CN114567009A - Equipment capacity configuration method and device for light-hydrogen storage integrated charging station - Google Patents
Equipment capacity configuration method and device for light-hydrogen storage integrated charging station Download PDFInfo
- Publication number
- CN114567009A CN114567009A CN202210209159.9A CN202210209159A CN114567009A CN 114567009 A CN114567009 A CN 114567009A CN 202210209159 A CN202210209159 A CN 202210209159A CN 114567009 A CN114567009 A CN 114567009A
- Authority
- CN
- China
- Prior art keywords
- charging station
- hydrogen
- capacity
- power
- comprehensive
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 239000001257 hydrogen Substances 0.000 title claims abstract description 180
- 229910052739 hydrogen Inorganic materials 0.000 title claims abstract description 177
- 238000003860 storage Methods 0.000 title claims abstract description 86
- 238000000034 method Methods 0.000 title claims abstract description 36
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 claims abstract description 157
- 239000000446 fuel Substances 0.000 claims abstract description 62
- 238000005457 optimization Methods 0.000 claims abstract description 34
- 238000004146 energy storage Methods 0.000 claims abstract description 32
- 238000011156 evaluation Methods 0.000 claims abstract description 15
- 230000006870 function Effects 0.000 claims description 23
- 230000003287 optical effect Effects 0.000 claims description 17
- 238000010248 power generation Methods 0.000 claims description 14
- 230000005611 electricity Effects 0.000 claims description 13
- 150000002431 hydrogen Chemical class 0.000 claims description 9
- 238000012423 maintenance Methods 0.000 claims description 6
- 238000004519 manufacturing process Methods 0.000 claims description 5
- 230000005855 radiation Effects 0.000 claims description 4
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 238000004590 computer program Methods 0.000 claims description 3
- -1 hydrogen Chemical class 0.000 claims description 3
- 238000002485 combustion reaction Methods 0.000 claims description 2
- 238000007599 discharging Methods 0.000 description 4
- 238000005868 electrolysis reaction Methods 0.000 description 4
- 238000005286 illumination Methods 0.000 description 4
- 238000009826 distribution Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 230000035772 mutation Effects 0.000 description 2
- 238000013486 operation strategy Methods 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 230000001502 supplementing effect Effects 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000037435 normal mutation Effects 0.000 description 1
- 230000008929 regeneration Effects 0.000 description 1
- 238000011069 regeneration method Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
-
- C—CHEMISTRY; METALLURGY
- C25—ELECTROLYTIC OR ELECTROPHORETIC PROCESSES; APPARATUS THEREFOR
- C25B—ELECTROLYTIC OR ELECTROPHORETIC PROCESSES FOR THE PRODUCTION OF COMPOUNDS OR NON-METALS; APPARATUS THEREFOR
- C25B1/00—Electrolytic production of inorganic compounds or non-metals
- C25B1/01—Products
- C25B1/02—Hydrogen or oxygen
- C25B1/04—Hydrogen or oxygen by electrolysis of water
-
- C—CHEMISTRY; METALLURGY
- C25—ELECTROLYTIC OR ELECTROPHORETIC PROCESSES; APPARATUS THEREFOR
- C25B—ELECTROLYTIC OR ELECTROPHORETIC PROCESSES FOR THE PRODUCTION OF COMPOUNDS OR NON-METALS; APPARATUS THEREFOR
- C25B9/00—Cells or assemblies of cells; Constructional parts of cells; Assemblies of constructional parts, e.g. electrode-diaphragm assemblies; Process-related cell features
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06313—Resource planning in a project environment
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
-
- 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
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0047—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
- H02J7/0048—Detection of remaining charge capacity or state of charge [SOC]
-
- 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
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0068—Battery or charger load switching, e.g. concurrent charging and load supply
-
- 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
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/32—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries for charging batteries from a charging set comprising a non-electric prime mover rotating at constant speed
-
- 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
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/34—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering
- H02J7/35—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering with light sensitive cells
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/04—Constraint-based 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
- G06F2119/06—Power analysis or power optimisation
-
- 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
- G06F2119/08—Thermal analysis or thermal optimisation
-
- 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
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
-
- 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
- H02J2300/30—The power source being a fuel cell
-
- 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
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/12—Electric charging stations
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Power Engineering (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- Entrepreneurship & Innovation (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Game Theory and Decision Science (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Organic Chemistry (AREA)
- Metallurgy (AREA)
- Materials Engineering (AREA)
- Electrochemistry (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biodiversity & Conservation Biology (AREA)
- Public Health (AREA)
- Primary Health Care (AREA)
- General Health & Medical Sciences (AREA)
- Water Supply & Treatment (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- Inorganic Chemistry (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention discloses a method and a device for configuring the capacity of equipment of a photo-hydrogen storage comprehensive charging station, wherein the method comprises the following steps: establishing a comprehensive charging station system model which can provide services for an electric automobile and a hydrogen fuel electric automobile and comprises a photovoltaic system, an energy storage battery and a hydrogen energy storage system; determining an energy flow strategy of the comprehensive charging station based on an energy flow coordination relation among all devices of the comprehensive charging station according to the system model of the comprehensive charging station; according to an energy flow strategy, based on energy balance constraint, element constraint and power grid power supply constraint, establishing an equipment capacity configuration optimization model taking annual operation cost and autonomous operation capacity of the comprehensive charging station as targets; and solving the equipment capacity configuration optimization model by adopting an NSGA-II algorithm and fuzzy comprehensive evaluation to determine a capacity configuration scheme of the comprehensive charging station equipment. The invention improves the autonomous operation capacity of the comprehensive charging station and can provide reference for the optimization of the charging station equipment.
