Distributed Energy Storage Scheduling Optimization of Micro Grid Based on Particle Swarm Optimization Algorithm

Abstract

Distributed generation is a new type of environment-friendly energy, which has a good development prospect. However, due to its inherent defects, it is still difficult to be widely used in large-scale access points and loads. As a new type of distributed power supply, Micro grid is decentralized and intermittent in power supply. In order to make rational use of power grid resources, it is necessary to optimize the distributed energy storage scheduling. The particle swarm optimization algorithm proposed in this paper is also to improve the data processing capacity of the scheduling system. This paper mainly uses the experimental comparison method and particle swarm optimization algorithm to study the micro grid distributed energy storage scheduling optimization. The experimental results show that the FCPSO algorithm takes the least time, only 7s, and has the lowest cost. Therefore, this algorithm can be used for in-depth analysis of micro grid scheduling.

Optimal Allocation of Energy Storage Capacity in Distributed Micro-grid Based on Improved Whale Algorithm

“…As the main body of a small power station, the setting of electric energy storage and capacity is very important and critical, which is directly related to the daily small power generation module and circuit control effect. In this context, the micro-grid can be operated on an isolated island and also connected to the grid [2].…”

“…To solve the power supply and security problems of the power system, it is necessary to optimize the overall energy storage capacity [3]. In [2] and [3], the optimal allocation method of micro-grid energy storage under the traditional particle swarm algorithm and the optimal allocation method of micro-grid energy storage under the traditional particle swarm algorithm can achieve the expected processing tasks, but the overall pertinence is not strong, and the positioning of the change of energy storage capacity is not accurate enough. To a certain extent, the actual control sensitivity will be reduced, which is not conducive to the implementation of the subsequent optimal configuration of energy storage [4].…”