Dynamic determination of optimal location for electric vehicle charging stations based on battery energy storage system sizing

The growing trend of electric vehicles (EVs) puts tremendous strain on power grids, leading to increased power loss, voltage instability, and the possibility of overloading of infrastructure. To mitigate these adverse effects, battery energy storage systems (BESSs) are identified as a crucial solution due to their quick response, efficiency, and modularity. This paper addresses the fundamental challenge of integrating EV charging stations (EVCSs) into radial distribution systems (RDSs) through the development of an optimal sizing and placement method incorporating BESSs. The multi-objective optimization problem in this paper aims to minimize the active power loss from the distribution network operator (DNO) perspective and installation costs from the charging station owner (CSO) perspective. A genetic algorithm is utilized to solve this non-linear integer problem with some modifications to the standard crossover and mutation operators to ensure feasibility. Two main scenarios are considered: first, allocation of BESS to fixed, assigned locations of EVCS; and second, optimization of EVCS placement and BESS capacity together. This study demonstrates a novel dynamic optimal placement strategy where increasing BESS capacity changes the dominant objective factors from the cost function, consequently altering the optimal location of EVCSs. Results also show that co-optimizing the placement of EVCS and BESS enhances the voltage stability performance compared to static placement. Moreover, an economic analysis covering energy arbitrage demonstrates 40 % BESS penetration against the connector power has the highest profitability, roughly over $155,000 during the lifespan of the project. This approach better considers the initial 18.2 % increase in power losses due to EVCS integration, reducing it by 6.36 %, and increases the minimum bus voltage by 0.1 %. The findings emphasize the necessity of dynamic and adaptive deployment strategies of BESS, offering valuable insights for policymakers, network operators, and investors, enabling them to make informed decisions regarding the optimal deployment of BESS for EVCSs. • A novel method is presented for optimal placement of EV charging station with BESS. • The non-linear integer problem is solved by a modified genetic algorithm. • A dynamic placement strategy considering BESS sizing is compared with a static one. • An economic evaluation is performed with optimal BESS capacity selection.