储能锂电池行业分析

储能锂电池行业分析（精选5篇）

篇1：储能锂电池行业分析

基于先进三元材料锂电池的储能系统

储能系统在应用领域上可以分为小型无间断备用电源（UPS）和大型储能电站(ESS)。UPS在停电时给计算机/服务器、存储设备、网络设备等计算机、通信网络系统或工业控制系统、需要持续运转的工业设备等提供不间断的电力供应。储能电站的目的是“削峰填谷”，可以把用电低谷期低价的富余的电储存起来，在用电高峰电价较贵的时候再拿出来用，可以为用户节约用电成本，也能在用电高峰期缓解电网的用电压力。储能电站还可存储太阳能和风能电站产生的电能，将光能和风能与储能电站完美结合，实现可再生电能的有效储存，突破时间和气候限制，解决了太阳能和风能由于缺乏稳定性而造成的并网难题。

目前市场上的储能系统多是基于传统的铅酸电池，铅酸电池虽然价格低廉，但是它主要有由金属铅构成，对环境危害很大，而且它们寿命很短，通常2年左右就要更换全部电池。在低碳和环保背景下，用新型锂离子电池代替传统铅酸电池是大势所趋。市场上虽然有基于磷酸铁锂电池的储能系统，但是磷酸铁锂电池价格高昂，是铅酸电池的3倍以上，在市场上缺乏竞争力。本项目的目的是设计和制造基于廉价三元锂电池的储能系统，可以用于备用电源也可以用于储能电站，比基于磷酸铁锂的储能系统在成本上能降低30%以上，而且能量密度更高，重量和占地面积都显著降低。崔博士已经和敦煌力波能源科技有限公司合作在敦煌市的国家级光电基地建造了一个0.5MWH的储能电站系统，这个储能电站主要服务于一个光伏电厂，在光照不足时为辅助光伏板以产生稳定的输出功率。

以下是崔立峰博士课题组设计并加工的用于储能系统的三元材料电芯和电池组模块：

3.7V-50AH电芯

48V-100AH锂电模块

篇2：储能锂电池行业分析

姓名：李浩杰

学号：2014050101018

摘要：出于对环境友好、高转换效率、高功率、高能量密度的能源技术的需求，世界各国纷纷开展对于性能优良的燃料电池的研究。其研究重点主要集中在四个方面：电解质膜、电极、燃料、系统结构。其中又以前三个为热点。目前，由于在燃料大规模制备上的困难以及其在工作时需要的一些昂贵的贵金属，燃料电池大规模商业应用受到一定限制。关键字：燃料电池、电解质膜、储能

一、燃料电池原理

燃料电池是一种使用燃料进行化学反应产生电能的装臵。所用的燃料主要包括氢气、甲醇、乙醇、天然气、汽油以及一些含氢有机物。氢气可以直接作为燃料电池的燃料，其他气体一般需要处理为含氢气的重整气。由于其燃料来源广泛，发电后产生纯水和热，能量转换效率高达80%~90%，对环境无污染，所以广泛受到各国科学家的关注，被认为是继火电、水电、核电之后的第四代发电方式。

燃料电池的工作原理图如上所示。在阳极，氢气与碱中氢氧根的在电催化剂的作用下，发生氧化反应生成水和电子：

电子通过外电路到达阴极，在阴极电催化剂的作用下，参与氧的还原反应：

生成的氢氧根通过多孔石棉膜迁移到氢电极。

为保持电池连续工作，除需与电池消耗氢气、氧气等速地供应氢气和氧气外，还需连续、等速地从阳极（氢电极）排出电池反应生成的水，以维持电解液浓度的恒定；排除电池反应的废热以维持电池工作温度的恒定。

容易看出，与其他电池相比，燃料电池内部并不储能，它只是高效地将从外部源源不断通入的燃料转换成电能，所以，它更像是一个微型的发电站。

二、燃料电池发展历程

1、国外

1839年，格罗夫发表世界上第一篇关于燃料电池的报告。初期的燃料电池使用气体为氧化剂和燃料，但因为气体在电解质溶液中溶解度很小，导致电池的工作电流密度极低。后来，多孔气体扩散电极和电化学反应三相界面概念的提出以及实际材料的突破，使燃料电池具备了走向实用化的必备条件。

60年代，由于载人航天器对于大功率、高比功率与高比能量电池的迫切需求，燃料电池开始引起一些国家与军工部门的高度重视。其典型成果为阿波罗登月飞船上的主电源—培根型中温氢氧燃料电池。

70~80 年代，由于出现世界性的能源危机和燃料电池在航天上成功应用及其高的能量转化效率，促使世界上以美国为首的发达国家大力支持民用燃料电池的开发，进而使磷酸型及熔融碳酸盐型燃料电池发展到兆瓦级试验电站的阶段。

20世纪90年代以来，出于可持续发展、保护地球、造福子孙后代等目的，基于质子交换膜的燃料电池开始高度发展。特别是在电动车行业，世界上所有的大汽车公司与石油公司均已介入燃料电池汽车的开发。

总的来说，燃料电池主要经历了经历了第1代碱性燃料电池（AFC），第2代磷酸燃料电池（PAFC），第3代熔融碳酸盐燃料电池（MCFC）后，在20世纪80年代迅速发展起了新型固体氧化物燃料电池(SOFC)。

2、国内

中国燃料电池的研究始于1958年。

1970年前后,开始了燃料电池产品开发工作并在70年代形成了燃料电池产品的研制高潮。主要开发项目是由国家投资的航天用碱性氢氧燃料电池,该产品的研制目标是为了配合中国航天技术发展计划的一个项目。

