CN114566982A - Micro-grid secondary frequency self-adaptive system and control method thereof - Google Patents

Micro-grid secondary frequency self-adaptive system and control method thereof Download PDF

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CN114566982A
CN114566982A CN202210085945.2A CN202210085945A CN114566982A CN 114566982 A CN114566982 A CN 114566982A CN 202210085945 A CN202210085945 A CN 202210085945A CN 114566982 A CN114566982 A CN 114566982A
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施永
程寅
苏建徽
赖纪东
解宝
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Abstract

The embodiment of the invention provides a micro-grid secondary frequency self-adaptive system and a control method thereof, belonging to the technical field of micro-grid secondary frequency control. The control method comprises the steps of obtaining an initial equivalent coefficient, a proportional coefficient and an integral coefficient of the microgrid; coding according to the initial equivalent coefficient to obtain an initial population; and calculating the optimal solution of the equivalent coefficient, the proportional coefficient and the integral coefficient according to the population by adopting a genetic algorithm. According to the method, the initial equivalent coefficient of the microgrid is obtained and encoded to form a population, the fitness function value of the population is calculated by adopting a genetic algorithm, the optimal solution of the equivalent coefficient, the proportional coefficient and the integral coefficient is obtained according to the fitness function value, and finally the distributed power of the microgrid is regulated according to the optimal demodulation, so that the rapid frequency modulation of the microgrid system is realized, the rapid recovery of the frequency of the microgrid is facilitated, and the stability of the microgrid system is guaranteed.

Description

Micro-grid secondary frequency self-adaptive system and control method thereof
Technical Field
The invention relates to the technical field of micro-grid secondary frequency control, in particular to a micro-grid secondary frequency self-adaptive system and a control method thereof.
Background
When a microgrid island runs, an energy storage converter usually adopts control methods such as droop control and virtual synchronous generator control to stabilize the frequency of a system. Because the control method of the energy storage converter enables the frequency in the system to change along with the change of the load, a secondary frequency modulation strategy is needed to realize the frequency differential-free control so as to improve the power supply quality of the micro-grid system. However, the switching of power generation equipment in the micro-grid is frequent, the network structure is constantly changed, the adaptability of the secondary frequency modulation controller is poor, and the parameter setting of the controller is difficult.
At present, proportional-integral control, model predictive control, dynamic matrix control and the like are mainly used, but no matter which control method is used, the parameters of the controlled object need to be designed by using a model of the controlled object, so that modeling of a micro-grid system is the basis of optimal design of a secondary frequency modulation controller. In the prior art, according to the characteristics of virtual synchronous machine control, a parameter identification method based on a least square method and a genetic algorithm is provided to identify the damping coefficient of the virtual synchronous machine, but the method focuses on parameter identification of a single converter model, is not applied to identification of the whole microgrid, and is poor in universality.
The inventor of the present application finds, in the process of implementing the present invention, that the above-described solution of the prior art has a problem that it cannot be applied to the entire microgrid, and thus the versatility is poor.
Disclosure of Invention
The embodiment of the invention aims to provide a micro-grid secondary frequency self-adaptive system and a control method thereof, and the micro-grid secondary frequency self-adaptive system and the control method thereof can realize the setting of controller parameters of the whole micro-grid.
In order to achieve the above object, an aspect of the embodiments of the present invention provides a microgrid secondary frequency adaptive system and a control method thereof, where the control method includes:
acquiring an initial equivalent coefficient, a proportional coefficient and an integral coefficient of the microgrid;
coding according to the initial equivalent coefficient to obtain an initial population;
calculating the optimal solution of the equivalence coefficient, the proportionality coefficient and the integral coefficient according to the population by adopting a genetic algorithm, wherein the genetic algorithm comprises the following steps:
calculating a fitness function value of the population according to the formulas (2) to (4),
Figure BDA0003486896180000021
wherein Fitness is the Fitness function value, i is an integer number, f (i) is a frequency value calculated according to the ith individual, f' (i) is a frequency value obtained by sampling, and n is the number of the individuals in the population;
f(i)＝Af(i-2)+Bf(i-1)+CP(i-1)+DP(i-2)， (2)
Figure BDA0003486896180000022
wherein, KmIs an initial constant coefficient, KpIs an initial proportionality coefficient, KiIs an initial integral coefficient, h is a sampling step length, tau is a sampling period, and P (i-1) is an i-1 th individual power value;
and adjusting the distributed power of the microgrid according to the optimal solution.
Optionally, the encoding according to the initial equivalent coefficient to obtain an initial population includes:
randomly initializing a plurality of values according to the initial equivalent coefficient to form an equivalent coefficient set;
binary coding each value in the set of equivalent coefficients to form a basic population;
calculating a Gray code for each individual in the population according to equation (8),
B＝bmbm-1...b2b1，
G＝gmgm-1…g2g1，
Figure BDA0003486896180000031
wherein B is a binary code, G is a Gray code, m is a coding bit number and is an integer number, GmIs the coded value of the mth bit of the Gray code, bmIs the coded value of the m-th bit of the binary code, giIs the coded value of i-th bit of the Gray code, biIs the coded value of the ith bit of the binary code, bi+1The coded value of the (i + 1) th bit of the binary code is represented, wherein i is an integer number;
and forming an initial population according to the gray code of each individual in the population.
Optionally, calculating an optimal solution of the equivalence coefficient, the proportionality coefficient, and the integral coefficient according to the population by using a genetic algorithm includes:
calculating a fitness function value of the population;
judging whether the fitness function value of the population is less than or equal to a preset threshold value or not;
under the condition that the fitness function value of the population is judged to be larger than a preset threshold value, selecting a new generation of individuals with the same quantity as the population according to the fitness function value of each individual in the population;
performing copy operation, cross operation and mutation operation on the selected new generation individuals to form a new generation population;
and returning to the step of calculating the fitness function value of the population again.
