CN108683211A - A kind of virtual power plant combined optimization method and model considering distributed generation resource fluctuation - Google Patents
A kind of virtual power plant combined optimization method and model considering distributed generation resource fluctuation Download PDFInfo
- Publication number
- CN108683211A CN108683211A CN201810353496.9A CN201810353496A CN108683211A CN 108683211 A CN108683211 A CN 108683211A CN 201810353496 A CN201810353496 A CN 201810353496A CN 108683211 A CN108683211 A CN 108683211A
- Authority
- CN
- China
- Prior art keywords
- distributed generation
- generation resource
- power plant
- virtual power
- virtual
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000005457 optimization Methods 0.000 title claims abstract description 22
- 238000000034 method Methods 0.000 title claims abstract description 20
- 230000005611 electricity Effects 0.000 claims abstract description 34
- 238000009826 distribution Methods 0.000 claims abstract description 29
- 238000004364 calculation method Methods 0.000 claims abstract description 8
- 230000000694 effects Effects 0.000 claims abstract description 6
- 230000007613 environmental effect Effects 0.000 claims abstract description 5
- 238000010248 power generation Methods 0.000 claims description 13
- 238000007599 discharging Methods 0.000 claims description 8
- 239000004744 fabric Substances 0.000 claims description 6
- 230000006870 function Effects 0.000 claims description 6
- PEDCQBHIVMGVHV-UHFFFAOYSA-N Glycerine Chemical compound OCC(O)CO PEDCQBHIVMGVHV-UHFFFAOYSA-N 0.000 claims description 2
- 238000012544 monitoring process Methods 0.000 abstract description 2
- 238000004458 analytical method Methods 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 239000006185 dispersion Substances 0.000 description 1
- 238000004146 energy storage Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000006116 polymerization reaction Methods 0.000 description 1
- 230000001172 regenerating effect Effects 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
-
- 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]
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a kind of virtual power plant combined optimization methods and model considering distributed generation resource fluctuation, include reasonably selecting distributed generation resource according to comprehensive considering various effects such as the geographical location of different regions, environmental aspect, resource distributions, and predict the output of each distributed generation resource；According to matching degree calculation formula, the matching degree that each distributed generation resource prediction is contributed with virtual power plant hair electricity plan is calculated；Each distributed generation resource for obtaining when smallest match is spent is included in virtual power plant；Judge whether the virtual power plant formed by distributed generation resource meets the scheduling needs of system；Form virtual power plant.The present invention establishes Uncertainty virtual power plant Combinatorial Optimization Model using multistage nonlinear integer programming method, the model in a distributed manner resource operating status be decision variable, it, can be according to system hair electricity plan real-time monitoring virtual power plant combination decision to minimize matching degree as target.
Description
Technical field
The present invention relates to electric power system optimization methods, more particularly to a kind of virtual hair considering distributed generation resource fluctuation
Power plant's combined optimization method and model.
Background technology
In recent years, a kind of new form that virtual power plant develops as distributed generation resource is increasingly by the weight of academia
Depending on.Virtual plant under intelligent grid background has been extended and extension, it can be understood as is by distributed electrical power management
Distributed Generation in Distribution System, controllable burden and energy-storage system are incorporated as a special power plant and participate in power grid fortune by system
Row, to coordinate the contradiction between intelligent grid and distributed generation resource well, it is power grid and user fully to excavate distributed energy
Caused value and benefit；It is different from micro-capacitance sensor, virtual plant can not islet operation, the distributed generation resource in virtual plant needs
Actual operational network could be purchased by external electrical network.The it is proposed of virtual power plant concept so that the big model of distributed generation resource
It encloses input operation of power networks to be possibly realized, or the management of Transmission system provides service.
As user side power supply is fast-developing, the virtual plant technology efficiently used as user side distributed generation resource will
Be widely used, the research to user side virtual plant domestic at present integrally just starts to walk, to the composition of virtual plant,
The mechanism study of characteristic morphology, access and operation characteristic also lacks very much, therefore this method has very important realistic meaning.
