Samenvatting Een groot deel van de opwekking van fotovoltaïsche energie zal nadelige effecten hebben op de stabiliteit van het elektriciteitssysteem, en energieopslag wordt beschouwd als een van de effectieve middelen om deze effecten te elimineren. Dit artikel analyseert de invloed van fotovoltaïsche energieopwekking op het energiesysteem vanuit het perspectief van de stroomstroom, en analyseert vervolgens het effect van energieopslag op het beperken van de invloed. Ten eerste worden het kansverdelingsmodel en het energieopslagmodel van componenten in het energiesysteem geïntroduceerd, en worden de Latijnse hypercube-bemonsteringsmethode en gram-Schmidt-sequentienormalisatiemethode geïntroduceerd. Ten tweede werd een multi-objectief optimalisatiemodel opgesteld, dat rekening hield met de kosten van het energieopslagsysteem, de off-limit waarschijnlijkheid van de stroom van aftakkingen en het netwerkverlies van het elektriciteitsnet. De optimale oplossing van de objectieve functie werd verkregen door een genetisch algoritme. Ten slotte wordt de simulatie uitgevoerd in het IEEE24-knooppunttestsysteem om de invloed van verschillende fotovoltaïsche toegangscapaciteit en toegangslocatie op het voedingssysteem en het effect van energieopslag op het voedingssysteem te analyseren, en de optimale energieopslagconfiguratie die overeenkomt met verschillende fotovoltaïsche capaciteit is verkregen.

Trefwoorden fotovoltaïsche energieopwekking; Energieopslagsysteem; Geoptimaliseerde configuratie; Waarschijnlijkheid krachtstroom; Genetisch algoritme (ga)

Photovoltaic power generation has the advantages of green environmental protection and renewable, and is considered to be one of the most potential renewable energy. By 2020, China’s cumulative installed capacity of photovoltaic power generation has reached 253 million kw. The intermittency and uncertainty of large-scale PV power affect the power system, including issues of peak shaving, stability and light discarding, and the grid needs to adopt more flexible measures to cope with these issues. Energy storage is considered to be an effective way to solve these problems. The application of energy storage system brings a new solution for large-scale photovoltaic grid connection.

At present, there are many researches on photovoltaic power generation, energy storage system and probability power flow at home and abroad. A large number of literature studies show that energy storage can improve the utilization rate of photovoltaic and solve the stability of photovoltaic grid connection. In the configuration of energy storage system in new energy power station, attention should be paid not only to the control strategy of optical storage and wind storage, but also to the economy of energy storage system. In addition, for the optimization of multiple energy storage power stations in the power system, it is necessary to study the economic model of the operation of energy storage power stations, the site selection of the starting point and end point of photovoltaic transmission channels and the site selection of energy storage. However, the existing research on optimal configuration of energy storage system does not consider the specific impact on power system, and the research on multi-point system does not involve large-scale optical storage operation characteristics.

With the large-scale development of uncertain new energy power generation such as wind power and photovoltaic, it is necessary to calculate the power flow of the power system in the operation planning of the power system. For example, the literature studies the optimal location and capacity allocation of energy storage in the power system with wind power. In addition, the correlation between multiple new energy sources should also be considered in the calculation of power flow. However, all the above studies are based on deterministic power flow methods, which do not consider the uncertainty of new energy generation. The literature considers the uncertainty of wind power and applies the probabilistic optimal power flow method to optimize the site selection of energy storage system, which improves the operation economy.

Op dit moment zijn er verschillende probabilistische powerflow-algoritmen voorgesteld door wetenschappers, en dataminingmethoden van niet-lineaire probabilistische powerflow op basis van de Monte Carlo-simulatiemethode zijn voorgesteld in de literatuur, maar de tijdigheid van de Monte Carlo-methode is erg slecht. In de literatuur wordt voorgesteld om de probabilistische optimale krachtstroom te gebruiken om de locatie van energieopslag te bestuderen, en er wordt een 2 m-puntmethode gebruikt, maar de berekeningsnauwkeurigheid van deze methode is niet ideaal. De toepassing van de Latijnse hypercube-bemonsteringsmethode bij de berekening van de vermogensstroom wordt in dit artikel bestudeerd en de superioriteit van de Latijnse hypercube-bemonsteringsmethode wordt geïllustreerd aan de hand van numerieke voorbeelden.

