A Distributed Framework for Solving and Benchmarking Security Constrained Unit Commitment With Warm Start

This paper discusses several methods to improve commercial optimization solver performance on day-ahead security constrained unit commitment through warm start and lazy constraint settings. Data analytics is performed to greatly improve the quality of the initial commitment solution and lazy constraint setting. A distributed optimization framework is proposed to take advantage of the diversity from prevalent solvers (GUROBI and CPLEX) and different warm start strategies. A systematic distribution profile based benchmarking method is also proposed.