Transactive-Based Day-Ahead Electric Vehicles Charging Scheduling
Yahya Kabiri‐Renani, Ali Arjomandi‐Nezhad, Mahmud Fotuhi‐Firuzabad, Mohammad Shahidehpour
Abstract
In this paper, a transactive-based scheduling approach is proposed to optimize EVs charging/discharging scheduling taking into account technical requirements of EVs with different State-Of-Charge (SOC) levels and EV owners’ preferences. In the proposed approach, EV aggregator (EVA) solves an optimization problem to determine the charging/discharging schedule of each individual EV in the EV Parking Lot (PL) in which the response curves of individual EVs are used to consider the EV owners’ charging/discharging preferences. Then, the EVAs provide their optimum day-ahead bids to the corresponding DSO based on calculated Distribution Locational Marginal Prices (DLMPs). The DSO’s transactive market-clearing procedure is simulated to iteratively calculate DLMPs in the local distribution area (LDA) nodes. The Monte Carlo (MC) scenarios are used to model the uncertainties associated with the EVs’ parameters and the driving behavior of the EV owners. Also, the robust optimization method is used to model the uncertainties associated with LMPs of the Transmission Network (TN) bus, Distributed Renewable Energy Resources (DRERs), and load demand. The proposed model is implemented on the modified IEEE-33 node distribution system and effectiveness of the model is investigated and presented.