A Two Stage Adjustable Robust Optimization Strategy for Active Distribution Networks

With the increasing energy crisis and environmental pollution, renewable energy sources, such as wind power and photovoltaics, are widely utilized in distribution networks. Moreover, to achieve the "Dual Carbon" goal as soon as possible, active distribution networks are gradually becoming the main development direction of distribution networks. However, the natural strong uncertainty of wind and solar power poses great challenges to the economic operation of active distribution networks. Therefore, this article presents a two-stage adjustable robust optimization model for active distribution networks, aimed at achieving economic dispatch of active distribution networks under the uncertain wind and solar prediction error. Firstly, in the day-ahead stage, assuming no error in wind and solar forecast, the initial day-ahead scheduling strategy is obtained. Subsequently, if there is a deviation between the actual output of the renewable energy sources and the previous predicted value, intraday adjustments will be made in the second stage. For the convenience of solving, the column constraint generation algorithm is utilized to decompose the complex min-max-min model, and the model is linearized by dual theory, Big-M method, and second-order cone relaxation method. The calculation results show that the method proposed in this article can effectively reduce system operating costs, improve wind and solar energy consumption rate, and achieve a reasonable balance between robustness and economy.