CN113468843A - Construction method of equivalent model of active power distribution network - Google Patents
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Abstract
The invention discloses a method for constructing an equivalent model of an active power distribution network, which comprises the following steps: step S1, determining historical data of the active power distribution network to be equivalent, wherein the historical data comprises power grid network data to be equivalent and renewable energy historical data; step S2, preprocessing the data; s3, performing equivalent modeling on the to-be-equivalent network according to the active power distribution network equivalent model considering the randomness of the renewable energy power generation on the preprocessed data sample; and step S4, analyzing the accuracy of the network equivalence before and after equivalence based on the equivalence result. According to the invention, the active power distribution network equivalent model considering the randomness of the renewable energy power generation is adopted, the result comparison is carried out on the active power distribution network equivalent model and the network to be equivalent, the test result shows that the active power distribution network equivalent model considering the randomness of the renewable energy power generation can accurately equivalent the original network, the randomness of the renewable energy power generation is considered, and the active power distribution network equivalent model has practical value and wide application prospect.
Description
Technical Field
The invention relates to the technical field of active power distribution networks, in particular to a construction method of an equivalent model of an active power distribution network.
Background
Scholars at home and abroad have already made a great deal of research work on the equivalent model of the active power distribution network. The equivalent model of the active power distribution network comprises a static model and a dynamic model. For the static model, the distribution network can be simplified into a ZIP model (constant impedance Z, constant current I, constant power P) or a constant power load model (PQ model), but the models cannot reflect the characteristics of different distributed power generation alone. Therefore, many scholars are beginning to consider the impact of different types of distributed generation and the uncertainty of renewable energy sources. Some documents describe distributed generation with an equivalent generator that can provide reactive support. Some documents replace distributed wind turbines and photovoltaic power generation systems with a load that consumes negative power. Zhang et al propose a distributed generation planning method for an active power distribution network, which converts a planning model into a three-layer planning model considering demand side management and network reconstruction according to the decomposition and coordination ideas. Part of scientific research workers provide a double-layer static equivalent model consisting of an equivalent generator, branches and loads. The upper layer equivalent model considers the consistency of sensitivity and power loss, and the lower layer equivalent model considers the dead load characteristic. In part of documents, a parameter identification model is used for replacing an inverter generator and a load, and parameters are optimized according to historical data. Some documents use previous wind and solar load data to predict uncertainty. However, when equivalent modeling of a cross-border network is considered, an overseas network contains a large number of fans and photovoltaic power generation systems, and the scale is large, so that renewable energy power generation not only has uncertainty in time but also uncertainty in space, which brings new challenges to network equivalence and is a problem to be solved.
Disclosure of Invention
The invention aims to solve the technical problem of providing a construction method of an active power distribution network model considering the randomness of renewable energy power generation so as to improve the engineering practical value and widen the application prospect.
In order to solve the technical problem, an embodiment of the present invention provides a method for constructing an equivalent model of an active power distribution network, including:
step S1, determining historical data of the active power distribution network to be equivalent, wherein the historical data comprises power grid network data to be equivalent and renewable energy historical data;
step S2, preprocessing the data;
s3, performing equivalent modeling on the to-be-equivalent network according to the active power distribution network equivalent model considering the randomness of the renewable energy power generation on the preprocessed data sample;
and step S4, analyzing the accuracy of the network equivalence before and after equivalence based on the equivalence result.
Further, the step S3 specifically includes:
step S31, calculating a power balance equation of the active power distribution network;
s32, respectively establishing an equivalent fan model, an equivalent photovoltaic power generation system and an equivalent ZIP load model;
in step S33, the uncertainty component is equalized.
