摘要
热电联产机组能够有效提高电厂能源利用率、降低碳排放,优化热电负荷在电厂内不同机组间的分配方式能够有效降低电厂能耗,提升经济效益。以国电泉州电厂两台670 MW机组为研究对象,采用Ebsilon软件进行建模仿真,得到单台机组的供热域,同时分析不同热电负荷分配方式对总能耗的影响,最后以某日热电负荷为例,采用遗传算法优化热电负荷在两台机组间的分配方式。结果表明:对于单台机组,随着供热负荷的增大,其最大电负荷减小,而最小电负荷增大,机组的可调范围变小,可调性变差。不同热电负荷分配方式对机组总能耗的影响较大,总能耗随着电负荷分配不均匀程度的增加而减小,当热电负荷在两台机组间平均分配时,总能耗最大。以泉州电厂某日典型热电负荷为例,采用遗传算法优化热电负荷分配后,两台机组的总能耗减少1512.107 MW。
The cogeneration unit can effectively improve energy utilizationand reduce carbon emissions for power plant.Optimization the distribution of heat and power load among different units in the power plant can effectively reduce the power plant energy consumption and improve the economic benefits.Taking two 670 MW units of Guodian Quanzhou power plant as the research object,Ebsilon software was adopted for modeling and simulation,and the heating area of single unit is obtained.At the same time,the influence of different heat and power load distribution modes on total energy consumption was analyzed.Finally,taking a certain day’s thermal load as an example,the genetic algorithm was used to optimize the heat and power load distribution between the two units.The results show that,for a single unit,with the increase of heating load,the maximum electrical load decreases,while the minimum electrical load increases,the adjustable range of the unit becomes smaller,and the adjustability becomes worse.The total energy consumption decreases with the increase of the uneven degree of electrical load distribution.When the thermal load is evenly distributed between two units,the total energy consumption is the largest.Taking a typical thermal load of Quanzhou power plant as an example,the total energy consumption of the two units is reduced by 1512.107 MW after the heat and power load distribution was optimized by genetic algorithm.
作者
严晓生
吴振华
殷戈
伍仁杰
郭良丹
YAN Xiaosheng;WU Zhenhua;YIN Ge;WU Renjie;GUO Liangdan(Guoneng(Quanzhou)Thermal Power Limited,Quanzhou 362804,China;Guodian Nanjing Electric Power Test and Research Ltd.,Nanjing 210046,China)
出处
《中国测试》
CAS
北大核心
2022年第2期148-153,共6页
China Measurement & Test
基金
国家科技支撑计划(2014BAA06B01)。
关键词
供热机组
供热域
负荷分配
遗传算法
heating units
heating region
load distribution
genetic algorithm
作者简介
严晓生(1981-),男,福建闽清县人,高级工程师,硕士,主要研究方向为火电机组优化运行及节能技术。
