CN105354655B - Confidence capacity evaluation method of photovoltaic power station group considering power correlation - Google Patents

Abstract

Aiming at the problem that the large-scale photovoltaic power station group in my country cannot accurately evaluate the confidence capacity of its power generation due to the randomness, intermittentness and periodicity of photovoltaic power generation, the present invention proposes a method for evaluating the confidence capacity of a photovoltaic power station group, which is characterized in that it includes Power characteristics analysis of photovoltaic power station groups, power modeling of photovoltaic power station groups taking into account power correlation, and photovoltaic power generation confidence capacity evaluation. The power model of photovoltaic power station group based on temperature, irradiance change and power correlation is used to simulate the power of photovoltaic power station and evaluate the confidence capacity of photovoltaic power station group. It is scientific, reasonable and has strong applicability.

Description

Confidence capacity evaluation method of photovoltaic power station group considering power correlation

technical field

The invention relates to the technical field of photovoltaic power generation, and relates to a method for evaluating the confidence capacity of photovoltaic power station groups in consideration of power correlation.

Background technique

The pressure of energy security and the environment has made people continuously reduce their dependence on fossil energy. Renewable energy such as photovoltaic power generation and wind power generation is gradually replacing traditional energy generation. With the continuous development of solar technology and the continuous reduction of the price of solar panels per unit capacity, large-scale development and utilization of solar energy has become a trend.

The areas with relatively rich solar energy resources in my country are mainly distributed in western provinces such as Tibet, Qinghai, Gansu and Xinjiang. These areas have flat terrain and low population density, which are suitable for the development of large-scale centralized photovoltaic power generation. However, due to the small power load and the weak grid structure, it is necessary to build a supporting power transmission project to send the excess photovoltaic power to the load center for consumption.

At present, photovoltaic power generation in the above-mentioned areas is usually integrated into the power grid by means of power aggregation and transmission. Due to the influence of weather conditions, photovoltaic power fluctuations are obvious. To evaluate the confidence of photovoltaic power generation capacity, it is not possible to simply equate a group of photovoltaic power stations to a photovoltaic power station with the same capacity. It is necessary to use the group of photovoltaic power stations as the basis to study the fluctuation characteristics of the power after aggregation, so as to evaluate the confidence capacity of the group of photovoltaic power stations.

The construction of large-scale photovoltaic power station groups in my country poses new challenges to the stability of the power grid. The power grid must not only accept more photovoltaic power generation installed capacity, but also deal with its impact on the stability of the power grid. It is necessary to study the confidence capacity evaluation method of photovoltaic power station group that comprehensively considers the temperature, irradiance change and photovoltaic power station power correlation.

Contents of the invention

The technical problem to be solved by the present invention is to propose a method for evaluating the confidence capacity of photovoltaic power station groups that takes power correlation into consideration. Power correlation, a more accurate assessment of the trusted capacity of photovoltaic power plant groups and their impact on the grid.

The method adopted to solve the technical problem is: a method for evaluating the confidence capacity of photovoltaic power station groups considering power correlation, which is characterized in that it includes the following steps:

1) Analysis of power characteristics of photovoltaic power station groups

The power correlation coefficient between photovoltaic power stations can reflect the closeness of the power correlation of photovoltaic power stations; based on the analysis of the measured historical data of photovoltaic power station groups, it can be seen that the correlation coefficient of output power of photovoltaic power stations in the same station group is relatively high, indicating that the power of photovoltaic power stations in the same station group has High correlation, and there are obvious differences in correlation with different station groups of photovoltaic power plants;

Due to the different spatial distances of photovoltaic power stations, the influence of temperature, light intensity, and weather conditions on photovoltaic power stations is not the same; therefore, there are differences in the output power of different photovoltaic power stations in the same photovoltaic power station group;

In the process of evaluating the trusted capacity of photovoltaic power station groups, the photovoltaic power station group cannot simply be replaced by a single photovoltaic power station with the same capacity, and the power correlation between photovoltaic power stations needs to be considered;

2) Power modeling of photovoltaic power station group considering power correlation

The power of photovoltaic power generation is affected by the intensity of solar radiation and temperature at the same time. The equivalent power calculation model of photovoltaic power station is (1):

Where: P _ST is the power of the photovoltaic power station;

η is the efficiency of the conversion and transformation system of the photovoltaic power station;

N is the number of equivalent photovoltaic cell components in the photovoltaic power station;

U is the voltage of the solar cell module;

