Biblio

As a part of the global decarbonization agenda, the electrification of the transport sector involving the large-scale integration of electric vehicles (EV) constitues one of the key initiatives. However, the introduction of EV loads results in more variable electrical demand profiles and higher demand peaks, challenging power system balancing, voltage and network congestion management. In this paper, a novel optimal load scheduling approach for a coupled power and transportation network is proposed. It employs an EV charging demand forecasting model to generate the temporal-spatial distribution of the aggregate EV loads taking into account the uncertainties stemmed from the traffic condition. An AC optimal power flow (ACOPF) problem is formulated and solved to determine the scheduling decisions for the EVs, energy storage units as well as other types of flexible loads, taking into account their operational characteristics. Convex relaxation is performed to convert the original non-convex ACOPF problem to a second order conic program. Case studies demonstrate the effectiveness of the proposed scheduling strategy in accurately forecasting the EV load distribution as well as effectively alleviating the voltage deviation and network congestion in the distribution network through optimal load scheduling control decisions.