Economic and low-carbon coordinated optimization scheduling of hydrogen-integrated multi-energy system based on NSGA-Ⅱ

doi:10.3969/j.issn.2097-0706.2025.10.004

Abstract

Abstract:

To address the operational safety issues of hydrogen production equipment in electricity-heat-hydrogen multi-carrier energy systems（MES） and the challenge of balancing economic efficiency with low-carbon operation in scheduling， an optimized scheduling strategy was proposed， integrating the nonlinear dynamic model of the electrolyzer with the non-dominated sorting genetic algorithm-Ⅱ（NSGA-Ⅱ） for multi-objective optimization of the MES. To ensure that the hydrogen production process operates within a safe temperature range， a dynamic model of alkaline electrolyzer was developed， considering the nonlinear effect of stack temperature on electrolysis efficiency. To make use of the strong coupling relationships among various energy resources and the dispatchability of loads in the MES， an integrated demand response mechanism and carbon trading mechanism were introduced to improve the system's economic efficiency and low-carbon operational performance. By integrating the complex nonlinear model of the electrolyzer， a multi-objective optimization algorithm based on NSGA-Ⅱ was proposed， and the optimal solution was comprehensively evaluated using the technique for order preference by similarity to ideal solution to achieve the optimization scheduling of the MES. Through simulation experiments under different scenarios， the daily total operating costs and carbon emissions of MES were compared and analyzed. The results showed that the multi-objective optimization method combined with NSGA-Ⅱ could significantly reduce the daily total operating costs and carbon emissions of the system. Compared with optimizing the system's economic costs alone， the daily total costs and carbon emissions of the system were reduced by 33.5% and 57.7%， respectively. This validated the effectiveness of the proposed strategy in improving the economic efficiency and low-carbon operation of the electricity-heat-hydrogen MES.

Key words: carbon emission reduction, carbon trading mechanism, hydrogen energy, integrated demand response, multi-energy systems, multi-objective optimization, non-dominated sorting genetic algorithm Ⅱ

CLC Number:

TK019

QIU Wenting, DONG Jiale, WU Di, SU Wenjing, ZONG Yi. Economic and low-carbon coordinated optimization scheduling of hydrogen-integrated multi-energy system based on NSGA-Ⅱ[J]. Integrated Intelligent Energy, 2025, 47(10): 34-44.