Optionally, calculating an optimal solution of the equivalence coefficient, the proportionality coefficient, and the integral coefficient according to the population by using a genetic algorithm includes:
selecting the minimum value of the fitness function values of the single individuals in the population under the condition that the fitness function value of the population is judged to be smaller than or equal to a preset threshold value;
identifying the optimal equivalent coefficient corresponding to the minimum value;
calculating corresponding proportional coefficients and integral coefficients according to the optimal equivalent coefficients;
and adjusting the distribution power of the microgrid according to the equivalent coefficient, the proportional coefficient and the integral coefficient.
Optionally, calculating an optimal solution of the equivalence coefficient, the proportionality coefficient, and the integral coefficient according to the population by using a genetic algorithm includes:
presetting an iteration time threshold;
judging whether the iteration times at the moment is larger than or equal to the iteration time threshold value or not;
under the condition that the iteration times at the moment are judged to be smaller than the iteration time threshold value, selecting a new generation of individuals with the same quantity as the population according to the fitness function value of each individual in the population;
performing copy operation, cross operation and mutation operation on the selected new generation individuals to form a new generation population;
and returning to the step of calculating the fitness function value of the population again.
Optionally, calculating an optimal solution of the equivalence coefficient, the proportionality coefficient, and the integral coefficient according to the population by using a genetic algorithm includes:
selecting the minimum value of the fitness function values of the single individuals in the population under the condition that the iteration times at the moment is judged to be greater than or equal to the iteration time threshold;
identifying the optimal equivalent coefficient corresponding to the minimum value;
and adjusting the distribution power of the microgrid according to the equivalent coefficient, the proportional coefficient and the integral coefficient.
Optionally, the copying operation and the interleaving operation include:
the population is duplicated to form a new generation of population according to equation (11),
Gi+1＝Gi， (11)
wherein G isi+1Is the i +1 th gray code, GiIs the ith code of GrayI is an integer number;
the population is cross-operated according to equation (12) to form a new generation of population,
Gij＝gmijg(m-1)ij…g2ijg1ij，
Gin＝gming(m-1)in…g2ing1in，
G(i+1)j＝gming(m-1)in…g2ijg1ij，
G(i+1)n＝gmijg(m-1)ij…g2ing1in， (12)
wherein G isijIs the jth Gray code in the ith generation group, GinIs the nth Gray code in the ith generation population, G(i+1)jIs the jth Gray code in the i +1 generation group, G(i+1)nIs the nth Gray code in the i +1 generation population, and j and n are integer numbers.
Optionally, the mutation operation comprises:
randomly selecting a plurality of individuals from the current population as individuals to be mutated;
randomly selecting a plurality of coding bits from the individuals to be mutated as the positions to be mutated aiming at each individual to be mutated;
and replacing the current coding value by the anti-code of the corresponding coding value at the position to be mutated.
Optionally, calculating a corresponding proportional coefficient and an integral coefficient according to the equivalent coefficient corresponding to each individual in the new generation of population includes:
calculating a scaling factor according to equation (13);
Figure BDA0003486896180000051
wherein, KpIs the proportionality coefficient, Kmτ is a time constant for the identified equivalence coefficients;
the integral coefficient is calculated according to the formula (14),
Figure BDA0003486896180000052
wherein, KiIs the integral coefficient;
the open loop transfer function of the quadric frequency modulation control system is obtained according to the formula (15),
Figure BDA0003486896180000053
wherein G isOP(s) is the open loop transfer function, KzIn order to achieve an equivalent output characteristic,
Figure BDA0003486896180000054
Figure BDA0003486896180000055
in another aspect, the present invention further provides a micro-grid secondary frequency adaptive system, including:
the photovoltaic unit is used for photovoltaic power generation;
the wind power unit is used for wind power generation;
the energy storage converters are connected in parallel and are respectively connected with the photovoltaic unit and the wind power unit;
the storage device is connected with the plurality of energy storage converters and is used for storing electric energy generated by the photovoltaic unit and the wind power unit;
one end of the intelligent switch is connected with one of the energy storage converters, and the other end of the intelligent switch is used for being connected with a power grid;
a plurality of loads connected to the energy storage converter;
and the microgrid central controller is used for executing the control method.
According to the technical scheme, the microgrid secondary frequency self-adaptive system and the control method thereof provided by the invention form a population by acquiring the initial equivalent coefficient of the microgrid and coding the initial equivalent coefficient, calculate the fitness function value of the population by adopting a genetic algorithm, acquire the optimal solution of the equivalent coefficient, the proportionality coefficient and the integral coefficient according to the fitness function value, and finally regulate the distribution power of the microgrid according to the optimal demodulation so as to realize the rapid frequency modulation of the microgrid system, facilitate the rapid recovery of the microgrid frequency and ensure the stability of the microgrid system.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.
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The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the embodiments of the invention without limiting the embodiments of the invention. In the drawings:
fig. 1 is a flowchart of a microgrid secondary frequency adaptive control method according to one embodiment of the present invention;
fig. 2 is a schematic flow chart of codes in a control method for micro-grid secondary frequency adaptation according to an embodiment of the invention;
fig. 3 is a schematic flow chart of a genetic algorithm in a microgrid secondary frequency adaptive control method according to an embodiment of the present invention;
fig. 4 is a schematic flow chart of a genetic algorithm in a microgrid secondary frequency adaptive control method according to an embodiment of the present invention;
fig. 5 is a schematic flow chart illustrating a variant operation in a method for controlling adaptive control of secondary frequency of a microgrid according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a microgrid secondary frequency adaptation system according to an embodiment of the present invention;
FIG. 7 is a diagram of the identification results of the genetic algorithm optimization process of the microgrid secondary frequency adaptive system according to one embodiment of the present invention;
fig. 8 is an identification value of an equivalent coefficient of a frequency modulation control system of a microgrid secondary frequency adaptive system according to an embodiment of the present invention;
fig. 9 is a frequency output waveform after online optimization by a controller of a microgrid secondary frequency adaptive system according to an embodiment of the present invention.
Description of the reference numerals
01. Photovoltaic unit 02 and wind power unit
03. Load 04 and energy storage device
05. Microgrid central controller 06 and energy storage converter
07. Intelligent switch 08 and power grid
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating embodiments of the invention, are given by way of illustration and explanation only, not limitation.
