CN113962101A - Reliability assessment method for new energy automobile comprehensive energy charging station - Google Patents
Reliability assessment method for new energy automobile comprehensive energy charging station Download PDFInfo
- Publication number
- CN113962101A CN113962101A CN202111258945.XA CN202111258945A CN113962101A CN 113962101 A CN113962101 A CN 113962101A CN 202111258945 A CN202111258945 A CN 202111258945A CN 113962101 A CN113962101 A CN 113962101A
- Authority
- CN
- China
- Prior art keywords
- charging
- hydrogen
- charging station
- bev
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 46
- 239000001257 hydrogen Substances 0.000 claims abstract description 152
- 229910052739 hydrogen Inorganic materials 0.000 claims abstract description 152
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 claims abstract description 147
- 238000011156 evaluation Methods 0.000 claims abstract description 44
- 238000011835 investigation Methods 0.000 claims abstract description 8
- 238000003860 storage Methods 0.000 claims description 40
- 241000709691 Enterovirus E Species 0.000 claims description 39
- 239000000446 fuel Substances 0.000 claims description 37
- 238000009826 distribution Methods 0.000 claims description 14
- 238000010168 coupling process Methods 0.000 claims description 10
- 238000005859 coupling reaction Methods 0.000 claims description 10
- 230000009467 reduction Effects 0.000 claims description 10
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 9
- 230000008878 coupling Effects 0.000 claims description 7
- 150000002431 hydrogen Chemical class 0.000 claims description 7
- 230000002354 daily effect Effects 0.000 claims description 6
- 238000005457 optimization Methods 0.000 claims description 5
- 238000005868 electrolysis reaction Methods 0.000 claims description 4
- 230000003203 everyday effect Effects 0.000 claims description 4
- 238000004519 manufacturing process Methods 0.000 claims description 4
- 238000007476 Maximum Likelihood Methods 0.000 claims description 3
- 230000005540 biological transmission Effects 0.000 claims description 3
- PQYUGUXEJHLOIL-UHFFFAOYSA-N diethoxysilyl triethyl silicate Chemical compound C(C)O[SiH](O[Si](OCC)(OCC)OCC)OCC PQYUGUXEJHLOIL-UHFFFAOYSA-N 0.000 claims description 3
- 230000005611 electricity Effects 0.000 claims description 3
- 239000007789 gas Substances 0.000 claims description 3
- 238000010438 heat treatment Methods 0.000 claims description 3
- 125000004435 hydrogen atom Chemical group [H]* 0.000 claims description 3
- 230000008569 process Effects 0.000 claims description 3
- 230000004044 response Effects 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims description 3
- 239000004576 sand Substances 0.000 claims description 3
- 230000000638 stimulation Effects 0.000 claims description 3
- 238000009827 uniform distribution Methods 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000000342 Monte Carlo simulation Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- 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
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/08—Probabilistic or stochastic CAD
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
-
- 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]
-
- 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
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Power Engineering (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Fuel Cell (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention provides a reliability evaluation method for a comprehensive energy charging station of a new energy automobile, which comprises the following steps of; the method comprises the following steps: acquiring relevant parameters required by reliability evaluation of the comprehensive charging station, including equipment capacity, the number of users in a service area and the like, and acquiring local annual distributed power supply electric energy output according to historical data; step two: acquiring the charging and hydrogen charging requirements of BEV and HFCV for one day based on a BEV/HFCV charging model; the hydrogen demand of the HFCV is determined according to the number of vehicles and a charging protocol; the BEV charging demand is determined according to the BEV number and BEV running rule data on the same day; solving an optimized scheduling model of the comprehensive energy charging station to obtain and record the energy charging demand shortage condition of the current day; checking whether the total days reach the upper limit of the reliability evaluation total investigation time; if the result is reached, the next step is carried out, otherwise, the step two is returned; step five, comprehensively evaluating the relevant data in the total investigation time by reliability, and calculating a reliability index; the invention can make the reliability evaluation result more accurate.
Description
Technical Field
The invention relates to the technical field of traffic facilities, in particular to a reliability evaluation method for a comprehensive energy charging station of a new energy automobile.
Background
The wide application of new energy automobiles is an important way for solving the problems of fossil energy shortage and environmental pollution improvement. With the continuous improvement of the technology and the implementation of the infrastructure service, the market share of the new energy automobile is continuously improved. Battery Electric Vehicles (BEV) have long development time and mature technology, but have long charging time, are limited by Battery manufacturing technology, and have limited cruising ability; the Hydrogen Fuel Cell Vehicle (HFCV) requires a short time for charging Hydrogen, has a strong cruising ability, but is slow to develop, and is not widely popularized at present. BEV and HFCV will exist in the market for a long time in the future. In order to meet the energy charging requirements of different forms of energy sources such as BEV and HFCV, the concept of a comprehensive energy charging station of a new energy automobile is provided. On the basis that the existing BEV charging station provides charging service, an electric-hydrogen energy conversion device, a hydrogen storage device and the like are arranged, meanwhile, the charging service is provided for the BEV and the HFCV, and distributed power supplies such as photovoltaic power, wind power and the like are used as main power input of the comprehensive charging station, so that the dependence on a public power grid is reduced, and the consumption of renewable energy sources is promoted. The comprehensive energy charging station is used as the most important infrastructure of new energy automobiles, and the energy supply reliability of the comprehensive energy charging station is a major focus of attention of automobile users and is also an important factor to be considered for planning and constructing the energy charging station. Therefore, the reliability evaluation of the comprehensive energy charging station is of great significance.
At present, research aiming at a new energy automobile comprehensive energy charging station mainly focuses on the impact influence of the optimized operation of the energy charging station and the quick charging of the automobile on the stable operation of a power distribution network. In the prior art, the fact that electric energy and hydrogen cannot be provided is considered as punishment for influencing the expected income of an energy charging station by setting a punishment coefficient, the operation of the electric/hydrogen hybrid energy charging station is optimized, but the influence of the shortage of energy charging requirements on the energy charging experience of an owner is not considered, so that the energy charging behavior of the owner and the reliability of the energy charging station are influenced; for the impact problem caused by rapid charging of the electric vehicle, a method such as sequential monte carlo simulation is also researched, the reliability evaluation is carried out on the power distribution network containing the charging station by considering the fault state of the charging station, but the reliability evaluation of the charging station is not deeply researched.
