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

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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.