CN109659963A - A kind of distributed energy storage participates in the control method and device of power grid peak load shifting - Google Patents
A kind of distributed energy storage participates in the control method and device of power grid peak load shifting Download PDFInfo
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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]
Abstract
A kind of distributed energy storage participates in the control method and device of power grid peak load shifting.This application involves a kind of power distribution network battery energy storage Optimal Configuration Method of power grid peak load shifting and devices, for position of the battery energy storage in power distribution network, the optimization allocation of capacity, to reduce, be reduced to starting point, it establishes using minimum voltage offset, minimum active power loss as the energy storage system capacity of target, position optimization allocation models, finally realizes and prepare optimal battery energy storage capacity for each node or branch.
Description
Technical field
The application belongs to energy internet area technical field, participates in power grid peak clipping more particularly, to a kind of distributed energy storage
Valley-fill control method and device.
Background technique
Energy storage technology is supported as the important technology of construction of energy internet, is increasingly becoming multiple national energy scientific and technical innovations
The focus supported with industry.Energy storage promotes the essence of power network development mode to become by way of increasing time variable to energy
Leather.Energy storage technology can be widely used in Generation Side, the power grid of electric system as the key technology in global energy internet
Side and user side for sufficiently consumption clean energy resource, ensure power grid security, stabilize load fluctuation and distributed generation resource is grid-connected etc. provides
Support.First is that the fm capacity of coal unit can be improved in conjunction with conventional electric power generation technology in energy storage in Generation Side, improve coal-fired
The economic operation level of unit, the fluctuation of smooth new energy play the new energy of auxiliary to optimize the power supply architecture of whole system
The key effect of source electricity generation grid-connecting, research application is more at this stage.Randomness, the unstability of generation of electricity by new energy are unfavorable for
Large-scale electricity generation grid-connecting, and match energy storage device and carry out flat volatility, auxiliary online then provides surely for electric system
Fixed, safety power supply environment, and achieve better effects.Second is that power energy storage can enrich peak load regulation network regulation in grid side
Means improve power grid power supply reliability, improve power quality, delay electric grid investment, improve grid equipment utilization rate, utilize power
Energy-storage system can quickly demand of the support load to power supply is contributed be applied in power grid by type energy-storage system, can be in peak-trough electricity
Obtain the high-incidence income of low storage under the mechanism of valence, while can also be from delaying equipment for power transmission and distribution upgrading, reduce electric line
Network loss, reduction electric network reliability cost etc. benefit.Its generate economy income be it is various, be not limited only to directly
Income is planned so carrying out analysis spininess to the main application of energy-storage battery to its economy by target of investment subject
Analysis has realistic meaning.Third is that energy-storage system is conducive to power consumer and is actively engaged in operation of power networks in user side, promote to use
The reduction of energy cost can exert advantages of oneself since battery energy storage system has quick-reaction capability in quick adjust,
Therefore it is installed in user side, for adjusting load, offer uninterrupted power supply etc., the focus studied at present still concentrates on storing up
Energy system adjusts load bring electricity charge benefit, offer uninterrupted power supply, reduction customer outage cost etc. and is accordingly ground
Study carefully.Therefore, load when peak can be effectively reduced there is an urgent need to one kind, increased the load of low ebb, guaranteed the steady of load, subtract
The start and stop of small unit, while the investment of system installed capacity can also be delayed, it is also beneficial to reduce the electric energy on transformer and route
Loss, achievees the purpose that saving energy and decreasing loss control strategy and method.
Summary of the invention
The technical problem to be solved by the present invention is to solve deficiency in the prior art, to provide a kind of power grid peak clipping
Valley-fill power distribution network battery energy storage Optimal Configuration Method and device.
