Optimal vehicle-to-grid and grid-to-vehicle scheduling strategy with uncertainty management using improved marine predator algorithm. (May 2022) - R, Sowmya; Sankaranarayanan, V.
Highlights: Proposed work schedules EV in both V2G and G2V modes by considering dynamic electricity pricing. The optimization model consists of mandatory and user-preferable constraints, such as SoC limits, c-rating/charging profiles, etc. that maintain system robustness. Uncertainty management due to dynamic electricity pricing and battery performance is addressed. A new improved marine predator algorithm is proposed and compared with the state-of-art algorithms. Statistical metrics, Friedman's ranking test, and runtime are utilized to assess the performance of the IMPA. Abstract: This paper presents a new solution to the problem of scheduling electric vehicles over a period of time. The primary objective is to minimize the total cost of the electricity price for charging/discharging by considering the battery capacity, C-rating, and other physical constraints. The battery life is improved by minimizing the charging/discharging cycles based on the minimum allowable State-of-Charge (SoC) deviation limit. A new Improved Marine Predator Algorithm (IMPA) is proposed by employing an opposition-based learning scheme to a recent marine predator algorithm to solve the proposed charge/discharge scheduling model, and the performance is compared with other state-of-the-art algorithms. Uncertainties in the end SoC mismatch and electricity price are considered to reiterate the scheduling plan. The statistical test results show that the proposed IMPA is better among other algorithms with the rank value of 2.33 under normal conditions and 1.667 with uncertainties at different intervals. Graphical abstract: Image, graphical abstract