基于可再生能源的插电式电动汽车负荷设计在负荷跟随策略下的技术经济评估与敏感性分析

Abstract This work explores the deployment of renewable based energy systems considering solar panels, wind turbines and battery energy storage for the charging of Plug-in Electric Vehicles (PEVs) in the Noida region. The main concern of this article is to establish the optimal sizing of system components in order to reduce energy costs and the possibility of power outages . To accomplish these goals, this research employs a unique metaheuristic-based optimization strategy known as the Giza Pyramid Construction technique (GPCA). The superiority of the solution provided by the GPCA is proven by comparing the results obtained using Grey Wolf Optimization (GWO), Flower Pollination Algorithm (FPA), Salp Swarm Algorithm (SSA) and Moth Flame Optimization (MFO). The algorithms used in the study are simulated 50 times with various values of loss of power supply probability (LPSP) such as 0 %, 1 %, 3 %, and 5 %. The simulation results show that the GPCA achieves the desired objectives with high accuracy and resilience. The study also examined how varying grid tariffs influenced the levelized cost of energy . The findings revealed that, when compared to other options, the solar/wind/battery combination had a significantly lower levelized cost of energy and overall net present cost. The total net present cost estimated by GPCA is lower by 7.9 %, 14.1 %, 17.9 %, and 24.5 % compared to the costs calculated using GWO, MFO, SSA, and FPA, respectively. Similarly, the GPCA provides optimized value of LCOE (0.3697 $/kWh) which is 3.1 %, 7.3 %, 8.8 % and 11.5 % less than GWO, MFO, SSA and FPA respectively. The outcomes of this research will provide valuable insights for researchers aiming to determine the most effective strategy for powering PEV charging through a multi-energy system approach. This information can be beneficial for other cities seeking to establish a similar strategy. The proposed system holds the potential to reduce dependence on overloaded grids, especially in developing cities, and aid researchers in identifying the optimal technique for optimizing an efficient energy system.