Self-scheduling of EV aggregators in Energy market based on Time-of-Use Pricing

This paper presents a new solution for the self-planning task of private EV aggregators to realize higher profits in distribution networks. Since the model is proposed from the EV aggregator's viewpoint, the corresponding effects of the aggregator's profit-seeking approach on the grid such as the power losses and voltage levels might be unknown or even negative. To address this issue, a commercial pricing scheme by the grid operator (DSO) based on time-of-use (TOU) program is proposed such that the aggregator plans for the charge and discharge task in a way to also improve the grid indices. Probability density functions are used to account for uncertainties related to the EV owners' behavior and the associated parameters. The proposed mechanism is implemented on a sample 33-bus test system with an EV aggregator employing genetic algorithm optimization. The results indicate that the proposed approach, beside maximizing the aggregator's profit, flattens the grid load curve leading to improved voltage profile and power loss indices.