计及多工况对机组寿命损耗影响的机组组合优化模型

doi:10.16183/j.cnki.jsjtu.2023.401

摘要/Abstract

摘要：

火电机组面临寿命损耗加快但服役期限延长的矛盾现状.一方面,大规模新能源并网导致机组调峰工况增多,损耗加快;另一方面,现役机组将在碳中和之前达到设计寿命,电力系统灵活运行要求延长机组服役期限.因此,在调度模拟过程中考虑不同工况对机组寿命造成的损耗,优化机组运行结构,对实现机组延寿,助力碳减排意义重大.为尽可能使理论研究时的机组寿命损耗接近实际值,摒弃传统模型中仅对深调峰工况机组寿命损耗进行平均化计算的思路,区分并建立火电机组常规工况及多种特殊工况判定条件,将机组寿命损耗成本融入目标函数中并修改对应约束条件,构建考虑火电机组多工况寿命损耗问题的机组组合模型.算例验证表明:常规模型低估了机组实际损耗成本,而所提模型在考虑多工况寿命损耗的基础上,能降低运行成本,减少机组寿命损耗,同时进一步发挥火电机组调峰能力,促进风电消纳.

关键词: 火电机组, 运行损耗, 机组组合, 寿命损耗, 服役期限

Abstract:

Thermal power units face a dilemma of accelerated lifespan degradation and extended service duration. On one hand, large-scale integration of new energy sources has increased peak shaving conditions and accelerated losses of the units. On the other hand, service units will reach designed lifespan before carbon neutrality is achieved, while flexible operation of the power system necessitates extending their service life of units. Therefore, it is of great significance to consider the losses caused by varicus operating conditions on the lifespan of the unit and optimize the operating structure of the unit in scheduling simulation for unit longevity and carbon reduction efforts. To make unit life losses in theoretical research more practical, the traditional model that averages the losses in deep peak shaving conditions has been discarded. Instead, new judgment criteria for conventional and various special operating conditions of thermal power units are established. The lifespan loss cost of the unit is integrated into the operating objective function and the corresponding constraint conditions are modified. Finally, a unit commitment model considering the multi-operating condition lifespan losses of thermal power units is constructed. Example simulations indicate that the conventional model underestimates the actual loss cost of the units. In constrast, the proposed model can not only reduce the operating cost and unit life loss by considering the lifespan impacts of multi-operating conditions, but also enhance the peak shaving capacity of thermal power units and promote wind power consumption.

Key words: thermal power unit, operation loss, unit commitment, lifespan degradation, service duration

中图分类号:

TM744

罗逸夫, 胡秦然, 钱涛, 陈涛, 张远实, 章飞, 王琦. 计及多工况对机组寿命损耗影响的机组组合优化模型[J]. 上海交通大学学报, 2025, 59(6): 768-779.

LUO Yifu, HU Qinran, QIAN Tao, CHEN Tao, ZHANG Yuanshi, ZHANG Fei, WANG Qi. Unit Commitment Optimization Model Considering Impact of Multiple Operating Conditions on Unit Life Loss[J]. Journal of Shanghai Jiao Tong University, 2025, 59(6): 768-779.