The technical solution adopted by the present invention to solve the technical problems is:
A kind of power distribution network battery energy storage Optimal Configuration Method of power grid peak load shifting, comprising the following steps:
S1: each node voltage and/or the active loss of transmission line of electricity are obtained；
S2: building is based on variation and based on the optimization object function of active loss；
S3: setting constraint condition simultaneously solves objective function；
S4: determine that the battery energy storage of access node or branch holds according to the voltage offset values of solution and active loss value
Amount.
Preferably, the power distribution network battery energy storage Optimal Configuration Method of power grid peak load shifting of the invention,
In S2 step,
Objective function based on variation are as follows:
Wherein T is the simulation time of setting, and Nbus is system node number, VjtFor the width of the voltage of jth node t moment
Value, VnReference voltage；
Based on active loss objective function are as follows:
Wherein Nbranch is system branch number,Indicate the active loss of i-th article of branch t moment.
Preferably, the power distribution network battery energy storage Optimal Configuration Method of power grid peak load shifting of the invention, in S3 step,
Constraint condition one,
P in formulai, QiIt is the active power and reactive power for being injected into node i, G respectivelyij、BijIt is node i and section respectively
Conductance and susceptance between point j, the i-th node voltage Vi=ei+jfi, jth node voltage Vj=ej+jfj, f in the two formulaj
Preceding j is imaginary unit；
Constraint condition two,
Node voltage range constraint Vmin≤Vjt≤Vmax；
Active power, which is positive, constrains Pi≥0；
Battery energy storage system charge and discharge limitation:
Wherein Pch、PdisRespectively the charge power of energy-storage system and put
Electrical power, taAnd tbFor charge and discharge duration bound；
Branch current limits I and is less than 0.1IMax；
I in formulaMaxRefer to the maximum current value that the circuit allows.
Preferably, the power distribution network battery energy storage Optimal Configuration Method of power grid peak load shifting of the invention uses in S3 step
PSO Algorithm objective function.
Preferably, the power distribution network battery energy storage Optimal Configuration Method of power grid peak load shifting of the invention in S4 step, obtains
To after optimal active loss value, battery stores up the maximum value that capacity configuration is the electricity consumption in all charge and discharge time limits.
The present invention also provides a kind of power distribution network battery energy storages of power grid peak load shifting to distribute device rationally,
Data acquisition module, for obtaining each node voltage and/or the active loss of transmission line of electricity；
Model construction module, for constructing based on variation and based on the optimization object function of active loss；
Model solution module, for constraint condition to be arranged and solves objective function；
Stored energy capacitance configuration module, for determining access jth node according to the voltage offset values and active loss value of solution
Or the battery energy storage capacity of i-th branch.
Preferably, the power distribution network battery energy storage of power grid peak load shifting of the invention distributes device rationally,
In model construction module,
Objective function based on variation are as follows:
Wherein T is the simulation time of setting, and Nbus is system node number, VjtFor the width of the voltage of jth node t moment
Value, VnReference voltage,
Based on active loss objective function are as follows:
Wherein Nbranch is system branch number,Indicate the active loss of i-th article of branch t moment；
Preferably, the power distribution network battery energy storage of power grid peak load shifting of the invention distributes device, model solution module rationally
In,
Constraint condition one,
Constraint condition one,
P in formulai, QiIt is the active power and reactive power for being injected into node i, G respectivelyij、BijIt is node i and section respectively
Conductance and susceptance between point j, the i-th node voltage Vi=ei+jfi, jth node voltage Vj=ej+jfj；
Constraint condition two,
Node voltage range constraint Vmin≤Vjt≤Vmax；
Active power, which is positive, constrains Pi≥0；
Battery energy storage system charge and discharge limitation:
Wherein Pch、PdisRespectively the charge power of energy-storage system and put
Electrical power, taAnd tbFor charge and discharge duration bound；
Branch current limits I and is less than 0.1IMax；
I in formulaMaxRefer to the maximum current value that the circuit allows.
Preferably, the power distribution network battery energy storage of power grid peak load shifting of the invention distributes device, model solution module rationally
It is middle to use PSO Algorithm objective function.
