CN108964099A - A kind of distributed energy storage system layout method and system - Google Patents
A kind of distributed energy storage system layout method and system Download PDFInfo
- Publication number
- CN108964099A CN108964099A CN201810644435.8A CN201810644435A CN108964099A CN 108964099 A CN108964099 A CN 108964099A CN 201810644435 A CN201810644435 A CN 201810644435A CN 108964099 A CN108964099 A CN 108964099A
- Authority
- CN
- China
- Prior art keywords
- storage system
- energy storage
- distributed energy
- individual
- value
- 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.)
- Granted
Links
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
-
- 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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
A kind of distributed energy storage system layout method, the described method includes: the individual amount of the cloth energy-storage system based on acquisition, distributed energy storage system mobile maximum distance in position in power grid, there is no the distances influenced between distributed energy storage system, the individual status number optional in its visual field of distributed energy storage system, the density of the individual amount of distributed energy storage system, it is solved using objective function of the artificial fish-swarm algorithm to the minimum target of cost of investment of preset energy-storage system in a distributed manner, obtains optimal solution；System is laid out based on the optimal solution.The present invention has the characteristics that easy to operate, control parameter is small, search precision is higher and robustness is stronger using artificial fish-swarm algorithm, and the complexity solved can be made to reduce, and solves distributed energy storage system layout complexity, computationally intensive problem.
Description
Technical field
The present invention relates to technical field of energy storage, and in particular to a kind of distributed energy storage system layout method and system.
Background technique
In recent years, with a large amount of grid-connected of distributed energy and its to a large amount of problems of stable operation bring of power distribution network,
The access of energy-storage system can play a degree of support and adjustment effect to system, and layout is made rational planning in power grid not only
Demand side management can be effectively realized, eliminate peak-valley difference, smooth load, be also used as improving operation of power networks stability and can
By property, adjustment frequency, a kind of means for compensating load fluctuation, however, the research in relation to distributed energy storage system layout method is also
It is more single.
Existing stage subsidy policy is simultaneously not known, and the usual price of energy storage device is higher.In the total tune of distributed energy storage
In layout, it is contemplated that distributed energy storage system, layout is complicated, computationally intensive.
Summary of the invention
In order to solve the above-mentioned deficiency in the presence of the prior art, the present invention provides a kind of synthesis of cool and thermal power equilibrium of supply and demand
Energy resource system classification regulation method and system.
Present invention provide the technical scheme that
A kind of distributed energy storage system layout method, which comprises
The individual amount of cloth energy-storage system based on acquisition, distributed energy storage system mobile maximum in position in power grid
Distance, there is no the distance influenced, the individual states optional in its visual field of distributed energy storage system between distributed energy storage system
Number, the density of the individual amount of distributed energy storage system are stored up using the artificial fish-swarm algorithm to preset in a distributed manner
The objective function of the minimum target of cost of investment of energy system is solved, and optimal solution is obtained；
System is laid out based on the optimal solution.
Preferably, the individual amount of the cloth energy-storage system based on acquisition, distributed energy storage system position in power grid
Mobile maximum distance is set, the distance influenced is not present between distributed energy storage system, the individual of distributed energy storage system is in its view
Optional status number in open country, the density of the individual amount of distributed energy storage system, using the artificial fish-swarm algorithm to presetting
The objective function of the minimum target of cost of investment of energy-storage system in a distributed manner solved, comprising:
The individual amount of the cloth energy-storage system is set as in artificial fish-swarm algorithm: the number of individuals of artificial fish-swarm；
By the distributed energy storage system, the mobile maximum distance in position is set as in artificial fish-swarm algorithm in power grid: mermaid
The mobile maximum step-length of group；
By there is no the distances influenced to be set as in artificial fish-swarm algorithm between the distributed energy storage system: perceived distance；
The density of the individual amount of the distributed energy storage system is positioned as in artificial fish-swarm algorithm: crowding；
Calculating is iterated to the objective function based on artificial fish-swarm algorithm, obtains the value of billboard；
Optimal location using the value of the billboard as distributed energy storage system in cost of investment minimum.
Preferably, described that calculating is iterated to the objective function based on artificial fish-swarm algorithm, the value of billboard is obtained,
Include:
Using the objective function as the input of artificial fish-swarm algorithm, it is iterated calculating, obtains the adaptation of each individual
Spend function；
The execution method to objective function is selected according to the fitness function of the individual and body position, after obtaining optimization
Value, value after the optimization is distributed energy storage system in cost of investment；
By the value after the optimization compared with the value on billboard, if being better than the value of billboard, after the optimization
Value update billboard, otherwise do not update the value of billboard；
When artificial fish-swarm algorithm reaches the current value that maximum number of iterations then exports billboard, otherwise continue to iterate to calculate.