I is the battery component current;

I ₀ is the diode saturation current;

R _S is the intrinsic resistance;

n is the ideal constant of the diode;

V _T is the thermal potential energy of battery components;

I _SC is the short-circuit current of electrical components, its value is related to temperature and irradiance, and the solution is formula (2):

Where: I _SC0 is the short-circuit current of photovoltaic modules under standard test conditions;

I _β is the irradiance;

I _ref is the irradiance under standard test conditions;

α _T is the short-circuit current temperature coefficient;

T is the component temperature;

T _ref is the temperature under standard test conditions;

Under the condition of given temperature and light intensity, different voltages correspond to different powers, where the irradiance I _β is a function of the clear sky index k _t and time t, k _t obeys a certain probability distribution, and the solution is formula (3):

Where: f is the probability density function of k _t ;

C and λ are coefficients related to the monthly mean clear sky index;

k _{t max} is the maximum value of the monthly clear sky index;

By constructing the joint k _t distribution, the power of the photovoltaic power station group with certain correlation can be simulated, and the multidimensional Copula function is used to construct the joint probability density function of k _t of different photovoltaic power stations in the photovoltaic power station group as formula (4);

C(u ₁ ,u ₂ ,…,u _n ; ρ)=Φ _ρ (Φ ^-1 (u ₁ ),Φ ^-1 (u ₂ ),…,Φ ^-1 (u _n )) (4)

Where: C is the multidimensional Gaussian Copula function;

ρ is the equivalent correlation coefficient matrix;

Φ and Φ ^-1 are the standard normal distribution and its inverse function respectively;

n represents the dimension of the function, here is the number of photovoltaic power plants;

3) Evaluation of photovoltaic power generation confidence capacity

The payload capacity is used to define the confidence capacity of the new energy unit, that is, the additional load that the new power supply can bear under the condition that the system reliability index remains unchanged, which is calculated as formula (5):

R ₀ ＝R(G,L)＝R(G+G _pv ,L+ΔL) (5)

Among them: R ₀ is the system reliability index;

R is the reliability evaluation function;

G and G _pv are the installed capacity of conventional units and the installed capacity of photovoltaic power generation respectively;

L is the system load;

ΔL is the added load of the system, that is, the credit capacity of photovoltaic power generation.

The photovoltaic power station group confidence capacity evaluation method considering power correlation of the present invention uses power correlation coefficient to characterize the power correlation of different photovoltaic power stations in the same photovoltaic power station group, and constructs a photovoltaic power station group that takes into account temperature, irradiance changes and power correlation. The power model of the power station group is used to simulate the power of the photovoltaic power station and evaluate the confidence capacity of the photovoltaic power station group. The present invention provides an effective method for evaluating the reliability of the large-scale photovoltaic power station group to the power system, which is scientific and reasonable, and has strong applicability specialty.

Description of drawings

Fig. 1 is a schematic diagram of correlation coefficients of photovoltaic power plants in different photovoltaic power plant groups in the embodiment;

Fig. 2 is a flow chart of the calculation principle of the inventive method;

Fig. 3 is a schematic diagram of the confidence capacity of photovoltaic power station groups with different installed capacities and the same capacity photovoltaic power station confidence capacity determined by the method of the present invention.

Detailed ways

The method for evaluating the confidence capacity of photovoltaic power station groups in the present invention, which takes power correlation into account, will be further described below using the drawings and embodiments.

A specific embodiment of the present invention is: taking the location of a large-scale photovoltaic power station group in Northwest my country as an example, the data used for analysis comes from the actual measurement data of the photovoltaic power station group, and the data can be obtained by using a commercially available product data acquisition device familiar to those skilled in the art to fulfill.

The calculation conditions of the embodiment are described as follows:

1) The scale of the simulated photovoltaic power station group is 200MW, 300MW, 400MW, 500MW, 600MW, 700MW and 800MW, of which the 200MW photovoltaic power station group is composed of 100MW, 50MW, 30MW and 20MW photovoltaic power station. Add 100MW photovoltaic power station;

2) The solar panel adopts 300W components; under standard test conditions, the short-circuit current I _SC0 of photovoltaic components = 8.81A; the temperature coefficient of short-circuit current α _T = 0.006%/°C; under standard test conditions, the temperature T _ref = 25°C; the diode ideal constant n is 1.5; the intrinsic resistance R _S is 0.054; the efficiency η of the photovoltaic power station's current and transformation system is 0.8; the irradiance I _ref under standard test conditions is 1000W/m ² ;