Preferably, the power distribution network battery energy storage of power grid peak load shifting of the invention distributes device, stored energy capacitance configuration rationally
In module, after obtaining optimal active loss value, battery stores up the maximum value that capacity configuration is the electricity consumption in a hour.
The beneficial effects of the present invention are:
The power distribution network battery energy storage Optimal Configuration Method and device of power grid peak load shifting of the invention, exist for battery energy storage
The optimization allocation of position, capacity in power distribution network is established with minimum voltage offset, most with reducing, being reduced to starting point
Small active power loss is energy storage system capacity, the position optimization allocation models of target, is finally realized as each node or branch
Prepare optimal battery energy storage capacity in road.
By optimizing the solution of allocation models based on particle swarm algorithm, the capacity configuration optimal solution of energy storage is obtained.
Detailed description of the invention
The technical solution of the application is further illustrated with reference to the accompanying drawings and examples.
Fig. 1 is particle swarm algorithm flow chart；
Fig. 2 is the flow chart that configuration operation is optimized using particle swarm algorithm；
Fig. 3 is European standard low pressure distribution net work structure figure in effect example；
Fig. 4 is energy storage power curve in effect example；
Fig. 5 is active loss comparison curves in effect example；
Fig. 6 is the flow chart of the power distribution network battery energy storage Optimal Configuration Method of power grid peak load shifting；
Fig. 7 is that the power distribution network battery energy storage of power grid peak load shifting distributes the structural block diagram of device rationally.
Specific embodiment
It should be noted that in the absence of conflict, the features in the embodiments and the embodiments of the present application can phase
Mutually combination.
It is described in detail the technical solution of the application below with reference to the accompanying drawings and in conjunction with the embodiments.Embodiment
The present embodiment provides a kind of power distribution network battery energy storage Optimal Configuration Methods of power grid peak load shifting, including following step
It is rapid:
S1: each node voltage and/or the active loss of transmission line of electricity are obtained；
S2: building is based on variation and based on the optimization object function of active loss；
S3: setting constraint condition simultaneously solves objective function；
S4: determine that the battery energy storage of access node or branch holds according to the voltage offset values of solution and active loss value
Amount.
In S2 step,
Objective function based on variation are as follows:
Wherein T is the simulation time of setting, and Nbus is system node number, VjtFor the width of the voltage of jth node t moment
Value, VnReference voltage；
Based on active loss objective function are as follows:
Wherein Nbranch is system branch number,Indicate the active loss of i-th article of branch t moment.
In S3 step,
Constraint condition one,
P in formulai, QiIt is the active power and reactive power for being injected into node i, G respectivelyij、BijIt is node i and section respectively
Conductance and susceptance between point j, the i-th node voltage Vi=ei+jfi, jth node voltage Vj=ej+jfj；
Constraint condition two,
Node voltage range constraint Vmin≤Vjt≤Vmax；
Active power, which is positive, constrains Pi≥0；
Battery energy storage system charge and discharge limitation:
Wherein Pch、PdisRespectively the charge power of energy-storage system and put
Electrical power, taAnd tbFor charge and discharge duration bound；
Branch current limits I and is less than 0.1IMax；
I in formulaMaxRefer to the maximum current value that the circuit allows.
PSO Algorithm objective function is used in S3 step.
In S4 step, after obtaining optimal active loss value, it is the use in all charge and discharge time limits that battery, which stores up capacity configuration,
The maximum value of electricity, can calculate separately each hour 24 hours one day electricity consumption, and battery storage capacity takes maximum value therein.
The power distribution network battery energy storage that the present embodiment also provides a kind of power grid peak load shifting distributes device rationally, comprising:
Data acquisition module, for obtaining each node voltage and/or the active loss of transmission line of electricity；
Model construction module, for constructing based on variation and based on the optimization object function of active loss；
Model solution module, for constraint condition to be arranged and solves objective function:
Stored energy capacitance configuration module, for determining access jth node according to the voltage offset values and active loss value of solution
Or the battery energy storage capacity of i-th branch.
