demanding energy savings, the interests of companies have shifted to avoid penalties. Given this tension, the quality of energy audits has become a central issue. The definition of quality is laid out by additional guidelines and regulations. Existing literature hardly covers mandatory energy auditing and the quality of audits, demonstrating that there is a need for further research. The new framework presented by the EED has created a high demand for energy audits in almost all commercial enterprises. Among service providers, a new economic sector is growing to meet this demand. The number of energy auditors is increasing, and a training market for the qualification of auditors and a market for devices and tools for the practical implementation of energy audits are developing. The data model presented in this book could serve as a theoretical basis for new software tools in these markets, which reflect the concrete economic interests of this researcher. Energy efficiency plays an important role in science, including the field of energy auditing. Existing publications discuss the effectiveness of audit programs or give concrete recommendations for future policy guidelines, but they also point to a great need for further research in numerous sub-areas. The extensive research conducted for this work found that certain topics, such as the quality aspects of energy audits, have been investigated very little or not at all. The few existing studies on modeling concern mostly characterization schemes, but their practical value beyond evaluation has not been critically questioned. In fact, many terms in the field of energy auditing have not been precisely defined. Further scientific examination should concretize these terms as a preliminary step.

4

1

Introduction

There is still an open gap in research on the comparability of energy audits and audit programs. In several studies, solutions are being sought to better structure energy audits. There are only incomplete approaches to modeling for energy audits, which have not yet been validated in practice. In addition to this explicitly formulated need, there is also a large gap in the definition of the quality concept for energy audits when they are used as a political instrument. If one paper had to be chosen to represent the motivation for this research, the choice would be “A study of the comparability of energy audit program evaluations” by the Swedish research team comprised of Andersson, Arfwidsson, Bergstrand, and Patrik Thollander (Andersson, Arfwidsson, Bergstrand, et al. 2017). By comparing five evaluations of energy audit funding programs, this paper examines the similarities and differences in evaluation criteria that have led to limitations in comparisons. For example, the incompatibilities lie in the use of different energy performance indicators (EnPIs) or the different calculations used for similar EnPIs. The use of different categories was also identified as a problem for cross-cutting evaluations, as standards such as ISO 50002 do not define categories themselves. According to Andersson et al. (Andersson, Arfwidsson, Bergstrand, et al. 2017), the demand for more comprehensive audits should also increase the quality of energy data and EEM. The present work continues this research by creating a uniform framework for energy balances and EEM using a data model. This model will make more detailed evaluations of audits down to the consumer level possible. If the “protocol for energy audit program design” as described by Andersson et al. (Andersson, Arfwidsson, Bergstrand, et al. 2017) were to include such a model or offer tools with this data model for the execution of audits, this would represent a large step toward more quality and comparability of energy audits. Fortunately, one study does not need to be chosen, which allows the authors to mention other publications that have led to the present work. Various articles on modeling and characterization in the field of energy efficiency have particularly inspired this text.

1.3

Objectives

The aim of this book is to investigate whether a generally applicable data model for energy audits exists. The challenge is to ensure that the quality of an individual audit is not lost with the application of a general data model. Since the quality is based on individual detailing, there is a trade-off given the desired generalization. The following hypothesis was formulated for this study:

1.3 Objectives

5

“An energy audit cannot be generalized into a universally applicable data model without (significantly) affecting the quality of the audit.”

In order for this study to verify this hypothesis, a data model was checked for the desired properties. Before this data model was created, however, the following questions needed to be addressed: • • • • •

How is the term “energy audit” defined? What is an “energy audit” in a scientific context? What is “quality” in the context of energy audits, and how can it be measured? What possibilities exist for modeling? What evaluation possibilities are offered by a modeled energy audit?

Figure 1.1 presents the main objective and associated sub-objectives of this book. The main value of developing a data model lies in the two areas of possibilities for processing multiple energy audits. On the one hand, tools for auditors can be created, enabling standardized and structured energy audits. On the other hand, evaluations would benefit greatly from a universal data structure as all possible database analyses would be available for cross-audit evaluation.

Figure 1.1 Objectives and sub-objectives of this work

6

1

Introduction

In order to achieve this study’s main objective, the author developed four subobjectives. One of these sub-goals is to present a comprehensive review of all qualityrelated aspects in the context of energy audits. With respect to mandatory energy audits, quality assurance plays a particularly important role. It is necessary for this study to determine what quality actually means. A set of criteria for quality is required in order for the quality of an audit to be determined objectively. The data model must not be limited to the specifications of the ISO 50002 standard, but should rather take into account other factors that determine quality. The guidelines for the implementation of mandatory energy audits, which were issued by the national authorities as supplementary regulations to ensure quality, should therefore be examined, and the criteria that are defined might be included in the model. This study’s investigation of quality starts with a measurement of individual energy consumers and explores national quality requirements in the context of subsidy programs and obligations. Two further sub-objectives are to develop data models for energy efficiency measures (EEM) and energy balances. These two models can be investigated independently. Studies on auditing in the domain of financial accounting demonstrate that there are, one the one hand, analogies between corporate balance sheets and energy balances. On the other hand, EEM are an additional component that is only relevant for energy audits. Research on the scientific fundamentals of energy audits has made it clear that there is neither a clear definition nor a universal calculation for the term “energy balance.” Every professional domain uses the term in a different way, making its usage inconsistent. This concept of a balance sheet needs to be analyzed more closely. The aim of this text is to find a suitable definition for the energy balances of organizations in the context of an energy audit. Based on this definition, a data model for energy balances can then be developed. This model should consider energy balances over several periods on an annual basis so that an audit can also demonstrate energy flows over several years. As for the submodel of the balances, it is necessary to include both period-independent assets, such as consumers, as well as periodic values of energy flows. When modeling EEM, they can be clearly defined in business terms, as they appear as an investment project. The challenge is to consider the profitability calculations, which, in the case of EEM, can be carried out either according to simple standardized methods or through the use of specific parameters and calculation procedures. It is helpful to decouple the calculation of energy savings from the calculation of economic viability. Energy saving—which brings to mind the insulation of buildings, for example—often involves complex physical and thermodynamic calculations that cannot be generalized for EEM. The resulting savings in energy or costs can then be subjected to an examination of economic efficiency through the

1.3 Objectives

7

use of simple, standardized methods. The parameters required for this need to be considered in the model. The fourth sub-objective is to validate the model based on data from real energy audits. A validation can prove that the model is applicable for energy audits of different types in a variety of sectors. In order to achieve this goal, the model must be implemented within an application for collecting this data. The hypothesis can only be rejected and the validity of the model thus proven only if the energy audit to be included in the data model is accepted. The data model thus requires a significantly large number of audits to be entered without a loss of quality. Consequently, the development and improvement of the application also forms part of the validation. The statistical evaluations of the recorded energy audits indicate the various possibilities for the analysis of audit data when the model is used. Besides the four sub-objectives, two more side objectives are pursued, which were not essential for the fulfillment of the main objective, but which have improved the quality of the research. First, in addition to the EEM, the effects beyond energy savings of the adoption of EEM are investigated. These effects are also called nonenergy benefits (NEB). If these NEB are taken into account in an assessment of the profitability of EEM, the quality of the analysis of measures increases, and thus increasing the quality of the entire energy audit. Within the scope of the NEB study, a modeling was also carried out. However, this was conducted via different methods, and the resulting model is therefore separate from the data model for energy audits. A further sub-goal is also dedicated to quality, specifically the quality of the calculation of energy balances. Through a comprehensive literature review and practical case studies, modern radio technologies and current industry standards are examined for their suitability for the conduct of consumption measurements within a company. The more consumers of an organization are measured and the shorter the measuring intervals, the more meaningful and the higher the quality of the data basis that is represented in an energy balance. A cost-efficient, networked metering technology provides the foundation for such a data basis. The main objective is to demonstrate that the modeling of energy audits is possible and to present how such a model can be developed. In order for the authors to ensure the quality of the results, an empirical investigation was conducted. Another ancillary objective is the general scientific gain of knowledge on the topic of energy audits, as this topic has not yet been deeply investigated or clearly defined. The development of an application for creating the energy audit reports is not a goal of this work, but forms a necessary step of the iterative modeling process according to this study’s methodology. Many factors concerning the application itself are not further examined in this work.

8

1.4

1

Introduction

Methodology

To test the hypothesis, the authors used both qualitative and quantitative research methods. The qualitative methods, including expert interviews, qualitative content analysis, comparative studies, and individual case studies, were used for model development. The quantitative methods were required for the evaluation of the datasets, which is presented in this paper through an analysis of descriptive statistics. A summary of the methodologies related to the objectives of this work is presented in Figure 1.2.

Figure 1.2 Methodology of this work

A comprehensive literature review of scientific studies on energy auditing supports this study’s analysis of the requirements for the data model. This review includes the general audit theory as a foundation for an examination of energy audits . The origins of the audit theory can be traced to the field of financial, which allows the authors to delimit the concept of energy auditing, a concept that is not well defined in science. For the investigation of the economic efficiency calculations of efficiency

1.4 Methodology

9

measures, the standard methods of investment theory are analyzed for their applicability to energy audits. A comparison of the legislation regarding mandatory energy audits between two EU countries is used to provide more qualitative information. This comparison also avoids a one-sided approach, since the audits based on which the model was developed follow only German laws and guidelines. The literature review also examines studies on the evaluation of audit programs from numerous European and non-European countries. Cross-audit evaluation is a form of multiple energy audit processing and therefore makes up a core part of this work. The literature review includes additional works that cover topics related to the analysis of audit quality. These include scientific papers on non-energy benefits (Mills and A. Rosenfeld 1996), as well as numerous scientific articles and technical documents on current network technologies (Bardyn et al. 2016) and standards on cyber-physical systems (Wolf 2009). This comprehensive review enabled the author to economically realize the automated, networked measurement of energy flows, which can significantly increase the quality of energy balances. The literature review also includes several scientific publications on case studies of individual energy audits featuring a wide range of technologies and fields of application. This broad range is necessary to ensure the general validity of the data model across all sectors, company sizes, and organizational forms. The two case studies on sensor technologies presented in this text are intended to prove the feasibility of using the technologies and standards researched. In one case at an industrial company and in another case in a laboratory, electricity meters were networked and automatically read. The setup in the company is discussed to prove the suitability of Long Range Wide Area Network (LoRaWAN) (Sornin et al. 2015) for the field of energy management. The case study in the laboratory is presented to verify the suitability of the standard OPC Unified Architecture (OPC UA) (Rinaldi 2016) for the retrofit of an electricity meter. The development of the data model itself followed a dynamic-iterative process, as is illustrated in Figure 1.3. The audit report was generated based only on the data from the model and was used to verify the suitability of the data model. This method can be used to clearly determine whether a model is “compatible” with an audit. If an auditor is able to use the application with the data model for an audit and accepts the generated report, the model would be verified for this audit. If this validation can be repeated successfully with a significant number of other audits, the general validity of the model would also be confirmed, and the hypothesis would be rejected. The entire process of modeling until this significant number of energy audits is achieved took about 4,5 years. For the model development, the cycles as illustrated in Figure 1.3. were quite short in the first few months, about two to four weeks. However, as the model matured, these cycles were extended to about three

10

Figure 1.3 Methodology of the iterative model development

1

Introduction

1.4 Methodology

11

months, and the optimizations of the data model became smaller with each cycle. The model of and data collected from the energy audits were then extracted and analyzed for this study on the cut-off date: 19.11.2019. The following section explains the individual steps of iterative model development, as presented in Figure 1.3, in more detail. The upper part of the figure describes the linear process that took place until the application was released: • Based on an audit report that meets the requirements of the energy audits outlined in the law from 2015, a five-member team of experts of energy consultants with expertise in energy audits and software engineers was assembled. This team defined an energy audit report that documents audits according to ISO 50002 and contains all necessary information according to the legal guidelines. The information for the data model was derived based on this prototypical audit report. • In addition to the implementation of the initial data model in the form of a structured query language (SQL) database, the application for recording this data and for the automatic generation of the energy audit report was also created. In order for this application to be used by a large number of energy auditors, it was implemented as a commercial software as a service (SaaS) on the internet and launched for use in July of 2015. • The application could then be used by energy auditors. Due to the acute need for energy audits and tools for efficient audit report generation, the application has was used by several hundred energy auditors after four months of its release. These auditors entered their audit data into the database. At this stage, a continuous cycle of improvement cycle was started. During this cycle, the data model, as well as the application and the SaaS functions, were permanently improved and expanded. The lower part of figure 1.3 illustrates the methodology for optimizing the data model: • At irregular intervals, the data collected by the auditors was analyzed, and a varying number of auditors were interviewed about their experience and the suitability of the system. This method of interviewing was necessary because it was also possible to question whether audit data did not fit into the data model and were therefore not recorded. These interviews also included suggestions for improving the data model. These interviews also included suggestions for improving the data model. With these suggestions, the data model could be improved and the number of incompatible audits reduced.

12

1

Introduction

• The suggestions were presented to the above-mentioned team of experts so that a decision could be made as whether to take them into account. The consideration of a suggestion as a new requirement in the model was based on the criterion of relevance. Proposals made by a single auditor must always be relevant to all other audits and auditors in order to be considered. The more often a suggestion was mentioned by the auditors, the more relevant it became. In the case of similar but not identical suggestions, the team of experts was able to homogenize the suggestions into one requirement. Members of the expert team were also able to make their own suggestions for consideration by the team. The result of this proposal was a set of requirements for the data model. • The new requirements for improvements, changes, and extensions were incorporated into the data model. Changes to the data model also required changes to the application. Thus, after the implementation of new requirements, the application was updated to contain the new data model. It should be noted that these updates were not allowed to hinder auditors’ continuous use of the system, so it was necessary to ensure data compatibility with the previous version. The compromises made due to these compatibility requirements resulted in a sub-optimal data structure that is not always normalized. The development process of the application followed an agile, scrum-like approach (Schwaber and Beedle 2002). The energy auditors took on the role of the users; the expert team occupied the position of the product owner. With the continuous improvement of the data model, it became increasingly static and the cycle of system development focused increasingly on the application. The expert team for the data model became smaller, the cycles became longer, and the number of interviews with the auditors decreased. As of November 19, 2019, the data model for the audits was considered mature. At this point, the data was extracted for analysis, and the resulting data model is described here. The data extracted on this cut-off date resulted in a snapshot of 25.724 energy balances and 7.030 EEM from over four years. As the system will continue to be used operationally, a key problem had to be solved: the fact that some of the data originated from incomplete audits. The data structure of an audit does not provide reliable information about whether an audit has actually been completed and whether the generated report can be assigned to a real audit. Therefore, before data analysis, a comprehensive cleansing process had to be carried out in order to remove all records suspected of incompleteness or any implausible data from the set of samples. After this data cleansing, the samples of 3.861 energy balances and 6.768 EEM were analyzed to validate the model.

1.4 Methodology

13

The validation of the data model is based on empirical methods. On the cutoff date of the evaluation, all of the data was extracted and evaluated through the statistics program PSPP (Free Software Foundation 2020), an open-source replica of the widely used commercial software SPSS. A comparison of both systems is given in (Rybenska, Sedivy, and Kudova 2014). The graphical presentation was created directly with PSPP. In addition, for a better representation, a chart was generated based on PSPP data via Microsoft Excel. The data of the energy balances and the data of the EEM was extracted and analyzed separately. This is because the energy balances are periodically available as annual balances, whereas the EEM data is not tied to a specific period. The way of data extraction was also different. The data of the energy balances was extracted directly from the model, while an economic efficiency calculation was performed on the data of all EEM before the analysis and the results are included in the analysis. A cleansing process was applied to both sets of data before the analysis. For the analysis of the data, an additional web-based analysis tool was created— apart from the application for energy audits—to filter the samples according to various factors and to clean up the data (Maletic and Marcus 2000). This data cleansing was intended to filter out implausible datasets, which came from test data and incomplete audits, from the set of samples in order to increase the quality of the database. This analysis tool made no changes to the data. The screenshots in Figure 1.4 present a visual impression of the tool. The analysis tool provides a user interface for easy selection and filtering of data. For example, the user can filter the total amount of data by year of energy balance, audit program, or branch of the company. Moreover, the cleansing filters can be switched on, activating a plausibility check of the data. Numerous other filters can further restrict the results. The output is a summary and a results list. This list can be exported in comma-separated values (CSV) format in order to be transferred as samples to PSPP. The extraction of the EEM was carried out through a different methodology, as more calculations were required, in contrast to the energy balances. Calculated variables such as redundant data were not included in the data model. A programmed script (without a graphical user interface) was made and used. This script extracts the data from the database, automatically performs a simple plausibility check of the EEM data, and then adds these calculated variables to the dataset of the sample for each measure. The script performs multiple EEM processing of the economic efficiency calculation, which is otherwise done by the application for individual audits. The CSV file generated by the script serves as an import source for PSPP.

