Mathematical Frameworks for Dynamic Reserve Policies

动态储备政策的数学框架

• 批准号: 1333646
• 负责人: Kory Hedman
• 金额: $ 30万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-15 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1333646&HistoricalAwards=false
• 关键词: Mathematical; Frameworks; Dynamic; Reserve; Policies

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.

该奖项的目的是为确定日常单位承诺（UC）模型或可靠性UC模型的储备要求开发新的范式，以维护可靠的电力系统。具体而言，这项工作将1）开发新的数学编程框架，以确定储备区和嵌入单位承诺模型中的储备水平； 2）开发数学框架，这些框架可自适应地确定各种可靠性标准的储备要求； 3）在随机编程算法中嵌入动态储备策略，以提高可扩展性和溶液质量； 4）将技术与现有的储备规则以及随机编程技术进行比较； 5）通过证明可靠性，经济效率和可扩展性的提高来验证这些概念； 6）通过检查它们促进可变生成（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，储备金政策的数学框架将通过提高效率和系统可靠性，并减少电网中断，从而改善电力系统操作和计划。该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，并有助于发展可持续经济以实现能源独立性和安全性。平衡储备政策与随机编程算法的过程将有助于电力系统中随机编程的整合和采用。开发的数学模型和多面体研究还将有助于大规模网络流或计划问题，这些问题具有不确定性和可靠性标准，例如运输系统和电信系统。