CN110048405A - Microgrid energy optimization method based on electric power spring - Google Patents
Microgrid energy optimization method based on electric power spring Download PDFInfo
- Publication number
- CN110048405A CN110048405A CN201910264175.6A CN201910264175A CN110048405A CN 110048405 A CN110048405 A CN 110048405A CN 201910264175 A CN201910264175 A CN 201910264175A CN 110048405 A CN110048405 A CN 110048405A
- Authority
- CN
- China
- Prior art keywords
- optimization
- electric power
- constraint condition
- energy storage
- power generation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000005457 optimization Methods 0.000 title claims abstract description 138
- 238000000034 method Methods 0.000 title claims abstract description 54
- 238000004146 energy storage Methods 0.000 claims abstract description 122
- 238000010248 power generation Methods 0.000 claims abstract description 108
- 230000005611 electricity Effects 0.000 claims description 74
- 240000002853 Nelumbo nucifera Species 0.000 claims description 27
- 235000006508 Nelumbo nucifera Nutrition 0.000 claims description 27
- 235000006510 Nelumbo pentapetala Nutrition 0.000 claims description 27
- 230000008859 change Effects 0.000 claims description 14
- 230000009467 reduction Effects 0.000 claims description 13
- 230000009194 climbing Effects 0.000 claims description 12
- 230000033228 biological regulation Effects 0.000 claims description 8
- 230000007774 longterm Effects 0.000 claims description 8
- 238000013178 mathematical model Methods 0.000 claims description 8
- 238000012423 maintenance Methods 0.000 claims description 7
- 230000001939 inductive effect Effects 0.000 claims description 6
- 230000001105 regulatory effect Effects 0.000 claims description 6
- 238000004458 analytical method Methods 0.000 claims description 3
- 230000001276 controlling effect Effects 0.000 claims description 2
- 238000007599 discharging Methods 0.000 claims description 2
- 239000004744 fabric Substances 0.000 claims description 2
- 238000010187 selection method Methods 0.000 claims 1
- 230000006641 stabilisation Effects 0.000 abstract description 10
- 238000011105 stabilization Methods 0.000 abstract description 10
- 238000007726 management method Methods 0.000 description 10
- 239000013598 vector Substances 0.000 description 5
- 230000000694 effects Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 238000011002 quantification Methods 0.000 description 3
- 230000032258 transport Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000002146 bilateral effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000009795 derivation Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 230000005284 excitation Effects 0.000 description 1
- 238000007654 immersion Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000007493 shaping process Methods 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
Classifications
-
- 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
-
- 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
-
- H02J3/382—
-
- 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]
-
- 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
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A30/00—Adapting or protecting infrastructure or their operation
- Y02A30/60—Planning or developing urban green infrastructure
Abstract
The present invention provides a kind of microgrid energy optimization methods based on electric power spring, analyze electric power spring topological structure, obtain the optimization constraint condition of electric power spring stable operation, using electric power spring operational efficiency as optimization aim, obtain electric power spring optimization part；According to storage energy operation factor, the optimization constraint condition of energy storage stable operation is determined, using energy-storage system charge and discharge number as optimization aim, obtain energy storage optimization part；Controlled distribution formula energy source optimization part is obtained using controllable distributed power generation operational efficiency as optimization aim using the constraint of the power output bound of controllable distributed power generation and Ramp Rate as the optimization constraint condition of the optimization part of controllable distributed power generation；On the basis of the optimization constraint condition of S1, S2 and S3, the extra optimization factor of optimization aim and micro-capacitance sensor in S1, S2 and S3 is combined, realizes the power-balance of entire micro-capacitance sensor.The present invention makes micro-capacitance sensor work in stabilization, efficiently operating status.
Description
Technical field
The present invention relates to micro-capacitance sensor technical fields, and in particular, to a kind of microgrid energy optimization based on electric power spring
Method.
Background technique
The renewable energy such as wind energy, solar energy have the characteristics that intermittent, unstability.When wind energy, solar energy etc. can be again
When raw energy large-scale grid connection, the power output situation of disturbance will cause the mismatch of generated energy and loading demand, influence system
It operates normally, in some instances it may even be possible to cause the unstability of system.
In the uncontrollable situation of this generated energy, generated energy can only be realized by adjusting the electricity consumption at loading demand
With the matching of electricity consumption.Largely practice have shown that, the load of electric system can be divided into the critical loads sensitive to voltage fluctuation
With the non-critical loads for showing passivity to voltage fluctuation.Typical critical loads have medical instrument, precision instrument, measuring instrumentss
Deng typical non-critical loads have the hot types instrument such as immersion heater, electric heater.For above-mentioned status, Hong Kong University is proposed
" electric power spring " conception based on power electronic technique, the concept of mechanical spring is introduced into electric system.Electric power spring passes through
The power of non-critical loads is adjusted, to maintain the normal operation of critical loads.
Compared with other existing raising micro-capacitance sensor power quality methods, electric power spring and non-critical loads are in series
Intelligent load has stronger load response ability, can fluctuate distributed energy and be transferred to non-critical loads, can also participate in frequency
Rate is adjusted.And other micro-capacitance sensor voltage methods of adjustment, generally directly connect with critical loads, as static synchronous series compensator,
Dynamic voltage compensator, or directly in parallel with critical loads, such as Static Var Compensator, in some cases, electric power spring
Regulated efficiency is higher than common reactive power compensator.
Corresponding to gradually popularizing for electric power spring technology, the operation of micro-capacitance sensor is for the microgrid energy containing electric power spring
Optimized model also proposed corresponding requirement.Microgrid energy optimization containing electric power spring is excellent relative to traditional microgrid energy
The particularity of change is embodied in: electric power spring provides guarantee to the critical loads for having highly reliable power demand,；Intelligent load is simultaneously
With adjustable range up and down, abandons light for reducing energy storage charge-discharge electric power and abandonment and there is positive effect.
Currently without the explanation or report for finding technology similar to the present invention, it is also not yet collected into money similar both at home and abroad
Material.
Summary of the invention
Aiming at the above shortcomings existing in the prior art, the object of the present invention is to provide a kind of micro- electricity based on electric power spring
Network energy optimization method, this method to electric power spring and intelligent load application model and the basis analyzed of shaping modes
On, electric power spring is analyzed in the micro- of meter and energy storage charge and discharge factor, abandonment factor, subsidy factor and electric power spring depreciation factor
The promotion of bring stability and operational efficiency in power grid energy management operating.It is purchased in conjunction with active Constraints of Equilibrium, from electricity market
Electricity constraint, controllable distributed power generation constraint, the constraint of uncontrollable distributed power generation, can interrupt load constraint, intelligent load constraint, anti-
The constraint conditions such as load restraint, the constraint of long market power purchase are played, enables stable operation of the electric power spring in micro-capacitance sensor, reduces
Stored energy capacitance；Meanwhile this method can make the generation of electricity by new energy in micro-capacitance sensor generate fluctuation when, micro-capacitance sensor can dissolve new energy
Possible disturbance is accessed, micro-capacitance sensor is made to realize power-balance, works in stable, efficient operating status.
The present invention is achieved through the following technical solutions.
A kind of microgrid energy optimization method based on electric power spring, includes the following steps:
S1 analyzes electric power spring topological structure, according to intelligent load reduction amount, in conjunction with general three ranks rebound load mould
Type establishes electric power spring stable operation constraint condition；On the basis of electric power spring stable operation, by electric power spring operational efficiency
As optimization aim；Obtain electric power spring optimization part；
S2 determines the energy storage optimization constraint condition of energy storage stable operation, in energy storage stable operation according to storage energy operation factor
On the basis of, using energy-storage system charge and discharge number as optimization aim, obtain energy storage optimization part；
S3, by controllable distributed power generation power output bound constraint, controllable distributed power generation climb upwards rate limitation and
The downward Ramp Rate limitation of controllable distributed power generation is used as controllable distributed power generation running optimizatin constraint condition, in controlled distribution formula
On the basis of power generation stabilization operation, using controllable distributed power generation operational efficiency as optimization aim, the controlled distribution formula energy is obtained
Optimize part；
The power-balance constraint condition of S4, active balance constraint condition based on micro-capacitance sensor and source storage lotus three, S1,
On the basis of the optimization constraint condition of S2 and S3, be added auxiliary constraint condition, while by S1, S2 and S3 optimization aim with it is micro-
The extra optimization factor of power grid is combined, and the optimization aim for combining extra optimization factor is integrated, and is realized entire micro-
The power-balance of power grid works in stable operating status.
