Multi-Microgrid Distribution Network Hierarchical Collaborative Scheduling Strategy Under Network Intelligence Algorithms

In recent years, microgrids, as an important component of distributed energy access distribution networks, have become a key strategy for promoting the evolution of traditional distribution networks towards active distribution networks. However, the economic issues caused by the integration of microgrids into distribution networks still need to be further addressed. This article takes active distribution networks and microgrids as research objects, and constructs an innovative hierarchical optimization model by analyzing the interest relationship between the two. The model aims to minimize the total cost of microgrids and distribution networks, including energy storage, distributed generators, distributed loads, and distributed solar panels. It takes the safety constraints of node voltage, power balance constraints, and power interaction constraints of the superior power grid during distribution as the constraint function. This article obtains the scheduling strategy with the minimum cost that satisfies the constraint conditions through the economic operation and hierarchical collaborative scheduling strategy of multi-microgrids and distribution networks. In order to solve this multi-level problem, this paper introduces genetic algorithms and linear programming methods to achieve the overall optimization solution of the system. The experimental data shows that the investment cost of scheduling optimization based on genetic algorithm is $\mathbf{6 8 1. 7 2}$ million yuan, which is 0.4502 million yuan less than the investment cost of particle swarm algorithm. The power outage loss of scheduling optimization based on genetic algorithm is 892.8709 million yuan, which is 409.1635 million yuan higher than the power outage loss of PSO (PSO) algorithm.