EVCS demand management using multi-objective power flow analysis with renewable distributed generators

Abstract Smart grids have made considerable strides in the optimal integration of electrical units such as distributed generation units and electric vehicle charging station. The conventional grid system has been successfully revolutionized by this advancement, which has great promise for cost savings and independent energy source management. This study obtains techno-economic benefits by minimizing the total economic losses of the system while reducing power losses, optimizing DG capacity and location and also reducing the fuel costs of thermal power plants. For this purpose, two multi-objective Grey Wolf Optimizer (MOGWO) and multi-Objective Differential Evolution (MODE) techniques have been merged with NR power flow analysis and pareto optimal solutions are also presented. This paper includes two scenarios: the base case under normal conditions and with EVCS where the load is increased by 1.1 times the base load. The results demonstrate that the MOGWO approach outperforms the MODE method, yielding more favourable outcomes in both scenarios. Notably, the MOGWO method results in smaller DG sizes compared to MODE in both normal and EVCS loading conditions. Transmission losses are reduced by 68.12% under normal conditions and by 72.7% under EVCS loading conditions with the MOGWO approach. The effectiveness of the proposed method has been evaluated using MATLAB software on the standard IEEE 57-bus system. Additionally, the base case results have been compared with other algorithms, including SCA, DA, NSGA-II, and MOCS, to validate the performance of the proposed MOGWO approach.