In recent years the expansion of renewable energy supplies has triggered an increased interest in stochastic optimization models for generation unit commitment. Solving this problem via Branch & Cut is computationally intractable for large instances. In this talk I will describe a stabilised Dantzig-Wolfe scenario decomposition algorithm for this problem class. Test results on a central scheduling model of the British power system confirm that optimality gaps as low as 0.1% can be achieved quickly with this method. Additionally, I will report results of a two-year evaluation of stochastic and deterministic rolling horizon scheduling and discuss the added value of stochastic planning.
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