CN117913827B - Optimization method of complex power distribution network considering trigger function - Google Patents
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Abstract
The invention particularly relates to an optimization method of a complex power distribution network considering a triggering function, which belongs to the technical field of power distribution networks and comprises the following steps: step 1): constructing a network state evaluation system model which comprises indexes of main transformer load rate, line load rate, network loss, load balance index and voltage quality; step 2): constructing a comprehensive evaluation model, wherein a CRITIC weight method is adopted to evaluate the correlation between indexes and the relation between the indexes and the decision targets; step 3): constructing an integrated threshold model, wherein an integrated threshold is set through a fuzzy set in the integrated threshold, and the integrated threshold comprises a main transformer load rate, a line load rate, a network loss, a load balancing index and voltage quality; step 4): constructing a complex power distribution network linear optimization model which is used for searching a switch combination mode with highest load balance degree; the invention has the beneficial effects that: the method and the device realize quick solution for the operation optimization of the complex power distribution network, thereby achieving the purpose of the operation optimization of the complex power distribution network.
Description
Technical Field
The invention relates to the technical field of power distribution networks with trigger functions, in particular to an optimization method of a complex power distribution network with trigger functions.
Background
Along with the continuous development of society, the demand for electric power is also increasing, meanwhile, an electric power system is also becoming more complex, a power distribution network is used as a bridge for connecting a power grid and users, and particularly, the complexity of the power distribution network such as 220kV is gradually increased due to the large area of the applicable electric power demand, and the operation optimization of the power distribution network is also gradually important. At present, in the aspect of the research of the operation optimization method of the power distribution network, most of researches have the conditions of single operation mode, incomplete consideration index, insufficient soundness of an evaluation system and the like.
Most of researches are on the aspect of optimizing the power distribution network, such as the research of only considering the safety and stability control of the operation in the process of optimizing the power distribution network, a safety evaluation model is established, but the economic benefit is poor, and the state of the power distribution network is continuously changed along with the increase of the complexity of the power distribution network.
Therefore, an optimization method of the complex power distribution network considering the triggering function needs to be designed at present to solve the problems.
Disclosure of Invention
The invention provides an optimization method of a complex power distribution network taking trigger functions into account, which aims to solve the technical problem of how to enable the inside of the power distribution network to flexibly and accurately run after reconstruction.
The aim of the invention is realized by the following technical scheme:
an optimization method of a complex power distribution network considering a trigger function comprises the following steps:
step 1): constructing a network state evaluation system model, wherein the network state evaluation system model comprises indexes of main transformer load rate, line load rate, network loss, load balance index and voltage quality, so as to comprehensively evaluate three aspects of safety, economy and reliability of a power distribution network;
Step 2): constructing a comprehensive evaluation model, wherein the comprehensive evaluation model adopts CRITIC weight method to evaluate the correlation between indexes and the relation between the indexes and decision targets;
Step 3): building an integrated threshold model, wherein an integrated threshold is set in the integrated threshold model through a fuzzy set, the integrated threshold comprises a main transformer load rate, a line load rate, a network loss, a load balancing index and voltage quality, and different operations are triggered through the integrated threshold;
Step 4): the method comprises the steps of constructing a complex power distribution network linear optimization model, wherein the complex power distribution network linear optimization model is used for finding a switch combination mode with highest load balance degree, and realizing load balance among a plurality of partitions based on power flow constraint of a line and regulation limitation of a switch.
Optionally, in step 1), the network state evaluation index system is evaluated by constructing the network state evaluation system model, where the network state evaluation system model is used for comprehensively evaluating the power distribution network.
Optionally, the specific steps of constructing the network state evaluation system model are as follows:
step a): determining the structure of a network state evaluation system model;
step b): introducing indexes;
Taking the main transformer load rate, the line load rate, the network loss, the load balancing index and the voltage quality as indexes to formulate a network state evaluation system;
(1) Main transformer load factor:
The current electric load occupation condition of the main transformer is used, the load rate of the main transformer is used as an index for evaluating the electric load occupation condition of the main transformer, the ratio of the current load born by the main transformer relative to the rated capacity of the main transformer is reflected, and the load rate of the main transformer can be expressed as a formula (1):
(1)；
Wherein, Representing the load factor of the main transformer,The load of the main transformer is represented,Representing the rated capacity of the main transformer;
(2) Line load factor:
The line load factor is used for evaluating the electrical load occupation condition of the power line, and represents the ratio of the load currently born by the power line to the rated capacity thereof, and the calculation formula (2) of the line load factor is as follows:
(2)；
Wherein, Representing the load rate of the line and,Representing the load on the line and,Representing the rated capacity of the line;
(3) Network loss:
The network loss is energy loss in the transmission and distribution process of the power system, and is expressed by power, and a calculation formula (3) of the network loss is as follows:
(3)；
Wherein, The net loss is indicated by the number of net points,Indicating the total power generation amount,Representing the total load;
(4) Load balancing index:
The load balance index is used for evaluating whether the load distribution among all the partitions or nodes in the power system is balanced or not, the distribution condition of the electric load among different areas is reflected, the high load balance index represents more uniform load distribution, and the calculation of the load balance index is as follows in the formula (4):
(4)；
Wherein, Representing a load balancing index; Representing the maximum load; representing a minimum load; Representing the average load;
(5) Voltage quality:
The voltage quality is an index for measuring the voltage stability provided by the power system, the voltage quality can cause equipment faults and power quality, the voltage quality is measured by adopting power supply voltage deviation, and the measuring mode of the voltage quality adopts the following formula (5):
(5)；
Wherein: Representing the measured value of the voltage, Representing the system nominal voltage.
