Data-Driven DG Capacity Assessment Method for Active Distribution Networks

This paper proposes a data-driven method based on distributionally robust optimization to determine the maximum penetration level of distributed generation (DG) for active distribution networks. In our method, the uncertain DG outputs and load demands are formulated as stochastic variables following some ambiguous distributions. In addition to the given expectations and variances, the polyhedral uncertainty intervals are employed for the construction of the probability distribution set to restrict possible distributions. Then, we decide the optimal sizes and locations of DG to maximize the total DG hosting capacity under the worst-case probability distributions among this set. Since more information is utilized, our proposed model is expected to be less conservative than the robust optimization method and the traditional distributionally robust method. Using the CVaR (Conditional Value at Risk) reformulation technique and strong duality, we transform the proposed model into an equivalent bilinear matrix inequality problem, and a sequential convex optimization algorithm is applied for solution. Our model guarantees that the probability of security constraints being violated will not exceed a given risk threshold. Besides, the predefined risk level can be tuned to control the conservativeness of our model in a physically meaningful way. The effectiveness and robustness of this proposed method are demonstrated numerically on the two modified IEEE test systems.