Data-driven distributionally robust management of distribution systems

Due to the remarkable share of renewable energy sources (RESs) in local energy systems, it is crucial to develop new approaches to manage these local resources efficiently and economically. Therefore, we have presented a data-driven distributionally robust optimization (DRO) approach for the centralized operation of a multi-MG distribution network with taking the operational constraints of the network into account. Furthermore, the Wasserstein metric (WM) has been employed to create the ambiguity set containing all of the uncertain probability distributions (PDs) to handle the uncertainty of the RESs using the DRO model. As a result, only historical sample sets are needed without any earlier information about the true PDs. In addition, to ensure computational tractability, equivalent linear programming reformulations of the DRO approach are obtained. At last, the proposed scheme is applied to the IEEE ۳۷-bus distribution system consisting of solar sources, distributed generation units, load demands, and battery energy storage systems, to examine its effectiveness in the centralized energy operation of multi-MG systems. Moreover, a comparison between the results of the proposed DRO model and the stochastic programming (SP) method has been made to determine their performance differences in handling the uncertainties.

Keywords:

renewable energy , Distributionally robust optimization , centralized , management , uncertainty , stochastic programming.