Distributionally Robust Dynamic Economic Dispatch With Energy Storage and Renewables

The integration of renewable generation which is uncertain and fluctuates over time challenges the economic dispatch of power systems. This paper proposes a distributionally robust dynamic programming framework to make economic dispatch decisions in a sequential manner. Compared to the stochastic DP that assumes the stage-wise independence of uncertainty and applies the sample average approximation, the proposed framework is improved from two main aspects: first, the temporal dependence of uncertain variable is exploited and the value functions in Bellman’s equation are taken conditional expectation; second, the inexactness of estimated conditional distribution is compensated by considering the worst distribution within an ambiguity set. A sampling-based algorithm with efficient samples is proposed to calculate the value functions. Case studies conducted on the modified IEEE 118-bus system verify the effectiveness of the proposed method.