CN108695868A - Power distribution network energy storage addressing constant volume method based on electric power electric transformer - Google Patents
Power distribution network energy storage addressing constant volume method based on electric power electric transformer Download PDFInfo
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- CN108695868A CN108695868A CN201810667774.8A CN201810667774A CN108695868A CN 108695868 A CN108695868 A CN 108695868A CN 201810667774 A CN201810667774 A CN 201810667774A CN 108695868 A CN108695868 A CN 108695868A
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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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- Charge And Discharge Circuits For Batteries Or The Like (AREA)
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Abstract
A kind of energy storage addressing constant volume method in the power distribution network based on electric power electric transformer, initially set up the dual-layer optimization allocation models of energy storage in the power distribution network based on electric power electric transformer, optimize the optimal capacity for acquiring energy storage device and the energy storage output under optimal capacity and dominant eigenvalues by internal layer capacity, and then object function related with via net loss in outer layer position optimization is obtained by Load flow calculation, energy storage device optimal location, the i.e. port position of place PET are obtained finally by particle swarm optimization algorithm.The present invention can significantly reduce whole network loss, while improve operation of power networks net profit.
Description
Technical field
It is specifically a kind of based on electric power electric transformer the present invention relates to a kind of technology in Distribution system design field
Energy storage addressing constant volume method in power distribution network.
Background technology
The friendly connection of existing micro-capacitance sensor and its regenerative resource and power distribution network utilizes the power regulation hand of energy storage device
Industrial frequency AC electric rectification is direct current by the power dividing function of section and electric power electric transformer, electric power electric transformer (PET),
Then inversion is high-frequency alternating current, and the transformation of voltage and current is realized with high frequency transformer, finally converts high-frequency alternating current to work
Frequency alternating current and direct current;The characteristics of energy-storage system adjusts and has both for accumulation of energy power by its fast power, smooth intermittent
Energy power swing, peak load shifting improve quality of voltage and have all played huge effect in terms of providing stand-by power supply.
When using accumulator as energy-storage system, the capacity configuration of accumulator is very big on photovoltaic generation influence, and Capacity Selection obtains too
Greatly, investment can not only be increased, battery can also be chronically at the state of undercharge, influence using effect and the service life of energy storage, no
It can preferably realize its economy;When Capacity Selection is too small, photovoltaic system cannot fully realize economic benefit, and the power supply of power grid
Reliability reduces.
Invention content
The present invention is directed to deficiencies of the prior art, proposes in a kind of power distribution network based on electric power electric transformer
Energy storage addressing constant volume method.
The present invention is achieved by the following technical solutions:
The present invention initially sets up the dual-layer optimization allocation models of energy storage in the power distribution network based on electric power electric transformer, passes through
Internal layer capacity optimizes the optimal capacity for acquiring energy storage device and the energy storage output under optimal capacity and dominant eigenvalues, in turn
Object function related with via net loss in outer layer position optimization is obtained by Load flow calculation, finally by particle swarm optimization algorithm
Obtain energy storage device optimal location, the i.e. port position of place PET.
The dual-layer optimization allocation models includes:Realize that energy storage constant volume and outer layer optimization part are real in internal layer optimization part
Existing energy storage addressing, wherein:Internal layer optimization part with the minimum object function of purchases strategies, outer layer optimization part with via net loss and
Electric power electric transformer port loss is object function.
The via net loss includes:Line loss and electric power electric transformer port (PET) loss.
1. line loss refers to:The calculation formula consumed is lost in distribution lineWherein:PlossIt is
Via net loss, PiIt is the injection active power of node i, QiIt is the injection reactive power of node i, ViIt is the voltage magnitude of node i,
RijIt is the resistance of circuit ij.
2. PET losses refer to:Work as Pgrid>0, then the ports 10kV input power, the ports 380V output power, then port loss
ForWherein:η is operational efficiency;β is load factor.
