CN107591794A - Active distribution network source storage capacity configuration optimizing method based on load classification - Google Patents
Active distribution network source storage capacity configuration optimizing method based on load classification Download PDFInfo
- Publication number
- CN107591794A CN107591794A CN201610539866.9A CN201610539866A CN107591794A CN 107591794 A CN107591794 A CN 107591794A CN 201610539866 A CN201610539866 A CN 201610539866A CN 107591794 A CN107591794 A CN 107591794A
- Authority
- CN
- China
- Prior art keywords
- load
- distribution network
- source
- power
- energy
- 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
Classifications
-
- 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
Landscapes
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a kind of active distribution network source based on load classification to store up capacity configuration optimizing method, and the load in active distribution network is classified, is divided into resident load, Commercial Load and industrial load, step：1) load is analyzed, is that each loading zone selects optimal collocation point and distributed power source capacity by foundation of optimal load flow；2) gather and analyze the average of the whole year air speed data of each loading zone and average photometric data；3) storage capacity Optimal Allocation Model in source is established to loading zone and respective target of distributing rationally respectively, capacity optimization is carried out simultaneously to energy storage and distributed power source, realizes the common coordination configuration of storage lotus in source in active distribution network；4) according to the respective average of the whole year air speed data of loading zone and average of the whole year photometric data, coordination control is carried out to each overloaded partition using cycle charging strategy.On the basis of power distribution network global optimization configuration is realized with optimal load flow, distributing rationally for each overloaded partition distributed energy and stored energy capacitance is realized.
Description
Technical field
The invention belongs to intelligent grid field, more particularly to a kind of active distribution network source-storage capacity based on load classification
Optimal Configuration Method.
Background technology
As economy continues to develop, sustainable growth of the society to electricity needs, but the shortage problem of traditional energy is more
Seriously, constantly opened there is an urgent need to electricity market to drive power network towards developing in a manner of efficient, flexible, intelligent and sustainable, with
Adapt to following technical need.Sustainability is the foundation characteristic of following power network, and it is mainly shown as distributed power source especially
The scale access and application of regenerative resource, such as wind-power electricity generation, photovoltaic generation, biomass energy.
At present grid side especially power distribution network still have regenerative resource digestion capability deficiency, a rack weakness, from
The problems such as dynamicization be not horizontal high, scheduling mode is backward and the interactive level of electricity consumption is relatively low, seriously constrains regenerative resource
Hypersynchronous, it is unfavorable for optimizing and revising for energy resource structure.For these problems, proposed in 2008 on international conference on large HV electric systems
Active distribution network concept, it is intended to solve power network compatibility and apply extensive batch (-type) regenerative resource, lifting green energy resource utilizes
Rate, the problems such as optimizing primary energy structure, conventional electrical distribution net is set to be changed into actively, connecing for extensive new energy being adapted to this from passive
Enter.
The initiative of power distribution network is embodied in it and possesses combination control various distributed energy (DG, controllable burden, energy storage, need
Seek side management etc.) ability, the purpose is to increase receiving ability of the power distribution network for regenerative resource, lifting power distribution network assets
Utilization rate, delay the upgrading of power distribution network to invest, and improve the power quality and power supply reliability of user.At present both at home and abroad for
The research that active distribution network capacity is distributed rationally is mostly for distributed power source or the sole disposition of energy storage, is lacked for distribution
Formula power supply and energy storage integrally carry out coordinating the research of configuration.
The content of the invention
The purpose of the present invention is to consider batch (-type) renewable energy power generation, part throttle characteristics demand and future source of energy
Under the uncertain factors such as market, source-storage open space planning of active distribution network is considered, mainly include distributed energy and energy-storage system
Capacity distribute rationally, with net investment cost minimization, the multiple objective programming that power supply reliability is high and green energy resource utilization rate is high
Problem, the on-site elimination of distributed energy is realized, and complemented one another with bulk power grid.
