CN106451504A - Control method and device for configuration cost of hybrid energy storage system - Google Patents
Control method and device for configuration cost of hybrid energy storage system Download PDFInfo
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- CN106451504A CN106451504A CN201610911483.XA CN201610911483A CN106451504A CN 106451504 A CN106451504 A CN 106451504A CN 201610911483 A CN201610911483 A CN 201610911483A CN 106451504 A CN106451504 A CN 106451504A
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- 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
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- 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]
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Abstract
The invention discloses a control method and device for the configuration cost of a hybrid energy storage system. The control method comprises the steps of: acquiring a target cost function required for calculating the configuration cost of the hybrid energy storage system and a constraint condition corresponding to the target cost function, as a configuration cost control model of the hybrid energy storage system; and calculating the configuration cost control model regarding a configuration parameter in the configuration cost control model till satisfying a configuration cost condition of the hybrid energy storage system to determine a target parameter value of the configuration parameter. By using the control method, optimal configuration cost required for configuring the hybrid energy storage system can be ensured while the hybrid energy storage system is ensured to have a good power output smoothing effect in comprehensive consideration with objective conditions such as energy density, service life and the like of an energy storage device in the hybrid energy storage system, and the purpose of reducing the construction investment cost of a grid system is thus fulfilled.
Description
Technical field
The present embodiments relate to technical field of electric power, more particularly to a kind of controlling party of mixed energy storage system deployment cost
Method and device.
Background technology
Increasingly serious with energy and environment problem, energy-saving and emission-reduction problem has obtained extensive concern, wind energy, solar energy etc.
Proportion of the clean energy resource in China's primary energy is stepped up, and corresponding wind-power electricity generation, photovoltaic generation are also obtained in recent years
Greatly develop.However, wind-force and photovoltaic generation depend on the meteorological condition of change, the output which generates electricity has fluctuation
Property and intermittence, in order to solve the above problems, can build clean energy resource generate electricity while configure certain energy-storage system, lead to
Cross configured energy-storage system to charge when generating electricity more than needed, discharge during generation deficiency, thus reach smooth wind, light generating output work
The purpose of rate, improves receiving ability of the system to clean energy resource.
General, the device in energy-storage system for energy storage is divided into two types, and one kind is energy type energy storage, such as electric power storage
Pond, another kind is power-type energy storage, such as super capacitor.The energy storage device of both types respectively has pluses and minuses, with accumulator is such as
The energy type energy storage of representative has the advantages that energy density is high, but frequently discharge and recharge can quickly reduce battery；As with
Super capacitor is that power-type energy storage although its energy density of representative is relatively low, but power density is high, and can discharge and recharge often.
At present, in the process of construction of energy-storage system, generally energy type energy storage and power-type energy storage are combined together and make
With, the mixed energy storage system that formation has complementary advantages, and then better ensure that wind, the smooth effect of light generated output output.However,
When configuring to mixed energy storage system, traditional collocation method simply considers the installed capacity of energy-storage system, merely
The impact of benefit in terms of the access of energy-accumulating power station being analyzed to electrical network, when specifically considering energy-storage system mixed configuration
Deployment cost, causes the situation that cost of investment is too high, therefore, on the premise of mixed energy storage system work efficiency is ensured, also needs
Consider how preferably to be controlled deployment cost.
Content of the invention
The invention provides a kind of control method of mixed energy storage system deployment cost and device, are ensureing that generated output is defeated
The purpose of deployment cost optimum has been reached while going out smooth effect.
The embodiment of the present invention is employed the following technical solutions：
In a first aspect, embodiments providing a kind of control method of mixed energy storage system deployment cost, the method
Including：
Obtain the objective cost function needed for calculating mixed energy storage system deployment cost and the objective cost function pair
The constraints that answers, used as the deployment cost Controlling model of the mixed energy storage system；
For the configuration parameter in the deployment cost Controlling model, the deployment cost Controlling model is carried out calculating directly
To the deployment cost condition for reaching the mixed energy storage system, to determine the targeted parameter value of the configuration parameter.
Second aspect, the embodiment of the present invention additionally provides a kind of control device of mixed energy storage system deployment cost, the dress
Put including：
Data obtaining module, for obtaining objective cost function and institute needed for calculating mixed energy storage system deployment cost
The corresponding constraints of objective cost function is stated, as the deployment cost Controlling model of the mixed energy storage system；
Targeted parameter value determining module, for for the configuration parameter in the deployment cost Controlling model, joining to described
Putting cost control model carries out calculating the deployment cost condition up to the mixed energy storage system is reached, to determine the configuration ginseng
The targeted parameter value of number.
The invention provides a kind of control method of mixed energy storage system deployment cost and device.The control method is obtained first
The objective cost function needed for calculating mixed energy storage system deployment cost and constraints corresponding with objective cost function is taken,
Deployment cost Controlling model as mixed energy storage system；Then, for the configuration parameter of objective cost function, to deployment cost
Controlling model carries out calculating the deployment cost condition up to mixed energy storage system is reached, and thereby determines that out the target ginseng of configuration parameter
Numerical value, such that it is able to configure mixed energy storage system based on determined targeted parameter value.Using the control method, mixed considering
Close in energy-storage system on the premise of the objective condition such as the energy density of energy storage device, service life, both ensured mixed energy storage system
With preferable power output smooth effect, the deployment cost optimum needed for configuration mixed energy storage system, Jin Erda is in turn ensure that
To the purpose for reducing network system Installed capital cost.
Description of the drawings
Fig. 1 is a kind of flow chart of the control method of mixed energy storage system deployment cost of the offer of the embodiment of the present invention one；
Fig. 2 is a kind of flow chart of the control method of mixed energy storage system deployment cost of the offer of the embodiment of the present invention two；
Fig. 3 a is a kind of preferred reality of the control method of mixed energy storage system deployment cost of the offer of the embodiment of the present invention three
Apply example；
Fig. 3 b is the configuration diagram of constructed network system in the embodiment of the present invention three；
Fig. 4 is a kind of structural frames of the control device of mixed energy storage system deployment cost of the offer of the embodiment of the present invention four
Figure.
Specific embodiment
The present invention is described in further detail with reference to the accompanying drawings and examples.It is understood that this place is retouched
The specific embodiment that states is used only for explaining the present invention, rather than limitation of the invention.It also should be noted that, in order to just
Part related to the present invention rather than full content is illustrate only in description, accompanying drawing.Exemplary reality is being discussed in greater detail
It should be mentioned that some exemplary embodiments are described as process or the method that describes as flow chart before applying example.Although
Operations (or step) are described as flow chart the process of order, but many of which operation can be by concurrently, concurrently
Ground or while enforcement.Additionally, the order of operations can be rearranged.The process when its operations are completed can be by
Terminate, it is also possible to have the additional step being not included in accompanying drawing.Described process can correspond to method, function, code,
Subroutine, subprogram etc..
