CN114825326A - Power distribution network optimization method - Google Patents
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Abstract
The invention discloses a power distribution network optimization method, which comprises the following steps: carrying out simplified analysis on the network; randomly initializing the position of an internal proton group through proton coding; substituting the proton positions into a plurality of objective functions to be optimized; performing a network radial check on each proton in the internal proton population; selecting a proton position corresponding to a solution set in the internal proton group and storing the proton position in the external proton group; evaluating the spatial distribution density of the solution set; the position of the protons in the internal proton group is updated. According to the power distribution network optimization method, the opening and closing states of the switches in the network are controlled less, the trend distribution in the network is changed, the network loss is reduced, the load balance degree is improved, the voltage quality is improved, the operation parameters are closer to the rated values, the performance of equipment is optimized, and the operation of the whole power distribution network is optimized.
Description
The application is a divisional application, the original application is an invention patent named as 'a power distribution network optimization method', the application number of the original application is '2020103089243', and the application date is 2020 and 4 months and 19 days.
Technical Field
The invention relates to the field of power grids, in particular to a power distribution network optimization method.
Background
The distribution network has the characteristics of mesh structure design and open-loop mode operation, a large number of section switches and interconnection switches exist in the network, and under a general condition, the section switches are closed and the interconnection switches are opened, so that the distribution network is in a radial operation structure under the condition that the action of the distributed power supply is not considered. Because the load properties of the connected power distribution network are different and the types of distributed power sources are different, if a certain mode is fixed, the operation can not be carried out in consideration of various conditions and can be kept in the optimal operation mode, and therefore, the operation of the power distribution network is more optimized by carrying out open loop in different places according to different operation working conditions.
Compared with a power transmission network, the load type division of the access power distribution network is finer, the single load capacity is smaller, the load change is more obvious and frequent, the change randomness is very large, and the change characteristic of the load causes the optimal network structure adapting to the load distribution to change with time. Therefore, in order to ensure that the power distribution network runs safely, reliably, high-quality and economically within a period of time, the overall optimal target of the power distribution network within the corresponding period of time is optimized by considering the variation trend and the randomness of the load, and the optimization method is particularly necessary.
Disclosure of Invention
In order to solve the defects and shortcomings in the prior art, the invention provides an optimal control method for a power distribution network, which comprises the following steps.
S1, simplifying and analyzing the network.
All fundamental loops in the distribution network are determined.
All legs not on the basic loop are deleted.
And combining the branches with the same ring-opening effect.
S2, randomly initializing the positions of internal proton groups through proton coding to obtain the feasible solutions.
The position of the internal proton group is randomly initialized by proton encoding, including.
And numbering the switches contained in each basic ring, then taking the number of the basic rings of the power distribution network as the dimension of the proton in the search space, and taking the code number of the switches in the basic rings as the content of the proton for proton coding.
Firstly, any switch in the first loop is set to be disconnected, and the switch is set to be inoperable in other loops; then, any switch in the second loop is set to be disconnected, and the switch is set to be inoperable in the rest loops; the above steps are repeated until all the loops have switches open, at which point one proton initialization is complete. All protons are initialized following this procedure.
For power distribution network optimization under a multi-period condition, the random initialization of the positions of internal proton groups through proton encoding further comprises the following steps.
And separating and storing each substring in the optimal solution set and the second optimal solution which are subjected to the previous multi-period optimization control in a feasible solution library, wherein each substring represents a feasible and better network topology structure.
And randomly selecting substrings from the feasible solution library to form a proton coding form meeting the current multi-period optimization control requirement, and forming an initial proton group during the current multi-period optimization control.
And S3, substituting the proton positions into a plurality of objective functions to be optimized, and calculating the fitness value.