Preferably, the electric power spring uses the generation electric power spring with resistance inductive load, described negative with resistance sense
The generation electric power spring topological structure of load are as follows: converter is connected with resistance sense non-critical loads, and collectively constituting can be with power grid end
Voltage change and the intelligent load changed；The intelligent load is connected in parallel with critical loads, maintains the voltage of critical loads
It is constant.
Preferably, the method for electric power spring topological structure is analyzed are as follows: utilize Kirchhoff's laws of electric circuit, obtain electric power spring
The mathematical model of topological structure.
Preferably, the storage energy operation factor include: charge-discharge electric power bound, energy storage climb upwards rate limitation, storage
It can Ramp Rate limitation downwards, state-of-charge bounded and odd-numbered day state-of-charge variation bounded.
Preferably, in the S1: electric power spring stable operation constraint condition includes: that intelligent load constraint condition and rebound are negative
Carry constraint condition；Electric power spring operational efficiency is by regulating and controlling coefficient and electric power spring equipment for electrostrictive coefficient, micro-capacitance sensor operator
Depreciation and maintenance coefficient embody.
Preferably, in the S4: auxiliary constraint condition includes following any one or any multinomial: electricity market power purchase is about
Beam condition, uncontrollable distributed power generation constraint condition, can interrupt load constraint condition and long market power purchase constraint condition；Volume
Outer optimizing factors include following any one or any multinomial: electricity market power purchase factor, abandonment abandon light factor and conventional electricity
Take factor.
Preferably, in the S4:
Using the method for mathematical modeling, on the basis of the optimization constraint condition of S1, S2 and S3, auxiliary constraint item is added
Part, realize uncontrollable distributed power generation stable operation, can interrupt load stable operation and source storage lotus power supply and electricity consumption
Balance；
Using the method for linear weighted function, the extra optimization factor of optimization aim and micro-capacitance sensor in S1, S2 and S3 is carried out
In conjunction with lotus operating status is stored up in pool micro-capacitance sensor source；
Using the method for linear-weighted, the optimization aim of selection is integrated.
Preferably, using the method for mathematical modeling, the friendship of the optimization constraint condition and auxiliary constraint condition of S1, S2, S3 is taken
Collection is realized on the basis of the optimization constraint condition of S1, S2 and S3, and auxiliary constraint condition is added.
Preferably, using the method for linear weighted function, according to the important journey of extra optimization factor different piece assert in practice
Degree, invests weight to the optimization of various pieces；Optimization aim after power of assigning is overlapped, total optimization aim is formed, is realized
The extra optimization factor of optimization aim and micro-capacitance sensor in S1, S2 and S3 is combined.
Preferably, using the method for linear-weighted, the optimization aim of selection is integrated method particularly includes:
A1 is obtained in optimization aim and is regulated and controled coefficient, the depreciation of electric power spring equipment and dimension for electrostrictive coefficient, micro-capacitance sensor operator
Repair coefficient, energy storage optimized coefficients, controllable distributed power generation running optimizatin coefficient, micro-capacitance sensor from electricity market power purchase penalty factor,
Abandonment abandons light penalty factor and can interrupt load interruption penalty factor；
A2 chooses optimization weight according to the service requirement of each result obtained in A1, and each after optimizing weight by assigning
As a result superimposed, the optimization aim after being integrated.
Preferably,
Active balance constraint condition realizes micro-capacitance sensor for power supply-coulomb balance between the source storage lotus in guarantee system
Power-balance；
Electricity market power purchase constraint condition, for guaranteeing that microgrid can be realized power self-produced personal, reduce to bulk power grid according to
Rely, realizes the self-produced personal of distributed power generation；
Controllable distributed power generation running optimizatin constraint condition, for guaranteeing the stable of controllable distributed power generation；Wherein,
Controllable distributed power generation power output bound constraint, for guaranteeing the safe and stable operation of controllable distributed power generation；Controlled distribution formula
Ramp Rate limits up and down for power generation, for guaranteeing the service life and reliability of equipment；
Uncontrollable distributed power generation constraint condition, for guaranteeing the stable of uncontrollable distributed power generation；
Energy storage optimizes constraint condition, for guaranteeing the stable of energy storage；
Can interrupt load constraint condition, for guarantee can interrupt load prime power demand；
Intelligent load constraint condition, for guaranteeing the stable of intelligent load；
Rebound load restraint condition, for guaranteeing the stable of rebound load；
Long market power purchase constraint condition, is used to form Long-term planning, provides reference for bulk power grid dispatcher scheduling.
Preferably,
Active balance constraint condition are as follows:
Electricity market power purchase constraint condition are as follows: Gt≥0；
Controllable distributed power generation running optimizatin constraint condition are as follows:-RD,down≤Di,t+1-Di,t≤RD,up；
Uncontrollable distributed power generation constraint condition are as follows: 0≤Wt≤kWfWt f,-Rw,down≤Wt+1-Wt≤Rw,up；
Energy storage optimizes constraint condition are as follows:
-Rs,down≤(Sd,t+1-Sc,t+1)-(Sd,t-Sc,t)≤Rs,up, |SOC(N)-SOC(1)|≤δ；
It can interrupt load constraint condition are as follows:
Intelligent load constraint condition are as follows:
Rebound load restraint condition are as follows:
Long market power purchase constraint condition are as follows: Lmin≤kbm≤Lmax；
Wherein: GtIt is the total electricity for entering micro-capacitance sensor by long market and ahead market；Di,tIt is i-th controlled distribution
The power output that formula power generation optimizes in section at t-th；WtIt is the practical online power of uncontrollable distributed power generation；Sc,tAnd Sd,tIt is respectively
The charge and discharge power of energy-storage battery；LtIt is the total load in t-th of section；It is j-th of interruptible load amount；It is k-th
Receive the non-critical loads reduction amount of ES regulation；It is the rebound load capacity that k-th of non-critical loads receives ES adjusting；
State for i-th controllable distributed power generation in the t period is 0/1 variable, respectively indicates controllable distributed power generation and stops transport and normal
Work；It is the upper limit of i-th controllable distributed power generation active power of output；RD,upAnd RD,downIt is positive value, being respectively can
Control the upward climbing rate limitation and the limitation of downward Ramp Rate of distributed power generation；kWf>=1, it is wind-powered electricity generation precision of prediction, precision is got over
Height is closer to 1；Wt fIt is wind power prediction expectation；Rw,upAnd Rw,downIt is positive value, is uncontrollable distributed power generation respectively
Climbing rate limitation and the limitation of downward Ramp Rate upwards；WithIt is 0/1 variable, 0, which represents energy storage, does not work, and 1 represents energy storage
Work；ScmaxAnd SdmaxIt is positive value, is the bound of energy storage charge-discharge electric power respectively；RS,upAnd RS,downIt is positive value, respectively
It is the upward climbing rate limitation and the limitation of downward Ramp Rate of energy storage；EbIt is the capacity of energy storage；η,ηdAnd ηcEnergy storage respectively from
Discharge rate, discharging efficiency and charge efficiency；SOC (t) is energy storage charge state (State of when t-th of section starts
charge,SOC)；Δ t is the duration for optimizing section；δ is that SOC maximum changes ratio in the odd-numbered day；Be can interrupt for j-th it is negative
Lotus amount；Pup% and Pdown% is that ES adjusts non-key workload demand percentage change up and down respectively,It is
The active rated value of the t non-key load in k-th of section；It is 0/1 variable, 0, which represents ES, does not work, and 1 represents ES work；Lmax
And LminIt is upper and lower bound of the micro-capacitance sensor from long market power purchase ratio respectively；kbmIt is from the hundred of long-term trade market purchase of electricity
Divide ratio, is optimized variable.
