Abstract A high proportion of photovoltaic power generation will have adverse effects on the stability of power system, and energy storage is considered to be one of the effective means to eliminate these effects. This paper analyzes the influence of photovoltaic power generation on the power system from the perspective of power flow, and then analyzes the effect of energy storage on restraining the influence. Firstly, the probability distribution model and energy storage model of components in power system are introduced, and the Latin hypercube sampling method and gram-Schmidt sequence normalization method are introduced. Secondly, a multi-objective optimization model was established, which considered the cost of the energy storage system, the off-limit probability of branch power flow and the network loss of the power grid. The optimal solution of the objective function was obtained by genetic algorithm. Finally, the simulation is carried out in IEEE24 node test system to analyze the influence of different photovoltaic access capacity and access location on the power system and the effect of energy storage on the power system, and the optimal energy storage configuration corresponding to different photovoltaic capacity is obtained.

Kalit so’zlar fotovoltaik energiya ishlab chiqarish; Energiyani saqlash tizimi; Optimallashtirilgan konfiguratsiya; Ehtimoliy quvvat oqimi; Genetik algoritm (ga)

Fotovoltaik energiya ishlab chiqarish yashil atrof-muhitni muhofaza qilish va qayta tiklanadigan afzalliklarga ega va eng potentsial qayta tiklanadigan energiyadan biri hisoblanadi. 2020 yilga kelib, Xitoyning fotovoltaik energiya ishlab chiqarishning umumiy o’rnatilgan quvvati 253 million kVt ga etdi. Keng miqyosdagi PV quvvatining uzilishlari va noaniqligi energiya tizimiga ta’sir qiladi, shu jumladan cho’qqi soqolini olish, barqarorlik va yorug’likni yo’qotish masalalari va tarmoq ushbu muammolarni engish uchun yanada moslashuvchan choralarni ko’rishi kerak. Energiyani saqlash bu muammolarni hal qilishning samarali usuli hisoblanadi. Energiyani saqlash tizimini qo’llash keng ko’lamli fotovoltaik tarmoq ulanishi uchun yangi echimni olib keladi.

Hozirgi vaqtda uyda va chet elda fotovoltaik energiya ishlab chiqarish, energiya saqlash tizimi va ehtimollik quvvati oqimi bo’yicha ko’plab tadqiqotlar mavjud. Ko’p sonli adabiyot tadqiqotlari shuni ko’rsatadiki, energiyani saqlash fotovoltaikdan foydalanish tezligini oshirishi va fotovoltaik tarmoq ulanishining barqarorligini hal qilishi mumkin. Yangi energiya elektr stantsiyasida energiya saqlash tizimini konfiguratsiya qilishda nafaqat optik saqlash va shamolni saqlashni boshqarish strategiyasiga, balki energiya saqlash tizimining iqtisodiga ham e’tibor qaratish lozim. Bundan tashqari, energiya tizimidagi bir nechta energiya saqlash elektr stantsiyalarini optimallashtirish uchun energiyani saqlaydigan elektr stantsiyalari ishlashining iqtisodiy modelini, fotovoltaik uzatish kanallarining boshlang’ich va yakuniy nuqtalarini tanlashni o’rganish kerak. energiyani saqlash joyini tanlash. Biroq, energiya saqlash tizimining optimal konfiguratsiyasi bo’yicha mavjud tadqiqotlar energiya tizimiga o’ziga xos ta’sirni hisobga olmaydi va ko’p nuqtali tizim bo’yicha tadqiqotlar keng ko’lamli optik saqlash operatsion xususiyatlarini o’z ichiga olmaydi.

Shamol energiyasi va fotovoltaik kabi noaniq yangi energiya ishlab chiqarishning keng miqyosli rivojlanishi bilan energiya tizimining ishlashini rejalashtirishda energiya tizimining quvvat oqimini hisoblash kerak. Misol uchun, adabiyotlarda shamol energiyasi bilan energiya tizimida energiyani saqlashning optimal joylashuvi va quvvatlarini taqsimlash o’rganiladi. Bundan tashqari, quvvat oqimini hisoblashda bir nechta yangi energiya manbalari o’rtasidagi korrelyatsiya ham hisobga olinishi kerak. Biroq, yuqoridagi barcha tadqiqotlar deterministik quvvat oqimi usullariga asoslangan bo’lib, ular yangi energiya ishlab chiqarishning noaniqligi hisobga olinmaydi. Adabiyotda shamol energetikasining noaniqligi ko’rib chiqiladi va energiya saqlash tizimini tanlashni optimallashtirish uchun ehtimollik optimal quvvat oqimi usulini qo’llaydi, bu esa operatsion iqtisodiyotni yaxshilaydi.

