Economic emission dispatch considering electric vehicles and wind power using enhanced multi-objective exchange market algorithm

Abstract

Due to the environmental concerns and the high penetration of wind power (WP) and electric vehicles (EVs), reducing the generation costs and emissions, coordinating EV charging in the distribution networks, and addressing the WP uncertainty, are among the most challenging issues in power system control and operation. Hence, the multi-objective dynamic economic emission dispatch problem (MDEEDP) integrated with EVs and wind farms has become one of the hottest topics in this research area. In this paper, the MDEEDP in the presence of EVs, the random behavior of EV drivers, and WP uncertainty is solved using a new multi-objective evolutionary algorithm called the enhanced multi-objective exchange market algorithm. In the proposed algorithm, the novel point-to-point distance technique is introduced to find the Pareto front solutions set (PFSS) with a uniform and diverse distribution. Also, smart strategy management for charging and discharging EVs is proposed to smooth the load curve. The effectiveness of the proposed method and performance of the proposed smart strategy are investigated on two single-objective and multi-objective test systems, including EVs and wind farms, considering uncertainty and system constraints such as ramp rate limits. The simulation results show that the PFSS of the MDEEDP is well extracted by the proposed method while maintaining its uniformity and diversity. The presence of EVs and the application of the proposed smart strategy lead to a reduction of 2,926,497 ($) in the operation costs for test system 1, and, 38,694,698.28 ($) and 7,832,876.64 (kg), respectively in the operation costs and emissions for test system 2, assuming the same load curve throughout the year. In addition, the load factor in test systems 1, and 2 is improved by 11 and 19 percent, respectively.