CN111598294A - Active power distribution network reconstruction algorithm and device based on improved teaching optimization - Google Patents
Active power distribution network reconstruction algorithm and device based on improved teaching optimization Download PDFInfo
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- CN111598294A CN111598294A CN202010284990.1A CN202010284990A CN111598294A CN 111598294 A CN111598294 A CN 111598294A CN 202010284990 A CN202010284990 A CN 202010284990A CN 111598294 A CN111598294 A CN 111598294A
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
An active power distribution network reconstruction algorithm and device based on improved teaching optimization, wherein the algorithm comprises the following steps: (1) defining the minimization of the expected value of the active network loss as a target function to obtain the expected value of the active network loss; (2) introducing a self-adaptive teaching factor and a self-learning mechanism on the basis of a traditional teaching optimization algorithm; (3) and a non-repetitive spanning tree strategy is adopted to quickly optimize and reconstruct the power supply path of the distributed power supply. The apparatus includes a computer readable storage medium having a computer program stored thereon for execution by a processor of an associated program. The invention considers the output fluctuation of the wind driven generator and the photovoltaic system in the active power distribution network containing the distributed power supply, takes the minimum network loss as a target function, provides an improved teaching optimization algorithm introducing a self-adaptive teaching factor and a self-learning mechanism, avoids the situation of falling into local optimization when processing complex problems, can improve the operation speed and the global search capability, and is more suitable for the optimization of the power supply path of the power distribution network.
Description
Technical Field
The invention relates to an active power distribution network reconstruction algorithm and device based on improved teaching optimization, and belongs to the technical field of distributed power supply protection and control.
Background
As more and more distributed power sources are plugged into the grid, the uncertainty of the distributed power sources may cause the distribution grid voltage to fluctuate significantly, resulting in overvoltage. This increases the complexity of the distribution network and makes reconfiguration of the distribution network difficult. However, the grid does not exclude these distributed energy sources. The active power distribution network can efficiently utilize renewable energy sources and realize flexible control of bidirectional tide, so that the key is how to realize the active power distribution network reconstruction strategy containing distributed power generation.
Disclosure of Invention
The invention aims to provide an active power distribution network reconstruction algorithm and device based on improved teaching optimization aiming at the output fluctuation of a wind driven generator and a photovoltaic system in an active power distribution network containing a distributed power supply and avoiding the situation that the output fluctuation falls into local optimization when complex problems are processed, so that the operation speed and the global search capability are improved, and the active power distribution network reconstruction algorithm and device are more suitable for optimization of a power supply path of the power distribution network.
The technical scheme of the invention comprises the following steps that an active power distribution network reconstruction algorithm based on improved teaching optimization comprises the following steps:
(1) defining the minimization of the expected value of the active network loss as an objective function, taking the optimization and reconstruction of a power supply path into consideration to reduce the active network loss to the maximum extent, and taking branch current, node voltage, power balance and distributed power supply power constraint into consideration to obtain the expected value L of the active network loss in the scene jj：
In the formula, E is the total expected value of the system active network loss; m is the number of scenes; p is a radical ofjAnd LjProbability and active network loss expected value in scene j are respectively; l is the total number of branches; r isiIs the impedance of branch i; pij、QijAnd UijRespectively the active, reactive and voltage of branch i in scene j.
(2) Introducing a self-adaptive teaching factor and a self-learning mechanism on the basis of a traditional teaching optimization algorithm;
the teaching factor can only be 1 or 0 before improvement, and students can either completely understand or not understand in teaching. But each student has different learning abilities and different knowledge acquired from the teacher. The learning ability is stronger, the learning is faster, the distance between teachers and students is larger in the early stage, the learning speed is high, but the learning speed is reduced along with the improvement of the level of students. The teaching factor TF determines the average variation, with larger searches being faster and smaller searches being finer. Therefore, an adaptation factor is proposed, which decays linearly with iteration. TF is improved to enable the early search to quickly converge to the optimal solution, and accuracy is improved through simplified search in the later period, so that the search of the algorithm can be dynamically adjusted in a self-adaptive mode.
The adaptive factor linearly attenuates along with iteration, the adaptive factor is improved to enable early search to quickly converge to an optimal solution, accuracy is improved through simplified search in the later period, the search of an algorithm can be dynamically and adaptively adjusted, and the expression of the adaptive factor TF is as follows:
in the formula, itermaxAnd iter are respectively the maximum iteration number and the current iteration number; TF is an adaptive factor, TFmaxAnd TFminRespectively the maximum value and the minimum value of the teaching factor;
the learning method of the self-learning mechanism comprises the following steps:
in the formula (I), the compound is shown in the specification,
and
respectively shows the scores, r, of the subject k of the student i before and after teachingiFor learning the step factor, Progress is the iteration increment, and t iterations are performed.
