考虑多灵活性资源联合运行的综合能源系统优化配置方法

doi:10.16183/j.cnki.jsjtu.2023.457

摘要/Abstract

摘要：

“双碳”战略背景下,可再生能源渗透比例不断提升,灵活性资源缺乏问题日益严重.为构建安全、高效、低碳的清洁能源系统以应对这一挑战,提出一种考虑多灵活性资源联合运行的综合能源系统优化配置方法.首先,对电转气设备的两阶段运行进行精细化建模,引入掺氢燃气轮机和电转气设备协同运行,以充分利用H₂的低碳特性.同时,通过碳捕集设备向电转气设备提供碳原料,实现了CO₂的循环利用,从而构建了以氢能为核心的灵活性资源联合运行框架.然后,针对可再生能源出力不确定性问题,通过Elbow法确定了最优聚类数目,并利用K-means聚类算法得到风速的典型场景.在此基础上,以投资成本、运维成本、置换成本、环境惩罚和弃风惩罚成本之和最小为目标,综合考虑设备约束、能量平衡约束及灵活性约束,建立优化配置模型.为解决模型的非线性问题,采用大M法将其线性化处理并完成模型求解.最后,基于中国西南某地区实测数据进行算例验证,结果表明:所提方法使综合能源系统的总成本降低了10.22%、新能源渗透率提高了6.01%、环境惩罚成本降低了2.65%,有效提升了系统的经济性和新能源消纳量,同时显著降低了系统碳排放水平.

关键词: 灵活性资源, 综合能源系统, 优化配置, 氢能, 不确定性

Abstract:

Under the “carbon peaking and carbon neutrality” strategy, the penetration ratio of renewable energy is increasing, while the lack of flexible resources becomes a growing challenge. To address this and build a safe, efficient, low-carbon, and clean energy system, an integrated energy system (IES) optimization allocation method is proposed considering the joint operation of multiple flexibility resources. First, the modeling of the two stages of the power-to-gas equipment is refined, with the introducation of the coordinated operation of the hydrogen-doped gas turbine and the power-to-gas equipment to make full use of the low-carbon characteristics of H₂. Carbon raw materials are provided for the power-to-gas facilities through carbon capture equipment realizing the recycling of CO₂, thereby establishing a coordinated operation framework for flexible resource with hydrogen energy as the core. Then, aimed at the uncertainty of renewable energy output, the optimal clustering number is determined by Elbow method, and typical wind speed scenarios are obtained by K-means clustering algorithm. On this basis, an optimal allocation model is established with the objective of minimizing the sum of investment cost, operation and maintenance cost, replacement cost, environmental penalty, and wind abandonment penalty cost, taking into account equipment constraints, energy balance constraints, and flexibility constraints. To solve the nonlinearity, the large M method is adopted to linearize the model and complete the model solution. Finally, the method proposed is validated through an example based on measured data from a region in southwest China. The results show that the total cost of the IES is reduced by 10.22%, the penetration rate of new energy is increased by 6.01%, and the cost of environmental penalties is reduced by 2.65%. The proposed method effectively improves the economy of the system and the consumption of new energy, and significantly reduces system carbon emissions.

Key words: flexibility resources, integrated energy system (IES), optimal configuration, hydrogen energy, uncertainty

中图分类号:

TM732

邓倩文, 李奇, 邱宜彬, 李豆萌, 霍莎莎, 陈维荣. 考虑多灵活性资源联合运行的综合能源系统优化配置方法[J]. 上海交通大学学报, 2025, 59(7): 912-922.

DENG Qianwen, LI Qi, QIU Yibin, LI Doumeng, HUO Shasha, CHEN Weirong. Optimal Allocation Method of Integrated Energy System Considering Joint Operation of Multiple Flexible Resources[J]. Journal of Shanghai Jiao Tong University, 2025, 59(7): 912-922.

