Topology Aware Resource Optimization and Uncertainty Quantification Energy Models for the Power Grid

该项目的重点是开发，实施和评估新的和有效的政策，拓扑感知资源分配的能源资源不确定性。当电力供应系统发生故障时，快速诊断问题并采取纠正措施以防止停电至关重要。该项目将支持基础研究，以增强智能电网的主动和被动可靠运行，而无需昂贵的基础设施投资。具体地说，这个研究项目将表明，控制网格的拓扑结构可以提高网格的可靠性和更好地管理资源。此外，这项研究将开发所需的程序，以找到最可靠的电网拓扑结构，以应对能源需求的变化。因此，这项研究的主要社会影响是提高预防和解决意外停电的能力，这些停电每年为美国企业和消费者造成约900亿美元的损失。该研究涉及电力系统、并行计算和优化等多个学科，其中线性规划模型可以克服现有拓扑感知模型在容量规划、资源再分配和调度等方面的局限性。研究小组将研究一系列混合整数线性规划模型，这些模型旨在确定供应源、服务需求站点以及重新分配的电力应沿沿着流动的路径的最佳组合。这些模型明确支持与风力发电等替代能源相关的不确定性。一个模拟器配置有多个智能分布式软件代理将开发，以支持模型解决方案的评估。感兴趣的应用程序包括（但不限于）发电机和负荷调度应用在能源管理和服务系统;定价和收入管理问题;和库存控制。

The project focuses on the development, implementation, and evaluation of new and effective policies for topology aware resource allocation of energy resources under uncertainty. When a malfunction occurs in an electricity provisioning system, it is vitally important to quickly diagnose the problem and take corrective action to prevent outages. This project will support fundamental research to enhance both the proactive and reactive reliable operation of the smart grid without costly infrastructure investments. Specifically, this research project will show that controlling the grid's topology can enhance the grid's reliability and better manage resources. In addition, this research will develop the procedures required to find the most reliable grid topology in response to changes in energy demand. Thus, the primary societal impact of this research is to increase the capability to prevent and resolve unexpected blackouts, which account for approximately $90 billion in losses each year for U.S. businesses and consumers. This research involves several disciplines including power systems, parallel computing and optimization.Integer Linear Programming models can overcome several limitations in the current topological aware models such as capacity planning, re-allocation and scheduling of resources. The research team will study a collection of mixed integer linear programming models designed to identify optimal combinations of supply sources, demand sites to serve, and the pathways along which the reallocated power should flow. The models explicitly support the uncertainty associated with alternative sources such as wind power. A simulator configured with multiple intelligent distributed software agents will be developed to support the evaluation of the model solutions. Applications of interest include (but are not restricted to) generator and load scheduling applications in energy management and service systems; pricing and revenue management problems; and inventory control.