Aiming at the defects, the invention provides a comprehensive energy charging station reliability assessment method considering the experience of the car owner. According to the method and the system, the influence of the experience of the vehicle owner on the behavior of the vehicle owner and the energy charging requirement of the energy charging station is considered, and the energy supply reliability of the comprehensive energy charging station is evaluated, so that the reliability evaluation result is more accurate.
Disclosure of Invention
The invention provides a reliability evaluation method for a new energy automobile comprehensive energy charging station, which evaluates the energy supply reliability of the comprehensive energy charging station by considering the influence of the experience of an automobile owner on the behavior of the automobile owner and the energy charging requirement of the energy charging station, and enables the reliability evaluation result to be more accurate.
The invention adopts the following technical scheme.
A reliability evaluation method for a new energy automobile comprehensive energy charging station can evaluate the reliability of the charging station on the automobile charging capacity, wherein the comprehensive energy charging station is used for charging BEV and charging HFCV; BEV is a battery electric vehicle; HFCV is hydrogen fuel cell vehicles; in the evaluation method, the power for charging the BEV comes from a public power grid coupling point or a fuel cell in a comprehensive charging station; hydrogen for charging HFCV is prepared by an electrolytic cell, and the hydrogen produced by the electrolytic cell is stored in a hydrogen storage tank; the evaluation method includes the following steps;
the method comprises the following steps: acquiring relevant parameters required by reliability evaluation of the comprehensive charging station, including equipment capacity, the number of users in a service area and the like, and acquiring local annual distributed power supply electric energy output according to historical data;
step two: acquiring the charging and hydrogen charging requirements of BEV and HFCV for one day based on a BEV/HFCV charging model; the hydrogen demand of the HFCV is determined according to the number of vehicles and a charging protocol; determining BEV charging requirements according to the number of BEVs on the same day determined by the vehicle quantity estimation model and BEV running rule data which are simulated and met by adopting Latin hypercube sampling;
solving an optimized scheduling model of the comprehensive energy charging station to obtain and record the energy charging demand shortage condition of the current day;
checking whether the total days reach the upper limit of the reliability evaluation total investigation time; if the result is reached, the next step is carried out, otherwise, the step two is returned;
and fifthly, comprehensively evaluating the relevant data of the energy-charging demand shortage condition in the total investigation time, and calculating the reliability index of the comprehensive energy-charging station.
The method for solving the optimal scheduling model of the comprehensive energy charging station in the third step comprises an electrolytic cell modeling method, a hydrogen storage tank modeling method and a fuel cell modeling method aiming at the comprehensive energy charging station;
the modeling method of the electrolytic cell comprises the following steps: the electrolytic cell is used for realizing the hydrogen production process by electrolyzing water and consuming electric energy to produce hydrogen, and the expression of the model and the constraint condition is as follows:
wherein etaelzFor the efficiency of the cell, Pt elz、mt elzRespectively representing the consumed electric power and the generated hydrogen quality in the t period; Δ t is the duration of each time period, and is set to 1 hour; LHV is the lower heating value of hydrogen and is a constant;
andP elzis the maximum and minimum electrical power consumed by the electrolytic cell;
the modeling method of the hydrogen storage tank comprises the following steps: the hydrogen storage tank stores hydrogen generated by water electrolysis of the electrolytic cell, and is used for hydrogen supply of a hydrogen fuel cell vehicle or for a fuel cell to convert the hydrogen into electric energy, and the amount of the hydrogen stored in the hydrogen storage tank can be represented by the following formula:
the amount of hydrogen gas stored in the hydrogen storage tank depends on the amount of hydrogen gas contained in the tank at the end of the previous period
And the amount of hydrogen produced and consumed during that period
At any time, the hydrogen content in the hydrogen storage tank cannot exceed the limit of the capacity of the hydrogen storage tank, namely, the following conditions should be met:
the fuel cell modeling method comprises the following steps: the fuel cell consumes part of the hydrogen from the hydrogen storage tank to produce electrical energy to supply BEv the vehicle's charging requirements along with electrical energy directly from the point of common coupling; the model expression is as follows:
wherein eta isfcIn order to achieve an operational efficiency of the fuel cell,
electric energy generated for the fuel cell during the period t andhydrogen consumed;
andP fcrespectively, the upper and lower limits of the generated power of the fuel cell.
The modeling method of the BEV/HFCV energy charging model comprises the following steps: the BEV adopts a disordered charging mode, and if the driving mileage end time of the vehicle in one day is the time for starting charging of the access charging station, the charging of a single BEVi can be modeled as follows:
wherein liIs BEViMileage on day of (E)hkmPower consumption per hundred kilometers, ηBEVFor BEV charging efficiency, Ei BEVIs BEViA fully charged power demand;
is t period BEViThe charging power of the battery pack is set,
assuming that the vehicle is charged with the maximum charging power in the disordered charging mode until the vehicle is fully charged;
is BEViThe length of time required for full charging,
respectively charge start and end times；
Based on single BEViThe charging model of (1) can obtain the electric power required by the charging station BEV for charging as follows:
a formula eleven;
wherein,
charging power required for BEV at time t, Nt，BEVThe number of BEVs charged by the charging station for accessing the moment;
the time required for charging the HFCV is short, and each HFCV can complete hydrogen supply in one period of time, so the hydrogen quality required for charging the HFCV at the charging station in the period t is as follows:
wherein,
is a single HFCViRequired quality of hydrogen, Nt，HFCVThe amount of HFCV needed to be charged at the charging station for that period.
In the evaluation method, when the HFCV is a bus or a transportation truck with a relatively fixed travel, a protocol hydrogen charging mode is adopted, a hydrogen charging protocol is signed with an HFCV user to charge the HFCV according to a fixed requirement, and when the hydrogen charging requirement can not be completely met by a charging station according to an optimized dispatching result, a vehicle owner is informed in advance and half of the sale price corresponding to the reduced hydrogen amount is paid as a compensation default fund.