Hozirgi vaqtda olimlar tomonidan turli ehtimollik quvvat oqimi algoritmlari taklif qilingan va adabiyotlarda Monte-Karlo simulyatsiyasi usuliga asoslangan chiziqli bo’lmagan ehtimollik quvvat oqimining ma’lumotlarini qazib olish usullari taklif qilingan, ammo Monte Karlo usulining o’z vaqtidaligi juda yomon. Adabiyotda energiyani saqlash joyini o’rganish uchun ehtimollik optimal quvvat oqimidan foydalanish taklif etiladi va 2 m nuqta usuli qo’llaniladi, ammo bu usulning hisoblash aniqligi ideal emas. Quvvat oqimini hisoblashda lotincha giperkub namuna olish usulining qo‘llanilishi ushbu maqolada o‘rganilgan va lotincha giperkub namuna olish usulining ustunligi raqamli misollar bilan ko‘rsatilgan.

Based on the above research, this paper uses the probabilistic power flow method to study the optimal allocation of energy storage in the power system with large-scale photovoltaic power generation. Firstly, the probability distribution model and Latin hypercube sampling method of components in power system are introduced. Secondly, a multi-objective optimization model is established considering the energy storage cost, power flow over limit probability and network loss. Finally, the simulation analysis is carried out in IEEE24 node test system.

1. Probabilistic power flow model

1.1 Komponentlarning noaniqlik modeli

Fotovoltaik, yuk va generator noaniqlik bilan tasodifiy o’zgaruvchilardir. Tarqatish tarmog’ining ehtimollik quvvat oqimini hisoblashda ehtimollik modeli adabiyotda tushuntirilgan. Tarixiy ma’lumotlarni tahlil qilish orqali fotovoltaik energiya ishlab chiqarishning chiqish quvvati BETA taqsimotiga mos keladi. Yuk kuchining ehtimollik taqsimotini o’rnatgan holda, yuk normal taqsimotdan keyin keladi deb taxmin qilinadi va uning ehtimollik zichligi taqsimoti funktsiyasi

Picture (1)

Where, Pl is the load power; μ L and σ L are the expectation and variance of load respectively.

Generatorning ehtimollik modeli odatda ikki nuqtali taqsimotni qabul qiladi va uning ehtimollik zichligini taqsimlash funktsiyasi

(2)

Bu erda, P – generatorning normal ishlashi ehtimoli; PG – generatorning chiqish quvvati.

Tushda yorug’lik etarli bo’lsa, fotovoltaik elektr stantsiyasining faol quvvati katta bo’lib, o’z vaqtida ishlatish qiyin bo’lgan quvvat energiya saqlash akkumulyatorida saqlanadi. Yuk kuchi yuqori bo’lsa, energiya saqlash batareyasi saqlangan energiyani chiqaradi. Energiyani saqlash tizimining oniy energiya balansi tenglamasi

Zaryad olayotganda

(3)

Bo’shatilganda

(4)

Cheklov

Rasmlar,

Rasmlar,

Rasm, rasm

Where, St is the energy stored at time T; Pt is the charge and discharge power of energy storage; SL and SG are the energy of charging and discharging respectively. η C and η D are charging and discharging efficiency respectively. Ds is the self-discharge rate of energy storage.

1.2 Lotin giperkub namuna olish usuli

There are simulation method, approximate method and analytical method which can be used to analyze system power flow under uncertain factors. Monte Carlo simulation is one of the most accurate methods in probabilistic power flow algorithms, but its timeliness is low compared with high precision. In the case of low sampling times, this method usually ignores the tail of the probability distribution curve, but in order to improve the accuracy, it needs to increase the sampling times. Latin hypercube sampling method avoids this problem. It is a hierarchical sampling method, which can ensure that the sampling points reflect the probability distribution effectively and reduce the sampling times effectively.

1-rasmda lotincha giperkub namuna olish usuli va Monte-Karlo simulyatsiya usulining kutilishi va farqi ko’rsatilgan. Namuna olish vaqtlari 10 dan 200 gacha. Ikki usul bilan olingan natijalarning umumiy tendentsiyasi pasaymoqda. Biroq, monte-Karlo usuli bilan olingan kutish va dispersiya juda beqaror va bir nechta simulyatsiyalar natijasida olingan natijalar bir xil namuna olish vaqtlari bilan bir xil emas. Lotin giperkubini tanlash usulining dispersiyasi namuna olish vaqtlari ortishi bilan barqaror ravishda kamayadi va 5 dan ortiq bo’lsa, nisbiy xatolik 150% dan kamroqgacha kamayadi. Y o’qiga nisbatan simmetrik, shuning uchun uning kutilgan xatosi 0 ga teng, bu ham uning afzalligi.

