CN105354655B - Photovoltaic power station group confidence capacity evaluation method considering power correlation - Google Patents
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Abstract
the invention provides a method for evaluating the confidence capacity of a photovoltaic power station group, aiming at the problem that the generated confidence capacity of a large-scale photovoltaic power station group in China cannot be accurately evaluated due to randomness, intermittence and periodicity of photovoltaic power generation.
Description
Technical Field
The invention relates to the technical field of photovoltaic power generation, in particular to a power correlation-based photovoltaic power station group confidence capacity evaluation method.
Background
the energy safety and the environmental pressure make people reduce the dependence on fossil energy continuously, and renewable energy sources such as photovoltaic power generation and wind power generation are gradually replacing the traditional energy source power generation. With the continuous development of solar energy technology and the continuous reduction of the price of a solar cell panel in unit capacity, the development and utilization of solar energy on a large scale have become a trend.
the areas with relatively rich solar energy resources in China are mainly distributed in the western provinces such as Tibet, Qinghai, Gansu and Xinjiang. The terrain of the areas is flat, the population density is low, and the method is suitable for developing large-scale centralized photovoltaic power generation. However, due to the small power load, weak grid structure of the power grid and the like, a matched power transmission project must be built to transmit the surplus photovoltaic power to the load center for consumption.
at present, photovoltaic power generation in the areas is usually merged into a power grid in a power convergence and outward delivery mode, and the photovoltaic power fluctuation is obvious under the influence of weather conditions. The confidence coefficient of the photovoltaic power generation capacity is evaluated, the photovoltaic power station group cannot be simply equivalent to a photovoltaic power station with the same capacity, and the fluctuation characteristics after power convergence need to be researched on the basis of the photovoltaic power station group so as to evaluate the confidence capacity of the photovoltaic power station group.
The construction of large-scale photovoltaic power station groups in China provides new challenges for the stability of a power grid. The power grid not only needs to accommodate more photovoltaic power generation installations, but also needs to cope with the influence of the photovoltaic power generation installations on the stability of the power grid. It is necessary to research a method for evaluating the confidence capacity of a photovoltaic power station group by comprehensively considering temperature, irradiance change and power correlation of a photovoltaic power station.
Disclosure of Invention
the invention aims to solve the technical problem of providing a power correlation-considered photovoltaic power station group confidence capacity assessment method, which comprehensively considers photovoltaic power generation temperature, irradiance time-varying property and power correlation of different photovoltaic power stations in the same photovoltaic power station group and more accurately assesses the confidence capacity of the photovoltaic power station group and the influence of the confidence capacity on a power grid.
the method for solving the technical problem comprises the following steps: 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: pSTIs 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;
RSIs an inherent resistance;
n is the ideal constant of the diode;
VTIs the thermal potential energy of the battery assembly;
ISCfor 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 isSC0The short-circuit current of the photovoltaic module under the standard test condition is obtained;
Iβis the irradiance;
IrefIrradiance under standard test conditions;
αTis the short circuit current temperature coefficient;
t is the component temperature;
TrefIs the temperature under standard test conditions;
Under the conditions of given temperature and illumination intensity, different voltages correspond to different powers, wherein irradiance IβIs clear sky index ktAnd a function of time t, ktObeying a certain probability distribution, solving as formula (3):
wherein: f is kta probability density function of;
c and lambda are coefficients related to the average clear sky index of the month;
kt maxis the maximum value of the clear sky index;
By constructing a union ktdistributing, 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 functiontthe joint probability density function is formula (4);
C(u1,u2,…,un；ρ)＝Φρ(Φ-1(u1),Φ-1(u2),…,Φ-1(un)) (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+Gpv,L+ΔL) (5)
wherein: r0Is a system reliability index;
r is a reliability evaluation function;
G、GpvRespectively 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.
According to the power correlation-considered photovoltaic power station group confidence capacity evaluation method, power correlation coefficients are used for representing power correlations of different photovoltaic power stations in the same photovoltaic power station group, a photovoltaic power station group power model considering temperature, irradiance change and power correlation is constructed, power of the photovoltaic power stations is simulated by the model, and the confidence capacity of the photovoltaic power station group is evaluated.
