Risk-Aware Market Clearing for Power Systems (RAMC)



Risk-Aware Market Clearing

The increasing role of renewable energy sources is challenging grid operations, which have traditionally relied on highly predictable load and generation. Future grid operations must balance generation costs and systemlevel risk, shifting from deterministic to stochastic optimization and risk management. The Risk-Aware Market Clearing (RAMC) project will provide a blueprint for an end-to-end, data-driven approach where risk is explicitly modeled, quantified, and optimized, striking a tradeoff between cost and system-level risk minimization. The RAMC project focuses on challenges arising from increased stochasticity in generation, load, flow interchanges with adjacent markets, and extreme weather. RAMC addresses these challenges through innovations in machine learning, sampling, and optimization. Starting with the risk quantification of each individual asset obtained from historical data, RAMC learns the correlations between the performance and risk of individual assets, optimizes the selection of asset bundles, and quantifies the system-level risk.

Roshan Joseph

Roshan Joseph is an A. Russell Chandler III Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech.

Joseph's research interests are in the broad areas of applied and computational statistics. A major focus of his research is in developing novel statistical methods for solving complex engineering problems. He has several years of consulting experience in solving quality-related problems in industries.

C. F. Jeff Wu

C. F. Jeff Wu is the Coca-Cola Chair in Engineering Statistics and Professor in the H. Milton School of Industrial and Systems Engineering at Georgia Tech.

Wu has made fundamental contributions to the methodological and theoretical developments of a wide variety of statistical and application areas such as design and analysis of experiments (optimal, sequential, factorial), computer experiments, robust parameter design, statistical computing, re-sampling methods, complex surveys, nonlinear least squares, and uncertainty quantification. 

Pascal Van Hentenryck

Pascal Van Hentenryck is the Associate Chair for Innovation and Entrepreneurship and an A. Russell Chandler III Chair and Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. He has taught undergraduate and graduate classes in computer science, operations research, and computational biology at Brown University, National Interactive College of Technology Australia, University of Michigan, and Georgia Tech. In addition, he leads the Seth Bonder Camp in Computational and Data Science for high school students each summer.

Van Hentenryck's research focuses in artificial intelligence, data science, and operations research. His current focus is to develop methodologies, algorithms, and systems for addressing challenging problems in mobility, energy systems, resilience, and privacy. He is also leading the Socially Aware Mobility project to foster an equitable and accessible transit system in Atlanta.

Andy Sun

Andy Sun is an Anderson-Interface Early Career Professor and Associate Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. 

Sun is interested in building new bridges between the theory of optimization under uncertainty, distributed optimization, convexification of nonconvex structures, and control of dynamical systems, and in developing fundamental understanding and new analytical tools for renewable energy integration, power grid optimization, and stability and resiliency of interconnected energy systems and transportation systems.