In order to solve the above-mentioned technical problem, the present invention provides a kind of active distribution network source-storage capacity based on load classification
Distribute rationally, comprise the following steps：
Step 1: classifying to the load in active distribution network, it is divided into resident load, Commercial Load and industrial load,
It is analyzed, is that each loading zone selects optimal collocation point and distributed power source capacity by foundation of optimal load flow；
Step 2: collection and analysis resident load area, the respective average of the whole year wind speed in Commercial Load area and industrial load area
Data and average of the whole year photometric data；
Step 3: to resident load area, Commercial Load area and industrial load area and respective distribute target rationally respectively
Establish source-storage capacity Optimal Allocation Model, power generating source side and power load side are to energy storage and distributed electrical in overall power distribution net
Source carries out capacity optimization simultaneously, realizes the common coordination configuration of source-storage-lotus in active distribution network.Wherein distributed power source includes
Wind-power electricity generation, photovoltaic generation and small diesel engine generate electricity, and energy-storage system uses widely used lead-acid accumulator；
Step 4: according to resident load area, the respective average of the whole year air speed data in Commercial Load area and industrial load area and
Average of the whole year photometric data, coordination control is carried out to each overloaded partition using cycle charging strategy, realized with optimal load flow
On the basis of power distribution network global optimization configuration, distributing rationally for each overloaded partition distributed energy and stored energy capacitance is realized.
Further, in step 1, the load in active distribution network is divided into resident load, Commercial Load and industrial load,
Draw the annual load data of three kinds of load types respectively according to the load data over the years of respective load, all kinds of load datas are adopted
It is 1 hour to collect time interval, and whole year gathers at 8760 points altogether based on 365 days, per type load data, and probability is used to each type load
Density function carries out load Analysis.
Further, in step 1, Load flow calculation analysis is carried out with certain 10kV distribution network, distribution network structure includes 23
Node and 21 branch roads, certain node is each selected to access distributed energy in three kinds of loading zones respectively.Optimal load flow computational methods
For Newton-Raphson methods, using active distribution network overall operation loss minimization as target.
Further, in step 2, analysis resident load area, the respective average of the whole year wind in Commercial Load area and industrial load area
Fast data and average of the whole year photometric data, air speed data and photometric data sampling interval are 1 hour, annual based on 365 days, always
Gather altogether at 8760 points, specificity analysis is carried out using probability density function.
Further, in step 3, wind-power electricity generation, photovoltaic generation and small diesel engine is generated electricity and establish generating mould respectively
Type, energy-storage system use lead-acid accumulator, establish Kinetic battery models, and inverter establishes two-way inverter model.
Further, in step 4, it is respective annual flat to consider resident load area, Commercial Load area and industrial load area
Equal air speed data and average of the whole year photometric data and corresponding average of the whole year load data, source-storage is established using Homer softwares
Capacity Optimal Allocation Model, distributed power source and energy storage are carried out with year pure cost least cost and the minimum target of year short of electricity rate
Capacity optimizes.
Compared with prior art, its remarkable advantage is the present invention：(1) present invention is divided load according to part throttle characteristics
Class, to meet the electricity needs of different load, it is ensured that the power supply reliability of distributed power source；(2) present invention is by natural money
The assessment in source and consideration load condition, while configuration, Neng Gougeng are optimized to distributed power source capacity and energy storage system capacity
Good utilizes distributed energy, overcomes the single limitation that configuration is optimized to distributed power source capacity or energy storage system capacity
Property and one-sidedness；(3) present invention can improve the digestion capability to distributed power source, avoid the excessive wave to natural resources
Take, reduce distributed energy generating abandons electric rate, increases economic efficiency；(4) source-storage of active distribution network proposed by the invention
Capacity configuration optimizing method, realize that active distribution network dissolves in whole distribution stratum reticulare to regenerative resource, it is for can be again
The access radius of the raw energy is bigger, power distribution network is implemented active management, can have a rest formula new energy and energy storage from primal coordination control room
The DG units such as device, actively dissolve regenerative resource and ensure the safe operation of network.
Brief description of the drawings
Fig. 1 is the active distribution network source based on load classification-storage capacity configuration optimizing method flow chart of the invention.