Further, the power balance equation of the active power distribution network is as follows:
Pin+jQin＝-(Pw+jQw)-(PPv+jQpv)+(Pl+Ploss)+j(Ql+Qloss)
wherein, PinActive power, Q, injected for the gridinReactive power injected for the transmission grid; pwThe active power is output by the distributed fans, and Qw is the reactive power output by the distributed fans; ppvActive power, Q, for distributed photovoltaic power generation outputpvReactive power output for distributed photovoltaic power generation; pl、PlossActive power, Q, being load and network loss, respectivelyl、QlossRespectively the reactive power of the load and the network losses.
Further, the step S32 of establishing the equivalent fan model specifically includes:
step S321, acquiring a fan power curve function:
wherein v isciFor cutting into the wind speed, vcoTo cut out wind speed, vrRated wind speed; prFor a rated power of the fan, q (v) is a function of flow and wind speed v;
step S322, fitting v with an arcsine functionr＜v≤vcoAnd (3) obtaining a wind speed curve of the section, obtaining the wind speed v of the observation fan, and calculating the active power of the equivalent fan according to the following formula:
Pw＝ω1arcsin(ω2v+ω3)+ω4
wherein ω isi(i ═ 1,2,3,4) are parameters fitted through the history data.
Further, the step S32 of establishing the equivalent photovoltaic power generation system specifically includes:
the model of the photovoltaic power generation is expressed by adopting a model of maximum power tracking control, and the active power of the model is determined by the illumination intensity and the temperature:
Vmp(G，T)＝Vmp，STC+KV(T-TSTC)+Vtln(G/GSTC)+αlog(G/GSTC)
Vmp(G，T)＝Vmp，STC+KV(T-TSTC)+Vtln(G/GSTC)+αlog(G/GSTC)
Pmp(G，T)＝Vmp(G，T)Imp(G，T)
Ppv(G，T)＝ηPmp(G，T)
wherein, KV，KI，Vmp，STC，Imp，STCAnd α, η are parameters fitted through historical data, GSTCAnd TSTCRespectively the illumination intensity and the temperature under standard test conditions; kVAnd KIAre the voltage and current coefficients, V, respectivelytIs the thermal voltage of a diode, and VtK is the boltzmann constant, q is the electronic charge, α is the photovoltaic panel coefficient, and η is the conversion efficiency of the inverter.
Further, the step S32 of establishing the ZIP load model specifically includes:
the complex load of the active power distribution network is replaced by the static ZIP load:
wherein, api，aqi(i ═ 1,2,3) are parameters fitted by historical data, and V is node powerAnd (6) pressing.
Further, the step S33 specifically includes:
step S331, calculating to obtain the output power of the equivalent fan at a given wind speed according to the fan power curve function and the active power of the equivalent fan;
step S332 of calculating the error between the deterministic component and the measured value
Drawing a probability density histogram PDH of errors;
step S333, fitting error by probability distribution
And obtaining the probability density function PDF of the power output randomness component of the equivalent model.
Further, a Gaussian distribution model is adopted to describe the probability characteristic of the random component of the power generation power of the renewable energy sources:
where μ and σ are the mean and variance, respectively, of the error from the actual measurement, and are parameters fitted through the historical data.
Further, the known steps of observing the observed wind speed of the fan, and solving the sampled values of the wind speeds of the n fans at different installation sites and the active power of the fan are as follows:
assuming that the wind speed of each fan obeys Weibull distribution, fitting the wind speed v according to historical wind speed datat(t ═ 1,2, …, n) edge distribution;
assuming that the relation between the probability distribution of the equivalent wind speed at a certain moment and the probability distribution of the unknown wind speed at the same moment obeys a Gaussian joint distribution function, and fitting the joint distribution function according to historical wind speed data;
when the equivalent wind speed is given, the conditional distribution function of the wind speed is calculated according to the joint distribution function and the edge distribution function;
sampling the condition distribution of the wind speed by using an LHS sampling method to obtain a plurality of groups of wind speed samples;
and inputting the wind speed samples into respective fan models to obtain the active power of each distributed fan.