The energy storage device optimal location obtains in the following manner:
1) initialization is the position and speed parameter of the population of N with population size, and the value of N is preferably 20;
2) target function value of each particle in population is calculated, i.e. outer layer optimization part neutralizes the related target of via net loss
Function;
3) via net loss to each particle under different location is preferentially updated;
4) by all particles are lived through in the via net loss of each particle desired positions and population desired positions
Via net loss compares and preferentially updates;
5) corresponding position is lost with particle oneself history optimal network and corresponding position is lost in particle entirety optimal network
It sets and the position and speed parameter of particle is updated, when having reached greatest iteration algebraically, then obtain optimum optimization scheme;Otherwise
Return to step 2) continue to optimize.
The internal layer optimization part refers to:Using purchases strategies as object function, i.e.,
Its constraints includes:
1) power-balance constraint Pgrid=Paggregate-Pbattery=Pload-Ppv-Pbattery, wherein:PgridIt is inputted for power grid
The power of microgrid;PaggregateFor net load power;PpvFor the output of photovoltaic generation;PloadFor load power;PbatteryFor energy storage
The output of system;
2) energy storage power constraint Pbattery≤Pbatterymax;
3) self-balancing rate constrains:Power distribution network is connected with bulk power grid, and certain electric power support can be provided by bulk power grid.It will match
Power grid is in some cycles, and the workload demand ratio being met by by itself distributed generation resource is defined as self-balancing rate, specifically
For:
Wherein:RselfIt is self-balancing rate;EselfIt is power distribution network
The load electricity consumption itself being met by;EtotalIt is the aggregate demand of load;Egrid-inIt is the load electricity consumption met by bulk power grid
Amount, i.e. power purchase electricity;
4) rate of generating power for their own use constrains:Distributed generation resource in net can not only power to load, superfluous in generating capacity
In the case of, it can also be to bulk power grid power transmission.By power distribution network in some cycles, the distributed generation resource for meeting workload demand is sent out
Electricity ratio is defined as the rate of generating power for their own use, specially:Wherein:RsuffIt is the rate of generating power for their own use;EselfIt is
The load electricity consumption that power distribution network is met by;EDGIt is the distributed generation resource gross generation of power distribution network;
5) it is constrained from smooth rate:It is also known as interconnection tie power fluctuation rate from smooth rate
Wherein:δlineIt is from smooth rate, Pline,iIt is i-th of moment dominant eigenvalues,It is the mean power of interconnection in one day.
Power relation in the power-balance constraint between photovoltaic, accumulator and major network three is according to operation reserve
It is determined, specially:
1) as net load Paggregate(t)=Pload(t)-Ppv(t)<When 0, photovoltaic generation is the case where meeting load power supply
Under, it charges to accumulator, load level is relatively low at this time, and electricity price is also low, therefore accumulator storage is low-price electricity.
1.1) battery charging but underfill, then have Pbat(t)=|Paggregate(t)|
Storage battery charge state updates, SOC (t+1)=SOC (t) (1- σ)+Pbat(t)/Ebat, wherein:EbatFor accumulator
Stored energy capacitance, σ be accumulator self-discharge rate hourly.
1.2) when accumulator group has been filled with, still there is surplus generation, can transmit electricity to major network, i.e.,:
Pgrid(t)=- |Paggregate(t)|+Pchmax, wherein:PchmaxFor the maximum charge power of accumulator.
2) as net load Paggregate(t)=Pload(t)-Ppv(t)=0 when, storage battery charge state is:SOC (t+1)=
SOC(t)(1-σ);
3) as net load Paggregate(t)=Pload(t)-Ppv(t)>When 0, selection is stored with accumulator first low-price electricity
Supplementary power vacancy.
3.1) when the low-price electricity of accumulator storage can supplement, accumulator group state-of-charge is:Pbat(t)=- (Pload
(t)-Ppv(t));SOC (t+1)=SOC (t) (1- σ)+Pbat(t)/Ebat
3.2) when the low-price electricity of accumulator storage is insufficient for power supply vacancy, then to major network power purchase, purchase of electricity is:
Pgrid(t)=Paggregate(t)-Pdhmax, wherein:PdhmaxFor the maximum discharge power of accumulator.
