Master-Slave Game Optimization Scheduling of Multi-Microgrid Market with Cloud Energy Storage Based on Probabilistic Model Integration

To promote efficient utilization of energy storage and demand-side resources, improve the efficiency of new energy consumption, a multi-microgrid master-slave game optimization scheduling strategy that takes into account user demand response and cloud energy storage was proposed. Firstly, a novel probabilistic model integration method was proposed, which was applied to source and load power forecasting and electricity price forecasting. An evidence correction method was built based on kappa coefficient and accuracy rate weight to improve Dempster Shafer (D-S) evidence theory information integration framework, integrate multiple probabilistic model and generate distributed energy output scenario sets. Secondly, a dual layer optimized scheduling architecture for multi-microgrid systems based on demand response and cloud energy storage operation mode was designed; a two-layer master-slave game model with the optimal joint operation cost of multi-microgrids and the lowest user purchase cost was established: The lower level model determines the market quotation strategy and distributed power equipment output adjustment strategy based on the generation probability integration method, and feeds back to the upper level model. The optimization of multi-microgrid master-slave game equilibrium operation was achieved by iteratively solving the upper and lower layers. Finally, case analysis shows that cloud energy storage and user demand response in a multi microgrid system have a synergistic effect on improving the output of distributed power generation, effectively improving the economy of the multi microgrid system and reducing user energy purchasing costs.