Stochastic Operation of Multi-Terminal Soft Open Points in Distribution Networks With Distributionally Robust Chance-Constrained Optimization

To achieve flexible operation of modern distribution networks with increasing renewable energy sources (RESs), this study proposes a stochastic operation method for a novel energy management platform that combines energy storage with a power flow controller, which is presented as a multi-terminal soft open point (MT-SOP). A Wasserstein metric-based distributionally robust chance-constrained optimization for the operation of the MT-SOP is proposed under the assumption that there is no prior knowledge about the distribution of forecast errors for both the load and RES outputs. The proposed method stochastically relaxes the reliability constraints on the bus voltage and branch power flow to maximize the cost-benefit of the distribution system operator (DSO). The proposed problem was reformulated into a tractable convex program using the concept of conditional value-at-risk. Based on case studies using modified IEEE-33 systems, the trade-offs between operational risks and cost–benefits were analyzed using parameters on the risk level and radius of the ambiguity set. Through numerical comparisons of the simulation results, we show that the DSO can achieve economic efficiency for imported electricity by relaxing conservativeness in distribution network reliability.