基于NSGA-Ⅱ的含氢多能源系统经济与低碳协同优化调度

doi:10.3969/j.issn.2097-0706.2025.10.004

综合智慧能源 ›› 2025, Vol. 47 ›› Issue (10): 34-44.doi: 10.3969/j.issn.2097-0706.2025.10.004

基于NSGA-Ⅱ的含氢多能源系统经济与低碳协同优化调度

邱文婷¹(), 董家乐¹(), 吴笛¹(), 苏文静¹^,^*(), 宗毅²()

1.武汉工程大学电气信息学院，武汉 430200
2.丹麦技术大学风力与能源系统，罗斯基勒 4000

收稿日期:2024-11-06 修回日期:2024-12-06 出版日期:2025-01-21
通讯作者: *苏文静（1975），女，副教授，硕士，从事嵌入式系统开发、综合能源系统需求侧响应等方面的研究，04004037@wit.edu.cn。
作者简介:邱文婷（1996），女，硕士生，从事综合能源系统优化运行方面的研究，22203010142@stu.wit.edu.cn；
董家乐（2000），男，硕士生，从事新能源并网方面的研究，1464338590@qq.com；
吴笛（2001），男，硕士生，从事新能源预测方面的研究，956807493@qq.com；
宗毅（1971），女，教授，博士，从事新能源并网、智能电网、智能楼宇、区域性多能源网络的规划、控制、管理和运营等方面的研究，yizo@dtu.dk。
基金资助:
武汉工程大学研究生教育创新基金项目(CX2023575)

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

QIU Wenting¹(), DONG Jiale¹(), WU Di¹(), SU Wenjing¹^,^*(), ZONG Yi²()

1. School of Electrical and Information Engineering， Wuhan Institute of Technology， Wuhan 430200， China
2. Department of Wind and Energy Systems， Technical University of Denmark， Roskilde 4000， Denmark

Received:2024-11-06 Revised:2024-12-06 Published:2025-01-21
Supported by:
Graduate Innovative Fund of Wuhan Institute of Technology(CX2023575)

摘要/Abstract

摘要：

为应对电-热-氢多能源系统（MES）中电制氢设备运行安全问题以及运行调度难以兼顾经济性和低碳性的问题，提出一种结合电解槽非线性动态模型和非支配遗传算法Ⅱ（NSGA-Ⅱ）的优化调度策略，以实现MES的多目标优化。为确保电制氢过程在安全温度范围内，构建了一个考虑堆温对电解效率非线性影响的碱性电解槽动态模型；为利用MES中各类能源的强耦合关系和负荷的可调度性，引入综合需求响应机制和碳交易机制以提高系统的经济性和低碳运行能力；融合电解槽的复杂非线性模型，提出了基于NSGA-Ⅱ的多目标优化算法，并使用优劣解距离法综合评价出最优解，以实现对MES的优化调度。通过不同算例的仿真试验，对MES的日总运行成本和碳排放量进行比较分析。结果表明，结合了NSGA-Ⅱ的多目标优化方法可显著降低系统的日总运行成本和碳排放总量，相较于单一优化系统经济成本，系统的日总成本和碳排放分别减少33.5%和57.7%，验证了所提策略在提升电-热-氢MES经济性与低碳性方面的有效性。

关键词: 碳减排, 碳交易机制, 氢能, 综合需求响应, 多能源系统, 多目标优化, 非支配排序遗传算法Ⅱ

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

中图分类号:

TK019

邱文婷, 董家乐, 吴笛, 苏文静, 宗毅. 基于NSGA-Ⅱ的含氢多能源系统经济与低碳协同优化调度[J]. 综合智慧能源, 2025, 47(10): 34-44.

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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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

基于NSGA-Ⅱ的含氢多能源系统经济与低碳协同优化调度

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

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