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

在过去的几年里，可再生能源越来越多地渗透到电网系统中，因为它对清洁和低污染的美国能源做出了贡献。但同时，其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获得一个具有成本效益和可靠的发电调度的热单位，以适应可再生能源发电的不确定性。另一个突出的问题是，可再生能源设施和智能设备收集的数据如何在决策过程中转化为有价值的信息和可操作的见解。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，以实现具有成本效益和强大的日常电力系统运营。如果成功，拟议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运营实践。研究成果的结果将被纳入课程作品，这将培养学生利用先进的数据驱动的优化方法来解决与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推广。所提出的创造性的方法集成了统计和优化方法，以获得创新的不确定性模型下的最优潮流和机组组合问题，将需求响应和可再生能源的决策。它提供了一个数据驱动的优化解决电力系统运行的分布模糊性的第一个研究。从一组给定的历史数据开始，构造真实未知分布的置信度集，并相应地为系统运营商和市场参与者开发数据驱动的风险规避优化模型。除了保证系统的鲁棒性，这种方法的优点是，所提出的模型的保守性是可调的基础上的历史数据的量，并最终消失的历史数据的大小走向无穷大。此外，所提出的先进技术，在加强制定探索的问题结构和分解算法，可在高性能计算设施，可以帮助提高计算效率，以解决衍生模型。最后，创新的数据驱动的优化模型和高效算法的开发的集成将丰富工具集，并推进前沿技术，以解决不确定性下的发电调度问题。

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.