Microgrid energy optimization method provided by the present invention based on electric power spring:
(1) the access micro-capacitance sensor stable operation of electric power spring is optimized:
Using Kirchhoff's laws of electric circuit, in conjunction with the topological structure of the electric power spring loaded with resistance sense, it can be deduced that band
The mathematical model for the generation electric power spring for thering is resistance sense to load, and then the available generation electric power spring with resistance sense load has
The function demand upper limit.It may insure the stable operation of electric power spring to the active demand upper limit, guarantee that electric power spring can operate at
Stable range of operation.
Meanwhile to extend electric power spring service life, electric power spring operational efficiency is improved, using electric power spring equipment depreciation
With maintenance coefficient, micro-capacitance sensor operator regulation coefficient and high reliability for electrostrictive coefficient, to extension electric power spring service life, improve
The targets such as electric power spring operational efficiency carry out quantification treatment, guarantee that electric power spring can operate at efficiently operating status.
(2) optimize energy storage charge and discharge number, optimized to the storage energy operation service life is improved:
Climb upwards the downward Ramp Rate limitation of rate limitation, energy storage, lotus in conjunction with energy storage charge-discharge electric power bound, energy storage
Electricity condition bounded and the accumulator stable operation that state-of-charge changes bounded in the odd-numbered day require, and want to the operation of energy storage stability
Carry out mathematical modeling is sought, the mathematical constraint of energy storage stable operation in micro-capacitance sensor is established, it is stable to guarantee that energy storage can operate at
Range of operation.
Meanwhile to be optimized to energy storage charge and discharge number, extends energy storage device service life, improve energy storage device operation
Efficiency, construction energy-storage system charge and discharge measure coefficient and carry out quantification treatment to targets such as optimization energy storage charge and discharge numbers, guarantee storage
Efficient operating status can be worked in.
(3) optimization controllable distributed power generation power output, improves controllable distributed power generation equipment service life:
In conjunction with the stable operation requirement that the constraint of controllable distributed power generation bound and Ramp Rate constrain, energy storage is stablized
Property service requirement carry out mathematical modeling, establish the mathematical constraint of controllable distributed power generation stable operation in micro-capacitance sensor, guarantee can
Control distributed power generation works in stable range of operation.
Meanwhile to improve the operational efficiency of controllable distributed power generation, safeguards controllable distributed power generation equipment service life, protect
The maximum of new energy is demonstrate,proved to utilize, using controllable distributed power generation operating factor, operational efficiency, reduction to distributed power generation is improved
Abandonment amount and abandon light quantity, guarantee new energy maximum using etc. targets carry out quantification treatment, guarantee that energy storage can operate at efficiently
Operating status.
(4) disturbance of new energy power output can be dissolved, abandonment is reduced and abandons light, guarantees that lotus stable operation is stored up in micro-capacitance sensor source:
It is fixed from the conservation of energy based on above-mentioned electric power spring, energy storage, the stable operation of controllable distributed power generation
Rule is set out, and further considers the constraint of micro-capacitance sensor active balance, microgrid power is made to reach balance.And further combined with from electric power city
The constraint of power purchase, can interrupt load constraint, the constraint of controllable distributed power generation power output bound, controllable generation of electricity by new energy Ramp Rate
Stable operation requirement, carry out mathematical modeling processing, guarantee that the source in micro-capacitance sensor, storage, lotus various energy resources form can operate at
Stable range of operation.
Lotus operational efficiency is stored up to plan as a whole source in micro-capacitance sensor, it is ensured that the coordinated operation of source storage lotus.To electric power spring, energy storage,
After the factors such as controllable distributed power generation operational efficiency are quantified, further utilizes and consider micro-capacitance sensor from electricity market power purchase system
The factors such as the regulation subsidy that the operating factor of several, uncontrollable distributed power generation, micro-capacitance sensor operator pay to intelligent load user,
After carrying out linear weighted function to each coefficient, source storage lotus operational efficiency is evaluated, there is multiple kinds of energy form in micro-capacitance sensor
Effect integration, and the stabilization of micro-capacitance sensor, even running is allowed to be protected.
Compared with prior art, the invention has the following beneficial effects:
(1) the microgrid energy optimization method provided by the invention based on electric power spring ensure that the access of electric power spring is micro-
The stable operation of power grid.
(2) the microgrid energy optimization method provided by the invention based on electric power spring, reduces energy storage charge-discharge electric power
The effective way of light is abandoned with abandonment；
(3) the microgrid energy optimization method provided by the invention based on electric power spring can dissolve new energy power output
Disturbance, provides feasible management scheme for the large-scale grid connection of generation of electricity by new energy mode；
(4) the microgrid energy optimization method provided by the invention based on electric power spring, proposes a kind of stable operation side
Formula effectively integrates micro-capacitance sensor multiple kinds of energy form, provides energy management for the stabilization even running of micro-capacitance sensor；
(5) the microgrid energy optimization method provided by the invention based on electric power spring, by the energy based on electric power spring
Optimization problem is as a kind of convex feasibility problem, in energy-optimised problem, non-linear, non-convex constraint, therefore mould does not occur
Mature business solver can be used in the solution of type, to ensure that computational efficiency and optimality.
(6) the microgrid energy optimization method provided by the invention based on electric power spring, can be realized electric power spring micro-
Stable operation, reduction stored energy capacitance in power grid；Meanwhile the generation of electricity by new energy in micro-capacitance sensor generates when disturbing, and makes micro-capacitance sensor
New energy can be dissolved and access possible disturbance, micro-capacitance sensor is made to realize power-balance, worked in and stablized, efficiently run shape
State.
Detailed description of the invention
Upon reading the detailed description of non-limiting embodiments with reference to the following drawings, other feature of the invention,
Objects and advantages will become more apparent upon:
Fig. 1 is the Optimization Steps of the microgrid energy optimization method based on electric power spring.
Fig. 2 is the topological structure schematic diagram of the generation electric power spring of the inductive load containing resistance.
Fig. 3 is the microgrid topology structure schematic diagram of the spring containing electric power.
Specific embodiment
Elaborate below to the embodiment of the present invention: the present embodiment carries out under the premise of the technical scheme of the present invention
Implement, the detailed implementation method and specific operation process are given.It should be pointed out that those skilled in the art
For, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to guarantor of the invention
Protect range.
Embodiment
A kind of microgrid energy optimization method based on electric power spring is present embodiments provided, is included the following steps:
S1 analyzes electric power spring topological structure, show that intelligent load constrains.According to intelligent load reduction amount, in conjunction with general
Three ranks rebound load module, establish rebound load restraint (i.e. the optimization constraint condition of electric power spring stable operation).And at it
On the basis of stable operation, consider highly reliable for electrostrictive coefficient, micro-capacitance sensor operator regulation coefficient, the depreciation of electric power spring equipment and dimension
Coefficient is repaired, i.e., from its operational efficiency (i.e. optimization aim), obtains electric power spring optimization part；
S2 is limited according to energy storage charge-discharge electric power bound, the energy storage downward Ramp Rate of rate limitation, energy storage of climbing upwards
The factors (i.e. storage energy operation factor) such as system, state-of-charge bounded, odd-numbered day state-of-charge variation bounded, determine energy storage stable operation
Optimize constraint condition.And on the basis of its stable operation, optimize energy storage charge and discharge number (i.e. optimization aim), improves energy storage fortune
The row service life obtains energy storage optimization part；
S3, the power output bound constraint for considering controllable distributed power generation, Ramp Rate are as the excellent of controllable distributed power generation
Change constraint condition.And on the basis of its stable operation, optimizes the operational efficiency (i.e. optimization aim) of controllable distributed power generation, obtain
To controlled distribution formula energy source optimization part；
S4 stores up the power-balance of lotus three about from the source for maintaining the active balance of entire micro-capacitance sensor to constrain, realize micro-capacitance sensor
Beam sets out, and reaches the stable operation of micro-capacitance sensor, combines the operational efficiency between each equipment, constrains in the optimization of S1, S2, S3
On the basis of condition, further consider from the constraint of electricity market power purchase, controllable distributed power generation constraint, uncontrollable distributed power generation
Constraint, can the factors (i.e. auxiliary constraint) such as interrupt load constraint, the constraint of long market power purchase.Meanwhile by S1, S2, S3
Optimization aim and micro-capacitance sensor abandon light factor, conventional electricity charge factor, micro-capacitance sensor to critical loads from electricity market power purchase factor, abandonment
The factors (extra optimization factor) such as the extra factor of high reliability power supply carry out comprehensive consideration, in a manner of linear weighted function, choose
Suitable optimization aim is planned as a whole, and realizes the power-balance of entire micro-capacitance sensor, guarantees that entire micro-capacitance sensor is stable, efficiently transports
Row.