General, when being generated electricity based on clean energy resource, needing to build new network system, the clean energy resource is concrete
The non-polluting energy sources of nature generation can be depended on for wind energy and solar energy etc..Specifically, include in the network system
Distributed power generation unit, energy-storage system and outside distribution region, wherein, the distributed power generation unit is particularly used in and is based on
Clean energy resource carries out generated output；The energy-storage system specifically can be utilized for discharge and recharge and exert oneself, and smooth described distributed
The output of group of motors；The outside distribution region is mainly used in distributing and transmits electric power, so that electric consumer uses.
Additionally, build the energy-storage system for being adopted during network system mixed energy storage system is usually, the hybrid energy-storing system
System specifically can be regarded as the energy-storage system constructed by the mixed configuration based on energy type energy-storage system and power-type energy-storage system.
General, the mixed energy storage system is made up of accumulator and super capacitor, based on the self-characteristic that the two has, incite somebody to action the two
The effect that mixed configuration serves mutual supplement with each other's advantages is carried out, thereby ensures that the work efficiency of the mixed energy storage system.However,
After ensureing mixed energy storage system work efficiency, in addition it is also necessary to consider the deployment cost of the mixed energy storage system, if its configuration
High cost, it is likely that be not suitable for actual network system construction because cost of investment is too high, therefore, it can based on this
A kind of control method of mixed energy storage system deployment cost that bright embodiment is provided is controlling the deployment cost.
Embodiment one
Fig. 1 is a kind of flow chart of the control method of mixed energy storage system deployment cost of the offer of the embodiment of the present invention one,
The present embodiment is applicable to what deployment cost when building to mixed energy storage system in network system process of construction was planned
Situation, the method can be executed by a kind of control device of mixed energy storage system deployment cost.The device can pass through hardware and/
Or the mode of software is realized, and can typically be integrated in the planning system for network system planning construction.
As shown in figure 1, a kind of control method of mixed energy storage system deployment cost of the offer of the present embodiment one, concrete bag
Include：
S110, acquisition calculate objective cost function and the objective cost letter needed for mixed energy storage system deployment cost
The corresponding constraints of number, used as the deployment cost Controlling model of the mixed energy storage system.
In the present embodiment, can be adapted in actual network system construction to ensure the mixed energy storage system,
In the planning stage of the network system construction, planning control can be carried out to the deployment cost of the mixed energy storage system, so that
The deployment cost optimum of the mixed energy storage system.In the present embodiment, the control to the deployment cost to be realized, is needed first
Obtain computing formula and the corresponding constraints with deployment cost correlation.
Specifically, the computing formula related to the deployment cost is referred to as objective cost function by the present embodiment, the mesh
Mark cost function can be set in advance, can before deployment cost control is carried out direct access；Additionally, described obtaining
After objective cost function, in addition it is also necessary to obtain corresponding constraints, the constraints specifically can be regarded as hybrid energy-storing system
Under unified central planning putting middle needs the qualificationss that are subject to；Finally, acquired objective cost function and the constraints can be made
For the deployment cost Controlling model of the mixed energy storage system, to control the mixing based on the deployment cost Controlling model
The deployment cost of energy-storage system, and ensure deployment cost optimum.
Further, the objective cost function is expressed as：
Wherein, CsumStore up for the mixing
Total deployment cost value of energy system, Cenergy(PE,EE) for energy type energy-storage system deployment cost, Cpower(PP,EP) it is power
The deployment cost of type energy-storage system, PE,EERepresent rated power (kW) and the rated capacity (kWh) of energy type energy-storage system respectively,
PP,EPRepresent rated power (kW) and the rated capacity (kWh) of power-type energy-storage system, and (P respectivelyE,EE,PP,EP) represent
The corresponding one group of parameter value of configuration parameter described in the deployment cost Controlling model, CoperaterOnce fill for mixed energy storage system
Operating cost produced by electric discharge, n is the total degree that during mixed energy storage system discharge and recharge, its depth of discharge reaches set depth.
Specifically, C can be based onenergy(PE,EE)=K1×PE+K2×EECalculate the configuration of the energy type energy-storage system
Cost, wherein, K1It is unit power cost (unit/kW), K2It is unit capacity cost (unit/kWh), PEIt is energy type energy-storage system
Rated power (kW), EEIt is the rated capacity (kWh) of energy type energy-storage system；It is also based on Cpower(PP,EP)=K3×PP
+K4×EPCalculate the deployment cost of the power-type energy-storage system, wherein, K3It is unit power cost (unit/kW), K4It is unit
Capacity Cost (unit/kWh), PPIt is the rated power (kW) of power-type energy-storage system, EPIt is the rated capacity of power-type energy-storage system
(kWh).
In the present embodiment, based on objective cost function given herein above it is found that the mixed energy storage system total
Deployment cost value can this depending on the configuration of energy type energy-storage system and power-type energy-storage system in energy system in mixing, and institute
State the deployment cost of energy type energy-storage system and power-type energy-storage system rated power again depending on respective energy-storage system and
Rated capacity.Additionally, expression formula based on the objective cost function it is also found that the size of total deployment cost value also
Related to produced operating cost during mixed energy storage system discharge and recharge, but due to produced during mixed energy storage system discharge and recharge
Operating cost need based on mixed energy storage system real work situation obtain, so the present embodiment is to the hybrid energy-storing system
When the deployment cost of system is controlled, directly the operating cost for producing during the mixed energy storage system discharge and recharge can be defined as
One historical experience value.In sum, total deployment cost value of the mixed energy storage system mainly with the energy type energy storage system
The value correlation of the rated power and rated capacity corresponding to system and power-type energy-storage system.Therefore, it can the energy
Rated power and rated capacity corresponding to type energy-storage system and power-type energy-storage system controls mould as the deployment cost
The configuration parameter of type, and total deployment cost value is determined by the parameter value of the determination configuration parameter.
The constraints includes：The power constraint in physical constraint, the energy type energy-storage system in network system
With state-of-charge SOC constraint and the power constraint in the power-type energy-storage system and SOC constraint；The network system includes
Distributed power generation unit, mixed energy storage system and outside distribution region.
In the present embodiment, before total deployment cost is determined based on the objective cost function, it is desirable to the mesh
In mark cost function, the value of configuration parameter disclosure satisfy that the corresponding constraints of the objective cost function.Specifically, described
Constraints is mainly limited to mixed energy storage system configuration in terms of three, and wherein, first aspect is network system
In physical constraint, second aspect is energy type energy-storage system and the power constraint in power-type energy-storage system, the third aspect
It is energy type energy-storage system and the constraint of the state-of-charge (state of charge, SOC) in power-type energy-storage system.