到70年代末,由于总体计划的变更而中止。但与该项计划实施的同时,一些由地方政府投资与使用部门合作的应用碱性燃料电池项目也进行了开发，只是尚未形成应用。

80年代初、中期,中国燃料电池的研究及开发工作处于低潮。

进入90年代以来,在国外先进国家燃料电池技术取得巨大进展,一些产品已进入准商品化阶段的形势影响下,中国又一次掀起了燃料电池研究开发热潮。

三、几种燃料电池简介

1、分类

（1）按燃料电池的运行机理可分为酸性燃料电池和碱性燃料电池。

（2）按电解质的种类不同，燃料电池可分为碱性燃料电池、磷酸燃料电池、熔融碳酸盐燃料电池、固体氧化物燃料电池、质子交换膜燃料电池等。在燃料电池中，磷酸燃料电池、质子交换膜燃料电池可以冷起动和快起动，可以作为移动电源，满足特殊情况的使用要求，更加具有竞争力。

（3）按燃料类型分，有氢气、甲烷、乙烷、丁烯、丁烷和天然气等气体燃料；甲醇、甲苯、汽油、柴油等有机液体燃料。有机液体燃料和气体燃料必须经过重整器“重整”为氢气后，才能成为燃料电池的燃料。（4）按燃料电池工作温度分，有低温型，工作温度低于200℃；中温型，工作温度为200～750℃；高温型，工作温度高于750℃。

上图为几种常见燃料电池各种性能，应用环境的简单对比，现主要以电解质分类形式介绍几种常见的燃料电池。

2、质子交换膜燃料电池

质子交换膜燃料电池是最接近商业化的一种燃料电池，最有希望作为未来电动汽车的发动机。在各种燃料电池中，它的工作温度是最低的，也是目前发展规模最大的一种。

上图为典型的单结质子交换膜燃料电池结构。由质子交换膜、催化层、气体扩散层、密封圈、双极板等关键部件组成。通常以全氟磺酸型质子交换膜为电解质膜，用Pt/C或者PtRu/C作为催化剂。以阴阳极催化剂层和电解质膜所组成的三合一组件统称为膜电极，是 它的核心部件。

实际应用的燃料电池电站是一个很复杂的系统，它包括燃料供应、氧化剂供应、电池反应、水热管理等多个子系统。

它的工作原理是是氢气和氧化剂分别由燃料电池的阳极和阴极流道进入电池内部，经过气体扩散层后到达电极催化层。阳极侧的氢气在催化剂的作用下，解离成氢离子和电子，氢离子穿过质子交换膜到达阴极侧，电子则经过外电路形成电流后到达阴极；在阴极催化剂的作用下，氧气接受质子和电子生成水分子，在整个过程中，外电路的电子流动形成电流。

目前限制质子交换膜燃料电池进入商业化的最主要原因是成本和寿命两大问题，寻找和开发新型材料成为解决这两大问题、推进商业化进程的必然选择，也是质子交换膜燃料电池近些年来的研究重点和热点。

3、熔融碳酸盐燃料电池

熔融碳酸盐燃料电池（MCFC）在高温下工作（约 650℃），可以利用排气余热和燃气轮机混合发电，发电效率通常高达50%以上，,可用多种燃料(如天然气和煤)，不需要用铂等贵重金属作为催化剂，有望应用到中心电站，工业化或商业化联合发电，是目前燃料电池研究的主流之一，上图为平板式熔融碳酸盐燃料电池单体结构示意。它由电极-电解质、燃料流通道、氧化剂流通道和上下隔板组成。目前,MCFC的主要技术问题已经基本解决。美国、日本等正在进行十万瓦和兆瓦级的实用演示试验,预计距商业化为期不远。

4、固体氧化物燃料电池

固体氧化物燃料电池是20世纪八九十年代燃料电池研究的成果，该燃料电池具有诸多优点。比如避免了使用液态电解质所带来的腐蚀和电解质流失等问题，反应迅速，无须贵金属催化剂，能量利用率高达80%以上，燃料广泛，可以承受较高浓度的硫化物和CO的毒害，因此对电极的要求大大降低。基于此，目前世界各国都在积极投入SOFC技术的研发。

上图为固体氧化物燃料电池的工作原理图。它主要由阴极、阳极、电解质和连接材料组 成。在阳极和阴极分别送入还原、氧化气体后，氧气在多孔的阴极上发生还原反应，生成氧负离子。氧负离子在电解质中通过氧离子空位和氧离子之间的换位跃迁达到阳极，然后与燃料反应，生成水和二氧化碳，因而形成了带电离子的定向流动。

四、燃料电池的应用

1、航天领域

早在上个世纪60年代，燃料电池就成功地应用于航天技术，这种轻质、高效的动力源一直是美国航天技术的首选。比如，以燃料电池为动力的 Gemini宇宙飞船1965年研制成功，采用的是聚苯乙烯磺酸膜，完成了8天的飞行。后来在Apollo宇宙飞船采用了碱性电解质燃料电池，从此开启了燃料电池航天应用的新纪元。

中国科学院大连化学物理研究所早在70年代就成功研制了以航天应用为背景的碱性燃料电池系统。A型额定功率为 500 W，B型额定功率为 300 W，燃料分别采用氢气和肼在线分解氢，整个系统均经过环境模拟实验，接近实际应用。这一航天用燃料电池研制成果为我国此后燃料电池在航天领域应用奠定了一定的技术基础。