Further, the step S4 specifically includes:
sampling uncertainty components based on Gaussian probability distribution by adopting an LHS method to obtain uncertainty power components of a plurality of groups of equivalent fans and equivalent photovoltaic power generation;
after the equivalent wind speed, the equivalent illumination intensity and the equivalent temperature are given, the deterministic power components of the equivalent fan and the equivalent photovoltaic power generation are obtained, and the two components are added to obtain the active power;
respectively measuring the voltage amplitude V of the corresponding nodes in the multiple groups of renewable energy power generation samples in the original model and the equivalent modelbAnd apparent power S injected by grid side nodebThen comparing V in the two modelsbAnd SbProbability distribution of (2).
The embodiment of the invention has the beneficial effects that: the method can realize equivalent modeling of random renewable energy sources such as wind power, photovoltaic and the like connected into the power distribution network, has obvious engineering practical value and has wide application prospect; the active power distribution network equivalent model considering the randomness of the renewable energy sources can consider the randomness of the spatial distribution, and the equivalent precision is superior to that of the equivalent model not considering the randomness of the spatial distribution.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flow diagram of a method for constructing an equivalent model of an active power distribution network according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of an equivalent model of the active power distribution network in the embodiment of the invention.
FIG. 3 is a schematic diagram of a typical operating curve of a wind turbine in an embodiment of the present invention.
Fig. 4 is a schematic diagram of an improved IEEE33 node power distribution network system according to an embodiment of the present invention.
Fig. 5 is a schematic diagram of an IEEE 30 node grid system with an ADN connected to node 20 according to an embodiment of the present invention.
FIG. 6 is a schematic diagram of the boundary voltage amplitudes of the equivalent system and the actual system in the embodiment of the present invention.
FIG. 7 is a schematic diagram of injected apparent power of boundary nodes of an equivalence system and an actual system according to an embodiment of the present invention.
FIG. 8 is a schematic diagram of a probability density function of voltage amplitudes of an equivalent system and an actual system according to an embodiment of the present invention.
FIG. 9 is a diagram illustrating probability density functions of the injected apparent power of the equivalence system and the actual system in the embodiment of the invention.
Detailed Description
The following description of the embodiments refers to the accompanying drawings, which are included to illustrate specific embodiments in which the invention may be practiced.
Referring to fig. 1, an embodiment of the present invention provides a method for constructing an equivalent model of an active power distribution network, including:
step S1, determining historical data of the active power distribution network to be equivalent, wherein the historical data comprises power grid network data to be equivalent and renewable energy historical data;
step S2, preprocessing the data;
s3, performing equivalent modeling on the to-be-equivalent network according to the active power distribution network equivalent model considering the randomness of the renewable energy power generation on the preprocessed data sample;
and step S4, analyzing the accuracy of the network equivalence before and after equivalence based on the equivalence result.
Specifically, fig. 2 shows an equivalent model of an active power distribution network, which includes an equivalent fan module, an equivalent photovoltaic power generation module, and an equivalent ZIP load module. The modules respectively reflect the integration effect of the distributed fans, the distributed photovoltaic power generation system and the network internal load and network loss in the active power distribution network.
Assuming that the total power injected into the active power distribution network by the power transmission network, the total output power of the distributed fans in the active power distribution network, the total output power of the distributed photovoltaic power generation and the sum of the load and the network loss are known, the power balance equation of the active power distribution network is as follows:
Pin+jQin＝-(Pw+jQw)-(PPv+jQpv)+(Pl+Ploss)+j(Ql+Qloss) (1)
wherein, PinActive power, Q, injected for the gridinReactive power injected for the transmission grid; pwActive power, Q, for distributed fan outputwThe reactive power is output by the distributed fans; ppvActive power, Q, for distributed photovoltaic power generation outputpvReactive power output for distributed photovoltaic power generation; pl、PlossActive power, Q, being load and network loss, respectivelyl、QlossRespectively the reactive power of the load and the network losses.