In the comprehensive energy charging station optimized scheduling model, one day is taken as a scheduling period, the energy charging station is optimally scheduled, the running process of the energy charging station is optimized, and corresponding energy charging demand shortage data are recorded and serve as reliability evaluation bases; assuming a total of D days within the total time of the reliability assessment, for any day D, the energy charging requirement reduction is accounted for, the objective function of which to optimize the scheduling is shown below,
wherein T is the number of time periods in one day, omegaDGIs a collection of distributed power sources; r is the corresponding income required by all the charging of BEV and HFCV, and EP and HP are the selling price of unit electric energy and hydrogen; c1Purchasing electricity costs for distributed power sources and grids, wherein
Actual power supply to the charging station for distributed generation and grid, CDGi、
The unit price of the corresponding electric energy cost; c2To lose revenue due to a lack of supply of energy,
respectively the electric energy and the hydrogen quantity which are not supplied in the t period; c3Defaulting money paid to the HFCV vehicle owner according to the charging protocol; namely, the optimal scheduling model of the comprehensive energy charging station has the following formula;
as shown in formulas fourteen to fifteen, the distributed power supply electric energy actually consumed by the comprehensive energy charging station cannot exceed the actual output, and the input power purchased from the power grid and passing through the public power grid coupling point is also limited by the capacity of the power transmission line;
as shown in formulas sixteen to seventeen, the shortage of the BEV and HFCV charging requirements cannot be greater than the actual charging requirements;
as shown in the formula eighteen, the sum of the power consumption of the comprehensive energy charging station is used for electrically producing hydrogen by the electrolytic cell in the energy charging station and directly charging the BEV;
as shown in the nineteenth equation, the actual power supplied to the BEV is equal to the fuel cell output and a portion of the electrical energy from the point of common coupling
Summing up;
as shown in the formula twenty, the hydrogen stored in the hydrogen storage tank is obtained by electrolyzing water in the electrolytic cell;
as shown in the formula twenty-one, the hydrogen stored in the hydrogen storage tank can be directly supplied to the HFCV, or can be converted into electric energy by the fuel cell;
as shown in the formula twenty-two, the optimal scheduling model of the comprehensive energy charging station sets the minimum value of the hydrogen amount in the gas storage tank at the initial time of a dayTo find
Amount of hydrogen in hydrogen storage tank at end of day
Immediately not less than this value.
Calculating a reliability index of the comprehensive energy charging station in the step five, namely establishing a reliability index system of the comprehensive energy charging station based on the shortage condition of the vehicle energy charging requirement, and evaluating the energy supply reliability of the comprehensive energy charging station, wherein the reliability index system comprises the following formula;
as shown in formulas twenty-three to twenty-eight, D.T time intervals are counted in the total reliability evaluation time, and the related data of energy charging demand reduction in the daily optimization scheduling operation result are recorded as
PBDS and PHDS represent the probability that BEV and HFCV charging requirements are curtailed; the PEDS represents the probability that the energy charging station reduces the energy charging requirement of the new energy automobile; EBDNS and EHDNS respectively represent the expected shortage power and hydrogen of BEV and HFCV, EEDNS represents the expected shortage energy, namely the sum of EBDNS and EHDNS, and EBDNS, EHDNS and EEDNS all take the expected date; in the formulas twenty-three to twenty-eight, the hydrogen amount is converted into the unit of electric power by combining with the low heat value of the hydrogen.
When a charging agreement is signed by the comprehensive charging station and the HFCV with fixed charging demand and compensation is given if hydrogen supply shortage occurs, in the vehicle quantity estimation model in the step two, only the influence of charging demand reduction on the selection of the charging station by a BEV owner is considered, a psychophysics field W-F law is introduced to establish a vehicle quantity estimation model considering the influence of owner charging experience, and the number of BEV vehicles on the day is estimated according to the charging demand shortage condition of the recent days, wherein the specific method comprises the following steps of:
in a service area of a position of a comprehensive energy charging station of the new energy automobile, the number of BEVs possibly served is NsumThe owners of these BEVs are divided into fixed users, general users and free users, and the number is respectively expressed as Nl、NsAnd Nr(ii) a The fixed user refers to a customer group with high loyalty and consuming by charging at the charging station for a fixed or long time; the general users refer to a customer group which is influenced by the short-term shortage of charging requirements and is not charged at the charging station with a certain probability; an errant user refers to a group of customers who are first or infrequently consuming at the charging station, but who are not yet stable, then the number of BEVs selected to charge the charging station on any day d may be expressed as:
as shown in the formula twenty-nine, the charging station has N all day on day dd，BEVsThe BEV is charged, wherein the number of users N is fixedd，lIs stable, i.e. is Nl(ii) a The number of free users fluctuates greatly, so that the uniform distribution is considered to be obeyed; the number of general users is estimated by introducing W-F law; the W-F law is a law which can quantitatively establish a functional relationship between human response and objective stimulus quantity in the field of psychology;
as shown in formula thirty, according to the W-F law, the probability that the general user refuses to select the charging station in the current day is sd，
Is the smallest perceptible difference, k0Is the Weber coefficient, s0Is the stimulus constant;
as shown in the formula thirty-one,
PBDS1 is the stimulation dose, i.e., the total situation of the shortage of the charging demand in n days before day diIs a percentage of the charging demand that is curtailed during the day.
