CN114744658B - 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 PDFInfo
- Publication number
- CN114744658B CN114744658B CN202210409307.1A CN202210409307A CN114744658B CN 114744658 B CN114744658 B CN 114744658B CN 202210409307 A CN202210409307 A CN 202210409307A CN 114744658 B CN114744658 B CN 114744658B
- Authority
- CN
- China
- Prior art keywords
- energy storage
- battery energy
- storage system
- wolf
- micro
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000004146 energy storage Methods 0.000 title claims abstract description 215
- 238000000034 method Methods 0.000 title claims abstract description 37
- 241000282461 Canis lupus Species 0.000 claims abstract description 148
- 239000011159 matrix material Substances 0.000 claims abstract description 33
- 238000005457 optimization Methods 0.000 claims abstract description 17
- 241000282421 Canidae Species 0.000 claims description 67
- 230000000694 effects Effects 0.000 claims description 24
- 230000004044 response Effects 0.000 claims description 14
- 230000008569 process Effects 0.000 claims description 7
- 238000005070 sampling Methods 0.000 claims description 7
- 238000004513 sizing Methods 0.000 claims description 7
- 239000013598 vector Substances 0.000 claims description 6
- 230000007123 defense Effects 0.000 claims description 3
- 238000010248 power generation Methods 0.000 abstract description 8
- 230000002159 abnormal effect Effects 0.000 abstract description 3
- 230000008901 benefit Effects 0.000 abstract description 3
- 230000003044 adaptive effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 241001260012 Bursa Species 0.000 description 1
- 241000282465 Canis Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000008094 contradictory effect Effects 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000000474 nursing effect Effects 0.000 description 1
- 238000009304 pastoral farming Methods 0.000 description 1
- 230000035515 penetration Effects 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 230000036578 sleeping time Effects 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
- H02J3/241—The oscillation concerning frequency
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/10—Flexible AC transmission systems [FACTS]
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention discloses a method and a system for dividing the size of a battery energy storage system based on a micro-grid, which relate to the field of micro-grids, wherein a coding matrix is obtained according to the micro-grid and parameter data of each battery energy storage system in the micro-grid, and input parameters of a wolf optimization algorithm and the positions of an initialized wolf group are set according to the coding matrix; the method has the advantages that the wolf group is divided through the fitness objective function, after iteration of the wolf optimization algorithm is entered, the position of the last alpha wolf is output to be the target size of the battery energy storage system, the wolf optimization algorithm is embedded into the battery energy storage system size division method to obtain the battery energy storage system with the target size, and the purposes that when the micro grid fails, frequency instability is caused by unbalance among power generation and load demands, enough frequency support is provided for the micro grid to keep the safety of the power system, and the cost of the battery energy storage system can be reasonably controlled while the power system can stably run under normal and abnormal conditions are achieved.
Description
Technical Field
The invention relates to the field of micro-grids, in particular to a method and a system for dividing the size of a battery energy storage system based on a micro-grid.
Background
As a single controllable network, the micro-grid cluster load and micro-sources (SGs and NS-RES) can provide reliable power supply to remote communities (including islands) and industrial complexes. The broad replacement 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, an inertially limited power system has a significant impact on the frequency behaviour of the system, for example, due to the loss of a large generator set, an unacceptable frequency level may result, possibly resulting in grid collapse, so that stable control of the system frequency is particularly important when the NS-RES penetration level is high.
In addition, in the interconnection mode of the micro-grid, it is mainly controlled and maintained by the utility grid that the voltage and frequency of the micro-grid are within the allowable range of the frequency and voltage deviation. However, unlike the case of an independent microgrid, the NS-RES permeability of the grid is high, and there is an imbalance between the power generation and load demands, and therefore, the frequency deviation of the independent microgrid is faster.