After the internal proton group is initialized, power flow calculation is needed to obtain the fitness function value of the proton which accords with radial constraint. The value of the fitness function is the basis of the proton group algorithm for guiding the search direction, the objective functions of the power distribution network optimization control are a network loss function, a voltage offset rate function and a switching action time function, and the fitness function is the three objective functions. The calculating the fitness value includes calculating a network loss, a maximum voltage offset, and a number of switching actions for the proton position that satisfy the radial constraint.
And S4, carrying out network radial verification on each proton in the internal proton group, and if the verification condition is not met, indicating that the proton is an infeasible solution.
And checking the radial structure, and gradually traversing the downstream nodes from the root node. The switch which starts to be searched is a first-layer switch, if the searched switch is closed, the area connected with the downstream of the switch can be searched and is marked as '1'; if a switch is open, the area downstream of this switch is not traversable, denoted as "0". After all the switches of the first layer are searched, the area marked as "1" is stored in a list. And recording the switches directly connected with the live areas in the list as second-layer switches, traversing all the second-layer switches, determining the live areas, updating the list, recording the switches directly connected with the live areas in the list as third-layer switches, and continuously repeating the process until all the switches are traversed.
If any area is searched more than once in the searching process, a loop exists, and the radial test is not established.
And S5, selecting a proton position corresponding to the solution set in the internal proton group, storing the proton position in the external proton group, and constructing a three-dimensional space to store the external proton group.
S6, evaluating the spatial distribution density of the solution set, and selecting the position of the proton with the minimum spatial distribution density as a first optimal solution; and selecting a second optimal solution of each proton in the internal proton group according to a second optimal solution updating strategy.
The target space is equally divided into a plurality of areas by a multi-dimensional stereo grid, and the number of protons contained in each area is used as the density value of the protons. The density value of the proton is larger when the number of protons contained in the grid where the protons are located is larger, and vice versa. If the newly added solution exceeds the boundaries of the current grid, the grid information will be recalculated and each proton will be repositioned. Selecting the position of a first optimal solution according to the obtained internal proton space distribution density; the lower the density value of the spatial distribution of the proton position is, the greater the probability that the proton position is selected, and vice versa.
The selection method of the first optimal solution comprises the following steps: firstly, calculating the number of protons contained in each three-dimensional space grid, taking the number of protons as the density of the grid, and selecting the grid with the lowest density by using a roulette method; a proton position is then randomly selected in this spatial grid as the first optimal solution.
Selecting a second optimal solution for each proton in the internal proton group according to a second optimal solution update strategy, comprising: if the current proton's position is dominated by its second optimal solution, then the second optimal solution is not updated; if the second optimal solution is dominated by the position of the current proton, the position of the current proton replaces the second optimal solution; and if the two solutions are not mutually dominant, randomly selecting one of the solutions as a second optimal solution.
S7, updating the position of the proton in the internal proton group, judging whether a preset stopping criterion (usually set as the maximum iteration number) is reached, and if so, stopping iteration; otherwise, return to step S4.
Preferably, the coded information of the proton code comprises three parts, wherein the first part represents the position of the proton in the search space and has the function of updating the proton group through the position of the proton group and the movement speed of the proton group; the second part represents the position of the protons in the target space, i.e. the value of the respective objective function, whose role is to determine the solution set in the internal proton groups; the third part represents density information of protons, the density information comprises proton density and the number of grids where the protons are located, and the density information is used for selecting a first optimal solution of the protons and deleting redundant protons in the external proton group.
According to the power distribution network optimization method, the opening and closing states of the switches in the network are controlled less, the trend distribution in the network is changed, the network loss is reduced, the load balance degree is improved, the voltage quality is improved, the operation parameters are closer to the rated values, the performance of equipment is optimized, and the operation of the whole power distribution network is optimized.
Drawings
Fig. 1 is a flow chart of a power distribution network optimization method of the present invention.
Detailed Description
As shown in fig. 1, the method for optimizing a power distribution network of the present invention includes.
S1, simplifying and analyzing the network.