In the present embodiment, the first aspect of the constraints mainly each node and branch road from whole network system
The design level of physical circuit limited, wherein, the network system includes distributed power generation unit, mixed energy storage system
And outside distribution region；The second aspect of the constraints is mainly from energy type energy-storage system and power-type energy-storage system
In output aspect limited, to ensure the output of the energy type energy-storage system and power-type energy-storage system
In power limited scope；The third aspect of the constraints is mainly from energy type energy-storage system and power-type energy storage system
The SOC aspect of system is defined, to ensure the SOC of the energy type energy-storage system and power-type energy-storage system in restriction model
In enclosing.Further, the SOC is particularly used in the dump energy in description energy-storage system, is that energy-storage system was deposited in certain moment
The ratio of the electricity of storage and its specified storing electricity, its mathematic(al) representation is：
SOC (t)=E (t)/EN, in the expression formula, variable E (t) is the electricity that energy storage was stored t-th moment
(kWh), ENRated capacity for energy-storage system.Additionally, the process that the energy-storage system carries out discharge and recharge can be retouched based on expression formula
State for：
In the expression formula, P (t) represents that energy-storage system is corresponding charge-discharge electric power (kW) t-th moment, and works as P (t)
Value for "+" when be in charged state, when value for "-" when be in discharge condition；ηdDischarging efficiency for energy-storage system；ηcFor storage
The charge efficiency of energy system；Δ t is the time interval (h) between the t-1 moment～t-th moment.
In the present embodiment, the constraints is can be expressed as with formula：
Umin≤Ui(t)≤Umaxi∈SB(3)
Ii(t)≤Imaxi∈SL(4)
PE min≤PE(t)≤PE,PP min≤PP(t)≤PP(5)
SOCE min≤SOCE(PE,EE)≤SOCE max,SOCP min≤SOCP(PP,EP)≤SOCP max(6)
Wherein, formula (1), formula (2), formula (3) and formula (4) corresponding to network system in the constraints physics about
Bundle, specifically, formula (1) and formula (2) are power-balance equality constraint during network system real work, PGi、PDiRespectively generate electricity
Machine node and active (kW) of load bus, QGi、QDiIdle (kVar) of respectively electromotor node and load bus, UiFor section
Point voltage (kV), Gij、BijAdmittance (S) and impedance (Ω) for branch road, SBFor the node set in outside distribution region, formula (1) and
Formula (2) is the basic formula of field of power, repeats no more here；Formula (3) be network system in physical circuit voltage about
Bundle, formula (4) is the restriction of current of subcircuits in network system, and SLSet of fingers for outside distribution region；Additionally, formula
(5) constrain corresponding to the output constraint in energy type energy-storage system and power-type energy-storage system and SOC with formula (6), and
P in formula (5) and formula (6)E,EE,PP,EPFor the configuration parameter in the deployment cost Controlling model.
S120, the configuration parameter being directed in the deployment cost Controlling model, are carried out to the deployment cost Controlling model
Calculate up to the deployment cost condition for reaching the mixed energy storage system, to determine the targeted parameter value of the configuration parameter.
In the present embodiment, can be based on the configuration parameter in the deployment cost Controlling model to the deployment cost control
Simulation is calculated, and finally obtains the targeted parameter value for meeting the mixed energy storage system deployment cost condition.Can manage
Solution, after the targeted parameter value is determined, can determine the configuration hybrid energy-storing based on the targeted parameter value
The concrete number of required dissimilar energy storage device during system, thereby determines that the concrete configuration scheme of the mixed energy storage system,
Wherein, the energy storage device generally comprises energy type energy-storage system (as accumulator) and functional type energy-storage system (as super electricity
Hold).
It should be noted that being chosen most based on configuration parameter for one to the calculating process of the deployment cost Controlling model
The figure of merit or the iterative process of the secondary figure of merit, therefore, in the present embodiment, the calculating to the deployment cost Controlling model is permissible
Algorithm based on the iterative such as simulated annealing, genetic algorithm or particle swarm optimization algorithm is realizing.
Further, for the configuration parameter in the deployment cost Controlling model, to the deployment cost Controlling model
Carry out calculating the deployment cost condition up to the mixed energy storage system is reached, to determine the target component of the configuration parameter
Value, including：
For the configuration parameter in the deployment cost Controlling model, based on particle swarm optimization algorithm to the deployment cost
Controlling model carries out calculating the deployment cost condition up to the mixed energy storage system is reached, to determine the mesh of the configuration parameter
Mark parameter value.
In the present embodiment, each corresponding to above-mentioned simulated annealing, genetic algorithm and particle swarm optimization algorithm
Algorithm principle and after realizing process analyses, it is found that the particle swarm optimization algorithm is compared with genetic algorithm, and which sets rule
Then more simple, without genetic algorithm " intersection " (Crossover) and " variation " (Mutation) operation, can be only by working as
Before the optimal value that searches finding global optimum, the particle swarm optimization algorithm is compared with simulated annealing, and the algorithm is again
Fast have the advantages that computational accuracy height, iteration convergence.Therefore, the present embodiment is based preferably on the particle swarm optimization algorithm to institute
State deployment cost Controlling model to be calculated, and then determine the targeted parameter value of the configuration parameter.
A kind of control method of mixed energy storage system deployment cost that the embodiment of the present invention one is provided, obtains first to calculate and mixes
The objective cost function needed for energy-storage system deployment cost and constraints corresponding with objective cost function is closed, as mixing
The deployment cost Controlling model of energy-storage system；Then, for the configuration parameter of objective cost function, to deployment cost Controlling model
Carry out the deployment cost condition up to mixed energy storage system is reached is calculated, the targeted parameter value of configuration parameter is thereby determined that out, from
And mixed energy storage system can be configured based on determined targeted parameter value.Using the control method, hybrid energy-storing is being considered
In system on the premise of the objective condition such as the energy density of energy storage device, service life, both ensured that mixed energy storage system had relatively
Good power output smooth effect, in turn ensure that the deployment cost optimum needed for configuration mixed energy storage system, and then reduces
The purpose of network system Installed capital cost.
