CAREER: Stochastic Multiple Time-Scale Co-Optimized Resource Planning of Future Power Systems with Renewable Generation, Demand Response, and Energy Storage

职业：可再生能源发电、需求响应和储能的未来电力系统的随机多时间尺度协同优化资源规划

• 批准号: 1906532
• 负责人: Lei Wu
• 金额: $ 8.99万
• 依托单位: Stevens Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-01-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1906532&HistoricalAwards=false
• 关键词: CAREER; Stochastic; Multiple; Time; Scale

This PI will develop co-optimized generation, transmission, and DR planning solutions to cope with the impacts of short-term variability and uncertainty of renewable generation (RG) and demand response (DR) as well as hourly chronological operation details of energy storage (ES) and generators. The approach is to (1) propose a stochastic multiple time-scale co-optimized planning model that explicitly integrates short-term variability and uncertainty as well as hourly chronological operation into long-term planning; (2) develop efficient solution methodologies and implement on high performance parallel computing facilities.Intellectual Merit: The interaction among variability, uncertainty, and constraints from long-term planning and hourly chronological operation will be quantified for enhancing security and sustainability of power systems with significant RG, DR, and ES. This research can be used to evaluate effective load carrying capability (ELCC) of variable energy sources, and to study policies on portfolios of energy production and storage techniques. This study is of practical importance since RG, DR, and ES are being implemented worldwide and their distinctive contributions to energy security and sustainability need to be well understood. Broader Impacts: This project has profound impacts on the sound deployment of RG, DR, ES, and smart grid. For example, it allows better treatment of investment options which require transmission and generation together, in order to exploit favorable sites for wind or solar. The research and educational findings would help educate engineers to meet challenges of the secure and sustainable electricity infrastructure. The project will increase public awareness and understanding of the complexity of power system planning, and appeal to researchers and educators with interests in power systems-based research and education.

该PI将开发共同优化的发电、输电和灾难恢复规划解决方案，以应对可再生发电(RG)和需求响应(DR)的短期变异性和不确定性的影响，以及储能(ES)和发电机的每小时时序运行细节。该方法是：(1)提出一种随机多时间尺度联合优化规划模型，该模型明确地将短期变异性和不确定性以及按小时进行的时序操作集成到长期计划中；(2)开发高效的求解方法并在高性能并行计算设施上实施；智力优势：将变异性、不确定性和来自长期计划和按小时时序操作的约束之间的相互作用进行量化，以提高具有重要RG、DR和ES的电力系统的安全性和可持续性。这项研究可用于评估可变能源的有效承载能力，以及研究能源生产和储存技术组合的政策。这项研究具有实际意义，因为RG、DR和ES正在世界范围内实施，它们对能源安全和可持续发展的独特贡献需要得到很好的理解。更广泛的影响：该项目对RG、DR、ES和智能电网的合理部署具有深远影响。例如，它允许更好地对待需要同时传输和发电的投资选择，以便开发有利的风能或太阳能区位。研究和教育成果将有助于教育工程师应对安全和可持续的电力基础设施的挑战。该项目将提高公众对电力系统规划复杂性的认识和理解，并吸引对电力系统研究和教育感兴趣的研究人员和教育工作者。