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Stimulus-response control strategy based on autonomous decentralized system theory for exploitation of flexibility by virtual power plant

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  • Zhou, Huan
  • Fan, Shuai
  • Wu, Qing
  • Dong, Lianxin
  • Li, Zuyi
  • He, Guangyu

Abstract

To adequately utilize flexible resources on demand side, Virtual Power Plant (VPP) is an effective solution through the aggregation and application of distributed energy resources (DER). While centralized control approaches are easy to achieve global optimum for the scheduling of every DER, they have limitations when dealing with massive number of complex and heterogeneous DERs with time-varying states. Existing decentralized control approaches are mainly based on the assumption that all DERs are completely rational, which is quite far from the reality. In this paper, using a bottom-up approach, we propose a stimulus–response control strategy to realize exploitation of flexibility by VPP. In such a strategy, DERs are dynamically aggregated through autonomous decentralized system, and interact with each other via subscription and publication of topics, regardless of the source and recipient of the messages, thus removing the direct coupling relationship between VPP Operator and DERs. Furthermore, each DER makes an independent decision through edge computing at an agent that has a general End-to-End structure and is driven by the stimulus message received from VPP Operator. We develop a simple yet efficient double deep q-network (DDQN) algorithm to optimize the state sequence of DER agents. A simulation study is conducted with over 1000 DERs including photovoltaics, electric vehicles and air conditioners. Results indicate that the proposed approach can dynamically aggregate DERs and exploit their flexibility with each DER agent dynamically adapting to the change of stimulus signals, thus achieving dynamic, automatic and adaptive exploitation of flexibility by VPP.

Suggested Citation

  • Zhou, Huan & Fan, Shuai & Wu, Qing & Dong, Lianxin & Li, Zuyi & He, Guangyu, 2021. "Stimulus-response control strategy based on autonomous decentralized system theory for exploitation of flexibility by virtual power plant," Applied Energy, Elsevier, vol. 285(C).
    • Handle: RePEc:eee:appene:v:285:y:2021:i:c:s0306261920317876
    DOI: 10.1016/j.apenergy.2020.116424
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    5. Gengsheng He & Yu Huang & Guori Huang & Xi Liu & Pei Li & Yan Zhang, 2024. "Assessment of Low-Carbon Flexibility in Self-Organized Virtual Power Plants Using Multi-Agent Reinforcement Learning," Energies, MDPI, vol. 17(15), pages 1-20, July.
    6. 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).
    7. Ju, Liwei & Yin, Zhe & Lu, Xiaolong & Yang, Shenbo & Li, Peng & Rao, Rao & Tan, Zhongfu, 2022. "A Tri-dimensional Equilibrium-based stochastic optimal dispatching model for a novel virtual power plant incorporating carbon Capture, Power-to-Gas and electric vehicle aggregator," Applied Energy, Elsevier, vol. 324(C).
    8. Chen, Siqi & Zhang, Kuan & Liu, Nian & Xie, Yawen, 2024. "Unlock the aggregated flexibility of electricity-hydrogen integrated virtual power plant for peak-regulation," Applied Energy, Elsevier, vol. 360(C).
    9. Wafa Nafkha-Tayari & Seifeddine Ben Elghali & Ehsan Heydarian-Forushani & Mohamed Benbouzid, 2022. "Virtual Power Plants Optimization Issue: A Comprehensive Review on Methods, Solutions, and Prospects," Energies, MDPI, vol. 15(10), pages 1-20, May.
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