图/表 15

图1

表1

图2

表2

图3

表3

表4

表5

图4

表6

表7

图5

表8

图6

图7

参考文献 24

[1]	BROUWER A S, VAN DEN BROEK M, SEEBREGTS A, et al. Operational flexibility and economics of power plants in future low-carbon power systems[J]. Applied Energy, 2015, 156: 107-128.
[2]	刘永奇, 陈龙翔, 韩小琪. 能源转型下我国新能源替代的关键问题分析[J]. 中国电机工程学报, 2022, 42(2): 515-524.
	LIU Yongqi, CHEN Longxiang, HAN Xiaoqi. The key problem analysis on the alternative new energy under the energy transition[J]. Proceedings of the CSEE, 2022, 42(2): 515-524.
[3]	金震杰, 纪冬梅, 吴凌轩. 某超超临界汽轮机转子蠕变-疲劳损伤分析及寿命评估[J]. 汽轮机技术, 2022, 64(2): 123-128.
	JIN Zhenjie, JI Dongmei, WU Lingxuan. Creep-fatigue damage analysis and life assessment for a ultra-supercritical stream turbine rotor[J]. Turbine Technology, 2022, 64(2): 123-128.
[4]	赵乃龙, 吴穹, 王炜哲, 等. 超超临界汽轮机高压转子低周疲劳及损伤分析[J]. 上海交通大学学报, 2015, 49(5): 590-594.
	ZHAO Nailong, WU Qiong, WANG Weizhe, et al. Numerical analysis of low-cycle fatigue and damage of a ultra-supercritical steam turbine high-pressure rotor[J]. Journal of Shanghai Jiao Tong University, 2015, 49(5): 590-594.
[5]	周新灵, 田宇, 李海霞, 等. 超超临界汽轮机高中压转子钢高温低周疲劳性能研究[J]. 机械工程师, 2013(7): 51-53.
	ZHOU Xinling, TIAN Yu, LI Haixia, et al. Study on the high temperature low cycle fatigue behavior of ultra-supercritical turbine HP/IP rotor steel[J]. Mechanical Engineer, 2013(7): 51-53.
[6]	ZHAO X, RU D H, WANG P, et al. Fatigue life prediction of a supercritical steam turbine rotor based on neural networks[J]. Engineering Failure Analysis, 2021, 127: 105435.
[7]	刘华锋, 王炜哲, 蒋浦宁, 等. 超超临界汽轮机转子蠕变对低周疲劳应变的影响[J]. 动力工程学报, 2010, 30(9): 715-719.
	LIU Huafeng, WANG Weizhe, JIANG Puning, et al. Influence of creep on low-cycle fatigue strain of ultra-supercritical steam turbine rotor[J]. Journal of Chinese Society of Power Engineering, 2010, 30(9): 715-719.
[8]	吴瑞康, 华敏, 秦攀, 等. 燃煤机组深度调峰对汽轮机设备的影响[J]. 热力发电, 2018, 47(5): 89-94.
	WU Ruikang, HUA Min, QIN Pan, et al. Influence of deep peak load regulation of coal-fired units on turbine equipment[J]. Thermal Power Generation, 2018, 47(5): 89-94.
[9]	JI D M, SUN J Q, SUN Q, et al. Optimization of start-up scheduling and life assessment for a steam turbine[J]. Energy, 2018, 160: 19-32.
[10]	刘盛龙. 汽轮机转子损伤分析与启动过程优化研究[D]. 杭州: 浙江大学, 2020.
	LIU Shenglong. Steam turbine rotor damage analysis and startup process optimization study[D]. Hangzhou: Zhejiang University, 2020.
[11]	程鹏, 钟芳源. 汽轮机转子蠕变-疲劳耦合寿命精细分析与传统方法的比较[J]. 动力工程, 1995, 15(6): 12-17.
	CHENG Peng, ZHONG Fangyuan. Rotor life expenditure under the concurrent action of creep and fatigue: A precise analysis as compared with the conventional[J]. Journal of Chinese Society of Power Engineering, 1995, 15(6): 12-17.
[12]	荆建平, 孟光. 汽轮机转子疲劳强度理论研究现状与展望[J]. 汽轮机技术, 2003, 45(5): 260-264.
	JING Jianping, MENG Guang. Survey of fatigue strength theory of turbine rotor[J]. Turbine Technology, 2003, 45(5): 260-264.
[13]	李一铭, 李文沅, 颜伟, 等. 基于机会约束规划模型降低机组寿命损耗的日调度计划[J]. 电网技术, 2014, 38(7): 1885-1890.
	LI Yiming, LI Wenyuan, YAN Wei, et al. Daily generation scheduling for reducing losses of unit’s lives based on the chance constrained programming model[J]. Power System Technology, 2014, 38(7): 1885-1890.
[14]	陟晶, 张高航, 邵冲, 等. 含大规模风电及光热电站的电力系统优化调度方法[J]. 电力工程技术, 2021, 40(1): 79-85.
	ZHI Jing, ZHANG Gaohang, SHAO Chong, et al. Optimal dispatching method for power system with large scale wind power and concentrated solar power plant[J]. Electric Power Engineering Technology, 2021, 40(1): 79-85.
[15]	邓婷婷, 娄素华, 田旭, 等. 计及需求响应与火电深度调峰的含风电系统优化调度[J]. 电力系统自动化, 2019, 43(15): 34-41.
	DENG Tingting, LOU Suhua, TIAN Xu, et al. Optimal dispatch of power system integrated with wind power considering demand response and deep peak regulation of thermal power units[J]. Automation of Electric Power Systems, 2019, 43(15): 34-41.
[16]	程韧俐, 李江南, 周保荣, 等. 含碳捕集-电转气的风光火储一体化系统优化运行[J]. 上海交通大学学报, 2024, 58(5): 709-718. doi: 10.16183/j.cnki.jsjtu.2022.270
	CHENG Renli, LI Jiangnan, ZHOU Baorong, et al. Operation optimization for integrated system of wind-PV-thermal-storage with CC-P2G[J]. Journal of Shanghai Jiao Tong University, 2024, 58(5): 709-718.
[17]	宋晓辉, 李晓飞, 蔺奕存, 等. 二次再热1 000 MW燃煤机组(极)热态启动过程切缸与并缸故障分析及处理[J]. 热力发电, 2022, 51(4): 141-149.
	SONG Xiaohui, LI Xiaofei, LIN Yicun, et al. Analysis and treatment of cylinder cutting and cylinder merging during(extremely)hot start-up in double-reheat 1 000 MW coal-fired unit[J]. Thermal Power Generation, 2022, 51(4): 141-149.
[18]	赵振锐, 朱忠芳. 350 MW超临界机组跳闸后极热态启动过程的探讨[J]. 山西电力, 2018(1): 48-51.
	ZHAO Zhenrui, ZHU Zhongfang. Discussion on 350 MW supercritical unit extreme hot start-up process after tripping[J]. Shanxi Electric Power, 2018(1): 48-51.
[19]	夏芹芹, 罗永捷, 王荣茂, 等. 考虑新能源爬坡的风光火耦合系统源荷匹配性分析及容量优化配置[J]. 上海交通大学学报, 2024, 58(1): 69-81. doi: 10.16183/j.cnki.jsjtu.2022.260
	XIA Qinqin, LUO Yongjie, WANG Rongmao, et al. Source-load matching analysis and optimal planning of wind-solar-thermal coupled system considering renewable energy ramps[J]. Journal of Shanghai Jiao Tong University, 2024, 58(1): 69-81.
[20]	赵振宙, 王同光, 郑源. 风力机原理[M]. 北京: 中国水利水电出版社, 2016.
	ZHAO Zhenzhou, WANG Tongguang, ZHENG Yuan. Wind turbine principle[M]. Beijing: China Water & Power Press, 2016.
[21]	于强. 新建火力发电厂精细化调试及管理[D]. 南京: 东南大学, 2018.
	YU Qiang. Fine debugging and management of new thermal power plant[D]. Nanjing: Southeast University, 2018.
[22]	郭力, 杨书强, 刘一欣, 等. 风光储微电网容量规划中的典型日选取方法[J]. 中国电机工程学报, 2020, 40(8): 2468-2479.
	GUO Li, YANG Shuqiang, LIU Yixin, et al. Typical day selection method for capacity planning of microgrid with wind turbine-photovoltaic and energy storage[J]. Proceedings of the CSEE, 2020, 40(8): 2468-2479.
[23]	刘子旭, 米阳, 卢长坤, 等. 计及需求响应和风力发电消纳的电-热系统低碳优化调度[J]. 上海交通大学学报, 2023, 57(7): 835-844. doi: 10.16183/j.cnki.jsjtu.2022.056
	LIU Zixu, MI Yang, LU Changkun, et al. Low-carbon optimal dispatch of electric-thermal system considering demand response and wind power consumption[J]. Journal of Shanghai Jiao Tong University, 2023, 57(7): 835-844.
[24]	顾慧杰, 周华锋, 彭超逸, 等. 含抽水蓄能电站的高比例新能源发电系统多时间尺度调度模型[J]. 上海交通大学学报, 2024, 58(12): 1957-1967. doi: 10.16183/j.cnki.jsjtu.2023.123
	GU Huijie, ZHOU Huafeng, PENG Chaoyi, et al. A multi-time scale scheduling model for power generation system with high proportion of new energy including pumped storage power station[J]. Journal of Shanghai Jiao Tong University, 2024, 58(12): 1957-1967.