Fig. 1 is a flowchart of a method for controlling adaptive secondary frequency of a microgrid according to one embodiment of the present invention. In fig. 1, the control method may include:
in step S10, an initial equivalence coefficient, a proportionality coefficient, and an integration coefficient of the microgrid are acquired. The initial equivalent coefficient, the proportional coefficient and the integral coefficient are preset values, and the micro-grid central controller can carry out droop control on the frequency of the micro-grid according to the initial equivalent coefficient, the initial proportional coefficient and the initial integral coefficient so as to guarantee the stability of the micro-grid system. The initial equivalence coefficient of the microgrid system can be calculated according to the formula (1),
Figure BDA0003486896180000081
wherein, KmIs an initial equivalent coefficient, f is the real-time frequency of the microgrid system, foIs the reference frequency, f, of the microgrid systemoiFor the i-th converter in the microgrid systemoiThe reference power of the ith converter in the micro-grid system, P is the real-time power of the micro-grid system, KmiThe coefficient is the equivalent coefficient of the ith converter.
In step S11, encoding is performed according to the initial equivalent coefficients to obtain an initial population. After the initial equivalent coefficients are obtained, the initial equivalent coefficients are subjected to randomization processing to obtain an initial equivalent coefficient set, and each individual in the initial equivalent coefficient set is encoded to obtain an initial population.
In step S12, an optimal solution of the equivalence coefficient, the proportionality coefficient, and the integral coefficient is calculated from the population using a genetic algorithm, wherein the genetic algorithm includes calculating fitness function values of the population according to equations (2) to (4),
f(i)＝Af(i-2)+Bf(i-1)+CP(i-1)+DP(i-2)， (2)
Figure BDA0003486896180000082
wherein, KpIs an initial proportionality coefficient, KiTaking 10 as the initial integral coefficient and h as the sampling step length-3τ is the sampling period, 20ms is taken, P (i-1) is the power value of the i-1 th individual, and f (i) is the frequency value calculated according to the i-th individual.
A fitness function is calculated according to equation (4),
Figure BDA0003486896180000091
wherein, Fitness is a Fitness function value, i is an integer number, f' (i) is a frequency value obtained by sampling, and n is the number of individuals in the population.
In this embodiment of the present invention, the calculation formula of the fitness function value may be obtained by:
the transfer function of the microgrid system is obtained according to the equivalent control block diagram of the traditional equivalent model as shown in formula (5), and the equivalent control block diagram of the traditional equivalent model is the prior art and is not described in detail herein.
Figure BDA0003486896180000092
Wherein, KmxIs an equivalent coefficient, K, of the microgrid systemzxThe equivalent output power characteristic of the micro-grid system.
A new P-F transfer function is constructed according to equation (6),
Figure BDA0003486896180000093
wherein G isPF(s) is the P-F transfer function of the microgrid system, KmIs an equivalent coefficient, K, of the microgrid systempIs the proportionality coefficient, K, of the microgrid systemiIs the integral coefficient of the microgrid system.
Expressing equation (6) in a discrete domain, a differential equation shown in equation (7) is obtained,
Figure BDA0003486896180000094
the formula (7) is processed by a three-point interpolation derivation formula, and a recursion formula shown in the formula (2) and the formula (3) can be obtained.
In step S13, the distributed power of the microgrid is adjusted according to the optimal demodulation. After the optimal solution of the equivalent coefficient, the proportional coefficient and the integral coefficient is obtained, the optimal solution is brought into a secondary frequency transfer function of the microgrid system, the distributed power of each converter in the microgrid system is obtained, and the distributed power is distributed to the energy storage converters in proportion to maintain the stability of the frequency of the microgrid system.
In steps S10 to S13, encoding is performed according to the obtained initial equivalent coefficient to obtain an initial population, a fitness function of the population is calculated by using a genetic algorithm to obtain an optimal solution of the equivalent coefficient, the proportionality coefficient and the integral coefficient, and finally, the microgrid system calculates the distribution power of each converter according to the optimal solution of the equivalent coefficient, the proportionality coefficient and the integral coefficient and distributes the distribution power to each converter to maintain the stability of the microgrid system.
The traditional control method for the secondary frequency modulation of the converter in the microgrid system mainly identifies the damping coefficient of a virtual synchronous machine according to the characteristics of the control of the virtual synchronous machine and a parameter identification method based on a least square method and a genetic algorithm, so that the aim of secondary frequency modulation of the converter is fulfilled. However, the method mainly aims at identifying parameters of a single converter, and for a plurality of converters in the whole microgrid system, a plurality of parameters need to be identified, the identification logic is complex and cannot be uniformly regulated, so that the method is poor in universality. In the embodiment of the invention, the optimal solution of the equivalent coefficient, the proportional coefficient and the integral coefficient of the whole microgrid system is obtained by adopting a mode of forming a population according to the initial equivalent coefficient code of the microgrid system and calculating the fitness function value of the population by a genetic algorithm, so that the rapid frequency modulation of the microgrid system is realized, the rapid recovery of the microgrid frequency is facilitated, and the stability of the microgrid system is ensured.
In this embodiment of the present invention, in order to obtain the initial population of the genetic algorithm, the initial equivalent coefficients are also encoded. Specifically, the control method may further include the steps as shown in fig. 2.
In fig. 2, the control method may further include:
in step S20, a plurality of values are randomly initialized based on the initial equivalent coefficients to form an equivalent coefficient set. After the initial equivalent coefficient is obtained, a preset number of values are randomized according to the initial equivalent coefficient, an equivalent coefficient set is formed by the values and the initial equivalent coefficient, and the numbers in the set are real numbers.
In step S21, each value in the set of value coefficients is binary coded to form a basic population. When each real number in the equivalence coefficient set is binary-coded, the digits of each binary code in the basic population are consistent, and the digits of the binary codes are determined by the digits of the binary codes corresponding to the maximum real number in the equivalence coefficient set.
In step S22, the gray code of each individual in the population is calculated according to formula (8),
B＝bmbm-1...b2b1，
G＝gmgm-1…g2g1，
Figure BDA0003486896180000111
wherein B is a binary code, G is a Gray code, m is a coding bit number and is an integer number, GmCoded value of mth bit of Gray code, bmIs the coded value of the m-th bit of the binary code, giCoded value of i-th bit of Gray code, biIs the coded value of the ith bit of the binary code, bi+1Is the coded value of the i +1 th bit of the binary code, i is an integer number.
