CN107394820B - Method for solving output probability model of controllable photovoltaic system - Google Patents

Method for solving output probability model of controllable photovoltaic system Download PDF

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CN107394820B
CN107394820B CN201710740351.XA CN201710740351A CN107394820B CN 107394820 B CN107394820 B CN 107394820B CN 201710740351 A CN201710740351 A CN 201710740351A CN 107394820 B CN107394820 B CN 107394820B
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power
active
value
reference value
photovoltaic system
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CN107394820A (en
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孙永辉
张世达
王加强
张博文
翟苏巍
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Hohai University HHU
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/48—Controlling the sharing of the in-phase component
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • H02J3/385—
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B10/00—Integration of renewable energy sources in buildings
    • Y02B10/10—Photovoltaic [PV]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers

Abstract

The invention discloses a method for solving a controllable photovoltaic system output probability model, which comprises the following steps: firstly, determining power control strategies of a DC-DC converter and a DC-AC grid-connected inverter of a photovoltaic system; a simulation system is built on a Simulink simulation platform and a power control strategy is realized; generating a photovoltaic sequence, testing a photovoltaic system on a simulation platform, and adjusting an input sequence to explore a power probability rule of the photovoltaic system; generating a large number of samples on MATLAB, and obtaining the active probability density function and the mean value of the photovoltaic system when different active reference values exist; and obtaining a probability model of the power of the controllable photovoltaic system by utilizing two-dimensional interpolation and quadratic regression.

