Integrated multi-objective optimization of distributed generation, battery energy storage, and electric vehicle charging for enhanced distribution network performance

Introduction. The increasing penetration of distributed generation (DG), electric vehicles (EVs), and battery energy storage systems (BESS) in modern power distribution networks necessitates advanced optimization techniques to ensure efficient and reliable operation. Problem. Traditional distribution networks face challenges such as increased power losses, voltage instability, and uncoordinated operation of flexible resources like DG units, EV charging, and BESS. Goal. To enhance the performance of modern distribution systems by minimizing active power losses and improving voltage profiles through coordinated optimization of DG placement and sizing, BESS scheduling, and EV charging management. Methodology. Efficient multi-objective optimization framework based on the non-dominated sorting genetic algorithm II (NSGA-II) is developed. Four scenarios are analyzed: 1) base case without optimization, 2) optimal DG placement and sizing only, 3) DG optimization combined with BESS scheduling, 4) fully integrated optimization including DG, BESS, and coordinated EV charging. Results. The proposed approach, applied to the IEEE 33-bus distribution system, significantly reduces active power losses and decreases voltage deviation compared to the base case. The best performance is achieved in case 4, where full coordination of DG, BESS, and EV charging leads to the most efficient network operation. Scientific novelty. Enhancing distribution grid performance through the simultaneous integration of DG, BESS scheduling, and EV charging coordination using an effective multi-objective optimization algorithm (NSGA-II). Practical value. The proposed NSGA-II approach reduces active power losses from 201.8925 kW to 14.8802 kW (92.6 %) and reactive power losses from 134.6413 kVAr to 22.4226 kVAr (83.3 %), significantly improving overall system performance. References 25, tables 4, figures 8.