Currently, with the development of energy materials, battery Energy Storage Systems (BESS) are widely used in power grids that provide frequency support by injecting instantaneous power into micro-grids, however, the oversized battery energy storage systems increase costs, while undersized battery capacity may result in poor performance. Therefore, the battery energy storage system with proper size is arranged to provide enough frequency support for the micro-grid to stabilize the frequency response of the micro-grid when the micro-grid fails, the power generation and the load demand are unbalanced, and maintain the safety of the power system, so that the power system can stably operate 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 micro-grid when the micro-grid fails and the frequency is unstable due to unbalance between power generation and load demands, the invention provides a micro-grid-based battery energy storage system size division method, which obtains the target size of the battery energy storage system through a gray wolf optimization algorithm so as to stabilize the frequency response of the micro-grid when the micro-grid fails; the size division method comprises the following steps:
S1: acquiring a coding matrix according to the micro-grid and parameter data of each battery energy storage system in the micro-grid, wherein elements in the coding matrix Representing the selectable size of the battery energy storage system in the micro-grid, wherein X in the element represents the battery energy storage system, n in the element represents the number of the battery energy storage systems, and p represents the position of the battery energy storage system X;
S2: the number of the wolves in the wolves, namely the number of the battery energy storage systems X, the maximum iteration times and the range formed by the minimum value of the wolves position and the maximum value of the wolves position of the battery energy storage system size parameter variable are set through the coding matrix, and the wolves position, namely the position of the battery energy storage system X, is initialized through the range;
S3: judging whether each battery energy storage system meets preset constraint conditions or not; if yes, entering the next step, otherwise, returning to the step S2 to reinitialize the wolf group position;
s4: acquiring an 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 frequency modulation through a battery energy storage system;
s5: dividing the wolf group into alpha, beta, delta and omega according to the fitness objective function value, wherein alpha wolf is the wolf with the optimal frequency modulation effect of the battery energy storage system, beta wolf is the wolf with the optimal frequency modulation effect of the battery energy storage system, delta wolf is the wolf with the third optimal frequency modulation effect of the battery energy storage system, and omega wolf is the rest wolf;
S6: initializing iteration times and starting to count 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 number, judging whether the iteration number is greater than or equal to the maximum iteration number, 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 of the encoding matrix in the step S1 is as follows:
in the formula, the element Represents the selectable size of the battery energy storage system in the micro-grid, X represents the battery energy storage system, n represents the number of lateral elements in the encoding matrix, i.e. the number of battery energy storage systems, and p represents the position of the battery energy storage system X.
Further, the formula for initializing the wolf group position in the step S2 is as follows:
Position＝Pmin+rand()*(Pmax-Pmin)；
Where P min denotes the wolf's Position minimum, P max denotes the wolf's Position maximum, rand () denotes the random function, and Position denotes the wolf's random Position.
Further, the preset constraint condition includes:
Constraint equation 1:
0≤Pd(t)≤Pd；
Wherein 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 moment t;
constraint equation 2:
S1≤SOC(t)≤S2；
s1 and S2 are respectively the standard upper limit and the standard lower limit of the energy storage charge quantity of the battery energy storage system, and SOC (t) is the energy storage charge state of the battery energy storage system at the moment t;
constraint condition formula 3, namely, the relation between the energy storage charge state and the output power of the battery energy storage system, needs to satisfy the equation:
Wherein SOC (0) is the energy storage charge state at the moment of failure occurrence, and eta f is the discharge efficiency of the battery energy storage system; e d is the energy storage capacity of the battery energy storage system;
Constraint equation 4:
Wherein, deltaf is the frequency deviation of the battery energy storage system, deltaf max is the maximum frequency deviation of the battery energy storage system; for slip of battery energy storage system,/> Maximum slip for the battery energy storage system;
Constraint equation 4:
wherein H is an inertia time constant, K L is a comprehensive frequency adjustment coefficient of a load, deltaP is the maximum possible power shortage fault disturbance quantity under the defense standard in the micro-grid, deltaP d is the rated discharge power of the battery energy storage system.
Further, the formula expression of the fitness objective function in the step S4 is:
wherein, N represents the frequency of sampling the battery energy storage system frequency in a preset period, F i represents the battery energy storage system frequency obtained by the ith sampling, F 0 represents the rated frequency of the battery energy storage system, and F is the fitness objective function value of the battery energy storage system.
Further, the step S7 of updating the position of the wolf group includes the steps of:
S71: the distance between each wolf and alpha, beta and delta wolves is obtained, and the obtaining formula is as follows:
In the method, in the process of the invention, Respectively representing the distance between the position of each wolf and the positions of alpha, beta and delta wolves; /(I)Respectively representing the current positions of alpha, beta and delta wolves; /(I) Are all random vectors, saidWherein/>Is a random value between (0, 1); /(I)Representing the current wolf's position;
s72: the position of the wolf group is updated according to the distance between each wolf and alpha, beta and delta wolves, and the formula is as follows:
In the method, in the process of the invention, Wherein/>Is a constant vector that decays from 2 to 0 with iteration number,/>Is a random value between (0, 1)/>Is a random coefficient; /(I)For each wolf, according to the updated position of alpha, beta, delta wolf,/>To update the position of the back wolf.