The combined method of existing virtual power plant be according to region zones, be by distributed resource all in region into
Row polymerization expression, the obtained single virtual power plant model for representing the region do not consider active optimization problem, certain scenes
In cannot be satisfied the requirement of Economical Operation of Power Systems.
Invention content
Goal of the invention：Consider distributed electrical to optimize distributed power source combination mode, the present invention inside virtual power plant
The fluctuation in source establishes virtual power plant optimized combination model, provides a kind of virtual hair considering distributed generation resource fluctuation
Power plant's combined optimization method and model.
Technical solution：The present invention provides a kind of virtual power plant Combinatorial Optimization sides considering distributed generation resource fluctuation
Method includes the following steps：
(1) it is reasonably selected according to comprehensive considering various effects such as the geographical location of different regions, environmental aspect, resource distributions
Distributed generation resource, and the output of each distributed generation resource is predicted；
(2) it according to matching degree calculation formula, calculates each distributed generation resource prediction and contributes and virtual power plant hair electricity plan
Matching degree；
(3) each distributed generation resource for obtaining when smallest match is spent is included in virtual power plant；
(4) judge whether the virtual power plant formed by distributed generation resource meets the scheduling needs of system；
(5) virtual power plant is formed.
Further, that each distributed generation resource prediction is contributed with virtual power plant hair electricity plan in the step (2)
It is calculated using following formula with degree：
It is in the specific formula for calculation of the matching degree of t periods：
In formula, StIndicate that t period fits degree, N are distributed electrical source category, i numbers for distributed generation resource, Pi tFor distribution
Power supply i predicts to contribute in the t periods, PeFor virtual power plant hair electricity plan electricity；
Entire dispatching cycle (1,2 ... t ... T) in the matching degree of the distributed generation resource based on renewable resource be：
In formula, S indicates that the matching degree of entire dispatching cycle, T are period serial number, N by taking period, t dispatching cycle
For distributed electrical source category, i numbers for distributed generation resource, Pi tIt predicts to contribute in the t periods for distributed generation resource i, PeVirtually to send out
Power plant's hair electricity plan electricity.
Further, the target letter of the Combinatorial Optimization Model of each distributed generation resource when smallest match is spent in the step (3)
Number is：
Wherein, T takes the period by dispatching cycle；T is time segment number；N is distributed electrical source category；I is distribution
Power supply type is numbered；Pi tIt contributes for the prediction of t moment distributed generation resource i；PeFor virtual power plant hair electricity plan electricity；uiTable
Show the 0-1 variables of distributed generation resource state, ui=1 indicates generator units of the distributed generation resource i as virtual power plant, ui=0 table
Show generator units of the distributed generation resource i not as virtual power plant.
Further, according to demand, select a few days ago, monthly, season or year scheduling as virtual power plant planning tune
The period is spent, corresponding S is day matching degree, moon matching degree, season matching degree or year matching degree.
Further, the scheduling in the step (4) needs for constraints, including：
(41) generation schedule constrains：
Wherein,The charge and discharge power of t moment accumulator is indicated respectively；Respectively store
The charge and discharge efficiency of battery；For t moment virtual power plant workload demand,The hair submitted to power grid for virtual power plant
Electricity plan；uiIndicate the 0-1 variables of distributed generation resource state, ui=1 indicates power generations of the distributed generation resource i as virtual power plant
Unit, ui=0 indicates generator units of the distributed generation resource i not as virtual power plant；Pi tFor the pre- of t moment distributed generation resource i
Measure power；
(42) controlled distribution formula power supply output bound constrains：
Wherein,Indicate controlled distribution formula power supply minimum load,Indicate controlled distribution formula power supply maximum output；
(43) accumulator cell charging and discharging power upper and lower limit constrains：
SOCmin≤SOCt≤SOCmax(9)；
Wherein,Respectively accumulator t moment charge and discharge power；Respectively store
Battery minimum charge and discharge power；Respectively accumulator maximum charge and discharge power；SOCtFor accumulator t when
The memory capacity at quarter, SOCminFor accumulators store capacity minimum value, SOCmaxFor accumulators store maximum capacity；
If meeting scheduling needs, above-mentioned distributed generation resource is selected, step (5) is executed and forms virtual power plant；If discontented
Foot scheduling needs, then return to step (3) adjusts the distributed generation resource of selection, and needs are dispatched until meeting.
