CN106992549A - The capacity configuration optimizing method and device of a kind of independent micro-grid system - Google Patents
The capacity configuration optimizing method and device of a kind of independent micro-grid system Download PDFInfo
- Publication number
- CN106992549A CN106992549A CN201710365308.XA CN201710365308A CN106992549A CN 106992549 A CN106992549 A CN 106992549A CN 201710365308 A CN201710365308 A CN 201710365308A CN 106992549 A CN106992549 A CN 106992549A
- Authority
- CN
- China
- Prior art keywords
- constraint
- grid system
- capacity
- generating set
- optimal solution
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 50
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 52
- 238000004146 energy storage Methods 0.000 claims abstract description 19
- 238000012423 maintenance Methods 0.000 claims abstract description 7
- 238000013486 operation strategy Methods 0.000 claims abstract description 7
- 230000005611 electricity Effects 0.000 claims description 18
- 239000002245 particle Substances 0.000 claims description 15
- 238000003860 storage Methods 0.000 claims description 12
- 230000000694 effects Effects 0.000 claims description 3
- 230000008569 process Effects 0.000 claims description 3
- 230000008929 regeneration Effects 0.000 claims description 3
- 238000011069 regeneration method Methods 0.000 claims description 3
- 230000000295 complement effect Effects 0.000 claims description 2
- 230000005291 magnetic effect Effects 0.000 claims 2
- 230000009467 reduction Effects 0.000 abstract description 7
- 230000001172 regenerating effect Effects 0.000 abstract description 3
- 230000006870 function Effects 0.000 description 36
- 238000005457 optimization Methods 0.000 description 8
- 238000007599 discharging Methods 0.000 description 7
- 230000008859 change Effects 0.000 description 5
- 238000012804 iterative process Methods 0.000 description 5
- 238000011156 evaluation Methods 0.000 description 4
- 238000011160 research Methods 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 230000002068 genetic effect Effects 0.000 description 3
- 230000001343 mnemonic effect Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 208000002193 Pain Diseases 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000002153 concerted effect Effects 0.000 description 1
- 238000005520 cutting process Methods 0.000 description 1
- 238000009795 derivation Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000011068 loading method Methods 0.000 description 1
- 230000035800 maturation Effects 0.000 description 1
- 230000006855 networking Effects 0.000 description 1
- 230000036407 pain Effects 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 238000005303 weighing Methods 0.000 description 1
Classifications
-
- H02J3/383—
-
- 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
-
- 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
-
- H02J3/386—
-
- 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
- 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
- Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
-
- 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
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
-
- 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
-
- 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
- Y02E70/00—Other energy conversion or management systems reducing GHG emissions
- Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin
-
- 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
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
- Y04S20/20—End-user application control systems
- Y04S20/222—Demand response systems, e.g. load shedding, peak shaving
Abstract
The present invention relates to a kind of capacity configuration optimizing method of independent micro-grid system and device, according to the constraints of independent micro-grid system, propose the Optimized Operation strategy maximally utilized as principle fully to meet customer charge demand He realize regenerative resource, set up with the object function of the minimum target of integrated operation cost, the solution of model, the final optimal solution of acquisition and target function value are carried out using imitative electromagnetism algorithm.Operating cost needed for the not crucial load power of operation cost of electricity-generating, operation expense, the maintenance cost of energy storage device and the excision of the invention for comprehensively considering generating set, single-goal function only need to be set up, it just can fully reflect the capacity configuration model of independent micro-grid system, introduce power supply reliability, wind light mutual complementing characteristic and wind light generation utilization rate these three Performance Evaluating Indexes, ensure the power supply reliability of micro-grid system, the capacity that energy storage device is configured in reduction system, improves the wind light generation utilization rate of micro-grid system.
Description
Technical field
The invention belongs to microgrid energy Optimized Operation field, and in particular to a kind of capacity optimization of independent micro-grid system is matched somebody with somebody
Put method and device.
Background technology
Micro-grid system has merged a variety of distributed power sources, load, energy storage device because of it, can neatly grid-connected or off-network fortune
OK, improve reliability that the utilization rate and load of distributed power source power and possess very big development potentiality.Wherein, microgrid system
The economic optimization operation study of system is that microgrid studies one of major issue urgently to be resolved hurrily.
At present, for the optimization traffic control problem of microgrid, different networking modes and different mode of operations, meeting
There are different Optimized model and constraints limitation.Existing patent document and research work are also more to operate to target with systematic economy
To set up Optimized model, and using conventional Non-Linear Programming, intelligent algorithm such as particle cluster algorithm, genetic algorithm etc. to being modeled
Type is solved.But either still all there is various ask in conventional intelligent algorithm in solution procedure for Non-Linear Programming
Topic.For example, in March, 2016《Power technology》Periodical is in research with designing the entitled of plate《Independence based on wind light mutual complementing is micro-
Net power system capacity optimizes》Paper disclose using power supply reliability, wind light mutual complementing, accumulator cell charging and discharging number of times as constraints,
The economical model that gross investment is at least object function is set up, using each power supply in genetic algorithm discussion system in given scheduling
Optimal capacity configuration problem under strategy, this method has certain reasonability in terms of model is set up with derivation algorithm, but deposits
The shortcomings of Restriction condition treat is inconvenient, algorithm the convergence speed is slow.