Description
Technical Field
The invention relates to charging station equipment configuration optimization, in particular to a method and a device for configuring capacity of optical hydrogen storage integrated charging station equipment.
Background
With the rapid development of new energy vehicles, charging facilities for providing services are also gaining attention. The charging station with the combination of the distributed renewable energy sources has a prospect in consideration of the fact that the power grid of the charging station also indirectly causes very high carbon emission. Although renewable energy sources offer us the convenience they also have some drawbacks, due to the randomness of the output, they are difficult to exploit. How to combine it with the charging station in order to realize consuming on the spot, reduce the impact to the electric wire netting, alleviate the problem that electric wire netting upgrade pressure needs to be solved.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a method and a device for configuring the equipment capacity of a light-hydrogen storage comprehensive charging station, which can serve electric vehicles and hydrogen fuel electric vehicles simultaneously and improve the autonomous operation capacity of the comprehensive charging station.
The technical scheme is as follows: in order to achieve the above object, the present invention provides a method for configuring capacity of an integrated optical hydrogen storage charging station device, including the following steps:
establishing a comprehensive charging station system model which can provide services for an electric automobile and a hydrogen fuel electric automobile and comprises a photovoltaic system, an energy storage battery and a hydrogen energy storage system;
determining an energy flow strategy of the comprehensive charging station based on an energy flow coordination relation among all devices of the comprehensive charging station according to the system model of the comprehensive charging station;
according to an energy flow strategy, based on energy balance constraint, element constraint and power grid power supply constraint, establishing an equipment capacity configuration optimization model taking annual operation cost and autonomous operation capacity of the comprehensive charging station as targets;
and solving the equipment capacity configuration optimization model by adopting an NSGA-II algorithm and fuzzy comprehensive evaluation to determine a capacity configuration scheme of the comprehensive charging station equipment.
Further, the integrated charging station system model includes:
photovoltaic output model:
wherein G isTIs the intensity of solar radiation, T represents the time T, TCIs the ambient temperature, GSTCAnd TSTCIntensity and temperature of light under standard conditions, Ppv-STCIs the rated power of the photovoltaic;
the energy storage battery state of charge model:
wherein EBIs a rated capacity of stored energy, PBIs the charge-discharge power of the stored energy, eta is the operating efficiency of the stored energy, and delta t is the time step;
the hydrogen energy storage system consists of an electrolytic cell, a hydrogen storage tank and a fuel cell, wherein the model of the electrolytic cell is as follows:
Hwe＝Pweηwe/HHV
wherein P isweOutput power, eta, for hydrogen production by photovoltaic power generationweThe working efficiency of the electrolytic cell, HHV is the electro-hydrogen energy conversion coefficient;
the stored hydrogen capacity model for each period of the hydrogen storage tank is as follows:
H(t)＝H(t-1)+Hwe(t-1)-Hl(t-1)-Hfc(t-1)
wherein H is the hydrogen content of the hydrogen storage tank, Hwe、HfcThe amounts of hydrogen supplied to the hydrogen-fueled electric vehicle and the fuel cell, respectively;
the fuel cell model is as follows:
Pfc＝HfcηfcHHV
wherein P isfcOutput power, eta, for generating electricity for combustion of hydrogenfcIs the operating efficiency of the fuel cell.
Further, the energy flow strategy of the integrated charging station comprises: obtaining photovoltaic output power PpvJudging the photovoltaic output power PpvWhether the electric load demand P is more than the electric load demand P of the electric automobile user in each periodlLet DP be equal to Ppv-PlIf DP is present>0, then enter the first policy branch, if DP<0, then enter the second strategy branch; in the first strategy branch and the second strategy branch, whether the hydrogen content H of the hydrogen storage tank is larger than the hydrogen content H required by the user of the hydrogen fuel electric vehicle is judgedlWhen DP is present>0 and H-HlWhen > 0, the A strategy is implemented, when DP>0 and H-HlIf the number is less than 0, implementing the strategy B; when DP is present<0 and H-HlWhen > 0, the C strategy is implemented, when DP<0 and H-HlIf < 0, the D strategy is implemented.
