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.

Tembung kunci pembangkit listrik fotovoltaik; Sistem panyimpenan energi; Konfigurasi sing dioptimalake; Aliran daya kemungkinan; Algoritma genetika (ga)

Pembangkit listrik fotovoltaik nduweni kaluwihan perlindungan lingkungan ijo lan bisa dianyari, lan dianggep minangka salah sawijining energi sing bisa dianyari paling potensial. Ing taun 2020, kapasitas terpasang kumulatif pembangkit listrik fotovoltaik China wis tekan 253 yuta kw. Intermittency lan kahanan sing durung mesthi saka daya PV gedhe-ukuran mengaruhi sistem daya, kalebu masalah puncak cukur, stabilitas lan cahya discarding, lan kothak kudu nganggo langkah luwih fleksibel kanggo ngrampungake karo masalah iki. Panyimpenan energi dianggep minangka cara sing efektif kanggo ngatasi masalah kasebut. Aplikasi sistem panyimpenan energi nggawa solusi anyar kanggo sambungan jaringan fotovoltaik skala gedhe.

Saiki, ana akeh riset babagan pembangkit listrik fotovoltaik, sistem panyimpenan energi lan aliran daya kemungkinan ing omah lan ing luar negeri. A nomer akeh studi literatur nuduhake yen panyimpenan energi bisa nambah tingkat pemanfaatan saka photovoltaic lan ngatasi stabilitas sambungan kothak fotovoltaik. Ing konfigurasi sistem panyimpenan energi ing stasiun daya energi anyar, manungsa waé kudu mbayar ora mung kanggo strategi kontrol panyimpenan optik lan panyimpenan angin, nanging uga kanggo ekonomi sistem panyimpenan energi. Kajaba iku, kanggo optimalisasi macem-macem stasiun daya panyimpenan energi ing sistem tenaga, perlu kanggo nyinaoni model ekonomi operasi stasiun daya panyimpenan energi, pilihan situs saka titik wiwitan lan titik pungkasan saluran transmisi fotovoltaik lan pilihan situs panyimpenan energi. Nanging, riset ana ing konfigurasi optimal saka sistem panyimpenan energi ora nimbang impact tartamtu ing sistem daya, lan riset ing sistem multi-titik ora ndherek ciri operasi panyimpenan optik ukuran gedhe.

Kanthi pangembangan gedhe-gedhe generasi daya energi anyar boten mesthi kayata daya angin lan photovoltaic, iku perlu kanggo ngetung aliran daya saka sistem daya ing planning operasi saka sistem daya. Contone, literatur nyinaoni lokasi optimal lan alokasi kapasitas panyimpenan energi ing sistem tenaga kanthi tenaga angin. Kajaba iku, korélasi antarane sawetara sumber energi anyar uga kudu dianggep ing pitungan aliran daya. Nanging, kabeh pasinaon ing ndhuwur adhedhasar metode aliran daya deterministik, sing ora nganggep kahanan sing durung mesthi generasi energi anyar. Literatur nganggep kahanan sing durung mesthi kekuwatan angin lan nggunakake metode aliran daya optimal probabilistik kanggo ngoptimalake pilihan situs sistem panyimpenan energi, sing nambah ekonomi operasi.

At present, different probabilistic power flow algorithms have been proposed by scholars, and data mining methods of nonlinear probabilistic power flow based on Monte Carlo simulation method have been proposed in literatures, but the timeliness of Monte Carlo method is very poor. It is proposed in the literature to use the probabilistic optimal power flow to study the location of energy storage, and 2 m point method is used, but the calculation accuracy of this method is not ideal. The application of Latin hypercube sampling method in power flow calculation is studied in this paper, and the superiority of Latin hypercube sampling method is illustrated by numerical examples.