2、潜艇

燃料电池作为潜艇AIP动力源，从2002年第一艘燃料电池AIP潜艇下水至今已经有6艘在役。FC-AIP 潜艇具有续航时间长、安静、隐蔽性好等优点，通常柴油机驱动的潜艇水下一次潜航时间仅为 2天，而FC-AIP潜艇一次潜航时间可达3周。

3、电动汽车

随着汽车保有量的增加，传统燃油内燃机汽车造成的环境污染日益加剧，同时，也面临着对石油的依存度日益增加的严重问题．燃料电池作为汽车动力源是解决因汽车而产生的环境、能源问题的可行方案之一。燃料电池汽车示范在国内外不断兴起，较著名的是欧洲城市清洁交通示范项目。

4、固定式分散电站

篇3：储能锂电池行业分析

门座式起重机在通用件杂货码头中广泛应用,是该类型码头中重要的生产设备,同时吊装货物时消耗大量的电能。门座式起重机在提升货物过程中,存储了大量的势能;而在货物下降或停止时,起升电机运行在第2或第4象限,产生再生制动现象[1,2],传统方法采用制动电阻消耗这部分势能转化成的再生电能,造成这部分电能白白浪费,采用锂电池或超级电容技术能够有效利用再生电能,从而提升门座式起重机的电能利用效率,达到节能的目的。

《国家中长期科技发展规划纲要》(2006-2020年)能源领域第一优先主题提出特种设备节能降耗空间和潜力巨大,建设特种设备安全监察与节能监管相结合的道路,全面高效推进高耗能特种设备节能降耗,促进节约发展,清洁发展。对通用件杂货码头中广泛使用的门座式起重机节能减排技术的研究是贯彻落实《国家中长期科技发展规划纲要》的现实需要,对减少能源消耗,建设绿色港口具有重要意义。

本文在分析门座式起重机电气结构特点的基础上,分析锂电池储能技术原理和特点,并通过实际测试验证了该技术具有明显的节能效果,值得在全国港口推广应用。

1 锂电池储能系统结构

近年来,超级电容在港口装卸机械储能环节中广泛采用,特别是在集装箱岸边起重机和ERTG中。虽然超级电容能快速储能,但由于体积大,价格昂贵,因此不宜在门座式起重机上采用。新型电容型锂电池不但具有类似超级电容快充快放的性能,还具有储能容量大的特点,用在门机上进行能量回收,组成新型节能门机的电控系统,是门座式起重机储能技术未来的发展方向之一。

锂电池技术与公共直流母线技术既可以回收电机发再生制动状态下产生的能量,并且与电气控制技术有机结合,将实现门座式起重机的平滑、稳定调速以及能量的高充分利用,原理图如图1所示[3]。

锂电池储能技术特点见表1所示。

由港口电网三相交流电接入门座式起重机进线变压器,经过变压整流和大电容滤波,得到脉冲直流,即为公共直流母线[4]。采用锂电池储能的门座式起重机的公共直流母线电压为540 V,电路包括两部分[5,6]:电机负载回路和锂电池储能回路。电机负载回路是由门座式起重机的直流总线并联,然后逆变器将脉动的直流电逆变为频率和幅值可调的交流电供给交流感应电机;锂电池储能回路由充放电控制单元和电池组组成。在门座式起重机原机型中增加的部件主要有:电压互感器、电流互感器、DC-DC变换器、锂电池组、监视显示器等,如图2所示。

2 节能效果测试方法

2.1 设备参数

在国内某港口对1台门座式起重机进行锂电池系统改造后,对未改造和混合动力(锂电池)门座式起重机进行了实际工况对比测试,其机械参数和电气参数分别见表2和表3所示。

锂电池储能单元采用某公司技术方案,选用50 AH、3.2 V的锂电池2并186串,共372节单体电池,额定电压为540 V,额定电流490 A。

2.2 测试分析

测试分为分解操作和联动操作两种工况进行,并对比两种工况的节能效果。加装锂电池储能装置前后,门座式起重机按规定动作起吊不同重量载荷的能耗,判断应用势能回收装置后是否具有节能效果。

2.2.1 分解操作

势能回收装置不工作,测量门座式起重机(吊钩)按照规定动作分别吊运12 t和6 t砝码的能耗。规定动作:

(1)起吊载荷,幅度取最大幅度的50%,载荷起升高度10 m(重载起升);

(2)以50%幅度为旋转半径,旋转180°(重载旋转);

(3)幅度从50%变到100%(重载变幅);

(4)将载荷下降至地面,解钩(重载下降);

(5)空钩起升,起升高度10 m(空载起升);

(6)幅度由100%变化为50%(空载变幅);

(7)空载旋转,以50%幅度为旋转半径,旋转180°(空载旋转);

(8)吊钩空载下降至地面,幅度为50%幅度(空载下降)。

以上作业工况为门座式起重机的一个作业周期,司机以正常操作方式进行起升、变幅、下降操作,各工况采用分解操作,每种载荷重量重复三个作业周期。电能测试仪器记录每次测试的功率、能耗和谐波等数据。

2.2.2 联动操作

门座式起重机起吊的高度、变幅的幅度、旋转的角度不变,以联动操作方式重复上述测试,测试仪器记录每次测试的功率、能耗和谐波数据,计算节能效果。

3 节能效果分析

为了得到锂电池储能技术实际的节能效果,考虑到通用件杂货码头的实际情况,设计2种测试工况(分解及联动),分别进行3次测试。6 t砝码、12 t砝码分解操作有功功率和有功电能趋势图(取平均值)分别如图3至图6所示。6 t砝码、12 t砝码联动操作有功功率和有功电能趋势图(取平均值)分别如图7至图10所示。