In the second step, assuming that all vehicles are accessed to the comprehensive energy charging station for charging at most once every day, the distribution of BEV driving rule data is obtained by fitting actual statistical data through a maximum likelihood estimation method;
the distribution of the BEV travel law data is as shown in the following equations thirty-two and thirty-three,
the vehicle charge start time follows the distribution shown in the formula thirty-two, where
The daily mileage of the vehicle is in accordance with the distribution shown by the formula thirty-three, wherein
Aiming at the reliability evaluation of the new energy automobile comprehensive energy charging station, the influence of the charging experience of an automobile owner needs to be considered, the Weber Fechner law is introduced, the influence of electric energy and hydrogen which cannot be provided by the energy charging station on the behavior of the automobile owner and the load of the energy charging station is considered, and the reliability of the new energy automobile comprehensive energy charging station is evaluated, so that the reliability evaluation method has the following advantages:
(1) according to the method, the optimization scheduling model can be established by taking the maximization of the benefits as an objective function through establishing the new energy automobile comprehensive energy charging station model and the BEV/HFCV energy charging model, meanwhile, the reduction of the automobile energy charging requirements is considered in the assessment method, the reliability index system is established, and the energy supply reliability of the new energy automobile comprehensive energy charging station can be assessed on the basis of the scheduling result of taking the year as a unit for a long time.
(2) According to the vehicle energy charging demand shortage condition, the influence of the vehicle energy charging demand shortage condition on the vehicle owner energy charging experience is counted, a Weber-Fechh (W-F) law is introduced to establish a quantity model, the number of vehicles which are accessed into the energy charging station for energy charging every day can be estimated, and the influence of the vehicle owner experience on the vehicle owner behavior and the energy charging demand of the energy charging station is considered.
(3) According to the method, the day-ahead prediction data is obtained based on the quantity model, the vehicle access time, the mileage probability density function and other information, the optimized scheduling model is solved, the reliability index of the comprehensive energy charging station is calculated, and the reliability evaluation result is more accurate.
Drawings
The invention is described in further detail below with reference to the following figures and detailed description:
FIG. 1 is a schematic diagram of an integrated charging station;
FIG. 2 is a schematic flow diagram of the present invention.
Detailed Description
As shown in the figure, the reliability evaluation method for the new energy automobile comprehensive charging station can evaluate the reliability of the charging station on the automobile charging capacity, wherein the comprehensive charging station is used for charging BEV and charging HFCV; BEV is a battery electric vehicle; HFCV is hydrogen fuel cell vehicles; in the evaluation method, the power for charging the BEV comes from a public power grid coupling point or a fuel cell in a comprehensive charging station; hydrogen for charging HFCV is prepared by an electrolytic cell, and the hydrogen produced by the electrolytic cell is stored in a hydrogen storage tank; the evaluation method includes the following steps;
the method comprises the following steps: acquiring relevant parameters required by reliability evaluation of the comprehensive charging station, including equipment capacity, the number of users in a service area and the like, and acquiring local annual distributed power supply electric energy output according to historical data;
step two: acquiring the charging and hydrogen charging requirements of BEV and HFCV for one day based on a BEV/HFCV charging model; the hydrogen demand of the HFCV is determined according to the number of vehicles and a charging protocol; determining BEV charging requirements according to the number of BEVs on the same day determined by the vehicle quantity estimation model and BEV running rule data which are simulated and met by adopting Latin hypercube sampling;
solving an optimized scheduling model of the comprehensive energy charging station to obtain and record the energy charging demand shortage condition of the current day;
checking whether the total days reach the upper limit of the reliability evaluation total investigation time; if the result is reached, the next step is carried out, otherwise, the step two is returned;
and fifthly, comprehensively evaluating the relevant data of the energy-charging demand shortage condition in the total investigation time, and calculating the reliability index of the comprehensive energy-charging station.
The method for solving the optimal scheduling model of the comprehensive energy charging station in the third step comprises an electrolytic cell modeling method, a hydrogen storage tank modeling method and a fuel cell modeling method aiming at the comprehensive energy charging station;
the modeling method of the electrolytic cell comprises the following steps: the electrolytic cell is used for realizing the hydrogen production process by electrolyzing water and consuming electric energy to produce hydrogen, and the expression of the model and the constraint condition is as follows:
wherein etaelzFor the efficiency of the cell, Pt elz、mt elzRespectively representing the consumed electric power and the generated hydrogen quality in the t period; Δ t is the duration of each time period, and is set to 1 hour; LHV is the lower heating value of hydrogen and is a constant;
andP elzis the maximum and minimum electrical power consumed by the electrolytic cell;
the modeling method of the hydrogen storage tank comprises the following steps: the hydrogen storage tank stores hydrogen generated by water electrolysis of the electrolytic cell, and is used for hydrogen supply of a hydrogen fuel cell vehicle or for a fuel cell to convert the hydrogen into electric energy, and the amount of the hydrogen stored in the hydrogen storage tank can be represented by the following formula:
the amount of hydrogen gas stored in the hydrogen storage tank depends on the amount of hydrogen gas contained in the tank at the end of the previous period
And the amount of hydrogen produced and consumed during that period
At any time, the hydrogen content in the hydrogen storage tank cannot exceed the limit of the capacity of the hydrogen storage tank, namely, the following conditions should be met:
the fuel cell modeling method comprises the following steps: the fuel cell consumes part of the hydrogen from the hydrogen storage tank to generate electrical energy to supply the charging demand of the BEV vehicle along with electrical energy directly from the pcc; the model expression is as follows:
wherein eta isfcIn order to achieve an operational efficiency of the fuel cell,
the electric energy generated and the hydrogen consumed by the fuel cell during the period t;
andP fcrespectively, the upper and lower limits of the generated power of the fuel cell.
The modeling method of the BEV/HFCV energy charging model comprises the following steps: the BEV adopts a disordered charging mode, and if the driving mileage end time of the vehicle in one day is the time for starting charging of the access charging station, the charging of a single BEVi can be modeled as follows:
wherein liIs BEViMileage on day of (E)hkmPower consumption per hundred kilometers, ηBEVFor BEV charging efficiency, Ei BEVIs BEViA fully charged power demand;
is t period BEViThe charging power of the battery pack is set,
assuming that the vehicle is charged with the maximum charging power in the disordered charging mode until the vehicle is fully charged;
is BEViThe length of time required for full charging,
respectively charging start and end times;
based on single BEViThe charging model of (1) can obtain the electric power required by the charging station BEV for charging as follows:
a formula eleven;
wherein,
charging power required for BEV at time t, Nt，BEVThe number of BEVs charged by the charging station for accessing the moment;
the time required for charging the HFCV is short, and each HFCV can complete hydrogen supply in one period of time, so the hydrogen quality required for charging the HFCV at the charging station in the period t is as follows:
wherein,
is a single HFCViRequired quality of hydrogen, Nt，HFCVThe amount of HFCV needed to be charged at the charging station for that period.
