Dynamic optimization of solar DG and shunt capacitor placement to mitigate the impact of EV charging stations on power distribution network - T. Yuvaraj; M. Thirumalai; T.D. Suresh; Sudhakar Babu Thanikanti; Mohammad Khishe (Results in Engineering)
The sudden surge in electric vehicle (EV) adoption has significantly increased electricity demand, posing new challenges for radial distribution networks (RDNs). The large-scale deployment of electric vehicle charging stations (EVCS) introduces operational issues such as elevated power losses, voltage instability, and line overloading, thereby testing the grid's robustness and efficiency. To address these challenges, this research proposes a novel optimization framework that integrates the quasi-refined slime mould algorithm (QRSMA) with conventional slime mould algorithm (SMA), particle swarm optimization (PSO), and genetic algorithm (GA) techniques for the optimal placement of solar-based distributed generators (SDGs) and shunt capacitors (SCs). The proposed method aims to minimize a multi-objective function incorporating real and reactive power losses, voltage deviation, and voltage stability, thereby enhancing the stability and reliability of RDN operations. The framework employs stochastic modeling based on four years of hourly meteorological data to account for uncertainties in solar irradiance and temperature. Additionally, the EVCS model includes dynamic operational aspects such as mean queue lengths, waiting times, and demand response (DR) load control. The methodology is validated using both a practical Indian 28-bus RDN and the large-scale IEEE 118-bus system to assess scalability and generalizability. Simulation results confirm the superior performance of QRSMA in improving voltage profiles, reducing power losses, and achieving better computational efficiency compared to conventional optimization algorithms. Sensitivity analyses further demonstrate the robustness of QRSMA under varying objective priorities. Moreover, the economic assessment using the levelized cost of energy (LCOE) indicates strong financial viability for real-world implementation. This work underscores the importance of coordinated planning of SDGs and SCs to mitigate EVCS-induced challenges and provides a scalable, efficient solution for modern, EV-integrated smart grids.