Two-level, two-objective evolutionary algorithms for solving unit commitment problems

A two-level, two-objective optimization scheme based on evolutionary algorithms (EAs) is proposed for solving power generating Unit Commitment (UC) problems by considering stochastic power demand variations. Apart from the total operating cost to cover a known power demand distribution over the scheduling horizon, which is the first objective, the risk of not fulfilling possible demand variations forms the second objective to be minimized. For this kind of problems with a high number of decision variables, conventional EAs become inefficient optimization tools, since they require a high number of evaluations before reaching the optimal solution(s). To considerably reduce the computational burden, a two-level algorithm is proposed. At the low level, a coarsened UC problem is defined and solved using EAs to locate promising solutions at low cost: a strategy for coarsening the UC problem is proposed. Promising solutions migrate upwards to be injected into the high level EA population for further refinement. In addition, at the high level, the scheduling horizon is partitioned in a small number of subperiods of time which are optimized iteratively using EAs, based on objective function(s) penalized to ensure smooth transition from/to the adjacent subperiods. Handling shorter chromosomes due to partitioning increases method’s efficiency despite the need for iterating. The proposed two-level method and conventional EAs are compared on representative test problems.