Fig. 2 is the line assumption diagram of the active distribution network of the present invention.
Fig. 3 is the probability density function histogram of industrial load of the present invention.
Fig. 4 is the annual mean wind speed probability density function figure in industrial load area of the present invention.
Fig. 5 is the annual illumination probability density function figure in industrial load area of the present invention.
Fig. 6 is active distribution network source-storage capacity Optimal Allocation Model structure chart of the present invention.
In figure：1 is that load data is analyzed, and addressing constant volume is carried out to distributed power source, and 2 be distributed energy generating and energy storage
Model is established, and 3 hold to establish active distribution network source-storage capacity Optimized model, 4 for determination optimal distribution formula power supply capacity and energy storage
Amount.
Embodiment
Active distribution network source-storage capacity configuration optimizing method of the invention based on load classification, collocation method flow chart is such as
Shown in Fig. 1, comprise the following steps：
Step 1: classifying to the load in active distribution network, it is divided into resident load, Commercial Load and industrial load,
It is analyzed, is that each loading zone selects optimal collocation point and distributed power source capacity by foundation of optimal load flow；
Step 2: collection and analysis resident load area, the respective average of the whole year wind speed in Commercial Load area and industrial load area
Data and average of the whole year photometric data；
Step 3: to resident load area, Commercial Load area and industrial load area and respective distribute target rationally respectively
Establish source-storage capacity Optimal Allocation Model, power generating source side and power load side are to energy storage and distributed electrical in overall power distribution net
Source carries out capacity optimization simultaneously, realizes the common coordination configuration of source-storage-lotus in active distribution network.Wherein distributed power source includes
Wind-power electricity generation, photovoltaic generation and small diesel engine generate electricity, and energy-storage system uses widely used lead-acid accumulator；
Step 4: according to resident load area, the respective average of the whole year air speed data in Commercial Load area and industrial load area and
Average of the whole year photometric data, coordination control is carried out to each overloaded partition using cycle charging strategy, realized with optimal load flow
On the basis of power distribution network global optimization configuration, distributing rationally for each overloaded partition distributed energy and stored energy capacitance is realized.
Further, in step 1, the load in active distribution network is divided into resident load, Commercial Load and industrial load,
Draw the annual load data of three kinds of load types respectively according to the load data over the years of respective load, all kinds of load datas are adopted
It is 1 hour to collect time interval, and whole year gathers at 8760 points altogether based on 365 days, per type load data, and probability is used to each type load
Density function is analyzed.
Further, in step 2, analysis resident load area, the respective average of the whole year wind in Commercial Load area and industrial load area
Fast data and average of the whole year photometric data, air speed data and photometric data sampling interval are 1 hour, annual based on 365 days, always
Gather altogether at 8760 points, specificity analysis is carried out using probability density function.The real output of blower fan is mainly influenceed by wind speed,
In project study, the expression formula of the probability density function of long-term wind speed profile is
In formula (1), v is wind speed；K and c are referred to as form parameter and scale parameter.
Further, in step 3, distributed power source in active distribution network includes wind-power electricity generation, photovoltaic generation and small-sized
Diesel power generation, energy-storage system use widely used lead-acid accumulator, carry out the foundation of model to it respectively.
According to wind speed-power characteristic, wind-power electricity generation model is
In formula (2),With vratedRespectively cut wind speed, cut-out wind speed and rated wind speed；WithRespectively
For blower fan quantity and the rated power of separate unit blower fan.
Photovoltaic generation model is
In formula (3), fpvFor the power deratng factor of photovoltaic generating system, 0.9 is typically taken；YpvFor photovoltaic array capacity；IT
For actual intensity of illumination；ISFor the intensity of illumination under standard test condition, 1kW/m is typically taken as2。
Small diesel engine mainly considers diesel engine power output-fuel curve characteristic, and its generation model is
F=F0Ygen+F1Pgen (4)
In formula (4), F is the per hour factor of diesel-driven generator；F0For the unloaded consumption of the unit power of diesel-driven generator
Oil mass；YgenFor the rated power of diesel-driven generator；F1For the fuel oil slope of curve of diesel-driven generator；PgenFor diesel-driven generator
Reality output rated power.
