This paper proposes a distance-based distributionally robust energy and reserve (DBDRER)\ndispatch model via Kullbackâ??Leibler (KL) divergence, considering the volatile of renewable\nenergy generation. Firstly, a two-stage optimization model is formulated to minimize the expected\ntotal cost of energy and reserve (ER) dispatch. Then, KL divergence is adopted to establish the\nambiguity set. Distinguished from conventional robust optimization methodology, the volatile\noutput of renewable power generation is assumed to follow the unknown probability distribution\nthat is restricted in the ambiguity set. DB-DRER aims at minimizing the expected total cost in the\nworst-case probability distributions of renewables. Combining with the designed empirical\ndistribution function, the proposed DB-DRER model can be reformulated into a mixed integer\nnonlinear programming (MINLP) problem. Furthermore, using the generalized Benders\ndecomposition, a decomposition method is proposed and sample average approximation (SAA)\nmethod is applied to solve this problem. Finally, simulation result of the proposed method is\ncompared with those of stochastic optimization and conventional robust optimization methods on\nthe 6-bus system and IEEE 118-bus system, which demonstrates the effectiveness and advantages\nof the method proposed.
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