Biomass electricity plant allocation through non-linear modeling and mixed integer optimization

Electricity generation from the combustion of biomass feedstocks provides low-carbon energy that is not as geographically constricted as other renewable technologies. This dissertation uses non-linear programming to provide policymakers with scenarios of possible sources of biomass for power generation as well as locations and types of electricity generation facilities utilizing biomass, consistent with a set of policy, technology and economic assumptions. The scenarios are obtained by combining the output from existing agricultural optimization models with a non-linear mathematical program that calculates the least-cost ways of meeting an assumed biomass electricity standard. The non-linear program considers region-specific cultivation and transportation costs of biomass fuels as well as the costs of building and operating both coal plants capable of co-firing biomass and new dedicated biomass combustion power plants. The results of the model provide geographically-detailed power plant allocation patterns that minimize the total cost of meeting the generation requirements, which are varying proportions of total U.S. electric power generation, under the assumptions made. The amount of each cost component comprising the objective functions of the various requirements are discussed in the results chapter for all cases, and the results show that approximately two-thirds of the total cost of meeting a biomass electricity standard occurs on the farms and forests that produce the biomass. Plant capital costs and biomass transportation costs comprise the largest share of the remaining costs. A conclusion section that places the findings in a policy-setting context follows the results. The most important policy conclusion is that biomass use in power plants will require significant subsidies, perhaps as much as half of their cost, if they are to achieve significant penetrations in U.S. electricity markets.