Squirrel search-based optimization of energy storage systems for electric vehicle charging stations

Battery Energy Storage System (BESS) is essential for regular and backup power supply in Electric Vehicle Charging Infrastructure (EVCI). Determining an appropriate BESS size is a key to cost-effective and efficient power supply solutions, particularly when incorporating renewable energy sources. The present work offers a novel Squirrel Search-Based Recursive Control Model (SSbRCM) to optimize the BESS size, considering practical aspects such as net metering, feed-in tariffs, net purchase and sale mechanisms, and Time-of-Use (ToU) pricing. In addition, the work involves solar PV integration and thereby it is capable of an optimal battery size of 39.51 kWh with a minimal cost of $ 29.13. The key novelty of this study is designing the squirrel based recursive control model for optimizing the BESS size. Here, the squirrel function was operated continuous till the desired optimal BESS size was found, that provided the better outcome than the previous model. The findings are compared to standard approaches such as Genetic Optimization (GO), Particle Swarm Model (PSM), and Modified Alternate Direction Multiplier Method (MADMA), and it is shown that the proposed model outperforms all of the aforementioned models. The Proposed optimization model exhibits a lower calculation time of 14.6 s and much-reduced error rate of 5.75 x 10−4. Thus, SSbRCM offers a faster convergence rate, reduced calculation time, and determination of the optimal battery size, making it a leading solution for BESS optimization in EVCI.