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改进烟花算法在火电厂负荷优化分配中的应用 被引量:6

Application of Improved Fireworks Algorithm in Optimal Load Distribution of Thermal Power Plant
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摘要 现有的负荷分配解决办法较少考虑机组快速响应网调速率与机组少变动策略,并且没有很好地解决厂级分配中快速性与经济性的矛盾.针对这些问题,利用改进的烟花算法,通过自适应动态改变负荷边界和结合爬坡速率约束,实现对负荷变化的快速响应;同时按规定的优先顺序对机组进行排序,当机组负荷发生变化时,在满足约束条件的前提下,实现最少机组变动负荷;通过引入运行切换的方法,使最低煤耗、快速调节和少机组变动之间的矛盾得到一定解决,实现柔性优化.最后给出了厂级负荷在线优化分配的具体算法步骤,并进行了4个机组的算例分析,仿真结果对比验证了算法的有效性. In the existing load distribution solutions, there is little consideration on the rapid response of units to network speed and on the small change strategy of units. Moreover,the contradiction between the speed and the economy in the factory-level distribution has not been solved yet. To deal these problems, this paper proposes an algorithm based on the improved fireworks algorithm, which changes the load boundary and climbs constraint adaptively, to dynamically achieve rapid response of load changes. At the same time, the algorithm sorts the units according to a predetermined priority order to achieve the minimum unit load changes under the certain constraints when unit load changing occurs. Finally, it introduces a switching mechanism to solve the contradiction among the lowest coal-consumption, quick adjustment and the little unit-change with flexible optimization.The paper presents a specific algorithm of plant-level real-time online optimization of load distribution and conducts the simulation for an example with four units. The simulation results show the effectiveness of the algorithm.
作者 曾德良 邓志光 陈彦桥 ZENG De-liang;DENG Zhi-guang;CHEN Yan-qiao(Beijing Key Laboratory of New Technology and System on Measuring and Control for Industrial Process, North China Electric Power University, Beijing 102206, China;Guo Dian Science and Technology Research Institute, Beijing 100023, China)
出处 《应用科学学报》 CAS CSCD 北大核心 2018年第3期542-552,共11页 Journal of Applied Sciences
基金 国家重点研发计划项目基金(No.2017YFB0602105)资助
关键词 负荷实时在线分配 改进烟花算法 快速调节 少机组变动 柔性优化 load real time and online dispatch improved firework algorithm fast regulation minimum unit change flexibility optimization
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