In the evaluation method, when the HFCV is a bus or a transportation truck with a relatively fixed travel, a protocol hydrogen charging mode is adopted, a hydrogen charging protocol is signed with an HFCV user to charge the HFCV according to a fixed requirement, and when the hydrogen charging requirement can not be completely met by a charging station according to an optimized dispatching result, a vehicle owner is informed in advance and half of the sale price corresponding to the reduced hydrogen amount is paid as a compensation default fund.
In the comprehensive energy charging station optimized scheduling model, one day is taken as a scheduling period, the energy charging station is optimally scheduled, the running process of the energy charging station is optimized, and corresponding energy charging demand shortage data are recorded and serve as reliability evaluation bases; assuming a total of D days within the total time of the reliability assessment, for any day D, the energy charging requirement reduction is accounted for, the objective function of which to optimize the scheduling is shown below,
wherein T is the number of time periods in one day, omegaDGIs a collection of distributed power sources; r is the corresponding income required by all the charging of BEV and HFCV, and EP and HP are the selling price of unit electric energy and hydrogen; c1Purchasing electricity costs for distributed power sources and grids, wherein
Actual power supply to the charging station for distributed generation and grid, CDGi、
The unit price of the corresponding electric energy cost; c2To lose revenue due to a lack of supply of energy,
respectively the electric energy and the hydrogen quantity which are not supplied in the t period; c3Defaulting money paid to the HFCV vehicle owner according to the charging protocol; namely, the optimal scheduling model of the comprehensive energy charging station has the following formula;
as shown in formulas fourteen to fifteen, the distributed power supply electric energy actually consumed by the comprehensive energy charging station cannot exceed the actual output, and the input power purchased from the power grid and passing through the public power grid coupling point is also limited by the capacity of the power transmission line;
as shown in formulas sixteen to seventeen, the shortage of the BEV and HFCV charging requirements cannot be greater than the actual charging requirements;
as shown in the formula eighteen, the sum of the power consumption of the comprehensive energy charging station is used for electrically producing hydrogen by the electrolytic cell in the energy charging station and directly charging the BEV;
as shown in the nineteenth equation, the actual power supplied to the BEV is equal to the fuel cell output and a portion of the electrical energy from the point of common coupling
Summing up;
as shown in the formula twenty, the hydrogen stored in the hydrogen storage tank is obtained by electrolyzing water in the electrolytic cell;
as shown in the formula twenty-one, the hydrogen stored in the hydrogen storage tank can be directly supplied to the HFCV, or can be converted into electric energy by the fuel cell;
as shown in the formula twenty two, the comprehensive energy charging station optimization scheduling model sets the minimum value of the hydrogen amount in the gas storage tank at the initial time of a day to be learned
Amount of hydrogen in hydrogen storage tank at end of day
Immediately not less than this value.
Calculating a reliability index of the comprehensive energy charging station in the step five, namely establishing a reliability index system of the comprehensive energy charging station based on the shortage condition of the vehicle energy charging requirement, and evaluating the energy supply reliability of the comprehensive energy charging station, wherein the reliability index system comprises the following formula;
as shown in formulas twenty-three to twenty-eight, D.T time intervals are counted in the total reliability evaluation time, and the related data of energy charging demand reduction in the daily optimization scheduling operation result are recorded as
PBDS and PHDS represent the probability that BEV and HFCV charging requirements are curtailed; the PEDS represents the probability that the energy charging station reduces the energy charging requirement of the new energy automobile; EBDNS and EHDNS respectively represent the expected shortage power and hydrogen of BEV and HFCV, EEDNS represents the expected shortage energy, namely the sum of EBDNS and EHDNS, and EBDNS, EHDNS and EEDNS all take the expected date; in the formulas twenty-three to twenty-eight, the hydrogen amount is converted into the unit of electric power by combining with the low heat value of the hydrogen.
When a charging agreement is signed by the comprehensive charging station and the HFCV with fixed charging demand and compensation is given if hydrogen supply shortage occurs, in the vehicle quantity estimation model in the step two, only the influence of charging demand reduction on the selection of the charging station by a BEV owner is considered, a psychophysics field W-F law is introduced to establish a vehicle quantity estimation model considering the influence of owner charging experience, and the number of BEV vehicles on the day is estimated according to the charging demand shortage condition of the recent days, wherein the specific method comprises the following steps of:
in a service area of a position of a comprehensive energy charging station of the new energy automobile, the number of BEVs possibly served is NsumThe owners of these BEVs are divided into fixed users, general users and free users, and the number is respectively expressed as Nl、NsAnd Nr(ii) a The fixed user refers to a customer group with high loyalty and consuming by charging at the charging station for a fixed or long time; the general users refer to a customer group which is influenced by the short-term shortage of charging requirements and is not charged at the charging station with a certain probability; an errant user refers to a group of customers who are first or infrequently consuming at the charging station, but who are not yet stable, then the number of BEVs selected to charge the charging station on any day d may be expressed as:
as shown in the formula twenty-nine, the charging station has N all day on day dd，BEVsThe BEV is charged, wherein the number of users N is fixedd，lIs stable, i.e. is Nl(ii) a The number of free users fluctuates greatly, so that the uniform distribution is considered to be obeyed; the number of general users is estimated by introducing W-F law; the W-F law is a law which can quantitatively establish a functional relationship between human response and objective stimulus quantity in the field of psychology;
as shown in formula thirty, according to the W-F law, the probability that the general user refuses to select the charging station in the current day is sd，
Is the smallest perceptible difference, k0Is prepared fromBoss coefficient, s0Is the stimulus constant;
as shown in the formula thirty-one,
PBDS1 is the stimulation dose, i.e., the total situation of the shortage of the charging demand in n days before day diIs a percentage of the charging demand that is curtailed during the day.