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References 31

[1]	KAKOULAKI G, KOUGIAS I, TAYLOR N, et al. Green hydrogen in Europe—A regional assessment: Substituting existing production with electrolysis powered by renewables[J]. Energy Conversion and Management, 2021, 228: 113649.
[2]	GÖTZ M, LEFEBVRE J, MÖRS F, et al. Renewable power-to-gas: A technological and economic review[J]. Renewable Energy, 2016, 85: 1371-1390.
[3]	TENG Y, WANG Z D, LI Y, et al. Multi-energy storage system model based on electricity heat and hydrogen coordinated optimization for power grid flexibility[J]. CSEE Journal of Power and Energy Systems, 2019, 5(2): 266-274.
[4]	CHENG Y, LIU M B, CHEN H L, et al. Optimization of multi-carrier energy system based on new operation mechanism modelling of power-to-gas integrated with CO₂-based electrothermal energy storage[J]. Energy, 2021, 216: 119269.
[5]	DING X Y, SUN W, HARRISON G P, et al. Multi-objective optimization for an integrated renewable, power-to-gas and solid oxide fuel cell/gas turbine hybrid system in microgrid[J]. Energy, 2020, 213: 118804.
[6]	WANG Y L, LIU C, QIN Y M, et al. Synergistic planning of an integrated energy system containing hydrogen storage with the coupled use of electric-thermal energy[J]. International Journal of Hydrogen Energy, 2023, 48(40): 15154-15178.
[7]	刘敦楠, 徐尔丰, 刘明光, 等. 面向分布式电源就地消纳的园区分时电价定价方法[J]. 电力系统自动化, 2020, 44(20): 19-28.
	LIU Dunnan, XU Erfeng, LIU Mingguang, et al. TOU pricing method for park considering local consumption of distributed generator[J]. Automation of Electric Power Systems, 2020, 44(20): 19-28.
[8]	张伊宁, 何宇斌, 晏鸣宇, 等. 计及需求响应与动态气潮流的电-气综合能源系统优化调度[J]. 电力系统自动化, 2018, 42(20): 1-8.
	ZHANG Yining, HE Yubin, YAN Mingyu, et al. Optimal dispatch of integrated electricity-natural gas system considering demand response and dynamic natural gas flow[J]. Automation of Electric Power Systems, 2018, 42(20): 1-8.
[9]	PONOĆKO J, MILANOVIĆ J V. Multi-objective demand side management at distribution network level in support of transmission network operation[J]. IEEE Transactions on Power Systems, 2020, 35(3): 1822-1833.
[10]	ZHANG Y N, HE Y B, YAN M Y, et al. Linearized stochastic scheduling of interconnected energy hubs considering integrated demand response and wind uncertainty[J]. Energies, 2018, 11(9): 2448.
[11]	王仕炬, 刘天琪, 何川, 等. 基于舒适度的需求响应与碳交易的园区综合能源经济调度[J]. 电测与仪表, 2022, 59(11):1-7.
	WANG Shiju, LIU Tianqi, HE Chuan, et al. Comfort demand response and carbon trading based comprehensive energy economic dispatching in industrial parks[J]. Electrical Measurement & Instrumentation, 2022, 59(11): 1-7.
[12]	葛磊蛟, 于惟坤, 朱若源, 等. 考虑改进阶梯式碳交易机制与需求响应的综合能源系统优化调度[J]. 综合智慧能源, 2023, 45(7):97-106. doi: 10.3969/j.issn.2097-0706.2023.07.011
	GE Leijiao, YU Weikun, ZHU Ruoyuan, et al. Integrated energy system optimization scheduling considering improved stepped carbon trading mechanism and demand responses[J]. Integrated Intelligent Energy, 2023, 45(7): 97-106. doi: 10.3969/j.issn.2097-0706.2023.07.011
[13]	TROTTER P, TOTH F. The impact of EU ETS on energy system optimization: Insights from integrated assessment models[J]. Energy Economics, 2015, 52: 103-115.
[14]	BELLORA C, FONTAGNÉ L. EU in search of a carbon border adjustment mechanism[J]. Energy Economics, 2023, 123: 106673.
[15]	AMBEC S. The European Union's carbon border adjustment mechanism: Challenges and perspectives[R]. TSE Working Paper,2022: 1-27.
[16]	崔杨, 曾鹏, 仲悟之, 等. 考虑阶梯式碳交易的电-气-热综合能源系统低碳经济调度[J]. 电力自动化设备, 2021, 41(3): 10-17.
	CUI Yang, ZENG Peng, ZHONG Wuzhi, et al. Low-carbon economic dispatch of electricity-gas-heat integrated energy system based on ladder-type carbon trading[J]. Electric Power Automation Equipment, 2021, 41(3): 10-17.
[17]	GUO R, YE H W, ZHAO Y. Low carbon dispatch of electricity-gas-thermal-storage integrated energy system based on stepped carbon trading[J]. Energy Reports, 2022, 8: 449-455.
[18]	陈志, 胡志坚, 翁菖宏, 等. 基于阶梯碳交易机制的园区综合能源系统多阶段规划[J]. 电力自动化设备, 2021, 41(9): 148-155.
	CHEN Zhi, HUZhijian, WENG Changhong, et al. Multi-stage planning of park-level integrated energy system based on ladder-type carbon trading mechanism[J]. Electric Power Automation Equipment, 2021, 41(9): 148-155.
[19]	陈锦鹏, 胡志坚, 陈颖光, 等. 考虑阶梯式碳交易机制与电制氢的综合能源系统热电优化[J]. 电力自动化设备, 2021, 41(9): 48-55.
	CHEN Jinpeng, HU Zhijian, CHEN Yingguang, et al. Thermoelectric optimization of integrated energy system considering ladder-type carbon trading mechanism and electric hydrogen production[J]. Electric Power Automation Equipment, 2021, 41(9): 48-55.
[20]	SUN H B, SUN X M, KOU L, et al. Optimal scheduling of park-level integrated energy system considering ladder-type carbon trading mechanism and flexible load[J]. Energy Reports, 2023, 9:3417-3430.
[21]	陈浩, 马刚, 钱达, 等. 绿证-碳交易机制下考虑阶梯需求响应的区域综合能源系统优化调度[J/OL]. 综合智慧能源,1-12(2024-11-19)[2024-12-05]. http://kns.cnki.net/kcms/detail/41.1461.tk.20241118.1910.003.html.
	CHEN Hao, MA Gang, QIAN Da, et al. Optimized dispatch of integrated regional energy system considering stepped demand response under green certificate-carbon trading mechanisms[J/OL]. Integrated Intelligent Energy,1-12(2024-11-19)[2024-12-05]. http://kns.cnki.net/kcms/detail/41.1461.tk.20241118.1910.003.html.
[22]	ULLEBERG Ø. Modeling of advanced alkaline electrolyzers: A system simulation approach[J]. Hydrogen Energy, 2003; 28:21-33.
[23]	FU C, LIN J, SONG Y H, et al. Optimal operation of an integrated energy system incorporated with HCNG distribution networks[J]. IEEE Transactions on Sustainable Energy, 2020, 11(4): 2141-2151.
[24]	Ursúa A, Sanchis P. Static-dynamic modelling of the electrical behaviour of a commercial advanced alkaline water electrolyse[J]. International Journal of Hydrogen Energy, 2012, 37(24): 18598-18614.
[25]	HUANG C J, ZONG Y, YOU S et al. Economic model predictive control for multi-energy system considering hydrogen-thermal-electric dynamics and waste heat recovery of MW-level alkaline electrolyzer[J]. Energy Conversion and Management, 2022, 265: 115697.
[26]	陈朝旭, 张亚超, 朱蜀, 等. 考虑多电解槽多工况组合运行的电-氢-热综合能源系统优化调度[J/OL]. 电网技术,1-10(2024-01-11)[2024-11-05]. https://doi.org/10.13335/j.1000-3673.pst.2023.2140.
	CHEN Zhaoxu, ZHANG Yachao, ZHU Shu, et al. Optimal scheduling of electricity-hydrogen-heat lntegrated energy system considering combined operation of multi-electrolyzers under multiple conditions[J/OL]. Power Grid Technology,1-10(2024-01-11)[2024-11-05]. https://doi.org/10.13335/j.1000-3673.pst.2023.2140.
[27]	叶幸, 邱辰, 艾琳, 等. “双碳”目标下可再生能源绿色电力证书与碳交易衔接机制的思考[J/OL]. 综合智慧能源, 1-9(2024-09-05)[2024-11-05].http://kns.cnki.net/kcms/detail/41.1461.TK.20240904.1056.006.html.
	YE Xing, QIU Chen, AI Lin, et al. Connection mechanism between green electricity certificates and carbon tradingmechanism under the "double carbon" target[J/OL]. Integrated Intelligent Energy,1-9(2024-09-05)[2024-11-05].http://kns.cnki.net/kcms/detail/41.1461.TK.20240904.1056.006.html.
[28]	生态环境部办公厅. 企业温室气体排放核算方法与报告指南:发电设施[R]. 北京: 生态环境部办公厅, 2022.
[29]	QIU W T, SU W J, ZONG Y. Energy price-driven integrated demand response for optimal operation of multi-carrier energy systems with hydrogen facility[C]// Proceedings of 2023 3rd Power System and Green Energy Conference (PSGEC),IEEE, 2023: 599-603.
[30]	DEB K, AGRAWAL S, PRATAP A, et al. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-Ⅱ[M]// Parallel Problem Solving from Nature PPSN VI. Heidelberg: Springer Berlin Heidelberg, 2000: 849-858.
[31]	张晓捷. 基于熵权TOPSIS法的长租公寓财务风险评价及防范研究:以青客公寓为例[D]. 南宁: 广西大学, 2022.
	ZHANG Xiaojie. Research on financial risk evaluation and preventive measures of long-time rental apartments based on entropy weight TOPSIS method[D]. Nanning: Guangxi University, 2022.