Optionally, in step 2), the following steps are used by the CRITIC weighting method:
Step A): establishing a decision;
Comprehensively measuring standard deviation and correlation coefficient based on historical index values by adopting CRITIC weight method according to the established network state evaluation system to objectively assign weight to each index;
Defining multiple criteria decisions explicitly, wherein the multiple criteria decisions comprise explicit decision targets and evaluation indexes related to the decision targets, and the evaluation indexes comprise main transformer load rates, line load rates, network losses, load balancing indexes and voltage quality under the situation of a power distribution network;
Step B): establishing a contrast matrix;
For each pair of evaluation indexes, a comparison matrix is constructed, the relative importance between the two indexes is represented, the element values in the comparison matrix are 1, 3 and 5, and then a comparison matrix is constructed by singular element values, namely 7, 9 and 11 and the like;
step C): calculating relative weights;
calculating the relative weight of each evaluation index relative to other indexes by using the comparison matrix, and calculating a consistency index to ensure the consistency of the comparison matrix, wherein for the calculation of the relative weight, a consistency matrix CM and a consistency index CI are used;
The expression of the consistency matrix CM is formula (6):
(6)；
Wherein, Is the value of the j-th element of the i-th row in the comparison matrix,Is the sum of the j-th columns of the comparison matrix;
The consistency index CI is used for measuring consistency, and the expression is formula (7):
(7)；
Wherein, Is the maximum eigenvalue of the consistency matrix, and n is the number of evaluation indexes;
step D): establishing a conflict matrix;
for each pair of evaluation indexes, the conflict between the indexes needs to be calculated, the conflict represents the mutual relation between the indexes, namely whether the indexes have conflict or not, the elements of the conflict matrix are determined according to the relation between the indexes, the value is between 0 and 1, and the degree of the conflict degree is represented;
Step E): calculating objective weights;
Calculating objective weights of all the evaluation indexes by using the relative weights and the conflict matrix, wherein the objective weights comprehensively consider the conflict between the relative weights and the evaluation indexes so as to better reflect the actual importance of the indexes;
for objective weight calculation, the collision matrix and the relative weight can be used for calculation, and the expression is formula (8):
(8)；
Wherein, An objective weight representing the evaluation index i,The j-th element representing the i-th row of the consistency matrix,The j-th element of the i-th row of the collision matrix is represented.
Optionally, in step 3), the setting of the comprehensive threshold employs the following method:
Step ①: defining a fuzzy number set;
For each single index and composite index, fuzzy sets, i.e. "low", "medium" and "high", are defined, for a certain index X, three fuzzy sets are defined, as follows (9):
(9)；
In the formula (9), the amino acid sequence of the compound, ，AndFuzzy sets respectively representing membership degrees with different degrees, namely load rates comprise a low load rate, a medium load rate and a high load rate;
Step ②: determining a membership function;
Membership functions describe the relationship between the input value and the membership of fuzzy sets, and for each fuzzy set the appropriate membership function is selected, i.e. triangular, trapezoidal or Gaussian membership functions, which are generally expressed as Wherein x represents an input value;
step ③: setting a membership range;
a membership range is defined for each fuzzy set, typically between 0 and 1, expressed as the following formula (10):
(10)；
step ④: defining a threshold fuzzy set;
the threshold fuzzy sets represent thresholds of different degrees, and fuzzy sets representing the thresholds are defined for each single index and the comprehensive index, namely " Is of low threshold ","/>Is a medium threshold value 'and'/>For a high threshold ", for a certain index X, three threshold fuzzy sets can be defined, as follows (11):
(11)；
step ⑤: determining a threshold membership function;
the threshold membership function describes the relationship between the input value and the membership of the threshold fuzzy set, and selects the appropriate membership function for the threshold fuzzy set, which is typically expressed as Y represents an input value;
step ⑥: setting a threshold membership range:
The threshold membership range represents the degree to which the input value corresponds to the membership of the threshold fuzzy set, and a membership range is defined for each threshold fuzzy set, typically between 0 and 1, the threshold membership range being represented by the following formula (12):
(12)；
step ⑦: calculating membership degree;
For each index, calculating the membership degree of which the value corresponds to the fuzzy set and the threshold fuzzy set, and calculating the membership degree by using the corresponding membership function;
Step ⑧: comprehensive membership degree;
Synthesizing the membership degree of each single index by using fuzzy logic operation, namely minimum or maximum operation, and obtaining the membership degree of the comprehensive index;
step ⑨: determining a threshold value;
Based on the membership of the comprehensive index, determining a threshold of the comprehensive index, determining by using a fuzzy reasoning method, selecting a high threshold if the membership of the comprehensive index is high, and selecting a low threshold if the membership of the comprehensive index is low.
