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WKS‐type distributionally robust optimisation for optimal sub‐hourly look‐ahead economic dispatch

Renewable energy sources, particularly wind power, are being increasingly deployed in power systems to reduce environmental contamination. However, the uncertainty of wind power significantly influences power system economy. Therefore, this study presents Wiesemann–Kuhn–Sim (WKS)-type distributionally robust optimisation for performing an optimal two-stage sub-hourly look-ahead economic dispatch considering uncertain wind power. The dispatch considers the scheduling of thermal generators, wind power generators, and fast-response resources. The optimisation aims at minimising the expected total operational cost, including the costs of generation, generation shedding, and wind power curtailment. The hourly stage determines the thermal generation that can withstand the worst-case wind power distribution. The sub-hourly stage schedules the fast-response operations to correct the hourly dispatch. To characterise the uncertainty, a novel WKS-format ambiguity set that can obtain wind power distribution from historical data is constructed using the lifting theorem. Based on the ambiguity set and linear decision rule, a general lemma is employed to convert the model into a tractable conic optimisation that can be directly solved. Experimental results demonstrate the effectiveness of the presented approach compared to adjustable robust optimisation and sample-based stochastic optimisation.