Due to uncertainty of meteorological conditions such as wind speed, illumination intensity and temperature, uncertainty exists in wind turbines and photovoltaic power generation. In an active power distribution network, taking a fan as an example, the installation site of renewable energy power generation equipment is determined along with the position distribution of users in the distribution network; and the active power distribution network model shown in FIG. 2 represents the integrated effect of all distributed wind power generation by using an equivalent fan module installed at the boundary node. Because of the few meteorological observation points in the distribution network, it is difficult to install wind speed measurement points at each distributed fan, and transmitting real-time wind speed data of each installation point to a power transmission network dispatcher also involves excessive data volume. Thus, the wind speed at each distributed fan is unknown. In this case, the equivalent modeling work first requires specifying an observation fan among all the fans of the distribution network, the wind speed at the installation point of which is observable. However, according to the wake effect of the wind speed, the wind speed actually absorbed by each distributed wind turbine is usually different from the equivalent wind speed. The difference between the actual wind speed of each distributed wind turbine and the equivalent wind speed leads to the spatial randomness of the distributed wind power generation.
Therefore, the injection power of the equivalent model of the active power distribution network is not a constant value, but is composed of a deterministic power component and an uncertain power component.
Deterministic power component
(1) Equivalent fan
The output power of the fan is determined by the characteristics of the fan. A typical fan power curve is a three-segment curve with cut-in wind speed (cut-in wind speed), rated wind speed (rated wind speed), and cut-out wind speed (cut-out wind speed) as segment points, as shown in fig. 3, and the function is expressed as follows:
wherein v isciFor cutting into the wind speed, vcoTo cut out wind speed, vrRated wind speed;
and fitting a second section of wind speed curve by adopting an arcsine function, and after obtaining the wind speed v of the observed fan, expressing the active power of the equivalent fan as follows:
Pw＝ω1arcsin(ω2v+ω3)+ω4 (3)
wherein ω isi(i ═ 1,2,3,4) are parameters that need to be fitted by the least squares method from the historical data. It is assumed that the distributed fans are all operating at unity power factor, and therefore the output reactive power of these fans is zero.
(2) Equivalent photovoltaic power generation system
Although the artificial neural network method is commonly used for modeling photovoltaic power generation, the mechanism type model can describe the actual behavior of the photovoltaic power generation better. Therefore, a model of maximum power tracking control is used to represent a model of photovoltaic power generation, whose active power is determined by the intensity of illumination and temperature.
Vmp(G，T)＝Vmp，STC+KV(T-TSTC)+Vtln(G/GSTC)+αlog(G/GSTC) (4)
Vmp(G，T)＝Vmp，STC+KV(T-TSTC)+Vtln(G/GSTC)+αlog(G/GSTC) (5)
Pmp(G，T)＝Vmp(G，T)Imp(G，T) (6)
Ppv(G，T)＝ηPmp(G，T) (7)
Wherein KV，KI，Vmp，STC，Imp，STCα, η are parameters that need to be fitted through historical data, GSTCAnd TSTCRespectively, the intensity of light and the temperature under standard test conditions, and GSTC＝1000W/m2，TSTC＝25℃；KVAnd KIAre the voltage and current coefficients, V, respectivelytIs the thermal voltage of a diode, and VtK is Boltzmann (Boltzmann) constant, q is the electronic charge, α is the photovoltaic panel coefficient, which is related to the intensity of illumination and temperature, and η is the conversion efficiency of the inverter.
Distributed photovoltaic power generation is assumed to operate at unity power factor, and therefore the output reactive power of these photovoltaic power generation is zero.
(3) Equivalent ZIP load
The complex load of an active power distribution network is replaced by a static ZIP load, and a commonly used ZIP load model is as follows:
wherein, api，aqi(i ═ 1,2,3) is a parameter that needs to be fitted through the history data, and V is the node voltage.
