CN106156921A - Based on the electric automobile photovoltaic charge station energy storage selection of configuration method that Copula is theoretical - Google Patents
Based on the electric automobile photovoltaic charge station energy storage selection of configuration method that Copula is theoretical Download PDFInfo
- Publication number
- CN106156921A CN106156921A CN201510166069.6A CN201510166069A CN106156921A CN 106156921 A CN106156921 A CN 106156921A CN 201510166069 A CN201510166069 A CN 201510166069A CN 106156921 A CN106156921 A CN 106156921A
- Authority
- CN
- China
- Prior art keywords
- theta
- copula
- energy storage
- photovoltaic
- charge station
- 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
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
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
-
- 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
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
-
- 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
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
- Photovoltaic Devices (AREA)
Abstract
A kind of electric automobile photovoltaic charge station energy storage selection of configuration method theoretical based on Copula is disclosed herein, comprise the following steps: choose photovoltaic cells and charging electric vehicle load to go out power rate be stochastic variable, by measured data normalization, build the marginal distribution of each variable;Theoretical based on Copula, choose Gumbel-Copula and Clayton-Copula and build mixing Copula function to describe the dependency of asymmetric rear tail characteristic between variable;The year net load amount of sampled analog photovoltaic charge station on the basis of both combine probability density function of exerting oneself;Under the constraint of the condition such as stability bandwidth, confidence level, set up the energy storage Optimal Allocation Model with the photovoltaic electric minimum object function of vehicle charging station annual operating and maintenance cost;Matlab programming is optimized calculating to optimum stored energy capacitance.Consider runnability and economy, the integrated system energy storage allocation models correctness and feasibility considering dependency is analyzed, selects optimal allocation scheme, thus improve the efficiency of light energy utilization and systematic economy benefit.
Description
Technical field
The present invention relates to energy storage selection of configuration field, a kind of electricity theoretical based on Copula
Electrical automobile photovoltaic charge station energy storage selection of configuration method.
Background technology
Energy crisis and environmental problem are day by day serious, regenerative resource and electric automobile (Electric Vehicles,
EVs) utilization shows great potential in terms of energy-saving and emission-reduction.Photovoltaic generation undulatory property, intermittent feature
Make it dissolve on a large scale to acquire a certain degree of difficulty.A large amount of electric automobile chargings at random can increase the weight of the burden of electrical network, and
The carbon emission amount indirectly produced is not preponderated, and improves the energy and environmental problem inconspicuous.In city
Under environment, build this typical integrated mode of electric automobile photovoltaic charge station and both can realize regenerative resource
On-site elimination reduces again the adverse effect of charging electric vehicle Load on Electric Power Grid, has certain development prospect
With exploration meaning.
Energy storage technology possesses the time-shift ability to power and energy, but high energy storage cost always stores up
Technology can develop very important problem.The energy-storage system of configuration optimum can improve the raising light of photovoltaic charge station
Can utilization rate, reduce the impact of bulk power grid, increase economic efficiency.For the capacity configuration of energy-storage system,
Some achievements in research have been there are in terms of improving operation of power networks economy stability.
The stroke characteristic of electric automobile has randomness, but the charging load of a large amount of electric automobile produces cluster
Effect i.e. has statistical property, provides auxiliary clothes even with V2G (vehicle to grid) technology to electrical network
Business, photovoltaic is exerted oneself and charging electric vehicle load has the typical feature that certain dependency is integrated system.Mesh
Front traditional energy storage configuration method does not all consider the dependency impact on energy-storage system optimization allocation.
Summary of the invention
It is an object of the invention to, for this typical integrated system of photovoltaic electric vehicle charging station, propose one
Plant the energy storage configuration method that consider dependency theoretical based on Copula, to improve the efficiency of light energy utilization and system
Business efficiency.