采用锂电池储能技术前后电能消耗和节能率测试,统计结果见表4和表5所示。

根据测试结果,采用标准规定的分解操作,在起吊12 t负载时,锂电池储能技术的节能效果为40.91%,在起吊6 t负载时,锂电池储能技术的节能效果为38.10%;采用联动操作,在起吊12 t负载时,锂电池储能技术的节能效果为48.86%,在起吊6 t负载时,锂电池储能技术的节能效果为47.76%。该技术节能效果十分明显。

4研究结论

将锂电池储能和公共直流母线供电技术引入门座式起重机节能控制系统设计,可以实现门座式起重机下放货物的再生能量被锂电池组储存,在起升货物时利用或者将多余的电能反馈到电网。

本文在分析门座式起重机电气结构特点的基础上,分析锂电池储能技术原理,并通过实际测试得到节能量,验证了该技术具有明显的节能效果,在传统变频调速节能的基础上进一步提升了节能空间,值得在全国港口推广应用。

参考文献

[1]姚立柱,刘晋川.基于公共直流母线的轻型电动轮胎式集装箱门式起重机控制系统设计[J].港口装卸,2009,6(188):24-26.

[2]常晓清.应用超级电容的轮胎式集装箱起重机节能特性研究[D].上海:同济大学,2007.

[3]李继方,汤天浩,姚刚.共直流母线交流传动节能系统的混杂系统建模与分析[J].电气传动,2011,7(26):20-26.

[4]常春贺,徐平勇,王路,等.超级电容器在高效节能轮胎吊中的应用研究[J].空军雷达学院学报,2008,22(3):198-203.

[5]金一,丁宋强,刘文华.基于公共直流母线的链式可拓展电池储能系统及控制[J].电力系统自动化,2010,15(34):66-70.

篇4：储能锂电池行业分析

关键词：磷酸铁锂电池；铅酸蓄电池；微电网；储能系统

中图分类号：TM912 文献标识码：A 文章编号：1009-2374（2014）03-0068-02

1 概述

微电网是一组负荷和微能源的集合，正常情况下运行在联网模式，紧急等情况下能够独立运行。在这两种模式下，作为微电网系统一个重要单元，高性能的储能系统能够把多余的能量储存起来，当需要的时候送给负荷，充分利用各种能源，对整个微电网起到了至关重要的作用，磷酸铁锂电池以其优越的性能应用到微电网当中，可以显著提高微电网系统的整体性能及各种能源利用率。

2 微电网的功能及特性

微电网是区别于传统电网的一种电网形式。随着世界各国对微电网研究的不断深入，对其定义的表述虽然不尽相同，但其目的却大同小异，如提高电力系统安全性、保证供电的可靠性、改善电能质量、提高各种能源的利用率。基于这些功能需求，要求微电网在能连接到电网并网运行的同时，也可以从电网中切除出来独立运行，这样就大大提高了电网的供电可靠性及电力系统的安全性。对电网而言，微电网不仅可以吸取电网能量，还可以根据电网运行状态来快速切换自身的状态，来选择是切断与电网的连接，还是吸收电网的能量补充自身需要；对电力使用者而言，微电网满足了他们一些特定的需求，如增加供电的可靠性，减少供电距离，进而降低电力输电线路损耗。

3 储能系统的必要性

电能存储技术对于实现微电网的基本功能是非常重要的。为什么微电网需要对电能进行存储，主要是因为以下四方面的原因：（1）为了保障供电系统的可靠性；（2）为保证供电能质量；（3）为了提高电能的综合利用效率；（4）为提高各种新能源的并网性能。所以，高性能的储能系统，是微电网实现其自身功能的有力保障。

3.1 储能系统的种类

储能技术已经发展了相当长的一段时间，形式也多种多样，除了日常生活中可以经常见到的铅酸蓄电池、锂电池等，还有抽水储能、飞轮储能、超导磁储能、超导电容储能、钒液流电池、钠硫电池、镍氢电池等等。而国际主流电池包括：铅酸蓄电池，锂离子电池，镍氢电池等。飞轮储能、超导磁储能、超导电容储能还处于研究阶段，而没有大规模应用。铅酸电池已经在微电网储能系统中得到一定规模的应用，但是由于其自身特性的制约，一些不足正在慢慢凸现出来。

3.2 传统蓄能系统特性——铅酸蓄电池

铅酸蓄电池对环境温度要求比较高：传统储能系统的代表是铅酸蓄电池，铅酸蓄电池对环境温度要求比较高，也就是提高了其使用场合的环境要求。

铅酸蓄电池对配电房要求高：一个系统的配电设备中，主要都是一些电源设备，而电源设备中电池房的面积及重量也是不容忽视的。

铅酸蓄电池的高倍率放电性能较差：在微电网的运行模式从连接电网切换到独立运行时，储能系统会在短时间内，流过很大的电流，这就要求储能系统具有高倍率放电的性能，而传统储能系统的铅酸蓄电池此性能

较差。

铅酸蓄电池的监控不准确：铅酸蓄电池的监控系大都是根据电压来进行判断，而此方法判断的准确性很有限，导致长时间使用后储能系统中所存储的电能计量会有很大偏差。

铅酸蓄电池对环境污染大：铅酸蓄电池中因为含有对环境造成污染的铅，所以在对其生产及废品处理不当时，均会造成对环境的严重污染。

3.3 磷酸铁锂电池在微电网中应用前景

3.3.1 磷酸铁锂电池电化学原理：磷酸铁锂电池的正极材料是磷酸铁锂（锂的过氧化合物），负极材料是石墨或焦炭，磷酸铁锂电池是锂电池的一种，其得名于磷酸铁锂电池的正极材料。

