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Article(id=1222513211748901704, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1222513210519970621, articleNumber=null, orderNo=null, doi=10.19666/j.rlfd.202302031, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1676217600000, receivedDateStr=2023-02-13, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1769399462878, onlineDateStr=2026-01-26, pubDate=1700841600000, pubDateStr=2023-11-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769399462878, onlineIssueDateStr=2026-01-26, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769399462878, creator=13701087609, updateTime=1769399462878, updator=13701087609, issue=Issue{id=1222513210519970621, tenantId=1146029695717560320, journalId=1210938733613449225, year='2023', volume='52', issue='11', pageStart='1', pageEnd='198', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769399462585, creator=13701087609, updateTime=1769405983425, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1222540560984957089, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1222513210519970621, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1222540560984957090, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1222513210519970621, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=115, endPage=122, ext={EN=ArticleExt(id=1222513212004754254, articleId=1222513211748901704, tenantId=1146029695717560320, journalId=1210938733613449225, language=EN, title=Operation control strategy of hydrogen production electrolytic cell based on particle swarm optimization algorithm, columnId=1213164439017276071, journalTitle=Thermal Power Generation, columnName=Special topic on new energy power generation technology, runingTitle=null, highlight=null, articleAbstract=

The optimal scheduling and economy of new energy hydrogen production systems are closely related to hydrogen production efficiency. Aiming at the problem of low hydrogen production efficiency in existing new energy hydrogen production systems, this paper proposes a control strategy for new energy hydrogen production systems based on particle swarm optimization (PSO). Firstly, based on the polymer electrolyte membrane (PEM) electrolytic cell model, the relationship between the operating point of the electrolytic cell and the hydrogen production efficiency is analyzed. Secondly, a hydrogen production system operation control method based on particle swarm optimization algorithm is proposed to improve the hydrogen production efficiency of the hydrogen production system. Furthermore, an optimal scheduling model for new energy hydrogen production systems considering the efficiency of system hydrogen production was established, and particle swarm optimization algorithm was also used to solve the optimal hydrogen production power. Finally, through simulation analysis of actual power grid operation data, it is proved that the proposed control strategy can effectively improve the hydrogen production capacity and system revenue compared to traditional startup and shutdown strategies, providing a theoretical basis for the large-scale application of hydrogen production systems in power grids.

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新能源制氢系统的优化调度、经济性与制氢效率密切相关，针对现有新能源制氢系统制氢效率低的问题，提出一种基于粒子群算法的新能源制氢系统控制策略。首先，基于质子交换膜电解槽模型，分析电解槽工作特点与制氢效率的关系，提出一种基于粒子群算法的制氢系统运行控制策略，提高制氢效率；然后，建立了考虑系统制氢效率的新能源制氢系统优化调度模型，并采用粒子群算法求解最佳制氢功率；最后通过电网实际运行数据的仿真分析，证明所提出制氢系统控制策略相较于传统的启停控制策略能够有效提高系统制氢量，提高系统收益。这为制氢系统在电网中的大规模应用提供了理论依据。

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姚红雨（2000），男，硕士研究生，主要研究方向为人工智能在电力系统中的应用，。
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张潇桐（1989），男，硕士，高级工程师，主要研究方向为新能源并网及检测技术，。

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张潇桐（1989），男，硕士，高级工程师，主要研究方向为新能源并网及检测技术，。

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张潇桐（1989），男，硕士，高级工程师，主要研究方向为新能源并网及检测技术，。

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hydrogen production system under different hydrogen selling prices, figureFileSmall=ea/qRMcGJOvzkQ5KCzl0Qw==, figureFileBig=oPCajX/hYsM9RAJvGL3y3g==, tableContent=null), ArticleFig(id=1241137060757696686, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1222513211748901704, language=CN, label=图10, caption=不同售氢价格下制氢系统功率曲线, figureFileSmall=ea/qRMcGJOvzkQ5KCzl0Qw==, figureFileBig=oPCajX/hYsM9RAJvGL3y3g==, tableContent=null), ArticleFig(id=1241137060875137209, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1222513211748901704, language=EN, label=Tab.1, caption=

