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Stochastic unit commitment models typically handle uncertainties in forecastdemand by considering a finite number of realizations from a stochastic processmodel for loads. Accurate evaluations of expectations or higher moments for thequantities of interest require a prohibitively large number of modelevaluations. In this paper we propose an alternative approach based on usingsurrogate models valid over the range of the forecast uncertainty. We considersurrogate models based on Polynomial Chaos expansions, constructed using sparsequadrature methods. Considering expected generation cost, we demonstrate theapproach can lead to several orders of magnitude reduction in computationalcost relative to using Monte Carlo sampling on the original model, for a giventarget error threshold.

Toward Using Surrogates to Accelerate Solution of Stochastic Electricity Grid Operations Problems