Embodiment two
Fig. 2 is a kind of flow chart of the control method of mixed energy storage system deployment cost of the offer of the embodiment of the present invention two,
The present embodiment is optimized on the basis of above-described embodiment, in the present embodiment, will be " for the deployment cost Controlling model
In configuration parameter, based on particle swarm optimization algorithm, the deployment cost Controlling model is carried out calculating until reach the mixing
The deployment cost condition of energy-storage system, to determine the targeted parameter value of the configuration parameter " it is optimized for further：A, setting iteration
The value of the iteration variable is simultaneously initialized as 0 by variable；B, determine candidate's configuration parameter set of the deployment cost Controlling model,
Wherein, least one set candidate parameter value of the candidate's configuration parameter set comprising the configuration parameter；C, for candidate configuration
Each group candidate parameter value in parameter set determines corresponding renewal coefficient；D, the objective cost function is based on, calculates the time
In arrangement parameter set, least one set candidate parameter is worth corresponding total deployment cost value；E, determine that described at least one is always configured to
Minima in this value, remembers that the minima is candidate's value at cost, and by candidate's value at cost and corresponding candidate parameter value
Deposit in setting caching；F, determine whether meet set deployment cost condition, if it is not, then execution step g；If so, then hold
Row step h；G, the iteration variable is carried out from increase operation, and based on described renewal coefficient to candidate's configuration parameter set
In corresponding candidate parameter value be updated operation, form new candidate's configuration parameter set, afterwards return to step c；H, determine institute
The minima for setting candidate's value at cost in caching is stated, using corresponding for minima candidate parameter value as the configuration parameter
Targeted parameter value is exported, and end loop operation.
As shown in Fig. 2 a kind of control method of mixed energy storage system deployment cost of the offer of the embodiment of the present invention two, specifically
Including：
S210, acquisition calculate objective cost function and the objective cost letter needed for mixed energy storage system deployment cost
The corresponding constraints of number, used as the deployment cost Controlling model of the mixed energy storage system.
The value of the iteration variable is simultaneously initialized as 0 by S220, setting iteration variable.
Exemplary, the particle swarm optimization algorithm is the algorithm of an iterative, therefore, the needs when algorithm starts
Set iteration variable and its value is initially 0.
S230, determine candidate's configuration parameter set of the deployment cost Controlling model, wherein, candidate's configuration parameter set
Least one set candidate parameter value comprising the configuration parameter.
Specifically, the particle swarm optimization algorithm is a kind of parallel algorithm, and therefore the particle cluster algorithm generally there are many
Individual input value, and can be to the plurality of input value while calculating.In the present embodiment, the deployment cost can be controlled
In model, the corresponding one group of parameter value of configuration parameter regards an input value as, therefore, it can to choose the configuration parameter corresponding
Multigroup parameter value is simultaneously as the input value of algorithm.For the ease of statement, the present embodiment is using one group of ginseng as algorithm input value
Numerical value is referred to as one group of candidate parameter value, and is added to candidate's configuration parameter concentration of setting.Exemplary, candidate's configuration ginseng
I-th group of candidate parameter value in manifold can be expressed as：(PEi,EEi,PPi,EPi).
In the present embodiment, the targeted parameter value of algorithm output to be determined, it is necessary first to determine and include multigroup candidate parameter
Candidate's configuration parameter set of value.Further, the candidate's configuration parameter set for determining the deployment cost Controlling model, bag
Include：
Setting with the generating output of the setting time interval collection distributed power generation unit, shape in sampling duration
Become to generate electricity and output power curve be designated as generating sampled data；Data spectrum analysis is carried out to the generating sampled data, respectively
Obtain the energy storage output power curve of the energy type energy-storage system and the power-type energy-storage system；Based on the energy type
Energy-storage system and the energy storage output power curve of the power-type energy-storage system, determine respectively corresponding rated operating range and
Rated capacity scope；Rated power span based on the energy type energy-storage system and the power-type energy-storage system and
Rated capacity span, determines least one set power-handling capability and corresponding rated capacity value respectively, forms the configuration ginseng
The least one set candidate parameter value of number；If it is corresponding about that the least one set candidate parameter value meets the objective cost function
Bundle condition, then add the candidate's configuration parameter for setting to concentrate by the least one set candidate parameter value.
In the present embodiment, the value for setting sampling duration was at least above 15 minutes；The distributed power generation unit
For clean energy resource generating set, because generated output undulatory property when clean energy resource generates electricity is larger, so the generating of collection gained is defeated
The peak value fluctuation for going out power curve is also very big, thus needs based on the energy-storage system in network system by discharge and recharge to smooth
State generating output power curve.General, the discharge and recharge based on energy-storage system is smoothed to the generating output power curve
During operation, data spectrum analysis can be carried out to the generating output power curve and determines one preferably for the energy-storage system
Energy storage output power curve.
In the present embodiment, for the energy storage output power curve, energy storage output power curve is put as will be described
In a plane coordinate system, then the axis of abscissas express time of the coordinate system, axis of ordinates represents energy storage output, wherein
Corresponding energy storage output does not also correspond to energy-storage system in not corresponding rated power in the same time in the same time, therefore, is based on
The energy storage output power curve can determine the rated operating range of the energy-storage system.Additionally, based on the energy storage output
Power curve it may also be determined that rated capacity corresponding to the rated power in each moment, exemplary, the volume corresponding to t
Constant volume is specifically regarded as the energy storage output power curve in the corresponding integrated value of t, therefore, defeated based on the energy storage
Go out power curve it may also be determined that the rated capacity scope of the energy-storage system.In the present embodiment, determining the specified work(
After rate scope and the rated capacity scope, multiple rated power can be determined in the rated operating range, acceptable
Based on determined by rated power determining corresponding rated capacity, it is possible thereby to obtain multigroup time for meeting the constraints
Radix Ginseng selection numerical value.It should be noted that the energy-storage system for referring in the present embodiment is all regarded as power-type energy-storage system or energy
Type energy-storage system.
Further, described data spectrum analysis is carried out to the generating sampled data, obtain respectively the energy type storage
Energy system and the energy storage output power curve of the power-type energy-storage system, including：
Fast Fourier transform is carried out to the generating sampled data and obtains result of spectrum analysis；Determine that the energy type is stored up
Corresponding energy optimum frequency band during energy system discharge and recharge, and corresponding power when determining the power-type energy-storage system discharge and recharge
Optimum frequency band；At least one frequency values for belonging to the energy optimum frequency band are determined in the result of spectrum analysis, and to institute
Stating the corresponding amplitude of at least one frequency values carries out inverse Fourier transform, obtains the energy storage output work of the energy type energy-storage system
Rate curve；At least one frequency values for belonging to the power optimum frequency band are determined in the result of spectrum analysis, and to described
The corresponding amplitude of at least one frequency values carries out inverse Fourier transform, obtains the energy storage output of the power-type energy-storage system
Curve.
Exemplary, if the generating sampled data is expressed as PW, then quick Fu is carried out to the generating sampled data
In result after leaf transformation be represented by：
Wherein, N is the number for setting sampled point in sampling duration；fpRepresent the set of frequency, fpN () represents n-th frequency
Rate, fp(n)=(n-1)/(TS× N), TSTime span (s) for the neighbouring sample moment；XpN () is fast Fourier transform after
N-th frequency fpAmplitude (n=1～N) corresponding to (n).