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. Model aliran daya probabilistik

1.1 Model kahanan sing durung mesthi komponen

Photovoltaic, beban lan generator kabeh variabel acak karo kahanan sing durung mesthi. Ing pitungan aliran daya probabilistik jaringan distribusi, model probabilistik diterangake ing literatur. Liwat analisis data historis, daya output pembangkit listrik fotovoltaik nderek distribusi BETA. Kanthi pas distribusi probabilitas daya beban, dianggep beban kasebut ngetutake distribusi normal, lan fungsi distribusi kerapatan probabilitas yaiku

Gambar (1)

Where, Pl punika daya mbukak; μ L lan σ L minangka pangarepan lan variasi beban.

Model probabilitas generator biasane nganggo distribusi rong titik, lan fungsi distribusi kapadhetan probabilitas yaiku

(2)

Ing endi, P minangka kemungkinan operasi normal generator; PG punika daya output generator.

Nalika cahya cukup ing wayah awan, daya aktif saka stasiun daya photovoltaic gedhe, lan daya sing angel digunakake ing wektu bakal disimpen ing baterei panyimpenan energi. Nalika daya mbukak dhuwur, baterei panyimpenan energi bakal ngeculake energi sing disimpen. Persamaan imbangan energi instan saka sistem panyimpenan energi yaiku

Nalika ngisi daya

(3)

Nalika discharge

(4)

Watesan

Gambar,

Gambar,

Gambar, gambar

Where, St punika energi disimpen ing wektu T; Pt minangka daya pangisian daya lan discharge saka panyimpenan energi; SL lan SG minangka energi ngisi daya lan ngisi daya. η C lan η D minangka efisiensi ngisi lan mbuwang. Ds minangka tingkat panyimpenan energi dhewe.

1.2 Metode Latin hypercube sampling

Ana metode simulasi, metode perkiraan lan metode analitik sing bisa digunakake kanggo nganalisa aliran daya sistem ing faktor sing ora mesthi. simulasi Monte Carlo iku salah siji saka cara paling akurat ing algoritma aliran daya probabilistik, nanging timeliness kurang dibandhingake karo presisi dhuwur. Ing kasus kaping sampling kurang, cara iki biasane nglirwakake buntut saka kurva distribusi probabilitas, nanging kanggo nambah akurasi, iku perlu kanggo nambah kaping sampling. Cara Latin hypercube sampling ngindhari masalah iki. Iki minangka cara sampling hirarkis, sing bisa mesthekake yen titik sampling nggambarake distribusi probabilitas kanthi efektif lan nyuda wektu sampling kanthi efektif.

Figure 1 shows the expectation and variance of Latin hypercube sampling method and Monte Carlo simulation method with sampling times ranging from 10 to 200. The overall trend of results obtained by the two methods is decreasing. However, the expectation and variance obtained by monte Carlo method are very unstable, and the results obtained by multiple simulations are not the same with the same sampling times. The variance of Latin hypercube sampling method decreases steadily with the increase of sampling times, and the relative error decreases to less than 5% when the sampling times are more than 150. It is worth noting that the sampling point of the Latin hypercube sampling method is symmetric about the Y-axis, so its expected error is 0, which is also its advantage.

Gambar kasebut

FIG. 1 Comparison of different sampling times between MC and LHS

Metode Latin hypercube sampling yaiku metode sampling berlapis. Kanthi ningkatake proses nggawe sampel variabel acak input, nilai sampling bisa kanthi efektif nggambarake distribusi sakabèhé variabel acak. Proses sampling dipérang dadi rong tahap.

(1) Sampling

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.

Gambar kasebut

Gambar 2 diagram skematik LHS

(2) Permutasi

The sampling values of random variables obtained from (1) are sequentially arranged, so the correlation between m random variables is 1, which cannot be calculated. The gram-Schmidt sequence orthogonalization method can be adopted to reduce the correlation between the sampling values of random variables. Firstly, a matrix of K×M order I=[I1, I2…, IK]T is generated. Elements in each row are randomly arranged from 1 to M, and they represent the position of the sampling value of the original random variable.

