CN114744658A - Battery energy storage system size division method and system based on micro-grid - Google Patents

Battery energy storage system size division method and system based on micro-grid Download PDF

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CN114744658A
CN114744658A CN202210409307.1A CN202210409307A CN114744658A CN 114744658 A CN114744658 A CN 114744658A CN 202210409307 A CN202210409307 A CN 202210409307A CN 114744658 A CN114744658 A CN 114744658A
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storage system
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CN114744658B (en
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王蒋静
叶佳青
徐杰
杨建立
张力
俞佳捷
曹雅素
曹欣凯
毛倩倩
黄湘云
郑瑞云
徐科兵
汪溥
秦桑
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Ningbo Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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    • H—ELECTRICITY
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Abstract

The invention discloses a battery energy storage system size division method and system based on a microgrid, and relates to the field of the microgrid, wherein an encoding matrix is obtained according to parameter data of each battery energy storage system in the microgrid and the microgrid, and input parameters of a gray wolf optimization algorithm and the position of a wolf group are set according to the encoding matrix; the wolf pack is divided through a fitness objective function, the position of the last alpha wolf is output as the target size of the battery energy storage system after the iteration of the grey wolf optimization algorithm is entered, the grey wolf optimization algorithm is embedded into the battery energy storage system size dividing method, the battery energy storage system with the target size is obtained, and the goal of reasonably controlling the cost of the battery energy storage system is achieved when the micro-grid fails and the frequency is unstable due to imbalance between power generation and load requirements, enough frequency support is provided for the micro-grid to keep the safety of the power system, so that the power system can stably run under normal and abnormal conditions.

Description

Battery energy storage system size division method and system based on micro-grid
Technical Field
The invention relates to the field of micro-grids, in particular to a battery energy storage system size division method and system based on a micro-grid.
Background
As a single controllable network, microgrid cluster loads and micro-sources (SGs and NS-RES) can provide reliable power supply to remote communities (including islands) and industrial complexes. Extensive substitution of Conventional Power Plants (CPPs) by NS-RES has a significant impact on the frequency response of the power system, which can significantly alter the frequency behavior of the system. In other words, power systems with limited inertia have a significant impact on the frequency behavior of the system, for example, loss of a large generator set may result in unacceptable frequency levels, possibly leading to grid collapse, and therefore stable control of the system frequency is particularly important when the penetration level of NS-RES is high.
In addition, in the interconnection mode of the microgrid, it controls and maintains the voltage and frequency of the microgrid within the allowable ranges of frequency and voltage deviations, mainly through the utility grid. However, in the case of independent micro-grids, the NS-RES penetration of the grid is high, and there is an imbalance between the power generation and load demands, so that the frequency deviation of the independent micro-grids is fast.
Currently, with the development of energy materials, Battery Energy Storage Systems (BESS) are widely used in power grids, which provide frequency support by injecting transient power into a microgrid, however, an oversize of a battery energy storage system increases costs, and an undersize results in insufficient battery capacity and thus poor performance. Therefore, the invention provides a battery energy storage system with a proper size, so that when the frequency is unstable due to the failure of the micro-grid and the imbalance between the power generation and the load demand, enough frequency support is provided for the micro-grid to stabilize the frequency response of the micro-grid, the safety of the power system is kept, the power system can stably run under normal and abnormal conditions, and the cost of the battery energy storage system is controlled.