In the present embodiment, based on energy type energy-storage system and the feature of power-type energy-storage system, it may be determined that the energy
When the discharge and recharge of amount type energy-storage system smooths generating output, corresponding energy optimum frequency band is 0.0011Hz～0.003Hz；Also
Can determine power-type energy-storage system discharge and recharge smooth generating output when corresponding power optimum frequency band be more than
0.003Hz.Connect above-mentioned example, it may be determined that the frequency range belonging to above-mentioned n frequency, belong to energy if there is k frequency optimal
The scope of frequency range, then can determine the corresponding amplitude of k frequency difference, carry out Fu to the amplitude of the k frequency afterwards
In leaf inverse transformation, it is possible to obtain the energy type energy-storage system corresponding ideal energy storage output power curve；In the same manner, it is also possible to
Draw the corresponding ideal energy storage output power curve of the power-type energy-storage system.
S240, each group candidate parameter value for candidate's configuration parameter concentration determine corresponding renewal coefficient.
In the present embodiment, in addition it is also necessary to determine accordingly more for each group candidate parameter value that candidate's configuration parameter is concentrated
New coefficient, with an iterative process based on the corresponding candidate parameter value of the renewal coefficient update.Exemplary, can be by the time
I-th group of candidate parameter value (P in Radix Ginseng selection manifoldEi,EEi,PPi,EPi) corresponding renewal coefficient be expressed as (Δ PEi,ΔEEi,ΔPPi,
ΔEPi).
It should be noted that it is the first of a setting at the beginning of algorithm iteration that the candidate parameter is worth the corresponding coefficient that updates
Initial value, afterwards with the continuous iteration of algorithm, which updates coefficient and can also change, and the change for updating coefficient meets institute
The more new regulation for setting in particle swarm optimization algorithm is stated, the description to the more new regulation may be referred to the Particle Swarm Optimization
The algorithm principle of method, I will not elaborate.
S250, the objective cost function is based on, calculates candidate's configuration parameter and concentrate least one set candidate parameter value
Corresponding total deployment cost value.
S260, the minima for determining in described at least one total deployment cost value, remember that the minima is candidate's value at cost,
And deposit in candidate's value at cost and corresponding candidate parameter value in setting caching.
In the present embodiment, current optimum of the algorithm in current iteration can determine based on step S250 and step S260
Value.Exemplary, the current optimal value concretely calculates the minima in total deployment cost value of gained in the present embodiment,
The minima is designated as candidate's value at cost.
General, for particle swarm optimization algorithm, need the current optimal value for determining each iteration to deposit in and set
In fixed caching, in order to determine final optimal objective value.Therefore, the present embodiment is by candidate's value at cost and corresponding time
Radix Ginseng selection numerical value is deposited in setting caching.
S270, determine whether meet set deployment cost condition, if it is not, then execution step S280；If so, step is then executed
Rapid S290.
General, need to terminate the loop iteration of algorithm based on the termination condition for setting.The present embodiment is joined setting
Cost conditions are put as termination condition.
Further, the deployment cost condition includes：The value of the iteration variable more than given threshold and/or described extremely
Minima in a few total deployment cost value is not more than setting and terminates threshold value.
S280, the iteration variable is carried out from increase operation, and based on described renewal coefficient to the candidate configuration ginseng
In manifold, corresponding candidate parameter value is updated operation, forms new candidate's configuration parameter set, afterwards return to step S240.
In the present embodiment, when the deployment cost condition for being unsatisfactory for setting, need to carry out iteration variable from increase behaviour
Make (as iteration variable k=k+1), and proceed iterative calculation next time.Before next iteration calculating is carried out, need
Determine the input value of next iteration, the determination of the input value can pass through input of the coefficient to current iteration to be updated based on set
Value is updated realizing, exemplary, and the input value of next iteration is equal to the input value of current iteration and the renewal coefficient
Sum.In the present embodiment, can with determined by next iteration input value will replace current iteration input value, shape
Candidate's configuration parameter set of Cheng Xin.After the input value for determining next iteration, it is possible to return to step S240, carry out next
Secondary iteration updates the determination operation of coefficient and its operation of calculating afterwards.
S290, determine described set caching in candidate's value at cost minima, by corresponding for minima candidate parameter
The targeted parameter value output being worth as the configuration parameter, and end loop operation.
In the present embodiment, after the deployment cost condition for meeting setting is determined based on step S270, it is possible to stop
The iterative calculation of algorithm, and the candidate's value at cost in the setting caching is compared, the value of candidate's value at cost minimum is chosen,
And corresponding for minima candidate parameter value can be exported as the targeted parameter value of the configuration parameter and terminate its circulation
Operation.
It should be noted that after the targeted parameter value is exported, the present embodiment can also be based on the targeted parameter value
During determining the configuration mixed energy storage system, the quantity of used energy type energy storage device and used power-type energy storage are filled
The quantity that puts.
A kind of control method of mixed energy storage system deployment cost that the embodiment of the present invention two is provided, embodies based on grain
Sub- optimized algorithm determines the operating process of the targeted parameter value for meeting mixed energy storage system deployment cost condition.Using the controlling party
Method, in mixed energy storage system is considered on the premise of the constraints such as the energy density of energy storage device, service life, it is ensured that
While mixed energy storage system has preferable power output smooth effect, it is ensured that joining needed for configuration mixed energy storage system
Optimum cost is put, and then reduces the purpose of network system Installed capital cost.
Embodiment three
Fig. 3 a is a kind of preferred reality of the control method of mixed energy storage system deployment cost of the offer of the embodiment of the present invention three
Example is applied, the embodiment of the present invention is with wind-power electricity generation as application background, and Fig. 3 b is the configuration diagram of constructed network system, such as schemes
Shown in 3b, the network system includes Wind turbines 31, mixed energy storage system 32 and external electrical network 33.In the present embodiment,
A length of 30 minutes during to Wind turbines 31 to set sampling, set sampling time interval and sampled as 1 minute, obtain wind-force and send out
The generating sampled data of electricity；It is then based on accumulator and super capacitor to configure mixed energy storage system 32, it is possible to based on this
The control method of the mixed energy storage system deployment cost that bright embodiment is provided is determining selected accumulator and ultracapacitor
Concrete number；Finally, the electric energy after mixed energy storage system 32 is smooth is distributed to electric consumer based on external electrical network 33.
As shown in Figure 3 a, the control method of a kind of preferred mixed energy storage system deployment cost that the present embodiment three is provided,
Specifically include：
S310, determine the objective cost function and constraints, the deployment cost as mixed energy storage system controls
Model.
S320, to determined by generating sampled data carry out data spectrum analysis, obtain energy type energy-storage system and work(
The corresponding preferable energy storage output power curve of rate type energy-storage system.
Exemplary, by carrying out fast Fourier transform and inverse Fourier transform to the generating sampled data, obtain
By the energy storage output power curve of the formed energy type energy-storage system of accumulator, while obtaining by the formed power-type of super capacitor
The energy storage output power curve of energy-storage system.