The invention also provides a micro-grid-based battery energy storage system size dividing system, which obtains the target size of the battery energy storage system through a gray wolf optimization algorithm so as to stabilize the frequency response of the micro-grid when faults occur; the system comprises:
The code matrix acquisition module is used for acquiring a code matrix according to the micro-grid and the parameter data of each battery energy storage system in the micro-grid, wherein the elements in the code matrix Representing the selectable size of the battery energy storage system in the micro-grid, wherein X in the element represents the battery energy storage system, n in the element 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 the wolves in the wolves, namely the number of the battery energy storage systems X, the maximum iteration times and the range formed by the minimum value of the wolves position and the maximum value of the wolves position, namely the range of the battery energy storage system size parameter variable, through the coding matrix, and initializing the wolves position, namely the position of the battery energy storage system X through the range of the values;
The judging module is used for reinitializing the position of the wolf group through the setting module when the energy storage systems of the batteries do not meet the preset constraint conditions;
The fitness objective function value acquisition module is used for acquiring fitness objective function values corresponding to the positions of the wolves through the fitness objective function when the battery energy storage systems meet preset constraint conditions, namely frequency deviation effect objective function values of the micro-grid after the micro-grid is subjected to frequency modulation through the battery energy storage systems;
the dividing module is used for dividing the wolf group into alpha, beta, delta and omega according to the fitness objective function value, wherein alpha wolf is the wolf with the optimal frequency modulation effect of the battery energy storage system, beta wolf is the wolf with the optimal frequency modulation effect of the battery energy storage system, delta wolf is the wolf with the third optimal frequency modulation effect of the battery energy storage system, and omega wolf is the rest wolf;
The iteration module is used for initializing the iteration times and starting to count the iteration times;
the position updating module is used for updating the position of the wolf group;
The checking module is used for updating the position of the wolf group again through the position updating module when the updated wolf does not meet the preset constraint condition;
The output module is used for adding 1 to the iteration times when the updated wolves meet the preset constraint condition, outputting the position of the alpha wolves, namely the target size of the battery energy storage system when the current iteration times are larger than or equal to the maximum iteration times, and updating the position of the wolves again through the position updating module when the current iteration times are smaller than the maximum iteration times.
Compared with the prior art, the invention at least has the following beneficial effects:
(1) According to the invention, a coding matrix is obtained according to the micro-grid and the parameter data of each battery energy storage system in the micro-grid, and the input parameters (the number of wolves, the maximum iteration number and the value range) of a wolf optimization algorithm and the positions of initialized wolves are set according to the coding matrix; dividing wolves by using a fitness objective function, after entering iteration of a wolf optimization algorithm, outputting the position of the last alpha wolf as the target size of the battery energy storage system, and embedding the wolf optimization algorithm into the battery energy storage system size dividing method to obtain a battery energy storage system with a proper target size, thereby realizing the goal 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 unbalance between power generation and load demands causes the frequency instability;
(2) In the size dividing method, whether each battery energy storage system meets the preset constraint condition is judged, after the positions of the wolves are updated, whether the updated wolves meet the preset constraint condition is checked, and 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 method flow diagram of a micro-grid based battery energy storage system sizing method;
FIG. 2 is a system block diagram of a micro-grid based battery energy storage system sizing system;
FIG. 3 is a graph of the level of the wolf in the wolf optimization algorithm.
Detailed Description
The following are specific embodiments of the present invention and the technical solutions of the present invention will be further described with reference to the accompanying drawings, but the present invention is not limited to these embodiments.
Example 1
Battery Energy Storage Systems (BESS) are widely used in power grids to provide frequency support by injecting transient power into the micro-grid, however, oversized battery energy storage systems can increase cost and undersized battery capacity can result in poor performance. In order to solve the technical problem, as shown in fig. 1, the invention provides a method for dividing the size of a battery energy storage system based on a micro-grid, which obtains the target size of the battery energy storage system through a gray 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 the micro-grid and parameter data of each battery energy storage system in the micro-grid, wherein elements in the coding matrix Representing the selectable size of the battery energy storage system in the micro-grid, wherein X in the element represents the battery energy storage system, n in the element 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 encoding matrix in the step S1 is:
in the formula, the element Represents the selectable size of the battery energy storage system in the micro-grid, X represents the battery energy storage system, n represents the number of lateral elements in the encoding matrix, i.e. the number of battery energy storage systems, and p represents the position of the battery energy storage system X.
S2: the number of the wolves in the wolves, namely the number of the battery energy storage systems X, the maximum iteration times and the range formed by the minimum value of the wolves position and the maximum value of the wolves position of the battery energy storage system size parameter variable are set through the coding matrix, and the wolves position, namely the position of the battery energy storage system X, is initialized through the 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 micro-grid.