PEMEC parameter table

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参数类型取值
pH2常量3.5 MPa
pO2常量0.1 MPa
Tc变量与Tel一致
Ta变量与Tel一致
αa常量2.0
αc常量0.5
jo,c常量10–3 A/cm2
jo,a常量10–6 A/cm2
δm常量200 μm
λ常量20
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PEMEC参数

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参数类型取值
pH2常量3.5 MPa
pO2常量0.1 MPa
Tc变量与Tel一致
Ta变量与Tel一致
αa常量2.0
αc常量0.5
jo,c常量10–3 A/cm2
jo,a常量10–6 A/cm2
δm常量200 μm
λ常量20
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System economy comparison of two strategies

, figureFileSmall=null, figureFileBig=null, tableContent=
经济性指标启停控制策略PSO控制策略
购电量/(MW·h)302.16313.12
购电费用/万元10.109.99
制氢量/t12.3513.50
制氢系统成本/万元18.5320.25
售氢收益/万元44.1648.60
制氢系统利用率/%54.2100.0
制氢系统总用电量/(MW·h)834.79845.48
弃电量/(MW·h)262.90262.90
), ArticleFig(id=1241137061193904330, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1222513211748901704, language=CN, label=表2, caption=

2种控制策略下系统经济性对比

, figureFileSmall=null, figureFileBig=null, tableContent=
经济性指标启停控制策略PSO控制策略
购电量/(MW·h)302.16313.12
购电费用/万元10.109.99
制氢量/t12.3513.50
制氢系统成本/万元18.5320.25
售氢收益/万元44.1648.60
制氢系统利用率/%54.2100.0
制氢系统总用电量/(MW·h)834.79845.48
弃电量/(MW·h)262.90262.90
), ArticleFig(id=1241137061323927764, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1222513211748901704, language=EN, label=Tab.3, caption=

System economy comparison under different electricity prices

, figureFileSmall=null, figureFileBig=null, tableContent=
经济性指标不同电价启停策略/(元·(kW·h)–1)不同电价PSO策略/(元·(kW·h)–1)
0.280.350.280.35
购电量/(MW·h)963.18163.85746.11190.94
购电费用/万元26.975.7320.896.68
制氢量/t22.4910.3819.7511.60
制氢系统成本/万元33.7415.5729.62517.4
售氢收益/万元80.9637.3871.0941.77
制氢系统利用率/%10050100100
制氢系统总用电量/(MW·h)1 495.69696.061 278.70723.63
弃电量/(MW·h)262.90262.90262.90262.90
), ArticleFig(id=1241137061441368282, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1222513211748901704, language=CN, label=表3, caption=

不同电价下系统经济性比较

, figureFileSmall=null, figureFileBig=null, tableContent=
经济性指标不同电价启停策略/(元·(kW·h)–1)不同电价PSO策略/(元·(kW·h)–1)
0.280.350.280.35
购电量/(MW·h)963.18163.85746.11190.94
购电费用/万元26.975.7320.896.68
制氢量/t22.4910.3819.7511.60
制氢系统成本/万元33.7415.5729.62517.4
售氢收益/万元80.9637.3871.0941.77
制氢系统利用率/%10050100100
制氢系统总用电量/(MW·h)1 495.69696.061 278.70723.63
弃电量/(MW·h)262.90262.90262.90262.90
), ArticleFig(id=1241137061567197408, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1222513211748901704, language=EN, label=Tab.4, caption=

