CN107394820B - Method for solving output probability model of controllable photovoltaic system - Google Patents
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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
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)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;
(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 interpolatedSet at any value within an adjustable rangeCan be considered to exist more than oneAs a function of parametersUnique determinationWhile obtaining by quadratic regressionAndthe functional relationship of (a). The above relationship can be expressed as
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. 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:
in the formula:an active power reference value set for the PV at the tth time period throughout the day;the active power prediction value in the time period is obtained. Reference value not less thanBecause less than this would result in excessive clipping of the photovoltaic; when the reference value isSince 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 abandoningPush buttonWhile using control belowMPPT 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%,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 buttonAfter per unit, PDF obtained in different time interval and the time intervalThe 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.
According to 10429W and 5220W respectively,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 segmentThe 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 differentTo obtain nsAn output power value Pi(i=1,...,ns). Then using the nuclear density estimation to respectively obtain eachCorresponding toProbability density function of (1):
in the formula: h is the bandwidth; k is a kernel function. To pairThe integration can obtain theLower partIs/are as followsThen draw outThe CDF three-dimensional image ofMean value ofCurve line.
(5) Performing two-dimensional interpolation on the three-dimensional graph to obtain a graph to be interpolatedSet at any value within an adjustable rangeCan be considered to exist more than oneAs a function of parametersUnique determinationWhile obtaining by quadratic regressionAndthe 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 1.3puAnd inputting a sequence of illuminations for simulation, eachTo obtain nsAn output power value Pi(i=1,...,ns). Then 31 nuclear density estimates are used to respectively obtainLower partIs 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 theLower partIs/are as follows
MATLAB simulation is performed to drawThe CDF three-dimensional image ofMean value ofThe 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 regressionAndthe functional relationship of (a). The above relationship can be expressed as
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)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-mentionedPrediction of active power for the full-day tth-period PV system, theFor each oneCorresponding toA 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 interpolatedSet at any value within an adjustable range
The above-mentionedThere is more than one active power reference value for the PV system in the tth time period of the whole dayAs a function of parametersUnique determinationWhile obtaining by quadratic regressionAndthe functional relationship of (a); the above relationship can be expressed as:
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:
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 differentTo obtain nsAn output power value Pi(i=1,...,ns) (ii) a Then using the nuclear density estimation to respectively obtain eachCorresponding toProbability density function of (1):
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