Risk-based dynamic pricing by metaheuristic optimization approach for electric vehicle charging infrastructure powered by grid integrated microgrid system

This paper presents a risk-based dynamic pricing strategy for the microgrid based electric vehicle charging station. The microgrid is composed of renewable resources such as solar energy, wind energy and fuel cells. The proposed microgrid-based charging station operates off-grid and on-grid depending on the charging demand and trades the electricity with the main grid when needed. In this work, to increase the reliability and to decrease the detrimental impact of electric vehicle load on the distribution network electric vehicles act as a source of flexibility through grid to vehicle and vehicle to grid provisions. The uncertainty of solar and wind sources is a key issue that is efficiently addressed in this work along with one of the electric vehicles load uncertain factors such as state of charge variation using a probabilistic method. Two types of competent time-based dynamic pricing strategies are employed in this work to enhance the collective profit and to curtail the peak-to-valley ratio of charging station and electricity purchase cost of the electric vehicle owner. To deal with the uncertainty in the electricity market and its associated risk, the average value-at-risk is applied. The proposed multi-objective problem is tested on an IEEE 33 bus-modified system using MATLAB software. A metaheuristic fire hawk optimization technique is employed to implement dynamic pricing on charging station for two cases. To observe the efficiency of the applied algorithm, the findings of this work are compared with the other techniques described in the literature.