14

1

Introduction

Figure 1.4 Screenshots of the analyzing tool

1.5

Structure of the Work

This book follows a structure of consecutive chapters, as is presented in Figure 1.5. The chapters do not present content in a strictly linear order. For the modeling in Chapter 6, different aspects, including energy balances, EEM, and the principles of multiple energy audit processing, are investigated in detail. These aspects are only weakly related to each other, so a sequential reading direction between chapters 3 to 5 is not required. Chapter 2 examines the basics of energy audits through a comprehensive literature review. This section investigates the term “audit” itself by comparing financial audits, as auditing has existed much longer in the financial sector and thus has a clearer framework for definition. The energy audit as a political instrument is examined with a focus on the mandatory energy audits that have been in place in the EU since 2015. In addition, a comparative study between Romania and Germany is presented based on the national regulations of these mandatory energy audits. Research into

1.5 Structure of the Work

Figure 1.5 Structure diagram of the book

15

16

1

Introduction

quality aspects in energy audits is an important prerequisite for modeling, as official standards such as ISO 50002 only take very few quality criteria into account. Among the indicators of the quality of energy audits are NEB, which are also dealt with in detail in this text. At the end of the chapter, several scientific papers of particular relevance for this work are also listed. Chapter 3 examines the properties of energy balances in preparation for modeling. Since the literature review provides only a small number of usable findings, separate definitions for energy balances, balance limits, and energy input had to be developed. The data model for energy audits aligns with these definitions. This chapter also discusses the qualitative aspects of energy balances, which are mainly influenced by the completeness and method of recording of the consumption data. Because data from short-term measurements of networked measuring devices lead to better energy balances, new types of radio networks and industrial internet of things (IIoT) standards are examined for their suitability for measurements and discussed through two case studies. Chapter 4 is dedicated to EEM, another important component of energy audits. This chapter is limited to the business aspects of EEM, as the physical-technical calculation of energy savings has an unlimited spectrum and cannot be defined by a fixed pattern. However, the economic efficiency can be determined through standardized methods of investment calculation. In this chapter, the most well known standard methods are analyzed for their application and suitability for EEM, and their qualities are discussed. Chapter 5 lays the groundwork for the scientifically unexamined field of the multiple energy audit processing. This section aims to define this topic for future discussions. The multiple energy audit processing is applied in two situations, each of which has its own dedicated section in this chapter: cross-audit evaluation, which is already used in the evaluation of audit programs, and the field of making multiple energy audits. The efficient conduct of a larger number of energy audits is a topic that has not yet been investigated and has become more relevant since the introduction of mandatory auditing in the EU in 2015, as energy audits must now be carried out in large numbers. Given these changes, auditors and companies are looking for tools to carry out large numbers of audits efficiently while maintaining their high quality. Chapter 6 presents the developed data model for energy audits in detail. The model is divided into submodels for corporate structures, companies, energy balances and EEM. Each of these submodels is illustrated separately in the form of an entity-relationship (ER) diagram and the individual data are described with their intended use. The description of the data is accompanied by references to their practical use through the application and through the audit report. The ER diagrams presented in this text were reduced to their essentials so that the presentation of the

1.6 Conventions

17

models could still be arranged well. The modeling of NEB is dealt with in a separate section. Chapter 7 carries out the validation of the data model by analyzing 3.861 energy balances and 6.768 EEM from energy audits effectively conducted in companies. In this way, the general validity of the data model is proven. The analysis is done separately for the data of the energy balances and for the data of the EEM. These batches of data are evaluated according to different aspects, and their descriptive statistics are presented in detail. In the first section, the multi-stage cleansing procedure is described, with which the originally far larger number of samples has been reduced by deleting implausible data sets. Some of the evaluations with less interesting information have been moved to the appendix. Chapter 8 presents a summary by comparing the results of this study with the original hypothesis. In this section, the model and the results of the sub-objectives are evaluated. In addition, numerous suggestions for further research are outlined.

1.6

Conventions

The following conventions have been applied in this work: • All texts, images, and Figures from external sources are provided with corresponding source references, which are listed in the bibliography. • All pictures, tables, and graphics without explicit source references were created by the authors. • Numbers follow the format commonly used in Germany, with a dot as a separation for units of thousands and a comma as a separator for decimal units (e.g. 2.208,71 kWh).

2

Theoretical Consideration of Energy Audits

This chapter examines the theoretical foundations of energy audits and establishes a link to scientific work in this field. To get to the roots of audit theory, a look is taken at the outside domain of financial audits to transfer the concept of auditing from there to the domain of energy. The energy audit is also described in its role as a political instrument, especially in connection with European energy policy. In preparation for the modeling in the further chapters, aspects relating to the quality of energy audits will also be compiled. In the chapter’s final section, two scientific papers are presented that have a particularly strong relation to this work.

2.1

Literature Research on General Audit Theory

The term “audit” comes from the Latin “audire” (to hear, listen) and stands—as there is always a speaker for a hearing—symbolically for a second person as the recipient of information. This bilateral communication serves to eliminate ambiguities. The fundamental task of an audit is to eliminate doubts and uncertainties. Tom Lee (Lee 1993, p. 19) expresses the origin of the need for audits using two connected propositions: “First, certain identifiable but not necessarily observable factors in human activities appear to trigger doubts and uncertainties in the mind of individuals affected by them. Second, these doubts appear to create a need for some form of verification function to either reduce or remove them” (Lee 1993, p. 19). Electronic supplementary material The online version of this chapter (https://doi.org/10.1007/978-3-658-33167-2_2) contains supplementary material, which is available to authorized users. © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2021 M. Krutwig und A. Dumitru Tan¸ta˘ u, Energy Audits, Sustainable Management, Wertschöpfung und Effizienz, https://doi.org/10.1007/978-3-658-33167-2_2

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2 Theoretical Consideration of Energy Audits

The verification of these factors must then be carried out by an independent authority, which brings with it a more precise and expert examination of these human activities. The result of the verification process is a qualitative and/or quantitative opinion by the auditor on the questioned object. However, the audit has far more elements than the term “hearing” suggests. Simplified, an audit can be seen as a procedure of detailed verification and recording of facts using standardized procedures. The procedures themselves are always application or domain-specific and each audit is subject to a specific theory—the so-called auditing theory (Mautz and Sharaf 1961). In searching for the theoretical basics for audits, it makes sense to switch to the specialist domain of accounting, as auditing is much more widespread here, has been applied earlier, and has, therefore, been scientifically investigated much more extensively. Financial audits appear much more frequently, as they are carried out in practically every large company for auditing purposes. A definition of auditing by the American Accounting Association is given in an article from 1972 (Silvoso 1972), according to which an audit is: “… a systematic process of objectively obtaining and evaluating evidence regarding assertions about economic actions and events to ascertain the degree of correspondence between those assertions and established criteria and communicating the results to interested users” (Silvoso 1972).

Another common definition of “auditing” can be found in a textbook from the year 1997 (Arens et al. 1997): “The accumulation and evaluation of evidence about information to determine and report on the degree of correspondence between the information and established criteria. Auditing should be done by a competent independent person.” (Arens et al. 1997)

The roots of auditing lie much earlier. In their search for the philosophy of auditing, Mautz and Sharaf describe sources that take up the concepts of auditing, dating back to 1905 (Mautz and Sharaf 1961). The first work on the term “auditing” and the associated accounting standards dates from the early 1950s, as a study from Australia indicates (Gibson and Arnold 1981). The authors (Mautz and Sharaf 1961) put a relationship between philosophy and auditing. According to this, auditing is an independent discipline that complements an existing domain but is not part of it. Contrary to the constructive tasks of accounting, the task of auditing lies in analysis: “Thus auditing has its principal roots, not in accounting, which it reviews, but in logic on which it leans heavily for ideas and methods” (Mautz and Sharaf 1961, p. 14).

2.1 Literature Research on General Audit Theory

21

A good overview of the history of auditing, which also describes the changes in audits over time, is provided in (Lee 1993, pp. 57–71). Ian Dennis examines audit theory and the associated terms such as “practices,” “conceptional frameworks,” “concepts,” and “conceptual inquiry” (Dennis 2018). According to its definition, an audit theory is “normative theory” expressed in a “conceptual framework” (Dennis 2018, p. 38), which illustrates the close relationship between the conceptual framework and the regulatory norms of an audit. To develop such a conceptual framework, the audit’s objectives must be determined. Dennis illustrates this (Dennis 2018, pp. 38–43) using the objectives of ISA 200 (International Auditing and Assurance Standards Board (IAASB) 2008) as an example. Audit Standards To ensure the quality of audits, there are standards to which audits are subject. In the age of global business, global standards are also required. In accounting, the International Standards on Auditing (ISA) are internationally recognized principles that are also observed by the International Federation of Accountants (IFAC), an international association of auditors established in 123 countries, taking into account national and local peculiarities. The ISA rules are being developed by the IFAC’s International Auditing and Assurance Standards Board (IAASB) (Humphrey and Loft 2009). Generally Accepted Accounting Principles (GAAP), US accounting regulations, and generally accepted accounting practices (Flood 2012) are also widely used in the USA. Another well-known US example of accounting standards is the Generally Accepted Government Auditing Standards (GAGAS) ((United States Government Accountability Office (GAO) 2018) published by the United States Government Accountability Office (GAO). This GAGAS, also commonly referred to as the “Yellow Book,” is divided into three areas: 1. General Standards: This part (Chapter 1–7) describes the philosophy behind the audits in terms of foundation, principles, and requirements. It covers ethics, independence, professional judgment, competence, quality assurance, and the basic standards for financial audits and for attestation engagements and reviews. 2. Standards of Field Work (Chapter 8): Here, specifications are made for the planning, the practical execution of the audit, and the presentation of evidence. 3. Standards of Reporting: These standards set out the format and content of the written audit report. A part of the report is the results’ evaluation, which the audit sorts into one of four types:

22

2 Theoretical Consideration of Energy Audits

a) unqualified “clean” opinion: this is the best possible assessment. In this case, the auditor certifies that the financial situation and (in the case of companies) the compliance with all prescribed standards is presented flawlessly. b) qualified opinion: Here, the auditor sees a fundamentally positive fulfillment of the standards, but with individual reservations and concerns about the prerequisites. c) adverse opinion: Here, the auditor certifies that the company’s financial statements have been distorted or falsified and that the actual situation of the company differs significantly from the statements made. d) disclaimer of opinion: In this case, the auditor refuses an opinion because the basic requirements for issuing an opinion are not met. A detailed discussion of these opinions can be found at (Hermanson, J. R. Strawser, and R. H. Strawser 1989, pp. 644–673). In searching for the roots of audits, this information from the field of accounting should help us to better understand the character of energy audits. It should not remain unmentioned that audits are also carried out in numerous other specialist domains, such as medicine, information technology, security technology, and quality assurance. Independence of the Auditor An auditor shall conduct an audit in a generally competent, responsible, honest and independent manner. In particular, the auditor must resist any influence by a company’s management and accept possible conflicts of interest between management’s objectives and his own assessment in the report. The personal relationship between the auditor and the company has a significant influence on the audit’s outcome (F. Ball, Tyler, and Wells 2015). This work suggests a regular rotation of auditors or audit firms to avoid excessive bias and to increase audit quality. Incentives, as well as regulations, contribute to ensuring the auditor’s independence; both mechanisms are discussed in detail in (Lee 1993). According to this, regulations must, above all, eliminate the influence of financial interests (e.g., by setting a fee scale) and rule out personal bias (e.g., the auditor’s equity interest). Regulations also guarantee the auditor’s competence by prescribing certain training courses and certifications as prerequisites for admission as an auditor.

2.2 Investigations on Energy Audits

2.2

23

Investigations on Energy Audits

An energy audit is the systematic inspection and analysis of the energy usage and energy consumption of a plant, building, system, or organization to identify and report on energy flows and the potential for energy savings. The goal of an energy audit is to improve energy efficiency. What is energy efficiency? Energy efficiency is always expressed in terms of a relative quantity to a reference value and can be defined generally as the following ratio (Patterson 1996): Useful output of a process Useful input into a process

(2.1)

An increase in energy efficiency can, therefore, be linked to an increase in this ratio. According to Patterson, this reference value can be divided into four basic categories: thermodynamics, physical-thermodynamics, economic-thermodynamics, and economics (Patterson 1996). An elaborate classification of energy efficiency is provided by (Xia and Zhang 2010). The acronym POET describes a general classification scheme for the four factors (P)erformance, (O)peration, (E)quipment and (T)echnology. In the context of an energy audit, according to (Xia and Zhang 2010), technology efficiency describes, for example, the optimization of a heating system, while the replacement of the complete heating system falls into the category equipment efficiency.The optimization of the heating control based on external weather data would be in the category operation efficiency, the performance efficiency would be determined by additional factors via so-called energy power indicators such as heating energy per capita. According to the article “The energy-efficiency gap” (Jaffe and Stavins 1994), there is no way to achieve 100% optimal energy use. The authors describe several levels for the achievable potential for energy efficiency, which are only partially achievable in real-life situations. The fact that even economically viable EEM are not always implemented is referred to here as the “energy paradox” (Jaffe and Stavins 1994). An energy audit can—but does not need to—be conducted following the requirements of the European Standard EN 16247-1. This standard aims to define a good quality energy audit and tries to harmonize common aspects of energy auditing to bring more transparency to the market of energy services. Numerous government subsidy programs and obligations for energy audits are based on this standard as a prerequisite for maintaining quality. The standard targets commercial and public sector organizations while explicitly excluding private households.

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2 Theoretical Consideration of Energy Audits

The above mentioned standard specifies the quality requirements for the energy auditor and the energy audit process (DIN EN 16247-1:2012 2012): The auditor must have experience in the audit field and be appropriately qualified for the task to be performed. No specific proof of qualification is stated in the standard. Moreover, confidential handling of all information obtained by the auditor during the performance is required. In addition, the auditor must deal with the interests of the organization objectively and with priority (DIN EN 16247-1:2012 2012, p. 6). The standard sets out six essential requirements for the energy audit process (DIN EN 16247-1:2012 2012, p. 7): appropriateness, completeness, representativeness, traceability, expediency, and verifiability. Further explanations of these aspects can be found below in Section 2.6. The audit process itself is also defined by the standard and comprises seven consecutive stages 2.1: 1. During preliminary contact, several agreements are made between the auditor and the organization and initial questions about the energy audit are clarified. Further to defining expectations and objectives, the planned time frame, and the concrete scope of application, the balance sheet limits, and the thoroughness of the investigation are also determined. The thoroughness concerns the selection of the examined objects, as well as the decision on the consumption measurements to be carried out. The involvement of the organization by providing

Figure 2.1 The energy audit procedure according to EN 16247-1

2.2 Investigations on Energy Audits

2.

3.

4.

5.

6.

25

access possibilities, personnel resources, and existing data is also determined in this step (DIN EN 16247-1:2012 2012, p. 7). In the kick-off meeting, the auditor will advise all persons involved in the audit’s concrete implementation. At this point, cooperation between the auditor and the organization is regulated, dates for the on-site examination are set, and persons from the organization who will actively accompany the audit are determined. Further procedures, such as the installation of a measurement system, are also agreed upon here (DIN EN 16247-1:2012 2012, p. 8). The data collection for the preparation of the energy balance establishes a list of all energy-relevant buildings, consumers, processes, and facilities. All current and historical consumption values, data from purchasing, tariffs, and other energy-relevant data are collected. In addition, any existing adjustment factors and non-energy-related data are also recorded, as far as they are required for creating energy performance indicators (EnPIs) (DIN EN 16247-1:2012 2012, p. 9). External operation describes the examination of all objects to be tested onsite. This fieldwork helps the auditor to understand the processes and the user behavior and for a first assessment of possible efficiency measures. Here, the list of objects is validated and, if necessary, supplemented and further consumption data is obtained, e.g., via temporary measurements. During the site visit, the company must support the auditor with accompanying persons and the provision of all necessary accesses and documents (DIN EN 16247-1:2012 2012, pp. 9, 10). The analysis produces the energy balance and energy description of the organization on the one hand, and the possible efficiency measures (EEM) are determined and calculated on their economic viability on the other. Besides comparing energy input and energy consumption on a balance sheet, the energy description also documents the adjustment factors and defines the EnPIs, which are used to make the energy efficiency comparable. When calculating the EEM, an energyrelated investment calculation is carried out to determine the economic value of individual measures and then prioritize them (DIN EN 16247-1:2012 2012, p. 10). A report summarizes the results of the previous steps and aligns them with the original objectives. The standard specifies the components that must be contained in the report (DIN EN 16247-1:2012 2012, pp. 11, 12): a) A summary with the prioritized list of EEM. b) Background information on the auditor and the audit. Description of the organization, objects, relevant standards, and regulations.