In step S23, an initial population is formed based on the gray code of each individual in the population. The randomness of the equivalence coefficient set causes the local searching capability of binary coding to be poor, and the problem that the calculation precision is influenced exists. After the population is calculated by the genetic algorithm for multiple generations, when the solution of a new generation population approaches to the optimal solution, the variation of binary codes after variation is large and discontinuous, so that the next generation population is far away from the optimal solution, and the optimal solution of the equivalent coefficient calculated by the genetic algorithm is not stable enough. Because only one code bit is different between the code values corresponding to two consecutive integers of the Gray code, the Gray code can effectively prevent the phenomenon.
In steps S20 to S23, a set of a plurality of real numbers is generated according to the initial equivalence coefficient randomization, and the plurality of real numbers in the set are binary-coded to form a basic population. In order to ensure that the genetic algorithm can make the solution of the new generation population approach the optimal solution after performing multi-generation calculation, it is necessary to convert each binary code in the basic population into a corresponding gray code to form an initial population. By adopting a Gray code encoding mode, the iteration times of a genetic algorithm can be reduced, and the stable output of an optimal solution can be ensured.
In this embodiment of the present invention, in order to obtain the optimal solution of the equivalence coefficient, the proportionality coefficient, and the integral coefficient, it is necessary to perform calculation using a genetic algorithm. Specifically, the control method may further include the steps shown in fig. 3. Specifically, in fig. 3, the control method may further include:
in step S30, fitness function values of the population are calculated. After the population of the current generation is input, the fitness function value corresponding to the population of the current generation is calculated according to the formulas (2) to (4).
In step S31, it is determined whether the fitness function value of the population is less than or equal to a preset threshold. After the fitness function value corresponding to the current-generation population is calculated, in order to determine whether an optimal solution exists in the current-generation population, the fitness function value needs to be compared with a preset threshold.
In step S32, when the fitness function value of the population is determined to be greater than the preset threshold, the same number of new-generation individuals as the population is selected according to the fitness function value of each individual in the population. If the fitness function value is larger than a preset threshold value, the current generation population does not meet the requirement of existence of an optimal solution. In order to make the population approach to the optimal solution, the same number of new generation individuals need to be selected according to the fitness function value of each individual in the population.
In step S33, the selected new generation individuals are subjected to copy operation, crossover operation, and mutation operation to form a new generation population. After the new generation individuals are selected, each individual in the current generation population needs to be subjected to copying operation, cross operation, mutation operation and the like so as to improve the fitness of each individual, namely the fitness function value of each individual is gradually reduced, so that the new generation population gradually tends to an optimal solution.
In step S34, the process returns to the step of calculating the fitness function value of the population again. After the new generation population is formed, the fitness function value of the new generation population is calculated to judge whether the fitness function value of the new generation population meets a preset threshold value, and the process is repeated until the optimal solution of the population is obtained.
In steps S30 to S34, it is necessary to calculate a fitness function value of the current generation population, and compare the fitness function value of the current generation population with a preset threshold value to determine whether there are individuals of the optimal solution in the current generation population. If the fitness function value of the current-generation population is larger than the preset threshold, it indicates that there is no individual with the optimal solution in the current-generation population, and the individuals with high fitness in the current-generation population need to be screened out and subjected to copying operation, cross operation, mutation operation and the like, so as to form a new-generation population. By adopting the modes of screening, copying operation, cross operation and mutation operation, the individual fitness in the population can be higher and higher, so that the individuals in the population of the current generation gradually approach to the optimal solution, and the optimal solution can be conveniently obtained.
In this embodiment of the present invention, when the fitness function value of the population meets the requirement of the preset threshold, the optimal solution of the equivalence coefficient, the proportionality coefficient and the integral coefficient also needs to be calculated. Specifically, the control method may further include the steps shown in fig. 3. Specifically, in fig. 3, the control method may include:
in step S35, in the case that the fitness function value of the population is determined to be less than or equal to the preset threshold, the minimum value of the fitness function values of the individual individuals in the population is selected. If the fitness function value of the population is smaller than or equal to a preset threshold value, the population of the generation is indicated to have the individual as the optimal solution, and in order to improve the accuracy and the accuracy of selecting the individual as the optimal solution, the individual with the minimum individual fitness function value in the population of the generation is selected as the optimal solution.
In step S36, the optimal equivalence coefficient corresponding to the minimum value is identified. When obtaining the gray code corresponding to the individual with the fitness function value as the minimum value, the gray code needs to be converted into a binary code. Gray code is converted into binary code according to equation (9),
B＝bmbm-1...b2b1，
G＝gmgm-1…g2g1，
Figure BDA0003486896180000131
and then the binary code is transcoded into a real number, and the real number is the optimal equivalent coefficient.
In step S37, the corresponding proportional coefficient and integral coefficient are calculated from the optimal equivalence coefficient. After the optimal equivalent coefficient is obtained, the optimal proportional coefficient and the optimal integral coefficient need to be calculated so as to meet the stability and accuracy of the secondary frequency modulation of the micro-grid system.
In step S38, the distributed power of the microgrid is adjusted based on the equivalence coefficient, the proportionality coefficient, and the integral coefficient. After the optimal values of the equivalent coefficient, the proportional coefficient and the integral coefficient are calculated, the micro-grid system can perform self-adaptive secondary frequency modulation according to the optimal values so as to ensure the stability of the micro-grid system.
In steps S35 to S38, if the fitness function value of the population is less than or equal to the preset threshold, it indicates that the population meets the requirement of having the individual with the optimal solution, and the individual corresponding to the minimum fitness function value in the population is used as the optimal solution of the equivalence coefficient and is decoded. And calculating corresponding proportional coefficients and integral coefficients according to the optimal solution of the equivalent coefficients to form a complete quadratic frequency modulation transfer function of the microgrid system, and performing rapid and stable frequency modulation on the microgrid system.
