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

功率流可能具有许多操作限制违反。当这种情况发生时，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。最优潮流（Optimal Power Flow，OPF）是从运筹学中的最优化原理发展起来的一种最有效的方法，它可以减少或减轻违规行为，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/校正控制的优化问题观察到几个约束，同时优化加权目标和导致多目标OPF。已经开发了许多方法来解决这种类型的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，下一代的最优潮流，它可以处理动态问题，前瞻性和噪声是迫切需要的电力系统社区。这种需求导致了一个创新的OPF称为动态随机最优潮流（DSOPF）。在这里，自适应动态规划（ADP）被用来作为一种新的方法来解决DSOPF问题。新的ADP技术在前瞻性很重要的动态变化环境中增强了传统优化的能力和鲁棒性。该奖项提出了一个扩展的最优潮流作为ADP的情况下，输入数据的随机性，拓扑结构，和苛刻的系统故障控制使用先进的运筹学原理的ADP计算工具。该项目将集中在DSOPF解决一个大规模的系统，不能解决使用传统的优化技术。ADP -不同于现有的OPF技术-将有助于电力系统的优化，通过扩展当前的工作在时间尺度调度的控制，资源和服务。它可以处理系统的变化和随机扰动在连续的时间间隔。ADP使“全局优化”成为可能，在任何时间间隔内解决机组组合，VAR规划和安全等系统挑战。在这个建议中，三种不同的时间相关的最优潮流问题相结合，以解决DSOPF。ADP是在运筹学中使用的几个神经网络和一个训练算法上开发的，该算法将期望的输出与实际输出进行比较，并生成一个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网络的训练参数和输入。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，并在数据的不确定性和随机性的不同条件下更好地预测和保护系统。

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.