Low-Carbon Robust Optimization Scheduling for Multi-Virtual Power Plants Considering P2P Trading

Virtual power plant large-scale grid connection brings a new direction for studying the scheduling strategy of multi-VPP systems. However, in practical application, the uncertainty of the output of renewable energy, how to trade VPP efficiently on the untrusted trading platform, and the operation problems caused by the large-scale grid connection of VPP are all challenges that need to be faced. Therefore, this paper focuses on the coordination and optimization scheduling strategy of point-to-point (Peer to Peer, P2P) Trading of multi-VPP systems. First, it studies the structural model, uncertainty model and stepped carbon price model of multi-VPP systems. Given the unpredictability of renewable energy generation in the VPP, we have employed robust optimization to address this issue. Introduce the ladder carbon price type carbon trading into the multi-VPP system for research. Based on the given analysis example, robust optimization offers a solution to address output uncertainty. Additionally, a proposed structural model for P2P trading in VPP system tackles the scheduling problem. Moreover, to promote dynamic and effective Trading among VPP participants, a game theory-inspired incentive mechanism known as Nash bargaining is implemented. The simulation comparative analysis shows that when the multi-VPP system is in the P2P trading market, the overall economy of the system is relatively much improved compared with the traditional trading market.