国产日韩欧美一区二区三区三州_亚洲少妇熟女av_久久久久亚洲av国产精品_波多野结衣网站一区二区_亚洲欧美色片在线91_国产亚洲精品精品国产优播av_日本一区二区三区波多野结衣 _久久国产av不卡

?

電動(dòng)汽車集群可調(diào)控潛力分析與互動(dòng)控制研究綜述

2022-03-22 04:26王明深董曉紅戴強(qiáng)晟
新能源汽車供能技術(shù) 2022年2期
關(guān)鍵詞:調(diào)頻控制策略集群

王明深,董曉紅,戴強(qiáng)晟

電動(dòng)汽車集群可調(diào)控潛力分析與互動(dòng)控制研究綜述

王明深1,董曉紅2,戴強(qiáng)晟1

(1.國(guó)網(wǎng)江蘇省電力有限公司電力科學(xué)研究院,江蘇 南京 211101;2.省部共建電工裝備可靠性與智能化國(guó)家重點(diǎn)實(shí)驗(yàn)室(河北工業(yè)大學(xué)),天津 300000)

隨著電動(dòng)汽車(electric vehicle, EV)和充電設(shè)施產(chǎn)業(yè)的持續(xù)推廣普及,未來(lái)EV將會(huì)逐步取代燃油汽車,而EV接入電網(wǎng)將對(duì)電網(wǎng)產(chǎn)生不可忽視的影響。首先,總結(jié)EV接入電網(wǎng)的特點(diǎn),分析EV車接入電網(wǎng)帶來(lái)的機(jī)遇與挑戰(zhàn)。其次,從EV集群建模分析其可調(diào)控潛力與互動(dòng)控制策略的2個(gè)應(yīng)用場(chǎng)景,進(jìn)行國(guó)內(nèi)外研究現(xiàn)狀綜述,總結(jié)現(xiàn)有研究不足。最后從EV集群建模和控制策略方面,對(duì)未來(lái)開(kāi)展進(jìn)一步研究提供一些思路與方向。

電動(dòng)汽車;集群建模;可調(diào)控潛力;互動(dòng)控制策略

0 引言

近年來(lái),隨著經(jīng)濟(jì)社會(huì)的快速發(fā)展,世界各國(guó)對(duì)能源的需求與日俱增。交通運(yùn)輸業(yè)的能源消耗在各行業(yè)中十分突出[1],交通領(lǐng)域用能向清潔化、低碳化轉(zhuǎn)變成為未來(lái)發(fā)展的必然趨勢(shì)。電動(dòng)汽車(electric vehicle, EV)以其節(jié)能、減排、低碳、環(huán)保的巨大優(yōu)勢(shì),成為汽車發(fā)展的新形式。近年來(lái),在各國(guó)政府和工業(yè)界的支持下,EV與充電設(shè)施產(chǎn)業(yè)得到快速發(fā)展。2019年全球電動(dòng)汽車銷量已達(dá)到224萬(wàn)輛,其中中國(guó)的EV銷量已占全球市場(chǎng)的50.5%。截止到2019年,全球EV充電樁保有量已達(dá)到736.2萬(wàn)輛,而中國(guó)充電樁保有量占全球市場(chǎng)的39.6%[2]。

隨著汽車技術(shù)的不斷革新和動(dòng)力電池技術(shù)的不斷突破,EV已經(jīng)進(jìn)入了高速發(fā)展階段,未來(lái)EV將會(huì)逐步取代燃油汽車,EV的市場(chǎng)占比將會(huì)越來(lái)越高。大量EV接入電網(wǎng)獲取電能,會(huì)產(chǎn)生新的負(fù)荷,這將會(huì)改變電網(wǎng)負(fù)荷的時(shí)空分布特性,繼而改變電網(wǎng)的潮流分布,對(duì)源荷平衡[3]、輸電網(wǎng)[4]、配電網(wǎng)[5]、電網(wǎng)規(guī)劃[6]、碳排放[7]等多方面產(chǎn)生不容忽視的影響。作為新增負(fù)荷,EV集群接入電網(wǎng)可帶來(lái)以下機(jī)遇:提高終端設(shè)備利用率,降低出行成本和污染排放等。

由于受車輛類型、用戶交通出行規(guī)律、EV數(shù)量、電池特性、政策法規(guī)[8]等影響,EV接入電網(wǎng)具有以下幾個(gè)方面的特點(diǎn):?jiǎn)误w容量小、EV數(shù)量多、乘用車??繒r(shí)間長(zhǎng)、EV充電時(shí)間相對(duì)短、EV接入電網(wǎng)具有時(shí)空分布特性、EV與電網(wǎng)互動(dòng)需要兼顧用戶的利益、EV與電網(wǎng)互動(dòng)的實(shí)現(xiàn)產(chǎn)生大量信息流、EV與電網(wǎng)互動(dòng)需要用戶的廣泛參與等特點(diǎn)。

由于每一輛EV都有一定的電能存儲(chǔ)能力,理論上也就具備一定的調(diào)控能力。盡管單臺(tái)EV的調(diào)控容量很小,完全可以忽略,但數(shù)以萬(wàn)計(jì)乃至百萬(wàn)計(jì)的EV出現(xiàn)后,經(jīng)過(guò)有機(jī)協(xié)調(diào)(如有序充電,甚至在理想情況下向電網(wǎng)反送電),則可在用戶側(cè)形成可觀調(diào)控能力,對(duì)促進(jìn)電網(wǎng)安全經(jīng)濟(jì)運(yùn)行有重要意義。通過(guò)利用EV的可調(diào)控潛力,實(shí)現(xiàn)EV與電網(wǎng)的友好互動(dòng)。

作為可調(diào)節(jié)手段,EV集群接入電網(wǎng)可帶來(lái)以下機(jī)遇:實(shí)現(xiàn)電力系統(tǒng)削峰填谷,電力系統(tǒng)頻率調(diào)控,電力系統(tǒng)緊急控制手段,增加電力系統(tǒng)的備用容量,改善電力系統(tǒng)穩(wěn)定性和可靠性,促進(jìn)可再生能源的消納等。EV集群入網(wǎng)潛力是機(jī)遇與挑戰(zhàn)并存的。由于單體EV接入電網(wǎng)獲取電能的過(guò)程,受用戶交通出行規(guī)律、用戶用能需求、電池特征參數(shù)、電力市場(chǎng)價(jià)格等因素的影響,在對(duì)大規(guī)模EV群體進(jìn)行建模評(píng)估其可調(diào)控潛力時(shí),這些因素會(huì)直接增加模型構(gòu)建的復(fù)雜度;此外,考慮到用戶不同的參與度,通信技術(shù)水平的不斷改善,市場(chǎng)政策的不斷調(diào)整,不同應(yīng)用場(chǎng)景對(duì)EV集群建模方法、控制和競(jìng)價(jià)策略的要求不同,因此EV互動(dòng)控制時(shí)需要充分考慮應(yīng)用場(chǎng)景的基礎(chǔ)條件、技術(shù)可行性和實(shí)現(xiàn)目標(biāo)。