Comparison results of economies of different hydrogen selling price systems

, figureFileSmall=null, figureFileBig=null, tableContent=
经济性指标不同氢价启停策略/(元·(kW·h)–1)不同氢价PSO策略/(元·(kW·h)–1)
34383438
购电量/(MW·h)35.93854.31139.86509.71
购电费用/万元1.5028.364.3716.34
制氢量/t8.0520.7310.6816.39
制氢系统成本/万元12.0831.1016.0224.59
售氢收益/万元27.3878.7736.3162.27
系统总收益/万元13.8019.3115.9221.34
制氢系统利用率/%45.8100.087.5100.0
制氢系统总用电量/(MW·h)568.431 386.68672.561 042.31
弃电量/(MW·h)262.90262.90262.90262.90
), ArticleFig(id=1241137061663666405, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1222513211748901704, language=CN, label=表4, caption=

不同售氢价格制氢系统经济性

, figureFileSmall=null, figureFileBig=null, tableContent=
经济性指标不同氢价启停策略/(元·(kW·h)–1)不同氢价PSO策略/(元·(kW·h)–1)
34383438
购电量/(MW·h)35.93854.31139.86509.71
购电费用/万元1.5028.364.3716.34
制氢量/t8.0520.7310.6816.39
制氢系统成本/万元12.0831.1016.0224.59
售氢收益/万元27.3878.7736.3162.27
系统总收益/万元13.8019.3115.9221.34
制氢系统利用率/%45.8100.087.5100.0
制氢系统总用电量/(MW·h)568.431 386.68672.561 042.31
弃电量/(MW·h)262.90262.90262.90262.90
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基于粒子群算法的制氢电解槽运行控制策略
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张潇桐 1 , 戈阳阳 1 , 姚红雨 2 , 袁铁江 2
热力发电 | 新能源发电技术专题 2023,52(11): 115-122
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热力发电 | 新能源发电技术专题 2023, 52(11): 115-122
基于粒子群算法的制氢电解槽运行控制策略
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张潇桐1 , 戈阳阳1, 姚红雨2 , 袁铁江2
作者信息
  • 1.国网辽宁省电力科学研究院，辽宁　沈阳　116023
  • 2.大连理工大学电气工程学院，大连　辽宁　116081

    • 张潇桐（1989），男，硕士，高级工程师，主要研究方向为新能源并网及检测技术，。

通讯作者:

姚红雨（2000），男，硕士研究生，主要研究方向为人工智能在电力系统中的应用，。
Operation control strategy of hydrogen production electrolytic cell based on particle swarm optimization algorithm
Xiaotong ZHANG1 , Yangyang GE1, Hongyu YAO2 , Tiejiang YUAN2
Affiliations
  • 1.State Grid Liaoning Electric Power Research Institute, Shenyang 116023, China
  • 2.School of Electrical Engineering, Dalian University of Technology, Dalian 116081, China
出版时间: 2023-11-25 doi: 10.19666/j.rlfd.202302031
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摘要
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新能源制氢系统的优化调度、经济性与制氢效率密切相关，针对现有新能源制氢系统制氢效率低的问题，提出一种基于粒子群算法的新能源制氢系统控制策略。首先，基于质子交换膜电解槽模型，分析电解槽工作特点与制氢效率的关系，提出一种基于粒子群算法的制氢系统运行控制策略，提高制氢效率；然后，建立了考虑系统制氢效率的新能源制氢系统优化调度模型，并采用粒子群算法求解最佳制氢功率；最后通过电网实际运行数据的仿真分析，证明所提出制氢系统控制策略相较于传统的启停控制策略能够有效提高系统制氢量，提高系统收益。这为制氢系统在电网中的大规模应用提供了理论依据。

制氢效率  /  粒子群优化算法  /  电解水制氢  /  优化调度

The optimal scheduling and economy of new energy hydrogen production systems are closely related to hydrogen production efficiency. Aiming at the problem of low hydrogen production efficiency in existing new energy hydrogen production systems, this paper proposes a control strategy for new energy hydrogen production systems based on particle swarm optimization (PSO). Firstly, based on the polymer electrolyte membrane (PEM) electrolytic cell model, the relationship between the operating point of the electrolytic cell and the hydrogen production efficiency is analyzed. Secondly, a hydrogen production system operation control method based on particle swarm optimization algorithm is proposed to improve the hydrogen production efficiency of the hydrogen production system. Furthermore, an optimal scheduling model for new energy hydrogen production systems considering the efficiency of system hydrogen production was established, and particle swarm optimization algorithm was also used to solve the optimal hydrogen production power. Finally, through simulation analysis of actual power grid operation data, it is proved that the proposed control strategy can effectively improve the hydrogen production capacity and system revenue compared to traditional startup and shutdown strategies, providing a theoretical basis for the large-scale application of hydrogen production systems in power grids.

