Energy Smart Appliances E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Energy Smart Appliances Enables designers and manufacturers to manage real-world energy performance and expectations by covering a range of potential scenarios and challenges Energy Smart Appliances provides utilities and appliance manufacturers, and designers with new approaches to better understand real-world performance, assess actual energy benefits, and tailor each technology to the needs of their customers. With contributions from a fully international group of experts, including heads of prestigious research organizations and leading universities, and innovation managers of the main appliance manufacturers, Energy Smart Appliances includes discussion on: * Enabling technologies for energy smart appliances, covering IoT devices and technology and active energy efficiency measures in residential environments * Smart home and appliances, answering questions like 'Where are we heading in terms of the overall smart homes' future?' and 'What's the energy impact from smart home devices?' * Demand-side management and demand response, covering overall system/ appliances readiness and ideal energy management scenario to drive demand response * Energy smart appliances' best practices and success stories, including refrigerators, washers, dryers, and more With practical coverage of a wide range of potential scenarios and existing and future challenges, Energy Smart Appliances is an essential learning resource for electrical engineering professionals, equipment manufacturers, and designers, along with postgraduate electrical engineering students and researchers in related fields and programs of study.
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Seitenzahl: 556
Veröffentlichungsjahr: 2023
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Table of Contents
Cover
Title Page
Copyright
Dedication
About the Editors
List of Contributors
1 Demand‐Side Flexibility in Smart Grids
1.1 The Energy Sector
1.2 The Power Grid
1.3 The Smart Grid
1.4 Power Grid Flexibility
1.5 Power Quality, Reliability, and Resilience
1.6 Economic Implications and Issues of Poor Power Quality
1.7 Internet of Things
1.8 The Relevance of Submetering
1.9 Energy Smart Appliances
Symbols and Abbreviations
References
2 A Deep Dive into the Smart Energy Home
2.1 Smart Home Ecosystem
2.2 Enabling Technologies
2.3 Limitations
2.4 A Look into a Future Anchored in the Past
2.5 Conclusion
Symbols and Abbreviations
Glossary
References
3 Household Energy Demand Management
3.1 Introduction
3.2 Technical Opportunities and Challenges for DSM
3.3 Pilots and Experimental Settings
3.4 Conclusions
Symbols and Abbreviations
Glossary
References
Notes
4 Demand‐Side Management and Demand Response
4.1 Introduction
4.2 Demand Response vs. Demand‐Side Management
4.3 The Need for Demand Response/Demand‐Side Management
4.4 DSM Strategies
4.5 Demand Response Programs
4.6 Smallest Communication Subsystem Enabling DSM: HAN
4.7 Smart Metering
4.8 Energy Usage Patterns of Households
4.9 Energy Consumption Scheduling
4.10 Demand Response Options for Appliances
4.11 Bidirectional Effects of Demand Response
4.12 Consumer Objections and Wishes Related to Smart Appliances and Demand Response
4.13 Costs and Benefits of Demand‐Side Management
Symbols and Abbreviations
Glossary
References
5 Standardizing Demand‐Side Management: The OpenADR Standard and Complementary Protocols
5.1 History and Creation of OpenADR
5.2 Re‐development of OpenADR 2.0
5.3 How OpenADR Works
5.4 Cybersecurity
5.5 Other Standards and Their Interaction with OpenADR and Energy Smart Appliances
5.6 Energy Market Aspects for Appliances
5.7 Typical DR and DSM Use Cases
Symbols and Abbreviations
Glossary
References
Notes
6 Energy Smart Appliances
6.1 Energy Smart Appliances
6.2 Which Appliances?
6.3 Smart Energy Controller
6.4 Large Home Appliances
6.5 Small Appliances
6.6 Monitoring
6.7 Health, Comfort, and Care
6.8 House Automation
6.9 Non‐appliances
6.10 Entertainment
6.11 Security
6.12 Conclusion
Symbols and Abbreviations
Glossary
References
Notes
7 The ETSI SAREF Ontology for Smart Applications: A Long Path of Development and Evolution
7.1 Introduction
7.2 IoT Ontologies for Semantic Interoperability
7.3 The SAREF Initiative
7.4 Specification and Design of the SAREF Ontology
7.5 Overview of the SAREF Ontology
7.6 The SAREF Ontology in the Smart Home Environment
7.7 The SAREF Ontology in Use
7.8 Lessons Learnt
7.9 Conclusions and Future Work
Acknowledgments
References
Notes
8 Scheduling of Residential Shiftable Smart Appliances by Metaheuristic Approaches
8.1 Introduction
8.2 Demand Response Programs in Demand‐Side Management
8.3 Time‐Shiftable and Smart Appliances in Residences
8.4 Smart Metaheuristic Algorithms
8.5 Scheduling of Time‐Shiftable Appliances by Smart Metaheuristic Algorithms
Symbols and Abbreviations
Glossary
References
9 Distributed Operation of an Electric Vehicle Fleet in a Residential Area
9.1 Introduction
9.2 EV Charging Stations
9.3 EV Services
9.4 Dispatching Strategies for EVs
9.5 Proposed Distributed EV Dispatching Strategy
9.6 Conclusions
Acknowledgments
References
Note
10 Electric Vehicles as Smart Appliances for Residential Energy Management
10.1 Introduction
10.2 EV Charging Standards and Charging Protocols
10.3 Communication Protocols Used in EV Ecosystem
10.4 Residential EV Charging Infrastructure
10.5 Impacts of EV Charging
10.6 Smart Charging for Home Charging
10.7 Residential Smart Energy Management
10.8 Conclusion
List of Abbreviations
Glossary
References
Notes
11 Induction Heating Appliances: Toward More Sustainable and Smart Home Appliances
11.1 Introduction to Induction Heating
11.2 Domestic Induction Heating Technology
11.3 Advanced Features and Connectivity
11.4 Conclusion and Future Challenges
Symbols and Abbreviations
References
Index
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Categories and typical characteristics of power system electromag...
Table 1.2 Average costs by type of poor PQ event from the survey results....
Table 1.3 Direct cost per voltage sag.
Chapter 2
Table 2.1 Number of Internet of Things (IoT) connected devices worldwide fr...
Table 2.2 List of connected devices available for Smart Homes in the year o...
Chapter 3
Table 3.1 Hardware platforms (a benchmark study – May 2022).
Table 3.2 Communication infrastructures (benchmark study – May 2022).
Chapter 4
Table 4.1 Comparison of smart meter to conventional meter.
Chapter 6
Table 6.1 List of appliances and other connected devices available in 2022....
Chapter 8
Table 8.1 The parameters of utilized algorithms and obtained results.
