Two-Stage Distributional Robust Optimization for the Expansion Planning of Photovoltaic Cluster

With the deepening of the two-carbon target, the penetration rate of renewable energy is increasing year by year, and its consumption problem has attracted much attention. Distributed renewable energy cluster is a new mode of accommodating renewable energy. It is necessary to consider the influence of source-load uncertainty in planning and operation. In this paper, based on the new photovoltaic grid-connected planning of distributed photovoltaic cluster, considering the uncertainty of source and load, a distributed photovoltaic cluster expansion planning method based on two-stage robust optimization was proposed. Considering the difference between the planning stage and the operation stage, it established a two-stage distributed robust optimization model, which takes the minimum annual equivalent cost as the objective and considers the unit output constraint and the power grid carrying capacity. In order to improve the computational efficiency, the historical data of regional distributed renewable energy and random load were reduced and modified by combining K-means clustering with extreme scenario method. Based on the modified scenario set, a probability distribution fuzzy set based on Wasserstein distance was constructed. The column and constraint generation algorithm was used to decompose the two-stage distributed robust optimization model into the main problem and the sub-problem. The main problem and the sub-problem were solved by iteration, which further improves the efficiency of the solution. In order to solve the sub-problem, Lagrange duality was introduced to transform the sub-problem into a deterministic optimization problem. Finally, a distributed photovoltaic cluster was taken as an example to carry out an example analysis. The results show that the proposed two-stage distributional robust optimization method for distributed photovoltaic clusters can coordinate the economy and robustness of the planning operation scheme. Model control parameters can be flexibly adjusted according to the size and reliability of historical scene sets to meet the different requirements of reliability and economy in various engineering application scenarios. © 2024 South China University of Technology. All rights reserved.