Disclosure of Invention
In order to control the cost of a battery energy storage system while providing enough frequency support for a power grid to stabilize the frequency response of the microgrid when the frequency is unstable due to the fact that the microgrid has a fault and the requirements for power generation and load are unbalanced, the invention provides a battery energy storage system size division method based on the microgrid, which obtains the target size of the battery energy storage system through a wolf optimization algorithm so as to stabilize the frequency response of the microgrid when the microgrid has a fault; the size division method comprises the following steps:
s1: acquiring a coding matrix according to parameter data of each battery energy storage system in the microgrid and the microgrid, wherein elements in the coding matrix
Figure BDA0003603479380000021
The optional size of a battery energy storage system in the micro-grid is represented, X in elements represents the battery energy storage system, n in elements represents the number of the battery energy storage systems, and p represents the position of the battery energy storage system X;
s2: setting the number of wolves in a wolve group, namely the number of the battery energy storage systems X, the maximum iteration times and the value range of the size parameter variable of the battery energy storage system, namely the range formed by the minimum value of the position of the wolve group and the maximum value of the position of the wolve group through the coding matrix, and initializing the position of the wolve group, namely the position of the battery energy storage system X through the value range;
s3: judging whether each battery energy storage system meets a preset constraint condition or not; if yes, the next step is carried out, if not, the step S2 is returned to reinitialize the wolf pack position;
s4: acquiring a fitness objective function value corresponding to the position of each wolf through the fitness objective function, namely acquiring a frequency deviation effect objective function value of the micro-grid after the micro-grid is subjected to frequency modulation through a battery energy storage system;
s5: dividing a wolf group into alpha, beta, delta and omega according to a fitness objective function value, wherein the alpha wolf is a wolf with the optimal frequency modulation effect of the battery energy storage system, the beta wolf is a wolf with the suboptimal frequency modulation effect of the battery energy storage system, the delta wolf is a wolf with the third best frequency modulation effect of the battery energy storage system, and the omega wolf is the rest wolf;
s6: initializing iteration times and starting counting the iteration times;
s7: updating the position of the wolf group;
s8: checking whether the updated wolf meets the preset constraint condition, if so, entering the next step, and if not, returning to the step S7;
s9: and adding 1 to the iteration times, judging whether the iteration times are larger than or equal to the maximum iteration times, if not, returning to the step S7, and if so, outputting the position of the alpha wolf, namely the target size of the battery energy storage system.
Further, the formula expression of the coding matrix in step S1 is:
Figure BDA0003603479380000031
in the formula (II)
Figure BDA0003603479380000032
The optional size of the battery energy storage system in the microgrid is represented, X represents the battery energy storage system, n represents the number of transverse elements in the coding matrix, namely the number of the battery energy storage systems, and p represents the position of the battery energy storage system X.
Further, the formula for initializing the wolf pack position in step S2 is:
Position＝Pmin+rand()*(Pmax-Pmin)；
in the formula, PminRepresents the minimum value of the position of the wolf pack, PmaxRepresents the maximum value of the Position of the wolf pack, rand () represents a random function, and Position represents the random Position of the wolf pack.
Further, the preset constraint condition includes:
constraint equation 1:
0≤Pd(t)≤Pd；
in the formula, Pd is the standard output power of the battery energy storage system, and Pd (t) is the output power of the battery energy storage system at the time t;
constraint equation 2:
S1≤SOC(t)≤S2；
in the formula, S1 and S2 respectively represent a standard upper limit and a standard lower limit of the energy storage charge amount of the battery energy storage system, and soc (t) represents the energy storage state of charge of the battery energy storage system at time t;
constraint condition formula 3, that is, the relationship between the energy storage state of charge and the output power of the battery energy storage system needs to satisfy the equation:
Figure BDA0003603479380000033
in the formula, SOC (0) is the energy storage state of charge at the moment of fault occurrence, etafThe discharge efficiency of the battery energy storage system; edThe energy storage capacity of the battery energy storage system is obtained;
constraint equation 4:
Figure BDA0003603479380000041
where Δ f is the frequency deviation of the battery energy storage system and Δ fmaxThe maximum frequency deviation of the battery energy storage system;
Figure BDA0003603479380000042
is the slip of the battery energy storage system,
Figure BDA0003603479380000043
the maximum slip of the battery energy storage system;
constraint equation 4:
Figure BDA0003603479380000044
wherein H is an inertia time constant, KLAdjusting the comprehensive frequency of the load, wherein the delta P is the maximum possible power shortage fault disturbance quantity under the defense standard in the micro-grid, and the delta PdThe rated discharge power of the battery energy storage system.
Further, the formula expression of the fitness objective function in step S4 is as follows:
Figure BDA0003603479380000045
wherein N represents the frequency of sampling the battery energy storage system within a preset time period, fiRepresenting the frequency f of the battery energy storage system obtained by the ith sampling0And F is a fitness objective function value of the battery energy storage system.
