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

功率流可能有许多运行限制违规。当这种情况发生时，操作人员可能希望确定是否可以通过适当的纠正措施来减轻故障。最优潮流（OPF）是从运筹学中完善的优化原则发展而来的，是一种最有用的方法，可以减少/减轻违规行为，并确定控制/资源的最佳分配，以实现给定的目标。这种基于成本的规划或预防/纠正控制优化问题在优化加权目标和以获得多目标OPF时观察到几个约束条件。目前已经开发了许多方法来解决这类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、前瞻问题和噪声问题的下一代OPF。这种需求导致了一种创新的OPF，称为动态随机最优潮流（DSOPF）。本文将自适应动态规划（ADP）作为解决DSOPF问题的一种新方法。在具有前瞻性的动态变化环境中，新型ADP技术比经典优化具有更强的性能和鲁棒性。该奖项为ADP提供了一个扩展的最优功率流，其中输入数据的随机性、拓扑结构和严重的系统故障使用ADP计算工具的先进运运学原理进行控制。该项目将专注于DSOPF，以解决传统优化技术无法解决的大规模系统。与现有的OPF技术不同，ADP将通过扩展控制、资源和服务的时间调度来促进电力系统优化。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，在任何时间间隔内解决系统挑战，如机组承诺、VAR规划和安全性。在此方案中，将三种不同的时变OPF问题组合在一起，由DSOPF来解决。ADP是在运筹学中使用的几个神经网络和一个训练算法的基础上开发的，该算法将期望输出与实际输出进行比较，并产生一个误差以允许网络学习。将使用反向传播来获得必要的误差导数，并训练网络的参数和输入。总的来说，这种新的运筹学方法将使规划者和决策者能够更好地洞察，更好地预测和保护系统在不同条件下的不确定性和随机数据。

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.