Collaborative Research: AMPS: Robust Failure Probability Minimization for Grid Operational Planning with Non-Gaussian Uncertainties

合作研究：AMPS：具有非高斯不确定性的电网运行规划的鲁棒故障概率最小化

• 批准号: 2229408
• 负责人: Anirudh Subramanyam
• 金额: $ 29万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-12-01 至 2025-11-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2229408&HistoricalAwards=false
• 关键词: Collaborative; Research; AMPS; Robust; Failure

The electric power industry accounted for the second-largest portion of all carbon emissions across economic sectors in 2020. Renewable energy resources, particularly wind and solar, are critical to decarbonizing the grid and ensuring the nation's future prosperity and welfare. However, because of their inherent and unavoidable intermittency and variability, successful integration of renewable energy resources in the nation's energy mix poses fundamental challenges for day-to-day grid operations. Failure to account for this uncertainty during planning can result in loss of service and grid de-stabilization, thus jeopardizing not only the achievement of decarbonization targets but also system reliability. This project develops the next generation of mathematical methods, computer models, and algorithms for grid operational planning, which accurately and systematically take into account the non-normal and multi-modal nature of renewable uncertainty, as well as the nonlinear and often counter-intuitive physical laws that govern electric power networks. The project's methods and computer implementations shall benefit and inform diverse planning tools, both within the electric power sector as well as the broader energy sector, including those of private companies and vendors who specialize in power systems software. The project further impacts education and the broader society by training undergraduate and graduate STEM students in energy systems optimization and the foundations of electric power grid operations, thereby enabling them to apply their analytical skills to design more environmentally- and economically-efficient future energy systems.The project contributes a general methodology, including new mathematical models, theory, and algorithms, to systematically account for non-Gaussian error distributions of renewable energy forecasts, in one of the most fundamental power system planning problems called AC Optimal Power Flow. A general treatment of non-Gaussian errors in electric load and renewable energy forecasts has not been considered before in grid planning, despite being exhibited in data. The project rigorously integrates risk and uncertainty in this context by developing a novel methodology for optimization under non-Gaussian probabilistic constraints. This is achieved by exploiting the representability and analyticity of Gaussian mixture models and by designing algorithms that are modular enough to allow current methods which are proven to work well for Gaussian errors to be reusable with only minor modifications. The generality of the approach is expected to spur new algorithms in the broader field of chance-constrained optimization, including nonlinear nonconvex problems whose constraints are affected by Gaussian mixture uncertainties. The project also rigorously accounts for misspecification of the mixture model parameters by designing novel non-Gaussian ambiguity sets, which have not been studied before but have the potential to enable the discovery of robust network operating points with improved out-of-sample performance and reliability. The project uses real utility data to guide model validation and experimentation and also provides a set of practical recommendations for system operators to facilitate the adoption of the developed methods.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.

2020年，电力行业占所有经济部门碳排放的第二大部分。可再生能源，特别是风能和太阳能，对于电网脱碳和确保国家未来的繁荣和福利至关重要。然而，由于其固有的和不可避免的不稳定性和可变性，可再生能源资源在国家能源结构中的成功整合对日常电网运营构成了根本挑战。如果在规划过程中不考虑这种不确定性，可能会导致服务中断和电网不稳定，从而不仅危及脱碳目标的实现，而且危及系统的可靠性。该项目开发了下一代数学方法，计算机模型和算法，用于电网运营规划，准确，系统地考虑到可再生不确定性的非正常和多模态性质，以及管理电力网络的非线性和往往违反直觉的物理定律。该项目的方法和计算机实现将有利于并通知不同的规划工具，无论是在电力部门以及更广泛的能源部门，包括那些专门从事电力系统软件的私营公司和供应商。该项目通过对本科生和研究生STEM学生进行能源系统优化和电网运行基础方面的培训，进一步影响教育和更广泛的社会，从而使他们能够应用他们的分析技能来设计更环保和更经济的未来能源系统。该项目提供了一种通用方法，包括新的数学模型，理论和算法，系统地考虑可再生能源预测的非高斯误差分布，在最基本的电力系统规划问题之一，称为AC最优潮流。电力负荷和可再生能源预测中的非高斯误差的一般处理在电网规划中以前没有考虑过，尽管在数据中有所表现。该项目通过开发一种新的非高斯概率约束下的优化方法，在这种情况下严格集成了风险和不确定性。这是通过利用高斯混合模型的可表示性和分析性，并通过设计足够模块化的算法来实现的，这些算法被证明可以很好地用于高斯误差，只需进行微小的修改即可重复使用。该方法的通用性，预计将刺激新的算法在更广泛的领域的机会约束优化，包括非线性非凸问题，其约束受高斯混合不确定性。该项目还通过设计新颖的非高斯模糊集来严格解释混合模型参数的错误指定，这些模糊集以前没有研究过，但有可能发现具有改进的样本外性能和可靠性的鲁棒网络操作点。该项目使用真实的公用事业数据来指导模型验证和实验，并为系统操作员提供一套实用建议，以促进采用所开发的方法。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。