基于退火演化算法和遗传算法的机组优化组合算法

机组组合问题是编制短期发电计划时首先要解决的问题，合理的开停机方案将带来很大的经济效益。现代电力系统对机组优化组合算法的收敛速度和解的质量要求越来越高，作者从改善传统算法这两方面着手，根据退火演化算法和遗传算法各自的特点，提出了一种用于机组优化组合的组合算法。与传统的一些优化算法相比，该组合算法具有搜索速度快，收敛性好，而且解的质量相当高。通过对实际系统的测算，验证了该方法的有效性和优越性。该方法具有良好的并行性，易于在并行计算机上实现。

OPTIMIZATION OF UNIT COMMITMENT BASED ON ANNEALING EVOLUTIONARY ALGORITHM AND GENETIC ALGORITHM

Unit commitment is a very important issue of generation scheduling in electric power system. A rational generation schedule can bring obvious economic profit. Modern power system requires the higher convergence speed and better solution quality of the generation scheduling algorithm. Starting with the improvement of the above mentioned two issues in traditional algorithm and according to the respective characteristics of annealing evolutionary algorithm and genetic algorithm, a combinatorial algorithm for optimal generation unit commitment is proposed. Comparing with traditional optimal algorithm the proposed algorithm and testing this algorithm in the unit commitment of a real power system, its effectiveness and advantages are proved. Besides, because this algorithm possesses good parallelism and is easy to implement by parallel computer, so it is promising to apply this algorithm to practical power system.