Further, the electric power spring uses the generation electric power spring with resistance inductive load, topological structure are as follows: such as
Shown in Fig. 2, converter is connected with resistance sense non-critical loads, collectively constitutes the intelligence that can change with power grid end voltage change
Load.The intelligent load is connected in parallel with critical loads, plays the effect for maintaining critical loads voltage constant.
Further, the method for electric power spring topological structure is analyzed are as follows: utilize Kirchhoff's laws of electric circuit, obtain electric power bullet
The mathematical model of spring topological structure.
Further, above each constraint condition is defined as follows:
Active balance constraint condition realizes micro-capacitance sensor for power supply-coulomb balance between the source storage lotus in guarantee system
Power-balance；
Electricity market power purchase constraint condition, for guaranteeing that microgrid can be realized power self-produced personal, reduce to bulk power grid according to
Rely, realizes the self-produced personal of distributed power generation；
Controllable distributed power generation running optimizatin constraint condition, for guaranteeing the stable of controllable distributed power generation；
Uncontrollable distributed power generation constraint condition, for guaranteeing the stable of uncontrollable distributed power generation；
Energy storage optimizes constraint condition, for guaranteeing the stable of energy storage；
Can interrupt load constraint condition, for guarantee can interrupt load prime power demand；
Intelligent load constraint condition, for guaranteeing the stable of intelligent load；
Rebound load restraint condition, for guaranteeing the stable of rebound load；
Long market power purchase constraint condition, is used to form Long-term planning, provides reference for bulk power grid dispatcher scheduling.
Further, the expression formula of above each constraint condition is as follows:
Active balance constraint condition are as follows:
Electricity market power purchase constraint condition are as follows: Gt≥0；
Controllable distributed power generation running optimizatin constraint condition are as follows:-RD,down≤Di,t+1-Di,t≤RD,up；
Uncontrollable distributed power generation constraint condition are as follows: 0≤Wt≤kWfWt f,-Rw,down≤Wt+1-Wt≤Rw,up；
Energy storage optimizes constraint condition are as follows:
-Rs,down≤(Sd,t+1-Sc,t+1)-(Sd,t-Sc,t)≤Rs,up, |SOC(N)-SOC(1)|≤δ；
It can interrupt load constraint condition are as follows:
Intelligent load constraint condition are as follows:
Rebound load restraint condition are as follows:
Long market power purchase constraint condition are as follows: Lmin≤kbm≤Lmax。
The above embodiment of the present invention is described in further detail below in conjunction with attached drawing.
The microgrid energy optimization method based on electric power spring that the above embodiment of the present invention provides, can be divided into four steps
Suddenly.As shown in Figure 1, entire optimization method resolves into 4 steps: S1 is the optimization part of electric power spring, from opening up for electric power spring
Structure is flutterred to set out, derive electric power spring stable operation electrical constraints, obtained electric power spring stable operation optimization constraint
Condition.Using the raising of electric power spring operational efficiency as electric power spring section, the factors such as the life cycle of electric power spring are carried out
Consider, keeps the operation of electric power spring more reliable；S2 is the optimization part of energy storage, considers energy storage charge-discharge electric power bound, energy storage
The factors such as climbing rate limitation, the downward Ramp Rate limitation of energy storage, state-of-charge bounded, odd-numbered day state-of-charge variation bounded upwards
In the case where, to guarantee that the normal operation of energy storage is constrained accordingly.Simultaneously in order to optimize energy storage charge and discharge number, storage is improved
Energy service life optimizes part using the optimization of energy-storage system charge and discharge number, efficiency for charge-discharge as energy storage；S3 is controlled distribution formula
The optimization part of power generation, the power output bound constraint for considering controllable distributed power generation, Ramp Rate are as controllable distributed power generation
Optimization part optimization constraint.And optimized using controllable distributed power generation operational efficiency as optimization aim, improve controllable point
The operational efficiency of cloth power generation.In S4, in order to maintain the active balance of entire micro-capacitance sensor, the source storage lotus three of micro-capacitance sensor is realized
Power-balance, reach the stable operation of micro-capacitance sensor, combine the operational efficiency between each equipment, S1, S2, S3 optimization
On the basis of constraint condition, further consideration is constrained from electricity market power purchase, uncontrollable distributed power generation constrains, can interrupt load
The factors such as constraint, the constraint of long market power purchase.Meanwhile by the optimization aim of S1, S2, S3 and micro-capacitance sensor from electricity market power purchase because
Element, abandonment are abandoned the factors such as the extra factor that light factor, conventional electricity charge factor, micro-capacitance sensor power to critical loads high reliability and are carried out
Comprehensive consideration is chosen suitable optimization aim and is planned as a whole in a manner of linear weighted function, guarantees that entire micro-capacitance sensor is stable, efficient
Ground operation.
Specifically:
(1) electric power spring access micro-capacitance sensor stable operation optimization
1. electric power spring stable operation constrains
Electric power spring topology diagram is as shown in Figure 2.Converter is connected with non-critical loads, and forming one can be with grid-connected
The intelligent load putting voltage change and changing.Intelligent load is in parallel with critical loads, when grid entry point voltage fluctuates, intelligence
Load can adjust itself power output, to guarantee the power demand of critical loads, maintain critical loads voltage constant.
The present embodiment carries out research to the steady-state characteristic of resistance sense intelligent load (installing generation electric power spring additional) and formula pushes away
It leads:
For the electricity consumption group 1 in Fig. 3 micro-capacitance sensor, generation electric power spring is assembled, ifIt is the electricity for flowing through non-critical loads
Stream,It is the voltage of intelligent load,It is non-critical loads voltage,It is electric power spring voltage.If above four vectors without
Vector dot subscript then indicates the size of the vector.It is easy to get, the voltage relationship of purely resistive non-critical loads and electric power spring is more
Simply.Resistance sense and the load of two class of capacitance-resistance are similar with the voltage vector relationship of electric power spring, therefore analysis has representative here
Property resistance sense non-critical loads voltage amount relationship, it is as follows:
Vs 2=(Vo cosψ)2+(Vosinψ±Va)2 (1)
In formula, ψ is the power-factor angle of non-critical loads.Non-critical loads voltage V is pushed away to obtain by above formulaoDisappear with intelligent load
The active-power P of consumptionsIt is as follows with the relationship of electric power spring voltage:
In formula, ZNLFor the impedance magnitude of non-critical loads.Analytical formula (2), (3) obtain, VoWhen taking maximum, PsIt takes greatly
Value；VoWith VaIt is related, therefore to VaDerivation obtains:
It enables above formula be solved for 0, takes negative value in conjunction with direction vector, obtain:
Va=-Vs tanψ (5)
In conclusion working as VaWhen meeting the condition of formula (5), VoTake maximum, i.e.-VsTan ψ is function Vo=f (Va)
Maximum point, maximum:
P simultaneouslysMaximum is taken, i.e. the active load of intelligent load adjusts upper limit Psmax, such as following formula:
It is easy to get, PsmaxIt is inversely proportional with cos ψ.It has obtained the theoretical active demand upper limit of intelligent load, has been shown in Table 1.