Optionally, in step 4), a complex power distribution network linear optimization model is constructed, specifically as follows:
In order to quickly reconstruct and solve the distribution network, neglecting network loss and voltage offset of the lines, an objective function is to maximize the sum of active loads carried by all main transformers in the adjacent partition transferable areas, namely, the load balance degree of a formula (13) and each feeder line, namely, a formula (14):
(13)；
(14)；
represents the active load of the 220kV main transformer in the rotatable supply area under the s-th typical scene, The variance of the active power of all feeder lines in the transferable region is represented, and S represents the number of typical scenes in normal operation; representing the active power carried by the mth feeder, The average power of all feeder lines is represented, and M represents all line-out numbers of the partition transformer substation;
constraint conditions of the linear optimization model of the power distribution network are as follows:
(1) Power balance constraints, such as equation (15):
(15)；
Representing the active power flow of the upstream line of the simplified power distribution network node j, Representing the active power flow of the downstream line of the simplified power distribution network node j,An upstream node set corresponding to the reduced power distribution network node j, pi (j) a downstream node set corresponding to the reduced power distribution network node j, B representing all node sets of the reduced power distribution network,Representing the active load of node j,Representing the unified multiplying power of the active load to be solved in the s-th typical scene;
(2) Safety constraints, such as formulas (16) - (18):
(16)；
(17)；
(18)；
represents the upper limit value of the active power flow of the line, Representing the upper limit value of the active power carried by the transformer substation, solving the two values through the apparent upper limit value of the active power and the load power factor carried by the transformer substation,Representing the lower limit of the active power flow of the line, the flow of the branch ij may be diverted after the switch is changed, soIs thatNegative values of (2); representing a collection of branches without switches in a reduced distribution network, Representing a simplified collection of branches with switches of the distribution network,Representing a switch state to be solved;
(3) Topological radial constraints:
The radial constraint is ensured by the following two constraints, line number = node number-substation number, as in equation (19); the sub-connectivity graph containing substation nodes, as shown in formulas (20) - (23), ensures that the network has neither island nor ring network, and is specifically shown as follows:
(19)；
(20)；
(21)；
(22)；
(23)；
Wherein q represents the switch state of the power distribution network which is not to be solved, Representing the number of nodes of the reduced distribution network,The number of substations of the simplified power distribution network is represented, and the number of the substations is 1; And (3) with Respectively representing the active power and the virtual active load of the virtual transformer substation; is a small constant; And (3) with All are virtual branch active power flows; the switch combination mode is limited, so that the radiation performance can be ensured;
active load multiplying power is obtained according to available transfer capacity model of adjacent feeder line group partitions of power distribution network Its corresponding maximum available transfer capability，Representing the total load of the adjacent partition, the adjacent partition may be able to transfer capacityIs formula (24):
(24)；
Since the objective function is nonlinear, linearizing the target uses the following formulas (25) - (26):
(25)；
(26)；
Wherein, Is an auxiliary variable introduced.
The invention has the beneficial effects that:
1. According to the invention, through construction of the whole complex power distribution network, namely, a network state evaluation system model, a comprehensive evaluation model, a comprehensive threshold model and a complex power distribution network linear optimization model, operation optimization can be effectively carried out on the complex power distribution network such as 220kV, flexible and rapid solution of operation optimization of the complex power distribution network can be realized, and efficient operation and load balancing of a power system are realized, wherein when the state evaluation and optimization of the power distribution network are determined, a threshold value is determined by adopting a threshold value setting method, and setting of the threshold value based on a fuzzy set is a flexible and highly applicable method, so that the inside of the power distribution network can be flexibly and accurately operated after reconstruction.
2. The invention provides an optimization method for complex power distribution network operation of trigger rules, which firstly relates to three aspects of safety, economy, reliability and the like, and takes main transformer load rate, line load rate, network loss, load balance index, voltage quality and the like as indexes to formulate a network state evaluation mechanism; secondly, establishing CRITIC comprehensive evaluation model based on CRITIC objective weighting method, carrying out objective weighting on each index by comprehensively measuring standard deviation and correlation coefficient based on historical index values to obtain comprehensive evaluation value, thereby realizing network state evaluation of the distribution network, and providing comprehensive threshold setting method based on fuzzy set, wherein not only single index threshold setting is considered, but also the threshold of the comprehensive index is mapped to the fuzzy set, which is helpful for coping with the complexity of threshold setting in multi-index comprehensive evaluation; thirdly, carrying out state evaluation on the power distribution network, and optimizing and calculating the operation mode of the power distribution network feeder line group through threshold triggering and regular triggering of set indexes; and finally, establishing a linear optimization model of the complex power distribution network, carrying out quick mutual-aid support through a plurality of adjacent partitioned feeder line component partitions, and considering the flow constraint of a line and the regulation limit of a switch, so that load balance among the plurality of partitions is realized, and finally, the optimal operation of the complex power distribution network such as 220kV is achieved.
Drawings
FIG. 1 is a structural framework diagram of a complex power distribution network operation optimization method of the triggering rule of the invention;
FIG. 2 is an architecture diagram of a complex power distribution network operation optimization method of the triggering rules of the present invention;
FIG. 3 is a diagram of a network state evaluation architecture of the present invention;
FIG. 4 is a flow chart of a composite evaluation model based on the CRITIC objective weighting method of the present invention;
FIG. 5 is a flow chart of a comprehensive threshold setting method based on fuzzy sets;
fig. 6 is a flow chart of a complex linear optimization model of a power distribution network according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples. It should be understood that the particular embodiments described herein are illustrative only and are not intended to limit the invention, i.e., the embodiments described are merely some, but not all, of the embodiments of the invention.