A. The requirements of electric automobiles and hydrogen fuel electric automobiles are met, the residual power of photovoltaic power generation sequentially charges a storage battery, an electrolytic cell electrolyzes and stores hydrogen, and finally the residual power is on line;
B. the demand of the electric automobile is met, the demand of the hydrogen fuel electric automobile is in shortage, the residual power of the photovoltaic power generation is firstly used for electrolytic hydrogen storage in an electrolytic cell and then used for charging a storage battery, and the hydrogen fuel electric automobile is still in shortage and is supplemented by purchased standby hydrogen;
C. the demand of the hydrogen fuel electric automobile is met, the demand of the electric automobile is in shortage, the storage battery and the fuel cell discharge once to provide the demand for the electric automobile, and finally the demand is supplemented by a power grid;
D. the electric automobile and the hydrogen fuel electric automobile are in shortage in demand, the electric automobile is in shortage in demand by discharging the storage battery first and then supplementing power by a power grid, and the hydrogen fuel electric automobile is in demand by supplementing purchased standby hydrogen.
Further, determining an energy flow strategy for the integrated charging station includes:
further, the optimization model of the equipment capacity configuration is as follows:
the annual running cost of the comprehensive charging station is lowest:
minF1＝(C1+C2)CRF+C3-C4
C1＝Ppvepv+Pbaeba+Pweewe+Pfcefc+HeH
C2＝C1K
wherein C is1、C2、C3、C4Respectively the equipment purchase cost, the equipment maintenance cost, the operation cost of the comprehensive charging station and the internet access income; CRF is the annual factor of each device; ppv、Pba、Pwe、PfcRespectively the installed capacities of a photovoltaic cell, a storage battery, an electrolytic cell and a fuel cell; h is the capacity of the hydrogen storage tank; e.g. of the typepv、eba、ewe、efc、eH1Unit price of corresponding equipment respectively; k is the maintenance cost of the equipment;r is the actual annual rate, and y is the equipment operating life; e.g. of the typeH、eGi、eGoRespectively the prices of standby hydrogen purchase, power grid purchase and surplus power on-line; hG、PGi、PGoRespectively standby hydrogen purchasing amount, power purchasing power of a power grid and surplus power on-line power in each time period;
the automatic operation capacity of the comprehensive charging station, and the ratio of the electricity purchasing quantity to the standby hydrogen purchasing quantity:
the exchange of electric power and hydrogen quantities of the integrated charging station system should be constantly balanced:
Pl＝Ppv+Pwe+Pba+Pfc+PG
Hl＝H+HG+Hwe-Hfc
the transformer has capacity limitation, and the power supply power of the power grid to the comprehensive charging station is restricted:
PG≤PGmax
the state of charge SOC of the battery is within a reasonable constraint range:
SOCmin≤SOC(t)≤SOCmax
further, solving the equipment capacity configuration optimization model specifically includes:
(a) inputting basic data of the comprehensive charging station, and setting initial parameters of NSGA-II and upper and lower limits of decision variables;
(b) randomly initializing a population with the size of N;
(c) calculating the fitness value of the target function, and then performing rapid non-dominated sorting on the result;
(d) calculating the crowding degree distance of the individuals, selecting and reserving excellent individuals by using an elite strategy to form a new population, and returning to the step (c);
(e) judging the iteration times, stopping iteration when the iteration times meet the requirements, outputting a Pareto front edge, and otherwise, continuing to carry out circular iteration;
(f) and calculating the membership of each Pareto solution by using a fuzzy membership function of fuzzy comprehensive evaluation, wherein the optimization scheme with the highest membership is the final scheme.
Further, the membership function of the fuzzy comprehensive evaluation is as follows:
wherein
The function value of the mth non-inferior solution of the nth objective function,
the degree of membership of the mth non-inferior solution of the nth objective function,
the maximum solution and the minimum solution of the Pareto solution are respectively.
The invention also provides a device for configuring the capacity of the equipment of the photo-hydrogen storage comprehensive charging station, which comprises the following components:
the comprehensive charging station system model building module is used for building a comprehensive charging station system model which can provide services for the electric automobile and the hydrogen fuel electric automobile and comprises a photovoltaic system, an energy storage battery and a hydrogen energy storage system;
the energy flow strategy determination module is used for determining an energy flow strategy of the comprehensive charging station based on the energy flow coordination relation among all equipment of the comprehensive charging station according to the system model of the comprehensive charging station;
the capacity configuration optimization model building module is used for building an equipment capacity configuration optimization model which takes the annual operation cost and the autonomous operation capacity of the comprehensive charging station as targets on the basis of energy balance constraint, element constraint and power supply power constraint of a power grid according to an energy flow strategy;
and the capacity allocation scheme solving module is used for solving the equipment capacity allocation optimization model by adopting an NSGA-II algorithm and fuzzy comprehensive evaluation to determine a capacity allocation scheme of the comprehensive charging station equipment.
The present invention also provides a computer apparatus comprising:
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs when executed by the processors implement the steps of the optical hydrogen storage integrated charging station device capacity configuration method as described above.
The present invention also provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the optical hydrogen storage integrated charging station device capacity configuration method as described above.