S330, the value for setting iteration variable and initializing the iteration variable are as 0.
S340, based on the energy type energy-storage system and the corresponding energy storage output power curve of power-type energy-storage system,
Determine candidate's configuration parameter set of the deployment cost Controlling model.
Exemplary, in the energy type energy-storage system and the corresponding energy storage output song of power-type energy-storage system
Line, determines rated operating range and the rated capacity scope for stating energy type energy-storage system and power-type energy-storage system, most respectively
Eventually multigroup rated power and rated capacity are selected based on respective rated operating range and rated capacity scope, form candidate's configuration
Multigroup candidate parameter value in parameter set.
S350, determine candidate's configuration parameter concentrate candidate parameter value renewal coefficient.
S360, the corresponding result of calculation of each group candidate parameter value is determined based on the deployment cost Controlling model.
Exemplary, table 1 lists the cost performance parameter of accumulator and super capacitor.
The performance parameter of the dissimilar energy storage device of table 1
Based on the performance parameter and one group of given candidate parameter value, it may be determined that the deployment cost Controlling model
In total deployment cost value.Assume one group of candidate parameter value (PEi,EEi,PPi,EPi) (20,20,5,0.05) are equal to, the class value table
Show that by the rated power of the formed energy type energy-storage system of accumulator be 20kW, rated capacity is 20kWh, by super capacitor institute shape
The rated power of success rate type energy-storage system is 5kW, and rated capacity is 0.05kWh, then to substitute into the deployment cost Controlling model
After obtain corresponding total deployment cost value be.
S370, minima in the result of calculation is determined as candidate's value at cost, and by candidate's value at cost and right
The candidate parameter value that answers is deposited to setting caching.
Exemplary, it is assumed that based on several groups of candidate parameter values difference that candidate's configuration parameter that step S340 determines is concentrated
For：(PE1,EE1,PP1,EP1) (20,20,5,0.05) are equal to, (PE2,EE2,PP2,EP2) it is equal to (5,10,15,0.45) and (PE3,
EE3,PP3,EP3) be equal to (15,15,5,0.15), based on step S340 calculate after gained result of calculation be respectively 23517 yuan,
29135 yuan and 21322 yuan.It can thus be appreciated that the minima in the result of calculation is 21322 yuan, it is possible to by the minima
21322 and corresponding candidate parameter value (15,15,5,0.15) deposit to set caching in.
If S380 is unsatisfactory for iteration termination condition, formed based on the corresponding candidate parameter value of each renewal coefficient update
New candidate's configuration parameter set, and return S350；Otherwise, the comparison candidate's value at cost for setting in caching, by minimum candidate
The corresponding candidate parameter value of value at cost is exported as targeted parameter value, and end loop.
In the present embodiment, preferably set the iteration termination condition and reach given threshold for iterationses, then can be to setting
Fixed iteration termination condition is judged, and executes corresponding operation based on result of determination.Exemplary, if algorithm meets
Iteration termination condition, then choose minima in the candidate's value at cost that can deposit in caching is set, and will be corresponding for the minima
Candidate parameter value is exported as targeted parameter value, it is assumed that the minima in candidate's value at cost is 21322, then can be by corresponding time
Radix Ginseng selection numerical value (15,15,5,0.15) is exported as targeted parameter value.
S390, determine the accumulator and super capacitor configuration number based on the targeted parameter value, described to configure
Mixed energy storage system.
Exemplary, if the targeted parameter value of output is (15,15,5,0.15), then it is believed that the hybrid energy-storing system
The rated power of the energy type energy-storage system for being formed based on the accumulator in system needs to reach 15kW, and ensures that rated capacity is
15kWh, while the rated power of the power-type energy-storage system for being formed based on the super capacitor in the mixed energy storage system is needed
5kW to be reached, and ensure that final rated capacity is 0.15kWh.It follows that when the mixed energy storage system is configured, optional
1 rated power is taken for 15kW, rated capacity for 15kWh accumulator forming energy type energy-storage system and choose 5 volumes
Determine power for 1kW, rated capacity for 0.15kWh super capacitor forming power-type energy-storage system, ultimately form deployment cost
Optimum mixed energy storage system.
The control method of a kind of preferred mixed energy storage system deployment cost that the embodiment of the present invention three is provided, specifically describes
The control process of deployment cost when configuring to mixed energy storage system in the network system of wind-power electricity generation.Using the controlling party
Method, it is ensured that while mixed energy storage system has preferable power output smooth effect, it is ensured that configuration mixed energy storage system
Required deployment cost optimum, and then reduce the purpose of network system Installed capital cost.
Example IV
Fig. 4 is a kind of structural frames of the control device of mixed energy storage system deployment cost of the offer of the embodiment of the present invention four
Figure.The device is applicable to what deployment cost when building to mixed energy storage system in network system process of construction was planned
Situation, can be realized by way of hardware and/or software, and the planning system that can be typically integrated in for network system planning construction
In system.As shown in figure 4, the device includes：Data obtaining module 41 and targeted parameter value determining module 42.
Wherein, data obtaining module 41, for obtaining the objective cost letter needed for calculating mixed energy storage system deployment cost
The corresponding constraints of the several and objective cost function, used as the deployment cost Controlling model of the mixed energy storage system.
Targeted parameter value determining module 42, for for the configuration parameter in the deployment cost Controlling model, to described
Deployment cost Controlling model carries out calculating the deployment cost condition up to the mixed energy storage system is reached, to determine the configuration
The targeted parameter value of parameter.
In the present embodiment, the control device is obtained by data obtaining module 41 first and calculates mixed energy storage system configuration
Objective cost function needed for cost and the corresponding constraints of the objective cost function, used as the mixed energy storage system
Deployment cost Controlling model；Then by targeted parameter value determining module 42 for joining in the deployment cost Controlling model
Parameter is put, the deployment cost Controlling model is carried out the deployment cost condition up to the mixed energy storage system is reached is calculated,
To determine the targeted parameter value of the configuration parameter.
A kind of control device of mixed energy storage system deployment cost that the embodiment of the present invention four is provided, is considering mixing
In energy-storage system on the premise of the objective condition such as the energy density of energy storage device, service life, both ensured that mixed energy storage system had
There is preferable power output smooth effect, the deployment cost optimum needed for configuration mixed energy storage system is in turn ensure that, and then is reached
Reduce the purpose of network system Installed capital cost.
