CN108074035B - Multi-scene distributed photovoltaic access power distribution network operation risk assessment method - Google Patents
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Abstract
The evaluation method provided by the invention mainly describes power loss caused by the influence of node voltage deviation rate and harmonic distortion rate brought by the power distribution network after distributed photovoltaic access, simultaneously considers the characteristics of photovoltaic output, and comprehensively evaluates the operation risk of the distributed photovoltaic access system under multiple scenes.
Description
Technical Field
The invention relates to the technical field of operation risk assessment of a distributed photovoltaic access power system, in particular to an operation risk assessment method of a multi-scene distributed photovoltaic access power distribution network.
Background
At present, a smart power grid taking distribution network intelligence as an important sign is widely researched, and safely and reliably receiving distributed power supplies such as wind power and photovoltaic power generation is one of important functions of a future intelligent distribution network.
The access of distributed photovoltaic will change and join in marriage net network structure and trend, and traditional radial distribution network becomes many power systems, will bring certain risk for the operation of distribution network, control. Due to the characteristics of the distributed photovoltaic, the reliability and risk evaluation of the distributed photovoltaic cannot be completely equal to that of a traditional standby power supply, the existing power distribution network reliability and risk evaluation method is difficult to continue to be applicable, and it is necessary to establish a power distribution network risk evaluation index containing the distributed photovoltaic and research the power distribution network risk evaluation method of the distributed photovoltaic.
On the other hand, the photovoltaic is an intermittent renewable energy source, and the power generation of the photovoltaic is fluctuant and random along with the influence of external weather factors, so that the traditional deterministic research method is not applicable any more.
Disclosure of Invention
There is a need to provide a multi-scenario distributed photovoltaic access power distribution network operation risk assessment method.
A multi-scene distributed photovoltaic access power distribution network operation risk assessment method comprises the following steps:
firstly, determining photovoltaic output scenes and the probability of occurrence of each scene, and numbering the scenes (i is 1, … S);
secondly, calculating the probability distribution parameters of the photovoltaic output curve of the scene i by adopting a parameter identification method so as to obtain the photovoltaic output probability distribution function f of the scene ii；
Thirdly, sampling according to the photovoltaic output probability distribution function of the scene i to obtain photovoltaic output, and setting the photovoltaic output obtained by sampling for the jth time as Pj；
Fourthly, photovoltaic output P obtained by sampling for the j timejPhotovoltaic output P obtained according to j samplingjRespectively carrying out load flow calculation and harmonic load flow calculation on the power distribution network to obtain a node voltage deviation ratio and a voltage distortion ratio of the power distribution network, calculating node voltage power loss and voltage distortion ratio power loss caused by voltage deviation ratio out-of-limit and voltage distortion ratio out-of-limit, and further calculating to obtain an operation risk index value r of the j-th photovoltaic access power distribution networkj；
Step five, judging whether the sampling frequency reaches the maximum value, if so, carrying out the next step, if not, changing j to j +1, and returning to the step three;
the sixth step, judging whether the scene number reaches the maximum value, if so, the next step, if not, i is i +1, and returning to the second step;
seventhly, calculating to obtain a distributed photovoltaic access power distribution network operation risk index r considering multiple scenes under each scene according to the following formula;
in the formula, r is a comprehensive evaluation index of the power grid operation risk considering the voltage quality under multiple scenes, i is the ith scene, and piThe scene occurrence probability corresponding to the ith scene is obtained by local meteorological department big data statistics for a certain specific area, and is constant, delta UiIs the node voltage deviation ratio, THD, of the PCC pointiNode voltage distortion rate, P (Δ U), for PCC pointi) And P (THD)i) The node voltage power loss caused by node voltage deviation rate out-of-limit and the power loss caused by voltage distortion rate out-of-limit are respectively.