The formula for initializing the wolf group position in the step S2 is as follows:
Position＝Pmin+rand()*(Pmax-Pmin)；
Where P min denotes the wolf's Position minimum, P max denotes the wolf's Position maximum, rand () denotes the random function, and Position denotes the wolf's random Position.
S3: judging whether each battery energy storage system meets preset constraint conditions or not; if yes, entering the next step, otherwise, returning to the step S2 to reinitialize the wolf group position;
s4: acquiring an 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 frequency modulation through a battery energy storage system;
The formula expression of the fitness objective function in the step S4 is as follows:
wherein, N represents the frequency of sampling the battery energy storage system frequency in a preset period, F i represents the battery energy storage system frequency obtained by the ith sampling, F 0 represents the rated frequency of the battery energy storage system, and F is the fitness objective function value of the battery energy storage system.
The adaptive objective function value corresponding to the battery energy storage system is obtained through the adaptive objective function designed by the invention after the frequency sampling is carried out on the battery energy storage system for N times within the preset period, so that wolves of different levels are accurately divided, and the obtaining precision of the target size is improved.
For the gray wolf optimization algorithm (GWO), it should be noted that:
The gray wolves belong to the canine family, and are considered top grazing, they are at the top of the biosphere food chain. The gray wolves mostly like to live in groups, and 5-12 wolves are on average in each group. Of particular interest are those having a very strict social level hierarchy, as shown in figure 3. The first layer of the pyramid is the leader in the population and is called α. Alpha is an individual with management capability in the wolf group and is mainly responsible for the matters about hunting, sleeping time and place, decisions in the group of food distribution and the like. The second layer of the pyramid is the bursa team of alpha, called beta, which is primarily responsible for assisting alpha in decision making. When alpha of the whole wolf group is empty, beta takes over the alpha position. The dominance of beta in the wolf group is next to alpha, which gives the command of alpha to other members and feeds back the execution of other members to alpha to act as a bridge. The third layer of pyramid is delta, which listens to the decision commands of alpha and beta and is mainly responsible for investigation, whistle, nursing and other matters. The poorly adapted α and β will also be reduced to δ. The bottommost layer of the pyramid is omega and is mainly responsible for balancing the internal relationship of the population. Wolf hunting has several troublesome processes, such as tracking, chasing, and wrapping, all of which must be performed accurately in order to allow perfect hunting.
GWO has the advantage of having a high level of robustness, and the ability to handle high dimensional, non-linear and non-convex problems, is an ideal tool to achieve the target dimensions of the present invention.
S5: dividing the wolf group into alpha, beta, delta and omega according to the fitness objective function value, wherein alpha wolf is the wolf with the optimal frequency modulation effect of the battery energy storage system, beta wolf is the wolf with the optimal frequency modulation effect of the battery energy storage system, delta wolf is the wolf with the third optimal frequency modulation effect of the battery energy storage system, and omega wolf is the rest wolf;
S6: initializing iteration times and starting to count the iteration times;
the initial value of the number of iterations in this embodiment is 1.
S7: updating the position of the wolf group;
the step S7 of updating the position of the wolf group includes the steps of:
S71: the distance between each wolf and alpha, beta and delta wolves is obtained, and the obtaining formula is as follows:
In the method, in the process of the invention, Respectively representing the distance between the position of each wolf and the positions of alpha, beta and delta wolves; /(I)Respectively representing the current positions of alpha, beta and delta wolves; /(I) Are all random vectors, saidWherein/>Is a random value between (0, 1); /(I)Representing the current wolf's position;
s72: the position of the wolf group is updated according to the distance between each wolf and alpha, beta and delta wolves, and the formula is as follows:
In the method, in the process of the invention, Wherein/>Is a constant vector that decays from 2 to 0 with iteration number,/>Is a random value between (0, 1)/>Is a random coefficient; /(I)For each wolf, according to the updated position of alpha, beta, delta wolf,/>To update the position of the back wolf.
It should be noted that the initialization is also included between the steps S6 to S7
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 dividing method, whether each battery energy storage system meets the preset constraint condition is judged, and after the positions of the wolves are updated, whether the updated wolves meet the preset constraint condition is checked, so that the accuracy of the target size is further improved.