For achieving the above object, the technical solution used in the present invention is:
1) choose photovoltaic cells and charging electric vehicle load to go out power rate be stochastic variable, measured data is entered
Row normalized, builds the marginal distribution of each variable;
2) theoretical based on Copula, choose Gumbel-Copula and Clayton-Copula and build mixing
Copula function describes photovoltaic and exerts oneself and the dependency of the asymmetric rear tail characteristic of charging electric vehicle load;
3) sampled analog photovoltaic charge station year net load amount on the basis of both combine probability density function of exerting oneself;
4) under the constraint of the conditions such as power-balance, state-of-charge, stability bandwidth, confidence level, set up with photovoltaic
The energy storage Optimal Allocation Model of the minimum object function of electric automobile charging station annual operating and maintenance cost;
5) program in matlab environment, stored energy capacitance is optimized calculating.
Selective allocation plan specifically includes: considers the single batteries to store energy model of dependency, consider phase
Close property hybrid energy-storing model, do not consider the single batteries to store energy model of dependency and do not consider dependency
Hybrid energy-storing model.
Technical scheme has the advantages that
Technical scheme, theoretical on the basis of dependency based on Copula, by considering operation
Performance and Financial cost, the feasibility running photovoltaic electric vehicle charging station integrated system is analyzed, and gives
Go out the allocation plan of practicality, thus reached to improve the efficiency of light energy utilization and the purpose of business efficiency.
Below by drawings and Examples, technical scheme is described in further detail.
Accompanying drawing explanation
Fig. 1 is the photovoltaic electric vehicle charging station energy storage configuration choosing considering dependency described in the embodiment of the present invention
Selection method flow chart
Fig. 2 is that photovoltaic is exerted oneself frequency histogram and probability density curve comparison diagram
Fig. 3 is that electric automobile load is exerted oneself frequency histogram and probability density curve comparison diagram
Fig. 4 is that photovoltaic is exerted oneself and the bivariate frequency rectangular histogram of the load that charges
Fig. 5 is that photovoltaic is exerted oneself and the joint probability density of the load that charges
Fig. 6 is electric automobile photovoltaic charge station year net load analog data
Detailed description of the invention
Below in conjunction with the drawings and specific embodiments, the present invention is further elaborated.
This example electric automobile charging station is provided with 120 charging piles, and the rated power of separate unit charging pile is 10kW;
The rated capacity of photovoltaic generation unit is 800kW;Energy-storage units uses lead-acid accumulator and ultracapacitor to constitute,
Component parameters is shown in Table 1.
Table 1 energy storage device parameter
1) choose photovoltaic cells and charging electric vehicle load to go out power rate be stochastic variable, number will be surveyed
According to being normalized.By frequency histogram, it is nonnormal distribution that Fig. 2, Fig. 3 understand sample data,
Then estimate marginal distribution with non parametric tests, use Density Estimator method herein.If x1, x2 ...,
Xn is the sample of stochastic variable X, and probability density function f (x) computing formula of stochastic variable X is as follows:
2) photovoltaic is exerted oneself and the dependency that charges between load has asymmetrical rear tail characteristic such as Fig. 4 institute
Show.Characteristic according to each function choose the relevant Gumbel-Copula of Asymmetric Tail and
Clayton-Copula linear combination builds mixing Copula function and is fitted.Form is as follows:
C (u, v, θ)=ω1CG(u, v;θ1)+ω2CC(u, v;θ2)
U=F in formulaPV(PPV), v=FEV(PEV);ω1, ω2For the weight coefficient of single Copula function, and
ω1+ω2=1;θ1, θ2Relevance parameter for Gumbel-Copula and Clayton-Copula.Use maximum
Expect that (EM) algorithm estimates parameter ω1, ω2。
3) joint probability density function formula is obtained as follows:
H (x, y)=[ω1cG(u, v;θ1)+ω2cC(u, v;θ2)]fpv(x)fev(y)
ω1=0.3038, ω2=0.6962, θ1=4.2788, θ2=8.6953.Fig. 5 be both combine probability of exerting oneself
Density function, the year net load amount of sampled analog photovoltaic charge station on this functional foundations, as shown in Figure 6.
4) energy storage Optimal Allocation Model is set up.
A, set up object function in terms of following two, calculate cost.
