Robust Optimization for PV and BESS Configurations in Distribution Network with EV Load Uncertainties - Deeum, Saksit; Bhumkittipich, Pimnapat; Janjamraj, Natin; Romphochai, Sillawat; Kongjeen, Yuttana; Bhumkittipich, Krischonme (Engineering Access, 2025)
The integration of electric vehicles (EVs) into electrical distribution systems introduces significant challenges and opportunities for optimization, particularly in the context of incorporating renewable energy sources such as photovoltaic (PV) systems and battery energy storage systems (BESS). This paper presents a comprehensive study on optimizing PV and BESS configurations within the IEEE 33 Bus distribution system, focusing on addressing the uncertainties of varying EV loads. Advanced optimization techniques such as particle swarm optimization (PSO), genetic algorithms (GA), and quantum-inspired evolutionary algorithms (QEA) are employed to determine optimal sizes and placements for PV and BESS installations. Monte Carlo simulation models EV load variability, ensuring that the optimization framework accounts for real-time data and forecasted demand. Results demonstrate that incorporating EV load uncertainty into optimization significantly enhances system resilience, efficiency, and cost-effectiveness. This research provides valuable insights for utilities and system operators, offering guidance on deploying renewable energy resources and storage solutions to build a more reliable and sustainable energy infrastructure.