S9: and adding 1 to the iteration number, judging whether the iteration number is greater than or equal to the maximum iteration number, 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 condition comprises:
Constraint equation 1:
0≤Pd(t)≤Pd；
Wherein t represents the 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 the time t;
constraint equation 2:
S1≤SOC(t)≤S2；
s1 and S2 are respectively the standard upper limit and the standard lower limit of the energy storage charge quantity of the battery energy storage system, and SOC (t) is the energy storage charge state of the battery energy storage system at the moment t;
constraint condition formula 3, namely, the relation between the energy storage charge state and the output power of the battery energy storage system, needs to satisfy the equation:
Wherein SOC (0) is the energy storage charge state at the moment of failure occurrence, and eta f is the discharge efficiency of the battery energy storage system; e d is the energy storage capacity of the battery energy storage system;
Constraint equation 4:
Wherein, deltaf is the frequency deviation of the battery energy storage system, deltaf max is the maximum frequency deviation of the battery energy storage system; for slip of battery energy storage system,/> Maximum slip for the battery energy storage system;
Constraint equation 4:
wherein H is an inertia time constant, K L is a comprehensive frequency adjustment coefficient of a load, deltaP is the maximum possible power shortage fault disturbance quantity under the defense standard in the micro-grid, deltaP d is the rated discharge power of the battery energy storage system.
According to the invention, a coding matrix is obtained according to the micro-grid and the parameter data of each battery energy storage system in the micro-grid, and the input parameters (the number of wolves, the maximum iteration number and the value range) of a wolf optimization algorithm and the positions of initialized wolves are set according to the coding matrix; the method has the advantages that the wolf group is divided through the fitness objective function, after iteration of the wolf optimization algorithm is entered, the position of the last alpha wolf is output to be the target size of the battery energy storage system, and the battery energy storage system with the proper target size is obtained through embedding the wolf optimization algorithm into the battery energy storage system size dividing method, so that when the micro-grid fails, the frequency is unstable due to unbalance among power generation and load demands, sufficient frequency support is provided for the micro-grid to stabilize the frequency response of the micro-grid, the safety of the power system is maintained, and the cost of the battery energy storage system is reasonably controlled while the power system can stably operate under normal and abnormal conditions.
Example two
The invention also provides a micro-grid-based battery energy storage system size dividing system, which obtains the target size of the battery energy storage system through a gray wolf optimization algorithm so as to stabilize the frequency response of the micro-grid when faults occur; the system comprises:
The code matrix acquisition module is used for acquiring a code matrix according to the micro-grid and the parameter data of each battery energy storage system in the micro-grid, wherein the elements in the code matrix Representing the selectable size of the battery energy storage system in the micro-grid, wherein X in the element represents the battery energy storage system, n in the element 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 the wolves in the wolves, namely the number of the battery energy storage systems X, the maximum iteration times and the range formed by the minimum value of the wolves position and the maximum value of the wolves position, namely the range of the battery energy storage system size parameter variable, through the coding matrix, and initializing the wolves position, namely the position of the battery energy storage system X through the range of the values;
The judging module is used for reinitializing the position of the wolf group through the setting module when the energy storage systems of the batteries do not meet the preset constraint conditions;
The fitness objective function value acquisition module is used for acquiring fitness objective function values corresponding to the positions of the wolves through the fitness objective function when the battery energy storage systems meet preset constraint conditions, namely frequency deviation effect objective function values of the micro-grid after the micro-grid is subjected to frequency modulation through the battery energy storage systems;
the dividing module is used for dividing the wolf group into alpha, beta, delta and omega according to the fitness objective function value, wherein alpha wolf is the wolf with the optimal frequency modulation effect of the battery energy storage system, beta wolf is the wolf with the optimal frequency modulation effect of the battery energy storage system, delta wolf is the wolf with the third optimal frequency modulation effect of the battery energy storage system, and omega wolf is the rest wolf;
The iteration module is used for initializing the iteration times and starting to count the iteration times;
the position updating module is used for updating the position of the wolf group;
The checking module is used for updating the position of the wolf group again through the position updating module when the updated wolf does not meet the preset constraint condition;
The output module is used for adding 1 to the iteration times when the updated wolves meet the preset constraint condition, outputting the position of the alpha wolves, namely the target size of the battery energy storage system when the current iteration times are larger than or equal to the maximum iteration times, and updating the position of the wolves again through the position updating module when the current iteration times are smaller than the maximum iteration times.
The system provides frequency support for the micro-grid by acquiring the battery energy storage system with the optimal size (namely the target size), reduces high-frequency fluctuation and reduces the conventional power generation burden involved in inertial response and primary frequency control.
It should be noted that all directional indicators (such as up, down, left, right, front, and rear … …) in the embodiments of the present invention are merely used to explain the relative positional relationship, movement, etc. between the components in a particular posture (as shown in the drawings), and if the particular posture is changed, the directional indicator is changed accordingly.