In model construction module,
Objective function based on variation are as follows:
Wherein T is the simulation time of setting, and Nbus is system node number, VjtFor the width of the voltage of jth node t moment
Value, VnReference voltage,
Based on active loss objective function are as follows:
Wherein Nbranch is system branch number,Indicate the active loss of i-th article of branch t moment；
In model solution module,
Constraint condition one,
Constraint condition one,
P in formulai, QiIt is the active power and reactive power for being injected into node i, G respectivelyij、BijIt is node i and section respectively
Conductance and susceptance between point j, the i-th node voltage Vi=ei+jfi, jth node voltage Vj=ej+jfj；
Constraint condition two,
Node voltage range constraint Vmin≤Vjt≤Vmax；
Active power, which is positive, constrains Pi≥0；
Battery energy storage system charge and discharge limitation:
Wherein Pch、PdisRespectively the charge power of energy-storage system and put
Electrical power, taAnd tbFor charge and discharge duration bound；
Branch current limits I and is less than 0.1IMax；
I in formulaMaxRefer to the maximum current value that the circuit allows.
PSO Algorithm objective function is used in model solution module.
In stored energy capacitance configuration module, after obtaining optimal active loss value, it is in a hour that battery, which stores up capacity configuration,
Electricity consumption maximum value, can calculate separately each hour 24 hours one day electricity consumption, battery storage capacity take it is therein most
Big value.
The theory deduction of the application is as follows:
(1) variation objective function
The objective function configured using square minimum of the difference of node voltage and a reference value as energy storage, guarantees each node
Voltage as close as voltage rating, maintain the voltage level of power distribution network.
Wherein T is the simulation time of setting, and Nbus is system node number, VjtFor the width of the voltage of jth node t moment
Value, VnReference voltage；
(2) active loss objective function
Energy-storage system accesses power distribution network, and voltage class is not high, and the trend in feeder line can cause via net loss, energy-storage system
Access can be played the role of reducing via net loss, that is, play lifting node voltage, and bring certain economic benefit.
Here the objective function configured using power distribution network active loss minimum as energy storage.
Wherein Nbranch is system branch number,Indicate the active loss of i-th article of branch t moment.
Constraint condition:
(1) equality constraint:
P in formula, Q refer to the power for being injected into node i, Gij、BijRefer to the conductance between node i and node j
And susceptance.
(2) inequality constraints condition
Node voltage range constraint Vmin≤Vjt≤Vmax (5)
Active power, which is positive, constrains P >=0 (6)
Battery energy storage system charge and discharge limitation
Branch current limits I < < IMax (8)
I in formulaMaxRefer to the maximum current value that the circuit allows
Particle group optimizing placement algorithm -- particle swarm optimization algorithm
Particle swarm optimization algorithm (particle swarm optimization, PSO) is a kind of searching at random based on population
Rope algorithm has many advantages, such as that easy to operate, search efficiency is high and convergence is quick, is suitable for solving nonlinear constrained optimization model.
Population optimization algorithm is containing there are two elements: suboptimization particle and global optimization's particle.Local optimum
The effect of value and global optimum is that particle is instructed to adjust the flying speed of oneself and the position to be gone, after n step search
Reach near defined globally optimal solution, operation finishes.XiFor the position vector of i-th of particle, the fitness of setting is utilized
Function F (Pbesti) the current adaptive value of particle i is calculated, the excellent or bad of current location is judged according to calculated result；ViFor particle i
Velocity vector, determine the moving distance of the particle；PbestiThe optimum position obtained for particle i current search；PGestIt is all
The optimum position that population current search obtains.After this particle i proceeds to kth time flight, by the fitness letter of this search
Numerical value is compared with defined fitness function value, the more position of new particle i.F(PGest) effect of function is to record every suboptimum
The fitness function value of position carries out continuous result update.