The stochastic global optimization surpassed between a kind of simulation charged particle proposed by doctor Birbil apart from force principal is calculated
Method --- imitative electromagnetism algorithm, its principle is simple, solves rapidly, is suitable for solving nonconvex nonlinear programming problems.
In the prior art, one is published in《Electric power system protection and control》Periodical the 8th phase of volume 44 it is entitled《It is based on
Wind/light/bavin/storage independent micro-grid distributed power source multiple target the capacity for improving ELM is distributed rationally》Paper disclose a kind of utilization
The capacity configuration optimizing method of imitative electromagnetism Algorithm for Solving distributed power source, this method is used as constraint bar using system power supply reliability
Part, sets up the Model for Multi-Objective Optimization for considering micro-capacitance sensor economy, the feature of environmental protection and energy utilization rate, and using adaptive iteration step
The measures such as long, search space reduction improve emulation electromagnetism convergence, and this method ensure that calculation to a certain extent
The convergence of method, but convergence rate is unhappy, locally optimal solution this problem is absorbed in order to improve in an iterative process, and this method is drawn
The TSP question thought of genetic algorithm is entered.
The content of the invention
It is an object of the invention to provide a kind of capacity configuration optimizing method of independent micro-grid system and device, for solving
In existing independent micro-grid system, set up with the irrational problem of object function of the minimum target of integrated operation cost, solve
By set up multiple objective function to micro-grid system carry out capacity distribute rationally generation solving complexity, it is computationally intensive the problem of.
In order to solve the above technical problems, the present invention proposes a kind of capacity configuration optimizing method of independent micro-grid system, bag
Include solution below：
Method scheme one, comprises the following steps：
Set up with the object function of the minimum target of integrated operation cost, integrated operation cost includes the operation of generating set
Operating cost needed for cost of electricity-generating, operation expense, the maintenance cost of energy storage device and the not crucial load power of excision；
Bound for objective function includes electric energy balance state constraint, generating set units limits, energy storage device discharge and recharge constraint, property
Can evaluation index constraint, wherein, Performance Evaluating Indexes constraint include the constraint of power supply reliability, the constraint of wind light mutual complementing characteristic and
The constraint of wind light generation utilization rate；
The object function is solved using imitative electromagnetism algorithm, optimal solution and its target function value is obtained.
Method scheme two, on the basis of method scheme one, optimal solution mistake is being found when utilizing the imitative electromagnetism algorithm
Occur in journey the target function value tried to achieve it is constant when, using in the EIAJ, minimum load and current iteration of each generating set
The distance between optimal solution reduces search space.
Method scheme three, on the basis of method scheme one, the wind light generation utilization rate is total output of generating set
Power account for the demand power of load and energy storage device charge-discharge electric power and ratio.
Method scheme four, on the basis of method scheme one, using imitative electromagnetism algorithm, the colony in each iteration is gone through
History optimal solution, is modified to the mobile formula of population in the imitative electromagnetism algorithm.
Method scheme five, on the basis of method scheme one, the generating set units limits include the higher limit of setting
And lower limit, wherein, higher limit is to calculate the maximum allowable of each generating set using the model of exerting oneself of each generating set to exert oneself
What power was obtained.
Method scheme six, on the basis of method scheme five, the process solved using imitative electromagnetism algorithm to object function
Including following sub-step：
S1, according to the generating set units limits generate initial population particle, sentence with reference to the Optimized Operation strategy of system
The running status of disconnected energy storage device and cut-out not critical load whether is needed, and will be put down using penalty function method with electric energy
The object function of weighing apparatus state constraint condition is converted into the object function of no electric energy balance state constraint condition；
S2, the object function without electric energy balance state constraint condition is solved, calculate individual target function value in population, individual
Volume charge value and its total stress size, obtain the optimal solution of current population；
S3, Population Regeneration and its optimal solution, selection power supply reliability, wind light mutual complementing characteristic and wind light generation utilization rate are met
The optimal solution of the condition of setting is used as optimal combined capacity.
In order to solve the above technical problems, the present invention also proposes that a kind of capacity of independent micro-grid system distributes device rationally,
Including solution below：
Device scheme one, including：
Model sets up unit：For setting up with the object function of the minimum target of integrated operation cost, integrated operation cost
The not critical load of operation cost of electricity-generating, operation expense, the maintenance cost of energy storage device and excision including generating set
Operating cost needed for power；Bound for objective function includes electric energy balance state constraint, generating set units limits, storage
Can device discharge and recharge constraint, Performance Evaluating Indexes constraint, wherein, Performance Evaluating Indexes constraint include power supply reliability constraint,
The constraint of wind light mutual complementing characteristic and the constraint of wind light generation utilization rate；
Computing unit：For being solved using imitative electromagnetism algorithm to the object function, optimal solution and its mesh are obtained
Offer of tender numerical value.