In this embodiment of the present invention, in order to ensure the stability and accuracy of the optimal value of the equivalence coefficient, the number of iterations of the population needs to be limited. Specifically, the control method may further include the steps shown in fig. 4. Specifically, in fig. 4, the control method may further include:
in step S40, a threshold number of iterations is preset. In order to ensure that the population can stably obtain the optimal solution of the equivalent coefficient after multiple iterations, the minimum iteration times of the population can be preset, so that the population must be subjected to iteration operation of the preset iteration times.
In step S41, it is determined whether the number of iterations at this time is greater than or equal to an iteration number threshold. In the population, in a few iterations, individuals which cannot meet the requirement of the optimal solution of the equivalent coefficient may exist in the formed new generation of population, so that the population needs to be ensured to be iterated for multiple times, namely, to be iterated for a preset iteration number.
In step S42, when it is determined that the iteration count at this time is smaller than the iteration count threshold, a new generation of individuals having the same number as the population is selected according to the fitness function value of each individual in the population. If the iteration number is smaller than the iteration number threshold, the iteration number of the current generation population is not enough, and the population may not have the optimal solution. In order to make the population approach to the optimal solution, the same number of new generation individuals need to be selected according to the fitness function value of each individual in the population.
In step S43, the selected new generation individuals are subjected to copy operation, crossover operation, and mutation operation to form a new generation population. After the new generation individuals are selected, each individual in the current generation population needs to be subjected to copying operation, cross operation, mutation operation and the like so as to improve the fitness of each individual, namely the fitness function value of each individual is gradually reduced, so that the new generation population gradually tends to an optimal solution.
In step S44, the process returns to the step of calculating the fitness function value of the population again. After the new generation population is formed, the iteration times of the new generation population are calculated to judge whether the iteration times of the new generation population meet a preset iteration time threshold value, and the iteration of the iteration time threshold value is completed through the loop.
In steps S40 to S44, a threshold value of the number of iterations required for the population is preset, and the population is iterated for a limited number of iterations until the number of iterations of the population reaches the threshold value of the number of iterations. If the iteration times of the population do not reach the iteration time threshold, the population may not meet the requirement of existence of the optimal solution, so that iteration of the population with a preset iteration time threshold is required to ensure that the population can stably obtain the optimal solution.
In this embodiment of the present invention, in order to obtain the optimal solution of the proportional coefficient and the integral coefficient, it is also necessary to calculate the optimal solution of the identified equivalent coefficient. Specifically, the control method may further include the steps shown in fig. 4. Specifically, in fig. 4, the control method may further include:
in step S45, when it is determined that the iteration count at this time is greater than or equal to the iteration count threshold, the minimum value of the individual fitness function values in the population is selected. If the iteration times of the population are greater than or equal to the iteration time threshold, the population iteration is completed, the optimal solution of the equivalent coefficient stably exists in the population of the current generation, and in order to improve the accuracy and the precision of selecting the individuals as the optimal solution, the individuals with the minimum individual fitness function values in the population of the current generation are selected as the optimal solution.
In step S46, the optimal equivalence coefficient corresponding to the minimum value is identified. When obtaining the gray code corresponding to the individual with the fitness function value as the minimum value, the gray code needs to be converted into a binary code. Gray codes are converted into binary codes according to a formula (8), and then the binary codes are transcoded into real numbers, wherein the real numbers are the optimal equivalent coefficients.
In step S47, the corresponding proportional coefficient and integral coefficient are calculated from the optimum equivalent coefficient. After the optimal equivalent coefficient is obtained, the optimal proportional coefficient and the optimal integral coefficient are required to be calculated so as to meet the stability and accuracy of the secondary frequency modulation of the micro-grid system.
In step S48, the distributed power of the microgrid is adjusted based on the equivalence coefficient, the proportionality coefficient, and the integral coefficient. After the optimal values of the equivalent coefficient, the proportional coefficient and the integral coefficient are calculated, the micro-grid system can perform self-adaptive secondary frequency modulation according to the optimal values so as to ensure the stability of the micro-grid system.
In steps S45 to S48, if the iteration number of the population is greater than or equal to the iteration number threshold, it indicates that the population meets the requirement of having the optimal solution individual, and the individual corresponding to the minimum value of the fitness function value in the population is used as the optimal solution of the equivalence coefficient and is decoded. And calculating corresponding proportional coefficients and integral coefficients according to the optimal solution of the equivalent coefficients to form a complete quadratic frequency modulation transfer function of the micro-grid system, and performing rapid and stable frequency modulation on the micro-grid system.
In this embodiment of the invention, a roulette selection method is used to select a new generation of individuals. Specifically, the fitness function value of each individual in the population is calculated according to the formula (2) and the formula (3), namely the probability of each individual in the population being selected can be obtained according to the formula (10),
Figure BDA0003486896180000161
wherein, KmiIs the ith individual in the population, i is the integer number, f (K)mi) Is the fitness function value of the ith individual in the population, P { KmiThe probability of the ith individual being selected in the population, n is the number of individuals in the population, and n is the integer number.
As can be seen from equation (10), the smaller the fitness function value is, the greater the probability of being selected is, i.e., the higher the fitness is, the greater the probability of being selected is. Individuals with high fitness are bred, the more the number of the next generations is, the individuals with low fitness are bred, the less the number of the next generations is, even the next generations are eliminated, and after multiple iterations, all the generations with high fitness are obtained in the population.
When a new generation of individuals are selected, the wheel disc is divided into n parts, wherein n is the number of the individuals in the population, the reciprocal of the fitness function value of each individual in the population is distributed on the wheel disc in proportion, two fixed pointers are arranged, the wheel disc is rotated to obtain two individuals, and the process is repeated until the number of the new generation of individuals is the same as that of the population.
In this embodiment of the invention, the replication operation may include replicating the operational population according to equation (11) to form a new generation of population,
Gi+1＝Gi， (11)
wherein, Gi+1Is the i +1 th gray code, GiIs the ith generation gray code, i is an integer number.
