CN111342458A - Method and device for two-stage reconstruction of power distribution network based on ordered optimization algorithm - Google Patents
Method and device for two-stage reconstruction of power distribution network based on ordered optimization algorithm Download PDFInfo
- Publication number
- CN111342458A CN111342458A CN202010221029.8A CN202010221029A CN111342458A CN 111342458 A CN111342458 A CN 111342458A CN 202010221029 A CN202010221029 A CN 202010221029A CN 111342458 A CN111342458 A CN 111342458A
- Authority
- CN
- China
- Prior art keywords
- distribution network
- power distribution
- load data
- time period
- reconstruction
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The application provides a method and a device for two-stage reconstruction of a power distribution network based on an ordered optimization algorithm, and relates to the field of electric power. The method comprises the following steps: obtaining load data in a future time period; load clustering is carried out on load data in a future time period, a static reconstruction model of the power distribution network is constructed, an optimal solution corresponding to each clustering center is obtained, short-time prediction of the load data is carried out on the power distribution network in the future time period, the dissimilarity degree between the short-time prediction load data and load data of the C clustering centers is calculated by taking the dissimilarity degree as an index, the static reconstruction model corresponding to the clustering center with the minimum dissimilarity degree is determined as a result of power distribution network reconstruction in a first preset time period, and the power distribution network is reconstructed according to the result. The invention minimizes the network loss after the dynamic reconfiguration of the power distribution network in actual operation, has less reconfiguration calculation amount, simple operation and less reconfiguration calculation time, can well adapt to the dynamically changed load and has higher practical value.
Description
Technical Field
The invention relates to the field of electric power, in particular to a method and a device for two-stage reconstruction of a power distribution network based on an ordered optimization algorithm.
Background
The reconstruction of the power distribution network achieves the aim of optimizing the power distribution network by changing the states of the section switch and the interconnection switch in the power distribution network so as to control the network topology structure of the power distribution network. Power distribution network reconfiguration can be generally divided into static reconfiguration based on a certain time point and dynamic reconfiguration based on a time interval (generally one day). Intermittent power sources such as photovoltaic power, wind power and the like have obvious time-varying property and uncertainty, and the time-varying property of the load makes dynamic reconstruction more complicated.
Most of the existing researches on the dynamic reconstruction of the power distribution network consider the optimal division of reconstruction time periods under different conditions, and all the researches are dynamic reconstruction performed under a known daily load curve. However, in actual operation, the real-time values of the load and the distributed power sources (intermittent power sources such as photovoltaic power, wind power and the like) are greatly different from the predicted values.
This may result in that the dynamic reconfiguration of the distribution network in actual operation is not optimal, or the network loss of the distribution network is high, or the time for calculating the optimal reconfiguration is long, and the dynamically changing load cannot be well adapted.
Disclosure of Invention
In view of the above problems, the present invention provides a method and an apparatus for two-stage reconstruction of a power distribution network based on an ordered optimization algorithm, which partially solve the above problems.
The embodiment of the invention provides a method for two-stage reconstruction of a power distribution network based on an ordered optimization algorithm, which is applied to a power distribution network server, wherein the power distribution network server is used for controlling the reconstruction of the power distribution network, and the method comprises the following steps:
load prediction is carried out on the load of the power distribution network in the future time period, and load data in the future time period are obtained;
carrying out load clustering on load data in a future time period by using an optimal fuzzy C-means clustering method to obtain load data of C clustering centers;
constructing a static reconstruction model of the power distribution network aiming at the load data of the C clustering centers, wherein the static reconstruction model is an optimal static reconstruction model of the power distribution network with the minimum network loss as a target;
solving the static reconstruction model by using an ordered optimization algorithm to obtain an optimal solution corresponding to each clustering center, wherein the optimal solution represents a static reconstruction result of the power distribution network with the minimum network loss as a target;
in a future time period, periodically performing short-time prediction on load data of the power distribution network by taking a preset time period as a cycle, wherein the short-time prediction is prediction on dynamic load data of the power distribution network in a first preset time period before the first preset time;
calculating the dissimilarity degree between the load data of the first preset time period predicted in short time and the load data of the C clustering centers by taking the dissimilarity degree as an index;
determining a static reconstruction model corresponding to the clustering center with the minimum dissimilarity degree as a result of the power distribution network reconstruction within the first preset time period;
and when the power distribution network runs to the first preset time period, reconstructing the power distribution network according to the result.
Optionally, the power distribution network comprises: and the interconnection switch solves the static reconstruction model by using an ordered optimization algorithm to obtain an optimal solution corresponding to each clustering center, and comprises the following steps:
for each cluster center, performing the following steps:
step 1: obtaining N samples based on the static reconstruction model, wherein each sample in the N samples represents the combination condition of a contact switch during one static reconstruction of the power distribution network;
step 2: estimating the performance of the N samples through a rough and fast calculation model, wherein the performance is estimated by taking the minimum network loss of the power distribution network as a target;
and step 3: the performance obtained by estimation is arranged according to the ascending order, an order performance curve is drawn, and the order performance curve category of the problem and the noise level of the original model are estimated;
and 4, step 4: the first S observed samples of N estimated by the original model are selected as the selected subset S. The number of samples of the subset S depends on a regression constant of the noise level k of the ordered performance curve and the number G of subsets G, the subset G being a set of true best solutions of the static reconstruction model, the solutions of the N samples that accurately calculate performance and are ordered first G% being defined as the true best solutions;
and 5: and taking the intersection of the subset S and the subset G, wherein the sample S in the intersection is the optimal solution corresponding to the clustering center.
Optionally, the forming of the subset G comprises the steps of:
step 1: calculating the performance of the N samples by rough model estimation;
step 2: performance calculated according to ascending order;
optionally, the intersection of the subset S and the subset G is determined by the number of alignment elements i, and the expression of the probability that the subset S and the subset G have at least k alignment elements is:
wherein G is the number of said subsets G; s is the number of subsets S.
Optionally, the value of the number of samples in the N samples is obtained according to the following formula:
N≥ln(1-P)/ln(1-α％)
in this equation, P represents the probability of an event in which at least one of the N samples falls within the top α% of the entire solution space;
the expression of P is:
P＝1-(1-α％)N
optionally, the number of subsets S is obtained according to the following formula:
in the formula, Z represents the number of the subsets S, Z1、Z2、Z3、Z4A regression constant depending on the noise level k of the ordered performance curve and the number G of subsets G.