图/表 9

图1

图2

表1

表2

表3

图3

图4

图5

图6

参考文献 25

[1]	张倩, 朱新超. 能源绿色低碳转型助推“双碳” 战略实施[J]. 煤炭经济研究, 2023, 43(1): 17-25.
	ZHANG Qian, ZHU Xinchao. Green low-carbon transformation of energy accelerates the strategy of carbon-peak and carbon-neutralization[J]. Coal Economic Research, 2023, 43(1): 17-25.
[2]	QIU Y B, LI Q, AI Y X, et al. Two-stage distributionally robust optimization-based coordinated scheduling of integrated energy system with electricity-hydrogen hybrid energy storage[J]. Protection and Control of Modern Power Systems, 2023, 8(2): 1-14.
[3]	马洲俊, 樊飞龙, 王勇, 等. 基于多源异构数据的配电网故障信息挖掘与诊断[J]. 供用电, 2018, 35(8): 31-39.
	MA Zhoujun, FAN Feilong, WANG Yong, et al. Distribution network fault information mining and diagnosis based on multi-source heterogeneous data[J]. Distribution & Utilization, 2018, 35(8): 31-39.
[4]	FLORES-QUIROZ A, STRUNZ K. A distributed computing framework for multi-stage stochastic planning of renewable power systems with energy storage as flexibility option[J]. Applied Energy, 2021, 291: 116736.
[5]	程杉, 徐建宇, 何畅, 等. 计及不确定性的综合能源系统容量规划方法[J]. 电力系统保护与控制, 2021, 49(18): 17-24.
	CHENG Shan, XU Jianyu, HE Chang, et al. Optimal capacity planning of an integrated energy system considering uncertainty[J]. Power System Protection and Control, 2021, 49(18): 17-24.
[6]	张程, 匡宇, 陈文兴, 等. 计及电动汽车充电方式与多能耦合的综合能源系统低碳经济优化运行[J]. 上海交通大学学报, 2024, 58 (5): 669-681. doi: 10.16183/j.cnki.jsjtu.2022.364
	ZHANG Cheng, KUANG Yu, CHEN Wenxing, et al. Low carbon economy optimization of integrated energy system considering electric vehicle charging mode and multi-energy coupling[J]. Journal of Shanghai Jiao Tong University, 2024, 58 (5): 669-681.
[7]	李哲, 王成福, 梁军, 等. 计及风电不确定性的电-气-热综合能源系统扩展规划方法[J]. 电网技术, 2018, 42(11): 3477-3487.
	LI Zhe, WANG Chengfu, LIANG Jun, et al. Expansion planning method of integrated energy system considering uncertainty of wind power[J]. Power System Technology, 2018, 42(11): 3477-3487.
[8]	朱海南, 王娟娟, 陈兵兵, 等. 考虑经济性与碳排放的电-气综合能源系统多目标规划[J]. 上海交通大学学报, 2023, 57(4): 422-431. doi: 10.16183/j.cnki.jsjtu.2021.513
	ZHU Hainan, WANG Juanjuan, CHEN Bingbing, et al. Multi-objective planning of power-gas integrated energy system considering economy and carbon emission[J]. Journal of Shanghai Jiao Tong University, 2023, 57(4): 422-431.
[9]	ZHANG D D, ZHU H Y, ZHANG H C, et al. An optimized design of residential integrated energy system considering the power-to-gas technology with multi-functional characteristics[J]. Energy, 2022, 238: 121774.
[10]	刘天琪, 曾红, 何川, 等. 考虑电转气设备和风电场协同扩建的气电互联综合能源系统规划[J]. 电力自动化设备, 2019, 39(8): 144-151.
	LIU Tianqi, ZENG Hong, HE Chuan, et al. Planning of integrated gas and electricity system considering coordinated expansion of power-to-gas facilities and wind farms[J]. Electric Power Automation Equipment, 2019, 39(8): 144-151.
[11]	张儒峰, 姜涛, 李国庆, 等. 考虑电转气消纳风电的电-气综合能源系统双层优化调度[J]. 中国电机工程学报, 2018, 38(19): 5668-5678.
	ZHANG Rufeng, JIANG Tao, LI Guoqing, et al. Bi-level optimization dispatch of integrated electricity-natural gas systems considering P2G for wind power accommodation[J]. Proceedings of the CSEE, 2018, 38(19): 5668-5678.
[12]	ZHANG G M, WANG W, CHEN Z Y, et al. Modeling and optimal dispatch of a carbon-cycle integrated energy system for low-carbon and economic operation[J]. Energy, 2022, 240: 122795.
[13]	袁桂丽, 钟飞, 张睿, 等. 考虑碳捕集及需求响应的虚拟电厂热电联合优化调度[J]. 电网技术, 2023, 47(11): 4458-4467.
	YUAN Guili, ZHONG Fei, ZHANG Rui, et al. Combined heat and power scheduling optimization for virtual power plants considering carbon capture and demand response[J]. Power System Technology, 2023, 47(11): 4458-4467.
[14]	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.
[15]	WU Q L, LI C X. Modeling and operation optimization of hydrogen-based integrated energy system with refined power-to-gas and carbon-capture-storage technologies under carbon trading[J]. Energy, 2023, 270: 126832.
[16]	田丰, 贾燕冰, 任海泉, 等. 考虑碳捕集系统的综合能源系统“源-荷”低碳经济调度[J]. 电网技术, 2020, 44(9): 3346-3355.
	TIAN Feng, JIA Yanbing, REN Haiquan, et al. “Source-load” low-carbon economic dispatch of integrated energy system considering carbon capture system[J]. Power System Technology, 2020, 44(9): 3346-3355.
[17]	崔耀欣, 刘晓佩, 陈明敏. F级重型燃气轮机燃烧器天然气掺氢全压试验研究[J]. 燃气轮机技术, 2021, 34(2): 38-42.
	CUI Yaoxin, LIU Xiaopei, CHEN Mingmin. Experimental study of natural gas mixed with hydrogen under full pressure of F-class heavy duty gas turbine burner[J]. Gas Turbine Technology, 2021, 34(2): 38-42.
[18]	马勤勇, 钱白云, 董利江, 等. 掺氢比例对氢混天然气燃气轮机运行特性影响的研究[J]. 热能动力工程, 2022, 37(9): 41-49.
	MA Qinyong, QIAN Baiyun, DONG Lijiang, et al. Research on the influence of hydrogen blending ratio on the operation characteristics of hydrogen blended fuel gas turbine[J]. Journal of Engineering for Thermal Energy and Power, 2022, 37(9): 41-49.
[19]	王开艳, 梁岩, 贾嵘, 等. 不确定环境下基于纳什谈判的含掺氢燃气综合能源多微网两阶段优化调度[J]. 电网技术, 2023, 47(8): 3141-3159.
	WANG Kaiyan, LIANG Yan, JIA Rong, et al. Two-stage optimal scheduling of Nash negotiation-based integrated energy multi-microgrids with hydrogen-doped gas under uncertain environment[J]. Power System Technology, 2023, 47(8): 3141-3159.
[20]	SAMMOUDA R, EL-ZAART A. An optimized approach for prostate image segmentation using K-means clustering algorithm with elbow method[J]. Computational Intelligence and Neuroscience, 2021, 2021: 4553832.
[21]	左逢源, 张玉琼, 赵强, 等. 计及源荷不确定性的综合能源生产单元运行调度与容量配置两阶段随机优化[J]. 中国电机工程学报, 2022, 42(22): 8205-8215.
	ZUO Fengyuan, ZHANG Yuqiong, ZHAO Qiang, et al. Two-stage stochastic optimization for operation scheduling and capacity allocation of integrated energy production unit considering supply and demand uncertainty[J]. Proceedings of the CSEE, 2022, 42(22): 8205-8215.
[22]	樊飞龙, 邰能灵, 郑晓冬, 等. 基于前推回代优化算法的社区能源网络有功实时调配策略[J]. 中国电机工程学报, 2017, 37(14): 4098-4108.
	FAN Feilong, TAI Nengling, ZHENG Xiaodong, et al. Real-time deployment strategy of the active power in community energy networks based on back/forward optimization algorithm[J]. Proceedings of the CSEE, 2017, 37(14): 4098-4108.
[23]	宁光涛, 李琳玮, 何礼鹏, 等. 面向绿色海岛微型综合能源系统的储能系统容量规划方法[J]. 电力自动化设备, 2021, 41(2): 8-15.
	NING Guangtao, LI Linwei, HE Lipeng, et al. Capacity planning method of energy storage system for micro integrated energy system in environmental friendly islands[J]. Electric Power Automation Equipment, 2021, 41(2): 8-15.
[24]	孙彩, 李奇, 邱宜彬, 等. 余电上网/制氢方式下微电网系统全生命周期经济性评估[J]. 电网技术, 2021, 45(12): 4650-4660.
	SUN Cai, LI Qi, QIU Yibin, et al. Economic evaluation of whole life cycle of the micro-grid system under the mode of residual power connection/hydrogen production[J]. Power System Technology, 2021, 45(12): 4650-4660.
[25]	杨金海, 武家辉, 王海云, 等. 不同渗透率下多种新能源电力系统动态安全域分析[J]. 电力建设, 2022, 43(4): 58-68. doi: 10.12204/j.issn.1000-7229.2022.04.007
	YANG Jinhai, WU Jiahui, WANG Haiyun, et al. Dynamic security region analysis of power system under different penetration rate of new energy[J]. Electric Power Construction, 2022, 43(4): 58-68. doi: 10.12204/j.issn.1000-7229.2022.04.007

