摘要
在峰谷分时电价背景下建立梯级水电站短期发电效益最大模型,通过自适应混沌蜂群算法(ACABC)对模型进行求解。为兼顾算法全局搜索能力及局部求解精度,引入搜索步长和蜜源选择概率自适应修正策略。考虑到模型求解过程中涉及的多维复杂约束问题,提出一种基于双向廊道的约束处理技术,用于规范解寻优空间。以峰谷分时电价下清江梯级短期优化调度为背景进行仿真建模,将计算结果与普通人工蜂群算法(ABC)和差分进化算法(DE)所得结果进行比较。实例研究表明,ACABC处理灵活且具有更强的全局搜索能力,工程实用价值较高。
Taking peak-load regulation and time-of-use tariff as research background,this paper established a short-term scheduling model of cascade hydropower stations,and proposed an adaptive chaotic artificial bee colony( ACABC) algorithm as a solution strategy. To balance the global and local searching ability,this paper brought adaptive step size and source selective probability into ACABC. Considering the multi-dimensional complex constraint,the author introduced a bidirectional gallery constraint handling method to limit searching space.Qingjiang cascade station was taken as a calculation example to approve this strategy,and,ABC and DE algorithm were carried out as a comparison. The result indicates that ACABC has much stronger global searching ability than that of ABC and DE,and shows high practical utility.
出处
《人民黄河》
CAS
北大核心
2017年第2期102-106,111,共6页
Yellow River
基金
国家自然科学基金资助项目(51109088)
教育部博士点基金新教师项目(20110142120020)
关键词
梯级电站
调峰
分时电价
混沌搜索
人工蜂群
发电效益
cascaded hydropower stations
peak-load regulation
time-of-use tariff
chaotic search
artificial bee colony
generation benefit
作者简介
方仍存（1980-）,男,湖北麻城人,高级工程师,博士,主要研究方向为电网规划.
李超顺（1983-）,男,湖北武汉人,博士,研究方向为水电能源优化运行.E-mail：hustfrc@163.com