Preferably, the objective function is shown below:
Minf=Cdess+Cmainainace+Crce
In formula: minf: distributed energy storage system is in cost of investment；CdessFor the mounting cost of energy storage device；CmainainaceFor
The operation and maintenance cost of energy storage device；CrceFor reactive-load compensation equipment expense.
Preferably, the installing reactive-load compensation equipment expense CrceIt is calculated as follows:
In formula, C0To install the revised unit reactive capability price of reactive-load compensation equipment, unit is member/kvar；QgiFor
The reactive compensation capacity of i-th of node, unit kvar, Qgi> 0 indicates installing capacitor or phase modifier, Qgi< 0 indicates installing simultaneously
Join reactor；N is node number.
Preferably, the objective function further includes constraint condition；Wherein the constraint condition include: node voltage constraint,
Branch current constraint, equality constraint, the constraint of energy-storage system active power output, idle units limits and state-of-charge constraint；
The node voltage constraint is shown below:
Uimin≤U’i≤Uimax
In formula, Uimin: the upper voltage limit of node i；Uimax: the lower voltage limit of node i, wherein i is that node is installed in energy storage；
U'i: the voltage of node i；
The branch current constrains
I’k≤Ikmax
In formula, IkFor the electric current of branch k；IkmaxPass through electric current for branch k is maximum allowable；
The equality constraint includes: active power and reactive power equilibrium constraint；
The active power balance constraint is shown below:
In formula: PGi: the active-power P that node i generator issuesDi；The active power of load consumption；Vi: the electricity of node i
Pressure；Vj: the voltage of node j；Gij: the conductance on route ij；BijSusceptance on route ij；
The reactive power equilibrium constraint is shown below:
In formula, Vi: the voltage of node i；Vj: the voltage of node j；Gij: the conductance on route ij；BijElectricity on route ij
It receives；QiThe reactive power of node i；QDiThe reactive power of load consumption；
The energy-storage system active power output constraint is shown below:
Pcmin≤Pc≤Pcmax
Wherein: PcFor the active output of energy-storage system；Wherein Pcmax PcminRespectively the active power output of energy storage device it is upper,
Lower limit；
Qcmin≤Qc≤Qcmax
Wherein, QcFor the idle output of energy-storage system；Wherein Qcmax QcminRespectively the idle power output of energy storage device it is upper,
Lower limit.
Preferably, the execution method includes looking for food, bunching, knocking into the back and random behavior.
A kind of system of distributed energy storage system layout, the system comprises:
Obtain module: for obtaining the individual amount of cloth energy-storage system, distributed energy storage system position in power grid is moved
Dynamic maximum distance, there is no the distance influenced, the density of the individual amount of distributed energy storage system between distributed energy storage system；
Computing module: based on distributed energy storage system in the minimum target of cost of investment, using artificial fish-swarm algorithm to mesh
Scalar functions, which solve, obtains optimal solution；
Layout modules: system is laid out based on the optimal solution.
Preferably, the computing module, comprising:
Computational submodule: based on being iterated using objective function and constraint condition as the input of artificial fish-swarm algorithm
It calculates, obtains the fitness function of each individual；
Optimize submodule: selecting the execution to objective function for the fitness function and body position according to the individual
Method, the value after being optimized；
First judging submodule: for by the value after the optimization compared with the value on billboard, if better than billboard
Value then updates billboard according to the value after the optimization, does not otherwise update the value of billboard；
Second judgment submodule: for then exporting the current of billboard when artificial fish-swarm algorithm reaches maximum number of iterations
Value, otherwise continues to iterate to calculate.
Preferably, the optimization submodule includes function calculating unit and execution unit；
The function calculating unit is calculate by the following formula objective function:
Minf=Cdess+Cmainainace+Crce
In formula: minf: for objective function；CdessFor the mounting cost of energy storage device；CmainainaceFor the operation of energy storage device
Maintenance cost；CrceFor reactive-load compensation equipment expense.
The execution unit includes:
Foraging behavior subelement, for individual in its visual field randomly choose a state, calculate separately it is described individual and
The target function value of the state, if it find that the target function value of the state is better than the target function value of the individual, then
The individual is moved to the direction of the state to move a step；
Bunch behavior subelement, in individual search present viewing field other individual amounts and center, if having by
In the center of individual, then moves and move a step towards center；
Knock into the back behavior subelement, includes: individual search present viewing field inner function optimum individual for the behavior of knocking into the back, such as
There are optimum individuals for fruit, then Xi moves a step towards optimum individual shifting；
Random behavior subelement reaches a new state for one step of individual random movement.