Device scheme two, on the basis of device scheme one, in addition to reduction search space unit：For when described in
Imitative electromagnetism algorithm utilizes the maximum of each generating set when the target function value for occurring trying to achieve during finding optimal solution is constant
Exert oneself, the distance between optimal solution reduces search space in minimum load and current iteration.
Device scheme three, on the basis of device scheme one, the wind light generation utilization rate is total output of generating set
Power account for the demand power of load and energy storage device charge-discharge electric power and ratio.
Device scheme four, on the basis of device scheme one, in addition to for utilizing imitative electromagnetism algorithm in each iteration
When colony's history optimal solution, the unit being modified to the mobile formula of population in the imitative electromagnetism algorithm.
The beneficial effects of the invention are as follows：According to the power supply reliability of independent micro-grid system, wind light mutual complementing characteristic and scene
The constraints of capacity factor, sets up with the object function of the minimum target of integrated operation cost, using imitative electromagnetism algorithm
Carry out the solution of model, the final optimal solution of acquisition and target function value.The operation of the invention for comprehensively considering generating set
Operating cost needed for cost of electricity-generating, operation expense, the maintenance cost of energy storage device and the not crucial load power of excision,
Single-goal function only need to be set up, just can fully reflect the capacity configuration model of independent micro-grid system, power supply reliability, wind is introduced
Light complementary characteristic and wind light generation utilization rate these three Performance Evaluating Indexes, it is ensured that the power supply reliability of micro-grid system, reduction system
The capacity configured in system to energy storage device, improves the wind light generation utilization rate of micro-grid system, for research wind light mutual complementing microgrid system
For system, it is more beneficial for solving the maximum capacity configuration scheme of honourable utilization rate.
The present invention is on the premise of the difficulty that huge profit is solved with the imitative electromagnetism algorithm of improvement to institute's established model is not added with, profit
Individual memory and colony's communication function with particle cluster algorithm, record population history optimal solution, and population movement formula is carried out
Amendment, this method solving speed is fast, convenience of calculation, and avoids locally optimal solution is absorbed in iterative process.
Brief description of the drawings
Fig. 1 is the structure chart of wind/light/storage independent microgrid system in the embodiment of the present invention；
Fig. 2 is the optimization based on the wind/light/storage independent microgrid system for improving imitative electromagnetism algorithm in the embodiment of the present invention
Operation method flow chart.
Embodiment
The embodiment to the present invention is further described below in conjunction with the accompanying drawings.
A kind of embodiment of the capacity configuration optimizing method of independent micro-grid system of the present invention：
The present invention illustrates that the capacity of the present invention is excellent by taking the micro-grid system for the dc bus type being made up of wind/light/storage as an example
Change collocation method, as shown in figure 1, generating set is wind-power electricity generation, photovoltaic generation in system, energy-storage units are battery, user
Load is divided into critical load and not critical load.
The operation principle of the system be the photovoltaic generation, wind-power electricity generation and battery respectively through corresponding DC/DC,
The tandem of AC/DC, DC/DC converter can power to dc bus, then by DC/AC inverters directly to load, also can be through transformation
Device feed-in power distribution network.
The present invention takes pains research wind/light/storage micro-grid system in intraday running situation, different periods natural cause with
Under conditions of machine change, selected according to the carrying capacity state of wind-power electricity generation, the electric energy of photovoltaic generation and battery optimal wind/
Light/storage operating scheme combination, with the change of follow load demand in real time, it is ensured that the equilibrium of supply and demand of energy in system, and can be real
The operating cost of existing system is minimum.It is proposed to this end that following Optimized Operation strategy：
1) to maximally utilize regenerative resource as principle, preferential exerting oneself electric energy and meet using wind/light generator group
Workload demand inside microgrid.
2) the t periods, if the electric energy sum of exerting oneself of wind/light generator group is more than workload demand and battery fullcharging electricity shape
State, then consider that the optimal wind of selection/light generator group combination puts into operation, battery not discharge and recharge.
3) the t periods, if the electric energy sum of exerting oneself of wind/light generator group is more than workload demand and the discontented carrying capacity shape of battery
State, then consider unnecessary electric energy (the electric energy sum of exerting oneself that unnecessary electric energy is equal to wind/light generator group subtracts workload demand)
For being charged to battery.Now, three kinds of situation considerations can be divided into：1. unnecessary electric energy, which is just met for battery, can realize fullcharging
The demand of state of charge, the then wind currently put into operation/light generator group as optimal unit combination；2. unnecessary electric energy is completely used for
Charged for battery, the carrying capacity state of battery can not still reach fullcharging state of charge, then wind/the light generator currently put into operation
Group is optimal unit combination；3. whole unnecessary electric energy is not charged for battery, battery is to have reached fullcharging electricity
State, then reach that required charge capacity is incorporated to needed for load during fullcharging electricity by battery, optimal wind/light generator group selected afterwards
Combination puts into operation.