3.3.2 磷酸铁锂电池的特性：

能量密度高：能量密度可以表示出单位体积或者重量中存储能量的多少。而电化学电池的能量密度是指电池的单位体积或质量所能提供给外部负载的电能。在相同重量下，磷酸铁锂电池的能量密度是铅酸蓄电池的3～5倍，也就是说电池额定容量一样的情况下，磷酸铁锂电池就会比铅酸蓄电池轻很多，也相对减少了对支撑物强度的要求。

使用寿命长：可循环电池的寿命是指电池可以正常充放电的循环次数，铅酸蓄电池的寿命在500次左右，而磷酸铁锂电池寿命可达到1600次，容量还能保持在80%，磷酸铁锂电池的寿命明显优于铅酸蓄电池。

安全：锂电池的的安全问题一直是阻碍其发展的关键原因，而磷酸铁锂电池完全解决了锂化合物的不稳定因素，钴酸锂和锰酸锂在特定的条件下会产生爆炸，如碰撞等，而磷酸铁锂电池经过精心的设计，并通过了严格的性能测试及安全测试，在剧烈的碰撞、穿透等情况下都不会发生爆炸。

没有记忆效应：可循环充放电电池长时间充满电而不使用，其容量会相对低于额定容量值，这即为记忆效应。铅酸蓄电池存在着明显的记忆效应，经过测试，磷酸铁锂电池基本上没有记忆效应。

3.3.3 磷酸铁锂电池的不足。磷酸铁锂电池因为其材料为锂金属以及加工工艺的成熟度不高，导致其成本居高不下，除此之外磷酸铁锂电池的容量还需要进一步改进。磷酸铁锂电池的能量管理系统、电池充放电均衡功能在未完善之前，还不宜在微电网系统中大规模

应用。

4 结语

在燃料电池、钒电池、飞轮储能等技术尚未成熟，传统铅酸蓄电池的缺点也越来越明显之际，磷酸铁锂电池作为目前电化学电池技术的代表，技术已逐渐走向成熟，应用也慢慢的得到普及。国家政策扶持更加快了磷酸铁锂电池的发展。磷酸铁锂电池的价格也会在将来的发展普及中慢慢走低，它将以其优越的性能成为未来储能系统中的主流电池，给微电网储能系统注入新鲜血液，更进一步提升微电网的整体性能。

参考文献

[1] 张建华.微电网运行控制与保护技术[M].北京：中国电力出版社，2010.

[2] 赵波，王成山，张雪松.海岛独立型微电网储能类型选择与商业运营模式探讨[J].电力系统自动化，2013，（4）：27-33.

[3] 刘冬升，陈宝林.磷酸铁锂电池特性的研究[J].河南科技学院学报，2012，40（1）：65-68.

[4] 赵晏强，李金坡.基于中国专利的锂电池发展趋势分析[J].情报杂志，2012，（1）：20，39-44.

篇5：储能锂电池行业分析

In recent decades,wind power is showing a rapid development all around the world due to escalating fuel prices as well as increasing public concern on environment protection[1].An energy policy called the renewable portfolio standard(RPS)is widely accepted by more and more countries and regions[2].RPS is indeed a promise of producing a specified percentage of the total power generation from renewable sources within a certain date.As a result,more and more large-scaled wind farms will be integrated into power systems.Unfortunately,wind power depends on climatic conditions and tends to be intermittent.

In many utilities,the intermittent wind power is considered as a negative load that cannot be dispatched and is excluded from the process of unit commitment(UC).On the basis of load and wind power predictions,conventional generator units are scheduled for serving the net load(i.e.,load demand minus wind power)at the minimum cost by means of UC tool.Due to the limitation of present prediction tools,the wind power prediction is far more difficult than the load prediction,and the prediction error increase dramatically with the prediction time horizon[3].Under this condition,the uncertainties of the net load increase due to the uncertainties of the wind power,so the operation risk of power systems consequently increase.In this paper,the operation risk refers to the probability in which the scheduled generating capacity will fail to carry the net load over a scheduled time horizon(24 h or less).Loss of load probability(LOLP)is a widely used reliability index and quantifies the probability of the load loss resulted from insufficient generation capacity.So it is utilized to measure the operation risk in this work.Scheduling sufficient spinning reserve(SR)capacity is considered as an effective means for reducing the operation risk,but it comes at some cost,because additional generator units might be synchronized and other units might operate at their suboptimal output points[4].

Since the early 1980s,the utility-scaled battery energy storage system(BESS)has been applied widely[5,6].It has been used for load leveling,peak shaving,voltage-frequency stabilizing,and other purposes.In recent years,BESS has been incorporated into wind farms for mitigating the disadvantages resulted from the integrated wind power.In Refs.[7-10],BESS has been incorporated into wind farms as an energy storage medium for dispatching the wind power.By controlling the charging/discharging behavior of BESS,the wind power can then be dispatched by system operators to some extent.The wind power is also intermittent.Its reliability contribution to generation systems is consequently far less than that of conventional generator units.In Ref.[11],the case study on a test system reported in Ref.[12]demonstrates that the reliability contribution of a90 MW wind farm is only equal to that of a 10 MW conventional generator unit.Refs.[13-14]incorporated BESS into wind farms for mitigating the intermittency of wind power,so the reliability contribution of wind power can be dramatically improved[13,14].