In this embodiment of the invention, the crossover operation may include crossover operating the population according to equation (12) to form a new generation of population,
Gij＝gmijg(m-1)ij…g2ijg1ij，
Gin＝gming(m-1)in…g2ing1in，
G(i+1)j＝gming(m-1)in…g2ijg1ij，
G(i+1)n＝gmijg(m-1)ij…g2ing1in， (12)
wherein G isijIs the jth Gray code in the ith generation group, GinIs the nth Gray code in the ith generation population, G(i+1)jIs the jth Gray code in the i +1 generation group, G(i+1)nIs the nth Gray code in the i +1 generation population, and j and n are integer numbers.
In this embodiment of the present invention, mutation operation is also performed in order to obtain a new generation population with high fitness. Specifically, the control method may further include the steps shown in fig. 5. Specifically, in fig. 5, the control method may further include:
in step S50, a plurality of individuals are randomly selected from the current population as the individuals to be mutated. Wherein, the mutation probability P is based on the principle of gene mutation in biogenetic inheritancemVery small, due to random variation, according to the variation probability PmAnd randomly selecting a plurality of individuals in the population to wait for variation.
In step S51, for each individual to be mutated, a plurality of code bits are randomly selected from the individual to be mutated as the positions to be mutated. The mutation probability of each code bit of each individual is small, and the random property is provided, so that a plurality of code bits in the individual to be mutated are randomly selected to wait for mutation.
In step S52, the current code value is replaced with the complement of the code value corresponding to the position to be mutated. Since each coded bit of the gray code consists of 0 or 1, when a change occurs at a position to be mutated of the gray code, the coded bit is replaced by 0 to 1 or by 1 to 0, i.e. an inverse code.
In steps S50 to S52, an individual in the population is randomly selected as an individual to be mutated, a certain coding bit on the individual to be mutated is randomly selected as a position to be mutated, and the position to be mutated is replaced with a code-reversal to realize the mutation operation during the mutation. The method can not obtain benefits in the solution by means of mutation operations alone, but can ensure that the genetic algorithm cannot generate a single population which cannot be evolved, because when each individual in the population is the same, new individuals cannot be generated by cross operations, and only the mutation operations can generate the new individuals, so that the mutation operations increase the characteristics of global optimization.
In this embodiment of the invention, the calculation of the optimal value of the scaling factor comprises calculating the scaling factor according to equation (13);
Figure BDA0003486896180000171
wherein, KpIs a proportionality coefficient, KmTo identify the equivalent coefficients, τ is the time constant, h is the bandwidth, and h is 5.
In this embodiment of the invention, the calculation of the optimal value of the integral coefficient comprises calculating the integral coefficient according to equation (14),
Figure BDA0003486896180000181
wherein, KiIs an integral coefficient.
In this embodiment of the invention, the tuning of the microgrid central controller parameters in the microgrid system comprises obtaining an open-loop transfer function of the quadric frequency modulation control system according to equation (15),
Figure BDA0003486896180000182
wherein G isOP(s) is the open loop transfer function, KzIn order to achieve an equivalent output power characteristic,
Figure BDA0003486896180000183
Figure BDA0003486896180000184
the denominator of the open loop transfer function of the chirp-quadratic control system can be extracted as s, as shown in equation (16),
Figure BDA0003486896180000185
equation (16) and typical type II system transfer function
Figure BDA0003486896180000186
Compare the excess constant term KmKzIf the constant term KmKzCan be omitted, then GOP(s) is consistent with a typical type II system transfer function form. Obtained according to the root-finding formula if
Figure BDA0003486896180000187
Time, constant term KmKzThe pole distribution of the typical type II system transfer function is hardly changed.
Of open-loop transfer functions of quadric-frequency control systems
Figure BDA0003486896180000188
The value of (a) can be obtained by the following steps:
droop coefficient K of single inverterm＝Δf/PnIn which P isnFor the rated power, Δ f is chosen to be the maximum frequency offset allowed, usually Δ f ≦ 0.5 Hz. In practical engineering, KmValue of 10-6～10-5Order of magnitude, since increasing the value of the droop coefficient reduces the stability of the system, so 10-5The worst working condition is adopted.
KzThe calculation of (1) needs to consider the value difference of output impedance in different droop modes, and the lower the impedance is, the K iszThe larger. According to the actual situation,substituting Z of 0.8 Ω into the worst condition (the filter inductance of the high-power inverter is usually less than 3mH, the resistance in the low-voltage system is about 0.6 Ω per kilometer, and the resistance is calculated according to a 3km line), and calculating to obtain the worst condition
Figure BDA0003486896180000191
Satisfy the above
Figure BDA0003486896180000192
And (4) conditions. When a plurality of converters are operated in parallel, the equivalent droop coefficient KmAnd equivalent output characteristic KzThere is no order of magnitude crossover compared to a single machine, so an approximation can still be made in this way.
On the other hand, the invention also provides a microgrid secondary frequency adaptive system, as shown in fig. 6, the adaptive system may include a photovoltaic unit 01, a wind power unit 02, a plurality of energy storage converters 06, an intelligent switch 07, a plurality of loads 03, an energy storage device 04, and a microgrid central controller 05.
The photovoltaic unit 01 is used for photovoltaic power generation, the wind power unit 02 is used for wind power generation, and the energy storage converters 06 are connected in parallel and are respectively connected with the photovoltaic unit 01 and the wind power unit 02. The energy storage device 04 is connected with the plurality of energy storage inverters 06 and is used for storing electric energy generated by the photovoltaic unit 01 and the wind power unit 02. One end of the intelligent switch 07 is connected with one of the energy storage converters 06, and the other end is used for being connected with a power grid 08. A plurality of loads 03 are connected to the energy storage converter 06, and the microgrid central controller 05 is configured to execute any one of the above control methods.
In this embodiment of the present invention, in order to verify the capability of genetic algorithm to identify optimal coefficient searching of equivalent coefficients in the present secondary frequency modulation strategy, the following simulation scheme is designed: the simulation parameter of three converter droop control inverters is set to be Km1＝8×10-6、Km2＝5×10-6、Km3＝3×10-6Theoretical K can be obtained by substituting formula (1) for calculationm＝1.52×10-6. The power grid disturbance frequency of 0.2Hz is applied, and the sampling step length h is 10-3And s. The simulation sampling data is sent to a genetic algorithm programThe population setting size is 100, the number of elite is 2 (namely, the two individuals with the minimum fitness function value in each generation of population), the crossing proportion is 0.8, the stopping algebra is 50, and the deviation of the fitness function value is 10-6The simulation results are shown in fig. 7 below.