4) the t periods, if the electric energy sum of exerting oneself of wind/light generator group is less than workload demand, the wind currently put into operation/light hair
Group of motors is optimal unit combination.Now, three kinds of situation considerations can be divided into：1. the carrying capacity state of battery is charged less than minimum
When measuring limit value, it is considered to cut-out not critical load, fully powered for critical load；2. the carrying capacity state of battery is not less than most
During small carrying capacity limit value, by battery discharging, load institute can not be still fully met during battery discharging to minimum carrying capacity state
Need, then consider cut-out not critical load；3. when the carrying capacity state of battery is not less than minimum carrying capacity limit value, and electric power storage
The current carrying capacity state in pond is met needed for electric discharge afterload.
According to above-mentioned Optimized Operation strategy, set up and mould is optimized with the capacity configuration of the minimum target of system total operating cost
Type, and introduce the index factor for evaluation system runnability.Object function is：
Wherein, C (t) is t period system integrated operation costs；C1For the operation cost of electricity-generating of generating set；C2For generator
The operation expense of group；CbFor battery service cost；CqfhThe punishment of the not crucial workload demand power cut off for the t periods
Cost；For the generated output of i-th unit of t periods；For the variable quantity of t period separate unit accumulator cell charging and discharging power, on the occasion of
Electric discharge, negative value charging；The not crucial load power cut off for the t periods；W is the penalty coefficient for cutting off not critical load；N is
Generating set sum；I is machine group #；T=24.
The relevant constraint of above-mentioned capacity configuration Optimized model includes electric energy balance state constraint, generating set and exerted oneself about
Beam, accumulator cell charging and discharging constraint, Performance Evaluating Indexes constraint.
Wherein, electric energy balance state constraint is in the case where ignoring the power electronic devices loss of system and network loss, during t
Needed for needing to meet load in section
N=a+b
In formula, a, b are respectively the unit number of wind-power electricity generation, photovoltaic generation, and N is total for the unit generated electricity,For the t periods
The active power output power of interior i-th wind-power electricity generation,For the active power output power of jth platform photovoltaic generation in the t periods,For t
The changed power of separate unit battery in period.
Generating set units limits areWherein, pimin、pimaxRespectively i-th generating set
The minimum of permission, EIAJ.
The carrying capacity state constraint of battery isWherein battery is charged
Measuring state change is：
Accumulator cell charging and discharging is constrained toWherein, δ is the self discharge efficiency of battery,
0.01%/h；pbmin、pbmaxThe respectively minimum of battery, maximum charge-discharge electric power, η c, ηdRespectively the charging of battery, put
Electrical efficiency, is taken as 90%；For the carrying capacity of t period batteries；WbrFor battery rating；Wbsocmin、Wbsocmax
Respectively battery is minimum, maximum carrying capacity, and herein, selection battery initial capacity is the 60% of fullcharging electricity.
Performance Evaluating Indexes include power supply reliability fLPSP, wind light mutual complementing characteristic DLWith wind light generation utilization rate τ, wherein, supply
Electric reliability fLPSPSystem power supply is characterized with load short of electricity rate (loss of power suppy probability, LPSP)
Reliability, then the system power supply Calculation of Reliability formula in the t periods be：
In formula, N is the sum of wind power generating set and photovoltaic generation unit,For wind power generating set and light in the t periods
The power output of overhead generator group,The power for needed for t period internal loadings,For the charge and discharge electric work of the battery in the t periods
Rate；fLPSPSmaller, power supply reliability is higher, fLPSPShort of electricity rate f can be born no more than its peak loadmax, fmaxValue is 0.1%.
Wind light mutual complementing characteristic DLWith the power output sum of wind power generating set in the t periods and photovoltaic generation unit relative to
The stability bandwidth D of power needed for loadLTo represent, its calculating formula is：
In above formula, DLNo more than maximum occurrences boundary DLmax, DLmaxDesirable 1.5.DLIt is smaller, wind power generating set and photovoltaic
The curve of the power output sum of generating set and load curve are closer to then illustrating that wind light mutual complementing characteristic is better.
Wind light generation utilization rate wind power generating set in the t periods and the power output sum and load of photovoltaic generation unit
The ratio τ of required power represents that its calculating formula is：
In above formula, τ is not more than 1, and τ is bigger, and wind light generation utilization rate is higher.
The wind power generating set mathematics of power output for calculating above-mentioned wind power generating set model of exerting oneself is：
In formula, v (t) is the actual wind speed (m/s) of t periods；vcFor incision wind speed (m/s)；vfFor cut off wind speed (m/s)；vr
For rated wind speed (m/s)；prFor the rated power (kW) of the different wind-driven generator models of correspondence.
