CN108667054A - A kind of energy storage method and device for planning - Google Patents
A kind of energy storage method and device for planning Download PDFInfo
- Publication number
- CN108667054A CN108667054A CN201710190530.0A CN201710190530A CN108667054A CN 108667054 A CN108667054 A CN 108667054A CN 201710190530 A CN201710190530 A CN 201710190530A CN 108667054 A CN108667054 A CN 108667054A
- Authority
- CN
- China
- Prior art keywords
- energy
- power
- storage system
- storage
- grid
- 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
- 238000004146 energy storage Methods 0.000 title claims abstract description 209
- 238000000034 method Methods 0.000 title claims abstract description 25
- 238000005457 optimization Methods 0.000 claims abstract description 18
- 230000006870 function Effects 0.000 claims description 92
- 238000001914 filtration Methods 0.000 claims description 30
- 238000009499 grossing Methods 0.000 claims description 30
- 230000001172 regenerating effect Effects 0.000 claims description 30
- 238000004422 calculation algorithm Methods 0.000 claims description 21
- 230000002068 genetic effect Effects 0.000 claims description 8
- 230000005611 electricity Effects 0.000 claims description 7
- 230000008859 change Effects 0.000 claims description 5
- 238000005070 sampling Methods 0.000 claims description 5
- 230000005540 biological transmission Effects 0.000 claims description 4
- 238000003860 storage Methods 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 9
- 238000004590 computer program Methods 0.000 description 7
- 238000012545 processing Methods 0.000 description 6
- 230000000694 effects Effects 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000035772 mutation Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 229910002056 binary alloy Inorganic materials 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000010187 selection method Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000006641 stabilisation Effects 0.000 description 1
- 238000011105 stabilization Methods 0.000 description 1
- 230000007704 transition 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
-
- H02J3/382—
-
- 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]
Abstract
The present invention relates to a kind of energy storage method and device for planning, the method includes：Establish the multiple objective programming object function and its constraints of energy-storage system；Determine the optimal solution of the multiple objective programming object function of energy-storage system；Energy storage planning is carried out to new energy power station according to the optimal solution；Technical solution provided by the invention, by the programme of the method optimizing energy-storage system of multi-objective optimization, to ensure that network system efficient stable is run.
Description
Technical field
The present invention relates to new energy energy storage control technology fields, and in particular to a kind of energy storage method and device for planning.
Background technology
With the rapid development of social economy, the shortage of fossil energy and problem of environmental pollution are increasingly serious, photovoltaic, wind-powered electricity generation
Etc. regenerative resources with its cleaning, environmental protection advantage become numerous scholars research hot spot, with wide research and application
Foreground.The regenerative resource electric power energy resource important by China is increasingly becoming is near completion from the energy is supplemented to alternative energy source
The transition in field.
However, the regenerative resource with wind-powered electricity generation, photovoltaic etc. for representative has significant randomness, intermittence and fluctuation special
Sign, can bring great challenge, using rational energy storage in large-scale grid connection to the stabilization safety and Effec-tive Function of power grid
Programme can be effectively improved the output power in regenerative resource power station, alleviate the negative effect generated to electric system.Mesh
Before, when formulating new energy power station energy storage programme, majority is only single excellent to limit grid-connected power swing or other factors
Change target, wherein portion of techniques establishes the multiple objective function for considering multiple influence factors, however exists between multiple desired values
Restricting relation, it is difficult to obtain globally optimal solution.
Invention content
The present invention provides a kind of energy storage method and device for planning, and the purpose is to the method optimizing energy storage by multi-objective optimization
The programme of system, to ensure that network system efficient stable is run.
The purpose of the present invention is what is realized using following technical proposals：
A kind of energy storage planing method, it is improved in that including：
The multiple objective programming object function and its constraints of energy-storage system are established respectively；
Determine the optimal solution of the multiple objective programming function；
Energy storage planning is carried out to power station according to the optimal solution.
Preferably, the object function includes：The second object function of energy storage first object function and energy storage；
Wherein, the energy storage first object function takes the fluctuating range minimum value of grid-connected power；
The second object function of the energy storage takes energy-storage battery economic index maximum value；
The constraints includes the bound data of energy-storage system.