The method mainly describes the power loss caused by the influence of the node voltage deviation rate and the harmonic distortion rate brought by the power distribution network after the distributed photovoltaic access, and simultaneously, comprehensively evaluates the operation risk of the distributed photovoltaic access system under multiple scenes by considering the characteristic of photovoltaic output. The risk assessment method considers a probability model of photovoltaic output, obtains the photovoltaic output through Monte Carlo sampling according to typical photovoltaic output models of different types, further performs comprehensive calculation on the operation risk of the power distribution network in different scenes, and achieves comprehensive assessment on the operation risk of the distributed photovoltaic access power distribution network in multiple scenes.
Drawings
Fig. 1 is a schematic diagram of a power distribution network including distributed power sources in a region according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a distributed photovoltaic access distribution network operation risk calculation process considering multiple scenes in the present invention.
Fig. 3 is a single line diagram of a power distribution network employed by the test system of the present invention.
Fig. 4 is a graph of a typical sunrise force for a first typical scenario for a region in an embodiment of the invention.
Fig. 5 is a graph of a typical sunrise force for a second exemplary scenario for a region in an embodiment of the present invention.
Fig. 6 is a graph of an exemplary sunrise force for a third exemplary scenario for a region in an embodiment of the present invention.
Fig. 7 is a graph of an exemplary sunrise force for a fourth exemplary scenario for a region in an embodiment of the present invention.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will further describe the embodiments, and those skilled in the art can also obtain other drawings according to these drawings without creative efforts.
The invention is based on the fact that on one hand, the most obvious influence of the distributed photovoltaic access power distribution network on the power distribution network is the influence on the voltage quality, and mainly comprises two aspects of node voltage deviation rate and voltage distortion rate, and on the other hand, because the photovoltaic power generation is intermittent energy, the output of the photovoltaic power generation has random fluctuation, has important relations with weather conditions, seasons, moments and the like, and has various output scenes, and therefore, the comprehensive consideration and analysis are carried out after the operation conditions under different scenes need to be considered.
(1) Node voltage deviation ratio of system
Most of the power distribution networks in China are designed to operate in an open loop mode in a closed loop mode, so that the power distribution networks in normal operation can be equivalent to a single-power-supply radiation network, as shown in figure 1. Neglecting the admittance of the feeder line, the load adopts a three-phase symmetrical load model with constant power. The distributed power supply has the characteristics of economy and environmental protection, and active power is output as much as possible. Meanwhile, the distributed power supply connected into the power distribution network has small single machine capacity and generally does not have power regulation capacity, and the distributed fan and the photovoltaic generally work in a constant power control mode, so that the distributed power supply is used as a PQ node with a constant power factor to be analyzed.
In the equivalent single-power-supply radiation power distribution network shown in fig. 1, node 0 is a common connection point (i.e., PCC point) between the power distribution network and a higher-level transmission network, and nodes 1 to n are nodes in the power distribution network. Each node of the model shown in the figure is connected with a distributed power supply and a load, and if the power of the distributed power supply and the load of a certain node is set to be 0, the node is not connected with the distributed power supply and the load.
The distributed power supply is directly connected to the load node, and the direction of the distributed power supply is opposite to the load flow direction, so that the distributed power supply has a certain effect of offsetting the load, the distributed power supply can be regarded as a load with a negative value, and the voltage deviation ratio at the node k after the distributed power supply is connected can be represented as shown in the following formula:
in the formula: r and X represent the resistance and reactance of the feed line, PDG+jQDGIs the output power of the distributed power supply, PL+jQLIs the power of the load, Pj+jQjThe feeder ij is a feeder between two nodes in the power grid for the power flowing through the feeder ij.
The limit value of the power supply voltage deviation rate in the national standard GB/T12325-2008 'Power quality-Power supply Voltage deviation' is specified as follows:
1) the sum of the absolute values of the positive and negative deviations of the supply voltages of 35kV and above does not exceed 10% of the nominal voltage. (for example, when the upper and lower deviation of the power supply voltage is in the same sign (positive or negative), the absolute value of the larger deviation is used as the measurement basis);
2) the deviation ratio of the three-phase power supply voltage of 20kV or below is +/-7% of the nominal voltage;
3) the deviation rate of the 220V single-phase power supply voltage is + 7% and-10% of the nominal voltage;
the users with small short circuit capacity of the power supply point, long power supply distance and special requirements on the deviation rate of the power supply voltage are determined by the agreement of the power supply party and the power utilization party.
