Comprehensive Analysis of Multi-Objective Optimization Algorithms for Sustainable Hybrid Electric Vehicle Charging Systems

Abstract

This study presents a multi-objective optimization approach for designing hybrid renewable energy systems for electric vehicle (EV) charging stations that considers both economic and reliability factors as well as seasonal variations in energy production and consumption. Four algorithms, MOPSO, NSGA-II, NSGA-III, and MOEA/D, were evaluated in terms of their convergence, diversity, efficiency, and robustness. Unlike previous studies that focused on single-objective optimization or ignored seasonal variations, our approach results in a more comprehensive and sustainable design for EV charging systems. The proposed system includes a 223-kW photovoltaic system, an 80-kW wind turbine, and seven Lithium-Ion battery banks, achieving a total net present cost of USD 564,846, a levelized cost of electricity of 0.2521 USD/kWh, and a loss of power supply probability of 1.21%. NSGA-II outperforms the other algorithms in terms of convergence and diversity, while NSGA-III is the most efficient, and MOEA/D has the highest robustness. The findings contribute to the development of efficient and reliable renewable energy systems for urban areas, emphasizing the importance of considering both economic and reliability factors in the design process. Our study represents a significant advance in the field of hybrid renewable energy systems for EV charging stations.

Optimal Sizing and Techno-Economic Analysis of an Off-Grid PV/Battery System for Fruit Farm Electrification

“…Increasingly employed for rural electrification in areas with restricted access to a reliable power supply, off-grid RE systems necessitate optimization to ensure efficient and sustainable electricity generation [12]- [14]. Past research has extensively examined the utilization of HOMER software for the simulation and economic analysis of off-grid systems.…”

A Hybrid Initialization and Effective Reproduction-Based Evolutionary Algorithm for Tackling Bi-Objective Large-Scale Feature Selection in Classification

“…Moreover, owing to their flexible architecture and versatile capability, MOEAs have also been applied into many real-world complex optimization problems [28][29][30][31][32] including discrete optimization problems such as network community detection problems [33], neural network search problems [34], task offload problems [35,36], and feature selection problems [37][38][39]. In particular, feature selection has been widely used as a data preprocessing and dimensionality reduction technique for tackling large-scale classification datasets by selecting only a subset of useful features [40].…”

A Multi-Task Decomposition-Based Evolutionary Algorithm for Tackling High-Dimensional Bi-Objective Feature Selection

“…There are also many other kinds of excellent MOEAs [27][28][29], including the novel multi-objective particle swarm optimization algorithm proposed by Leung et al [30], which adopted a hybrid global leader selection strategy with two leaders: one for exploration and the other for exploitation. Moreover, MOEAs have also been used to solve many real-world optimization problems [31][32][33], such as system control [34,35], community detection [36,37], network construction [38][39][40], task allocation [41,42], and feature selection [43,44]. Generally speaking, feature selection is normally used to select useful feature subsets for classification [45], while the bi-objective feature selection problem usually seeks to minimize both the classification error and the number of selected features [46].…”