為分析大規(guī)模EV入網(wǎng)的影響,同時(shí)實(shí)現(xiàn)大規(guī)模入網(wǎng)EV在不同應(yīng)用場(chǎng)景下的利用,構(gòu)建合適的EV集群模型,是所有后續(xù)研究的基礎(chǔ)。面向不同應(yīng)用場(chǎng)景時(shí)的控制策略或市場(chǎng)應(yīng)用所構(gòu)建的EV集群模型,在考慮EV特點(diǎn)的基礎(chǔ)上,還需滿足不同應(yīng)用需求。EV集群模型構(gòu)建為控制策略和市場(chǎng)應(yīng)用的實(shí)現(xiàn)提供所需要的模型、參數(shù)、約束條件等。本文從EV集群建模分析其可調(diào)控潛力與互動(dòng)控制的2個(gè)應(yīng)用場(chǎng)景,進(jìn)行國(guó)內(nèi)外研究現(xiàn)狀綜述,總結(jié)現(xiàn)有研究不足。最后從EV集群建模和控制策略方面,對(duì)未來(lái)開(kāi)展進(jìn)一步研究提供一些思路與方向。

1 電動(dòng)汽車集群模型的國(guó)內(nèi)外研究現(xiàn)狀

單臺(tái)EV的能量存儲(chǔ)和調(diào)節(jié)能力都很小,只有大量EV(稱為EV集群)接入電網(wǎng)后才能具有足夠的調(diào)控容量,從而為電網(wǎng)提供調(diào)控能力,為此需要研究和構(gòu)建EV的集群模型。EV集群的建模過(guò)程需要精細(xì)化分析EV接入電網(wǎng)過(guò)程,其過(guò)程受到“用戶出行相關(guān)因素”、“車輛與充電設(shè)施相關(guān)因素”的影響,EV接入電網(wǎng)具有時(shí)間和空間上的隨機(jī)性。

用戶出行相關(guān)因素包括出行時(shí)間、出行距離、用戶出行和入網(wǎng)決策選擇等因素,而用戶出行和入網(wǎng)決策選擇涉及多個(gè)方面,包括出行路徑選擇、充電方式選擇、充電需求選擇、互動(dòng)決策選擇。EV充電方式包括慢充和快充兩種方式,快充的優(yōu)勢(shì)在于充電時(shí)間短,但是由于目前電池技術(shù)手段的限制,快充對(duì)電池?fù)p傷較大,一般作為應(yīng)急充電方式,且由于快充要求盡快完成充電,快充的EV處于不可控狀態(tài),不能實(shí)現(xiàn)與電網(wǎng)互動(dòng),故本文只討論EV慢充方式。

1.1 研究現(xiàn)狀

文獻(xiàn)[9-11]根據(jù)交通統(tǒng)計(jì)數(shù)據(jù),獲取EV出行時(shí)間的分布規(guī)律,在此基礎(chǔ)上,利用抽樣算法獲取單體EV的出行參數(shù)值,從而構(gòu)建了EV集群的統(tǒng)計(jì)學(xué)模型,用以評(píng)估EV集群充電負(fù)荷的時(shí)間分布特性。文獻(xiàn)[12-14]分析了不同類型車輛日出行距離的分布,假設(shè)EV采用一日一充的模式,根據(jù)日出行距離來(lái)估算EV接入電網(wǎng)時(shí)的電池荷電狀態(tài)值,在此基礎(chǔ)上構(gòu)建了考慮出行距離的EV集群模型。文獻(xiàn)[15-17]采用Origin Destination 矩陣分析方法來(lái)刻畫(huà)EV用戶對(duì)出行路徑的選擇,獲取EV出行終點(diǎn)和入網(wǎng)接入點(diǎn),從而達(dá)到交通網(wǎng)和電網(wǎng)耦合的目的,動(dòng)態(tài)模擬EV出行和入網(wǎng)過(guò)程,通過(guò)模擬所有EV的動(dòng)態(tài)入網(wǎng)過(guò)程,獲得EV集群模型以評(píng)估充電負(fù)荷的時(shí)空分布規(guī)律和分析EV動(dòng)態(tài)接入電網(wǎng)的影響;文獻(xiàn)[18-20]充分考慮了用戶對(duì)充電需求的差異性,在保證EV用戶用能需求的基礎(chǔ)上,建立了EV集群模型,分析了用戶充電需求對(duì)集群模型的影響。文獻(xiàn)[21-23]則考慮了EV與電網(wǎng)的互動(dòng)能力,研究了充電和放電兩種電網(wǎng)功率交換形式,建立了EV集群模型來(lái)獲取集群在不同時(shí)刻的可調(diào)節(jié)容量。

車輛與充電設(shè)施相關(guān)因素包括EV類型和電池特征,其中電池特征包括電池類型、電池容量、出行能耗、充放電效率等。文獻(xiàn)[24]介紹了最具應(yīng)用前景的3種EV電池,包括鉛酸電池、鎳氫電池和鋰離子電池,并從電化學(xué)性能和經(jīng)濟(jì)性角度比較了不同類型電池的發(fā)展趨勢(shì)。文獻(xiàn)[25]表明電池容量直接影響EV用戶接入電網(wǎng)充電行為,電容容量越大,電池續(xù)航里程越大,用戶對(duì)充電的焦慮程度越低,充電的頻率越低。文獻(xiàn)[26-28]分析了EV出行過(guò)程中的能耗問(wèn)題,如受溫度、交通擁堵等影響,能耗直接影響EV接入電網(wǎng)時(shí)的初始狀態(tài),在此基礎(chǔ)上,建立了EV集群模型,分析能耗對(duì)EV充電負(fù)荷的影響;文獻(xiàn)[29-30]考慮了電池類型、電池容量、充電效率的影響,根據(jù)各EV的參數(shù)信息,可以獲取單體EV的充電過(guò)程和響應(yīng)能力,進(jìn)而通過(guò)求和的方法獲得EV集群在不同時(shí)刻的充電負(fù)荷和響應(yīng)能力。

1.2 研究不足

已有文獻(xiàn)在研究EV集群建模過(guò)程時(shí),分析了來(lái)自用戶出行相關(guān)因素、車輛與充電設(shè)施相關(guān)因素的影響,建模時(shí)需要參照相關(guān)因素提取EV參數(shù),并根據(jù)歷史統(tǒng)計(jì)數(shù)據(jù)獲取EV參數(shù)的分布規(guī)律,進(jìn)而獲取各單體EV的參數(shù)值,模擬每輛EV接入電網(wǎng)的充電過(guò)程,通過(guò)求和方法獲取EV集群的充電負(fù)荷和響應(yīng)能力。已有研究在EV集群建模時(shí),未能綜合考慮用戶出行相關(guān)因素、車輛與充電設(shè)施相關(guān)因素的影響,忽略了這些因素對(duì)建模過(guò)程的交互影響。已有EV集群模型需要獲取各獨(dú)立EV的參數(shù)值,對(duì)于大規(guī)模EV集群,建模過(guò)程的計(jì)算量極大且模型實(shí)際應(yīng)用時(shí)對(duì)通信設(shè)施的性能要求高,需要提出考慮現(xiàn)有基礎(chǔ)條件和實(shí)際可行性的有效建模方法以簡(jiǎn)化集群建模的復(fù)雜度。EV集群建模方法還需要考慮具體的實(shí)現(xiàn)目標(biāo),以滿足具體的實(shí)際應(yīng)用需求,如利用EV集群平抑控制可再生能源功率波動(dòng)、調(diào)節(jié)系統(tǒng)頻率、參與日前市場(chǎng)競(jìng)價(jià)等,在各自建模過(guò)程中,需重點(diǎn)考慮的因素會(huì)有所不同。