Optionally, calculating a dissimilarity degree between the load data of the first preset time period predicted in the short time and the load data of the C cluster centers includes:
standardizing the load data of the C clustering centers, and compressing the load data in a [0, 1] ratio space;
calculating the complex power X of each node of the power distribution network in the first preset time period by using an Euclidean distance formulakObtaining C dissimilarity values through the similarity between the C clustering centers and the C clustering centers;
wherein the normalized formula is:
′
in the formula: x is the number oftiRepresenting load data, x, corresponding to node i during a period ttiIndicating normalized load data corresponding to the node i in the t period;
the Euclidean distance formula is as follows:
in the formula: dmIndicating Euclidean distance, VmIs the m-th cluster center, Vm∈Cn，T，n。
Optionally, the static reconstruction model targets minimum loss and satisfies a constraint condition, where the constraint condition includes: the method comprises the following steps of (1) power flow constraint conditions, voltage constraint conditions of all nodes in the power distribution network, capacity constraint conditions of all branches in the power distribution network and network topology constraint conditions of the power distribution network;
the function expression taking the minimum network loss as a target is as follows:
in the formula: plossRepresents the loss of the network, NlRepresenting each branch set, and l representing each branch number; k is a radical oflRepresents the switching state of branch l; r islRepresents the resistance of branch l; plAnd QlRespectively representing active power and reactive power flowing through a branch circuit l; u shapelRepresenting the node voltage of the end node of branch l.
Optionally, the power flow constraint condition is:
in the formula, PiAnd QiIs the injected active and reactive power of node i; viAnd deltaiIs the magnitude and phase angle of the voltage at node i; vjIs the magnitude and phase angle of the voltage at node j; y isijAnd thetaijThe branch admittance and the phase angle difference between the node i and the node j are respectively;
the voltage constraint conditions of each node in the power distribution network are as follows:
Vmin≤Vn≤Vmax
in the formula, VminAnd VmaxRespectively the minimum value and the maximum value of the node voltage amplitude, and the values are [0.9, 1.1 ]]。
The capacity constraint conditions of each branch in the power distribution network are as follows:
|Sl|≤Sl max
in the formula, SlDenotes, | SlI and SlmaxRespectively the transmission power of the branch circuit l and the maximum transmission power of the branch circuit l;
the network topology constraint conditions of the power distribution network are as follows:
the power distribution network is designed in a closed loop mode and operates in an open loop mode, and the power distribution network after reconstruction must be radial.
The embodiment of the present invention further provides a device, which is applied to a grid server, where the grid server is configured to control reconfiguration of the power distribution network, and the device includes:
the future load prediction module is used for predicting the load of the power distribution network in the future time period to obtain load data in the future time period;
the clustering module is used for carrying out load clustering on the load data in the future time period by using an optimal fuzzy C-means clustering method to obtain load data of C clustering centers;
the construction model module is used for constructing a static reconstruction model of the power distribution network aiming at the load data of the C clustering centers, and the static reconstruction model is an optimal static reconstruction model of the power distribution network with the minimum network loss as a target;
the solving module is used for solving the static reconstruction model by using an ordered optimization algorithm to obtain an optimal solution corresponding to each clustering center, and the optimal solution represents a static reconstruction result of the power distribution network with the minimum network loss as a target;
the short-time load prediction module is used for periodically performing short-time prediction on load data of the power distribution network within a future time period by taking a preset time period as a cycle, wherein the short-time prediction is prediction on dynamic load data of the power distribution network within a first preset time period before the first preset time;
the dissimilarity degree calculating module is used for calculating dissimilarity degrees between the load data of the first preset time period predicted in short time and the load data of the C clustering centers by taking the dissimilarity degrees as indexes;
a result determining module, configured to determine that the static reconstruction model corresponding to the cluster center with the minimum dissimilarity is a result of reconstruction of the power distribution network within the first preset time period;
and the reconstruction module is used for reconstructing the power distribution network according to the result when the power distribution network runs to the first preset time period.
Optionally, the power distribution network comprises: and the communication switch is used for specifically executing the following steps by the solving module aiming at each clustering center:
step 1: obtaining N samples based on the static reconstruction model, wherein each sample in the N samples represents the combination condition of a contact switch during one static reconstruction of the power distribution network;
step 2: estimating the performance of the N samples through a rough and fast calculation model, wherein the performance is estimated by taking the minimum network loss of the power distribution network as a target;
and step 3: the performance obtained by estimation is arranged according to the ascending order, an order performance curve is drawn, and the order performance curve category of the problem and the noise level of the original model are estimated;
and 4, step 4: the first S observed samples of N estimated by the original model are selected as the selected subset S. The number of samples of the subset S depends on a regression constant of the noise level k of the ordered performance curve and the number G of subsets G, the subset G being a set of true best solutions of the static reconstruction model, the solutions of the N samples that accurately calculate performance and are ordered first G% being defined as the true best solutions;
and 5: and taking the intersection of the subset S and the subset G, wherein the sample S in the intersection is the optimal solution corresponding to the clustering center.
Optionally, the forming of the subset G comprises the steps of:
step 1: calculating the performance of the N samples by rough model estimation;
step 2: the calculated properties are arranged in ascending order.
By adopting the two-stage reconstruction method of the power distribution network based on the ordered optimization algorithm, the dynamic reconstruction of the power distribution network in the future time period is converted into the static reconstruction model of the corresponding C clustering centers before the future time period to obtain the optimal solution of the future time period, namely, the reconstruction with the minimum network loss of the power distribution network in the future time period, short-time prediction is periodically carried out in the future time period, the dissimilarity degree of the load data predicted in the short time period and the load data of the C clustering centers is taken as an index, and the states of all switches of the power distribution network in the next time period are reconstructed according to the static reconstruction model corresponding to the clustering center with the minimum dissimilarity degree. According to the method, reconstruction calculation with complex process and great calculation amount is not needed for real-time load data, only the load state of the cluster center with the minimum corresponding dissimilarity degree needs to be found out, and then the power distribution network is reconstructed by utilizing the scheme of the static reconstruction model corresponding to the cluster center, so that the network loss after the dynamic reconstruction of the power distribution network in actual operation is minimized, the reconstruction calculation amount is small, the operation is simple, the reconstruction calculation time is short, the dynamically-changed load can be well adapted, and the method has high practical value.