Further, the objective cost function is expressed as：
Wherein, CsumStore up for the mixing
Total deployment cost value of energy system, Cenergy(PE,EE) for energy type energy-storage system deployment cost, Cpower(PP,EP) it is power
The deployment cost of type energy-storage system, PE,EERepresent rated power (kW) and the rated capacity (kWh) of energy type energy-storage system respectively,
PP,EPRepresent rated power (kW) and the rated capacity (kWh) of power-type energy-storage system, and (P respectivelyE,EE,PP,EP) represent institute
State configuration parameter corresponding one group of parameter value, C in the deployment cost Controlling modeloperaterOnce fill for mixed energy storage system
Operating cost produced by electric discharge, n is the total degree that during mixed energy storage system discharge and recharge, its depth of discharge reaches set depth；
The constraints includes：The power constraint in physical constraint, the energy type energy-storage system in network system
With state-of-charge SOC constraint and the power constraint in the power-type energy-storage system and SOC constraint；The network system includes
Distributed power generation unit, mixed energy storage system and outside distribution region.
Further, the targeted parameter value determining module 42, specifically for：
For the configuration parameter in the deployment cost Controlling model, based on particle swarm optimization algorithm to the deployment cost
Controlling model carries out calculating the deployment cost condition up to the mixed energy storage system is reached, to determine the mesh of the configuration parameter
Mark parameter value.
Further, for the configuration parameter in the deployment cost Controlling model, based on particle swarm optimization algorithm to institute
Stating deployment cost Controlling model carries out calculating the deployment cost condition up to the mixed energy storage system is reached, to determine described joining
The targeted parameter value of parameter is put, including：
The value of the iteration variable is simultaneously initialized as 0 by a, setting iteration variable；B, determine the deployment cost control mould
Candidate's configuration parameter set of type, wherein, least one set candidate parameter of the candidate's configuration parameter set comprising the configuration parameter
Value；C, each group candidate parameter value for candidate's configuration parameter concentration determine corresponding renewal coefficient；D, be based on the target
Cost function, calculates candidate's configuration parameter and concentrates least one set candidate parameter to be worth corresponding total deployment cost value；E, determination
Minima at least one total deployment cost value, remembers that the minima is candidate's value at cost, and by candidate's cost
Value and corresponding candidate parameter value are deposited in setting caching；F, determine whether meet set deployment cost condition, if it is not, then
Execution step g；If so, then execution step h；G, the iteration variable is carried out from increase operation, and be based on the renewal coefficient
Concentrate corresponding candidate parameter value to be updated operation candidate's configuration parameter, new candidate's configuration parameter set is formed, it
Return to step c afterwards；H, determine described set caching in candidate's value at cost minima, by corresponding for minima candidate parameter
The targeted parameter value output being worth as the configuration parameter, and end loop operation.
On the basis of above-described embodiment, the candidate's configuration parameter set for determining the deployment cost Controlling model, bag
Include：
Setting with the generating output of the setting time interval collection distributed power generation unit, shape in sampling duration
Become to generate electricity and output power curve be designated as generating sampled data；Data spectrum analysis is carried out to the generating sampled data, respectively
Obtain the energy storage output power curve of the energy type energy-storage system and the power-type energy-storage system；Based on the energy type
Energy-storage system and the energy storage output power curve of the power-type energy-storage system, determine respectively corresponding rated operating range and
Rated capacity scope；Rated power span based on the energy type energy-storage system and the power-type energy-storage system and
Rated capacity span, determines least one set power-handling capability and corresponding rated capacity value respectively, forms the configuration ginseng
The least one set candidate parameter value of number；If it is corresponding about that the least one set candidate parameter value meets the objective cost function
Bundle condition, then add the candidate's configuration parameter for setting to concentrate by the least one set candidate parameter value.
Further, described data spectrum analysis is carried out to the generating sampled data, obtain respectively the energy type storage
Energy system and the energy storage output power curve of the power-type energy-storage system, including：
Fast Fourier transform is carried out to the generating sampled data and obtains result of spectrum analysis；Determine that the energy type is stored up
Corresponding energy optimum frequency band during energy system discharge and recharge, and corresponding power when determining the power-type energy-storage system discharge and recharge
Optimum frequency band；At least one frequency values for belonging to the energy optimum frequency band are determined in the result of spectrum analysis, and to institute
Stating the corresponding amplitude of at least one frequency values carries out inverse Fourier transform, obtains the energy storage output work of the energy type energy-storage system
Rate curve；At least one frequency values for belonging to the power optimum frequency band are determined in the result of spectrum analysis, and to described
The corresponding amplitude of at least one frequency values carries out inverse Fourier transform, obtains the energy storage output of the power-type energy-storage system
Curve.
On the basis of above-described embodiment, the deployment cost condition includes：The value of the iteration variable is more than setting threshold
Minima in value and/or at least one total deployment cost value is not more than setting and terminates threshold value.
Note, above are only presently preferred embodiments of the present invention and institute's application technology principle.It will be appreciated by those skilled in the art that
The invention is not restricted to specific embodiment described here, can carry out for a person skilled in the art various obvious changes,
Readjust and substitute without departing from protection scope of the present invention.Therefore, although by above example, the present invention is carried out
It is described in further detail, but the present invention is not limited only to above example, without departing from the inventive concept, also
Other Equivalent embodiments more can be included, and the scope of the present invention is determined by scope of the appended claims.
Claims (10)
1. a kind of control method of mixed energy storage system deployment cost, it is characterised in that include：
Acquisition calculates the objective cost function needed for mixed energy storage system deployment cost and the objective cost function is corresponding
Constraints, used as the deployment cost Controlling model of the mixed energy storage system；
For the configuration parameter in the deployment cost Controlling model, the deployment cost Controlling model is carried out calculating until reaching
To the deployment cost condition of the mixed energy storage system, to determine the targeted parameter value of the configuration parameter.
2. method according to claim 1, it is characterised in that：
The objective cost function is expressed as：
Wherein, CsumFor the hybrid energy-storing system
Total deployment cost value of system, Cenergy(PE,EE) for energy type energy-storage system deployment cost, Cpower(PP,EP) store up for power-type
The deployment cost of energy system, PE,EERepresent rated power (kW) and the rated capacity (kWh) of energy type energy-storage system, P respectivelyP,
EPRepresent rated power (kW) and the rated capacity (kWh) of power-type energy-storage system, and (P respectivelyE,EE,PP,EP) represent described
The corresponding one group of parameter value of configuration parameter described in deployment cost Controlling model, CoperaterFor discharge and recharge of mixed energy storage system
Produced operating cost, n is the total degree that during mixed energy storage system discharge and recharge, its depth of discharge reaches set depth；
The constraints includes：The power constraint in physical constraint, the energy type energy-storage system and lotus in network system
Power constraint in electricity condition SOC constraint and the power-type energy-storage system and SOC constraint；The network system includes distribution
Formula generating set, mixed energy storage system and outside distribution region.
