Research Progress and Prospect of Gallium-Based Liquid Metals in Electrical-Thermal-Mechanics Field
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摘要:
液态金属兼顾液体和金属的特性,其研究与应用在工学各领域兴起. 镓基液态金属常温下呈液态,具有高沸点、高电导率、高热导率、安全无毒等优良特性,在电学、热学、力学和生物医疗等诸多领域取得了广泛的应用. 目前,镓基液态金属已成为前沿研究热点. 通过综合对比国内外研究现状,介绍镓基液态金属的制备方法及其性能改善的措施,分析几种典型镓基液态金属的理化性质,总结镓基液态金属在电力设备、柔性电子、电源储能、散热冷却、载流摩擦、极压润滑等应用领域的功能原理与研究进展,并提出其未来研究重点. 基于镓基液态金属合金的特性,对其在材料改性、新型电子器件、太阳能电池、轨道交通、电磁弹射等领域具有的应用潜力进行分析和展望.
Abstract:Liquid metal takes into account the characteristics of both liquid and metal, and it is increasingly studied and applied in various fields of engineering. Gallium-based liquid metal is liquid at room temperature, with a high boiling point, high electrical conductivity, high thermal conductivity, safety, non-toxicity, and other excellent characteristics, and it has been widely used in many fields such as electricity, heat, mechanics, and biomedicine. At present, gallium-based liquid metals have become a frontier research hotspot. By comprehensively comparing the research status in China and abroad, the preparation methods and performance improvement measures of gallium-based liquid metals were introduced, and the physical and chemical properties of several typical gallium-based liquid metals were analyzed. The functional principles and research progress of gallium-based liquid metals in the fields of power equipment, flexible electronics, power storage, heat dissipation cooling, current-carrying friction, and extreme pressure lubrication were summarized, and its future research focus was put forward. Based on the characteristics of gallium-based liquid metals, their potential applications in material modification, new electronic devices, solar cells, rail transit, electromagnetic ejection, and other fields were analyzed and prospected.
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能源是人类社会赖以生存和发展的物质基础. 针对能源型经济社会建设的目标,提出了开发新能源、减少环境污染、提高能源利用率的理念,区域综合能源系统已成为了近年研究热点[1-3]. 相较于传统单一能源系统,综合能源系统含有多种能源类型,不同能源互补耦合,基于能量梯级利用原则进行协调优化[4-5]. 目前,针对含有电转气(P2G)环节的综合能源系统已有较多研究:文献[6]建立了含P2G的电-热系统风电消纳优化运行模型,对比采用P2G设备前后和不同风电占比下风电利用率及效益;文献[7]分析了电、气、热需求响应及P2G、储能设备接入对区域综合能源系统运行优化的影响;文献[8]建立了电-气-热-冷联供的微型能源网模型,并在考虑P2G收益的基础上进行日前经济调度;文献[9]提出了一种考虑需求响应的含P2G 电-气综合能源系统优化调度模型. 上述研究中,P2G环节均采用电转天然气模型,利用P2G产生的天然气供给系统中气负荷使用. 实际上P2G环节从原理上分为2个阶段:一为电解水制氢气,二为氢气进一步与碳基原料反应合成甲烷[10]. 而电转氢气这一中间过程具有更高的转换效率[11]. 且随着氢燃料电池汽车等的推广,含有氢负荷的区域综合能源系统(RIES)日益受到重视[12]. 因此,本文建立“电-气-热-储-氢”区域综合能源系统,并综合考虑P2G的2阶段模型,将一阶段制得的氢气供给区内的氢负荷使用.
随着新能源的日益发展,风电光伏发电场的建设装机容量不断增加,但由于其与电网建设不同步、调峰能力不匹配,导致弃风弃光现象较为明显[13-14]. 同时,区域综合能源系统耦合情况复杂,因此优化调度问题成为核心问题之一. 针对不同系统结构的优化调度问题,文献[15]对“以热定电”导致的弃风现象及运行成本较高问题,提出基于热电联供(CHP)灵活热电比的多目标优化调度方法;文献[16]提出了一种多目标电热综合能源系统低碳经济调度模型,采用多目标细菌群体驱药性算法进行求解,对系统风电消纳的效果进行了分析;文献[17]针对风电消纳问题进行研究,考虑电锅炉、电转气技术、储能设备,利用Mosek求解器求解. 文献[18-21]介绍了可转移负荷以及需求侧响应在系统能量管理方面的相关研究. 由于甲烷化反应器、电解槽、储氢罐和储气罐等装置的引入,本文“电-气-热-储-氢”RIES的拓扑结构与能量耦合情况将更为复杂,结合系统结构与运行模式的特殊性,需求侧响应影响以及建立优化调度模型问题有待深入研究.