In the second step, assuming that all vehicles are accessed to the comprehensive energy charging station for charging at most once every day, the distribution of BEV driving rule data is obtained by fitting actual statistical data through a maximum likelihood estimation method;
the distribution of the BEV travel law data is as shown in the following equations thirty-two and thirty-three,
the vehicle charge start time follows the distribution shown in the formula thirty-two, where
The daily mileage of the vehicle is in accordance with the distribution shown by the formula thirty-three, wherein
Claims (8)
1. A reliability assessment method for a new energy automobile comprehensive energy charging station can assess the reliability of the energy charging station on the automobile energy charging capacity, and is characterized in that: the integrated charging station is used for charging BEV and charging HFCV; BEV is a battery electric vehicle; HFCV is hydrogen fuel cell vehicles; in the evaluation method, the power for charging the BEV comes from a public power grid coupling point or a fuel cell in a comprehensive charging station; hydrogen for charging HFCV is prepared by an electrolytic cell, and the hydrogen produced by the electrolytic cell is stored in a hydrogen storage tank; the evaluation method includes the following steps;
the method comprises the following steps: acquiring relevant parameters required by reliability evaluation of the comprehensive charging station, including equipment capacity, the number of users in a service area and the like, and acquiring local annual distributed power supply electric energy output according to historical data;
step two: acquiring the charging and hydrogen charging requirements of BEV and HFCV for one day based on a BEV/HFCV charging model; the hydrogen demand of the HFCV is determined according to the number of vehicles and a charging protocol; determining BEV charging requirements according to the number of BEVs on the same day determined by the vehicle quantity estimation model and BEV running rule data which are simulated and met by adopting Latin hypercube sampling;
solving an optimized scheduling model of the comprehensive energy charging station to obtain and record the energy charging demand shortage condition of the current day;
checking whether the total days reach the upper limit of the reliability evaluation total investigation time; if the result is reached, the next step is carried out, otherwise, the step two is returned;
and fifthly, comprehensively evaluating the relevant data of the energy-charging demand shortage condition in the total investigation time, and calculating the reliability index of the comprehensive energy-charging station.
2. The reliability evaluation method for the comprehensive energy charging station of the new energy automobile according to claim 1, characterized in that: the method for solving the optimal scheduling model of the comprehensive energy charging station in the third step comprises an electrolytic cell modeling method, a hydrogen storage tank modeling method and a fuel cell modeling method aiming at the comprehensive energy charging station;
the modeling method of the electrolytic cell comprises the following steps: the electrolytic cell is used for realizing the hydrogen production process by electrolyzing water and consuming electric energy to produce hydrogen, and the expression of the model and the constraint condition is as follows:
wherein etaelzIn order to be able to achieve the efficiency of the electrolysis cell,
mt elzrespectively representing the consumed electric power and the generated hydrogen quality in the t period; Δ t is the duration of each time period, and is set to 1 hour; LHV is the lower heating value of hydrogen and is a constant;
andP elzis the maximum and minimum electrical power consumed by the electrolytic cell;
the modeling method of the hydrogen storage tank comprises the following steps: the hydrogen storage tank stores hydrogen generated by water electrolysis of the electrolytic cell, and is used for hydrogen supply of a hydrogen fuel cell vehicle or for a fuel cell to convert the hydrogen into electric energy, and the amount of the hydrogen stored in the hydrogen storage tank can be represented by the following formula:
the amount of hydrogen gas stored in the hydrogen storage tank depends on the amount of hydrogen gas contained in the tank at the end of the previous period
And the amount of hydrogen produced and consumed during that period
At any time, the hydrogen content in the hydrogen storage tank cannot exceed the limit of the capacity of the hydrogen storage tank, namely, the following conditions should be met:
the fuel cell modeling method comprises the following steps: the fuel cell consumes part of the hydrogen from the hydrogen storage tank to generate electrical energy to supply the charging demand of the BEV vehicle along with electrical energy directly from the pcc; the model expression is as follows:
wherein eta isfcIn order to achieve an operational efficiency of the fuel cell,
the electric energy generated and the hydrogen consumed by the fuel cell during the period t;
andP fcrespectively, the upper and lower limits of the generated power of the fuel cell.
3. The reliability evaluation method for the comprehensive energy charging station of the new energy automobile according to claim 2, characterized in that: the modeling method of the BEV/HFCV energy charging model comprises the following steps: the BEV adopts a disordered charging mode, and if the travel mileage end time of the vehicle in one day is the time for starting charging of the access charging station, a single BEViThe charging of (a) can be modeled as:
wherein liIs BEViMileage on day of (E)hkmPower consumption per hundred kilometers, ηBEVFor BEV charging efficiency, Ei BEVIs BEViA fully charged power demand;
is t period BEViThe charging power of the battery pack is set,
assuming that the vehicle is charged with the maximum charging power in the disordered charging mode until the vehicle is fully charged;
is BEViThe length of time required for full charging,
respectively charging start and end times;
based on single BEViThe charging model of (1) can obtain the electric power required by the charging station BEV for charging as follows:
wherein,
charging power required for BEV at time t, Nt，BEVThe number of BEVs charged by the charging station for accessing the moment;
the time required for charging the HFCV is short, and each HFCV can complete hydrogen supply in one period of time, so the hydrogen quality required for charging the HFCV at the charging station in the period t is as follows:
wherein,
is a single HFCViRequired quality of hydrogen, Nt，HFCVThe amount of HFCV needed to be charged at the charging station for that period.
4. The reliability evaluation method for the comprehensive energy charging station of the new energy automobile according to claim 3, characterized in that: in the evaluation method, when the HFCV is a bus or a transportation truck with a relatively fixed travel, a protocol hydrogen charging mode is adopted, a hydrogen charging protocol is signed with an HFCV user to charge the HFCV according to a fixed requirement, and when the hydrogen charging requirement can not be completely met by a charging station according to an optimized dispatching result, a vehicle owner is informed in advance and half of the sale price corresponding to the reduced hydrogen amount is paid as a compensation default fund.
