Research on Multi-objective Collaborative Optimization Strategies for Multi microgrid Systems Based on Data Feature Analysis

A multi microgrid system can interconnect adjacent microgrids for overall scheduling, effectively increasing the proportion of renewable energy consumption and improving the economic and stability of the operation of microgrids. However, the uncertainty of distributed renewable generation bring challenges to the operation of multi microgrid systems. Moreover, the current collaborative strategy of multi microgrid systems has not taken the optimization goals of different types of microgrids into account. This paper proposes a multi-objective optimization model for multi microgrid systems that considers the operational objectives of each microgrid. A scenario feature analysis method which combines Latin hypercube sampling and cluster analysis is applied to simulate the impact of uncertainty in wind and photovoltaic output on multi microgrid systems which can greatly reduce the scale of optimization problem. Adaptive grid particle swarm optimization algorithm is applied to solve the objective problem and the simulation results show that the collaborative optimization algorithm can effectively improve the renewable energy consumption rate of the system, reduce the external energy dependence of the system, and lower operating costs.