CAREER: Frequency-Constrained Energy Scheduling for Renewable-Dominated Low-Inertia Power Systems

这个NSF职业项目旨在确保风能和太阳能等可再生能源渗透率高的未来低惯性电力系统的可靠性和稳定性。虽然可再生能源的快速增长可以显著降低电网的碳含量，但它也会导致电网的低惯性问题，从而对电网的频率稳定性产生很大影响。解决这一低惯性挑战对电网运营至关重要。该项目将在提高电力系统频率稳定性方面带来革命性的变化，同时确保有足够的电力容量来满足电力需求。这将通过利用先进的机器学习技术来准确预测关键频率稳定指标来实现，这些指标将被集成到前一天的能源调度模型中。该项目的智能优点包括开发了一种新的频率受限的能量调度模型，并使用机器学习来降低模型的复杂性和提高计算效率。该项目的更广泛影响包括促进清洁能源的电网整合，开发开源课程，鼓励女性、代表性不足和少数族裔学生参与研究和教育活动。在大多数实际电力系统中，传统的同步发电机正逐渐被风能和太阳能等逆变器资源所取代。这一过渡将给电网运行和稳定带来低惯性挑战。然而，传统的提前一天机组组合模型不能有效地考虑这一新挑战的影响。为了弥补这一差距，该项目将利用优化方法和机器学习技术，特别是图形神经网络，开发一种创新的频率约束机组组合(FCUC)模型。该项目将首先开发一个频率稳定性能指标估计模型，然后将其作为附加约束集成到FCUC中，以执行频率稳定要求。此外，该项目将开发机器学习辅助方法，通过将变量子集转换为常量并消除不必要的非绑定约束来降低FCUC的模型复杂性。最后，该项目将开发机器学习辅助的加速分解算法，以进一步提高计算效率，并确保在指定的时间框架内获得高质量的解决方案。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This NSF CAREER project aims to ensure the reliability and stability of future low-inertia power systems with high penetration of renewable generation resources such as wind and solar power. Although the fast growth of renewable energy could significantly decarbonize the power grid, it will lead to the low-inertia issue that substantially impacts the grid frequency stability. Addressing this low-inertia challenge is crucial for grid operations. The project will bring transformative change in enhancing power system frequency stability while ensuring sufficient power capacities to meet the electrical demand. This will be achieved by leveraging advanced machine learning technologies to accurately predict critical frequency stability metrics, which will be integrated into the day-ahead energy scheduling model. The intellectual merits of the project include developing a novel frequency-constrained energy scheduling model, and using machine learning to reduce model complexity and enhance computational efficiency. The broader impacts of the project include promoting the grid integration of clean energy, developing open-source curriculum, and encouraging the engagement of female, underrepresented and minority students in research and educational activities.In most practical power systems, traditional synchronous generators are gradually being replaced by inverter-based resources such as wind and solar power. This transition will introduce the low-inertia challenge to grid operation and stability. However, traditional day-ahead unit commitment models cannot effectively consider the impact of this emerging challenge. To bridge the gap, this project will develop an innovative frequency-constrained unit commitment (FCUC) model by leveraging optimization methods and machine learning technologies especially graph neural networks. This project will first develop a frequency stability performance metric estimation model and then integrate it into FCUC as additional constraints to enforce frequency stability requirements. Moreover, this project will develop machine learning-assisted approaches to reduce the model complexity of FCUC by converting a subset of variables into constants and eliminating unnecessary nonbinding constraints. Lastly, this project will develop machine learning-assisted accelerated decomposition algorithms to further enhance computational efficiency and ensure quality solutions can be obtained within the specified timeframe.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.