hydrogen production efficiency  /  particle swarm optimization algorithm  /  hydrogen production by electrolysis of water  /  optimize scheduling
张潇桐, 戈阳阳, 姚红雨, 袁铁江. 基于粒子群算法的制氢电解槽运行控制策略. 热力发电, 2023 , 52 (11) : 115 -122 . DOI: 10.19666/j.rlfd.202302031
Xiaotong ZHANG, Yangyang GE, Hongyu YAO, Tiejiang YUAN. Operation control strategy of hydrogen production electrolytic cell based on particle swarm optimization algorithm[J]. Thermal Power Generation, 2023 , 52 (11) : 115 -122 . DOI: 10.19666/j.rlfd.202302031
正文
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构建新型电力系统，对于实现“双碳”目标具有重要意义[1]。以风、光为代表的新型能源资源丰富、清洁、效率高，可供人类永久使用[2]，但由于其固有的波动性和间歇性为电网带来了更多挑战[3]。当调峰困难时，必须弃风弃光以维持电网安全稳定[4]，导致能源的极大浪费，而储能技术的发展对于解决该问题具有重要意义。在现有储能技术中，氢能凭借热值高、来源广泛、无污染等特点被誉为21世纪最具潜力的能源[5]，因此氢储能技术受到了国内外学者的广泛关注。
当前，我国已是世界第一产氢国，然而由于电解水制氢需要消耗大量电能导致较高的制氢成本，我国“绿氢”产量仅占总产氢量的1%，利用弃风弃光产生的富裕电能制氢，不仅降低了制氢的成本而且减少了生产过程中的碳排放，同时解决了能源浪费的问题。
目前，电解水制氢技术主要有碱性水电解、质子交换膜电解槽水电解（polymer electrolyte mem-brane electrolytic cell，PEMEC）、固体氧化物水电解3种[6]，其中PEMEC具有调节范围宽、启动速度快、安全性能好等特点，更适用于具有高渗透可再生能源发电的电力系统[7]。因此，本文对其进行详细研究。在电解槽控制策略方面，国内外诸多学者采用启停控制策略，如文献[8-9]提出了电解槽启停特性的出力优化模型，建立了以系统运行成本最低的电解槽运行优化模型。然而传统的启停策略未考虑电解槽工作点对于其制氢效率的影响，使得制氢系统虽能实现新能源消纳，却未能提高产氢量。在新能源制氢系统优化调度方面，文献[10]基于改进郊狼优化算法，以微电网功率过剩和不足量最小为目标函数，优化制氢系统的运行功率。文献[11]考虑电网经济性、电解槽安全性、供电可靠性，制定了电解槽运行策略。文献[12]建立了风电制氢系统模型，研究了电解槽功率和氢气价格对回收期的影响，验证了水电解制氢的经济可行性。文献[13]根据乌克兰风电的特点，提出风电制氢的计算方法，并对风电制氢系统的经济效益进行了研究。然而上述研究均未考虑制氢系统效率对于调度结果的影响。
综上所述，可以发现现有研究对于制氢系统控制策略均采用传统的启停控制方法，且未考虑电解槽产氢效率对于系统优化调度的影响。本文在现有研究的基础上主要工作为：1）基于PEMEC产氢效率模型，提出基于粒子群优化算法的制氢系统控制策略；2）建立新能源制氢系统优化调度模型，制氢系统分别采用启停控制策略与基于粒子群优化[14]（particle swarm optimization algorithm，PSO）的控制策略并做出对比，通过实际运行数据的仿真分析，证明所提控制策略相较于传统的启停策略能够有效提高系统产氢量，提高系统收益，为制氢系统在电网中的大规模应用提供了理论依据。
1　PEMEC数学模型