Drawings
FIG. 1 is a flowchart of a method for two-stage reconstruction of a power distribution network based on an ordered optimization algorithm according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an architecture of a power distribution network according to an embodiment of the present invention;
FIG. 3 is a graph showing the real component of the load of 9 cluster centers according to an embodiment of the present invention;
FIG. 4 is a reactive component of the load for 9 cluster centers according to an embodiment of the present invention;
fig. 5 is a block diagram of a device for two-stage reconstruction of a power distribution network based on an ordered optimization algorithm.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below. It should be understood that the specific embodiments described herein are merely illustrative of the invention, but do not limit the invention to only some, but not all embodiments.
Referring to fig. 1, a flowchart of a method for two-stage reconstruction of a power distribution network based on an ordered optimization algorithm in an embodiment of the present invention is shown, the method is applied to a power distribution network server, the power distribution network server is used for controlling reconstruction of the power distribution network, and the method for two-stage reconstruction of the power distribution network based on the ordered optimization algorithm includes:
step 101: and load prediction is carried out on the load of the power distribution network in the future time period to obtain load data in the future time period.
In the embodiment of the invention, the reconstruction of the power distribution network is generally completed by a power grid server or a controller and the like, and the purpose of optimizing the power distribution network is achieved by changing the states of a section switch and a contact switch in the power distribution network so as to control the network topology structure of the power distribution network, so that the network loss of the power distribution network in the current operation period is minimum.
In the embodiment of the present invention, load prediction needs to be performed on loads of a power distribution network in a future time period to obtain load data in the future time period, for example: according to a big data system, or historical data, curves and the like, for a future week of the power distribution network: load prediction is carried out on the load in a time period of 7 by 24 hours to 168 hours, and then load data in the future 168 hours can be obtained.
Step 102: and carrying out load clustering on the load data in the future time period by using an optimal fuzzy C-means clustering method to obtain the load data of C clustering centers.
In the embodiment of the invention, after the load data in the future time period is obtained, the load data in the future time period can be subjected to load clustering by using an optimal fuzzy C-means clustering method to obtain the load data of C clustering centers. For example: load clustering is carried out on the load data in the future 168 hours by using an optimal fuzzy C-means clustering method to obtain load data of 9 clustering centers, and the 9 clustering centers represent the types of the load data of all nodes in the distribution network in each time period in the future 168 hours. Specifically, the method comprises the following steps: if 0-30 hours exist, the 1 st clustering center represents a first state that the load data of all nodes in the power distribution network are similar in the 30 hours, and only one No. 1 clustering center is needed to represent the load data; 31-83 hours, the 2 nd clustering center represents that the load data of all nodes in the power distribution network are in a similar second state in the 52 hours, and only one No. 2 clustering center is needed to represent the load data; and so on until cluster center number 9. Certainly, the number of the clustering centers can be defined according to the requirements of the users, if the users need to perform power distribution network reconstruction more accurately, more clustering centers can be obtained, and it can be understood that the more clustering centers, the larger the corresponding calculation amount is.
Step 103: and constructing a static reconstruction model of the power distribution network aiming at the load data of the C clustering centers, wherein the static reconstruction model is an optimal static reconstruction model of the power distribution network with the minimum network loss as a target.
In the embodiment of the invention, after the clustering centers are obtained, the static reconstruction model of the power distribution network is constructed according to the load data of the clustering centers, and the model is the optimal static reconstruction model of the power distribution network with the minimum network loss as the target. For example: the distribution network has 5 interconnection switches: contact switch 1, contact switch 2, contact switch 3, contact switch 4, contact switch 5, at a certain period, the hypothesis distribution network moves according to closed contact switch 1, contact switch 3's mode earlier, and the net loss is 100kW this moment, if the adjustment distribution network moves according to closed contact switch 2, contact switch 5's mode, and the net loss is 90kW this moment, then the distribution network moves according to closed contact switch 2, contact switch 5's mode and is the optimum static reconstruction model of this period promptly. However, in an actual power distribution network architecture, the reconstruction problem of the power distribution network is caused by the complexity of the power distribution network, and becomes an NP problem with discrete control variables, and although a plurality of clustered cluster centers can represent load data of the same type of power distribution network, for such load data, how to minimize the network loss of the whole power distribution network is a plurality of solutions, that is, a plurality of reconstruction modes are provided, so that a static reconstruction model of the power distribution network needs to be solved to obtain an optimal reconstruction structure, that is, an optimal solution is obtained, and a specific method is shown in a corresponding place below.
In the embodiment of the invention, the static reconstruction model takes the minimum network loss as a target and needs to meet constraint conditions, and the constraint conditions comprise: the method comprises the following steps of (1) carrying out load flow constraint conditions, voltage constraint conditions of all nodes in the power distribution network, capacity constraint conditions of all branches in the power distribution network and network topology constraint conditions of the power distribution network;
the function expression targeting the minimum loss is:
in the formula: plossRepresents the loss of the network, NlRepresenting each branch set, and l representing each branch number; k is a radical oflRepresents the switching state of branch l; r islRepresents the resistance of branch l; plAnd QlRespectively representing active power and reactive power flowing through a branch circuit l; u shapelRepresenting the node voltage of the end node of branch l.
The power flow constraint conditions are as follows:
in the formula, PiAnd QiIs the injected active and reactive power of node i; viAnd deltaiIs the magnitude and phase angle of the voltage at node i; vjIs the magnitude and phase angle of the voltage at node j; y isijAnd thetaijThe branch admittance and phase angle difference between node i and node j, respectively.
The voltage constraint conditions of each node in the power distribution network are as follows:
Vmin≤Vn≤Vmax
in the formula, VminAnd VmaxRespectively the minimum value and the maximum value of the node voltage amplitude, and the values are [0.9, 1.1 ]]。
The capacity constraint conditions of each branch in the power distribution network are as follows:
|Sl|≤Sl max
in the formula, | SlI and SlmaxRespectively the transmission power of branch l and the maximum transmission power of branch l.
The network topology constraint conditions of the power distribution network are as follows:
the power distribution network is designed in a closed loop mode and operates in an open loop mode, and the reconstructed power distribution network must be radial.
And calculating and solving the minimum network loss of the static reconstruction model by using the function expression and the constraint condition.
