CN109063950B - Dynamic time warping association assessment method for controllability of intelligent power distribution network - Google Patents
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Abstract
The invention provides a controllability dynamic time bending association assessment method for an intelligent power distribution network, which comprises the following steps of: step 1: constructing a controllability evaluation index system of the intelligent power distribution network; step 2: evaluating data acquisition and scoring of the index; and step 3: evaluating the weight assignment of the index; and 4, step 4: carrying out controllability DTW (delay tolerant W) correlation evaluation on the intelligent power distribution network; and 5: and carrying out controllability DTW (delay tolerant W) correlation evaluation on the intelligent power distribution network. The method comprehensively analyzes various key factors influencing the controllability of the power distribution network, and completes the construction of the controllability evaluation index system of the intelligent power distribution network, so that the evaluation index system is more scientific and comprehensive.
Description
Technical Field
The invention relates to the field of controllability assessment of intelligent power distribution networks, in particular to a controllability assessment method of an intelligent power distribution network based on dynamic time bending correlation analysis.
Background
With the large-scale grid connection of a Distributed Generation (DG), the large-scale access of a distributed energy storage Device (DESS) and the rapid development of an Electric Vehicle (EV), the social environment is greatly improved, and meanwhile, the power quality problems such as harmonic waves generated by frequent actions of a switch device due to the voltage rise of a load node and the like bring huge challenges to a power distribution network. Meanwhile, the intelligent power distribution network is produced by the company, the intelligent power distribution network can rapidly sense the running state of the power distribution network in real time, and various distributed power supplies, energy storage devices and controllable loads in the power distribution network are actively controlled and managed. In order to ensure safe and reliable operation of the power distribution network, comprehensive and accurate comprehensive evaluation must be carried out on the controllability of the intelligent power distribution network.
At present, in the aspect of evaluating the operation state of the intelligent power distribution network, domestic and foreign researches mainly focus on the aspects of operation safety, reliability and economy of the intelligent power distribution network. The controllability of some distributed power sources in the intelligent power distribution network is poor, and energy storage equipment and controllable loads also need to be uniformly controlled, so that the controllability research on the intelligent power distribution network is imperative in order to fully exert the controllability of the intelligent power distribution network and the distributed power sources. In order to realize comprehensive evaluation of the controllability of the intelligent power distribution network, the invention provides a universal evaluation index system and an evaluation method. In order to solve the problem of comprehensive evaluation of the controllability of the intelligent power distribution network, the solution is proposed as follows: 1) comprehensively analyzing various key factors influencing the stable operation of the power distribution network, and completing the construction of an intelligent power distribution network controllability evaluation index system, so that the evaluation index system is more scientific and comprehensive; 2) the evaluation of the weight is completed by using an analytic hierarchy process, and the rationality and objectivity of an evaluation index system are improved; 3) and finally, carrying out comprehensive controllability evaluation on the distributed power supply by using a dynamic time warping method, carrying out controllability grade evaluation by analyzing the similarity of the index to be evaluated and the standard index, and finally carrying out comprehensive controllability evaluation on the intelligent power distribution network.
Disclosure of Invention
In order to make up for the defects of the existing evaluation surface, the invention provides a controllability dynamic time warping correlation evaluation method for an intelligent power distribution network. The technical scheme of the invention is as follows:
a controllability dynamic time warping association assessment method for an intelligent power distribution network comprises the following steps: step 1: constructing a controllability evaluation index system of the intelligent power distribution network; step 2: evaluating data acquisition and scoring of the index; and step 3: evaluating the weight assignment of the index; and 4, step 4: carrying out controllability DTW (delay tolerant W) correlation evaluation on the intelligent power distribution network; and 5: and carrying out controllability DTW (delay tolerant W) correlation evaluation on the intelligent power distribution network.