Chapter 9
Table 9.1 Data for the distributed algorithm for the charging management of ...
Table 9.2 Data for the distributed algorithm for the discharging management ...
Chapter 10
Table 10.1 Charging levels as defined in SAEJ1772.
Table 10.2 Other relevant standards on EV charging.
Table 10.3 Smart charging and communication protocols and the supporting rol...
Table 10.4 General requirements.
Table 10.5 Standards for charging interface.
Table 10.6 Different types of smart charging.
Table 10.7 Requirements to enable residential smart charging.
Chapter 11
Table 11.1 Efficiency comparison.
List of Illustrations
Chapter 1
Figure 1.1 Evolution of the primary energy production by fuel in EU‐27 from ...
Figure 1.2 Evolution of the net electricity generation in EU‐27 from 2010 to...
Figure 1.3 Evolution of the final energy consumption in EU‐27 from 2010 to 2...
Figure 1.4 Classification of demand‐side management measures.
Figure 1.5 Demand response programs timescale.
Figure 1.6 Impact of power quality disturbances.
Figure 1.7 Example of location of the Virtual Transfer Points.
Chapter 2
Figure 2.1 Traditional smart home in the year of 2020.
Figure 2.2 Number of Smart Homes forecast in the World from 2017 to 2025 (in...
Figure 2.3 Smart Home penetration rate forecast in the World from 2017 to 20...
Figure 2.4 Diagram of what a heavily connected Smart Home looks like in the ...
Figure 2.5 Ownership rate of Smart Home devices in the United States 2021. W...
Figure 2.6 Smart home technology concerns that affect consumer adoption in t...
Figure 2.7 Average duration of total annual electric power interruptions, Un...
Figure 2.8 Average total annual electric power interruption duration and fre...
Figure 2.9 Smart Energy Home ecosystem showing extended scenario.
Figure 2.10 Metering penetration by technology type in the United States....
Figure 2.11 Percentage of Advanced Metering Infrastructure (AMI) per state a...
Chapter 3
Figure 3.1 Protocols for IoT systems, adapted from Cruz et al. (2020).
Figure 3.2 UDP communication clientserver, adapted from Cruz et al. (2020)....
Figure 3.3 MQTT
Handshake
communication clientserver, adapted from Cruz et a...
Figure 3.4 CoAP DTLS
Handshake
communication clientserver, adapted from Cruz...
Chapter 4
Figure 4.1 World electricity final consumption by sector between 1979 and 20...
Figure 4.2 General components of a home area network.
Chapter 5
Figure 5.1 OpenADR Alliance logo.
Figure 5.2 The seal of compliance to OpenADR 2.0.
Figure 5.3 A relationship diagram between the parts of the system.
Figure 5.4 The relationship of the OpenADR profiles and OASIS Energy Interop...
Figure 5.5 The Typical OpenADR Exchange Pattern.
Figure 5.6 The structure of a typical OpenADR Event.
Figure 5.7 The typical information flow of the OpenADR signal.
Figure 5.8 USNAP add‐on module.
Figure 5.9 An AC Modular Communications Interface use case.
Figure 5.10 Example of a Home Energy Manager that supports DC Modular Commun...
Figure 5.11 Block Diagram of the MCI (Socket Interface is defined in CTA‐204...
Figure 5.12 CTA‐2045 brand name.
Figure 5.13 EEBUS overview.
Figure 5.14 Sacramento Municipal Utility District summer rates.
Figure 5.15 SCE Bring Your Own Thermostat ecosystem.
Chapter 6
Figure 6.1 Smart Energy Controller block diagram.
Figure 6.2 Ownership rate of major household appliances in selected countrie...
Figure 6.3 Dishwasher macro elements for smart energy applications.
Figure 6.4 Typical Electric Vented Dryer macro elements for smart energy app...
Figure 6.5 Standard HVAC Split System macro elements for smart energy applic...
Figure 6.6 Microwave with broil and convect features macro elements for smar...
Figure 6.7 Refrigerator macro elements for smart energy applications.
Figure 6.8 Oven macro elements for smart energy applications.
Figure 6.9 Washing machine macro elements for smart energy applications.
Figure 6.10 Electric tank‐based water heater macro elements for smart energy...
Figure 6.11 Virtual Assistant block diagram.
Chapter 7
Figure 7.1 The SAREF suite of ontologies with its different modules.
Figure 7.2 The SAREF project version development workflow (adapted from ETSI...
Figure 7.3 The SAREF pipeline checks the compliance of a SAREF project with ...
Figure 7.4 Overview of the SAREF ontology (adapted from ETSI (2020j), ©ETSI ...
Figure 7.5 Main SAREF classes that are relevant for the smart home environme...
Figure 7.6 Main classes of SAREF4ENER (adapted from ETSI (2020e), ©ETSI 2020...
Figure 7.7 Water‐related terms of SAREF4WATR (adapted from ETSI (2020h), ©ET...
Figure 7.8 Overview of SAREF4BLDG (adapted from ETSI (2020b), ©ETSI 2020, al...
Figure 7.9 Device hierarchy in SAREF4BLDG. (a) Hierarchy of s4bldg:BuildingO...
Figure 7.10 Overview of SAREF4CITY (adapted from ETSI (2020c), ©ETSI 2020, a...
Figure 7.11 Overview of the SAREF4SYST ontology pattern (adapted from ETSI (...
Chapter 8
Figure 8.1 Six major DSM objectives proposed by Gellings (1985).
Figure 8.2 Price‐based and incentive‐based demand response programs.
Figure 8.3 Representative operating cycles for dishwashing machine, cloth wa...
Figure 8.4 Nature‐inspired computing techniques in artificial intelligence....
Figure 8.5 Smart appliance scheduling via centralized pricing and SEC.
Figure 8.6 Smart appliance scheduling via distributed demand‐side management...
Figure 8.7 Request aggregating and scheduling mechanism.
Figure 8.8 Aggregated consumption curves when smart appliances are scheduled...
Chapter 9
Figure 9.1 V2X interactions.
Figure 9.2 Charging modes.
Figure 9.3 Home Energy Management System (HEMS).
Figure 9.4 Grid‐controlled and aggregator methods. (a) Grid‐controlled syste...
Figure 9.5 Basic scenario tested for the distributed management of EV chargi...
Figure 9.6 values for every iteration for the distributed charging algorit...
Figure 9.7 Scenario with home PV and storage tested for the distributed mana...
Figure 9.8 values for every iteration for the distributed discharging algo...
Chapter 10
Figure 10.1 Impact of high EV charging load on the distribution network.
Figure 10.2 Applications of EV charger.
