Mathematical Frameworks for Dynamic Reserve Policies

该奖项的目的是开发一种新的范式，用于确定日前机组组合（UC）模型或可靠性UC模型的备用要求，以保持可靠的电力系统。具体而言，这项工作将1）开发新的数学规划框架，用于确定嵌入在机组组合模型中的备用区和备用水平; 2）开发数学框架，自适应地确定各种可靠性标准的备用要求; 3）将动态备用策略嵌入随机规划算法中，以提高可扩展性和解决方案的质量; 4）将这些技术与现有的备用规则以及随机规划技术进行比较; 5）通过展示可靠性、经济效率和可扩展性的改进来验证这些概念; 6）通过检查这些技术促进高水平可变发电（可再生）资源的集成和管理的能力来验证这些技术。如果成功的话，备用政策的数学框架将通过提高效率和系统可靠性，减少电网停电，从而改善电力系统的运营和规划。这一框架还将提高管理可再生资源的能力，从而允许更高水平的可变可再生资源，这将有助于减少排放，并将促进可持续经济的发展，以实现能源独立和安全。利用随机规划算法来平衡备用策略的过程将促进随机规划在电力系统中的集成和采用。数学模型和多面体的研究也将有助于大规模网络流或调度问题的不确定性和可靠性标准，例如，运输系统和电信系统。

The objective of this award is to develop a new paradigm for the determination of reserve requirements for either day-ahead unit commitment (UC) models or reliability UC models to maintain a reliable electric power system. Specifically, this work will 1) develop novel mathematical programming frameworks for the determination of reserve zones and reserve levels embedded in unit commitment models; 2) develop mathematical frameworks that adaptively determine reserve requirements for various reliability standards; 3) embed dynamic reserve policies inside stochastic programming algorithms in order to improve scalability and solution quality; 4) compare the techniques to existing reserve rules as well as stochastic programming techniques; 5) validate the concepts by demonstrating improvements in reliability, economic efficiency, and scalability; 6) validate these techniques by examining their ability to facilitate the integration and management of high levels of variable generation (renewable) resources. If successful, the mathematical frameworks for reserve policies will lead to improvements in electric power systems operations and planning by improving efficiency and systems reliability, with a reduction in grid outages. This framework will also improve the ability to manage renewable resources and, thus, allow for higher levels of variable renewable resources, which will help reduce emissions and it will facilitate the development of a sustainable economy for energy independence and security. The process to balance reserve policies with stochastic programming algorithms will facilitate the integration and adoption of stochastic programming in electric power systems. The mathematical models and polyhedral studies developed will also contribute to large scale network flow or scheduling problems with uncertainties and reliability standards, e.g., transportation systems and telecommunication systems.