The comparison of 1 non-critical loads different capacity factor of table
Intelligent load electricity consumption lower limit is codetermined by user's by the operation demand for adjusting wish and place micro-capacitance sensor.
The electric power spring control measures of the present embodiment meaning are different from existing tunable load, and tunable load is negative including that can interrupt
It carries and directly load control load.Intelligent load has adjusting space up and down simultaneously, can interrupt load and this implementation
The unmodeled directly load control load of example, can only reduce electricity consumption to respond energy-optimised target.A part of tunable load
It can equally be used as regulation medium using electric power spring with the non-critical loads of discontinuous adjusting, receive to continuously adjust.
By analyzing above, the following constraint condition of available electric power spring stable operation:
(1) intelligent load constrains
In formula: Pup% and Pdown% is that ES adjusts non-critical loads demand percentage change up and down respectively,It is the active rated value in t-th of k-th of section non-critical loads；It is 0/1 variable, 0, which represents ES, does not work, and 1 represents
ES work.
(2) rebound load restraint
Receive the rebound load capacity of ES adjusting, third-order model is loaded using general rebound here, as follows:
In formula: a1、a2And a3It is intelligent load reduction amount respectivelyIn t-1, the rebound coefficient of t-2 and t-3 period.
2. electric power spring efficient operation optimizes
When in view of the operation of electric power spring, non-critical loads electric power can be sacrificed, and can be provided simultaneously for critical loads
Higher power quality.In operation, to reach preferably operational effect, it is desirable to the higher power quality of critical loads is obtained,
And wish to reduce the power sacrifice of non-critical loads.Therefore the highly reliable reward for electrostrictive coefficient H, as high reliability power supply is defined
The factor；It defines micro-capacitance sensor operator and regulates and controls coefficient F1, as the penalty factor for sacrificing non-critical loads.Simultaneously in view of wishing electricity
Its service life of power spring extend as far as possible defines the depreciation of electric power spring equipment and maintenance coefficient F2, used as electric power spring
Penalty factor.It takes into account at above-mentioned 3 points, for the efficient operation for reaching the operation of electric power spring, following optimization aim can be obtained:
max H-F1-F2 (10)
Wherein,
μESIt is that intelligent load unit adjusts penalty coefficient.NESIt is the life cycle of electric power spring, kES,aIt is by electric power spring
Initial value characterizes coefficient coefficient, kES,IIt is that electric power spring adjusting unit intelligent load electricity consumption reduction amount corresponds to loss factor, kES,mIt is
The operation of electric power spring and maintenance factor.It is k-th of non-critical loads reduction amount for receiving ES regulation,It is k-th
Non-critical loads receive the rebound load capacity of ES adjusting, and K is the number of intelligent load, and N is optimization section sum, and Δ t is optimization
The duration in section.
(2) energy storage access micro-capacitance sensor stable operation optimization
1. energy storage stable operation constrains
To ensure stable operation of the energy storage in micro-capacitance sensor, realizes that energy storage charge-discharge electric power is not out-of-limit, construct following mathematics
Model:
In formula: ScmaxAnd SdmaxIt is positive value, is the bound of energy storage charge-discharge electric power respectively.WithIt is 0/1 variable,
0, which represents energy storage, does not work, and 1 represents energy storage work.
To ensure that energy storage for power supply is as steady as possible, change rate is contributed in energy storage should not be too fast.For limitation energy storage for power supply climbing speed
Rate constructs following mathematical model:
-Rs,down≤(Sd,t+1-Sc,t+1)-(Sd,t-Sc,t)≤Rs,up (14)
Wherein, RS,upAnd RS,downIt is positive value, is the upward climbing rate limitation and downward Ramp Rate limit of energy storage respectively
System.In addition, energy storage is energy type device.The power output that energy storage provides is by itself state-of-charge (State of charge, SOC) shadow
It rings.When system mode shifts, energy storage charge state will limit energy storage power output situation.To ensure that energy storage can be always to outside
Variation is responded, and state-of-charge variable quantity should be made in the odd-numbered day to be no more than and give threshold value δ:
SOCmin≤SOC(t)≤SOCmax (15)
In formula: SOCmaxAnd SOCminRespectively energy storage charge state bound.Sc,tAnd Sd,tBe respectively the filling of energy-storage battery,
Discharge power.In order to guarantee lower day regulating power of energy-storage system, the SOC variable quantity in the odd-numbered day is no more than energy storage total amount
One ratio delta:
|SOC(N)-SOC(1)|≤δ (16)
In order to guarantee the space with charge and discharge in starting in one day, the starting SOC setting of energy storage is as follows:
SOC (1)=50% (17)
2. energy storage efficient operation optimizes
To reduce energy storage charge and discharge number, improving storage energy operation efficiency, comprehensively considers raising energy storage and utilize service life and charging
Number chooses following optimization aim:
Wherein,
F3It indicates energy storage optimized coefficients, is optimized for quantifying energy storage using service life and charging times.NSIt is the longevity of energy-storage battery
It orders the period, r is the constant between 0.01~0.1, kS,aIt is to be converted by energy storage investment initial value to annual annuity coefficient, kS,IIt is single
Position energy storage charge-discharge electric power penalty coefficient, kS,mIt is the operation and the maintenance factor of energy storage, Sc,tAnd Sd,tIt is energy-storage battery respectively
Charge and discharge power.Energy storage electric discharge penalty coefficient is the K of charging penalty coefficientdcTimes.If operator possesses energy storage, charge and discharge
Electric penalty coefficient is consistent, i.e. Kdc=1；If the energy storage of third company is leased by operator, electric discharge penalty coefficient is chosen high
In charging penalty coefficient.
(3) controllable distributed power generation access micro-capacitance sensor stable operation optimization
1. controllable distributed power generation stable operation constrains
To ensure that controllable distributed power generation works in stable operating status, consider that the power output of controllable distributed power generation is not got over
Limit, and to consider that distributed power generation power output change rate is no more than the permission upper limit of system, controllable distributed power generation is established in micro- electricity
The mathematical constraint of stable operation in net guarantees that controllable distributed power generation works in stable range of operation:
-RD,down≤Di,t+1-Di,t≤RD,up (21)
In formulaState for i-th controllable distributed power generation in the t period is 0/1 variable, respectively indicates controlled distribution formula
Power generation is stopped transport and is worked normally；It is the upper limit of i-th controllable distributed power generation active power of output.R in formulaD,upAnd RD,down
It is positive value, is the upward climbing rate limitation and the limitation of downward Ramp Rate of controllable distributed power generation respectively.
2. controllable distributed power generation efficient operation optimizes
In view of the consumption to generate electricity to equipment, under the premise of being able to maintain that load operates normally, it is desirable to reduce as far as possible
Output power improves the service life of equipment.
Choose following optimization aim:
Wherein, F4For controllable distributed power generation optimized coefficients.μDIt is punishing for controllable distributed power generation equipment unit power generation
Penalty factor, Di,tIt is the power output that i-th controllable distributed power generation optimizes in section at t-th, I is the number of controllable distributed power generation
Amount.
(4) lotus stable coordination running optimizatin is stored up in micro-capacitance sensor source
In above-mentioned S1, S2, S3, electric power spring, energy storage, controllable distributed power generation stabilization, efficient operation have been discussed
Management method.On the basis of the above-described procedure, to reduce to bulk power grid degree of dependence, improving micro-capacitance sensor robustness, consumption can not
The disturbance of generation of electricity by new energy bring is controlled, micro-capacitance sensor inside sources storage lotus stabilization, efficient operation is realized, further considers micro-capacitance sensor from electricity
Power market power purchase factor, the operation factor of uncontrollable distributed power generation, and it is sent out with electric power spring, energy storage, controlled distribution formula
Electricity combines, and obtains micro-capacitance sensor source storage lotus stabilization, coordinates efficient operation management method.
