Leveraging PSO algorithms to achieve optimal stand-alone microgrid performance with a focus on battery lifetime

Abstract

This research endeavors to increase the lifespan of a battery utilized in a standalone microgrid system, a self-sufficient electrical system that consists of multiple generators that are not connected to the main power grid. This type of system is ideal for use in remote locations or areas where the grid connection is not possible. The sources of energy for this system include photovoltaic panels, wind turbines, diesel generators, and batteries. The state of charge (SOC) of the battery is used to determine the amount of energy stored in it. The particle swarm optimization (PSO) method is applied to minimize energy generation costs and maximize battery life. The results show that battery optimization can decrease energy generation costs from Rp 5,271,523.03 ($338.64 in USD) to Rp 13,064,979.20 ($839.30 in USD) while increasing the battery's lifespan by 0.42%, with losses of 7.22 kW and 433.29 kVAR, and also a life loss cost of Rp 5,499/$0.35.

Sizing and analysis of a standalone photovoltaics system for a three-bedroom residence in Nigeria

“…This offers a valuable tool for researchers and engineers to analyse and optimize the behaviour of PV systems [16], [17]. The minimization of energy generation cost and enhanced battery life is achieved using the particle swarm optimization method [18]. Adhikari et al [19] focuses on the design, control, and performance evaluation of a low power solar-PV energy generating system by assessing the system's performance under diverse conditions.…”