Has the advantages that: the invention provides a capacity configuration method of a light-hydrogen storage comprehensive charging station device, which comprises the steps of establishing a comprehensive charging station model comprising a photovoltaic system, an energy storage battery and a hydrogen energy storage system, designing an energy flow strategy of the comprehensive charging station, taking the minimum annual cost of the comprehensive charging station and the minimum energy purchasing occupation ratio of the charging station as targets, considering the operation benefits of the comprehensive charging station and the autonomous operation capacity of the comprehensive charging station, establishing an objective function, and solving an optimal scheme by adopting an NSGA-II algorithm and fuzzy comprehensive evaluation. The invention can simultaneously serve the electric automobile and the hydrogen fuel electric automobile. Based on the NSGA-II algorithm and fuzzy comprehensive evaluation, the provided equipment configuration scheme improves the autonomous operation capacity of the comprehensive charging station and can provide reference for the optimization of the charging station equipment.
Drawings
FIG. 1 is a flow chart of a capacity allocation method of the present invention;
FIG. 2 is a total flow diagram of the energy flow of the present invention;
FIG. 3 is a detailed flow chart of the energy flow of the present invention;
FIG. 4 is a flow chart of the optimization established by the present invention;
FIGS. 5(a) -5(c) are graphs of the photovoltaic, EV, HV data, respectively, required for the present invention;
FIG. 6 is a graph of the optimized Pareto set of the present invention;
fig. 7(a) -7(d) are respectively power, hydrogen amount, SOC, and SOHC capability balance maps corresponding to the optimization results of the present invention.
Detailed Description
The technical scheme of the invention is further explained by combining the attached drawings.
The invention provides a capacity configuration method of a light-hydrogen storage integrated charging station device, which enables the integrated charging station to have certain autonomous energy supply capacity and can serve electric vehicles and hydrogen fuel electric vehicles simultaneously. Referring to fig. 1, the method includes the steps of:
step 1: and constructing a light hydrogen storage comprehensive charging station system, and modeling each device to be configured in the comprehensive charging station.
And (1-1) constructing a light-hydrogen storage integrated charging station system.
In the embodiment of the invention, the comprehensive charging station system mainly comprises equipment devices such as a photovoltaic array, a storage battery energy storage device, an electrolytic cell, a hydrogen storage tank, a fuel cell and a converter, wherein the electrolytic cell, the hydrogen storage tank and the fuel cell form a hydrogen energy storage system. The converters include a DC-DC converter (for photovoltaic power generation supply compliance and energy storage) and a DC-AC converter (for photovoltaic power generation grid). The charging station is incorporated into the power grid, and two kinds of energy, namely electricity and hydrogen, flow in the station. The station can provide service for electric vehicles and hydrogen fuel electric vehicles at the same time. Electric vehicles are demanded by power grids, photovoltaics, fuel cells and storage batteries. Hydrogen fueled electric vehicles are demanded by hydrogen storage tanks and backup hydrogen.
And (1-2) establishing each equipment model of the comprehensive charging station.
Photovoltaic power generation is a process for converting solar radiation into electric energy by utilizing a photovoltaic effect, and has the advantages of cleanness, no pollution and reproducibility. The photovoltaic output power depends on various factors, but mainly takes the illumination intensity and the ambient temperature into consideration. In an embodiment of the invention, the photovoltaic output model is as follows:
wherein is GTIntensity of solar radiation, TCIs the ambient temperature. GSTCAnd TSTCIllumination intensity and temperature under standard conditions. P ispv-STCIs the rated power of the photovoltaic given by the supplier.
The State of Charge (SOC) of the energy storage battery is related to the Charge amount in the (t-1) period, the Charge and discharge amount of the energy storage battery in the (t-1, t) period and the Charge attenuation amount per hour, and the SOC model is as follows:
wherein EBIs the rated capacity of the stored energy, PBIs the charge and discharge power of the stored energy, eta is the operating efficiency of the stored energy, and the time step of delta t is 1 hour.
The hydrogen energy storage system consists of an electrolytic cell, a hydrogen storage tank and a fuel cell. Photovoltaic power generation passes through the electrolysis of electrolysis trough and produces hydrogen, and hydrogen stores in the hydrogen storage tank, and hydrogen in the hydrogen storage tank can provide hydrogen fuel electric automobile user hydrogen, also can provide the electric energy for electric automobile user through fuel cell regeneration. The hydrogen energy storage system has two functions, namely, on one hand, the hydrogen energy storage system provides hydrogen requirements for the hydrogen fuel electric automobile, and on the other hand, the built electrolytic cell-hydrogen storage tank-fuel cell model of the storage battery has the same function as the storage battery.
The cell model was as follows:
Hwe＝Pweηwe/HHV (3)
wherein P isweIs the output power, eta, of the photovoltaic power generation for hydrogen productionweIs the working efficiency of the electrolyzer, and HHV is the electro-hydrogen energy conversion coefficient.
The electrolytic cell and the fuel cell work independently of each other, and different from a storage battery, hydrogen can be charged and discharged simultaneously, and the comprehensive modeling is realized by combining the requirements of a hydrogen fuel electric automobile. The stored hydrogen capacity model for each period of the hydrogen storage tank is as follows:
H(t)＝H(t-1)+Hwe(t-1)-Hl(t-1)-Hfc(t-1) (4)
wherein H is the hydrogen content of the hydrogen storage tank, Hwe、HfcThe amounts of hydrogen provided to the hydrogen-fueled electric vehicle and the fuel cell, respectively. The capacity of the hydrogen storage tank at the initial time is not zero. HlIs the amount of hydrogen required by the hydrogen-fueled electric vehicle user.