Drawings
FIG. 1 is a schematic diagram of correlation coefficients of photovoltaic power plants in different photovoltaic power plant groups in an embodiment;
FIG. 2 is a flow chart of the computational principle of the method of the present invention;
fig. 3 is a schematic diagram of the confidence capacities of a group of photovoltaic power stations with different installed capacities and the confidence capacities of photovoltaic power stations with the same capacity determined by the method of the present invention.
Detailed Description
The method for evaluating the confidence capacity of the photovoltaic power station group, which takes power dependence into account, is further described with the attached drawings and the embodiments.
the specific embodiment of the invention is as follows: taking the position of a certain large photovoltaic power station group in northwest of China as an example, the analysis data comes from actually measured data of the photovoltaic power station group, and the data can be obtained by adopting a commercially available product data acquisition device familiar to the technical personnel in the field.
Example the calculation conditions are illustrated below:
1) the simulation photovoltaic power station groups are respectively 200MW, 300MW, 400MW, 500MW, 600MW, 700MW and 800MW in scale, wherein the 200MW photovoltaic power station group is composed of 100MW, 50MW, 30MW and 20MW photovoltaic power stations, and then the 100MW photovoltaic power stations are added to the photovoltaic power station group when the total capacity is increased by 100 MW;
2) the solar panel adopts a 300W component; photovoltaic module short-circuit current I under standard test conditionSC08.81A; temperature coefficient of short-circuit current alphaT0.006%/deg.c; temperature T under Standard test conditionsrefsetting the temperature at 25 ℃; the ideal constant n of the diode is 1.5; intrinsic resistance RSis 0.054; photovoltaic power station current transformation and voltage transformation systemthe system efficiency eta is 0.8; irradiance I under standard test conditionsrefIs 1000W/m2；
3) the longitude and latitude of the photovoltaic power station group for calculation are 36 degrees 24 'N and 94 degrees 53' E; the average clear sky index of each month is 0.670, 0.679, 0.650, 0.643, 0.636, 0.611, 0.593, 0.607, 0.645, 0.685, 0.689 and 0.675; the equivalent correlation coefficient rho is 0.8; taking the maximum clear sky index of 0.9 per month;
under the above calculation conditions, the evaluation results of the confidence capacity of the photovoltaic power station group of the embodiment by applying the method of the present invention are as follows:
1) photovoltaic power station group power characteristic analysis
in the embodiment, when the photovoltaic power generation power is removed to be 0, the power correlation coefficients of the No. 1 photovoltaic power station of the photovoltaic power station groups 1 and 2 and the photovoltaic power stations of the group and other groups are respectively calculated, and the correlation coefficient pair of the photovoltaic power stations of different photovoltaic power station groups and the photovoltaic power stations of the group and other groups is shown in figure 1; as can be seen from fig. 1, the power correlation coefficient of the photovoltaic power station in the local area is high, and is obviously different from the power correlation coefficients of other photovoltaic power station groups, and the power correlation coefficient between the photovoltaic power stations can be used as a basis for distinguishing the photovoltaic power station groups and can also be used as a basis for simulating the power of the photovoltaic power station groups;
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):
the specific calculation of the photovoltaic power station power under the given calculation parameters is converted from the formula (1) to the formula (6):
I0、VTThe calculation of (2):
VT＝1.35×10-3×T
the compound represented by the formula (2):
Conversion of incoming data into formula (7) pair ISCThe calculation of (2):
The photovoltaic cell is similar to a controlled current source, different cell assembly voltages correspond to different output currents and powers, and the optimal working point voltage of the photovoltaic cell is obtained by means of an improved particle swarm algorithm;
irradiance Iβand (3) calculating:
Wherein: n isdStarting from 1 month and 1 day, the date corresponds to the number of days in a year, i.e. ndTaking 1-365; theta is the incident angle of sunlight on the cell panel, and is measured by a sun incident angle tracking measuring instrument at different moments;
the compound represented by the formula (3):
conversion of substituted data into formula (8) pair ktthe probability density function f of (a):
Wherein the calculation formula of C and lambda is as follows:
C＝λ2×0.9/(exp(λ×0.9)-1-λ×0.9)
Clear sky index is according to formula (4)
C(u1,u2,…,un；ρ)＝Φρ(Φ-1(u1),Φ-1(u2),…,Φ-1(un)) (4)