Figure 10.3 Modes of charging as defined by IEC 61851.
Figure 10.4 Typical power vs. voltage curve.
Figure 10.5 Distribution feeder.
Figure 10.6 Static loads in the feeder.
Figure 10.7 Feeder voltage at different nodes under different EV penetration...
Figure 10.8 Voltages at ND 17 for different levels of EV penetration.
Figure 10.9 Total active power losses for different penetration levels of EV...
Figure 10.10 Classification of smart charging strategies.
Figure 10.11 Architecture for time‐based V1G (the sub‐meter is optional base...
Figure 10.12 Architecture for demand limited V1G (the requirement of sub‐met...
Figure 10.13 Architecture for minimization of cost of charging using V1G (th...
Figure 10.14 Architecture for optimization of local generation for self‐use ...
Figure 10.15 Architecture for using V2H/V2B to avoid consumption during peak...
Figure 10.16 Architecture for using V2H/V2B for maximization of local energy...
Figure 10.17 Architecture for using V2H/V2B in islanded mode.
Figure 10.18 Schematic while using vehicle as storage.
Figure 10.19 Architecture for V2G application of EV.
Chapter 11
Figure 11.1 Induction heating fundamentals.
Figure 11.2 Domestic induction heating appliance: a technology enabler towar...
Figure 11.3 Induction heating history: main enabling technologies and techno...
Figure 11.4 Built‐in domestic induction heating appliances.
Figure 11.5 Induction heating appliance power conversion block diagram.
Figure 11.6 Typical power flow in an induction heating appliance.
Figure 11.7 Single‐switch quasi‐resonant inverters for induction heating app...
Figure 11.8 Operation areas of a ZVS single‐switch quasi‐resonant inverter f...
Figure 11.9 Half‐bridge (a) and full‐bridge (b) series resonant inverters....
Figure 11.10 Output power,
P
o
, vs. switching frequency,
f
sw
, in a series res...
Figure 11.11 Multiple‐output induction heating architectures: (a) single‐inv...
Figure 11.12 ZVS series resonant matrix inverter that enables to power multi...
Figure 11.13 Direct AC–AC boost series resonant inverter that achieves highe...
Figure 11.14 Three‐phase series resonant inverter with power factor correcti...
Figure 11.15 Structure of an induction heating coil system.
Figure 11.16 Inductor wire evolution: efficiency vs. cost.
Figure 11.17 Digital control architecture of an induction heating appliance....
Figure 11.18 Some silicon carbide power devices applied to domestic inductio...
Figure 11.19 Deep neural network implementation workflow for a model predict...
Figure 11.20 Flexible induction heating appliance using concentric windings....
Figure 11.21 Flexible induction heating appliance using multiple coils: (a) ...
Figure 11.22 Wireless power transfer using IH appliances.
Guide
Cover Page
Series Page
Title Page
Copyright
Dedication
About the Editors
List of Contributors
Table of Contents
Begin Reading
Index
Wiley End User License Agreement
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Ahmet Murat Tekalp
Energy Smart Appliances
Applications, Methodologies, and Challenges
Edited byAntonio Moreno‐MunozUniversidad de CordobaCórdoba, Spain
Neomar GiacominiWhirlpool CorporationBenton Harbor, USA
Copyright © 2023 by The Institute of Electrical and Electronics Engineers, Inc.
All rights reserved.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
Published simultaneously in Canada.
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Library of Congress Cataloging‐in‐Publication Data Applied for:
Hardback ISBN: 9781119899426
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Dedicated to our ever patient, supportive, and loving families.
About the Editors
Antonio Moreno‐Munoz is a professor at the Department of Electronics and Computer Engineering, Universidad de Córdoba, Spain, where he is the chair of the Industrial Electronics and Instrumentation R&D Group. He received his PhD and MSc degrees from UNED, Spain, in 1998 and 1992, respectively. From 1981 to 1992, he was with RENFE maintenance service, the Spanish National Railways Company, where he received a scholarship for his university studies. Since 1992, he has been with the University of Cordoba, where he has been the director of its department and academic director of the Master in Distributed Renewable Energies. His research focuses on Smart Cities, Smart Grids, Power Quality, and Internet of energy. He has participated in 22 R&D projects and/or contracts and has more than 200 publications on these topics.
He is currently a member of European Technology & Innovation Platforms (ETIP) Smart Networks for Energy Transition (SNET) WG‐4; WG Member of the Spanish Railways Technological Platform (PTFE); WG Member of the IEEE P3001.9 Recommended Practice for the Lighting of Industrial and Commercial Facilities; a member of the Technical Committee on Smart Grids of the IEEE Industrial Electronics Society. He has been a member of the CIGRÉ/CIRED JWG‐C4.24 committee “Power Quality and EMC Issues associated with future electricity networks.” He has been a member of the IEC/CENELEC TC‐77/SC‐77A/WG‐9 committee. He has been a member of the ISO International Organization for Standardization AEN/CTN‐208/SC‐77‐210.
He is an evaluator of R&D&I projects for the Estonian Research Council; the Fund for Scientific and Technological Research (FONCYT) of the National Agency for the Promotion of Science and Technology in Argentina; the Directorate General of Research, Development, and Innovation of the Ministry of Science, Innovation, and Universities of Spain; and academic promotion at Qatar University. He is also an evaluator for European Quality Assurance (EQA) and DNV‐GL. He is the Section Board Member of the journal Electronics published by MDPI, an associate editor of Elsevier's e‐Prime journal, the Section Editor in Chief of MDPI's Smart Cities journal, an associate editor of Electronics journal published by MDPI, an editor of Intelligent Industrial Systems journal published by Springer Nature Science, and an editor of Frontiers in Energy Research, Sustainable Energy Systems, and Policies. Also, he is a guest editor of and reviewer for numerous journals of IEEE, IET, MDPI, and Elsevier.
Neomar Giacomini is a Senior Manager for Electronics Hardware Development at Whirlpool Corporation, USA. He is an accomplished inventor, developer, and technology aficionado who has been in electronics for more than 20 years, developing hardware, firmware, sensors, and user interfaces. Neomar has filed more than 50 patents, with over 20 already granted. In his current position, he is leading a team focused on electronics hardware development for refrigeration and cooking home appliances. His work at Whirlpool Corporation has actualized into component and module deployment on a global scale and across multiple home appliance platforms. In the technology space, Neomar has faced numerous challenges while working to apply and debug electromechanical and electronic‐based systems. Through this work, he has gained an understanding of the complexity and key points associated with the technologies involved on a very elevated scale.