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1.1　PEMEC电化学模型
单个PEMEC电解总电压Uel[15]可表示为：
Uel=Urev+Uohm+Uact+Udiff
式中：Urev为可逆电压；Uohm为欧姆过电势；Uact为活化过电势；Udiff为扩散过电势。其中，Udiff占比较小，通常忽略[16]。
参考文献[17]，Urev计算公式为：
Urev=1.229−0.009(Tel−298.15)+RTel2Fln(pH2pO20.5αH2O)
式中：pH2与pO2分别为氢气与氧气的分压；R为气体常数；αH2O为水的活度，本文取1；F为法拉第常数；Tel为电解温度。
Uact由阳极过电势Uact-a和阴极过电势Uact-c构成，计算公式[17]为：
Uact=Uact-a+Uact-c
Uact-a=RTaαaFln(j2jO,a+1+(j2jO,a)2)
Uact-c=RTcαcFln(j2jO,c+1+(j2jO,c)2)
式中：Ta、Tc分别为阳极与阴极的温度；αa、αc分别为阳极与阴极的电荷转移系数；jo,a、jo,c分别为阳极与阴极的交换电流密度；j为电流密度。
PEMEC通常以膜电阻为主而忽略其他电阻，于是得到Uohm计算方法[16]为：
Uohm=jδm(0.005 139λ−0.003 26)exp(1303−1Tel)
式中：δm为质子交换膜厚度；λ为质子交换膜含水量。上面公式中所涉及参数取值见表1[16]。
综合式(1)—式(6)与表1，可以得到PEMEC在不同电解温度下电流密度j与电解总电压Uel关系，结果如图1所示。
1.2　PEMEC产氢效率分析
参考文献[17]，PEMEC产氢量计算公式为：
nH2=ηF3 600VH2jA2F
ηF=(j)2f1+(j)2f2 
式中：nH2为制氢量，mol/h；A为PEMEC活性面积，取100 cm2；VH2为氢气的摩尔体积；ηF[17]为电流效率；f1与f2均为法拉第相关参数。
综合PEMEC电化学模型，可得其制氢量与运行功率关系如图2所示。
由图2可见，PEMEC制氢量与运行功率并非线性关系，因此当给定制氢系统功率时，使其开启部分电解槽并以额定功率运行并非最好选择，因此本文提出一种基于粒子群算法的制氢系统运行控制策略，来提高制氢量。
2　基于PSO算法的电解槽控制策略
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并网型制氢系统结构如图3所示[18]，通过AC/DC变换器，可控制电解槽开启的数目以及电解槽的运行功率。目前大多数制氢系统均采取启停控制策略，本文主要与该控制策略进行对比。
2.1　PEMEC启停控制策略
假设PEMEC的额定功率为Prate，最小安全运行功率为Pmin，制氢系统功率为Pel，启停控制策略过程如下[19]：
1）当Pel≤Pmin时，无电解槽开启；
2）当Pmin<Pel≤Prate时，开启1个电解槽，运行功率为Pel；
3）当Prate<Pel≤Prate+Pmin时，仍仅有1个电解槽工作，运行功率为Prate；
4）当Prate+Pmin<Pel≤2Prate时，开启第2个电解槽，运行功率分别为Prate和Pel–Prate，最终，直到全部Pel分配完毕或所有电解槽均以Prate运行。
该控制策略下，电解槽启动方式大多为冷启动，其运行温度需运行一段时间才能达到预期，使得产氢效率降低。
2.2　基于PSO的电解槽控制策略
当给定制氢系统功率Pel，系统可控变量为接入PEMEC的数量n与PEMEC的工作点，由于PEMEC产氢量、工作电压等均与电流密度j有关，因此PSO算法的控制变量可表示为[n, j]。
系统优化的目标为最大化制氢量，因此，基于式(7)、式(8)目标函数可表示为：
f=−max(ηFn3 600VH2jA2F)
同时，为保证系统正常运行，仍需设立以下约束条件。
1）PEMEC安全运行约束
当PEMEC运行在较低电流密度点时易发生爆炸问题[20-21]，当其工作在较高电流密度下时会加剧电解槽的不可逆衰减[22-23]，减少其使用寿命，因此对PEMEC电流密度加以限制：0.2<j<2.0。