Further, the energy storage first object function：
In above formula, PC(k) it is the grid-connected power at k moment, λdFor the unit penalty factor beyond power swing range, N is to adopt
Sample moment sum,Wherein, PClimitFor grid-connected rate of power change limit value；
Further, the second object function of the energy storage：
In above formula, PmaxFor energy-storage system maximum power, λPFor the cost coefficient of energy-storage system maximum power, EmaxFor energy storage
System maximum capacity, λEFor the cost coefficient of energy-storage system maximum capacity, EcycleElectricity, λ are recycled in the period in order to controlmvIn order to control
The cost coefficient of cycle electricity in period, wherein：
In above formula,PS(k) it is k moment energy storage power, Δ T is in order to control between the time
Every.
Further, the energy-storage system bound data includes：The grid-connected power constraint in regenerative resource power station, low pass filtered
The constraint of wave smoothing time constant, the state-of-charge of energy-storage system and charge-discharge electric power constraint；
Regenerative resource electric station grid connection power constraint is established as the following formula：
Pc(k)≤min{Pcref,Pcline, k=1 ..., N
In above formula, PC(k) it is the grid-connected power at k moment, PcrefFor the regenerative resource electric station grid connection upper limit of the power, PclineFor
Grid-connected circuit maximum transmission power, N are sampling instant sum；
The constraint of low-pass filtering smoothing time constant is established as the following formula：
τmin≤τ≤τmax
In above formula, τ is low-pass filtering smoothing time constant, τminFor low-pass filtering smoothing time constant lower limit, τmaxIt is low
The pass filter smoothing time constant upper limit；
The state-of-charge constraint of energy-storage system is established as the following formula：
SOCmin≤SOC(k)≤SOCmax, k=1 ..., N
In above formula, SOC (k) is k moment energy-storage system state-of-charges, SOCminFor under k moment energy-storage system state-of-charges
Limit, SOCmaxFor the k moment energy-storage system state-of-charge upper limits；
Charge-discharge electric power constraint is established as the following formula：
In above formula, PS(k) it is k moment energy storage power, PRFor energy-storage system rated power, ERFor energy-storage system rated capacity,
PROFor current time energy-storage system peak power output, Δ T time intervals in order to control.
Further, the optimal solution of the multiple objective programming object function of the determining energy-storage system is：
Introduce low-pass filtering smoothing time constant τ and k moment energy storage power PS(k) multiple target as the energy-storage system
The variable of object of planning function, and determine using multi-objective optimization algorithm the multiple objective programming object function of the energy-storage system
Optimal solution.
Further, the multi-objective optimization algorithm includes：
Utilize the quick non-dominated sorted genetic algorithm of the second generation.
Further, described that energy storage planning is carried out to new energy power station according to the optimal solution, including：
Grid-connected power is determined according to the low-pass filtering smoothing time constant τ in the optimal solution；
According in the optimal solution energy storage power and the grid-connected power, in real time plan energy-storage system charge-discharge electric power
And the grid-connected power of regenerative resource.
Further, the k moment grid-connected power PC(k)：
In above formula, τ is low-pass filtering smoothing time constant in optimal solution, Δ T time intervals in order to control, PPV(k) it is the k moment
Regenerative resource station output.
A kind of energy storage device for planning, it is improved in that described device includes：
Build module, the multiple objective programming object function for establishing energy-storage system respectively and its constraints；
Determining module, the optimal solution for determining the multiple objective programming function；
Planning module, for carrying out energy storage planning to power station according to the optimal solution.
Preferably, in the structure module, the object function includes：The second target of energy storage first object function and energy storage
Function；
Wherein, the energy storage first object function takes the fluctuating range minimum value of grid-connected power；
The second object function of the energy storage takes energy-storage battery economic index maximum value；
The constraints includes the bound data of energy-storage system.
Preferably, the determining module, including：
First determination unit, for introducing low-pass filtering smoothing time constant τ and k moment energy storage power PS(k) it is used as institute
The variable of the multiple objective programming object function of energy-storage system is stated, and the more of the energy-storage system are determined using multi-objective optimization algorithm
The optimal solution of goal programming object function.
Preferably, the planning module, including：
Second determination unit, for determining grid-connected power according to the low-pass filtering smoothing time constant τ in the optimal solution；
Planning unit, for according in the optimal solution energy storage power and the grid-connected power, plan energy storage system in real time
The charge-discharge electric power of system and the grid-connected power of regenerative resource.
