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

机组承诺是电力公司在管制环境下经济调度发电资源以实现成本最小化的重要优化问题。虽然电力行业正在朝着放松管制的方向发展，但UC的重要性并没有随着结构调整的趋势而减弱。另一方面，现在在UC模型中考虑了新的特性和要求，例如价格不确定性，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等事件，该行业最近正在考虑“重新监管”，并强调集中单位的承诺。本研究将探讨火力发电厂对价格和网路不确定性的最佳反应。在该问题中，发电厂的运营商通过将发电机组的最佳承诺通过输电网络在现货市场上出售，从而使总利润最大化。单位承诺受物理约束，包括斜坡约束。这些操作限制可能会影响该单位如何快速响应盈利机会的能力。技术优点本研究的挑战是双重的：斜坡约束已经臭名昭著，即使不考虑不确定性，也使单元运行问题在时间上不可分离，并且考虑了网络不确定性的大维度，如节点和传输价格，以及传输拥塞。我们打算开发一种具有多项式时间复杂度的高效且理论上合理的方法来解决确定性斜坡约束操作优化问题。我们还将开发一种将蒙特卡罗方法与最小二乘回归相结合的新方法来解决这种随机单元响应优化问题。该方法适用于大规模多阶段随机优化，具有通用性、有效性和高效性。拟议活动的更广泛影响通过拟议的研究，将研究在解除管制的电力市场中的容量扩张过程。这项研究有很多好处：1。本文提出的解决方案程序可用于帮助独立发电商在竞争激烈的市场中实现最优承诺和调度决策。它也有助于传统的UC优化。2. 本研究将提供一种有效且理论上合理的方法来处理机组承诺问题中臭名昭著的斜坡约束。最近，由于加州危机和安然等事件，电力行业被认为是“重新监管”，并重新关注集中的机组承诺。这项研究将对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算法，它将是实用的，适用于大规模的实际情况。我们将从技术和财政两方面着手这项研究。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，帮助投资者做出适当的投资决策。从长远来看，客户也会从社会效率的提高中受益。

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.