Therefore, in a distribution network system accessed by the distributed photovoltaic system, the node voltage should meet the following constraint condition,
UN(1-ε1)≤Ui≤UN(1+ε2) (2)
in the formula: u shapeNIs the nominal voltage of the system; epsilon1、ε2The allowable deviation ratio specified for the national standard.
(2) Voltage distortion rate due to harmonics
A photovoltaic power supply can be regarded as a nonlinear load for injecting harmonic waves into a feeder line of a power distribution network, the photovoltaic power connected into the power distribution network through a power converter can generate harmonic current, and if the harmonic current generated by the photovoltaic power supply at a certain moment is large enough, the voltage distortion of the power distribution network can exceed a distortion limit value specified by a standard.
After the distributed power supplies except the synchronous machine are connected into the power grid, the harmonic voltage of the public connection point should meet the regulation of GB/T14549 & 1993 'harmonic wave of power quality public power grid', as shown in Table 1.
Table 1:
by combining the system node voltage deviation rate and the voltage distortion rate index, the comprehensive evaluation index of the operation risk of the distributed photovoltaic access power distribution network under the multi-scene consideration and the calculation formula thereof are provided as follows:
in the formula, r is a comprehensive evaluation index of the power grid operation risk considering the voltage quality under multiple scenes, i is the ith scene, and piThe scene occurrence probability corresponding to the ith scene is obtained by local meteorological department big data statistics for a certain specific area, and is constant, delta UiIs the node voltage deviation ratio, THD, of the PCC pointiNode voltage distortion rate, P (Δ U), for PCC pointi) And P (THD)i) The node voltage power loss caused by node voltage deviation rate out-of-limit and the power loss caused by voltage distortion rate out-of-limit are respectively.
The operation risk of the multi-scene distributed photovoltaic access power distribution network can be evaluated based on a Monte Carlo method, and the method comprises the following specific steps:
firstly, determining photovoltaic output scenes and the probability of occurrence of each scene, and numbering the scenes (i is 1, … S);
secondly, calculating the probability distribution parameters of the photovoltaic output curve of the scene i by adopting a parameter identification method so as to obtain a photovoltaic output probability distribution function fi of the scene i;
thirdly, sampling according to the photovoltaic output probability distribution function of the scene i to obtain photovoltaic output, and setting the photovoltaic output obtained by sampling for the jth time as Pj；
Fourthly, photovoltaic output P obtained by sampling for the j timejPhotovoltaic output P obtained according to j samplingjRespectively carrying out load flow calculation and harmonic load flow calculation on the power distribution network to obtain a node voltage deviation ratio and a voltage distortion ratio of the power distribution network, calculating node voltage power loss and voltage distortion ratio power loss caused by voltage deviation ratio out-of-limit and voltage distortion ratio out-of-limit, and further calculating to obtain an operation risk index value r of the j-th photovoltaic access power distribution networkj；
Step five, judging whether the sampling frequency reaches the maximum value, if so, carrying out the next step, if not, changing j to j +1, and returning to the step three;
the sixth step, judging whether the scene number reaches the maximum value, if so, the next step, if not, i is i +1, and returning to the second step;
and seventhly, calculating to obtain a distributed photovoltaic access power distribution network operation risk index r considering multiple scenes under each scene according to the following formula.
The typical scene of photovoltaic output can be obtained by analyzing through a clustering method, and the output curve under the typical weather condition can also be obtained by converting the output characteristic of a light irradiance curve under the typical weather condition provided by a meteorological department. A typical scenario of known photovoltaic contribution is a prerequisite and basic condition for the analysis herein.