Further, the step 1 specifically comprises:
step 1-1: after the influence modes and the influence degrees of the influence factors of real-time output of the distributed power supply, the charge state of the energy storage power station, the power factor and the voltage deviation on the controllability of the intelligent power distribution network are fully considered, an intelligent power distribution network controllability evaluation index system is formed and comprises a distributed power supply utilization rate index, an energy storage power station charge state index, a power factor index and a voltage deviation index;
step 1-2: establishing a standard sample sequence according to the controllability evaluation index system of the intelligent power distribution network, and setting a standard sample as X1＝[x11,x12,x13]And respectively corresponding to the real-time output of the distributed power supply: the utilization rate of a distributed power supply or the charge state of an energy storage power station; reactive power control capability of the distributed power supply: a power factor; voltage regulation capability: the voltage offset is calculated by the following formula:
distributed power utilization index:
the charge state index of the energy storage power station is as follows:
power factor index:
voltage offset index: delta Ui＝|1-Ui|
In the above formula, PDG,iActive power, Q, from distributed power or energy-storage power stationsDG,iReactive power, S, absorbed or emitted by distributed power or energy storage stationsN,iRated capacity, E, of grid-connected inverters of distributed power or energy storage stationsC,iResidual capacity, U, of energy storage power stationsiThe grid connection point voltage standard value;
step 1-3: establishing a standard sample reference influence scale sequence A1＝[a11,a12,a13]While A is1The first row as the final evaluation sample matrix a; a is11Is an index of the utilization rate of the distributed power supply, a12Is a power factor index, a13Is an indication of voltage deviation.
Further, the step 2 specifically includes:
step 2-1: acquiring data required by controllability evaluation of the intelligent power distribution network according to the calculation mode of indexes in the standard sample sequence in the step 1 to form a reference data sample sequence and a data sample sequence to be evaluated; the standard sample score obtained by the standard sample scoring function is 1, and the scoring function of each index is shown as the following formula:
distributed power utilization scoring function: a isi1＝1-η 0≤η≤1
Energy storage power station state of charge scoring function:
power factor scoring function:
voltage biasMoving the scoring function:
step 2-2: and forming each evaluation index by the acquired data, establishing a scoring function according to the influence factors of each index, and scoring the reference sample and the sample to be evaluated by using the scoring function.
Further, the step 3 specifically includes:
the index weight assignment method of the AHP can be used for subjectively assigning a distributed power supply utilization index, a power factor index and a voltage offset index in an evaluation index system;
step 3-1: according to the influence degree of each evaluation index on the controllability of the intelligent power distribution network, comparing the evaluation indexes with each other to obtain the importance degree of each index, and selecting the size of a comparison scale to further form a comparison judgment matrix;
step 3-2: obtaining the maximum eigenvalue of the matrix and the corresponding eigenvector thereof through pairwise comparison of the matrixes, completing consistency check, and if the consistency check is not met, reselecting a comparison scale;
step 3-3: carrying out normalization processing on the judgment matrix meeting the conditions by utilizing the obtained maximum eigenvalue and the eigenvector thereof to complete subjective weight assignment; and solving the weighted standard sample distance sequence, the weighted reference sample distance sequence and the weighted sample distance sequence to be evaluated by utilizing the subjective weight.
Further, the step 4 specifically includes:
calculating and solving the minimum accumulation distance of the two groups of sample sequences, and measuring the similarity between the two samples by using the minimum accumulation distance;
step 4-1: calculating a distance matrix: the Euclidean distance lambda of corresponding elements in the two sample sequences can be obtained by using the following formulaij，
Wherein u and v represent two sample sequences to be compared, and i is 2,3, …, m and j are 2,3, …, n; the Euclidean distances are arranged according to a certain rule to obtain a distance matrix as shown in the following formula:
step 4-2: calculating a cumulative distance matrix: the calculation method is shown as the following formula, and the minimum bending distance, namely the correlation matching coefficient, is obtained by accumulating the distance matrix; d (i, j) represents the minimum warping distance from (1,1) to (i, j), and the value of D (m, n), i.e., the minimum warping distance of two sample sequences, reflects the degree of similarity of the two samples.
Step 4-3: correlation matching coefficient C by reference sample sequenceiTo determine a range of different controllability levels; and comparing the correlation matching coefficient of the sample sequence to be evaluated and the standard sample sequence with the range to finally obtain the controllability grade of the sample to be evaluated.
Further, the step 5 specifically includes:
calculating to obtain the correlation matching coefficient of the sample to be evaluated by using a DTW correlation evaluation method, and forming the correlation matching coefficient of each distributed power supply and the energy storage power station under different load conditions; according to the obtained correlation matching coefficients of the distributed power sources and the energy storage power stations in the power distribution network, and by combining key elements influencing the controllability of the intelligent power distribution network, the relative capacities of the distributed power sources and the energy storage power stations have a crucial influence on the controllability, so that the controllability evaluation of the intelligent power distribution network is obtained;
the calculation formula is shown as follows:
in the formula, n is intelligent power distributionNumber of distributed power sources and energy storage power stations in the network, STFor the total capacity of distributed power and energy storage plants, SiFor the capacity of distributed power or energy-storage power stations, CiThe coefficients are matched for the respective associations.