The photovoltaic generation unit mathematics of power output for calculating above-mentioned photovoltaic generation unit model of exerting oneself is：
In formula, η is the conversion efficiency (%) of photovoltaic cell；YpvFor photovoltaic battery panel rated capacity (W)；IT(t) it is the t periods
Amount of radiation (the kW/m that photovoltaic battery panel is received2), time to time change；Is=1kW/m2, under being standard test condition (STC)
Irradiation intensity.
Application enhancements imitate electromagnetism algorithm and above-mentioned capacity configuration Optimized model are solved, and step is：
(1) information parameter such as needed for input wind speed, illumination and load；The population scale for setting algorithm is m, and dimension is n, most
Big iterations Itermax, the parameter such as evolutionary generation initial value k=0.
(2) setting wind/light generator group is operated under maximal power tracing dotted state, utilizes described wind-powered electricity generation, photovoltaic
Model of exerting oneself, calculate each generating set it is maximum allowable go out activity of force.
(3) it is random that initial population particle X is produced between the maximum of each generating set, minimum load power bracketint；Should
Scope is the units limits of each generating set, that is, the higher limit and lower limit set, lower limit is set to zero, and higher limit utilizes each hair
The model of exerting oneself of group of motors calculate each generating set it is maximum allowable go out activity of force obtain.
(4) battery running status is judged according to foregoing Optimized Operation strategy and whether needs cut-out not crucial
Load, and penalty function method is introduced by the capacity configuration model with electric energy balance state constraint condition, it is converted into no electric energy balance
The capacity configuration model of state constraint condition, calculates evaluation function value individual in population, individual charge value and its total stress big
It is small.
(5) new population is produced using the particle more new formula after improving, by new population and the target function value of previous generation population
It is compared, Population Regeneration, and writes down current optimal solution Xk,best。
(6) f during current optimal solution is calculatedLPSP、DL, τ, selection meets the constraint of above-mentioned wind light mutual complementing characteristic and power supply is reliable
Property constraint the minimum and honourable utilization rate highest wind/light of system operation cost/storage combination；Make k=k+1, evaluation algorithm iteration
Condition termination is no, if not being transferred to step (3) iteration step, optimal solution is exported if end condition has been met and its final
Target function value, exports accordingly result.
The mobile formula that the imitative electromagnetism algorithm that the present invention is used carries out the population that searching process is related to includes：
Wherein,
In formula, n is particle dimension, i.e. wind light generation unit variable number；M is total number of particles in population,For kth time
The solution vector of i-th particle in iteration；Xk,bestFor the particle that fitness function in kth time iteration is optimal, i.e. kth time iteration most
Excellent solution；For the i-th particle in kth time iterationCharge value；Make a concerted effort for i-th particle in k iteration；λ is weight
The factor, is the random number between [0,1], directly reflects the mobile degree of population；F () is the fitness function of particle；This is imitated
The optimizing formula and optimizing principle of electromagnetism algorithm are prior art, are specifically shown in《It is only based on the wind/light/bavin/storage for improving ELM
Vertical micro-grid distributed generation multiple target capacity is distributed rationally》Paper.
The present invention is carried out using dynamic iterative search method, reduction search space method and global mnemonics to imitative electromagnetism algorithm
Improve, concretely comprise the following steps：
P1) dynamic iterative search method
Due to optimizing formulaλ is the weight for influenceing algorithmic statement precision and ensureing population diversity
Want parameter.Choose rational λ value and be conducive to the ability of searching optimum and local search ability of balanced algorithm, so as to seek optimal
Solution.When being defined on algorithm iteration and starting, λ=λmax；By iterative process, at the end of algorithm, λ=λmin.λ iterative formula
For：
In formula, Iter, ItermaxCurrent and maximum iterations is represented respectively.
P2 search space method) is reduced
For the convergence that accelerating algorithm is solved, in the iterative process of algorithm, if target function value is constant, using each
Optimal solution X in maximum, minimum load and kth time iteration of generating setk,bestThe distance between come adjust reduction search space,
So as to the convergence of accelerating algorithm.The tactful mathematical notation formula is：
In formula, α is the random number between [0 1].
P3) global mnemonics
In order to prevent algorithm in an iterative process, to be absorbed in locally optimal solution.Individual memory and the group for using for reference particle cluster algorithm
Body communication function, when imitative electromagnetism algorithm reaches kth time iteration, colony's history optimal solution is designated as Xk,gbest, to formulaIt is modified, is modified to：
In formula, λ1For constant of the value between [0 1], r is the random number between [0 1].
It is to follow current natural environmental condition first, according to existing maturation in the capacity of the present invention is distributed rationally
The mathematical modeling of generating set calculate the EIAJ power of current each unit.Then reinitialize population, utilizes imitative electromagnetism
Algorithm is learned to be solved.But, the premise of solution is also contemplated that proposed Optimized Operation strategy, and a point situation judges whether
Battery is needed to carry out discharge and recharge, influence of the discharge and recharge to battery carrying capacity state；Simultaneously by constrained optimization aim letter
Number is converted into unconfined optimization object function and solved.