Beneficial effects of the present invention：
Technical solution provided by the invention, it is maximum with the fluctuating range minimum of grid-connected power and energy-storage battery economic index
Energy storage object of planning function is established respectively for target, by way of multi-objective optimization, to grid-connected power swing and energy-storage battery
Two indexs of economy are carried out at the same time non-dominant optimization, a series of Pareto optimal solution sets are obtained, to obtain multi-objective optimization
Energy storage programme, be based on energy-storage system programme provided by the invention, regenerative resource power station can be effectively improved
Output power alleviates the negative effect that is generated to electric system, ensures the efficient stable operation of network system, is policymaker right
Network system carries out providing solid reference when energy storage planning.
Description of the drawings
Fig. 1 is a kind of flow chart of energy storage planing method of the present invention；
Fig. 2 is application scenarios structural schematic diagram in the embodiment of the present invention；
Fig. 3 is second generation non-dominated sorted genetic algorithm flow chart in the embodiment of the present invention；
Fig. 4 is a kind of structural schematic diagram of energy storage device for planning of the present invention.
Specific implementation mode
It elaborates below in conjunction with the accompanying drawings to the specific implementation mode of the present invention.
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art
The all other embodiment obtained without making creative work, shall fall within the protection scope of the present invention.
A kind of energy storage planing method provided by the invention, passes through the planning side of the method optimizing energy-storage system of multi-objective optimization
Case, to ensure that network system efficient stable is run, as shown in Figure 1, including：
101. establishing the multiple objective programming object function and its constraints of energy-storage system respectively；
Wherein, the object function includes：The second object function of energy storage first object function and energy storage；
The energy storage first object function takes the fluctuating range minimum value of grid-connected power；
The second object function of the energy storage takes energy-storage battery economic index maximum value；
The constraints includes the bound data of energy-storage system.
102. determining the optimal solution of the multiple objective programming function；
103. carrying out energy storage planning to power station according to the optimal solution.
For example, in a kind of application scenarios provided in the embodiment of the present invention regenerative resource power station and energy-storage system joint
Capability diagram, as shown in Fig. 2, by regenerative resource power station electricity generation system, energy-storage system, two current transformers, a grid-connected transformer
It being formed with a central control unit, regenerative resource power station and energy-storage system are connect by current transformer with ac bus respectively,
Ac bus is connected to the grid by transformer.Grid-connected power is the charging of the generated output and energy-storage system in regenerative resource power station
The difference of power.Central processing unit receives the output power data of regenerative resource power station prediction model, electric station grid connection dispatches number
According to the bound data with energy-storage system, these data are brought into two object functions, pass through second generation non-dominated ranking heredity
Two object functions of algorithm pair are carried out at the same time optimization, obtain a series of Pareto optimal solutions, and then obtain the optimal of energy-storage system
Programme.
Specifically, being referred to the fluctuating range minimum of grid-connected power and energy-storage battery economy in embodiment provided by the invention
Mark is up to target and establishes energy storage object of planning function respectively, the step 101, including：
Energy storage first object function is established with the minimum target of the fluctuating range of grid-connected power；
It is up to target with energy-storage battery economic index and establishes the second object function of energy storage；
The multiple objective programming bound for objective function of energy-storage system is established using the bound data of energy-storage system.
Wherein, the minimum target of the fluctuating range with grid-connected power establishes energy storage first object function, including：
The energy storage first object function is established as the following formula：
In above formula, PC(k) it is the grid-connected power at k moment, λdFor the unit penalty factor beyond power swing range, N is to adopt
Sample moment sum,Wherein, PClimitFor grid-connected rate of power change limit value；
It is described target is up to energy-storage battery economic index to establish the second object function of energy storage, including：
The second object function of the energy storage is established as the following formula：
In above formula, PmaxFor energy-storage system maximum power, λPFor the cost coefficient of energy-storage system maximum power, EmaxFor energy storage
System maximum capacity, λEFor the cost coefficient of energy-storage system maximum capacity, EcycleElectricity, λ are recycled in the period in order to controlmvIn order to control
The cost coefficient of cycle electricity in period, wherein：
In above formula,PS(k) it is k moment energy storage power, Δ T is in order to control between the time
Every.
