Optimization Strategies and Benefit Allocation of Virtual Power Plants Considering Different Risk Preferences

By aggregating and optimizing the management of demand-side resources (distributed energy, flexible load, and energy storage) in the form of virtual power plant (VPP), the overall competitiveness and economy can be effectively improved. On this basis, considering the uncertainty faced by VPP and its internal profit distribution problem, a VPP day-ahead scheduling model with consideration of risk preference is established to figure out the operational strategies and returns of VPP under different risk levels. In this model, the conditional value at risk theory (CVaR) is adopted to quantitatively deal with the risks of uncertain factors, and Fuzzy Decision Making is used to deal with different operational strategies, which provides reference scheme for VPP managers. At the same time, taking the risk level and marginal benefit of each subject within the virtual power plant into full consideration, the model is based on Nash negotiation model, combined with Shapley value method and standalone contribution to allocate the total revenue of VPP. Finally, the validity of the operation management model is verified by arithmetic simulation, and the rationality of this profit allocation system is proved by analyzing the risk magnitude and profit magnitude of demand-side resources.