Two-way inverter model is
In formula (5),For the power output of inverter；For the input power of inverter；ηinvFor inverter
Conversion efficiency；For the power output of rectifier；For the input power of rectifier；ηrecImitated for the conversion of rectifier
Rate.
Energy-storage system model uses KiBaM models.The gross energy that battery stores at any time be equal to utilisable energy with
Energy sum is fettered, i.e.,
Q=Q1+Q2 (6)
In formula (6), Q is the gross energy that battery stores at any time；Q1For utilisable energy；Q2To fetter energy.
The foundation actual charge-discharge electric power of battery, the utilisable energy and constraint energy of battery after discharge and recharge can be calculated
Respectively
In formula (7), (8), Q1For the utilisable energy of initial time battery；Q2For the constraint energy of initial time battery；
Q1,endFor the utilisable energy of end time battery；Q2,endFor the constraint energy of end time battery；P charges for battery
(just) or electric discharge (negative) power；Δ t is time interval；C is battery capacity ratio, represents that under battery fully charged state energy can be used
The ratio of amount and gross energy；K is battery speed constant.
According to the utilisable energy of KiBaM models, maximum allowable discharge power can be obtained
According to the utilisable energy of KiBaM models, maximum allowable charge power can be obtained
In formula (10), QmaxFor battery maximum possible storage energy.
Maximum charge power corresponding to the maximum charge rate limit of battery is
In formula (11), αcFor the maximum charge speed of battery.
Battery maximum charging current limitation corresponding to maximum charge power be
In formula (12), NbattFor battery connection in series-parallel sum；ImaxFor the maximum charging current of battery, A；VnomFor electric power storage
The rated voltage in pond, V.
It can thus be concluded that the final charge-discharge electric power of battery is limited to
Pbat,dmax=ηbat,dPbatt,dmax,kbm
In formula (13), ηbat,cFor battery charge efficiency；ηbat,dFor battery discharging efficiency.
Further, in step 3, the object function of source-storage capacity Optimal Allocation Model of active distribution network, with year only into
This least cost and the minimum target of year short of electricity rate carry out the capacity optimization of distributed power source and energy storage.
Using the object function of life cycle management pure cost least cost as
Cann,tot=Cbattery+Cwind+Cpv+Cconverter+Cgenerator (16)
In formula, CNPCSpent for life cycle management pure cost；Cann,totFor total system year cost expenses；CRF(i,Rproj) be
Capital recovery factor；I is annual true rate of interest；RprojFor total system life cycle；N is year.CbatteryIt is total for energy-storage system
Cost；CwindFor wind generator system totle drilling cost；CpvFor photovoltaic generating system totle drilling cost；CconverterFor two-way inverter assembly
This；CgeneratorFor diesel generating system totle drilling cost.Wherein, energy-storage system, wind generator system, photovoltaic generating system and inversion
Device cost includes cost of investment, replacement cost, operation and maintenance cost and remanent value of equipment, and diesel generating system includes investment
Cost, replacement cost, fuel cost, operation and maintenance cost and remanent value of equipment.
The object function minimum using system year short of electricity rate as
In formula (17), fcsFor system year short of electricity rate；EcsCapacity is always lacked for year；LtotFor year total load capacity.
Further, in step 4, according to resident load area, the respective average of the whole year wind in Commercial Load area and industrial load area
Fast data and average of the whole year photometric data and source-storage capacity Optimal Allocation Model, set cycle charging control strategy respectively to occupying
The distributed energy capacity and stored energy capacitance of people's loading zone, Commercial Load area and industrial load area optimize configuration.Circulation is filled
Electric control strategy：When net load is bears, meet the workload demand at this moment, diesel engine utilizes regenerative resource without starting
Charged for energy-storage system, and consider the charged constant and maximum charge power limit of current battery, if being unsatisfactory for limiting
Condition, then need to abandon this part renewable energy source power；When net load is timing, the workload demand at this moment is unsatisfactory for, then
Need energy-storage system or diesel engine to carry out power supplement, compare the power cost curve of battery and diesel engine, work as diesel engine
Power cost it is relatively low when, open diesel engine follow net load, surplus power can consider whether the constraints of battery is carried out
Charging；When the power cost of energy-storage system is relatively low, and charged constant is not below electric discharge lower limit or is not above battery
Maximum discharge power limitation, then energy-storage system follows net load, otherwise opens diesel engine and follows net load.