Compared with prior art, the invention has the benefit that
1, the present invention provides a kind of distributed energy storage system layout method, which is characterized in that the described method includes: based on obtaining
The individual amount of the cloth energy-storage system taken, distributed energy storage system mobile maximum distance in position in power grid, distribution storage
The distance influenced, the individual status number optional in its visual field of distributed energy storage system, distributed energy storage can be not present between system
The density of the individual amount of system, using the artificial fish-swarm algorithm to the investment of preset energy-storage system in a distributed manner at
The objective function of this minimum target is solved, and optimal solution is obtained；System is laid out based on the optimal solution, there is behaviour
Work is simple, control parameter is small, search precision is higher and the stronger feature of robustness, and the complexity solved reduction can be made to realize
In the total tune layout of distributed energy storage, solve distributed energy storage system layout complexity, computationally intensive problem.
2, the present invention provides a kind of distributed energy storage system layout method and is laid out distributed energy storage combines with economical,
So that both having met cost of investment minimum, in turn ensure that the power grid after energy storage is added being capable of safe and stable and reliable operation.
3, the present invention provides a kind of distributed energy storage system layout method and to store up using the smallest investment as objective function
The practical feasibility that power distribution network is added in energy system greatly promotes.Meanwhile energy-storage system addition so that energy-storage system itself it is excellent
Gesture is played, and plays a degree of support and adjustment effect to network system, is also used as raising operation of power networks and is stablized
Property and reliability, adjustment frequency, compensate load fluctuation a kind of means.
Detailed description of the invention
Fig. 1 is a kind of distributed energy storage system layout method flow diagram of the invention；
Fig. 2 is a kind of distributed energy storage system layout flow diagram of the specific embodiment of the invention.
Specific embodiment
In the total tune layout of distributed energy storage, it is contemplated that distributed energy storage system, layout is complicated, computationally intensive,
And artificial fish-swarm algorithm has the characteristics that easy to operate, control parameter is small, search precision is higher and robustness is stronger, can make to ask
The complexity of solution substantially reduces.This method is minimised as target value based on artificial fish-swarm algorithm with cost of investment, while with
Node voltage, branch current, the state-of-charge of the active and idle power output of power-balance, the energy-storage system of each node, energy-storage system
The layout of energy-storage system is realized for constraint condition.
For a better understanding of the present invention, the contents of the present invention are done further with example with reference to the accompanying drawings of the specification
Explanation.
Specific embodiment one
A kind of distributed energy storage system layout method as shown in Figure 1, the number of individuals of the cloth energy-storage system based on acquisition
Mesh, the mobile maximum distance in distributed energy storage system position in power grid, there is no the distance influenced between distributed energy storage system,
The individual status number optional in its visual field of distributed energy storage system, the density of the individual amount of distributed energy storage system use
The artificial fish-swarm algorithm to the objective function of the minimum target of cost of investment of preset energy-storage system in a distributed manner into
Row solves, and obtains optimal solution；
System is laid out based on the optimal solution.
The individual amount of the cloth energy-storage system based on acquisition, the position movement in power grid of distributed energy storage system
Maximum distance, there is no the distance influenced between distributed energy storage system, the individual of distributed energy storage system is optional in its visual field
Status number, the density of the individual amount of distributed energy storage system use the artificial fish-swarm algorithm to preset with distribution
The objective function of the minimum target of the cost of investment of formula energy-storage system is solved, comprising:
The individual amount of the cloth energy-storage system is set as in artificial fish-swarm algorithm: the number of individuals of artificial fish-swarm；
By the distributed energy storage system, the mobile maximum distance in position is set as in artificial fish-swarm algorithm in power grid: mermaid
The mobile maximum step-length of group；
By there is no the distances influenced to be set as in artificial fish-swarm algorithm between the distributed energy storage system: perceived distance；
The density of the individual amount of the distributed energy storage system is positioned as in artificial fish-swarm algorithm: crowding；
Calculating is iterated to the objective function based on artificial fish-swarm algorithm, obtains the value of billboard；
Optimal location using the value of the billboard as distributed energy storage system in cost of investment minimum.
It is described that calculating is iterated to the objective function based on artificial fish-swarm algorithm, obtain the value of billboard, comprising:
Using the objective function as the input of artificial fish-swarm algorithm, it is iterated calculating, obtains the adaptation of each individual
Spend function；
The execution method to objective function is selected according to the fitness function of the individual and body position, after obtaining optimization
Value, value after the optimization is distributed energy storage system in cost of investment；
By the value after the optimization compared with the value on billboard, if being better than the value of billboard, after the optimization
Value update billboard, otherwise do not update the value of billboard；
When artificial fish-swarm algorithm reaches the current value that maximum number of iterations then exports billboard, otherwise continue to iterate to calculate.
