Generation planning for electric power utilities under market uncertainties: a real options approach

The generation business in the U.S. is currently undergoing a transition from a regulated monopoly toward an uncertain, competitive market. Under the competitive market, the price of electric power as well as the corresponding revenue may be much less certain than before. These market uncertainties have increased the significance of two critical factors in generation planning. These factors are financial risks and managerial flexibilities. In order to quantitatively and objectively address these two factors in generation planning, in this dissertation, we design and analyze a series of mathematical models based on the real options approach for generation planning. Hence, this dissertation can be viewed as a comprehensive study of the real options approach in generation planning. The dissertation begins with a simple multiple-project single-option model based on the Black-Scholes option-pricing formula. This is followed by a single-project multipleoption model based on geometric Brownian motion process, binomial lattice, and backward dynamic programming. Next, we design and analyze sophisticated multiple-project multiple-option models where the market values of the projects are assumed to be correlated. As before, we employ the backward dynamic programming over the lattice to determine the optimal options for the multiple projects and the corresponding values of the investment. Also, we investigate the roles of the correlation coefficients among projects in decision making and the value of an option. In addition, we construct and analyze a traditional generation planning model that incorporates forced customer outage costs and forced utility outage costs. By incorporating