The present invention also provides a kind of virtual power plant Combinatorial Optimization Model considering distributed generation resource fluctuation, the models
Including object function and constraints, the object function is：
Wherein, T takes the period by dispatching cycle；T is time segment number；N is distributed electrical source category；I is distribution
Power supply type is numbered；Pi tIt contributes for the prediction of t moment distributed generation resource i；PeFor virtual power plant hair electricity plan electricity；uiTable
Show the 0-1 variables of distributed generation resource state, ui=1 indicates generator units of the distributed generation resource i as virtual power plant, ui=0 table
Show generator units of the distributed generation resource i not as virtual power plant；
The constraints includes：
(a) generation schedule constrains：
Wherein,The charge and discharge power of t moment accumulator is indicated respectively；Respectively store
The charge and discharge efficiency of battery；For t moment virtual power plant workload demand,The hair submitted to power grid for virtual power plant
Electricity plan；uiIndicate the 0-1 variables of distributed generation resource state, ui=1 indicates power generations of the distributed generation resource i as virtual power plant
Unit, ui=0 indicates generator units of the distributed generation resource i not as virtual power plant；Pi tFor the pre- of t moment distributed generation resource i
Measure power；
(b) controlled distribution formula power supply output bound constrains：
Wherein,Indicate controlled distribution formula power supply minimum load,Indicate controlled distribution formula power supply maximum output；
(c) accumulator cell charging and discharging power upper and lower limit constrains：
SOCmin≤SOCt≤SOCmax(9)；
Wherein,Respectively accumulator t moment charge and discharge power；Respectively store
Battery minimum charge and discharge power；Respectively accumulator maximum charge and discharge power；SOCtFor accumulator t when
The memory capacity at quarter, SOCminFor accumulators store capacity minimum value, SOCmaxFor accumulators store maximum capacity.
Advantageous effect：Compared with prior art, the present invention is established uncertain using multistage nonlinear integer programming method
Situation virtual power plant Combinatorial Optimization Model, the model in a distributed manner resource operating status be decision variable, with minimize
It is target with degree, it can be according to system hair electricity plan real-time monitoring virtual power plant combination decision.According to the ground of different regions
It manages the comprehensive considering various effects such as position, environmental aspect and resource distribution and reasonably selects distributed generation resource, make full use of local money
Source plays the advantage of distributed power generation.Reasonably selection distributed power generation power supply type can be developed and used fully various available
The existing energy of dispersion simultaneously improves the utilization ratio in source, reduces cost of investment.
Description of the drawings
Fig. 1 is the method for the present invention flow chart.
Specific implementation mode
In the following with reference to the drawings and specific embodiments, technical scheme of the present invention is described in detail.
The present invention is based on the concepts of distributed generation resource matching degree, establish distributed generation resource Combinatorial Optimization Model.The model exists
The minimum target of matching variance of power supply in a distributed manner in the virtual power plant programming dispatching period, meets certain constraints,
Optimum combinational scheme is obtained from the combination of the distributed generation resource of different type, different capabilities.The matching degree that the method for the present invention defines
For the output matching degree of distributed generation resource, the i.e. matching degree of distributed generation resource prediction curve and practical power curve.
In conjunction with variance concept mathematically, the method for the present invention proposes the concept of distributed generation resource output matching degree, and gives
Go out specific formula for calculation, which is the characterization that is quantified when selecting suitable distributed generation resource to form virtual power plant
Value.
