COLLABORATIVE RESEARCH: Data-Driven Risk-Averse Models and Algorithms for Power Generation Scheduling with Renewable Energy Integration

合作研究：数据驱动的可再生能源发电调度风险规避模型和算法

• 批准号: 1610935
• 负责人: Chaoyue Zhao
• 金额: $ 19.93万
• 依托单位: Oklahoma State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1610935&HistoricalAwards=false
• 关键词: COLLABORATIVE; RESEARCH; Data; Driven; Risk

Renewable energy has been increasingly penetrating into the power grid system during the past years due to its contribution toward cleaner and lower-polluting American energy. Meanwhile, however, its intermittent nature brings challenges to power system operators. One challenging problem is how to derive a cost-effective and reliable power generation scheduling for thermal units in a short time to accommodate renewable generation uncertainties. The other outstanding question is how the data collected by the renewable facilities and intelligent devices can be transformed into valuable information and actionable insights in the decision-making process. To help address these challenges, this project aims to explore innovative data-driven optimization models and develop corresponding intelligent algorithms, as well as the implementation of the algorithms in high-performance computing facilities, to achieve cost-effective and robust daily power system operations. If successful, the proposed innovative approaches can be implemented in the industry in a short time and help improve current operations practices. The results of research outcomes will be incorporated into course works, which will train students to utilize cutting edge data-driven optimization methods to solve upfront power system problems with renewable energy integration. Educational activities also include outreach to K-12 students to promote science and engineering and to under-represented minorities in all aspects of this research effort. The proposed creative approach integrates statistical and optimization methods to derive innovative decision-making under uncertainty models for optimal power flow and unit commitment problems incorporating demand response and renewable energy. It provides one of the first studies on data-driven optimization addressing distributional ambiguity for power system operations. Starting from a given set of historical data, a confidence set for the true unknown distribution is constructed and accordingly data-driven risk-averse optimization models are developed for both system operators and market participants. Besides ensuring system robustness, the advantage of this approach is that the conservatism of the proposed model is adjustable based on the amount of historical data and eventually vanishes as the size of historical data goes to infinity. Also, the proposed advanced techniques in strengthening the formulation by exploring the problem structure and decomposition algorithms implementable at high-performance computing facilities can help improve the computational efficiency to solve the derived models. Finally, integration of innovative data-driven optimization models and development of efficient algorithms will enrich the tool set and advance the cutting edge technology to solve power generation scheduling problems under uncertainty.

由于可再生能源对清洁低污染的美国能源的贡献，在过去几年里，可再生能源越来越多地渗透到电网系统中。但与此同时，它的间歇性也给电力系统运营商带来了挑战。一个具有挑战性的问题是如何在短时间内制定一个经济可靠的火电机组发电计划，以适应可再生能源发电的不确定性。另一个突出的问题是，如何将可再生能源设施和智能设备收集的数据转化为决策过程中有价值的信息和可操作的见解。为了帮助解决这些挑战，本项目旨在探索创新的数据驱动优化模型，开发相应的智能算法，并在高性能计算设施中实现算法，以实现成本效益和稳健的日常电力系统运行。如果成功，所提出的创新方法可以在短时间内在行业中实施，并有助于改善当前的操作实践。研究成果将纳入课程作业，训练学生利用尖端的数据驱动优化方法解决可再生能源整合的前期电力系统问题。教育活动还包括向K-12学生推广科学和工程，并在这一研究工作的各个方面向代表性不足的少数民族伸出援手。该方法将统计和优化方法相结合，在不确定模型下推导出包含需求响应和可再生能源的最优潮流和机组承诺问题的创新决策。它提供了数据驱动优化解决电力系统运行中的分布模糊问题的首批研究之一。从给定的一组历史数据出发，构建了真实未知分布的置信集，并相应地为系统运营商和市场参与者建立了数据驱动的风险规避优化模型。除了确保系统的鲁棒性外，该方法的优点是所提出模型的保守性可以根据历史数据的数量进行调整，并最终随着历史数据的大小趋于无穷大而消失。此外，通过探索可在高性能计算设备上实现的问题结构和分解算法，提出了加强公式的先进技术，有助于提高求解衍生模型的计算效率。最后，整合创新的数据驱动优化模型和开发高效算法，将丰富工具集，推进前沿技术，以解决不确定条件下的发电调度问题。