方案	风电不确定性	CCS与 P2G联合	P2G向掺氢 GT提供H₂	P2G向掺氢 GT提供天然气
1	否	是	是	是
2	是	否	否	是
3	是	是	否	是
4	是	是	是	是

方案	容量							新能源渗透率/%
方案	风电机组/MW	掺氢燃气轮机/MW	碳捕集设备/MW	储碳罐/ (kN·m³)	电解槽/MW	甲烷化设备/MW	储氢罐/ (kN·m³)	新能源渗透率/%
1	150.00	127.24	1.39	12.61	82.77	15.56	76.28	34.46
2	107.88	106, 51	2.24	0	77.70	13.57	41.08	46.62
3	116.97	80.27	1.60	15.99	75.80	16.55	68.44	48.84
4	125.93	82.16	1.76	15.56	88.69	15.56	59.52	49.42

方案	投资成本/ 万元	运维成本/ 万元	置换成本/ 万元	环境惩罚/ 万元	弃风惩罚/ 万元	综合成本/ 万元	风电机组年发电/GW	天然气年购买量/ (MN·m³)	CO₂年排放量/ (MN·m³)
1	19316	4176	11636	15446	206	50780	260.93	610.54	257.44
2	14569	3254	12443	8289	207	38762	332.59	589.19	207.22
3	15374	3298	9253	7632	403	35962	357.72	591.04	190.79
4	16164	3481	6704	8069	384	34802	386.12	593.92	201.73