(3) In order to achieve the aim of minimizing network faults, a non-repetitive spanning tree strategy is adopted, and the power supply path of the distributed power supply is rapidly optimized and reconstructed.
The step of reconstructing the power supply path of the distributed power supply comprises the following steps:
(1) initializing power grid parameters and inputting related data;
(2) initializing power grid parameters and inputting related data;
(3) generating various radial network structures by adopting a non-repetitive spanning tree strategy based on the simplified power distribution network; that is, students of different classes determine an initial population and encode the initial population;
(4) initializing codes, and calculating a fitness value by using the power flow; then finding out students with the best learning experience;
(5) calculating the average number of shifts, implementing a 'teaching' stage, gradually increasing the knowledge of students through teaching, and continuously updating the positions; otherwise, abandoning the teaching process;
(6) in the learning stage, the positions of the students are updated along with the improvement of the knowledge of the students; otherwise, the process is terminated;
(7) in the self-learning stage, the position is continuously updated along with the improvement of the knowledge of the students; otherwise, the self-learning process is terminated;
(8) and (4) outputting the optimal fitness and determining the current optimal reconstruction scheme in order to verify whether the search termination condition is met, otherwise, turning to the step (4).
The method considers the output fluctuation of the wind driven generator and the photovoltaic system, avoids the situation of falling into local optimization when complex problems are processed, can improve the operation speed and the global search capability, and is more suitable for the optimization of the power supply path of the power distribution network.
An apparatus for improved pedagogically optimized active power distribution network reconstruction algorithm, comprising a computer readable storage medium having a computer program stored thereon, the program when executed by a processor implementing the steps of:
(1) inputting power data and related parameters;
(2) reasonably processing the distributed power supply according to time period division;
(3) determining an initial population by using a no-repeat spanning tree strategy;
(4) finding out an optimal solution according to power distribution network load flow calculation;
(5) entering a teaching stage, calculating the average value of the class, comparing the previous value and the next value to generate a new solution, and calculating a target function;
(6) entering a learning stage, receiving a new solution of the teaching stage, comparing the previous value and the next value to generate a new solution, and calculating a target function;
(7) entering an improvement stage, and updating the individuals by utilizing self-learning; and calculating an objective function, iterating until the maximum iteration is reached, and outputting a result.
The method has the advantages that the method considers the output fluctuation of the wind driven generator and the photovoltaic system in the active power distribution network containing the distributed power supply, takes the minimized network loss as a target function, provides an improved teaching optimization algorithm introducing a self-adaptive teaching factor and a self-learning mechanism, avoids the situation that the local optimization is involved when complex problems are processed, can improve the operation speed and the global search capability, and is more suitable for the optimization of the power supply path of the power distribution network.
Drawings
FIG. 1 is a power distribution network reconfiguration optimization process of the present invention;
fig. 2 is a diagram of a power distribution network contact system.
Detailed Description
A specific embodiment of the present invention is shown in fig. 1.
The embodiment of the invention provides an active power distribution network reconstruction algorithm based on improved teaching optimization, which comprises the following steps:
(1) defining the minimization of the expected value of the loss of the active power network as a target function;
(2) considering constraint conditions such as branch current, node voltage, power balance and distributed power supply power, and the like, establishing a target constraint function;
(3) initializing power grid parameters and inputting related data by adopting an improved teaching optimization algorithm; and processing the distributed power supply by adopting a corresponding method according to the divided time periods.
(4) Various radial network structures are generated based on a simplified power distribution network using a no-duplication spanning tree strategy. I.e. students of different classes determine and code the initial population.
(5) And initializing codes and calculating a fitness value by using the power flow. Then the students who learn the best show are found.
(6) And calculating the average number of shifts, gradually increasing the knowledge of students through teaching, and continuously updating the positions. Otherwise, the teaching process is abandoned.
(7) In the "learning" stage, the student's location is updated as his knowledge improves. Otherwise, the process is terminated.
(8) In the self-learning stage, the position is continuously updated along with the improvement of the knowledge of the students. Otherwise, the self-learning process is terminated.
(9) And outputting the optimal fitness and determining the current optimal reconstruction scheme.
As shown in fig. 2, taking the node power distribution network system diagram in fig. 2 as an example, 3 1MW fans are incorporated at nodes 10, 27 and 68, and 1MW photovoltaic power is connected at nodes 34, 52 and 56.
Monthly average wind speed and luminosity data for a region are simulated. Taking a certain day as an example, one day is divided into six scenes according to photovoltaic and wind energy loads and access conditions, as shown in table 1.
TABLE 1 wind-solar Access situation at times
The method provided by the embodiment can optimize the network power supply path. Where 0 indicates no distributed power access, 1 indicates distributed power access, the lowest node voltage is the lower limit of the lowest voltage confidence interval at 95% confidence, and the data pair ratio is shown in table 2.