Positive iteration

Gambar kasebut

A iteratif mbalikke

Gambar kasebut

“Gambar” nggantosi assignment, takeout (Ik, Ij) nggantosi pitungan nilai ampas ing regresi linear Ik=a+bIj, pangkat (Ik) nggantosi vektor anyar kawangun dening nomer urutan unsur ing orientasi Ik saka cilik kanggo gedhe.

Sawise iterasi bidirectional nganti nilai RMS ρ, sing nuduhake korelasi, ora suda, matriks posisi saben variabel acak sawise permutasi dipikolehi, banjur matriks permutasi variabel acak kanthi korelasi paling sithik bisa dipikolehi.

(5)

Ing ngendi, gambar kasebut minangka koefisien korelasi antarane Ik lan Ij, cov minangka kovarian, lan VAR minangka varian.

2. Multi-objective optimization configuration of energy storage system

2.1 Objective function

In order to optimize the power and capacity of the energy storage system, a multi-objective optimization function is established considering the cost of the energy storage system, the power off-limit probability and the network loss. Due to the different dimensions of each indicator, deviation standardization is carried out for each indicator. After deviation standardization, the value range of observed values of various variables will be between (0,1), and the standardized data are pure quantities without units. In the actual situation, there may be differences in the emphasis on each indicator. If each indicator is given a certain weight, different emphases can be analyzed and studied.

(6)

Where, w indeks kanggo optimized; Wmin lan wmax minangka minimal lan maksimal fungsi asli tanpa standarisasi.

Fungsi objektif yaiku

(7)

In the formula, λ1 ~ λ3 are weight coefficients, Eloss, PE and CESS are standardized branch network loss, branch active power crossing probability and energy storage investment cost respectively.

2.2 Algoritma genetik

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.

Ing makalah iki, sistem tenaga kalebu fotovoltaik lan panyimpenan energi pisanan diitung kanthi algoritma aliran daya probabilistik, lan data sing dipikolehi digunakake minangka variabel input algoritma genetika kanggo ngatasi masalah kasebut. Proses pitungan ditampilake ing Gambar 3, sing utamane dipérang dadi langkah-langkah ing ngisor iki:

Gambar kasebut

FIG. 3 Algorithm flow

(1) Sistem input, data panyimpenan fotovoltaik lan energi, lan nindakake sampling hypercube Latin lan orthogonalization urutan Gram-Schmidt;

(2) Lebokake data sampel menyang model pitungan aliran daya lan cathet asil pitungan;

(3) Asil output dienkode dening kromosom kanggo ngasilake populasi awal sing cocog karo nilai sampling;

(4) Ngitung fitness saben individu ing populasi;

(5) select, cross and mutate to produce a new generation of population;

(6) Judge whether the requirements are met, if not, return step (4); If yes, the optimal solution is output after decoding.

3. Example analysis

Cara aliran daya probabilistik simulasi lan analisa ing sistem test IEEE24-simpul ditampilake ing FIG. 4, ing tingkat voltase 1-10 node yaiku 138 kV, lan 11-24 node yaiku 230 kV.

Gambar kasebut

Gambar 4 Sistem uji simpul IEEE24

3.1 Pengaruh pembangkit listrik fotovoltaik ing sistem tenaga

Pembangkit listrik fotovoltaik ing sistem tenaga, lokasi lan kapasitas sistem tenaga bakal mengaruhi voltase simpul lan daya cabang, mula sadurunge analisa pengaruh sistem panyimpenan energi kanggo jaringan listrik, bagean iki pisanan nganalisa pengaruh tenaga fotovoltaik. stasiun ing sistem, akses photovoltaic sistem ing kertas iki, gaya watesan saka kemungkinan, mundhut jaringan lan ing wis digawa ing analisis simulasi.

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.

