The concept of Demand Response (DR) generally concerns methodologies, technologies and commercial arrangements that could allow active participation of consumers in the power system operation. The primary aim of DR is thus to overcome the "traditional" inflexibility of electrical demand and, amongst others, create a new powerful tool to maximize deployment of renewable energy sources as well as provide active network management solutions to help reducing the impact of limited grid capabilities.
DR allows consumers to actively participate in power system operation, thus bringing new opportunities in emerging energy markets as well as tangible system benefits. In this sense, DR is considered one of the key enablers of the Smart Grid concept. However, DR also poses a number of challenges, particularly when "active demand" is connected to the Low Voltage network, thus affecting all the actors involved in the electricity chain.
This book presents for the first time a comprehensive view on technical methodologies and architectures, commercial arrangements, and socio-economic and regulatory factors that could facilitate the uptake of DR. The work is developed in a systematic way so as to create a comprehensive picture of challenges, benefits and opportunities involved with DR. The reader will thus be provided with a clear understanding of the complexity deriving from a demand becoming active, as well as with a quantitative assessment of the techno-economic value of the proposed solutions in a Smart Grid context.
Many research contributions have appeared in recent years in the field of DR, both in journals and conference proceedings. However, most publications focus on individual aspects of the problem. A systematic treatment of the issues to be tackled to introduce DR in existing electricity grids, involving the extended value chain in terms of technical and commercial aspects, is still missing. Also, several books have recently been published about Smart Grid, in which there is some mention to DR. However, again while DR is seen as a key pillar for the Smart Grid, there is no dedicated, comprehensive and systematic contribution in this respect.
Autorentext
Arturo Losi, Professor of Power Systems, Dipartimento di Ingegneria Elettrica e dell'Informazione "M. Scarano", University of Cassino and LM, Italy.
Pierluigi Mancarella, Lecturer in Future Energy Networks, School of Electrical and Electronic Engineering, The University of Manchester, Ferranti Building, Manchester, UK.
Antonio Vicino, Professor of Control Systems, Dipartimento di Ingegneria dell'Informazione e Scienze Matematiche, Università di Siena, Italy.
Inhalt
Preface xi Arturo LOSI, Pierluigi MANCARELLA and Antonio VICINO
List of Acronyms xvii
Chapter 1. Demand Response in Smart Grids 1 Amir ABIRI-JAHROMI, Navdeep DHALIWAL and François BOUFFARD
1.1. Introduction 1
1.2. Background on demand side management and demand response 2
1.3. Benefits offered by demand-side management 4
1.4. Types of demand response programs 5
1.4.1. Price-based programs 5
1.4.2. Incentive-based programs 6
1.5. Demand response performance, measurement and verification 8
1.6. The challenges: aligning economics and intelligence 8
1.7. Bibliography 9
Chapter 2. Active Consumer Characterization and Aggregation 11 Alessandro AGNETIS, Ignacio DELGADO ESPINÓS, Joseba JIMENO HUARTE, Marco PRANZO and Antonio VICINO
2.1. Introduction 11
2.2. Overview of the interaction between aggregator and other system players 13
2.2.1. Markets 13
2.2.2. Regulated players 14
2.2.3. Deregulated players 14
2.2.4. Consumers 15
2.3. Consumption modeling and flexibility forecasting 15
2.3.1. Consumer segmentation 16
2.3.2. Forecasting baseline demand 18
2.3.3. Forecasting flexibility under a dynamic pricing scheme 19
2.3.4. Calibration of price sensitivity parameters 21
2.4. Algorithms for electricity market price forecasting 21
2.4.1. Short-term energy price forecasting 22
2.4.2. Short-term energy price volatility forecasting 25
2.5. Optimization algorithm for designing demand response-based offers for the market 26
2.5.1. Aggregator toolbox optimization model for the day-ahead market 28
2.6. Software architecture of the aggregator toolbox 31
2.7. Numerical results on simulation experiments 32
2.7.1. Flexibility forecasting 32
2.7.2. Generating market offers 34
2.8. Bibliography 37
Chapter 3. Distributed Intelligence at the Consumer's Premises 41 Alessandro AGNETIS, Colin BROWN, Paolo DETTI, Joseba JIMENO HUARTE and Antonio VICINO
3.1. Introduction 41
3.2. Functional architecture 43
3.2.1. User interface 44
3.2.2. Other interfaces 44
3.3. Software architecture 45
3.3.1. Software modules 46
3.3.2. Types of daemons 46
3.3.3. Software architecture layers 47
3.4. Classification of distributed energy resources 48
3.4.1. Non-controllable loads 48
3.4.2. Shiftable loads 48
3.4.3. Thermal loads 49
3.4.4. Curtailable loads 49
3.4.5. Non-dispatchable generation sources 50
3.4.6. Dispatchable generation sources 50
3.4.7. Storage systems 50
3.5. Optimization algorithm for appliance scheduling 51
3.5.1. The optimization problem solved by the energy box 52
3.5.2. A mathematical model for energy box scheduling problems 53
3.5.3. A heuristic algorithm for energy box scheduling problems 57
3.6. Results on testing the implementation of the software architecture 59
3.7. Bibliography 61
Chapter 4. Distribution Control Center: New Requirements and Functionalities 65 Lilia CONSIGLIO, Anna Rita DI FAZIO, Simone PAOLETTI, Mario RUSSO, Adrian TIMBUS and Giovanni VALTORTA
4.1. Introduction 65
4.2. Functional specifications, including strategies 67
4.2.1. Distribution system operator's algorithms and prototypes to enable and exploit demand response 68
4.3. Architectures of distribution system automation and control 70
4.3.1. Centralized approach 71
4.3.2. Decentralized approach 72
4.4. Active and reactive power control in medium-voltage active distribution grids 75
4.5. Validation of demand response products 76
4.5.1. Ex ante validation 77
4.5.2. Real-time validation 78
4.6. New operational planning applications for the medium-voltage control center 79
4.6.1. Forecasting tools 79
4.6.2. Market tools 82
4.7. Bibliography 84
Chapter 5. Distribution Network Representation in the Presence of Demand Response 89 Giovanni M. CASOLINO, Arturo LOSI, Christian NOCE and Giovanni VALTORTA
5.1. Introduction 89
5.2. Requirements for distribution network monitoring and control 90
5.2.1. Functionalities at the distribution system operator control center level 90
5.2.2. Functionalities at the high-voltage/medium-voltage substation level 91
5.2.3. Functionalities at the medium voltage/low voltage level 92
5.3. Load areas 92
5.3.1. Identification 93
5.3.2. Modeling 96
5.4. Load areas: study cases 100
5.4.1. Small-size grid 100
5.4.2. Medium-size grid 103
5.4.3. Large-size grid 105
5.5. Appendix: active-reactive relationships 107
5.5.1. Pure loads107
5.5.2. Distributed generation 107
5.6. Bibliography 108
Chapter 6. Communication Needs and Solutions for the Deployment of Demand Response 111 Tatjana KOSTIC, Dacfey DZUNG and Adrian TIMBUS
6.1. Introduction 111
6.2. Requirements 111
6.2.1. System requirements 111
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