In containing the distributed system based on regenerative resource distributed generation resource, the stronger wind of fluctuation is typically contained
Power, photovoltaic etc. clean distributed generation resource；Also it will necessarily contain the relatively higher distributed generation resource of some other output stability such as
Miniature gas turbine etc., to obtain more good electric energy.So containing the stronger distributed power supply system of output fluctuation
Certain difference is had between gross capability curve and total expection expectation curve, and the present invention utilizes the concept of matching degree, in conjunction with
The prediction of distributed generation resource is contributed, and the optimum combination of virtual power plant is determined with this.
As shown in Figure 1, a kind of virtual power plant combined optimization method of consideration distributed generation resource fluctuation of the present invention, packet
Include following steps：
(1) it is reasonably selected according to comprehensive considering various effects such as the geographical location of different regions, environmental aspect, resource distributions
Distributed generation resource predicts the output of each distributed generation resource in conjunction with existing power generation prediction data and power generation probabilistic model；
(2) it according to matching degree calculation formula, calculates each distributed generation resource prediction and contributes and virtual power plant hair electricity plan
Matching degree；
It is in the specific formula for calculation of the matching degree of t periods：
In formula, StIndicate that t period fits degree, N are distributed electrical source category, i numbers for distributed generation resource, Pi tFor distribution
Power supply i predicts to contribute in the t periods, PeFor virtual power plant hair electricity plan electricity.
Power generation-electricity consumption ability is closer, i.e. StCloser to zero, the value of matching degree is smaller, illustrates not true included in it
It is fewer to determine information.The value of matching degree is smaller, illustrates generate electricity-more to be matched with electrical characteristics, proper combination is at affiliate.
Entire dispatching cycle (1,2 ... t ... T) in the matching degree of the distributed generation resource based on renewable resource be：
In formula, S indicates that the matching degree of entire dispatching cycle, T are period serial number, N by taking period, t dispatching cycle
For distributed electrical source category, i numbers for distributed generation resource, Pi tIt predicts to contribute in the t periods for distributed generation resource i, PeVirtually to send out
Power plant's hair electricity plan electricity.
(3) according to the concept of above-mentioned distributed generation resource matching degree, distributed generation resource Combinatorial Optimization Model is established, can be obtained
Each distributed generation resource when being spent to smallest match is included in virtual power plant；
The object function of the model is：
Wherein, T takes the period by dispatching cycle；T is time segment number；N is distributed electrical source category；I is distribution
Power supply type is numbered；Pi tIt contributes for the prediction of t moment distributed generation resource i；PeFor virtual power plant hair electricity plan electricity；uiTable
Show the 0-1 variables of distributed generation resource state, ui=1 indicates generator units of the distributed generation resource i as virtual power plant, ui=0 table
Show generator units of the distributed generation resource i not as virtual power plant.S is distributed generation resource matching variance, and S is smaller, practical to send out
Electricity is more matched with power generation curve it is expected, similarity is higher.
According to demand, can select a few days ago, monthly, season or year scheduling as the virtual power plant programming dispatching period,
As to dispatch a few days ago, then using hour as scale, T is taken as 24 hours, and corresponding S is defined as a day matching degree, i.e. distributed generation resource is daily
It is expected that generated energy summation and day that the matching degree between power generation curve, other dispatching cycles are similar.It is originated from according to each distributed electrical
Body characteristic should ensure that when choosing virtual power plant distributed electrical Source Type and at least contain a kind of schedulable distributed generation resource, to carry
High system overall stability.
(4) judge whether the virtual power plant formed by above-mentioned distributed generation resource meets the scheduling needs of system, that is, constrain
Condition；
Wherein, constraints is to include：
(41) generation schedule constrains：
Wherein,The charge and discharge power of t moment accumulator is indicated respectively；Respectively store
The charge and discharge efficiency of battery；For t moment virtual power plant workload demand,The hair submitted to power grid for virtual power plant
Electricity plan；uiIndicate the 0-1 variables of distributed generation resource state, ui=1 indicates power generations of the distributed generation resource i as virtual power plant
Unit, ui=0 indicates generator units of the distributed generation resource i not as virtual power plant；Pi tFor the pre- of t moment distributed generation resource i
Measure power.
(42) controlled distribution formula power supply output bound constrains：
Wherein,Indicate controlled distribution formula power supply minimum load,Indicate controlled distribution formula power supply maximum output.
(43) accumulator cell charging and discharging power upper and lower limit constrains：
SOCmin≤SOCt≤SOCmax(9)；
Wherein,Respectively accumulator t moment charge and discharge power；Respectively store
Battery minimum charge and discharge power；Respectively accumulator maximum charge and discharge power；SOCtFor accumulator t when
The memory capacity at quarter, SOCminFor accumulators store capacity minimum value, SOCmaxFor accumulators store maximum capacity.
If meeting scheduling needs, above-mentioned distributed generation resource is selected, step (5) is executed and forms virtual power plant；If discontented
Foot scheduling needs, then return to step (3) is adjusted, and distributed generation resource is selected according to the incremental order of matching degree from small to large,
Needs are dispatched until meeting.
(5) virtual power plant is formed.
A kind of virtual power plant combination that can be obtained distributed generation resource and contribute with prediction best match.Virtually to send out
Power plant plans that the minimum target of matching degree of power supply establishes Optimized model in a distributed manner in dispatching cycle a few days ago, and meets power generation meter
Constraint, the constraint of controlled distribution formula power supply output bound, the constraint of accumulator cell charging and discharging power bound are drawn, from different type, no
With capacity distributed generation resource combination in obtain Optimum combinational scheme.
Based on the fluctuation analysis to distributed generation resource, to the prediction curve and reality of each distributed generation resource in virtual power plant
Border power curve carries out matching degree and calculates analysis, obtains its matching degree degree.
It selects to be dispatched a few days ago as the virtual power plant programming dispatching period, using hour as scale, defines and day match variance, i.e.,
Matching degree of the distributed generation resource per daily generation summation between day expectation power generation curve.From different type, different capabilities
Optimum combinational scheme is obtained in distributed generation resource combination.According to each distributed generation resource self-characteristic, virtual power plant point is being chosen
When cloth power supply type, it should ensure that and at least contain a kind of schedulable distributed generation resource, to improve system overall stability.
Generation schedule constrains：The generation schedule that virtual power plant is submitted to power grid, which will be equal to, removes virtual power plant internal load
The charge and discharge of distributed generation resource and accumulator other than demand are contributed.
Controlled distribution formula power supply output bound constrains：Controlled distribution formula power supply output wants boundary between output bound.
Accumulator cell charging and discharging power bound constrains：It is characterized in that accumulator cell charging and discharging power wants boundary in charge-discharge electric power
In bound constraint, accumulator storage volume Ye Yao circle is between storage volume bound.
Claims (6)
1. a kind of virtual power plant combined optimization method considering distributed generation resource fluctuation, which is characterized in that including following step
Suddenly：
(1) it is reasonably selected and is distributed according to the geographical location of different regions, environmental aspect and resource distribution comprehensive considering various effects
Formula power supply, and the output of each distributed generation resource is predicted；
(2) according to matching degree calculation formula, that each distributed generation resource prediction is contributed with virtual power plant hair electricity plan is calculated
With degree；
(3) each distributed generation resource for obtaining when smallest match is spent is included in virtual power plant；
(4) judge whether the virtual power plant formed by distributed generation resource meets the scheduling needs of system；
(5) virtual power plant is formed.
2. a kind of virtual power plant combined optimization method considering distributed generation resource fluctuation according to claim 1,
It is characterized in that, each distributed generation resource prediction is contributed in the step (2) uses with the matching degree of virtual power plant hair electricity plan
Following formula calculates：
It is in the specific formula for calculation of the matching degree of t periods：
In formula, StIndicate that t period fits degree, N are distributed electrical source category, i numbers for distributed generation resource, Pi tFor distributed generation resource
I predicts to contribute in the t periods, PeFor virtual power plant hair electricity plan electricity；
Entire dispatching cycle (1,2 ... t ... T) in the matching degree of the distributed generation resource based on renewable resource be：
In formula, S indicates that the matching degree of entire dispatching cycle, T are period serial number by taking period, t dispatching cycle, and N is point
Cloth power type, i number for distributed generation resource, Pi tIt predicts to contribute in the t periods for distributed generation resource i, PeFor virtual power plant
Hair electricity plan electricity.
3. a kind of virtual power plant combined optimization method considering distributed generation resource fluctuation according to claim 1,
It is characterized in that, the object function of the Combinatorial Optimization Model of each distributed generation resource is when smallest match is spent in the step (3)：
Wherein, T takes the period by dispatching cycle；T is time segment number；N is distributed electrical source category；I is distributed generation resource
Type number；Pi tIt contributes for the prediction of t moment distributed generation resource i；PeFor virtual power plant hair electricity plan electricity；uiIt indicates to divide
The 0-1 variables of cloth power supply status, ui=1 indicates generator units of the distributed generation resource i as virtual power plant, ui=0 indicates to divide
Generator units of the cloth power supply i not as virtual power plant.
4. a kind of virtual power plant combined optimization method considering distributed generation resource fluctuation according to claim 3,
It is characterized in that：According to demand, select a few days ago, monthly, season or year scheduling as the virtual power plant programming dispatching period, accordingly
S is day matching degree, moon matching degree, season matching degree or year matching degree.
5. a kind of virtual power plant combined optimization method considering distributed generation resource fluctuation according to claim 1,
It being characterized in that, the scheduling in the step (4) needs for constraints, including：
(41) generation schedule constrains：
Wherein,The charge and discharge power of t moment accumulator is indicated respectively；Respectively accumulator
Charge and discharge efficiency；For t moment virtual power plant workload demand,The power generation meter submitted to power grid for virtual power plant
It draws；uiIndicate the 0-1 variables of distributed generation resource state, ui=1 indicates generator units of the distributed generation resource i as virtual power plant,
ui=0 indicates generator units of the distributed generation resource i not as virtual power plant；Pi tFor predicting for t moment distributed generation resource i
Power；
(42) controlled distribution formula power supply output bound constrains：
Wherein,Indicate controlled distribution formula power supply minimum load,Indicate controlled distribution formula power supply maximum output；
(43) accumulator cell charging and discharging power upper and lower limit constrains：
SOC min≤SOCt≤SOC max(9)；
Wherein,Respectively accumulator t moment charge and discharge power；Respectively accumulator
Minimum charge and discharge power；Respectively accumulator maximum charge and discharge power；SOCtFor accumulator t moment
Memory capacity, SOCminFor accumulators store capacity minimum value, SOCmaxFor accumulators store maximum capacity；
If meeting scheduling needs, above-mentioned distributed generation resource is selected, step (5) is executed and forms virtual power plant；If being unsatisfactory for adjusting
Degree needs, then return to step (3) adjusts the distributed generation resource of selection, and needs are dispatched until meeting.
6. a kind of virtual power plant Combinatorial Optimization Model considering distributed generation resource fluctuation, which is characterized in that the model includes
Object function and constraints, the object function are：
Wherein, T takes the period by dispatching cycle；T is time segment number；N is distributed electrical source category；I is distributed generation resource
Type number；Pi tIt contributes for the prediction of t moment distributed generation resource i；PeFor virtual power plant hair electricity plan electricity；uiIt indicates to divide
The 0-1 variables of cloth power supply status, ui=1 indicates generator units of the distributed generation resource i as virtual power plant, ui=0 indicates to divide
Generator units of the cloth power supply i not as virtual power plant；
The constraints includes：
(a) generation schedule constrains：
Wherein,The charge and discharge power of t moment accumulator is indicated respectively；Respectively accumulator
Charge and discharge efficiency；For t moment virtual power plant workload demand,The power generation meter submitted to power grid for virtual power plant
It draws；uiIndicate the 0-1 variables of distributed generation resource state, ui=1 indicates generator units of the distributed generation resource i as virtual power plant,
ui=0 indicates generator units of the distributed generation resource i not as virtual power plant；Pi tFor predicting for t moment distributed generation resource i
Power；
(b) controlled distribution formula power supply output bound constrains：
Wherein,Indicate controlled distribution formula power supply minimum load,Indicate controlled distribution formula power supply maximum output；
(c) accumulator cell charging and discharging power upper and lower limit constrains：
SOC min≤SOCt≤SOC max(9)；
Wherein,Respectively accumulator t moment charge and discharge power；Respectively accumulator
Minimum charge and discharge power；Respectively accumulator maximum charge and discharge power；SOCtFor accumulator t moment
Memory capacity, SOCminFor accumulators store capacity minimum value, SOCmaxFor accumulators store maximum capacity.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810353496.9A CN108683211B (en) | 2018-04-19 | 2018-04-19 | Virtual power plant combination optimization method and model considering distributed power supply volatility |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810353496.9A CN108683211B (en) | 2018-04-19 | 2018-04-19 | Virtual power plant combination optimization method and model considering distributed power supply volatility |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108683211A true CN108683211A (en) | 2018-10-19 |
| CN108683211B CN108683211B (en) | 2021-04-20 |
Family
ID=63801212
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810353496.9A Expired - Fee Related CN108683211B (en) | 2018-04-19 | 2018-04-19 | Virtual power plant combination optimization method and model considering distributed power supply volatility |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108683211B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111125877A (en) * | 2019-11-19 | 2020-05-08 | 广西电网有限责任公司 | Active power distribution network reliability evaluation method based on Monte Carlo simulation |
| CN111915125A (en) * | 2020-06-08 | 2020-11-10 | 清华大学 | Multi-type resource optimal combination method and system for virtual power plant |
Family Cites Families (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9300141B2 (en) * | 2010-11-18 | 2016-03-29 | John J. Marhoefer | Virtual power plant system and method incorporating renewal energy, storage and scalable value-based optimization |
| CN103020738A (en) * | 2012-12-17 | 2013-04-03 | 河海大学 | Method for predicting disturbed trajectory of electric power system based on WDSE (wavelet decomposed signal energy) |
| CN103107558B (en) * | 2013-01-31 | 2015-04-22 | 东南大学 | Multi-modal customizable green energy concentrator and method thereof |
| US9755430B2 (en) * | 2013-04-11 | 2017-09-05 | Solantro Semiconductor Corp. | Virtual inverter for power generation units |
| CN103617455A (en) * | 2013-11-29 | 2014-03-05 | 广东电网公司电力科学研究院 | Power network and plant two-stage optimal load scheduling method based on virtual machine set subgroup |
| CN104517161B (en) * | 2014-12-25 | 2017-08-15 | 东南大学 | The distributed power source combinatorial programming system and method for virtual power plant |
| CN105260824A (en) * | 2015-09-24 | 2016-01-20 | 华中科技大学 | Virtual power plant optimization scheduling method containing storage batteries based on unified electricity market |
| CN105514986A (en) * | 2015-12-07 | 2016-04-20 | 国网上海市电力公司 | DER user bidding grid-connection method based on virtual power plant technology |
| CN106972550A (en) * | 2017-03-20 | 2017-07-21 | 国网浙江省电力公司嘉兴供电公司 | A kind of virtual plant power regulating method based on tide energy and luminous energy |
| CN106972545A (en) * | 2017-03-20 | 2017-07-21 | 国网浙江省电力公司嘉兴供电公司 | A kind of virtual plant power regulating method |
| CN107341574A (en) * | 2017-07-10 | 2017-11-10 | 华北电力大学 | The virtual plant multistage of meter and demand response bids optimization method and computing device |
| CN107887932A (en) * | 2017-11-13 | 2018-04-06 | 天津大学 | Virtual power plant is bidded method for organizing in the production of ahead market |
-
2018
- 2018-04-19 CN CN201810353496.9A patent/CN108683211B/en not_active Expired - Fee Related
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111125877A (en) * | 2019-11-19 | 2020-05-08 | 广西电网有限责任公司 | Active power distribution network reliability evaluation method based on Monte Carlo simulation |
| CN111915125A (en) * | 2020-06-08 | 2020-11-10 | 清华大学 | Multi-type resource optimal combination method and system for virtual power plant |
| CN111915125B (en) * | 2020-06-08 | 2022-07-29 | 清华大学 | Multi-type resource optimal combination method and system for virtual power plant |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108683211B (en) | 2021-04-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Dinh et al. | A home energy management system with renewable energy and energy storage utilizing main grid and electricity selling | |
| CN108667052B (en) | Multi-type energy storage system planning configuration method and system for virtual power plant optimized operation | |
| Bhattacharya et al. | Managing energy storage in microgrids: A multistage stochastic programming approach | |
| CN104200297A (en) | Energy optimizing dispatching method of home hybrid power supply system in real-time power price environment | |
| CN103795079A (en) | Off-grid grid-connected hybrid photovoltaic power generation control system and economical operation optimization method thereof | |
| Makhadmeh et al. | An optimal power scheduling for smart home appliances with smart battery using grey wolf optimizer | |
| Gbadega et al. | Impact of incorporating disturbance prediction on the performance of energy management systems in micro-grid | |
| CN112800658A (en) | Active power distribution network scheduling method considering source storage load interaction | |
| Cheng et al. | A particle swarm optimization based power dispatch algorithm with roulette wheel re-distribution mechanism for equality constraint | |
| CN113988444A (en) | Electricity charge optimization control system and method for light storage system | |
| CN108551176B (en) | Energy storage battery system capacity configuration method combined with energy storage balancing technology | |
| CN110350512A (en) | A kind of Itellectualized uptown generation of electricity by new energy station method for optimizing scheduling and system | |
| CN108512238B (en) | Two-stage optimal scheduling method for smart home based on demand side response | |
| CN107273968A (en) | A kind of Multiobjective Scheduling method and device based on dynamic fuzzy Chaos-Particle Swarm Optimization | |
| CN105896580B (en) | A kind of micro-capacitance sensor multiobjective optimization control method and device | |
| Alvarez et al. | A generic storage model based on a future cost piecewise-linear approximation | |
| Niknam et al. | Probabilistic model of polymer exchange fuel cell power plants for hydrogen, thermal and electrical energy management | |
| CN112836849A (en) | Virtual power plant scheduling method considering wind power uncertainty | |
| Georgiou et al. | A genetic algorithm driven linear programming for battery optimal scheduling in nearly zero energy buildings | |
| Yuan et al. | Bess aided renewable energy supply using deep reinforcement learning for 5g and beyond | |
| CN108683211A (en) | A kind of virtual power plant combined optimization method and model considering distributed generation resource fluctuation | |
| TWI639962B (en) | Particle Swarm Optimization Fuzzy Logic Control Charging Method Applied to Smart Grid | |
| CN117559526A (en) | Router-simulated energy regulation and control method based on optical storage and charging integrated charging station | |
| CN110224397B (en) | User-side battery energy storage cost benefit analysis method under wind and light access background | |
| CN105119315B (en) | Energy storage converter combination optimization method based on energy efficiency maximization |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20210420 |