The highlight of the present work is to utilize BESS to reduce the operation risk of power systems.BESS is designed to discharge for serving some load in case of power shortage resulted from unpredictable events,such as generator outages,sudden load/wind power changes or a combination of both.In this instance,BESS can be considered as another kind of reserve.Regarding expensive BESS,an important consideration for design is the finite number of charge-discharge cycles that BESS can undertake over its lifetime[15].A special operation strategy is designed to make full use of the battery.A new index,called the expected cycle life consumption(ECLC),is proposed to measure the cycle life consumption of BESS during the scheduled time horizon.Sequential Monte Carlo simulation(SMCS),described in Ref.[16],is adopted to assess the system operation risk,in which,both stochastic factors associated with power systems and effects of BESS are considered.Simulations on a case system are done to justify the feasibility of the designed BESS.

1 BESS Integrated as Reserve

1.1 System Configuration

In Refs.[7-8],BESS is incorporated into a system at the point of common coupling(PCC)for dispatching wind power by adjusting the charging/discharging state of BESS.In the present work,BESS designed for reducing the operation risk of power systems is also incorporated into a wind farm according to the same technical scheme in Refs.[7-8],as shown in Fig.1.

In Fig.1,Pw,tand Pb,tare wind power output and charging/discharging power of BESS at hour t respectively(1≤t≤T,Tis the total number of hours over the scheduled time horizon).If BESS discharges at hour t,Pb,tis positive;if BESS charges at hour t,Pb,tis negative.Pd,trepresents the output power from the wind farm and the BESS power station.Assuming that power losses of converters are ignored,it can be computed as

In many utilities,the wind power is considered as a negative load when scheduling power systems.The precision of wind power prediction is far less than that of load prediction,so the integration of wind power increases the operation risk of power systems.In case of an emergency that SR fails to compensate power shortage entirely,the energy stored in BESS discharges to serve some load to reduce the operation risk of power systems.In this instance,BESS can be considered as another kind of reserve.Subsequently,SR provided by operating generator units starts to charge BESS.Under this condition,Pd,tcan be negative,i.e.,the wind farm and BESS power station absorb energy from the grid.After the charging period,the recharged BESS has a capability of compensating unpredictable power shortage again and discharges as needed.

1.2 Technical Characteristics of BESS

BESS is mainly comprised of batteries,the control and power conversion system(C-PCS),and the rest of the plant.The rest of the plant is designed to provide good protection for batteries and C-PCS[6].Batteries are the most important and expensive component of BESS.The performances of BESS depend on the battery characteristics.

Regarding BESS,an important characteristic is the finite number of charge-discharge cycles that the battery can undertake over its lifetime[15].The number of such cycles,called the cycle life,is of nonlinear and complex relationships with various factors,such as the depth of discharge(dDOD)that the battery has to undergo,the ambient temperature and the charging/discharging rate of the battery.For example,the cycle life of a certain type of battery,as reported in Ref.[17],decreases dramatically from 4 200for dDOD=20%to 2000for dDOD=80%.However,too deep discharge(e.g.,dDOD>80%)should be avoided as it may lead to permanent physical damage to the battery and an exceedingly low cycle life[18].In the present work,if the battery is fully charged,its state of charge(SOC)is 1.Hence,when the battery is subsequently fully discharged,i.e.,its dDODreaches the allowable maximum value,denoted as dmax,thus SOC of the battery is 1-dmax.In order to obtain a judicious balance between the discharging level of battery energy and the battery lifetime,a reasonable value of dmaxis 0.8[9].

Due to the nonlinear relationship among SOC,charging/discharging rate,internal losses of battery,and other factors,only nonlinear model[19]can accurately represent battery charging/discharging behaviors.Because the scheduled time horizon is short-term,a simplified linear battery model can be adopted,in which a fixed charging/discharging efficiency,denoted asη,is utilized to express internal losses of the battery.

2 Operation Strategies of BESS

To date,BESS is still a very expensive appliance,and its cycle life is finite.To make full use of BESS in an economic way,three principles must be observed in the operation as follows:

1)To avoid damages to BESS,the charging/discharging rate at any time must be less than its allowable maximum value,denoted as Pbm.

2)The charged/discharged energy must be confined strictly to avoid an over-charge/discharge.

3)Any incomplete charge-discharge cycle must be avoided for making full use of BESS and prolonging its lifetime,i.e.,as long as a BESS starts to charge/discharge,it must be fully charged/discharged.

During hour t,it is assumed that some power shortage occurs due to generator unit outages,sudden load/wind power changes or a combination of both.Subsequently,SR provided by operating generator units starts voluntarily to compensate the power shortage,but fails to compensate entirely.The uncompensated power shortage is denoted as Ps,t.Under this condition,BESS designed for reducing the operation risk needs to be discharged to serve some load.The allowable maximum discharging power,denoted as Pdm,t,depends on the current charging/discharging state and SOC of BESS.It can be computed as

where St-1is the SOC of BESS at the previous hour;Ebthe BESS capacity;Tsthe studied time span of this work(1h);and Utis the charging/discharging state of BESS during hour t.

Uthas two different values,i.e.,0 means discharging state,while 1 means charging state.If BESS is unfortunately in charging state during hour t(i.e.,Ut=1),it cannot be discharged to reduce the operation risk.Otherwise,an incomplete charge-discharge cycle will happen and a charge-discharge cycle of BESS will be wasted.