The objective function is shown below:
Minf=Cdess+Cmainainace+Crce
In formula: minf: distributed energy storage system is in cost of investment；CdessFor the mounting cost of energy storage device；CmainainaceFor
The operation and maintenance cost of energy storage device；CrceFor reactive-load compensation equipment expense.
The installing reactive-load compensation equipment expense CrceIt is calculated as follows:
In formula, C0To install the revised unit reactive capability price of reactive-load compensation equipment, unit is member/kvar；QgiFor
The reactive compensation capacity of i-th of node, unit kvar, Qgi> 0 indicates installing capacitor or phase modifier, Qgi< 0 indicates installing simultaneously
Join reactor；N is node number.
The objective function further includes constraint condition；Wherein the constraint condition includes: node voltage constraint, branch current
Constraint, equality constraint, the constraint of energy-storage system active power output, idle units limits and state-of-charge constraint；
The node voltage constraint is shown below:
Uimin≤U’i≤Uimax
In formula, Uimin: the upper voltage limit of node i；Uimax: the lower voltage limit of node i, wherein i is that node is installed in energy storage；
U'i: the voltage of node i；
The branch current constrains
I’k≤Ikmax
In formula, IkFor the electric current of branch k；IkmaxPass through electric current for branch k is maximum allowable；
The equality constraint includes: active power and reactive power equilibrium constraint；
The active power balance constraint is shown below:
In formula: PGi: the active-power P that node i generator issuesDi；The active power of load consumption；Vi: the electricity of node i
Pressure；Vj: the voltage of node j；Gij: the conductance on route ij；BijSusceptance on route ij；
The reactive power equilibrium constraint is shown below:
In formula, Vi: the voltage of node i；Vj: the voltage of node j；Gij: the conductance on route ij；BijElectricity on route ij
It receives；QiThe reactive power of node i；QDiThe reactive power of load consumption；
The energy-storage system active power output constraint is shown below:
Pcmin≤Pc≤Pcmax
Wherein: PcFor the active output of energy-storage system；Wherein Pcmax PcminRespectively the active power output of energy storage device it is upper,
Lower limit；
Qcmin≤Qc≤Qcmax
Wherein, QcFor the idle output of energy-storage system；Wherein Qcmax QcminRespectively the idle power output of energy storage device it is upper,
Lower limit.
The execution method includes looking for food, bunching, knocking into the back and random behavior.
Specific embodiment two
A kind of system of distributed energy storage system layout obtains module: for obtaining the individual amount of cloth energy-storage system,
The mobile maximum distance in distributed energy storage system position in power grid, there is no the distance influenced between distributed energy storage system, point
The density of the individual amount of cloth energy-storage system；
Computing module: based on distributed energy storage system in the minimum target of cost of investment, using artificial fish-swarm algorithm to mesh
Scalar functions, which solve, obtains optimal solution；
Layout modules: system is laid out based on the optimal solution.
The computing module, comprising:
Computational submodule: based on being iterated using objective function and constraint condition as the input of artificial fish-swarm algorithm
It calculates, obtains the fitness function of each individual；
Optimize submodule: selecting the execution to objective function for the fitness function and body position according to the individual
Method, the value after being optimized；
First judging submodule: for by the value after the optimization compared with the value on billboard, if better than billboard
Value then updates billboard according to the value after the optimization, does not otherwise update the value of billboard；
Second judgment submodule: for then exporting the current of billboard when artificial fish-swarm algorithm reaches maximum number of iterations
Value, otherwise continues to iterate to calculate.
The optimization submodule includes function calculating unit and execution unit；
The function calculating unit is calculate by the following formula objective function:
Minf=Cdess+Cmainainace+Crce
In formula: minf: for objective function；CdessFor the mounting cost of energy storage device；CmainainaceFor the operation of energy storage device
Maintenance cost；CrceFor reactive-load compensation equipment expense.
The execution unit includes:
Foraging behavior subelement, for individual in its visual field randomly choose a state, calculate separately it is described individual and
The target function value of the state, if it find that the target function value of the state is better than the target function value of the individual, then
The individual is moved to the direction of the state to move a step；
Bunch behavior subelement, in individual search present viewing field other individual amounts and center, if having by
In the center of individual, then moves and move a step towards center；
Knock into the back behavior subelement, includes: individual search present viewing field inner function optimum individual for the behavior of knocking into the back, such as
There are optimum individuals for fruit, then Xi moves a step towards optimum individual shifting；
Random behavior subelement reaches a new state for one step of individual random movement.