Further, the step S7 of updating the position of the wolf pack includes the steps of:
s71: the distances between each wolf and alpha, beta and delta wolfs are obtained, and the obtaining formula is as follows:
Figure BDA0003603479380000046
Figure BDA0003603479380000047
Figure BDA0003603479380000051
in the formula,
Figure BDA0003603479380000052
respectively representing the distance between the position of each wolf and the positions of alpha, beta and delta wolfs;
Figure BDA0003603479380000053
respectively representing the current positions of alpha, beta and delta wolfs;
Figure BDA0003603479380000054
Figure BDA0003603479380000055
are all random vectors, said
Figure BDA0003603479380000056
Wherein
Figure BDA0003603479380000057
Is a random value between (0, 1);
Figure BDA0003603479380000058
represents the position of the current wolf;
s72: updating the positions of the wolf groups according to the distances between each wolf and alpha, beta and delta wolfs respectively, wherein the formula is as follows:
Figure BDA0003603479380000059
Figure BDA00036034793800000510
Figure BDA00036034793800000511
Figure BDA00036034793800000512
in the formula,
Figure BDA00036034793800000513
wherein,
Figure BDA00036034793800000514
is a constant vector that decays from 2 to 0 with the number of iterations,
Figure BDA00036034793800000515
is a random value between (0,1),
Figure BDA00036034793800000516
is a random coefficient;
Figure BDA00036034793800000517
the updated position of each wolf according to alpha, beta and delta wolfs,
Figure BDA00036034793800000518
is the updated position of the wolf.
The invention also provides a battery energy storage system size division system based on the microgrid, which obtains the target size of the battery energy storage system through a wolf optimization algorithm so as to stabilize the frequency response of the microgrid when a fault occurs; the system comprises:
the coding matrix acquisition module is used for acquiring the energy storage system of each battery in the micro-grid according to the micro-gridObtaining a coding matrix from the parametric data, the elements of the coding matrix
Figure BDA0003603479380000061
The optional size of a battery energy storage system in the micro-grid is represented, X in elements represents the battery energy storage system, n in elements represents the number of the battery energy storage systems, and p represents the position of the battery energy storage system X;
the setting module is used for setting the number of wolves in the wolves, namely the number and the maximum iteration times of the battery energy storage system X, and the value range of the size parameter variable of the battery energy storage system, namely the range formed by the minimum value of the positions of the wolves and the maximum value of the positions of the wolves through the coding matrix, and initializing the positions of the wolves, namely the positions of the battery energy storage system X through the value range;
the judgment module is used for reinitializing the wolf pack positions through the setting module when each battery energy storage system does not meet the preset constraint condition;
the fitness objective function value acquisition module is used for acquiring a fitness objective function value corresponding to the position of each wolf through the fitness objective function when each battery energy storage system meets a preset constraint condition, namely a frequency deviation effect objective function value of the micro-grid after the micro-grid passes through the battery energy storage system for frequency modulation;
the dividing module is used for dividing the wolf group into alpha, beta, delta and omega according to the fitness objective function value, wherein the alpha wolf is the wolf with the optimal frequency modulation effect of the battery energy storage system, the beta wolf is the wolf with the suboptimal frequency modulation effect of the battery energy storage system, the delta wolf is the wolf with the third best frequency modulation effect of the battery energy storage system, and the omega wolf is the rest wolf;
the iteration module is used for initializing the iteration times and starting counting the iteration times;
the position updating module is used for updating the position of the wolf pack;
the checking module is used for updating the position of the wolf pack again through the position updating module when the updated wolf does not meet the preset constraint condition;
and the output module is used for adding 1 to the iteration times when the updated wolf meets the preset constraint condition, outputting the position of the alpha wolf when the current iteration times is greater than or equal to the maximum iteration times, namely the target size of the battery energy storage system, and updating the position of the wolf cluster through the position updating module when the current iteration times is less than the maximum iteration times.
Compared with the prior art, the invention at least has the following beneficial effects:
(1) the method comprises the steps of obtaining an encoding matrix according to parameter data of a micro-grid and each battery energy storage system in the micro-grid, setting input parameters (the number, the maximum iteration times and the value range of a wolf) of a wolf optimization algorithm according to the encoding matrix, and initializing the position of a wolf group; the method comprises the steps of dividing a wolf group through a fitness objective function, outputting the position of the last alpha wolf as the target size of a battery energy storage system after entering the iteration of a wolf optimization algorithm, embedding the wolf optimization algorithm into a battery energy storage system size dividing method to obtain a battery energy storage system with a proper target size, and achieving the purpose of reasonably controlling the cost of the battery energy storage system while providing enough frequency support for a micro-grid to stabilize the frequency response of the micro-grid and keeping the safety of the power system when the micro-grid fails and the frequency is unstable due to imbalance between power generation and load requirements;
(2) in the size division method, whether each battery energy storage system meets the preset constraint condition or not is judged, and after the position of the wolf pack is updated, whether the updated wolf meets the preset constraint condition or not is checked, so that the accuracy of the target size is further improved;
(3) according to the invention, the battery energy storage system with the optimal size (namely the target size) is obtained to provide frequency support for the micro-grid, so that the conventional power generation burden participating in inertial response and primary frequency control is reduced while high-frequency fluctuation is reduced;
(4) the battery energy storage system with the optimal size obtained by the invention stabilizes the frequency response of the micro-grid and improves the robustness of the power system.