The bound data using energy-storage system establishes the multiple objective programming bound for objective function of energy-storage system,
Including：
The energy-storage system bound data includes：Grid-connected power, the low-pass filtering smoothingtime in regenerative resource power station are normal
The state-of-charge and charge-discharge electric power of number, energy-storage system；
The regenerative resource electric station grid connection of the multiple objective programming bound for objective function of energy-storage system is established as the following formula
Power constraint：
Pc(k)≤min{Pcref,Pcline, k=1 ..., N
In above formula, PC(k) it is the grid-connected power at k moment, PcrefFor the regenerative resource electric station grid connection upper limit of the power, PclineFor
Grid-connected circuit maximum transmission power, N are sampling instant sum；
The low-pass filtering smoothingtime for establishing the multiple objective programming bound for objective function of energy-storage system as the following formula is normal
Number constraint：
τmin≤τ≤τmax
In above formula, τ is low-pass filtering smoothing time constant, τminFor low-pass filtering smoothing time constant lower limit, τmaxIt is low
The pass filter smoothing time constant upper limit；
The state-of-charge of the energy-storage system of the multiple objective programming bound for objective function of energy-storage system is established as the following formula
Constraint：
SOCmin≤SOC(k)≤SOCmax, k=1 ..., N
In above formula, SOC (k) is k moment energy-storage system state-of-charges, SOCminFor under k moment energy-storage system state-of-charges
Limit, SOCmaxFor the k moment energy-storage system state-of-charge upper limits；
The charge-discharge electric power constraint of the multiple objective programming bound for objective function of energy-storage system is established as the following formula：
In above formula, PS(k) it is k moment energy storage power, PRFor energy-storage system rated power, ERFor energy-storage system rated capacity,
PROFor current time energy-storage system peak power output, Δ T time intervals in order to control.
In embodiment provided by the invention, by way of multi-objective optimization, grid-connected power swing and energy-storage battery are passed through
Two indexs of Ji property are carried out at the same time non-dominant optimization, the step 102, including：
Introduce low-pass filtering smoothing time constant τ and k moment energy storage power PS(k) multiple target as the energy-storage system
The variable of object of planning function, and determine using multi-objective optimization algorithm the multiple objective programming object function of the energy-storage system
Optimal solution.
Wherein, in optimal embodiment provided by the invention, as shown in figure 3, being lost using the quick non-dominated ranking of the second generation
Propagation algorithm determines the optimal solution of the multiple objective programming object function of the energy-storage system.
The quick non-dominated sorted genetic algorithm of the second generation is that the effect of solution multi-objective optimization question in the prior art is more excellent
One of evolution multi-objective Algorithm, efficiently solve original non-dominated ranking genetic method it is existing calculate it is complicated, lack elite
Strategy and the defect that need to think specified shared radius, therefore multiple objective function model is optimized using this algorithm, such as：Root
Two object functions are solved according to the quick non-dominated sorted genetic algorithm of the second generation, algorithm uses real coding, selection mechanism to use
Algorithm of tournament selection method, crossover operation use multinomial mutation operator using simulation binary system crossover operator, mutation operation, are based on institute
The bound data of grid-connected system is stated, low-pass filtering smoothing time constant τ and k moment energy storage power P is introducedS(k) two variables, are waited for
Solve problems are expressed as：
(minf1,maxf2)=f (τ, PS(k)), k=1,2 ..., N
Above-mentioned multi-objective problem is solved using the quick non-dominated sorted genetic algorithm of the second generation, obtains a series of Pareto most
Excellent solution, to provide important references when carrying out energy storage planning to network system for policymaker.
Finally, the programme of energy-storage system is determined using the optimal solution of object function, described 103, including：
Grid-connected power is determined according to the low-pass filtering smoothing time constant τ in the optimal solution；
According in the optimal solution energy storage power and the grid-connected power, in real time plan energy-storage system charge-discharge electric power
And the grid-connected power of regenerative resource.
Wherein, k moment grid-connected power P as the following formulaC(k)：
In above formula, Δ T time intervals in order to control, PPV(k) it is k moment regenerative resource station outputs.
The present invention also provides a kind of energy storage device for planning, as shown in figure 4, described device includes：
Build module, the multiple objective programming object function for establishing energy-storage system respectively and its constraints；
Determining module, the optimal solution for determining the multiple objective programming function；
Planning module, for carrying out energy storage planning to power station according to the optimal solution.