Technique effect
Compared with prior art, the present invention is planned using dual-layer optimization, and inside and outside bilayer is all made of improvement particle cluster algorithm, outside
Layer realizes that addressing, internal layer determine that optimal capacity, ectonexine are connected by photovoltaic and the power of energy storage, fully considered electric power
The energy flow mode and port loss of electronic transformer reduce whole network loss.
Description of the drawings
Fig. 1 is the distribution network system figure based on electric power electric transformer in embodiment;
In figure:Grid is power grid, PET is electric power electric transformer, PV is photovoltaic, ES is accumulator, DC Load are direct current
Load, AC Load are AC load;
Fig. 2 is operational plan curve synoptic diagram in embodiment;
Fig. 3 is purchases strategies Optimal Curve schematic diagram in embodiment.
Specific implementation mode
As shown in Figure 1, the present embodiment is the power distribution network based on electric power electric transformer, electric power electric transformer is three ports
Structure, one of port connection 10kV exchange major network, other two port is separately connected 380V ac bus and ± 375V is straight
Flow busbar.Photovoltaic is to exchange access way with energy storage device, because the position of energy storage can influence the trend to distribution network, in turn
Line loss is influenced, therefore the object function of outer layer position optimization is line loss and electric power electric transformer end in the present embodiment
Mouth loss, wherein loss calculation needs the real-time charging and discharging state of clear energy storage device and the real time execution shape of entire power distribution network
State, and the charge and discharge of energy storage and the operation of power distribution network are related with stored energy capacitance, optimize in being the introduction of internal layer stored energy capacitance, target
Function is purchases strategies, and the real-time running state of the power distribution network based on electric power electric transformer is determined in internal layer.
The line loss, when energy storage, which is connected on 380V, exchanges node, Wherein:Ploss1It is connected on total line loss when 380V exchanges node, P for energy storagePVIt contributes for photovoltaic, PESIt contributes for energy storage,
P when energy storage device chargesESIt is negative, P when energy storage device dischargesESFor just, PACFor AC load, R1For the electricity of the alternating current circuits 380V
Resistance;PDCFor DC load, R2For the resistance of ± 375V DC lines;PgridFor major network input power distribution network power, when power from
Power distribution network inputs major network, then PgridIt is negative.
The electric power electric transformer port loss refers to:(assuming that energy storage is connected on by taking the ports 380V~10kVAC as an example
380V exchanges node), work as Pgrid>0, then the ports 10kV input power, the ports 380V output power, then port loss beWherein:η is operational efficiency;β is load factor.
The operational efficiency of 1 PET of table
In order to weigh the degree of line loss variation, the present embodiment introduces line loss Sensitivity Analysis Method.Line loss
Sensitivity (loss sensitivity factor, LSF) refers to the electric line loss consumption caused by one specific power of every increase
Variable quantity,Wherein:LSFiIt is the line loss sensitivity of node i, LSFiIt is bigger, illustrate to save
After increasing a specific power, via net loss declines more apparent point i.Therefore, the object function of outer layer position optimization is set to min
(Ploss+1/LSF)。
By taking industrial park shown in FIG. 1 as an example, containing photovoltaic, industrial class AC and DC load, pass through three port electric power electricity
Sub- transformer realizes source, the access of lotus and complementary cooperation, realizes that the reliable access of photovoltaic and industrial the economic of class load supply
Energy.Self-discharge rate is 0.01% to accumulator per hour, initial state-of-charge SOC (0)=0.4, SOCmax=0.9, SOCmin=
0.2, the maximum exchange power P gridmax=500kW of microgrid and major network.The electricity price of different periods is as shown in table 2.
2 tou power price of table
The purchase of electricity and energy storage charge state change curve that step 1) internal layer capacity optimizes are illustrated in fig. 2 shown below.Fig. 3 is
Purchases strategies change Optimal Curve.Analysis chart 2 can obtain:
1:00-12:00, net load is more than 0, i.e. photovoltaic generation is unsatisfactory for load power supply, at this time since electricity price is relatively low, simultaneously
With accumulator and to power grid power purchase come supplementary power vacancy;
13:00-17:00, net load is less than 0, i.e. photovoltaic generation meets load power supply, remaining first to charge a battery,
There are certain spare, then surplus online.At this time also purchases strategies are effectively reduced on the occasion of electricity price highest period;
18:00-24:00, although net load is more than 0, its value is little, mainly by power grid for electronic compensating, so that energy storage is put
It is electric few, stay the spare of enough abundances for second day power shortage of compensation.
The results are shown in Table 3 for capacity configuration.
3 capacity configuration result of table
| Model | Single-machine capacity | Configure number of units | |
| Accumulator | Holy energy VRB-50 | 50kWh | 21 |
± 375V the DC ports that the best configuration position that step 2) outer layer position optimization obtains energy storage is PET.
Above-mentioned specific implementation can by those skilled in the art under the premise of without departing substantially from the principle of the invention and objective with difference
Mode carry out local directed complete set to it, protection scope of the present invention is subject to claims and not by above-mentioned specific implementation institute
Limit, each implementation within its scope is by the constraint of the present invention.
Claims (8)
1. a kind of energy storage addressing constant volume method in power distribution network based on electric power electric transformer, which is characterized in that initially set up base
The dual-layer optimization allocation models of the energy storage in the power distribution network of electric power electric transformer acquires energy storage device by the optimization of internal layer capacity
Optimal capacity and the energy storage output under optimal capacity and dominant eigenvalues, and then outer layer position is obtained by Load flow calculation
Optimization neutralizes the related object function of via net loss, obtains energy storage device optimal location finally by particle swarm optimization algorithm, i.e.,
The port position of place PET;
The dual-layer optimization allocation models includes:Realize that energy storage constant volume and outer layer optimization part realize storage in internal layer optimization part
Energy addressing, wherein:Internal layer optimization part is with the minimum object function of purchases strategies, and outer layer optimization part is with via net loss and electric power
Electronic transformer port loss is object function.
2. according to the method described in claim 1, it is characterized in that, the via net loss includes:Line loss and power electronics
Transformer port loss.
3. according to the method described in claim 2, it is characterized in that, the line loss refers to:The meter of distribution line loss consumption
Calculating formula isWherein:PlossIt is via net loss, PiIt is the injection active power of node i, QiIt is node
The injection reactive power of i, ViIt is the voltage magnitude of node i, RijIt is the resistance of circuit ij;The PET is lost:Work as Pgrid
>0, then the ports 10kV input power, the ports 380V output power, then port loss be Wherein:η is operational efficiency;β is load factor.
4. according to the method described in claim 1, it is characterized in that, the energy storage device optimal location is in the following manner
It arrives:
1) initialization is the position and speed parameter of the population of N with population size;
2) target function value of each particle in population is calculated, i.e. outer layer optimization part neutralizes the related target letter of via net loss
Number;
3) via net loss to each particle under different location is preferentially updated;
4) by the network for the desired positions that all particles are lived through in the via net loss of each particle desired positions and population
Loss is compared and is preferentially updated;
5) corresponding position is lost with particle oneself history optimal network and corresponding position pair is lost in particle entirety optimal network
The position and speed parameter of particle is updated, and when having reached greatest iteration algebraically, then obtains optimum optimization scheme;Otherwise it returns
Step 2) continues to optimize.
5. according to the method described in claim 1, it is characterized in that, the internal layer optimization part refers to:Using purchases strategies as mesh
Scalar functions, i.e.,Its constraints includes:
1) power-balance constraint Pgrid=Paggregate-Pbattery=Pload-Ppv-Pbattery, wherein:PgridMicrogrid is inputted for power grid
Power;PaggregateFor net load power;PpvFor the output of photovoltaic generation;PloadFor load power;PbatteryFor energy-storage system
Output;
2) energy storage power constraint
3) self-balancing rate constrains:Power distribution network is connected with bulk power grid, and certain electric power support can be provided by bulk power grid.By power distribution network
In some cycles, the workload demand ratio being met by by itself distributed generation resource is defined as self-balancing rate, specially:
Wherein:RselfIt is self-balancing rate;EselfIt is power distribution network itself
The load electricity consumption being met by;EtotalIt is the aggregate demand of load;Egrid-inIt is the load electricity consumption met by bulk power grid, i.e.,
Power purchase electricity;
4) rate of generating power for their own use constrains:Distributed generation resource in net can not only power to load, in the situation of generating capacity surplus
Under, it can also be to bulk power grid power transmission.By power distribution network in some cycles, the distributed generation resource generated energy for meeting workload demand
Ratio is defined as the rate of generating power for their own use, specially:Wherein:RsuffIt is the rate of generating power for their own use;EselfIt is distribution
The load electricity consumption that net is met by;EDGIt is the distributed generation resource gross generation of power distribution network;
5) it is constrained from smooth rate:It is also known as interconnection tie power fluctuation rate from smooth rate
Wherein:δlineIt is from smooth rate, Pline,iIt is i-th of moment dominant eigenvalues,It is the mean power of interconnection in one day.
6. according to the method described in claim 5, it is characterized in that, photovoltaic, accumulator and master in the power-balance constraint
Power relation between net three is determined according to operation reserve, specially:
1) as net load Paggregate(t)=Pload(t)-Ppv(t)<When 0, photovoltaic generation meet load power supply in the case of, to
Accumulator charges, and load level is relatively low at this time, and electricity price is also low, therefore accumulator storage is low-price electricity;
2) as net load Paggregate(t)=Pload(t)-Ppv(t)=0 when, storage battery charge state is:SOC (t+1)=SOC
(t)(1-σ);
3) as net load Paggregate(t)=Pload(t)-Ppv(t)>When 0, the low-price electricity that selection accumulator stores first supplements
Power supply vacancy.
7. according to the method described in claim 6, it is characterized in that, the step 1 specifically includes:
1.1) battery charging but underfill, then there is Pbat(t)=|Paggregate(t)|
Storage battery charge state updates, SOC (t+1)=SOC (t) (1- σ)+Pbat(t)/Ebat, wherein:EbatFor the storage of accumulator
Energy capacity, σ are accumulator self-discharge rate hourly;
1.2) when accumulator group has been filled with, still there is surplus generation, can transmit electricity to major network, i.e.,:Pgrid(t)=- |Paggregate(t)|
+Pchmax, wherein:PchmaxFor the maximum charge power of accumulator.
8. according to the method described in claim 6, it is characterized in that, the step 3 specifically includes:
3.1) when the low-price electricity of accumulator storage can supplement, accumulator group state-of-charge is:Pbat(t)=- (Pload(t)-Ppv
(t));SOC (t+1)=SOC (t) (1- σ)+Pbat(t)/Ebat;
3.2) when the low-price electricity of accumulator storage is insufficient for power supply vacancy, then to major network power purchase, purchase of electricity is:Pgrid(t)=
Paggregate(t)-Pdhmax, wherein:PdhmaxFor the maximum discharge power of accumulator.
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| CN109510224A (en) * | 2018-11-16 | 2019-03-22 | 上海交通大学 | Photovoltaic energy storage and the united capacity configuration of distributed energy and running optimizatin method |
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| CN112134286A (en) * | 2020-09-22 | 2020-12-25 | 广东电网有限责任公司 | Alternating current-direct current hybrid micro-grid |
| CN112152219A (en) * | 2020-09-23 | 2020-12-29 | 广东电网有限责任公司东莞供电局 | Alternating current-direct current hybrid power distribution network system applied to office living park |
| CN114825451A (en) * | 2022-06-29 | 2022-07-29 | 西安热工研究院有限公司 | Light-storage micro-grid flexible networking system for thermal power plant |
| CN114825451B (en) * | 2022-06-29 | 2022-10-11 | 西安热工研究院有限公司 | Light-storage micro-grid flexible networking system for thermal power plant |
| CN117559507A (en) * | 2024-01-04 | 2024-02-13 | 武汉理工大学 | Constant-volume and site-selection optimization configuration method and system for network-structured energy storage power station |
| CN117559507B (en) * | 2024-01-04 | 2024-05-24 | 武汉理工大学 | Constant-volume and site-selection optimization configuration method and system for network-structured energy storage power station |
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