CN106022970B - A measurement and configuration method for active distribution network considering the impact of distributed power generation - Google Patents

Abstract

The invention discloses an active distribution network measurement and configuration method that takes into account the influence of distributed power sources, estimates the operating state of the distribution network, determines the relationship between measurement and state quantities; determines the access position of distributed power sources, and obtains each Historical data of distributed power output, simulate the probability density function of distributed power output; judge whether there is correlation between adjacent distributed The weighted minimum of the two sub-objectives of configuration economy and system estimation accuracy is the goal, the maximum allowable system estimation error constraint and the installation quantity of measurement equipment are the upper limit constraints, establish a mathematical model of measurement configuration, and traverse all alternatives until the maximum is satisfied. Allows the system to estimate error constraints or reach the upper limit of the installed number of measurement devices to determine the final measurement configuration. The present invention coordinates the relationship between measurement configuration economy and estimation accuracy on the basis of considering the influence of DG.

Description

A measurement and configuration method for active distribution network considering the impact of distributed power generation

technical field

The invention relates to an active distribution network measurement and configuration method considering the influence of distributed power sources.

Background technique

The massive access of distributed generation (DG) poses new challenges to the operation and scheduling of distribution networks, and traditional distribution networks will gradually transform into active distribution networks (ADN). As a development model of the future intelligent distribution network, ADN needs to actively manage the access of distributed power sources according to the actual operating status of the power system, and adaptively adjust the network, power source, and load to achieve safe and economical operation of the system.

The construction of ADN must first realize the real-time perception of system operation status, and the state estimation as the core module of the situation awareness tool is an important way to obtain the operation status of the entire network of ADN, which is indispensable in ADN. The access of DG in ADN increases the uncertainty factor and raises the requirement for estimation accuracy. As an effective method to improve the accuracy of state estimation, measurement configuration is particularly important in ADN. The characteristics of ADN make the traditional distribution network measurement and configuration method that ignores the influence of DG no longer applicable. Therefore, it is of great significance to deeply study the measurement configuration method of ADN considering the influence of DG.

At present, the measurement configuration method of distribution network has the following problems:

(1) The influence of DG is not considered, or the same processing method as the load is adopted, and the DG output without measurement is regarded as a pseudo-measurement. Although this method considers the influence of DG to a certain extent, it does not take The certainty is transformed into the uncertainty of the measurement system, which does not reflect the impact of DG itself on ADN;

(2) The influence of the output correlation between DG in adjacent areas is not considered;

(3) With the single goal of state estimation accuracy, the relationship between measurement configuration economy and estimation accuracy is not well coordinated.

Contents of the invention

In order to solve the above problems, the present invention proposes an active distribution network measurement configuration method that takes into account the influence of distributed power sources. This method enables the measurement configuration scheme to coordinate the measurement configuration economy and Estimated accuracy relationship.

In order to achieve the above object, the present invention adopts the following technical solutions:

A method for measuring and configuring an active distribution network considering the influence of distributed power sources, comprising the following steps:

(1) Estimate the operating state of the distribution network, and determine the relationship between the quantity measurement and the state quantity;

(2) Determine the location of the distributed power supply, obtain the output history data of each distributed power supply, and simulate the probability density function of the distributed power supply output;

(3) Determine whether adjacent distributed power sources are related, and use the off-diagonal elements of the measurement covariance matrix to characterize the correlation;

(4) Taking the weighted minimum of the two sub-objectives of measurement configuration economy and system estimation accuracy as the goal, the maximum allowable system estimation error constraint and the installation quantity of measurement equipment as the upper limit constraint, establish a mathematical model of measurement configuration;

(5) Traverse all the alternatives until the maximum allowable system estimation error constraint is satisfied or the upper limit of the installed quantity of measurement equipment is reached, and the final measurement configuration scheme is determined.

In described step (1), utilize weighted least squares method as the relationship between state estimator estimator measurement and state quantity, wherein, state vector selects the magnitude and phase angle of each voltage except the phase angle of balance node as State quantities and measurement vectors include real-time power measurement and voltage amplitude measurement, virtual measurement and pseudo measurement.

In the step (2), the Gaussian mixture model is used to simulate the probability density function of the distributed power output to characterize the uncertainty of the distributed power output.

In described step (2), described Gaussian mixture model is the weighting of a plurality of Gaussian components, for a multidimensional random variable, the probability density function of its Gaussian mixture model is the product of the weight of each Gaussian component and corresponding probability density function, The weight, mean and covariance of each Gaussian component are obtained by the maximum expectation algorithm.

In the step (3), the adjacent zone refers to the distance between DGs within 100km.

