A Robust Aggregate Model of Virtual Power Plant Considering Uncertainty and Acceleration Strategies

Virtual power plants can help active distribution networks fully utilize and efficiently manage large amounts of distributed energy resources by calculating the aggregate flexibility of VPPs at the point of common coupling, which is usually described as a two-stage robust optimization problem. Column and constraint generation algorithm has become a mature method for solving two-stage robust optimization problem, which decomposes the original problem into master problem and sub-problem. However, solving sub-problem is usually time-consuming. In order to improve the solving efficiency of sub-problem, two acceleration strategies are proposed in this paper. Firstly, we linearize the bilinear term in the SP objective function using a binary expansion. Then, an extreme scenarios method is adopted to deal with the randomness of renewables, which allows sub-problem to be solved in parallel and reduces unnecessary conservativeness. In addition, a max-min model is constructed to obtain an upper bound of the actual operating cost of virtual power plants. The cost functions of virtual power plants can be expressed as a group of piecewise-linear functions based on convex hull. Finally, the correctness and effectiveness of the proposed method are verified by numerical test results of three different distribution systems.