The fuel cell model is as follows:
Pfc＝HfcηfcHHV (5)
wherein P isfcIs the output power, eta, of the electricity generated by burning hydrogenfcIs the operating efficiency of the fuel cell.
Step 2: and designing an energy flow strategy of the comprehensive charging station based on the energy flow coordination relation among all the devices of the comprehensive charging station.
The energy flow strategy of the comprehensive charging station influences the output force among all devices. In the embodiment of the invention, four operation strategies are formulated for the difference between the requirements of the electric vehicle and the hydrogen fuel electric vehicle, and the working conditions of the storage battery and the hydrogen energy storage system are determined in each time period t. Fig. 2 shows four flow strategies of the integrated charging station, which are specifically as follows:
A. the requirements of electric automobiles and hydrogen fuel electric automobiles are met, the residual power of photovoltaic power generation sequentially charges a storage battery, an electrolytic cell carries out electrolysis to store hydrogen, and finally the residual power is on line.
B. The demand of the electric automobile is met, the demand of the hydrogen fuel electric automobile is in shortage, the residual power of the photovoltaic power generation is firstly used for electrolytic hydrogen storage in an electrolytic cell and is used for charging a storage battery, and the hydrogen fuel electric automobile is still in shortage and is supplemented by purchased standby hydrogen.
C. The hydrogen fuel electric automobile demand is satisfied, and the electric automobile demand has the shortage, and battery, fuel cell once discharge provide the demand for electric automobile, and finally are complemented by the electric wire netting.
D. The electric automobile and the hydrogen fuel electric automobile have shortage in demand, the electric automobile is required to be discharged by a storage battery, and the power shortage is supplemented by a power grid. Hydrogen fueled electric vehicles are required to be supplemented with purchased backup hydrogen.
Fig. 3 shows a detailed flow chart of energy flow, and with reference to fig. 2 and 3, the strategy of energy flow is generally as follows: reading meteorological data to obtain photovoltaic output power PpvJudging the photovoltaic output power PpvWhether the electric load demand P of the electric automobile user is more than each time periodlLet DP be equal to Ppv-PlIf DP is>0, enter the A or B strategy branch, if DP<0, entering a C or D strategy branch; then judging whether the hydrogen content H of the hydrogen storage tank is larger than the hydrogen content H required by the hydrogen fuel electric automobile userlWhen DP is present>0 and H-HlWhen > 0, the A strategy is implemented, when DP>0 and H-HlIf the number is less than 0, implementing the strategy B; when DP is present<0 and H-HlWhen > 0, the C strategy is implemented, when DP<0 and H-HlIf < 0, the D strategy is implemented.
Further, the specific method of the strategy A is as follows: firstly, judging whether DP is larger than the capacity P of the storage batterybaIf yes, charging the energy storage, otherwise, judging whether DP is less than the storage battery capacity PbaAnd cell capacity PweSum of if Pba＜DP＜Pba+PweCharging for energy storage and simultaneously producing hydrogen by the electrolytic cell; if DP < Pba+PweThe energy storage and charging are carried out, meanwhile, the electrolytic cell produces hydrogen, and the rest electricity is on line.
The specific method of the strategy B is as follows: firstly, judging whether DP is less than the capacity P of the electrolytic cellweIf the difference is less than the preset value, hydrogen is produced by the electrolytic cell, otherwise, whether the DP is less than the capacity P of the storage battery or not is judgedbaAnd cell capacity PweSum if Pwe＜DP＜Pba+PweThen the electrolytic cell produces hydrogen and charges the energy storage; if DP > Pba+PweThen the electrolytic cell produces hydrogen, and simultaneously charges the stored energy, and the rest power is on line. And if the hydrogen production amount of the electrolytic cell does not meet the difference of the hydrogen demand, purchasing hydrogen.
The strategy C is specifically as follows: due to DP<0, and is therefore represented by the absolute value | DP |. First, it is determined whether | DP | is less than the battery capacity PbaIf the voltage is less than the preset value, the stored energy is discharged, otherwise, whether the absolute value of DP is greater or not is judgedLess than the battery capacity PbaAnd fuel cell capacity PfcSum of if Pba＜|DP|＜Pba+PfcIf so, storing energy and discharging, and simultaneously discharging the fuel cell; if | DP | > Pba+PfcAnd the stored energy is discharged, the fuel cell is discharged, and the electricity is purchased from the power grid at the same time.
The strategy D is specifically as follows: first, it is determined whether | DP | is less than the battery capacity PbaIf the value is less than the preset value, storing energy, discharging and purchasing hydrogen, otherwise, judging whether the value is greater than the capacity P of the storage batterybaIf P isbaIf < DP, the energy is stored, discharged and hydrogen is purchased, and at the same time, electricity is purchased from the power grid.
And step 3: under the constraints of energy balance, element constraint, power grid constraint and the like, an optimized equipment configuration model which aims at integrating the annual operation cost and the autonomous operation capacity of the charging station is established.