In the step (4), the mathematical model of the measurement configuration is established, and the objective function is the weighting of the two sub-objectives of measurement configuration economy and system estimation accuracy: the measurement configuration economy considers both power measurement and current measurement Measurement type, the system estimation accuracy is characterized by the total error of the system estimation.

In the step (4), the objective function is the product of the weight of the measurement expense and the total measurement allocation expense, and the sum of the product of the weight of the system estimation accuracy and the total error of the system state estimation.

In the step (4), the total measurement allocation cost is the sum of the product of the relative price of a single power measurement and the installed quantity of power measurement and the product of the relative price of a single current measurement and the installed quantity of current measurement.

In the step (4), the mean value calculated by M _t Monte Carlo method is used as the estimated value of each state variable to characterize the uncertainty of the measurement system.

In described step (5), concrete method is:

(5-1) Fix a measurement type, calculate the measurement in all The value of the objective function in the case and save;

(5-2) Change the measurement type, calculate the measurement at this time in all The value of the objective function in the case and save;

(5-3) Comparing all the above objective function values, the situation where the objective function is minimized is the installation type and location of the new incremental measurement;

(5-4) Repeat steps (5-1)-step (5-3) until the maximum allowable system estimation error constraint is met or the upper limit of the installed quantity of measuring equipment is reached;

Among them, s represents the type and location of the new measuring equipment, S is the set of installed measuring equipment; the complete set is all alternatives of the measuring equipment type and location.

The beneficial effects of the present invention are:

(1) In the process of measurement configuration, the present invention considers the influence of DG output uncertainty on measurement configuration, and the Gaussian mixture model can better simulate the uncertainty of DG output;

(2) In the process of measurement configuration, the present invention considers the correlation of output between adjacent zones DG, and the final measurement configuration result is more realistic;

(3) The measurement configuration method of the present invention coordinates the relationship between the measurement configuration economy and the system estimation accuracy, and improves the measurement configuration economy.

Description of drawings

Fig. 1 is the flow chart of the design scheme provided by the present invention;

Fig. 2 is a sample histogram of a certain DG active power output provided by the present invention and a probability density function approximated by GMM;

Fig. 3 is a flow chart of determining a measurement configuration scheme based on a heuristic algorithm provided by the present invention;

Fig. 4 IEEE33 node system wiring diagram provided by the present invention;

Fig. 5 provides the true value and estimated value of each voltage amplitude based on the final measurement configuration provided by the present invention;

FIG. 6 shows the true value and estimated value of each voltage phase angle based on the final measurement configuration provided by the present invention.

Detailed ways:

The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

As shown in Figure 1, the active distribution network measurement configuration method considering the impact of distributed power includes the following steps:

(1) Select Weighted Least Squares (WLS) as the state estimator;

(2) Determine the location where the DG is connected to the active distribution network, obtain the historical data of each DG output, and use the Gaussian mixture model (GMM) to simulate the probability density function of the DG output to represent the uncertainty of the DG output;

(3) If there is a correlation between the DG output in the adjacent zone, it is represented by the off-diagonal elements of the measurement covariance matrix;

(4) Establish a mathematical model of measurement configuration. The objective function is the weighting of the two sub-objectives of measurement configuration economy and system estimation accuracy: measurement configuration economy takes into account two types of measurement, power measurement and current measurement, and the system The estimation accuracy is characterized by the system estimation total error. The constraints include the maximum allowable system estimation error constraint and the upper limit constraint of the installed quantity of measuring equipment.

(5) Determine the final measurement configuration scheme based on the heuristic algorithm, that is, go through all the optional schemes, and select the location and type of the new measurement to minimize the objective function of the step (4). Repeat the process until the maximum allowable system estimation error constraint is met or the upper limit of the installed number of measuring devices is reached;

(6) Outputting the final measurement configuration scheme.

The relationship between the quantity measurement and the state quantity in the aforementioned step (1) state estimation is:

z=h(x)+ε

In the formula: x is the state vector, and the amplitude and phase angle of each voltage are selected as the state quantity (except the voltage phase angle of the balance node); z is the measurement vector, and the measurement includes real-time power measurement and voltage amplitude measurement, Virtual measurement (zero injection node) and pseudo measurement (node load); h(x) is the measurement equation; ε is the measurement error vector, ε～N(0, R), and R is the covariance matrix of the measurement error.

The Gaussian mixture model in the aforementioned step (2) is the weighting of multiple Gaussian components. For a multidimensional random variable, the probability density function of its GMM is:

In the formula: K is the total number of Gaussian components; Θ={w _i ,μ _i ,Σ _i ,i=1,2,…,K} is the parameter of each Gaussian component, and w _i , μ _i and Σ _i are respectively The weight, mean and covariance of each Gaussian component, these parameters can be obtained by the maximum expectation algorithm (Expectation maximization, EM).