因此,本文针对电-气-热-储-氢耦合的区域综合能源系统,提出考虑两阶段P2G模型的RIES优化调度方法,以最小日运行成本为优化目标,充分考虑各方面约束条件,利用混合整数线性规划方法求解优化调度结果,并开展不同季节、是否含P2G环节、是否考虑需求侧响应影响因素下的技术经济分析,为RIES合理运行提供科学可靠的依据.
1. 考虑P2G两阶段模型的电-气-热-储-氢RIES模型
1.1 系统模型
考虑含P2G两阶段模型的“电-气-热-储-氢”区域综合能源系统可用图1结构描述,主要包括可再生能源发电系统、燃气轮机、燃气锅炉、电锅炉、电(气、热、氢)能源类型的负荷和储能设备、P2G环节.
1.1.1 传统设备模型
1) 燃气锅炉
燃气锅炉是以天然气为燃料,并将其转化为热能的设备. 其数学模型如式(1)所示.
Ph,GB(t)=Pg,GB(t)ηGB, (1) 式中:Ph,GB(t)、Pg,GB(t)分别为燃气锅炉的产热功率、天然气消耗功率,ηGB为燃气轮机的制热效率,t为时刻.
2) 电锅炉
电能经电锅炉转换成热能,其制热功率为
Ph,EB(t)=Pe,EB(t)ηEB, (2) 式中:Ph,EB(t)、Pe,EB(t)分别为电锅炉的产热功率、耗电功率,ηEB为电锅炉的制热效率.
3) 燃气轮机
燃气轮机能够将天然气的能量转化为电能及热能,其电、热输出功率与天然气输入功率间均存在近似线性的关系,数学模型如式(3)所示.
{Pe,GT(t)=Pg,GT(t)ηe,GT,Ph,GT(t)=Pg,GT(t)ηh,GT,ηh,GT=1−ηe,GT−ηloss,GT, (3) 式中:Pg,GT(t)为燃气轮机消耗的天然气功率,Pe,GT(t)、Ph,GT(t)分别为时刻t燃气轮机的发电功率和产热功率,ηe,GT、ηh,GT分别为燃气轮机输出电能、热能的效率,ηloss,GT为热能自耗散率.
1.1.2 电、热储能设备模型
储能设备在系统运行过程中对多余能量进行储存,并在功率缺额时及时进行补偿,具有能量缓冲、平抑功率波动、保障系统稳定运行的特点,有助于实现系统的经济调度.
电、热储能设备的充放能量为
Ek(t)=Ek(t−1)(1−ηloss,k)+Δt(ηch,kPch,k(t)−Pdis,k(t)/ηdis,k), (4) 式中:k∈{e,h},e、h分别代表电、热储能设备;ηloss,k为自耗能率;Pch, k(t)、Pdis, k(t)分别为时刻t的设备充、放电功率;ηch,k、ηdis,k分别为储能的充、放能效率;∆t为步长,取1 h.
1.1.3 P2G 2阶段模型
本文构建了一种联合制氢与制甲烷的2阶段P2G模型,其流程如图2所示. 第1阶段为电转氢阶段,进行电解水反应,使用的电能主要是风力发电高峰期而电负荷相对较低时富余的电能,生成的H2中一部分注入储氢罐并供给氢燃料电池车负荷;另一部分富余H2进入第2阶段,即制甲烷阶段,进行甲烷化反应进一步制得CH4,注入储气罐并供应系统内的天然气负荷.
1) 制氢阶段
电解槽制氢量的数学模型如式(5)所示.
VH2,EL(t)=0.0224PEL(t)ρH24νELMH2, (5) 式中:VH2,EL(t)为时刻t电解槽制氢量(m3),PEL(t)为时刻t电解槽耗电功率,ρH2为H2密度(g/m3),νEL为电解氢速率,MH2为H2摩尔质量(g/mol).
由于储氢罐前、后压缩机的精细模型对系统优化运行的影响较小,因此,主要关注氢气的存储过程. 储氢设备数学模型表示为
EHT(t)=EHT(t−1)+(Ein,HT(t)−Eout,HT(t)), (6) 式中:EHT(t)为时刻t氢储存容量(kmol);Ein,HT(t)、Eout,HT(t)分别为时刻t存入、输出氢气的量(kmol).