Embodiment
Step 1: classifying to the load in active distribution network, it is divided into resident load, Commercial Load and industrial load,
It is analyzed, is that each loading zone selects optimal collocation point and distributed power source capacity by foundation of optimal load flow.
The line assumption diagram of active distribution network is as shown in Fig. 2 part-structure for a certain regional 10kV power distribution networks.With network loss
Minimum object function, it is as shown in table 1 to obtain optimal load flow result of calculation, and resident load area collocation point is node 7, Commercial Load
Area's collocation point is node 12, and industrial load area collocation point is node 18.
The optimal load flow configuration result of table 1
Draw the annual load data of three kinds of load types respectively according to the load data over the years of respective load, it is all kinds of negative
Lotus data collection interval is 1 hour, and whole year gathers at 8760 points altogether based on 365 days, per type load data.To each type load
Load Characteristic Analysis is carried out using probability-distribution function.By taking industrial load as an example, the probability density function histogram of industrial load
As shown in Figure 3.As can be seen that industrial load active power hourly accounts for the 80% of annual load in 200kW-400kW
A left side, the foundation of load data is provided for distributed energy and stored energy capacitance optimization.
Step 2: collection and analysis resident load area, the respective average of the whole year wind speed in Commercial Load area and industrial load area
Data and average of the whole year photometric data.
By taking industrial load area as an example, annual mean wind speed probability density function figure is as shown in figure 4, annual illumination probability density
Functional arrangement is as shown in Figure 5.The wind resource in industrial load area is in spring and winter compared with horn of plenty, and illumination resource is in summer and autumn
Season forms resource complementation compared with horn of plenty, wind resource and illumination resource.From fig. 4, it can be seen that wind speed resource is 0 to 20m/s
In the range of, 1m/s is concentrated mainly on between 10m/s.From fig. 5, it can be seen that illumination resource is 0 to 1.2kW/m2In the range of, it is main
0 is concentrated on to 0.2kW/m2Between.
Step 3: to resident load area, Commercial Load area and industrial load area and respective distribute target rationally respectively
Establish source-storage capacity Optimal Allocation Model, power generating source side and power load side are to energy storage and distributed electrical in overall power distribution net
Source carries out capacity optimization simultaneously, realizes the common coordination configuration of source-storage-lotus in active distribution network.Wherein distributed power source includes
Wind-power electricity generation, photovoltaic generation and small diesel engine generate electricity, and energy-storage system uses widely used lead-acid accumulator；
Active distribution network source-storage capacity Optimal Allocation Model structure chart is as shown in Figure 6.Blower fan separate unit rated power 20kW,
Cut wind speed 4m/s, cut-out wind speed 24m/s, rated wind speed 14m/s.Photovoltaic panel power deratng factor 90%, ignores photovoltaic panel table
The influence of face temperature.The efficiency of rectification and the inversion of two-way inverter is 90%.The minimum lotus of energy-storage system cell batteries
Electric constant 30%, efficiency for charge-discharge 93%, maximum charging current 610A, maximum charge speed 1A/Ah.The fuel oil curve of diesel engine
Intercept coefficient 0.08L/hr/kW, slope 0.25L/hr/kW.Active distribution network source-storage capacity Optimal Allocation Model is using circulation
Charge control strategy, diesel-driven generator and battery meet required net load jointly.
Step 4: according to resident load area, the respective average of the whole year air speed data in Commercial Load area and industrial load area and
Average of the whole year photometric data, coordination control is carried out to each overloaded partition using cycle charging strategy, realized with optimal load flow
On the basis of power distribution network global optimization configuration, distributing rationally for each overloaded partition distributed energy and stored energy capacitance is realized.
