A Two-Level Method for Solving Power Generating Unit Commitment Problems

This paper is concerned with the solution of unit commitment problems by means of a two-level evolutionary algorithm (EA), handling the encoded operational states of units. The EA is assisted by the augmented Lagrange relaxation method to compute the optimal units´ loading, for each candidate configuration. Emphasis is laid on the reduction of the CPU cost of the proposed method and, for this purpose, a two- level EA has been devised. At the first-preparatory level, the time units (hours) are grouped, based on heuristics. This gives rise to a coarse-grained optimization problem with a reduced number of unknowns which can readily be solved using EAs, without taking into consideration constraints related to units´ start-up and shutdown. The so-obtained, sub-optimal and, likely, infeasible solution is then processed by the second level for further refinement. At this level, instead of optimizing the unit commitment over the whole period of time, a small number of consecutive sub-periods are formed and the corresponding optimization sub-problems are solved iteratively. For each sub- problem, an EA coupled with chromosome repairing and cost function penalization to account for inconsistencies between the current sub-problem solution and those computed at the adjacent sub-intervals.