In the Internet of Things space, Neomar has experience both delivering connected products and also as a user with nearly 120 connected devices to experience what a heavily connected home brings to the consumer. This experience enabled him to speak at events such as Sensors & IoT Virtual World Week 2020, Sensors Converge 2021, Sensors Converge 2022, and Digital Manufacturing Summit North America 2022.
From an educational perspective, Neomar holds an Executive MBA from Fundação Getúlio Vargas, Brazil, a Master's of Science in Electronics, and a Bachelor of Science in Electrical Engineering, both from the Santa Catarina State University, Brazil.
He is also an Advisory Board Member for Sensors Converge 2022/2023, holds a Certified Professional in Management® certification from the American Management Association®, and is a certified Six Sigma Black Belt by Whirlpool Corporation.
List of Contributors
Jesús Acero
Department of Electronic Engineering and Communications
Instituto de Investigación en Ingeniería de Aragón
University of Zaragoza
Zaragoza
Spain
José A. Aguado
Escuela de Ingenierías Industriales
University of Málaga
Málaga
Spain
Rolf Bienert
OpenADR Alliance
San Ramon, CA
USA
Ignacio Bravo
Department of Electronics
University of Alcalá
Madrid
Spain
José M. Burdío
Department of Electronic Engineering and Communications
Instituto de Investigación en Ingeniería de Aragón
University of Zaragoza
Zaragoza
Spain
Recep Çakmak
Samsun University
Department of Electrical‐Electronics Engineering
Faculty of Engineering
Samsun
Turkey
Inmaculada Casaucao
Escuela de Ingenierías Industriales
University of Málaga
Málaga
Spain
Carlos Cruz
Department of Electronics
University of Alcalá
Madrid
Spain
Laura Daniele
TNO (Netherlands Organization for Applied Scientific Research)
The Hague
The Netherlands
Raúl García‐Castro
Ontology Engineering Group
Universidad Politécnica de Madrid
Madrid
Spain
Joaquin Garrido‐Zafra
Department of Electronics and Computer Engineering
Universidad de Córdoba
Córdoba
Spain
Neomar Giacomini
Senior Engineering Manager for Electronics Hardware Development at Whirlpool Corporation
Benton Harbor
Michigan
USA
Maxime Lefrançois
Mines Saint‐Étienne
Univ. Clermont Auvergne
INP Clermont Auvergne
CNRS
Saint‐Étienne
France
Sahana Lokesh
Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ)
New Delhi
India
Óscar Lucía
Department of Electronic Engineering and Communications
Instituto de Investigación en Ingeniería de Aragón
University of Zaragoza
Zaragoza
Spain
Indradip Mitra
Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ)
New Delhi
India
Antonio Moreno‐Munoz
Department of Electronics and Computer Engineering
Universidad de Córdoba
Córdoba
Spain
and
Industrial Electronics and Instrumentation R&D Group
Córdoba
Spain
Angshu Nath
Indian Institute of Technology Bombay
Mumbai
Maharashtra
India
Esther Palomar
Department of Electronics
University of Alcalá
Madrid
Spain
María Poveda‐Villalón
Ontology Engineering Group
Universidad Politécnica de Madrid
Madrid
Spain
Zakir Rather
Indian Institute of Technology Bombay
Mumbai
Maharashtra
India
Héctor Sarnago
Department of Electronic Engineering and Communications
Instituto de Investigación en Ingeniería de Aragón
University of Zaragoza
Zaragoza
Spain
Neyre Tekbıyık‐Ersoy
Energy Systems Engineering
Faculty of Engineering
Cyprus International University
Nicosia
Turkey
Alicia Triviño
Escuela de Ingenierías Industriales
University of Málaga
Málaga
Spain
1Demand‐Side Flexibility in Smart Grids
Antonio Moreno‐Munoz1,2 and Joaquin Garrido‐Zafra1
1Department of Electronics and Computer Engineering, Universidad de Córdoba, Córdoba, Spain
2Industrial Electronics and Instrumentation R&D Group, Córdoba, Spain
1.1 The Energy Sector
In the last decades, western economies have been making decisive efforts to promote and encourage the use of renewable energies to decarbonize the energy system. In this sense, the commitments resulting from the last 26th Conference Of the Parties (COP26) based on the Paris Agreement (UNFCCC 2015) are a clear driver of this policy. In the case of the European Union (EU), for example, the countries involved agreed upon several objectives such as increasing the ability to adapt to the adverse impacts of extreme climate events, curbing greenhouse gas emissions, and providing the necessary funding to support these measures. These actions reinforce the ambition for Europe to remain a world leader also in terms of the so‐called energy transition.
This evolution is also reflected in the historical primary energy production data collected by the statistical office of the EU, Eurostat (Energy, transport and environment statistics – Publications Office of the EU 2020). Figure 1.1 depicts the relative evolution (to levels of 2010) of the primary energy production by fuel within the EU‐27 during the period of 2010–2020 and a detailed distribution of primary energy sources in 2020 as a donut chart, and trends are quite evident. Production of primary energy within the EU was 573.8 million tons of oil equivalent (TOE) in 2020, 17.5% and 7.1% lower than in 2010 and 2019, respectively (see dashed line in Figure 1.1). The distribution in 2020 was as follows: Renewable energies and biofuels (40.9%), nuclear (30.6%), solid fossil fuels (14.6%), natural gas (7.2%), oil and petroleum products (3.8%), non‐renewable wastes (2.4%), as well as oil shale, oil sands, and peat products (0.5%).
Figure 1.1 Evolution of the primary energy production by fuel in EU‐27 from 2010 to 2020, and primary energy production in 2020.
Source: Adapted from Energy, transport and environment statistics – Publications Office of the EU (2020).
In general terms, the trend in primary energy production has been downward in recent years due to the descending trends of solid fossil fuels, natural gas, and oil petroleum products, with 16.5%, 21.1%, and 5.2% of reductions, respectively, in the period 2019–2020. However, this decline did not exclusively take place in the last year, the trend of these energy sources is predominantly negative since 1990 but with minor increases. The primary energy produced by solid fossil fuels, natural gas, and oil petroleum products was 43.0%, 62.4%, and 35.1% lower than in 2010.
By contrast, the highest growth was reported by renewable energies and biofuels, as well as non‐renewable wastes, with 3.0% and 1.6% of variation, respectively, in 2020 (2019 as baseline). The energy production from renewable sources has increased significantly in recent decades (IEA 2021) and accounted for the highest share in primary energy production since 2015. Nuclear energy also shows a downward trend over the studied decade with a decrease of 10.7% and 20.2% in 2020 (2019 and 2010, respectively, as baseline).
Oil shale, oil sand, and peat products have had a more unstable trend over this decade. Oil shale and oil sand with maximum of 108.0% in 2017 and minimum of 63.1% in 2020 compared to 2010 levels. Concerning peat products, these peaks took place in 2012 (104.7%) and 2020 (24.8%). Although both energy sources have experienced a decrease compared to 2010 (37.0% and 75.2% of decrease, respectively), the reduction during the period 2019–2020 has been quite considerable: 18.0% and 50.2%, respectively.
1.2 The Power Grid
Power systems around the world are undergoing significant changes in response to several key drivers: The increasing availability of low‐cost variable renewable energy sources (VRES), the deployment of distributed energy resources (DER), advances in digitalization, and growing opportunities for electrification (International Energy Agency 2019), since electricity is playing an ever‐more central role in the lives of citizens and expected to be the energy source on which people rely for almost all their everyday needs such as mobility, cooking, lighting, heating, or cooling.
In terms of electricity generation, Figure 1.2 illustrates the evolution of the net production in the decade of 2010–2020 relative to 2010 levels within the EU‐27, as well as the breakdown of the different energy sources at the end of this period using a donut chart. Net electricity generation has remained about the same over the studied decade, as shown by the dashed line, but there have been two notable declines in 2014 and 2020 accounting for 95.9 and 94.1 of the 2010 levels. The EU‐27 reached 2664 TWh in 2020, 4.03% lower than in 2019 and 5.91% lower than in 2010. Nearly a half of the generation (41.3%) was covered by combustible fuels such as natural gas, coal, and oil, and almost a quarter (24.3%) came from nuclear power plants. Concerning renewable energy sources, the highest share of net electricity generation in 2020 was from wind power plants (14.7%), followed by hydropower plants (13.8%) and solar power (5.3%). Furthermore, geothermal and other sources accounted for 0.23% and 0.18%, respectively.
Figure 1.2 Evolution of the net electricity generation in EU‐27 from 2010 to 2020, and net electricity generation in 2020.
Source: Adapted from Energy, transport and environment statistics – Publications Office of the EU (2020).
The electricity coming from combustible fuels was at historical lows in 2020. It has followed a general downward trend since 2010, accounting for 75.8% of the electricity generated from this source in 2010, which means a 24.23% of reduction. The electricity from nuclear power plants shows a similar behavior and has never returned to 2010 levels representing 79.9% of the 2010 levels. The production of solar plants and wind turbines has reported the highest growth: From 0.82% and 4.86% of the total production in 2010 to 5.3% and 14.7%, respectively, in 2020. However, the electricity produced by hydropower plants has remained stable (11.3–14.4%) in this period. Furthermore, in 2020 the group of hydropower, wind, and solar plants reached almost 170% of the 2010 production, and the electricity generated by wind turbines has surpassed levels like those of the hydropower plants. Geothermal and other electricity sources also illustrate a significant growth; however, they are still a minority in the total breakdown.
In summary, the relative weight of the renewable energy sources in the EU's electricity portfolio has undergone strong growth in parallel with a large decrease in the significance of combustible fuels as well as a significant decline in the amount of nuclear energy utilization. Concretely, the renewable energy sources of electricity have increased their importance by more than 14% points in the period 2010–2020. By contrast, both the electricity coming from combustible fuels and nuclear power plants registered a reduction of 10.0% and 4.3% points over the same period.
Finally, Figure 1.3 shows the evolution of the final energy consumption by sector in EU‐27 over the previously considered period as well as a detailed breakdown of the different sectors in 2018. The electricity available for final consumption within the EU‐27 reached 2462 TWh in 2020, practically the same as in 2019 (−3.93%), and has experienced periods of growth and decline with fluctuations between 95% and 100% throughout the period under analysis as can be seen from the figure (see dashed line). The distribution of electricity consumption among the different sectors in 2020 is depicted by the donut chart: Industry (35.9%), transport (2.2%), services (27.5%), household (29.0%), and others (5.4%). The final consumption and the consumption of the different sectors account for 94.3, 95.6, 95.2, 90.9, 97.7, and 86.0 of the 2010 levels. The highest variation in the studied decade occurred in electricity consumption of the transport sector, as well as in the “others” category. Moreover, it should be noted the generalized fall in most sectors in 2020 agree with the global health crisis resulting from the COVID‐19. The only sector that increased was the residential sector, probably due to the lockdowns. The final electricity consumption decreased moderately by 5.7% concerning the levels of 2010 as well as the electricity demanded by all sectors: Industry (−4.4%), transport (−4.8%), services (−9.1%), household (−2.2%), others (−13.9%), leading to the conclusion that the weight of this fall was mainly in the services sector and others category.
Figure 1.3 Evolution of the final energy consumption in EU‐27 from 2010 to 2020, and final energy consumption in 2020.
Source: Adapted from Energy, transport and environment statistics – Publications Office of the EU (2020).
1.3 The Smart Grid
The classical power system was originally built to deliver the electrical energy generated by central power plants to the relatively nearby end‐users safely and reliably. For this purpose, the voltage level is increased up to 60–750 kV at the source to be transmitted over high‐voltage transmission lines and then is gradually reduced to be delivered to consumers in a two‐stage distribution process: First, from substations to transformation centers at medium voltage (5–20 kV) and finally from this point to the end‐users at low‐voltage (120 or 230 V in America or Europe, respectively) (ENTSO‐E Transmission System Grid Map n.d.; Mapa del sistema eléctrico ibérico n.d.; Carr 1996). The structure of a conventional grid can be summarized as follows: Power plants that generate the electrical power, high‐voltage transmission lines that transport the power from power plants to power stations which then outputs medium‐ and low‐voltage distribution lines that interconnect individual consumers. Notice that the energy flow is thus unidirectional from power plants to end‐users. This architecture has remained practically unaltered since its conception as it has been highly effective for decades in covering the initial needs of providing electrical energy to end‐users reliably and safely. However, this vision of the power system is being forced to face new conditions and more demanding requirements in both the industrial and residential sectors because of the current digital revolution as has already been mentioned at the beginning of Section 1.2. Some of them are detailed as follows (Colak 2016):
The increased energy demand due to population growth, increase in manufacturing capability all over the globe, the trend in household appliances that traditionally used gas as a fuel and now moving electricity, and the proliferation of new technologies such as the electric vehicle.
The need to increase the production capacity in the current power plants as well as the reduction of the transmission and distribution energy losses.
The challenge of reducing the operational costs, while improving the management of the existing transmission and distribution infrastructures.
The rapid growth of
distributed generation
(
DG
) due to grid‐connected DER and VRES in addition to conventional power plants. These resources are mainly solar
photovoltaic
(
PV
) panels and wind turbines.
– The need to replace equipment and deploy new technologies over existing infrastructure. In most cases, the power devices employed in the transmission and distribution systems are transformers, power switches, power breakers, utility meters, and relays. These components have low reliability due to the old technology in use. Moreover, the grid capacity for collecting information and measurements during these stages is still quite limited today.
With the intend to overcome such challenges, the power grid has evolved and must continue doing so. This new paradigm of the power grid has been called Smart Grid. The European technology platform (ETP) for Smart Grids provides the following definition in its documentation for the strategic deployment of the European electricity networks of the future (European Technology Platform [ETP] Smart Grids 2010): “A Smart Grid is an electricity network that can intelligently integrate the actions of all users connected to it – generator, consumers and those that do both – to efficiently deliver sustainable, economic and secure electricity supplies.” This concept has also been widely discussed in the United States (US Department of Energy: Office of Electricity 2011). Notice that now end‐users take an active role and can act as energy producers and consumers, becoming what is known as prosumers (Dai et al. 2020). The Smart Grid could be seen as a digital upgrade of both transmission and distribution grids, in which the idea of a one‐way flow of energy and information from energy providers to end‐users turns into a complex scheme with a bidirectional flow. Moreover, aspects such as scalability, maintainability, security, and interoperability between devices are central to the Smart Grid concept (Colak et al. 2020). To this end, the Smart Grid must be undoubtedly linked to several concepts such as the information and communication technologies (ICTs) to ensure the exhaustive coordination of stakeholders, the use of renewable energy sources (RES), and the decentralization of them through the DG, the deployment of smart meters or an advanced metering infrastructure (AMI) toward the monitoring of the consumption and the creation of statistics, and the demand‐side management (DSM) to achieve a better balance between generation and consumption as will be discussed later (Cecati et al. 2010).
1.4 Power Grid Flexibility
The concept of power grid flexibility has been introduced recently by academics and international organizations. Although a global definition has not been reached yet, as a rule, flexibility describes the capacity of the power grid to respond to changes in demand or supply while preserving the stability of the system. Thus, from a technical viewpoint, flexibility is essential to address the generation‐demand imbalances; however, other aspects need to be considered. A more complete definition is provided by the International Energy Agency (IEA): “Flexibility is the ability of a power system to reliably and cost‐effectively manage the variability and uncertainty of demand and supply across all relevant timescales, from ensuring instantaneous stability of the power system to supporting long‐term security of supply” (International Energy Agency 2019). Notice how flexibility extends to other dimensions such as time, management, uncertainty, and cost (Akrami et al. 2019). These points are further detailed in the following lines:
Time
: Indicates how fast the system can be restored to a given state when it undergoes a deviation. Control actions are often classified into short‐term, mid‐term, and long‐term measures.
Management measures
or control procedures are performed by the power grid operator to deal not only with day‐to‐day but also with unexpected events. These corrective actions depend directly on the time interval available to be applied.
Uncertainty
or absence of information about future condition. The more uncertainty in the system, the more flexibility is required for its proper operation.
Cost
: Although the power system scheduler should always offer flexibility, this concept implies an extra charge as the marginal cost or marginal risk is considered to serve system flexibility and, therefore, high marginal cost control actions are required to ensure low marginal risk and vice versa. Accordingly, a level of commitment must be found between the amount of flexibility and its associated cost.
1.4.1 The Need for Flexibility
All power systems have a certain degree of flexibility aiming to continuously balance the generation and consumption and ensure system stability. This flexibility is employed to maintain the foremost power grid parameters (i.e. voltage and frequency) within the safe range specified by several international standards addressed later. Although variability and uncertainty have always been considered during the power systems operation, these inherent flexibility mechanisms have demonstrated to be insufficient to perform a successful system regulation when dealing with the presence of large quantities of grid‐connected VRES, as is being experienced in recent years since VRES are now cheaper to acquire for electricity generation due to the government funding and the absence of fuel costs. These VRES refers mostly to solar, wind, or hydro resources. In this regard, achieving an acceptable balance between generation and demand turns out to be a major challenge due to the intermittent and variable dynamic that characterizes these energy sources. Therefore, given these reasons, making the power planning and operation more flexible has become a global priority to achieve the power system transformation in response to these novel trends. Moreover, the current context brought by the COVID‐19 pandemic has revealed that a flexible and well‐functioning power system is crucial to maintaining the operation of critical infrastructures such as those in the healthcare sector (Heffron et al. 2021).
1.4.2 Sources of Flexibility
Regulators and system operators recognize that flexibility in all power systems must be addressed by ensuring the following elements (Mohandes et al. 2019; Babatunde et al. 2020; Cochran et al. 2014; Nikoobakht et al. 2019):
1.4.2.1 Flexible Generation
Flexibility is often offered by power plants with fast start‐up and shut‐down operation and high‐power ramp capabilities. Moreover, one of the main features of these flexible sources is an efficient operation at a lower minimum level in periods with high penetration of VRES or even the ability to perform deep turndowns. In this regard, it is crucial to ensure a minimum marginal cost so that these power plants can compete in the market as a source of flexibility. Some of these conventional plants include hydro plants, gas‐fired, coal‐fired, and fuel‐fired power plants, as well as dispatchable renewable power plants (i.e. biomass, geothermal plants, etc.). Currently, conventional power plants are the predominant source of flexibility in modern power systems. DG can also perform a fast response to power mismatches to provide local flexibility by modulating their production.
1.4.2.2 Flexible Transmission and Grid Interconnection
Transmission networks are responsible for this kind of flexibility. Among other features, transmission networks must avoid bottlenecks and have the capability to take advantage of a wide range of resources that support achieving the generation‐demand needs. These resources include the use of smart network technologies that better optimize the energy transmission and the interconnection between neighboring power systems. Furthermore, grid interconnection opens the door for electricity trade which could be highly advantageous for power systems extended over multiple time zones. Consequently, their peak‐load intervals take place at different times, and their renewable energy sources with a strong dependency on the time, such as the photovoltaic plants, also reach their maximum production at different times. Therefore, a coordinated strategy can contribute to smoothing out peak demand periods and making use of the energy surpluses.
1.4.2.3 Control Over VRES
Uncertainty and variability are part of the VRES's nature and often limit the amount of flexibility that can be provided or sometimes even contribute to the opposite. Therefore, greater control over the use of these resources can help alleviate the situation. A scenario with congestion of the transmission lines or when the produced power exceeds the required power system demand may be the best example to understand this issue. In such a case, flexibility can be offered via the renewable generation curtailment although this action is the least preferred choice, as it can lead to a suboptimal operation from both viewpoints: Owner's revenues or savings and loss of renewable energy in the absence of energy storage facilities.
1.4.2.4 Energy Storage Facilities
The spread of storage systems throughout the power grid is undoubtedly another source of flexibility and is especially relevant when considering a context with high penetration of generation coming from VRES. These storage infrastructures can help the power system to absorb the energy surpluses or inject the required energy to solve a momentary mismatch between supply and demand. Currently, pumped hydro energy storage accounts for the highest amount of total storage capacity worldwide. Nevertheless, other technologies such as batteries, ultracapacitors, flywheels, and compressed air are also becoming popular.
1.4.2.5 Demand‐Side Management
DSM is a portfolio of measures to improve the energy system on the side of consumption and evolved during the 1970s because of economic, political, social, technological, and resource supply factors (Gellings 2017). The US Department of Energy (DoE) provides the following definition of DSM (Loughran and Kulick 2004): “DSM is the planning, implementation and monitoring activities of electric utilities that are designed to encourage consumers to modify their level and pattern of electricity usage.” DSM includes both energy efficiency (EE) and demand response (DR) measures as can be depicted in Figure 1.4. These measures range from improving the EE by using less energy while providing the same or even better level of service to the consumers to the implementation of DR techniques such as the use of smart energy tariffs with incentives for certain consumption patterns or sophisticated real‐time control of DER. More specifically, EE includes both the use of high‐efficiency equipment and energy conservation strategies, while DR is divided into explicit and implicit measures.
Figure 1.4 Classification of demand‐side management measures.
Source: A. Rezaee Jordehi (2019).
Regarding the concept of DR, the US DoE (Qdr 2006) also defines it as “Changes in electric usage by end‐use customers from their normal consumption patterns in response to changes in the price of electricity over time, or to incentive payments designed to induce lower electricity use at the time of high wholesale market prices or when system reliability is jeopardized.” DR has already proven to be a resource that the energy provider can offer to improve system reliability, stability, and security services. As shown in Figure 1.4, DR services are normally classified into two groups attending to the mechanism used to promote the response: Explicit and implicit DR. Explicit DR is a committed and dispatchable DR action traded on the energy market and is usually provided by an independent aggregator, virtual power plants (VPPs), or the energy provider. In this case, consumers receive an incentive to change their consumption in certain scenarios such as the grid congestion or balance problems among others. This is referred to as “incentive‐driven” DR. The following programs can be found within this category:
Demand bidding/buyback programs
(
DBP
)
: The utility pays an incentive to reduce electric load when notified of a DR event day. Customers submit load reduction bids for a DBP event, which can be called on a day‐ahead or day‐of basis. For any event, the customer may elect to submit or not submit a bid.
Direct load control
(
DLC
)
: Allows the aggregator control over certain equipment, e.g. switching‐off noncritical loads or modifying devices' setpoint to reduce net electrical load.
Emergency demand response program
s (
ERDP
)
: Customers receive incentive payments for load reductions when needed to ensure reliability.
Interruptible/curtailable (I/C)
: Customers receive a discounted rate for agreeing to reduce the load on request.
Ancillary services market programs
: Customers receive payments from a grid operator for committing to restrict load when needed to support the operation of the electric grid (i.e. auxiliary services).
Capacity market program
s (
CMP
)
: Customers offer load curtailments as system capacity to replace conventional generation or delivery resources. Customers typically receive day‐of notice of events and face penalties for failure to curtail when called upon to do so. Incentives usually consist of up‐front reservation payments.
Concerning implicit DR, some of the most common DR products are summarized below. Under this scheme, consumers agree to be exposed to hourly or shorter‐term tariffs in which the price of the electricity varies depending on production costs. Therefore, consumers adapt their consumption (through automation or personal choice) to save on the electricity bill. Implicit DR is also known as priced‐based DR.
Time‐of‐use
(
TOU
)
: A rate with different unit prices for usage during different blocks of time, for a 24‐hour day. Daily pricing blocks include an on‐peak, partial‐peak, and off‐peak price for non‐holiday weekdays, the on‐peak price being the highest, and the off‐peak price the lowest. These tariffs include diurnal and seasonal variations in electricity cost but are fixed several months before. It can be integrated within the operations planning stage.
Real‐time pricing
(
RTP
)
: A retail rate in which the price fluctuates hourly reflecting changes in the wholesale price of electricity. These are typically known to customers on a day‐ahead or hour‐ahead basis.
Critical peak pricing
(
CPP
)
: Hybrid of the TOU and RTP. The basic rate structure is TOU. However, the normal peak price is replaced with a much higher CPP event price under specified trigger conditions (e.g. when system reliability is compromised, or supply prices are very high). It is called on the day of economic dispatch.
Finally, Figure 1.5 describes the potential impact of DR measures on customer service levels. The opportunities and potential depend on the existing building and appliances infrastructure. This figure also summarizes the load commitment timescales over which these DR schemes operate.
Figure 1.5 Demand response programs timescale.
Source: Adapted from Qdr (2006).
1.4.2.6 Other Sources of Flexibility
Other flexibility resources include ancillary services. The power grid requires ancillary services to ensure reliability and support its main function of delivering electrical energy to consumers. These services are employed by system operators as a flexibility mechanism to preserve the instantaneous and continuous balance between generation and consumption. Although most balance requirements are being covered by regulation, spinning, and non‐spinning ancillary services, new ancillary technologies such as load following, frequency response reserve, or inertia response are also proliferating.
Moreover, on a smaller scale, the utilization of electric vehicles and multi‐mode operation of combined cycle units have also been revealed as another source of flexibility by providing a particular case of energy storage system or recovering exhaust heat from thermal units to drive a steam turbine and generate more electricity, respectively.
1.5 Power Quality, Reliability, and Resilience
The main goal of modern power systems is to deliver the required electrical energy to its customers as economically as possible with an acceptable level of reliability (Billinton and Allan 2003). Nowadays, the working and social habits of modern society have led end‐users to expect the supply to be continuously available on demand. Although a power system with high reliability is possible, a risk‐free power system is not. In this context, engineers and power system managers try to maintain reliability as high as possible within their socioeconomic constraints.