Example 1
As shown in fig. 1-2, the present invention provides an optimization method for a complex power distribution network, which takes trigger functions into account, comprising the following steps:
step 1): constructing a network state evaluation system model, wherein the network state evaluation system model comprises indexes of main transformer load rate, line load rate, network loss, load balance index and voltage quality, so as to comprehensively evaluate three aspects of safety, economy and reliability of a power distribution network;
Step 2): constructing a comprehensive evaluation model, wherein the comprehensive evaluation model adopts CRITIC weight method to evaluate the correlation between indexes and the relation between the indexes and decision targets;
Step 3): building an integrated threshold model, wherein an integrated threshold is set in the integrated threshold model through a fuzzy set, the integrated threshold comprises a main transformer load rate, a line load rate, a network loss, a load balancing index and voltage quality, and different operations are triggered through the integrated threshold;
Step 4): the method comprises the steps of constructing a complex power distribution network linear optimization model, wherein the complex power distribution network linear optimization model is used for finding a switch combination mode with highest load balance degree, and realizing load balance among a plurality of partitions based on power flow constraint of a line and regulation limitation of a switch.
Specifically, the invention is used for optimizing the operation of a complex power distribution network, the core steps of the invention are to establish a network state evaluation system for comprehensively evaluating the power distribution network, the network state evaluation index system is evaluated through the construction of a network state evaluation system model, the network state evaluation system model is used for comprehensively evaluating the power distribution network, simultaneously, the CRITIC weight method is adopted for evaluating each index system, and the fuzzy set is used for carrying out operation judgment so as to optimize the complex power distribution network, and the following is the detailed steps and key contents of the invention, and the construction of the network state evaluation system model comprises the following specific steps:
As shown in fig. 3, step a): determining the structure of a network state evaluation system;
firstly, a network state evaluation system is established from three aspects of safety, economy and reliability, wherein the safety is a primary target of planning operation of a power distribution network, the economy is an important index for evaluating whether the design of the power distribution network is reasonable, the reliability is an important index for evaluating whether the power distribution network can stably supply power, and the network state evaluation index is established based on the three aspects and is determined by the internal requirements of the power distribution network;
Step b): introducing indexes;
Taking the main transformer load rate, the line load rate, the network loss, the load balancing index and the voltage quality as indexes to formulate a network state evaluation system;
(1) Main transformer load factor:
considering the current electrical load occupation situation of the main transformer, the main transformer load factor is used as an index for evaluating the electrical load occupation situation of the main transformer, which reflects the ratio of the current load born by the main transformer to the rated capacity thereof, and the main transformer load factor can be expressed as formula (1):
(1)；
Wherein, Representing the load factor of the main transformer,The load of the main transformer is represented,Representing the main transformer rated capacity. The load condition of the main transformer can be found in time by monitoring the load rate of the main transformer, overload is prevented from being used, and the service life of equipment is prolonged.
(2) Line load factor:
The line load factor is used to evaluate the electrical load occupancy of the electrical line. It represents the ratio of the load currently experienced by the power line relative to its rated capacity. The calculation formula (2) of the line load factor is as follows:
(2)；
Wherein, Representing the load rate of the line and,Representing the load on the line and,Representing the line rated capacity. High line load rates may lead to overheating of the power line and increased energy losses.
(3) Network loss:
the network loss is energy loss in the transmission and distribution process of the power system, and is generally expressed by power, and the calculation formula (3) of the network loss is as follows:
(3)；
Wherein, The net loss is indicated by the number of net points,Indicating the total power generation amount,Indicating the total load. The network loss represents the energy loss condition in the system, and the reduction of the network loss is beneficial to improving the energy efficiency of the power system.
(4) Load balancing index:
The load balancing index is used to evaluate whether the load distribution among the various partitions or nodes in the power system is balanced. It reflects the distribution of electrical loads between different regions, with a high load balancing index indicating a more uniform load distribution. Calculation formula (4) of load balancing index:
(4)；
Wherein: representing a load balancing index; Representing the maximum load; representing a minimum load; Representing the average load. The load balancing index may help system operators to better understand the load distribution across different regions in order to more efficiently allocate power resources.
(5) Voltage quality:
voltage quality is an index for measuring voltage stability provided by a power system, equipment faults and power quality problems can be caused by voltage quality problems, power supply voltage deviation is used for measuring voltage quality, and the measuring mode of the voltage quality adopts the following formula (5):
(5)；
Wherein: Representing the measured value of the voltage, Representing the nominal voltage of the system, the result of this formula calculation is a ratio representing the relative deviation between the actual voltage and the nominal voltage-the supply voltage deviation being used to evaluate the voltage quality problem to ensure the stability of the power system and the proper functioning of the equipment.
As shown in fig. 4, CRITIC weighting methods are established:
Step A): establishing a decision;
Carrying out objective weighting on each index by comprehensively measuring a standard deviation and a correlation coefficient based on a historical index value by adopting a CRITIC weight method according to the established network state evaluation system, and definitely defining a multi-criterion decision, wherein the multi-criterion decision comprises a definite decision target and an evaluation index related to the decision target, and the evaluation index comprises a main transformer load rate, a line load rate, a network loss, a load balance index and voltage quality under the situation of a power distribution network;
Specifically, the index system adopts CRITIC weight method to comprehensively measure standard deviation and correlation coefficient based on historical index values to objectively weight each index, firstly, a multi-criterion decision problem is definitely defined, the multi-criterion decision problem comprises a definite decision target and evaluation indexes related to the decision target, and under the situation of a power distribution network, the evaluation indexes may comprise main transformer load rate, line load rate, network loss, load balancing index and voltage quality.