Drawings
Fig. 1 is a flow chart of a method for partitioning the size of a battery energy storage system based on a microgrid;
FIG. 2 is a system block diagram of a microgrid-based battery energy storage system sizing system;
FIG. 3 is a gray wolf level diagram in the gray wolf optimization algorithm.
Detailed Description
The following are specific embodiments of the present invention and are further described with reference to the accompanying drawings, but the present invention is not limited to these embodiments.
Example one
Battery Energy Storage Systems (BESS) are widely used in electrical grids to provide frequency support by injecting transient power into the microgrid, however, overdimensioning of battery energy storage systems increases cost, while undersizing results in insufficient battery capacity and thus poor performance. In order to solve the technical problem, as shown in fig. 1, the invention provides a battery energy storage system size division method based on a microgrid, which obtains a target size of the battery energy storage system through a wolf optimization algorithm so as to stabilize the frequency response of the microgrid when a fault occurs; the size division method comprises the following steps:
s1: acquiring a coding matrix according to the parameter data of the microgrid and each battery energy storage system in the microgrid, wherein elements in the coding matrix
Figure BDA0003603479380000081
The optional size of a battery energy storage system in the micro-grid is represented, X in elements represents the battery energy storage system, n in elements represents the number of the battery energy storage systems, and p represents the position of the battery energy storage system X;
the formula expression of the coding matrix in step S1 is:
Figure BDA0003603479380000082
in the formula (II)
Figure BDA0003603479380000083
Indicating selectable size of battery energy storage system in microgridX denotes the battery energy storage system, n denotes the number of transverse elements in the coding matrix, i.e. the number of battery energy storage systems, and p denotes the position of the battery energy storage system X.
S2: setting the number of wolves in a wolve group, namely the number of the battery energy storage systems X, the maximum iteration times and the value range of the size parameter variable of the battery energy storage system, namely the range formed by the minimum value of the position of the wolve group and the maximum value of the position of the wolve group through the coding matrix, and initializing the position of the wolve group, namely the position of the battery energy storage system X through the value range;
the value range of the size parameter variable of the battery energy storage system is determined by the optional size of the battery energy storage system in the microgrid.
The formula for initializing the wolf pack position in step S2 is:
Position＝Pmin+rand()*(Pmax-Pmin)；
in the formula, PminRepresents the minimum value of the position of the wolf pack, PmaxRepresents the maximum value of the wolf pack Position, rand () represents a random function, and Position represents the wolf pack random Position.
S3: judging whether each battery energy storage system meets a preset constraint condition or not; if yes, the next step is carried out, if not, the step S2 is returned to reinitialize the wolf pack position;
s4: acquiring a fitness objective function value corresponding to the position of each wolf through the fitness objective function, namely acquiring a frequency deviation effect objective function value of the micro-grid after the micro-grid is subjected to frequency modulation through a battery energy storage system;
the formula expression of the fitness objective function in step S4 is as follows:
Figure BDA0003603479380000091
wherein N represents the number of times the frequency of the battery energy storage system is sampled within a preset time period, fiRepresenting the frequency f of the battery energy storage system obtained by the ith sampling0And F is a fitness objective function value of the battery energy storage system.
The battery energy storage system is subjected to N times of frequency sampling within a preset time period, and then the fitness objective function value corresponding to the battery energy storage system is obtained through the fitness objective function designed by the invention, so that wolves of different levels are accurately divided, and the target size obtaining precision is improved.
For the gray wolf optimization algorithm (GWO), it should be noted that:
the sirius is a canine, considered a top predator, who is at the top of the biosphere food chain. The wolf is most like the group, and there are 5-12 wolfs on average in each group. Of particular interest are those having a very strict hierarchy of social levels, as shown in fig. 3. The first level of the pyramid is the leader in the population, called α. In the wolf pack, α is an individual with management ability, and is mainly responsible for matters related to various decisions in hunting, sleeping time and place, food distribution and other groups. The second level of the pyramid is the brain team of α, called β, which is primarily responsible for assisting α in making decisions. When alpha of the whole wolf group is vacant, beta will take over the position of alpha. The dominance of beta in the wolf group is next to alpha, and the wolf group gives the command of alpha to other members and feeds back the execution conditions of the other members to alpha to play the role of a bridge. The third layer of the pyramid is delta, which follows decision commands of alpha and beta and is mainly responsible for investigation, sentry, nursing and other matters. Poorly adapted α and β will also be reduced to δ. The bottom layer of the pyramid is omega, which is mainly responsible for the balance of the relationships within the population. Wolves have several tricky processes such as tracking, chasing and embracing, all of which must be performed accurately to create conditions for a perfect hunt.