26

2 Theoretical Consideration of Energy Audits

c) Description of the energy audit as defined in step 1 and step 2 and how it was performed. Information on data acquisition must also be included here and the way consumption values are determined by measurements or estimates must be documented. d) A detailed description of the EEM. Presentation of EEM calculations, recommendations, suggestions, and timing for the adoption of EEM, information on funding opportunities and advice on how to prove the effectiveness of adopted EEM afterward. e) Conclusions. The standard does not specify these components as a fixed content structure. 7. There must be a final presentation in which the report is handed over and the results of the energy audit are presented in such a way that the organization can decide on the adoption of EEM (DIN EN 16247-1:2012 2012, p. 12). The global standard ISO 50002 (ISO 2014) has been in existence since July 1, 2014, as an offshoot of the European standard EN 16247-1. These two closely related documents are already consistent in numerous sections, such as the basic requirements, when the audit is carried out, and the required content for the audit report. However, the ISO standard clarifies many organizational issues, such as responsible persons, their roles, and tasks right at the beginning of the audit. The ISO standard also becomes more concrete in the qualification of the auditor: Anyone who is not familiar with the energy uses audited, does not know the important regulations and standards, and has no technical skills in the relevant areas may not carry out energy audits. In an additional section (ISO 2014, pp. 8, 9), specifications for a measurement plan are made. Here, the data acquisition by energy measurements on-site is defined. The plan contains a list of all measurement points, their consumers, the measuring devices used and further details on the duration, accuracy, and frequency of the measurement over a representative period. If a measurement is not economically feasible or even technically impossible, sampling procedures for determining the data are also available. For sampling, reference is made to the Guidelines for Auditing Management Systems in ISO 19011:2011 (ISO 2011), Clause B.3. Another difference between ISO 50002 and EN 16247-1 is the more intensive presentation of the identification of EEM (ISO 2014, pp. 10, 11). According to the ISO standard, the final report must also show more transparency and plausibility in measurement and data analysis. Beyond the standards, further regulatory requirements can ensure the quality of energy audits. Whether and which requirements exist depends on the context of the energy audit. If audits are funded by subsidized programs, their performance can be linked to the EN 16247-1 standard and auditors must prove their qualification,

2.2 Investigations on Energy Audits

27

as is the case with the SME funding program in Germany (BAFA 2017a). The promotion of concrete measures for efficiency improvements could also be linked to the systematic auditing and analysis of processes (partial audit), for example, in the German program for the promotion of cross-sector technologies (BAFA 2018). Classification of Energy Audits Energy audits can be classified according to several parameters. One parameter is the motivation for the energy audit, i.e., whether the audit is voluntary or based on a legal obligation. In the case of audits carried out voluntarily, the interest lies in reducing energy costs. Government support programs, such as the program to promote audits in small and medium-sized companies in Germany (BAFA 2017a), strengthen this motivation. A legal obligation for energy audits, as introduced in all European member states for large companies, provides a completely different motivation, which is the avoidance of penalties for non-compliance. Another parameter is the Completeness, where a distinction is made between an overall and partial energy audit. An overall energy audit examines all areas of application of the energy balance while a partial energy audit focuses on sub-areas, processes, or technologies. Examples are building insulation, lighting, or audits for production processes in special industries such as agriculture. For partial energy audits, government support programs create targeted incentives for savings measures in specific areas. In Germany, for example, the program for the promotion of crosssector technologies (BAFA 2018) is a well-known support measure. The overall energy audits require a qualified person as auditor with broad competence in many energy-related areas. Here, it is apparent that this auditor can not have in-depth specialist knowledge in all technological areas and is, therefore, dependent on the help of other experts for calculating measures in these specialist domains. Thoroughness in accordance with coverage of energy consumption is another parameter, especially in general energy audits. Energy consumption coverage is the percentage ratio of purchased and self-produced energy to the sum of all consumption values recorded in the audit as given in Equation 3.3 on page 60. It is practically impossible to achieve 100% coverage in large companies, as the effort required to record each individual, small consumer is not economically proportional to the effort required for the audit. In mandatory energy audits, this ratio is usually prescribed as a minimum criterion for thoroughness; in Germany, for example, it lies at 90% (BAFA 2019d). In (Kumbhar and Joshi 2012), energy audits are classified into three types according to the thoroughness of their implementation: A walkthrough audit is a quick inspection and a rudimentary data collection to get a first overview. This form of audit is used to prepare for a subsequent, detailed energy audit. The intermediate audit takes a closer look at the systems and focuses on EEM that can

28

2 Theoretical Consideration of Energy Audits

be discovered and performed without deep technical specialization. The comprehensive audit also looks at processes and subsystems and evaluates the secondary energy consumers. This approach is relatively fuzzy and incomplete. For example (Kumbhar and Joshi 2012), the walkthrough audit gives a quantification of the effort from three days to one week, although this factor depends strongly on the size of the object to be examined. A similarly pragmatic approach is described (E. Cagno et al. 2010), which divides audits into three types: walkthrough audits, mini-audits, and maxi-audits. The distribution of the audit is another parameter for classification, a distinction can be drawn between single-site and multi-site energy audits. Companies with a single location are managed with a single energy audit. If the company has several sites that are geographically distributed, the audits are also conducted separately because of the separate energy balance. In addition, these audits can also be transferred and presented in a joint energy balance. For companies with a high number of identical or similar locations, as is typical for chain stores, the auditing effort can be reduced by forming clusters of similar locations—i.e., locations with a similar consumer structure and with potential for similar savings measures. In this case, only a single representative location per cluster is audited. In the case of mandatory energy audits, the respective authorities prescribe the mode according to which relief may be applied in the case of multi-sites. In Germany, these requirements are found in (BAFA 2019c). Energy Audits vs. Energy Management Systems The energy audit is a basic instrument for organizations to get transparency in energy flows and to get the opportunity to improve energy efficiency. A much more comprehensive and sustainable measure to achieve these goals is given by means of an energy management system (EnMS). The associated international standard is the ISO 50001 (ISO 2018), which was established as the successor to the European Standard European Standard EN 16001 (EN 2009). The evolution of ISO 50001 from this predecessor and a comparison between the two standards can be found in (Duglio 2011). An energy audit is a non-recurring, linear process that can be conducted with the support of an expert and with moderate internal effort. An EnMS, on the other hand, establishes a continuous process with the aim of steadily improving energy efficiency. The management system is the entirety of interrelated or interacting elements of an organization to establish an energy policy and strategic goals and for achieving these goals (EN 2009). The standard defines the requirements for the introduction, implementation, maintenance, and improvement of energy manage-

2.2 Investigations on Energy Audits

29

ment. It is intended to enable an organization to adopt a systematic approach to the continuous improvement of energy efficiency (ISO 2018). The ISO 50001 model is based on a PDCA (Plan-Do-Check-Act) cycle that originated in Japan in the 1950s (Moen and Norman 2006). In this cycle, energy policy to improve transparency or efficiency is regularly planned (Plan). These measures are introduced and implemented (Do), later checked for their effectiveness (Check), and finally subjected to a management review (Act) (ISO 2018). Besides measuring the effects on energy consumption, the review also includes tests for nonconformities and the review of the management system itself through an internal audit. The process is shown in Figure 2.2.

Figure 2.2 PDCA-cycle of ISO 50001. Source author, based on (ISO 2018)

Compared to an energy audit, the effort and costs for a certified EnMS are significantly higher. A non-quantifiable minimum level of energy consumption and savings potential is, therefore, required for an organization to use an EnMS economically. Besides the internal costs for the introduction and organization of the

30

2 Theoretical Consideration of Energy Audits

management process and costs for the certification required every three years, costs for software and hardware for energy monitoring and measurement technology must also be calculated. In addition, there are costs for the adoption of the EEM, whose investment level is very individual. Costs can be saved by combining the EnMS with other already existing management systems, such as systems for quality management according to ISO 9001 (Hoyle 2017) or an environmental management system according to ISO 14001 (MacDonald 2005), and by using their organizational PDCA processes and document management. The duration for the introduction of an EnMS depends on synergies with other management systems, the size of the organization, and the number of energyrelevant objects. Martin Howell proposes a timetable in his ISO 50001 (Howell 2014, pp. 153–156) implementation manual, according to which the EnMS can be implemented in less than four months. An approach for an efficient energy assessment and reporting methodology for the implementation of an EnMS according to ISO 50001 offers (Kanneganti et al. 2017) in a sector-independent process modeling. This model focuses with sections 4.4.3. to 4.4.6. on the sub-area of the management system that has the character of an energy audit. Therefore, this approach can also be of interest for implementing energy audits. A method for metering assessment to identify submetering needs at a manufacturing facility (Rao, Muller, and Gunn 2017) is a supplement to this, as the question of the need for cost-intensive submetering is also relevant for energy audits. There can be various motivations for companies to introduce a management system according to ISO 50001. In addition to the economically driven goal of cost savings, three further motives for adopting a management system according to ISO 50001 can be named: social requirements, ecology drivers, and competitive advantage (Marimon and Casadesús 2017). Another motivation for large companies to adopt a certified EnMS can also be the exemption from the obligation to carry out energy audits, which, according to the European Energy Efficiency Directive (European Parliament 2018) and the corresponding national legislation (Deutscher Bundestag 2019), must be carried out by all non-SMEs every four years. On the other hand, there are numerous challenges that an organization must overcome when implementing an EnMS according to ISO 50001. In a recent literature review of 17 articles from 2012 to 2019, a total of eleven major challenges were identified (frequency of citations in brackets) (Rampasso et al. 2019): • Lack of resources–limitations (time, financial, staff, technologies, infrastructure) (9) • Difficulty in determining the energy baseline and energy performance indicators (6)

2.2 Investigations on Energy Audits

• • • • • • • • •

31

Human resources deficiencies (knowledge, competencies, and abilities) (4) Lack of management support and/or commitment (3) Lack of clear policies (governmental or organizational) (2) Difficulty with properly evaluating the benefits generated by the adoption of ISO 50001 (2) Difficulty with fully reaching the energy and carbon efficiency enabled by ISO 50001 (1) Barrier in acquiring external consultants (1) Difficulty in managing third-party international certifications (1) Lack of proper management of documentation (1) Difficulty in maintaining the certification (1)

No comparable study was found for the challenges of carrying out an energy audit. If one reduces the arguments of (Rampasso et al. 2019) to the domain of audits, most of these challenges are not applicable.

Table 2.1 Comparison between energy audits and energy management systems Energy audit

Energy Management System ISO 50001

Estimated duration

EN 16247-1 ISO 50002 Audit process, report contents, and requirements for the auditor 2–4 months

Result Management system

Audit report none

Cost factors

External auditor, internal personnel resources, and in some cases costs for measurements One-time Report on saving potentials.

Standard Main contents

Costs incurring Benefits

Requirements for the introduction, implementation, maintenance, and continuous improvement of an EnMS 3–18 months for introduction, then continuously Certificate Management system is based on the PDCA cycle. Can be combined with other PDCA management systems. External consultant or internal expert, external certifier, internal organization team, energy monitoring system and measurement equipment Regular Continuous improvement of energy efficiency is possible. Positive public image through certificate

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2 Theoretical Consideration of Energy Audits

A comparative overview of the differences between energy audits and energy management systems is given in Table 2.1. Often, an energy audit without analysis of EEM can also be used as a preliminary stage for introducing an EnMS, as the determination of the energy balance and the consumers is an important part of energy planning (chapter 4.4.3 of ISO 50001).

2.3

Comparison between Financial Audit and Energy Audit

Historically, energy audits have been much younger than financial audits and their number and importance are much smaller. The literature search for this work did not reveal any sources before 1975. The first records of energy audits for this were found in (C. E. Anderson 1975) and (Hora 1975); thus the origins of energy audits can be assessed to the ’70s. Compared to the general term “audit” introduced above and to auditing in the domain of business accounting, there are clear differences in energy audits. The triangular constellation between company, skeptic, and auditor also exists here, but the terms “doubt” and “verification” are fading into the background. The focus here is on proving a situation the company itself is often not aware of in detail before the audit. Here, the (energy) balance is not checked, but is created with the help of an auditor and possible savings measures are worked out by the auditor. What remains is obtaining evidence of energy flows, which the company would not be able to obtain itself in many cases without the auditor’s support because of a lack of knowledge and equipment. Especially when comparing auditing theory, it becomes clear how low the concepts of energy audits are elaborated in comparison to financial audits. The objectives required in (Dennis 2018) are not laid down in any official standard. Therefore, the assertion is made here that there is no universal, conceptual framework for energy audits and that they cannot be derived from both known standards DIN EN 16247-1 (DIN EN 16247-1:2012 2012) and its successor ISO 50002 (ISO 2014). In principle, the auditor itself must fulfill the same characteristics as in the financial audit. On closer inspection, however, there are differences, because the auditor does not act in the interests of shareholders or other private sector actors. It, therefore, does not pose a potential danger to management by uncovering grievances and irregularities. Nor does it provide any assessment that could endanger the company. A conflict of interest with management is, therefore, less likely. Based on the existing literature, it can be assumed that energy audits are almost always carried out as a measure within funded audit programs or as part of an

2.4 Research on the Political Significance of Energy Audits

33

audit obligation. In the latter case, a public, state authority assumes the role of a skeptic who has an interest in thorough, honest, and high-quality auditing. While funded audits are supposed to provide incentives for energy savings and are in the interest of the company because of their voluntary nature, mandatory audits such as those prescribed by EED (European Parliament 2012) offer little motivation for companies. The study in (M. C. Krutwig and Tan¸ta˘ u 2018) shows clear differences in quality between voluntary and mandatory audits with regard to the savings potential found in the audit. In financial audits, there are experts for different types of audits. Auditors specialize in areas such as auditing, business valuation, transaction valuation, risk assessment, and in specific industries. In the case of energy auditors, this is done in the same way, with the auditors also specializing in specific energy and technology areas, processes, or sectors and company types. A special case here is the mandatory energy audits, which require an almost holistic examination of the company. For companies with a large variety of products, energy sources, systems, and consumers, this can lead to excessive demands on the auditor. There is a potential risk that additional experts will be dispensed with for cost reasons and that an incomplete result will be accepted concerning the recommendations for efficiency measures.

2.4

Research on the Political Significance of Energy Audits

Energy efficiency was and is a strongly politically motivated topic. The general interest here is to save energy to secure the overall economic energy supply and to increase independence from imports of fossil energy sources. Further, the protection of the environment from harm caused by emissions resulting from producing and using primary energy is also a central concern. During roughly the last 20 years, especially in Europe, the reduction of carbon dioxide emissions to protect the global climate has also been a major focus of energy policy. Programs for energy conservation and energy efficiency improvement have been in place since about 1970 in response to the energy crisis in the US (York et al. 2012). These exist in the form of subsidy programs or energy efficiency obligations (EEO) for companies and private households. The macroeconomic study (Rosenow, Cowart, and Thomas 2019) examines a total of 52 market-based instruments (MBIs) around the world for energy efficiency in terms of costs and benefits. The authors estimate a worldwide investment volume of $26 billion in EEM. In this study, the costs of these programs are estimated to average $0,013 USD/kWh and energy savings of about $0,026/kWh (Rosenow, Cowart, and Thomas 2019). A similar study limited to European EEOs (Rosenow and Bayer 2017) comes up with

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program costs of up to $0.011/kWh lifetime savings. The results of both studies identify the costs as significantly lower than the production costs for energy. A toolkit for setting up EEOs is presented in (Lees and Bayer 2016). This report provides a detailed introduction to many aspects of EEOs using examples. The concrete implementation of energy efficiency measures (EEM) is preceded by transparency. Energy audits are, therefore, one of the many instruments with which policymakers aim to achieve the objectives mentioned in this section’s introduction. Other possible instruments for achieving the goals are energy taxes, funding schemes for EEM, funding of energy networks (Durand et al. 2018) or tradeable certificates such as “white certificates” (Langniss and Praetorius 2006). A review of these policy instruments in the EU plus Japan and Norway provides numerous examples (Thollander, Rohde, et al. 2019). Another study (H. Lu and Price 2011) compares 22 programs on EEO and subsidies in 15 countries worldwide. Already in 2006, the EU Energy Service Directive (ESD) (Parliament and Council of the European Union 2006; E.-E. Commission et al. 2006) triggered the establishment of funding programs for energy audits in companies to exploit the untapped potential for energy efficiency. With the Energy Efficiency Directive (EED) 2012/27/EU (European Parliament 2012) amended by (European Parliament 2018), the European Parliament raised energy audits to a high level of importance in 2012. The EED provides a framework applicable to all member states, which obliges them to use energy more efficiently at all stages of the supply chain. The EED pursues the main goal of a 20% reduction in primary energy consumption, a 20% expansion of renewable energies, and a 20% reduction of greenhouse gases (GHG) by 2020. In (Commission 2013), these goals were increased to include a 30% reduction in primary energy consumption by 2030. Not all of the EU-28 member states fully comply with the EED. The study (Pereira and Silva 2017) examines different aspects of energy efficiency governance concerning their national implementation in the EU-28, divided into political support, financial capacity, human capacity and institutional structure. Figure 2.3 shows the result of this study (Pereira and Silva 2017). Concerning energy audits, Article 8 “Energy audits and energy management systems” of the EED is of high relevance for this work. This article demands energy audits: “2. Member States shall develop programs to encourage SMEs to undergo energy audits and the subsequent implementation of the recommendations from these audits” (European Parliament 2012, Art. 8.2). […]

2.4 Research on the Political Significance of Energy Audits

35

Figure 2.3 EU governance analysis

“4. Member States shall ensure that enterprises that are not SMEs are subject to an energy audit carried out in an independent and cost-effective manner by qualified and/or accredited experts or implemented and supervised by independent authorities under national legislation by 5 December 2015 and at least every four years from the date of the previous energy audit.” (European Parliament 2012, Art. 8.4).

In the further sections of Article 8, exceptions are mentioned to release large companies from the obligation to carry out energy audits in specific instances. This is the case if companies have established a certified energy management system or environmental management system (European Parliament 2012). Article 8 is not affected by the EED extension of 2018 (European Parliament 2018). SME funding programs are designed to lower the financial hurdle of having an energy audit performed by a qualified expert. The study (Fresner et al. 2017) examines 280 SMEs from seven member states and compiles the results of 140 audits. In addition to a satisfaction rate of 88% according to the survey, the audits

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revealed an energy-saving potential of 464 MWh per audit (5.580 toe in total) over the entire lifetime. The study (M. Krutwig and Tan¸ta˘ u 2019) examines an SME support program in Germany using the same methodology as in this book. With 567 audits of 476 companies, the potential savings amount to an average of 527 MWh per audit (111.660 toe for all audits). Data from European-wide surveys in 2017 and 2018 of the European Investment Bank on energy audits (Kalantzis and Revoltella 2019) confirms the effectiveness of energy audits and additionally points out the importance of available financing for EEM. Three notable studies for subsidized audit programs, which were initiated before the EED and which provide figures to prove the success of this instrument, are: • (Backlund and Thollander 2015) examines the Swedish Energy Audit Program (SEAP) established in 2010. Here, 241 audit reports from the period 2010 to 2012 are evaluated. As SEAP requires minimum energy consumption and not only SMEs are eligible, the total savings potential per company is between 6.980 and 11.130 MWh. • (Fleiter, Gruber, et al. 2012; Frahm et al. 2010) analyses a German SME subsidy program for energy audits to be carried out in the period 2008–2010. Here, 542 companies were examined for potential savings, taking into account EEM already carried out. Including the EEM that are still planned, this study comes to a saving of 711 GJ (198 MWh) per year. Based on the same samples, a second study (Fleiter, Schleich, and Ravivanpong 2012) was also prepared, which examines the adoption of EEM from energy audits. • (Thollander, Danestig, and Rohdin 2007) examines the results of 47 SMEs from the manufacturing industry, a Swedish SME program from the “Highland” project before 2006, where an average savings potential of 40 MWh per year was achieved. Audit programs have also been used successfully as a political instrument beyond the European Union for many decades. Exemplary reference is made to two studies: (S. T. Anderson and Newell 2004) examines in the United States the adoption of EEM of a program over a long period. In Australia (Harris, J. Anderson, and Shafron 2000) analyzes the Australian Audit Program (EEAP) between 1991 and 1997. Another requirement of the EED is the obligation of large companies to conduct energy audits. According to Article 8.4, the member states are required to establish a legal basis that obliges non-SMEs to carry out an energy audit every four years (European Parliament 2012). Penalties are to be imposed for non-compliance. This directive marks the first time a new form of energy audit—a mandatory audit— has been introduced. Whereas the motivation for an energy audit was previously

2.4 Research on the Political Significance of Energy Audits

37

always to achieve improved energy efficiency, the objective for companies here is now to avoid penalties by fulfilling this obligation at the lowest possible expense. It is obvious that with this new motivation, the topic of quality in energy audits will shift into focus. Although the EN 16247-1 standard provides specifications for conducting energy audits (DIN EN 16247-1:2012 2012), it is not sufficiently detailed for this particular form of audit to ensure minimum quality. Article 8 of the EED leaves a lot of leeway to the individual member states when adopting the directive into national legislation. In (Nabitz and S. Hirzel 2019) this transposition is described as a kind of mutual learning process. In Figure 2.4, the legislative process is presented as a cycle, which, on the one hand, feeds back the results and experiences from the respective national laws into the European legislation and, thereby, enters the directive in the form of improvements, amendments, and concretizations. Such extensions, as for example the amendment of the EED from 2018 (European Parliament 2018), in turn lead to new editions of national legislation, such as the new version of the EDL-G in Germany (Deutscher Bundestag 2019). An authority in each country is responsible for the execution and monitoring of national legislation on energy audits. As the process of the cycle, shown in Figure 2.4, and the parliamentary legislation usually takes several years, the national authority can make further non-parliamentary requirements and additions to the legislation. For example, it defines the precise regulations for subsidy programs and the requirements for mandatory energy audits. To maintain a minimum quality of the audits, a reference to the EN 16247-1 standard is insufficient. Therefore, the national authority issues further additional rules and guidelines for the conduct of energy audits. These non-parliamentary regulations have economically and legally significant importance for companies and, in contrast to parliamentary legislation, can be extended and adapted on short notice. A comparison between the different national transpositions of the EED has already been carried out in several studies, each of which is, however, limited to national parliamentary legislation. Non-parliamentary regulations are not considered in these studies. For instance, (Bertoldi, Zancanella, and Boza-Kiss 2016) compares the transpositions of Article 15 to the demand side report. A comparison with the transposition of Article 7 on EEO schemes is offered by (Forster 2016). In (Hirzel et al. 2016), a comprehensive comparison is made on the implementation of Article 8. The following subsection summarizes the results of a comparative study (M. C. Krutwig, Starosta, and Tan¸ta˘ u 2020), which exemplarily examines Romania and Germany concerning the national implementation of Article 8. This work also considers aspects of non-parliamentary regulations.