2）PEMEC投入数量约束
PEMEC同时工作的数量应不超过其最大规模：0<n<nmax。nmax为PEMEC数量上限值，本文取2 000。
3）制氢系统运行功率约束
PEMEC运行功率的总和应不超过制氢系统的功率Pel，基于式(1)—式(6)与j可计算Uel，于是得该约束条件：njAUel<Pel。
上述约束中，约束1）与约束2）为对控制变量的约束，可通过其取值范围对其约束，约束3）为对状态变量的约束，当约束条件不满足时，该粒子的适应度函数将增加1个较大的惩罚项M。
2.3　控制策略对比
制氢系统功率由0变化到62 MW时，2种策略下系统制氢量对比如图4所示。
由图4可见，当制氢功率为20、30、40、50 MW时，本文基于PSO的制氢系统控制策略制氢量比传统启停策略制氢量分别多190.11、211.49、191.76、133.48 kg，可见当给定制氢系统运行功率时，本文控制策略能够有效提升制氢系统的制氢量。
3　多电解槽协同控制
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为增加制氢系统收益并验证所提控制策略在并网条件下的工作效果，建立新能源制氢系统优化调度模型。在该场景中，制氢系统电力来源有弃风弃光的电能Pp和购买的火电电能Pqf，优化调度问题的控制变量为火电购电量Pqf。优化调度的目标为系统的总收益最高，由于系统的初始投资不影响调度结果[24]，因此不考虑该部分支出。于是建立目标函数：
fop=min(∑i=124Pi,qfei−MH2ich+MH2ica)
式中：Pi,qf为i时刻购电量；ei为i时刻电价；MH2i为i时刻的制氢量；ch为氢气售价；ca为生产1 kg氢气所需的制氢系统操作成本及维护成本。
对于各个时刻，制氢系统的功率Pel为：
Pel=Pqf+Pp
进而，基于前述的启停控制策略和本文的PSO算法控制策略得到该时刻下制氢系统的最优结构及PEMEC工作点，从而计算产氢量Mi,H2，优化调度求解算法同样采取PSO算法。
综上，本文完整优化调度流程包含2个优化过程，外层为基于PSO算法的优化调度，内层为基于PSO算法的制氢系统优化，具体流程如图5所示。
4　算例分析
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4.1　算例设置
选取新疆某地1天内实际负荷数据、新能源发电数据、火电发电数据为例，3种数据如图6所示。
为验证所提控制策略的优越性，分别在电价固定、电价实时变化、售氢价格变化3种情况下进行典型日运行模拟。算例中售氢价格[25]为36元/kg，2组对照实验售氢价格分别为34、38元/kg，典型日的售电电价如图7所示。
4.2　实时电价结果分析
在实时电价下，当售氢价格ch为36元/kg时，2种控制策略的制氢系统功率曲线如图8所示。在该场景下，在弃电量较大时间段（12:00—17:00），制氢系统几乎满负荷工作，尤其在11:00—14:00。由图3的制氢量对比可知，启停控制策略下，制氢系统满负荷工作时其制氢效率最高，因此启停控制策略在此时购电能够增加系统的总收益。而在其他弃电量较少的时段，由于电价较高，通过购电制氢并不会为系统带来正收益，因此制氢系统处于关闭状态。对于PSO算法控制策略，由图4可知，制氢系统满负荷工作制氢效率并不是最高，因此在电价较高时仅利用弃电制氢，在其他电价较低的时间段，由于PSO算法控制策略在较低制氢功率时制氢效率更高，因此通过购电制氢更符合经济性。
实时电价下制氢系统经济性对比见表2，表2中2种控制策略的弃电量均为262.90 MW·h。这是由于在11:00—12:00、16:00—18:00时段，富裕的新能源发电功率大于制氢系统最大运行功率，因此只能通过弃风弃光以维持系统稳定。
基于PSO的控制策略购电花费9.99万元，售氢获得收益48.60万元，系统操作成本18.53万元，不考虑系统初始投资的情况下，系统总收益为18.36万元。而传统的启停控制策略系统总收益为15.53万元，即本文控制系统增加了2.83万元的收益。此外，PSO控制策略下制氢系统的利用率为100%，而传统启停控制策略仅在08:00—19:00、01:00—3:00开启，系统利用率为54.2%，且在某些工作状态下，部分PEMEC还会处于未开启状态，实际利用率会更低。
