Risk-Sensitive Market Design for the Power Grid

Integration of variable renewable and distributed energy resources in the grid makes demand and supply conditions uncertain. In this talk, we explore customized algorithm to tackle risk-sensitive electricity market clearing problems, where power delivery risk is modeled via the conditional value at risk (CVaR) measure. The market clearing formulations are such that they allow a system operator to effectively explore the cost-reliability tradeoff. We discuss algorithmic architectures and their convergence properties to solve these risk-sensitive optimization problems at scale. The first half of this talk will focus on a CVaR-sensitive optimization problem that can be cast as a large linear program. For this problem, we propose and analyze an algorithm that shares parallels and differences with Benders decomposition. The second half of this talk will focus on another CVaR-sensitive problem for which we propose and analyze sample complexity of a stochastic primal-dual algorithm.