Advantageous effects
Compared with the closest prior art, the invention has the following characteristics:
1. various key factors influencing the controllability of the power distribution network are comprehensively analyzed, and the construction of an intelligent power distribution network controllability evaluation index system is completed, so that the evaluation index system is more scientific and comprehensive;
2. in view of the difficulty in directly solving the controllability of the power distribution network, the invention innovatively provides that the Score of the evaluation index is obtained through a scoring Function (SF, Score Function), and after the influence mode and the influence degree of each key factor on the intelligent power distribution network are fully considered, the scoring Function of the related index is established;
3. the evaluation of the weight is completed by using an analytic hierarchy process, and the rationality and objectivity of an evaluation index system are improved;
4. and performing controllability comprehensive evaluation on the distributed power supply by using a Dynamic Time Warping (DTW) method, performing controllability grade evaluation by analyzing the similarity of the index to be evaluated and the standard index, and finally performing comprehensive evaluation on the controllability of the intelligent power distribution network.
Drawings
FIG. 1 is a flow chart of the present invention.
FIG. 2 is a schematic diagram of a simulation system of the present invention.
Detailed Description
In order to make the technical scheme, the purpose and the meaning of the invention more clearly expressed, the invention is explained in detail in the following through the attached drawings and the corresponding implementation examples. The present embodiment is only for illustrative purposes and is not limited to the present embodiment.
The flow of the controllability dynamic time warping association assessment method for the intelligent power distribution network provided by the invention is shown in fig. 1.
Example of the implementation
The method is characterized in that an intelligent power distribution network controllability simulation system is built and is divided into 3 parts as shown in figure 2, wherein the parts are respectively a direct-current distributed power supply 1-k, a direct-current energy storage power station (k +1) -h and a load (h +1) -j.
In order to achieve diversity of collected data and simplicity of program simulation, a distributed photovoltaic power supply, a distributed fan power supply, a micro gas turbine power supply and an energy storage power station are selected, and rated capacities are 40kVA, 100kVA, 70kVA and 60kVA respectively. And various data of the distributed power supply and the energy storage power station are acquired by adjusting the load degree of the load.
1) Assessment data collection
According to the established simulation system and the power distribution network controllability evaluation index system determined in the step 1, the distributed power supply utilization rate index x can be acquired according to the formulas (1) to (4)1Power factor index x2Voltage deviation index x3And finally obtaining a standard sample index, a reference sample index and a sample index to be evaluated, which are required by controllability evaluation.
Under the condition of fully considering different load degrees, the simulation system carries out a large number of simulation experiments, provides data support for making scientific and reasonable reference samples, enables the classification level to be more scientific and reasonable, and enables the described controllability level of the intelligent power distribution network to be more objective.
2) Construction of standard sample and weight assignment of index
The controllability standard sample of the intelligent power distribution network is a sample for enabling the controllability of the intelligent power distribution network to reach the optimal state. Determining a reference scale A according to the influence degree of each index on the power distribution network1＝[a11,a12,a13]，A1The medium elements are scoring functions of a distributed power utilization index, a power factor index and a voltage deviation index respectively. The scores were all 1 as seen from the scoring functions (5) to (8).
And calculating the weight of each index by using an AHP weight assignment method, and performing normalization processing on the weight to finally obtain a weighted standard sample vector.