Gambar kasebut

Gambar 5. (a) jumlah probabilitas off-limit aliran daya (b) fluktuasi voltase simpul (c) total jaringan sistem mundhut titik akses PV beda

Saliyane ngluwihi aliran daya, makalah iki uga nganalisa pengaruh photovoltaic ing voltase simpul, kaya sing dituduhake ing FIG. 5(b). Panyimpangan standar amplitudo voltase simpul 1, 3, 8, 13, 14, 15 lan 19 dipilih kanggo mbandhingake. Sakabèhé, sambungan stasiun listrik fotovoltaik menyang jaringan listrik ora duwe pengaruh gedhe marang voltase simpul, nanging stasiun tenaga fotovoltaik duwe pengaruh gedhe marang voltase a-Node lan simpul sing cedhak. Kajaba iku, ing sistem sing diadopsi dening conto pitungan, liwat perbandingan, ditemokake yen stasiun tenaga fotovoltaik luwih cocok kanggo akses menyang jinis simpul: ① simpul kanthi tingkat voltase sing luwih dhuwur, kayata 14, 15, 16, lan liya-liyane. voltase meh ora owah; (2) simpul didhukung dening generator utawa nyetel kamera, kayata 1, 2, 7, etc.; (3) ing resistance baris gedhe ing mburi simpul.

In order to analyze the influence of PV access point on the total network loss of power system, this paper makes a comparison as shown in Figure 5(c). It can be seen that if some nodes with large load power and no power supply are connected to pv power station, the network loss of the system will be reduced. On the contrary, nodes 21, 22 and 23 are the power supply end, which is responsible for centralized power transmission. The photovoltaic power station connected to these nodes will cause large network loss. Therefore, the pv power station access point should be selected at the receiving end of power or the node with large load. This access mode can make the power flow distribution of the system more balanced and reduce the network loss of the system.

Adhedhasar telung faktor ing analisis asil ing ndhuwur, simpul 14 dijupuk minangka titik akses stasiun daya fotovoltaik ing kertas iki, lan banjur pengaruh kapasitas saka stasiun daya photovoltaic beda ing sistem daya sinau.

Gambar 6(a) nganalisa pengaruh kapasitas fotovoltaik ing sistem. Bisa dideleng yen standar deviasi daya aktif saben cabang mundhak kanthi nambah kapasitas fotovoltaik, lan ana hubungan linear positif antarane loro kasebut. Kajaba kanggo sawetara cabang ditampilake ing tokoh, standar deviasi saka cabang liyane kabeh kurang saka 5 lan nuduhake hubungan linear, kang digatèkaké kanggo penak saka drawing. Bisa dideleng yen sambungan grid fotovoltaik nduweni pengaruh gedhe marang daya sing disambungake langsung karo titik akses fotovoltaik utawa cabang sing cedhak. Amarga transmisi line transmisi daya winates, garis transmisi saka jumlah construction lan investasi ageng, supaya nginstall stasiun daya photovoltaic, kudu nimbang watesan kapasitas transportasi, milih pengaruh paling cilik ing akses line kanggo lokasi paling apik, saliyane, milih kapasitas paling apik saka stasiun daya photovoltaic bakal muter bagean penting kanggo ngurangi efek iki.

Gambar kasebut

Gambar 6. (a) Panyimpangan standar daya aktif cabang (b) probabilitas aliran daya cabang metu saka watesan (c) kerugian jaringan sistem total ing kapasitas fotovoltaik sing beda

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.

Ing Gbr. 6 (c), mundhut jaringan total sistem mundhak karo nambah kapasitas photovoltaic, nanging efek iki ora ketok. Nalika kapasitas fotovoltaik mundhak 60 MW, kerugian jaringan total mung mundhak 0.5%, yaiku 0.75 MW. Mulane, nalika nginstal stasiun daya pv, mundhut jaringan kudu dijupuk minangka faktor secondary, lan faktor sing duwe impact luwih ing operasi stabil saka sistem kudu dianggep pisanan, kayata fluktuasi daya baris transmisi lan kemungkinan metu-saka-watesan. .

3.2 Dampak akses panyimpenan energi ing sistem

Bagean 3.1 Posisi akses lan kapasitas pembangkit listrik fotovoltaik gumantung saka sistem tenaga