Management of large energy storage power plants: optimization of charging and discharging with cuckoo search algorithm

Abstract

They are directly integrated into smart distribution networks and can supply stored energy during peak demand periods, while absorbing and storing energy during periods of low demand. This capability helps maintain a balance between supply and demand in power grids, preventing voltage fluctuations and the inability to meet peak loads during high-demand hours. Thanks to technological advancements, it is now possible to upgrade large-scale energy storage facilities. The modern architecture and technology of these facilities facilitate the efficient utilization of renewable energy sources, significantly reducing energy costs and increasing energy efficiency. Additionally, through the use of artificial intelligence algorithms and optimization techniques, the performance and operations of large-scale energy storage facilities can be enhanced. This article focuses on the management of large-scale energy storage facilities, introducing innovative measures that include constraints on the number of charge and discharge processes. Furthermore, the use of the advanced Fakete search algorithm is employed as a powerful and efficient method for solving the proposed model. This algorithm has the capability to find global optimal solutions and can significantly improve the efficiency and profitability of large-scale energy storage facilities. Simulation results demonstrate that adopting this approach in managing large-scale energy storage facilities leads to significant economic impacts. These impacts include reduced energy costs, increased efficiency, greater independence from fossil fuel resources, the preservation of grid stability, and improved performance of the power transmission system.

Keywords

• distribution substation
• large-scale energy storage power plants
• evolutionary algorithm
• optimization