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将开发合作的生成，传输和DR计划解决方案，以应对短期可再生能源生成（RG）和需求响应（DR）的影响以及每小时的储能存储（ES）和发电机的时间顺序运行细节。 该方法是（1）提出一个随机的多个时间尺度的合作计划模型，该模型将短期可变性和不确定性以及每小时的年代顺序运作明确整合到长期计划中； （2）开发有效的解决方案方法并在高性能并行计算设施上实施。智能优点：长期计划和每小时按时间顺序的可变性，不确定性和约束之间的相互作用将被量化，以增强与RG，DR和ES的重要功率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（ELCC），并研究有关能源生产和存储技术组合的政策。 这项研究至关重要，因为RG，DR和ES在全球范围内实施，并且需要充分理解它们对能源安全和可持续性的独特贡献。 更广泛的影响：该项目对RG，DR，ES和智能电网的合理部署产生了深远的影响。例如，它可以更好地处理需要传输和产生的投资选择，以便利用有利的场地进行风或太阳能。研究和教育发现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计划复杂性的认识和理解，并吸引对基于电力系统的研究和教育感兴趣的研究人员和教育者。