Step 104: and solving the static reconstruction model by using an ordered optimization algorithm to obtain an optimal solution corresponding to each clustering center, wherein the optimal solution represents a static reconstruction result of the power distribution network with the minimum network loss as a target.
In the embodiment of the invention, after the static reconstruction model of the power distribution network is constructed, the static reconstruction model needs to be solved by using an ordered optimization algorithm to obtain the optimal solution corresponding to each clustering center, and the optimal solution represents the static reconstruction result of the power distribution network with the minimum network loss as the target. Specifically, the method comprises the following steps:
for each cluster center, performing the following steps:
step 1: and obtaining N samples based on the static reconstruction model, wherein each sample in the N samples represents the combination condition of the interconnection switches when the power distribution network is statically reconstructed.
In the embodiment of the invention, N samples are obtained based on a static reconstruction model, each sample in the N samples represents the combination condition of a contact switch in the static reconstruction of the power distribution network, and the N samples are obtained by using a spanning tree algorithm.
Specifically, all contact switches in the power distribution network are closed to form a mesh grid of the power distribution network; the N different trees are obtained using a spanning tree algorithm that represents N combinations of reconfiguration switches, which ensures a proper network structure of the distribution network, which is radial and without isolated nodes.
Step 2: and estimating the performance of the N samples by roughly and quickly calculating a model, wherein the performance is estimated by taking the minimum network loss of the power distribution network as a target.
In the embodiment of the invention, after the N samples are obtained, the performance of the N samples is estimated through a rough and fast calculation model, and the performance is estimated by taking the minimum network loss of the power distribution network as a target.
And step 3: and (4) arranging and estimating the obtained performance according to ascending order, drawing an ordered performance curve, and estimating the ordered performance curve category of the problem and the noise level of the original model.
And 4, step 4: the first S observed samples of N estimated by the original model are selected as the selected subset S. The number of samples of the subset S depends on the regression constant of the noise level k of the ordered performance curve and the number G of the subsets G, the subset G is a set of the true best solutions of the static reconstruction model, and the solution with the N samples accurately calculating the performance and the top G% after sorting is defined as the true best solution.
In the embodiment of the invention, after the performance is estimated, the performance obtained by estimation is arranged according to the ascending order, and an ordered performance curve is drawn. Naturally, the smaller the performance value is, the minimum loss of the distribution network is represented, and then, the samples corresponding to the first S performances in the ascending order are taken to form a subset S, for example: estimating the performance of N samples through a coarse and computationally fast model may yield N performance results, assuming that the results are: 1. 6, 3, and 5 … …, then 1, 3, 5, and 6 … … are in ascending order, and the samples corresponding to the first 4 performance results are samples 10, 8, 36, and 98, then the subset S includes samples: 10. the number s of 4 samples is 4 for 4 samples 8, 36 and 98.
In general, the number S of samples of the subset S depends on the noise level k of the ordered performance curve that has been drawn, and the regression constant of the number G of subsets G, where the subset G is the set of true best solutions of the static reconstruction model, and the solution with N samples that are exactly calculated and ordered in the top G% is defined as the true best solution.
Subset G is formed by the following steps:
step 1': calculating the performance of the N samples by using the rough model estimation;
step 2': the calculated properties are arranged in ascending order.
And 5: and taking the intersection of the subset S and the subset G, wherein the sample S in the intersection is the optimal solution corresponding to the clustering center.
In the embodiment of the invention, after the subset S is obtained, the intersection of the subset S and the subset G is taken, and the sample S in the intersection is the optimal solution corresponding to the clustering center.
Because the ordered optimization algorithm is an algorithm for considering both the calculation speed and the result, the obtained solution is a good enough solution, but not an optimal solution, because it requires a lot of calculations and a long enough time to solve the optimal solution, i.e. the solution corresponding to the sample S in the subset S is an estimated good enough solution obtained by fast calculation, and is not a true optimal solution.
Since the subset G is the true best solution and the subset S is the estimated sufficiently good solution, the intersection of the two is taken, and the sample S in the intersection is the optimal solution corresponding to the clustering center, that is, the sample S can be determined to be the true best solution corresponding to the clustering center. Therefore, the calculation amount is greatly reduced, the calculation amount of the solution is greatly reduced, and the solution obtained by the solution is ensured to be the optimal solution.
In order for the solution corresponding to sample S in subset S to contain at least one true best solution with a probability of at least α%, the intersection of subset S with subset G is determined by the number of alignment elements i, and the expression for the probability that subset S and subset G have at least k alignment elements is:
wherein G is the number of subsets G; s is the number of subsets S.
To ensure the probability of an event where at least one of the N samples falls within the first α% of the subset G, it is necessary to ensure that the number of N samples is obtained according to the following formula:
N≥ln(1-P)/ln(1-α％)
in this equation, P represents the probability of an event where at least one of the N samples falls within the top α% of the entire solution space;
in addition, the expression of P is:
P＝1-(1-α％)N
for example, if we take N2000 and α% 0.5%, then the probability of at least one of the 2000 samples falling within the first 0.5% of the subset G is 99.9956%, and if we expect the probability of at least 1 of the N samples falling within the first 0.5% of the subset G to be 99.999%, then N should be greater than 2297.
Step 105: in the future time period, the preset time period is taken as a cycle, load data of the power distribution network are periodically predicted in a short time mode, and the short time prediction is prediction of dynamic load data of the power distribution network in the first preset time period before the first preset time.
In the embodiment of the invention, after the optimal solution corresponding to each clustering center is obtained, the load data of the power distribution network is periodically predicted in a future time period by taking a preset time period as a cycle, wherein the short-time prediction is the prediction of the dynamic load data of the power distribution network in a first preset time period before the first preset time.
For example: processing power distribution network load data for 168 hours in the future, obtaining an optimal solution corresponding to each clustering center, and then performing short-time prediction on the load data of 0-2 hours coming from the power distribution network in a period of 2 hours in a preset time before 168 hours, wherein the short-time prediction is only on the dynamic load data of the power distribution network in 0-2 hours coming from the power distribution network. Naturally, if the selection of the preset time is small, the operation of naturally performing short-term prediction is more, and the amount of naturally subsequent calculation is larger, if the selection of the preset time is large, if 24 hours is taken as the preset time, only 7 times of short-term prediction are needed.