Under a certain photovoltaic output typical scene, the photovoltaic output correspondingly obeys a certain determined probability distribution, and the research of more scholars at home and abroad proves that the photovoltaic output is distributed from Beta within a few hours. Probability description of photovoltaic output is also used herein with Beta distributions. According to the specific probability distribution, a Monte Carlo simulation method can be adopted to perform load flow calculation and harmonic load flow calculation on the power distribution network accessed by the photovoltaic in the scene, so that a voltage deviation rate and a voltage distortion rate are obtained, and the operation risk index of the power distribution network in the scene is obtained.
The basic principle and steps of the Monte Carlo simulation method are mainly as follows:
the monte carlo method is based on the probability-quantity theory, and the measurement of data forms the most intuitive concept of probability theory, namely, the occurrence of an event is possibly accompanied by a plurality of possible results, so that the probability of the event can be defined as the ratio of the occurrence quantity of the event to all possible results. The monte carlo method has the same principle as this method, but is the reverse of this principle, i.e. the number of times an event occurs is measured and is taken as the probability of the event occurring. To measure the proportion of a certain block area in the whole space, only points need to be randomly drawn in the space arbitrarily, and the proportion of the number of the points falling into the block area in all the points is calculated. Such as the values calculated by the monte carlo method, the results of the simulation calculations have been infinitely close. The reliability of the monte carlo method is based on two very important theorems in probability theory, namely the law of large numbers and the central limit theorem. The majority theorem ensures that the estimated value calculated by the Monte Carlo method can be converged to the true value along with the increase of the simulation times; the central limit theorem gives the range of simulation errors after a finite number of simulation experiments.
The convergence rate of the Monte Carlo method is
Convergence speed is relatively slow, which means that the number of simulations needs to be increased by four times if the standard deviation is to be reduced to half; increasing the accuracy by one decimal point requires that the number of experiments be increased by 100 times. The method mainly comprises the following steps:
(1) constructing or describing probabilistic processes
For problems which are inherently random, such as particle transport problems, the probability process is mainly described and modeled correctly, and for deterministic problems which are not random in nature, such as calculating a definite integral, an artificial probability process must be constructed in advance, and certain parameters of the artificial probability process are exactly the solution of the required problem. I.e. to convert the problem of not having random properties into the problem of random properties.
(2) Enabling sampling from a known probability distribution
After the probability model is constructed, since each probability model can be regarded as being composed of various probability distributions, generating random variables (or random vectors) of known probability distributions becomes a basic means for realizing a simulation experiment of the monte carlo method, which is also called random sampling. One of the simplest, most basic, and most important probability distributions is a uniform distribution (or rectangular distribution) over (0, 1). Random numbers are random variables with such a uniform distribution. A random number sequence is a simple sub-instance of a population having such a distribution, i.e. a sequence of mutually independent random variables having such a distribution. The problem of generating random numbers is the sampling problem from this distribution. On a computer, random numbers can be generated by a physical method, but the random numbers are expensive, cannot be repeated and are inconvenient to use. Another method is to generate with a mathematical recurrence formula. The sequence thus generated is different from a truly random number sequence and is therefore called a pseudo-random number, or pseudo-random number sequence. However, it has been shown by various statistical tests to have properties similar to a true random number, or sequence of random numbers, and can therefore be used as a true random number. From the known distributed random sampling there are various methods, which, unlike the uniformly distributed sampling from (0,1), are realized by means of random sequences, that is to say, on the premise of generating random numbers. It follows that random numbers are the basic tool we implement monte carlo simulations.
(3) Establishing various estimators
Generally, after a probabilistic model is constructed and sampled, i.e. after a simulation experiment is implemented, a random variable is determined as a solution to the required problem, which is called unbiased estimation. Establishing various estimators is equivalent to investigating and registering the results of the simulation experiment to obtain a solution to the problem.