Wherein, the object function includes：The second object function of energy storage first object function and energy storage；
The energy storage first object function takes the fluctuating range minimum value of grid-connected power；
The second object function of the energy storage takes energy-storage battery economic index maximum value；
The constraints includes the bound data of energy-storage system.
The energy storage first object function is established as the following formula：
In above formula, PC(k) it is the grid-connected power at k moment, λdFor the unit penalty factor beyond power swing range, N is to adopt
Sample moment sum,Wherein, PClimitFor grid-connected rate of power change limit value；
The second object function of the energy storage is established as the following formula：
In above formula, PmaxFor energy-storage system maximum power, λPFor the cost coefficient of energy-storage system maximum power, EmaxFor energy storage
System maximum capacity, λEFor the cost coefficient of energy-storage system maximum capacity, EcycleElectricity, λ are recycled in the period in order to controlmvIn order to control
The cost coefficient of cycle electricity in period, wherein：
In above formula,PS(k) it is k moment energy storage power, Δ T is in order to control between the time
Every.
The energy-storage system bound data includes：Grid-connected power, the low-pass filtering smoothingtime in regenerative resource power station are normal
The state-of-charge and charge-discharge electric power of number, energy-storage system；
The regenerative resource electric station grid connection of the multiple objective programming bound for objective function of energy-storage system is established as the following formula
Power constraint：
Pc(k)≤min{Pcref,Pcline, k=1 ..., N
In above formula, PC(k) it is the grid-connected power at k moment, PcrefFor the regenerative resource electric station grid connection upper limit of the power, PclineFor
Grid-connected circuit maximum transmission power, N are sampling instant sum；
The low-pass filtering smoothingtime for establishing the multiple objective programming bound for objective function of energy-storage system as the following formula is normal
Number constraint：
τmin≤τ≤τmax
In above formula, τ is low-pass filtering smoothing time constant, τminFor low-pass filtering smoothing time constant lower limit, τmaxIt is low
The pass filter smoothing time constant upper limit；
The state-of-charge of the energy-storage system of the multiple objective programming bound for objective function of energy-storage system is established as the following formula
Constraint：
SOCmin≤SOC(k)≤SOCmax, k=1 ..., N
In above formula, SOC (k) is k moment energy-storage system state-of-charges, SOCminFor under k moment energy-storage system state-of-charges
Limit, SOCmaxFor the k moment energy-storage system state-of-charge upper limits；
The charge-discharge electric power constraint of the multiple objective programming bound for objective function of energy-storage system is established as the following formula：
In above formula, PS(k) it is k moment energy storage power, PRFor energy-storage system rated power, ERFor energy-storage system rated capacity,
PROFor current time energy-storage system peak power output, Δ T time intervals in order to control.
The determining module, including：
First determination unit, for introducing low-pass filtering smoothing time constant τ and k moment energy storage power PS(k) it is used as institute
The variable of the multiple objective programming object function of energy-storage system is stated, and the more of the energy-storage system are determined using multi-objective optimization algorithm
The optimal solution of goal programming object function.
Wherein, the multiple objective programming target of the energy-storage system is determined using the quick non-dominated sorted genetic algorithm of the second generation
The optimal solution of function.
The planning module, including：
Second determination unit, for determining grid-connected power according to the low-pass filtering smoothing time constant τ in the optimal solution；
Planning unit, for according in the optimal solution energy storage power and the grid-connected power, plan energy storage system in real time
The charge-discharge electric power of system and the grid-connected power of regenerative resource.
Wherein, k moment grid-connected power P as the following formulaC(k)：
In above formula, Δ T time intervals in order to control, PPV(k) it is k moment regenerative resource station outputs.
It should be understood by those skilled in the art that, embodiments herein can be provided 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, the application can be used in one or more wherein include computer usable program code computer
The computer program production implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.)
The form of product.
The application is with reference to method, the flow 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 can be realized by computer program instructions every first-class in flowchart and/or the block diagram
The combination of flow and/or box in journey and/or box and flowchart and/or the block diagram.These computer programs can be provided
Instruct the processor of all-purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine so that the instruction executed by computer or the processor of other programmable data processing devices is generated for real
The device for the function of being specified in present one flow of flow chart or one box of multiple flows and/or block diagram or multiple boxes.