1. lotus stable operation constraint is stored up in micro-capacitance sensor source
(1) consider that micro-capacitance sensor internal power conservation, source storage lotus power need to reach active balance:
GtIt is the total electricity for entering micro-capacitance sensor by long market and ahead market, WtIt is that uncontrollable distributed power generation is practical
Online power, LtIt is the total load in t-th of section,Be j-th can interrupt load reduction amount.
(2) to meet new energy on-site elimination, realization is marketed one's own products, it is contemplated herein that micro-capacitance sensor does not send power outside:
Gt≥0 (24)
(3) to guarantee uncontrollable new energy power output electrical constraints, uncontrollable new energy power output is not out-of-limit, while Ramp Rate
Also it is limited:
0≤Wt≤kWfWt f (25)
-Rw,down≤Wt+1-Wt≤Rw,up (26)
kWf>=1, it is wind-powered electricity generation precision of prediction, precision is higher closer to 1.Wt fIt is wind power prediction expectation.Rw,upWith
Rw,downIt is positive value, is the upward climbing rate limitation and the limitation of downward Ramp Rate of uncontrollable distributed power generation respectively.
(4) in micro-capacitance sensor, existing can interrupt load.When system power deficiency, the mode of low-frequency load shedding can be taken,
Maintain power generation-coulomb balance and frequency stabilization in micro-capacitance sensor.But in micro-capacitance sensor can interrupt load be bounded:
In formula:It is j-th of interruptible load amount,It is the maximum permissible value of j-th of interruptible load amount.
(5) further, it is contemplated that micro-capacitance sensor is limited from the long-term purchase of electricity of electricity market:
Lmin≤kbm≤Lmax (28)
In formula: kbmIt is from the percentage of long-term trade market purchase of electricity, is optimized variable, optimum results depends on city a few days ago
The height of field electricity price level.LmaxAnd LminIt is upper and lower bound of the micro-capacitance sensor from long market power purchase ratio respectively.
2. the optimization of lotus efficient operation is stored up in micro-capacitance sensor source
For the self-produced personal for realizing micro-capacitance sensor generation of electricity by new energy, it is desirable to reduce from electricity market purchase of electricity, choose micro-capacitance sensor
From electricity market power purchase penalty factor F5；In order to dissolve generation of electricity by new energy amount as far as possible, reducing abandonment amount and abandon light quantity, improve new
Energy utilization rate chooses abandonment and abandons light penalty factor F6；In addition, reducing stopping for interruptible load to improve power supply reliability
Electricity chooses interruptible load and interrupts penalty factor F7。
In formula:WithIt is t-th of section from long-term Bilateral power market and a few days ago list of spot market respectively
Position electricity penalty factor.σWIt is abandonment penalty factor, μILIt is the unit power penalty factor of interruptible load.
3. lotus coordinated operation management method is stored up in micro-capacitance sensor source
The operation of lotus stable coordination is stored up to reach source, while reaching electric power spring efficiency, energy storage efficiency, controllable distributed power generation
The coordinated operation of the various micro-capacitance sensor component parts such as efficiency reaches the optimal of entire micro-capacitance sensor operation.Meanwhile it is micro- from encouraging to promote
Net development, pushes new energy use to set out, and defines:
E is the micro-capacitance sensor decision-making coefficient formed with linear weighted function, for realizing electric power spring in micro-capacitance sensor, energy storage, controllable
The coordination of the component parts such as distributed power generation, the microgrid grade operational efficiency being optimal are optimal.The reward factor that M is positive, is used for
Encourage the generation of electricity by new energy in microgrid.It is the sale of electricity excitation factor in t-th of optimization section, LtIt is the total negative of t-th of section
Lotus, it is available by predicting a few days ago.J is the number of interruptible load,It is the reduction amount of j-th of interruptible load,
It is k-th of non-critical loads reduction amount for receiving ES regulation,It is the payback load that k-th of non-critical loads receives ES adjusting
Amount.
Comprehensively consider, the optimization aim of system are as follows:
(34) formula is for making micro-capacitance sensor run on optimal whole efficiency, the management effect being optimal.And (8), (9),
(11-17), (20), (21), (23-28) are as optimization constraint, for ensuring that system runs on stable range of operation.
The optimized mathematical model mathematics essence is convex feasibility problem.In energy above Optimized model, do not occur non-thread
Property, non-convex constraint, therefore the solution of model can be used mature business solver and solve, such as Cplex and Gurobi, from
And guarantee the computational efficiency and optimality that understand.
Microgrid energy optimization method based on electric power spring provided by the above embodiment of the present invention, provides electric power bullet
The energy management method of spring access micro-capacitance sensor stable operation.The active of the intelligent load of inductive load is hindered by analysis electric power spring
The upper limit is run, the electrical constraints of electric power spring stable operation are obtained.And the factors such as electric power spring life period are combined, obtain electric power
Spring accesses the energy management of the stabilization, efficient operation of power grid.
Bound, climb upwards rate limitation, the limitation of downward Ramp Rate, state-of-charge in conjunction with energy storage charge-discharge electric power
Bounded, the stable operations requirement such as state-of-charge variation bounded in the odd-numbered day, obtain the operation constraint of energy storage.And further consider energy storage
The optimization of charge and discharge number obtains the energy management for making the stabilization, efficient operation of energy storage access power grid.
Consider uncontrollable generation of electricity by new energy factor, establish and consider under uncontrollable generation of electricity by new energy, bound constraint and
The uncontrollable new energy units limits of Ramp Rate constraint.The micro-capacitance sensor of the spring containing electric power can be made to dissolve uncontrollable new energy to go out
The disturbance of power provides feasible scheme for the large-scale grid connection of new energy.
In the case that obtain electric power spring, energy storage, uncontrollable generation of electricity by new energy constraint condition, further consider micro-capacitance sensor
Active balance constraint, from electricity market power purchase constraint, can interrupt load constraint, controllable generation of electricity by new energy power output bound constraint,
Controllable generation of electricity by new energy Ramp Rate constraint；In the feelings for considering the factors such as electric power spring life period, energy storage charge and discharge number
Under condition, further consider from electricity market power purchase factor, the operation factor of controllable generation of electricity by new energy, micro-capacitance sensor operator to intelligence
The many factors such as the Control factors of load demand payment, and effectively integrated with the corresponding reward factor, penalty factor,
Keep the multiple kinds of energy in micro-capacitance sensor mutually coordinated, stablizes, efficient operation.
In conjunction with the specific electrical constraints of the micro-capacitance sensor of the spring containing electric power, uncertain factor electrical quantity is considered, foundation contains
The electrical constraints condition of certainty factor and uncertain factor.According to the mathematical model for considering its efficiency, it is established as realizing each energy
Measure the mathematical model of coordinated operation.This method is gone out by the controllable generation of electricity by new energy of control, electric power spring intelligent load power, energy storage
The decision variables such as power enable whole system to reach stable in conjunction with specific electrical constraints condition；Optimizing to optimization aim,
Realize the efficient operation of entire micro-capacitance sensor.
Specific embodiments of the present invention are described above.It is to be appreciated that the invention is not limited to above-mentioned
Particular implementation, those skilled in the art can make various deformations or amendments within the scope of the claims, this not shadow
Ring substantive content of the invention.
Claims (10)
1. a kind of microgrid energy optimization method based on electric power spring, which comprises the steps of:
S1 analyzes electric power spring topological structure, is built according to intelligent load reduction amount in conjunction with general three ranks rebound load module
Vertical electric power spring stable operation constraint condition；On the basis of electric power spring stable operation, using electric power spring operational efficiency as
Optimization aim；Obtain electric power spring optimization part；
S2 determines the energy storage optimization constraint condition of energy storage stable operation, in the base of energy storage stable operation according to storage energy operation factor
On plinth, using energy-storage system charge and discharge number as optimization aim, energy storage optimization part is obtained；
S3 climbs the constraint of controllable distributed power generation power output bound, controllable distributed power generation rate limitation and controllable upwards
The downward Ramp Rate limitation of distributed power generation is used as controllable distributed power generation running optimizatin constraint condition, in controllable distributed power generation
On the basis of stable operation, using controllable distributed power generation operational efficiency as optimization aim, controlled distribution formula energy source optimization is obtained
Part；
The power-balance constraint condition of S4, active balance constraint condition based on micro-capacitance sensor and source storage lotus three, in S1, S2 and
On the basis of the optimization constraint condition of S3, auxiliary constraint condition is added, while by the optimization aim and micro-capacitance sensor in S1, S2 and S3
Extra optimization factor be combined, and the optimization aim for combining extra optimization factor is integrated, and realizes entire micro-capacitance sensor
Power-balance, work in stable operating status.