(II) uncertainty component
Due to the uncertainty in time and space of renewable energy power generation, uncertainty components are defined as follows:
wherein, less e { w, pv }, which respectively represent a wind turbine and a photovoltaic power generation system, PyRepresenting a deterministic component, derived from the associated mathematical equation,
an uncertainty component is represented. Taking a distributed fan as an example, fitting uncertainty components according to measured values comprises the following steps:
1) according to the formulas (2) and (3), the output power of the equivalent fan at a given wind speed can be obtained;
2) from equation (9), the error between the deterministic component and the measured value can be found
(i.e., uncertainty components), and then a Probability Density Histogram (PDH) of the errors is plotted;
3) fitting errors with probability distributions
The PDH of (1) can obtain a Probability Density Function (PDF) of the power output randomness component of the equivalent model.
Adopting a Gaussian distribution model to describe the probability characteristic of the random component of the power generation power of the renewable energy sources:
where μ and σ are the mean and variance, respectively, of the error from the actual measurement, parameters that need to be fitted through the historical data.
The active power distribution network model considering the randomness of the renewable energy power generation is specifically described below by taking two types of real data of a power network in a certain area as simulation objects.
The test system selected was an improved 63-node system based on an IEEE33 node distribution network and an IEEE 30 node transmission network. Wherein 10 fans and 10 photovoltaic power generation systems are connected to an IEEE33 node power distribution network, the numbers of the nodes where the fans and the photovoltaic power generation systems are located are shown in bold, and the power distribution network becomes an active power distribution network at the moment, as shown in FIG. 4. Meanwhile, the 33-node active distribution network is connected to the 20 nodes in the IEEE 30-node transmission network through a step-down transformer, as shown in fig. 5. The internal load of the active power distribution network is set as a ZIP load.
Wind speed data used for testing is from domestic wind power plants, and illumination and temperature data are from national renewable energy laboratory websites. The sampling interval time of the data samples is 5 minutes, and the simulation time is 24 h. Suppose that the equivalent model of the 33-node active power distribution network is replaced by the equivalent model shown in fig. 2. Meanwhile, the total power injected into the power distribution network, the total output power of the distributed fans, the total output power of the distributed photovoltaic power generation and the voltage amplitude of the node 31 are assumed. The node 42 is selected as an observation node, so that the wind speed of the fan at the node 42 and the illumination intensity and the ambient temperature of the photovoltaic power generation can be observed.
(1) Uncertainty of real system
The randomness of the renewable energy power generation in the actual system is obtained according to the existing wind speed and illumination intensity fitting of each place. Taking the wind speed as an example, the steps of knowing the observed wind speed of the observed wind turbine, and solving the sampling values of the wind speeds of n wind turbines at different installation sites and the active power of the wind turbines are as follows:
1) assuming that the wind speed of each fan obeys Weibull distribution, fitting the wind speed v according to historical wind speed datat(t ═ 1,2, …, n) edge distribution;
2) assuming that the relation between the probability distribution of the equivalent wind speed at a certain moment and the probability distribution of the unknown wind speed at the same moment obeys a Gaussian joint distribution function, and fitting the joint distribution function according to historical wind speed data;
3) when the equivalent wind speed is given, the conditional distribution function of the wind speed is calculated according to the joint distribution function and the edge distribution function;
4) sampling the condition distribution of the wind speed by using an LHS sampling method to obtain a plurality of groups of wind speed samples;
5) and inputting the wind speed samples into respective fan models to obtain the active power of each distributed fan.
(2) Precision evaluation
In order to evaluate the accuracy of the equivalence active power distribution network model considering randomness, an LHS method is adopted to sample uncertainty components based on Gaussian probability distribution, and uncertainty power components of 500 groups of equivalent fans and equivalent photovoltaic power generation are obtained. After the equivalent wind speed and the equivalent illumination intensity and temperature are given, the deterministic power components of the equivalent fan and the equivalent photovoltaic power generation can be obtained. The two components are added to obtain the active power.
