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: 1609794
• 负责人: Yongpei Guan
• 金额: $ 20.43万
• 依托单位: University of Florida
• 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 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1609794&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.

在过去几年中，由于其对清洁和降低的美国能源的贡献，可再生能源在过去几年中越来越多地渗透到电网系统中。但是，与此同时，其间歇性的性质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为热单元提供具有成本效益且可靠的发电计划，以适应可再生生成的不确定性。另一个杰出的问题是，如何将可再生设施和智能设备收集的数据转换为决策过程中有价值的信息和可行的见解。为了帮助应对这些挑战，该项目旨在探索创新的数据驱动优化模型，并开发相应的智能算法，以及在高性能计算设施中实施算法，以实现成本效益和强大的日常电力系统操作。如果成功，则可以在短时间内在行业中实施拟议的创新方法，并有助于改善当前的运营实践。研究成果的结果将纳入课程工作，该课程将培训学生利用尖端数据驱动的优化方法来解决可再生能源集成的前期电力系统问题。教育活动还包括向K-12学生推广，以促进科学和工程学以及在这项研究工作的各个方面的代表性不足的少数民族。提出的创意方法集成了统计和优化方法，以在不确定性模型下得出创新的决策，以实现最佳功率流和单位承诺问题，并结合了需求响应和可再生能源。它提供了有关数据驱动优化的最早研究，以解决电力系统操作的分布歧义。从给定的一组历史数据开始，构建了针对真实未知分布的置信度集合，并为系统运营商和市场参与者开发了相应的数据驱动风险优化模型。除了确保系统鲁棒性外，这种方法的优点是，根据历史数据的数量，该模型的保守性是可以调节的，并且随着历史数据的规模流向无穷大。同样，通过探索可在高性能计算设施中实现的问题结构和分解算法来增强配方的提议高级技术可以帮助提高计算效率以解决衍生模型。最后，创新数据驱动的优化模型和有效算法的开发的整合将丰富工具集并推进尖端技术，以解决不确定性下的发电计划问题。