Data-driven static voltage chance constrained security region modeling and application for MV DNs with high PV penetration

In the optimal power flow problem of medium voltage distribution networks with high penetration of renewable energy, it is challenging to achieve efficient voltage management with incomplete network parameters and uncertainties, while also maintaining computational efficiency. To this end, this paper proposes a nonlinear adaptive data-driven method for constructing a static voltage security region model for medium voltage distribution networks. In the nodal power injection space, the linear hyperplane expression for the static voltage security region is derived through a data-driven power flow model, achieving the visualization of the static voltage security region in scenarios where network parameters are incomplete. By further considering the uncertainty in nodal power injections and utilizing the adjustments of controllable node power as variables, we convert the nodal voltage chance constraints into a simple linear combination of nodal power injections based on the static voltage security region. This approach simplifies the handling of the impacts caused by uncertainties in nodal power injections and reduces the computational burden of probabilistic safety analysis. Finally, the case analysis demonstrates that the maximum boundary error of the proposed method is only 0.83 % compared to the static voltage security region constructed with accurate parameters of the medium voltage distribution networks, confirming that the proposed method achieves high computational accuracy and solving efficiency.