Date of Award
Master of Science
Industrial and Manufacturing Systems Engineering
Sarah M. Ryan
The research is motivated by the need for economic efficiency and risk management in the national electric system. Stochastic costs of natural gas are introduced in a generalized network flow model of the integrated power energy system to explore the effects of uncertain fuel costs on the optimal energy flows in U.S. The fuel costs are modeled as discretely distributed random variables and a rolling two-stage approach is applied to solve the stochastic recourse problem. All the data are derived from publicly available information for the year 2002. The natural gas price forecasts by the Energy Information Administration are adapted to generate scenarios that are considered in the stochastic problem. Compared to the expected value solution from the deterministic model, the recourse problem solution obtained from the stochastic model has higher total cost, lower natural gas consumption and less subregional power trade but a flow mix which is closer to the 2002 real data. Surprisingly, increasing the uncertainty level of the scenarios leads to a recourse problem solution with slightly lower total cost but this effect may be distributed to the inaccuracy of the forecasts. The comparison demonstrates the stochastic model's capability of forecasting energy flows. The stochastic model assists decision makers to better understand how the uncertain fuel costs would affect future flows within the national electric energy system.
Digital Repository @ Iowa State University, http://lib.dr.iastate.edu/
Wang, Yan, "Effects of fuel cost uncertainty on optimal energy flows in U.S." (2007). Retrospective Theses and Dissertations. 14822.