CN110311396A - A kind of alternating current-direct current mixing micro-capacitance sensor hybrid energy-storing capacity configuration optimizing method - Google Patents
A kind of alternating current-direct current mixing micro-capacitance sensor hybrid energy-storing capacity configuration optimizing method Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 13
- 238000004146 energy storage Methods 0.000 claims abstract description 104
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 claims abstract description 78
- 229910052744 lithium Inorganic materials 0.000 claims abstract description 78
- 238000000354 decomposition reaction Methods 0.000 claims abstract description 16
- 230000000694 effects Effects 0.000 claims description 15
- 238000005457 optimization Methods 0.000 claims description 9
- 238000010248 power generation Methods 0.000 claims description 9
- 239000003990 capacitor Substances 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 6
- 230000005611 electricity Effects 0.000 claims description 6
- 230000005540 biological transmission Effects 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims description 3
- 239000013256 coordination polymer Substances 0.000 claims description 2
- 238000007599 discharging Methods 0.000 claims description 2
- 239000000463 material Substances 0.000 claims description 2
- 238000012544 monitoring process Methods 0.000 claims description 2
- 239000010410 layer Substances 0.000 claims 5
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- 239000002245 particle Substances 0.000 description 1
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Classifications
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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
- H02J3/02—Circuit arrangements for ac mains or ac distribution networks using a single network for simultaneous distribution of power at different frequencies; using a single network for simultaneous distribution of ac power and of dc power
-
- 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
- H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
-
- 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
-
- 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 alternating current-direct current mixing micro-capacitance sensor hybrid energy-storing capacity configuration optimizing method, belongs to alternating current-direct current mixing micro-capacitance sensor field, it includes: to establish hybrid energy-storing capacitance double layer Optimal Allocation Model；Power distribution verifying based on set empirical mode decomposition；The configuration of mixed energy storage system capacity.The power instruction of lithium battery and supercapacitor is obtained by filter order, using the rated power and rated capacity of lithium battery and supercapacitor as optimized variable, using system year overall cost as target, comprehensively consider energy storage service life and inverter loss, it is optimized using APSO algorithm, obtains the rated power and rated capacity of optimal system year overall cost and corresponding lithium battery and supercapacitor.The present invention solves the problems, such as in alternating current-direct current mixing micro-capacitance sensor since scene goes out interconnection tie power fluctuation caused by fluctuation and load fluctuation.
Description
Technical field
The present invention relates to alternating current-direct current mixing micro-capacitance sensor fields, are a kind of alternating current-direct current mixing micro-capacitance sensor hybrid energy-storing capacity optimization
Configuration method.
Background technique
Alternating current-direct current mixing micro-capacitance sensor combines the advantage of exchange micro-capacitance sensor and direct-current grid, and which omits many change of current rings
Section, reduces system running wastage, so that micro-capacitance sensor control is more flexible.But since the renewable energy power generations such as wind, light have
The disadvantages of fluctuation, uncontrollability, certain energy storage need to be configured in alternating current-direct current mixing micro-capacitance sensor just can be reduced renewable energy
Generate electricity bring adverse effect.Mixed energy storage system is higher the characteristics of integrating energy type energy storage and power-type energy storage
Effect, economic energy-storage system.Lithium battery can provide prolonged power shortage as energy type energy storage, and supercapacitor
As the power-type energy-storage travelling wave tube being most widely used at present, it is responsible for stabilizing frequent power swing in short-term, extends energy storage system
The service life of system improves the overall performance of energy-storage system.
Since the system structure of alternating current-direct current mixing micro-capacitance sensor exchanges micro-capacitance sensor there are larger difference, existing micro- electricity with single
Net energy storage Optimal Configuration Method not can be used directly in alternating current-direct current mixing micro-capacitance sensor.Compared to conventional AC micro-capacitance sensor, hand over straight
Stream mixing micro-capacitance sensor energy storage distributes rationally and also needs to consider the power interaction problems between exchange subnet and direct current subnet.Therefore, to friendship
The mixed energy storage system that direct current mixing micro-capacitance sensor configures appropriate capacity is of great significance.
Summary of the invention
The present invention is in order to solve to go out power in alternating current-direct current mixing micro-capacitance sensor caused by fluctuation and load fluctuation as scene
Imbalance problem establishes the alternating current-direct current mixing micro-capacitance sensor hybrid energy-storing capacitance double layer optimization based on set empirical mode decomposition and matches
Set model.
A kind of alternating current-direct current mixing micro-capacitance sensor hybrid energy-storing capacity configuration optimizing method, including the optimization of hybrid energy-storing capacitance double layer
The foundation of allocation models；Power distribution verifying based on set empirical mode decomposition；The configuration of mixed energy storage system capacity.
1. the foundation of hybrid energy-storing capacitance double layer Optimal Allocation Model:
(1) comprehensively consider energy storage service life and inverter loss, the optimization of alternating current-direct current mixing micro-capacitance sensor mixed energy storage system capacity
The target of allocation models is that system year overall cost is minimum:
min C0=min { CB+CC+CIC}
Wherein, C0For system year overall cost；CB、CC、CICRespectively lithium battery investment operating cost, supercapacitor are thrown
Money operating cost and inverter cost depletions (having converted to wait years value).
(2) constraint condition being all satisfied in each period: the constraint of energy-storage system residual capacity, energy-storage system charge and discharge electric work
Rate constraint, energy deviation constraint.
(3) model is divided into two layers, first layer is Filled function, is in the case where determining filter order, use is adaptive
Answer particle swarm algorithm to solve energy storage system capacity Optimal Allocation Model, obtain system year under each configuration it is comprehensive at
This；The second layer is integer optimization, by corresponding to different filter orders system year overall cost be ranked up, it is final to determine
System year the smallest filter order of overall cost and energy storage configuration scheme.
2. the power distribution verifying based on set empirical mode decomposition
(1) with certain alternating current-direct current mixing micro-capacitance sensor typical case's day operation data instance, sampling interval 1min, one day totally 1440
Sampled point, computing system net load power determine interconnection according to the 60min average value of system net load power when no energy storage
Agreement power, and be modified according to tie-line power transmission bound, further according to system net load power and contact wire protocol
Power calculation obtains mixed energy storage system general power, is divided by gathering empirical mode decomposition mixed energy storage system general power
Solution；
(2) mixing is stored up by choosing suitable filter order d using a kind of new space time filter of IMF component layout
Energy system total power resolves into two parts, and the sum of the IMF component of filter order less than or equal to d is high frequency section, and order is greater than d's
The sum of IMF component is low frequency part.The characteristics of according to lithium battery and supercapacitor, low frequency wave in mixed energy storage system general power
Dynamic part is stabilized by lithium battery, and high-frequency fluctuation part is then stabilized by supercapacitor, to realize mixed energy storage system
Power distribution；
(3) power instruction of lithium battery and supercapacitor is obtained in the case where filter order determines, is substituted into lithium electricity
The rated power and rated capacity of pond and supercapacitor are optimized variable, using system year overall cost as the hybrid energy-storing of target
In capacitance double layer Optimal Allocation Model, comprehensively considers energy storage service life and inverter loss, carried out using APSO algorithm
Optimization Solution, obtain optimal system year overall cost and corresponding lithium battery and supercapacitor rated power and specified appearance
Amount；
(4) the corresponding system year overall cost of different filter orders is ranked up, it is final to determine system year overall cost
The smallest filter order and corresponding mixed energy storage system configuration scheme；
(5) gone out according to the model solution of foundation: charging and discharging lithium battery power, lithium battery SOC value, supercapacitor charge and discharge
Power, supercapacitor SOC value, system year overall cost, mixed energy storage system allocation plan etc..
3. the configuration of mixed energy storage system capacity
(1) it is supervised according to the history of this area's whole year photovoltaic power generation power output, wind turbine power generation power output, AC load and DC load
Measured value chooses 12 months typical day operation data, optimizes configuration to this alternating current-direct current mixing micro-capacitance sensor mixed energy storage system；
(2) since wind light generation and load condition all have stronger seasonality, an allusion quotation is chosen respectively in 4 seasons
Type day is run, and the reliability of configuration result is verified；
(3) influence of the analysis filter order to mixed energy storage system configuration result.
12 months typical days were exactly the same day of every month, such as January 3, and 3 days 2 months, March 3, April 3, May 3
Day, June 3, July 3, August 3rd, September 3rd, October 3, November 3, December 3.
The present invention has the advantage that as follows in contrast to existing research institute:
(1) comprehensively consider that alternating current-direct current mixing micro-capacitance sensor exchanges power with bulk power grid, exchange subnet with direct current subnet exchanges function
Rate is contributed, the Historical Monitoring value of AC load and DC load according to this area's whole year photovoltaic power generation power output, wind turbine power generation, with
The systems year overall costs such as investment operating cost, inverter cost depletions comprising lithium battery and supercapacitor are objective function
It carries out alternating current-direct current mixing micro-capacitance sensor hybrid energy-storing capacity to distribute rationally, it is more accurate to configure micro-capacitance sensor energy storage；
(2) present invention carries out power distribution to mixed energy storage system general power using set empirical mode decomposition, will mix
Low frequency component in energy-storage system general power with large energy distributes to lithium battery, will be comprising more back and forth changing power
High fdrequency component distribute to supercapacitor, given full play to supercapacitor and stabilized the effect on high-frequency fluctuation power, benefit
With the complementary advantage between supercapacitor and lithium battery, lithium battery service life is improved, reduces system year overall cost；
(3) present invention is lithium battery/super capacitor mixed energy storage system that alternating current-direct current mixing micro-capacitance sensor configures suitable capacity
System, reduces the peak-valley difference of micro-grid connection dominant eigenvalues, reduces the fluctuation of micro-grid connection dominant eigenvalues, reduces
Influence of the micro-capacitance sensor to bulk power grid.
Detailed description of the invention
Fig. 1 is alternating current-direct current mixing micro-capacitance sensor system construction drawing according to the present invention；
Fig. 2 is that mixed energy storage system capacitance double layer according to the present invention distributes flow chart rationally；
Fig. 3 is typical daylight volt, blower power output and load curve according to the present invention；
Fig. 4 is typical day system net load power, contact wire protocol power and mixed energy storage system according to the present invention
Total power curve；
Fig. 5 be it is according to the present invention set empirical mode decomposition is carried out to typical day mixed energy storage system general power after
IMF component and remainder curve；
Fig. 6 is the charge-discharge electric power curve of typical day mixed energy storage system according to the present invention；
Fig. 7 is typical day mixed energy storage system SOC curve according to the present invention；
Fig. 8 is that typical day mixed energy storage system according to the present invention stabilizes effect curve；
Fig. 9 is that spring according to the present invention typical day mixed energy storage system stabilizes effect curve；
Figure 10 is that summer according to the present invention typical day mixed energy storage system stabilizes effect curve；
Figure 11 is that autumn according to the present invention typical day mixed energy storage system stabilizes effect curve；
Figure 12 is that winter according to the present invention typical day mixed energy storage system stabilizes effect curve；
Figure 13 is allocation plan according to the present invention and filter order relation curve；
Figure 14 is system year overall cost involved in the present invention and filter order relation curve；
Figure 15 is system year overall cost growth rate according to the present invention and filter order relation curve.
Specific embodiment
Shown in Fig. 1, alternating current-direct current mixing micro-capacitance sensor system includes exchange subnet and direct current subnet.Exchange the distributed generation resource of side
For blower, the distributed generation resource of DC side is photovoltaic and mixed energy storage system.
Contain ac bus and DC bus in micro-capacitance sensor simultaneously, can directly power, hand over to AC load and DC load
It is attached between DC bus by two-way interconnection inverter, alternating current-direct current mixing micro-capacitance sensor is connect by interconnection with bulk power grid.
In embodiment, lithium battery unit power cost coefficient is 9000 yuan/kW, and unit capacity cost coefficient is 2500
Member/kWh, operation expense coefficient are 0.05 yuan/kWh, and efficiency for charge-discharge 90%, initial capacity is the 0.6 of rated capacity
Times, the residual capacity upper limit is 0.8 times of rated capacity, and residual capacity lower limit is 0.2 times of rated capacity, and service life cycle is
1000 times；Supercapacitor unit power cost coefficient is 1000 yuan/kW, and unit capacity cost coefficient is 100000 yuan/kWh,
Operation expense coefficient is 0.05 yuan/kWh, and efficiency for charge-discharge 95%, initial capacity is 0.6 times of rated capacity, remaining
Maximum size is 0.9 times of rated capacity, and residual capacity lower limit is 0.1 times of rated capacity, and operation is limited to 20 years in year；Discount rate
It is 6%；Inverter change of current efficiency is 90%, and year cost depletions coefficient is 0.4 yuan/kWh；Ceiling capacity root-mean-square-deviation is
0.1；Tie-line power transmission is limited to 5kW.
The foundation of hybrid energy-storing capacitance double layer Optimal Allocation Model:
(1) mixed energy storage system investment operating cost and inverter cost depletions, alternating current-direct current mixing micro-capacitance sensor are considered
The target of mixed energy storage system capacity Optimal Allocation Model is that system year overall cost is minimum, be may be expressed as:
min C0=min { CB+CC+CIC}
Wherein, C0For system year overall cost；CB、CC、CICRespectively lithium battery investment operating cost, supercapacitor are thrown
Money operating cost and inverter cost depletions (having converted to wait years value).
A. lithium battery invests operating cost
The service life of lithium battery and working method are closely related, mainly by the depth of discharge of lithium battery, recycling time
Several influences.The depth of discharge (depth of discharge, DOD) of lithium battery is the discharge capacity and its rated capacity of lithium battery
Ratio.According to the corresponding relationship of lithium battery depth of discharge and energy storage service life cycle, its functional relation can be fitted are as follows:
Wherein, DbFor the reference discharge depth of battery；NB(Di) it be depth of discharge is DiWhen corresponding battery use the longevity
Life.
It can thus be concluded that depth of discharge is DiWhen i-th circulation equivalent cycle number are as follows:
The actual motion time limit of lithium battery are as follows:
Wherein, Nj(Di) it is that i-th recycles corresponding equivalent cycle number in jth day；L is the charge and discharge number in 1d；p
For the number of days for the operation that works in 1a.
The investment operating cost of lithium battery may be expressed as:
Wherein, PBNFor lithium battery rated power；EBNFor lithium battery rated capacity；YBFor the service life of lithium battery；kBPFor
The unit power cost coefficient of lithium battery；kBEFor the unit capacity cost coefficient of lithium battery；kBYFor lithium battery operation and maintenance at
This coefficient；λ is discount rate.
B. supercapacitor invests operating cost
Wherein, PCNFor supercapacitor rated power；ECNFor supercapacitor rated capacity；YCFor making for supercapacitor
With the service life, it is typically set to fixed value；kCPFor the unit power cost coefficient of supercapacitor；kCEFor the unit of supercapacitor
Capacity Cost coefficient；kCYFor the operation expense coefficient of supercapacitor.
C. inverter year cost depletions
CIC=kac/dc·(1-ηac/dc)∫1 T(PAC(t)+PDC(t))
Wherein, kac/dcFor inverter year cost depletions coefficient；T is research cycle.
(2) it is the safe and reliable operation for guaranteeing micro-capacitance sensor, is both needed to meet certain constraint condition in each period, wraps
It includes:
A. energy-storage system residual capacity constrains:
For the normal operation for guaranteeing energy-storage system, extend the service life of energy-storage system, the remaining of energy-storage system any time is held
Amount should meet following constraint condition:
Wherein, EB(t)、EB(t- Δ t) is respectively lithium battery t moment and t- time Δt remaining capacity；EC(t)、EC(t-Δ
It t) is respectively supercapacitor t moment and t- time Δt residual capacity；EBmin、EBmaxThe respectively residual capacity lower limit of lithium battery
And the upper limit；ECmin、ECmaxThe respectively residual capacity lower and upper limit of supercapacitor；PB(t)、PCIt (t) is lithium battery and super
The practical charge-discharge electric power of capacitor t moment (takes timing for electric discharge, to be charging when taking negative)；ηB、ηCIt is lithium battery and super electricity respectively
The efficiency for charge-discharge of container；Δ t is material calculation, takes 1min.
For the periodicity for ensuring system continuous operation, each whole story research cycle memory capacity of energy-storage system is consistent,
I.e.
Wherein, EB(0)、EC(0) be respectively lithium battery and supercapacitor initial capacity.
B. energy-storage system charge-discharge electric power constrains:
The constraint of t moment energy storage device charge-discharge electric power may be expressed as:
When lithium battery and supercapacitor residual capacity are not able to satisfy required discharge power, need to lithium battery and super
The discharge power of capacitor is adjusted as follows:
When lithium battery and supercapacitor residual capacity are not able to satisfy required charge power, need to lithium battery and super
The charge power of capacitor is adjusted as follows:
C. energy deviation constrains:
With energy root-mean-square-deviation indicate plus energy storage after stabilize effect, it is desirable that energy storage stabilize rear net load power and contact
Wire protocol power energy root-mean-square-deviation R is not greater than certain threshold value, i.e.,
Wherein, RmaxFor maximum square deviation.
(3) in view of filter order is different, the low frequency component and high frequency division that mixed energy storage system general power obtains after decomposing
Amount is different, and obtained hybrid energy-storing configuration result is also different, i.e., model is divided into two layers, and first layer is Filled function, is
In the case where determining filter order, energy storage system capacity Optimal Allocation Model is asked using APSO algorithm
Solution, obtains the system year overall cost under each configuration；The second layer is integer optimization, by corresponding to different filter orders
System year, overall cost was ranked up, final to determine the smallest filter order of system year overall cost and the energy storage side of distributing rationally
Case.Specifically distributing process rationally sees Fig. 2.
Power distribution verifying based on set empirical mode decomposition:
(1) with certain alternating current-direct current mixing micro-capacitance sensor typical case's day operation data instance, typical day operation data are shown in Fig. 3, between sampling
It is divided into 1min, one day totally 1440 sampled point；
(2) calculate separately DC side net load power may be expressed as: with side system net load power, calculation formula is exchanged
PJ-ac(t)=PL-ac(t)-PWT(t)
PJ-dc(t)=PL-dc(t)-PPV(t)
Wherein, t is time, 1≤t≤T；PWTIt (t) is exchange crosswind machine power output；PPV(t) it contributes for DC side photovoltaic；PL-ac
(t)、PL-dcIt (t) is respectively exchange side, DC side load consumption power；PJ-ac(t)、PJ-dcIt (t) is respectively exchange side, DC side
Net load power.
(3) it is born according to DC side net load power with side net load power calculation alternating current-direct current mixing micro-capacitance sensor system net is exchanged
Lotus power, calculating power may be expressed as:
Wherein, ηac/dcFor the change of current efficiency of AC/DC inverter.
(4) the 60min average value of net load power, determines when considering dominant eigenvalues limitation and micro-grid system without energy storage
Get in touch with wire protocol power；
(5) mixed energy storage system general power is calculated according to contact wire protocol power and system net load power, calculates power
It may be expressed as:
In formula: PHIt (t) is mixed energy storage system general power；PJIt (t) is system net load power；PAIt (t) is contact wire protocol
Power.Mixed energy storage system general power, system net load power, contact wire protocol power are shown in Fig. 4
(6) mixed energy storage system general power is decomposed by gathering empirical mode decomposition, the result after decomposition is as follows
It is shown:
In formula: PHIt (t) is mixed energy storage system general power；hiIt (t) is the i-th rank IMF of mixed energy storage system general power points
Amount；rnIt (t) is decomposition surplus.It decomposes acquired results and sees Fig. 5.
(7) mixing is stored up by choosing suitable filter order d using a kind of new space time filter of IMF component layout
Energy system total power resolves into two parts, and the sum of the IMF component of filter order less than or equal to d is high frequency section, and order is greater than d's
The sum of IMF component is low frequency part.The characteristics of according to lithium battery and supercapacitor, low frequency wave in mixed energy storage system general power
Dynamic part is stabilized by lithium battery, and high-frequency fluctuation part is then stabilized by supercapacitor, to realize mixed energy storage system
Power distribution.Therefore, the power instruction P of lithium batteryB0(t) and the power instruction P of supercapacitorC0(t) it respectively indicates are as follows:
In formula: d is filter order, and 0≤d≤n.
(8) power instruction of lithium battery and supercapacitor is obtained in the case where filter order determines, is substituted into lithium electricity
The rated power and rated capacity of pond and supercapacitor are optimized variable, using system year overall cost as the hybrid energy-storing of target
In capacitance double layer Optimal Allocation Model, comprehensively considers energy storage service life and inverter loss, carried out using APSO algorithm
Optimization Solution, obtain optimal system year overall cost and corresponding lithium battery and supercapacitor rated power and specified appearance
Amount；
(9) the corresponding system year overall cost of different filter orders is ranked up, it is final to determine system year overall cost
The smallest filter order is d=3, and year overall cost is 2.78 ten thousand yuan, mixed energy storage system configuration scheme, that is, lithium battery volume
Determine power and rated capacity is respectively 4.18kW and 8.40kWh, supercapacitor rated power and rated capacity are respectively
3.16kW and 0.82kWh；
(10) gone out according to the model solution of foundation: lithium battery and the charge-discharge electric power of supercapacitor are shown in Fig. 6, lithium battery and
The SOC value of supercapacitor is shown in that Fig. 7, mixed energy storage system stabilize effect and see Fig. 8.
The configuration of mixed energy storage system capacity:
(1) it is supervised according to the history of this area's whole year photovoltaic power generation power output, wind turbine power generation power output, AC load and DC load
Measured value chooses 12 months typical day operation data, optimizes configuration to this alternating current-direct current mixing micro-capacitance sensor mixed energy storage system, match
It is respectively 5.49kW and 14.28kWh that set result, which be lithium battery rated power and rated capacity, supercapacitor rated power and volume
Constant volume is respectively 4.15kW and 1.24kWh, and system year overall cost is 4.05 ten thousand yuan；
(2) since wind light generation and load condition all have stronger seasonality, an allusion quotation is chosen respectively in 4 seasons
Type day is run, and the reliability of configuration result is verified, and spring typical case's day mixed energy storage system stabilizes effect curve and sees Fig. 9, summer
Typical case's day mixed energy storage system stabilized effect curve and saw Figure 10 season, and autumn typical case's day mixed energy storage system stabilizes effect curve and sees figure
11, winter typical case's day mixed energy storage system stabilizes effect curve and sees Figure 12；
(3) influence of the analysis filter order to energy storage configuration result, allocation plan and filter order relational graph are shown in Figure 13, are
System year overall cost and filter order relational graph are shown in Figure 14, system year overall cost growth rate and filter order relational graph see figure
15。
Claims (4)
1. a kind of alternating current-direct current mixing micro-capacitance sensor hybrid energy-storing capacity configuration optimizing method, characterized by comprising: establish mixing storage
It can capacitance double layer Optimal Allocation Model；Power distribution verifying based on set empirical mode decomposition；Mixed energy storage system capacity
Configuration.
2. a kind of alternating current-direct current mixing micro-capacitance sensor hybrid energy-storing capacity configuration optimizing method, feature exist according to claim 1
Include the following steps: in establishing hybrid energy-storing capacitance double layer Optimal Allocation Model
(1) system year overall cost is minimum, indicates are as follows:
min C0=min { CB+CC+CIC}
Wherein, C0For system year overall cost；CB、CC、CICRespectively lithium battery investment operating cost, supercapacitor investment fortune
Row cost and inverter cost depletions；
A. lithium battery invests operating cost
PBNFor lithium battery rated power；EBNFor lithium battery rated capacity；YBFor the service life of lithium battery；kBPFor the list of lithium battery
Position power cost coefficient；kBEFor the unit capacity cost coefficient of lithium battery；kBYFor the operation expense coefficient of lithium battery；λ is
Discount rate；
According to the corresponding relationship of lithium battery depth of discharge and energy storage service life cycle, its functional relation can be fitted are as follows:
Wherein, DbFor the reference discharge depth of battery；NB(Di) it be depth of discharge is DiWhen corresponding service lifetime of accumulator；
It can thus be concluded that depth of discharge is DiWhen i-th circulation equivalent cycle number are as follows:
The service life of lithium battery are as follows:
Wherein, Nj(Di) it is that i-th recycles corresponding equivalent cycle number in jth day；L is the charge and discharge number in 1d；P is 1a
The number of days of interior work operation；
B. supercapacitor invests operating cost
Wherein, PCNFor supercapacitor rated power；ECNFor supercapacitor rated capacity；YCThe longevity is used for supercapacitor
Life, is typically set to fixed value；kCPFor the unit power cost coefficient of supercapacitor；kCEFor the unit capacity of supercapacitor
Cost coefficient；kCYFor the operation expense coefficient of supercapacitor；
C. inverter year cost depletions
Wherein, kac/dcFor inverter year cost depletions coefficient；T is research cycle；
(2) energy-storage system residual capacity constrains:
The residual capacity of energy-storage system any time should meet following constraint condition:
Wherein, EB(t)、EB(t- Δ t) is respectively lithium battery t moment and t- time Δt remaining capacity；EC(t)、EC(t- Δ t) points
It Wei not supercapacitor t moment and t- time Δt residual capacity；EBmin、EBmaxThe respectively residual capacity lower limit of lithium battery and upper
Limit；ECmin、ECmaxThe respectively residual capacity lower and upper limit of supercapacitor；PB(t)、PCIt (t) is lithium battery and super capacitor
The practical charge-discharge electric power of device t moment takes timing for electric discharge, is charging when taking negative；ηB、ηCIt is lithium battery and supercapacitor respectively
Efficiency for charge-discharge；Δ t is material calculation, takes 1min；
The each whole story research cycle memory capacity of energy-storage system is consistent, i.e.,
Wherein, EB(0)、EC(0) be respectively lithium battery and supercapacitor initial capacity；
The constraint of energy-storage system charge-discharge electric power:
The constraint of t moment energy storage device charge-discharge electric power may be expressed as:
When lithium battery and supercapacitor residual capacity are not able to satisfy required discharge power, need to lithium battery and super capacitor
The discharge power of device is adjusted as follows:
When lithium battery and supercapacitor residual capacity are not able to satisfy required charge power, need to lithium battery and super capacitor
The charge power of device is adjusted as follows:
Energy deviation constraint:
With energy root-mean-square-deviation indicate plus energy storage after stabilize effect, it is desirable that energy storage is stabilized rear net load power and interconnection and is assisted
View power energy root-mean-square-deviation R is not greater than certain threshold value, i.e.,
Wherein, RmaxFor maximum square deviation；
(3) model is divided into two layers, and first layer is in the case where determining filter order, using APSO algorithm to energy storage
Power system capacity Optimal Allocation Model is solved, and the system year overall cost under each configuration is obtained；The second layer is by not
With corresponding to filter order system year overall cost be ranked up, it is final to determine the smallest filter order of system year overall cost
With energy storage configuration scheme.
3. a kind of alternating current-direct current mixing micro-capacitance sensor hybrid energy-storing capacity configuration optimizing method, feature exist according to claim 1
Include the following steps: in the power distribution verifying based on set empirical mode decomposition
(1) one day totally 1440 sampled point, sampling interval 1min, computing system net load power, according to system when no energy storage
The 60min average value of net load power gets in touch with wire protocol power to determine, and is repaired according to tie-line power transmission upper and lower limit
Just, mixed energy storage system general power is obtained further according to system net load power and contact wire protocol power calculation, is passed through by set
It tests mode decomposition to decompose mixed energy storage system general power, the result after decomposition is as follows:
In formula: PHIt (t) is mixed energy storage system general power；hiIt (t) is the i-th rank IMF component of mixed energy storage system general power；rn
It (t) is decomposition surplus；
(2) using a kind of new space time filter of IMF component layout, by choosing suitable filter order d, by hybrid energy-storing system
System general power resolves into two parts, and the sum of the IMF component of filter order less than or equal to d is high frequency section, and order is greater than the IMF of d
The sum of component is low frequency part；Low-frequency fluctuation part is stabilized by lithium battery in mixed energy storage system general power, high-frequency fluctuation portion
Divide and stabilized by supercapacitor, realizes the power distribution of mixed energy storage system；The power instruction P of lithium batteryB0(t) and super electricity
The power instruction P of containerC0(t) it respectively indicates are as follows:
In formula: d is filter order, and 0≤d≤n；
(3) obtain the power instruction of lithium battery and supercapacitor in the case where filter order determines, substitute into lithium battery and
The rated power and rated capacity of supercapacitor are optimized variable, using system year overall cost as the hybrid energy-storing capacity of target
In dual-layer optimization allocation models, comprehensively considers energy storage service life and inverter loss, optimized using APSO algorithm
It solves, obtains the rated power and rated capacity of optimal system year overall cost and corresponding lithium battery and supercapacitor；
(4) the corresponding system year overall cost of different filter orders is ranked up, it is final to determine that system year overall cost is minimum
Filter order and corresponding mixed energy storage system configuration scheme；
(5) gone out according to the model solution of foundation: charging and discharging lithium battery power, lithium battery SOC value, supercapacitor charge and discharge electric work
Rate, supercapacitor SOC value, system year overall cost, mixed energy storage system allocation plan etc..
4. a kind of alternating current-direct current mixing micro-capacitance sensor hybrid energy-storing capacity configuration optimizing method, feature exist according to claim 1
In mixed energy storage system capacity configuration be according to annual photovoltaic power generation power output, wind turbine power generation power output, AC load and DC load
Historical Monitoring value, choose 12 months typical day operation data, alternating current-direct current mixing micro-capacitance sensor mixed energy storage system optimized
Configuration.
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110880777A (en) * | 2019-12-20 | 2020-03-13 | 国电南瑞科技股份有限公司 | Method and device for evaluating emergency peak regulation standby capacity of energy storage at power grid side |
| CN111654028A (en) * | 2020-06-15 | 2020-09-11 | 湘潭大学 | Final benefit hybrid energy storage optimized capacity allocation ratio based on optimization algorithm |
| CN112072655A (en) * | 2020-09-10 | 2020-12-11 | 天津大学 | Hybrid energy storage optimal configuration method for grid-connected wind energy storage power generation system |
| CN112242709A (en) * | 2020-10-19 | 2021-01-19 | 华翔翔能科技股份有限公司 | Hybrid energy storage capacity determination method with reliable load power supply of micro-grid system |
| CN112290596A (en) * | 2020-11-12 | 2021-01-29 | 合肥工业大学 | Wind power plant hybrid energy storage configuration self-adaptive double-layer optimization method based on Haar wavelet |
| CN112365299A (en) * | 2020-12-03 | 2021-02-12 | 天津大学 | Comprehensive energy source electricity/heat mixed energy storage configuration method considering battery life loss |
| CN112653163A (en) * | 2020-12-21 | 2021-04-13 | 合肥阳光新能源科技有限公司 | Energy storage system power distribution method and energy storage system |
| CN114217530A (en) * | 2021-12-13 | 2022-03-22 | 中山东菱威力电器有限公司 | Hybrid energy storage control system based on lithium battery power estimation |
| CN114665510A (en) * | 2022-05-27 | 2022-06-24 | 西安海联石化科技有限公司 | Photoelectric energy source direct current power supply pumping unit well group energy-saving control system |
| CN114912848A (en) * | 2022-06-27 | 2022-08-16 | 南通大学 | Full-life-cycle hybrid energy storage capacity configuration method based on adaptive filtering |
| CN115764849A (en) * | 2022-11-11 | 2023-03-07 | 国网安徽省电力有限公司芜湖市繁昌区供电公司 | Hybrid energy storage capacity optimal configuration method and configuration system thereof |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160105020A1 (en) * | 2014-10-09 | 2016-04-14 | Nec Laboratories America, Inc. | Modular multilvel converter and control framework for hybrid energy storage |
| CN105576685A (en) * | 2016-02-19 | 2016-05-11 | 安徽工程大学 | Energy storage system for new energy micro-grid |
| CN105896582A (en) * | 2016-06-16 | 2016-08-24 | 南京工程学院 | Micro-grid energy storage capacity optimization configuration method |
| CN106451508A (en) * | 2016-10-13 | 2017-02-22 | 深圳职业技术学院 | Configuration, charge and discharge method and device of distributed hybrid energy storage system |
| 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 |
| CN109301853A (en) * | 2018-12-17 | 2019-02-01 | 国网江苏省电力公司经济技术研究院 | A kind of micro-capacitance sensor Multiple Time Scales energy management method for stabilizing power swing |
-
2019
- 2019-07-30 CN CN201910696857.4A patent/CN110311396B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160105020A1 (en) * | 2014-10-09 | 2016-04-14 | Nec Laboratories America, Inc. | Modular multilvel converter and control framework for hybrid energy storage |
| CN105576685A (en) * | 2016-02-19 | 2016-05-11 | 安徽工程大学 | Energy storage system for new energy micro-grid |
| CN105896582A (en) * | 2016-06-16 | 2016-08-24 | 南京工程学院 | Micro-grid energy storage capacity optimization configuration method |
| CN106451508A (en) * | 2016-10-13 | 2017-02-22 | 深圳职业技术学院 | Configuration, charge and discharge method and device of distributed hybrid energy storage system |
| 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 |
| CN109301853A (en) * | 2018-12-17 | 2019-02-01 | 国网江苏省电力公司经济技术研究院 | A kind of micro-capacitance sensor Multiple Time Scales energy management method for stabilizing power swing |
Non-Patent Citations (3)
| Title |
|---|
| 曹超等: ""基于经验模态分解和模糊机会约束的混合储能容量配置方法"", 《分布式能源》, vol. 1, no. 3, 31 December 2016 (2016-12-31), pages 43 - 48 * |
| 曹超等: ""基于经验模态分解和模糊机会约束的混合储能容量配置方法"", 《分布式能源》, vol. 1, no. 3, pages 43 - 48 * |
| 贾燕冰等: ""基于集合经验模态分解的火-储联合调度调频储能容量优化配置"", 《电网技术》, vol. 42, no. 9, pages 2930 - 2937 * |
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110880777A (en) * | 2019-12-20 | 2020-03-13 | 国电南瑞科技股份有限公司 | Method and device for evaluating emergency peak regulation standby capacity of energy storage at power grid side |
| CN111654028A (en) * | 2020-06-15 | 2020-09-11 | 湘潭大学 | Final benefit hybrid energy storage optimized capacity allocation ratio based on optimization algorithm |
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| CN112290596A (en) * | 2020-11-12 | 2021-01-29 | 合肥工业大学 | Wind power plant hybrid energy storage configuration self-adaptive double-layer optimization method based on Haar wavelet |
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| CN112365299A (en) * | 2020-12-03 | 2021-02-12 | 天津大学 | Comprehensive energy source electricity/heat mixed energy storage configuration method considering battery life loss |
| CN112653163A (en) * | 2020-12-21 | 2021-04-13 | 合肥阳光新能源科技有限公司 | Energy storage system power distribution method and energy storage system |
| CN112653163B (en) * | 2020-12-21 | 2023-09-15 | 阳光新能源开发股份有限公司 | Energy storage system power distribution method and energy storage system |
| CN114217530A (en) * | 2021-12-13 | 2022-03-22 | 中山东菱威力电器有限公司 | Hybrid energy storage control system based on lithium battery power estimation |
| CN114217530B (en) * | 2021-12-13 | 2022-11-08 | 中山东菱威力电器有限公司 | Hybrid energy storage control system based on lithium battery power estimation |
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| CN114912848B (en) * | 2022-06-27 | 2023-05-26 | 南通大学 | Full life cycle hybrid energy storage capacity configuration method based on adaptive filtering |
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