Bi-level planning and scheduling of electric vehicle charging stations for peak shaving and congestion management in low voltage distribution networks

Abstract

Most existing algorithms for electric vehicle charging station (EVCS) planning and scheduling of electric vehicles (EVs) do not consider the uncertain behavior of EVs and distribution networks, including the EV arrival and departure times, user driving needs, and realistic residential load and generation profiles. This paper proposes a bi-level planning and operation framework that determines the optimal locations of EVCS and scheduling of EVs to reduce peak load and network congestion in low voltage distribution networks by considering uncertain EV and distribution network behavior. Planning of EVCS is performed to minimize voltage deviations and distribution network losses. The priority-based scheduling algorithm uses the vehicle-to-grid (V2G) and grid-to-vehicle (G2V) technology to charge the EVs during high priority periods to meet the user requirements and discharge during low or medium priority periods to reduce the peak load and thermal overloading in distribution feeders. Based on an Australian scenario, the proposed method has been tested on the IEEE low voltage test feeder. Uncoordinated scheduling of EVs is also implemented and compared with the proposed strategy. The results show that installing EVCS at the optimal locations can reduce the network losses and voltage deviations by 39.38 per cent and 15.32 per cent, respectively. It is also shown that the proposed scheduling of EVs can reduce the peak load of distribution substations and lower the network congestion without affecting the EV charging requirements. With a 55 percent penetration of EVs, the peak load and thermal overload in distribution lines are reduced by 20.53 per cent and 5.88 per cent, respectively.