Resident load area and Commercial Load area are using life cycle management pure cost least cost as object function, industrial load area
With the minimum object function of system year short of electricity rate.Each system cost parameter below, all in accordance with existing market situation, in reasonable model
Enclose interior value.Dollar/kilowatt of acquisition cost 600 of wind generator system, dollar/kilowatt of replacement cost 550, operation and maintenance expense
With 12.5 dollars/kilowatt/year.Dollar/kilowatt of acquisition cost 1200 of photovoltaic generating system, dollar/kilowatt of replacement cost 1000,
Dollar/kilowatt of operation and maintenance cost 25/year.Dollar/kilowatt of acquisition cost 360 of diesel generating system, replacement cost 330 are beautiful
Member/kilowatt, dollar/kilowatt of operation and maintenance cost 5/year.Dollar/kilowatt of acquisition cost 1200 of two-way inverter system, more
Change this 1000 dollar/kilowatt into, dollar/kilowatt of operation and maintenance cost 40/year.The capacity of energy-storage system cell batteries is
3000Ah, dollar/of acquisition cost 2000, dollar/of replacement cost 1900, dollar/of operation and maintenance cost 40/year.
By simulation analysis, obtain resident load area, Commercial Load area and industrial load area distributed energy capacity and
Stored energy capacitance distributes result rationally, as shown in table 2.
The capacity of table 2 distributes result rationally
It can be drawn by table 2, resident load area and Commercial Load area are using life cycle management pure cost least cost as target
Function, in order to reduce cost expenses as far as possible, the application of extensive regenerative resource is realized, does not access diesel generating system,
Because generation of electricity by new energy is influenceed by environment, there are certain fluctuation and uncertainty, so system year short of electricity rate be present, but
It is respectively less than 10% to ensure the reliability of system power supply, the sub-load needs that can not meet are carried out more by power network to provide
Mend.Generation of electricity by new energy abandons electric rate 30% or so, it may be considered that feedback grid is some electrically-charging equipments such as electric automobile
Small-power power supply is carried out, further improves economic well-being of workers and staff and energy utilization rate.Industrial load area is minimum with system year short of electricity rate
Object function, in order to make up the uncontrollability of generation of electricity by new energy and uncertainty, therefore consider to access controllable diesel generation system
System, it is ensured that continual and steady power supply, it is 0% that can realize system year short of electricity rate, and generation of electricity by new energy abandons electric rate it is also contemplated that returning
Transmission network carries out small-power power supply for some electrically-charging equipments such as electric automobiles.
Active distribution network based on load classification source-storage capacity configuration optimizing method proposed by the invention, from power distribution network
The overall situation it is overall optimize configuration, consider distributed power source output situation and part throttle characteristics to configure stored energy capacitance,
To realize the extensive access of distributed energy, cost of investment is reduced, improves natural resource utilizing rate, electric rate is abandoned in reduction, is realized
The active management to distributed energy and load of power distribution network, for active distribution network stored energy capacitance distribute rationally provide it is certain
Directive significance.
Claims (7)
- A kind of 1. active distribution network source-storage capacity configuration optimizing method based on load classification, to the load in active distribution network Classified, be divided into resident load, Commercial Load and the class of industrial load three, it is characterised in that specific steps：It is that each loading zone selects optimal configuration by foundation of optimal load flow Step 1: analyzing respectively three type loads Point and distributed power source capacity；Step 2: the respective average of the whole year air speed data of collection and three type load areas of analysis and average of the whole year photometric data；Step 3: source-storage capacity Optimal Allocation Model is established to three type load areas and respective target of distributing rationally respectively, it is comprehensive Close power generating source side and power load side in power distribution network and capacity optimization is carried out simultaneously to energy storage and distributed power source, realize and actively match somebody with somebody The common coordination configuration of source-storage-lotus in power network；Step 4: according to the respective average of the whole year air speed data in Ju Sanlei areas and average of the whole year photometric data, using cycle charging Strategy carries out coordination control to each overloaded partition, real on the basis of power distribution network global optimization configuration is realized with optimal load flow Now each overloaded partition distributed energy and stored energy capacitance are distributed rationally.
