Distributed Optimization of Virtual Power Plant Clusters Considering a Carbon Incentive and Penalty Mechanism in Uncertain Environments

The diversification of distributed energy resources (DERs) brings fluctuations and impacts to the power grid, as well as the urgent need to achieve the dual carbon goals. The focal point of this study lies in the virtual power plant (VPP), delving into the optimization of power sharing within the virtual power plant cluster (VPPC). In this study, a framework of energy flow trading system is proposed in the first place. Based on cooperative game theory, a VPPC cooperative game model considering a ladder-type carbon price mechanism is formed to transform the problem of maximizing profits into a problem of minimizing costs. Then, a carbon incentive and penalty mechanism is introduced to improve the allocation coefficient of the Shapley value method. Moreover, taking into account the impact of uncertainty in renewable energy sources such as wind power and photovoltaics on the VPPC energy flow trading, the discretized conditional value at risk (CVaR) is employed to quantify uncertainty risk. To solve the cooperative game model considering uncertainty, the alternating direction method of multipliers (ADMM) algorithm is used for distributed solution. Finally, the effectiveness of the proposed model in contributing to the energy complementation and carbon reduction of the VPPC is verified through the analysis of several cases.