2 電動(dòng)汽車集群平抑控制研究現(xiàn)狀

近年來(lái),為應(yīng)對(duì)能源危機(jī),風(fēng)力發(fā)電、光伏發(fā)電等可再生能源發(fā)電以其節(jié)能減排的巨大優(yōu)勢(shì),在世界范圍內(nèi)快速發(fā)展。隨著可再生能源發(fā)電在電網(wǎng)中的大規(guī)模接入,可再生能源發(fā)電隨機(jī)間歇性的特點(diǎn),將會(huì)給電網(wǎng)的安全穩(wěn)定運(yùn)行產(chǎn)生深刻影響[31-33]??稍偕茉窗l(fā)電功率具有強(qiáng)波動(dòng)性,而傳統(tǒng)發(fā)電機(jī)由于受到爬坡率的限制,難以追蹤功率的快速變化,該因素已成為制約可再生能源發(fā)電大規(guī)模入網(wǎng)的主要障礙[34-36]。以電池為代表的傳統(tǒng)儲(chǔ)能資源能夠有效追蹤功率波動(dòng),然而,目前大規(guī)模配置傳統(tǒng)儲(chǔ)能資源成本很高。隨著需求響應(yīng)技術(shù)的不斷發(fā)展,以EV為代表的需求側(cè)資源擁有快速的功率調(diào)節(jié)能力,有取代傳統(tǒng)儲(chǔ)能資源的潛力,成為平抑可再生能源功率波動(dòng)的新選擇[37-39]。

2.1 研究現(xiàn)狀

利用EV集群的可調(diào)節(jié)潛力,已有研究提出了平抑再生能源功率波動(dòng)的控制策略。文獻(xiàn)[40]針對(duì)配網(wǎng)中的EV集群提出了三層能量管理模型,分析了網(wǎng)側(cè)調(diào)度中心與集群運(yùn)營(yíng)商的職責(zé)與分工,探究了EV集群用于平抑可再生能源功率波動(dòng)的可行性。文獻(xiàn)[41]提出了EV在多種運(yùn)行模式下靈活控制策略,給出了利用EV為電網(wǎng)提供功率支撐的實(shí)現(xiàn)架構(gòu),驗(yàn)證了EV平抑功率波動(dòng)實(shí)際應(yīng)用的可能性。文獻(xiàn)[42]面向風(fēng)電大規(guī)模接入的背景,設(shè)計(jì)了EV集群平抑風(fēng)電功率波動(dòng)的雙層控制器,上層計(jì)算功率波動(dòng)率并發(fā)送相應(yīng)的控制信號(hào),下層在考慮EV荷電狀態(tài)和用戶出行的基礎(chǔ)上提出了平抑功率波動(dòng)的模糊控制器。文獻(xiàn)[43]在考慮非工作日和工作日負(fù)荷在不同區(qū)域分布差異性的基礎(chǔ)上,分析了微網(wǎng)中EV集群充放電和可再生能源的協(xié)同互補(bǔ)特性,能夠降低可再生能源接入對(duì)電網(wǎng)的沖擊。文獻(xiàn)[44]提出了用于限制光伏功率波動(dòng)的EV集群有序充放電策略,根據(jù)系統(tǒng)凈負(fù)荷功率的波動(dòng)量,按照篩選條件選擇滿足要求的EV進(jìn)行充電或放電的操作,從而提高電網(wǎng)中光伏的滲透率。文獻(xiàn)[45]提出了考慮EV集群的微網(wǎng)聯(lián)絡(luò)線功率平滑方法,在可調(diào)節(jié)容量約束下,通過(guò)在短和長(zhǎng)時(shí)間尺度上分別對(duì)EV集群和樓宇虛擬儲(chǔ)能進(jìn)行優(yōu)化調(diào)度,從而達(dá)到有效平抑聯(lián)絡(luò)線功率波動(dòng)的目的。文獻(xiàn)[46]提出了EV集群參與平抑光伏功率波動(dòng)的實(shí)時(shí)調(diào)度策略,在考慮用戶出行和充放電約束的基礎(chǔ)上,建立了利用EV集群跟蹤光伏功率的凸優(yōu)化模型,通過(guò)日內(nèi)實(shí)時(shí)滾動(dòng)優(yōu)化實(shí)現(xiàn)了對(duì)光伏功率的有效平抑。

2.2 研究不足

已有文獻(xiàn)在研究EV集群平抑控制策略時(shí),EV集群從整體上作為一個(gè)虛擬電廠參與平抑控制時(shí),未能充分考慮EV集群和傳統(tǒng)發(fā)電機(jī)的協(xié)同控制,未能充分計(jì)及EV集群在不同電網(wǎng)節(jié)點(diǎn)上響應(yīng)能力的差異性。EV集群在響應(yīng)平抑目標(biāo)功率時(shí),集群中EV運(yùn)行狀態(tài)具有差異性,針對(duì)大規(guī)模EV集群采用優(yōu)化方法會(huì)增加模型的復(fù)雜度,難以滿足實(shí)時(shí)控制的要求,需要提出有效的算法,根據(jù)各EV運(yùn)行狀態(tài)的差異設(shè)計(jì)相應(yīng)的控制信號(hào),在保證用戶出行和用能需求的基礎(chǔ)上,達(dá)到快速控制和充分利用EV可調(diào)節(jié)潛力的目的。

3 電動(dòng)汽車集群頻率控制研究現(xiàn)狀

在可再生能源大規(guī)模接入背景下,源荷功率不平衡造成系統(tǒng)頻率波動(dòng)受到越來(lái)越多的關(guān)注[47-50],現(xiàn)階段儲(chǔ)能資源的缺乏以及傳統(tǒng)發(fā)電機(jī)爬坡率的限制,頻率波動(dòng)問(wèn)題難以獲得有效支撐[51-54]。隨著需求響應(yīng)技術(shù)的快速發(fā)展,以EV為代表的需求側(cè)資源能夠參與系統(tǒng)頻率調(diào)節(jié)[55-57]。