5. The reliability evaluation method for the comprehensive energy charging station of the new energy automobile according to claim 4, characterized in that: in the comprehensive energy charging station optimized scheduling model, one day is taken as a scheduling period, the energy charging station is optimally scheduled, the running process of the energy charging station is optimized, and corresponding energy charging demand shortage data are recorded and serve as reliability evaluation bases; assuming a total of D days within the total time of the reliability assessment, for any day D, the energy charging requirement reduction is accounted for, the objective function of which to optimize the scheduling is shown below,
wherein T is the number of time periods in a day，ΩDGIs a collection of distributed power sources; r is the corresponding income required by all the charging of BEV and HFCV, and EP and HP are the selling price of unit electric energy and hydrogen; c1Purchasing electricity costs for distributed power sources and grids, wherein
Actual power supply to the charging station for distributed generation and grid, CDGi、
The unit price of the corresponding electric energy cost; c2To lose revenue due to a lack of supply of energy,
respectively the electric energy and the hydrogen quantity which are not supplied in the t period; c3Defaulting money paid to the HFCV vehicle owner according to the charging protocol; namely, the optimal scheduling model of the comprehensive energy charging station has the following formula;
as shown in formulas fourteen to fifteen, the distributed power supply electric energy actually consumed by the comprehensive energy charging station cannot exceed the actual output, and the input power purchased from the power grid and passing through the public power grid coupling point is also limited by the capacity of the power transmission line;
as shown in formulas sixteen to seventeen, the shortage of the BEV and HFCV charging requirements cannot be greater than the actual charging requirements;
as shown in the formula eighteen, the sum of the power consumption of the comprehensive energy charging station is used for electrically producing hydrogen by the electrolytic cell in the energy charging station and directly charging the BEV;
as shown in the nineteenth equation, the actual power supplied to the BEV is equal to the fuel cell output and a portion of the electrical energy from the point of common coupling
Summing up;
as shown in the formula twenty, the hydrogen stored in the hydrogen storage tank is obtained by electrolyzing water in the electrolytic cell;
as shown in the formula twenty-one, the hydrogen stored in the hydrogen storage tank can be directly supplied to the HFCV, or can be converted into electric energy by the fuel cell;
setting an initial time storage in one day by the optimized scheduling model of the comprehensive energy charging station as shown by a formula twenty twoMinimum hydrogen requirement in gas tank
Amount of hydrogen in hydrogen storage tank at end of day
This value should not be exceeded.
6. The reliability evaluation method for the comprehensive energy charging station of the new energy automobile according to claim 5, characterized in that: calculating a reliability index of the comprehensive energy charging station in the step five, namely establishing a reliability index system of the comprehensive energy charging station based on the shortage condition of the vehicle energy charging requirement, and evaluating the energy supply reliability of the comprehensive energy charging station, wherein the reliability index system comprises the following formula;
as shown in formulas twenty-three to twenty-eight, D.T time intervals are counted in the total reliability evaluation time, and the related data of energy charging demand reduction in the daily optimization scheduling operation result are recorded as
PBDS and PHDS represent the probability that BEV and HFCV charging requirements are curtailed; the PEDS represents the probability that the energy charging station reduces the energy charging requirement of the new energy automobile; EBDNS and EHDNS respectively represent the expected shortage power and hydrogen of BEV and HFCV, EEDNS represents the expected shortage energy, namely the sum of EBDNS and EHDNS, and EBDNS, EHDNS and EEDNS all take the expected date; in the formulas twenty-three to twenty-eight, the hydrogen amount is converted into the unit of electric power by combining with the low heat value of the hydrogen.
7. The reliability evaluation method for the comprehensive energy charging station of the new energy automobile according to claim 6, characterized in that: when a charging agreement is signed by the comprehensive charging station and the HFCV with fixed charging demand and compensation is given if hydrogen supply shortage occurs, in the vehicle quantity estimation model in the step two, only the influence of charging demand reduction on the selection of the charging station by a BEV owner is considered, a psychophysics field W-F law is introduced to establish a vehicle quantity estimation model considering the influence of owner charging experience, and the number of BEV vehicles on the day is estimated according to the charging demand shortage condition of the recent days, wherein the specific method comprises the following steps of:
in a service area of a position of a comprehensive energy charging station of the new energy automobile, the number of BEVs possibly served is NsumThe owners of these BEVs are divided into fixed users, general users and free users, and the number is respectively expressed as N1、NsAnd Nr(ii) a The fixed user refers to a customer group with high loyalty and consuming by charging at the charging station for a fixed or long time; the general users refer to a customer group which is influenced by the short-term shortage of charging requirements and is not charged at the charging station with a certain probability; an errant user refers to a group of customers who are first or infrequently consuming at the charging station, but who are not yet stable, then the number of BEVs selected to charge the charging station on any day d may be expressed as:
as shown in the formula twenty-nine, the charging station has N all day on day dd，BEVsThe BEV is charged, wherein the number of users N is fixedd，1Is stable, i.e. is N1(ii) a The number of free users fluctuates greatly, so that the uniform distribution is considered to be obeyed; the number of general users is estimated by introducing W-F law; the W-F law is a law which can quantitatively establish a functional relationship between human response and objective stimulus quantity in the field of psychology;
as shown in formula thirty, according to the W-F law, the probability that the general user refuses to select the charging station in the current day is sd，
Is the smallest perceptible difference, k0Is the Weber coefficient, s0Is the stimulus constant;
as shown in the formula thirty-one,
PBDS1 is the stimulation dose, i.e., the total situation of the shortage of the charging demand in n days before day diIs a percentage of the charging demand that is curtailed during the day.
