Monte Carlo Based Optimal Placement of Multiple PV/BESS Units in Power Distribution System with Varying Electric Vehicle Load Penetration

This article presents the Monte Carlo based optimal placement of multiple PV/BESS units in a power distribution system with varying Electric Vehicle (EV) load penetration. The popularity of EVs brings the need for power supply to integrate renewable energy resources and energy storage devices to enhance the operational efficiency of the power system. This article presents a static simulation approach that tries to improve the placement and sizing of PV and BESS systems in the IEEE 33 bus test system for different penetration levels of EVs. The suggested method utilizes a Multi-Objective Monte Carlo (MOMC) simulation technique to assess various scenarios, specifically reducing total power losses, system cost, and carbon dioxide (CO2) emissions while guaranteeing voltage stability within predetermined thresholds. The simulation scheme combines dynamic PV output, accurately representing the daily solar generating cycles. In addition, it contains extra EV loads to model the increased penetration on the power distribution system. The research proposes specific spots within the power grid to place PV and BESS systems and applies zoning restrictions during the optimization computation step. The objective function imposes voltage violation fines; therefore, compliance with operational requirements is ensured. The findings indicate substantial decreases in power wastage and CO2 emissions at various levels of EV adoption (30, 50, and 70%). The most efficient PV and BESS systems setup change depending on the level of adoption of EVs, emphasizing the need for flexible approaches in constructing distribution networks. The findings provide valuable insights for power grid operators and regulators who aspire to improve the resilience and efficiency of power distribution networks in the face of increased EV integration. This contributes to the creation of strong and sustainable energy management policies.