The present invention is to maximally utilize regenerative resource and improve workload demand satisfaction as principle, by reasonably optimizing
Scheduling strategy, determines accumulator charging and discharging state and switching load condition, sets up with the minimum target of system integrated operation cost
Capacity configuration Optimized model, institute's established model is solved using imitative electromagnetism algorithm is improved, the optimal satisfaction of selection is certain
The honourable hair long electricity combination of Performance Evaluating Indexes factor.Also, the present invention is using dynamic iterative search method, reduction search space method
And global mnemonics improves imitative electromagnetism convergence, while accelerating convergence, it is to avoid be absorbed in asking for locally optimal solution
Topic.
A kind of capacity of independent micro-grid system of the present invention distributes the embodiment of device rationally：
Including such as lower unit：
Model sets up unit：For setting up with the object function of the minimum target of integrated operation cost, integrated operation cost
The not critical load of operation cost of electricity-generating, operation expense, the maintenance cost of energy storage device and excision including generating set
Operating cost needed for power；Bound for objective function includes electric energy balance state constraint, generating set units limits, storage
Can device discharge and recharge constraint, Performance Evaluating Indexes constraint, wherein, Performance Evaluating Indexes constraint include power supply reliability constraint,
The constraint of wind light mutual complementing characteristic and the constraint of wind light generation utilization rate.
Computing unit：For being solved using imitative electromagnetism algorithm to the object function, optimal solution and its mesh are obtained
Offer of tender numerical value.
The device is additionally operable to work as there is the target function value tried to achieve using imitative electromagnetism algorithm during optimal solution is found
It is constant when, reduced using the distance between optimal solution in EIAJ, minimum load and kth time iteration of each generating set
Search space.
The capacity of signified independent micro-grid system distributes device rationally in above-described embodiment, is actually based on the present invention
A kind of computer solution of method flow, i.e., a kind of software architecture may apply to the controller of independent micro-grid system
In, said apparatus is the treatment progress corresponding with method flow.Due to the introduction to the above method, sufficiently clear is complete
It is whole, therefore be no longer described in detail.
Claims (10)
1. a kind of capacity configuration optimizing method of independent micro-grid system, it is characterised in that comprise the following steps：
Set up with the object function of the minimum target of integrated operation cost, the operation that integrated operation cost includes generating set generates electricity
Operating cost needed for cost, operation expense, the maintenance cost of energy storage device and the not crucial load power of excision；Target
The constraints of function is commented including electric energy balance state constraint, generating set units limits, energy storage device discharge and recharge constraint, performance
Valency Index Constraints, wherein, Performance Evaluating Indexes constraint includes the constraint, the constraint of wind light mutual complementing characteristic and scene of power supply reliability
The constraint of capacity factor；
The object function is solved using imitative electromagnetism algorithm, optimal solution and its target function value is obtained.
2. the capacity configuration optimizing method of independent micro-grid system according to claim 1, it is characterised in that when utilizing
Imitative electromagnetism algorithm is stated when the target function value for occurring trying to achieve during finding optimal solution is constant, using each generating set most
Exert oneself greatly, the distance between optimal solution reduces search space in minimum load and current iteration.
3. the capacity configuration optimizing method of independent micro-grid system according to claim 1, it is characterised in that the scene
Capacity factor be generating set gross output account for the demand power of load and energy storage device charge-discharge electric power and ratio
Value.
4. the capacity configuration optimizing method of independent micro-grid system according to claim 1, it is characterised in that utilize imitative electricity
Colony history optimal solution of the magnetics algorithm in each iteration, is repaiied to the mobile formula of population in the imitative electromagnetism algorithm
Just.
5. the capacity configuration optimizing method of independent micro-grid system according to claim 1, it is characterised in that the generating
Unit output constraint includes the higher limit and lower limit of setting, wherein, higher limit is the model meter of exerting oneself using each generating set
Calculate each generating set it is maximum allowable go out activity of force obtain.
6. the capacity configuration optimizing method of independent micro-grid system according to claim 5, it is characterised in that using imitative electricity
The process that magnetics algorithm is solved to object function includes following sub-step：
S1, according to the generating set units limits generate initial population particle, with reference to system Optimized Operation strategy judge storage
Can device running status and whether need cut-out not critical load, and will have electric energy balance shape using penalty function method
The object function of modal constraint condition is converted into the object function of no electric energy balance state constraint condition；
S2, object function of the solution without electric energy balance state constraint condition, calculate target function value individual in population, individual electricity
Charge values and its total stress size, obtain the optimal solution of current population；
S3, Population Regeneration and its optimal solution, selection power supply reliability, wind light mutual complementing characteristic and wind light generation utilization rate meet setting
The optimal solution of condition be used as optimal combined capacity.
