Determining maximum hosting capacity for PV systems in distribution grids. (February 2022) - Yuan, Jingyi; Weng, Yang; Tan, Chin-Woo
Highlights: PV hosting capacity determination of distribution systems is important for planning. Existing methods suffer from lack of theoretical guarantees, time inefficiency, and non-convex problem model. We provide a bottom-up analysis, generalizing the geometric intuition to find optimal hosting capacity. We adapt the analytical solution with respect to different operational constraints. We use parallel computation to speed up hosting capacity calculation for large system. Abstract: Power systems face increasing challenges on reliable operations due to the widespread distributed generators (DGs), e.g., rooftop PV systems in distribution grids. Characterizing the hosting capacity (HC) is vital for utilities to assess the total amount of distributed generations that a grid can deploy efficiently before upgrading. Some methods conduct extensive simulations for analyzing HC, which lack theoretical guarantees and can be time-consuming. Therefore, there are also methods employing optimization over all necessary operation constraints. However, the complexity and inherent non-convexity lead to non-optimal solutions. To solve the problems, this paper provides a constructive model for HC determination. Based on geometrical understanding, HC solutions are constructed sequentially according to realistic constraints so that the optimal solution can be reserved even with a non-convex model. For practical adaption, the three-phase unbalance condition is considered, and parallel computation is developed for speed-up. The method is validated by extensive numerical results on IEEE standard systems, such as 8-bus and 123-bus radial distribution grids. In comparison, the proposed approach obtains analytical hosting capacity solutions of larger values and uses less computation time. The results of HC verify that the proposed method reserves optimality and thus has good performance in complex scenarios, including more operational constraints, different PV deployment, and larger system size.