Collaborative Research: AMPS: Deep-Learning-Enabled Distributed Optimization Algorithms for Stochastic Security Constrained Unit Commitment

电力系统的运营格局目前正经历着由各种因素驱动的深刻变革，包括可再生能源的整合，对清洁能源经济的需求以及应对气候危机的紧迫性。该项目旨在为充分利用深度机器学习方法在增强电力系统运营方面的潜力奠定必要的数学基础，特别是在风能和太阳能发电等可再生能源方面。该研究将开发一套新的分布式优化工具，使大规模电力系统运营能够管理不确定性，同时有效地整合可再生能源。新算法将潜在地改变电力系统内的操作实践。与此同时，这些成果将提高公众意识，并增进利益攸关方、监管机构、政策制定者和市场参与者的理解。该项目的成功完成将使电力系统运营商能够采用先进的算法，大大提高其可再生能源发电的运营实践。该项目将为来自这两个机构的学生，特别是来自STEM代表性不足群体的学生提供培训和推广机会。该项目旨在开发和验证支持深度学习的分布式随机算法。这些算法将解决大规模的，随机的安全约束下的机组组合问题的电力系统。具体而言，该项目将专注于以下目标：（i）设计一种整体的，三阶段的，基于深度神经网络的机器学习方法;（ii）基于混合分布参数系统控制理论的解决方案策略;以及（iii）使用大规模真实世界电力系统数据集对所提出的算法进行广泛验证。该研究将通过引入创新技术来解决与电力系统在不确定性下运行相关的挑战，从而推动该领域的发展。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The operational landscape of electric power systems is currently experiencing a profound transformation driven by various factors, including the integration of renewable energy sources, the need for a cleaner energy economy, and the urgency to address the climate crisis. This project aims to lay the mathematical groundwork necessary to harness the full potential of deep machine learning approaches in enhancing power system operations, particularly in relation to renewable energy, such as wind and solar generation. The research will develop a new suite of distributed optimization tools that will empower large-scale power system operations to manage uncertainty while incorporating renewable energy resources effectively. The new algorithms will potentially transform operational practices within the power system. At the same time, the results will increase public awareness and understanding among stakeholders, regulators, policymakers, and market participants. The successful completion of this project will enable power system operators to adopt cutting-edge algorithms that significantly enhance their operational practices with renewable generation. The project will provide training and outreach opportunities to students from both institutions, particularly those from underrepresented groups in STEM. The project aims to develop and validate deep-learning-enabled distributed stochastic algorithms. These algorithms will solve large-scale, stochastic security-constrained unit commitment problems within power systems. Specifically, the project will focus on the following objectives: (i) the design of a holistic, three-stage, deep neural network-based machine learning approach; (ii) the solution strategies based on the hybrid distributed parameter system control theory; and (iii) extensive validations of the proposed algorithms using large-scale real-world power system datasets. The research will advance the field by introducing innovative techniques to address the challenges associated with power system operation under uncertainty.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.