GWO has the advantage of having a high level of robustness and the ability to handle high dimensional, non-linear and non-convex problems, and is an ideal tool for achieving the target dimensions of the present invention.
S5: dividing a wolf group into alpha, beta, delta and omega according to the fitness objective function value, wherein the alpha wolf is the wolf with the optimal frequency modulation effect of the battery energy storage system, the beta wolf is the wolf with the suboptimal frequency modulation effect of the battery energy storage system, the delta wolf is the wolf with the third best frequency modulation effect of the battery energy storage system, and the omega wolf is the rest wolf;
s6: initializing iteration times and starting counting the iteration times;
the initial value of the number of iterations in this example is 1.
S7: updating the position of the wolf group;
the updating of the position of the wolf pack in the step S7 includes the steps of:
s71: obtaining the distance between each wolf and alpha, beta and delta wolfs respectively, wherein the obtaining formula is as follows:
Figure BDA0003603479380000101
Figure BDA0003603479380000102
Figure BDA0003603479380000103
in the formula,
Figure BDA0003603479380000104
respectively representing the distance between the position of each wolf and the positions of alpha, beta and delta wolfs;
Figure BDA0003603479380000105
respectively representing the current positions of alpha, beta and delta wolfs;
Figure BDA0003603479380000106
Figure BDA0003603479380000107
are all random vectors, said
Figure BDA0003603479380000108
Wherein
Figure BDA0003603479380000109
Is a random value between (0, 1);
Figure BDA00036034793800001010
represents the position of the current wolf;
s72: updating the positions of the wolf clusters according to the distances between each wolf and alpha, beta and delta wolf respectively, wherein the formula is as follows:
Figure BDA00036034793800001011
Figure BDA00036034793800001012
Figure BDA0003603479380000111
Figure BDA0003603479380000112
in the formula,
Figure BDA0003603479380000113
wherein,
Figure BDA0003603479380000114
is a constant vector that decays from 2 to 0 with the number of iterations,
Figure BDA0003603479380000115
is a random value between (0,1),
Figure BDA0003603479380000116
is a random coefficient;
Figure BDA0003603479380000117
for each wolf, the updated position is based on alpha, beta, delta wolf,
Figure BDA0003603479380000118
to updateThe position of the rear wolf.
It should be noted that initialization is also included between steps S6 and S7
Figure BDA0003603479380000119
S8: checking whether the updated wolf meets the preset constraint condition, if so, entering the next step, and if not, returning to the step S7;
in the size division method, whether each battery energy storage system meets the preset constraint condition or not is judged, and after the position of the wolf pack is updated, whether the updated wolf meets the preset constraint condition or not is checked, so that the accuracy of the target size is further improved.
S9: and adding 1 to the iteration times, judging whether the iteration times are larger than or equal to the maximum iteration times, if not, returning to the step S7, and if so, outputting the position of the alpha wolf, namely the target size of the battery energy storage system.
The preset constraint conditions comprise:
constraint equation 1:
0≤Pd(t)≤Pd；
in the formula, t represents time t, Pd is the standard output power of the battery energy storage system, and Pd (t) is the output power of the battery energy storage system at time t;
constraint equation 2:
S1≤SOC(t)≤S2；
in the formula, S1 and S2 respectively represent a standard upper limit and a standard lower limit of the energy storage charge amount of the battery energy storage system, and soc (t) represents the energy storage state of charge of the battery energy storage system at time t;
constraint condition formula 3, that is, the relationship between the energy storage state of charge and the output power of the battery energy storage system needs to satisfy the equation:
Figure BDA0003603479380000121
in the formula, SOC (0) is the energy storage state of charge at the moment of fault occurrence, etafThe discharge efficiency of the battery energy storage system; edIs electricityThe energy storage capacity of the battery energy storage system is large;
constraint equation 4:
Figure BDA0003603479380000122
where Δ f is the frequency deviation of the battery energy storage system and Δ fmaxThe maximum frequency deviation of the battery energy storage system;
Figure BDA0003603479380000123
is the slip of the battery energy storage system,
Figure BDA0003603479380000124
the maximum slip of the battery energy storage system;
constraint equation 4:
Figure BDA0003603479380000125
wherein H is an inertia time constant, KLThe comprehensive frequency regulation coefficient of the load is adopted, the delta P is the maximum possible power shortage fault disturbance quantity under the defense standard in the micro-grid, and the delta PdThe rated discharge power of the battery energy storage system.