2) 制甲烷阶段
氢气通过甲烷化反应器转化为天然气,储气罐模型同式(4),同时采用式(7)的固定效率简化模型描述甲烷化反应.
Pg,M(t)=4ηMEH2,M(t)HLκ, (7) 式中:Pg,M(t)为时刻t甲烷化反应的天然气功率(kW),EH2,M(t)为时刻t输入甲烷化反应器的氢量(kmol),HL为天然气低热值(kW/m3),κ为气体密度(kg/m3),ηM为运行效率.
1.2 激励型需求侧响应模型
1.2.1 需求侧响应模型的目标函数
采用激励型需求侧响应,通过调整可转移负荷的用电时间,使其与可再生能源发电在时序上更加贴合,进一步减少能源购买成本,提高系统经济性. 其目标函数为
{minT∑t=1|PDG(t)−Ldr(t)|,Ldr(t)=Lload(t)+Ldr_in(t)−Ldr_out(t), (8) 式中:T为调度周期,取T = 24 h,PDG(t)为时刻t可再生能源的输出功率,Lload(t)、Ldr(t)分别为时刻t响应前、后负荷大小,Ldr_in(t)、Ldr_out(t)分别为时刻t负荷转入量、转出量.
1.2.2 需求侧响应模型的约束条件
1) 负荷转移量约束
在系统实际运行中,任一时刻下的实际负荷转移量均应不大于最大负荷转移量,且在一个调度周期内转出与转入的负荷总量应相等,以维持总负荷需求不变,故约束条件可表示为
{mdr(t)⩽Mdr(t),Ldr_in(T)=Ldr_out(T), (9) 式中:mdr(t)、Mdr(t)分别为时刻t的负荷转移容量和最大负荷可转移容量.
2) 负荷转移时段约束
负荷转移只能在一个调度周期T内进行,即
{tin∈T,tout∈T, (10) 式中:tin、tout分别为负荷转入、转出时刻.
2. 考虑P2G 2阶段模型的“电-气-热-储-氢”RIES优化调度方法
2.1 目标函数
针对电-气-热-氢耦合的区域综合能源系统的日前优化调度问题,从系统运营商角度出发,以日运行经济成本最优为目标. 目标函数F除包括购电成本Cbuye(t)、购气成本Cbuyg(t)外,还考虑弃风惩罚成本Ccurw(t)、弃光惩罚成本Ccurp(t),以体现弃风弃光现象对系统的影响,以及针对需求侧响应的补贴成本Cdr(t),如式(11)所示.
F=minT∑t=1[Cbuye(t)+Cbuyg(t)+Ccurw(t)+Ccurp(t)+Cdr(t)]. (11) 2.2 约束条件
1) 系统功率平衡约束
系统运行时需满足电、气、热、氢4种能源的能量平衡约束,即
{Pe,net(t)+Pwt(t)+Ppv(t)+Pe,GT(t)+Pdis,e(t)=PEL(t)+Pe,EB(t)+Pch,e(t)+Le(t),Pg,net(t)+Pg,M(t)+Pg,dis(t)=Pg,GT(t)+Pg,GB(t)+Pg,ch(t)+Lg(t),Ph,GB(t)+Ph,GT(t)+Ph,EB(t)+Ph,dis(t)=Ph,ch(t)+Lh(t),Ein,HT(t)+EH2,M(t)=EH2,EL(t),2Eout,HT(t)=LH2(t), (12) 式中:Le(t)、Lg(t)、Lh(t)、LH2(t)分别为电负荷、天然气负荷、热负荷、氢负荷功率;Pe,net(t)、Pwt(t)、Ppv(t)、Pe,GT(t)、Pe,EB(t)、PEL(t)、Pch,e(t)、Pdis,e(t)分别为购电功率、风力发电机功率、光伏系统功率、燃气轮机发电功率、电锅炉耗电功率、电解槽耗电功率、蓄电池充电功率和放电功率;Pg,net(t)、Pg,GT(t) (Pg,GB(t)/Pg,M(t))、Pg,ch(t)、Pg,dis(t)分别为购气功率、燃气轮机(燃气锅炉/甲烷化反应)耗气功率、储气设备充气功率和放气功率;Ph,GB(t)(Ph,GT(t)/Ph,EB(t))、Ph,ch(t)、Ph,dis(t)分别为燃气锅炉(燃气轮机/电锅炉)产热功率、储热设备充热功率和放热功率;EH2,EL为甲烷化反应电解槽产氢量.