Description

Method for solving output probability model of controllable photovoltaic system
Technical Field
The invention relates to a method for solving a controllable photovoltaic system output probability model, which can provide an accumulative distribution function of active power of a photovoltaic system in consideration of light abandon and is suitable for calculation of photovoltaic power systems in consideration of randomness.
Background
The power system calculation considering randomness has penetrated into each branch direction of the electrical discipline, and the probability model of each element in the system is the basis of the randomness calculation and optimization. In the existing research of the probability model of the photovoltaic system, the probability model of the illumination intensity is determined firstly, and then the photovoltaic system is considered to operate according to the MPPT control strategy, so that the probability model is obtained. However, when light abandon exists, the probability model of the photovoltaic system is bound to change, and the probability model is not researched sufficiently.
Disclosure of Invention
The purpose of the invention is as follows: based on the analysis, the invention provides a method for solving a controllable photovoltaic system output probability model, which can give out an accumulative distribution function of active power of a photovoltaic system in consideration of light abandonment and is suitable for calculation of photovoltaic power systems in consideration of randomness.
The technical scheme is as follows: a method for solving a controllable photovoltaic system output probability model is provided. The method is realized by the following steps:
(1) a control strategy for the photovoltaic system is determined. The DC-DC link of the controllable PV system adopts a strategy of combining the set active reference value and the MPPT, namely the PV panel generates power according to the reference value when the power obtained by the MPPT is higher than the reference value, and generates power according to the MPPT when the power obtained by the MPPT is lower than the reference value. The grid-connected inverter adopts a constant power control strategy, an active reference value is given by the power of the DC-DC link, and a reactive reference value is set in addition. Therefore, the power control strategy can be determined by determining the active DC-DC link and the reactive DC-AC reference value of the PV system;
(2) building a PV system simulation system on a Simulink simulation platform;
(3) considering the medium-term prediction error of the illumination intensity and the volatility of a short time scale comprehensively, considering that an illumination intensity sequence in a certain time period with a known predicted value obeys normal division (the mean value is the predicted value and the standard deviation is 20% of the predicted value), generating the illumination sequence at a certain time interval, inputting the illumination sequence into a simulation platform, obtaining an active power probability density function by utilizing kernel density estimation, and analyzing to obtain the following conclusion: 1)
Figure BDA0001388980700000011
the time interval of the illumination sequence is irrelevant to the length of the time interval; 2) different at any time interval
Figure BDA0001388980700000012
Obtained under the numerical value
Figure BDA0001388980700000013
The data are approximately the same after the data are unified;
(4) simulating according to different active reference values to obtain a result, and obtaining an accumulated distribution function and an average value when the active reference values are different, wherein the active reference value-active probability density function is a three-dimensional image;
(5) performing two-dimensional interpolation on the three-dimensional graph to obtain a graph to be interpolated
Figure BDA00013889807000000214
Set at any value within an adjustable range
Figure BDA0001388980700000022
Can be considered to exist more than oneAs a function of parameters
Figure BDA0001388980700000024
Unique determination
Figure BDA0001388980700000025
While obtaining by quadratic regression
Figure BDA0001388980700000026
And
Figure BDA0001388980700000027
the functional relationship of (a). The above relationship can be expressed as
Figure BDA0001388980700000028
Drawings
FIG. 1 is a flow chart of a method of an embodiment of the present invention;
FIG. 2 is a simulation result of the simulation system;
FIG. 3 is a probability density function obtained from simulation results with different interval times;
FIG. 4 is a probability density function obtained from simulation results with different input powers;
FIG. 5 is a drawing showing
Figure BDA00013889807000000210
A three-dimensional image of the CDF;
fig. 6 is an image of the active reference value of the controllable PV system as a function of the mean value.
Detailed Description
The present invention is further illustrated by the following examples, which are intended to be purely exemplary and are not intended to limit the scope of the invention, as various equivalent modifications of the invention will occur to those skilled in the art upon reading the present disclosure and fall within the scope of the appended claims.
(1) The DC-DC link of the controllable PV system considered by the invention adopts a strategy of combining the set active reference value and the MPPT, namely the PV panel generates power according to the reference value when the power obtained by the MPPT is higher than the reference value and generates power according to the MPPT when the power obtained by the MPPT is lower than the reference value. The grid-connected inverter adopts a constant power control strategy, an active reference value is given by the power of the DC-DC link, and a reactive reference value is set in addition. Therefore, the power control strategy can be determined by determining the DC-DC link active and DC-AC reactive reference values of the PV system. The present invention only considers the active power,
considering that the power of a PV system may fluctuate above and below a predicted value when the PV system is not abandoned, an active reference value set by a certain PV system needs to satisfy the following constraints:
Figure BDA00013889807000000211
in the formula:
Figure BDA00013889807000000212
an active power reference value set for the PV at the tth time period throughout the day;
Figure BDA00013889807000000213
the active power prediction value in the time period is obtained. Reference value not less than
Figure BDA0001388980700000031
Because less than this would result in excessive clipping of the photovoltaic; when the reference value is
Figure BDA0001388980700000032
Since light is not discarded, the upper limit is set.
(2) And building a PV system simulation system on the Simulink simulation platform according to the control strategy.
PV system is connected with load through inverter to simulate constant voltage grid connection and inversionThe device adopts a constant power control mode, and the optical power of the DC-DC link is higher than that of the DC-DC link without abandoning
Figure BDA0001388980700000033
Push button
Figure BDA0001388980700000034
While using control below
Figure BDA0001388980700000035
MPPT control is adopted.
(3) The medium-term prediction error of the illumination intensity and the volatility of a short time scale are comprehensively considered, the illumination intensity sequence in a certain period of a known predicted value is considered to be subjected to normal division (the mean value is the predicted value, and the standard deviation is 20% of the predicted value), and the illumination sequence is generated at a certain time interval and input into the simulation platform. PV system power prediction 10429W, the standard deviation is set at 20%,
Figure BDA0001388980700000037
a random value is generated for 0.93pu (9400W) and 0.1s, simulation is performed for 30s, and simulation step length is 1^10-6s, which are respectively simulated according to the whole-process MPPT control strategy and the control strategy provided by the invention, and one section is shown in FIG. 2.
It can be seen that, except for the transient process of switching the control mode under the strategy, the control strategy provided by the invention is stable and feasible, and the output power is kept above or below 9400W. A probability model (probability density function PDF) of the controlled PV system can be derived from the simulation results using kernel density estimation.
Two points are verified as follows: 1) the PDF obtained by estimation is irrelevant to the random value generation time interval; 2) push button
Figure BDA0001388980700000038
After per unit, PDF obtained in different time interval and the time interval
Figure BDA0001388980700000039
The magnitude of the value is irrelevant.
The interval of 0.1s and 0.5s respectively produces random values, 30s is simulated, 10429W is used for per unit, PDF is shown in figure 3, and two curves are similar and can be explained to be independent of the interval time.
Figure BDA00013889807000000310
According to 10429W and 5220W respectively,
Figure BDA00013889807000000311
9400W and 4700W respectively, and PDF is drawn by simulation and is calculated according to the respectivePer unit, the result is shown in FIG. 4, which can prove that the obtained PDF is according to the time segment
Figure BDA00013889807000000313
The real probability characteristic can be reflected after the reference value is set.
(4) And taking the illumination intensity as a predicted value of the corresponding time interval. For a certain period, firstly generating an illumination sequence according to a certain time interval, setting different
Figure BDA00013889807000000314
To obtain nsAn output power value Pi(i＝1,...,ns). Then using the nuclear density estimation to respectively obtain each
Figure BDA00013889807000000315
Corresponding to
Figure BDA00013889807000000316
Probability density function of (1):
Figure BDA00013889807000000317
in the formula: h is the bandwidth; k is a kernel function. To pair
Figure BDA0001388980700000041
The integration can obtain the
Figure BDA0001388980700000042
Lower part
Figure BDA0001388980700000043
Is/are as follows
Figure BDA0001388980700000044
Then draw outThe CDF three-dimensional image ofMean value of
Figure BDA0001388980700000048
Curve line.
(5) Performing two-dimensional interpolation on the three-dimensional graph to obtain a graph to be interpolated
Figure BDA0001388980700000049
Set at any value within an adjustable rangeCan be considered to exist more than one
Figure BDA00013889807000000411
As a function of parameters
Figure BDA00013889807000000412
Unique determination
Figure BDA00013889807000000413
While obtaining by quadratic regressionAnd
Figure BDA00013889807000000415
the functional relationship of (a). The above relationship can be expressed as
(6) One embodiment of the invention is described below:
and solving an active power probability model of the per-unit system on the basis of exploring the random output rule of the photovoltaic system. Arranged at intervals of 0.02pu from 0.7pu to 1.3pu
Figure BDA00013889807000000417
And inputting a sequence of illuminations for simulation, each
Figure BDA00013889807000000418
To obtain nsAn output power value Pi(i＝1,...,ns). Then 31 nuclear density estimates are used to respectively obtain
Figure BDA00013889807000000419
Lower part
Figure BDA00013889807000000420
Is determined.
Setting nuclear density estimation parameters: ref is 0.7pu to 1.3 pu; h is 0.025 bandwidth; a gaussian kernel function is used. To pairThe integration can obtain the
Figure BDA00013889807000000432
Lower part
Figure BDA00013889807000000423
Is/are as follows
MATLAB simulation is performed to drawThe CDF three-dimensional image of
Figure BDA00013889807000000427
Mean value of
Figure BDA00013889807000000428
The curves are shown in fig. 5 and 6, respectively.
Performing two-dimensional interpolation on the three-dimensional graph, and obtaining the three-dimensional graph by utilizing quadratic regression
Figure BDA00013889807000000429
And
Figure BDA00013889807000000433
the functional relationship of (a). The above relationship can be expressed as
Figure BDA00013889807000000431
From the above conclusions, the above formula for per-unit value representation can be applied to PV systems of different capacities at any time period.

