Stochastic OPF and Transaction Monitoring in Deregulated Power Markets

电力行业的放松管制需要新的最优潮流（OPF）和状态估计工具，以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。 随机最优潮流问题包含了运营突发事件和随机效应，如需求或能源价格的不确定性。 在今天的市场上，没有一个经营者能够负担得起经营成本，使其系统能够完全承受每一种意外事故，无论其发生的概率如何。由于包含了如此多的偶然性，这些问题是巨大的，对于直接方法来说是巨大的。我们建议使用基于蒙特卡洛的重要性抽样加上本德分解，技术，产生易于处理的大小的子问题。 建议的工作将首先开发基于蒙特卡洛的随机最优潮流算法的直流网络受到随机故障的线路和发电机。 第二阶段将增加负荷的不确定性，需要对两个不确定性来源进行单独的重要性抽样。 最后，随机模型将扩展到包括前一天购买旋转备用面对不确定的负荷和能源价格。 这三类最优潮流的算法方法，然后将扩展到一个完整的交流网络。 一些公众讨论已经确定了一个日益严重的问题，所产生的未公开的双边能源交易扭曲网络的计划操作点，因为他们通过it. The任务，确定这种“粗糙”的交易可以制定为一个问题，在电力系统状态估计的假设检验。 潜在的令人生畏的数量未公开的买方-卖方组合进行测试，可以容纳仔细的问题制定和有效的计算技术，如低排名更新。 核心概念步骤是正确使用所有的先验信息。提案者将利用他们在状态估计的经验-可预测性、有效计算、不良数据检测和识别-以及在时间耦合动态调度、机组组合和价格响应负荷建模方面的经验，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，强大的方法随机最优潮流，高效，实用的计算算法。

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.