2) 可再生能源出力约束
光伏、风机在实际运行过程中的输出功率Pwt(t)、Ppv(t)均不能超过其预测输出功率值,故约束条件为
{0⩽Pwt(t)⩽Pf,wt(t),0⩽Ppv(t)⩽Pf,pv(t), (13) 式中:Pf,wt(t)、Pf,pv(t)分别为系统光伏、风机的预测最大输出功率.
3) 电/燃气锅炉约束
电锅炉、燃气锅炉在实际运行过程中,需满足额定功率约束及爬坡约束,即
{0⩽Pe,EB(t)⩽Pe,EB,rated(t),−Rg,EB,downΔt⩽Pe,EB(t)−Pe,EB(t−1)⩽Rg,EB,upΔt, (14) {0⩽Pg,GB(t)⩽Pg,GB,rated(t),−Rg,GB,downΔt⩽Pg,GB(t)−Pg,GB(t−1)⩽Rg,GB,upΔt, (15) 式中:Pe,EB,rated(t)、Pg,GB,rated(t)分别指系统中电锅炉、燃气锅炉的额定功率,Rg,EB,up、Rg,EB,down分别为电锅炉的爬坡功率上、下限,Rg,GB,up、Rg,GB,down分别为燃气锅炉的爬坡功率上、下限.
4) 燃气轮机约束
燃气轮机运行过程中需满足额定功率约束及爬坡约束,即
{0⩽Pg,GT(t)⩽Pg,GT,rated(t),−Rg,GT,downΔt⩽Pg,GT(t)−Pg,GT(t−1)⩽Rg,GT,upΔt, (16) 式中:Pg,GT,rated(t)为系统中燃气轮机的额定功率,Rg,GT,up、Rg,GT,down分别指燃气轮机的爬坡上、下限.
5) 电解槽约束
电解槽运行过程中需满足其上、下限功率约束,即
uEL(t)PEL,min⩽PEL(t)⩽uEL(t)PEL,max, (17) 式中:PEL,min、PEL,max分别为电解槽运行功率的下、上限;uEL(t)为0-1变量,表示电解槽时刻t的启停状态,uEL(t) = 1即表示电解槽此时处于运行状态,uEL(t) = 0即表示电解槽此时处于停机状态.
6) 甲烷化反应器约束
uM(t)EH2,M,min⩽EH2,M(t)⩽uM(t)EH2,M,max, (18) 式中:EH2,M,min、EH2,M,max分别指甲烷化反应器耗氢量的下、上限;uM(t)为0-1变量,表示甲烷化反应器t时刻的启停状态,uM(t) = 1表示启动,反之表示停机.
7) 储电/热/气设备
电、热、气储能设备需满足上、下限约束,避免过度充放能影响设备寿命. 同时,为了给下一调度周期预留一定的调节裕量,使得在下一个调度周期开始时储能设备能够满足系统充放能要求,所以将运行一个调度周期T后的储能容量恢复到初始状态. 此外,考虑到同一时刻储能装置不能同时充放能,所以任一时刻充放能功率都必有一个为0. 故相关约束条件为
{0⩽Pch,κ(t)⩽uκ(t)Pch,κ,max,0⩽Pdis,κ(t)⩽vκ(t)Pdis,κ,max,0⩽uκ(t)+vκ(t)⩽1, (19) Eκ,min⩽Eκ(t)⩽Eκ,max, (20) Eκ(0)=Eκ(T), (21) 式中:κ∈{e,h,g},g代表气设备;Pch,κ(t)、Pdis,κ(t)分别为储能设备κ充、放能功率;uκ(t)、vκ(t)均为0-1变量,表示时刻t储能设备κ的充、放能状态,当uκ(t)=0时,储能设备不充能,反之则充能;vκ(t)=0时,储能设备不放能,反之则放能;同一时刻储能设备不能同时充、放能;Eκ,min、Eκ,max分别为κ储能设备允许容量下、上限;Pch, κ,max、Pdis, κ,max分别为储能设备κ充能和放能功率的最大值.
8) 储氢罐
储氢罐也需满足上、下限约束及调度周期T内充放氢总量相等,即
{0⩽Eout,HT(t)⩽Eout,HT,max,0⩽Ein,HT(t)⩽Ein,HT,max, (22) EHT,min⩽EHT(t)⩽EHT,max, (23) EHT(0)=EHT(T), (24) 式中:Eout,HT,max、Ein,HT,max分别为各时刻储氢罐注入、输出的氢气最大值,EHT,min、EHT,max分别为储氢罐储存容量上、下限,EHT(0)和EHT(T)分别为当前优化调度周期的初时刻储氢量与末时刻储氢量.
