摘要
电力市场环境下,水电站短期优化调度对优化发电企业向电力市场申报的次日发电计划和最大化企业发电收益具有重要意义。为了提高短期优化调度的计算精度和效率,针对模拟退火算法和遗传算法的优缺点将两者结合起来形成退火遗传算法,改善其计算精度和速度。实例计算表明该方法是可行的。
In power market, the short-term optimal operation（STOO） of hydropower station is an important method for the electricity schedule fo maximize the income of the company. In order to improve the precision and efficiency of STOO, the annealing genetic algorithm（AGA）is presented which is based on simulated annealing algorithm and genetic algorithm, the convergence and solution quality are improved. The precision of AGA is better than standard genetic algorithm, the computed speed is faster than simulated annealing algorithm. The simulated computing result shows that the proposed method is effective.
出处
《水力发电学报》
EI
CSCD
北大核心
2008年第6期18-21,共4页
Journal of Hydroelectric Engineering
基金
国家自然科学基金重点项目资助(50539140)
关键词
水电站
短期优化调度
退火遗传算法
hydropower station
short-term optimal operation
annealing genetic algorithm