Specific embodiment three
As shown in Fig. 2, being a kind of distributed energy storage system layout method flow diagram provided by the invention, it is added ensure that
Power grid after energy storage can utilize easy to operate, the control parameter of artificial fish-swarm algorithm under safe and stable and reliable operation
Small, the features such as search precision is higher and robustness is stronger, the complexity of solution is substantially reduced, while also meeting cost of investment
The optimal location of energy storage when minimum.It is specifically described as follows:
1, a kind of distributed energy storage system layout method, which is characterized in that the described method comprises the following steps:
Step 1: carrying out initializing set in artificial fish-swarm algorithm；
Step 2: objective function, network constraint condition are inputted in artificial fish-swarm algorithm；
Step 3: fish-swarm algorithm starts to be iterated, and calculates the fitness function of each individual, assesses housing choice behavior, that is, root
Method is executed to the optimization of objective function according to the selection of a body position.These execution methods include looking for food, bunching, knocking into the back and random row
For；
Step 4: completing the optimization of a target addressing, the value after evaluation optimization, if being better than billboard, more by billboard
Newly in the individual；
Step 5: the value of billboard is exported if fish-swarm algorithm reaches greatest iteration number, if not reaching return step 3,
The final optimal location for realizing the energy storage in cost of investment minimum.
2, a kind of distributed energy storage system layout method according to claim 1, which is characterized in that the step 1 is right
The individual amount size of artificial fish-swarm algorithm, mobile maximum step-length, perceived distance, foraging behavior attempt maximum times, crowding,
Maximum number of iterations carries out initializing set.
3, a kind of distributed energy storage system layout method according to claim 1, which is characterized in that the step 2 is built
It stands using total least cost as the distributed energy storage construction mode of objective function, energy storage layout is provided according to the comparison of catalogue scalar functions
Optimal location.Specific calculation method is as follows:
Objective function
Objective function, that is, total least cost, the operation and maintenance cost of mounting cost, energy storage device including energy storage device with
And the expense of installation reactive-load compensation equipment, expression formula are
Minf=Cdess+Cmainainace+Crce (1)
In formula:
CdessFor the mounting cost of energy storage device；
CmainainaceFor the operation and maintenance cost of energy storage device；
CrceTo consider to install reactive-load compensation equipment expense, wherein
C0(member/kvar) is to consider the installing revised unit reactive capability price of reactive-load compensation equipment.Qgi(kvar)
For the reactive compensation capacity of i-th of load point, Qgi>0 indicates that installing capacitor or phase modifier, Qgi<0 indicate installing parallel reactance
Device；
N is node number；
Constraint condition
Node voltage constraint
Uimin≤U'i≤Uimax (2)
Branch current constraint
I'k≤Ikmax (3)
I is that node is installed in energy storage；Wherein Uimin UimaxThe respectively voltage of node i and voltage upper and lower limit；
IkmaxPass through electric current for branch k is maximum allowable；
Equality constraint is that each node injects active power and reactive power equilibrium constraint, i.e. trend constraint:
The active and idle units limits of energy-storage system
PcFor the active output of energy-storage system；Wherein Pcmax PcminThe respectively upper and lower limit of the active power output of energy storage device；
QcFor the idle output of energy-storage system；Wherein Qcmax QcminThe respectively upper and lower limit of the idle power output of energy storage device；
State-of-charge constraint:
SOCxmin≤SOCx≤SOCxmax (6)
SOCxmin、SOCxmaxThe respectively minimum and maximum state-of-charge of energy-storage system x.
4, a kind of distributed energy storage system layout method according to claim 1, which is characterized in that in the step 3
The fitness function for calculating each individual takes when the optimal location for meeting the smallest investment and its value is given to billboard.So
The optimization method of corresponding objective function is selected according to body position afterwards, these execution methods include looking for food, bunching, knocking into the back
And random behavior, it is characterised in that:
(1) foraging behavior: individual Xi randomly chooses a state Xj in its visual field, calculates separately the target letter of Xi and Xj
Numerical value Yi and Yj, if it find that Yj is better than Yi, then Xi is moved to the direction of Xj and is moved a step.
(2) bunch behavior: other individual amounts nf and centre bit of (dij < Vaisual) in individual Xi search present viewing field
Xc is set, if Yc/nf > δ Yi, has the center due to individual, then Xi is moved towards center and moved a step.
(3) it knocks into the back behavior: (dij < Vaisual) function Yj optimum individual Xj in individual Xi search present viewing field, if Yj/
Nf > δ Yi, then Xi moves a step towards optimum individual shifting
(4) random behavior: individual one step of Xi random movement reaches a new state
5, a kind of distributed energy storage system layout method according to claim 1, which is characterized in that selected by step 3
The optimization that a target addressing of step 4 is completed after optimal way is selected, if objective function individual after optimization is better than billboard
Target function value corresponding to upper individual, then update billboard in the individual.
6, a kind of distributed energy storage system layout method according to claim 1, which is characterized in that in the step 5
The value of billboard is exported if the number of iterations of fish-swarm algorithm reaches the greatest iteration number of setting, if not reaching repeatedly step
3, the final optimal location for realizing the energy storage in cost of investment minimum.