Furthermore, descriptions such as those referred to herein as "first," "second," "a," and the like are provided for descriptive purposes only and are not to be construed as indicating or implying a relative importance or an implicit indication of the number of features being indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
In the present invention, unless specifically stated and limited otherwise, the terms "connected," "affixed," and the like are to be construed broadly, and for example, "affixed" may be a fixed connection, a removable connection, or an integral body; can be mechanically or electrically connected; either directly or indirectly, through intermediaries, or both, may be in communication with each other or in interaction with each other, unless expressly defined otherwise. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
In addition, the technical solutions of the embodiments of the present invention may be combined with each other, but it is necessary to be based on the fact that those skilled in the art can implement the technical solutions, and when the technical solutions are contradictory or cannot be implemented, the combination of the technical solutions should be considered as not existing, and not falling within the scope of protection claimed by the present invention.
Claims (7)
1. The method is characterized in that the target size of the battery energy storage system is obtained through a gray wolf optimization algorithm so as to stabilize the frequency response of the micro-grid when faults occur; the size division method comprises the following steps:
S1: acquiring a coding matrix according to the micro-grid and parameter data of each battery energy storage system in the micro-grid, wherein elements in the coding matrix Representing the selectable size of the battery energy storage system in the micro-grid, wherein X in the element represents the battery energy storage system, n in the element represents the number of the battery energy storage systems, and p represents the position of the battery energy storage system X;
S2: the number of the wolves in the wolves, namely the number of the battery energy storage systems X, the maximum iteration times and the range formed by the minimum value of the wolves position and the maximum value of the wolves position of the battery energy storage system size parameter variable are set through the coding matrix, and the wolves position, namely the position of the battery energy storage system X, is initialized through the range;
S3: judging whether each battery energy storage system meets preset constraint conditions or not; if yes, entering the next step, otherwise, returning to the step S2 to reinitialize the wolf group position;
s4: acquiring an 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 frequency modulation through a battery energy storage system;
s5: dividing the wolf group into alpha, beta, delta and omega according to the fitness objective function value, wherein alpha wolf is the wolf with the optimal frequency modulation effect of the battery energy storage system, beta wolf is the wolf with the optimal frequency modulation effect of the battery energy storage system, delta wolf is the wolf with the third optimal frequency modulation effect of the battery energy storage system, and omega wolf is the rest wolf;
S6: initializing iteration times and starting to count 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 number, judging whether the iteration number is greater than or equal to the maximum iteration number, 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 method for sizing a micro-grid-based battery energy storage system according to claim 1, wherein the formula of the encoding matrix in step S1 is:
in the formula, the element Represents the selectable size of the battery energy storage system in the micro-grid, X represents the battery energy storage system, n represents the number of lateral elements in the encoding matrix, i.e. the number of battery energy storage systems, and p represents the position of the battery energy storage system X.
3. The method for sizing a micro-grid-based battery energy storage system according to claim 2, wherein the formula for initializing the wolf group position in step S2 is:
Position＝Pmin+rand()*(Pmax-Pmin)；
Where P min denotes the wolf's Position minimum, P max denotes the wolf's Position maximum, rand () denotes the random function, and Position denotes the wolf's random Position.
4. The micro-grid-based battery energy storage system sizing method of claim 1, wherein the preset constraint condition comprises:
Constraint equation 1:
0≤Pd(t)≤Pd；
Wherein 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 moment t;
constraint equation 2:
S1≤SOC(t)≤S2；
s1 and S2 are respectively the standard upper limit and the standard lower limit of the energy storage charge quantity of the battery energy storage system, and SOC (t) is the energy storage charge state of the battery energy storage system at the moment t;
constraint condition formula 3, namely, the relation between the energy storage charge state and the output power of the battery energy storage system, needs to satisfy the equation:
Wherein SOC (0) is the energy storage charge state at the moment of failure occurrence, and eta f is the discharge efficiency of the battery energy storage system; e d is the energy storage capacity of the battery energy storage system;
Constraint equation 4:
Wherein, deltaf is the frequency deviation of the battery energy storage system, deltaf max is the maximum frequency deviation of the battery energy storage system; for slip of battery energy storage system,/> Maximum slip for the battery energy storage system;
Constraint equation 4:
wherein H is an inertia time constant, K L is a comprehensive frequency adjustment coefficient of a load, deltaP is the maximum possible power shortage fault disturbance quantity under the defense standard in the micro-grid, deltaP d is the rated discharge power of the battery energy storage system.
