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Distributed Optimal Coordination of a Virtual Power Plant with Residential Regenerative Electric Heating Systems

Author

Listed:
  • Guixing Yang

    (Engineering Research Center of Ministry of Education for Renewable Energy Generation and Grid Connection Technology, Xinjiang University, Urumqi 830046, China
    State Grid Xinjiang Electric Power Co., Ltd., Urumqi 830002, China)

  • Haoran Liu

    (School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China)

  • Weiqing Wang

    (Engineering Research Center of Ministry of Education for Renewable Energy Generation and Grid Connection Technology, Xinjiang University, Urumqi 830046, China)

  • Junru Chen

    (Engineering Research Center of Ministry of Education for Renewable Energy Generation and Grid Connection Technology, Xinjiang University, Urumqi 830046, China)

  • Shunbo Lei

    (School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China)

Abstract

Renewable energy sources play a key role in the transition towards clean and affordable energy. However, grid integration of renewable energy sources faces many challenges due to its intermittent nature. The controllability of aggregated regenerative electric heating load provides a method for the consumption of renewable energy sources. Based on the concept of a virtual power plant (VPP), this paper considers the cooperative energy management of aggregated residential regenerative electric heating systems. First, considering physical constraints, network constraints, and user comfort, comprehensive modeling of a VPP is given to maximize its social benefits. In addition, this VPP is investigated as a participant in day-ahead energy and reserve markets. Then, to solve this problem, a distributed coordination approach based on an alternating direction method of multipliers (ADMM) is proposed, which can respect the independence of users and preserve their privacy. Finally, the simulation results illustrate the effectiveness of our algorithm.

Suggested Citation

  • Guixing Yang & Haoran Liu & Weiqing Wang & Junru Chen & Shunbo Lei, 2023. "Distributed Optimal Coordination of a Virtual Power Plant with Residential Regenerative Electric Heating Systems," Energies, MDPI, vol. 16(11), pages 1-15, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:11:p:4314-:d:1155291
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    References listed on IDEAS

    as
    1. Yang, Qing & Wang, Hao & Wang, Taotao & Zhang, Shengli & Wu, Xiaoxiao & Wang, Hui, 2021. "Blockchain-based decentralized energy management platform for residential distributed energy resources in a virtual power plant," Applied Energy, Elsevier, vol. 294(C).
    2. Zhong, Shengyuan & Wang, Xiaoyuan & Zhao, Jun & Li, Wenjia & Li, Hao & Wang, Yongzhen & Deng, Shuai & Zhu, Jiebei, 2021. "Deep reinforcement learning framework for dynamic pricing demand response of regenerative electric heating," Applied Energy, Elsevier, vol. 288(C).
    3. Wang, Xuejie & zhao, Huiru & Lu, Hao & Zhang, Yuanyuan & Wang, Yuwei & Wang, Jingbo, 2022. "Decentralized coordinated operation model of VPP and P2H systems based on stochastic-bargaining game considering multiple uncertainties and carbon cost," Applied Energy, Elsevier, vol. 312(C).
    4. Pandžić, Hrvoje & Morales, Juan M. & Conejo, Antonio J. & Kuzle, Igor, 2013. "Offering model for a virtual power plant based on stochastic programming," Applied Energy, Elsevier, vol. 105(C), pages 282-292.
    5. Ju, Liwei & Tan, Zhongfu & Yuan, Jinyun & Tan, Qingkun & Li, Huanhuan & Dong, Fugui, 2016. "A bi-level stochastic scheduling optimization model for a virtual power plant connected to a wind–photovoltaic–energy storage system considering the uncertainty and demand response," Applied Energy, Elsevier, vol. 171(C), pages 184-199.
    6. Fan, Shuai & Liu, Jiang & Wu, Qing & Cui, Mingjian & Zhou, Huan & He, Guangyu, 2020. "Optimal coordination of virtual power plant with photovoltaics and electric vehicles: A temporally coupled distributed online algorithm," Applied Energy, Elsevier, vol. 277(C).
    7. Thomas Morstyn & Niall Farrell & Sarah J. Darby & Malcolm D. McCulloch, 2018. "Using peer-to-peer energy-trading platforms to incentivize prosumers to form federated power plants," Nature Energy, Nature, vol. 3(2), pages 94-101, February.
    8. Dong, Lianxin & Fan, Shuai & Wang, Zhihua & Xiao, Jucheng & Zhou, Huan & Li, Zuyi & He, Guangyu, 2021. "An adaptive decentralized economic dispatch method for virtual power plant," Applied Energy, Elsevier, vol. 300(C).
    9. Tarroja, Brian & Chiang, Felicia & AghaKouchak, Amir & Samuelsen, Scott & Raghavan, Shuba V. & Wei, Max & Sun, Kaiyu & Hong, Tianzhen, 2018. "Translating climate change and heating system electrification impacts on building energy use to future greenhouse gas emissions and electric grid capacity requirements in California," Applied Energy, Elsevier, vol. 225(C), pages 522-534.
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