It should be understood by those skilled in the art that, embodiments herein can provide as method, system or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the application
Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the application, which can be used in one or more,
The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces
The form of product.
The application is referring to method, the process of equipment (system) and computer program product according to the embodiment of the present application
Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions
The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs
Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real
The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
The above is only the embodiment of the present invention, are not intended to restrict the invention, all in the spirit and principles in the present invention
Within, any modification, equivalent substitution, improvement and etc. done, be all contained in apply pending scope of the presently claimed invention it
It is interior.
Claims (10)
1. a kind of distributed energy storage system layout method, which is characterized in that the described method includes:
The individual amount of cloth energy-storage system based on acquisition, distributed energy storage system position in power grid it is mobile it is maximum away from
From, there is no the distance influenced between distributed energy storage system, optional in its visual field status number of individual of distributed energy storage system,
The density of the individual amount of distributed energy storage system, using the artificial fish-swarm algorithm to preset energy storage system in a distributed manner
The objective function of the minimum target of the cost of investment of system is solved, and optimal solution is obtained；
System is laid out based on the optimal solution.
2. a kind of distributed energy storage system layout method as described in claim 1, which is characterized in that the cloth based on acquisition
The individual amount of formula energy-storage system, distributed energy storage the system mobile maximum distance in position, distributed energy storage system in power grid
Between there is no the distance influenced, optional in its visual field status number of individual of distributed energy storage system, distributed energy storage system
The density of individual amount, it is minimum using cost of investment of the artificial fish-swarm algorithm to preset energy-storage system in a distributed manner
It is solved for the objective function of target, comprising:
The individual amount of the cloth energy-storage system is set as in artificial fish-swarm algorithm: the number of individuals of artificial fish-swarm；
By the distributed energy storage system, the mobile maximum distance in position is set as in artificial fish-swarm algorithm in power grid: mermaid group's
Mobile maximum step-length；
By there is no the distances influenced to be set as in artificial fish-swarm algorithm between the distributed energy storage system: perceived distance；
The density of the individual amount of the distributed energy storage system is positioned as in artificial fish-swarm algorithm: crowding；
Calculating is iterated to the objective function based on artificial fish-swarm algorithm, obtains the value of billboard；
Optimal location using the value of the billboard as distributed energy storage system in cost of investment minimum.
3. a kind of distributed energy storage system layout method as claimed in claim 2, which is characterized in that described to be based on artificial fish-swarm
Algorithm is iterated calculating to the objective function, obtains the value of billboard, comprising:
Using the objective function as the input of artificial fish-swarm algorithm, it is iterated calculating, obtains the fitness letter of each individual
Number；
The execution method to objective function is selected according to the fitness function of the individual and body position, after being optimized
Value, the value after the optimization are distributed energy storage system in cost of investment；
By the value after the optimization compared with the value on billboard, if being better than the value of billboard, according to the value after the optimization
Billboard is updated, does not otherwise update the value of billboard；
When artificial fish-swarm algorithm reaches the current value that maximum number of iterations then exports billboard, otherwise continue to iterate to calculate.
4. a kind of distributed energy storage system layout method as claimed in claim 2, which is characterized in that the objective function is as follows
Shown in formula:
Min f=Cdess+Cmainainace+Crce
In formula: min f: distributed energy storage system is in cost of investment；CdessFor the mounting cost of energy storage device；CmainainaceFor storage
The operation and maintenance cost of energy device；CrceFor reactive-load compensation equipment expense.
5. a kind of distributed energy storage system layout method as claimed in claim 4, which is characterized in that the installing reactive compensation
Cost of equipment CrceIt is calculated as follows:
In formula, C0To install the revised unit reactive capability price of reactive-load compensation equipment, unit is member/kvar；QgiIt is i-th
The reactive compensation capacity of node, unit kvar, Qgi> 0 indicates installing capacitor or phase modifier, Qgi< 0 indicates that installing is in parallel
Reactor；N is node number.
