ABSTRACT Coordination between transmission system operators (TSOs) and distribution system operators (DSOs) can support TSOs in using the distribution system (DS) flexibility while ensuring feasible operation. Flexibility areas (FAs) can support TSO‐DSO coordination, aggregating the total feasible flexibility within the DS. However, existing real‐time estimation approaches do not consider the limited measurements within DS. This paper proposes a Bayesian neural network (BNN) to estimate the operating conditions that bound the operational flexibility, including epistemic and aleatoric uncertainties. These uncertainties stem from the limited real‐time measurements in DSs and the measurement noise. TSOs can select a threshold that confirms a probability of safety, considering uncertainty margins. The paper also provides FA estimation in DS topologies with points of common coupling (PCC) with the transmission system. Case studies in the CIGRE and Oberrhein networks compare the proposed BNNs to baseline statistic‐based approaches for forecast and measurement uncertainty in FAs. The case studies show the proposed FA estimation under various safety margins and systems with 2‐PCC. Case studies also assess various measurement noise levels and evaluate model performance for different DS topologies.
Chrysostomou et al. (Thu,) studied this question.