Obtain the P of particlebestiAnd PGestAfterwards, each particle can follow the two extreme values according to formula (9) and (10)
Update respective speed and position.
Wherein population more new formula (9) is divided into three parts, and what first part embodied is the current speed of particle to lower a moment
The influence of speed can balance part and global search capability；Second partEmbody oneself of particle
My elaborative faculty encourages particle to fly to the history optimal location of oneself；Part IIIWhat is embodied is grain
Cognition of the son to locating group, realizes interparticle information sharing, and each particle is mobile to the optimal location of population according to it.
The particle renewal speed formula obtained after inertia weight factor W is added are as follows:
K in formula is the number of iteration；c1And c2It is accelerated factor, r1And r2It is the random value in [0,1] section.
The solution of particle swarm optimization algorithm
The solution procedure of the particle swarm algorithm of the factor containing inertia weight is as follows:
(1) initialization of each particle position and speed in population is carried out；
(2) fitness function value of particle is evaluated, then the fitness of current particle and position is stored in
PbestiAmong, PbestiIn possess adaptive optimal control angle value individual particles position and fitness be stored in PGestAmong:
(3) according to formula (9) and 10) carry out particle position and speed update, k=k+1；
(4) fitness function value instantly for recalculating each particle in population, judges PGestWhether need to carry out more
Newly；
(5) by instantly all Pbesti, with PGestExpansion is compared, and new P is obtainedGest；
(6) if reaching the operational precision of setting or the number of iterations has reached the upper limit, operation stops, and exports corresponding result:
Otherwise step (3) is returned to continue to search for；
The flow chart of PSO Algorithm process is as shown in Figure 1:
Optimal Allocation Model solution-constraint condition is crossed the border processing
In the solution procedure of particle swarm algorithm, equality constraint can carry out operation according to power flow equation, in iterative process
Convergence do not influence.Inequality constraints is then different, these inequality may go out solution in the iterative process of particle optimizing
The constraint in space, obtains trivial solution.Once this occurs, convergence will be unfolded in the current position of particle in algorithm, differ
The constraint of formula is no longer worked, so that the result of optimization algorithm be made to become without in all senses.
Therefore the particle to cross the border in population must be handled, the method for use is that " penalty factor " is added.Specifically
Process be: by punishing that those jump out the particle of constraint condition, making the adaptation of the particle to penalty term is added in objective function
Value is deteriorated, and algorithm is avoided to be restrained in the current position of the particle.It needs to introduce in the objective function of this paper particle swarm algorithm
Penalty factor have: energy-storage system maximum capacity, each node voltage and branch current.
The solution distributed rationally
Step 1: initialization.Start the parameter initialization in particle swarm algorithm, the interior scale ginseng for having population for including
Number m, the dimension n of particle, accelerated factor c1And c2, the inertia weight factor most value Wmax and Wmin and algorithm setting maximum change
Generation number K；
Step 2: the loading of electricity distribution network model.Carry out the input of the topological structure and load power curve for 24 hours of power distribution network；
Step 3: carrying out initial Load flow calculation, obtain the objective functions such as variation, via net loss and correspond to numerical value；
Step 4: numerical value is corresponded to objective function and is judged, determines whether need to access energy-storage system, if so, into
Enter step 5, otherwise enters step 8；
Step 5: the access point selection of energy-storage system；
Step 6: the Load flow calculation after carrying out access energy-storage system obtains each fitness function numerical value；
Step 7: judging whether the fitness function calculated reaches requirement, if it is entering step 8, otherwise to energy storage system
System parameter enters step 6 after updating；
Step 8: the optimization for completing energy storage power curve calculates, and exports stored energy capacitance configuration result, operation knot by calculating
Beam.
Effect example
It is example, branch and Node distribution such as Fig. 3 of the system that the present embodiment, which selects European standard low-voltage distribution pessimistic concurrency control,
Shown, table 1 is the line parameter circuit value of all types of transmission lines in model.