9) 外部网络交互功率约束
在本研究中,仅考虑从外部电网、天然气网购入能量,不考虑向外部电网售出能量. 系统与外部网络间联络线上的传输功率有如下关系:
{0⩽Pg,net(t)⩽Pg,net,max(t),0⩽Pe,net(t)⩽Pe,net,max(t), (25) 式中:Pg,net(t)、Pe,net(t)分别为购气功率和购电功率;Pg,net,max(t)、Pe,net,max(t)分别为购气和购电功率的最大值.
2.3 求解流程
电-气-热-储-氢耦合的区域综合能源系统中耦合了多种类型的设备,其优化变量众多,本质为混合整数规划(MILP)问题. 因此,搭建系统优化调度模型后,利用数学优化工具包YALMIP建模求解,实现系统内多种设备的优化调度.
1) 输入数据:根据冬季、夏季典型日的风光资源条件,输入其风力、光伏发电预测值及其相应的各类型负荷数据;输入各设备模型的相关数据信息.
2) 定义优化调度决策变量:定义一个决策变量矩阵P,第1~24列分别代表1 d的24个时段.
3) 设定优化调度目标函数:日运行成本最优.
4) 设定优化调度约束条件:结合各设备模型及系统功率平衡原则分别建立相应的约束条件,并输入微电网优化调度模型.
5) 求解参数设置:设置求解算法,对决策变量矩阵P进行求解.
6) 输出结果:通过优化算法求解输出满足系统所有约束条件下的最低日运行成本以及相应的系统最佳优化调度策略.
3. 算例分析
3.1 系统参数
本文采用的冬季典型日电、气、热、氢负荷数据如图3,夏、冬季典型日热负荷值比例取为1∶4. 分时电价与天然气价如图4所示. 风光出力预测数据如图5所示.
为研究P2G对系统的影响,本文按照是否含有P2G环节的冬、夏季节设置4个场景分析系统的优化调度结果,具体场景见表1.
表 1 算例场景设置Table 1. Scenario settings场景 季节 含电/热储能 含气储能 含 P2G 1 冬 √ × × 2 冬 √ √ √ 3 夏 √ × × 4 夏 √ √ √ 3.2 经济性分析
加入P2G环节显著减少了系统日运行成本,提升了系统的经济性,不同场景下的系统运行成本见表2.
表 2 不同场景下的系统运行成本Table 2. Operating costs in different scenarios元 场景 购电成本 购气成本 购氢成本 弃风成本 弃光成本 总成本 1 557.5 5863.6 1079.6 1099.4 56.6 8656.7 2 401.0 5420.8 0 10.7 0 5832.5 3 0 3025.6 1079.6 1426.6 207.7 5739.5 4 0 2152.3 0 49.5 0 2201.8 1) 购电购气成本
分别对比场景1、2、3、4,购电购气成本均有不同程度的降低. 其中购气成本降幅更大,场景2较场景1减少442.8元,场景4较场景3减少873.3元,分别节省总成本的7.55%和28.86%. 对比可知,加入P2G环节后,系统将多余电能转化为天然气以满足气负荷,降低购气成本,尤其是在10:00—15:00 的风光发电高峰时段. 由于夏季热负荷需求降低,锅炉出力减少,更多的弃风量被P2G环节利用,所以场景4购气成本较场景2节约比例更大.
2) 购氢成本
场景1和场景3不含P2G环节,系统内氢负荷需求全部由加氢站购氢满足,氢负荷一天内的总量约为53.98 kg,则购氢成本固定为1079.6元,分别占场景1和场景3总日运行成本的12.47%和18.81%.
3) 弃风弃光成本
由图3、5可知:夜间0:00—6:00电负荷需求较低,均小于1.0 MW,而此时风力发电出力最大可达3.5 MW,因此,夜间产生较大弃风量,系统经济性较差;日间电负荷需求于日间11:00—15:00明显降低,此时风电出力趋于稳定,且光伏出力处于高峰,故午间产生较严重的弃风弃光现象. 综上,场景1、3由于缺少P2G环节,都存在较严重的弃风现象和一定的弃光现象,而场景2、4加入P2G后,风光能源均得到良好利用,弃风弃光成本大幅降低.
