Optimal Management of Energy Storage Systems for Peak Shaving in a Smart Grid

In this paper, the installation of energy storage systems (EES) and their role in grid peak load shaving in two echelons, their distribution and generation are investigated. First, the optimal placement and capacity of the energy storage is taken into consideration, then, the charge-discharge strategy for this equipment is determined. Here, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to calculate the minimum and maximum load in the network with the presence of energy storage systems. The energy storage systems were utilized in a distribution system with the aid of a peak load shaving approach. Ultimately, the battery charge-discharge is managed at any time during the day, considering the load consumption at each hour. The results depict that the load curve reached a constant state by managing charge-discharge with no significant changes. This shows the significance of such matters in terms of economy and technicality.

Automated summary

This paper investigates the installation of energy storage systems (EES) and their role in grid peak load shaving in two echelons, exploring their distribution and generation. The optimal placement and capacity of the energy storage is considered, and the charge-discharge strategy is determined using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The results show the significance of managing battery charge-discharge for load consumptions in terms of economy and technicality.