Optimal economic management of an electric vehicles aggregator by using a stochastic p-robust optimization technique

Abstract

Electricity market price is one of the significant uncertainties of the electricity market, which imposes a severe pack of challenges on electric vehicle aggregators (EVAs). Optimal scheduling of the EVA in day-ahead energy and reserve markets can be challenging due to the electricity market price. In this paper, we proposed a robust and optimal scheduling method for an electric vehicle aggregator (EVA) to establish robustness parameters that are needed for the day-ahead scheduling. This proposed method is one of the newest optimization approaches, which is called stochastic p-robust optimization technique (SPROT), in which the purpose is to maximize the expected profit of the EVA and to minimize the maximum relative regret (MRR) in the worst case. In this regard, this proposed technique is applied through SPROT-based form versus the stochastic optimization technique (SOT) one. This problem forms mixed-integer linear programming (MILP) model, which is solved in GAMS optimization software using CPLEX solver. As results indicated, there is a slow reduction of 3.9% in the expected profit of the EVA and a sharp falling of 46.91% of MRR in the SPROT-based model in comparison with the SOT-based model. Therefore, results reveal the robustness and effectiveness of the proposed SPROT-based model.