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Fundamentals and business model for resource aggregator of demand response in electricity markets

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  • Lu, Xiaoxing
  • Li, Kangping
  • Xu, Hanchen
  • Wang, Fei
  • Zhou, Zhenyu
  • Zhang, Yagang

Abstract

Demand response (DR) is an effective means to help maintain the balance between power supply and demand, promote energy conservation and emission reduction. Nevertheless, the involvement of different flexible resources, including distributed generation (DG), energy storage system (EES) and especially controllable load of customers in DR programs becomes the critical element for the implementation of DR. The resource aggregators (RA), an emerging market participant, serves as the professional integrator of these widely-distributed adjustable resources and offers them convenient access to related market trading information. The participation of RAs plays a significant role in enhancing system flexibility, fully exploiting the potential of flexible resources and bridging the gap between suppliers and buyers of DR. This paper focuses on the fundamentals and business mechanism of RAs in the electricity market. First, a comprehensive review of recent literature and projects is presented, with particular attention on RAs’ roles in electricity markets as well as their difference from other market entities. Then, the business model for RA is analyzed systematically, involving resource aggregation, basic information prediction, market bidding strategy development, and settlement process. Besides, information about existing RAs around the world is collected and compared to understand the development status and existing challenges.

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  • Lu, Xiaoxing & Li, Kangping & Xu, Hanchen & Wang, Fei & Zhou, Zhenyu & Zhang, Yagang, 2020. "Fundamentals and business model for resource aggregator of demand response in electricity markets," Energy, Elsevier, vol. 204(C).
    • Handle: RePEc:eee:energy:v:204:y:2020:i:c:s0360544220309920
    DOI: 10.1016/j.energy.2020.117885
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    as
    1. Li, Kangping & Wang, Fei & Mi, Zengqiang & Fotuhi-Firuzabad, Mahmoud & Duić, Neven & Wang, Tieqiang, 2019. "Capacity and output power estimation approach of individual behind-the-meter distributed photovoltaic system for demand response baseline estimation," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    2. Behrangrad, Mahdi, 2015. "A review of demand side management business models in the electricity market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 270-283.
    3. Tabar, Vahid Sohrabi & Abbasi, Vahid, 2019. "Energy management in microgrid with considering high penetration of renewable resources and surplus power generation problem," Energy, Elsevier, vol. 189(C).
    4. Niu, Zhewen & Yu, Zeyuan & Tang, Wenhu & Wu, Qinghua & Reformat, Marek, 2020. "Wind power forecasting using attention-based gated recurrent unit network," Energy, Elsevier, vol. 196(C).
    5. Nikolaos Koltsaklis & Athanasios Dagoumas, 2018. "Policy Implications of Power Exchanges on Operational Scheduling: Evaluating EUPHEMIA’s Market Products in Case of Greece," Energies, MDPI, vol. 11(10), pages 1-26, October.
    6. Hu, Maomao & Xiao, Fu, 2020. "Quantifying uncertainty in the aggregate energy flexibility of high-rise residential building clusters considering stochastic occupancy and occupant behavior," Energy, Elsevier, vol. 194(C).
    7. Wang, Dan & Hu, Qing'e & Jia, Hongjie & Hou, Kai & Du, Wei & Chen, Ning & Wang, Xudong & Fan, Menghua, 2019. "Integrated demand response in district electricity-heating network considering double auction retail energy market based on demand-side energy stations," Applied Energy, Elsevier, vol. 248(C), pages 656-678.
    8. Stinner, Sebastian & Huchtemann, Kristian & Müller, Dirk, 2016. "Quantifying the operational flexibility of building energy systems with thermal energy storages," Applied Energy, Elsevier, vol. 181(C), pages 140-154.
    9. Paterakis, Nikolaos G. & Erdinç, Ozan & Catalão, João P.S., 2017. "An overview of Demand Response: Key-elements and international experience," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 871-891.
    10. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    11. Barbero, Mattia & Corchero, Cristina & Canals Casals, Lluc & Igualada, Lucia & Heredia, F.-Javier, 2020. "Critical evaluation of European balancing markets to enable the participation of Demand Aggregators," Applied Energy, Elsevier, vol. 264(C).
    12. Hu, Jing & Harmsen, Robert & Crijns-Graus, Wina & Worrell, Ernst & van den Broek, Machteld, 2018. "Identifying barriers to large-scale integration of variable renewable electricity into the electricity market: A literature review of market design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2181-2195.
    13. Hu, Maomao & Xiao, Fu, 2018. "Price-responsive model-based optimal demand response control of inverter air conditioners using genetic algorithm," Applied Energy, Elsevier, vol. 219(C), pages 151-164.
    14. Iria, José & Soares, Filipe & Matos, Manuel, 2019. "Optimal bidding strategy for an aggregator of prosumers in energy and secondary reserve markets," Applied Energy, Elsevier, vol. 238(C), pages 1361-1372.