8. The reliability evaluation method for the comprehensive energy charging station of the new energy automobile according to claim 7, characterized in that: in the second step, assuming that all vehicles are accessed to the comprehensive energy charging station for charging at most once every day, the distribution of BEV driving rule data is obtained by fitting actual statistical data through a maximum likelihood estimation method;
the distribution of the BEV travel law data is as shown in the following equations thirty-two and thirty-three,
the vehicle charge start time follows the distribution shown in the formula thirty-two, where
kT＝5.857，
The daily mileage of the vehicle is in accordance with the distribution shown by the formula thirty-three, wherein
kD＝1.048，
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111258945.XA CN113962101B (en) | 2021-10-28 | 2021-10-28 | New energy automobile comprehensive energy charging station reliability assessment method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111258945.XA CN113962101B (en) | 2021-10-28 | 2021-10-28 | New energy automobile comprehensive energy charging station reliability assessment method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113962101A true CN113962101A (en) | 2022-01-21 |
| CN113962101B CN113962101B (en) | 2024-08-30 |
Family
ID=79467727
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111258945.XA Active CN113962101B (en) | 2021-10-28 | 2021-10-28 | New energy automobile comprehensive energy charging station reliability assessment method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113962101B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110263435A (en) * | 2019-06-20 | 2019-09-20 | 燕山大学 | Dual-layer optimization fault recovery method based on electric-gas coupling integrated energy system |
| CN112070374A (en) * | 2020-08-25 | 2020-12-11 | 天津大学 | Regional energy Internet energy supply reliability assessment method |
| WO2021098352A1 (en) * | 2019-11-22 | 2021-05-27 | 国网福建省电力有限公司 | Active power distribution network planning model establishment method taking into consideration site selection and capacity determination of electric vehicle charging stations |
| CN113033868A (en) * | 2021-02-03 | 2021-06-25 | 浙江吉利控股集团有限公司 | Comprehensive energy management system and method based on energy Internet of things cloud platform |
| CN113131529A (en) * | 2021-04-25 | 2021-07-16 | 南京创智力合电力科技有限公司 | Renewable energy bearing capacity assessment method considering multiple flexible resources |
-
2021
- 2021-10-28 CN CN202111258945.XA patent/CN113962101B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110263435A (en) * | 2019-06-20 | 2019-09-20 | 燕山大学 | Dual-layer optimization fault recovery method based on electric-gas coupling integrated energy system |
| WO2021098352A1 (en) * | 2019-11-22 | 2021-05-27 | 国网福建省电力有限公司 | Active power distribution network planning model establishment method taking into consideration site selection and capacity determination of electric vehicle charging stations |
| CN112070374A (en) * | 2020-08-25 | 2020-12-11 | 天津大学 | Regional energy Internet energy supply reliability assessment method |
| CN113033868A (en) * | 2021-02-03 | 2021-06-25 | 浙江吉利控股集团有限公司 | Comprehensive energy management system and method based on energy Internet of things cloud platform |
| CN113131529A (en) * | 2021-04-25 | 2021-07-16 | 南京创智力合电力科技有限公司 | Renewable energy bearing capacity assessment method considering multiple flexible resources |
Non-Patent Citations (2)
| Title |
|---|
| WENDI ZHENG 等: "Reliability evaluation and analysis for NEV charging station considering the impact of charging experience", INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, vol. 47, no. 6, 19 January 2022 (2022-01-19), pages 3980 - 3993 * |
| 陈中;陈妍希;车松阳;: "新能源汽车一体充能站框架及能量优化调度方法", 电力系统自动化, no. 24, 31 December 2019 (2019-12-31), pages 64 - 74 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113962101B (en) | 2024-08-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Wang et al. | A scenario-based stochastic optimization model for charging scheduling of electric vehicles under uncertainties of vehicle availability and charging demand | |
| Liang et al. | Configuration and system operation for battery swapping stations in Beijing | |
| Gough et al. | Vehicle-to-grid feasibility: A techno-economic analysis of EV-based energy storage | |
| Kempton et al. | Electric vehicles as a new power source for electric utilities | |
| Jenkins et al. | Utilization and effect of plug-in hybrid electric vehicles in the United States power grid | |
| Riesz et al. | Quantifying the costs of a rapid transition to electric vehicles | |
| Wang et al. | Distribution system planning considering stochastic EV penetration and V2G behavior | |
| Qian et al. | Economic optimization and potential analysis of fuel cell vehicle-to-grid (FCV2G) system with large-scale buildings | |
| CN110556822B (en) | Combined calculation method for large-scale wind turbine generator set containing electric automobile absorption | |
| Brandt et al. | Road to 2020: IS-supported business models for electric mobility and electrical energy markets | |
| CN115239032B (en) | Highway service area microgrid planning method and system considering energy self-consistency rate | |
| Ye et al. | Decarbonizing regional multi-model transportation system with shared electric charging hubs | |
| Darabi et al. | Plug-in hybrid electric vehicles: Charging load profile extraction based on transportation data | |
| Beltramo et al. | Using electric vehicles as flexible resource in power systems: A case study in the Netherlands | |
| Zeng et al. | Optimal configuration planning of vehicle sharing station-based electro-hydrogen micro-energy systems for transportation decarbonization | |
| CN112613682A (en) | Electric vehicle charging load prediction method | |
| Zheng et al. | Reliability evaluation and analysis for NEV charging station considering the impact of charging experience | |
| CN109672199B (en) | Method for estimating peak clipping and valley filling capacity of electric vehicle based on energy balance | |
| Mouhy-Ud-Din et al. | Comparative techno-economic assessment of electric vehicle charging preferences: A quantitative study for sustainable EV policy in Pakistan | |
| Chauhan et al. | Scheduling of Electric Vehicle’s Power in V2G and G2V Modes Using an Improved Charge–Discharge Opportunity-Based Approach | |
| Englberger | Optimized energy management for battery energy storage via multi-use and multi-storage operation | |
| CN113962101A (en) | Reliability assessment method for new energy automobile comprehensive energy charging station | |
| Erdinç | Comfort violation minimization oriented energy management strategy for a renewable-assisted stand-alone multi-energy electric vehicle service station | |
| Coldwell et al. | Impact of electric vehicles on GB electricity demand and associated benefits for system control | |
| CN114784832A (en) | Scheduling method and system for electricity and hydrogen integrated charging station of new energy automobile |
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 |