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2 Theoretical Consideration of Energy Audits

Figure 2.4 Policy cycle of Article 8 EED. Source (Nabitz and S. Hirzel 2019)

2.5

Political Significance: A Comparison Study between Romania and Germany

The study (M. C. Krutwig, Starosta, and Tan¸ta˘ u 2020) compares the national transpositions and regulations on Article 8 in the two EU member states, Romania and Germany. In particular, it aims to examine whether there is a locational advantage for companies concerning this legal obligation. All EU member states report their status, activities, and plans for implementing the EDD in a so-called National Energy Efficiency Action Plan (NEEAP). In this study, the Romanian NEEAP 2014–2020 (Parlamentul României 2015) and the German NEEAP until 2020 (BAFA 2017b) were taken into account.

2.5 Political Significance: A Comparison Study between Romania and Germany

39

Table 2.2 Key energy figures and targets from 2018 for Germany and Romania. Data source (E. Commission 2020) Energy production Gross inland consumption Energy intensity (GAE/GDP) Energy per capita [GIC/pop] Final electricity per capita Primary energy consumption, 2020–2030 target Final energy consumption, 2020–2030 target Primary energy intensity, 2020–2030 target

Mtoe Mtoe toe/Me’10 kgoe KWh Mtoe Mtoe toe/Me’10

DE

RO

113,33 314,4 106 3.798 6.195 291,8

25,0 33,5 199 1.715 2.333 32,5

215,4

23,5

98,1

192,7

A general examination of the two national economies already gives a rough overview of the number of energy audits carried out in both countries. According to Eurostat TPS00002, Germany has 83 million inhabitants, about four times as many as Romania (19.4 million), and the number of companies in Germany is more than five times as large at 2,46 million compared to 0,48 million. The difference is even greater in the number of large (non-SME) companies. Here, Germany with 11.379 companies (E. Commission 2018a) is far ahead of Romania with 1.664 large companies (E. Commission 2018b). Table 2.2 summarizes the most important figures from (E. Commission 2019b) on the respective national energy balances. The energy intensity figures are formed by the ratio of gross available energy (GAE) to gross domestic product (GDPD) (E. Commission 2019a). The number of large (non-SME) companies is an indication of the number of mandatory energy audits to be carried out. From (Mai, Gruber, Ashley-Belbin, et al. 2017) it can be seen that in Germany, a share of about 52% of the large companies had carried out a mandatory energy audit. The rest had a certified management system according to ISO 50001 or EMAS or were in the process of implementing it. The average number of sites per company is 34 (Mai, Gruber, Ashley-Belbin, et al. 2017). With these figures as assumptions and the statistical data (M. C. Krutwig, Starosta, and Tan¸ta˘ u 2020), the theoretical number of energy audits in Germany is 193.443 and analogously 28.288 audits in Romania. In practice, this number is likely to be much smaller, as many sites do not need to be audited because of the application of further exemptions such as the multisite procedure for many similar sites. The NEEAP from Germany (BAFA 2017b) estimates the total number of energy audits

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2 Theoretical Consideration of Energy Audits

at 60.000, including both mandatory and subsidized audits for SMEs. The report on the progress recorded in achieving national energy efficiency objectives (Romanian Energy Regulatory Authority (ANRE) 2019d) in Romania provides a number of 1.323 mandatory energy audits conducted between 2015 and 2018. The transposition of Article 8 of the EED in Germany was achieved by amending the “Gesetz über Energiedienstleistungen und andere Energieeffizienzmaßnahmen” (EDL-G) (Deutscher Bundestag 2019) published on 04.11.2010. The competent and controlling authority in Germany is the Federal Office for Economic Affairs and Export Control (Bundesamt für Wirtschaft und Ausfuhrkontrolle, BAFA). This law lays down the obligation to carry out energy audits for large companies. This obligation exists every four years and was to be fulfilled for the first time on 5 December 2015. This law also considers the exemptions from the obligation and the requirements for energy auditors. With the amendment of 2019, a de minimis limit was also introduced in Germany for the first time, so companies with an energy consumption of less than 500.000 kWh no longer have to conduct energy audits. Also introduced in 2019 was the obligation for all companies to report their energy consumption; before this, compliance was only checked by random sampling. In Romania, Article 8 of the EED is reflected in law 121/2014 on energy efficiency, published in the Official Gazette of Romania, Part I No 574 of 1st August 2014 (Parlamentul României 2014). Here too, there have been extensions in the sense of the improvement cycle according to Figure 2.4 by the amendment by law 160/2016 published in July 2016 (Parlamentul României 2016). The competent authority in Romania for the application of the law is the National Energy Regulatory Authority (Autoritatea Nationala de Reglementare în Domeniul Energiei, ANRE). In terms of content, law 121/2014 is reduced to a translation of the EED in the national language without any significant concretization. The picture 2.5 illustrates the two transpositions of Article 8. In both countries, the respective parliamentary laws are supplemented by further regulations. In Germany, the EDL-G refers to the implementation according to the standard DIN EN 16247-1 (DIN EN 16247-1:2012 2012). BAFA has published further publications on the regulation of the audit obligation, which are adapted and extended at regular intervals. Since the end of 2019, an instruction sheet (BAFA 2019c) has been in existence to specify details of the interpretation of the EDL-G. This instruction sheet sets out the specific qualifications for energy auditors, as well as the exact criteria for deciding whether a company is obliged to carry out an energy audit or not. Details on the performance of the audit and the preparation of the audit report are also contained in (BAFA 2019c). In 2019, a detailed guideline (BAFA 2019b) was added, which deals with the elements to be fulfilled and the audit report and provides concrete methodological guidelines for calculating the EEM. Another

2.5 Political Significance: A Comparison Study between Romania and Germany

41

Figure 2.5 Transposition of Article 8 EED in Germany and Romania. Source (M. C. Krutwig, Starosta, and Tan¸ta˘ u 2020)

guideline specifically for calculating total energy consumption (BAFA 2019d) has also been in existence since 2019, which serves as the basis for the above-mentioned de minimis limit. In Romania, too, parliamentary law is being expanded to include nonparliamentary regulations. The ANRE Decision No. 2794 (Romanian Energy Regulatory Authority (ANRE) 2014b) with the extension from Decision 1111 (Romanian Energy Regulatory Authority (ANRE) 2017) regulates which qualifications of individuals and companies are required to carry out energy audits. The specifications for the performance of the audits and the required components of the audit report are defined in the guideline according to ANRE Decision 2123 (Romanian Energy Regulatory Authority (ANRE) 2014a). Further instruction sheets, such as ANRE Decision 1685 (Romanian Energy Regulatory Authority (ANRE) 2019a) and ANRE Decision 366 (Romanian Energy Regulatory Authority (ANRE) 2019b) are dedicated to the proof of verifying compliance with the minimum criteria when conducting energy audits, the determination of the total energy consumption and the contents of the report to be submitted with the audit. In (Romanian Energy Regulatory Authority (ANRE) 2019c) the legislative framework regarding the EED transposition for Romania is summarized in English. The implementation of the audit requirement in Germany and Romania can be further examined using the following criteria (M. C. Krutwig, Starosta, and Tan¸ta˘ u 2020):

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• In defining “non-SMEs,” both countries follow the EU definition with the three criteria: number of employees (250 or more), annual turnover (over 50 million e), and balance sheet total (43 million e) (European Parliament 2012, p. 2.26). This does not apply to all MS. Some countries, such as Croatia or Slovenia, take other thresholds into account (Nabitz and S. Hirzel 2019). • The penalty for non-compliance with the audit obligation is up to 50.000 e in Germany (BAFA 2019c, p. 22) and slightly lower in Romania at up to 200.000 lei (Parlamentul României 2016, Art. 18). • In Germany and Romania, the exemptions apply equally to companies with a certified energy management system according to ISO 50001 (see page 28 ff.) or a certified environmental management system according to EMAS (Hillary 1993) or ISO 14001 (MacDonald 2005). Municipalities and organizations with sovereign functions are also exempt from the audit requirement. However, the two countries make a difference in terms of the de minimis threshold: while, in Germany, a total annual consumption below 500 MWh exempts from the audit obligation, in Romania, this threshold is 11.630 MWh (1.000 toe). • The minimum coverage with the consumers shown in the energy audit concerning the energy used is 90% in Germany (BAFA 2019c, p. 14) and only 50% in Romania (Parlamentul României 2016, Art. 1(12)). This means that in Romania, considerably fewer consumers have to be considered in detail. The requirement reduces the audit to the main consumers. This factor has a great influence on the costs of the energy audit, but also on the quality of the entire audit. • A distinction between audit classes is only made in Romania. Here, audits are divided into two classes, depending on total consumption and the type of energy source mainly used (Romanian Energy Regulatory Authority (ANRE) 2014b). • A required notification of audits carried out or exceptional situations by the companies to the authorities has existed in Romania since 2016 (Romanian Energy Regulatory Authority (ANRE) 2019b) In Germany, this obligation has existed since 2019 (BAFA 2019c, pp. 20, 21). For numerous other comparison criteria, no sufficient information could be obtained from both countries (M. C. Krutwig, Starosta, and Tan¸ta˘ u 2020). These include criteria such as the use of multi-site procedures and the sampling approach, rules for the structure of the audit report, rules on determining measured consumption values, and rules on the allocation of energy consumption. The result of the study shows a similar process of transposition of Article 8, including the improvement cycle shown in Figure 2.4. However, in the case of nonparliamentary regulations, there are major differences in minimum coverage and de minimis limits to the detriment of companies in Germany.

2.6 Research on Quality Aspects of Energy Audits

2.6

43

Research on Quality Aspects of Energy Audits

Few sources have been found in the scientific literature on ensuring the quality of energy audits in general. This can be explained by the fact that until 2015, the interest in high-quality energy audits was always held by the entity that paid for the audit: the company. A shift in interest has only occurred since the mandatory audits came into force at the end of 2015 in accordance with the EED. In the case of a mandatory audit, the company’s interest could now also lie in avoiding fines and minimizing the costs of the energy audit. Interest in quality now rests with the authorities, which, for their part, must ensure sufficient quality through numerous regulations for the audit process and the necessary qualification of auditors. Standard EN 16247-1 (DIN EN 16247-1:2012 2012) by itself is too general to ensure sufficient quality in the case of mandatory audits. The entire chapter on quality requirements takes up less than a single page of text in the standard document (DIN EN 16247-1:2012 2012, pp. 6, 7). Anyway, the standard provides a first approach to further pursue the topic “Quality in energy audits”. The standard considers both the quality of the energy auditors and the quality of the energy audit process. Accordingly, the energy auditor must meet the characteristics of competence, confidentiality, objectivity, and transparency. However, a special education is not given. The energy audit process must be appropriate, comprehensive, representative, traceable, relevant, and verifiable according to the standard. Considering the numerous industries, the wide technological range of consumers and energy production equipment, and the classes of energy audits presented in Section 2.2, it is clear that a standard for energy audits can only cover the greatest common denominator. Ensuring the quality of the energy auditor in mandatory audits and in support programs for energy audits is done by the authorities. In Germany, the (Deutscher Bundestag 2019, Par. 8) specifies relevant skills: at least three years of professional experience in operational energy consulting and regular further training. Energy auditors can apply for registration in an expert list maintained by the authorities. This registration is a prerequisite for being allowed to conduct mandatory energy audits. The requirements for registration are listed in (BAFA 2019a). This document specifies concrete educational qualifications and activities that are accepted as professional experience. The objectivity of the energy auditor required by the standard must “… give priority to the interests of the organisation…” (DIN EN 16247-1:2012 2012, Par. 4.1.3). Thus, this standard does not consider authorities as stakeholders. Interestingly, in Germany, internal company employees are explicitly allowed to carry out energy audits (Deutscher Bundestag 2019, Par. 8), as long as they meet the same qualification requirements as external energy auditors. The conflict of interest between

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companies and authorities regarding mandatory energy audits remains unresolved at this point. A look at the domain of financial audits, for example, provides a study that identifies the personal relationship between the company and the auditor as a serious quality criterion (F. Ball, Tyler, and Wells 2015). The study suggests a regular exchange of auditors to prevent the development of too close a relationship. The EN 16247-1 standard defines some basic criteria for the audit process. As mentioned above, the process must meet the following requirements (DIN EN 16247-1:2012 2012, p. 7): a) appropriate: the process must pursue the objectives defined beforehand, it must fit the intended scope and it must meet the agreed level of detail. A measure of thoroughness can be defined, for example, as the minimum coverage of energy use by consumers investigated. The decision whether the consumption of consumers is technically measured or only estimated also influences the thoroughness. b) complete: the audited (sub)field must be examined to a sufficient extent to correctly capture the energetic character of the object to be audited. c) representative: the data collected in the audit process should be reliable and reflect the real-world situation. This means, for example, that a large consumer must not be measured in the event of an unscheduled failure. d) traceable: all data obtained and the calculation steps used should be disclosed and verifiable. e) relevant: the audit should focus on areas where it is possible to analyze EEM and their savings potential. f) verifiable: after the adoption of EEM, the organization should have the possibility to validate the pre-calculated savings against the actual savings. This means, for example, that equipment to carry out consumption measurements must still be available after the energy audit. Most of these points are “soft criteria” against which the quality of an audit cannot be specifically measured. The boundary between fulfillment and non-fulfillment of a criterion varies with the perspective of a consideration. Typically, the energy consumption of individual consumers is estimated based on parameters such as connected load, load, and average operating time. The fulfillment of the first three criteria can be achieved by carrying out technical consumption measurements, also called submetering. The measurements are either carried out temporarily (representative) or permanently by installing measuring devices and setting up monitoring systems. This results in a higher accuracy of the energy balance

2.6 Research on Quality Aspects of Energy Audits

45

and thus an improvement in the quality of the audit. Further aspects for realizing consumption measurements using modern technologies can be found in Section 3.4. As an actual measurable variable for the quality of energy audits, (Fleiter, Schleich, and Ravivanpong 2012) suggest the number of adopted EEM. In fact, this adoption rate can at least serve as a relative benchmark for quality if the conditions for energy audits are otherwise similar, for example, in comparable funding programs. Several suggestions have been made to improve the quality of energy audits (Fleiter, Schleich, and Ravivanpong 2012): • • • • •

The development of (further) standards Templates for mandatory reports that summarize the audit results The definition of qualification criteria for the auditors The use of software tools to standardize processes Clearly defined methods such as the lifecycle method for cost calculation of the EEM • Cooperation with the auditor beyond the audit, who also accompanies the implementation of the EEM and supports the success control Many of these suggestions have now become part of the extended regulations for mandatory energy audits in Germany (BAFA 2019b). Standardized methods for the calculation of EEM are discussed in Section 4.1 of this book. The present work is also closely related to the study (Fleiter, Schleich, and Ravivanpong 2012), not only because the paper explicitly addresses the topic of quality of energy audits, but also because almost all of the abovementioned proposals also aim at a better standardization and comparability of energy audits. It is precisely this type of measure that supports the multiple energy audits processing. If one allows an equation of effectiveness and quality, then energy performance indicators (EnPIs) are further measures of quality. However, the effectiveness can also be lacking despite high quality if there is too little potential for savings. These indicators are also referred to as energy efficiency indicators (EEI) in some sources such as (Andersson, Arfwidsson, and Thollander 2018). They can be used to measure actual energy and money savings after the adoption of EEM. The advantage of the EnPIs is the objective comparability within the organization. A disadvantage is the long time delay in determining the EnPIs. Further consideration of EnPIs is in Section 3.2.