2. the microgrid energy optimization method according to claim 1 based on electric power spring, which is characterized in that the electric power
Spring uses the generation electric power spring with resistance inductive load, the generation electric power spring topological structure with resistance inductive load
Are as follows: converter is connected with resistance sense non-critical loads, collectively constitutes the intelligent load that can change with power grid end voltage change；
The intelligent load is connected in parallel with critical loads, maintains the voltage of critical loads constant.
3. the microgrid energy optimization method according to claim 1 based on electric power spring, which is characterized in that analysis electric power
The method of spring topological structure are as follows: utilize Kirchhoff's laws of electric circuit, obtain the mathematical model of electric power spring topological structure.
4. the microgrid energy optimization method according to claim 1 based on electric power spring, which is characterized in that the energy storage
Operation factor includes: that charge-discharge electric power bound, energy storage are climbed the downward Ramp Rate limitation of rate limitation, energy storage, charged upwards
State bounded and odd-numbered day state-of-charge change bounded.
5. the microgrid energy optimization method according to claim 1 based on electric power spring, which is characterized in that the S1
In: electric power spring stable operation constraint condition includes: intelligent load constraint condition and rebound load restraint condition；Electric power spring fortune
Line efficiency is by regulating and controlling coefficient and the depreciation of electric power spring equipment and maintenance coefficient embodiment for electrostrictive coefficient, micro-capacitance sensor operator；Institute
State in S4: auxiliary constraint condition includes following any one or any multinomial: electricity market power purchase constraint condition, uncontrollable distribution
Formula generate electricity constraint condition, can interrupt load constraint condition and long market power purchase constraint condition；Extra optimization factor includes such as
Lower any one is any multinomial: electricity market power purchase factor, abandonment abandon light factor and conventional electricity charge factor.
6. the microgrid energy optimization method according to claim 5 based on electric power spring, which is characterized in that the S4
In:
Using the method for mathematical modeling, on the basis of the optimization constraint condition of S1, S2 and S3, auxiliary constraint condition is added, it is real
The stable operation of existing uncontrollable distributed power generation, can the stable operation of interrupt load and power supply and the coulomb balance of source storage lotus；
Using the method for linear weighted function, the extra optimization factor of optimization aim and micro-capacitance sensor in S1, S2 and S3 is combined,
Plan as a whole micro-capacitance sensor source and stores up lotus operating status；
Using the method for linear-weighted, the optimization aim of selection is integrated.
7. the microgrid energy optimization method according to claim 6 based on electric power spring, which is characterized in that use mathematics
The method of modeling takes the intersection of the optimization constraint condition and auxiliary constraint condition of S1, S2, S3, realizes the optimization in S1, S2 and S3
On the basis of constraint condition, auxiliary constraint condition is added.
8. the microgrid energy optimization method according to claim 6 based on electric power spring, which is characterized in that using linear
The method of weighting invests the optimization of various pieces according to the extra optimization factor different piece significance level assert in practice
Weight；Optimization aim after power of assigning is overlapped, total optimization aim is formed, is realized the optimization aim in S1, S2 and S3
It is combined with the extra optimization factor of micro-capacitance sensor.
9. the microgrid energy optimization method according to claim 6 based on electric power spring, which is characterized in that use line style
The method of weighting integrates the optimization aim of selection method particularly includes:
A1 is obtained in optimization aim and is regulated and controled coefficient, the depreciation of electric power spring equipment and maintenance system for electrostrictive coefficient, micro-capacitance sensor operator
Number, energy storage optimized coefficients, controllable distributed power generation running optimizatin coefficient, micro-capacitance sensor are from electricity market power purchase penalty factor, abandonment
Abandon light penalty factor and can interrupt load interrupt penalty factor；
A2 chooses optimization weight according to the service requirement of each result obtained in A1, and will assign each result after optimizing weight
It is superimposed, the optimization aim after being integrated.
10. the microgrid energy optimization method according to claim 5 based on electric power spring, it is characterised in that:
Active balance constraint condition are as follows:For in guarantee system
Source storage lotus between power supply-coulomb balance, realize the power-balance of micro-capacitance sensor；
Electricity market power purchase constraint condition are as follows: Gt>=0, for guaranteeing that microgrid can be realized power self-produced personal, reduce to bulk power grid
It relies on, realizes the self-produced personal of distributed power generation；
Controllable distributed power generation running optimizatin constraint condition are as follows:-RD,down≤Di,t+1-Di,t≤RD,up, it is used for
Guarantee the stable of controllable distributed power generation；Wherein, controllable distributed power generation power output bound constraint controllably divides for guaranteeing
The safe and stable operation of cloth power generation；Ramp Rate limits controllable distributed power generation up and down, for guaranteeing the fortune of equipment
Row service life and reliability；
Uncontrollable distributed power generation constraint condition are as follows: 0≤Wt≤kWfWt f,-Rw,down≤Wt+1-Wt≤Rw,up, uncontrollable for guaranteeing
Distributed power generation it is stable；
Energy storage optimizes constraint condition are as follows:
-Rs,down≤(Sd,t+1-Sc,t+1)-(Sd,t-Sc,t)≤Rs,up, | SOC (N)-SOC (1) |≤δ, for guaranteeing the stable of energy storage；
It can interrupt load constraint condition are as follows:For guarantee can interrupt load prime power demand；
Intelligent load constraint condition are as follows:For guaranteeing the operation of intelligent load
Stablize；
Rebound load restraint condition are as follows:For guaranteeing the stable of rebound load；
Long market power purchase constraint condition are as follows: Lmin≤kbm≤Lmax, it is used to form Long-term planning, is dispatched for bulk power grid dispatcher
Reference is provided；
Wherein: GtIt is the total electricity for entering micro-capacitance sensor by long market and ahead market；Di,tIt is i-th controllable distributed power generation
In the power output that t-th optimizes in section；WtIt is the practical online power of uncontrollable distributed power generation；Sc,tAnd Sd,tIt is energy storage electricity respectively
The charge and discharge power in pond；LtIt is the total load in t-th of section；It is j-th of interruptible load amount；It is to receive ES k-th
The non-critical loads reduction amount of regulation；It is the rebound load capacity that k-th of non-critical loads receives ES adjusting；It is i-th
State of the controllable distributed power generation in the t period is 0/1 variable, respectively indicates controllable distributed power generation and stops transport and work normally；It is the upper limit of i-th controllable distributed power generation active power of output；RD,upAnd RD,downIt is positive value, is controlled distribution respectively
The upward climbing rate limitation and the limitation of downward Ramp Rate of formula power generation；kWf>=1, it is wind-powered electricity generation precision of prediction, the precision the high more connects
It is bordering on 1；Wt fIt is wind power prediction expectation；Rw,upAnd Rw,downIt is positive value, is climbing for uncontrollable distributed power generation respectively
Slope rate limitation and the limitation of downward Ramp Rate；WithIt is 0/1 variable, 0, which represents energy storage, does not work, and 1 represents energy storage work；
ScmaxAnd SdmaxIt is positive value, is the bound of energy storage charge-discharge electric power respectively；RS,upAnd RS,downIt is positive value, is energy storage respectively
