Robust Allocation and Scheduling of Electric Parkings and Wind Resources in Distribution Networks Using Information Gap Decision Theory and Improved Flow Direction Algorithm

Abstract

This paper proposes a robust scheduling approach for electric parking lots (EPLs) integrated with battery storage and wind power sources in distribution networks, aiming to minimize the cost-to-revenue function. The method is based on information gap decision theory with a risk aversion strategy (IGDT-RAS) and takes into account uncertainties in network load and wind power. In deterministic scheduling, decision variables include the location and capacity of the EPLs and wind resources in the network, while in robust scheduling, the maximum uncertainty radius (UR) is determined using an improved flow direction optimization algorithm (IFDA), enhanced by an opposition learning strategy (OLS). The proposed method is applied to the 33- and 45-bus networks. The deterministic approach results in a lower cost-to-revenue ratio, reduced energy losses, and improved reliability compared to traditional FDA, whale optimization algorithm (WOA), and particle swarm optimizer. In robust scheduling, for the 33-bus network, the largest UR for load and wind power is 8.70% and 17.06%, respectively, while for the 45-bus network, it is 8.45% and 32.36%, respectively. The robustness of the network against the worst-case uncertainty scenario is demonstrated in the robust scheduling, and the superior performance of IGDT-RAS over Monte Carlo simulation (MCS) is confirmed in achieving a reliable cost level.