3) The latitude and longitude of the photovoltaic power station group used for calculation is 36°24′N, 94°53′E; 0.689, 0.675; the equivalent correlation coefficient ρ is 0.8; the maximum monthly clear sky index is 0.9;

Under the above calculation conditions, the application of the method of the present invention to the evaluation results of the confidence capacity of the photovoltaic power station group in the embodiment is as follows:

1) Analysis of power characteristics of photovoltaic power station groups

In the embodiment, when the photovoltaic power generation power is removed to be 0, the power correlation coefficients of No. 1 photovoltaic power station of photovoltaic power station groups 1 and 2 and the photovoltaic power stations of this group and other groups are calculated, and the photovoltaic power stations of different photovoltaic power station groups are related to the photovoltaic power stations of this group and Correlation coefficients of other groups of photovoltaic power stations are compared with attached figure 1; it can be seen from Figure 1 that the power correlation coefficients of photovoltaic power stations in this area are relatively high, and there are obvious differences with other photovoltaic power station groups. The power correlation coefficients between photovoltaic power stations can be used to distinguish photovoltaic power stations The basis of the group can also be used as the basis for simulating the power of the photovoltaic power station group;

2) Power modeling of photovoltaic power station group considering power correlation

The power of photovoltaic power generation is affected by the intensity of solar radiation and temperature at the same time. The equivalent power calculation model of photovoltaic power station is (1):

Under the given calculation parameters, the specific calculation of the photovoltaic power station power is converted from formula (1) to formula (6):

Calculation of I ₀ and V _T :

V _T ＝1.35×10 ^-3 ×T

From (2) formula:

Bring in the data and convert it to formula (7) for the calculation of _ISC :

Photovoltaic cells are similar to controlled current sources, and different battery module voltages correspond to different output currents and powers. The present invention obtains the optimal operating point voltage of photovoltaic cells by means of the improved particle swarm algorithm;

Calculation of irradiance I _β :

Among them: n _d is starting from January 1, and the date corresponds to the number of days in a year, that is, n _d ranges from 1 to 365; θ is the incident angle of sunlight on the panel, and its values at different times measured;

By (3) formula:

Substituting the data into equation (8) for the calculation of the probability density function f of k _t :

The calculation formulas of C and λ are as follows:

C＝λ ² ×0.9/(exp(λ×0.9)-1-λ×0.9)

Clear sky index according to formula (4)

C(u ₁ ,u ₂ ,…,u _n ; ρ)=Φ _ρ (Φ ^-1 (u ₁ ),Φ ^-1 (u ₂ ),…,Φ ^-1 (u _n )) (4)

Sampling, substituting the generated clear sky index sequence into formulas (6) and (7), combined with the optimal operating point calculation algorithm, the power of the photovoltaic power station group can be calculated;

3) Evaluation of photovoltaic power generation confidence capacity

The payload capacity is used to define the confidence capacity of the new energy unit, that is, the additional load that the new power supply can bear under the condition that the system reliability index remains unchanged, which is calculated as formula (5):

R ₀ ＝R(G,L)＝R(G+G _pv ,L+ΔL) (5)

Taking the RTS-79 stability test system as an example, MATLAB software is used to program to evaluate the power generation reliability of the system after photovoltaics are added; the system includes 32 generators with capacities ranging from 12MW to 400MW, with a total installed capacity of 3405MW, the largest in the system The load is 2850MW; the payload capacity is used to define the confidence capacity of the new energy unit. As shown in Figure 2, the method flow of the present invention is as follows: input system unit data and load data, evaluate the original system reliability index R ₀ , input the number of photovoltaic power station groups and monthly average clear sky index data, and simulate the photovoltaic power station group Clear sky index, input temperature data, input installed capacity of photovoltaic power station, calculate power of each photovoltaic power station, evaluate system reliability level R, when │R ₀ -R│≤ε is Y, to output ΔL, when │R ₀ -R│ >When ε is N, return to evaluate the system reliability level R by adjusting the load level L'=L+ΔL.

Fig. 3 is that the present invention calculates the confidence capacity of photovoltaic power station groups with different installed capacities, and calculates the confidence capacity calculated by the traditional method of photovoltaic power station equivalent to a photovoltaic power station of the same capacity at the same time; the result shows that the photovoltaic power station group confidence capacity considering correlation It is higher than the confidence capacity of the photovoltaic power station group without considering the correlation. The power correlation between photovoltaic power stations in the photovoltaic power station group has a positive impact on the reliability of the system, that is, under the same reliability index, the extra load borne by the system is higher than that of other systems using the model that considers the power correlation between photovoltaic power stations in this paper. An equal-capacity equivalent approach to account for dependencies.