7. a kind of capacity of independent micro-grid system distributes device rationally, it is characterised in that including such as lower unit：
Model sets up unit：For setting up with the object function of the minimum target of integrated operation cost, integrated operation cost includes
Operation cost of electricity-generating, operation expense, the maintenance cost of energy storage device and the not crucial load power of excision of generating set
Required operating cost；Bound for objective function includes electric energy balance state constraint, generating set units limits, energy storage dress
Discharge and recharge constraint, Performance Evaluating Indexes constraint are put, wherein, Performance Evaluating Indexes constraint includes the constraining of power supply reliability, honourable
The constraint of complementary characteristic and the constraint of wind light generation utilization rate；
Computing unit：For being solved using imitative electromagnetism algorithm to the object function, optimal solution and its target letter are obtained
Numerical value.
8. the capacity of independent micro-grid system according to claim 7 distributes device rationally, it is characterised in that also including contracting
Subtract search space unit：The imitative electromagnetism algorithm is utilized the object function tried to achieve occur during optimal solution is found for working as
When being worth constant, reduced using the distance between optimal solution in the EIAJ, minimum load and current iteration of each generating set
Search space.
9. the capacity of independent micro-grid system according to claim 7 distributes device rationally, it is characterised in that the scene
Capacity factor be generating set gross output account for the demand power of load and energy storage device charge-discharge electric power and ratio
Value.
10. the capacity of independent micro-grid system according to claim 7 distributes device rationally, it is characterised in that also include
For utilizing imitative colony history optimal solution of the electromagnetism algorithm in each iteration, the shifting to population in the imitative electromagnetism algorithm
The unit that dynamic formula is modified.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710365308.XA CN106992549A (en) | 2017-05-22 | 2017-05-22 | The capacity configuration optimizing method and device of a kind of independent micro-grid system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710365308.XA CN106992549A (en) | 2017-05-22 | 2017-05-22 | The capacity configuration optimizing method and device of a kind of independent micro-grid system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106992549A true CN106992549A (en) | 2017-07-28 |
Family
ID=59420017
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710365308.XA Pending CN106992549A (en) | 2017-05-22 | 2017-05-22 | The capacity configuration optimizing method and device of a kind of independent micro-grid system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106992549A (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110707754A (en) * | 2019-08-28 | 2020-01-17 | 广东工业大学 | Optimization method for water, wind and light power supply capacity configuration in micro-grid |
| CN110932317A (en) * | 2019-11-29 | 2020-03-27 | 国网新疆电力有限公司 | Design method of distributed energy system with complementary essential renewable energy sources |
| CN111080111A (en) * | 2019-12-09 | 2020-04-28 | 浙江工业大学 | Power system economic dispatching method based on distributed non-convex optimization algorithm |
| CN111564868A (en) * | 2020-06-09 | 2020-08-21 | 北方工业大学 | Off-grid type optical storage micro-grid system capacity configuration evaluation method and device |
| CN111585305A (en) * | 2020-06-12 | 2020-08-25 | 国网天津市电力公司 | Method suitable for multi-energy complementary linkage economic evaluation |
| CN113555908A (en) * | 2021-06-24 | 2021-10-26 | 国网山东省电力公司济宁市任城区供电公司 | Energy storage optimization configuration method for intelligent power distribution network |
| CN114188961A (en) * | 2021-12-13 | 2022-03-15 | 三峡大学 | Wind-solar complementary system capacity configuration optimization method |
| CN116131365A (en) * | 2023-04-18 | 2023-05-16 | 国网山东省电力公司聊城供电公司 | Flexible operation control management system and method for intelligent power distribution network |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106602591A (en) * | 2015-10-20 | 2017-04-26 | 上海交通大学 | Seawater pumped storage wind power combination control method for multi-target optimized control |
-
2017
- 2017-05-22 CN CN201710365308.XA patent/CN106992549A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106602591A (en) * | 2015-10-20 | 2017-04-26 | 上海交通大学 | Seawater pumped storage wind power combination control method for multi-target optimized control |
Non-Patent Citations (2)
| Title |
|---|
| 谭颖等: "基于改进ELM的风/光/柴/储独立微网分布式电源多目标容量优化配置", 《电力系统保护与控制》 * |
| 黎嘉明等: "独立海岛微网分布式电源容量优化设计", 《电工技术学报》 * |
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110707754B (en) * | 2019-08-28 | 2022-11-22 | 广东工业大学 | Optimization method for water-wind-light power supply capacity configuration in micro-grid |
| CN110707754A (en) * | 2019-08-28 | 2020-01-17 | 广东工业大学 | Optimization method for water, wind and light power supply capacity configuration in micro-grid |
| CN110932317A (en) * | 2019-11-29 | 2020-03-27 | 国网新疆电力有限公司 | Design method of distributed energy system with complementary essential renewable energy sources |
