Abstract
The pervasiveness of electric vehicles (EVs) has increased recently, which results in the interdependence of power and transport networks. Power outages may adversely impact the transportation sector, and the available energy may not be sufficient to meet the needs of all EVs during such events. In addition, EVs will be used for diverse purposes in the future, ranging from personal usage to emergency response. Therefore, the allocation of energy to different EVs may have different degrees of societal-, community-, and individual-level benefits. To capture these diverse aspects, the energy allocation problem to EVs during outages is modeled as a multiobjective optimization (MOO) problem in this study. Three indices are formulated to quantify the value of different EVs for societies, communities, and individuals during outages, and, correspondingly, three objective functions are formulated. The formulated MOO problem is solved using the five most widely used MOO solution methods, and their performance is evaluated. These methods include the weighted-sum method, lexicographic method, normal boundary intersection method, min–max method, and nondominated sorting genetic algorithm II. To compare the performance of these methods, two indices are proposed in this study, which include the demand fulfillment index and total demand fulfillment index. The former is for analyzing the demand fulfillment ratio of different priority EVs, while the latter is for the demand fulfillment analysis of the whole EV fleet requiring a recharge. In addition, the computational complexity, variance, and additional constraints required by each method are also analyzed. The simulation results have shown that the lexicographic method has the best performance when the relative priorities are known, while the min–max method is the most suitable method if the priorities are not known.
Highlights
Multi-objective optimization (MOO) is generally used to solve problems involving two or more conflicting objectives, and most of the problems in the real world are multiobjective in nature [1]
A multi-objective optimization problem was developed for allocating energy to electric vehicles during power outages
Three objective functions were developed based on three indices to quantify the benefits of different electric vehicles for societies, communities, and individuals
Summary
Multi-objective optimization (MOO) is generally used to solve problems involving two or more conflicting objectives, and most of the problems in the real world are multiobjective in nature [1]. In contrast to single-objective optimization problems, a single solution generally cannot minimize/maximize all the objective functions simultaneously in MOO problems [2]. A solution is known as Pareto optimal if none of the objective functions can be improved without deteriorating other objective functions. All Pareto optimal solutions are considered good if no preference information is provided [3]. Due to the ability to provide acceptable solutions for various complex real-world problems, MOO is used in several fields, including power systems and microgrids [4,5,6,7,8]
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