Step 106: and calculating the dissimilarity degree between the load data of the first preset time period predicted in short time and the load data of the C clustering centers by taking the dissimilarity degree as an index.
In the embodiment of the invention, after the short-time prediction, the dissimilarity degree between the load data of the first preset time period of the short-time prediction and the load data of the C clustering centers is calculated by taking the dissimilarity degree as an index.
For example: after the load data of the power distribution network of 0-2 hours are predicted, the dissimilarity degree is used as an index, and the dissimilarity degree between the load data of the 2 hours and the load data of the 9 clustering centers which are clustered previously is calculated.
Specifically, calculating the dissimilarity degree between the load data of the first preset time period predicted in a short time and the load data of the C cluster centers includes:
standardizing the load data of the C clustering centers, and compressing the load data in a [0, 1] ratio space;
calculating the complex power X of each node of the power distribution network in a first preset time period by using an Euclidean distance formulakObtaining C dissimilarity values through the similarity between the C clustering centers and the C clustering centers;
wherein the normalized formula is:
in the formula: x is the number oftiRepresenting load data, x, corresponding to node i during a period tti' represents normalized load data corresponding to the node i in the period t;
the Euclidean distance formula is as follows:
in the formula: dmIndicating Euclidean distance, VmIs the m-th cluster center, Vm∈Cn，T，n。
Step 107: and determining a static reconstruction model corresponding to the cluster center with the minimum dissimilarity as a result of the power distribution network reconstruction in the first preset time period.
In the embodiment of the invention, after the dissimilarity value is obtained, the static reconstruction model corresponding to the cluster center with the minimum dissimilarity is determined as the result of the reconstruction of the power distribution network within the first preset time period.
For example: and (3) calculating to obtain the minimum dissimilarity degree between the load data of the power distribution network in 0-2 hours and the load data of the No. 3 clustering center in the 9 clustering centers, determining a static reconstruction model of the power distribution network constructed by the No. 3 clustering center as a result of reconstruction of the power distribution network in 0-2 hours, namely, reconstructing the power distribution network by using a scheme corresponding to the static model of the power distribution network constructed by the No. 3 clustering center, so as to achieve the minimum network loss of the power distribution network. The scheme which is obtained according to the original 168-hour predicted load data clustering of the power distribution network and corresponds to the reconstruction of the power distribution network within 0-2 hours is a scheme corresponding to a static model of the power distribution network constructed by a No. 1 clustering center, but by adopting the scheme, the network loss of the power distribution network is not minimum.
Step 108: and when the power distribution network runs to a first preset time period, reconstructing the power distribution network according to the result.
In the embodiment of the invention, when the power distribution network really runs to the first preset section, the power distribution network is reconstructed according to the optimal solution.
For example: and when the power distribution network really runs to 0-2 hours of the next week, reconstructing the power distribution network according to the result of power distribution network reconstruction within the time period of 0-2 hours of the static reconstruction model corresponding to the clustering center with the minimum dissimilarity.
By the method, the reconstruction of the power distribution network can be realized, the network loss after the dynamic reconstruction of the power distribution network in actual operation is minimized, the reconstruction calculation amount is small, and the operation is simple.
In the following, reconstruction calculations are performed with the IEEE33 node power distribution network to verify the effectiveness of the method of the present invention.
Referring to fig. 2, there is shown a schematic diagram of the architecture of a power distribution network of an embodiment of the present invention, the power distribution network comprising 37 lines and 5 tie switches, i.e., branches 8-21, 9-15, 12-22, 18-33, and 25-29; rated voltage 12.66 kV; active load 3715 kW; reactive load 2300 kvar.
2297 samples are generated using a spanning tree algorithm and computed to form the subset G. After 2297 samples were evaluated with MATPOWER, 2297 samples could be sorted in ascending order. Regression value Z1Is 8.1200, Z2Is 1.0044, Z3Is-1.3698, Z4It was 9.00. The subset S is obtained. Assume that the alignment level k is 1 and the alignment probability P is 95% the first 15 of 2297 samples should be selected as the subset S. The following table shows the results after applying the process of the invention:
therefore, the method provided by the invention can minimize the network loss of the whole power distribution network.
The following table shows the comparison data of the method of the present invention and the genetic algorithm and the particle swarm algorithm in average time consumption:
therefore, the optimization time for convergence is short while the network loss value is the same as that of other algorithms by the ordered optimization algorithm, so that the algorithm can effectively balance global search and local search capabilities and has extremely high calculation rate.
Specifically, load data of one week is constructed on the basis of an IEEE33 test system, the resolution of the data is 1 hour, and 168 time sections (0-167) exist.
Carrying out optimal fuzzy clustering on the load data of one week by using a fuzzy C-means clustering method, wherein h is 2, and epsilon is 1 × 10-5α is 2.5, the load states of the 9 cluster centers are shown in fig. 3 and 4, wherein fig. 3 shows the active components of the load of the 9 cluster centers, and fig. 4 shows the reactive components of the load of the 9 cluster centers.
In fig. 3 and 4, the X axis represents a cluster number, the Y axis represents a node number, the Z axis in fig. 3 represents a load active component, which is represented by a per unit value, and the Z axis in fig. 4 represents a load reactive component, which is represented by a per unit value.
Performing 9 times of static reconstruction on 9 clustering centers by using an ordered optimization algorithm, wherein parameters are set to α of 0.5 percent, N of 2297, k of 1 and P of 95 percent, and Z is1Is 8.1200, Z2Is 1.0044, Z3Is-1.3698, Z4Was 9.00, thereby obtainingThe reconstruction results for the 9 cluster centers are shown in the following table:
and setting the complex power of each node of the power distribution network in the IEEE33 test system twice, wherein the first set complex power represents load prediction (represented by a scheme a) of the future time period of the power distribution network, and the second set complex power represents the short-time predicted load (represented by a scheme b).
In order to better reflect the dissimilarity degree, the values of the scheme a and the scheme b are set to be different, and then the dissimilarity degree is used as an index to compare with 9 cluster center loads for calculation. The dissimilarity degree between the scheme b and the No. 6 clustering center is minimum, the network loss calculated by utilizing the reconstruction scheme corresponding to the No. 6 clustering center is 139.93kW, and the network loss result calculated by utilizing the reconstruction scheme of the No. 3 clustering center in the scheme b is 166.2 kW.