In most countries, the electricity sector is currently a deregulated and competitive environment where accurate information on system performance must be provided to ensure adequate service to customer needs. As consequence, series of indexes have been proposed under the concept of reliability. In the electric power industry, reliability reflects the ability to supply electricity in the amount demanded by users and in the time required. Specifically, reliability has to do with total electrical interruptions (outages), that is, the complete loss of voltage. These reliability indexes include measurements such as the number of interruptions and how long they last, the customers affected, and the power interrupted. There are a wide variety of indexes to measure reliability, but the most common are SAIDI, SAIFI, and CAIDI as defined in IEEE Standard 1366 (Bollen 2003). SAIDI (System Average Interruption Duration Index) and SAIFI (System Average Interruption Frequency Index) values include sustained interruptions, which are defined as outages that last at least five minutes (although this is not uniform and may vary).
Another concept related to reliability is that of power quality (PQ), although they are two different issues. While the simplest idea of reliability is whether the power is available as it is needed, PQ can be defined as the degree to which current and voltage maintain their waveforms adjusted to a perfect sine wave with constant amplitude and frequency at a given point of the power system. An additional requirement of the current quality is that it must be in phase with the voltage waveform. Therefore, PQ is the combination of voltage and current quality (IEEE 2012). As will be detailed further in Section 1.5.1, a wide variety of electromagnetic disturbances are collected under this term and all of them can affect a critical installation to the extent that it depends on the sensitivity of each load.
Finally, the concept of power system resilience is currently attracting a lot of interest. The topic has become one of the most studied characteristics in the energy industry since Hurricane Katrina dramatically exposed the vulnerability of the power grid in Louisiana in 2005. The frequency of extreme weather events such as hurricanes, tsunamis, ice storms, and other natural disasters as well as man‐made cyber and physical attacks have increased in recent years and affect an increasing number of human and environmental victims worldwide (Bhusal et al. 2020). This term comes from the Latin root “resilire,” which means “the ability to spring back or rebound.” Assuming that disruptive events can occur regularly, for a system, resiliency would be the ability to anticipate, compensate, adapt, and recover from a potentially damaging event (Gholami et al. 2018).
1.5.1 Power Quality Disturbances
The increasing number of electronic equipment connected to the power grid that can generate electromagnetic disturbances or be affected by them has caused the community to become interested in the classification of these disturbances as a first step to subsequently decide on the appropriate strategy to address their mitigation. In this regard, prestigious international organizations such as the International Electrotechnical Commission (IEC) and the Institute of Electrical and Electronics Engineers (IEEE) have made decisive efforts by providing relevant standards and regulations related to PQ issues from several viewpoints. Although this section is mainly focused on standards that address the classification of the principal electromagnetic phenomena causing PQ disturbances within the power system, many others intended for specifying measurement techniques or limits for these disturbances are also included to bring a more detailed insight and introduce the required background for the understanding of the remaining chapters. Accordingly, the IEEE std. 1159‐2019 “Recommended practice for monitoring electric power quality” (“IEEE Recommended Practice for Monitoring Electric Power Quality” 2019) has been considered as a reference and thus the main body of this section follows its structure. This standard adopts a quantitative approach as opposed to the qualitative one assumed by other standards. Finally, electromagnetic phenomena and their characteristics are firstly classified in Table 1.1 and later discussed in the following lines.
Table 1.1 Categories and typical characteristics of power system electromagnetic phenomena.
Source: IEEE std 1159‐2019 (2019).
Categories
Typical spectral content
Typical duration
Typical voltage magnitude
Transients
Impulsive
Nanoseconds
5 ns rise
<50 ns
Microseconds
1 μ rise
50 ns–1 ms
Milliseconds
0.1 ms rise
>1 ms
Oscillatory
Low frequency
<5 kHz
0.3–50 ms
0–4 pu
a)
Medium frequency
5–500 kHz
20 μs
0–8 pu
High frequency
0.5–5 MHz
5 μs
0–4 pu
Short‐duration RMS variations
Instantaneous
Sag
0.5–30 cycles
0.1–0.9 pu
Swell
0.5–30 cycles
1.1–1.8 pu
Momentary
Interruption
0.5 cycles – 3 s
<0.1 pu
Sag
30 cycles – 3 s
0.1–0.9 pu
Swell
30 cycles – 3 s
1.1–1.4 pu
Temporary
Interruption
3 s–1 min
<0.1 pu
Sag
3 s–1 min
0.1–0.9 pu
Swell
3 s–1 min
1.1–1.4 pu
Long‐duration RMS variations
Sustained interruptions
>1 min
0 pu
Undervoltages
>1 min
0.8–0.9 pu
Overvoltages
>1 min
1.1–1.2 pu
Current overload
>1 min
Imbalance
Voltage
Steady state
0.5–2%
Current
Steady state
1–30%
Waveform distortion
DC offset
Steady state
0–0.1%
Harmonics
0–9 kHz
Steady state
0–20%
Interharmonics
0–9 kHz
Steady state
0–2%
Notching
Steady state
Noise
Broadband
Steady state
0–1%
Voltage fluctuations
<25 Hz
Intermittent
0.1–7% 0.2–2
P
st
b)
Power frequency variations
<10 s
±0.1 Hz
a) Per unit (pu).
b) Flicker severity index as defined in IEC 61000‐4‐15:2010 and IEEE Std. 1453.
1.5.1.1 Transients
Transients give a name to a phenomenon that is undesirable and momentary and can be classified into two categories: Impulsive and oscillatory transients depending on the waveshape of a current or voltage transient. IEEE Std. C62.41.1‐2002 (IEEE 2003) deals with defining standard impulsive and oscillatory transient test waves to test electrical equipment.
Impulsive transients
: Impulsive transients are sudden, non‐power frequency change from the nominal condition voltage, current, or both, that is unidirectional in polarity. The most common cause of impulsive transients is lightning and is often damped quickly by impedance circuit elements due to the high frequencies involved. There can be a significant difference in the transient characteristics from one location to another within the power system. Impulsive transients are often characterized by their peak value, rise, decay, or duration times.
Oscillatory transients
: Oscillatory transients are sudden, non‐power frequency change in the steady‐state condition of voltage, current, or both, that includes both positive and negative polarity values. An oscillatory transient consists of a voltage or current whose instantaneous value changes polarity rapidly and often decays within a fundamental‐frequency cycle. The subclasses are high, medium, and low frequency and have been chosen to coincide with typical types of oscillatory transients within the power system. High‐frequency oscillatory transients (>500 kHz) are normally provoked by switching events or can be the response of one point of the system to an impulsive transient. When the frequency of the primary frequency component of an oscillatory transient is within the range of 5–500 kHz, the category used is medium frequency. Back‐to‐back capacitor energization can give rise to this electromagnetic phenomenon. Finally, low‐frequency oscillatory transients (<5 kHz and duration between 0.3 and 50 ms) are normally found in sub‐transmission and distribution lines and can be the result of many types of events (e.g. capacitor, ferro resonance, or transformers energization).