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 Uncertainty model of components

Photovoltaic, load and generator are all random variables with uncertainty. In the calculation of probabilistic power flow of distribution network, the probabilistic model is explained in the literature. Through the analysis of historical data, the output power of photovoltaic power generation follows BETA distribution. By fitting the probability distribution of load power, it is assumed that load follows normal distribution, and its probability density distribution function is

Picture (1)

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

The probability model of generator usually adopts two-point distribution, and its probability density distribution function is

(2)

Waar, P is de kans op normale werking van de generator; PG is het uitgangsvermogen van de generator.

When the light is sufficient at noon, the active power of the photovoltaic power station is large, and the power that is difficult to use in time will be stored in the energy storage battery. When the load power is high, the energy storage battery will release the stored energy. The instantaneous energy balance equation of the energy storage system is

Bij het opladen

(3)

When the discharge

(4)

The constraint

Afbeeldingen,

Afbeeldingen,

Picture, picture

Waar, St is de energie die is opgeslagen op tijdstip T; Pt is het laad- en ontlaadvermogen van energieopslag; SL en SG zijn respectievelijk de energie van laden en ontladen. η C en η D zijn respectievelijk laad- en ontlaadefficiëntie. Ds is de zelfontladingssnelheid van energieopslag.

1.2 Latijnse hypercube-bemonsteringsmethode

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.

Figuur 1 toont de verwachting en variantie van de Latijnse hypercube-bemonsteringsmethode en de Monte Carlo-simulatiemethode met bemonsteringstijden variërend van 10 tot 200. De algemene trend van de resultaten die met de twee methoden worden verkregen, neemt af. De verwachting en variantie verkregen door de Monte Carlo-methode zijn echter erg onstabiel en de resultaten die worden verkregen door meerdere simulaties zijn niet hetzelfde met dezelfde bemonsteringstijden. De variantie van de Latijnse hypercube-bemonsteringsmethode neemt gestaag af met de toename van de bemonsteringstijden, en de relatieve fout neemt af tot minder dan 5% wanneer de bemonsteringstijden meer dan 150 zijn. Het is vermeldenswaard dat het bemonsteringspunt van de Latijnse hypercube-bemonsteringsmethode is symmetrisch rond de Y-as, dus de verwachte fout is 0, wat ook het voordeel is.

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Afb. 1 Vergelijking van verschillende bemonsteringstijden tussen MC en LHS

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) Bemonstering

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.

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Figuur 2 schematisch diagram van LHS

(2) Permutations

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

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A reverse iterative

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“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.

Na bidirectionele iteratie totdat de RMS-waarde ρ, die de correlatie vertegenwoordigt, niet afneemt, wordt de positiematrix van elke willekeurige variabele na permutatie verkregen en kan vervolgens de permutatiematrix van willekeurige variabelen met de minste correlatie worden verkregen.

(5)

Waarbij de afbeelding de correlatiecoëfficiënt is tussen Ik en Ij, cov covariantie is en VAR variantie is.

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 is the index to be optimized; Wmin and wmax are the minimum and maximum of the original function without standardization.

The objective function is

(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 Genetic algorithm

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 dit artikel wordt het energiesysteem, inclusief fotovoltaïsche en energieopslag, eerst berekend door het probabilistische stroomstroomalgoritme, en de verkregen gegevens worden gebruikt als de invoervariabele van het genetische algoritme om het probleem op te lossen. Het berekeningsproces wordt weergegeven in figuur 3, die voornamelijk is onderverdeeld in de volgende stappen:

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FIG. 3 Algorithm flow

(1) invoersysteem, fotovoltaïsche en energieopslaggegevens, en Latijnse hypercube-bemonstering en Gram-Schmidt-sequentie-orthogonalisatie uitvoeren;

(2) Input the sampled data into the power flow calculation model and record the calculation results;

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

(4) Bereken de fitness van elk individu in de populatie;

(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. Voorbeeldanalyse

The probabilistic power flow method is simulated and analyzed in the IEEE24-node test system shown in FIG. 4, in which the voltage level of 1-10 nodes is 138 kV, and that of 11-24 nodes is 230 kV.