Obtaining the optimal individual K meeting the optimization termination condition after the optimization solution of the genetic algorithmm＝1.5×10-6It can be seen from the figure that the genetic algorithm completes the identification of 50 generations in 2.5s, the identification speed is rapid, the identification value tends to be stable in 25 generations, and the identification result is accurate. In fig. 7, as the algebra increases, the individual optimal fitness value and the average fitness value gradually decrease and approach, which indicates that the difference between the calculated output frequency and the actual output frequency of the system when the identification value is substituted into the identification value becomes smaller, i.e. the optimal identification value approaches to the actual value. In FIG. 7, the identification values gradually stabilized at 1.5X 10 in 25 generations-6It shows that the identification result is accurate and the deviation is low. From the identified Km＝1.5×10-6And the calculated theoretical value Km＝1.52×10-6The comparison of the two results also shows that the error is 1.31%, and the accuracy of the error within 5% is satisfied. In the traditional fixed-order off-line identification, the identification time is long, the identification accuracy is low, and in the actual micro-grid application, the quick setting of the controller is not facilitated, so that the secondary frequency modulation speed is influenced, and the micro-grid frequency oscillation is caused.
In the embodiment of the invention, in order to test the online optimization effect of the frequency modulation control system of the microgrid, the following experiment is designed to verify that the frequency modulation control system has or does not have the influence of optimization on the system stability and the frequency modulation speed when the number of the converter access units is different: and (3) acquiring an equivalent coefficient of the microgrid model through the genetic algorithm, and then adaptively updating the equivalent coefficient to an optimal value according to a formula (13) and a formula (14). And in a P-F droop parallel mode of the work of the three converters, the system frequency is acquired by an intelligent network turnoff circuit device at the common connection point, uploaded to the MGCC for analysis and processing and sent to the MATLAB to draw a frequency waveform. The acquisition frequency of the gateway circuit breaker is 6.4kHz, the sliding filter values of 5 periods are used as frequency calculation results, and the sampling precision is 0.01 Hz. MGCC scheduling period is 20ms, and lower computer data are collected and a calculation instruction is issued in each periodThe time constant τ is consistent with the MGCC scheduling period. The direct-current bus voltage of the inverter is 700V, the effective value of the line voltage is 380V, and the filter inductance is 1.5 mH. If the inverter is stopped, identifying K identified by the genetic algorithm according to the 'stop' recordmK optimized on line by micro-grid central controller 05pThe values were sent from an oscilloscope, and the specific experimental data are shown in table 1.
TABLE 1 Experimental data
Figure BDA0003486896180000201
Figure BDA0003486896180000211
K of the entire Experimental proceduremThe identification values are shown in FIG. 8, and the corresponding frequency waveforms are shown in FIG. 9.
As can be seen from the figure:
period t 1: the 3 inverters run in no-load mode, and the system frequency is stabilized to 50 Hz.
Period t 2: and when a 30kW load is suddenly added, the system frequency drops and is recovered to be stable within 0.2s, which indicates that the system can perform secondary frequency modulation under the action of the PI controller.
Period t 3: the converter 1 is out of operation, a 30kW load is shared by the converters 2 and 3, the system output frequency oscillates when the original PI value is adopted, and the system output frequency converges to (50 +/-0.02) Hz after about 5 s.
Period t 4: and starting genetic algorithm on-line identification, adaptively optimizing regulator parameters, and adjusting the frequency of the micro-grid to be maintained at 50Hz after 1 s.
Period t 5: the converter 2 quits operation, the converter 3 is independently loaded with 30kW, the PI parameter maintains a t4 time period value, the output of the PI regulator is immediately saturated, the PI regulator jumps on the upper and lower limit amplitude values, and the corresponding frequency fluctuation also reaches the limit amplitude value +/-0.2 Hz. The drastic fluctuation of the frequency causes the fluctuation of the reference angle in dq conversion, so that the calculation of the instantaneous value of the active power generates deviation.
Period t 6: and optimizing the parameters of the regulator on line, after the PI parameter values are adaptively set, the frequency of the microgrid is converged to the given value of 50Hz again, and the calculation of the instantaneous value of the active power is stabilized to 30kW again.
Period t 7: the inverter 2 is restarted and the system frequency is slightly disturbed by the power redistribution during start-up but is quickly restored.
According to the technical scheme, the microgrid secondary frequency self-adaptive system and the control method thereof provided by the invention form a population by acquiring the initial equivalent coefficient of the microgrid and coding the initial equivalent coefficient, calculate the fitness function value of the population by adopting a genetic algorithm, acquire the optimal solution of the equivalent coefficient, the proportionality coefficient and the integral coefficient according to the fitness function value, and finally regulate the distribution power of the microgrid according to the optimal demodulation so as to realize the rapid frequency modulation of the microgrid system, facilitate the rapid recovery of the microgrid frequency and ensure the stability of the microgrid system.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims (10)

1. A method for controlling the secondary frequency adaptation of a micro-grid is characterized by comprising the following steps:
acquiring an initial equivalent coefficient, a proportional coefficient and an integral coefficient of the microgrid;
coding according to the initial equivalent coefficient to obtain an initial population;
calculating the optimal solution of the equivalence coefficient, the proportionality coefficient and the integral coefficient according to the population by adopting a genetic algorithm, wherein the genetic algorithm comprises the following steps:
calculating a fitness function value of the population according to the formulas (2) to (4),
Figure FDA0003486896170000011
wherein Fitness is the Fitness function value, i is an integer number, f (i) is a frequency value calculated according to the ith individual, f' (i) is a frequency value obtained by sampling, and n is the number of the individuals in the population;
f(i)＝Af(i-2)+Bf(i-1)+CP(i-1)+DP(i-2)， (2)
Figure FDA0003486896170000012
wherein, KmIs an initial constant coefficient, KpIs an initial proportionality factor, KiIs an initial integral coefficient, h is a sampling step length, tau is a sampling period, and P (i-1) is an i-1 th individual power value;
and adjusting the distributed power of the microgrid according to the optimal solution.