5. The method for sizing a micro-grid-based battery energy storage system according to claim 3, wherein the fitness objective function in step S4 has a formula expressed as:
wherein, N represents the frequency of sampling the battery energy storage system frequency in a preset period, F i represents the battery energy storage system frequency obtained by the ith sampling, F 0 represents the rated frequency of the battery energy storage system, and F is the fitness objective function value of the battery energy storage system.
6. The method for sizing a micro-grid based battery energy storage system according to claim 5, wherein the updating the position of the wolf group in step S7 comprises the steps of:
S71: the distance between each wolf and alpha, beta and delta wolves is obtained, and the obtaining formula is as follows:
In the method, in the process of the invention, Respectively representing the distance between the position of each wolf and the positions of alpha, beta and delta wolves; respectively representing the current positions of alpha, beta and delta wolves; /(I) Are all random vectors, saidWherein/>Is a random value between (0, 1); /(I)Representing the current wolf's position;
s72: the position of the wolf group is updated according to the distance between each wolf and alpha, beta and delta wolves, and the formula is as follows:
In the method, in the process of the invention, Wherein/>Is a constant vector that decays from 2 to 0 with iteration number,/>Is a random value between (0, 1)/>Is a random coefficient; /(I)For each wolf, according to the updated position of alpha, beta, delta wolf,/>To update the position of the back wolf.
7. The battery energy storage system size dividing system based on the micro-grid is characterized in that the target size of the battery energy storage system is obtained through a gray wolf optimization algorithm so as to stabilize the frequency response of the micro-grid when faults occur; the system comprises:
The code matrix acquisition module is used for acquiring a code matrix according to the micro-grid and the parameter data of each battery energy storage system in the micro-grid, wherein the elements in the code matrix Representing the selectable size of the battery energy storage system in the micro-grid, wherein X in the element represents the battery energy storage system, n in the element 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 the wolves in the wolves, namely the number of the battery energy storage systems X, the maximum iteration times and the range formed by the minimum value of the wolves position and the maximum value of the wolves position, namely the range of the battery energy storage system size parameter variable, through the coding matrix, and initializing the wolves position, namely the position of the battery energy storage system X through the range of the values;
The judging module is used for reinitializing the position of the wolf group through the setting module when the energy storage systems of the batteries do not meet the preset constraint conditions;
The fitness objective function value acquisition module is used for acquiring fitness objective function values corresponding to the positions of the wolves through the fitness objective function when the battery energy storage systems meet preset constraint conditions, namely frequency deviation effect objective function values of the micro-grid after the micro-grid is subjected to frequency modulation through the battery energy storage systems;
the dividing module is used for dividing the wolf group into alpha, beta, delta and omega according to the fitness objective function value, wherein alpha wolf is the wolf with the optimal frequency modulation effect of the battery energy storage system, beta wolf is the wolf with the optimal frequency modulation effect of the battery energy storage system, delta wolf is the wolf with the third optimal frequency modulation effect of the battery energy storage system, and omega wolf is the rest wolf;
The iteration module is used for initializing the iteration times and starting to count the iteration times;
the position updating module is used for updating the position of the wolf group;
The checking module is used for updating the position of the wolf group again through the position updating module when the updated wolf does not meet the preset constraint condition;
The output module is used for adding 1 to the iteration times when the updated wolves meet the preset constraint condition, outputting the position of the alpha wolves, namely the target size of the battery energy storage system when the current iteration times are larger than or equal to the maximum iteration times, and updating the position of the wolves again through the position updating module when the current iteration times are smaller than the maximum iteration times.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210409307.1A CN114744658B (en) | 2022-04-19 | 2022-04-19 | Battery energy storage system size division method and system based on micro-grid |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210409307.1A CN114744658B (en) | 2022-04-19 | 2022-04-19 | Battery energy storage system size division method and system based on micro-grid |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114744658A CN114744658A (en) | 2022-07-12 |
| CN114744658B true CN114744658B (en) | 2024-05-14 |
Family
ID=82281339