sampling, substituting the generated clear sky index sequence into the formulas (6) and (7), and calculating by combining an optimal working point calculation algorithm to obtain the power of the photovoltaic power station group;
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+Gpv,L+ΔL) (5)
taking an RTS-79 stability test system as an example, adopting MATLAB software to program, and evaluating the power generation reliability of the system after photovoltaic addition; the system comprises 32 generators, the capacity is different from 12MW to 400MW, the total installed capacity is 3405MW, and the maximum load of the system is 2850 MW; the payload capacity is used to define the confidence capacity of the new energy bank. As shown in the attached figure 2, the method of the invention comprises the following steps in sequence: inputting system unit data and load data, and evaluating original system reliability index R0Inputting the number of the photovoltaic power station group power stations and the monthly average clear sky index data, simulating the clear sky index of the photovoltaic power station group, inputting air temperature data, inputting the installed capacity of the photovoltaic power stations, calculating the power of each photovoltaic power station, evaluating the reliability level R of the system, and judging the reliability level R as |, R0When ≦ R ≦ ε is Y, then it is outputted Δ L0-R│>When epsilon is N, the system reliability level R is evaluated back by adjusting the load level L' ═ L + Δ L.
FIG. 3 is a graph showing the confidence capacities of groups of photovoltaic power stations with different installed capacities calculated by the present invention, and the confidence capacities calculated by a conventional method for equating the photovoltaic power stations to one photovoltaic power station with the same capacity are calculated at the same time; the result shows that the confidence capacity of the photovoltaic power station group considering the correlation is higher than that of the photovoltaic power station group not considering the correlation. The power correlation among the photovoltaic power stations in the photovoltaic power station group has positive influence on the reliability of the system, namely under the same reliability index, the extra load born by the model calculation system considering the power correlation among the photovoltaic power stations is higher than that of a same-capacity equivalent method without considering the correlation.
the computing conditions, illustrations and the like in the embodiments of the present invention are only used for further description of the present invention, are not exhaustive, and do not limit the scope of the claims, and those skilled in the art can conceive other substantially equivalent alternatives without inventive step in light of the teachings of the embodiments of the present invention, which are within the 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: pSTis 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;
RSis an inherent resistance;
n is the ideal constant of the diode;
VTis the thermal potential energy of the battery assembly;
ISCFor 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 isSC0The short-circuit current of the photovoltaic module under the standard test condition is obtained;
IβIs the irradiance;
Irefirradiance under standard test conditions;
αTis the short circuit current temperature coefficient;
t is the component temperature;
TrefIs the temperature under standard test conditions;
Under the conditions of given temperature and illumination intensity, different voltages correspond to different powers, wherein irradiance Iβis clear sky index ktand a function of time t, ktobeying a certain probability distribution, solving as formula (3):
wherein: f is kta probability density function of;
C and lambda are coefficients related to the average clear sky index of the month;
kt maxis the maximum value of the clear sky index;
by constructing a union ktDistributing, 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 functiontthe joint probability density function is formula (4);
C(u1,u2,…,un；ρ)＝Φρ(Φ-1(u1),Φ-1(u2),…,Φ-1(un)) (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+Gpv,L+ΔL) (5)
wherein: r0Is a system reliability index;
R is a reliability evaluation function;
G、Gpvrespectively 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.
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| CN109767041B (en) * | 2019-01-15 | 2022-04-08 | 阳光电源股份有限公司 | Method and device for acquiring effective temperature of photovoltaic power station string |
| CN110417057B (en) * | 2019-08-01 | 2020-10-09 | 河海大学常州校区 | Photovoltaic output fluctuation stabilizing method considering system capacity reliability |
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| CN113395041B (en) * | 2021-06-15 | 2022-07-26 | 齐殿全 | Photovoltaic module arrangement method based on constant power generation |
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