The mean μ _y and covariance Σ _y of the entire GMM are:

In order to characterize the uncertainty of DG output, each time the state estimation is calculated, the output of each DG is:

In the formula: mvnrnd(μ _i ,Σ _i ) means to take a set of random numbers from the multidimensional normal distribution with (μ _i ,Σ _i ) as parameters.

In state estimation, the diagonal elements of Σ _y are the corresponding diagonal elements in R.

In the aforementioned step (3), if there is a correlation between the output of DGs in the adjacent zone, it will be represented by the off-diagonal elements corresponding to each DG in the matrix R, namely:

R _off-diag = Σ _y,ij

In the formula: Σ _{y, ij} is the off-diagonal element of Σ _y , that is, the covariance of the i-th and j-th DG output.

The objective function F and constraints of the aforementioned step (4) are as follows:

min F＝w _cost C _cost +w _accu E _sys

stn _p +n _c ≤ N _m

E _sys ≤ E _sys,max

In the formula, w _cost ＝0.4, w _accu ＝0.6, which are the weights of measurement cost and system estimation accuracy respectively; C _cost is the total measurement configuration cost, C _cost ＝c _p n _p +c _c n _c , c _p and c _c are the relative prices of a single power measurement and current measurement respectively, n _p and n _c are the installation quantities of power measurement and current measurement respectively, and N _m =12 is the upper limit of the installation quantity of measurement equipment; E _sys is the total error of system state estimation, and E _sys,max =0.0005 is the maximum allowable system estimation error.

The present invention assumes that the relative prices of power measurement and current measurement are c _p = 1.0 and c _c = 0.5 respectively, and the relative prices of each measurement equipment are only used for illustration, and the actual prices of each equipment also depend on specific application scenarios, etc. factor is determined.

Among them, the total error of the system state estimation is represented by the sum of the estimation errors of each state variable:

In the formula: n is the number of state variables; x _i,true and x _i,est are the true value and estimated value of the i-th state variable respectively.

In order to characterize the uncertainty of the measurement system, the mean value calculated by M _t Monte Carlo method is used as the estimated value of each state variable, namely:

In the formula: M _t is the running times of Monte Carlo; x _i,j is the estimated value of the i-th state variable of the j-th Monte Carlo.

Considering that there is a large difference between the estimation error and the absolute value of the measurement expenditure, in order to ensure the role of the two sub-objectives in the target, in the calculation process, the sum of E _{sys, max} and the unit price of each measurement equipment is used as the benchmark value, and the The estimated accuracy and the unit price of the measuring equipment are unitized, namely:

In the formula: _E'sys is the per-unit integrated estimation error of the system.

In the formula: c' _i is the unit price of each measurement equipment per unit. Correspondingly, the C _cost in the target becomes C' _cost .

The above per unitization method has a guiding effect on the measurement configuration: in the early stage of the measurement configuration, the value of E'sys is large, and the value of C' _cost is small, and the measurement configuration is mainly based on the estimation accuracy; with the increase of measurement equipment, E' _sys ' _sys decreases, C' _cost increases, and the post-measurement configuration is mainly based on economy.

The steps for determining the measurement configuration scheme in the aforementioned step (5) are as follows:

1) Fix a measurement type and calculate the measurement in all The objective function value F _i in the case is saved;

2) Change the measurement type, and calculate the measurement at this time in all The objective function value F _i in the case is saved;

3) Comparing all the above F _i s, the condition that minimizes the objective function is the installation type and location of the new incremental measurement.

4) Repeat steps 1)-3) until the maximum allowable system estimation error constraint is met or the upper limit of the installed quantity of measuring equipment is reached.

in, s represents the type and location of the new measuring equipment, and S is the set of installed measuring equipment; is the complement of S, and the complete set is all alternatives for the type and location of the measuring equipment.

The sample histogram in Figure 2 is the simulated DG sample data obtained by sampling every half hour for a year, and the number of GMM Gaussian components of the simulated DG active output is 4. Figure 2 shows that the GMM curve is consistent with the fluctuation trend of the sample histogram, so GMM can better simulate the fluctuation of DG output.

In addition, the real-time measurement in the final measurement configuration determined by the present invention includes 6 power measurements and 3 voltage amplitude measurements. The power measurement is installed in branches 3, 8, 9, 15, 19 and 21 respectively; the voltage amplitude measurement is installed in 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 . Figures 5 and 6 show that based on the final measurement configuration determined by the present invention, the true values of the voltage amplitudes and phase angles are basically consistent with the estimated values, with high precision and satisfying constraints. It can be seen that, on the basis of considering the influence of DG, the present invention can take into account the estimation accuracy of the system, and at the same time ensure the economy of the measurement configuration.

