Risk-Averse Two-Stage Distributionally Robust Economic Dispatch Model Under Uncertain Renewable Energy

With the participation of large quantities of renewable energy in power system operations, their volatility and intermittence increases the difficulties and challenges of power system economic scheduling. Considering the uncertainty of renewable energy generation, based on the distributionally robust optimization method, a two-stage economic dispatch model is proposed to minimize the total operation costs. In this paper, it is assumed that the fluctuating of renewable power generation follows the unknown probability distribution that is restricted in an ambiguity set, which is established by utilizing the first-order moment information of available historical data. Furthermore, the theory of conditional value-at-risk is introduced to transform the model into a tractable model, which we call robust counterpart formulation. Based on the stochastic dual dynamic programming method, an improved iterative algorithm is proposed to solve the robust counterpart problem. Specifically, the convergence optimum can be obtained by the improved iterative algorithm, which performs a forward pass and backward pass repeatedly in each iterative process. Finally, by comparing with other methods, the results on the modified IEEE 6-bus, 118-bus, and 300-bus system show the effectiveness and advantages of the proposed model and method.