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Figure 4 IEEE24 node test system

3.1 Influence of photovoltaic power station on power system

Fotovoltaïsche krachtcentrale in het elektriciteitssysteem, de locatie en capaciteit van het elektriciteitssysteem zullen de knooppuntspanning en het vertakkingsvermogen beïnvloeden, daarom analyseert deze sectie eerst de invloed van fotovoltaïsche energie vóór de analyse van de invloed van het energieopslagsysteem op het elektriciteitsnet station op het systeem, fotovoltaïsche toegang tot het systeem in dit document, de trend van de limiet van de waarschijnlijkheid, het netwerkverlies, enzovoort, heeft de simulatieanalyse voortgezet.

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.

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

In addition to the exceedance of power flow, this paper also analyzes the influence of photovoltaic on node voltage, as shown in FIG. 5(b). The standard deviations of voltage amplitudes of nodes 1, 3, 8, 13, 14, 15 and 19 are selected for comparison. On the whole, the connection of photovoltaic power stations to the power grid does not have a great influence on the voltage of nodes, but the photovoltaic power stations have a great influence on the voltage of a-Nodes and their nearby nodes. In addition, in the system adopted by the calculation example, through comparison, it is found that photovoltaic power station is more suitable for access to the node types: ① nodes with higher voltage grade, such as 14, 15, 16, etc., the voltage almost does not change; (2) nodes supported by generators or adjusting cameras, such as 1, 2, 7, etc.; (3) in the line resistance is large at the end of the node.

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.

Op basis van de drie factoren in de analyse van de bovenstaande resultaten, wordt knooppunt 14 in dit artikel genomen als het toegangspunt van de fotovoltaïsche krachtcentrale en wordt vervolgens de invloed van de capaciteit van verschillende fotovoltaïsche krachtcentrales op het elektriciteitssysteem bestudeerd.

Figure 6(a) analyzes the influence of photovoltaic capacity on the system. It can be seen that the standard deviation of the active power of each branch increases with the increase of photovoltaic capacity, and there is a positive linear relationship between the two. Except for several branches shown in the figure, the standard deviations of other branches are all less than 5 and show a linear relationship, which are ignored for the convenience of drawing. It can be seen that photovoltaic grid connection has a great influence on the power of directly connected with photovoltaic access point or adjacent branches. Because of limited power transmission line transmission, the transmission lines of quantities of construction and investment is huge, so installing a photovoltaic power station, should consider the limitation of transportation capacity, choose the smallest influence on line access to the best location, in addition, selecting the best capacity of photovoltaic power station will play an important part to reduce this effect.

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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

Afb. 6(b) vergelijkt de waarschijnlijkheid dat het actief vermogen de limiet van elke tak overschrijdt bij verschillende pv-centralecapaciteiten. Behalve de takken die in de figuur worden getoond, overschreden de andere takken de limiet niet of was de kans erg klein. Vergeleken met FIG. 6(a), blijkt dat de kans op off-limit en standaarddeviatie niet noodzakelijk gerelateerd zijn. Het actieve vermogen van een lijn met grote fluctuatie van de standaarddeviatie is niet noodzakelijkerwijs off-limit, en de reden is gerelateerd aan de transmissierichting van het fotovoltaïsche uitgangsvermogen. Als het in dezelfde richting is als de oorspronkelijke stroomstroom van de aftakking, kan een kleine fotovoltaïsche stroom ook een off-limit veroorzaken. Wanneer het pv-vermogen erg groot is, mag de vermogensstroom de limiet niet overschrijden.

In FIG. 6(c), the total network loss of the system increases with the increase of photovoltaic capacity, but this effect is not obvious. When the photovoltaic capacity increases by 60 MW, the total network loss only increases by 0.5%, i.e. 0.75 MW. Therefore, when installing pv power stations, network loss should be taken as a secondary factor, and factors that have a greater impact on the stable operation of the system should be considered first, such as transmission line power fluctuation and out-of-limit probability.

3.2 Impact of energy storage access on the system

Section 3.1 The access position and capacity of photovoltaic power station depend on the power system