1 European standard voltage distribution pessimistic concurrency control line parameter circuit value of table
OL:Overheadline, UL:Undergroundline, SCServiceconnection
(1).Ohmicresistanceat50℃conductortemperature
(2): Ohmicresistanceattemperature90 DEG C
forphaseand20"Cfortheneutral
(3): Ohmicresistanceattemperature70 DEG C of forallconductors
In stored energy capacitance Optimal Allocation Model, the punishment of energy-storage system maximum capacity, node voltage and branch current because
Son all values are 1000: population scale is set as 50, and the dimensionality of particle for accessing energy storage is 24, and accelerated factor Cl and C2 are set
It is 2, maximum number of iterations is set as 2000.In conjunction with topological structure and the load power curve for 24 hours of power distribution network, Unified Power Flow is established
Equation optimizes to obtain 24 one-hour rating curves of energy storage as shown in Fig. 4 according to trend operation result.
After obtaining optimal power curve, the capacity configuration of battery energy storage, energy storage power curve are obtained using following company
Control target as current transformer simultaneously.
C in formulastFor battery energy storage capacity, PstFor power, this formula is used to calculate the electricity consumption in one day in each hour
Maximum value, t1To t2When being 0 to 1, t2To t3When being 1 to 2 ..., t11To t12When being 23 to 24.
(1) when target minimum with via net loss, access node selects N10, and the capacity configuration of battery energy storage is calculated
For 327.2736kWh.The comparison of active loss optimization front and back as shown in figure 5, before optimization total active loss be 5.3876MW, it is excellent
Total active loss is 5.3721MW after change.
(2) when target minimum with variation, access node selects N101, and the capacity configuration of battery energy storage is calculated
For 283.1721kWh, the variation for optimizing front and back compares as shown in Fig. 6, and the variation before optimization is 1.9577p.u.2,
Variation after optimization is 1.9476p.u.2。
It is enlightenment with the above-mentioned desirable embodiment according to the application, through the above description, relevant staff is complete
Full various changes and amendments can be carried out in the range of without departing from this item application technical idea.The technology of this item application
Property range is not limited to the contents of the specification, it is necessary to which the technical scope thereof is determined according to the scope of the claim.
Claims (10)
1. a kind of power distribution network battery energy storage Optimal Configuration Method of power grid peak load shifting, which comprises the following steps:
S1: each node voltage and/or the active loss of transmission line of electricity are obtained；
S2: building is based on variation and based on the optimization object function of active loss；
S3: setting constraint condition simultaneously solves objective function；
S4: the battery energy storage capacity of access node or branch is determined according to the voltage offset values of solution and active loss value.
2. the power distribution network battery energy storage Optimal Configuration Method of power grid peak load shifting according to claim 1, which is characterized in that
In S2 step,
Objective function based on variation are as follows:
Wherein T is the simulation time of setting, and Nbus is system node number, VjtFor the amplitude of the voltage of jth node t moment, Vn
Reference voltage；
Based on active loss objective function are as follows:
Wherein Nbranch is system branch number,Indicate the active loss of i-th article of branch t moment.
3. the power distribution network battery energy storage Optimal Configuration Method of power grid peak load shifting according to claim 1, which is characterized in that
In S3 step,
Constraint condition one,
P in formulai, QiIt is the active power and reactive power for being injected into node i, G respectivelyij、BijIt is node i and node j respectively
Between conductance and susceptance, the i-th node voltage Vi=ei+jfi, jth node voltage Vj=ej+jfj；
Constraint condition two,
Node voltage range constraint Vmin≤Vjt≤Vmax；
Active power, which is positive, constrains Pi≥0；
Battery energy storage system charge and discharge limitation:
Wherein Pch、PdisThe respectively charge power of energy-storage system and electric discharge function
Rate, taAnd tbFor charge and discharge duration bound；
Branch current limits I and is less than 0.1IMax；
I in formulaMaxRefer to the maximum current value that the circuit allows.
