Multi-objective planning of micro-grid system considering renewable energy and hydrogen storage systems with demand response

Abstract

Recently, researchers in the power system industry have considered the intelligent parking lot idea. The intelligent parking lots (IPL) try to exchange power with the upstream by electric vehicles (EVs) charge/discharge application. The mentioned process of charge/discharge can provide a solution model of regular problems in this field, i.e., peak time problems. Additionally, such vehicles' existence can play an essential role in environmental performance enhancement in power systems. The primary purpose of this paper is to consider such enhancements of EVs in power systems and environmental performance. For this purpose, a multi-objective optimization approach is suggested for the environmental performance and economical operation of IPL by considering the time-of-use rates of a demand response program. Since such a model is associated with various practical bounds, the multi-objective grasshopper optimization algorithm is suggested to solve this problem. The outcomes indicate the effectiveness of the compared techniques utilizing fuzzy decision-making strategies. Chaos theory is employed to increase this method and progress searching operators. Additionally, the proposed multi-objective technique is a model created using fuzzy theory, variable detection, strategy selection-based memory, and non-dominated sorting theory to choose the best Pareto from a list of solutions with robust functionality for resolving the problems mentioned above. The proposed technique is tested on a sample system that includes an intelligent parking lot, local production facilities, and non-renewable and renewable production systems under the proposed uncertainty strategy of upstream grid pricing. The study showed the effectiveness of the suggested method. The simulation findings indicate that the emission rate and overall cost of intelligent parking lots are reduced by 3.87 and 2%. It indicates that economic and environmental objectives are also satisfied due to the successful deployment of a demand response program.