Robust dynamic tariff method for day-ahead congestion management of distribution networks

With the large-scale deployment of distributed energy resources (DERs) in distribution networks, network congestion could occur due to the non-coordinated operation of DERs. The dynamic tariff (DT) method as a decentralized day-ahead congestion management method has been widely studied. In the DT method, it is assumed that the distribution system operator (DSO) and aggregators use the same energy requirement parameters, which is impractical due to the DSO's forecast error. The discrepancy between the DSO's forecast parameters and aggregator's accurate parameters leads to a certain level of uncertainty that needs to be handled when employing the DT method. Therefore, a robust DT method is proposed for day-ahead congestion management while dealing with the uncertainty in the DT framework. A three-level robust DT model is formulated to obtain a robust DT solution, based on which the network constraints are respected even in the worst-case scenario. Moreover, due to the nonconvexity of the three-level robust DT model, the robust DT model is reformulated as a two-level optimization model, and a heuristic solution method is developed to obtain the robust DT solution with an iterative procedure. The Roy Billinton Test System (RBTS) was used to conduct case studies to validate the effectiveness of the proposed robust DT method for day-ahead congestion management in distribution networks. The case study results demonstrate that the deterministic DT method may be ineffective due to the DSO's forecast errors whereas the proposed robust DT method can resolve congestion efficiently under the uncertain condition.