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Non-energy Benefits (NEB) as a Quality Characteristic

A further characteristic for the thoroughness and thus the quality of an energy audit is the consideration of non-energy benefits (NEB) in calculating and evaluating the EEM. These factors appear in the sources under various names, with the sources mentioned serving as examples: “non-energy benefit” (NEB) (Mills and A. Rosenfeld 1996), “productivity benefit” (Worrell et al. 2003), “non-energy impacts” (L. A. Skumatz and Gardner 2005), “ancillary benefit” (Jakob 2006), “multiple benefits” (Ryan and Campbell 2012), “synergy effects” (Kasprowicz and Schulz 2015), and “co-benefits” (Mayrhofer and Gupta 2016). The term “non-energy benefits” (NEB) used here is suggested as the most common (Rasmussen 2014), which also offers a detailed introduction to the concept of NEB. The inclusion of NEB in the efficiency calculation requires a high level of qualification and experience of the auditor and is a reliable, although not measurable, indication of a high-quality audit. In numerous studies, the inclusion of NEB leads to a significantly higher amortization of the EEM. In some cases, the economically positive effect of the NEB is even higher than the effect of the energy-related benefits themselves. In a meta-study of 52 case studies (Worrell et al. 2003), in 63% of the cases, the savings effect of NEB was identical or even greater than that of the energy benefits. By including NEB in the calculation of EEM, the payback period can be reduced from an average of 4,2 to 1,9 years (Worrell et al. 2003). In a further evaluation of 130 participants in a US program to promote energy efficiency in trade and industry (Dennis Pearson 2002), an average reduction in the payback time of an EEM from 4,1 to 3,0 years was calculated by including NEB; in this study, the percentage share of NEB in the cost savings was 40%. An average of 1,03 NEB was found per installed measure (Dennis Pearson 2002). In a study of 81 projects from a US program for the promotion of renewable energies, the payback period was also reduced from 1,42 to 0,99 years by taking NEB into account (Lung et al. 2005). According to (Pye and McKane 2000), extending energy efficiency to general efficiency or productivity increases by taking NEB into account correlates positively with a company’s stock market value and thus increases the attractiveness of efficiency measures that appear less appealing at first glance (= without NEB). A major barrier for EEM as a result of the generally low priority of energy efficiency is also seen (Thollander, Danestig, and Rohdin 2007), which can be overcome by the other advantages of NEB. A compilation of NEB discovered as a result of audits carried out is provided in (Kluczek and Olszewski 2017), although in the case studies of this paper, no concrete calculations on NEB are made. The research papers mentioned above deal with the calculative assessment of NEB.

2.7 Non-energy Benefits (NEB) as a Quality Characteristic

47

Further studies discuss a classification of NEB. Classifications are generally a helpful tool for multiple objects processing. In most of the studies mentioned above, the classification of NEB is part of an evaluation of audit programs. Therefore each study provides different classification suggestions. In (L. A. Skumatz and Dickerson 1998) a subsidy program for increasing energy efficiency in households is evaluated and the NEB found are classified. Further classifications for NEB can be found, for example, in the following studies: (L. Skumatz 2014; Lazar and Colburn 2013; Ryan and Campbell 2012; Fleiter, S. Hirzel, and Worrell 2012; Lung et al. 2005; L. A. Skumatz and Gardner 2005; Worrell et al. 2003; Pye and McKane 2000; Mills and A. Rosenfeld 1996). A meta-analysis of various NEB classifications can be found in (Trianni, Enrico Cagno, and Donatis 2014) and (Rasmussen 2014). A simple division into the three areas “utility system benefits,” “participants benefits,” and “societal benefits” is found in several papers, such as in (L. A. Skumatz and Dickerson 1998). This classification is also followed by the so-called “layer cake” model of (Lazar and Colburn 2013), which refines the three areas by six subareas each. At this point, it should be mentioned that the term “participant” is to be understood as a synonym for company or household. The societal related NEB makes a further distinction according to the dimension of the impact (Ryan and Campbell 2012). They distinguish between the individual, sectoral, national, and international levels. An alternative classification according to productivity benefit, product quality benefits and other benefits is provided by (Martin et al. 2000, pp. 29, 30) in a report on emerging energy-efficient technologies in industry. Another classification method is the distinction between monetary and nonmonetary benefits. In this case, the non-monetary benefits have a monetary influence, but only indirectly and are, therefore, not suitable for being included in an EEM calculation. While, for example, savings in maintenance costs can be directly quantified in monetary terms, a non-monetary benefit such as an improved corporate image cannot be calculated directly. In particular, a good reputation in the area of sustainability and environmental protection is also of great importance for companies and thus influences investment decisions, as 91% of participants in an audit program confirm (Harris, J. Anderson, and Shafron 2000). The study by (Small 2006) deals with these non-monetary aspects using the example of investment decisions in advanced manufacturing technologies (ATM). Improved load management to reduce peak loads is also an often overlooked added value for energy-intensive companies (Ankit 2015). Other examples of non-monetary NEB in energy efficiency projects are competitive advantages in the market (Catherine Cooremans 2011), the reduction of corporate risks and improved working conditions (Thollander and Ottosson 2008). The lack of understanding of the value of these additional

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factors removes the strategic nature of investment decisions in EEM (C. Cooremans 2012). The problem with all attempts to classify NEB is their incompleteness. Its usefulness, in general, may also be critically questioned. A completely different approach is taken by the expandable classification methodology using an open matrix (M. Krutwig and Starosta 2017). Starting from the problem of identifying NEB in an energy audit, this scheme offers a solution approach for practice. In the chapter on EEM starting on page 87 this method is presented in more detail. The monetary valuation of NEB is very individual and difficult to cover by general schemes. Both, the positive and the negative effects should be considered when calculating NEB. Taking these “negative benefits” into account, one also speaks of net benefits (L. A. Skumatz and Gardner 2005), which then have a positive or negative effect on the profitability of an EEM. To measure non-monetary and difficult-to-value NEB, (L. A. Skumatz and Gardner 2005) suggest methods such as the willingness to pay (WTP) method. This method uses questionnaires to ask test persons how much money they would be prepared to pay for a benefit. The average value of a WTP survey can then be calculated as a monetary NEB benefit. In addition to the WTP method, there are numerous other approaches to measuring difficult NEB, which are outlined in (L. A. Skumatz and Gardner 2005). In principle, three different valuation methods can be applied to measure nonmonetary NEB (L. Skumatz 2014): The first method is based on technical calculations or model-based estimates, such as the valuation of new jobs created using input-output models. The second method is based on estimates of an incremental frequency including calculation variables from secondary literature. An example is the valuation of a reduced number of absence days as a result of illness using marginal wage rates. The third method is based on statistical evaluations of surveys such as the WTP method mentioned above. The modeling in Section 6.5 also takes into account cash-flow factors that result from NEB. In doing so, an existing monetary valuation of the NEB is assumed. In the scientific discussion of NEB, (Freed and Felder 2017) observe a lack of consideration in funding programs and point out several shortcomings and inconsistencies in existing studies. In particular, the difficulty of calculating and the unclear allocation of effects is an obstacle to the use of NEB as a policy instrument (Freed and Felder 2017). Summary of the Factors Influencing the Quality of Energy Audits Summarizing the findings of the above factors, a total of five factors can be described, which influence the quality of energy audits:

2.7 Non-energy Benefits (NEB) as a Quality Characteristic

49

1. Ressources such as available time and budget always require a trade-off with quality. If these resources are defined at the beginning of the audit, quality becomes a stretchable factor. 2. The qualification of the auditor greatly affects the qualitative result. Companywide audits, in which the auditor is confronted with a wide range of technologies, are particularly critical. Indirectly, the availability of suitable auditors can thus also become a quality criterion. 3. The thoroughness of examination within the defined scope is often at the discretion of the auditor. It is difficult to check afterward whether all consumers, figures and measured values have been considered. 4. The willingness and availability of the organization to participate in the audit restricts the auditor in his thoroughness. Particularly in the case of mandatory energy audits, this tends to be perceived as annoying and is accordingly neglected by the company. 5. The availability of required documents, such as energy invoices also influences the thoroughness and thus the quality of the audit. The influencing factors directly raise the question of the measurability of the quality of an energy audit. The following indicators can be used to assess the quality: • Measurement of completeness by the ratio between energy used and the sum of the consumption values of the consumers examined. • Definition of annual consumption thresholds for individual consumptions, so that technical measurement (instead of estimation or extrapolation) is required when this threshold is exceeded. • The explicit inclusion of NEB in the calculation of the EEM is a clear indicator of the thoroughness and competence of the auditor. • The adoption rate of EEM can be taken as an indicator of a thorough calculation. • The adoption rate of EEM is a good comparison factor, especially when evaluating several audits of a program. • The deviation of the calculated savings according to the audit report from the actual savings after adoption and review of the EEM proves—albeit delayed—the quality of the EEM calculations. • The completeness of the audit report can be checked against the requirements of the standard. • Subjective quality factors for measuring the cooperation during the audit and overall satisfaction can be recorded via a questionnaire after completion.

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The total number of EEM and NEBs found and the calculated savings potential must explicitly not be a quality indicator, as these figures depend strongly on the initial situation in the company. An audit in an energy-intensive company with old technologies offers significantly more potential than a third repeat audit in a company that had already invested a large amount in EEM. Therefore, these factors should only be used for comparisons of a significant number of audits, so the potential for savings can be considered as an arithmetic mean.

2.8

Related Work

There are two closely related papers on the topic “Modeling of Energy Audits” and several other papers related to this work. A Paper on the Comparability of Energy Audit Programs This first paper, like many of the sources cited in this paper, was written by a working group from Linköping University, Sweden. They examine the comparability of energy audit program evaluations (Andersson, Arfwidsson, Bergstrand, et al. 2017). This paper examines five different studies evaluating funding programs for energy audits: the long-term IAC program from the USA (S. T. Anderson and Newell 2004), the Australian EEAP (Harris, J. Anderson, and Shafron 2000), an SME program from Germany (Fleiter, Gruber, et al. 2012), and from Sweden the Highland project (Thollander, Danestig, and Rohdin 2007), as well as another SME program (Thollander and Dotzauer 2010). The authors use a multiple-case holistic design methodology (Yin 2018, p. 60). The authors miss comparability between these evaluations because of incompatibilities in the categorization of EEM and consumption data (Andersson, Arfwidsson, Bergstrand, et al. 2017). The number and selection of categories for consumers and EEM are different in all evaluations, with only minor overlaps in some cases. Evaluation according to different performance indicators also stands in the way of comparability, as well as the only partial consideration of freerider effects. To achieve better comparability, the authors propose several elements that should or must be included in an evaluation, depending on the level of detail of the evaluation (three levels: minimum, recommended, and thorough). What is remarkable about (Andersson, Arfwidsson, Bergstrand, et al. 2017) is that the article (submitted in March 2016) does not yet refer to the EED and the associated sharp rise in the spread of energy audits and audit programs in the EU.

2.8 Related Work

51

All five studies concern audit programs that have already been evaluated before 2015. The importance of this work has increased significantly with the EED. The present work ties in with this paper on two points: • This work expands the area of applying the evaluation to the application area of multiple-processing, as with the increased need for energy audits comes the need for tools to produce energy audits in a systematic and high-quality manner. The target group for producing the audits is then rather the industry of energy consulting than politics. • The prerequisite for common parameters in entire audit programs is common parameters in individual audits. Therefore, a model is required that appropriately maps the parameters proposed in the paper to enable uniform evaluation. In other words: to homogenize audit programs, audits must be homogenized first. The challenge in the model developed here is to get energy audits in the most varied organizations, which are diverse depending on the nature and sector, into a common pattern without becoming too generalized and thus without compromising quality. Paper: Energy Efficiency in Small and Medium Enterprises: Lessons Learned from 280 Energy Audits across Euope The study (Fresner et al. 2017) provides an evaluation of over 280 energy audits from seven EU member states each with 40 SMEs. The remarkable thing about this study is the methodology, since a similar approach to model development was followed in this work. In comparison to other evaluation studies, a data model during the execution of the audits was applied interactively and the figures were determined. The data model is based on simple MS Excel tools and has not been published, so unfortunately no details about the data model are known. The model of this study takes into account categories for consumers and also the breakdown of business processes by business unit. Further information on the savings potential of each business unit was obtained via questionnaires. On the basis of the results of this study, it can be assumed that the data model used is far from being complete enough to produce an audit report according to EN 16247-1, but it already demonstrates the potential for detailed evaluations that results from this methodology of model development. In this study, the modeling was done only for the purpose of evaluation, whereas this work follows a complete structuring of the audit process and therefore ties in with a much more elaborate data model.

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Paper: The Characteristics of EEM—A Neglected Dimension The work by (Fleiter, S. Hirzel, and Worrell 2012) was found after a thorough literature search as the only work that deals with theoretical modeling in the context of energy audits. Using a morphological box (Zwicky and Wilson 1967) as a methodology for modeling, numerous characteristics of EEM were investigated and brought into a model according to Figure 2.6.

Figure 2.6 An EEM classification scheme. Image source (Fleiter, S. Hirzel, and Worrell 2012)

The model presented in this paper contains 16 characteristics grouped by economic, technical, and organizational parameters. The influence of the economic efficiency of NEB as a parameter can be found here with the attributes negative, none, small, and large. One goal of this work is the evaluation of audits according to this model in correlation to the adoption rate, to identify more EEM with

2.8 Related Work

53

a higher probability of adoption in the future or to make recommendations to the policymakers to promote EEM with certain characteristics more strongly. The paper has influenced the present work in the following points: • Modeling of the characteristics of NEB was carried out using an analogous methodology (M. Krutwig and Starosta 2017). This work is presented in more detail in Section 4.2.3. • The transformation of an abstract model into a detailed data model is an essential element of this work (see Section 6.5). However, the attributes used in the data model can no longer correspond to the attributes of the abstract model, as all numerical values are precisely recorded or calculated. The NEB are also extended in the data model as a form of general factors that influence the cash flow once or regularly. However, the concrete values of the data model can be reduced again to the attributes of the model by Fleiter et al. • Beyond the modeling of the EEM, this book deals with the modeling of the complete energy audits. The article (Fleiter, S. Hirzel, and Worrell 2012) was ahead of its time, respectively, the EED, and has grown in importance with the entry into force of the audit obligations in 2015. Another work very similar to Fleiter et al. but without a descriptive box scheme is offered by a framework proposed by (Trianni, Enrico Cagno, and Donatis 2014) for the characterization of EEM. Here, 17 attributes in six different categories are defined. Similarly, these attributes (yes, no, few, many, high, low, …) are qualified instead of quantified. It can be observed that the EEM used for validation, which were taken from previous publications, very often could not provide information (N/A) on attributes. Papers Dealing with a Classification of EEM and NEB More remotely related to modeling are all the works on classifications of EEM and NEB. Many of them have already been mentioned or described in the previous section on NEB and will not be listed here again. Papers Dealing with the Quality of Energy Audits The literature search did not find any work on the quality of energy audits that focused on this topic. At least (Fleiter, Schleich, and Ravivanpong 2012) established a relationship between the adoption rate of EEM and the quality of energy audits. The quality was measured by satisfaction surveys.

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Papers for Which This Book Would Have Been Helpful This includes all papers that evaluate and analyze audit programs. Many of these studies are mentioned on page 36. All studies of this kind implicitly also deal with the multiple energy audits processing. Most of the studies mentioned in the context of the NEB from page 46 evaluate energy audit programs.

3

Research on Energy Balances

The presented model for energy audits is based on an energy balance with annual values. The problem here is the lack of a standard for the energy balance in an energy audit. The first section of this chapter uses existing energy balances from other domains, as well as financial sector analogies, to narrow down the term energy balance so it is usable for a universal model. The following sections deal with quality in energy balancing and its assessment. Energy performance indicators also come into play in this context. Significant quality improvement in the energy balance of organizations can be achieved by carrying out measurements. As numerous new technologies and standards for intelligent factories and energy systems have emerged in the last few years, the most important of these standards are presented and their application is described in case studies.

3.1

Approaches to Definition and Typification of Energy Balances

In the literature, there is no clear definition of the term “energy balance.” Something like an energy balance can be found in numerous application domains. In nutrition science, for example, energy balance is understood as a comparison of the calories consumed by food and the energy burned by body functions and physical activity (Garrow et al. 1974). Concerning the production of energy carriers, this balance is made up of the amount of energy put into production and the energy contained in Electronic supplementary material The online version of this chapter (https://doi.org/10.1007/978-3-658-33167-2_3) contains supplementary material, which is available to authorized users.

© The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2021 M. Krutwig und A. Dumitru Tan¸ta˘ u, Energy Audits, Sustainable Management, Wertschöpfung und Effizienz, https://doi.org/10.1007/978-3-658-33167-2_3

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Research on Energy Balances

the energy carrier (Morris 2005). In the domain of energy generating units, such as a solar thermal collector, the energy balance describes the ratio between the energy saved by the collector and the employed energy during the collector’s life cycle (Ardente et al. 2005). Numerous other examples defining energy balance within a specific application context could be given. Common ground in understanding “energy balance” is always comparing the ratio or the difference between the energy input and the energy that is received in any form. For the modeling of energy audits, the context of this work is the energy flow of a company or organization. However, as modeling can also derive findings from the balancing of entire countries, these energy balances are also discussed below.