The method comprises the steps of obtaining an encoding matrix according to parameter data of a micro-grid and each battery energy storage system in the micro-grid, setting input parameters (the number, the maximum iteration times and the value range of a wolf) of a wolf optimization algorithm according to the encoding matrix, and initializing the position of a wolf group; the wolf pack is divided through a fitness objective function, the position of the last alpha wolf is output as the target size of the battery energy storage system after iteration of a wolf optimization algorithm is entered, the wolf optimization algorithm is embedded into the battery energy storage system size dividing method, the battery energy storage system with the proper target size is obtained, and the purposes that when the micro-grid fails, and frequency instability is caused by imbalance between power generation and load requirements, enough frequency support is provided for the micro-grid to stabilize the frequency response of the micro-grid, the safety of the power system is kept, the power system can stably run under normal and abnormal conditions, and meanwhile, the cost of the battery energy storage system is reasonably controlled are achieved.
Example two
The invention also provides a battery energy storage system size division system based on the microgrid, which obtains the target size of the battery energy storage system through a wolf optimization algorithm so as to stabilize the frequency response of the microgrid when a fault occurs; the system comprises:
the coding matrix acquisition module is used for acquiring a coding matrix according to the parameter data of the microgrid and each battery energy storage system in the microgrid, and elements in the coding matrix
Figure BDA0003603479380000131
The optional size of a battery energy storage system in the micro-grid is represented, X in elements represents the battery energy storage system, n in elements represents the number of the battery energy storage systems, and p represents the position of the battery energy storage system X;
the setting module is used for setting the number of wolves in the wolves, namely the number and the maximum iteration times of the battery energy storage system X, and the value range of the size parameter variable of the battery energy storage system, namely the range formed by the minimum value of the positions of the wolves and the maximum value of the positions of the wolves through the coding matrix, and initializing the positions of the wolves, namely the positions of the battery energy storage system X through the value range;
the judgment module is used for reinitializing the wolf pack positions through the setting module when each battery energy storage system does not meet the preset constraint condition;
the fitness objective function value acquisition module is used for acquiring a fitness objective function value corresponding to the position of each wolf through the fitness objective function when each battery energy storage system meets a preset constraint condition, namely a frequency deviation effect objective function value of the micro-grid after the micro-grid passes through the battery energy storage system for frequency modulation;
the dividing module is used for dividing the wolf group into alpha, beta, delta and omega according to the fitness objective function value, wherein the alpha wolf is the wolf with the optimal frequency modulation effect of the battery energy storage system, the beta wolf is the wolf with the suboptimal frequency modulation effect of the battery energy storage system, the delta wolf is the wolf with the third best frequency modulation effect of the battery energy storage system, and the omega wolf is the rest wolf;
the iteration module is used for initializing the iteration times and starting counting the iteration times;
the position updating module is used for updating the position of the wolf pack;
the checking module is used for updating the position of the wolf pack again through the position updating module when the updated wolf does not meet the preset constraint condition;
and the output module is used for adding 1 to the iteration times when the updated wolf meets the preset constraint condition, outputting the position of the alpha wolf when the current iteration times is greater than or equal to the maximum iteration times, namely the target size of the battery energy storage system, and updating the position of the wolf cluster through the position updating module when the current iteration times is less than the maximum iteration times.
The system provides frequency support for the microgrid by acquiring a battery energy storage system with the optimal size (namely the target size), so that the conventional power generation burden of participating in inertial response and primary frequency control is reduced while high-frequency fluctuation is reduced.
It should be noted that all the directional indicators (such as up, down, left, right, front, and rear … …) in the embodiment of the present invention are only used to explain the relative position relationship between the components, the movement situation, etc. in a specific posture (as shown in the drawing), and if the specific posture is changed, the directional indicator is changed accordingly.
Moreover, descriptions of the present invention as relating to "first," "second," "a," etc. are for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicit ly indicating a number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the present invention, unless otherwise expressly stated or limited, the terms "connected," "secured," and the like are to be construed broadly, and for example, "secured" may be a fixed connection, a removable connection, or an integral part; can be mechanically or electrically connected; they may be directly connected or indirectly connected through intervening media, or they may be connected internally or in any other suitable relationship, unless expressly stated otherwise. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In addition, the technical solutions in the embodiments of the present invention may be combined with each other, but it must be based on the realization of those skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination of technical solutions should not be considered to exist, and is not within the protection scope of the present invention.