Respectively measuring the voltage amplitude V of the corresponding node 31 in 500 groups of renewable energy power generation samples in the original model and the equivalent modelbAnd apparent power S injected at grid side node 31b. Then, V in the two models is comparedbAnd SbProbability distribution of (2).
Fig. 6 and 7 show the voltage amplitude and apparent power at the boundary node for the equivalent model and the real model, respectively. As can be seen from the figure, after the randomness of the renewable energy device is considered, the error of the equivalent model at the boundary node is smaller, and the accuracy of the equivalent model is higher. In addition, the probability density function of 1000 sets of boundary node voltage amplitude and apparent power at 8 hours was also evaluated.
As can be seen from FIGS. 8 and 9, the probability density functions of the voltage amplitude and the injected apparent power of the equivalent model and the actual model almost coincide, which shows that the equivalent model can replace the active distribution network very accurately. Similarly, in order to evaluate the accuracy of the probability power flow of the equivalent model in the power transmission network, the voltage amplitudes of the 30 nodes of the power transmission network in the two models and the error mean value of the branch power flow are compared.
From the above, compared with the prior art, the embodiment of the invention has the following beneficial effects: the method can realize equivalent modeling of random renewable energy sources such as wind power, photovoltaic and the like connected into the power distribution network, has obvious engineering practical value and has wide application prospect; the active power distribution network equivalent model considering the randomness of the renewable energy sources can consider the randomness of the spatial distribution, and the equivalent precision is superior to that of the equivalent model not considering the randomness of the spatial distribution.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.
Claims (10)
1. A construction method of an equivalent model of an active power distribution network comprises the following steps:
step S1, determining historical data of the active power distribution network to be equivalent, wherein the historical data comprises power grid network data to be equivalent and renewable energy historical data;
step S2, preprocessing the data;
s3, performing equivalent modeling on the to-be-equivalent network according to the active power distribution network equivalent model considering the randomness of the renewable energy power generation on the preprocessed data sample;
and step S4, analyzing the accuracy of the network equivalence before and after equivalence based on the equivalence result.
2. The method for constructing the equivalent model of the active power distribution network according to claim 1, wherein the step S3 specifically includes:
step S31, calculating a power balance equation of the active power distribution network;
s32, respectively establishing an equivalent fan model, an equivalent photovoltaic power generation system and an equivalent ZIP load model;
in step S33, the uncertainty component is equalized.
3. The method for constructing the equivalent model of the active power distribution network according to claim 2, wherein the power balance equation of the active power distribution network is as follows:
Pin+jQin＝-(Pw+jQw)-(PPv+jQpv)+(Pl+Ploss)+j(Ql+Qloss)
wherein, PinActive power, Q, injected for the gridinReactive power injected for the transmission grid; pwActive power, Q, for distributed fan outputwThe reactive power is output by the distributed fans; ppvActive power, Q, for distributed photovoltaic power generation outputpvReactive power output for distributed photovoltaic power generation; pl、PlossActive power, Q, being load and network loss, respectivelyl、QlossRespectively the reactive power of the load and the network losses.
4. The method for constructing the equivalent model of the active power distribution network according to claim 3, wherein the step S32 of establishing the equivalent fan model specifically comprises:
step S321, acquiring a fan power curve function:
wherein v isciFor cutting into the wind speed, vcoTo cut out wind speed, vrRated wind speed; prFor a rated power of the fan, q (v) is a function of flow and wind speed v;
step S322, fitting v with an arcsine functionr<v≤vcoAnd (3) obtaining a wind speed curve of the section, obtaining the wind speed v of the observation fan, and calculating the active power of the equivalent fan according to the following formula:
Pw＝ω1arcsin(ω2v+ω3)+ω4
wherein ω isi(i ═ 1,2,3,4) are parameters fitted through the history data.