3.1.1

Energy Balance of a National Economy

Energy policy is a frequent context in energy balances. Here, the energy balance of an economy is drawn up to obtain an overview of the energy production, energy trade, and energy use of a country. There are no official standards for this form of energy balance either, but within the European Union there exists a common, uniform structure for recording and publishing these energy balances (E. Commission 2020). Some typical values from this publication were already used in Table 2.2. For economies, energy quantities are uniformly reported in joules, watt-hours, or oil equivalent (toe). In Germany, the Arbeitsgemeinschaft Energiebilanzen e. V. (AGEB) (AG Energiebilanzen e. V. 2020) is engaged in the national energy balance. This organization collects statistics from all areas of the energy industry, evaluates them according to uniform criteria, and publishes them in the form of energy balances (Ziesing, Maaßen and Nickel et al. 2019). These energy balances provide an overview of the country’s economic energy relations. The evaluations are made both by sector and by different primary energy sources. In the energy balance, the production, trade, conversion, and use of energy sources in an economy or economic area are documented for a certain period. The detailed energy balance is presented in a matrix form, a simplified form is also published by AGEB as an energy flow chart, as shown in Figure 3.1. The methodology of this presentation is a Sankey flowchart (Schmidt 2008), as it is often used for the visualization of energy flows. An AGEB energy balance consists of several individual sheets, so the term balance sheet must not be understood here as a strict comparison in the sense of financial accounting, but rather as a homogenized collection of complete statistics. There are individual balance sheets for various aspects (Ziesing, Maaßen and Nickel et al. 2019). The distinction between primary energy and final energy is

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Figure 3.1 Energy Flow Chart for the Federal Republic of Germany in 2019. Illustration by authors, data source: (Ziesing, Maaßen and Nickel et al. 2019)

significant here: all types of energy that can be taken from nature count as primary energy sources. These include oil, natural gas, hard and lignite coal, nuclear energy, and biomass. Forms of energy generated from processes, such as electricity from photovoltaic power, solar heat, wind power, hydro-power and tidal power, are also included among the primary energy sources. The primary energy consumption is an indicator of the use of resources, economic activity, and living conditions of an economy. The following list briefly explains the three most important terms in this context: • Primary energy supply: This includes the domestic production of primary energy, the import and export of primary energy from other countries, as well as stock removal, restocking, and marine bunkering. The balance of domestic production shows the relationship between own production and import/export of energy, but also the energy mix produced in the country. Besides the annual balancing by the AGEB, the Fraunhofer ISE publishes these data daily (Fraunhofer ISE 2020). • Primary energy consumption: Primary energy consumption excludes exports and stockpiling. The conversion of primary energy into final energy, for example, in generating electricity from lignite, is considered to be primary energy consumption. This results in losses as a result of the efficiency of the conversion plants. In addition, energy is required for the own consumption of these plants. Losses also

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occur during transport to the consumer. If this consumption and loss is subtracted from the primary energy consumption, one obtains the final energy consumption. • Final energy consumption: Total consumption outside the energy sector is considered final energy consumption. The AGEB keeps further statistics that classify this consumption according to various aspects, such as industry or application purposes. These application areas are space heating, hot water, other process heat, HVAC, mechanical energy, ICT, and lighting. The development of an energy audit model can be guided by such classifications according to energy sources and consumption areas. At the European level exists an energy balance guide (E. Commission 2019a), which provides member states with a standard for calculating their national energy balances. The aim is to publish these balances in an objectively comparable way in the Eurostat database (European Commission 2020). Key figures such as energy intensity (energy per GDP) are also part of these balances. These data then serve as a basis for European policy decisions. In addition to definitions, conversion tables, and calculation rules, the guide also offers questionnaires and calculation tools based on Excel. Above and beyond Europe, there is the international recommendation for energy statistics (IRES) of the United Nations, which also aims at harmonizing national energy balances (U. N. S. Commission et al. 2018). This document contains recommendations for defining and standardizing energy statistics. It includes basic principles, definitions, and classifications, energy balances, data collection strategies, data sources, aspects of data quality, and dissemination.

3.1.2

Complete Energy Balance of an Organization

Looking at organizations instead of nations, we arrive in the context of energy audits. Also, for organizations, the energy flow can be defined analog to picture 3.1. The energy consumption E consumption is here the sum of the purchased energy of all energy carriers E procurement and the energy produced E produced in the organization itself, for example through conversion in a combined heat and power plant or through generation by photovoltaic, wind, or geothermal energy (Equation 3.1). For the audit, it is useful to separate the energy flows of the energy production itself from the energy consumption of the company’s operational business activities. This operational consumption is referred to as energy use E use . The energy use is calculated by taking E consumption and subtracting all energy that was not used for the organi-

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zation’s own operations. Consequently, sold energy E sale and the self-production caused loss E loss and consumption E prodConsumption are subtracted (Equation 3.2). E consumption = E procurement + E produced

(3.1)

E use = E consumption − (E prodConsumption + E loss + E sale )

(3.2)

In balance form, this calculation corresponds to the scheme in Table 3.1. The energy use of an organization is comparable to the final energy consumption of a national energy balance. The determination of the energy use for an energy audit can be done easily and precisely, as the figures for purchasing can be taken from the bills of the suppliers, as well as the figures for energy sold and fed into the grid. Also, any existing plants for self-production are usually equipped with their own energy meters, so their production and consumption are also known. If the organization does not have its own energy production, then energy use and energy consumption are identical. No standard in the energy audit prescribes the definition of energy use as given here. It is assumed that the plants for own energy production are not the subject of the audit and are, therefore, outside the agreed balance borders. If this assumption is followed and the organization has its own energy production, the balance sheet of E consumption can also be mentioned in the audit report as shown in 3.1. Table 3.1 Scheme for an energy consumption balance of organizations IN

OUT

energy procurement own generation

energy sale energy feed consumption for own generation efficiency loss in own energy production energy use

If the entire organization is subject of the energy audit, then energy use defines the balance limit of the audit. This value is then compared to each consumption value of each consumer or consumer group that was measured or estimated individually during the audit. As, in practice, it is hardly possible to consider every existing consumer, an unallocated consumption remains. The representation in balance form then corresponds to Table 3.2.

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Table 3.2 Scheme for an energy use balance of organizations IN

OUT

energy use

consumption 1 consumption 2 ··· consumption n unallocated consumption

This now makes it possible to define the term coverage of an energy audit (in percent), which can also be understood as a transparency factor or thoroughness regarding the quality of an energy audit. The calculation is done according to the Equation 3.3: 100% · coverage =

n 

consumptioni

i=1

E use

(3.3)

The larger this ratio is, the more precisely the consumption was allocated in an energy audit. The transparency of the consumption describes the exact allocation of the consumption to individual consumers. A theoretically complete transparency per observation interval would be given if unallocated consumption was zero. In practice, this complete transparency is hardly achievable, because, in many cases, submetering of processes or parts of buildings already provides sufficient information about efficiency potential and a more detailed examination of individual consumers, especially small consumers, is not economically viable. Reducing the measuring intervals at the consumer also increases transparency. In this energy balance modeling, all energy data were set to annual values according to ISO 50002, Audit Type2 (ISO 2014, A.3). To evaluate the economic efficiency of an EEM, however, it may be useful to examine shorter measuring intervals in some areas or to incorporate a special form of presentation, such as carpet plots or Sankey diagrams in the audit for selected processes. When considering the data intervals for audit type 2 or 3 according to ISO 50002, it becomes clear that a traditional in-out balancing in the sense of financial accounting is not possible because of a lack of time normalization. The bills for energy purchases may vary from one energy source to another. If, for instance, a 15-minute load profile is received from the utility company, the purchase of heating oil is subject to monthly or annual billing at irregular intervals. The developed

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model complies with a compromise on annual figures. In addition, load profiles and submetering at load profile level can be attached as documents to the energy audit to comply with ISO 50002.

3.1.3

Partial Energy Balance of an Organization

The obligatory energy audits prescribed by the EED as well as many state support programs require a comprehensive audit of the organization with a minimum coverage according to the formula 3.3. However, if there is no such requirement, a partial audit can be set as a goal instead of a complete audit. Partial means that the audit is limited to certain energy carriers, to parts of the company, or even to the sphere of influence of a specific EEM. In Germany, there are numerous support programs that, for example, only concern certain cross-sectional technologies or the area of insulation, and an energy audit only needs to be carried out on this area. In this case, energy use is defined as the energy input into this sub-area. On the other side, only the consumption values from this subsection appear. The model presented in this book is not limited to comprehensive energy audits, it can also be used to balance a partial audit. For partial energy balances, it must be considered that in cross-audit evaluations, these balances can only be compared with balances that were prepared with an identical determination of energy use. The audit model developed in this work is not limited to comprehensive energy audits, it can also be used to balance a partial audit. It should be noted that partial energy balances can only be compared with balances that have been prepared with an identical definition of energy use, for example, when evaluating funding programs for specific EEM.

3.2

Literature Review on Energy Baseline and Energy Performance Indicators

Energy performance indicators (EnPIs) can be used to assess whether an organization had increased its energy efficiency after implementing EEM. The basic definition of efficiency according to (Patterson 1996) is already described on page 23. Efficiency indicators exist in the context of any kind of energy balance. In national energy balances (ISO 2014, p. 3.11), many of these EnPIs are established. These are, for example, energy productivity (real gross domestic product per unit of primary energy consumption), energy efficiency (primary energy consumption (observed) per unit of real gross domestic product and per inhabitant) and several EnPIs that describe the energy consumption of individual industries or energy sources.

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The standards on energy audits, such as ISO 50002, stipulate that appropriate EnPIs must be drawn up (ISO 2014, p. 3.11). These could be expressed as a simple metric, a ratio, or a more complex model. To obtain an objective evaluation with EnPIs, further quantifiable variables impacting energy consumption may have to be defined (ISO 2014, p. 3.14). The standard gives examples of such influencing factors as ambient weather indicators, operating parameters, and working hours. The model presented in this book refers to these parameters as adjustment factors. Usually, these energy indicators consist of a physical measured variable and a reference variable, optimally expressed in power and a utility unit. This can be an electricity consumption measured in kilowatt-hours [kWh] concerning a daily or monthly production in quantities or weight units. Often, the personnel employed is also used as a reference value. A methodology for EnPIs and numerous other examples are provided, for example, in (Tallini and Cedola 2016) study on the evaluation of EnPIs. There are three main types of energy performance indicators: • Measured energy value by a meter. This indicator gives the energy intensity of an organization. However, the sole use of these indicators is not suitable for evaluating efficiency (Proskuryakova and Kovalev 2015). • The ratio of measured energy values to non-energy costs or yield factors (energy efficiency) (Patterson 1996). • Model-based calculation or simulation: These statistical or engineering models can be derived by linear regression or nonlinear regression or can be created using engineering theory (ISO 2017). The so-called energy baseline (EB) is also used alongside energy management systems. The standard ISO 50006 (ISO 2017) is available for the use of EnPIS and EB. To identify improvements in energy-related performance, the organization must track changes between a reference period and a reporting period. Therefore, the EB is determined at the beginning based on the defined EnPIs. These EnPIs are regularly measured, monitored, interpreted, and compared with the EB. The baseline must be adjusted following a pre-defined method if EnPIs no longer reflect the organization’s energy use or energy consumption or if significant changes have been made to operational procedures, the process, or energy systems (ISO 2017). Usually, these energy indicators consist of an indicator of energy intensity and a reference value. This can be electricity consumption measured in kilowatt-hours [kWh] in relation to daily or monthly production in quantities or weight units. Often, the personnel employed is also used as a reference value. A methodology for EnPIs

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and numerous other examples are provided, for example, in the study (Tallini and Cedola 2016) on the evaluation of EnPIs. When defining suitable key figures, profound knowledge of the audited company must already have been gained. In some areas, the determination and calculation of these EnPIs is specific. For example, in (Henriques and Catarino 2017) study, a relationship between flow rate and energy consumption was established in sewage treatment plants and a so-called ‘sustainable value’ was defined as an indicator of this. When auditing a water supply system, (Mamade et al. 2014) describes key figures based on concepts, such as minimum energy and energy in excess. One of these ratios describes the energy in excess (kWh) per unit of input volume (m 3 ). These special indicators aim to find a higher potential for optimization. For a comprehensive assessment of energy efficiency in the context of an energy audit, (Zanardo et al. 2018) proposes an algorithmic procedure model, which calculates an overall level of energy performance from a set of different EnPIs of a company. In cross-audit evaluation, it is helpful to work with uniform key figures. Here, (Andersson, Arfwidsson, Bergstrand, et al. 2017) have already determined that EnPIs on firm cost-effectiveness, program cost-effectiveness, and energy savings were available for all five programs investigated, but were not calculated uniformly. For modeling purposes, the consideration remains whether such generally applicable EnPIs should already be included as a fixed component in the model. However, this idea was rejected, as the reference values required for this can then still be determined according to different criteria. The model is, therefore, limited to the free definition of EnPIs. A computational method for cross-audit EnPIs suggests (Andersson, Arfwidsson, and Thollander 2018). In this paper 11 companies in a specific industry are used to calculate an energy efficiency indicator (EEI).

3.3

Research on Quality in Energy Balancing

Evaluation of quality can only be done by using standards by which the degree of quality can be measured. In Chapter 2, this connection has already been shown by a comparison with financial audits. This analogy will be used again to better illuminate the concept of quality for energy audits. In accounting, the International Financial Reporting Standard (IFRS) is an internationally uniform, recognized standard for the accounting of companies (Nickenig 2018). It was issued by the International Accounting Standards Board (IASB) with the aim of general, worldwide harmonization of accounting. Described in a very simplified way, the annual balance sheets, according to the IFRS, show the non-current

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and current assets on the left side, on the right side the values of equity (capital and reserves) and liabilities are positioned with identical total assets (Nickenig 2018). In Germany, companies represented on the capital market have been required, since 2005, to prepare their consolidated financial statements in accordance with IFRS. This obligation exists in a large number of countries worldwide, but also the voluntary introduction of accounting under IFRS brings advantages to small and medium-sized companies concerning international investors (R. Ball 2006). Therefore, the introduction of the standard itself is a quality feature. If IFRS is applied correctly, quality lies in the correctness of the figures down to the cent amount. Thus, to put it simply, “correct or incorrect” is the only quality criterion for financial balance sheets using a standard. If one compares financial balance sheets with energy balancing, the accounts in finance correspond to the inventory of energy-relevant aggregates (consumers and producers). The annual balances in financial accounting correspond to the annual consumption values of these aggregates, as well as annual energy purchases and sales. In both domains, balancing is performed with annual values, so the model presented in this book should handle annual figures. With energy audits, on the other hand, there are many “soft factors” that affect quality. This is, in particular, already recognizable by the quite generally formulated standards (ISO 2014) but is also as a result of the impossibility to determine in practice all energy consumption values up to the joule exactly or in any way precisely. Thus, the application of a standard does not automatically lead to energy balances of acceptable quality. In Section 2.6, the quality criteria for energy audits are described, many of the factors listed there also apply to energy balances. For these, the quality lies in the completeness and correctness of the figures presented. The qualitative maximum for energy balances with annual values is achieved by two actions: • The measurement concept covers all energy-consuming and energy-producing aggregates within the defined balance borders. • All generation and consumption values of all aggregates were measured over the annual period using accurate technical measurements. In practice, this maximum is not achievable, as determining all measured data does not have a reasonable economic relationship to the benefit. For some consumers, it is often easier to group them together, especially if many small and similar devices are determined (e.g., lighting). It is also often permissible to determine the consumption of consumers and consumer groups by estimations or by temporary measurements

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with extrapolation to the year. The quality of these estimates then influences the overall quality of the energy balance. Thus, the economic assessment of using measuring equipment has a direct influence on the quality of the audit and the energy balance. The lower the costs of this type of metering, the more frequently measurements are taken. In Section 3.4, it is shown that retrofitting and new technologies make more cost-effective, networked measurements for energy audits and energy management systems possible. This application then leads to an increase in the quality of the energy balances.

3.4

Measurements for More Accurate Energy Balances

According to EN 16247-1 and ISO 50002 a measurement plan must be drawn up for energy audits and relevant measurement points must be measured with suitable equipment (ISO 2014, Sec. 5.5). Only in exceptional cases may consumption be estimated or calculated. Annual values are sufficient. An energy management system according to ISO 50001 requires the continuous tracking of consumptions, here an analysis of time series in short periods must even take place. The measuring intervals go into the minute range. This high time resolution is necessary to discover saving potentials through transparency, but also to prove savings in implemented measures. Continuous energy measurements can be done in two ways, by mobile or stationary measurement. With mobile measurement, a temporary observation over a relevant period is carried out using mobile equipment. This device includes a meter and a data recorder to record the consumption values. The evaluation is usually carried out after completion of the measurements. Stationary measurement involves permanent monitoring for an unlimited period. Here, the measuring devices are permanently integrated into the measuring point and the data is recorded by a central monitoring software. The sensors are directly or indirectly (via a data logger) networked with the monitoring. The analysis of these data can be done at any time. Intelligent energy networks, also known as smart grids or smart microgrids (Farhangi 2010), use internet of things (IoT) technologies for flexible monitoring and control of energy flows. According to this, an organization’s energy balance provides the energy component of a smart migrogrid. The components of such an IoT environment are called cyber-physical systems (CPS) (Wolf 2009) or, in the energy context, cyber-physical energy systems (CPES). In modern and future energy measurements, networked sensors as CPES represent the measurement infrastructure of a digitized organization. These CPES not only bring transparency to energy flows but they also offer great potential for added value beyond energy management.

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In the following subsections, technologies are examined for their suitability for CPES. The focus is on considering wireless networks that can be used for smart microgrids and on considering an industry standard that extends simple sensors to CPES.

3.4.1

Emerging Technologies for Energy Measurements

Radio Technologies for Sensor Networks In this section, some newer and emerging technologies are presented that enable wireless and thus cost-effective networking of sensors. Network technologies, such as wireless sensor networks (WSN) (Potdar, Sharif, and Chang 2009) for indoor applications and the class of low power wide area networks (LPWANs) (Buurman et al. 2020; Bardyn et al. 2016; Raza, Kulkarni, and Sooriyabandara 2017; Khalifeh et al. 2019) are optimized for the application of low data transmission, as is the case with the transmission of measured values. The low data rate and limited use of frequencies demand little energy, so battery-powered sensors can be used over a long time. The long range (LoRa) standard is of particular interest here, as this LPWAN operates in an unlicensed range of the frequency band, the industrial, scientific and medical (ISM) and short range device (SDR) radio band, and can, therefore, be operated by anyone. The unlicensed frequencies of the ISM band are country-specific; in Europe, they range between 863 and 870 MHz and in the USA between 902 and 928 MHz. For energy measurements, the WSN and LPWAN are used for communication between machines (m2m communication). In contrast to classic radio networks, such as WiFi or cellular networks, the required bandwidth plays only a minor role here. In a measurement infrastructure, there are special requirements that are fulfilled in particular by the LPWAN: • The radio network should transmit consumption values, status values, or meter readings in short cycles (daily, hourly, or even every minute). One sensor has only a small need for transmission capacity. • The energy consumption for radio transmission needs to be low, so batterypowered sensors can be operated over a long period without changing the batteries. • The radio network should cover the entire scope of energy management at a site with several buildings or housing units. This area corresponds to the agreed

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spatial balance limit. The network must also function reliably through several walls and buildings. • The network protocols must include security mechanisms to ensure the privacy and integrity of the transmitted data. Unauthorized persons must also not be able to introduce further components such as network nodes or base stations. • The radio gateway, also called the base station, should be able to operate a large number of sensors in order to minimize the number of required gateways. The quantity of sensors depends on the amount of measuring points and varies with the size of the company and with the number of separately measurable consumers. • The protocol standard of the radio network should be optimized for several different application scenarios. The 3GPP technical report no. 45.820 (The 3rd Generation Partnership Project 2016), for example, includes four categories of applications to specifically address devices with greatly reduced hardware complexity and limited functionality. The use of radio networks in energy management is made for cost reasons. The wired networking of measuring points, especially in the case of spatial spread, is work and cost-intensive and is thus often an economic hurdle in establishing a networked measuring infrastructure. Radio networks can overcome this barrier, so more detailed measurements can be made with more sensors. These networks thus help to increase the transparency of energy flows. Wireless Sensor Network Standards The WSM operate in the unlicensed frequency band, where the radio network is set up directly on site. This type of network is particularly widespread in home automation in the private and commercial sector. The range of a gateway is rather small at a maximum of 200 meters, but can be extended by repeaters or by special topologies such as mesh networks (Akyildiz and X. Wang 2005), in which nodes also act as transporters and forward data packets. WSM such as ZigBee can also be used in electric power system environments (Gungor, B. Lu, and Hancke 2010). The comparative overview in Table 3.3 also includes standards such as WiFi and Bluetooth 5, which do not include optimization for transmission energy for m2m communication. No definition exist, which allows a sharp distinction between WSN and LPWAN. For example, the Wi-SUN network, which is based on the IEEE 802.15.4g standard, lies in the free ISM band and achieves a range of up to five kilometers (Harada et al. 2017). The Weightless-P technology, which is also available but has hardly been scientifically investigated, is mentioned in (Adelantado et al. 2017).

