Distributionally Robust Optimal DG Allocation Model Considering Flexible Adjustment of Demand Response

The development of wind power and photovoltaic is mainly restricted by wind and solar curtailment. Demand response (DR) is an effective measure to improve the consumption of clean energy for distribution system. Traditionally, however, it has great limitations on the method of dealing with the uncertainty associated with distributed generation (DG) output or DR. In this paper, therefore, a two-stage distributionally robust optimal distributed generation allocation model considering DR flexible adjustment is formulated. The paper is aimed to maximize the annual profit of distribution system operator (DSO) considering various investment and operational constraints. In order to improve the consumption of clean energy for distribution system, the author regards the difference between clean energy output and load as the pricing basis of DR based on real-time price (RTP). Moreover, taking advantage of the historical data of DR and DG output, a data-driven two-stage distributionally robust optimal DG allocation model is presented. The first stage determines the investment plan of DG, and in the second stage, the system operation stage after investing DG, the norm-1 and norm-$\infty$ of the uncertainty probability distribution confidence set are incorporated. Finally, the proposed model is simulated on the IEEE33-node system and solved by column and constraint generation (CCG) algorithm. Numerical results demonstrate the effectiveness of the proposed model.