CAREER: Risk-Averse Decision Making via Chance-Constrained Programming for Power Systems

NSF CAREER项目旨在开发规避风险的决策模型和方法，以管理高度不确定的大规模可再生能源的整合，使社会受益于更清洁，更可靠和更具成本效益的电力系统。可再生能源的增加给电力系统带来了巨大的挑战，这是由于生产的不稳定性和有限的可预测性。在经济和可靠地平衡电力供应和需求时，传统的运营策略可能不足以应对这种增加的不确定性。该CAREER项目将弥合这一紧迫的不确定性差距，以有效整合可再生能源，从而有助于确保系统的可靠性和成本效益，这将为电力行业带来变革。该项目的智力优势包括开发新的风险规避决策模型，以适应电力系统的性质和决策者的风险偏好，并开发新的计算效率高的解决方案，为所有利益相关者和最终用户社区提供实用，有用的解决方案。该项目更广泛的影响包括：（一）通过指导本科生为这个职业生涯项目进行实践研究，并将研究结果与熟悉的应用程序联系起来，以激励下一代学生追求工程职业，将亚利桑那大学的研究和教育结合起来;（二）纳瓦霍民族能源挑战的可持续解决方案;以及（iii）在美国经济中具有社会重要性的行业（如运输和金融）存在高度不确定性的情况下，将技术转移到其他应用。本项目将采用机会约束规划（风险厌恶随机规划的一个重要变种，以对抗不利的随机性）来解决电力系统运行中的两个核心问题：实时调度和长期发电规划。研究目标和任务是：（1）应用基于采样的方法来开发机会约束的凸近似;（2）开发分解方法并在并行计算框架中实现分解算法，以解决不确定大规模电力系统的两个实际问题。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

This NSF CAREER project aims to develop risk-averse decision-making models and methodologies to manage the integration of highly uncertain, large-scale renewable energy for society to benefit from cleaner, more reliable, and cost-effective power systems. The increase in renewable energy presents power systems with significant challenges due to the intermittency and limited predictability of production. Traditional operational strategies may be inadequate in the presence of such increased uncertainty when balancing power supply and demand economically and reliably. This CAREER project will bridge this pressing uncertainty gap to effectively integrate renewable energy and thus help to ensure system reliability and cost-effectiveness, which will bring transformative change to the power industry. The intellectual merits of the project include developing new risk-averse decision-making models to accommodate the nature of power systems and decision makers’ risk preferences, and developing new computationally efficient solution approaches to enable practical, useful solutions for all stakeholder and end-user communities. The broader impacts of the project include: (i) integrating research and education at the University of Arizona through mentoring undergraduate students in hands-on research for this CAREER project and connecting the research results with familiar applications to inspire the next generation of students to pursue a career in engineering; (ii) sustainable solutions to energy challenges in the Navajo Nation; and (iii) technology transfer to other applications in the presence of highly uncertainty for societally important industries in the US economy, such as transportation and finance.This project will adopt chance-constrained programming (an important variant of risk-averse stochastic programming against unfavorable randomness) to solve two core problems in power system operations: real-time dispatch and long-term generation planning. The research objectives and tasks will: (1) apply sampling-based methods to develop convex approximations for the chance constraints; and (2) develop decomposition approaches and implement the decomposition algorithms in a parallel-computing framework to solve the two example practical problems for uncertain, large-scale power systems. The developed approaches are expected to overcome the inherent computational challenges faced in actionable situations.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.