The real discharge power of BESS depends on both uncompensated power shortage and allowable maximum discharge power.It can be expressed by

Then the SOC of BESS at hour t,denoted as St,can be calculated as

If BESS has been fully discharged at the end of hour t,i.e.,its SOC has reached 1-dmax,its state will change from discharge to charge immediately,i.e.,Ut+1will change to 1from 0.During the subsequent charging period,three principles should also be observed as the discharging period,i.e.,the charging rate and the total charging energy of BESS will be strictly confined for avoiding damages to batteries.As long as BESS has been fully charged,i.e.,its SOC has reached 1,the state of the recharged BESS will change from charge to discharge immediately.

According to the operation strategy described above,an hourly charging/discharging sequence of BESS,as shown in Fig.2,can be obtained to assess the operation risk of power systems.

The hourly charging/discharging sequence is comprised of a series of alternating charging/discharging periods,during which,BESS has been fully charged/discharged.If there are mcharging/discharging periods,it can be easily concluded that BESS has undergone m/2charge-discharge cycles over the studied time horizon.mis uncertain due to many random factors,such as stochastic fluctuations of load and wind power,random outage of generator units,etc.The ECLC index is defined to represent the expected value of cycle life consumption of BESS over the studied time horizon.

3 Assessment of Operation Risk

The objective of UC is to minimize the energy production cost over a scheduling period with constraints of generator operation and power systems.The detailed model can be found in Ref.[20].The UC problem is so difficult that only exhaustive enumeration can obtain its accurate solution[21].In recent years,genetic algorithm(GA)has been successfully utilized to solve this problem.In this work,GA is a choice.

When wind power is integrated into power systems,it is usually viewed as a negative load and is incorporated into load demand for obtaining a net load.Under this condition,the load demand considered in the UC process is consequently a net load prediction,i.e.,the load prediction minus the wind power prediction.

The constraint on the amount of SR is of importance in the UC process,as it is related to not only system operation risk,but also production cost.How to quantify the optimal SR amount is an important issue.In this paper,SR amount is predetermined to be 10%of the net load prediction.

3.1 Uncertainties in Assessment Process

The charging/discharging sequence of BESS is chronological,so SMCS is a suitable computational tool to assess the operation risk of power systems.In SMCS,both random factors associated with power systems and effects of BESS are considered.The random factors include stochastic outages of generator units and uncertainties of load/wind power.

The actual load demand La,tcan be expressed as the sum of load prediction Lf,tand an normally distributed prediction errorεt.

The mean ofεtis 0.The standard deviation ofεtis a function of the prediction tool.And it can be expressed as a percentage of the load prediction.

The actual wind power is also assumed to be the sum of wind power forecast Wf,tand a prediction errorεw,t.But being different with the load prediction error,the wind power prediction error does not follow the normal distribution.Instead,some scholars conclude that the prediction error follows aβ-distribution.However,the large number and the geographical dispersion of wind turbine generators allow the application of central limit theorem to justify the assumption of normally distributed wind power prediction error,which is a common practice[22,23,24].With the current prediction techniques,the wind power prediction is far more difficult than the load prediction and the time factor is of a primary concern.It is believed that the prediction error increases dramatically with the prediction time horizon.The studied time horizon in this work is short-term,so a zero-mean,normally distributed random variable is used to describe the wind power prediction error[24].The standard deviation of the prediction errorσw,tcan be estimated as[3]

where LTis a system lead time;k1and k2are adopted for quantifying the prediction errors.In Ref.[3],these two parameters are set to be 0.061 6and 0.228 5respectively according to a case of Danish systems.

3.2 Assessment Process

Step 1:Obtaining the optimum generation schedule by UC tool.

Step 2:Specifying the initial state of each generator unit.Here,it is assumed that all generator units are in the upstate for the convenience of description.

Step 3:Simulating the duration of each generator unit residing its present state by the inverse transformer model,and the distribution functions of the generator unit failure and repair rates.In this work,the failure and repair times are all supposed to follow an exponential distribution,so the sample value of the duration time is

where Diis a duration of unit i residing its present state;δian uniform-distributed random number in[0,1]for unit i;andλiis a constant repair/failure rate depending on the current state of unit i.

Step 4:Step 3is repeated to construct a chronological upand down-state of each generator unit during the scheduled time horizon.Then,the system operation cycle is determined by combining all the generator cycles.

Step 5:Randomly generating normally distributed prediction errors of load and wind power at each hour over the scheduled time horizon.These prediction errors are added to the load and wind power predictions for obtaining chronological hourly load and wind power models,respectively.

Step 6:According to the operation strategy,an hourly BESS charging/discharging power sequence is constructed and incorporated to calculate LOLP and ECLC indices over the scheduled time horizon.In order to obtain accurate results,Step 3and 4should be repeated for many times.In this paper,the repeated time denoted as nsimis set to be 106.Then,LOLP and ECLC indices can be calculated by

where VECLCis the value of ECLC index;VLOLP,tthe value of LOLP index at hour t;mjthe number of charging/discharging periods in the jth simulation;and nd,tis the number of load loss during hour t in nsimsimulations.

4 Case Study

A case study is given to justify the feasibility of the designed BESS for reducing the operation risk.

4.1 Case System Data

The case system is extended from a typical thermal system[25]by adding a 400 MW wind farm.Some key parameters of thermal units are given in Table 1,other parameters and hourly load demands can be found in Ref.[25].In Table 1,Pi,maxand Pi,minare the maximum and minimum output powers of unit i respectively,and ai,bi,ciare the cost coefficients of unit i.

