SGER: Optimal Stochastic Unit Response Subject to Ramp and Network Constraints

机组组合问题是电力系统在管制环境下实现发电成本最小化的重要优化问题。虽然电力行业正在走向放松管制，但UC的重要性并没有随着重组趋势而沿着减弱。另一方面，UC模型中考虑了新的特征和要求，如价格不确定性，这大大增加了问题的复杂性。一些集中式市场（如PJM）仍然执行类似UC的优化来进行电力拍卖。此外，由于加州危机和安然事件，业界最近正在考虑“重新管制”，并强调集中机组的承诺。本研究将解决热电厂的最佳响应价格和网络的不确定性。在该问题中，发电厂的运营商最大化的总利润最优承诺的单位，以产生电力，通过传输网络在现货市场上出售。机组组合受包括坡道约束在内的物理约束。这些操作的限制，可以影响的能力，该单位如何能够快速响应有利可图的opportunity.Technical MeritsThe挑战，这项研究是双重的：坡道的限制已经臭名昭著的单位操作问题的不可分离的时间，即使不考虑不确定性，和大维数的网络不确定性，如节点和传输价格，传输拥塞。我们打算开发一个有效的和理论上合理的方法，多项式时间复杂度来解决确定性斜坡约束的操作优化。我们还将开发一种新的方法，集成蒙特卡罗方法和最小二乘回归来解决这个随机单位响应优化。所提出的新方法是通用的，有效的，高效的大规模多阶段随机优化。更广泛的影响，建议的活动通过拟议的研究，容量扩张过程中，放松管制的电力市场将进行研究。这项研究有很多好处：1。在这个建议中开发的解决方案程序可以用来帮助独立的发电商实现最优的承诺和调度决策在竞争激烈的市场。它还可以为传统的UC优化做出贡献。2.这项研究将提供一个有效的，但理论上健全的方法来处理臭名昭著的斜坡约束的机组组合问题。最近，由于加州危机和安然事件，电力行业被认为是“重新管制”，并重新关注集中机组承诺。这项研究将为开发更有效的求解算法做出根本性的贡献。 此外，由于我们将开发一个多项式时间的算法，它将是实用的和适用于大规模的真实的情况.我们将从技术和财务两个角度来处理这项研究。在技术方面，我们将开发有效的算法来帮助优化操作。在财务方面，我们希望提供一个公平的估值工具，帮助投资者作出适当的投资决定。从长远来看，客户也受益于社会效率的提高。

Unit commitment (UC) is an important optimization problem for power utilities to economically schedule generating resources to achieve cost minimization in the regulated environment. Although the electricity industry is moving toward deregulation, the importance of the UC does not diminish along with the restructuring trend. On the other hand, new features and requirements are now considered in UC models such as price uncertainty, which significantly increase the complexity of the problem. Some centralized markets (such as the PJM) still perform UC-like optimization to conduct electricity auctions. Also the industry is recently considering "re-regulation" due to incidents such as California crisis and Enron and emphasizing centralized unit commitment.This research will address the optimal response of a thermal plant to price and network uncertainties. In the problem, the operator of a power plant maximizes total profit by optimally committing the unit to generate power to sell in spot markets via transmission network. The unit commitment is subject to physical constraints including the ramp constraints. These operational constraints can impact the capability of how the unit can quickly respond to profitable opportunities.Technical MeritsThe challenge of this research is twofold: the ramp constraints have been notorious for making the unit operation problem non-separable in time even without considering uncertainty, and the large dimensionality of the network uncertainties considered, such as nodal and transmission prices, and transmission congestion. We intend to develop an efficient and theoretically sound approach with a polynomial time complexity to solve the deterministic ramp-constrained operation optimization. We will also develop a new approach integrating the Monte Carlo method and the least squares regression to solve this stochastic unit response optimization. The proposed new method is general, effective, and efficient for large-scale multi-stage stochastic optimization.Broader Impact of the Proposed ActivityThrough the proposed research, the capacity expansion process in a deregulated power market will be studied. The research has many benefits:1. The solution procedure developed in this proposal can be used to help independent power producers achieve optimal commitment and dispatch decisions in the competitive marketplace. It can also contribute to the traditional UC optimization. 2. This research will provide an efficient yet theoretically sound approach to handle the notorious ramping constraints in the unit commitment problems. Recently the electric industry is being considered "re-regulated" due to incidents such as California crisis and Enron, and is refocusing on centralized unit commitment. This research will make a fundamental contribution to the development of more efficient solution algorithms. Furthermore, since we will develop a polynomial time algorithm, it will be practical and applicable for large-scale real cases.3. We will tackle this research from both technical and financial perspective. In the technical side, we will develop efficient algorithms to aid optimal operation. In the financial side, we hope to provide a fair valuation tool to help investors to make appropriate investment decisions. In the long run, customers also benefit from the improved societal efficiency.