Although the specific implementation of the present invention has been described above in conjunction with the accompanying drawings, it does not limit the protection scope of the present invention. Those skilled in the art should understand that on the basis of the technical solution of the present invention, those skilled in the art do not need to pay creative work Various modifications or variations that can be made are still within the protection scope of the present invention.

Claims (9)

1. A method for measuring and configuring active distribution networks considering the impact of distributed power sources, characterized in that: comprising the following steps: (1) Estimate the operating state of the distribution network, and determine the relationship between the quantity measurement and the state quantity; (2) Determine the location of the distributed power supply, obtain the output history data of each distributed power supply, and simulate the probability density function of the distributed power supply output; (3) Determine whether adjacent distributed power sources are related, and use the off-diagonal elements of the measurement covariance matrix to characterize the correlation; (4) Taking the weighted minimum of the two sub-objectives of measurement configuration economy and system estimation accuracy as the goal, the maximum allowable system estimation error constraint and the installation quantity of measurement equipment as the upper limit constraint, establish a mathematical model of measurement configuration; (5) Traverse all the alternatives until the maximum allowable system estimation error constraint is satisfied or the upper limit of the installed quantity of measurement equipment is reached, and the final measurement configuration scheme is determined. 2. A kind of active distribution network measurement and configuration method considering the impact of distributed power sources as claimed in claim 1, characterized in that: in the step (1), the weighted least squares method is used as the state estimator estimator The relationship between measurement and state quantity, where the state vector selects the amplitude and phase angle of each voltage except the phase angle of the balance node as the state quantity, and the quantity measurement includes real-time power measurement and voltage amplitude measurement, virtual quantity measurement and pseudo-measurement. 3. A method for measuring and configuring active distribution networks considering the impact of distributed power sources as claimed in claim 1, characterized in that: in the step (2), the Gaussian mixture model is used to simulate the probability of distributed power output Density function to represent the uncertainty of distributed power output. 4. A kind of active distribution network measurement and configuration method considering the impact of distributed power sources as claimed in claim 3, characterized in that: in the step (2), the Gaussian mixture model is a plurality of Gaussian components Weighting, for a multidimensional random variable, the probability density function of its Gaussian mixture model is the product of the weight of each Gaussian component and the corresponding probability density function, where the weight, mean and covariance of each Gaussian component are obtained by the maximum expectation algorithm. 5. A kind of active distribution network measurement and configuration method considering the impact of distributed power sources as claimed in claim 1, characterized in that: in the step (4), the mathematical model of the measurement configuration is established, and the objective function is The weighting of the two sub-goals of measurement configuration economy and system estimation accuracy. The measurement configuration economy considers two measurement types, power measurement and current measurement, and the system estimation accuracy is characterized by the total error of system estimation. 6. A method for measuring and configuring active distribution networks considering the influence of distributed power sources as claimed in claim 5, characterized in that: the objective function is the product of the weight of the measuring expense and the total measuring and configuring expense, And the sum of the product of the weight of the system estimation accuracy and the total error of the system state estimation. 7. An active distribution network measurement and configuration method that takes into account the influence of distributed power sources as claimed in claim 6, wherein the total measurement configuration cost is the relative price and power measurement of a single power measurement The sum of the product of the installation quantity of the current measurement and the product of the relative price of a single current measurement and the installation quantity of the current measurement. 8. A kind of active distribution network survey configuration method that considers the influence of distributed power supply as claimed in claim 1, is characterized in that: in described step (4), adopt the mean value calculated by M _t time Monte Carlo method As the estimated value of each state variable, to characterize the uncertainty of the measurement system. 9. A kind of active distribution network measurement and configuration method considering the impact of distributed power sources as claimed in claim 1, characterized in that: in the step (5), the specific method for determining the final measurement configuration scheme is: (5-1) Fix a measurement type, calculate the measurement in all The value of the objective function under the condition is saved, wherein, s represents the type and location of the new measuring equipment, and S is the set of installed measuring equipment; is the complement of S, and the complete set is all alternatives for the type and location of the measuring equipment; (5-2) Change the measurement type, calculate the measurement at this time in all The value of the objective function in the case and save; (5-3) Comparing all the above objective function values, the situation where the objective function is minimized is the installation type and location of the new incremental measurement; (5-4) Steps (5-1)-step (5-3) are repeated until the maximum allowable system estimation error constraint is satisfied or the upper limit of the installed quantity of measuring equipment is reached.