4. the power distribution network battery energy storage Optimal Configuration Method of power grid peak load shifting according to claim 1, which is characterized in that
PSO Algorithm objective function is used in S3 step.
5. the power distribution network battery energy storage Optimal Configuration Method of power grid peak load shifting according to claim 1-4,
It is characterized in that, in S4 step, after obtaining optimal active loss value, it is the use in all charge and discharge time limits that battery, which stores up capacity configuration,
The maximum value of electricity.
6. a kind of power distribution network battery energy storage of power grid peak load shifting distributes device rationally characterized by comprising
Data acquisition module, for obtaining each node voltage and/or the active loss of transmission line of electricity；
Model construction module, for constructing based on variation and based on the optimization object function of active loss；
Model solution module, for constraint condition to be arranged and solves objective function；
Stored energy capacitance configuration module, for according to the voltage offset values and active loss value of solution determine access jth node or
The battery energy storage capacity of i-th branch.
7. the power distribution network battery energy storage of power grid peak load shifting according to claim 6 distributes device rationally, which is characterized in that
In model construction module,
Objective function based on variation are as follows:
Wherein T is the simulation time of setting, and Nbus is system node number, VjtFor the amplitude of the voltage of jth node t moment, Vn
Reference voltage,
Based on active loss objective function are as follows:
Wherein Nbranch is system branch number,Indicate the active loss of i-th article of branch t moment.
8. the power distribution network battery energy storage of power grid peak load shifting according to claim 6 distributes device rationally, which is characterized in that
In model solution module,
Constraint condition one,
Constraint condition one,
P in formulai, QiIt is the active power and reactive power for being injected into node i, G respectivelyij、BijIt is node i and node j respectively
Between conductance and susceptance, the i-th node voltage Vi=ei+jfi, jth node voltage Vj=ej+jfj；
Constraint condition two,
Node voltage range constraint Vmin≤Vjt≤Vmax；
Active power, which is positive, constrains Pi≥0；
Battery energy storage system charge and discharge limitation:
Wherein Pch、PdisThe respectively charge power of energy-storage system and electric discharge function
Rate, taAnd tbFor charge and discharge duration bound；
Branch current limits I and is less than 0.1IMax；
I in formulaMaxRefer to the maximum current value that the circuit allows.
9. the power distribution network battery energy storage of power grid peak load shifting according to claim 6 distributes device rationally, which is characterized in that
PSO Algorithm objective function is used in model solution module.
10. device is distributed rationally according to the power distribution network battery energy storage of the described in any item power grid peak load shiftings of claim 6-9,
It is characterized in that, in stored energy capacitance configuration module, after obtaining optimal active loss value, it is in a hour that battery, which stores up capacity configuration,
Electricity consumption maximum value.
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112909930A (en) * | 2021-01-29 | 2021-06-04 | 国网河南省电力公司经济技术研究院 | Energy storage system optimal configuration method for stabilizing fluctuation of hybrid power distribution network |
| CN112994027A (en) * | 2021-02-03 | 2021-06-18 | 国网河南省电力公司电力科学研究院 | Method and device for configuring energy storage capacity under secondary reactive voltage control strategy |
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| CN113054674B (en) * | 2021-03-17 | 2023-05-23 | 云南电网有限责任公司电力科学研究院 | Control method and control system based on distributed small hydropower station combined energy storage |
| CN113344273A (en) * | 2021-06-08 | 2021-09-03 | 中国农业大学 | Building energy consumption based method and system for adjusting and optimizing peak-valley difference of regional distribution network |
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| CN116822107A (en) * | 2023-08-30 | 2023-09-29 | 国网江苏省电力有限公司镇江供电分公司 | Distributed new energy power distribution network configuration optimization method and system |
| CN116822107B (en) * | 2023-08-30 | 2023-11-21 | 国网江苏省电力有限公司镇江供电分公司 | Distributed new energy power distribution network configuration optimization method and system |
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