Multifactor optimization of electrical power plants

最近，世界各地的司法管辖区都出现了电力市场放松管制的趋势，这导致了个体电力生产商角色的根本转变。在这种新模式下，单个市场参与者必须承担市场价格和成本不确定性带来的唯一金融风险。此外，金融能源合约的创新正迅速成为一种管理风险的手段。运营决策再也不能脱离金融市场现实。然而，同样，发电机的运行限制并不会随着市场管制的解除而消失。发电厂的运营限制常常迫使公司不断做出决策，而这些决策将对未来的财务后果产生高度不确定的影响。一个简单的例子是，一个水储备有限的水力发电厂，或者一个煤库存有限的燃煤发电厂，必须决定以什么价格发电，因为今天消耗的资源在未来将不再可用。由于电力价格的高度不确定性以及可能影响这些价格的不同因素的数量，确定这种机会成本的大小变得复杂。仅在安大略省，煤炭库存决策就可能涉及数亿美元，因此这个问题远非微不足道。在面对不确定性时，其他数十种操作约束也会导致类似的决策问题。个体参与者要有效地操作和管理风险，需要强大的计算机模型和数学求解算法。然而，目前大多数这种基于财务的方法在它们可以考虑的变量数量上是有限的。该提案旨在开发一套新的数学算法，使行业从业者能够考虑到比目前计算可行更多的因素和变量，从而更有效地利用国家稀缺的能源资源。

The recent trend toward electricity market deregulation that has occurred in jurisdictions around the world has led to a fundamental shift in the role of individual power producers.  In this new paradigm, individual market participants must bear the sole financial risks of market price and cost uncertainties.  In addition, innovations in financial energy contracts are rapidly becoming more popular as a means of managing risk.  No longer can operational decisions be separated from financial market realities.  Similarly however, the operational constraints of electrical generators do not disappear when markets are deregulated.  The operational restrictions of electrical power plants often force companies to continuously make decisions that will have highly uncertain future financial consequences.  One simple example would be a hydro plant with a limited reserve of water or a coal plant with a limited inventory of coal that must decide at what price to produce power given that whatever resources are consumed today will no longer be available in the future.   Determining the size of this opportunity cost is complicated by the high degree of uncertainty in power prices as well as the number of different factors which may influence these prices.  As coal inventory decisions in Ontario alone can involve hundreds of millions of dollars, this problem is far from trivial.  Dozens of other operational constraints give rise to similar such decision making problems in the face of uncertainty.  For individual participants to operate and manage risk efficiently, powerful computer models and mathematical solution algorithms are required.  Currently however, most such financially based methods are limited in the number of variables they can consider.  This proposal aims at developing a new set of mathematical algorithms that will enable industry practitioners to take into account more factors and variables than is currently computationally feasible, thereby leading to a more efficient use of the nation's scarce energy resources.