Taking a rural power distribution network in a certain area as an example, the operation risk assessment index r of the distributed photovoltaic access power distribution network is calculated by combining multiple sunrise scenes in the local area.
The system branch parameters and bus load data of the regional power grid system are shown in table 2,
table 2:
the permeability of distributed photovoltaic is set to be 150%, the distributed photovoltaic is connected to the tail end of a power distribution network, and the numbers of access nodes are 18, 19, 22 and 23.
And analyzing the annual photovoltaic output curve of the region to obtain four types of typical photovoltaic output power curves, as shown in fig. 4-7. Given that the photovoltaic output obeys Beta probability distribution of two parameters, the least square method is adopted to analyze and obtain alpha and Beta parameters under four typical weather scenes, the result is shown in table 3, and table 3 is a photovoltaic output probability distribution model parameter under the typical weather scenes:
table 3:
the calculation results obtained by the operation risk indicators and the method provided by the invention are shown in table 4:
table 4:
specifically, the distributed photovoltaic operation risk in the local area under the multi-weather scene is calculated as follows:
the first step, selecting a scene as a typical scene 1, the probability of occurrence is 0.25, and sampling is carried out by a Monte Carlo methodObtaining a photovoltaic output value, and obtaining power loss P (delta U) caused by voltage deviation rate out-of-limit through load flow calculation and harmonic load flow calculationi) Power loss P (Δ THD) due to voltage distortion rate caused by harmonics at 1.4408MWi) 2.256MW, then the risk indicator in typical scenario 1 is
r＝pi·(P(ΔUi)+P(THDi))＝0.25×(1.4408+2.256)＝0.9242MW
And similarly, calculating the risk indexes under other scenes.
Finally, the distributed photovoltaic operation risk in a multi-weather scene is
The modules or units in the device of the embodiment of the invention can be combined, divided and deleted according to actual needs.
While the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention.
Claims (1)
1. A multi-scene distributed photovoltaic access power distribution network operation risk assessment method is characterized by comprising the following steps:
firstly, determining photovoltaic output scenes and the probability of occurrence of each scene, and numbering the scenes (i is 1, … S);
secondly, calculating the probability distribution parameters of the photovoltaic output curve of the scene i by adopting a parameter identification method so as to obtain the photovoltaic output probability distribution function f of the scene ii；
Thirdly, sampling according to the photovoltaic output probability distribution function of the scene i to obtain photovoltaic output, and setting the photovoltaic output obtained by sampling for the jth time as Pj；
The fourth step, photovoltaic output obtained by the jth samplingPjPhotovoltaic output P obtained according to j samplingjRespectively carrying out load flow calculation and harmonic load flow calculation on the power distribution network to obtain a node voltage deviation ratio and a voltage distortion ratio of the power distribution network, calculating node voltage power loss and voltage distortion ratio power loss caused by voltage deviation ratio out-of-limit and voltage distortion ratio out-of-limit, and further calculating to obtain an operation risk index value r of the j-th photovoltaic access power distribution networkj；
Step five, judging whether the sampling frequency reaches the maximum value, if so, carrying out the next step, if not, changing j to j +1, and returning to the step three;
the sixth step, judging whether the scene number reaches the maximum value, if so, the next step, if not, i is i +1, and returning to the second step;
seventhly, calculating to obtain a distributed photovoltaic access power distribution network operation risk index r considering multiple scenes under each scene according to the following formula;
in the formula, r is a comprehensive evaluation index of the power grid operation risk considering the voltage quality under multiple scenes, i is the ith scene, and piThe scene occurrence probability corresponding to the ith scene is obtained by local meteorological department big data statistics for a certain specific area, and is constant, delta UiIs the node voltage deviation ratio, THD, of the PCC pointiNode voltage distortion rate, P (Δ U), for PCC pointi) And P (THD)i) The node voltage power loss caused by node voltage deviation rate out-of-limit and the power loss caused by voltage distortion rate out-of-limit are respectively.
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