When an optimization algorithm is used for power distribution network optimization control decision making, due to the fact that generated solutions are random, a large number of invalid solutions can be generated in the initialization and iteration processes, the invalid solutions mean that a power distribution network structure restored through decoding does not meet network topology constraint conditions, namely a 'ring network' or an 'island' exists. The existence of the invalid solution greatly increases the search space of the power distribution network control and reduces the search efficiency. Therefore, it should be sought to avoid the generation of invalid solutions during the optimization process. In addition, because the actual distribution network is large in scale and multiple in branches, the number of optimization variables is huge, and when an algorithm is applied, the code is too long, and the network needs to be simplified to a certain extent.
All fundamental loops in the distribution network are determined.
All legs not on the basic loop are deleted.
And combining the branches with the same ring-opening effect.
S2, randomly initializing the positions of internal proton groups through proton coding to obtain the feasible solutions.
The position of the internal proton group is randomly initialized by proton encoding, including.
And numbering the switches contained in each basic ring, then taking the number of the basic rings of the power distribution network as the dimension of the proton in the search space, and taking the code number of the switches in the basic rings as the content of the proton for proton coding.
Firstly, any switch in the first loop is set to be disconnected, and the switch is set to be inoperable in other loops; then, any switch in the second loop is set to be disconnected, and the switch is set to be inoperable in the rest loops; the above steps are repeated until all the loops have switches open, at which point one proton initialization is complete. All protons are initialized following this procedure.
When a proton cluster algorithm is used for processing the control problem of the power distribution network, a large number of invalid solutions are easily generated by setting codes of all switch states, so that the search efficiency is reduced, and the search result is influenced. Therefore, proton encoding is required based on network simplification analysis.
When any one of the connection switches is closed, a ring network is inevitably generated, and a certain switch in the loop circuit must be disconnected in order to ensure the operation of the radial structure. Some switches are not on the loop and must be closed in order to ensure that all nodes are supplied, i.e. not islanded, and therefore cannot be used as control variables in control.
For power distribution network optimization under a multi-period condition, the random initialization of the positions of internal proton groups through proton encoding further comprises the following steps.
And separating the substrings in the optimal solution set and the second optimal solution of the previous multi-period optimization control and storing the substrings in a feasible solution library, wherein each substring represents a feasible and better network topology structure.
And randomly selecting substrings from the feasible solution library to form a proton coding form meeting the current multi-period optimization control requirement, and forming an initial proton group during the current multi-period optimization control.
Because the encoding of the protons is expanded from the time level, the optimization capability of the algorithm is reduced due to the increase of the proton dimension, but the efficiency and the effect of the algorithm can be effectively improved if the initial value of the protons is better.
Although the multi-period optimization control schemes under different time interval numbers are different, the control schemes of each time interval in the multi-period optimization control schemes are better solutions, and the network structure change caused by the multi-period optimization control performed twice before and after is not too large. If the optimal solution set obtained in the previous multi-period optimization control (namely the multi-period optimization control scheme stored in the proton optimal solution at the moment) is used as the initial population of the next multi-period optimization control, the solution meeting the requirements can be easily searched near the proton neighborhood by using the local searching capability of the proton population algorithm.
Before the positions of the internal proton groups are initialized randomly through proton coding to obtain a plurality of feasible solutions, parameters such as the internal proton group scale, the external proton group scale and the maximum iteration number need to be set.
And S3, substituting the proton positions into a plurality of objective functions to be optimized, and calculating the fitness value.
After the internal proton group is initialized, power flow calculation is needed to obtain the fitness function value of the proton which accords with radial constraint. The value of the fitness function is the basis of the proton group algorithm for guiding the search direction, the objective functions of the power distribution network optimization control are a network loss function, a voltage offset rate function and a switching action time function, and the fitness function is the three objective functions. The calculating the fitness value includes calculating a network loss, a maximum voltage offset, and a number of switching actions for the proton location that satisfy the radial constraint.
And S4, carrying out network radial verification on each proton in the internal proton group, and if the verification condition is not met, indicating that the proton is an infeasible solution.
And checking the radial structure, and gradually traversing the downstream nodes from the root node. The switch which starts to be searched is a first-layer switch, if the searched switch is closed, the area connected with the downstream of the switch can be searched and is marked as '1'; if the switch is open, the area downstream of this switch is not traversable, denoted as "0". After all the switches of the first layer are searched, the area marked as "1" is stored in a list. And recording the switches directly connected with the live areas in the list as second-layer switches, traversing all the second-layer switches, determining the live areas, updating the list, recording the switches directly connected with the live areas in the list as third-layer switches, and continuously repeating the process until all the switches are traversed.
If any area is searched more than once in the searching process, a loop exists, and the radial test is not established.
And S5, selecting a proton position corresponding to the solution set in the internal proton group, storing the proton position in the external proton group, and constructing a three-dimensional space to store the external proton group.
S6, evaluating the spatial distribution density of the solution set, and selecting the position of the proton with the minimum spatial distribution density as a first optimal solution; and selecting a second optimal solution of each proton in the internal proton group according to a second optimal solution updating strategy.
The spatial distribution density of the individual becomes the main basis for selecting the first optimal solution and deleting the redundant solution set, so the estimation of the spatial distribution density of the protons in the internal proton group is the basis for searching the solution set with good diversity by the algorithm, and is also the main basis for searching the optimal solution.
The target space is equally divided into a plurality of areas by a multi-dimensional stereo grid, and the number of protons contained in each area is used as the density value of the protons. The density value of the proton is larger when the number of protons contained in the grid where the protons are located is larger, and vice versa. If the newly added solution exceeds the boundaries of the current grid, the grid information will be recalculated and each proton will be repositioned. Selecting the position of a first optimal solution according to the obtained internal proton space distribution density; the lower the density value of the spatial distribution of the proton position is, the greater the probability that the proton position is selected, and vice versa.
The selection method of the first optimal solution comprises the following steps: firstly, calculating the number of protons contained in each three-dimensional space grid, taking the number of protons as the density of the grid, and selecting the grid with the lowest density by using a roulette method; a proton position is then randomly selected in this spatial grid as the first optimal solution.
Selecting a second optimal solution for each proton in the internal proton group according to a second optimal solution update strategy, comprising: if the current proton's position is dominated by its second optimal solution, then the second optimal solution is not updated; if the second optimal solution is dominated by the position of the current proton, the position of the current proton replaces the second optimal solution; and if the two solutions are not mutually dominant, randomly selecting one of the solutions as a second optimal solution.
S7, updating the position of the proton in the internal proton group, judging whether a preset stopping criterion (usually set as the maximum iteration number) is reached, and if so, stopping iteration; otherwise, return to step S4.
The coded information of the proton code comprises three parts, wherein the first part represents the position of the proton in the search space and has the function of updating the proton group through the position of the proton group and the movement speed of the proton group; the protons in the space are in a motion state, and the positions of the protons in the proton group can be updated according to the historical positions of the proton group, the motion speed and the motion rule of the protons; the second part represents the position of the protons in the target space, i.e. the value of the respective objective function, whose role is to determine the solution set in the internal proton groups; the third part represents density information of protons, the density information comprises proton density and the number of grids where the protons are located, and the density information is used for selecting a first optimal solution of the protons and deleting redundant protons in the external proton group.
According to the power distribution network optimization method, the opening and closing states of the switches in the network are controlled less, the trend distribution in the network is changed, the network loss is reduced, the load balance degree is improved, the voltage quality is improved, the operation parameters are closer to the rated values, the performance of equipment is optimized, and the operation of the whole power distribution network is optimized.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (10)
1. The power distribution network optimization method is characterized by comprising the following steps:
s1, simplifying and analyzing a network;
s2, randomly initializing the positions of internal proton groups through proton coding to obtain a plurality of feasible solutions;
s3, substituting the proton positions into a plurality of objective functions to be optimized, and calculating a fitness value;
s4, carrying out network radial verification on each proton in the internal proton group, and if the verification condition is not met, indicating that the proton is an infeasible solution;
s5, selecting a proton position corresponding to a solution set in the internal proton group, storing the proton position in the external proton group, and constructing a three-dimensional space to store the external proton group;
s6, evaluating the spatial distribution density of the solution set, and selecting the position of the proton with the minimum spatial distribution density as a first optimal solution; selecting a second optimal solution of each proton in the internal proton group according to a second optimal solution updating strategy;
s7, updating the position of the proton in the internal proton group, judging whether a preset stopping criterion (usually set as the maximum iteration number) is reached, and if so, stopping iteration; otherwise, return to step S4.
2. The method according to claim 1, characterized in that S1. simplified analysis of the network; the method comprises the following steps:
determining all basic loops in the power distribution network;
deleting all branches not on the basic loop;
and combining the branches with the same ring-opening effect.
3. The method of claim 1, wherein the randomly initializing the position of internal proton groups by proton encoding comprises:
and numbering the switches contained in each basic ring, then taking the number of the basic rings of the power distribution network as the dimension of the proton in the search space, and taking the code number of the switches in the basic rings as the content of the proton for proton coding.
4. The method of claim 3, wherein the randomly initializing the position of internal proton groups by proton coding for power distribution grid optimization under multi-period conditions further comprises:
separating the substrings in the optimal solution set and the second optimal solution of the previous multi-period optimization control and storing the substrings in a feasible solution library, wherein each substring represents a feasible and better network topology structure;
and randomly selecting substrings from the feasible solution library to form a proton coding form meeting the current multi-period optimization control requirement, and forming an initial proton group during the current multi-period optimization control.
5. The method of claim 1, wherein a power flow calculation is required after initialization of the internal proton population to obtain a fitness function value of protons that meet radial constraints; the value of the fitness function is the basis of the proton group algorithm for guiding the search direction, the objective functions of the power distribution network optimization control are a network loss function, a voltage offset rate function and a switching action frequency function, and the fitness function is the three objective functions; the calculating the fitness value includes calculating a network loss, a maximum voltage offset, and a number of switching actions for the proton position that satisfy the radial constraint.
6. The method of claim 1, wherein radial structures are examined, traversing downstream nodes step-by-step starting from a root node; if any area is searched more than once in the searching process, a loop exists, and the radial test is not established.
7. The method according to claim 1, characterized in that the target space is equally divided into several regions with a multidimensional stereo grid, and the number of protons contained in each region is taken as the density value of protons; the density value of the proton is larger when the number of protons contained in the grid where the protons are located is larger, and vice versa.
8. The method of claim 1, wherein the first optimal solution is selected by: firstly, calculating the number of protons contained in each three-dimensional space grid, taking the number of protons as the density of the grid, and selecting the grid with the lowest density by using a roulette method; a proton position is then randomly selected in this spatial grid as the first optimal solution.
9. The method of claim 1, wherein selecting a second optimal solution for each proton in the internal proton population according to a second optimal solution update strategy comprises: if the current proton's position is dominated by its second optimal solution, then the second optimal solution is not updated; if the second optimal solution is dominated by the position of the current proton, the position of the current proton replaces the second optimal solution; and if the two solutions are not mutually dominant, randomly selecting one of the solutions as a second optimal solution.
10. The method of claim 1,
the coded information of the proton code comprises three parts, wherein the first part represents the position of the proton in the search space and has the function of updating the proton group through the position of the proton group and the movement speed of the proton group; the second part represents the position of the protons in the target space, i.e. the value of the respective objective function, whose role is to determine the solution set in the internal proton groups; the third part represents density information of protons, the density information comprises proton density and the number of grids where the protons are located, and the density information is used for selecting a first optimal solution of the protons and deleting redundant protons in the external proton group.
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