Step B): establishing a contrast matrix;
For each pair of evaluation indexes, a comparison matrix is constructed, the relative importance between the two indexes is shown, the elements in the comparison matrix are usually valued as 1 (showing that the two indexes have the same importance), 3 (showing that one index is more important than the other index), 5 (showing that one index is far more important than the other index), and then a comparison matrix is constructed by singular element values, namely 7, 9 and 11 and so on.
Step C): calculating relative weights;
And calculating the relative weight of each evaluation index relative to other indexes by using the comparison matrix. This typically involves calculating a consistency index to ensure consistency of the contrast matrix. For the calculation of the relative weights, a consistency matrix CM and a consistency index CI may be used;
The expression of the consistency matrix CM is formula (6):
(6)；
Wherein, Is the value of the j-th element of the i-th row in the comparison matrix,Is the sum of the j-th columns of the comparison matrix;
The consistency index CI is used for measuring consistency, and the expression is formula (7):
(7)；
Wherein, Is the maximum eigenvalue of the consistency matrix, and n is the number of evaluation indexes;
step D): establishing a conflict matrix;
for each pair of evaluation indexes, the conflict between the indexes needs to be calculated, the conflict represents the mutual relation between the indexes, namely whether the indexes have conflict or not, the elements of the conflict matrix are determined according to the relation between the indexes, and the value is usually between 0 and 1, and represents the degree of the conflict degree;
Step E): calculating objective weights;
and calculating the objective weight of each evaluation index by using the relative weight and the conflict matrix. The objective weight comprehensively considers the conflict between the relative weight and the index so as to better reflect the actual importance of the index.
For objective weight calculation, the collision matrix and the relative weight can be used for calculation, and the expression is formula (8):
(8)；
Wherein, An objective weight representing the evaluation index i,The j-th element representing the i-th row of the consistency matrix,The j-th element of the i-th row of the collision matrix is represented.
As shown in fig. 5, the following method is used for setting the integration threshold value:
The method is a flexible and high-applicability method, and the threshold value is dynamically set according to the actual situation and the need of multi-index comprehensive evaluation and corresponding operation is triggered, so that the high efficiency and reliability of the power distribution network can be maintained.
Step ①: defining a fuzzy number set;
For each single index and composite index, fuzzy sets, i.e. "low", "medium" and "high", are defined, for a certain index X, three fuzzy sets are defined, as follows (9):
(9)；
In the formula (9), the amino acid sequence of the compound, ，AndThe fuzzy sets respectively represent membership degrees with different degrees, such as a load rate including a low load rate, a medium load rate and a high load rate;
Step ②: determining a membership function;
membership functions describe the relationship between the input value and the membership of fuzzy sets, and for each fuzzy set an appropriate membership function is selected, e.g. a triangular, trapezoidal or Gaussian membership function, which is usually expressed as Wherein x represents an input value;
step ③: setting a membership range;
a membership range is defined for each fuzzy set, typically between 0 and 1, expressed as the following formula (10):
(10)；
step ④: defining a threshold fuzzy set;
the threshold fuzzy sets represent thresholds of different degrees, and fuzzy sets representing the thresholds are defined for each single index and the comprehensive index, namely " Is of low threshold ","/>Is a medium threshold value 'and'/>For a high threshold ", for a certain index X, three threshold fuzzy sets can be defined, as follows (11):
(11)；
step ⑤: determining a threshold membership function;
the threshold membership function describes the relationship between the input value and the membership of the threshold fuzzy set, and selects the appropriate membership function for the threshold fuzzy set, which is typically expressed as Y represents an input value;
step ⑥: setting a threshold membership range:
The threshold membership range represents the degree to which the input value corresponds to the membership of the threshold fuzzy set, and a membership range is defined for each threshold fuzzy set, typically between 0 and 1, the threshold membership range being represented by the following formula (12):
(12)；
step ⑦: calculating membership degree;
For each index, calculating the membership degree of which the value corresponds to the fuzzy set and the threshold fuzzy set, and calculating the membership degree by using the corresponding membership function;
Step ⑧: comprehensive membership degree;
fuzzy logic operations (such as minimum or maximum operations) are used to synthesize the membership of each single index to obtain the membership of the synthesized index.
Step ⑨: determining a threshold value;
Based on the membership of the comprehensive index, determining a threshold of the comprehensive index, determining by using a fuzzy reasoning method, selecting a high threshold if the membership of the comprehensive index is high, and selecting a low threshold if the membership of the comprehensive index is low.
Through these steps, fuzzy set-based synthetic thresholding may help determine the thresholds for each single and synthetic index to support multi-criterion decision-making. Each step uses fuzzy sets and membership functions to describe the relationship between input values and membership, ultimately determining the appropriate threshold.
Establishing a complex power distribution network linear optimization model:
the complex linear optimization model of the power distribution network is used for finding a switch combination mode with highest load balance degree, and for quickly carrying out reconstruction solution of the power distribution network, the network loss and voltage offset objective function of a neglected line is used for maximizing the sum of active loads carried by all main transformers in a transferable area of an adjacent partition, namely, the load balance degree of a formula (13) and each feeder line is represented by a formula (14):
(13)；
(14)；
represents the active load of the 220kV main transformer in the rotatable supply area under the s-th typical scene, The variance of the active power of all feeder lines in the transferable region is represented, and S represents the number of typical scenes in normal operation; representing the active power carried by the mth feeder, And representing the average power of all feeder lines, and M represents the number of all lines of the subarea substation.