5. The method for constructing the equivalent model of the active power distribution network according to claim 4, wherein the step S32 of establishing the equivalent photovoltaic power generation system specifically comprises:
the model of the photovoltaic power generation is expressed by adopting a model of maximum power tracking control, and the active power of the model is determined by the illumination intensity and the temperature:
Vmp(G,T)＝Vmp,STC+KV(T-TSTC)+Vtln(G/GSTC)+αlog(G/GSTC)
Vmp(G,T)＝Vmp,STC+KV(T-TSTC)+Vtln(G/GSTC)+αlog(G/GSTC)
Pmp(G,T)＝Vmp(G,T)Imp(G,T)
Ppv(G,T)＝ηPmp(G,T)
wherein, KV,KI,Vmp,STC,Imp,STCAnd α, η are parameters fitted through historical data, GSTCAnd TSTCRespectively the illumination intensity and the temperature under standard test conditions; kVAnd KIAre the voltage and current coefficients, V, respectivelytIs the thermal voltage of a diode, and VtK is the boltzmann constant, q is the electronic charge, α is the photovoltaic panel coefficient, and η is the conversion efficiency of the inverter.
6. The method for constructing the equivalent model of the active power distribution network according to claim 5, wherein the step S32 of establishing the ZIP load model specifically comprises:
the complex load of the active power distribution network is replaced by the static ZIP load:
wherein, api,aqi(i ═ 1,2,3) is a parameter fitted through the history data, and V is the node voltage.
7. The method for constructing the equivalent model of the active power distribution network according to claim 6, wherein the step S33 specifically comprises:
step S331, calculating to obtain the output power of the equivalent fan at a given wind speed according to the fan power curve function and the active power of the equivalent fan;
step S332 of calculating the error between the deterministic component and the measured value
Drawing a probability density histogram PDH of errors;
step S333, fitting error by probability distribution
And obtaining the probability density function PDF of the power output randomness component of the equivalent model.
8. The method for constructing the equivalent model of the active power distribution network according to claim 7, wherein a Gaussian distribution model is adopted to describe the probability characteristic of the random component of the power generated by the renewable energy sources:
where μ and σ are the mean and variance, respectively, of the error from the actual measurement, and are parameters fitted through the historical data.
Wherein, PyRepresenting a deterministic component, derived from the associated mathematical equation,
an uncertainty component is represented.
9. The method for constructing the equivalent model of the active power distribution network according to claim 4, wherein the steps of knowing the observed wind speed of the observation fan, and solving the sampled values of the wind speeds of the n fans at different installation sites and the active power of the fans are as follows:
assuming that the wind speed of each fan obeys Weibull distribution, fitting the wind speed v according to historical wind speed datat(t ═ 1,2, …, n) edge distribution;
assuming that the relation between the probability distribution of the equivalent wind speed at a certain moment and the probability distribution of the unknown wind speed at the same moment obeys a Gaussian joint distribution function, and fitting the joint distribution function according to historical wind speed data;
when the equivalent wind speed is given, the conditional distribution function of the wind speed is calculated according to the joint distribution function and the edge distribution function;
sampling the condition distribution of the wind speed by using an LHS sampling method to obtain a plurality of groups of wind speed samples;
and inputting the wind speed samples into respective fan models to obtain the active power of each distributed fan.
10. The method for constructing the equivalent model of the active power distribution network according to claim 1, wherein the step S4 specifically includes:
sampling uncertainty components based on Gaussian probability distribution by adopting an LHS method to obtain uncertainty power components of a plurality of groups of equivalent fans and equivalent photovoltaic power generation;
after the equivalent wind speed, the equivalent illumination intensity and the equivalent temperature are given, the deterministic power components of the equivalent fan and the equivalent photovoltaic power generation are obtained, and the two components are added to obtain the active power;
respectively measuring the voltage amplitude V of the corresponding nodes in the multiple groups of renewable energy power generation samples in the original model and the equivalent modelbAnd apparent power S injected by grid side nodebThen comparing V in the two modelsbAnd SbProbability distribution of (2).
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