运行方式	损耗率/%	累计运行次数	寿命损耗累积/%
冷态启动	0.150 00	100	15
停机2 d后启动	0.010 00	500	5
停机8 h后启动	0.010 00	2 000	20
停机2 h后启动	0.050 00	100	5
大幅度变负荷	0.010 00	3 000	30
小幅度变负荷	0.000 25	20 000	5

机组编号	购机成本/ 万元	装机容量/ MW	常规最小出力/MW	最大深调峰/MW	最大爬坡率/ (%·min^-1)	日最大启停次数	发电耗量系数
机组编号	购机成本/ 万元	装机容量/ MW	常规最小出力/MW	最大深调峰/MW	最大爬坡率/ (%·min^-1)	日最大启停次数	a/(元·MW^-2)	b/(元·MW^-1)	c/元
1、2	17250	1000	480	300	5	2	6.76	175.35	12830
3、4	10068	350	160	140	3	2	30.38	182.50	5843

季节	模型	成本/ 万元	弃风电量/ (MW·h)	折算电价/ [元·(kW·h)^-1]
春季	A	745.7	171.6	0.223
	B	743.4	115.7	0.222
	C	746.4	171.6	0.223
夏季	A	1 191.1	190.2	0.263
	B	1 174.9	4.8	0.259
	C	1 250.5	190.2	0.276

模型	成本/ 万元	弃风电量/ (MW·h)	折算电价/[元· (kW·h)^-1]	1000 MW/350 MW 机型寿命损耗/%
A	10 609.9	2 189.4	0.267	0.30/0.51
B	8 979.5	879.9	0.226	0.18/0.49
C	11 003.6	2 189.4	0.277	0.30/0.51

模型	成本/ 万元	弃风电量/ (MW·h)	折算电价/[元· (kW·h)^-1]	1000 MW/350 MW 机型寿命损耗/%
A	16 602.7	1 273.1	0.285	0.89/0.91
B	15 892.4	237.6	0.273	0.49/0.45
C	16 263.7	1 273.1	0.279	0.89/0.91