Target 1: the annual operating cost of the comprehensive charging station is minimum:
minF1＝(C1+C2)CRF+C3-C4 (6)
C1＝Ppvepv+Pbaeba+Pweewe+Pfcefc+HeH (7)
C2＝C1K (8)
wherein C is1、C2、C3、C4Respectively equipment purchase cost, equipment maintenance cost, comprehensive charging station operating cost and net income. CRF is each equipmentAnnual coefficient of aging. Ppv、Pba、Pwe、PfcRespectively the installed capacities of photovoltaic cells, storage batteries, electrolytic cells and fuel cells. H is the capacity of the hydrogen storage tank. e.g. of the typepv、eba、ewe、efc、eH1Unit prices of the corresponding devices, respectively. K is the maintenance cost of the equipment. r is the actual annual rate and y is the equipment operating life. e.g. of the typeH、eGi、eGoThe prices of standby hydrogen purchase, power grid purchase and surplus power on-line are respectively. HG、PGi、PGoThe method comprises the steps of respectively purchasing hydrogen gas for standby in each time period, purchasing power of a power grid and remaining power on-line power.
Target 2: the automatic operation capacity of the charging station is integrated, and the ratio of the electricity purchasing quantity to the standby hydrogen purchasing quantity is integrated.
The ratio of the minF2 to the minF3 is smaller, the power is less purchased from a power grid, the spare hydrogen is less purchased from the outside, and the self-generating operation capability is stronger. T is the total time period of the year 8760. Pl(t) is the electrical load demand of the electric vehicle user for a period of t.
Constraint 1: the exchange of electric power and hydrogen gas quantities of the integrated charging station system should be constantly balanced.
Pl＝Ppv+Pwe+Pba+Pfc+PG (14)
Hl＝H+HG+Hwe-Hfc (15)
Constraint 2: the transformer has capacity limitation, and the power supply power of the power grid to the comprehensive charging station is restricted.
PG≤PGmax (16)
PGmaxIs the maximum power of the power network for charging stations, i.e. the network is chargedTransformer power limitations of the station.
Constraint 3: the battery is overcharged and overdischarged, which affects its service life, so its state of charge SOC should be within reasonable constraints.
SOCmin≤SOC(t)≤SOCmax (17)
SOCmaxAnd SOCminRespectively representing the upper and lower limits of the power stored by the storage battery.
And 4, step 4: and optimizing the capacity of the optimal equipment by adopting an NSGA-II algorithm and fuzzy comprehensive evaluation based on MATLAB programming.
Fig. 4 shows an optimization process of the NSGA-II and the embedded four operation strategies used in the embodiment of the present invention, which specifically includes the following steps:
step 401: and inputting basic data of the comprehensive charging station, and setting initial parameters of NSGA-II and upper and lower limits of decision variables.
Step 402: the random initialization scale size is N population.
Step 403: the fitness value of the objective function is calculated,
step 404: and (4) carrying out non-dominated sorting on the individuals of the population, and layering the advantages and the disadvantages of the individuals according to an objective function.
Step 405: a virtual fitness value, i.e. a congestion value, of the individual is calculated.
Step 406: individuals for each layer were selected on a round-robin basis. And carrying out cross mutation on the selected individuals, and carrying out cross mutation operation by adopting an SBX cross and normal mutation operator to form a new offspring population with the same size.
Step 407: and combining the parent population and the newly formed offspring population, carrying out non-dominant sorting on the combined population, and screening the first N individuals to form a new population.
Step 408: and judging the iteration times, stopping iteration when the iteration times meet the requirements, and outputting a Pareto front edge, otherwise, repeating the step 403 until the evolution times meet the designed number, and outputting a result. C indicates how many iterations are reached and Cmax is the maximum number of iterations.
Step 409: and calculating the membership degree of each Pareto solution by using a fuzzy membership degree function, wherein the optimization scheme with the highest membership degree is the finally determined scheme.
And selecting a membership function of fuzzy comprehensive evaluation, wherein the smaller the optimization target is, the better the optimization target is, and selecting smaller trapezoidal distribution.
The validity of the method of the invention is verified by an example below. The data for this example are as follows:
and carrying out capacity configuration on the charging stations which are operated at a certain place, and establishing the optical hydrogen storage comprehensive charging station. And selecting the illumination intensity data of the area and the daily requirements of the two types of new energy automobiles as data. And taking the operation of the comprehensive charging station as a period, and calculating the equipment investment, the operation cost and the energy purchase ratio of the comprehensive charging station. Specific light intensity and EV and HV demand parameters are shown in fig. 5(a) to 5(c), respectively.
The distributed photovoltaic grid price refers to a notice about relevant matters of a photovoltaic power generation grid electricity price policy in 2020 issued by relevant departments, the real-time electricity price for power purchase of a distribution network is 0.7962 yuan/kWh, and the market price of hydrogen is 35 yuan/kg. The parameters of the devices of the comprehensive charging station are as follows.