2. The control method of claim 1, wherein encoding according to the initial equivalent coefficients to obtain an initial population comprises:
randomly initializing a plurality of values according to the initial equivalent coefficient to form an equivalent coefficient set;
binary coding each value in the set of equivalent coefficients to form a basic population;
calculating a Gray code for each individual in the population according to equation (8),
B＝bmbm-1…b2b1，
G＝gmgm-1…g2g1，
Figure FDA0003486896170000021
wherein B is a binary code, G is a Gray code, m is a coding bit number and is an integer number, GmIs the coded value of the mth bit of the Gray code, bmFor the coded value of the mth bit of the binary code, giIs the coded value of the ith bit of the Gray code, biIs the coded value of the ith bit of the binary code, bi+1The coded value of the (i + 1) th bit of the binary code is represented, wherein i is an integer number;
and forming an initial population according to the gray code of each individual in the population.
3. The control method of claim 2, wherein calculating the optimal solution of the equivalence coefficients, the proportionality coefficients, and the integral coefficients from the population using a genetic algorithm comprises:
calculating a fitness function value of the population;
judging whether the fitness function value of the population is less than or equal to a preset threshold value or not;
under the condition that the fitness function value of the population is judged to be larger than a preset threshold value, selecting a new generation of individuals with the same quantity as the population according to the fitness function value of each individual in the population;
performing copy operation, cross operation and mutation operation on the selected new generation individuals to form a new generation population;
and returning to the step of calculating the fitness function value of the population again.
4. The control method of claim 3, wherein calculating the optimal solution of the equivalence coefficients, the proportionality coefficients, and the integral coefficients from the population using a genetic algorithm comprises:
selecting the minimum value of the fitness function values of the single individuals in the population under the condition that the fitness function value of the population is judged to be smaller than or equal to a preset threshold value;
identifying the optimal equivalent coefficient corresponding to the minimum value;
calculating corresponding proportional coefficients and integral coefficients according to the optimal equivalent coefficients;
and adjusting the distribution power of the microgrid according to the equivalent coefficient, the proportional coefficient and the integral coefficient.
5. The control method of claim 2, wherein calculating optimal solutions for the equivalence coefficients, the proportionality coefficients, and the integral coefficients from the population using a genetic algorithm comprises:
presetting an iteration time threshold;
judging whether the iteration number is larger than or equal to the iteration number threshold value or not;
under the condition that the iteration times at the moment are judged to be smaller than the iteration time threshold value, selecting a new generation of individuals with the same quantity as the population according to the fitness function value of each individual in the population;
performing copy operation, cross operation and mutation operation on the selected new generation individuals to form a new generation population;
and returning to the step of calculating the fitness function value of the population again.
6. The control method of claim 5, wherein calculating the optimal solution of the equivalence coefficients, the proportionality coefficients, and the integral coefficients from the population using a genetic algorithm comprises:
selecting the minimum value of the fitness function values of the single individuals in the population under the condition that the iteration times at the moment is judged to be greater than or equal to the iteration time threshold;
identifying the optimal equivalent coefficient corresponding to the minimum value;
calculating corresponding proportional coefficients and integral coefficients according to the optimal equivalent coefficients;
and adjusting the distribution power of the microgrid according to the equivalent coefficient, the proportional coefficient and the integral coefficient.
7. The control method according to claim 3 or 5, wherein the copy operation and the interleave operation include:
the population is duplicated to form a new generation of population according to equation (11),
Gi+1＝Gi， (11)
wherein G isi+1Is the i +1 th gray code, GiIs an ith generation gray code, i is an integer number;
the population is cross-operated according to equation (12) to form a new generation of population,
Gij＝gmijg(m-1)ij…g2ijg1ij，
Gin＝gming(m-1)in…g2ing1in，
G(i+1)j＝gming(m-1)in…g2ijg1ij，
G(i+1)n＝gmijg(m-1)ij…g2ing1in， (12)
wherein G isijIs the jth Gray code in the ith generation group, GinIs the nth Gray code in the ith generation population, G(i+1)jIs the jth Gray code in the i +1 generation group, G(i+1)nIs the nth Gray code in the i +1 generation population, and j and n are integer numbers.
8. The control method according to claim 3 or 5, characterized in that the mutation operation comprises:
randomly selecting a plurality of individuals from the current population as individuals to be mutated;
randomly selecting a plurality of coding bits from the individuals to be mutated as the positions to be mutated aiming at each individual to be mutated;
and replacing the current coding value by the code reversal of the corresponding coding value at the position to be mutated.
9. The control method according to claim 4 or 6, wherein calculating the proportional coefficient and the integral coefficient according to the equivalent coefficient corresponding to each individual in the new generation of population comprises:
calculating a scaling factor according to equation (13);
Figure FDA0003486896170000041
wherein, KpIs the proportionality coefficient, Kmτ is a time constant for the identified equivalence coefficients;
the integral coefficient is calculated according to the formula (14),
Figure FDA0003486896170000042
wherein, KiIs the integral coefficient;
the open-loop transfer function of the secondary frequency modulation control system is obtained according to the formula (15),
Figure FDA0003486896170000051
wherein G isOP(s) is the open loop transfer function, KzIn order to achieve an equivalent output characteristic,
Figure FDA0003486896170000052
Figure FDA0003486896170000053
10. a microgrid secondary frequency adaptive system, comprising:
a photovoltaic unit (01) for photovoltaic power generation;
a wind power unit (02) for wind power generation;
the energy storage converters (06) are connected in parallel and are respectively connected with the photovoltaic unit (01) and the wind power unit (02);
the storage device (04) is connected with the energy storage converters (06) and is used for storing electric energy generated by the photovoltaic unit (01) and the wind power unit (02);
one end of the intelligent switch (07) is connected with one of the energy storage converters (06), and the other end of the intelligent switch is used for being connected with a power grid (08);
a plurality of loads (03) connected to the energy storage converter (06);
a microgrid central controller (05) for performing the control method of any one of claims 1 to 9.
CN202210085945.2A 2022-01-25 2022-01-25 Micro-grid secondary frequency self-adaptive system and control method thereof Pending CN114566982A (en)

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