考虑火电机组深度调峰的实时发电计划模型及应用
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摘要
伴随我国大规模新能源接入和用电峰谷差日趋增大,调峰需求逐步提升,而由于我国电源结构不尽合理,调峰电源紧缺,造成电网调峰困难。深度挖掘火电机组深度调峰能力,分析其运行特性,在常规优化模型的基础上,引入深度调峰出力平稳段运行时间、最小深度调峰时间等约束,并对机组最大最小出力、爬坡等常规约束进行改进,建立考虑火电机组深度调峰的实时发电计划优化模型。采用广东电网实际运行数据进行算例分析,结果满足电网实际运行需求。
The demand of peak regulation is gradually growing along with the access of large-scale renewable energy and the gradual increase of electricity peak-valley difference,while the power source for peak regulation is insufficient due to the unreasonable power source structure in China,which causes difficulties in peak regulation of power grid. The deep peak regulation ability of thermal power units is deeply excavated,and their operation characteristics are analyzed. On the basis of conventional optimization model,the constraints such as operation time in stationary segment of deep peak regulation output and minimum deep peak regulation time are introduced,the conventional constraints such as maximum and minimum unit output and ramping are improved,and an optimal real-time generation scheduling model with the consideration of deep peak regulation of thermal power units is built. The practical operation data of Guangdong Power Grid is used for case analysis,and the results meet the requirement of practical power grid operation.
The demand of peak regulation is gradually growing along with the access of large-scale renewable energy and the gradual increase of electricity peak-valley difference,while the power source for peak regulation is insufficient due to the unreasonable power source structure in China,which causes difficulties in peak regulation of power grid. The deep peak regulation ability of thermal power units is deeply excavated,and their operation characteristics are analyzed. On the basis of conventional optimization model,the constraints such as operation time in stationary segment of deep peak regulation output and minimum deep peak regulation time are introduced,the conventional constraints such as maximum and minimum unit output and ramping are improved,and an optimal real-time generation scheduling model with the consideration of deep peak regulation of thermal power units is built. The practical operation data of Guangdong Power Grid is used for case analysis,and the results meet the requirement of practical power grid operation.
引文
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[2]谢丽荣,张勇,王岗,等.中短期多周期发电计划的协调方式和闭环控制[J].电力系统自动化,2014,38(10):126-130.XIE Lirong,ZHANG Yong,WANG Gang,et al. Coordination method and closed-loop control of medium-short term multi-cycle generation scheduling[J]. Automation of Electric Power Systems,2014,38(10):126-130.
[3]胡泽春,丁华杰,孔涛.风电-抽水蓄能联合日运行优化调度模型[J].电力系统自动化,2012,36(2):36-41.HU Zechun,DING Huajie,KONG Tao. A joint daily operational optimization model for wind power and pumped-storage plant[J]. Automation of Electric Power Systems,2012,36(2):36-41.
[4]牛林华,龚庆武,黄炳翔,等.大规模风电入网下的风气火电力系统联合优化调度[J].电力自动化设备,2015,35(11):52-58.NIU Linhua,GONG Qingwu,HUANG Bingxiang,et al. Optimal dispatch of wind-gas-coal power generation system with large-scale wind farm[J]. Electric Power Automation Equipment,2015,35(11):52-58.
[5]肖欣,周渝慧,何时有,等.含流域梯级水电的水火风互补发电系统联合运行优化[J].电力自动化设备,2018,38(2):100-108.XIAO Xin,ZHOU Yuhui,HE Shiyou,et al. Optimal joint operation of hydro-thermal-wind hybrid power system with cascaded hydro power[J]. Electric Power Automation Equipment,2018,38(2):100-108.
[6]牛哲文,余泽远,李波,等.基于深度门控循环单元神经网络的短期风功率预测模型[J].电力自动化设备,2018,38(5):36-42.NIU Zhewen,YU Zeyuan,LI Bo,et al. Short-term wind power forecasting model based on deep gated recurrent unit neural network[J]. Electric Power Automation Equipment,2018,38(5):36-42.
[7]叶林,杨丹萍,赵永宁.风电场风能资源评估的测量-关联-预测方法综述[J].电力系统自动化,2016,40(3):140-151.YE Lin,YANG Danping,ZHAO Yongning. Overview of wind resource measure-correlate-predict methods for wind farms[J]. Automation of Electric Power Systems,2016,40(3):140-151.
[8]龚莺飞,鲁宗相,乔颖,等.光伏功率预测技术[J].电力系统自动化,2016,40(4):140-151.GONG Yingfei,LU Zongxiang,QIAO Ying,et al. An overview of photovoltaic energy system output forecasting technology[J]. Auto-mation of Electric Power Systems,2016,40(4):140-151.
[9]王继东,冉冉,宋智林.基于改进深度受限玻尔兹曼机算法的光伏发电短期功率概率预测[J].电力自动化设备,2018,38(5):43-49.WANG Jidong,RAN Ran,SONG Zhilin. Probability forecast of shortterm photovoltaic power generation based on improved depth restricted Boltzmann machine algorithm[J]. Electric Power Automation Equipment,2018,38(5):43-49.
[10]张怡,张峰,朱炳铨,等.适用于大规模新能源接入的日内滚动计划闭环控制系统[J].电力自动化设备,2018,38(3):52-58.ZHANG Yi,ZHANG Feng,ZHU Bingquan,et al. Closed-loop control system of intraday rolling generation schedule for renewable energy generation integration[J]. Electric Power Automation Equipment,2018,38(3):52-58.
[11]徐帆,陈之栩,张勇,等.实时发电计划模型及其应用[J].电力系统自动化,2014,38(6):117-122.XU Fan,CHEN Zhixu,ZHANG Yong,et al. Real-time generation scheduling model and its application[J]. Automation of Electric Power Systems,2014,38(6):117-122.
[12]徐帆,丁恰,谢丽荣,等.实时发电计划闭环控制模型与应用[J].电网技术,2014,38(11):3187-3192.XU Fan,DING Qia,XIE Lirong,et al. A closed-loop control model for real-time generation scheduling and its application[J]. Power System Technology,2014,38(11):3187-3192.
[13]张彦涛,任景,孙骁强,等.基于广义联络线网省协调的两阶段实时发电计划模型[J].电力系统自动化,2015,39(6):128-133.ZHANG Yantao,REN Jing,SUN Xiaoqiang,et al. Two-stage realtime generation scheduling model based on coordination of regional and provincial power grid generalized tie-line[J]. Automation of Electric Power Systems,2015,39(6):128-133.
[14]陈浩,李战鹰.广东电网调峰分析[J].广东电力,2001,14(2):6-8.CHEN Hao,LI Zhanying. Analysis of peak shaving of Guangdong Province[J]. Guangdong Electric Power,2001,14(2):6-8.
[15]牛逸宁,艾欣,刘晓.广东电网调峰特性分析[J].广东电力,2012,25(10):1-4.NIU Yining,AI Xin,LIU Xiao. Analysis on peak shaving characteristic of Guangdong Power Grid[J]. Guangdong Electric Power,2012,25(10):1-4.