Optimization Models to Integrate Production and Transportation Planning for Biomass Co-Firing in Coal-Fired Power Plants

Co-firing biomass is a strategy that leads to reduced greenhouse gas emissions in coal-fired power plants. Incentives such as 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 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 which captures the loss in process efficiencies due to using biomass, a product which has lower heating value as compared to 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 which provide upper bounds and two linear approximations which 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 USA. Via numerical experiments we observe that: (a) Incentives such as PTC do facilitate renewable energy production. (b) 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. (c) There is a need for comprehensive tax credit schemes to encourage renewable electricity production and reduce GHG emissions

A multi-objective, hub-and-spoke model to design and manage biofuel supply chains

In this paper we propose a multi-objective, mixed integer linear programming model to design and manage the supply chain for biofuels. This model captures the trade-offs that exist between costs, environmental and social impacts of delivering biofuels. The in-bound supply chain for biofuel plants relies on a hub-and-spoke structure which optimizes transportation costs of biomass. The model proposed optimizes the CO2 style= position: relative; tabindex= 0 id= MathJax-Element-1-Frame \u3eCO2 emissions due to transportation-related activities in the supply chain. The model also optimizes the social impact of biofuels. The social impacts are evaluated by the number of jobs created. The multi-objective optimization model is solved using an augmented ϵ style= position: relative; tabindex= 0 id= MathJax-Element-2-Frame \u3eϵ-constraint method. The method provides a set of Pareto optimal solutions. We develop a case study using data from the Midwest region of the USA. The numerical analyses estimates the quantity and cost of cellulosic ethanol delivered under different scenarios generated. The insights we provide will help policy makers design policies which encourage and support renewable energy production

Developing spreadsheet-based decision support systems: Using Excel and VBA for Excel. 2nd edition

Belmont, Mass.xx, 956 p.: bibl. ref., index; 26 c

A SIMULATION MODEL OF PORT OPERATIONS DURING CRISIS CONDITIONS

We consider the supply chain for containerized items that arrive at a port in the U.S. whose final destination is also in the U.S. Ports are important entities in global supply chains. As such, when a port cannot operate because of a crisis, such as a natural or man-made disaster, it is critical that freight flow is not disrupted. We develop a simulation model that can be used to make effective re-routing decisions so that the time for freight to reach its final destination is not significantly increased in a crisis. The simulation model will evaluate and report the performance of the supply chain under different re-routing strategies. The output can be analyzed to find the best re-routing strategy that minimizes congestion and delays during crisis conditions. The model can also be used by various decision makers such as port managers, ocean carriers, or transportation companies for strategic decision making.