Multiobjective Day-Ahead Scheduling of Reconfigurable-Based Microgrids Through Electric Vehicles and Demand Response Integration

Abstract

Nowadays, as the demand for plug-in electric vehicles in microgrids is growing, there are various challenges that the network must face, including providing adequate electricity, addressing environmental concerns, and rescheduling the microgrid. In order to overcome these challenges, this paper introduces a novel multiobjective optimization model where the first objective is to minimize the total operation cost of the microgrid and the second objective is to maximize the reliability index by reducing the amount of system energy not supplied. Because of these two compromising objectives, the evolutionary multiobjective seagull optimization algorithm is utilized to find the best local solutions. In this regard, integrated plug-in electric vehicles and demand response programs are used to smooth distribution locational marginal pricing. Furthermore, the effect of the system’s various configurations is analyzed in the suggested method to smooth the amount of distribution locational marginal prices in comparison to the initial case. Two case studies including modified IEEE 33-bus and 69-bus distribution networks are applied to evaluate the efficiency of the proposed approach.