The hourly wind power predictions are adjusted proportionately according to the actual conditions in a typical spring day of Jiangsu power system in China,and are expressed as square symbols,as shown in Fig.3.For comparison,the hourly load and net load predictions are expressed as deltoid and diamond symbols in the same figure,respectively.In order to assess the operation risk of the case system,the constant failure/repair rate of each unit is shown in Table 2.The standard deviation of load forecast error is assumed to be 2%of the load prediction.The parameters k1and k2are assumed to be 0.061 6and 0.228 5respectively,and the value of LTis supposed to be 4h.

4.2 Simulation Results Without BESS

UC tool is adopted to schedule 10thermal units in the case system for meeting the net load at the minimum cost.The converged UC solution is illustrated in Fig.4,and the corresponding production cost is$443 390.

The operation risk can be measured by hourly LOLP index,which is expressed as square symbols,as shown in Fig.5.In order to highlight the impacts of wind power uncertainties on the operation risk of the case system,it is assumed that wind power can be predicted or dispatched,i.e.,the prediction error of wind power is 0.Under the assumption,the values of hourly LOLP index are decreased dramatically and expressed as deltoid symbols in Fig.5.

Fig.5indicates that the operation risk of the case system increases dramatically due to the uncertainties of integrated wind power.For example,the value of LOLP index during hour 9increases from 1.28%to 1.96%,the corresponding rise expressed in percentage is as high as 53%.

4.3 Simulation Results with BESS

Scheduling sufficient SR capacity is widely considered as an effective method to cope with the increased operation risk resulted from integrated wind power.But it comes at some cost.In the present work,BESS is connected to wind farms to reduce the operation risk.

From 1988 to 1997,a 10 MW/40 MW.h BESS had been successfully utilized for peak shaving,load leveling,load following,and other purposes as a demonstration project in Chino,California,USA[5].In this paper,it is also assumed that Pbmis 0.25 Eb.It is emphasized that BESS is scheduled according to the operation strategy.The 40 MW.h,80 MW.h,120 MW.h and 160 MW.h BESS is supposed here to be incorporated into wind farms.The operation risks for different BESS capacities are expressed by different symbols,as shown in Fig.6.

It can be found that BESS can dramatically reduce the operation risk of the case system.Moreover,the extent of the operation risk reduction depends on the BESS capacity.If a 40 MW.h BESS is incorporated,the maximum value of LOLP index decreases from 2.58% to 2.17%.When the BESS capacity increases from 40 MW.h to 160 MW.h,the maximum value of LOLP index decreases further from 2.17% to 1.62%.

In the present work,ECLC index is formulated to quantify the cycle life consumption of BESS.Fig.7 gives the values of ECLC index for different BESS capacities.It can be found that the value of ECLC index decreases with the increasing Eb,i.e.,the larger BESS will outperform the smaller BESS on the expected life.Moreover,the values of ECLC index for different BESS capacities are all less than 0.015.These values are tiny compared with the cycle life of BESS.For example,the cycle life of Chino BESS is as high as 2000[5].In addition,the value of cycle life will be gradually improved with the technical progress on batteries.In view of the above,BESS can reduce the operation risk of the case system without evident consumption of BESS.That is to say,BESS originally designed for reducing the operation risk can also be used for other purposes,such as achievement of wind power dispatching,which is the highlight of Refs.[7-10].

From the above case study,it can be concluded that larger BESS outperforms smaller BESS on the effectiveness of reducing operation risk under the expected lifetime.Unfortunately,the battery is so expensive that selecting larger BESS to reduce the operation risk is not optimal from the viewpoint of economy.In the future research,we wish to explore a calculation methodology of optimal BESS capacity.

4.4 Effectiveness of BESS Operation Strategy

If incomplete charge-discharge cycle of BESS is permitted,the operation risk of the case system incorporated with a 40 MW.h BESS is shown in Fig.8.For comparison,the case without any incomplete charge-discharge cycle(i.e.,BESS is scheduled according to the operation strategy proposed in this paper)is also illustrated in the same figure.It can be seen that the permission of incomplete chargedischarge cycle will not bring out more remarkable reduction in the operation risk.But the value of ECLC index increases dramatically from 0.0137 to 0.049,of which the corresponding rise expressed in percentage is as high as 257%,i,e.,the cycle life consumption of BESS has been increased dramatically.The comparison described above demonstrates that the operation strategy of BESS designed in this paper can make full use of BESS in an economic way.

5 Conclusions

In this paper,BESS is introduced as another reserve for reducing the increased operation risk resulted from the uncertainties of integrated wind power.To make full use of BESS in an economic way,a special operation strategy is designed,in which any incomplete charge-discharge cycle is avoided.Simulation results on a case system demonstrate that the designed BESS can effectively reduce the operation risk of power systems without evident consumption of BESS.

BESS is so expensive that using it only as a reserve for reducing the operation risk is not economic.The case study also indicates that the cycle life consumption of BESS is tiny compared with the cycle life of BESS.The designed BESS still has a capability of achieving other purposes simultaneously.

摘要：大规模风电接入电网后,风电功率固有的不确定性可能会显著增大系统的运行风险。该运行风险可用失负荷概率来衡量。文中将电池储能系统接入风电场,并利用其灵活的充放电能力来降低系统的运行风险。为充分利用昂贵的电池储能装置,设计了专门的电池运行策略,任何不完整的充放电循环在电池运行过程中均被严格禁止。考虑到电池运行的时序性,采用序贯蒙特卡洛模拟技术评估系统的运行风险。基于某算例系统的仿真试验证实了所提出的技术方案及电池运行策略的可行性。此外,仿真结果亦表明,电池用于降低系统运行风险的循环寿命消耗非常低,也就是说,电池储能系统在降低系统运行风险的同时仍有能力实现其他功能。