CN106022970B - A kind of active distribution network measure configuration method counted and distributed generation resource influences - Google Patents
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Abstract
The invention discloses a kind of active distribution network measure configuration methods that meter and distributed generation resource influence, and estimate the operating status of power distribution network, determine the relationship of measurement and quantity of state；It determines distributed generation resource on-position, obtains the power output historical data of each distributed generation resource, the probability density function of simulation distribution formula power supply power output；Judge off-diagonal element characterization correlation whether related between adjoining distributed generation resource, and utilizing measurement covariance matrix；With the minimum target of weighting of measure configuration economy and two sub-goals of system estimation precision, the installation number of maximum allowable system estimation error constraints and measurement equipment is upper limit constraint, establish the mathematical model of measure configuration, traverse all optinal plans, until meeting maximum allowable system estimation error constraints or reaching the installation number upper limit of measurement equipment, final measure configuration scheme is determined.The present invention coordinates the relationship of measure configuration economy and estimated accuracy on the basis of considering that DG influences.
Description
Technical Field
The invention relates to an active power distribution network measurement configuration method considering distributed power supply influence.
Background
The massive access of Distributed Generation (DG) provides new challenges to the operation and scheduling of the distribution network, and the conventional distribution network will gradually change to an Active Distribution Network (ADN). The ADN is used as a development mode of a future intelligent power distribution network, and needs to actively manage access of a distributed power supply according to the actual running state of a power system, adaptively adjust a network, a power supply and a load, and realize safe and economic running of the system.
The ADN is constructed by firstly realizing the real-time perception of the system running state, and the state estimation serving as a situation perception tool core module is an important way for obtaining the ADN whole-network running state and is indispensable in the ADN. And due to the access of DGs in the ADN, uncertainty factors are increased, and the requirement on estimation precision is improved. Measurement configuration is an effective method for improving the state estimation accuracy, and is especially important in ADN. Due to the characteristics of the ADN, the conventional power distribution network measurement configuration method that ignores the DG influence is not applicable. Therefore, it is significant to deeply research the ADN measurement configuration method considering the DG influence.
At present, the measurement configuration method of the power distribution network has the following problems:
(1) the influence of DG is not considered, or a processing mode which is the same as that of the load is adopted, the DG output which is not configured and measured is taken as pseudo measurement, although the influence of DG is considered to a certain extent, the uncertainty of the DG output is converted into the uncertainty of a measurement system, and the influence of DG on ADN is not reflected;
(2) the influence of the output correlation between the adjacent zones DG is not taken into account;
(3) the state estimation accuracy is taken as a single target, and the relation between the measurement configuration economy and the estimation accuracy is not well coordinated.
Disclosure of Invention
In order to solve the problems, the invention provides an active power distribution network measurement configuration method considering the distributed power supply influence, and the method enables the measurement configuration scheme to well coordinate the relationship between the measurement configuration economy and the estimation precision on the basis of considering the DG influence.
In order to achieve the purpose, the invention adopts the following technical scheme:
an active power distribution network measurement configuration method considering distributed power supply influence comprises the following steps:
(1) estimating the running state of the power distribution network, and determining the relation between the measurement and the state quantity;
(2) determining access positions of distributed power supplies, acquiring output historical data of each distributed power supply, and simulating a probability density function of the output of each distributed power supply;
(3) judging whether the adjacent distributed power supplies are related or not, and representing the correlation by using off-diagonal elements of a measurement covariance matrix;
(4) establishing a mathematical model of the measurement configuration by taking the weighted minimum of two sub-targets of the economy of the measurement configuration and the estimation precision of the system as a target and taking the maximum allowable system estimation error constraint and the installation quantity of the measurement equipment as an upper limit constraint;
(5) and traversing all the alternatives until the maximum allowable system estimation error constraint is met or the upper limit of the installation quantity of the measuring equipment is reached, and determining the final measuring configuration scheme.
In the step (1), a weighted least square method is used as a state estimator to estimate the relationship between the quantity measurement and the state quantity, wherein the state vector selects the amplitude and the phase angle of each voltage except the phase angle of the balance node as the state quantity, and the measurement vector comprises real-time power measurement, voltage amplitude measurement, virtual measurement and pseudo measurement.
In the step (2), a probability density function of the distributed power output is simulated by using a Gaussian mixture model to represent uncertainty of the distributed power output.
In the step (2), the gaussian mixture model is a weighting of a plurality of gaussian components, and for a multidimensional random variable, a probability density function of the gaussian mixture model is a product of a weight of each gaussian component and a corresponding probability density function, wherein the weight, a mean value and a covariance of each gaussian component are obtained by a maximum expectation algorithm.