    15. Vallés, Mercedes & Bello, Antonio & Reneses, Javier & Frías, Pablo, 2018. "Probabilistic characterization of electricity consumer responsiveness to economic incentives," Applied Energy, Elsevier, vol. 216(C), pages 296-310.
    16. van der Meer, D.W. & Widén, J. & Munkhammar, J., 2018. "Review on probabilistic forecasting of photovoltaic power production and electricity consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1484-1512.
    17. Gao, Xiang & Chan, Ka Wing & Xia, Shiwei & Zhou, Bin & Lu, Xi & Xu, Da, 2019. "Risk-constrained offering strategy for a hybrid power plant consisting of wind power producer and electric vehicle aggregator," Energy, Elsevier, vol. 177(C), pages 183-191.
    18. Iria, José & Soares, Filipe & Matos, Manuel, 2018. "Optimal supply and demand bidding strategy for an aggregator of small prosumers," Applied Energy, Elsevier, vol. 213(C), pages 658-669.
    19. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
    20. Lujano-Rojas, Juan M. & Zubi, Ghassan & Dufo-López, Rodolfo & Bernal-Agustín, José L. & García-Paricio, Eduardo & Catalão, João P.S., 2019. "Contract design of direct-load control programs and their optimal management by genetic algorithm," Energy, Elsevier, vol. 186(C).
    21. Koltsaklis, Nikolaos E. & Dagoumas, Athanasios S., 2018. "Incorporating unit commitment aspects to the European electricity markets algorithm: An optimization model for the joint clearing of energy and reserve markets," Applied Energy, Elsevier, vol. 231(C), pages 235-258.
    22. López-Rodríguez, M.A. & Santiago, I. & Trillo-Montero, D. & Torriti, J. & Moreno-Munoz, A., 2013. "Analysis and modeling of active occupancy of the residential sector in Spain: An indicator of residential electricity consumption," Energy Policy, Elsevier, vol. 62(C), pages 742-751.
    23. Ayón, X. & Gruber, J.K. & Hayes, B.P. & Usaola, J. & Prodanović, M., 2017. "An optimal day-ahead load scheduling approach based on the flexibility of aggregate demands," Applied Energy, Elsevier, vol. 198(C), pages 1-11.
    24. Lu, Renzhi & Hong, Seung Ho & Zhang, Xiongfeng, 2018. "A Dynamic pricing demand response algorithm for smart grid: Reinforcement learning approach," Applied Energy, Elsevier, vol. 220(C), pages 220-230.
    25. Pinto, Rui & Bessa, Ricardo J. & Matos, Manuel A., 2017. "Multi-period flexibility forecast for low voltage prosumers," Energy, Elsevier, vol. 141(C), pages 2251-2263.
    26. Carreiro, Andreia M. & Jorge, Humberto M. & Antunes, Carlos Henggeler, 2017. "Energy management systems aggregators: A literature survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1160-1172.
    27. He, Xian & Keyaerts, Nico & Azevedo, Isabel & Meeus, Leonardo & Hancher, Leigh & Glachant, Jean-Michel, 2013. "How to engage consumers in demand response: A contract perspective," Utilities Policy, Elsevier, vol. 27(C), pages 108-122.
    28. Chabok, Hossein & Roustaei, Mahmoud & Sheikh, Morteza & Kavousi-Fard, Abdollah, 2020. "On the assessment of the impact of a price-maker energy storage unit on the operation of power system: The ISO point of view," Energy, Elsevier, vol. 190(C).
    29. Li, Weilin & Xu, Peng & Lu, Xing & Wang, Huilong & Pang, Zhihong, 2016. "Electricity demand response in China: Status, feasible market schemes and pilots," Energy, Elsevier, vol. 114(C), pages 981-994.
    30. Aghaei, Jamshid & Alizadeh, Mohammad-Iman, 2013. "Demand response in smart electricity grids equipped with renewable energy sources: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 18(C), pages 64-72.
    31. Kandil, Sarah M. & Farag, Hany E.Z. & Shaaban, Mostafa F. & El-Sharafy, M. Zaki, 2018. "A combined resource allocation framework for PEVs charging stations, renewable energy resources and distributed energy storage systems," Energy, Elsevier, vol. 143(C), pages 961-972.
    32. Burger, Scott & Chaves-Ávila, Jose Pablo & Batlle, Carlos & Pérez-Arriaga, Ignacio J., 2017. "A review of the value of aggregators in electricity systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 395-405.
    33. Li, Bosong & Shen, Jingshuang & Wang, Xu & Jiang, Chuanwen, 2016. "From controllable loads to generalized demand-side resources: A review on developments of demand-side resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 936-944.
    34. Khan, Ahsan Raza & Mahmood, Anzar & Safdar, Awais & Khan, Zafar A. & Khan, Naveed Ahmed, 2016. "Load forecasting, dynamic pricing and DSM in smart grid: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1311-1322.
    35. Lockwood, Matthew & Mitchell, Catherine & Hoggett, Richard, 2020. "Incumbent lobbying as a barrier to forward-looking regulation: The case of demand-side response in the GB capacity market for electricity," Energy Policy, Elsevier, vol. 140(C).
    36. Katz, Jonas & Andersen, Frits Møller & Morthorst, Poul Erik, 2016. "Load-shift incentives for household demand response: Evaluation of hourly dynamic pricing and rebate schemes in a wind-based electricity system," Energy, Elsevier, vol. 115(P3), pages 1602-1616.
    37. Ottesen, Stig Ødegaard & Tomasgard, Asgeir & Fleten, Stein-Erik, 2018. "Multi market bidding strategies for demand side flexibility aggregators in electricity markets," Energy, Elsevier, vol. 149(C), pages 120-134.
    38. Iria, José & Soares, Filipe, 2019. "Real-time provision of multiple electricity market products by an aggregator of prosumers," Applied Energy, Elsevier, vol. 255(C).
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