TABLE 1 Equipment parameters table
In an MATLAB environment, the method provided by the invention is used for carrying out simulation optimization on the comprehensive charging station equipment optimization configuration model. The verification result of the embodiment shows that the method can reduce the operation cost of the comprehensive charging station and improve the autonomous supply capability. The Pareto solution set obtained by the simulation is shown in fig. 6. The electric quantity and hydrogen quantity balance of the optimal configuration is obtained by fuzzy comprehensive evaluation, as shown in fig. 7(a) -7 (d).
From fig. 6, it can be known that the optimal solution of the optimal configuration of the integrated charging station is distributed on the Pareto frontage, and the diversity and the distribution uniformity of the knowledge are reflected. Multiple optimization schemes may be provided in an optimized configuration. While non-dominant relationships exist between the various targets. If the operation cost of the comprehensive charging station is reduced, the ratio of electricity purchasing and standby hydrogen purchasing of the comprehensive charging station from the power grid is increased, and the autonomous operation capacity of the comprehensive charging station is reduced. Objective decisions are made when there is a need to comprehensively weigh the factors considered for each optimization objective.
From the operating result diagrams of the individual devices, it can be derived that the result of the optimized configuration enables an effective operation of the integrated charging station. As can be seen from fig. 7(a), in the period from 1 to 6, the electric vehicle is mainly supplied with the energy stored by the storage battery, the power supplied by the hydrogen energy storage system, and the supplementary power supplied by the power grid. In the period of 7 to 17, the illumination is sufficient. The photovoltaic output supplies power for the electric power storage and electrolyzes water to produce hydrogen on the premise of meeting the requirements of the electric automobile. In the period from 18 to 24, the electric automobile is mainly powered by a storage battery and a power grid. As can be seen from fig. 7(b), during the early morning and night hours, the hydrogen fuel vehicle is mainly supplied with the hydrogen remaining from the hydrogen storage tank and the hydrogen for standby purchased. The photovoltaic output electrolysis hydrogen production is carried out in the daytime, the net hydrogen amount can meet the requirement when the consumed hydrogen of the fuel cell is removed, and the requirement can be provided for the nighttime period by the residual hydrogen in the hydrogen storage tank. Fig. 7(c) and 7(d) show the operating state of the battery and the hydrogen storage tank for one day.
The invention also provides a device for configuring the capacity of the equipment of the photo-hydrogen storage comprehensive charging station, which comprises the following components:
the comprehensive charging station system model building module is used for building a comprehensive charging station system model which can provide services for the electric automobile and the hydrogen fuel electric automobile and comprises a photovoltaic system, an energy storage battery and a hydrogen energy storage system;
the energy flow strategy determination module is used for determining an energy flow strategy of the comprehensive charging station based on the energy flow coordination relation among all equipment of the comprehensive charging station according to the system model of the comprehensive charging station;
the capacity configuration optimization model building module is used for building an equipment capacity configuration optimization model which takes the annual operation cost and the autonomous operation capacity of the comprehensive charging station as targets on the basis of energy balance constraint, element constraint and power supply power constraint of a power grid according to an energy flow strategy;
and the capacity allocation scheme solving module is used for solving the equipment capacity allocation optimization model by adopting an NSGA-II algorithm and fuzzy comprehensive evaluation to determine a capacity allocation scheme of the comprehensive charging station equipment.
It should be understood that, in the embodiment of the present invention, the device for configuring capacity of an integrated optical hydrogen storage charging station device may implement all technical solutions in the above method embodiments, and functions of each functional module of the device may be implemented specifically according to the method in the above method embodiments, and a specific implementation process of the device may refer to relevant descriptions in the above embodiments, which is not described herein again.
The present invention also provides a computer apparatus comprising:
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs when executed by the processors implement the steps of the optical hydrogen storage integrated charging station device capacity configuration method as described above.
The present invention also provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the optical hydrogen storage integrated charging station device capacity configuration method as described above.
The above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. A method for configuring the capacity of an optical hydrogen storage integrated charging station device is characterized by comprising the following steps:
establishing a comprehensive charging station system model which can provide services for an electric automobile and a hydrogen fuel electric automobile and comprises a photovoltaic system, an energy storage battery and a hydrogen energy storage system;
determining an energy flow strategy of the comprehensive charging station based on an energy flow coordination relation among all devices of the comprehensive charging station according to the system model of the comprehensive charging station;
according to an energy flow strategy, based on energy balance constraint, element constraint and power supply power constraint of a power grid, establishing an equipment capacity configuration optimization model taking annual operation cost and autonomous operation capacity of a comprehensive charging station as targets;
and solving the equipment capacity configuration optimization model by adopting an NSGA-II algorithm and fuzzy comprehensive evaluation to determine a capacity configuration scheme of the comprehensive charging station equipment.