The dissimilarity degree of the No. 6 clustering center corresponding to the moment of the scheme a is larger, and the dissimilarity degree of the No. 3 clustering center is minimum. The network loss result calculated by the scheme a through the reconstruction scheme of the No. 6 clustering center is 157.29kW, and the network loss calculated by the scheme a through the reconstruction scheme of the No. 3 clustering center is 139.5 kW. Therefore, the reconstruction result using the cluster center No. 3 is the optimal reconstruction result under the scheme b at the moment, and is not the reconstruction scheme of the cluster center No. 6 corresponding to the first prediction result.
In conclusion, the method for two-stage reconstruction of the power distribution network based on the ordered optimization algorithm has high effectiveness and good practical value.
Referring to fig. 5, a block diagram of an apparatus for two-stage reconstruction of a power distribution network based on an ordered optimization algorithm according to an embodiment of the present invention is shown, and the apparatus is applied to a power distribution network server, where the power distribution network server is used to control reconstruction of the power distribution network, and the apparatus for two-stage reconstruction of the power distribution network based on the ordered optimization algorithm includes:
a future load prediction module 310, configured to perform load prediction on a load of the power distribution network in a future time period to obtain load data in the future time period;
the clustering module 320 is configured to perform load clustering on the load data in the future time period by using an optimal fuzzy C-means clustering method to obtain load data of C clustering centers;
a building model module 330, configured to build a static reconstruction model of the power distribution network for the load data of the C cluster centers, where the static reconstruction model is a model of optimal static reconstruction of the power distribution network that aims at minimizing network loss;
the solving module 340 is configured to solve the static reconstruction model by using an ordered optimization algorithm to obtain an optimal solution corresponding to each clustering center, where the optimal solution represents a static reconstruction result of the power distribution network that aims at minimizing network loss;
the short-time load prediction module 350 is configured to periodically perform short-time prediction on load data of the power distribution network within a future time period by using a preset time period as a cycle, where the short-time prediction is prediction on dynamic load data of the power distribution network within a first preset time period before a first preset time;
a dissimilarity degree calculating module 360, configured to calculate dissimilarity degrees between the load data of the first preset time period predicted in the short time and the load data of the C clustering centers, using the dissimilarity degrees as an index;
a result determining module 370, configured to determine that the static reconstruction model corresponding to the cluster center with the minimum dissimilarity is the result of the power distribution network reconstruction within the first preset time period;
and the reconfiguration module 380 is configured to reconfigure the power distribution network according to the result when the power distribution network runs to the first preset time period.
Optionally, the power distribution network comprises: and the communication switch is used for specifically executing the following steps by the solving module aiming at each clustering center:
step 1: obtaining N samples based on the static reconstruction model, wherein each sample in the N samples represents the combination condition of a contact switch during one static reconstruction of the power distribution network;
step 2: estimating the performance of the N samples through a rough and fast calculation model, wherein the performance is estimated by taking the minimum network loss of the power distribution network as a target;
and step 3: the performance obtained by estimation is arranged according to the ascending order, an order performance curve is drawn, and the order performance curve category of the problem and the noise level of the original model are estimated;
and 4, step 4: the first S observed samples of N estimated by the original model are selected as the selected subset S. The number of samples of the subset S depends on a regression constant of the noise level k of the ordered performance curve and the number G of subsets G, the subset G being a set of true best solutions of the static reconstruction model, the solutions of the N samples that accurately calculate performance and are ordered first G% being defined as the true best solutions;
and 5: and taking the intersection of the subset S and the subset G, wherein the sample S in the intersection is the optimal solution corresponding to the clustering center.
Optionally, the forming of the subset G comprises the steps of:
step 1: calculating the performance of the N samples by rough model estimation;
step 2: the calculated properties are arranged in ascending order.
Through the embodiment, the dynamic reconstruction of the power distribution network in the future time period is converted into the static reconstruction model of the corresponding C clustering centers before the future time period, the optimal solution of each time period in the future is obtained, namely the reconstruction with the minimum network loss of the power distribution network in each time period in the future, short-time prediction is periodically carried out in the future time period, the difference degree of load data of the short-time prediction and the load data of the C clustering centers is used as an index, and the states of all switches of the power distribution network in the next time period are reconstructed according to the static reconstruction model corresponding to the clustering center with the minimum difference degree. According to the method, reconstruction calculation with complex process and great calculation amount is not needed for real-time load data, only the load state of the cluster center with the minimum corresponding dissimilarity degree needs to be found out, and then the power distribution network is reconstructed by utilizing the scheme of the static reconstruction model corresponding to the cluster center, so that the network loss after the dynamic reconstruction of the power distribution network in actual operation is minimized, the reconstruction calculation amount is small, the operation is simple, the reconstruction calculation time is short, the dynamically-changed load can be well adapted, and the method has high practical value.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.
The embodiments of the present invention have been described in connection with the accompanying drawings, and the principles and embodiments of the present invention are described herein using specific examples, which are provided only to help understand the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. A method for two-stage reconstruction of a power distribution network based on an ordered optimization algorithm is applied to a power grid server, and the power grid server is used for controlling reconstruction of the power distribution network, and the method comprises the following steps:
load prediction is carried out on the load of the power distribution network in the future time period, and load data in the future time period are obtained;
carrying out load clustering on load data in a future time period by using an optimal fuzzy C-means clustering method to obtain load data of C clustering centers;
constructing a static reconstruction model of the power distribution network aiming at the load data of the C clustering centers, wherein the static reconstruction model is an optimal static reconstruction model of the power distribution network with the minimum network loss as a target;
solving the static reconstruction model by using an ordered optimization algorithm to obtain an optimal solution corresponding to each clustering center, wherein the optimal solution represents a static reconstruction result of the power distribution network with the minimum network loss as a target;
in a future time period, periodically performing short-time prediction on load data of the power distribution network by taking a preset time period as a cycle, wherein the short-time prediction is prediction on dynamic load data of the power distribution network in a first preset time period before the first preset time;
calculating the dissimilarity degree between the load data of the first preset time period predicted in short time and the load data of the C clustering centers by taking the dissimilarity degree as an index;
determining a static reconstruction model corresponding to the clustering center with the minimum dissimilarity degree as a result of the power distribution network reconstruction within the first preset time period;
and when the power distribution network runs to the first preset time period, reconstructing the power distribution network according to the result.