3. method according to claim 2, it is characterised in that for the configuration ginseng in the deployment cost Controlling model
Number, carries out calculating the deployment cost condition up to the mixed energy storage system is reached to the deployment cost Controlling model, with true
The targeted parameter value of the fixed configuration parameter, including：
For the configuration parameter in the deployment cost Controlling model, based on particle swarm optimization algorithm, the deployment cost is controlled
Model carries out calculating the deployment cost condition up to the mixed energy storage system is reached, to determine the target ginseng of the configuration parameter
Numerical value.
4. method according to claim 3, it is characterised in that for the configuration ginseng in the deployment cost Controlling model
Number, is carried out calculating until reaching the mixed energy storage system to the deployment cost Controlling model based on particle swarm optimization algorithm
Deployment cost condition, to determine the targeted parameter value of the configuration parameter, including：
The value of the iteration variable is simultaneously initialized as 0 by a, setting iteration variable；
B, determine candidate's configuration parameter set of the deployment cost Controlling model, wherein, candidate's configuration parameter set includes institute
State the least one set candidate parameter value of configuration parameter；
C, each group candidate parameter value for candidate's configuration parameter concentration determine corresponding renewal coefficient；
D, the objective cost function is based on, calculates candidate's configuration parameter and concentrate least one set candidate parameter value corresponding total
Deployment cost value；
E, the minima for determining in described at least one total deployment cost value, remember that the minima is candidate's value at cost, and by institute
State candidate's value at cost and corresponding candidate parameter value is deposited in setting caching；
F, determine whether meet set deployment cost condition, if it is not, then execution step g；If so, then execution step h；
G, the iteration variable is carried out from increase operation, and based on described renewal coefficient to candidate's configuration parameter concentrate phase
The candidate parameter value that answers is updated operation, forms new candidate's configuration parameter set, afterwards return to step c；
H, determine described set caching in candidate's value at cost minima, using corresponding for minima candidate parameter value as institute
State the targeted parameter value output of configuration parameter, and end loop operation.
5. method according to claim 4, it is characterised in that the candidate of the determination deployment cost Controlling model joins
Parameter set is put, including：
Setting with the generating output of the setting time interval collection distributed power generation unit in sampling duration, formed and send out
Electric output power curve is simultaneously designated as generating sampled data；
Data spectrum analysis is carried out to the generating sampled data, obtains the energy type energy-storage system and the power respectively
The energy storage output power curve of type energy-storage system；
Based on the energy type energy-storage system and the energy storage output power curve of the power-type energy-storage system, phase is determined respectively
The rated operating range that answers and rated capacity scope；
Rated power span and rated capacity based on the energy type energy-storage system and the power-type energy-storage system
Span, determines least one set power-handling capability and corresponding rated capacity value respectively, forms the configuration parameter at least
One group of candidate parameter value；
If the least one set candidate parameter value meets the corresponding constraints of the objective cost function, described in general at least
One group of candidate parameter value adds the candidate's configuration parameter for setting to concentrate.
6. method according to claim 5, it is characterised in that described data spectrum is carried out to the generating sampled data divide
Analysis, obtains the energy storage output power curve of the energy type energy-storage system and the power-type energy-storage system respectively, including：
Fast Fourier transform is carried out to the generating sampled data and obtains result of spectrum analysis；
Determine corresponding energy optimum frequency band during the energy type energy-storage system discharge and recharge, and determine the power-type energy storage system
Corresponding power optimum frequency band during system discharge and recharge；
Determine at least one frequency values for belonging to the energy optimum frequency band in the result of spectrum analysis, and to described at least
The corresponding amplitude of one frequency values carries out inverse Fourier transform, obtains the energy storage output song of the energy type energy-storage system
Line；
Determine at least one frequency values for belonging to the power optimum frequency band in the result of spectrum analysis, and to described at least
The corresponding amplitude of one frequency values carries out inverse Fourier transform, obtains the energy storage output song of the power-type energy-storage system
Line.
7. according to the arbitrary described method of claim 4-6, it is characterised in that the deployment cost condition includes：The iteration
The value of variable is not more than setting more than the minima in given threshold and/or at least one total deployment cost value and terminates threshold
Value.
8. a kind of control device of mixed energy storage system deployment cost, it is characterised in that include：
Data obtaining module, for obtaining objective cost function and the mesh needed for calculating mixed energy storage system deployment cost
The corresponding constraints of mark cost function, used as the deployment cost Controlling model of the mixed energy storage system；
Targeted parameter value determining module, for for the configuration parameter in the deployment cost Controlling model, being configured to described
This Controlling model carries out calculating the deployment cost condition up to the mixed energy storage system is reached, to determine the configuration parameter
Targeted parameter value.
9. method according to claim 1, it is characterised in that：
The objective cost function is expressed as：
Wherein, CsumFor the hybrid energy-storing system
Total deployment cost value of system, Cenergy(PE,EE) for energy type energy-storage system deployment cost, Cpower(PP,EP) store up for power-type
The deployment cost of energy system, PE,EERepresent rated power (kW) and the rated capacity (kWh) of energy type energy-storage system, P respectivelyP,
EPRepresent rated power (kW) and the rated capacity (kWh) of power-type energy-storage system, and (P respectivelyE,EE,PP,EP) represent described in join
Put parameter corresponding one group of parameter value, C in the deployment cost Controlling modeloperaterFor discharge and recharge of mixed energy storage system
Produced operating cost, n is the total degree that during mixed energy storage system discharge and recharge, its depth of discharge reaches set depth；
The constraints includes：The power constraint in physical constraint, the energy type energy-storage system and lotus in network system
Power constraint in electricity condition SOC constraint and the power-type energy-storage system and SOC constraint；The network system includes distribution
Formula generating set, mixed energy storage system and outside distribution region.
10. device according to claim 9, it is characterised in that the targeted parameter value determining module, specifically for：
For the configuration parameter in the deployment cost Controlling model, based on particle swarm optimization algorithm, the deployment cost is controlled
Model carries out calculating the deployment cost condition up to the mixed energy storage system is reached, to determine the target ginseng of the configuration parameter
Numerical value.
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| CN106998072A (en) * | 2017-05-15 | 2017-08-01 | 国网江苏省电力公司电力科学研究院 | A kind of mixed energy storage system capacity configuration optimizing method for optimizing operation towards power distribution network |
| CN108667057A (en) * | 2018-05-29 | 2018-10-16 | 天津大学 | One kind accessing power distribution network multiple target electric energy administering method for high power density distributed photovoltaic |
| CN109191026A (en) * | 2018-11-09 | 2019-01-11 | 浙江大学 | A kind of energy conversion device service life Explore of Unified Management Ideas based on simulated annealing |
| CN109191026B (en) * | 2018-11-09 | 2022-03-25 | 浙江大学 | Simulated annealing algorithm-based unified management method for service life of energy conversion device |
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Application publication date: 20170222 |