Optimization models to integrate production and transportation planning for biomass co-firing in coal-fired power plants
Optimization models to integrate production and transportation planning for biomass co-firing in coal-fired power plants
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DOI：
10.1080/0740817x.2015.1126004
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发表时间：
2016-01
期刊：
影响因子：
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通讯作者：
S. Eksioglu;H. Karimi;Burak Eksioglu
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文献类型：
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作者：
S. Eksioglu;H. Karimi;Burak Eksioglu
摘要生物质共燃是一种减少燃煤电厂温室气体排放的策略。生产税收抵免（PTC）等激励措施旨在帮助发电厂克服实施阶段面临的财务挑战。发电厂的决策者面临两大挑战。第一个挑战是确定PTC等激励措施的好处是否可以克服与共烧相关的成本。第二个挑战是确定一个工厂应该在多大程度上共同燃烧，以实现利润最大化。我们提出了一种新的数学模型，集成了发电厂的生产和运输决策。这种模型使决策者能够评估共烧对系统性能和产生可再生电力的成本的影响。所提出的模型是一个非线性混合整数规划，它捕获了由于使用生物质而导致的过程效率损失，与煤相比，生物质是一种具有较低热值的产品;支持生物质共烧所需的额外投资成本以及由于PTC而节省的成本。为了有效地解决这个问题的现实生活中的例子，我们提出了一个拉格朗日松弛模型，提供了上限和两个线性近似，提供了问题的下限。我们使用数值分析来评估这些边界的质量。我们开发了一个案例研究，使用的数据来自九个州位于美国东南部地区。通过数值实验，我们观察到：（i）PTC等激励措施确实促进了可再生能源生产;（ii）PTC不应“一刀切”;相反，税收抵免可以是工厂容量或可再生电力产量的函数;（iii）需要全面的税收抵免计划来鼓励可再生电力生产并减少温室气体排放。
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.