CyberSEES: Type 1: Dynamic Robust Optimization for Emerging Energy Systems

Cyber​​SEES：类型 1：新兴能源系统的动态鲁棒优化

• 批准号: 1331426
• 负责人: Shabbir Ahmed
• 金额: $ 30万
• 依托单位: Georgia Tech Research Corporation
• 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: 2013-08-01 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1331426&HistoricalAwards=false
• 关键词: CyberSEES; Type; Dynamic; Robust; Optimization

This project develops and analyzes dynamic robust optimization models and solution methods for electric power systems operation under uncertainty. In particular, this project will develop multi-stage robust optimization models and algorithms for the unit commitment problem, a critical decision-making tool for daily scheduling of power systems. In a multi-stage robust optimization model, both the generation commitment and power dispatch decisions are dynamically adaptive to sequential observation of uncertainty, as opposed to existing models where the decisions are either static or noncausal. Such models involve a large number of mixed integer variables and decision policies in infinite dimensional space. Finding an exact solution for these models is computationally intractable. The challenge is to develop theory and algorithms for large-scale problems along with performance guarantees. Our approach is to develop and apply finitely adaptive decision policies in properly designed multi-stage uncertainty models, and develop efficient decomposition methods for their resolution.As an effort toward building a sustainable national energy system, electric power systems in the US are experiencing fundamental changes, with large-scale integration of renewable energy resources and the deployment of demand response technologies. As a result, both the energy supply and demand sides have significantly increased uncertainty. Reliable operation of power systems under these uncertainties is critical. The results of this project, if successful, will significantly advance the state of the art in power systems operations under uncertainty, and will also significantly advance the techniques in solving general multi-stage robust optimization problems. Substantial effort will be committed to educating next generation academics and researchers who are proficient in advanced analytics and devoted to improve the national energy sustainability.

本计画针对不确定性下电力系统之动态强健最佳化模式及其求解方法进行研究与分析。特别是，本项目将开发多阶段鲁棒优化模型和算法的机组组合问题，一个关键的决策工具，电力系统的日常调度。在一个多阶段的鲁棒优化模型中，发电承诺和电力调度决策是动态自适应的顺序观察的不确定性，而不是现有的模型中的决定是静态的或非因果关系。这类模型涉及无穷维空间中的大量混合整数变量和决策策略。找到这些模型的精确解在计算上是困难的。挑战在于为大规模问题开发理论和算法沿着性能保证。我们的方法是在适当设计的多阶段不确定性模型中开发和应用自适应决策策略，并开发有效的分解方法来解决问题。作为建立可持续国家能源系统的努力，美国的电力系统正在经历根本性的变化，大规模整合可再生能源资源和部署需求响应技术。因此，能源供需双方的不确定性明显增加。电力系统在这些不确定性下的可靠运行是至关重要的。该项目的结果，如果成功的话，将显着推进在不确定性下的电力系统运行的最先进的状态，也将显着推进解决一般的多阶段鲁棒优化问题的技术。将致力于教育下一代学者和研究人员，他们精通高级分析，并致力于改善国家能源可持续性。