The calculation conditions, legends, etc. in the embodiments of the present invention are only used to further illustrate the present invention, are not exhaustive, and do not constitute a limitation to the scope of protection of the claims. Those skilled in the art can obtain the enlightenment according to the embodiments of the present invention. Other substantially equivalent substitutions can be conceived through creative work, all of which are within the protection scope of the present invention.

Claims (1)

1. A photovoltaic power station group confidence capacity assessment method considering power correlation is characterized by comprising the following steps:

1) Photovoltaic power station group power characteristic analysis

The power correlation coefficient between the photovoltaic power stations can reflect the degree of closeness of the power correlation relationship of the photovoltaic power stations; based on the analysis of the actually measured historical data of the photovoltaic power station group, the correlation coefficient of the output power of the photovoltaic power stations in the same station group is higher, which indicates that the power of the photovoltaic power stations in the same station group has higher correlation and is obviously different from the correlation of the photovoltaic power stations in different station groups;

Due to different space distances of the photovoltaic power stations, the photovoltaic power stations are affected by different factors such as temperature, illumination intensity and weather conditions; therefore, even if the output power of different photovoltaic power stations in the same photovoltaic power station group is different;

in the process of evaluating the confidence capacity of the photovoltaic power station group, the photovoltaic power station group cannot be simply replaced by a single photovoltaic power station with the same capacity, and the power correlation among the photovoltaic power stations needs to be considered;

2) photovoltaic power station group power modeling considering power correlation

The photovoltaic power generation power is simultaneously influenced by the solar radiation intensity and the temperature, and the equivalent power calculation model of the photovoltaic power station is a formula (1):

Wherein: p_STis the photovoltaic power station power;

Eta is the efficiency of a photovoltaic power station current and voltage transformation system;

n is the number of equivalent photovoltaic cell assemblies of the photovoltaic power station;

U is the voltage of the solar cell module;

I is the battery pack current;

I₀is a diode saturation current;

R_Sis an inherent resistance;

n is the ideal constant of the diode;

V_Tis the thermal potential energy of the battery assembly;

I_SCFor an electrical component short circuit current, the value of which is related to temperature and irradiance, the solution is given by equation (2):

wherein: i is_SC0The short-circuit current of the photovoltaic module under the standard test condition is obtained;

I_βIs the irradiance;

I_refirradiance under standard test conditions;

α_Tis the short circuit current temperature coefficient;

t is the component temperature;

T_refIs the temperature under standard test conditions;

Under the conditions of given temperature and illumination intensity, different voltages correspond to different powers, wherein irradiance I_βis clear sky index k_tand a function of time t, k_tobeying a certain probability distribution, solving as formula (3):

wherein: f is k_ta probability density function of;

C and lambda are coefficients related to the average clear sky index of the month;

k_{t max}is the maximum value of the clear sky index;

by constructing a union k_tDistributing, namely simulating the power of the photovoltaic power station group with certain correlation, and constructing different photovoltaic power stations k in the photovoltaic power station group by adopting a multidimensional Copula function_tthe joint probability density function is formula (4);

C(u₁,u₂,…,u_n；ρ)＝Φ_ρ(Φ^-1(u₁),Φ^-1(u₂),…,Φ^-1(u_n)) (4)

wherein: c is a multidimensional Gaussian Copula function;

rho is an equivalent correlation coefficient matrix;

Phi and phi^-1Respectively, standard normal distribution and its inverse function;

n represents the dimension of the function, here the number of photovoltaic power stations;

3) photovoltaic power generation confidence capacity assessment

the confidence capacity of the new energy source unit is defined by adopting the effective load capacity, namely under the condition that the reliability index of the system is not changed, the load quantity which can be additionally borne by the newly added power supply is calculated as the formula (5):

R₀＝R(G,L)＝R(G+G_pv,L+ΔL) (5)

wherein: r₀Is a system reliability index;

R is a reliability evaluation function;

G、G_pvrespectively the installed capacity of a conventional unit and the installed capacity of photovoltaic power generation;

l is the system load;

and deltaL is the added load of the system, namely the confidence capacity of photovoltaic power generation.