April 28, 2024
Ali Ahmadian

Ali Ahmadian

Academic rank: Associate professor
Address: Bonab-University of bonab
Education: Ph.D in electrical engineering
Phone: 04137745000
Faculty: Faculty of Engineering
Department: Electrical Engineering

Research

Title
The CVaR-based risk assessment of the electric vehicle's intelligent parking lots with energy storage devices including equalizers and fuel cell
Type Article
Keywords
Stochastic, optimization, conditional value-at-risk, scenario, vehicle to grid, grid to vehicle
Researchers Ramin Nourollahi، Nima Dolati، Ali Ahmadian

Abstract

The interest in utilizations of electric vehicles (EVs) causes to increase the penetration of intelligent parking lots (IPL). Also, vehicle-to-grid (V2G) technology next to grid-to-vehicle (G2V) technology improves the profitability of the IPLs. In addition to vehicle charge and discharge management, the IPLs can gain more profit by installing and accessing loads and resources. In this paper, the IPLs next to the hydrogen storage system (HSS) containing electrolysis, fuel cell and hydrogen storage tanks are considered to serve the loads in the existence of the upstream grid that is modeled through a scenario approach based on stochastic optimization. The uncertainties related to the electrical load, market price, vehicle's arrival and departure time, initial State-of-charge (SOC), and desired SOC of the car are considered in the proposed model. Besides, the financial risks related to the proposed hydrogen storage-based intelligent parking lot's uncertain parameters are modeled by the conditional value-at-risk (CVaR) method to get the risk-averse and risk-neutral strategies during the system operation horizon. The obtained results demonstrate that the uncertainties have a significant impact on smart parking operators' profitability, so considering uncertainty is a critical issue for parking lots. Risk results also represent that the variation of financial risk in the higher deviation of uncertain parameters is more than risk variation in lower deviations.