Collaborative Optimal Dispatch of Multi-virtual Power Plants Considering Low-carbon Goals and Master-Slave Game

To improve energy efficiency, increase the interests of virtual power plants (VPPs), and reduce carbon emissions, a collaborative optimal dispatch method for multi-VPPs considering low-carbon goals and a master-slave game was proposed. First, the power-heat collaborative game optimization framework for multi-VPPs was constructed. The distribution system operator (DSO) was introduced as the leader, which formulated energy trading prices according to the dispatch strategies of VPPs. VPPs were regarded as followers, which developed energy dispatch and electricity purchase-sales strategies based on the trading prices, thus forming a mutual game relationship between the two parties. Then, a stepped carbon trading mechanism and a dual-compensation demand response were incorporated to further explore the low-carbon potential of the master-slave game. Finally, the optimal dispatch mathematical models for DSO and VPPs were developed, and the particle swarm optimization (PSO) algorithm combined with mixed-integer nonlinear programming (MINLP) was adopted for solution. Four scenarios were set up for comparative analysis. Results show that the proposed game model can promote multi-energy collaborative complementarity among multi-VPPs while maximizing the interests of each VPP. Owing to the increased energy interaction between VPPs and the integration of the stepped carbon trading mechanism, the model can effectively reduce carbon emissions. Meanwhile, the dual-compensation demand response can guide load curtailment and improve low-carbon efficiency.