6. a kind of distributed energy storage system layout method as described in claim 1, which is characterized in that the objective function also wraps
Include constraint condition；Wherein the constraint condition includes: that node voltage constraint, branch current constraint, equality constraint, energy-storage system have
Function units limits, idle units limits and state-of-charge constraint；
The node voltage constraint is shown below:
Uimin≤U′i≤Uimax
In formula, Uimin: the upper voltage limit of node i；Uimax: the lower voltage limit of node i, wherein i is that node is installed in energy storage；U′i: section
The voltage of point i；
The branch current constrains
I′k≤Ikmax
In formula, IkFor the electric current of branch k；IkmaxPass through electric current for branch k is maximum allowable；
The equality constraint includes: active power and reactive power equilibrium constraint；
The active power balance constraint is shown below:
In formula: PGi: the active-power P that node i generator issuesDi；The active power of load consumption；Vi: the voltage of node i；Vj:
The voltage of node j；Gij: the conductance on route ij；BijSusceptance on route ij；
The reactive power equilibrium constraint is shown below:
In formula, Vi: the voltage of node i；Vj: the voltage of node j；Gij: the conductance on route ij；BijSusceptance on route ij；QiSection
The reactive power of point i；QDiThe reactive power of load consumption；
The energy-storage system active power output constraint is shown below:
Pcmin≤Pc≤Pcmax
Wherein: PcFor the active output of energy-storage system；Wherein PcmaxPcminThe respectively upper and lower limit of the active power output of energy storage device；
Qcmin≤Qc≤Qcmax
Wherein, QcFor the idle output of energy-storage system；Wherein QcmaxQcminThe respectively upper and lower limit of the idle power output of energy storage device.
7. a kind of distributed energy storage system layout method as claimed in claim 3, which is characterized in that the execution method includes
It looks for food, bunch, knocking into the back and random behavior.
8. a kind of system of distributed energy storage system layout, which is characterized in that the system comprises:
Obtain module: for obtaining the individual amount of cloth energy-storage system, the position movement in power grid of distributed energy storage system
Maximum distance, there is no the distance influenced, the density of the individual amount of distributed energy storage system between distributed energy storage system；
Computing module: based on distributed energy storage system in the minimum target of cost of investment, using artificial fish-swarm algorithm to target letter
Number, which solve, obtains optimal solution；
Layout modules: system is laid out based on the optimal solution.
9. a kind of system of distributed energy storage system layout as claimed in claim 8, which is characterized in that the computing module,
Include:
Computational submodule: it for being iterated calculating using objective function and constraint condition as the input of artificial fish-swarm algorithm, obtains
To the fitness function of each individual；
Optimize submodule: selecting the execution side to objective function for the fitness function and body position according to the individual
Method, the value after being optimized；
First judging submodule: for by the value after the optimization compared with the value on billboard, if be better than billboard value,
Billboard is updated according to the value after the optimization, does not otherwise update the value of billboard；
Second judgment submodule: no for reaching the current value that maximum number of iterations then exports billboard when artificial fish-swarm algorithm
Then continue to iterate to calculate.
10. a kind of system of distributed energy storage system layout as claimed in claim 9, which is characterized in that the optimization submodule
Block includes function calculating unit and execution unit；
The function calculating unit is calculate by the following formula objective function:
Min f=Cdess+Cmainainace+Crce
In formula: min f: for objective function；CdessFor the mounting cost of energy storage device；CmainainaceIt is tieed up for the operation of energy storage device
Shield expense；CrceFor reactive-load compensation equipment expense.
The execution unit includes:
Foraging behavior subelement randomly chooses a state for individual in its visual field, calculates separately described individual and described
The target function value of state, if it find that the target function value of the state is better than the target function value of the individual, then it is described
Individual is moved to the direction of the state and is moved a step；
Bunch behavior subelement, in individual search present viewing field other individual amounts and center, if having due to a
The center of body is then moved towards center and moves a step；
Knock into the back behavior subelement, includes: individual search present viewing field inner function optimum individual for the behavior of knocking into the back, if deposited
In optimum individual, then Xi moves a step towards optimum individual shifting；
Random behavior subelement reaches a new state for one step of individual random movement.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810644435.8A CN108964099B (en) | 2018-06-21 | 2018-06-21 | Distributed energy storage system layout method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810644435.8A CN108964099B (en) | 2018-06-21 | 2018-06-21 | Distributed energy storage system layout method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108964099A true CN108964099A (en) | 2018-12-07 |
| CN108964099B CN108964099B (en) | 2023-05-23 |
Family
ID=64492080