The average annual operating cost of A1, energy-storage system
C1=Cin+Cop+Cma+Cdi=(1+kopba+kmaba+kdiba)·kdebamfba+(1+kopsc+kdisc)kdesnfsc
Wherein Cin、Cop、Cma、CdiRespectively purchase, run, safeguard and processing cost, simply consider,
Converted herein as different proportionality coefficients.kopba、kmaba、kdibaRepresent that lead-acid accumulator runs, ties up respectively
Protect, processing cost coefficient.kopsc、kdiseRepresent ultracapacitor operation, processing cost coefficient respectively.
A2 system year power purchase expense.
Pr (t) and P in formulagT () represents t period bulk power grid and the mutual electricity price of system and mean power respectively;
Δ t is the duration of t period.
The whole integrated charge station minimum object function of annual operating and maintenance cost, it may be assumed that
Min C=min (C1+C2)
B constraints
The B1 system power equilibrium of supply and demand.
Pg(t)+Ppv(t)+Pev(t)+Pba(t)+Puc(t)=0
Exchange power P between integrated charge station and bulk power gridgT (), photovoltaic are exerted oneself PpvT (), EVs charging load is exerted oneself
PevT (), energy storage are exerted oneself Pba(t) and PucT () should keep balance the moment.
B2 power constraint.Battery be output as zero or rated value and the output of ultracapacitor should be less than
In its maximum.
Pba(t)=0 or Pba(t)=Pban
Psc(t)≤Pscmax
B3 super-charge super-discharge can reduce the SOC of the service life of energy-storage travelling wave tube, battery and ultracapacitor all
Should be in the range of reasonably limiting.
Ebamin≤Eba(t)≤Ebamax
Escmin≤Esc(t)≤Escmax
The constraint of the grid-connected power swing of B4
α≤αmax
The constraint of B5 confidence level.Grid-connected power-performance mutually restricts with cost, need to be in certain confidence level
The lower balance realizing performance and cost.
η≥ηmin
Alternative allocation plan specifically includes: consider the single batteries to store energy model of dependency, consideration
The hybrid energy-storing model of dependency, do not consider the single batteries to store energy model of dependency and do not consider dependency
Hybrid energy-storing model.
5) improvement invasive weed algorithm based on differential evolution strategy is used to be programmed meter in matlab environment
Calculate, choose the allocation plan of optimum.Set stability bandwidth limit value 4%, during confidence level 97%, consider dependency
As shown in table 2 with not considering to optimize result of calculation in the case of dependency.
Table 2 stored energy capacitance optimum results
Compared with not considering dependency, it is considered to dependency decreases energy storage on the premise of tallying with the actual situation
The configuration capacity of device, improves the economy of system.Hybrid energy-storing mode can slow down battery depreciation, subtracts
The alternative costs of little element, thus obtain higher whole economic efficiency.
The all data of the technical program are all that conversion is to considering as a whole in each year
The foregoing is only the preferred embodiments of the present invention, be not limited to the present invention, although with reference to aforementioned
The present invention has been described in detail by embodiment, and for a person skilled in the art, it is the most permissible
Technical scheme described in foregoing embodiments is modified, or wherein portion of techniques feature is carried out
With replacing.All within the spirit and principles in the present invention, any modification, equivalent substitution and improvement etc. made,
Should be included within the scope of the present invention.
Claims (6)
1. based on the electric automobile photovoltaic charge station energy storage selection of configuration method that Copula is theoretical, comprise with
Lower step:
1) choose photovoltaic cells and charging electric vehicle load to go out power rate be stochastic variable, measured data is entered
Row normalized, builds the marginal distribution of each variable;
2) theoretical based on Copula, choose Gumbel-Copula and Clayton-Copula and build mixing Copula
Function describes photovoltaic and exerts oneself and the dependency of the asymmetric rear tail characteristic of charging electric vehicle load;
3) the year net load amount of sampled analog photovoltaic charge station on the basis of both combine probability density function of exerting oneself;
4) under the constraint of the conditions such as power-balance, state-of-charge, stability bandwidth, confidence level, set up with photovoltaic
The energy storage Optimal Allocation Model of the minimum object function of electric automobile charging station annual operating and maintenance cost;
5) matlab programming is optimized calculating to optimum stored energy capacitance.
