International Journal of Applied Power Engineering (IJAPE) Vol. 12, No. 3, September 2023, pp. 293~299 ISSN: 2252-8792, DOI: 10.11591/ijape.v12.i3.pp293-299  293 Journal homepage: http://ijape.iaescore.com Leveraging PSO algorithms to achieve optimal stand-alone microgrid performance with a focus on battery lifetime Vicky Andria Kusuma 1 , Aji Akbar Firdaus 2 , Sena Sukmananda Suprapto 1 , Dimas Fajar Uman Putra 3 , Yuli Prasetyo 4 , Firillia Filliana 1 1 Department of Electrical Engineering, Institut Teknologi Kalimantan, Balikpapan, Indonesia 2 Department of Engineering, Faculty of Vocational, Airlangga University, Surabaya, Indonesia 3 Department of Electrical Engineering, Faculty of Intelligent Electrical and Informatics Technology, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia 4 Department of Engineering, Politeknik Negeri Madiun, Madiun, Indonesia Article Info ABSTRACT Article history: Received Jan 8, 2023 Revised Feb 9, 2023 Accepted Feb 17, 2023 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. Keywords: Battery management Cost-effectiveness Lifetime Optimization PSO algorithm This is an open access article under the CC BY-SA license. Corresponding Author: Aji Akbar Firdaus Department of Engineering, Faculty of Vocational, Airlangga University Surabaya, Indonesia Email: aa.firdaus@vokasi.unair.ac.id 1. INTRODUCTION Microgrids are systems that utilize renewable energy sources such as photovoltaic (PV) panels [1], wind turbines [2], and diesel generators (DG) [3], along with batteries for energy storage. The batteries in microgrids serve as backup power sources when renewable energy sources are unable to meet the energy demand [4], [5]. Ensuring the efficiency [6]–[14], stability [15], safety [16], and reliability [17] of the energy storage system is vital in microgrids. Optimizing battery performance can be challenging due to the limited lifespan and high cost of these systems [18]–[20]. An energy management system can be used to control energy optimization in microgrids [21]–[23]. This research involves the use of a modified IEEE 30 bus as a model for optimization, taking into consideration various factors such as battery lifespan cost, maintenance cost, and fuel cost in order to determine optimal operating parameters. The aim of this study is to analyze the comparison of battery lifespan through the implementation of an energy management system in microgrids by considering various factors such as battery lifespan cost, maintenance cost, and fuel cost. In the past, there have been difficulties in optimizing battery performance in microgrid systems. Conventional battery management methods, such as maximizing the state of charge (SOC) or controlling charging and discharging patterns, fail to consider the limited lifespan of batteries and often result in