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810644435.8A Active CN108964099B (en) | 2018-06-21 | 2018-06-21 | Distributed energy storage system layout method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108964099B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111509744A (en) * | 2020-04-21 | 2020-08-07 | 中国电力科学研究院有限公司 | Energy storage multifunctional application layout method and system |
| CN111931427A (en) * | 2020-10-19 | 2020-11-13 | 国网江西省电力有限公司电力科学研究院 | Method for determining induction motor model parameters in power distribution network load modeling |
| CN116488250A (en) * | 2023-03-17 | 2023-07-25 | 长电新能有限责任公司 | Capacity optimization configuration method for hybrid energy storage system |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105162151A (en) * | 2015-10-22 | 2015-12-16 | 国家电网公司 | Intelligent energy storage system grid-connected real-time control method based on artificial fish swarm algorithm |
| CN106712076A (en) * | 2016-11-18 | 2017-05-24 | 上海电力学院 | Power transmission system optimization method on offshore wind farm cluster scale |
| CN106845623A (en) * | 2016-12-13 | 2017-06-13 | 国网冀北电力有限公司信息通信分公司 | A kind of electric power wireless private network base station planning method based on artificial fish-swarm algorithm |
-
2018
- 2018-06-21 CN CN201810644435.8A patent/CN108964099B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105162151A (en) * | 2015-10-22 | 2015-12-16 | 国家电网公司 | Intelligent energy storage system grid-connected real-time control method based on artificial fish swarm algorithm |
| CN106712076A (en) * | 2016-11-18 | 2017-05-24 | 上海电力学院 | Power transmission system optimization method on offshore wind farm cluster scale |
| CN106845623A (en) * | 2016-12-13 | 2017-06-13 | 国网冀北电力有限公司信息通信分公司 | A kind of electric power wireless private network base station planning method based on artificial fish-swarm algorithm |
Non-Patent Citations (4)
| Title |
|---|
| 刘大贺等: "基于光伏电站场景下的梯次电池储能经济性分析", 《电力工程技术》 * |
| 刘小寨等: "含风电场的电网储能系统选址和优化配置", 《电网与清洁能源》 * |
| 夏娜等: "鱼群启发的水下传感器节点布置", 《自动化学报》 * |
| 杨文荣等: "配电网中基于人工鱼群算法的分布式发电规划", 《电力系统保护与控制》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111509744A (en) * | 2020-04-21 | 2020-08-07 | 中国电力科学研究院有限公司 | Energy storage multifunctional application layout method and system |
| CN111931427A (en) * | 2020-10-19 | 2020-11-13 | 国网江西省电力有限公司电力科学研究院 | Method for determining induction motor model parameters in power distribution network load modeling |
| CN116488250A (en) * | 2023-03-17 | 2023-07-25 | 长电新能有限责任公司 | Capacity optimization configuration method for hybrid energy storage system |
| CN116488250B (en) * | 2023-03-17 | 2023-12-15 | 长电新能有限责任公司 | Capacity optimization configuration method for hybrid energy storage system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108964099B (en) | 2023-05-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107301472B (en) | Distributed photovoltaic planning method based on scene analysis method and voltage regulation strategy | |
| Yammani et al. | Optimal placement and sizing of distributed generations using shuffled bat algorithm with future load enhancement | |
| CN111756050B (en) | Distributed voltage coordination control method and system for wind power plant group | |
| CN108964099A (en) | A kind of distributed energy storage system layout method and system | |
| CN108551176B (en) | Energy storage battery system capacity configuration method combined with energy storage balancing technology | |
| CN111490542B (en) | Site selection and volume fixing method of multi-end flexible multi-state switch | |
| CN110247404B (en) | Wind power grid-connected voltage hierarchical coordination control method, system, medium and equipment | |
| Askarzadeh | Solving electrical power system problems by harmony search: a review | |
| Kazemi et al. | On the use of harmony search algorithm in optimal placement of FACTS devices to improve power system security | |
| Sarda et al. | Genetic Algorithm Approach for Optimal location of FACTS devices to improve system loadability and minimization of losses | |
| CN115545290A (en) | Distributed energy storage economic optimization configuration method in power distribution network containing photovoltaic power generation | |
| CN109586278B (en) | Method for evaluating power supply capacity of alternating current-direct current hybrid power distribution network | |
| Ashtiani et al. | Optimal allocation of energy storage systems in connected microgrid to minimize the energy cost | |
| CN110490492A (en) | A kind of method and apparatus dividing distributed energy cluster | |
| US20200021131A1 (en) | Control for energy resources in a microgrid | |
| CN110137974B (en) | Distributed photovoltaic cluster voltage control method and system | |
| CN111146815B (en) | Distributed power generation planning configuration method for intelligent power distribution network | |
| CN114865649B (en) | Wind-solar-storage integrated station reactive power regulation method and device and electronic equipment | |
| CN105470976A (en) | Coordinated configuration method for SVC and TCSC under steady state condition | |
| Wang et al. | Decentralized and multi-objective coordinated optimization of hybrid AC/DC flexible distribution networks | |
| CN114172149B (en) | Distributed generation cluster autonomous control method and system | |
| CN109343650A (en) | A kind of maximum power point tracking method, solar controller and relevant device | |
| CN114417566A (en) | MOEA/D-based active power distribution network multi-region division optimization method | |
| Sakthivel et al. | Particle Swarm Optimization algorithm for voltage stability enhancement by optimal reactive power reserve management with multiple TCSCs | |
| Eissa et al. | A novel approach for optimum allocation of Flexible AC Transmission Systems using Harmony Search technique |
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 |