Two-stage robust optimization of a virtual power plant considering a refined demand response

Jinpeng Liu, Jinchun Peng, Hushihan Liu, Jiaming Deng and Xiaohua Song

Energy, 2025, vol. 322, issue C

Abstract: A reasonable demand response strategy and flexible resource planning technology are the key methods for constructing new power systems. Based on this, a virtual power plant economic optimization model considering a refined demand response strategy is proposed. First, considering the regulatory characteristics of flexible loads and the satisfaction degree of residents, flexible loads are finely classified, and a demand response model is constructed. Second, considering the power uncertainty of wind turbines and photovoltaics in virtual power plants, a two-stage robust optimization model with a min–max–min structure is constructed; then, a transformation method of fuzzy sets and subproblems is proposed to improve the solution efficiency. Finally, the total operating cost of virtual power plants under the deviation of power forecasts and fluctuations in intraday electricity prices is analysed. The simulation results reveal that refined load classification can reduce the system day-ahead operating cost by 7.12 %; the proposed two-stage robust optimization model reduces the total real time operating cost by 0.81–6.39 % compared to the deterministic optimization model.

Keywords: Virtual power plants; Demand response; Two-stage robust optimization; Economic scheduling (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:322:y:2025:i:c:s0360544225012022

DOI: 10.1016/j.energy.2025.135560

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