项目		数值	项目		数值
AE	安装容量/kW	500	储氢罐额定容量/(kW·h)		200
	单堆额定功率/kW	26.6	FC	安装容量/kW	250
	最大功率/kW	400		产电效率	0.85
	最小功率/kW	40		产热效率	1.2
	爬坡约束	0.2	蓄电池/储热罐额定容量/(kW·h)		400
	氢的高热值/[kW·h·kg^-1]	39.4	蓄电池/储热罐额定容量/(kW·h)		400
	氢密度/(kg·m^-3)	7.8	热泵	安装容量/kW	400
	最高堆叠温度/℃	80	热泵	效率	4.4
	最低堆叠温度/℃	60
	余热装置效率	0.8

项目	总运行成本/元	碳排放量/t
未考虑IDR和碳交易机制	42 507.224	19.862
仅考虑IDR	39 838.417	18.899
考虑IDR和碳交易机制	38 736.349	17.597

项目	情景1	情景2	情景3
总运行成本/元	38 736.349	25 921.737	25 766.618
购能成本/元	36 956.368	22 356.315	21 911.388
碳交易成本/元	242.661	217.152	178.738
碳排放量/t	17.597	8.164	7.451

情景	电解效率	总运行成本/元	日产氢量/m³
2	0.67	31 102.822	5.429
	0.69	25 921.737	6.701
	0.71	23 029.549	8.316
3	时变	25 766.618	8.414