- 2. active distribution network source-storage capacity configuration optimizing method as claimed in claim 1, it is characterised in that right in step 1 Three type loads carry out load Analysis using probability density function, and optimal load flow is using active distribution network overall operation loss minimization as mesh Mark is calculated.
- 3. active distribution network source-storage capacity configuration optimizing method as claimed in claim 1, it is characterised in that in step 2, point The respective average of the whole year air speed data in three type load areas and average of the whole year photometric data are analysed, using wind speed probability density function and light Specificity analysis is carried out according to probability density function.
- 4. active distribution network source-storage capacity configuration optimizing method as claimed in claim 1, it is characterised in that comprehensive in step 3 Close and consider the electricity consumption situation of mains side power generation situation and load side in power distribution network, to meet part throttle characteristics demand as configuration target, Capacity optimization is carried out to energy storage and distributed power source from for electricity consumption both ends, realizes the common association of source-storage-lotus in active distribution network Allotment is put.
- 5. active distribution network source-storage capacity configuration optimizing method as described in claim 1 or 4, it is characterised in that distributed electrical Source includes wind-power electricity generation, photovoltaic generation and small diesel engine and generated electricity, and energy-storage system uses widely used lead-acid accumulator, and Its Optimal Allocation Model includes wind-power electricity generation model, photovoltaic generation model, small diesel engine model, battery model and two-way inverse Become device model.
- 6. active distribution network source-storage capacity configuration optimizing method as claimed in claim 4, it is characterised in that active distribution network In the control strategy of source-storage system use cycle charging strategy, born only by energy-storage system and diesel generating system shared Lotus, coordination control is carried out according to respective service condition and constraints, realizes Optimum Economic output distribution.
- 7. active distribution network source-storage capacity configuration optimizing method as claimed in claim 1, it is characterised in that comprehensive in step 4 Close and consider the respective average of the whole year air speed data in three type load areas and average of the whole year photometric data and corresponding load data, with Year pure cost least cost and the minimum target of year short of electricity rate.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610539866.9A CN107591794A (en) | 2016-07-08 | 2016-07-08 | Active distribution network source storage capacity configuration optimizing method based on load classification |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610539866.9A CN107591794A (en) | 2016-07-08 | 2016-07-08 | Active distribution network source storage capacity configuration optimizing method based on load classification |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107591794A true CN107591794A (en) | 2018-01-16 |
Family
ID=61046599
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610539866.9A Pending CN107591794A (en) | 2016-07-08 | 2016-07-08 | Active distribution network source storage capacity configuration optimizing method based on load classification |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107591794A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110119886A (en) * | 2019-04-18 | 2019-08-13 | 深圳供电局有限公司 | Dynamic planning method for active distribution network |
| CN111754361A (en) * | 2020-06-29 | 2020-10-09 | 国网山西省电力公司电力科学研究院 | Energy storage capacity optimal configuration method and computing device of wind-storage combined frequency modulation system |
| CN117293954A (en) * | 2023-09-15 | 2023-12-26 | 三峡智控科技有限公司 | Storage battery energy storage method and device, electronic equipment and storage medium |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1734424A1 (en) * | 2004-03-11 | 2006-12-20 | Oleg Alexeevich Sukhanov | System for controlling processes of power distribution produced by an electric power system |
| CN104915900A (en) * | 2015-07-09 | 2015-09-16 | 国网四川省电力公司经济技术研究院 | Loading-zone-block-based site selection and volume determination method of distributed power supply |
-
2016
- 2016-07-08 CN CN201610539866.9A patent/CN107591794A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1734424A1 (en) * | 2004-03-11 | 2006-12-20 | Oleg Alexeevich Sukhanov | System for controlling processes of power distribution produced by an electric power system |
| CN104915900A (en) * | 2015-07-09 | 2015-09-16 | 国网四川省电力公司经济技术研究院 | Loading-zone-block-based site selection and volume determination method of distributed power supply |
Non-Patent Citations (2)
| Title |
|---|
| 郭力等: ""独立微网系统的多目标优化规划设计方法"", 《中国电机工程学报》 * |
| 马明等: ""分布式电源配置研究"", 《江苏电机工程》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110119886A (en) * | 2019-04-18 | 2019-08-13 | 深圳供电局有限公司 | Dynamic planning method for active distribution network |
| CN110119886B (en) * | 2019-04-18 | 2022-11-25 | 深圳供电局有限公司 | Active distribution network dynamic planning method |
| CN111754361A (en) * | 2020-06-29 | 2020-10-09 | 国网山西省电力公司电力科学研究院 | Energy storage capacity optimal configuration method and computing device of wind-storage combined frequency modulation system |
| CN111754361B (en) * | 2020-06-29 | 2022-05-03 | 国网山西省电力公司电力科学研究院 | Energy storage capacity optimal configuration method and computing device of wind-storage combined frequency modulation system |
| CN117293954A (en) * | 2023-09-15 | 2023-12-26 | 三峡智控科技有限公司 | Storage battery energy storage method and device, electronic equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110502814B (en) | Active power distribution network multi-target planning method considering energy storage and load management technology | |
| CN110661246B (en) | Capacity optimization configuration method for urban rail transit photovoltaic energy storage system | |
| CN105990838B (en) | A kind of island microgrid Optimization Scheduling of meter and schedulable and translatable load | |
| CN112734098B (en) | Power distribution network power dispatching method and system based on source-load-network balance | |
| CN109687444A (en) | A kind of micro-capacitance sensor power supply multiple target dual-layer optimization configuration method | |
| CN109599856A (en) | Electric car management of charging and discharging optimization method and device in a kind of more building of microgrid | |
| CN103151797A (en) | Multi-objective dispatching model-based microgrid energy control method under grid-connected operation mode | |
| CN110289622B (en) | Day-ahead economic optimization scheduling method for optical storage and energy charging router | |
| CN114243795A (en) | Comprehensive energy collaborative interaction optimization configuration method and system for typical charging station | |
| CN105226689A (en) | Consider polymorphic type energy-storage system energy management method and the system of operation and maintenance | |
| CN110417045A (en) | A kind of optimization method for alternating current-direct current mixing micro-capacitance sensor capacity configuration | |
| CN112886624B (en) | Three-station-in-one substation energy storage device planning and designing system and method | |
| CN107591794A (en) | Active distribution network source storage capacity configuration optimizing method based on load classification | |
| CN111762057A (en) | Intelligent charging and discharging management method for V2G electric vehicle in regional microgrid | |
| Li et al. | Micro-grid resource allocation based on multi-objective optimization in cloud platform | |
| Huang et al. | A Hierarchical Strategy for Multi-Objective Optimization of Distribution Network Considering DGs and V2G-Enabled EVs Integration | |
| CN116960939A (en) | Multi-target particle swarm algorithm-based wind, solar and diesel storage system optimal scheduling method, equipment and storage medium | |
| CN115940284B (en) | Operation control strategy of new energy hydrogen production system considering time-of-use electricity price | |
| Mulleriyawage et al. | Battery system selection in DC microgrids for residential applications: An Australian case study | |
| CN116131303A (en) | Comprehensive energy system collaborative optimization method based on energy storage, energy storage and photovoltaic cell | |
| Zec et al. | Load management in an off-grid hybrid PV–wind–battery system using the power flow control algorithm and fuzzy logic controller | |
| CN115375343A (en) | Power distribution network cooperative interaction method considering distributed power consumption benefits | |
| CN114123171A (en) | Incremental power distribution network distributed optimization planning method and medium based on potential game | |
| Gupta et al. | Hybrid energy system sizing incorporating battery storage: An analysis via simulation calculation | |
| CN108183498A (en) | A kind of vehicle storage mixed configuration method under the background of power distribution network containing wind-light storage |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20180116 |