The electric automobile photovoltaic charge station energy storage configuration theoretical based on Copula the most according to claim 1
System of selection, it is characterised in that step 1) in x1、x2、...、xnFor the sample of stochastic variable X, at random
Probability density function f (x) computing formula of variable X is:
The electric automobile photovoltaic charge station energy storage configuration theoretical based on Copula the most according to claim 1
System of selection, it is characterised in that step 2) in choose Asymmetric Tail according to the characteristic of each function relevant
Gumbel-Copula and Clayton-Copula linear combination builds mixing Copula function and is fitted.Shape
Formula is as follows:
C (u, v, θ)=ω1CG(u, v;θ1)+ω2CC(u, v;θ2)
U=F in formulaPV(PPV), v=FEV(PEV);ω1, ω2For the weight coefficient of single Copula function, and
ω1+ω2=1;θ1, θ2Relevance parameter for Gumbel-Copula and Clayton-Copula.Use maximum
Expect that (EM) algorithm estimates parameter ω1, ω2。
The electric automobile photovoltaic charge station energy storage configuration theoretical based on Copula the most according to claim 1
System of selection, it is characterised in that step 3) to obtain joint probability density function formula as follows:
H (x, y)=[ω1cG(u, v;θ1)+ω2cC(u, v;θ2)]fpv(x)fev(y)
The electric automobile photovoltaic charge station energy storage configuration theoretical based on Copula the most according to claim 1
System of selection, it is characterised in that step 4) set up object function in terms of following two, calculate cost.
1. the average annual operating cost of energy-storage system
C1=Cin+Cop+Cma+Cdi=(1+kopba+kmaba+kdiba)·kdebamfba+(1+kopsc+kdisc)kdescnfsc
Wherein Cin、Cop、Cma、CdtRespectively purchase, run, safeguard and processing cost, simply consider,
Converted herein as different proportionality coefficients.kopba、kmaba、kdtbaRepresent that lead-acid accumulator runs, ties up respectively
Protect, processing cost coefficient.kopsc、kdiscRepresent ultracapacitor operation, processing cost coefficient respectively.
2. system year power purchase expense.
Pr (t) and P in formulagT () represents t period bulk power grid and the mutual electricity price of system and mean power respectively;
Δ t is the duration of t period.
The whole integrated charge station minimum object function of annual operating and maintenance cost, it may be assumed that
Min C=min (C1+C2)
The electric automobile photovoltaic charge station energy storage configuration theoretical based on Copula the most according to claim 1
System of selection, it is characterised in that step 4) constraints includes following five aspects:
1. the system power equilibrium of supply and demand.
Pg(t)+Ppv(t)+Pev(t)+Pba(t)+Puc(t)=0
Exchange power P between integrated charge station and bulk power gridgT (), photovoltaic are exerted oneself PpvT (), EVs charging load is exerted oneself
PevT (), energy storage are exerted oneself Pba(t) and PucT () should keep balance the moment.
2. power constraint.Battery be output as zero or rated value and the output of ultracapacitor should be less than
In its maximum.
Pba(t)=0 or Pba(t)=Pban
Psc(t)≤Psc max
3. super-charge super-discharge can reduce the SOC of the service life of energy-storage travelling wave tube, battery and ultracapacitor all
Should be in the range of reasonably limiting.
Eba min≤Eba(t)≤Eba max
Esc min≤Esc(t)≤Esc max
The constraint of the most grid-connected power swing
α≤αmax
5. the constraint of confidence level.Grid-connected power-performance mutually restricts with cost, need to be in certain confidence level
The lower balance realizing performance and cost.
η≥ηmin
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201510166069.6A CN106156921B (en) | 2015-04-10 | 2015-04-10 | Electric vehicle photovoltaic charging station energy storage configuration selection method based on Copula theory |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201510166069.6A CN106156921B (en) | 2015-04-10 | 2015-04-10 | Electric vehicle photovoltaic charging station energy storage configuration selection method based on Copula theory |
Publications (2)
Publication Number | Publication Date |
---|---|
CN106156921A true CN106156921A (en) | 2016-11-23 |
CN106156921B CN106156921B (en) | 2021-11-09 |
Family
ID=57336082
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201510166069.6A Active CN106156921B (en) | 2015-04-10 | 2015-04-10 | Electric vehicle photovoltaic charging station energy storage configuration selection method based on Copula theory |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN106156921B (en) |
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107147110A (en) * | 2017-05-17 | 2017-09-08 | 山东大学 | Consider the stored energy capacitance Optimal Configuration Method of windy field prediction error temporal correlation |
CN109814178A (en) * | 2018-12-25 | 2019-05-28 | 河海大学 | Hydrological probability forecasting procedure based on Copula- Model Condition processor |
CN113435730A (en) * | 2021-06-23 | 2021-09-24 | 中国能源建设集团广东省电力设计研究院有限公司 | Collaborative configuration method, device and system for energy storage capacity of transformer substation |
CN114123354A (en) * | 2022-01-26 | 2022-03-01 | 湖北工业大学 | Wind storage integrated system optimal scheduling method based on t distribution weed algorithm |
Citations (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101960396A (en) * | 2008-02-29 | 2011-01-26 | 株式会社东芝 | Operation plan creatiion method and device for energy storage device |
CN102005771A (en) * | 2010-12-23 | 2011-04-06 | 天津电力设计院 | Energy storage capacity selecting method of wind, photovoltaic and storage micro-grid system |
CN102361334A (en) * | 2011-10-17 | 2012-02-22 | 广东电网公司深圳供电局 | Photovoltaic energy storage electric automobile charging station system and method for switching state of energy storage system |
CN102428621A (en) * | 2009-03-18 | 2012-04-25 | 瑞典电动马达有限公司 | System and method for controlling an energe storage pack |
CN102985883A (en) * | 2009-12-10 | 2013-03-20 | 埃森哲环球服务有限公司 | Energy facility control system |
CN103310306A (en) * | 2013-06-18 | 2013-09-18 | 国家电网公司 | Monitoring and managing system and monitoring and managing method for wind power storing station comprehensive information |
CN103903066A (en) * | 2014-04-04 | 2014-07-02 | 东南大学 | Virtual power plant stratified random optimized dispatching method |
CN104054228A (en) * | 2011-12-30 | 2014-09-17 | 汤姆逊许可公司 | Adaptation of a power generation capacity and determining of an energy storage unit size |
CN104092230A (en) * | 2014-07-02 | 2014-10-08 | 中国南方电网有限责任公司 | Method for achieving optimal configuration of capacity of energy storage equipment in islanding mode of regional power grid comprising DG |
CN104240040A (en) * | 2014-09-28 | 2014-12-24 | 华北电力大学 | City smart power grid planning method on basis of land conversion risk |
-
2015
- 2015-04-10 CN CN201510166069.6A patent/CN106156921B/en active Active
Patent Citations (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101960396A (en) * | 2008-02-29 | 2011-01-26 | 株式会社东芝 | Operation plan creatiion method and device for energy storage device |
CN102428621A (en) * | 2009-03-18 | 2012-04-25 | 瑞典电动马达有限公司 | System and method for controlling an energe storage pack |
CN102985883A (en) * | 2009-12-10 | 2013-03-20 | 埃森哲环球服务有限公司 | Energy facility control system |
CN102005771A (en) * | 2010-12-23 | 2011-04-06 | 天津电力设计院 | Energy storage capacity selecting method of wind, photovoltaic and storage micro-grid system |
CN102361334A (en) * | 2011-10-17 | 2012-02-22 | 广东电网公司深圳供电局 | Photovoltaic energy storage electric automobile charging station system and method for switching state of energy storage system |
CN104054228A (en) * | 2011-12-30 | 2014-09-17 | 汤姆逊许可公司 | Adaptation of a power generation capacity and determining of an energy storage unit size |
CN103310306A (en) * | 2013-06-18 | 2013-09-18 | 国家电网公司 | Monitoring and managing system and monitoring and managing method for wind power storing station comprehensive information |
CN103903066A (en) * | 2014-04-04 | 2014-07-02 | 东南大学 | Virtual power plant stratified random optimized dispatching method |
CN104092230A (en) * | 2014-07-02 | 2014-10-08 | 中国南方电网有限责任公司 | Method for achieving optimal configuration of capacity of energy storage equipment in islanding mode of regional power grid comprising DG |
CN104240040A (en) * | 2014-09-28 | 2014-12-24 | 华北电力大学 | City smart power grid planning method on basis of land conversion risk |
Non-Patent Citations (1)
Title |
---|
李梦玄: "《中国金融市场一体化度量的Copula模型及实证研究》", 31 October 2012, 湖北科学技术出版社 * |
Cited By (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107147110A (en) * | 2017-05-17 | 2017-09-08 | 山东大学 | Consider the stored energy capacitance Optimal Configuration Method of windy field prediction error temporal correlation |
CN107147110B (en) * | 2017-05-17 | 2020-01-17 | 山东大学 | Energy storage capacity optimal configuration method considering multi-wind-field prediction error space-time correlation |
CN109814178A (en) * | 2018-12-25 | 2019-05-28 | 河海大学 | Hydrological probability forecasting procedure based on Copula- Model Condition processor |
CN113435730A (en) * | 2021-06-23 | 2021-09-24 | 中国能源建设集团广东省电力设计研究院有限公司 | Collaborative configuration method, device and system for energy storage capacity of transformer substation |
CN114123354A (en) * | 2022-01-26 | 2022-03-01 | 湖北工业大学 | Wind storage integrated system optimal scheduling method based on t distribution weed algorithm |
Also Published As
Publication number | Publication date |
---|---|
CN106156921B (en) | 2021-11-09 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN109687444B (en) | Multi-objective double-layer optimal configuration method for micro-grid power supply | |
CN109301852B (en) | Micro-grid hierarchical multi-target combined economic dispatching method | |
CN109146201A (en) | Filling based on cooperative game changes the integrated power station micro-capacitance sensor Optimization Scheduling of storage | |
CN108090277A (en) | A kind of electric vehicle microgrid dual-layer optimization dispatching method for considering satisfaction and dispatching | |
CN109256800A (en) | A kind of region, which is filled, changes the integrated power station micro-capacitance sensor group's coordination optimization dispatching method of storage | |
CN103839109A (en) | Microgrid power source planning method based on game and Nash equilibrium | |
CN103904749B (en) | A kind ofly consider the orderly charge control method of the electric automobile of wind power output fluctuation | |
CN107769235A (en) | A kind of microgrid energy management method based on hybrid energy-storing and electric automobile | |
CN107482660B (en) | Active power distribution network energy storage configuration method based on double energy storage systems | |
CN106156921A (en) | Based on the electric automobile photovoltaic charge station energy storage selection of configuration method that Copula is theoretical | |
CN102969780A (en) | Off-grid wind/solar/battery hybrid power generation system capacity optimal configuration method | |
Beltran et al. | Lithium ion batteries ageing analysis when used in a PV power plant | |
CN111762057B (en) | Intelligent charging and discharging management method for V2G electric vehicle in regional microgrid | |
CN105356521A (en) | AC and Dc mixed micro-grid operation optimization method based on time-domain rolling control | |
Xie et al. | Optimal capacity configuration of hybrid energy storage for an isolated microgrid based on QPSO algorithm | |
CN111639866A (en) | Energy storage capacity configuration method of energy storage charging station based on genetic algorithm | |
CN107732937A (en) | The peak load shifting method of the grid type microgrid of the electric automobile containing wind-light storage | |
CN107482675A (en) | A kind of computational methods of the electric automobile consumption regenerative resource based on off-network type microgrid | |
Zheng et al. | Multi-objective optimization of hybrid energy management system for expressway chargers | |
CN105470947A (en) | Micro-power-grid scheduling method based on quantum-behaved particle swarm optimization | |
Skander-Mustapha et al. | Energy management of rooftop PV system including battery storage: case study of ENIT building | |
Tripathy et al. | Voltage profile enhancement of a 33 bus system integrated with renewable energy sources and electric vehicle | |
CN114971154A (en) | Renewable energy consumption method comprising carbon transaction mechanism | |
Koochaki et al. | Optimal design of solar-wind hybrid system using teaching-learning based optimization applied in charging station for electric vehicles | |
Thakre et al. | Potentially affect of a vehicle to grid on the electricity system |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | 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 |