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，因为它对更清洁、更低污染的美国能源做出了贡献。然而，它的间歇性也给电力系统运营者带来了挑战。一个具有挑战性的问题是如何在短时间内推导出具有成本效益和可靠性的火电机组发电计划，以适应可再生能源发电的不确定性。另一个悬而未决的问题是，如何将可再生设施和智能设备收集的数据转化为决策过程中的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，开发相应的智能算法，并在高性能计算设施中实施这些算法，以实现经济高效和稳健的日常电力系统运行。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。研究成果将被纳入课程工作，培训学生利用尖端数据驱动的优化方法来解决与可再生能源集成的电力系统前期问题。教育活动还包括向K-12学生进行宣传，以促进科学和工程学，并在这项研究工作的所有方面向代表性不足的少数群体宣传。该方法将统计方法和优化方法相结合，在不确定模型下对包含需求响应和可再生能源的最优潮流和机组组合问题进行创新决策。它提供了首批关于数据驱动优化的研究之一，以解决电力系统运行的分布模糊性。从一组给定的历史数据出发，构造了真实未知分布的置信度集，从而为系统运营商和市场参与者建立了数据驱动的风险厌恶优化模型。除了保证系统的稳健性外，该方法的优点是模型的保守性可以根据历史数据量进行调整，并随着历史数据量的增加而最终消失。此外，建议的高级技术通过探索问题结构和可在高性能计算设施中实现的分解算法来加强公式，有助于提高求解派生模型的计算效率。最后，集成创新的数据驱动优化模型，开发高效的算法，将丰富工具集，推进解决不确定条件下发电调度问题的前沿技术。

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.