Joint Chance-constrained Unit Commitment: Statistically Feasible Robust Optimization with Learning-to-Optimize Acceleration

Renewable energy penetration increases the power grid's operational uncertainty, threatening the economic effectiveness and reliability of the grid. In this paper, we examine how uncertainty affects unit commitment (UC), a classical electricity market procedure. Stochastic programming has helped handle uncertainty for UC and performed well with distribution knowledge, but the lack of such information in practice deteriorates the effectiveness. Such a dilemma becomes more pronounced when dealing with joint chance constraints solely based on samples. To address this issue, we introduce statistical feasibility into UC and develop robust sample-based algorithms employing appropriate uncertainty sets to hedge uncertainty without distribution dependence. We also propose a learn-to-optimize acceleration method to convexify UC. Furthermore, we construct an optimization kernel to boost computational efficiency.