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This paper addresses the problem of optimization operation strategy for a virtual power plant (VPP) consisting of a load aggregator (LA), an energy aggregator (EA), and a dispatch control center (DCC) participating in the day-ahead (DA) electricity market. A two-layer game approach is proposed to maximize DA profits while minimizing carbon emissions and ensuring customer satisfaction. In the inner layer, a demand response (DR) model based on price elasticity coefficients is created to manage flexible loads. Subsequently, a cooperative game model is established to optimize the power generation strategies of distributed thermal generators (TG), wind turbines (WT), and photovoltaic (PV) panels, with unified coordination by EA to maximize electricity sales revenue. On this basis, the impact of renewable energy source (RES) uncertainty on Nash equilibrium is analyzed based on interval estimation theory. In the outer layer, a Stackelberg game model is constructed to maximize the revenue of the DCC, which serves as the leader and influences the actions of the EA and LA. The case study in the pilot area of Xiongan New Area, China validates the effectiveness of the proposed method in improving profits for VPP, reducing carbon emissions, and promoting the utilization of renewable energy.
Wu et al. (Wed,) studied this question.