In the step (3), the adjacent zones mean that the DGs are within 100km from each other.
In the step (4), a mathematical model of the measurement configuration is established, and the objective function is the weighting of two sub-targets of the economy of the measurement configuration and the estimation precision of the system: the measurement configuration economy considers two measurement types of power measurement and current measurement, and the system estimation precision is represented by the total system estimation error.
In the step (4), the objective function is the sum of the product of the weight of the measurement expense and the total measurement configuration expense and the product of the weight of the system estimation precision and the total error of the system state estimation.
In the step (4), the total measurement allocation expense is the sum of the product of the relative price of the single power measurement and the installation quantity of the power measurement and the product of the relative price of the single current measurement and the installation quantity of the current measurement.
In the step (4), M is adoptedtThe mean value calculated by the sub-Monte Carlo method is used as the estimation value of each state variable to represent the uncertainty of the measuring system.
In the step (5), the specific method is as follows:
(5-1) fixing a measurement type, and calculating the measurement in allAnd storing the objective function value under the condition;
(5-2) changing the measurement type, calculating the measurement at the time when all the measurements are performedAnd storing the objective function value under the condition;
(5-3) comparing all the objective function values to ensure that the condition of the minimum objective function is the installation type and the position of the newly added measurement;
(5-4) repeating the step (5-1) to the step (5-3) until the maximum allowable system estimation error constraint is met or the upper limit of the installation quantity of the measuring equipment is reached;
wherein S represents the type and position of the new incremental measurement equipment, and S is the set of the installed incremental measurement equipment; the full set is all alternatives for metrology device type and location.
The invention has the beneficial effects that:
(1) in the measurement configuration process, the influence of the uncertainty of the DG output on the measurement configuration is considered, and the Gaussian mixture model can better simulate the uncertainty of the DG output;
(2) in the measurement configuration process, the invention considers the correlation of the output force between adjacent zones DG, and the final measurement configuration result is more practical;
(3) the measurement configuration method of the invention coordinates the relationship between the economy of measurement configuration and the estimation precision of the system, and improves the economy of measurement configuration.
Drawings
FIG. 1 is a flow chart of a design scheme provided by the present invention;
FIG. 2 is a sample histogram of a DG active output and a probability density function approximated by a GMM provided by the present invention;
FIG. 3 is a flow chart for determining a metrology configuration scheme based on heuristic algorithms as provided herein;
FIG. 4 is a wiring diagram of an IEEE33 node system provided by the present invention;
FIG. 5 illustrates configuring true and estimated values for each voltage amplitude based on final measurements;
FIG. 6 illustrates the present invention in which the true and estimated values for each voltage phase angle are configured based on final measurements.
The specific implementation mode is as follows:
the invention is further described with reference to the following figures and examples.
As shown in fig. 1, the active power distribution network measurement configuration method considering the distributed power source influence includes the following steps:
(1) selecting Weighted Least Square (WLS) as a state estimator;
(2) determining the position of the DG accessed to the active power distribution network, acquiring historical data of DG output, and simulating a probability density function of the DG output by using a Gaussian Mixture Model (GMM) to represent the uncertainty of the DG output;
(3) if the DG output forces of the adjacent zones have correlation, the DG output forces are represented by off-diagonal elements of a measurement covariance matrix;
(4) establishing a mathematical model of the measurement configuration, wherein an objective function is the weighting of two sub-targets of the economy of the measurement configuration and the estimation precision of the system: the measurement configuration economy considers two measurement types of power measurement and current measurement, and the system estimation precision is represented by the total system estimation error. The constraints include a maximum allowable system estimation error constraint and an upper limit constraint on the number of installations of the metrology equipment.
(5) And (4) determining a final measurement configuration scheme based on a heuristic algorithm, namely traversing all the alternatives, and selecting the newly added measurement positions and types to minimize the target function in the step (4). Repeating the process until the maximum allowable system estimation error constraint is met or the upper limit of the installation quantity of the measuring equipment is reached;
(6) and outputting the final measurement configuration scheme.
The relationship between the quantity measurement and the state quantity in the state estimation in the step (1) is as follows:
z＝h(x)+ε
in the formula: x is a state vector, and the amplitude and the phase angle of each voltage are selected as state quantities (except the voltage phase angle of a balance node); z is a measurement vector, and the measurement comprises real-time power measurement, voltage amplitude measurement, virtual measurement (zero injection node) and pseudo measurement (node load); h (x) is a measurement equation; ε is the measurement error vector, ε -N (0, R), and R is the covariance matrix of the measurement errors.