3.3 弃风弃光现象分析
图6和图7为各场景下风光利用情况. 对于场景1、3,午间时段光照较强,产生一定的弃光量,风机不出力;夜间低负荷时段,出现大量弃风,日弃风量分别为45.26%和58.73%. 场景2、4消除了弃光现象,仅在01:00—03:00和20:00—23:00时间段存在少量弃风,弃风率降低到0.44%和2.04%. 对比场景1、3,夏季热负荷需求较冬季更低,弃风弃光量更多,加入P2G环节后消纳弃风弃光效果更显著. 综上,加入P2G后电-气-氢-储-热RIES能有效消纳弃风弃光可再生能源,显著减少弃风弃光现象.
3.4 出力结果分析
以系统日运行成本最小为目标求解得优化调度结果,以冬季场景为例进行分析,图8、9分别为场景1、2的电、气、热功率平衡图.
由图8可以看出:场景1在此时段内尽管电锅炉也满载运行,但由于缺少电解水制氢,仍无法消纳大量的弃风弃光量;只有蓄电池少量储放电对系统进行调节;由于场景1缺少制甲烷环节和储气罐,系统日购气量一直保持在较高值,以满足气负荷的需求. 观察图9可以看出:10:00—15:00时段,由于光伏出力达高峰且电负荷需求有所减少,电转气设备出力增加满足气负荷需求,购气量减少;热负荷需求降低,燃气锅炉出力减少,购电仅出现在16:00—21:00时间段内,是由于此时段内电负荷需求达到峰值,各设备无法满足其需求,故从外部购电来满足此功率缺额;场景2在01:00—16:00及22:00—24:00时段系统均未从外部购电,燃气轮机不出力,电锅炉满载运行,同时P2G环节出力制氢制甲烷. 由图5可知:风光出力之和在此时段内均保持较高水平,因此,存在富余电能供给电锅炉和P2G环节以减少弃风弃光量,提高系统经济性;燃气锅炉出力满足剩余热负荷需求,甲烷化反应补充供气. 各储能系统灵活充放能调节各能量需求,提高系统缓冲能力.
图10为场景2、4电转气环节出力. 对比图10(a)和图5:场景2中电解槽产氢时段与场景1中的弃风弃光时段几乎重合,场景4与场景3亦然,即场景2、场景4中P2G环节均能为系统消纳多余可再生能源;由于储氢罐始末态一致,第1阶段电解水产氢输入储氢罐的氢量等于氢负荷日需求量. 由于夏季存在更多弃风弃光量,电转气制甲烷量更大,即图10(b)第2阶段甲烷化耗氢量更多,P2G环节运行时间略长.
3.5 考虑需求侧响应的经济性分析
场景5在场景2的基础上引入电力需求侧响应环节,图11为可再生能源出力曲线及需求响应前后电负荷曲线对比,表3为冬季各场景下的运行成本.
表 3 冬季不同场景下的系统运行成本Table 3. Operating costs in different scenarios in winter元 场景 购电成本 购气成本 购氢成本 弃风成本 弃光成本 补贴成本 总成本 节省成本/% 1 557.5 5863.6 1079.6 1099.4 56.6 0 8656.7 2 401.0 5420.8 0 10.7 0 0 5832.5 32.62 5 22.6 5152.3 0 17.7 0 547.8 5740.4 33.69 引入需求侧响应后,由于可转移负荷用电时段的改变,使得响应后负荷曲线更加平滑,且更加贴近可再生能源出力曲线,使得与外部电网的交互功率减少,大幅降低了购电成本;使得燃气轮机出力有所减少,购气成本降低;与场景2相比,弃风弃光成本仍处于较低水平. 因此,引入需求侧响应后,尽管新增了补贴成本,但总成本仍有所降低,相较场景2节省成本百分比进一步提升为33.69%.
4. 结 论
1) 引入P2G环节后,冬、夏2季节下的系统在购电购气成本、弃风弃光成本、购氢成本各方面都有所下降,使得系统总运行成本大幅降低,提升系统的经济性.
2) 引入P2G环节后,配合各储能设备灵活充放能,系统能有效消纳弃风弃光可再生能源,显著减少弃风弃光现象. 夏季弃风弃光现象更严重,P2G环节运行时间更长,充分提高了系统能源利用效率.
3) 考虑电负荷需求侧响应,通过负荷用电时间转移进行削峰填谷,使得与外部网络交互功率减少,通过减少能源购买成本,降低了系统的日运行成本.
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