Abstract

In recent years the expansion of energy supplies from volatile renewable sources has triggered an increased interest in stochastic optimization models for generation unit commitment. Solving this problem directly is computationally intractable for large instances. In this talk we outline how a Dantzig-Wolfe reformulation can be used to decompose multistage stochastic unit commitment problems by scenarios. We develop a dually stabilized column generation framework which can handle convex quadratic and piecewise linear generation costs and is capable of solving stochastic unit commitment problems to optimality. We use a dual initialization procedure to hot start our method. Numerical results are given to illustrate that convergence can be achieved within a few iterations of our method.