The gaussian mixture model in the step (2) is a weighting of a plurality of gaussian components, and for a multidimensional random variable, the probability density function of the GMM is:
in the formula: k is the total number of Gaussian components; Θ ═ wi,μi,ΣiI ═ 1,2, …, K }, which is a parameter of each gaussian component, wi、μiSum-sigmaiThe weight, mean and covariance of each gaussian component are determined by the maximum Expectation algorithm (EM).
Mean value μ of the entire GMMySum covariance ΣyRespectively as follows:
to characterize the uncertainty of DG contribution, each DG contribution is:
in the formula: mvnrnd (mu)i,Σi) Represents the order of (mu)i,Σi) A set of random numbers is taken for the multidimensional normal distribution of parameters.
In state estimation, ∑yThe diagonal elements of (a) are the corresponding diagonal elements in R.
In the step (3), if there is a correlation between the DG forces in the adjacent zones, the corresponding off-diagonal elements of the matrix R for each DG are characterized, that is:
Roff-diag＝Σy,ij
in the formula: sigmay，ijIs sigmayThe non-diagonal elements of (i), i.e., the covariance of the ith and jth DG contributions.
The objective function F and the constraint conditions in the step (4) are as follows:
min F＝wcost·Ccost+waccu·Esys
s.t.np+nc≤Nm
Esys≤Esys,max
in the formula, wcost＝0.4、waccu0.6, which is the weight of the measurement expense and the system estimation precision respectively; ccostAllocating expenses for the total measurement, Ccost＝cp·np+cc·nc，cp、ccRelative price, n, of a single power measurement and current measurement, respectivelypAnd ncThe number of installations, N, for power measurement and current measurement, respectivelym12, the upper limit of the installation quantity of the measuring equipment is; esysEstimating the total error for the system state, Esys,max0.0005, the maximum allowed systematic estimation error.
The present invention assumes that the relative prices of power measurement and current measurement are cp1.0 and ccThe relative price of each measuring device is only used for illustration, and in practice, the price of each device is determined according to factors such as a specific application scenario.
Wherein the total error of the system state estimation is represented by the sum of the estimation errors of the state variables:
in the formula: n is the number of state variables; x is the number ofi,trueAnd xi,estThe true value and the estimated value of the ith state variable are respectively.
To characterize the uncertainty of the metrology system, M is usedtThe mean value calculated by the sub-monte carlo method is used as an estimated value of each state variable, namely:
in the formula: mtThe number of Monte Carlo runs; x is the number ofi,jIs the estimate of the ith state variable of the jth Monte Carlo.
Considering that the estimation error is greatly different from the absolute value of the measurement expense, in order to ensure the functions of the two sub-targets in the target, respectively using E in the calculation processsys,maxAnd the sum of the unit prices of the measuring devices is used as a reference value, and the estimation precision and the unit prices of the measuring devices are unified, namely:
in the formula: e'sysThe error is comprehensively estimated for the per-unit system.
In the formula: c'iUnit price of each measuring equipment for per unit, corresponding to C in targetcostBecome C'cost。
The per-unit method has a guiding function to the measurement configuration: in the measurement configuration early stage, E'sysGreater numerical value, C'costSmaller, the measurement configuration is based on estimation accuracy; with the addition of measuring equipment, E'sysIs reduced by C'costThe later measurement configuration is economical.
The step of determining the measurement configuration scheme in the step (5) is as follows:
1) fixing a measurement type, calculating the measurement in allObjective function value F of the caseiAnd storing;
2) changing the measurement type, calculating the measurement at the timeObjective function value F of the caseiAnd storing;
3) comparison of all F aboveiThe condition that the objective function is minimum is the installation type and position of the newly added measurement.
4) And repeating the steps 1) -3) until the maximum allowable system estimation error constraint is met or the upper limit of the installation quantity of the measuring equipment is reached.
Wherein,s represents the type and position of the new incremental measurement equipment, and is the set of the installed measurement equipment;the complement of S, the full set is all alternatives for the type and location of the measurement device.
The sample histogram in fig. 2 is a simulated DG sample data obtained by sampling every half hour in a year, and the GMM gaussian component number of the simulated DG active power output is 4. Fig. 2 shows that the GMM curve is consistent with the fluctuation trend of the sample histogram, so that the GMM can better simulate the fluctuation of the DG output.