As shown in fig. 6, constraint conditions of the linear optimization model of the power distribution network are as follows:
(1) Power balance constraints, such as equation (15):
(15)；
Representing the active power flow of the upstream line of the simplified power distribution network node j, Representing the active power flow of the downstream line of the simplified power distribution network node j,An upstream node set corresponding to the reduced power distribution network node j, pi (j) a downstream node set corresponding to the reduced power distribution network node j, B representing all node sets of the reduced power distribution network,Representing the active load of node j,And representing the unified multiplying power of the active load to be solved in the s-th typical scene.
(2) Safety constraints, such as formulas (16) - (18):
(16)；
(17)；
(18)；
represents the upper limit value of the active power flow of the line, Representing the upper limit value of the active power carried by the transformer substation, solving the two values through the apparent upper limit value of the active power and the load power factor carried by the transformer substation,Representing the lower limit of the active power flow of the line, the flow of the branch ij may be diverted after the switch is changed, soUsually isNegative values of (2); representing a collection of branches without switches in a reduced distribution network, Representing a simplified collection of branches with switches of the distribution network,Representing the state of the switch to be solved.
(3) Topological radial constraint of power distribution network:
Because of the plurality of substations, the key of load distribution network reconstruction is how to effectively guarantee radial constraint, wherein the radial constraint is guaranteed by the following two constraints, namely the number of lines = the number of nodes-the number of substations, as shown in a formula (19); the sub-connectivity graph containing substation nodes, as shown in formulas (20) - (23), ensures that the network has neither island nor ring network, and is specifically shown as follows:
(19)；
(20)；
(21)；
(22)；
(23)；
Wherein q represents the switch state of the power distribution network which is not to be solved, Representing the number of nodes of the reduced distribution network,The number of substations of the simplified power distribution network is represented, and the number of the substations is 1; And (3) with Respectively representing the active power and the virtual active load of the virtual transformer substation; is a small constant; And (3) with All are virtual branch active power flows; the switch combination mode is limited, so that the radiation performance can be ensured;
active load multiplying power is obtained according to available transfer capacity model of adjacent feeder line group partitions of power distribution network Its corresponding maximum available transfer capability，Representing the total load of the adjacent partition, the adjacent partition may be able to transfer capacityIs formula (24):
(24)；
Since the objective function is nonlinear, linearizing the target uses the following formulas (25) - (26):
(25)；
(26)；
Wherein, Is an auxiliary variable introduced.
The complex power distribution network optimization method provided by the invention can be used for optimizing the operation of the power distribution network for 220KV multi-level power distribution network, finding the optimal operation structure of the power distribution network, and being beneficial to optimizing the technology and solving the problem by applying a linear programming mode so as to realize the efficient operation and load balancing of the power system.
Compared with the existing technology, the invention has the following beneficial effects: by constructing the whole complex power distribution network, namely the network state evaluation system model, the comprehensive evaluation model, the comprehensive threshold model and the complex power distribution network linear optimization model, the operation optimization of the complex power distribution network of 220kV can be effectively performed, and the flexible and rapid solution of the operation optimization of the complex power distribution network can be realized.
The invention provides an optimization method for complex power distribution network operation of trigger rules. Firstly, three aspects of safety, economy, reliability and the like are related, and a network state evaluation mechanism is formulated by taking main transformer load rate, line load rate, network loss, load balance index, voltage quality and the like as indexes; secondly, establishing CRITIC comprehensive evaluation model based on CRITIC objective weighting method, carrying out objective weighting on each index by comprehensively measuring standard deviation and correlation coefficient based on historical index values to obtain comprehensive evaluation value, thereby realizing network state evaluation of the distribution network, and providing comprehensive threshold setting method based on fuzzy set, wherein not only single index threshold setting is considered, but also the threshold of the comprehensive index is mapped to the fuzzy set, which is helpful for coping with the complexity of threshold setting in multi-index comprehensive evaluation; thirdly, carrying out state evaluation on the power distribution network, and optimizing and calculating the operation mode of the power distribution network feeder line group through threshold triggering and regular triggering of set indexes; and finally, establishing a linear optimization model of the complex power distribution network, carrying out quick mutual-aid support through a plurality of adjacent partitioned feeder line component partitions, and considering the flow constraint of a line and the regulation limit of a switch, so that load balance among the plurality of partitions is realized, and finally, the optimal operation of the complex power distribution network such as 220kV is achieved.
Example 2
Based on embodiment 1, it should be noted that, when determining the state evaluation and optimization of the power distribution network, the threshold setting method is adopted, and it is the most critical that the threshold is determined, and setting the threshold based on the fuzzy set is a flexible and applicable method, so that the inside of the power distribution network of the present invention can be flexibly and accurately operated after being reconstructed.
In addition, the triggering function related by the invention is embodied on the setting of the comprehensive threshold, and the threshold is dynamically set according to the actual situation and the requirement of multi-index comprehensive evaluation and the corresponding operation is triggered, so that the high efficiency and the reliability of the power distribution network can be maintained.