These computer program instructions, which may also be stored in, can guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works so that instruction generation stored in the computer readable memory includes referring to
Enable the manufacture of device, the command device realize in one flow of flow chart or multiple flows and/or one box of 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 count
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, in computer or
The instruction executed on other programmable devices is provided for realizing in one flow of flow chart or multiple flows and/or block diagram one
The step of function of being specified in a box or multiple boxes.
Finally it should be noted that：The above embodiments are merely illustrative of the technical scheme of the present invention and are not intended to be limiting thereof, to the greatest extent
Invention is explained in detail with reference to above-described embodiment for pipe, those of ordinary skills in the art should understand that：Still
The specific implementation mode of the present invention can be modified or replaced equivalently, and without departing from any of spirit and scope of the invention
Modification or equivalent replacement should all cover within the claims of the present invention.
Claims (13)
1. a kind of energy storage planing method, it is characterised in that：
The multiple objective programming object function and its constraints of energy-storage system are established respectively；
Determine the optimal solution of the multiple objective programming function；
Energy storage planning is carried out to power station according to the optimal solution.
2. the method as described in claim 1, which is characterized in that the object function includes：Energy storage first object function and storage
It can the second object function；
Wherein, the energy storage first object function takes the fluctuating range minimum value of grid-connected power；
The second object function of the energy storage takes energy-storage battery economic index maximum value；
The constraints includes the bound data of energy-storage system.
3. method as claimed in claim 2, which is characterized in that the energy storage first object function：
In above formula, PC(k) it is the grid-connected power at k moment, λdFor the unit penalty factor beyond power swing range, N is when sampling
Sum is carved,Wherein, PClimitFor grid-connected rate of power change limit value.
4. method as claimed in claim 2, which is characterized in that
The second object function of the energy storage：
In above formula, PmaxFor energy-storage system maximum power, λPFor the cost coefficient of energy-storage system maximum power, EmaxFor energy-storage system
Maximum capacity, λEFor the cost coefficient of energy-storage system maximum capacity, EcycleElectricity, λ are recycled in the period in order to controlmvPeriod in order to control
The cost coefficient of interior cycle electricity, wherein：
In above formula,PS(k) it is k moment energy storage power, Δ T time intervals in order to control.
5. method as claimed in claim 2, which is characterized in that
The energy-storage system bound data includes：Grid-connected power constraint, the low-pass filtering smoothingtime in regenerative resource power station are normal
Number constraint, the state-of-charge of energy-storage system and charge-discharge electric power constraint；
Regenerative resource electric station grid connection power constraint is established as the following formula：
Pc(k)≤min{Pcref,Pcline, k=1 ..., N
In above formula, PC(k) it is the grid-connected power at k moment, PcrefFor the regenerative resource electric station grid connection upper limit of the power, PclineIt is grid-connected
Circuit maximum transmission power, N are sampling instant sum；
The constraint of low-pass filtering smoothing time constant is established as the following formula：
τmin≤τ≤τmax
In above formula, τ is low-pass filtering smoothing time constant, τminFor low-pass filtering smoothing time constant lower limit, τmaxFor low pass filtered
The wave smoothing time constant upper limit；
The state-of-charge constraint of energy-storage system is established as the following formula：
SOCmin≤SOC(k)≤SOCmax, k=1 ..., N
In above formula, SOC (k) is k moment energy-storage system state-of-charges, SOCminFor k moment energy-storage system state-of-charge lower limits,
SOCmaxFor the k moment energy-storage system state-of-charge upper limits；
Charge-discharge electric power constraint is established as the following formula：
In above formula, PS(k) it is k moment energy storage power, PRFor energy-storage system rated power, ERFor energy-storage system rated capacity, PROFor
Current time energy-storage system peak power output, Δ T time intervals in order to control.
6. method as described in any one in claim 1-5, which is characterized in that the multiple objective programming mesh of the determining energy-storage system
The optimal solution of scalar functions is：
Introduce low-pass filtering smoothing time constant τ and k moment energy storage power PS(k) multiple objective programming as the energy-storage system
The variable of object function, and determined using multi-objective optimization algorithm the energy-storage system multiple objective programming object function it is optimal
Solution.
7. method as claimed in claim 6, which is characterized in that the multi-objective optimization algorithm includes：The quick non-branch of the second generation
With Sorting Genetic Algorithm.