Rasm

ANJIR. 1 MC va LHS o’rtasidagi turli xil namuna olish vaqtlarini solishtirish

Latin hypercube sampling method is a layered sampling method. By improving the sample generation process of input random variables, the sampling value can effectively reflect the overall distribution of random variables. The sampling process is divided into two steps.

(1) Namuna olish

Xi (I = 1, 2,… ,m) is m random variables, and the sampling times are N, as shown in FIG. 2. The cumulative probability distribution curve of Xi is divided into N interval with equal spacing and no overlap, the midpoint of each interval is selected as the sampling value of probability Y, and then the sampling value Xi= p-1 (Yi) is calculated by using inverse function, and the calculated Xi is the sampling value of random variable.

Rasm

2-rasm LHS ning sxematik diagrammasi

(2) almashtirishlar

(1) dan olingan tasodifiy miqdorlarning tanlanma qiymatlari ketma-ket joylashtirilgan, shuning uchun m tasodifiy o’zgaruvchilar orasidagi korrelyatsiya 1 ga teng, uni hisoblash mumkin emas. Tasodifiy o’zgaruvchilarning tanlanma qiymatlari o’rtasidagi korrelyatsiyani kamaytirish uchun gram-Shmidt ketma-ketligini ortogonallashtirish usuli qabul qilinishi mumkin. Birinchidan, K×M tartibli I=[I1, I2…, IK]T matritsa hosil bo‘ladi. Har bir qatordagi elementlar 1 dan M gacha tasodifiy joylashtirilgan va ular asl tasodifiy o’zgaruvchining tanlanma qiymatining o’rnini ifodalaydi.

Positive iteration

Rasm

Teskari iterativ

Rasm

“Picture” represents assignment, takeout(Ik,Ij) represents calculation of residual value in linear regression Ik=a+bIj, rank(Ik) represents new vector formed by the sequence number of elements in orientation Ik from small to large.

Ikki tomonlama takrorlashdan so’ng, korrelyatsiyani ifodalovchi RMS qiymati kamaymaguncha, har bir tasodifiy o’zgaruvchining almashtirishdan keyingi pozitsiyasi matritsasi olinadi, keyin esa eng kam korrelyatsiyaga ega bo’lgan tasodifiy o’zgaruvchilarning almashtirish matritsasi olinishi mumkin.

(5)

Bu erda rasm – Ik va Ij o’rtasidagi korrelyatsiya koeffitsienti, cov – kovariatsiya va VAR – dispersiya.

2. Energiyani saqlash tizimini ko’p maqsadli optimallashtirish konfiguratsiyasi

2.1 Objective function

Energiya saqlash tizimining quvvati va quvvatini optimallashtirish uchun energiya saqlash tizimining narxini, quvvatni cheklash ehtimoli va tarmoq yo’qolishini hisobga olgan holda ko’p maqsadli optimallashtirish funktsiyasi o’rnatiladi. Har bir ko’rsatkichning turli o’lchamlari tufayli har bir ko’rsatkich uchun og’ish standartlashtirish amalga oshiriladi. Og’ish standartlashtirilgandan so’ng, turli o’zgaruvchilarning kuzatilgan qiymatlarining qiymat diapazoni (0,1) orasida bo’ladi va standartlashtirilgan ma’lumotlar birliksiz sof miqdorlardir. Haqiqiy vaziyatda har bir ko’rsatkichga urg’u berishda farqlar bo’lishi mumkin. Har bir ko’rsatkichga ma’lum bir vazn berilsa, turli urg’ularni tahlil qilish va o’rganish mumkin.

(6)

Bu yerda, w – optimallashtiriladigan indeks; Wmin va wmax standartlashtirishsiz asl funktsiyaning minimal va maksimal qiymatidir.

The objective function is

(7)

Formulada l1 ~ l3 og’irlik koeffitsientlari, Eloss, PE va CESS standartlashtirilgan tarmoq yo’qotilishi, tarmoqning faol quvvatini kesib o’tish ehtimoli va energiyani saqlash uchun investitsion xarajatlardir.

2.2 Genetik algoritm

Genetic algorithm is a kind of optimization algorithm established by imitating the genetic and evolutionary laws of survival of the fittest and survival of the fittest in nature. It first to coding, initial population each coding on behalf of an individual (a feasible solution of the problem), so each feasible solution is from for genotype phenotype transformation, to undertake choosing according to the laws of nature for each individual, and selected in each generation to the next generation of computing environment to adapt to the strong individual, until the most adaptable to the environment of the individual, After decoding, it is the approximate optimal solution of the problem.

In this paper, the power system including photovoltaic and energy storage is firstly calculated by the probabilistic power flow algorithm, and the obtained data is used as the input variable of the genetic algorithm to solve the problem. The calculation process is shown in Figure 3, which is mainly divided into the following steps:

Rasm

ANJIR. 3 Algoritm oqimi

(1) Kirish tizimi, fotovoltaik va energiyani saqlash ma’lumotlari va Lotin giperkub namunalarini va Gram-Schmidt ketma-ketligini ortogonallashtirishni amalga oshirish;

(2) Namuna olingan ma’lumotlarni quvvat oqimini hisoblash modeliga kiriting va hisoblash natijalarini yozing;

(3) The output results were encoded by chromosome to generate the initial population corresponding to the sampling value;

(4) Calculate the fitness of each individual in the population;

(5) populyatsiyaning yangi avlodini yaratish uchun tanlash, kesish va mutatsiya qilish;

(6) Talablar bajarilganmi yoki yo’qligini baholang, agar bajarilmasa, qaytarish bosqichi (4); Ha bo’lsa, optimal yechim dekodlashdan keyin chiqariladi.

3. Misol tahlili

Ehtimoliy quvvat oqimi usuli shaklda ko’rsatilgan IEEE24-tugun sinov tizimida simulyatsiya qilingan va tahlil qilingan. 4, bunda 1-10 tugunlarning kuchlanish darajasi 138 kV, 11-24 tugunlari esa 230 kV.

Rasm

Figure 4 IEEE24 node test system

3.1 Fotovoltaik elektr stantsiyasining energiya tizimiga ta’siri

Elektr tizimidagi fotovoltaik elektr stantsiyasi, energiya tizimining joylashuvi va quvvati tugun kuchlanishiga va tarmoq quvvatiga ta’sir qiladi, shuning uchun elektr tarmog’i uchun energiya saqlash tizimining ta’sirini tahlil qilishdan oldin, ushbu bo’lim birinchi navbatda fotovoltaik quvvatning ta’sirini tahlil qiladi. Ushbu maqolada tizimdagi stansiya, tizimga fotovoltaik kirish, ehtimollik chegarasi tendentsiyasi, tarmoq yo’qolishi va boshqalar simulyatsiya tahlilini o’tkazdi.

As can be seen from FIG. 5(a), after photovoltaic power station is connected, nodes with smaller branch power flow overlimit are as follows: 11, 12, 13, 23, 13 to balance the node node, the node voltage and the phase Angle is given, have the effect of stable power grid power balance, 11, 12 and 23 instead of directly connected, as a result, several nodes connected to the limit the probability of smaller and more power, photovoltaic power station will access the node with balance effect is less on the impact of power system.

Rasm

Figure 5. (a) sum of power flow off-limit probability (b) node voltage fluctuation (c) total system network loss of different PV access points

Quvvat oqimining oshib ketishiga qo’shimcha ravishda, ushbu maqola rasmda ko’rsatilganidek, fotovoltaikning tugun kuchlanishiga ta’sirini ham tahlil qiladi. 5(b). Taqqoslash uchun 1, 3, 8, 13, 14, 15 va 19 tugunlarining kuchlanish amplitudalarining standart og’ishlari tanlanadi. Umuman olganda, fotovoltaik elektr stantsiyalarining elektr tarmog’iga ulanishi tugunlarning kuchlanishiga katta ta’sir ko’rsatmaydi, lekin fotovoltaik elektr stantsiyalari a-tugunlari va ularning yaqinidagi tugunlarning kuchlanishiga katta ta’sir ko’rsatadi. Bundan tashqari, hisob-kitob misolida qabul qilingan tizimda, taqqoslash yo’li bilan, fotovoltaik elektr stantsiyasining tugun turlariga kirish uchun ko’proq mos kelishi aniqlandi: ① kuchlanish darajasi yuqori bo’lgan tugunlar, masalan, 14, 15, 16 va boshqalar, kuchlanish deyarli o’zgarmaydi; (2) generatorlar yoki sozlash kameralari tomonidan qo’llab-quvvatlanadigan tugunlar, masalan, 1, 2, 7 va boshqalar; (3) chiziqda qarshilik tugunning oxirida katta.

PV kirish nuqtasining energiya tizimining umumiy tarmoq yo’qotilishiga ta’sirini tahlil qilish uchun ushbu maqola 5 (c)-rasmda ko’rsatilganidek, taqqoslashni amalga oshiradi. Ko’rinib turibdiki, agar katta yuk kuchiga ega bo’lgan va quvvat manbai bo’lmagan ba’zi tugunlar pv elektr stantsiyasiga ulanmagan bo’lsa, tizimning tarmoq yo’qotilishi kamayadi. Aksincha, 21, 22 va 23-tugunlar markazlashtirilgan elektr uzatish uchun mas’ul bo’lgan quvvat manbai oxiri hisoblanadi. Ushbu tugunlarga ulangan fotovoltaik elektr stantsiyasi katta tarmoq yo’qotilishiga olib keladi. Shuning uchun, pv elektr stantsiyasiga kirish nuqtasi quvvatni qabul qilish uchida yoki katta yuk bilan tugunni tanlash kerak. Ushbu kirish rejimi tizimning quvvat oqimining taqsimlanishini yanada muvozanatli qilishi va tizimning tarmoq yo’qotilishini kamaytirishi mumkin.

Yuqoridagi natijalarni tahlil qilishda uchta omilga asoslanib, ushbu ishda fotoelektr stansiyasining kirish nuqtasi sifatida 14-tugun olinadi, so’ngra turli fotoelektr stansiyalarning quvvatlarining energiya tizimiga ta’siri o’rganiladi.

Shakl 6 (a) fotovoltaik quvvatning tizimga ta’sirini tahlil qiladi. Ko’rinib turibdiki, har bir tarmoqning faol quvvatining standart og’ishi fotovoltaik quvvatning oshishi bilan ortadi va ikkalasi o’rtasida ijobiy chiziqli munosabatlar mavjud. Rasmda ko’rsatilgan bir nechta shoxlardan tashqari, boshqa filiallarning standart og’ishlari hammasi 5 dan kichik va chiziqli munosabatlarni ko’rsatadi, ular chizish qulayligi uchun e’tiborga olinmaydi. Ko’rinib turibdiki, fotovoltaik tarmoq ulanishi to’g’ridan-to’g’ri fotovoltaik kirish nuqtasi yoki qo’shni tarmoqlar bilan bog’langan quvvatga katta ta’sir ko’rsatadi. Elektr uzatish liniyalarining cheklanganligi sababli, qurilish va investitsiyalar hajmini uzatish liniyalari juda katta, shuning uchun fotovoltaik elektr stantsiyasini o’rnatish transport imkoniyatlarini cheklashni hisobga olishi, liniyaning eng yaxshi joyga kirishiga eng kichik ta’sirni tanlashi kerak, bundan tashqari, fotovoltaik elektr stantsiyasining eng yaxshi quvvatini tanlash bu ta’sirni kamaytirish uchun muhim rol o’ynaydi.

Rasm

Figure 6. (a) Branch active power standard deviation (b) branch power flow out-of-limit probability (c) total system network loss under different photovoltaic capacities

FIG. 6(b) compares the probability of active power exceeding the limit of each branch under different pv power station capacities. Except for the branches shown in the figure, the other branches did not exceed the limit or the probability was very small. Compared with FIG. 6(a), it can be seen that the probability of off-limit and standard deviation are not necessarily related. The active power of a line with large standard deviation fluctuation does not necessarily off-limit, and the reason is related to the transmission direction of photovoltaic output power. If it is in the same direction as the original branch power flow, small photovoltaic power may also cause off-limit. When the pv power is very large, the power flow may not exceed the limit.

Shaklda. 6 (c), tizimning umumiy tarmoq yo’qotilishi fotovoltaik quvvatning oshishi bilan ortadi, ammo bu ta’sir aniq emas. Fotovoltaik quvvat 60 MVt ga oshganda, umumiy tarmoq yo’qotilishi faqat 0.5% ga, ya’ni 0.75 MVt ga oshadi. Shuning uchun, PV elektr stantsiyalarini o’rnatishda tarmoq yo’qotilishi ikkinchi darajali omil sifatida qabul qilinishi kerak va birinchi navbatda tizimning barqaror ishlashiga ko’proq ta’sir ko’rsatadigan omillarni hisobga olish kerak, masalan, elektr uzatish liniyasining quvvat o’zgarishi va chegaradan tashqari ehtimoli. .

3.2 Energiyani saqlashga kirishning tizimga ta’siri

3.1-bo’lim Fotovoltaik elektr stantsiyasining kirish joyi va quvvati energiya tizimiga bog’liq