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Table 3.3 Common standards for local sensor networks standard

frequency (EU)

data rate

indoor range references

WiFi

IEEE 802.11 2,4 GHz (a/b/g/n/ac)

>3 MBit/s

100 m

Z-Wave

(proprietary)

868 MHz

100 kbit/s

30 m

ZigBee

IEEE 802.15.4 IEEE 802.15.1 IEEE 802.15.1

868 MHz 2,4 GHz 2,4 GHz

250 kbit/s

10–75 m

>10 MBit/s

10 m

2,4 GHz

1 MBit/s

n/a

IETF RFC 4944

868 MHz 2,4 GHz

250 kbit/s

200 m

Bluetooth 5 Bluetooth LE

6LoWPAN

Yaqoob et al. (2017) Verma et al. (2013) Gomez et al. (2010) Mahmood et al. (2015) Bocker et al. (2017) Gomez et al. (2012) Darroudi et al. (2017) Mahmood et al. (2015)

LoRa and LoRaWAN for Metering Infrastructures For submetering applications, the small coverage of WSN is often insufficient, so LPWAN technologies are the better choice. The long range (LoRa) (Sornin et al. 2015), which operates in the unlicensed ISM or SDR band, is establishing as the most suitable network technology, if one subjectively considers the number of LPWAN devices on the market. LoRa was developed by Semtech Corporation and describes components of an IoT infrastructure (Figure 3.2). The area of the radio network is called LoRaWAN. Components of this infrastructure are LoRa devices (also called nodes), which communicate wirelessly with a LoRa network server (NS) via gateways using the LoRa protocol. The NS acts as an organizer of the network. This is where devices and gateways are registered. The connection between gateway and network server is IPbased. For remote gateways, the classical mobile network is often used for connection. The gateways are transparent for the node. From their point of view, they communicate directly with the NS, which, for example, also dynamically coordinates which gateway is assigned to which node. The transmitted sensor data are stored in the NS, which makes these data available via an API for the application servers such as our energy management system (Sornin et al. 2015).

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Figure 3.2 LoRa system components

Different needs for energy efficiency of the nodes are met by LoRa through three different device classes (Sornin et al. 2015). These classes (A, B, or C) define the downlink availability of the sensors, for example, for receiving configuration parameters or control commands. Devices from different classes can be operated in the same LoRa network. Class A devices are particularly energy-efficient. They use the ALOHA method when sending their packets. With this method, reception is only possible immediately after a packet has been sent. In between, there is no transmission or readiness to receive. This class is suitable for most devices that regularly send sensor data, such as temperature data or meter readings. In class B devices, the uplink and downlink are decoupled. Here, fixed time windows are defined for the reception of data in the NS, at which the node wakes up and receives data. This class is suitable if control instructions must be sent to the node regularly. Class C devices are permanently in receiving mode. For these devices, a power supply is required. Battery operation is not possible. LoRa uses Semtec’s patented chirp spread spectrum modulation method (Goursaud and Gorce 2015; Reynders and Pollin 2016) for radio transmission. The microchip for modulation is also referred to as the “LoRa concentrator”. This method involves a spreading factor (SF) to optimize the energy consumption of the radio components. Depending on the required transmission frequency and distance to the gateway, the SF can be set to an optimum value for the required data rate. The energy consumption per data packet and the range correlates with the adjustable data rate. This data rate Rb (in bit/s) of a LoRa transmission is calculated by formula 3.4: Rb =

SF · BW · 4 2SF · (4 + CR)

(3.4)

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With the following variables: • The spread factor SF in a range of 7 … 12. The SF is selected based on the attenuation of the radio network. The better the radio connection to the gateway, the lower the SF can be set. The SF can be adjusted manually, LoRaWAN also supports an automatic adjustment of the SF factors depending on the network configuration. • The bandwidth BW of the modulation frequency in kHz. Typical values are 125, 250 and 500 kHz. A larger bandwidth shortens the transmission time. • The coding rate CR influences the forward error correction (FEC) for each data packet, a value in the range of 1 to 4 can be used here. The FEC extends a 4-bit data packet by redundancies up to 8 bits. The larger the CR, the more robust the transmission and the longer the transmission time. Without CF, a theoretical data rate of up to 27 kbit/s could be achieved in Europe, but restrictions on using frequency bands must be considered. As in Europe in the ISM band, the permitted network occupancy is only a maximum of 1% of the total time (ETSI 2012), the data rate that can be used practically is significantly lower. A typical transmission in energy management is the transfer of a meter reading (2–4 bytes) in a cycle of 15 or 60 minutes. This requirement can be achieved by LoRa with no difficulty. Numerous different studies exist on the possible data rates and the range of LoRa networks. Inside buildings and through several walls (Petäjäjärvi et al. 2017) have measured a range of 300 meters, but with packet losses at all possible SF values. In another study, coverage of 3 km could be achieved with a gateway in a suburban area (Augustin et al. 2016). A less practical record of a 702 km transmission range of a data packet was measured in 2017 (TTN 2020). However, the LoRa device was flying in a balloon at an altitude of 39 km in perfect meteorological conditions. In addition to setting up a LoRaWAN infrastructure, LoRaWAN can also be used as a network-as-a-service (NaaS) via providers. In many European countries, providers promote a city-wide or nationwide coverage of gateways. If the EnMS in its role as application server can connect to the NS of these providers, no separate gateways and NS are required. A special approach based on crowd-sourcing is offered by The Things Network (TTN) (Blenn and Kuipers 2017). Here, an open, worldwide LoRaWAN was created, in which every participant can make his private gateway available to the public. In March 2020, 10.056 gateways in 149 countries were registered with TTN (TTN 2020). Although LoRa is now widely adopted, there is a need for further research to address weaknesses. For example, there is potential for improvement in the optimal

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placement of multiple gateways, link coordination, improvement of the communication range without substantial changes to the hardware, and a deep investigation of the security of LoRa transmissions (Sundaram, Du, and Zhao 2020). Further LPWAN Technologies for Submetering Apart from LoRaWAN, other LPWAN networks are suitable for submetering and use in smart microgrids. Not all of these networks operate in the unlicensed frequency band. The most common technologies besides LoRaWAN are SigFox (Sigfox 2020), NB-IoT (Release 13) (The 3rd Generation Partnership Project 2016), and LTE-M (Ratasuk, Mangalvedhe, et al. 2014). Sigfox is a global IoT network of the French Sigfox s.A. Corporation. The provider touts availability in 70 countries (as of March 2020), but without full coverage in these countries (sigfox.com 2020). Besides the operation of public gateways, Sigfox has been offering the possibility of building private networks by integrating its own gateways since 2020. Under optimal conditions, the range to a gateway can be up to a maximum of 50 km in lowland areas while a distance of 3 to 10 km is specified for urban environments (Vejlgaard et al. 2017), although various studies have come to different conclusions. Sensors for Sigfox are available for many commercial and residential applications such as supply chain and logistics, manufacturing, smart cities, utility and energy, smart buildings, retail, agriculture, and smart home (sigfox.com 2020). In Europe, Sigfox uses the ISM frequency band at 868,0 to 868,6 MHz for uplink and 869,4 to 869,65 MHz for downlink. It supports unidirectional and half-duplex communication, which can only be invoked by sensor. For the uplink, Sigfox uses differential binary phase-shift keying (D-BPSK) with 100 or 600 baud symbol rate as modulation technology. The downlink is modulated with Gaussian frequencyshift keying (GFSK) with a fixed 600 baud symbol rate (Sigfox 2020). The payload at Sigfox comprises 12 bytes, which is sufficient for a meter reading or status value within the scope of an EnMS. If the ISM restrictions regarding the duty cycle limitation of 1% in Europe are observed, Sigfox can transfer up to 140 packets daily (Vejlgaard et al. 2017). A time series with 15-minute values is thus feasible. A detailed research study on energy consumption in Sigfox networks and the associated possible runtimes of battery-powered sensors is available in (Gomez, Veras, et al. 2019). Sigfox nodes are often also used for the geographical tracking of palettes, boxes, or suitcases. An artificial intelligence (AI)-based approach for a more precise localization is presented in (S.-Y. Wang, Cheng, and Tarng 2019). LPWANs in the licensed radio frequencies are operated by the providers of these cellular networks, such as Telekom, Vodafone, and Telefónica, who are using their existing LTE infrastructure for IoT technologies. As not only the existing base

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stations but also numerous system features of the LTE standard can be used for these LPWANs, the financial and organizational hurdle for these providers is much lower than for providers without an existing infrastructure. To upgrade the base stations to NB-IoT, an additional hardware upgrade is required, allowing NB-IoT to operate in the GSM band, the LTE band, or within the LTE guard band, depending on the mode. The specification of NB-IoT takes into account simplifications compared to LTE to allow less complex and, therefore, cheaper hardware for the radio modules. For example, transmission is only in half-duplex mode and, unlike LTE, receivers can be equipped with only a single antenna. Up to 50.000 devices can be operated per radio cell. For improved range, NB-IoT uses additional mechanisms such as power boosting in the downlink and subframe repetition for both directions (Y.-P. E. Wang et al. 2017). The achievable data rate of about 200–250 kbit/s is fully sufficient for sensors in energy management. The operation of LTE-M in an LTE network requires only a software update, as LTE-M operates in full duplex mode within the LTE frequency band (Ratasuk, Mangalvedhe, et al. 2014). One radio cell can supply up to 10.000 devices, the possible data rate in the uplink is 1 Mbit/s. As NB-IoT and LTE-M are provided as NaaS, no separate gateways or network servers need to be installed when using such devices. However, this also has no influence on the reachability of the network, which, as is usual with mobile phone networks, is not available in the same quality at every location. In rural environments, various studies (Lauridsen et al. 2016; Vejlgaard et al. 2017) have demonstrated an availability of LTE-M of 99.9% both outdoors and indoors. For measurement points in buildings with greater attenuation, such as in basements, NB-IoT is the better choice, where at least 95% availability is still given. There are numerous scientific papers with more detailed system descriptions, comparative studies and investigations of performance, of which the following shall be mentioned as examples: (Ratasuk, Vejlgaard, et al. 2016; Y.-P. E. Wang et al. 2017; Zayas and Merino 2017). Another commercial LPWAN is a network technology based on random phase multiple access (RPMA) modulation in the 2.4 GHz band called “Ingenu,” which is mainly used in the USA. This network is only mentioned here for completeness. Further information can be found on the Ingenu Inc. website (Ingenu 2020) and a comparative study of several LPWANs (Raza, Kulkarni, and Sooriyabandara 2017). Standards to Turn Sensors into CPES Besides suitable power grids, electricity meters and sensors need another property so they can be used as participants in IoT: the “digital twin” (Wolf 2009). In CPES, this twin is not located in a distant control center, but is always closely linked to

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the respective component (Macana, Quijano, and Mojica-Nava 2011). This means the physical device exists in all relevant properties, access possibilities, states, and methods in a virtual representation in the network. To ensure each participant does not implement its digital twin in an individual scheme, there are standards for implementing these representations. The OPC unified architecture (OPC UA) framework (OPC Foundation 2017a) is one technology for standardization and thus for promoting interoperability in IoT. This standard enables the generation of digital twins as components of cyberphysical systems (CPS). Our energy meters in a factory not only form components of a CPES but also become participants in cyber-physical production systems (CPPS) (Monostori at al. 2016), in which they can develop added value beyond energy. In the field of automation engineering, the OLE for process control (OPC) has been around for decades (Rehbein and Pederson 1996). Here, components of a manufacturing process were mapped in a Microsoft Windows-based system and were controlled and monitored by this system. From the requirement for stronger vertical networking within a company, the OPC Foundation (Foundation 2020) developed the successor, OPC unified architecture (OPC UA) (Leitner and Mahnke 2006; Mahnke, Leitner, and Damm 2009). This framework, defined in the IEC 62541-3 standard, gave up its binding to Microsoft-based systems and now offers an open, manufacturer-independent platform for m2m communication in industrial processes. The hierarchical model of the classical automation pyramid in Figure 3.3 is now dissolving in favor of service-oriented communication across the entire enterprise.

Figure 3.3 Dissolving the automation pyramid with OPC UA. Source (M. C. Krutwig 2019)

This framework offers everything a sensor needs to grow into a CPES: a secure, internet-based communication model and a uniform data model for processing data,

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alarms, and historical data. An OPC UA server generates an information model of all physically existing devices and applications (OPC Foundation 2017e). The server provides various components such as objects, relationships, properties and methods in an address space (OPC Foundation 2017c). For standard objects, OPC UA already provides a set of nodes in a standard information model, which can then be extended by further information models, referred to as companion specifications (CS), depending on the application (OPC Foundation 2017e). These CS are aimed at specific application domains, such as robotic arms or RFDI technologies, and are intended to enable interoperability of the devices across manufacturers. In other words, the digital twins of the devices overcome manufacturer boundaries. For specific applications of a manufacturer, the information model can also be extended by a manufacturer’s own model. The upper part of Figure 3.4 shows the combination of the information models.

Figure 3.4 The OPC UA architecture stack. Source (Foundation 2020)

The basis of the OPC UA framework is shown in the lower part of Figure 3.4. This base consists of the data model with associated services (OPC Foundation 2017a; OPC Foundation 2017d; OPC Foundation 2017f; OPC Foundation 2017b), which is based on a transport layer (OPC Foundation 2017h) and a discovery service (OPC Foundation 2018c). The discovery service is helpful because a large number of OPC UA servers and clients can exist in an enterprise system. The transport layer offers several possibilities for access via connection-oriented internet protocols like TCP/IP or via UPD to a publisher-subscriber (pubsub) service (OPC Foundation 2018d). Specifically, the framework provides for the use of the message queue telemetry transport (MQTT) protocol (Hunkeler, Truong, and Stanford-Clark 2008) or the advanced message queuing protocol (AMQP) as pubsub services (OPC Foundation 2018d). Such connectionless exchanges are always feasible within an EnMS if a loss of individual values can be accepted.

