Kooperativer Bibliotheksverbund

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Umfang: 1 online resource (474 pages)

ISBN: 9780128226667 , 0128226668 , 9780128200742 , 012820074X

Anmerkung: Front Cover -- Local Electricity Markets -- Copyright Page -- Contents -- List of contributors -- Introduction -- 1 Introduction -- References -- I. Distributed energy resources as enablers of local electricity markets -- 1 New electricity markets. The challenges of variable renewable energy -- 1.1 Introduction -- 1.2 Physical characteristics of renewable generation -- 1.2.1 Time variability: daily, seasonal, and annual cycles -- 1.2.2 Natural complementarity of renewable energy resources -- 1.3 Enhance the VRE value: from the large European to small local electricity markets -- 1.3.1 Spatial smoothing effects -- 1.3.2 Aggregation and virtual renewable power plants -- 1.3.3 Emerging of local markets -- 1.4 The challenges of the current market designs for ~100% renewable power systems -- 1.5 The need for new market designs and trading models under ~100% renewable power systems -- 1.6 Final remarks -- Acknowledgment -- References -- 2 Integration of electric vehicles in local energy markets -- 2.1 Introduction -- 2.2 Electric vehicles -- 2.2.1 Types of electric vehicles -- 2.2.2 Electric car stock in the world by the end of 2019 -- 2.2.3 Electric vehicles market sales and market share in the world by the end of 2019 -- 2.2.4 Electric car stock and market sales by 2030 -- 2.2.5 Electric vehicles charging infrastructures in China and United States -- 2.2.6 Impact of EV penetration on residential homes -- 2.3 Demand response and electric vehicles flexibility -- 2.3.1 Concepts of demand response and electric vehicles flexibility -- 2.3.2 Current challenges -- 2.3.3 Specific demand response for electric vehicles -- 2.3.3.1 Time-of-use -- 2.3.3.2 Real-time pricing -- 2.3.3.3 Smart charging -- 2.3.3.4 Vehicle-to-grid -- 2.4 Electric vehicles in local energy markets -- 2.4.1 Local market -- 2.4.2 Business models -- 2.5 Conclusions -- References. , 3 From wholesale energy markets to local flexibility markets: structure, models and operation -- 3.1 Introduction -- 3.2 Wholesale markets: models and operation -- 3.2.1 Day-ahead market -- 3.2.2 Intraday market -- 3.2.3 Bilateral markets -- 3.2.4 Balancing markets -- 3.3 Key European markets -- 3.3.1 The Nordic and Baltic power market (Nord Pool) -- 3.3.2 The market for Central Western Europe (EPEX SPOT) -- 3.3.3 The Iberian market (MIBEL) -- 3.3.4 The Italian electricity market (GME) -- 3.4 Agent-based tools for energy markets -- 3.4.1 MATREM: overview of the simulated markets -- 3.5 The energy transition and flexibility markets -- 3.6 Key flexibility market platforms -- 3.6.1 The NODES marketplace -- 3.6.2 Enera and the EPEX SPOT local flexibility market -- 3.6.3 The Piclo flex marketplace -- 3.6.4 IREMEL and the Iberian electricity market -- 3.7 Conclusion -- Acknowledgment -- References -- 4 From the smart grid to the local electricity market -- 4.1 Introduction -- 4.2 Emergence and widespread of the smart grid -- 4.3 Technological and infrastructure developments -- 4.4 Transactive energy -- 4.5 The local electricity market concept -- 4.6 Conclusion -- References -- II. Local market models and opportunities -- 5 Local market models -- 5.1 Introduction -- 5.2 Local electricity trading -- 5.2.1 Decentralized approach -- 5.2.2 Distributed (peer-to-peer) approach -- 5.2.3 Centralized (community-based) approach -- 5.3 Simulation results -- Acknowledgments -- References -- 6 Peer-to-peer energy platforms -- 6.1 Introduction -- 6.2 Why do we need P2P? -- 6.3 How do we design a P2P platform? -- 6.3.1 Conceptual component of a P2P platform-P2P market structure -- 6.3.2 Technical component-network controls -- 6.3.3 Implementation component-information and communication -- 6.4 What do P2P platforms look like-now and the future?. , 6.4.1 Past and existing P2P platforms -- 6.4.2 Challenges and outlook -- 6.5 Concluding remarks -- References -- 7 Transmission system operator and distribution system operator interaction -- 7.1 Transmission system operator/distribution system operator cooperation -- 7.2 Cooperative market approaches -- 7.3 Information exchange needs -- 7.4 TSO/DSO coordination challenges -- References -- 8 Local electricity markets-practical implementations -- 8.1 Introduction -- 8.2 Search methodology -- 8.3 Local electricity market implementation -- 8.4 Discussion and future directions -- 8.5 Conclusion -- Acknowledgment -- References -- III. Enablers for local electricity markets -- 9 Local energy markets, commercially available tools -- 9.1 Local energy markets, commercially available tools -- 9.2 Enabling the local marketplace -- 9.2.1 The marketplace and messaging -- 9.2.2 Participant tools -- 9.2.3 Technical enablement of the market -- 9.2.3.1 Market tools -- 9.2.3.2 Messaging tools -- 9.2.3.3 Datahub capabilities -- 9.3 Participant architecture -- 9.3.1 Back office tools -- 9.3.2 Front office tools -- 9.3.3 Settlement tools -- 9.4 Community and end customer architecture -- 9.4.1 Smart meter enabled control -- 9.4.2 Smart device enabled control -- 9.4.3 Smart charging control -- 9.4.4 Microgrid tools -- 9.5 Conclusions -- References -- 10 Distributed energy resource management system -- 10.1 Energy management system in local energy market -- 10.2 Architecture and functionalities of EMS -- 10.3 EMS design and implementation -- 10.3.1 Production and consumption forecasting -- 10.3.2 Optimization problem formulation -- 10.3.3 Optimization methods and solvers -- 10.4 Case study 1: centralized EMS in a single microgrid -- 10.4.1 Formulation -- 10.4.2 Definition of management objectives -- 10.4.3 Optimization method: branch and bound. , 10.4.4 Implementation results -- 10.5 Case study 2: distributed EMS in a multimicrogrid network -- 10.5.1 Day ahead scheduling -- 10.5.2 Flexibilities and problem formulation -- 10.5.3 Implementation and results -- 10.6 Conclusion -- References -- 11 Modeling, simulation, and decision support -- 11.1 Introduction -- 11.2 Modeling approaches -- 11.3 Modeling electricity markets -- 11.3.1 Single firm optimization models -- 11.3.2 Multiple-firm equilibrium models -- 11.3.2.1 Cournot competition -- 11.3.2.2 Bertrand competition -- 11.3.2.3 Supply function equilibrium -- 11.3.2.4 Conjectural variation -- 11.3.2.5 Stackelberg and multileader-follower games -- 11.3.3 Simulation models -- 11.3.3.1 Equilibrium models -- 11.3.3.2 Agent-based models -- 11.4 Local electricity markets -- 11.4.1 Market designs -- 11.4.2 Renewable and energy storage systems -- 11.4.3 Demand side management -- 11.4.4 Power reliability and resilience -- 11.5 Concluding remarks -- References -- 12 Blockchain as messaging infrastructure for smart grids -- 12.1 Introduction -- 12.2 Current, emerging, and blockchain-based communication protocols for smart grids -- 12.2.1 Emerging standards for smart grids -- 12.2.1.1 IEC 61850 -- 12.2.1.2 Modbus -- 12.2.1.3 OPC UA -- 12.2.1.4 DNP3 -- 12.2.2 Blockchain as a communication protocol -- 12.3 Use case: renewable energy community pilots in the Netherlands -- 12.4 Discussion -- 12.4.1 Benefits -- 12.4.2 Challenges -- 12.4.2.1 Economic aspects -- 12.4.2.2 Technical aspects -- 12.4.2.3 Sociotechnical aspects -- 12.5 Conclusions and future work -- Acknowledgments -- References -- 13 Load profiling revisited: prosumer profiling for local energy markets -- 13.1 Introduction -- 13.2 Load profiling principles -- 13.2.1 Basic principles -- 13.2.2 Scopes of load profiling -- 13.2.2.1 Settlement -- 13.2.2.2 Tariff setting -- 13.2.2.3 Forecasting. , 13.2.2.4 Demand side management, demand response and flexibility -- 13.2.2.5 Energy not served -- 13.2.2.6 Aggregate load modeling, simulations, and benchmarking -- 13.2.3 Types of load profiles -- 13.2.4 How to obtain the load profiles -- 13.3 Timing and amplitude aspects of the electricity usage patterns -- 13.3.1 Timescales -- 13.3.2 Horizontal and vertical resolutions -- 13.3.3 Timing issues for net power analysis -- 13.3.4 Creation of time series with the same time step -- 13.3.5 Horizontal and vertical normalization -- 13.3.6 Data alignment and synchronization -- 13.4 Trends and opportunities for local energy systems and markets -- 13.4.1 From large-scale to local load profiling -- 13.4.2 Market opportunities -- 13.4.3 Not only electrical load profiling -- 13.5 Conclusions -- References -- 14 Forecasting -- 14.1 Introduction -- 14.2 Energy prediction: particularities -- 14.2.1 Prediction horizon and resolution -- 14.2.2 Level of aggregation -- 14.2.3 Influencing factors -- 14.3 Energy prediction: methods -- 14.3.1 From machine learning to neural networks -- 14.3.2 From neural networks to sparse neural networks -- 14.4 Experiments and results: country level -- 14.4.1 Implementation details -- 14.4.2 Metrics used for accuracy assessment -- 14.4.3 Total load forecast with 1-hour resolution -- 14.4.4 Total load forecast with 15-minute resolution -- 14.5 Forecasting: a glimpse into the future -- References -- 15 Mathematical models and optimization techniques to support local electricity markets -- 15.1 Introduction -- 15.2 Mathematical models for power flow and distributed energy resources -- 15.2.1 Power flow representation in unbalanced distribution networks -- 15.2.2 Representation of distributed energy resources -- 15.2.2.1 Electric vehicles -- 15.2.2.2 Energy storage devices -- 15.2.2.3 Renewable distributed generation. , 15.2.3 Representation of voltage control devices.

Sprache: Englisch