3.1 研究現(xiàn)狀

按照反應(yīng)時(shí)間的不同,目前EV集群的頻率響應(yīng)可以分為一次調(diào)頻和二次調(diào)頻[58-60]。一次調(diào)頻要求EV集群在頻率偏移發(fā)生后幾秒內(nèi)快速響應(yīng),實(shí)際應(yīng)用時(shí)要求集群控制中心能夠根據(jù)頻率偏差迅速計(jì)算得到頻率控制信號(hào),并向集群中的EV發(fā)送相應(yīng)的頻率控制信號(hào),一次調(diào)頻的實(shí)現(xiàn)對(duì)集群控制中心和通信設(shè)施的要求極高,需要集群控制中心能夠迅速處理大量的數(shù)據(jù),需要通信設(shè)施能夠?qū)崿F(xiàn)高質(zhì)量的實(shí)時(shí)通信;二次調(diào)頻要求EV集群在頻率偏移發(fā)生30秒后參與系統(tǒng)頻率響應(yīng),響應(yīng)持續(xù)時(shí)間為5~20分鐘,實(shí)際應(yīng)用允許的延時(shí)較長(zhǎng),該控制的實(shí)現(xiàn)對(duì)控制中心和通信設(shè)施的要求遠(yuǎn)低于一次調(diào)頻,但控制中心和通信設(shè)施的質(zhì)量越高,控制效果會(huì)越好[61-63]。文獻(xiàn)[64-69]將集中控制方式下的群體EV看作一個(gè)集群,利用EV集群的可調(diào)度潛力進(jìn)行系統(tǒng)一次調(diào)頻,但并未介紹如何利用現(xiàn)有通信設(shè)施傳輸EV的控制信號(hào),一次調(diào)頻的實(shí)現(xiàn)需要建立在通信設(shè)施質(zhì)量高和集群控制中心運(yùn)算能力強(qiáng)的假設(shè)條件上,很難在當(dāng)前通信條件下滿足實(shí)際應(yīng)用需求,目前關(guān)于EV集群的一次調(diào)頻還處于理論研究階段;文獻(xiàn)[70-72]利用分散控制方式下的EV進(jìn)行系統(tǒng)一次調(diào)頻,分散式控制不需要集群控制中心,要求終端設(shè)備能夠根據(jù)系統(tǒng)頻率偏移情況迅速做出功率調(diào)整,對(duì)通信設(shè)施要求低,但要求終端設(shè)備能夠采集系統(tǒng)頻率、采集EV運(yùn)行信息、實(shí)現(xiàn)智能算法等,目前終端設(shè)備投入的成本較高。

針對(duì)EV集群的頻率控制,系統(tǒng)二次調(diào)頻對(duì)通信設(shè)施和集群控制中心的要求遠(yuǎn)低于一次調(diào)頻,在現(xiàn)有的技術(shù)條件下,EV集群更適用于參與系統(tǒng)二次調(diào)頻[73]。文獻(xiàn)[74]給出了EV集群二次調(diào)頻的實(shí)現(xiàn)框架,提出了考慮網(wǎng)絡(luò)約束的模型預(yù)測(cè)控制策略,利用EV集群來(lái)追蹤系統(tǒng)的二次調(diào)頻信號(hào),并驗(yàn)證了EV集群進(jìn)行二次調(diào)頻的可行性。文獻(xiàn)[75]結(jié)合丹麥電網(wǎng)高滲透率風(fēng)電的特征,利用EV集群在長(zhǎng)時(shí)間尺度上提供的可調(diào)節(jié)容量,結(jié)合丹麥電網(wǎng)典型日的算例,有效評(píng)估了EV集群參與二次調(diào)頻的實(shí)際應(yīng)用價(jià)值。文獻(xiàn)[76]考慮了EV在行駛狀態(tài)、充電狀態(tài)、受控狀態(tài)之間的相互轉(zhuǎn)換,提出了EV集群儲(chǔ)能能力評(píng)估模型,并探究了EV集群在二次調(diào)頻過(guò)程中與熱泵負(fù)荷集群和電池儲(chǔ)能系統(tǒng)的互補(bǔ)特性。文獻(xiàn)[77]基于EV的快速響應(yīng)特性,在考慮各EV接入電網(wǎng)過(guò)程中實(shí)時(shí)SOC的基礎(chǔ)上,提出了一種功率分配方法來(lái)確定調(diào)頻過(guò)程中各EV的目標(biāo)控制功率。文獻(xiàn)[78]以EV集群運(yùn)營(yíng)商作為調(diào)度中心和EV之間的中間商為出發(fā)點(diǎn),闡述了EV集群參與二次調(diào)頻的實(shí)現(xiàn)過(guò)程,運(yùn)營(yíng)商需要上傳EV的特征參數(shù)和運(yùn)行數(shù)據(jù),同時(shí)需要保證EV在完成調(diào)頻任務(wù)時(shí),滿足EV用戶對(duì)電池荷電狀態(tài)的需求。文獻(xiàn)[79]提出了最優(yōu)模糊控制器來(lái)實(shí)現(xiàn)EV集群參與二次頻率調(diào)節(jié),通過(guò)選定SOC閾值的方法,保證SOC過(guò)低的EV不會(huì)參與放電控制,同時(shí)保證SOC過(guò)高的EV盡量減少充電控制,從而保證電池的SOC穩(wěn)定。文獻(xiàn)[80]針對(duì)EV集群參與系統(tǒng)頻率調(diào)節(jié),提出了面向EV集群控制中心的不確定性控制方法,該方法不需要獲取EV的詳細(xì)充放電信息,能夠?qū)⒄{(diào)頻功率分配給集群中的各EV,并通過(guò)集群控制中心與各EV之間的實(shí)時(shí)修正以保證用戶出行需求。

3.2 研究不足

已有文獻(xiàn)在產(chǎn)生頻率控制信號(hào)時(shí),不同EV由于運(yùn)行狀態(tài)的差異,各EV需要實(shí)現(xiàn)不同的功率調(diào)節(jié)目標(biāo),因此,需要針對(duì)每輛EV產(chǎn)生不同的控制信號(hào)。針對(duì)大規(guī)模EV集群,頻率控制需要產(chǎn)生大量的獨(dú)立控制信號(hào),實(shí)時(shí)計(jì)算量大,會(huì)造成一定的計(jì)算延時(shí)。同時(shí),發(fā)送獨(dú)立的控制信號(hào)需要集群控制中心與各EV建立獨(dú)立的通信通道,通信成本高,且大量獨(dú)立控制信號(hào)會(huì)造成實(shí)時(shí)通信壓力大。與此同時(shí),獨(dú)立控制信號(hào)需要獲取用戶的實(shí)時(shí)運(yùn)行信息和充電位置信息等用戶隱私,隱私暴露問(wèn)題會(huì)影響用戶積極參與頻率調(diào)節(jié)。頻率控制實(shí)際上影響了用戶的充電習(xí)慣和充電需求,目前尚未提出有效的控制策略來(lái)降低這種影響。

4 工作展望

關(guān)于EV集群的研究,盡管目前已做了一些研究,但研究深度和廣度還遠(yuǎn)遠(yuǎn)不夠,因此未來(lái)將可在以下幾個(gè)方面進(jìn)一步開(kāi)展深入探究:

1) 關(guān)于EV集群建模方面:EV集群模型在一定程度上仍然依賴用戶上傳的數(shù)據(jù),建模過(guò)程未能充分考慮用戶數(shù)據(jù)隱私保護(hù)的問(wèn)題,如數(shù)據(jù)上傳過(guò)程中如何考慮不同類型數(shù)據(jù)的隱私保護(hù),在大數(shù)據(jù)時(shí)代數(shù)據(jù)隱私保護(hù)背景下,如何建立合理有效的EV集群模型還需要進(jìn)一步深入研究。

2) 關(guān)于EV集群控制策略方面:考慮EV用戶數(shù)據(jù)隱私保護(hù)需求和EV運(yùn)行狀態(tài)差異性,同時(shí)考慮通信設(shè)施的技術(shù)條件,如何開(kāi)發(fā)適用于大規(guī)模EV集群的概率控制信號(hào),在降低通信成本的同時(shí)保證EV集群的控制精度。

[1] International Energy Agency. World energy balances [DB/OL]. [2020-02-11]. https://www.iea.org/statistics/.