8. method as claimed in claim 6, which is characterized in that described to carry out energy storage to new energy power station according to the optimal solution
Planning, including：
Grid-connected power is determined according to the low-pass filtering smoothing time constant τ in the optimal solution；
According in the optimal solution energy storage power and the grid-connected power, in real time plan energy-storage system charge-discharge electric power and can
The grid-connected power of the renewable sources of energy.
9. method as claimed in claim 8, which is characterized in that the k moment grid-connected power PC(k)：
In above formula, τ is low-pass filtering smoothing time constant in optimal solution, Δ T time intervals in order to control, PPV(k) it is that the k moment can be again
Raw energy station output.
10. a kind of energy storage device for planning, which is characterized in that described device includes：
Build module, the multiple objective programming object function for establishing energy-storage system respectively and its constraints；
Determining module, the optimal solution for determining the multiple objective programming function；
Planning module, for carrying out energy storage planning to power station according to the optimal solution.
11. device as claimed in claim 10, which is characterized in that in the structure module, the object function includes：Energy storage
The second object function of first object function and energy storage；
Wherein, the energy storage first object function takes the fluctuating range minimum value of grid-connected power；
The second object function of the energy storage takes energy-storage battery economic index maximum value；
The constraints includes the bound data of energy-storage system.
12. device as claimed in claim 10, which is characterized in that the determining module, including：
First determination unit, for introducing low-pass filtering smoothing time constant τ and k moment energy storage power PS(k) it is used as the energy storage
The variable of the multiple objective programming object function of system, and determine that the multiple target of the energy-storage system is advised using multi-objective optimization algorithm
Draw the optimal solution of object function.
13. device as claimed in claim 10, which is characterized in that the planning module, including：
Second determination unit, for determining grid-connected power according to the low-pass filtering smoothing time constant τ in the optimal solution；
Planning unit, for according in the optimal solution energy storage power and the grid-connected power, plan energy-storage system in real time
The grid-connected power of charge-discharge electric power and regenerative resource.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710190530.0A CN108667054B (en) | 2017-03-28 | 2017-03-28 | Energy storage planning method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710190530.0A CN108667054B (en) | 2017-03-28 | 2017-03-28 | Energy storage planning method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108667054A true CN108667054A (en) | 2018-10-16 |
| CN108667054B CN108667054B (en) | 2021-09-03 |
Family
ID=63785718
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710190530.0A Active CN108667054B (en) | 2017-03-28 | 2017-03-28 | Energy storage planning method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108667054B (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109787259A (en) * | 2019-01-23 | 2019-05-21 | 西安交通大学 | A kind of polymorphic type energy storage joint planing method based on new energy random fluctuation |
| CN109962485A (en) * | 2019-03-08 | 2019-07-02 | 台州宏创电力集团有限公司 | A kind of composite energy storing device addressing constant volume method towards source net lotus close friend interaction |
| CN113507134A (en) * | 2021-06-24 | 2021-10-15 | 东北电力大学 | Optimal planning method for planning target year new energy power supply installed capacity |
| CN115864429A (en) * | 2022-08-31 | 2023-03-28 | 湖北工业大学 | Multi-objective optimization AGC method for wind and fire storage cooperation under double-carbon target |
| CN116415799A (en) * | 2023-06-06 | 2023-07-11 | 南方电网调峰调频发电有限公司储能科研院 | Energy storage scale and structure planning method and system |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104701871A (en) * | 2015-02-13 | 2015-06-10 | 国家电网公司 | Wind, light and water-containing multi-source complementary micro-grid hybrid energy storage capacity optimal proportion method |
| CN106408131A (en) * | 2016-09-30 | 2017-02-15 | 安徽工程大学 | Photovoltaic microgrid multi-target scheduling method based on demand-side management |
-
2017
- 2017-03-28 CN CN201710190530.0A patent/CN108667054B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104701871A (en) * | 2015-02-13 | 2015-06-10 | 国家电网公司 | Wind, light and water-containing multi-source complementary micro-grid hybrid energy storage capacity optimal proportion method |
