Stochastic OPF and Transaction Monitoring in Deregulated Power Markets

放松管制的电力市场中的随机 OPF 和交易监控

• 批准号: 0086706
• 负责人: Kevin Clements
• 金额: $ 23.99万
• 依托单位: Worcester Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-06-01 至 2005-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0086706&HistoricalAwards=false
• 关键词: Stochastic; OPF; Transaction; Monitoring; Deregulated

The deregulated electric power industry requires new optimal power flow (OPF) and state estimation tools to maintain high levels of security while operating near full capacity in competitive markets. Stochastic OPF problems incorporate operating contingencies and random effects such as uncertainty in demand or energy prices. No operator in today's markets can afford the cost of operating so that its system can withstand fully every contingency, regardless of the probability of its occurrence. Because so many contingencies are included, these problems are massive, to large for direct methods. We propose the use of Monte Carlo-based importance sampling coupled with Benders decomposition, techniques that produce subproblems of tractable size. The proposed work will first develop Monte Carlo-based stochastic OPF algorithms for a DC network subject to random failures of lines and generators. The second stage will add uncertainty to loads, requiring separate importance sampling for the two sources of uncertainty. Finally, the stochastic model will be extended to include day-ahead purchase of spinning reserve in the face of uncertain loads and energy prices. The algorithmic methods for these three classes of OPF will then be extended to a full AC network. A number of public discussions have identified a growing problem arising from undisclosed bilateral energy transactions that distort a network's planned operating point as they pass through it. The task of identifying such "rough" transactions can be formulated as a problem in hypothesis testing in power system state estimation. The potentially daunting number of undisclosed buyer-seller combinations to be tested can be accommodated by careful problem formulation and by efficient computational techniques such as low rank updates. The central conceptual step is proper use of all a priori information.The proposers will draw on their experience in state estimation-observ-ability, efficient computation, bad data detection and identification-and in time-coupled dynamic dispatch, unit commitment, and price-responsive load modeling to develop efficient algorithms for both classes of problems by exploiting common aspects of their problem structure. This work will lead to new estimation and hypothesis-testing paradigms, robust approached to stochastic OPF, and efficient, practical computation algorithms.

放松管制的电力行业需要新的最优潮流（OPF）和状态估计工具来保持高水平的安全性，同时在竞争激烈的市场中接近满负荷运行。随机OPF问题包括操作偶然性和随机效应，如需求或能源价格的不确定性。在当今的市场上，没有一家运营商能够负担得起运营成本，使其系统能够完全承受所有突发事件，无论其发生的概率如何。由于包含了如此多的偶发事件，这些问题非常大，大到无法用直接方法解决。我们建议使用基于蒙特卡罗的重要性抽样与Benders分解相结合的技术，产生可处理大小的子问题。提出的工作将首先开发基于蒙特卡罗的随机OPF算法，用于受线路和发电机随机故障影响的直流网络。第二阶段将增加负载的不确定性，需要对两个不确定性源进行单独的重要性采样。最后，将随机模型扩展到考虑负荷和能源价格不确定情况下的日前购买旋转储备。然后将这三类OPF的算法方法扩展到整个交流网络。许多公开讨论已经发现，一个日益严重的问题是，未披露的双边能源交易在通过电网时扭曲了计划的运行点。识别这种“粗糙”交易的任务可以表述为电力系统状态估计中的假设检验问题。需要测试的未公开买卖组合的数量可能令人生畏，但可以通过仔细的问题表述和高效的计算技术（如低秩更新）来解决。核心概念步骤是正确使用所有先验信息。提议者将利用他们在状态估计——可观察性、高效计算、坏数据检测和识别——以及时间耦合动态调度、单位承诺和价格响应负载建模方面的经验，通过利用问题结构的共同方面，为这两类问题开发有效的算法。这项工作将导致新的估计和假设检验范式，随机OPF的鲁棒方法，以及高效实用的计算算法。