2. The method of claim 1, wherein the power distribution network comprises: and the interconnection switch solves the static reconstruction model by using an ordered optimization algorithm to obtain an optimal solution corresponding to each clustering center, and comprises the following steps:
for each cluster center, performing the following steps:
step 1: obtaining N samples based on the static reconstruction model, wherein each sample in the N samples represents the combination condition of a contact switch during one static reconstruction of the power distribution network;
step 2: estimating the performance of the N samples through a rough and fast calculation model, wherein the performance is estimated by taking the minimum network loss of the power distribution network as a target;
and step 3: the performance obtained by estimation is arranged according to the ascending order, an order performance curve is drawn, and the order performance curve category of the problem and the noise level of the original model are estimated;
and 4, step 4: selecting the first S observed samples of N estimated from the original model as a selected subset S, the number of samples of the subset S depending on the regression constant of the noise level k of the ordered performance curve and the number G of subsets G, the subset G being a set of true best solutions of the static reconstructed model, the solutions of the N samples that accurately calculate performance and are ordered first G% being defined as the true best solutions;
and 5: and taking the intersection of the subset S and the subset G, wherein the sample S in the intersection is the optimal solution corresponding to the clustering center.
3. The method according to claim 2, wherein the forming of the subset G comprises the steps of:
step 1: calculating the performance of the N samples by rough model estimation;
step 2: the calculated properties are arranged in ascending order.
4. The method of claim 3, wherein the intersection of the subset S with the subset G is determined by the number of alignment elements i, and wherein the probability that the subset S and the subset G have at least k alignment elements is expressed as:
wherein G is the number of said subsets G; s is the number of subsets S.
5. The method of claim 3, wherein the number of samples in the N samples is obtained according to the following formula:
N≥ln(1-P)/ln(1-α％)
in this equation, P represents the probability of an event in which at least one of the N samples falls within the top α% of the entire solution space;
the expression of P is:
P＝1-(1-α％)N。
6. a method according to claim 3, characterized in that the number of subsets S is obtained according to the following formula:
in the formula, Z represents the number of the subsets S, Z1、Z2、Z3、Z4A regression constant depending on the noise level k of the ordered performance curve and the number G of subsets G.
7. The method of claim 1, wherein calculating the dissimilarity between the load data of the short-time predicted first preset time period and the load data of the C cluster centers comprises:
standardizing the load data of the C clustering centers, and compressing the load data in a [0, 1] ratio space;
calculating the complex power X of each node of the power distribution network in the first preset time period by using an Euclidean distance formulakObtaining C dissimilarity values through the similarity between the C clustering centers and the C clustering centers;
wherein the normalized formula is:
in the formula: x is the number oftiRepresenting load data, x, corresponding to node i during a period tti' represents normalized load data corresponding to the node i in the period t;
the Euclidean distance formula is as follows:
in the formula: dmIndicating Euclidean distance, VmIs the m-th cluster center, Vm∈Cn，T，n。
8. The method of claim 1, wherein the static reconstruction model targets a minimum loss and satisfies constraints, the constraints comprising: the method comprises the following steps of (1) power flow constraint conditions, voltage constraint conditions of all nodes in the power distribution network, capacity constraint conditions of all branches in the power distribution network and network topology constraint conditions of the power distribution network;
the function expression taking the minimum network loss as a target is as follows:
in the formula: plossRepresents the loss of the network, NlRepresenting each branch set, and l representing each branch number; k is a radical oflRepresents the switching state of branch l; r islRepresents the resistance of branch l; plAnd QlRespectively representing active power and reactive power flowing through a branch circuit l; u shapelRepresenting the node voltage of the end node of branch l.
9. The method of claim 8, wherein the power flow constraint is:
in the formula, PiAnd QiIs the injected active and reactive power of node i; viAnd deltaiIs the magnitude and phase angle of the voltage at node i; vjIs the magnitude and phase angle of the voltage at node j; y isijAnd thetaijThe branch admittance and the phase angle difference between the node i and the node j are respectively;
the voltage constraint conditions of each node in the power distribution network are as follows:
Vmin≤Vn≤Vmax
in the formula, VminAnd VmaxRespectively the minimum value and the maximum value of the node voltage amplitude, and the values are [0.9, 1.1 ]]，
The capacity constraint conditions of each branch in the power distribution network are as follows:
|Sl|≤Sl max
in the formula, | S representslI and SlmaxRespectively the transmission power of the branch circuit l and the maximum transmission power of the branch circuit l;
the network topology constraint conditions of the power distribution network are as follows:
the power distribution network is designed in a closed loop mode and operates in an open loop mode, and the power distribution network after reconstruction must be radial.