4.3　固定电价结果分析
当电价固定为0.35、0.28元/(kW·h)，售氢价格ch为36元/kg时，2种控制策略制氢系统功率曲线如图9所示。对于PSO控制策略，根据不同电价，制氢系统会选择不同购电量以最大化系统效益（表3）。对于启停控制策略，由图4可知，当制氢系统满负荷工作时其制氢效率最高，因此仅当电价低于一定阈值时，制氢系统才会购电且满负荷工作。
4.4　售氢价格对系统影响
在实时电价下，当售氢价格ch为38、34元/kg时，2种控制策略的制氢系统功率曲线如图10所示。由图10可见，由于启停控制策略下制氢效率在满负荷工作时最高，因此售氢价格较高且电价较低时，制氢系统满负荷工作以最大化系统收益。而在售氢价格较低时购电制氢并不会为系统带来收益，因此制氢系统仅在存在弃电时开启，在其他时段系统关闭。对于PSO控制策略，由于其制氢效率高于启停控制策略，因此即使在售氢价格较低时仍可通过购电制氢为系统带来正收益。
不同售氢价格下制氢系统经济性见表4。
由表4可见，同样不考虑系统初始投资，当售氢价格为34元/kg时，启停控制策略下系统总收益为13.80万元，PSO控制策略下系统总收益为15.92万元，对比增加了2.12万元的收益。当售氢价格为38元/kg时，启停控制策略下系统总收益为19.31万元，PSO控制策略下系统总收益为21.34万元，对比增加了2.03万元收益，可见本文PSO控制策略能够有效提升制氢系统利用率，提高系统收益。
5　结论
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1）本文提出基于PSO的制氢系统控制策略在制氢系统运行功率固定时，相较于传统的启停控制策略制氢量更多，PEMEC利用率更高。
2）在实际工况下，由于本文控制策略制氢效率更高，因此在电价较低时会通过购电的方式制氢，增加系统总收益，PEMEC利用率更高。
3）氢气价格与电价信息对本文控制策略影响较大，当电价较低或氢气价格较高时，本文控制策略在经济性方面更优，启停控制策略在制氢量方面更优，因此需根据具体需求做出决策。当电价较高或氢气价格较低时，本文所提控制策略仍能通过购电制氢以增加系统收益，提高制氢系统利用率。
基金
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  • 国网辽宁省电力有限公司电力科学研究院项目(SLGNDK00XXJS2200064)
文献
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2023年第52卷第11期
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doi: 10.19666/j.rlfd.202302031
  • 接收时间：2023-02-13
  • 首发时间：2026-01-26
  • 出版时间：2023-11-25
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  • 收稿日期：2023-02-13
基金
State Grid Liaoning Electric Power Co., Ltd., Electric Power Research Institute Project(SLGNDK00XXJS2200064)
国网辽宁省电力有限公司电力科学研究院项目(SLGNDK00XXJS2200064)
作者信息
    1.国网辽宁省电力科学研究院，辽宁　沈阳　116023
    2.大连理工大学电气工程学院，大连　辽宁　116081

通讯作者:

姚红雨（2000），男，硕士研究生，主要研究方向为人工智能在电力系统中的应用，。
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