Claims (4)

1. A method for solving a controllable photovoltaic system output probability model is characterized by comprising the following steps:
(1) determining a control strategy of the photovoltaic system;
(2) building a PV system simulation system on a Simulink simulation platform;
(3) comprehensively considering the medium-term prediction error of the illumination intensity and the volatility of a short time scale, determining that an illumination intensity sequence in a certain time period with a known predicted value obeys normal distribution, generating the illumination sequence according to a certain time interval, inputting the illumination sequence into a simulation platform, obtaining an active power probability density function by utilizing kernel density estimation, and analyzing to obtain the following conclusion: 1)
Figure FDA0002233624040000011
the time interval of the illumination sequence is irrelevant to the length of the time interval; 2) different at any time intervalObtained under the numerical valueThe data are approximately the same after the data are unified;
the above-mentioned
Figure FDA0002233624040000014
Prediction of active power for the full-day tth-period PV system, the
Figure FDA0002233624040000015
For each one
Figure FDA0002233624040000016
Corresponding to
Figure FDA0002233624040000017
A probability density function of;
(4) simulating according to different active reference values to obtain a result, and obtaining an accumulated distribution function and an average value when the active reference values are different, wherein the active reference value-active probability density function is a three-dimensional image;
(5) performing two-dimensional interpolation on the three-dimensional graph to obtain a graph to be interpolated
Figure FDA0002233624040000018
Set at any value within an adjustable range
Figure FDA0002233624040000019
The above-mentioned
Figure FDA00022336240400000110
There is more than one active power reference value for the PV system in the tth time period of the whole day
Figure FDA00022336240400000111
As a function of parameters
Figure FDA00022336240400000112
Unique determination
Figure FDA00022336240400000113
While obtaining by quadratic regression
Figure FDA00022336240400000114
And
Figure FDA00022336240400000115
the functional relationship of (a); the above relationship can be expressed as:
Figure FDA00022336240400000116
2. the method according to claim 1, wherein the DC-DC link of the controllable PV system in step 2 adopts a strategy of setting an active reference value in combination with MPPT, that is, when the power obtained by MPPT is higher than the reference value, the PV panel generates power according to the reference value, and when the power obtained by MPPT is lower than the reference value, the PV panel generates power according to MPPT; the grid-connected inverter adopts a constant power control strategy, an active reference value is given by the power of a DC-DC link, and a reactive reference value is set in addition; therefore, the power control strategy can be determined by determining the DC-DC link active and DC-AC reactive reference values of the PV system.
3. The method of claim 1, wherein the PV system power is allowed to fluctuate above and below the predicted value when the PV system power is not curtailed, and the active reference value set by a PV system is required to satisfy the following constraints:
Figure FDA0002233624040000021
in the formula:
Figure FDA0002233624040000022
is the first daythe PV set active power reference value in t time periods;
Figure FDA0002233624040000023
the predicted value of the active power in the time period is obtained; is greater thanTo consider not to excessively cut, butThe light is basically not abandoned, and the waste caused by control is avoided.
4. The method of claim 1, wherein the intensity of light is used as a prediction value for the corresponding time period; for a certain period, firstly generating an illumination sequence according to a certain time interval, setting different
Figure FDA0002233624040000026
To obtain nsAn output power value Pi(i＝1,...,ns) (ii) a Then using the nuclear density estimation to respectively obtain eachCorresponding to
Figure FDA0002233624040000028
Probability density function of (1):
Figure FDA0002233624040000029
in the formula: h is the bandwidth; k is a kernel function; to pair
Figure FDA00022336240400000210
The integration can obtain theLower part
Figure FDA00022336240400000212
Is/are as follows
Figure FDA00022336240400000213
Then draw outThe CDF three-dimensional image of
Figure FDA00022336240400000215
Mean value of
Figure FDA00022336240400000216
Curve line.
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