TABLE 1 comparative Scale and its meanings
TABLE 2 AHP judge matrix
The maximum eigenvalue of the matrix and the corresponding eigenvector are obtained by comparing the matrixes in table 2, the maximum eigenvalue of the matrix is 3, the corresponding eigenvector is represented as gamma, the matrix is a consistency matrix, the random consistency ratio is 0<0.1, the consistency requirement is met, the eigenvector gamma with the maximum eigenvalue is normalized, and the vector of the subjective weight is obtained as follows:
γ＝[0.7074 0.6119 0.3537]
the resulting weighted standard sample vector is as follows:
A1′＝[a′11 a′12 a′13]
＝[0.4228 0.3658 0.2114]
3) reference sample selection and ranking
Through the analysis of the running state of the intelligent power distribution network, 3 groups of typical sample sequences in the simulation data are selected as reference sequences, and finally the reference sample sequences are formed as follows:
analyzing and calculating the reference sample through expressions (9) to (11) in the DTW association evaluation algorithm, taking the minimum bending distance of the accumulated distance matrix at the lower right corner as an association matching coefficient, and finally obtaining the evaluation results corresponding to the association matching coefficients of different levels as follows:
TABLE 3 controllability evaluation rankings
4) Construction of a sample to be evaluated
Firstly, according to different load conditions, respectively measuring controllability indexes of a distributed power supply and an energy storage device, and grading by using a grading function to further form a sample sequence to be evaluated, wherein A2～A5Scoring the evaluation indexes of the distributed power supply and the energy storage power station under the first load condition, A6～A9Scoring the evaluation indexes of the distributed power supply and the energy storage power station under the second load condition, A10～A13The evaluation index scores of the distributed power supply and the energy storage power station under the third load condition are obtained, and dimensionless scores are as follows:
and weighting the scores by using the weights to obtain dimensionless weighted scores of the sample to be evaluated, wherein the dimensionless weighted scores are as follows:
5) comprehensive evaluation of controllability of intelligent power distribution network
Firstly, the DTW association evaluation method is used for calculating the association matching coefficient of the sample to be evaluated, and the association matching coefficients under different load conditions of each distributed power supply are formed and are shown in the table 4:
TABLE 4 correlation matching coefficients for distributed power under different load conditions
According to the obtained correlation matching coefficients of the distributed power sources and the energy storage power stations in the power distribution network, and by combining key elements influencing the controllability of the intelligent power distribution network, the relative capacities of the distributed power sources and the energy storage power stations have a crucial influence on the controllability, and therefore controllability evaluation of the intelligent power distribution network is obtained.
According to the formula (12), controllability comprehensive evaluation can be performed on the operation states of the intelligent power distribution network under three different load conditions. The evaluation results are shown in table 5:
TABLE 5 comprehensive evaluation of controllability of smart distribution network
In conclusion, the invention provides a method for evaluating controllability dynamic time bending association of the intelligent power distribution network by analyzing the control capability of the intelligent power distribution network. Key indexes influencing the controllability of the intelligent power distribution network are found out, and a comprehensive controllability evaluation system is formed. Firstly, scoring the indexes by using a scoring function on the basis of combining the influence factors of the indexes; weighting the indexes by using the subjective weights of the indexes to obtain a reference sample and a sample to be evaluated; then, dynamic time bending correlation evaluation is provided in the field of evaluation of controllability of the intelligent power distribution network, a sample to be evaluated is compared with a standard sample to obtain an accumulated distance matrix, and correlation matching coefficients of the distributed power supply and the energy storage power station are obtained; and finally, obtaining the controllability comprehensive evaluation capability of the intelligent power distribution network by combining the capacity of the equipment. The comprehensive evaluation index system constructed by the invention has strong rationality and the DTW (delay tolerant spread) correlation evaluation of the intelligent power distribution network has high applicability.
Claims (2)
1. A controllability dynamic time warping correlation evaluation method for an intelligent power distribution network is characterized by comprising the following steps:
step 1: constructing a controllability evaluation index system of the intelligent power distribution network;
the method specifically comprises the following steps:
step 1-1: after the influence modes and the influence degrees of the influence factors of real-time output of the distributed power supply, the charge state of the energy storage power station, the power factor and the voltage deviation on the controllability of the intelligent power distribution network are fully considered, an intelligent power distribution network controllability evaluation index system is formed and comprises a distributed power supply utilization rate index, an energy storage power station charge state index, a power factor index and a voltage deviation index;
step 1-2: establishing a standard sample sequence according to the controllability evaluation index system of the intelligent power distribution network, and setting a standard sample as X1＝[x11，x12，x13]And respectively corresponding to the real-time output of the distributed power supply: the utilization rate of a distributed power supply or the charge state of an energy storage power station; reactive power control capability of the distributed power supply: a power factor; voltage regulation capability: the voltage offset is calculated by the following formula:
distributed power utilization index:
the charge state index of the energy storage power station is as follows:
power factor index:
voltage offset index: delta Ui＝|1-Ui|
In the above formula, PDG，iActive power, Q, from distributed power or energy-storage power stationsDG，iReactive power, S, absorbed or emitted by distributed power or energy storage stationsN，iRated capacity, E, of grid-connected inverters of distributed power or energy storage stationsC，iResidual capacity, U, of energy storage power stationsiThe grid connection point voltage standard value;
step 1-3: establishing a standard sample reference influence scale sequence A1＝[a11，a12，a13]While A is1The first row as the final evaluation sample matrix a; a is11Is an index of the utilization rate of the distributed power supply, a12Is a power factor index, a13Is an index of voltage deviation;
step 2: evaluating data acquisition and scoring of the index;
the method specifically comprises the following steps:
step 2-1: acquiring data required by controllability evaluation of the intelligent power distribution network according to the calculation mode of indexes in the standard sample sequence in the step 1 to form a reference data sample sequence and a data sample sequence to be evaluated; the standard sample score obtained by the standard sample scoring function is 1, and the scoring function of each index is shown as the following formula:
distributed power utilization scoring function: a isi1＝1-η 0≤η≤1
Energy storage power station state of charge scoring function:
power factor scoring function:
voltage offset scoring function:
step 2-2: forming each evaluation index by the collected data, establishing a scoring function according to the influence factors of each index, and scoring the reference sample and the sample to be evaluated by using the scoring function;
and step 3: evaluating the weight assignment of the index;
the method specifically comprises the following steps:
the index weight assignment method of the AHP can be used for subjectively assigning a distributed power supply utilization index, a power factor index and a voltage offset index in an evaluation index system;
step 3-1: according to the influence degree of each evaluation index on the controllability of the intelligent power distribution network, comparing the evaluation indexes with each other to obtain the importance degree of each index, and selecting the size of a comparison scale to further form a comparison judgment matrix;
step 3-2: obtaining the maximum eigenvalue of the matrix and the corresponding eigenvector thereof through pairwise comparison of the matrixes, completing consistency check, and if the consistency check is not met, reselecting a comparison scale;
step 3-3: carrying out normalization processing on the judgment matrix meeting the conditions by utilizing the obtained maximum eigenvalue and the eigenvector thereof to complete subjective weight assignment; calculating the weighted standard sample distance sequence, the weighted reference sample distance sequence and the weighted sample distance sequence to be evaluated by utilizing the subjective weight;
and 4, step 4: carrying out controllability DTW (delay tolerant W) correlation evaluation on the intelligent power distribution network;
and 5: controllability DTW correlation evaluation of the intelligent power distribution network specifically comprises the following steps:
calculating to obtain the correlation matching coefficient of the sample to be evaluated by using a DTW correlation evaluation method, and forming the correlation matching coefficient of each distributed power supply and the energy storage power station under different load conditions; according to the obtained correlation matching coefficients of the distributed power sources and the energy storage power stations in the power distribution network, and by combining key elements influencing the controllability of the intelligent power distribution network, the relative capacities of the distributed power sources and the energy storage power stations have a crucial influence on the controllability, so that the controllability evaluation of the intelligent power distribution network is obtained;
the calculation formula is shown as follows:
in the formula, n is the number of distributed power sources and energy storage power stations in the intelligent power distribution network, STFor the total capacity of distributed power and energy storage plants, SiFor the capacity of distributed power or energy-storage power stations, CiThe coefficients are matched for the respective associations.
2. The method for evaluating controllability dynamic time warping association of a smart distribution network according to claim 1, wherein the step 4 specifically comprises:
calculating and solving the minimum accumulation distance of the two groups of sample sequences, and measuring the similarity between the two samples by using the minimum accumulation distance;
step 4-1: calculating a distance matrix: the Euclidean distance lambda of corresponding elements in the two sample sequences can be obtained by using the following formulaij，
Wherein u and v represent two sample sequences to be compared, and i is 2,3, …, m and j are 2,3, …, n; the Euclidean distances are arranged according to a certain rule to obtain a distance matrix as shown in the following formula:
step 4-2: calculating a cumulative distance matrix: the calculation method is shown as the following formula, and the minimum bending distance, namely the correlation matching coefficient, is obtained by accumulating the distance matrix; d (i, j) represents the minimum bending distance from (1,1) to (i, j), and the value of D (m, n), i.e., the minimum bending distance of the two sample sequences, reflects the degree of similarity of the two samples;
step 4-3: correlation matching coefficient C by reference sample sequenceiTo determine a range of different controllability levels; and comparing the correlation matching coefficient of the sample sequence to be evaluated and the standard sample sequence with the range to finally obtain the controllability grade of the sample to be evaluated.
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Application publication date: 20181221 Assignee: Nanjing Qianyuan Electric Power Technology Co.,Ltd. Assignor: NANJING University OF SCIENCE AND TECHNOLOGY Contract record no.: X2022980027425 Denomination of invention: A Dynamic Time Bending Correlation Evaluation Method for Controllability of Intelligent Distribution Network Granted publication date: 20220315 License type: Exclusive License Record date: 20221229 |