TABLE 2 comparison before and after optimization of node switch combinations
Analysis of the results leads to the conclusion that distributed power access before reconstruction can reduce loss of the system network and improve node voltage distribution. After reconstruction, the total expected network loss of the system is reduced from 194.89kw to 80.53kw, the network loss is reduced by 58.68%, the minimum node voltage is improved from 0.961 zero to 1.018, and the economy and the reliability of the system are improved.
Therefore, the active power distribution network reconstruction algorithm based on the improved teaching optimization provided by the embodiment considers the output fluctuation of the wind driven generator and the photovoltaic system in the active power distribution network containing the distributed power supply, takes the minimized grid loss as a target function, and provides the improved teaching optimization algorithm introducing the self-adaptive teaching factor and the self-learning mechanism, so that the situation that the local optimization is involved in processing complex problems is avoided, the operation speed and the global search capability can be improved, and the algorithm is more suitable for the optimization of the power supply path of the power distribution network.
Claims (3)
1. An active power distribution network reconstruction algorithm based on improved teaching optimization is characterized by comprising the following steps:
(1) defining the expected value of the loss of the active networkThe method comprises the steps of reducing the power supply path as an objective function, considering the optimization and reconstruction of the power supply path to reduce the active network loss to the maximum extent, considering branch current, node voltage, power balance and distributed power supply power constraint, and obtaining the expected value L of the active network loss in the scene jj：
In the formula, E is the total expected value of the system active network loss; m is the number of scenes; p is a radical ofjAnd LjProbability and active network loss expected value in scene j are respectively; l is the total number of branches; r isiIs the impedance of branch i; pij、QijAnd UijRespectively the active power, the reactive power and the voltage of the branch i in the scene j;
(2) introducing a self-adaptive teaching factor and a self-learning mechanism on the basis of a traditional teaching optimization algorithm;
the adaptive factor linearly attenuates along with iteration, the adaptive factor is improved to enable early search to quickly converge to an optimal solution, accuracy is improved through simplified search in the later period, the search of an algorithm can be dynamically and adaptively adjusted, and the expression of the adaptive factor TF is as follows:
in the formula, itermaxAnd iter are respectively the maximum iteration number and the current iteration number; TF is an adaptive factor, TFmaxAnd TFminRespectively the maximum value and the minimum value of the teaching factor;
the learning method of the self-learning mechanism comprises the following steps:
in the formula (I), the compound is shown in the specification,
and
respectively shows the scores, r, of the subject k of the student i before and after teachingiFor learning step size factor, Progress is iteration increment, and t times of iteration;
(3) in order to achieve the aim of minimizing network faults, a non-repetitive spanning tree strategy is adopted, and the power supply path of the distributed power supply is rapidly optimized and reconstructed.
2. The improved teaching optimization-based active power distribution network reconstruction algorithm according to claim 1, wherein the step of reconstructing the power supply path of the distributed power supply is as follows:
(1) initializing power grid parameters and inputting related data;
(2) initializing power grid parameters and inputting related data;
(3) generating various radial network structures by adopting a non-repetitive spanning tree strategy based on the simplified power distribution network; that is, students of different classes determine an initial population and encode the initial population;
(4) initializing codes, and calculating a fitness value by using the power flow; then finding out students with the best learning experience;
(5) calculating the average number of shifts, implementing a 'teaching' stage, gradually increasing the knowledge of students through teaching, and continuously updating the positions; otherwise, abandoning the teaching process;
(6) in the learning stage, the positions of the students are updated along with the improvement of the knowledge of the students; otherwise, the process is terminated;
(7) in the self-learning stage, the position is continuously updated along with the improvement of the knowledge of the students; otherwise, the self-learning process is terminated;
(8) and (4) outputting the optimal fitness and determining the current optimal reconstruction scheme in order to verify whether the search termination condition is met, otherwise, turning to the step (4).
3. Apparatus for implementing an improved pedagogically optimized active power distribution grid reconfiguration algorithm according to claims 1-2, comprising a computer-readable storage medium having a computer program stored thereon, wherein the program when executed by a processor performs the steps of:
(1) inputting power data and related parameters;
(2) reasonably processing the distributed power supply according to time period division;
(3) determining an initial population by using a no-repeat spanning tree strategy;
(4) finding out an optimal solution according to power distribution network load flow calculation;
(5) entering a teaching stage, calculating the average value of the class, comparing the previous value and the next value to generate a new solution, and calculating a target function;
(6) entering a learning stage, receiving a new solution of the teaching stage, comparing the previous value and the next value to generate a new solution, and calculating a target function;
(7) entering an improvement stage, and updating the individuals by utilizing self-learning; and calculating an objective function, iterating until the maximum iteration is reached, and outputting a result.
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