Optimization models to integrate production and transportation planning for biomass co-firing in coal-fired power plants

Abstract

Co-firing biomass is a strategy that leads to reduced greenhouse gas emissions in coal-fired power plants. Incentives such as the Production Tax Credit (PTC) are designed to help power plants overcome the financial challenges faced during the implementation phase. Decision makers at power plants face two big challenges. The first challenge is identifying whether the benefits from incentives such as PTC can overcome the costs associated with co-firing. The second challenge is identifying the extent to which a plant should co-fire in order to maximize profits. We present a novel mathematical model that integrates production and transportation decisions at power plants. Such a model enables decision makers to evaluate the impacts of co-firing on the system performance and the cost of generating renewable electricity. The model presented is a nonlinear mixed integer program that captures the loss in process efficiencies due to using biomass, a product that has lower heating value as compared with coal; the additional investment costs necessary to support biomass co-firing as well as savings due to PTC. In order to solve efficiently real-life instances of this problem we present a Lagrangean relaxation model that provides upper bounds and two linear approximations that provide lower bounds for the problem in hand. We use numerical analysis to evaluate the quality of these bounds. We develop a case study using data from nine states located in the southeast region of the United States. Via numerical experiments we observe that (i) incentives such as PTC do facilitate renewable energy production; (ii) the PTC should not be “one size fits all”; instead, tax credits could be a function of plant capacity or the amount of renewable electricity produced; (iii) there is a need for comprehensive tax credit schemes to encourage renewable electricity production and reduce GHG emissions.