In addition, the real-time measurements in the final measurement configuration determined by the present invention include 6 power measurements and 3 voltage magnitude measurements. Power measurements are installed in branches 3, 8, 9, 15, 19 and 21, respectively; voltage amplitude measurements are installed at nodes 3, 7 and 12, respectively. The final relative measurement cost of the measurement configuration is 7.5, and the system estimation error is 4.0323 × 10-4. Fig. 5 and 6 show that the true values of the amplitude and phase angles of the voltages are substantially consistent with the estimated values, with higher accuracy, and satisfy the constraints, based on the final measurement configuration determined by the present invention. Therefore, the method and the device can give consideration to the estimation precision of the system on the basis of considering the DG influence, and simultaneously ensure the economy of the measurement configuration.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (9)
1. An active power distribution network measurement configuration method considering distributed power supply influence is characterized by comprising the following steps: the method comprises the following steps:
(1) estimating the running state of the power distribution network, and determining the relation between the measurement and the state quantity;
(2) determining access positions of distributed power supplies, acquiring output historical data of each distributed power supply, and simulating a probability density function of the output of each distributed power supply;
(3) judging whether the adjacent distributed power supplies are related or not, and representing the correlation by using off-diagonal elements of a measurement covariance matrix;
(4) establishing a mathematical model of the measurement configuration by taking the weighted minimum of two sub-targets of the economy of the measurement configuration and the estimation precision of the system as a target and taking the maximum allowable system estimation error constraint and the installation quantity of the measurement equipment as an upper limit constraint;
(5) and traversing all the alternatives until the maximum allowable system estimation error constraint is met or the upper limit of the installation quantity of the measuring equipment is reached, and determining the final measuring configuration scheme.
2. The method as claimed in claim 1, wherein the method comprises the following steps: in the step (1), a weighted least square method is used as a state estimator to estimate the relationship between the quantity measurement and the state quantity, wherein the state vector selects the amplitude and the phase angle of each voltage except the phase angle of the balance node as the state quantity, and the quantity measurement comprises real-time power measurement, voltage amplitude measurement, virtual measurement and pseudo measurement.
3. The method as claimed in claim 1, wherein the method comprises the following steps: in the step (2), a probability density function of the distributed power output is simulated by using a Gaussian mixture model to represent uncertainty of the distributed power output.
4. The method as claimed in claim 3, wherein the method comprises the following steps: in the step (2), the gaussian mixture model is a weighting of a plurality of gaussian components, and for a multidimensional random variable, a probability density function of the gaussian mixture model is a product of a weight of each gaussian component and a corresponding probability density function, wherein the weight, a mean value and a covariance of each gaussian component are obtained by a maximum expectation algorithm.
5. The method as claimed in claim 1, wherein the method comprises the following steps: in the step (4), a mathematical model of the measurement configuration is established, the objective function is the weighting of two sub-targets of the economy of the measurement configuration and the estimation precision of the system, the economy of the measurement configuration considers two measurement types of power measurement and current measurement, and the estimation precision of the system is represented by the total estimation error of the system.
6. The method as claimed in claim 5, wherein the method further comprises the following steps: the objective function is the product of the weight of the measurement expense and the total measurement configuration expense, and the sum of the product of the weight of the system estimation precision and the total error of the system state estimation.
7. The method as claimed in claim 6, wherein the method comprises the following steps: the total measurement allocation cost is the sum of the product of the relative price of a single power measurement and the installation quantity of the power measurement and the product of the relative price of a single current measurement and the installation quantity of the current measurement.
8. The method as claimed in claim 1, wherein the method comprises the following steps: in the step (4), M is adoptedtThe mean value calculated by the sub-Monte Carlo method is used as the estimation value of each state variable to represent the uncertainty of the measuring system.
9. The method as claimed in claim 1, wherein the method comprises the following steps: in the step (5), the specific method for determining the final measurement configuration scheme is as follows:
(5-1) fixing a measurement type, and calculating the measurement in allAnd storing an objective function value under the condition, wherein S represents the type and the position of the new increment measuring equipment, and S is the installed measuring equipmentA set of (a);the full set is all the optional schemes of the type and the position of the measuring equipment;
(5-2) changing the measurement type, calculating the measurement at the time when all the measurements are performedAnd storing the objective function value under the condition;
(5-3) comparing all the objective function values to ensure that the condition of the minimum objective function is the installation type and the position of the newly added measurement;
(5-4) repeating the steps (5-1) and (5-3) until the maximum allowable system estimation error constraint is met or the upper limit of the installation quantity of the measuring equipment is reached.
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