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Real-time operation strategy of virtual power plants with optimal power disaggregation among heterogeneous resources

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  • Chen, Qixin
  • Lyu, Ruike
  • Guo, Hongye
  • Su, Xiangbo

Abstract

The virtual power plant (VPP) can aggregate flexible resources on the demand side to provide frequency regulation for the grid, helping address the supply–demand balance challenges. When deploying regulation, the VPP disaggregates the requested power adjustment in real time among its internal heterogeneous resources. Achieving optimal power disaggregation in this process is challenging due to the temporal coupling characteristics of the resources, the uncertain regulation signals, and the requirement for fast response. Therefore, existing research relies on heuristic methods, such as proportional disaggregation, and fails to leverage the heterogeneity of multiple resources. Here, we propose an optimal operation strategy for VPPs to provide regulation, exploiting the complementary characteristics of heterogeneous resources by prioritising the use of low-cost resources while considering temporal coupling. To reduce the computational overhead of online deployment, we further propose a fast disaggregation algorithm to eliminate the reliance on optimisation solvers. We conducted case studies on the operation of a VPP composed of resources including thermostatically controlled loads and industrial production processes. The results verified the reduced operation cost and increased profit of the VPP under the proposed strategy, with only milliseconds of online computation time. We believe that our work can help better exploit demand-side flexibility.

Suggested Citation

  • Chen, Qixin & Lyu, Ruike & Guo, Hongye & Su, Xiangbo, 2024. "Real-time operation strategy of virtual power plants with optimal power disaggregation among heterogeneous resources," Applied Energy, Elsevier, vol. 361(C).
    • Handle: RePEc:eee:appene:v:361:y:2024:i:c:s0306261924002599
    DOI: 10.1016/j.apenergy.2024.122876
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    References listed on IDEAS

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    1. Lu, Renzhi & Bai, Ruichang & Huang, Yuan & Li, Yuting & Jiang, Junhui & Ding, Yuemin, 2021. "Data-driven real-time price-based demand response for industrial facilities energy management," Applied Energy, Elsevier, vol. 283(C).
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    7. Peng, Chao & Zou, Jianxiao & Lian, Lian & Li, Liying, 2017. "An optimal dispatching strategy for V2G aggregator participating in supplementary frequency regulation considering EV driving demand and aggregator’s benefits," Applied Energy, Elsevier, vol. 190(C), pages 591-599.
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    1. Lin, Mohan & Liu, Jia & Tang, Zao & Zhou, Yue & Jiang, Biao & Zeng, Pingliang & Zhou, Xinghua, 2025. "Coordinated DSO-VPP operation framework with energy and reserve integrated from shared energy storage: A mixed game method," Applied Energy, Elsevier, vol. 379(C).

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