Day-ahead transmission grid topology optimization considering renewable energy sources’ uncertainty

The increasing penetration of renewable energy sources (RES) introduces significant uncertainty in power system operations. At the same time, the existing transmission grid is often congested, and grid reinforcements are frequently delayed. To address these challenges, the transmission grid topology can be optimized as a non-costly remedy to enable a more efficient power transmission. Therefore, this paper proposes a multistep stochastic grid topology optimization model with busbar splitting for both AC and hybrid AC/DC grids. RES forecast uncertainty is represented via a scenario-based approach, using real offshore wind data and K-means clustering to generate representative forecast error scenarios. We then compare a plain optimal power flow (OPF) model with the proposed models which either optimizes the topology hourly, creates one optimal topology for a given time horizon (24 h), or allows a limited number of switching actions over a given time horizon. The optimization model is formulated as a mixed-integer quadratic convex problem, optimized based on the day-ahead (D-1) RES forecast and validated for AC-feasibility via a full nonlinear non-convex OPF formulation. Based on the generation setpoints of the feasibility check, a redispatch simulation based on the measured (D) RES realization is computed. The methodology is tested on a AC 30-bus test case and a hybrid AC/DC 50-bus test case, for a 24-hours (30-bus test case) and a 14-days (both test cases) time series. The results highlight the economic benefits brought by accounting for RES uncertainty by including 6 to 8 scenarios in day-ahead grid topology optimization models, compared to only a single deterministic one. We show how the proposed approach leads to lower (up to 1.55%) or comparable total (generation and redispatch) costs with respect to deterministic day-ahead forecasts, even when limiting the frequency of topological actions.