A Low-Carbon Economic Optimization Strategy for Multi-Energy Virtual Power Plant Considering Multiple Time Scales

With the proposal of net zero emission policies, virtual power plant has received extensive research, for its ability to integrate and optimize distributed renewable energy resources such as power generation, energy storage, and flexible loads, thereby encouraging the development and promotion of renewable energy. This paper proposes a multi-timescale control strategy for multi-energy virtual power plant (MEVPP) considering the carbon trading mechanism. First, in the virtual power plant, a multi-objective optimal dispatch model considering operation and maintenance costs, carbon trading costs and energy purchase costs is constructed. Secondly, in order to further solve the optimal scheduling model, a two-stage optimization algorithm including day-ahead scheduling and intra-day rolling optimization is employed, and uses multi-objective particle swarm optimization (MOPSO) to solve it. Finally, in the case study, a multi-network model comprising of IEEE39-node electricity network, Belgium 20-node gas network and 6-node thermal network is constructed to verify the proposed control strategy. This optimal dispatch strategy can further reduce the impact of forecast errors of uncertain renewable energy sources such as wind and solar on the optimal dispatch of the system.