Real-TimeWater and Energy Management In Power Plants and Implications In Electricity Markets

We propose a real-time optimization framework to integrate water and energy management
in power plants. The objective of the framework is to construct optimal bidding curves (power
output vs. price) using a first-principles model of the power plant and of the cooling towers
under uncertain weather conditions. The weather uncertainty is quantified in the form of ensembles
using the state-of-the-art numerical weather prediction model WRF running at Argonne
National Laboratory. The ensembles are used for real-time stochastic optimization performed by
the BONUS algorithm and coupled to a steady-state power plant model implemented as a CAPEOPEN
compliant capability. We analyze the effects of cooling capacity constraints and weather
forecasts on the market participation of power plants. We use a pulverized coal power plant case
study to demonstrate the potential of the framework.