[2] Global EV outlook 2020[R]. International Energy Agency, 2020.

[3] 馬玲玲, 楊軍, 付聰, 等. 電動(dòng)汽車充放電對(duì)電網(wǎng)影響研究綜述[J]. 電力系統(tǒng)保護(hù)與控制, 2013, 41(3): 140-148.

MA Lingling, YANG Jun, FU Cong, et al. Review on impact of electric car charging and discharging on power grid[J]. Power System Protection and Control, 2013, 41(3): 140-148.

[4] WANG Mingshen, MU Yunfei, LI Fangxing, et al. State space model of aggregated electric vehicles for frequency regulation[J]. IEEE Transactions on Smart Grid, 2020, 11(2): 981-994.

[5] 楊昕然, 呂林, 向月, 等. “車-路-網(wǎng)”耦合下電動(dòng)汽車惡劣充電場(chǎng)景及其對(duì)城市配電網(wǎng)電壓穩(wěn)定性影響[J]. 電力自動(dòng)化設(shè)備, 2019, 39(10): 102-108, 122.

YANG Xinran, Lü Lin, XIANG Yue, et al. Degradation charging scenarios and impacts on voltage stability of urban distribution network under “EV-road-grid” coupling[J]. Electric Power Automation Equipment, 2019, 39(10): 102-108, 122.

[6] 肖湘寧, 溫劍鋒, 陶順, 等. 電動(dòng)汽車充電基礎(chǔ)設(shè)施規(guī)劃中若干關(guān)鍵問(wèn)題的研究與建議[J]. 電工技術(shù)學(xué)報(bào), 2014, 29(8): 1-10.

XIAO Xiangning WEN Jianfeng TAO Shun, et al. Study and recommendations of the key issues in planning of electric vehicles’ charging facilities[J]. Transactions of China Electrotechnical Society, 2014, 29(8): 1-10.

[7] SABER A Y, VENAYAGAMOORTHY G K. Plug-in vehicles and renewable energy sources for cost and emission reductions [J]. IEEE Transactions on Industrial Electronics, 2011, 58(4): 1229-1238.

[8] 陳麗丹, 張堯, ANTONIO F. 電動(dòng)汽車充放電負(fù)荷預(yù)測(cè)研究綜述[J]. 電力系統(tǒng)自動(dòng)化, 2019, 43(10): 177-197.

CHEN Lidan, ZHANG Yao, ANTONIO F. Overview of charging and discharging load forecasting for electric vehicles[J]. Automation of Electric Power Systems, 2019, 43(10): 177-197.

[9] 羅卓偉, 胡澤春, 宋永華, 等. 電動(dòng)汽車充電負(fù)荷計(jì)算方法[J]. 電力系統(tǒng)自動(dòng)化, 2011, 35(14): 36-42.

LUO Zhuowei, HU Zhechun, SONG Yonghua, et al. Study on plug-in electric vehicles charging load calculating[J]. Automation of Electric Power Systems, 2011, 35(14): 36-42.

[10] 田立亭, 史雙龍, 賈卓. 電動(dòng)汽車充電功率需求的統(tǒng)計(jì)學(xué)建模方法[J]. 電網(wǎng)技術(shù), 2010, 34(11): 126-130.

TIAN Liting, SHI Shuanglong, JIA Zhuo. A statistical model for charging power demand of electric vehicles[J]. Power System Technology, 2010, 34(11): 126-130.

[11] 和敬涵, 謝毓毓, 張金國(guó). 電動(dòng)汽車充電負(fù)荷時(shí)空分布及其對(duì)配電網(wǎng)的影響[J]. 電力建設(shè), 2015, 36(7): 83-88.

HE Jinghan, XIE Yuyu, ZHANG Jinguo. Temporal and spatial distribution research for electric vehicle charging load and the impacts on distribution network[J]. Electric Power Construction, 2015, 36(7): 83-88.

[12] QIAN K, ZHOU C, ALLAN M, et al. Modeling of load demand due to EV battery charging in distribution systems [J]. IEEE Transactions on Power Systems, 2011, 26(2): 802-810.

[13] 徐立中, 楊光亞, 許昭, 等. 電動(dòng)汽車充電負(fù)荷對(duì)丹麥配電系統(tǒng)的影響[J]. 電力系統(tǒng)自動(dòng)化, 2011, 35(14): 18-23.

XU Lizhong, YANG Guangyan, XU Zhao, et al. Impacts of electric vehicle charging on distribution network in Denmark[J]. Automation of Electric Power Systems, 2011, 35(14): 18-23.

[14] 張聰. 規(guī)?;妱?dòng)汽車出行規(guī)律及充電負(fù)荷建模研究[C] // 江蘇省電機(jī)工程學(xué)會(huì)城市供用電專業(yè)委員會(huì), 2017: 9-15.

ZHANG Cong. Research on travel law and charging load modeling of large scale electric vehicles[C] // Urban power supply and consumption Professional Committee of Jiangsu Electrical Engineering Society, 2017: 9-15.

[15] 張晨彧, 丁明, 張晶晶. 基于交通出行矩陣的私家車充電負(fù)荷時(shí)空分布預(yù)測(cè)[J]. 電工技術(shù)學(xué)報(bào), 2017, 32(1): 78-87.

ZHANG Chenyu, DING Ming, ZHANG Jingjing. Temporal and spatial distribution forecasting of private car charging load based on origin-destination matrix[J]. Transactions of China Electrotechnical Society, 2017, 32(1): 78-87.

[16] 黃小慶, 陳頡, 陳永新, 等. 大數(shù)據(jù)背景下的充電站負(fù)荷預(yù)測(cè)方法[J]. 電力系統(tǒng)自動(dòng)化, 2016, 40(12): 68-74.

HUANG Xiaoqing, CHEN Jie, CHEN Yongxin, et al. Load forecasting method for electric vehicle charging station based on big data[J]. Automation of Electric Power Systems, 2016, 40(12): 68-74.

[17] 李曉輝, 李磊, 劉偉東, 等. 基于動(dòng)態(tài)交通信息的電動(dòng)汽車充電負(fù)荷時(shí)空分布預(yù)測(cè)[J]. 電力系統(tǒng)保護(hù)與控制, 2020, 48(1): 117-125.

LI Xiaohui, LI Lei, LIU Weidong, et al. Spatial-temporal distribution prediction of charging load for electric vehicles based on dynamic traffic information[J]. Power System Protection and Control, 2020, 48(1): 117-125.

[18] 張美霞, 蔡雅慧, 楊秀, 等. 考慮用戶充電差異性的家用電動(dòng)汽車充電需求分布分析方法[J]. 電力自動(dòng)化設(shè)備, 2020, 40(2): 154-161.

ZHANG Meixia, CAI Yahui, YANG Xiu, et al. Charging demand distribution analysis method of household electric vehicles considering users’charging difference[J]. Electric Power Automation Equipment, 2020, 40(2): 154-161.