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210409307.1A Active CN114744658B (en) | 2022-04-19 | 2022-04-19 | Battery energy storage system size division method and system based on micro-grid |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114744658B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017062908A1 (en) * | 2015-10-08 | 2017-04-13 | Johnson Controls Technology Company | Photovoltaic energy system with solar intensity prediction |
| CN106896716A (en) * | 2017-04-17 | 2017-06-27 | 华北电力大学（保定） | Micro-capacitance sensor alternating current-direct current section transverter pid parameter optimization method based on grey wolf algorithm |
| CN110401209A (en) * | 2019-05-14 | 2019-11-01 | 东华大学 | The energy management method of peak load shifting based on more random composite optimization grey wolf algorithms |
| CN113471989A (en) * | 2021-07-02 | 2021-10-01 | 东北电力大学 | Intelligent micro-grid secondary frequency control method based on wolf optimization method |
| CN114172176A (en) * | 2021-11-19 | 2022-03-11 | 国网内蒙古东部电力有限公司电力科学研究院 | Flywheel energy storage array control method and system applied to wind generating set |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10564610B2 (en) * | 2015-10-08 | 2020-02-18 | Con Edison Battery Storage, Llc | Photovoltaic energy system with preemptive ramp rate control |
| US10389136B2 (en) * | 2015-10-08 | 2019-08-20 | Con Edison Battery Storage, Llc | Photovoltaic energy system with value function optimization |
-
2022
- 2022-04-19 CN CN202210409307.1A patent/CN114744658B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017062908A1 (en) * | 2015-10-08 | 2017-04-13 | Johnson Controls Technology Company | Photovoltaic energy system with solar intensity prediction |
| CN106896716A (en) * | 2017-04-17 | 2017-06-27 | 华北电力大学（保定） | Micro-capacitance sensor alternating current-direct current section transverter pid parameter optimization method based on grey wolf algorithm |
| CN110401209A (en) * | 2019-05-14 | 2019-11-01 | 东华大学 | The energy management method of peak load shifting based on more random composite optimization grey wolf algorithms |
| CN113471989A (en) * | 2021-07-02 | 2021-10-01 | 东北电力大学 | Intelligent micro-grid secondary frequency control method based on wolf optimization method |
| CN114172176A (en) * | 2021-11-19 | 2022-03-11 | 国网内蒙古东部电力有限公司电力科学研究院 | Flywheel energy storage array control method and system applied to wind generating set |
Non-Patent Citations (3)
| Title |
|---|
| 基于灰狼优化算法的微电网并网控制器参数优化设计;牛成玉;傅仲文;;电器与能效管理技术;20171030(20);全文 * |
| 基于灰狼算法的分布式混合能源优化配置方法;杨灿;王勇;;微型电脑应用;20181219(12);全文 * |
| 邓长征 ; 冯朕 ; 邱立 ; 余利 ; 杨楠 ; .基于混沌灰狼算法的交直流混合微网经济调度.电测与仪表.(04),全文. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114744658A (en) | 2022-07-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101919134B (en) | Event-based control system for wind turbine generators and control method thereof | |
| KR101778309B1 (en) | Lead acid storage battery and lead acid storage battery system for a natural energy using system | |
| JP6790072B2 (en) | Power system, control system and power control method of power system | |
| CN107359378A (en) | Battery charging method, device and equipment | |
| WO2017006371A1 (en) | Renewable power system and sizing method for controllable plant associated with renewable power system | |
| CN104779607B (en) | One of direct-current micro-grid distributed and coordinated control method and system | |
| CN110556877B (en) | Distribution network distributed energy storage aggregation control method considering SOC balance | |
| CN111682563A (en) | Micro-grid intelligent frequency control method based on electric energy storage system | |
| JP2016034170A (en) | Power storage device charge/discharge system | |
| Usman et al. | Optimal under voltage load shedding scheme for a distribution network using EPSO algorithm | |
| CN114744658B (en) | Battery energy storage system size division method and system based on micro-grid | |
| CN114744657B (en) | Battery energy storage system size optimization method based on micro-grid | |
| KR20160078174A (en) | System and method for estimating soc of sodium rechargeable battery | |
| CN107919683A (en) | A kind of energy storage reduces the Study on Decision-making Method for Optimization that wind power plant abandons wind-powered electricity generation amount | |
| JP2016220450A (en) | Power supply controller, power supply system, power source control method, and program | |
| KR101736717B1 (en) | Energy storage apparatus and method for controlling therof | |
| CN112186764A (en) | Access optimization method and device for power distribution network equipment and electronic equipment | |
| CN107147139B (en) | Micro-grid load scheduling method considering line transmission loss | |
| US11050258B2 (en) | Battery control system | |
| CN115796492A (en) | Micro-grid scheduling method and terminal of hybrid energy storage system | |
| CN114914942A (en) | Polymorphic distributed energy storage primary frequency modulation control method based on consistency algorithm | |
| JP7513391B2 (en) | Secondary battery management system and secondary battery management method | |
| KR20180114739A (en) | Method for managing Energy Storage System | |
| KR101936293B1 (en) | An energy storage system | |
| CN108206561B (en) | Apparatus for updating charge/discharge efficiency factor of energy storage system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |