SGER: Framework for Dynamic Stochastic Optimal Power Flow (DSOPF) of the Grid

SGER：电网动态随机最优潮流（DSOPF）框架

• 批准号: 0549071
• 负责人: James Momoh
• 金额: --
• 依托单位: Howard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-10-01 至 2007-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0549071&HistoricalAwards=false
• 关键词: SGER; Framework; Dynamic; Stochastic; Optimal

A power flow may have many operating limit violations. When such conditions occur, the operator may wish to determine if the trouble could be alleviated by appropriate corrective actions. Optimal Power Flow (OPF), developed from well documented optimization principles in Operation Research, is a most useful method to diminish/alleviate violations, and determine the best allocation of controls/resources to achieve a given objective. This cost-based optimization problem for planning or preventive/corrective control observes several constraints while optimizing the weighted objective sum leading to a multi-objective OPF. Many methods have been developed to solve this type of OPF problem, but several of them fail to solve truly dynamic and noisy systems. As such, the next generation of OPF, which can deal with dynamic problems, foresight, and noise is urgently required by the power systems community. This need has led to an innovative OPF called Dynamic Stochastic Optimal Power Flow (DSOPF). Here, Adaptive Dynamic Programming (ADP) is used as a new method to solve the DSOPF problem. The new ADP technology has enhanced capability and robustness above classical optimization in dynamically changing environments where foresight is important. This award presents an extended optimal power flow as a case for ADP where stochasticity of input data, topology, and harsh system faults are controlled using advanced Operation Research principles of an ADP computational tool.This project will focus on DSOPF to solve a large scale system that cannot be solved using conventional optimization techniques. ADP - different from existing OPF technologies - will contribute to power systems optimization by extending current work in time-scale scheduling of controls, resources, and services. It can deal with system changes and stochastic perturbation over consecutive time intervals. ADP makes the "global optimization" possible where system challenges such as unit commitment, VAR planning and security are solved within any time interval. In this proposal, three different kinds of time-dependant OPF problems are combined to be solved by the DSOPF. The ADP develops on several neural networks used in Operation Research, and a training algorithm, which compares a desired output to the actual output, and generates an error to allow the network to learn. Back-propagation will be used to get necessary derivatives of the error and train parameters and inputs of the network. Overall, this new Operation Research methodology will enable planners and decision makers to gain better insight, and better predict and protect the system under different conditions of uncertainty and stochasticity in data.

潮流可能有许多操作限制违规。当出现这种情况时，操作员可能希望确定是否可以通过适当的纠正措施来缓解故障。最佳潮流 (OPF) 是根据运筹学中记录良好的优化原理开发的，是减少/减轻违规行为并确定控制/资源的最佳分配以实现给定目标的最有用的方法。这种用于规划或预防/纠正控制的基于成本的优化问题在优化加权目标总和时观察到多个约束，从而产生多目标 OPF。人们已经开发了许多方法来解决此类 OPF 问题，但其中一些方法无法解决真正的动态和噪声系统。因此，电力系统界迫切需要能够处理动态问题、预见性和噪声的下一代OPF。这种需求催生了一种称为动态随机最优功率流 (DSOPF) 的创新 OPF。这里，自适应动态规划（ADP）被用作解决DSOPF问题的新方法。在前瞻性非常重要的动态变化的环境中，新的 ADP 技术比传统优化具有更强的能力和稳健性。该奖项提出了一个扩展的最佳功率流作为 ADP 的案例，其中使用 ADP 计算工具的先进运筹学原理来控制输入数据、拓扑和恶劣​​系统故障的随机性。该项目将重点关注 DSOPF 来解决大型系统的问题使用传统的优化技术无法解决。 ADP 与现有的 OPF 技术不同，将通过扩展当前在控制、资源和服务的时间尺度调度方面的工作，为电力系统优化做出贡献。它可以处理连续时间间隔内的系统变化和随机扰动。 ADP 使“全局优化”成为可能，可以在任何时间间隔内解决机组承诺、VAR 规划和安全等系统挑战。在这个提案中，三种不同类型的时变 OPF 问题被组合起来由 DSOPF 来解决。 ADP 基于运筹学中使用的多个神经网络和训练算法而开发，该算法将所需输出与实际输出进行比较，并生成错误以允许网络学习。反向传播将用于获得误差的必要导数以及训练参数和网络的输入。总体而言，这种新的运筹学方法将使规划者和决策者能够获得更好的洞察力，并在不同的数据不确定性和随机性条件下更好地预测和保护系统。