[19] 劉洪, 張旭, 劉暢, 等. 考慮充電設(shè)施充裕性的電動(dòng)私家車出行與充電需求時(shí)序交互分析[J]. 中國(guó)電機(jī)工程學(xué)報(bào), 2018, 38(18): 5469-5478.

LIU Hong, ZHANG Xu, LIU Chang, et al. Timing interactive analysis of electric private vehicle traveling and charging demand considering the sufficiency of charging facilities[J]. Proceedings of the CSEE, 2018, 38(18): 5469-5478.

[20] SHAHIDINEJAD S, FILIZADEH S, BIBEAU E. Profile of charging load on the grid due to plug-in vehicles[J]. IEEE Transactions on Smart Grid, 2012, 3(1): 135-141.

[21] 葛少云, 連恒輝, 劉洪, 等. 電動(dòng)汽車時(shí)序響應(yīng)能力模型與控制策略[J]. 電力系統(tǒng)自動(dòng)化, 2016, 40(3): 33-38, 151.

GE Shaoyun, LIAN Henghui, LIU Hong, et al. Timing response capability model and control method of electric vehicle[J]. Automation of Electric Power Systems, 2016, 40(3): 33-38, 151.

[22] 張亞朋, 穆云飛, 賈宏杰, 等. 電動(dòng)汽車虛擬電廠的多時(shí)間尺度響應(yīng)能力評(píng)估模型[J]. 電力系統(tǒng)自動(dòng)化, 2019, 43(12): 94-110.

ZHANG Yanpeng, MU Yunfei, JIA Hongjie, et al. Response capacity evaluation model with multiple time scales for electric vehicle virtual plant[J]. Automation of Electric Power Systems, 2019, 43(12): 94-110.

[23] DONADEE J, SHAW R, GARNETT O, et al. Potential benefits of vehicle-to-grid technology in California: High value for capabilities beyond one-way managed charging[J]. IEEE Electrification Magazine, 2019, 7(2): 40-45.

[24] 宋永華, 陽(yáng)岳希, 胡澤春. 電動(dòng)汽車電池的現(xiàn)狀及發(fā)展趨勢(shì)[J]. 電網(wǎng)技術(shù), 2011, 35(4): 1-7.

SONG Yonghua, YANG Yuexi, HU Zechun. Present status and development trend of batteries for electric vehicles[J]. Power System Technology, 2011, 35(4): 1-7.

[25] KNUTSEN D, WILLéN O. A study of electric vehicle charging patterns and range anxiety[J]. Uppsala, Sweden: Uppsala University, 2013.

[26] 王海玲, 張美霞, 楊秀. 基于氣溫影響的電動(dòng)汽車充電需求預(yù)測(cè)[J]. 電測(cè)與儀表, 2017, 54(23): 123-128.

WANG Hailing, ZHANG Meixia, YANG Xiu. Electric vehicle charging demand forecasting based on influence of weather and temperature[J]. Electrical Measurement & Instrumentation, 2017, 54(23): 123-128.

[27] 陳麗丹, 聶涌泉, 鐘慶. 基于出行鏈的電動(dòng)汽車充電負(fù)荷預(yù)測(cè)模型[J]. 電工技術(shù)學(xué)報(bào), 2015, 30(4): 216-225.

CHEN Lidan, NIE Yongquan, ZHONG Qing. A model for electric vehicle charging load forecasting based on trip chains[J]. Transactions of China Electrotechnical Society, 2015, 30(4): 216-225.

[28] 蘇舒, 林湘寧, 張宏志, 等. 電動(dòng)汽車充電需求時(shí)空分布動(dòng)態(tài)演化模型[J]. 中國(guó)電機(jī)工程學(xué)報(bào), 2017, 37(16):4618-4629.

SU Shu, LIN Xiangning, ZHANG Hongzhi, et al. Spatial and temporal distribution model of electric vehicle charging demand[J]. Proceedings of the CSEE, 2017, 37(16): 4618-4629.

[29] MENG J, MU Y, JIA H, Et Al. Dynamic frequency response from electric vehicles considering travelling behavior in the Great Britain power system[J]. Applied Energy, 2016, 162: 966-979.

[30] MENG J, MU Y, WU J, et al. Dynamic frequency response from electric vehicles in the Great Britain power system[J]. Journal of Modern Power Systems and Clean Energy, 2015, 3(2): 203-211.

[31] PARSON B, MILLIGAN M, ZAVADIL B, et al. Grid impacts of wind power: a summary of recent studies in the United States[J]. Wind Energy, 2004, 7(2): 87-108.

[32] 魯宗相, 李海波, 喬穎. 含高比例可再生能源電力系統(tǒng)靈活性規(guī)劃及挑戰(zhàn)[J]. 電力系統(tǒng)自動(dòng)化, 2016, 40(13): 147-158.

LU Zhongxiang, LI Haibo, QIAO Ying. Power system flexibility planning and challenges considering high proportion of renewable energy[J]. Automation of Electric Power Systems, 2016, 40(13): 147-158.

[33] 豐穎, 贠志皓, 周瓊, 等. 考慮風(fēng)電接入的在線風(fēng)險(xiǎn)評(píng)估和預(yù)防控制[J]. 電力自動(dòng)化設(shè)備, 2017, 37(2): 61-68.

FENG Ying, YUN Zhihao, ZHOU Qiong, et al. Online risk assessment and preventive control considering wind-power integration[J]. Electric Power Automation Equipment, 2017, 37(2): 61-68.

[34] 楊建, 張利, 王明強(qiáng), 等. 計(jì)及出力水平影響與自相關(guān)性的風(fēng)電預(yù)測(cè)誤差模擬方法[J]. 電力自動(dòng)化設(shè)備, 2017, 37(9): 96-102.

YANG Jian, ZHANG Li, WANG Mingqiang, et al. Wind- power forecasting error simulation considering output level and self-correlation[J]. Electric Power Automation Equipment, 2017, 37(9): 96-102.

[35] 劉文龍. 儲(chǔ)能提升含高比例可再生能源電力系統(tǒng)頻率穩(wěn)定性研究[D]. 杭州: 浙江大學(xué), 2019.

LIU Wenlong. Improving frequency stability of large scale renewable energy integrated power system using energy storage system[D]. Hangzhou: Zhejiang University, 2019.

[36] 李本新, 韓學(xué)山, 劉國(guó)靜, 等. 風(fēng)電與儲(chǔ)能系統(tǒng)互補(bǔ)下的火電機(jī)組組合[J]. 電力自動(dòng)化設(shè)備, 2017, 37(7): 32-37, 54.

LI Benxin, HAN Xueshan, LIU Guojing, et al. Thermal unit commitment with complementary wind power and energy storage system[J]. Electric Power Automation Equipment, 2017, 37(7): 32-37, 54.

[37] 徐唐海, 魯宗相, 喬穎, 等. 源荷儲(chǔ)多類型靈活性資源協(xié)調(diào)的高比例可再生能源電源規(guī)劃[J]. 全球能源互聯(lián)網(wǎng), 2019, 2(1): 27-34.