| CN110932317B (en) * | 2019-11-29 | 2023-07-21 | 国网新疆电力有限公司 | Design method of essentially renewable energy complementary distributed energy system |
| CN111080111B (en) * | 2019-12-09 | 2022-04-05 | 浙江工业大学 | Power system economic dispatching method based on distributed non-convex optimization algorithm |
| CN111080111A (en) * | 2019-12-09 | 2020-04-28 | 浙江工业大学 | Power system economic dispatching method based on distributed non-convex optimization algorithm |
| CN111564868B (en) * | 2020-06-09 | 2022-05-06 | 北方工业大学 | Off-grid type optical storage micro-grid system capacity configuration evaluation method and device |
| CN111564868A (en) * | 2020-06-09 | 2020-08-21 | 北方工业大学 | Off-grid type optical storage micro-grid system capacity configuration evaluation method and device |
| CN111585305A (en) * | 2020-06-12 | 2020-08-25 | 国网天津市电力公司 | Method suitable for multi-energy complementary linkage economic evaluation |
| CN111585305B (en) * | 2020-06-12 | 2023-08-11 | 国网天津市电力公司 | Method suitable for multi-energy complementary linkage economy evaluation |
| CN113555908A (en) * | 2021-06-24 | 2021-10-26 | 国网山东省电力公司济宁市任城区供电公司 | Energy storage optimization configuration method for intelligent power distribution network |
| CN113555908B (en) * | 2021-06-24 | 2023-11-14 | 国网山东省电力公司济宁市任城区供电公司 | Intelligent power distribution network energy storage optimal configuration method |
| CN114188961A (en) * | 2021-12-13 | 2022-03-15 | 三峡大学 | Wind-solar complementary system capacity configuration optimization method |
| CN114188961B (en) * | 2021-12-13 | 2023-08-01 | 三峡大学 | Capacity configuration optimization method for wind-solar complementary system |
| CN116131365A (en) * | 2023-04-18 | 2023-05-16 | 国网山东省电力公司聊城供电公司 | Flexible operation control management system and method for intelligent power distribution network |
| CN116131365B (en) * | 2023-04-18 | 2023-07-18 | 国网山东省电力公司聊城供电公司 | Flexible operation control management system and method for intelligent power distribution network |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106992549A (en) | The capacity configuration optimizing method and device of a kind of independent micro-grid system | |
| Lu et al. | Multi-objective optimal load dispatch of microgrid with stochastic access of electric vehicles | |
| Ma et al. | Optimal allocation of hybrid energy storage systems for smoothing photovoltaic power fluctuations considering the active power curtailment of photovoltaic | |
| Qiao et al. | Multi-objective dynamic economic emission dispatch based on electric vehicles and wind power integrated system using differential evolution algorithm | |
| CN104037793B (en) | A kind of energy-storage units capacity collocation method being applied to active distribution network | |
| Falahati et al. | A new smart charging method for EVs for frequency control of smart grid | |
| CN103490410B (en) | Micro-grid planning and capacity allocation method based on multi-objective optimization | |
| CN103078340B (en) | Mixed energy storing capacity optimization method for optimizing micro-grid call wire power | |
| CN103280821B (en) | Multi-period dynamic reactive power optimization method of intelligent power distribution system | |
| CN108764552B (en) | Method for determining location and volume planning of distributed power supply of power distribution network | |
| CN107239847A (en) | A kind of active distribution network energy-storage system dynamic programming method | |
| CN105071389B (en) | The alternating current-direct current mixing micro-capacitance sensor optimizing operation method and device of meter and source net load interaction | |
| CN106058855A (en) | Active power distribution network multi-target optimization scheduling method of coordinating stored energy and flexible load | |
| CN108304972B (en) | Active power distribution network frame planning method based on supply and demand interaction and DG (distributed generation) operation characteristics | |
| CN112290601B (en) | Optimized scheduling method and system for flexible interconnection alternating current-direct current power distribution system | |
| CN105576677A (en) | Energy management control method of photovoltaic energy storage system | |
| CN107039975A (en) | A kind of distributed energy resource system energy management method | |
| Eid et al. | Efficient operation of battery energy storage systems, electric-vehicle charging stations and renewable energy sources linked to distribution systems | |
| CN106505558A (en) | A kind of energy conveying control method of DC distribution net and device | |
| CN109217352A (en) | Active distribution network source net lotus collaborative planning method based on DG initialization addressing | |
| Ren et al. | Multitime scale coordinated scheduling for electric vehicles considering photovoltaic/wind/battery generation in microgrid | |
| Mao et al. | Microgrid group control method based on deep learning under cloud edge collaboration | |
| Pan et al. | Dual-layer optimal dispatching strategy for microgrid energy management systems considering demand response | |
| Li et al. | Double-layer optimized configuration of distributed energy storage and transformer capacity in distribution network | |
| Wu et al. | Battery energy storage system (bess) sizing analysis of bess-assisted fast-charge station based on double-layer optimization method |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170728 |
|
| RJ01 | Rejection of invention patent application after publication |