Claims (7)

1. A battery energy storage system size division method based on a micro-grid is characterized in that a target size of the battery energy storage system is obtained through a wolf optimization algorithm so as to stabilize the frequency response of the micro-grid when a fault occurs; the size division method comprises the following steps:
s1: acquiring a coding matrix according to parameter data of each battery energy storage system in the microgrid and the microgrid, wherein elements in the coding matrix
Figure FDA0003603479370000011
The optional size of a battery energy storage system in the micro-grid is represented, X in elements represents the battery energy storage system, n in elements represents the number of the battery energy storage systems, and p represents the position of the battery energy storage system X;
s2: setting the number of wolves in a wolve group, namely the number of the battery energy storage systems X, the maximum iteration times and the value range of the size parameter variable of the battery energy storage system, namely the range formed by the minimum value of the position of the wolve group and the maximum value of the position of the wolve group through the coding matrix, and initializing the position of the wolve group, namely the position of the battery energy storage system X through the value range;
s3: judging whether each battery energy storage system meets a preset constraint condition or not; if yes, the next step is carried out, if not, the step S2 is returned to reinitialize the wolf pack position;
s4: acquiring a fitness objective function value corresponding to the position of each wolf through the fitness objective function, namely a frequency deviation effect objective function value of the micro-grid after frequency modulation through the battery energy storage system;
s5: dividing a wolf group into alpha, beta, delta and omega according to the fitness objective function value, wherein the alpha wolf is the wolf with the optimal frequency modulation effect of the battery energy storage system, the beta wolf is the wolf with the suboptimal frequency modulation effect of the battery energy storage system, the delta wolf is the wolf with the third best frequency modulation effect of the battery energy storage system, and the omega wolf is the rest wolf;
s6: initializing iteration times and starting counting the iteration times;
s7: updating the position of the wolf group;
s8: checking whether the updated wolf meets the preset constraint condition, if so, entering the next step, and if not, returning to the step S7;
s9: and adding 1 to the iteration times, judging whether the iteration times are larger than or equal to the maximum iteration times, if not, returning to the step S7, and if so, outputting the position of the alpha wolf, namely the target size of the battery energy storage system.
2. The microgrid-based battery energy storage system size division method as claimed in claim 1, wherein the formula expression of the coding matrix in the step S1 is as follows:
Figure FDA0003603479370000021
in the formula (II)
Figure FDA0003603479370000022
The optional size of the battery energy storage system in the micro-grid is represented, X represents the battery energy storage system, n represents the number of transverse elements in the coding matrix, namely the number of the battery energy storage systems, and p represents the position of the battery energy storage system X.
3. The microgrid-based battery energy storage system size division method as claimed in claim 2, wherein the formula for initializing the wolf pack position in step S2 is as follows:
Position＝Pmin+rand()*(Pmax-Pmin)；
in the formula, PminRepresents the minimum value of the wolf group position, PmaxRepresents the maximum value of the Position of the wolf pack, rand () represents a random function, and Position represents the random Position of the wolf pack.
4. The microgrid-based battery energy storage system size division method of claim 1, wherein the preset constraint conditions include:
constraint equation 1:
0≤Pd(t)≤Pd；
in the formula, Pd is the standard output power of the battery energy storage system, and Pd (t) is the output power of the battery energy storage system at the time t;
constraint equation 2:
S1≤SOC(t)≤S2；
in the formula, S1 and S2 respectively represent a standard upper limit and a standard lower limit of the energy storage charge amount of the battery energy storage system, and soc (t) represents the energy storage state of charge of the battery energy storage system at time t;
constraint condition formula 3, that is, the relationship between the energy storage state of charge and the output power of the battery energy storage system needs to satisfy the equation:
Figure FDA0003603479370000023
in the formula, SOC (0) is the energy storage state of charge at the moment of fault occurrence, etafThe discharge efficiency of the battery energy storage system; edThe energy storage capacity of the battery energy storage system is set;
constraint equation 4:
Figure FDA0003603479370000031
where Δ f is the frequency offset of the battery energy storage systemDifference,. DELTA.fmaxThe maximum frequency deviation of the battery energy storage system;
Figure FDA0003603479370000032
is the slip of the battery energy storage system,
Figure FDA0003603479370000033
the maximum slip of the battery energy storage system;
constraint equation 4:
Figure FDA0003603479370000034
wherein H is an inertia time constant, KLThe comprehensive frequency regulation coefficient of the load is adopted, the delta P is the maximum possible power shortage fault disturbance quantity under the defense standard in the micro-grid, and the delta PdThe rated discharge power of the battery energy storage system.