XU Tanghai, LU Zongxiang, QIAO Ying, et al. High penetration of renewable energy power planning considering coordination of source-load-storage multi-type flexible resources[J]. Journal of Global Energy Interconnection, 2019, 2(1): 27-34.

[38] 蔣效康. 計(jì)及需求響應(yīng)的含大規(guī)模風(fēng)電和電動(dòng)汽車電力系統(tǒng)優(yōu)化運(yùn)行研究[D]. 武漢: 華中科技大學(xué), 2018.

JIANG Xiaokang. Power system optimal operation with large-scale wind power and electric vehicle integrated considering demand response[D]. Wuhan: Huazhong University of Science and Technology, 2018.

[39] 于飛. 考慮風(fēng)電消納的電動(dòng)汽車分布式充放電調(diào)度策略研究[D]. 沈陽(yáng): 東北電力大學(xué), 2018.

YU Fei. Research on distributed charging and discharging dispatching strategies for electric vehicles considering wind power accommodation[D]. Shenyang: Northeast Dianli University, 2018.

[40] 劉其輝, 余培, 祝培鑫. 基于新能源消納/負(fù)荷平抑的電動(dòng)汽車能量管理策略[J]. 電力需求側(cè)管理, 2019, 21(5): 5-10, 16.

LIU Qihui, YU Pei, ZHU Peixin. Electric vehicle energy management strategies for renewable energy consumption/ load smoothing[J]. Power DSM, 2019, 21(5): 5-10, 16.

[41] KHEMAKHEM S, REKIK M, KRICHEN L. A flexible control strategy of plug-in electric vehicles operating in seven modes for smoothing load power curves in smart grid[J]. Energy, 2017, 118: 197-208.

[42] RAOOFAT M, SAAD M, LEFEBVRE S, et al. Wind power smoothing using demand response of electric vehicles[J]. International Journal of Electrical Power & Energy Systems, 2018, 99: 164-174.

[43] 劉祎玲. 微網(wǎng)中電動(dòng)汽車充放電策略與可再生能源協(xié)同互補(bǔ)研究[D]. 濟(jì)南: 山東大學(xué), 2019.

LIU Yiling. Charging/discharging strategies of electric vehicles and the synergistic complementary operation with renewable energy in a micro-grid[D]. Jinan: Shandong University, 2019.

[44] 謝小英, 李文軍, 宣文華, 等. 一種平抑光伏和負(fù)荷波動(dòng)的電動(dòng)汽車有序充放電策略[J]. 燕山大學(xué)學(xué)報(bào), 2015, 39(4): 352-356.

XIE Xiaoying, LI Wenjun, XUAN Wenhua, et al. A method of smoothing PV and load fluctuation by organized charging/discharging for electric vehicles[J]. Journal of Yanshan University, 2015, 39(4): 352-356.

[45] 杜麗佳, 靳小龍, 何偉, 等. 考慮電動(dòng)汽車和虛擬儲(chǔ)能系統(tǒng)優(yōu)化調(diào)度的樓宇微網(wǎng)聯(lián)絡(luò)線功率平滑控制方法[J]. 電力建設(shè), 2019, 40(8): 26-33.

DU Lijia, JIN Xiaolong, HE Wei, et al. A tie-line power smoothing control method for an office building microgrid by scheduling thermal mass of the building and plug-in electric vehicles[J]. Electric Power Construction, 2019, 40(8): 26-33.

[46] 胡俊杰, 周華嫣然, 李陽(yáng). 集群電動(dòng)汽車平抑光伏波動(dòng)實(shí)時(shí)調(diào)度策略[J]. 電網(wǎng)技術(shù), 2019, 43(7): 2552-2560.

HU Junjie, ZHOU Huayanran, LI Yang. Real-time dispatching strategy for aggregated electric vehicles to smooth power fluctuation of photovoltaics[J]. Power System Technology, 2019, 43(7): 2552-2560.

[47] NGUYEN N, MITRA J. An analysis of the effects and dependency of wind power penetration on system frequency regulation[J]. IEEE Transactions on Sustainable Energy, 2016, 7(1): 354-363.

[48] PRADHAN C, BHENDE C N, SRIVASTAVA A K. Frequency sensitivity analysis of dynamic demand response in wind farm integrated power system[J]. IET Renewable Power Generation, 2019, 13(6): 905-919.

[49] EL-HAMEED M A, ELKHOLY M M, EL-FERGANY A A. Efficient frequency regulation in highly penetrated power systems by renewable energy sources using stochastic fractal optimiser[J]. IET Renewable Power Generation, 2019, 13(12): 2174-2183.

[50] HUANG Y, XU Q S, ABEDI S, et al. Stochastic security assessment for power systems with high renewable energy penetration considering frequency regulation[J]. IEEE Access, 2019, 7: 6450-6460.

[51] TURK A, SANDELIC M, NOTO G, et al. Primary frequency regulation supported by battery storage systems in power system dominated by renewable energy sources[J]. Journal of Engineering-Joe, 2019, 1: 4986-4990.

[52] HENNINGER S, SCHROEDER M, JAEGER J. Combining frequency containment reserves and renewable power leveling in energy storage systems[J]. Energy Procedia, 2016, 99: 147-156.

[53] FERNáNDEZ-GUILLAMóN A, GóMEZ-LáZARO E, MULJADI E, et al. Power systems with high renewable energy sources: a review of inertia and frequency control strategies over time[J]. Renewable & Sustainable Energy Reviews, 2019, 115.

[54] BEVRANI H, GHOSH A, LEDWICH G. Renewable energy sources and frequency regulation: survey and new perspectives[J]. IET Renewable Power Generation, 2010, 4(5): 438-457.

[55] WU Y K, TANG K T. Frequency support by demand response—review and analysis[J]. Energy Procedia, 2019, 156: 327-331.

[56] MUHSSIN M T, CIPCIGAN L M, SAMI S S, et al. Potential of demand side response aggregation for the stabilization of the grids frequency[J]. Applied Energy, 2018, 220: 643-656.

[57] BABAHAJIANI P, SHAFIEE Q, BEVRANI H. Intelligent demand response contribution in frequency control of multi-area power systems[J]. IEEE Transactions on Smart Grid, 2018, 9(2): 1282-1291.

[58] SHI Q, LI F, HU Q, et al. Dynamic demand control for system frequency regulation: concept review, algorithm comparison, and future vision[J]. Electric Power Systems Research, 2018, 154: 75-87.

[59] XIAO G X, LI C B, YU Z C, et al. Review of the impact of electric vehicles participating in frequency regulation on power grid[C] // Chinese Automation Congress, 2013, Changsha, China.

[60] PENG C, ZOU J X, LIAN L. Dispatching strategies of electric vehicles participating in frequency regulation on power grid: a review[J]. Renewable & Sustainable Energy Reviews, 2017, 68(1): 147-152.

[61] HUI H, DING Y, SONG Y, et al. Modeling and control of flexible loads for frequency regulation services considering compensation of communication latency and detection error[J]. Applied Energy, 2019, 250: 161-174.

[62] LEDVA G S, VRETTOS E, MASTELLONE S, et al. Managing communication delays and model error in demand response for frequency regulation[J]. IEEE Transactions on Power Systems, 2018, 33(2): 1299-1308.