| CN106408131A (en) * | 2016-09-30 | 2017-02-15 | 安徽工程大学 | Photovoltaic microgrid multi-target scheduling method based on demand-side management |
Non-Patent Citations (2)
| Title |
|---|
| LI JIANLIN等: "Study on Energy storage system smoothing wind power fluctuations", 《2010 INTERNATIONAL CONFERENCE ON POWER SYSTEM TECHNOLOGY》 * |
| 李文斌等: "平抑风电场功率波动的储能容量选取方法", 《华东电力》 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109787259A (en) * | 2019-01-23 | 2019-05-21 | 西安交通大学 | A kind of polymorphic type energy storage joint planing method based on new energy random fluctuation |
| CN109787259B (en) * | 2019-01-23 | 2020-10-27 | 西安交通大学 | Multi-type energy storage joint planning method based on new energy random fluctuation |
| CN109962485A (en) * | 2019-03-08 | 2019-07-02 | 台州宏创电力集团有限公司 | A kind of composite energy storing device addressing constant volume method towards source net lotus close friend interaction |
| CN109962485B (en) * | 2019-03-08 | 2021-07-16 | 台州宏远电力设计院有限公司 | Source network charge-friendly interaction-oriented composite energy storage device site selection and volume fixing method |
| CN113507134A (en) * | 2021-06-24 | 2021-10-15 | 东北电力大学 | Optimal planning method for planning target year new energy power supply installed capacity |
| CN113507134B (en) * | 2021-06-24 | 2022-04-05 | 东北电力大学 | Optimal planning method for planning target year new energy power supply installed capacity |
| CN115864429A (en) * | 2022-08-31 | 2023-03-28 | 湖北工业大学 | Multi-objective optimization AGC method for wind and fire storage cooperation under double-carbon target |
| CN116415799A (en) * | 2023-06-06 | 2023-07-11 | 南方电网调峰调频发电有限公司储能科研院 | Energy storage scale and structure planning method and system |
| CN116415799B (en) * | 2023-06-06 | 2023-09-12 | 南方电网调峰调频发电有限公司储能科研院 | Energy storage scale and structure planning method and system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108667054B (en) | 2021-09-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Li et al. | Energy management and operational control methods for grid battery energy storage systems | |
| CN108667054A (en) | A kind of energy storage method and device for planning | |
| CN103020853B (en) | Method for checking short-term trade plan safety | |
| CN106451556A (en) | Method and apparatus for determining capability of receiving distributed power supply by distributed network feeder line | |
| Xu et al. | A decomposition-based practical approach to transient stability-constrained unit commitment | |
| CN103955777A (en) | Photovoltaic power generation access power distribution network scheme design and analysis assessment auxiliary system | |
| CN102244677B (en) | Green energy Cloud computing method and system | |
| CN104077494A (en) | Simulation evaluation method for access of distributed power source to power distribution network | |
| CN109672215A (en) | Based on load can time shift characteristic distributed photovoltaic dissolve control method | |
| CN115986834A (en) | Near-end strategy optimization algorithm-based optical storage charging station operation optimization method and system | |
| CN112163304A (en) | Transmission network redundancy constraint identification method, storage medium and computing device | |
| Liu et al. | Multi-objective mayfly optimization-based frequency regulation for power grid with wind energy penetration | |
| Saracoglu et al. | Initialization of a multi-objective evolutionary algorithms knowledge acquisition system for renewable energy power plants | |
| CN110649652B (en) | New energy sending-out system phase modulator configuration method and device based on risk quantitative evaluation | |
| CN109560568A (en) | Double-fed fan motor field maximum based on short circuit current nargin can access capacity determining methods | |
| CN109842121B (en) | Multi-stage multi-region cooperative control load reduction online simulation modeling method and system | |
| Cao et al. | Parallel algorithms for islanded microgrid with photovoltaic and energy storage systems planning optimization problem: Material selection and quantity demand optimization | |
| CN115954956A (en) | Method and system for evaluating access capacity of distributed power supply of power distribution network | |
| CN115425650A (en) | Power supply station microgrid configuration method, device, equipment and medium | |
| Sahay et al. | Risks in an Active Distribution Network: A Review of the Literature | |
| Hong et al. | A study of evaluation system based on large scale photovoltaic power generation | |
| CN109638835B (en) | AC/DC hybrid micro-grid optimal configuration method and device | |
| Yuan et al. | Passive power filter optimization problem based on adaptive multipopulation NSGA-II and CRITIC-TOPSIS | |
| CN112101708A (en) | Expert knowledge fused power grid short circuit safety intelligent decision method and system | |
| Sun et al. | Optimal operation strategy of wind-hydrogen integrated energy system based on NSGA-II algorithm |
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 |