10. A device for two-stage reconstruction of a power distribution network based on an ordered optimization algorithm is applied to a power grid server, the power grid server is used for controlling reconstruction of the power distribution network, and the device comprises:
the future load prediction module is used for predicting the load of the power distribution network in the future time period to obtain load data in the future time period;
the clustering module is used for carrying out load clustering on the load data in the future time period by using an optimal fuzzy C-means clustering method to obtain load data of C clustering centers;
the construction model module is used for constructing a static reconstruction model of the power distribution network aiming at the load data of the C clustering centers, and the static reconstruction model is an optimal static reconstruction model of the power distribution network with the minimum network loss as a target;
the solving module is used for solving the static reconstruction model by using an ordered optimization algorithm to obtain an optimal solution corresponding to each clustering center, and the optimal solution represents a static reconstruction result of the power distribution network with the minimum network loss as a target;
the short-time load prediction module is used for periodically performing short-time prediction on load data of the power distribution network within a future time period by taking a preset time period as a cycle, wherein the short-time prediction is prediction on dynamic load data of the power distribution network within a first preset time period before the first preset time;
the dissimilarity degree calculating module is used for calculating dissimilarity degrees between the load data of the first preset time period predicted in short time and the load data of the C clustering centers by taking the dissimilarity degrees as indexes;
a result determining module, configured to determine that the static reconstruction model corresponding to the cluster center with the minimum dissimilarity is a result of reconstruction of the power distribution network within the first preset time period;
and the reconstruction module is used for reconstructing the power distribution network according to the result when the power distribution network runs to the first preset time period.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010221029.8A CN111342458B (en) | 2020-03-25 | 2020-03-25 | Method and device for two-stage reconstruction of power distribution network based on ordered optimization algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010221029.8A CN111342458B (en) | 2020-03-25 | 2020-03-25 | Method and device for two-stage reconstruction of power distribution network based on ordered optimization algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111342458A true CN111342458A (en) | 2020-06-26 |
| CN111342458B CN111342458B (en) | 2021-11-19 |
Family
ID=71184602
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010221029.8A Active CN111342458B (en) | 2020-03-25 | 2020-03-25 | Method and device for two-stage reconstruction of power distribution network based on ordered optimization algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111342458B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111832836A (en) * | 2020-07-23 | 2020-10-27 | 广东电网有限责任公司 | Power distribution network reconstruction method and system considering load power utilization characteristics |
| CN112330042A (en) * | 2020-11-17 | 2021-02-05 | 合肥工业大学 | Power distribution network reconstruction method based on self-adaptive fuzzy C-means clustering scene division |
| CN112711895A (en) * | 2020-12-30 | 2021-04-27 | 上海电机学院 | Power distribution network reconstruction method based on time interval division and improved particle swarm algorithm |
| CN113890015A (en) * | 2021-09-25 | 2022-01-04 | 三峡大学 | Power distribution network dynamic reconstruction method based on improved fuzzy C-means clustering algorithm |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106329516A (en) * | 2015-07-09 | 2017-01-11 | 中国电力科学研究院 | Typical scene recognition based dynamic reconstruction method of power distribution network |
| CN109995075A (en) * | 2018-09-29 | 2019-07-09 | 国网上海市电力公司 | A kind of active distribution network dynamic reconfiguration method containing distributed generation resource |
-
2020
- 2020-03-25 CN CN202010221029.8A patent/CN111342458B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106329516A (en) * | 2015-07-09 | 2017-01-11 | 中国电力科学研究院 | Typical scene recognition based dynamic reconstruction method of power distribution network |
| CN109995075A (en) * | 2018-09-29 | 2019-07-09 | 国网上海市电力公司 | A kind of active distribution network dynamic reconfiguration method containing distributed generation resource |
Non-Patent Citations (3)
| Title |
|---|
| 李晶: "含有微电网的配电网络重构及其优化算法", 《中国优秀硕士学位论文全文数据库 工程科技II辑》 * |
| 王淳等: "采用最优模糊C均值聚类和改进化学反应算法的配电网络动态重构", 《中国电机工程学报》 * |
| 高山山等: "计及负荷时变性及分布式电源出力波动性的配电网两级实时重构", 《电网技术》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111832836A (en) * | 2020-07-23 | 2020-10-27 | 广东电网有限责任公司 | Power distribution network reconstruction method and system considering load power utilization characteristics |
| CN111832836B (en) * | 2020-07-23 | 2021-05-28 | 广东电网有限责任公司 | Power distribution network reconstruction method and system considering load power utilization characteristics |
| CN112330042A (en) * | 2020-11-17 | 2021-02-05 | 合肥工业大学 | Power distribution network reconstruction method based on self-adaptive fuzzy C-means clustering scene division |
| CN112330042B (en) * | 2020-11-17 | 2022-09-13 | 合肥工业大学 | Power distribution network reconstruction method based on self-adaptive fuzzy C-means clustering scene division |
| CN112711895A (en) * | 2020-12-30 | 2021-04-27 | 上海电机学院 | Power distribution network reconstruction method based on time interval division and improved particle swarm algorithm |
| CN113890015A (en) * | 2021-09-25 | 2022-01-04 | 三峡大学 | Power distribution network dynamic reconstruction method based on improved fuzzy C-means clustering algorithm |
| CN113890015B (en) * | 2021-09-25 | 2023-08-25 | 三峡大学 | Power distribution network dynamic reconstruction method based on improved fuzzy C-means clustering algorithm |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111342458B (en) | 2021-11-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111342458B (en) | Method and device for two-stage reconstruction of power distribution network based on ordered optimization algorithm | |
| Muthukumar et al. | Optimal placement and sizing of distributed generators and shunt capacitors for power loss minimization in radial distribution networks using hybrid heuristic search optimization technique | |
| CN109478848B (en) | Controlling power output using battery DC characteristics | |
| Peng et al. | Short-term load forecasting at different aggregation levels with predictability analysis | |
| Li et al. | Efficient resource scaling based on load fluctuation in edge-cloud computing environment | |
| CN113807589B (en) | Rolling optimization method and device for energy storage of operators based on model prediction | |
| KR20200123310A (en) | Apparatus and method for predicting short term electrical load | |
| Hantash et al. | An improved particle swarm optimization algorithm forOptimal allocation of distributed generation units in radial power systems | |
| CN106712050B (en) | Power grid reactive power optimization method and device based on improved leapfrog algorithm | |
| Alam et al. | Artificial bee colony algorithm with self-adaptive mutation: a novel approach for numeric optimization | |
| KR102628551B1 (en) | Apparatus and method for forecasting short-term electric load of hybrid type | |
| CN117353398A (en) | Power quality control method, storage medium and electronic equipment for power distribution area | |
| CN111832836B (en) | Power distribution network reconstruction method and system considering load power utilization characteristics | |
| CN116093995B (en) | Multi-target network reconstruction method and system for power distribution system | |
| CN117520818A (en) | Power fingerprint identification method and device, electronic equipment and storage medium | |
| Hong et al. | A novel adaptive elite‐based particle swarm optimization applied to var optimization in Electric Power Systems | |
| CN111276962B (en) | Operation method and system of power distribution network | |
| CN116976514A (en) | Distributed power supply location and volume-fixing method, device and storage medium | |
| CN116307111A (en) | Reactive load prediction method based on K-means clustering and random forest algorithm | |
| CN116683471A (en) | Configuration method, device and equipment of reactive power compensation resource | |
| CN115528684A (en) | Ultra-short-term load prediction method and device and electronic equipment | |
| Sulaima et al. | A 16kV distribution network reconfiguration by using evolutionaring programming for loss minimizing | |
| CN115663807B (en) | Strong power distribution network method based on predicted load and distributed power supply output | |
| Sanseverino et al. | A new representation of energy storage systems operation using Fourier theory in optimal smart grids management | |
| Fernández-Fernández et al. | A dynamic programming approach for power curtailment decision making on pv systems |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |