CN108695871A - The configuration method of the reduction stored energy capacitance demand of isolated island micro-capacitance sensor containing electric power spring - Google Patents
The configuration method of the reduction stored energy capacitance demand of isolated island micro-capacitance sensor containing electric power spring 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
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
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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
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
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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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- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
- Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
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- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
- Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
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- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
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Abstract
A kind of isolated island micro-capacitance sensor containing electric power spring reduces the configuration method to stored energy capacitance demand, and the isolated island micro-capacitance sensor includes conventional distributed generation resource, energy-storage system, diesel-driven generator, ac bus, power load, electric power spring.Conventional distributed generation resource includes wind-powered electricity generation, photovoltaic.Demand difference of the power load according to electrical appliance to power quality can be divided into critical load and non-key load.The present invention realizes the positive interaction of electric power spring and energy-storage system, can effectively reduce energy-storage system charging and discharging currents and power, improve the service life of energy-storage system;The demand that reduction system configures stored energy capacitance improves the economy of isolated island micro-capacitance sensor.It is equivalent to electric power spring and provides a part of virtual stored energy capacitance for micro-capacitance sensor.
Description
Technical field
The present invention relates to micro-grid energy storage system, a kind of reduction stored energy capacitance need of the isolated island micro-capacitance sensor containing electric power spring
The configuration method asked.
Background technology
In conventional electric power system running pattern, generated energy is determined by loading demand.As distribution type renewable energy is in electricity
Permeability in net is constantly promoted, and wind-powered electricity generation and Photovoltaic new energy are contributed with randomness and fluctuation, it is difficult to accurate prediction in real time
Generated energy, the realtime power to realize between workload demand and generated energy balance, and larger pressure is brought to frequency modulation and voltage modulation, so that
Voltage fluctuation and frequency flickering are easy to happen.Bulk power grid has certain self-healing ability, and the micro- electricity of small-sized isolated island to voltage fluctuation
The regulating power of net is poor.The difference between power generation and loading demand is offset using a variety of energy storage devices, is one of approach.So
And the energy storage systems such as chemical cell are expensive, cost is higher, and waste battery is one of source of pollutant.
The operational mode of micro-capacitance sensor changes to " generated energy determines workload demand ".Demand-side energy management is studied
It rises, including load scheduling, tou power price etc..However, these methods are suitable for small time interval loading demand management mostly, and
It is not suitable for real-time power balance." electric power spring " technology, for the load side energy pipe containing a large amount of new energy access power grid
Energy fluctuation is instantaneously passed to non-critical loads, and the power consumption of adjust automatically non-critical loads by reason, autobalance generated energy and
Electricity consumption.Generally intelligent load is collectively referred to as by electric power spring and non-critical loads are concatenated.
Invention content
In order to overcome the above-mentioned deficiencies of the prior art, the present invention proposes a kind of drop of the isolated island micro-capacitance sensor containing electric power spring
The configuration method of low stored energy capacitance demand, this method adjust non-critical loads in time by the interaction of energy-storage system and electric power spring
On power consumption, reduce the charge-discharge electric power of energy-storage system, to achieve the purpose that reduce stored energy capacitance configuration.This patent pair
The micro-capacitance sensor for meeting a certain scene has done quantitative analysis to the configuration of minimum stored energy capacitance, and the isolated island containing electric power spring of proposition is micro-
The energy-optimised algorithm of bilayer of power grid can quantitatively determine the minimum stored energy capacitance configuration for meeting a certain scene.Electric power spring and
The coordinated operation of energy-storage system can reduce the capacity configuration of energy-storage system to reduce cost of investment.Electric power spring can be with relatively low
Cost realize energy snubber, and isolated island micro-capacitance sensor can be run with the new model of " electricity consumption tracking generated energy ".Electric power bullet
The good characteristic of spring energy snubber will provide new thinking for the planning of the following micro-capacitance sensor and construction.
In order to achieve the above objectives, technical solution of the invention is as follows:
A kind of configuration method of the reduction stored energy capacitance demand of the isolated island micro-capacitance sensor containing electric power spring, the isolated island are micro-
Power grid includes conventional distributed generation resource, energy-storage system, diesel-driven generator, ac bus, power load, electric power spring, described
Conventional distributed generation resource includes wind-powered electricity generation, photovoltaic, and demand difference of the power load according to electrical appliance to power quality is divided into
Critical load and non-key load are generally collectively referred to as intelligent load, feature by electric power spring and non-critical loads are concatenated
It is that this method includes the following steps:
1) sufficient investigation is done to the regenerative resource of the local especially non-key load of load, wind-powered electricity generation, photovoltaic, is based on
The simulation result of historical data determines optimal stored energy capacitance configuration, and the core apparatus of the electric power spring includes inverter,
The capacity configuration of inverter is determined that the capacity configuration of inverter should be at least over non-key load by the capacity of non-key load
The 20% of peaking capacity, there are allowances;
2) first layer is energy-optimised, i.e., isolated island micro-capacitance sensor global energy optimizes:
First layer is energy-optimised similar to the optimization of traditional microgrid energy, and the object function of optimization is diesel-driven generator unit
The function of generating price minimum:
In formula, N indicate one day within scheduling interval number, λ and DtBe respectively diesel-driven generator unit generating price and
In the output of t-th of scheduling interval;In view of the power constraint of micro-capacitance sensor, capacity-constrained and real scene etc., operation constraint is such as
Under:
0≤Wt≤Wmax (17)
|Wt+1-Wt|≤Rwmax (18)
-Scmax≤St≤Sdmax (19)
|St+1-St|≤Rsmax (20)
0≤Dt≤Ddmax-Dr (21)
|Dt+1-Dt|≤Rdmax (22)
Wt+St+Dt=Pcl-t+kbasePnl-t (23)
Formula (2)-(3) are the bound constraint and Climing constant that wind turbine is contributed, WtIt is that the wind-powered electricity generation of t-th of scheduling interval goes out
Power, WmaxIt is the wind-powered electricity generation prediction maximum output of t-th of scheduling interval, RwmaxIt is the upper limit of wind turbine climbing power, formula (4)-(7)
It is energy-storage system and diesel-driven generator units limits and Climing constant, StIt is the discharge current of t-th of scheduling interval energy-storage system,
SdmaxIt is the maximum discharge current of energy-storage system, ScmaxIt is the maximum charging current of energy-storage system, RsmaxIt is the maximum of energy-storage system
Climbing power, DtIt is the output of t-th of scheduling interval diesel-driven generator, DdmaxIt is diesel-driven generator maximum discharge power, DrIt is bavin
The spare capacity of fry dried food ingredients motor, for resisting the extreme case of isolated island micro-capacitance sensor, usually, Dr=0.1Ddmax;RdmaxIt is diesel oil
The maximum climbing power of generator;Formula (8) is the realtime power balance of the different type load considered, Pcl-tIt is crucial at this time
The power of load, Pnl-tIt is the power of non-critical load, kbasePnl-tIt is that non-critical loads absorb wattful power after installing electric power spring additional
Rate.kbaseIt can flexibly choose, take 0.81 here;
When energy storage system discharges, i.e., S (t) >=0, the state-of-charge (SOC) of energy-storage system are expressed as:
When energy-storage system charges, i.e., S (t)≤0, the state-of-charge (SOC) of energy-storage system are expressed as:
Wherein, η is the self discharge efficiency of energy storage, ηdIt is discharging efficiency, ηcCharge efficiency, EbIt is the capacity of energy-storage system,
SOC (t) is the state-of-charge of t-th of scheduling interval;Change in safe range for the state-of-charge of energy-storage system:
SOCmin≤SOCt≤SOCmax (26)
Wherein, SOCminIt is the lower limit of energy storage charge state, SOCmaxIt is the upper limit of energy storage charge state;
In order to ensure lower day regulating power of energy-storage system, the SOC variable quantities in the odd-numbered day are no more than energy storage total amount
One percentage δ:
|SOCN-SOC1|≤δ (27)
The energy-optimised effect for not considering electric power spring of first layer, solving result can be used for determining that the warp of global minima
Ji operating cost;
3) second layer is energy-optimised, i.e., the collaboration based on electric power spring and energy-storage system interaction is energy-optimised:
The energy-optimised interaction for being primarily upon electric power spring and energy-storage system of the second layer, electric power spring are non-key to adjust
The demand of load, thus in a certain voltage class reduce energy-storage system charging and discharging currents and power;Second layer optimization aim
Function is the charge-discharge electric power minimum function of energy-storage system:
If the synergistic mechanism of electric power spring and energy-storage system is activated, the constraint of power-balance mechanism is corrected and strengthens
For:
S1-t+kbasePnl-t=S2-t+ktPnl-t (29)
In formula, S1-tAnd S2-tIt is the solving result that energy-storage system is contributed in the first energy optimizing model, second layer energy respectively
Measure the solving result that energy-storage system is contributed in Optimized model, the regulationing factor of power k of non-key loadtConstraint such as formula (15)
Shown namely intelligent load range of operation:
klow≤kt≤khigh (30)
Wherein, klowIt is the lower limit that non-critical loads are adjusted, value range is 0.64-1, is received by non-key load user
The wish of regulation and control is affected.khighIt is the upper limit that non-critical loads are adjusted, value range 1-1.25;Its theory adjust upper limit by
The power factor of non-key load is affected, and non-critical loads power factor is smaller, and it is bigger that theory adjusts the upper limit.
The klowAnd khighOptimal value be respectively 0.8 and 1.05.
The beneficial effects of the present invention are:
The present invention, the dispatch command generated by the double-deck energy-optimised algorithm, first layer is with the minimum optimization of operating cost
Target generates primary dispatch command.The second layer updates energy storage charge-discharge electric power and intelligence using minimum charge-discharge electric power as optimization aim
Portative active demand.The charging and discharging currents that energy-storage system can effectively be reduced, improve the service life of energy-storage battery;Reduce energy storage
The charge-discharge electric power of system reduces demand of the micro-capacitance sensor to energy storage system capacity, is equivalent to and provides a part of virtual energy storage.
Description of the drawings
Fig. 1 is conventional isolated island micro-capacitance sensor topological diagram of the invention.
Fig. 2 is the energy-optimised algorithm schematic diagram of bilayer of the present invention.
Fig. 3 is simulation result diagram energy-optimised in one day of the present invention, including:
Fig. 3 (a) be wind power output one day of the present invention, non-key load and critical load prediction curve;
Fig. 3 (b) is the energy-optimised solving result figure of the present invention for not considering electric power spring;
Fig. 3 (c) is the corresponding energy storage charge state figure line of different stored energy capacitances of the present invention;
Fig. 3 (d) is energy storage charge and discharge figure line before and after optimization of the present invention;
Fig. 3 (e) be it is of the present invention containing with without containing electric power spring when energy storage SOC change curves;
Fig. 3 (f) is the energy-optimised solving result figure of consideration electric power spring of the present invention.
Fig. 4 be consideration electric power spring of the present invention energy-optimised verification process in 4 kinds of typical scenes using
Wind power output data.
Specific implementation mode
The technical solution further illustrated the present invention with reference to embodiment and attached drawing, but should not limit the present invention's with this
Protection domain.Specific implementation object such as the present embodiment using the energy-storage system of isolated island micro-capacitance sensor as control strategy of the present invention, but
The application range of the present invention should not be limited with this.
The present invention is based on electric power springs to the active regulating power of non-key load electricity consumption, passes through two layers of energy-optimised calculation
Method realizes that intelligent load mitigates filling greatly for energy-storage system and puts greatly, reduces charge-discharge electric power, can effectively reduce energy-storage system charge and discharge
Electric current and power improve the service life of energy-storage system;The demand that reduction system configures stored energy capacitance improves isolated island micro-capacitance sensor
Economy.
The double-deck energy-optimised algorithm of isolated island micro-capacitance sensor with electric power spring is as shown in Figure 2.First layer is energy-optimised be
It reduces operating cost within one day to the greatest extent, while being constrained by isolated island micro-capacitance sensor run-limiting.Optimize the tune obtained based on first layer
Degree instruction, the energy-optimised energy snubber effect based on electric power spring of the second layer can be used for obtaining the storage for meeting current micro-capacitance sensor
The minimum capacity configuration of energy system.
1) first layer --- isolated island micro-capacitance sensor global energy optimizes
First layer is energy-optimised similar to the optimization of traditional microgrid energy.The object function of optimization is diesel-driven generator unit
The function of generating price minimum:
In formula, N indicate one day within scheduling interval number, λ and DtBe respectively diesel-driven generator unit generating price and
In the output of t-th of scheduling interval;In view of the power constraint of micro-capacitance sensor, capacity-constrained and real scene etc., operation constraint is such as
Under:
0≤Wt≤Wmax (2)
|Wt+1-Wt|≤Rwmax (3)
-Scmax≤St≤Sdmax (4)
|St+1-St|≤Rsmax (5)
0≤Dt≤Ddmax-Dr (6)
|Dt+1-Dt|≤Rdmax (7)
Wt+St+Dt=Pcl-t+kbasePnl-t (8)
Formula (2)-(3) are the bound constraint and Climing constant that wind turbine is contributed, WtIt is that the wind-powered electricity generation of t-th of scheduling interval goes out
Power, WmaxIt is the wind-powered electricity generation prediction maximum output of t-th of scheduling interval, RwmaxIt is the upper limit of wind turbine climbing power.Formula (4)-(7)
It is energy-storage system and diesel-driven generator units limits and Climing constant, StIt is t-th of scheduling interval energy storage maximum discharge current,
SdmaxIt is energy storage maximum discharge current, ScmaxIt is maximum charging current, RsmaxIt is energy storage maximum climbing power.DtIt is t-th of scheduling
The output of section diesel-driven generator, DdmaxIt is diesel generating set maximum discharge power, DrIt is the spare capacity of diesel-driven generator,
For resisting the extreme case of isolated island micro-capacitance sensor.Usually, Dr=0.1Ddmax。RdmaxIt is diesel-driven generator maximum climbing power.
Formula (8) is the realtime power balance of the different type load considered.Pcl-tIt is the power of critical load at this time, Pnl-tIt is non-pass
The power of key load, kbaseIt is the active power that non-critical loads absorb after installing electric power spring additional.
When energy storage system discharges, i.e., S (t) >=0, the state-of-charge (SOC) of energy-storage system are expressed as:
When energy-storage system charges, i.e., S (t)≤0, the state-of-charge (SOC) of energy-storage system are expressed as:
Wherein, η is the self discharge efficiency of energy-storage system, ηdIt is discharging efficiency, ηcCharge efficiency, EbIt is the appearance of energy-storage system
Amount, SOC (t) is the state-of-charge of t-th of scheduling interval, is changed in safe range for the state-of-charge of energy-storage system:
SOCmin≤SOCt≤SOCmax (11)
Wherein, SOCminIt is the lower limit of energy storage charge state, SOCmaxIt is the upper limit of energy storage charge state;In order to ensure energy storage
Lower day regulating power of system, the percentage δ of SOC variable quantities in the odd-numbered day no more than energy storage total amount:
|SOCN-SOC1|≤δ (12)
The energy-optimised effect for not considering electric power spring of first layer, solving result can be used for determining that the warp of global minima
Ji operating cost;
2) second layer is energy-optimised, i.e., the collaboration based on electric power spring and energy-storage system interaction is energy-optimised:
The energy-optimised interaction for being primarily upon electric power spring and energy-storage system of the second layer, electric power spring can adjust non-key
The demand of load, thus in a certain voltage class reduce energy-storage system charging and discharging currents and power.Second layer optimization aim
Function is that the function of the charge-discharge electric power of energy-storage system is minimum:
If the synergistic mechanism of electric power spring and energy-storage system is activated, the constraint of power-balance mechanism is corrected and strengthens
For:
S1-t+kbasePnl-t=S2-t+ktPnl-t (14)
In formula, S1-tAnd S2-tIt is the solving result that energy-storage system is contributed in first and second layer of energy optimizing model, non-pass respectively
The regulationing factor of power k of key loadtConstraint it is as follows:
klow≤kt≤khigh (15)
Wherein, klowIt is the lower limit that non-critical loads are adjusted, value range is 0.64-1, is received by non-key load user
The wish of regulation and control is affected.khighIt is the upper limit that non-critical loads are adjusted, value range 1-1.25;Its theory adjust upper limit by
The power factor of non-key load is affected, and non-critical loads power factor is smaller, and it is bigger that theory adjusts the upper limit.
In general, klowAnd khighOptimal value be respectively 0.8 and 1.05.
Essence applied to the energy-optimised algorithm of bilayer containing electric power spring isolated island micro-capacitance sensor can be attributed to second order cone
Problem can be solved effectively using the business such as Cplex or Gurobi solver.
The present embodiment isolated island micro-capacitance sensor is expected configuration wind-powered electricity generation 2MW, energy-storage system 17.3MWh, and diesel-driven generator maximum is defeated
Go out power 600kW.
Wind power output and variety classes load prediction curve such as Fig. 3 (a) are shown.The total load of the micro-capacitance sensor is by critical load
It is formed with non-key load.In one day different scheduling interval, the ratio of critical loads and total load is different.In order to protect
It is next day continued power to demonstrate,prove under isolated island micro-capacitance sensor normal operation mode, and the conditional that needs restraint (12), energy-storage system will be to fill
The mode operation of electricity-electric discharge-charging.
In the case where not considering electric power spring, based on wind turbine prediction contribute and safe operation constraints, at
This minimum object function, can obtain the solving result of conventional energy optimization, as shown in Fig. 3 (b).Energy-storage system is in non-peak
The load period still has Capacity Margin.15:00 to 20:During 00 peak load, energy-storage system reaches maximum power output,
Diesel-driven generator puts into operation.Wind turbine Maximum Power Output as far as possible in operation constraint.
As the capacity [ of energy-storage system;0.6Eb,Eb]In the range of when changing, the SOC variations that can obtain energy-storage system are bent
Shown in line such as Fig. 3 (c).By the solution to the double-deck energy-optimised algorithm, acquires and meet the current Run-time scenario of the micro-grid system
Minimum stored energy capacitance be 0.599Eb.Namely if the capacity configuration of energy-storage system ESS maintains 0.6EbOr more, isolated island is micro-
One day operating cost of power grid will be maintained at 4423.22 yuan.
But if the capacity of energy-storage battery is less than 0.6EbThreshold value, then operating cost will rise, efficiency will decline (this because
For when wind power is more sufficient it is unnecessary first electric discharge recharge, such as Fig. 3 (c) red circles.
If the interactive second layer based on energy-storage system and electric power spring cooperates with energy-optimised algorithm to start (energy storage at this time
Capacity configuration is 0.6Eb), then the charge-discharge electric power of energy-storage system reduces, and the variation range smaller of SOC, such as Fig. 3 (d)-
(e) shown in.In other words, if not considering the operation nargin of micro-capacitance sensor, current 0.6EbStored energy capacitance exist it is superfluous.
With 0.6EbStored energy capacitance configure and be added electric power spring operation, energy-optimised solving result can be obtained,
As shown in Fig. 3 (f).
When the capacity of energy-storage system drops to 0.553E when further simulation result shows containing electric power springb, isolated island
One day operating cost of micro-capacitance sensor still can maintain 4423.22 yuan.That is, electric power spring makes the capacity of energy-storage system
Have dropped 7.8%.However, if not configuring electric power spring, the capacity of energy-storage system drops to 0.553Eb, operation in one day
Cost has increased to 5488.89 yuan, it can be seen that the work that electric power spring is played for reducing energy storage system capacity configuration aspect
With.0.553EbIt is that can make under current context microgrid energy optimization there are the configurations of the minimum stored energy capacitance of feasible solution, according to this
Numerical value considers further that allowance, you can obtain final stored energy capacitance matches setting value.
In order to keep result more general, statistical analysis has been carried out to 4 season of the whole year wind power output level in somewhere,
Estimation obtains the typical day data of wind power output prediction under 4 typical days, as shown in Figure 4.
Two benches are energy-optimised before being carried out respectively under 4 typical scenes, can obtain remaining identical minimum under each scene
Cost does not consider and minimum stored energy capacitance when consideration electric power spring, such as the following table 1.Critical stored energy capacitance instigates optimization problem to exist
The minimum stored energy capacitance of feasible solution.
1 each typical scene lowest capacity allocative abilities relation table of table
Stored energy capacitance before and after equipping electric power spring under 4 typical scenes is compared to configure as can be seen that electric power spring can be big
The big demand for reducing system to energy storage system capacity, reduces the cost of investment of energy storage device, improves power grid total revenue.This be because
The limitation of energy storage charge-discharge electric power bound is alleviated for the energy snubber effect of electric power spring.
Claims (3)
1. a kind of isolated island micro-capacitance sensor containing electric power spring reduces the configuration method of stored energy capacitance demand, the isolated island micro-capacitance sensor
Including conventional distributed generation resource, energy-storage system, diesel-driven generator, ac bus, power load, electric power spring, the routine
Distributed generation resource includes wind-powered electricity generation, photovoltaic, and demand difference of the power load according to electrical appliance to power quality is divided into key
Load and non-key load are collectively referred to as intelligent load by electric power spring and non-critical loads are concatenated, it is characterised in that the party
Method includes the following steps:
1) imitative based on historical data to the regenerative resource investigation of local load, including non-key load, wind-powered electricity generation, photovoltaic
Very as a result, determining optimal stored energy capacitance configuration, the core apparatus of the electric power spring includes inverter, the capacity of inverter
Configuration determines by the capacity of non-key load, the capacity configuration of inverter at least over the 20% of non-key load peak capacity,
There are allowances;
2) first layer is energy-optimised, i.e., isolated island micro-capacitance sensor global energy optimizes:
First layer is energy-optimised similar to the optimization of traditional microgrid energy, and the object function of optimization is the power generation of diesel-driven generator unit
The function of price minimum:
In formula, N indicate one day within scheduling interval number, λ and DtIt is the unit generating price of diesel-driven generator and in t respectively
The output of a scheduling interval;In view of the power constraint of micro-capacitance sensor, capacity-constrained and real scene, operation constraint is as follows:
0≤Wt≤Wmax (2)
|Wt+1-Wt|≤Rwmax (3)
-Scmax≤St≤Sdmax (4)
|St+1-St|≤Rsmax (5)
0≤Dt≤Ddmax-Dr (6)
|Dt+1-Dt|≤Rdmax (7)
Wt+St+Dt=Pcl-t+kbasePnl-t (8)
Formula (2)-(3) are the bound constraint and Climing constant that wind turbine is contributed, WtIt is the wind power output of t-th of scheduling interval,
WmaxIt is the wind-powered electricity generation prediction maximum output of t-th of scheduling interval, RwmaxIt is the upper limit of wind turbine climbing power, formula (4)-(7) are storages
Energy system and diesel-driven generator units limits and Climing constant, StIt is the discharge current of t-th of scheduling interval energy-storage system, Sdmax
It is the maximum discharge current of energy-storage system, ScmaxIt is the maximum charging current of energy-storage system, RsmaxIt is the maximum climbing of energy-storage system
Power, DtIt is the output of t-th of scheduling interval diesel-driven generator, DdmaxIt is diesel-driven generator maximum discharge power, DrIt is diesel oil hair
The spare capacity of motor, for resisting the extreme case of isolated island micro-capacitance sensor;RdmaxIt is the maximum climbing power of diesel-driven generator;
Pcl-tIt is the power of critical load at this time, Pnl-tIt is the power of non-critical load, kbasePnl-tIt is non-key after installing electric power spring additional
Load absorption active power;
When energy storage system discharges, i.e., S (t) >=0, the state-of-charge (SOC) of energy-storage system are expressed as:
When energy-storage system charges, i.e., S (t)≤0, the state-of-charge (SOC) of energy-storage system are expressed as:
Wherein, η is the self discharge efficiency of energy storage, ηdIt is discharging efficiency, ηcCharge efficiency, EbIt is the capacity of energy-storage system, SOC (t)
It is the state-of-charge of t-th of scheduling interval;Change in safe range for the state-of-charge of energy-storage system:
SOCmin≤SOCt≤SOCmax (11)
Wherein, SOCminIt is the lower limit of energy storage charge state, SOCmaxIt is the upper limit of energy storage charge state;
The percentage δ of SOC variable quantities in odd-numbered day no more than energy storage total amount:
|SOCN-SOC1|≤δ (12)
The energy-optimised effect for not considering electric power spring of first layer, solving result can be used for determining that the economic fortune of global minima
Row cost;
3) second layer is energy-optimised, i.e., the collaboration based on electric power spring and energy-storage system interaction is energy-optimised:
In a certain voltage class, the charging and discharging currents and power of energy-storage system are reduced, second layer optimization object function is energy storage
The charge-discharge electric power minimum function of system:
If the synergistic mechanism of electric power spring and energy-storage system is activated, the constraint of power-balance mechanism is corrected and reinforcing is:
S1-t+kbasePnl-t=S2-t+ktPnl-t (14)
In formula, S1-tAnd S2-tBe respectively in the first energy optimizing model energy-storage system contribute solving result, second layer energy it is excellent
Change the solving result that energy-storage system is contributed in model, the regulationing factor of power k of non-key loadtConstraint such as formula (15) institute
Show namely the range of operation of intelligent load:
klow≤kt≤khigh (15)
Wherein, klowIt is the lower limit that non-critical loads are adjusted, value range is 0.64-1, is received regulation and control by non-key load user
Wish be affected;khighIt is the upper limit that non-critical loads are adjusted, value range 1-1.25;Its theory adjusts the upper limit by non-pass
The power factor of key load is affected, and non-critical loads power factor is smaller, and it is bigger that theory adjusts the upper limit.
2. the configuration side of the reduction stored energy capacitance demand of the isolated island micro-capacitance sensor according to claim 1 containing electric power spring
Method, it is characterised in that the klowAnd khighOptimal value be respectively 0.8 and 1.05.
3. the configuration side of the reduction stored energy capacitance demand of the isolated island micro-capacitance sensor according to claim 1 containing electric power spring
Method, it is characterised in that Dr=0.1Ddmax。
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