[63] QI J J, KIM Y, CHEN C, et al. Demand response and smart buildings: a survey of control, communication, and cyber-physical security[J]. ACM Transactions on Cyber- Physical Systems, 2018, 1(4): 18.

[64] 李承霖, 周宇, 胥崢, 等. 基于電動(dòng)汽車能效電廠的系統(tǒng)頻率控制策略[J]. 電力系統(tǒng)及其自動(dòng)化學(xué)報(bào), 2019, 31(11): 68-74.

LI Chenglin, ZHOU Yu, XU Zheng, et al. System frequency control strategy based on efficient power plant of electric vehicles[J]. Proceedings of the CSU-EPSA, 2019, 31(11): 68-74.

[65] IZADKHAST S, GARCIA-GONZALEZ P, FRíAS P. An aggregate model of plug-in electric vehicles for primary frequency control[J]. IEEE Transactions on Power Systems, 2015, 30(3): 1475-1482.

[66] MU Y, WU J, EKANAYAKE J, et al. Primary frequencyresponse from electric vehicles in the Great Britain power system[J]. IEEE Transactions on Smart Grid, 2013, 4(2): 1142-1150.

[67] KARIMINEJAD N, TAHER S A, SHAHIDEHPOUR M, et al. A hierarchical governor/turbine and electric vehicles optimal control framework for primary frequency support in power systems [J]. IEEE Transactions on Smart Grid, 2018, 9(6): 6702-6712.

[68] MARINELLI M, MARTINENAS S, KNEZOVIC K, et al. Validating a centralized approach to primary frequency control with series-produced electric vehicles[J]. Journal of Energy Storage, 2016, 7: 63-73.

[69] IZADKHAST S, GARCIA-GONZALEZ P, FRíAS P, et al. Design of plug-in electric vehicle’s frequency-droop controller for primary frequency control and performance assessment[J]. IEEE Transactions on Power Systems, 2017, 32(6): 4241-4254.

[70] LIU H, HU Z, SONG Y, et al. Decentralized vehicle-to-grid control for primary frequency regulation considering charging demands[J]. IEEE Transactions on Power Systems, 2013, 28(3): 3480-3489.

[71] XU X, ZHANG C, GU L. Decentralized primary frequency regulation control strategy for vehicle-to- grid[C] // International Conference on Systems and Informatics (ICSAI), 2016, Shanghai, China.

[72] MOLINA-GARCIA A, BOUFFARD F, KIRSCHEN D S. Decentralized demand-side contribution to primary frequency control[J]. IEEE Transactions on Power Systems, 2011, 26(1): 411-419.

[73] 何麗娜. 電動(dòng)汽車充電負(fù)荷頻率響應(yīng)控制策略研究[D]. 長(zhǎng)沙: 湖南大學(xué), 2014.

HE Lina. Research on the control strategy for electric vehicles charging load to participate in system frequency response[D]. Changsha: Hunan University, 2014.

[74] GALUS M D, KOCH S, ANDERSSON G. Provision of load frequency control by PHEVs, controllable loads, a cogeneration unit[J]. IEEE Transactions on Industrial Electronics, 2011, 58(10): 4568-4582.

[75] PILLAI J R, BAK-JENSEN B. Integration of vehicle-to- grid in The Western Danish Power System[J]. IEEE Transactions on Sustainable Energy, 2011, 2(1): 12-19.

[76] MASUTA T, YOKOYAMA A. Supplementary load frequency control by use of a number of both electric vehicles and heat pump water heaters[J]. IEEE Transactions on Smart Grid, 2012, 3(3): 1253-1262.

[77] LI J, AI X, HU J. Supplementary frequency regulation strategy considering electric vehicles[C] // IEEE Conference on Energy Internet and Energy System Integration, 2018, Beijing, China.

[78] 付文波. 電動(dòng)汽車參與電網(wǎng)二次頻率調(diào)節(jié)控制策略研究[D]. 南京: 江蘇大學(xué), 2016.

FU Wenbo. Vehicle-to-grid control for the supplement frequency regulation of a power grid[D]. Nanjing: Jiangshu University, 2016.

[79] FALAHATI S, TAHER S A, SHAHIDEHPOUR M. Grid secondary frequency control by optimized fuzzy control of electric vehicles[J]. IEEE Transactions on Smart Grid, 2018, 9(6): 5613-5621.

[80] LIU H, QI J, WANG J, et al. EV dispatch control for supplementary frequency regulation considering the expectation of EV owners[J]. IEEE Transactions on Smart Grid, 2018, 9(4): 3763-3772.

Overview of regulatory potential and interactive control of electric vehicle aggregator

WANG Mingshen1, DONG Xiaohong2, DAI Qiangsheng1

(1. Electric Power Research Institute, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 211101, China; 2. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300000, China)

With the continuous popularization of electric vehicles (EV) and charging facilities industry, EV will gradually replace fuel vehicles in the future, and EV access to the power grid will have a significant impact on the power grid. First, the characteristic of EV access to the power grid is summarized, and the opportunities and challenges brought by EV access to the power grid are analyzed. Next, the research status from the EV aggregator modeling and two application scenarios of interactive control strategies is summarized, and the existing research deficiencies are analyzed. Finally, some ideas and directions for further research from the aspects of EV aggregator modeling and control strategy are provided.

electric vehicle; EV aggregator modeling; regulatory potential; interactive control strategies

基于電動(dòng)汽車“交通流-信息流-能量流”協(xié)同仿真的城市區(qū)域充電設(shè)施與配電網(wǎng)協(xié)同規(guī)劃研究項(xiàng)目資助(E202020131)

2022-06-03;

2022-08-07

王明深(1990—),男,博士,工程師,研究方向?yàn)槿后w電動(dòng)汽車充電規(guī)劃、優(yōu)化調(diào)控與運(yùn)營(yíng)機(jī)制;E-mail: wmshtju@outlook.com

董曉紅(1989—),女,通信作者,博士,講師,研究方向?yàn)殡妱?dòng)汽車充電設(shè)施與配電網(wǎng)系統(tǒng)規(guī)劃,需求側(cè)響應(yīng);E-mail: dxh@hebut.edu.cn

戴強(qiáng)晟(1989—), 男 ,博士, 工程師,研究方向?yàn)橹悄芘潆娋W(wǎng)、電力系統(tǒng)規(guī)劃與運(yùn)行控制。E-mail: day_qs@ 163.com

猜你喜歡
調(diào)頻控制策略集群
考慮頻率二次跌落抑制的風(fēng)火聯(lián)合一次調(diào)頻控制
海上小型無(wú)人機(jī)集群的反制裝備需求與應(yīng)對(duì)之策研究
工程造價(jià)控制策略
現(xiàn)代企業(yè)會(huì)計(jì)的內(nèi)部控制策略探討
一種無(wú)人機(jī)集群發(fā)射回收裝置的控制系統(tǒng)設(shè)計(jì)
Python與Spark集群在收費(fèi)數(shù)據(jù)分析中的應(yīng)用
勤快又呆萌的集群機(jī)器人
容錯(cuò)逆變器直接轉(zhuǎn)矩控制策略
基于Z源逆變器的STATCOM/BESS控制策略研究
調(diào)頻發(fā)射機(jī)技術(shù)改造