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Also, concerning security, OPC UA offers several possibilities for implementation (OPC Foundation 2018e), depending on the individual requirements and the possibilities for implementation. For access protection, different security policies can be defined, so the complexity of the implementation for limited devices can remain low. This complexity can also be influenced by different features and performance requirements. For this purpose, there are four so-called “server profiles” for OPC UA (OPC Foundation 2017g), which are used to address different device classes. These profiles, which are based on each other, range from the small “nano embedded device server” to two further levels for embedded devices up to the “standard UA server” with full functionality. With the ability to store historical data (OPC Foundation 2018b) OPC UA can also take on the role of a data logger. Alarms and conditions (OPC Foundation 2017i) are also typical functions that can also be found in an EnMS. The case study in Section 3.4.2 is not the only proof that OPC UA can be used without problems for energy management. Earlier work has already confirmed the suitability of OPC UA for energy systems and smart grids (Matzler et al. 2013; Rohjans, D. Fensel, and A. Fensel 2011). For small, low-cost sensors, such as those typical in EnMS, there are studies describing an implementation of OPC UA at chip or sensor level (Veichtlbauer, Ortmayer, and Heistracher 2017; Imtiaz and Jasperneite 2013). The most significant characteristics that make up a CPES are listed in (Macana, Quijano, and Mojica-Nava 2011). A mapping of these properties on the standard OPC UA can be found in (M. Krutwig, Kölmel, et al. 2019). An extension of OPC UA to the use of time sensitive networks (TSN) increases the applicability of the framework for real-time applications in CPES. As communication via TCP/IP cannot be used in time-critical systems, an additional OPC UA TSN was created for real-time communication, e.g. in ethernet-based fieldbuses (Bruckner et al. 2019). Several studies exist on this topic, for example, on the use of TSN extensions of the ethernet protocol via a new pubsub extension (Pfrommer et al. 2018). In addition to OPC UA, other standards are relevant for implementing smart grids: • In the conventional power grid, the standard IEC 61850 (IEC 2013) of the International Electrotechnical Commission (IEC) serves to provide cross-manufacturer interoperability of all components found in substations, transformer stations, switchgear, and distribution networks (Mackiewicz 2006). In the digitization of power grids, this standard with its object-oriented data model and the logical separation of the data model and communication protocol can map these com-

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ponents as CPES and make a major contribution to the transformation of the grids into smart grids (Higgins et al. 2011; Zhabelova and Vyatkin 2012). • AutomationML identifies the standard IEC 62714 (IEC 2014), which comes from the domain of automation technology. The components of production systems are represented in an object model. The description language is based on the objectoriented programming paradigm with concepts such as objects, methods, classes, inheritance, polymorphism, and others. AutomationML is based on the description language XML and the Computer Aided Engineering Exchange (CAEX) description language, which is defined in the standard IEC 62424 (IEC 2008). CAEX is used to model plant topology, resource topologies, and communication topologies in AutomationML (IEC 2014). • IEC 61970/61968 sets out the standard for common information model (CIM). This specifies the integration of IT systems into the domain of the energy industry (McMorran 2007). As CIM covers a wide range of participants in energy systems, it is often used for simulations in the energy industry. Just like OPC UA and AutomationML, CIM uses the description language XML. The objectbased topology is exchanged by serializing the data with the resource description framework (RDF) (McMorran 2007). All three standards can also be combined with OPC UA. A bridge between UPC UA and IEC 61850 is provided by a dedicated companion specification (OPC Foundation 2018a), which enables the lossless mapping of object models into the address space of OPC UA. Another CS also exists for AutomationML to link its data model with the communication possibilities of OPC UA (OPC Foundation 2016). Further, the configuration of an OPC UA server itself can be standardized using AutomationML (Henßen and Schleipen 2014). With CIM, the UML descriptions used there can also be translated into the address space of OPC UA (Rohjans, Uslar, and Appelrath 2010). There are several comparative studies on such standards in smart grids. One focus is the investigation of communication technologies in smart grids (Gungor, Sahin, et al. 2011). The work (Rohjans, Uslar, Bleiker, et al. 2010) contains a comparative study and concrete recommendations for future standards in smart grids. Technologies for Retrofitting Energy Meters “Retrofitting” refers to modifying existing systems and equipment to achieve objectives such as extending the service life, increasing efficiency, or enabling old equipment to be moved into modern IT environments. Such a step is supposed to prevent a more cost-intensive new purchase. In the context of energy measurements, retrofitting means an upgrade of measuring instruments so they can be in a network or

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even inserted as CPES in an IoT environment. The upgrading of machines with appropriate measuring equipment can also be understood as a retrofit. Measuring instruments that are built to be read by a human can be converted into a machine-readable device using two methods: pulse detection or character recognition (OCR). In pulse detection, an electricity meter delivers a pulse after a fixed defined consumption quantity via an optical flashing signal, an electrical pulse signal (S0 pulse generator), or via a recognizable marking on a so-called Ferraris disc. The retrofit is done by adding a device for optical or electrical detection of these signals and for counting and transmission via a network. The advantage of this method is the low hardware complexity of the pulse detection, an inexpensive infrared LED e.g. can be used as sensor for optical pulses. A disadvantage is the incorrect measurement in case of errors: if a pulse is not detected, the calculated consumption does not increase. In addition, this method can only be used for meters with pulse transmitters, which are often not available, for instance, in flow quantity meters for gas, oil, and water. In a retrofit using the OCR method, the meter is photographed, the displayed meter reading is determined by a cognitive system, and this value is then regularly transmitted via the network to the EnMS. Such a setup is shown in Image 3.5. OCR is an established technology (Mori, Suen, and Yamamoto 1992) that is widely used for digitization and automation. As a result of the highly limited recognition context to just a few digits of a clearly defined font, the recognition error rate is practically zero. Modern OCR systems deliver good recognition performance even with handwritten characters and, therefore, simple counter readings do not cause a big challenge for the algorithms, especially if the camera is placed correctly during installation and the ORC software has configured the respective counter. Typical configuration parameters are foreground and background color, counter type (analog or digital), and the handling of possible partially visible digits as a result of the rotary movement. There are several scientific studies on the use of OCR for energy meters, such as (Castells-Rufas and Carrabina 2006; Cortez et al. 2014; Elrefaei et al. 2015), which can be used to deepen the topic. A retrofit with OCR has the advantage that almost every meter whose housing allows the retrofit to be installed can be read. If the OCR takes place directly in the camera, only a few bytes of numerical value instead of the image are transmitted, which enables networking via LPWAN. Even the loss of a measurement is not critical, as the absolute consumption value is correctly taken over again with the next successful reading. The disadvantage of this method is the complex and thus more expensive hardware, which includes a camera module and an embedded system for OCR in addition to the networking component.

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Figure 3.5 The mounted camera module on the analog energy meter

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Using LoRaWAN and OPC UA for Measurement—A Case Study

In this subsection, a case study for automated energy measurement is presented, which was first described in the study (M. Krutwig, Kölmel, et al. 2019). This case includes two standards that were discussed in the previous paragraphs: OPC UA (from page 73) and LoRaWAN (from page 68). The study describes an installation in a production facility of a German car manufacturer. As proof of concept, the suitability of LoRaWAN as a transmission technology was to be demonstrated. The current was to be measured in three phases and converted into consumption values at 15-minute intervals before transmission to the EnMS. The installation comprised several components: • To measure the power, folding current transformers (CT) were connected to the three phases (yellow cables) as shown in Figure 3.6, part [c]. Each CT measured a maximum power of 63A at 50 Hz. • The induced voltage of the CT was transmitted via the white cables to a so-called modbus CT bridge. Figure 3.6, part [a], shows the installation of this bridge. This voltage was also used to supply the bridge by energy harvesting, so no additional power supply was needed. The device calculated a meter reading from the continuous power measurement and transmitted this value via LoRaWAN to the gateway. The bridge is a class C LoRa device. • The LoRaWAN base server, as shown in Figure 3.6, part [b], was located in a network cabinet about 30 meters away from the CT bridge. The base server, like the CT bridge, is a device available on the market from the Swiss company comtac (comtac 2020). The embedded system uses an AM33x processor from Texas Instruments with a Yocto Linux operating system. The device operates in the European ISM band 868 MHz. The device was connected to a power supply and via Ethernet to the company’s data network. The base server integrates several components in one device: it acts as a LoRaWAN gateway, as a network server (NS), and it provides an application server in the form of an integrated OPC UA server. As an alternative to the NS, the device can also be operated as a pure packet forwarder to a LoRa NaaS operated nationwide in Switzerland, which was not relevant for the application shown. The base server received the data packets from the CT bridge, interpreted the LoRa payload, and stored the extracted meter values in the address space of its OPC UA server. Any authorized OPC UA client could then fetch these counter readings. • The software system enerchart (krumedia 2020a) was used to process and visualize the consumption data. The system is an EnMS for ISO 50001 with a built-in

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Figure 3.6 Measurement setup with current transformer [c], ct bridge [a] and LoRaWAN base server [b]

OPC UA client, so the software could connect directly to the OPC UA server of the base server without modification and retrieve the meter readings. Figure 3.7 shows the configuration mask of the OPC UA client, which was created as a data source for the EnMS. To transfer the consumption of a measured phase as a displayable measuring point into the EnMS, the address space of the OPC UA server could be browsed during configuration and the located variable could be associated with the measuring point.

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Figure 3.7 Configuration of the OPC UA client in the EnMS. Source (M. Krutwig, Kölmel, et al. 2019)

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As the CT bridge does not have its own user interface, a configuration file in XML format is stored in the device’s flash memory via a USB connection. This configuration contains parameters for current measurement and for the LoRaWAn transmission. For current measurement, the transformer ratio of each CT and the type of measurement (true root mean square (RMS) or—like here—50 Hz component I50 H z ) had to be set. The power factor cos ϕ could be taken from the data sheet of the measured units. The conversion from power to current consumption P with known voltage U is then done according to the formula 3.5. P = U · I50 H z · cos ϕ

(3.5)

The LoRaWAN parameters were set according to the local environment. The transmission interval was 900 seconds and the spread factor (SF) was 10. The values for bandwidth (125 kHz) and code rate (1) could not be changed. According to the formula 3.4 this resulted in a theoretical data rate of 0,9766 bit/s. The data package transmitted via LoRaWAN (payload) had a total length of 208 bits and contained six counter values of 32 bits each, as well as two values for temperature and humidity (8 bits each). As the LoRa standard does not define the payload itself, its structure must be made known to the network server of the base station via a configuration. With the device used here, this payload description is done via a file in JavaScript object notation (JSON) (Crockford 2006). The representation of this JSON file can be found in Annex B.

Figure 3.8 Screenshot: Visualization of the total consumption of all phases in the course of the day (15-minute intervals)

In EnMS, the three phases were now available as time series. The visualization could now be done in many ways using the interactive analysis tool. The chart in

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Figure 3.8 shows the calculated total consumption for one day. It can be observed that no data was available for the interval 2:45 h to 3:00 h in the morning; most likely, the transmission via LoRaWAN failed. The installation and configuration effort in this case study was perceived as high: the configuration of the CT bridge was done via USB file transfer and required the study of the documentation, as well as the configuration of the base server with the basic settings, network settings and the JSON file matching the CT bridge. A virtual private network (VPN) also had to be set up on-site between the base server and the EnMS in the cloud. Finally, the EnMS also had to be configured on the OPA UA server of the base station and the measuring points had to be brought to visualization. The required effort was caused by the lack of payload standardization of the LoRa standard, but also by the complexity of OPC UA and the not yet fully developed LoRa products. Nevertheless, the interoperability of the systems as a result of existing standards could be successfully proven. Another case study (M. C. Krutwig 2019) converts a simple energy meter into a CPES using inexpensive hardware and an open-source OPC UA implementation,

Figure 3.9 OPC UA case study setup with low-cost hardware. Source (M. C. Krutwig 2019)

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in this case without LoRaWAN. In this study, an energy meter with Modbus RTU (serial RS 485 bus) was connected by a USB serial converter to a Raspberry Pi single board computer (SBC) (Figure 3.9). The costs for the retrofit hardware (SBC and USB converter) were less than 50 e. For the implementation of the OPC UA server, the free software library open62541 (open62541 2019) was installed. The enerchart EnMS (krumedia 2020a) was used to visualize the data. The study shows that a retrofit with low financial investment is possible by using free software.

3.5

Findings for the Modeling

The term energy balance must be limited to the context of the audit. The European Energy Balance Guide (E. Commission 2019a) comes quite close to our model but is designed for energy balances of nations. An energy balance of an organization should be reflected in the audit report according to ISO 50002. This balance must, therefore, be much more detailed and variable regarding consumer listing. Potentially, every conceivable consumer should fit into the scheme of the model. Categories for consumers must be provided for better structuring and for subsequent evaluation. A list of possible categories for consumer areas can be found in numerous scientific papers—each with its own set of categories. The choice of categories for this model (see Annex A) was based on the guidelines of a German SME funding program (BAFA 2017a). For a more flexible application, the fixed categories were supplemented by categories that can be freely defined per audit. An energy audit should also be able to show energy balances for several years. Therefore, the energy balance consists of period-independent assets and of periodic energy data. Assets are, for example, consumers, buildings and energy generators. The validity of the assets is specified for multiple periods: the first year and—if the asset is no longer available—the last year. Within this validity period, energy data can be recorded for this asset. To homogenize the data, all energy values in purchasing, consumption, production and sales are recorded as annual values. The conversion of the calorific values of all energy carriers is done in kWh. The default conversions used in the model are listed in Annex A. The user can replace these default values with his own conversion parameters. Variables for EnPIs and adjustment factors must be taken into account. The EnPIs themselves are treated as assets, as their definition applies to all periods. The annual values of the reference data, on the other hand, are annual values just like the consumption data. The model, therefore, only considers annual key indicators. When recording consumption values, the type of measurement must also be considered. For each consumption value, it must be indicated whether this value was

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measured, temporarily measured, estimated or calculated from the performance data of the asset and a term. As more measured values are to be expected in the future, it should also be possible to specify a meter number for these consumption data. Each asset and energy value must have the ability to be supplemented by a file. This provides a back door for introducing measurement protocols, load profiles, and other information that does not fit into the homogenized data model itself. This additional information is not embedded in the generated report, but at least it is shown as an attachment.

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Studies on Business Aspects of Energy Efficiency Measures

The recording of a company’s energy situation and the associated energy balance is only the first part of an energy audit. The second task in conducting the audit is to discover and calculate energy efficiency measures (EEM). Therefore, data are needed in the data model which characterize the EEM and allow a calculation of the economic efficiency. The “organizational” data of the EEM (such as name, description, consumer category, implementation period, priority, etc.) are relatively trivial and can be found in the data model in Section 6.5. This chapter focuses on the calculation methods that can be used to decide on an investment in an EEM. Based on the knowledge of these calculation methods, a decision is first made as to which methods will be implemented in the application. This allows the input variables required for the calculation to be determined. The quality of an audit is also related to the completeness of the EEM found and the precision of their calculation. Therefore various calculation methods are considered. The more influencing factors a calculation contains, the more precisely the economic efficiency can be assessed. The findings of this chapter also apply, of course, to energy efficiency measure calculations within the framework of an energy management system.

Electronic supplementary material The online version of this chapter (https://doi.org/10.1007/978-3-658-33167-2_4) contains supplementary material, which is available to authorized users.

© The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2021 M. Krutwig und A. Dumitru Tan¸ta˘ u, Energy Audits, Sustainable Management, Wertschöpfung und Effizienz, https://doi.org/10.1007/978-3-658-33167-2_4

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Assessing Economic Efficiency

When deciding on the implementation of planned energy efficiency measures, the energy savings are calculated in monetary terms and compared with the required investment. The decision to adopt the EEM is then largely dependent on profitability. In this case, classical methods of investment calculation (Brigham and Houston 2014) are used. We first look at some general calculation methods in order to refine them later for use with EEM. To calculate the savings, it is necessary to know the useful life n of the investment I , as well as the cash flow CF in year t, whereby the benefit is given by the savings through the efficiency measure: CF t = savingst − costst

(4.1)

If fixed costs and savings are assumed for t = [1 . . . n], then the static cash flow is abbreviated with CF. In practice, the cash flow is not always homogeneous. The annual savings and capital costs vary, for example, because of irregular maintenance costs. The investment in the efficiency measure is included as zero year cash flow: CF 0 = Investment in the EEM

(4.2)

The salvage value R of the investment, as recommended in BAFA’s examples (BAFA 2019b), can also be considered as a further factor for the calculation. In this work, we do not follow this recommendation, as the indication of the useful life n usually corresponds to the duration of the value added. Moreover, according to BAFA example calculations, a reduction of the investment through a possible state subsidy should be considered in energy audits in Germany, which is also not regarded in this consideration.

4.1.1

Net Present Value

To determine the net present value (NPV) of future earnings, the investments are compared with alternative, low-risk investment opportunities at interest rates. This interest rate r is also referred to as the weighted average cost of capital (WACC). An investment is only worthwhile if the total cash flow is greater than the return generated by the interest on the investment sum. The cash-flow amounts are dis-

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counted over the entire term n. The NPV is calculated as (Brigham and Houston 2014, p. 374): NPV =

n  t=0

CF t (1 + r )t

(4.3)

The decision to accept or reject a measure can now be made dependent on whether the NPV is positive, i.e., greater than zero. If there are several efficiency measures to choose from, the NPV can be used to prioritize the measures. Various studies are critical of the recommendation to take a positive NVP as the green light for investment decisions, as the calculation does not take sufficient account of future uncertainties. In the study (Basher and Raboy 2018), for instance, an over-optimistic valuation of energy prices by the US Department of Energy, whose questioned recommendations are based on a positive NPV, is discussed.

4.1.2

Internal Rate of Return

The internal rate of return (IRR) of an investment can be a further indicator of the profitability of a measure and thus a basis for decision-making. This calculation method provides the return rate of an investment where the NPV is exactly zero (Brigham and Houston 2014, p. 377): NPV = 0 =

n  t=0

CF t (1 + IRR)t

(4.4)

This equation solves the interest rate IRR, which can now be compared with the company’s WACC. If the IRR is higher, the measure is worthwhile. It should be noted that an investment project can also deliver more than one IRR if further investments are required during the term, for example, for a cost-intensive dismantling of the measure at the end of the useful life. According to (Brigham and Houston 2014), there is an inherent overvaluation in the IRR as it erroneously assumes the cash flow is always invested at the IRR (and not at the WACC). A modified IRR (MIRR) therefore shows an alternative calculation in which the reinvestments are made at the WACC. A further advantage of the MIRR is that it is unique and, thus, there are no multiple MIRRs. In addition, (Short, Packey, and Holt 1995) recommend using MIRR instead of IRR if a negative cash flow occurs near the middle or at the end of the period.

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The NPV and the IRR are the preferred and recommended (BAFA 2019b) methods in Germany for obligatory energy audits, but other calculation methods are also permitted.

4.1.3

Return on Investment

The return on investment (ROI) is a financial percentage ratio used to calculate the benefit in relation to the investment cost. It is most commonly measured as net income divided by the original capital cost of the investment: n 

ROI =

CF t

t=1

CF 0

(4.5)

The higher the ROI, the better the investment can be evaluated. Depending on the type of calculation, the capital value can still be discounted or standardized to an average value. It is also left open whether the cash flow is reduced by income taxes. In practice, there are several methods for calculating ROI, such as net income, capital gain, the total return method, or the inclusion of annuities. Therefore, the ROI does not offer an optimal and objective method for evaluating efficiency measures.

4.1.4

Payback Period

The payback period (PBP) allows an efficiency measure to be evaluated over time and is, therefore, indirectly also a parameter for risk assessments. The shorter the amortization period, the more attractive the investment. The PBP can be calculated by equating the cumulative cash flow with the depreciable fixed capital investment (Brigham and Houston 2014). To do this, we first iteratively determine the period T from which the break-even is exceeded: CF 0 ≤

T 

CF t

t=1

We now know the PBP is somewhere between T − 1 and T years. To further specify T , we subtract the fraction of the period T and obtain

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PBP = T −

CF t

t=0

CF T

(4.6)

An example:

Assuming a cash flow CF t = {−1.000.000 e, 323.400 e, 243.400 e, 218.000 e, 181.600 e, 156.200 e, · · · }. At T = 5 the break even is exceeded, here the cumulative cash flow is 122.600 e. This calculates PBP = 5 − 122.600 156.200 = 4,2151 years.

To determine the PBP more exactly, (Brigham and Houston 2014) recommend discounting the cash flow at the WACC. This leads to an extended payback period: CF 0