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: 2229409
• 负责人: Daniel Maldonado
• 金额: $ 11.02万
• 依托单位: University of Chicago
• 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=2229409&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.

电力行业占2020年所有经济领域所有碳排放的第二大部分。可再生能源资源，尤其是风和太阳能，对于将网格脱碳并确保国家的未来繁荣和福利至关重要。但是，由于它们固有且不可避免的间歇性和可变性，因此在国家能源组合中成功整合可再生能源的能源对日常网格操作构成了根本挑战。在计划过程中不考虑这种不确定性会导致服务损失和电网去稳定化，从而危害了脱碳目标的实现，而且危害了系统的可靠性。该项目开发了用于电网操作计划的下一代数学方法，计算机模型和算法，这些算法准确地，系统地考虑了可再生不确定性的非正常和多模式性质，以及管理电力网络的非线性和通常的反直觉物理定律。该项目的方法和计算机实施应受益并为各种规划工具受益，包括电力部门以及更广泛的能源领域，包括专门从事Power Systems软件的私人公司和供应商。该项目通过培训能源系统优化和电力网格操作的基础来进一步影响教育和更广泛的社会，从而使他们能够运用其分析技能来设计更环保和经济上有效的未来能源系统。最基本的电力系统计划问题之一称为交流最佳功率流。尽管在数据中展示了电网计划，但在电网计划中尚未考虑过对电力负载和可再生能源预测中非高斯错误的一般处理。在这种情况下，该项目通过开发一种在非高斯概率约束下优化的新方法来严格整合风险和不确定性。这是通过利用高斯混合模型的可表示性和分析性以及设计模块化的算法来实现的，该算法足以允许当前方法可以很好地使用高斯错误，仅通过较小的修改可以重复使用高斯错误。预计该方法的通用性将在更广泛的机会受限优化领域中刺激新算法，包括非线性非凸问题，其约束受高斯混合物不确定性的影响。该项目还通过设计新颖的非高斯歧义集来严格地解释混合模型参数的错误指定，这些歧义集以前从未研究过，但有潜力能够发现可靠的网络操作点，并提高样本外的性能和可靠性。该项目使用真实的实用数据来指导模型验证和实验，并为系统操作员提供了一系列实用建议，以促进采用开发方法。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的审查标准通过评估来进行评估的。