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基于含储热双抽机组特性的NSGA-Ⅱ改进方法研究

Improved NSGA-II Method based on the Characteristics of Double-extraction Units with Heat Storage Equipment
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摘要 为热电机组配置储热设备是提高热电联产机组灵活性的重要手段,针对目前储热设备存在的时序耦合问题以及双抽供热机组运行的边界问题,原有的NSGA-Ⅱ方法在优化调度过程中计算结果难以达到预期。本文提出了基于NSGA-Ⅱ方法初始化、交叉、变异过程的改进方法,实现了时序耦合约束的解耦。在初始化方法中,依照时间序列依次生成参数使其满足爬坡约束,从而获得满足全部约束条件的初始化种群;对满足约束条件的种群,逐时依次对个体的基因片段进行交叉和变异处理,通过对前后两个时刻的同时优化,保障每次交叉变异后产生的子代均满足约束条件。研究结果表明,本文所提出的改进的NSGA-Ⅱ方法在处理含储热设备热电联产机组优化调度问题时,可以大幅缩短计算时间。 The configuration of heat storage equipment is an important means to improve the flexibility of cogeneration units.In view of the timing coupling problem of heat storage equipment and the boundary problem of double extraction heating unit operation,the original NSGA-II method is difficult to achieve expected results in the optimization scheduling process.In this paper,an improved method based on the initialization,crossover and mutation process of NSGA-II method is proposed to realize the decoupling of timing coupling constraints.In the initialization method,the parameters are generated in turn according to the time series to satisfy the climbing constraint,so as to obtain the initial population that satisfies all the constraints.For the population that meets the constraints,the individual s gene fragments are crossed and mutated in turn.By optimizing the two moments at the same time,the offspring generated after each crossover and mutation are guaranteed to meet the constraints.The results show that the improved NSGA-II method proposed in this paper can greatly shorten the calculation time when dealing with the optimal scheduling problem of cogeneration units with heat storage equipment.
作者 汤若鑫 王培红 王乾 王玟苈 TANG Ruo-xin;WANG Pei-hong;WANG Qian;WANG Min-li(School of Energy and Environment,Southeast University,Nanjing 210096,China;Jiangsu University of Science and Technology,Zhenjiang 212003,China)
出处 《节能技术》 CAS 2023年第4期315-323,共9页 Energy Conservation Technology
关键词 熔融盐储热 时序耦合约束 NAGA-Ⅱ算法 日前负荷优化调度 多目标优化 thermal storage equipment temporal coupling constraints NSGA-II method day-ahead load optimization scheduling multi-objectives optimization
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