5. The microgrid-based battery energy storage system size division method of claim 3, wherein the formula expression of the fitness objective function in the step S4 is as follows:
Figure FDA0003603479370000035
wherein N represents the number of times the frequency of the battery energy storage system is sampled within a preset time period, fiRepresenting the frequency f of the battery energy storage system obtained by the ith sampling0And F is a fitness objective function value of the battery energy storage system.
6. The microgrid-based battery energy storage system sizing method of claim 5, wherein the step S7 of updating the location of the wolf pack comprises the steps of:
s71: the distances between each wolf and alpha, beta and delta wolfs are obtained, and the obtaining formula is as follows:
Figure FDA0003603479370000041
Figure FDA0003603479370000042
Figure FDA0003603479370000043
in the formula,
Figure FDA0003603479370000044
respectively representing the distance between the position of each wolf and the positions of alpha, beta and delta wolfs;
Figure FDA0003603479370000045
respectively representing the current positions of alpha, beta and delta wolfs;
Figure FDA0003603479370000046
Figure FDA0003603479370000047
are all random vectors, said
Figure FDA0003603479370000048
Wherein
Figure FDA0003603479370000049
Is a random value between (0, 1);
Figure FDA00036034793700000410
indicating the position of the current wolf;
s72: updating the positions of the wolf clusters according to the distances between each wolf and alpha, beta and delta wolf respectively, wherein the formula is as follows:
Figure FDA00036034793700000411
Figure FDA00036034793700000412
Figure FDA00036034793700000413
Figure FDA00036034793700000414
in the formula,
Figure FDA0003603479370000051
wherein,
Figure FDA0003603479370000052
is a constant vector that decays from 2 to 0 with the number of iterations,
Figure FDA0003603479370000053
is a random value between (0,1),
Figure FDA0003603479370000054
is a random coefficient;
Figure FDA0003603479370000055
for each wolf, the updated position is based on alpha, beta, delta wolf,
Figure FDA0003603479370000056
is the updated position of the wolf.
7. A battery energy storage system size division system based on a micro-grid is characterized in that a target size of the battery energy storage system is obtained through a wolf optimization algorithm so as to stabilize the frequency response of the micro-grid when a fault occurs; the system comprises:
the coding matrix acquisition module is used for acquiring a coding matrix according to the parameter data of the micro-grid and each battery energy storage system in the micro-grid, and elements in the coding matrix
Figure FDA0003603479370000057
The optional size of a battery energy storage system in the micro-grid is represented, X in elements represents the battery energy storage system, n in elements represents the number of the battery energy storage systems, and p represents the position of the battery energy storage system X;
the setting module is used for setting the number of wolves in the wolves, namely the number and the maximum iteration times of the battery energy storage system X, and the value range of the size parameter variable of the battery energy storage system, namely the range formed by the minimum value of the positions of the wolves and the maximum value of the positions of the wolves through the coding matrix, and initializing the positions of the wolves, namely the positions of the battery energy storage system X through the value range;
the judgment module is used for reinitializing the wolf pack positions through the setting module when each battery energy storage system does not meet the preset constraint condition;
the fitness objective function value acquisition module is used for acquiring a fitness objective function value corresponding to the position of each wolf through the fitness objective function when each battery energy storage system meets a preset constraint condition, namely a frequency deviation effect objective function value of the micro-grid after the micro-grid passes through the battery energy storage system for frequency modulation;
the dividing module is used for dividing the wolf group into alpha, beta, delta and omega according to the fitness objective function value, wherein the alpha wolf is the wolf with the optimal frequency modulation effect of the battery energy storage system, the beta wolf is the wolf with the suboptimal frequency modulation effect of the battery energy storage system, the delta wolf is the wolf with the third best frequency modulation effect of the battery energy storage system, and the omega wolf is the rest wolf;
the iteration module is used for initializing the iteration times and starting counting the iteration times;
the position updating module is used for updating the position of the wolf pack;
the checking module is used for updating the position of the wolf pack again through the position updating module when the updated wolf does not meet the preset constraint condition;
and the output module is used for adding 1 to the iteration times when the updated wolf meets the preset constraint condition, outputting the position of the alpha wolf when the current iteration times is greater than or equal to the maximum iteration times, namely the target size of the battery energy storage system, and updating the position of the wolf cluster through the position updating module when the current iteration times is less than the maximum iteration times.
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