Quantification of Supply and Demand and Distribution Robust Optimal Scheduling Considering New Energy Power System Flexibility

The increase in the penetration rate of new energy in the power grid has led to a serious lack of flexibility in the power system during certain periods. In order to solve the problem that the existing methods for dealing with power system flexibility and supply and demand uncertainty are too conservative or risky, a data-driven distributed robust optimization dispatching model is proposed. Firstly, considering the temporal and spatial correlation of wind and photovoltaic power output, an output set is constructed based on Copula theory. The flexibility demand of the power system is quantified by combining the scenario method and the interval method, and the flexible adjustment factor is introduced to characterize the ability of various resources to participate in flexibility adjustment, and the flexibility supply and demand balance constraint is established. Secondly, considering the flexible supply capacity of demand-side resources such as electric vehicles, a data-driven two-stage distribution robust model is established with the optimal flexibility resource operating cost and grid flexibility deficit penalty cost as the objective function. In order to reduce conservatism, the comprehensive norm is used to constrain its probability distribution, which reduces the probability of extreme situations in flexibility demand. In order to solve the two-stage robust model problem, the zero-sum game idea is used to decouple the model into the main problem and sub-problems, and the column and constraint generation algorithm is used for iterative solution. Finally, the simulation example shows that the proposed model has a positive effect on improving the flexibility margin and economy of the power system compared with the traditional uncertainty model.