The above description is merely an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present invention, and it is intended to cover the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (5)
1. The optimizing method of the complex power distribution network considering the triggering function is characterized by comprising the following steps:
Step 1): constructing a network state evaluation system model, wherein the network state evaluation system model comprises indexes of main transformer load rate, line load rate, network loss, load balance index and voltage quality;
Step 2): constructing a comprehensive evaluation model, wherein the comprehensive evaluation model adopts CRITIC weight method to evaluate the correlation between indexes and the relation between the indexes and decision targets;
Step 3): building an integrated threshold model, wherein an integrated threshold is set in the integrated threshold model through a fuzzy set, the integrated threshold comprises a main transformer load rate, a line load rate, a network loss, a load balancing index and voltage quality, and different operations are triggered through the integrated threshold;
Step 4): constructing a complex power distribution network linear optimization model, wherein the complex power distribution network linear optimization model is used for finding a switch combination mode with highest load balance degree, and realizing load balance among a plurality of partitions based on the power flow constraint of a line and the regulation limit of a switch;
in the step 4), the complex power distribution network linear optimization model is constructed, specifically as follows:
In order to quickly reconstruct and solve the distribution network, neglecting network loss and voltage offset of the lines, an objective function is to maximize the sum of active loads carried by all main transformers in the adjacent partition transferable areas, namely, the load balance degree of a formula (13) and each feeder line, namely, a formula (14):
(13)；
(14)；
Represents the active load carried by 220kV main transformer in the transferable region in the s-th typical scene,/> The variance of the active power of all feeder lines in the transferable region is represented, and S represents the number of typical scenes in normal operation; /(I)Representing the active power carried by the mth feeder line,/>The average power of all feeder lines is represented, and M represents all line-out numbers of the partition transformer substation;
constraint conditions of the linear optimization model of the power distribution network are as follows:
(1) Power balance constraints, such as equation (15):
(15)；
Representing the active power flow of an upstream line of a simplified power distribution network node j,/> Representing the active power flow of a downstream line of a simplified power distribution network node j,/>Upstream node set corresponding to reduced power distribution network node j, pi (j) corresponding to reduced power distribution network node j, and B represents all node sets of the reduced power distribution network,/>Representing the active load of node j,/>Representing the unified multiplying power of the active load to be solved in the s-th typical scene;
(2) Safety constraints, such as formulas (16) - (18):
(16)；
(17)；
(18)；
representing the upper limit value of the active power flow of the line,/> Representing the upper limit value of the active power carried by the transformer substation, and obtaining the two values by looking at the upper limit value of the active power and the load power factor carried by the transformer substation,/>Representing the lower limit of the line active power flow, the power flow of the branch ij may be turned after the switch is changed, so/>Is/>Negative values of (2); /(I)Representing a collection of branches without switches of a reduced distribution network,/>Representing a simplified distribution network with a set of branches with switches,/>Representing a switch state to be solved;
(3) Topological radial constraints:
The radial constraint is ensured by the following two constraints, line number = node number-substation number, as in equation (19); the sub-connectivity graph containing substation nodes, as shown in formulas (20) - (23), ensures that the network has neither island nor ring network, and is specifically shown as follows:
(19)；
(20)；
(21)；
(22)；
(23)；
Wherein q represents the switch state of the power distribution network which is not to be solved, Representing the number of nodes of the simplified power distribution network,/>The number of substations of the simplified power distribution network is represented, and the number of the substations is 1; /(I)And/>Respectively representing the active power and the virtual active load of the virtual transformer substation; /(I)Is a small constant; /(I)And/>All are virtual branch active power flows; the switch combination mode is limited, so that the radiation performance can be ensured;
active load multiplying power is obtained according to available transfer capacity model of adjacent feeder line group partitions of power distribution network Its corresponding maximum available transfer capability/>，/>Representing the total load of the adjacent partition, then the adjacent partition may be available to transfer capacity/>Is formula (24):
(24)；
Since the objective function is nonlinear, linearizing the target uses the following formulas (25) - (26):
(25)；
(26)；
Wherein, Is an auxiliary variable introduced.
2. The optimizing method of complex distribution network according to claim 1, wherein in step 1), the network state evaluation index system is evaluated by constructing the network state evaluation system model, and the network state evaluation system model is used for comprehensively evaluating the distribution network.
3. The optimization method of a complex power distribution network with trigger function according to claim 2, wherein the specific steps of constructing the network state evaluation system model are as follows:
step a): determining the structure of a network state evaluation system model;
step b): introducing indexes;
Taking the main transformer load rate, the line load rate, the network loss, the load balancing index and the voltage quality as indexes to formulate a network state evaluation system;
(1) Main transformer load factor:
The current electric load occupation condition of the main transformer is used, the load rate of the main transformer is used as an index for evaluating the electric load occupation condition of the main transformer, the ratio of the current load born by the main transformer relative to the rated capacity of the main transformer is reflected, and the load rate of the main transformer can be expressed as a formula (1):
(1)；
Wherein, Representing the load factor of the main transformer,/>Representing the main transformer load,/>Representing the rated capacity of the main transformer;
(2) Line load factor:
The line load factor is used for evaluating the electrical load occupation condition of the power line, and represents the ratio of the load currently born by the power line to the rated capacity thereof, and the calculation formula (2) of the line load factor is as follows:
(2)；
Wherein, Representing line load factor,/>Representing line load,/>Representing the rated capacity of the line;
(3) Network loss:
The network loss is energy loss in the transmission and distribution process of the power system, and is expressed by power, and a calculation formula (3) of the network loss is as follows:
(3)；
Wherein, Representing net loss,/>Representing the total power generationRepresenting the total load;
(4) Load balancing index:
The load balance index is used for evaluating whether the load distribution among all the partitions or nodes in the power system is balanced or not, the distribution condition of the electric load among different areas is reflected, the high load balance index represents more uniform load distribution, and the calculation of the load balance index is as follows in the formula (4):
(4)；
Wherein, Representing a load balancing index; /(I)Representing the maximum load; /(I)Representing a minimum load; /(I)Representing the average load;
(5) Voltage quality:
The voltage quality is an index for measuring the voltage stability provided by the power system, the voltage quality can cause equipment faults and power quality, the voltage quality is measured by adopting power supply voltage deviation, and the measuring mode of the voltage quality adopts the following formula (5):
(5)；
Wherein: representing voltage measurements,/> Representing the system nominal voltage.
4. A method for optimizing a complex distribution network taking into account triggering functions according to claim 1, characterized in that in said step 2), the step of using said CRITIC weight method is as follows:
Step A): establishing a decision;
Comprehensively measuring standard deviation and correlation coefficient based on historical index values by adopting CRITIC weight method according to the established network state evaluation system to objectively assign weight to each index;
Defining multiple criteria decisions explicitly, wherein the multiple criteria decisions comprise explicit decision targets and evaluation indexes related to the decision targets, and the evaluation indexes comprise main transformer load rates, line load rates, network losses, load balancing indexes and voltage quality under the situation of a power distribution network;
Step B): establishing a contrast matrix;
for each pair of evaluation indexes, a comparison matrix is constructed, the relative importance between the two indexes is represented, the element values in the comparison matrix are 1, 3 and 5, and then a comparison matrix is constructed by singular element values;
step C): calculating relative weights;
calculating the relative weight of each evaluation index relative to other indexes by using the comparison matrix, and calculating a consistency index to ensure the consistency of the comparison matrix, wherein for the calculation of the relative weight, a consistency matrix CM and a consistency index CI are used;
The expression of the consistency matrix CM is formula (6):
(6)；
Wherein, Is the value of the j-th element of the i-th row in the comparison matrix,/>Is the sum of the j-th columns of the comparison matrix;
The consistency index CI is used for measuring consistency, and the expression is formula (7):
(7)；
Wherein, Is the maximum eigenvalue of the consistency matrix, and n is the number of evaluation indexes;
step D): establishing a conflict matrix;
for each pair of evaluation indexes, the conflict between the indexes needs to be calculated, the conflict represents the mutual relation between the indexes, namely whether the indexes have conflict or not, the elements of the conflict matrix are determined according to the relation between the indexes, the value is between 0 and 1, and the degree of the conflict degree is represented;
Step E): calculating objective weights;
Calculating objective weights of all the evaluation indexes by using the relative weights and the conflict matrix, wherein the objective weights comprehensively consider the conflict between the relative weights and the evaluation indexes so as to better reflect the actual importance of the indexes;
for objective weight calculation, the collision matrix and the relative weight can be used for calculation, and the expression is formula (8):
(8)；
Wherein, Objective weight representing evaluation index i,/>The j-th element representing the i-th row of the consistency matrix,The j-th element of the i-th row of the collision matrix is represented.
5. A method for optimizing a complex distribution network taking into account triggering functions according to claim 1, characterized in that in said step 3), said integration threshold is set by the following method:
Step ①: defining a fuzzy number set;
For each single index and composite index, fuzzy sets, i.e. "low", "medium" and "high", are defined, for a certain index X, three fuzzy sets are defined, as follows (9):
(9)；
In the formula (9), the amino acid sequence of the compound, ，/>And/>Fuzzy sets respectively representing membership degrees with different degrees, namely load rates comprise a low load rate, a medium load rate and a high load rate;
Step ②: determining a membership function;
Membership functions describe the relationship between the input value and the membership of fuzzy sets, and for each fuzzy set the appropriate membership function is selected, i.e. triangular, trapezoidal or Gaussian membership functions, expressed as Wherein x represents an input value;
step ③: setting a membership range;
A membership range is defined for each fuzzy set, between 0 and 1, the membership range being expressed by the following formula (10):
(10)；
step ④: defining a threshold fuzzy set;
the threshold fuzzy sets represent thresholds of different degrees, and fuzzy sets representing the thresholds are defined for each single index and the comprehensive index, namely " Is of low threshold ","/>Is a medium threshold value 'and'/>For a high threshold ", for a certain index X, three threshold fuzzy sets can be defined, as follows (11):
(11)；
step ⑤: determining a threshold membership function;
the threshold membership function describes the relationship between the input value and the membership of the threshold fuzzy set, selects the appropriate membership function for the threshold fuzzy set, and the threshold membership function is expressed as Y represents an input value;
step ⑥: setting a threshold membership range:
The threshold membership range represents the degree of membership of the input value to the threshold fuzzy set, a membership range is defined for each threshold fuzzy set, between 0 and 1, the threshold membership range is represented by the following formula (12):
(12)；
step ⑦: calculating membership degree;
For each index, calculating the membership degree of which the value corresponds to the fuzzy set and the threshold fuzzy set, and calculating the membership degree by using the corresponding membership function;
Step ⑧: comprehensive membership degree;
Synthesizing the membership degree of each single index by using fuzzy logic operation, namely minimum or maximum operation, and obtaining the membership degree of the comprehensive index;
step ⑨: determining a threshold value;
Based on the membership of the comprehensive index, determining a threshold of the comprehensive index, determining by using a fuzzy reasoning method, selecting a high threshold if the membership of the comprehensive index is high, and selecting a low threshold if the membership of the comprehensive index is low.
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