Upward climbing rate limitation and downward Ramp Rate limitation；EbIt is the capacity of energy storage；η,ηdAnd ηcIt is energy storage self discharge respectively
Rate, discharging efficiency and charge efficiency；SOC (t) be when t-th of section starts energy storage charge state (State of charge,
SOC)；Δ t is the duration for optimizing section；δ is that SOC maximum changes ratio in the odd-numbered day；It is j-th of interruptible load amount；
Pup% and Pdown% is that ES adjusts non-key workload demand percentage change up and down respectively,It is in t-th of area
Between k-th of non-key load active rated value；It is 0/1 variable, 0, which represents ES, does not work, and 1 represents ES work；LmaxAnd Lmin
It is upper and lower bound of the micro-capacitance sensor from long market power purchase ratio respectively；kbmBe from the percentage of long-term trade market purchase of electricity,
It is optimized variable.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910264175.6A CN110048405B (en) | 2019-04-03 | 2019-04-03 | Microgrid energy optimization method based on power spring |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910264175.6A CN110048405B (en) | 2019-04-03 | 2019-04-03 | Microgrid energy optimization method based on power spring |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110048405A true CN110048405A (en) | 2019-07-23 |
| CN110048405B CN110048405B (en) | 2023-03-28 |
Family
ID=67276019
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910264175.6A Active CN110048405B (en) | 2019-04-03 | 2019-04-03 | Microgrid energy optimization method based on power spring |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110048405B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110474317A (en) * | 2019-08-26 | 2019-11-19 | 上海电力大学 | A kind of more direct current power spring droop control methods |
| CN110867891A (en) * | 2019-11-12 | 2020-03-06 | 湖南大学 | Topological structure of multifunctional grid-connected inverter and control method |
| CN113872242A (en) * | 2021-10-26 | 2021-12-31 | 华北电力科学研究院有限责任公司 | Active power distribution network energy optimization method and device adopting power spring |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2013178054A1 (en) * | 2012-06-01 | 2013-12-05 | The University Of Hong Kong | Input ac voltage control bi-directional power converters |
| US20150039145A1 (en) * | 2013-07-31 | 2015-02-05 | Abb Technology Ag | Microgrid Energy Management System and Method for Controlling Operation of a Microgrid |
| CN107591837A (en) * | 2017-09-06 | 2018-01-16 | 南京理工大学 | A kind of method that electric power spring based on droop control participates in micro-capacitance sensor stability contorting |
| CN108695871A (en) * | 2018-05-03 | 2018-10-23 | 上海交通大学 | The configuration method of the reduction stored energy capacitance demand of isolated island micro-capacitance sensor containing electric power spring |
| CN108830026A (en) * | 2018-08-21 | 2018-11-16 | 广西电网有限责任公司电力科学研究院 | A kind of simulating analysis of energy-stored spring stress state and danger zone |
| CN108879783A (en) * | 2018-08-02 | 2018-11-23 | 广东电网有限责任公司 | A kind of electric power spring energy source consumption system |
-
2019
- 2019-04-03 CN CN201910264175.6A patent/CN110048405B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2013178054A1 (en) * | 2012-06-01 | 2013-12-05 | The University Of Hong Kong | Input ac voltage control bi-directional power converters |
| US20150039145A1 (en) * | 2013-07-31 | 2015-02-05 | Abb Technology Ag | Microgrid Energy Management System and Method for Controlling Operation of a Microgrid |
| CN107591837A (en) * | 2017-09-06 | 2018-01-16 | 南京理工大学 | A kind of method that electric power spring based on droop control participates in micro-capacitance sensor stability contorting |
| CN108695871A (en) * | 2018-05-03 | 2018-10-23 | 上海交通大学 | The configuration method of the reduction stored energy capacitance demand of isolated island micro-capacitance sensor containing electric power spring |
| CN108879783A (en) * | 2018-08-02 | 2018-11-23 | 广东电网有限责任公司 | A kind of electric power spring energy source consumption system |
| CN108830026A (en) * | 2018-08-21 | 2018-11-16 | 广西电网有限责任公司电力科学研究院 | A kind of simulating analysis of energy-stored spring stress state and danger zone |
Non-Patent Citations (4)
| Title |
|---|
| JIDONG WU: "Steady-State Operation Range Analysis and off-limits Optimization of Electric Spring", 《2018 THE 6TH IEEE INTERNATIONAL CONFERENCE ON SMART ENERGY GRID ENGINEERING》 * |
| ZHIYU ZHAO: "Research on optimal operation of distribution network with electric springs", 《THE 14TH IET INTERNATIONAL CONFERENCE ON AC AND DC POWER TRANSMISSION》 * |
| 赵志宇: "含有电力弹簧的主动配电网优化运行", 《现代电力》 * |
| 赵志宇: "含有电力弹簧的微电网能量优化模型", 《电力自动化设备》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110474317A (en) * | 2019-08-26 | 2019-11-19 | 上海电力大学 | A kind of more direct current power spring droop control methods |
| CN110867891A (en) * | 2019-11-12 | 2020-03-06 | 湖南大学 | Topological structure of multifunctional grid-connected inverter and control method |
| CN110867891B (en) * | 2019-11-12 | 2023-06-13 | 湖南大学 | Topological structure of multifunctional grid-connected inverter and control method |
| CN113872242A (en) * | 2021-10-26 | 2021-12-31 | 华北电力科学研究院有限责任公司 | Active power distribution network energy optimization method and device adopting power spring |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110048405B (en) | 2023-03-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Hossain et al. | Modified PSO algorithm for real-time energy management in grid-connected microgrids | |
| Ju et al. | A multi-objective robust scheduling model and solution algorithm for a novel virtual power plant connected with power-to-gas and gas storage tank considering uncertainty and demand response | |
| Hossain et al. | Energy management of community microgrids considering degradation cost of battery | |
| Teng et al. | Technical review on advanced approaches for electric vehicle charging demand management, part i: Applications in electric power market and renewable energy integration | |
| Lv et al. | Interactive energy management of networked microgrids-based active distribution system considering large-scale integration of renewable energy resources | |
| CN107301472B (en) | Distributed photovoltaic planning method based on scene analysis method and voltage regulation strategy | |
| Khatamianfar et al. | Improving wind farm dispatch in the Australian electricity market with battery energy storage using model predictive control | |
| CN113688567B (en) | Virtual power plant two-stage optimization scheduling method considering impact load | |
| Huang et al. | Robust coordination expansion planning for active distribution network in deregulated retail power market | |
| CN109858774B (en) | Source network load planning method for improving system safety and coordination | |
| CN110048405A (en) | Microgrid energy optimization method based on electric power spring | |
| CN110956324B (en) | Day-ahead high-dimensional target optimization scheduling method for active power distribution network based on improved MOEA/D | |
| CN105140941B (en) | Consider that lithium battery can be changed the micro-capacitance sensor Optimization Scheduling of depreciable cost and practical charge and discharge strategy | |
| CN111786422B (en) | Real-time optimization scheduling method for participating in upper-layer power grid by micro-power grid based on BP neural network | |
| CN109474013A (en) | The increment power distribution network of the distributed photovoltaic containing Thief zone Optimization Scheduling a few days ago | |
| Maulik | Probabilistic power management of a grid-connected microgrid considering electric vehicles, demand response, smart transformers, and soft open points | |
| CN112821470A (en) | Micro-grid group optimization scheduling strategy based on niche chaos particle swarm algorithm | |
| Suchetha et al. | Optimization techniques for operation and control of microgrids–Review | |
| Yi et al. | Dispatch-aware planning of energy storage systems in active distribution network | |
| CN110046780B (en) | Islanding micro-grid demand response economic dispatching method and system based on time-of-use electricity price | |
| Ma et al. | Energy storage capacity optimization for improving the autonomy of grid-connected microgrid | |
| Kumari et al. | Integration of solar and flexible resources into expected security cost with dynamic optimal power flow problem using a Novel DE algorithm | |
| Dai et al. | An equilibrium model of the electricity market considering the participation of virtual power plants | |
| CN116961008A (en) | Micro-grid capacity double-layer optimization method considering power spring and load demand response | |
| CN108683211B (en) | Virtual power plant combination optimization method and model considering distributed power supply volatility |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |