Stochastic optimization approach to water management in cooling-constrained power plants

Abstract

A stochastic optimization framework for water management in cooling-constrained power plants is proposed. The approach determines optimal set-points to maximize power output in the presence of uncertain weather conditions and water intake constraints. Weather uncertainty is quantified in the form of ensembles using the state-of-the-art numerical weather prediction model WRF. The framework enables the handling of first-principles black-box simulation models by using the reweighting scheme implemented in the BONUS solver. In addition, it enables the construction of empirical distributions from limited samples obtained from WRF. Using these computational capabilities, the effects of cooling constraints and weather conditions on generation capacity are investigated. In a pulverized coal power plant study it has been found that weather fluctuations make the maximum plant output vary in the range of 5–10% of the nominal capacity in intraday operations. In addition, it has been found that stochastic optimization can lead to daily capacity gains of as much as 245MWh over current practice and enables more robust bidding procedures. It is demonstrated that reweighting schemes can enable real-time implementations.