2. The method for configuring the capacity of an optical hydrogen storage integrated charging station device according to claim 1, wherein the integrated charging station system model comprises:
photovoltaic output model:
wherein G isTIs the intensity of solar radiation, T represents the time T, TCIs the ambient temperature, GSTCAnd TSTCIntensity and temperature of light under standard conditions, Ppv-STCIs the rated power of the photovoltaic;
the energy storage battery state of charge model:
wherein EBIs a rated capacity of stored energy, PBIs the charge-discharge power of the stored energy, eta is the operating efficiency of the stored energy, and delta t is the time step;
the hydrogen energy storage system consists of an electrolytic cell, a hydrogen storage tank and a fuel cell, wherein the model of the electrolytic cell is as follows:
Hwe＝Pweηwe/HHV
wherein P isweOutput power, eta, for hydrogen production by photovoltaic power generationweThe working efficiency of the electrolytic cell, HHV is the electro-hydrogen energy conversion coefficient;
the stored hydrogen capacity model for each period of the hydrogen storage tank is as follows:
H(t)＝H(t-1)+Hwe(t-1)-Hl(t-1)-Hfc(t-1)
wherein H is the hydrogen content of the hydrogen storage tank, Hwe、HfcThe amounts of hydrogen supplied to the hydrogen-fueled electric vehicle and the fuel cell, respectively;
the fuel cell model is as follows:
Pfc＝HfcηfcHHV
wherein P isfcOutput power, eta, for generating electricity for combustion of hydrogenfcIs the operating efficiency of the fuel cell.
3. The method for configuring the capacity of an optical hydrogen storage integrated charging station device according to claim 1, wherein the energy flow strategy of the integrated charging station comprises the following four strategies:
A. the requirements of electric automobiles and hydrogen fuel electric automobiles are met, the residual power of photovoltaic power generation sequentially charges a storage battery, an electrolytic cell electrolyzes and stores hydrogen, and finally the residual power is on line;
B. the demand of the electric automobile is met, the demand of the hydrogen fuel electric automobile is in shortage, the residual power of the photovoltaic power generation is firstly used for electrolytic hydrogen storage in an electrolytic cell and then used for charging a storage battery, and the hydrogen fuel electric automobile is still in shortage and is supplemented by purchased standby hydrogen;
C. the demand of the hydrogen fuel electric automobile is met, the demand of the electric automobile is in shortage, the storage battery and the fuel cell discharge once to provide the demand for the electric automobile, and finally the demand is supplemented by a power grid;
D. the electric automobile and the hydrogen fuel electric automobile have shortage in demand, the electric automobile is required to be discharged by a storage battery firstly, the power shortage is supplemented by a power grid, and the hydrogen fuel electric automobile is required to be supplemented by purchased standby hydrogen.
4. The method of claim 3, wherein determining the energy flow strategy for the integrated charging station comprises: obtaining photovoltaic output power PpvJudging the photovoltaic output power PpvWhether the electric load demand P is more than the electric load demand P of the electric automobile user in each periodlLet DP be equal to Ppv-PlIf DP is present>0, enter the first strategy branch, if DP<0, then enter the second strategy branch; in the first strategy branch and the second strategy branch, whether the hydrogen content H of the hydrogen storage tank is larger than the hydrogen content H required by the user of the hydrogen-fueled electric vehicle is judgedlWhen DP is present>0 and H-HlWhen > 0, the A strategy is implemented, when DP>0 and H-HlIf the number is less than 0, implementing the strategy B; when DP is present<0 and H-HlWhen > 0, the C strategy is implemented, when DP<0 and H-HlIf < 0, the D strategy is implemented.
5. The method for configuring the device capacity of the optical hydrogen storage integrated charging station according to claim 2, wherein the device capacity configuration optimization model is as follows:
the annual running cost of the comprehensive charging station is lowest:
minF1＝(C1+C2)CRF+C3-C4
C1＝Ppvepv+Pbaeba+Pweewe+Pfcefc+HeH
C2＝C1K
wherein C is1、C2、C3、C4Respectively the equipment purchase cost, the equipment maintenance cost, the operation cost of the comprehensive charging station and the internet access income; CRF is the annual factor of each device; ppv、Pba、Pwe、PfcRespectively the installed capacities of a photovoltaic cell, a storage battery, an electrolytic cell and a fuel cell; h is the capacity of the hydrogen storage tank; e.g. of the typepv、eba、ewe、efc、eH1Unit price of corresponding equipment respectively; k is the maintenance cost of the equipment; r is the actual annual rate, and y is the equipment operating life; e.g. of a cylinderH、eGi、eGoThe prices of standby hydrogen purchase, power grid purchase and surplus power on-line are respectively; hG、PGi、PGoRespectively standby hydrogen purchasing amount, power purchasing power of a power grid and surplus power on-line power in each time period;
the automatic operation capacity of the comprehensive charging station, and the ratio of the electricity purchasing quantity to the standby hydrogen purchasing quantity:
the exchange of electric power and hydrogen quantities of the integrated charging station system should be constantly balanced:
Pl＝Ppv+Pwe+Pba+Pfc+PG
Hl＝H+HG+Hwe-Hfc
the transformer has capacity limitation, and the power supply power of the power grid to the comprehensive charging station is restricted:
PG≤PGmax
the state of charge SOC of the battery is within a reasonable constraint range:
SOCmin≤SOC(t)≤SOCmax。
6. the method for configuring the device capacity of the optical hydrogen storage integrated charging station according to claim 1, wherein solving the device capacity configuration optimization model specifically comprises:
(a) inputting basic data of the comprehensive charging station, and setting initial parameters of NSGA-II and upper and lower limits of decision variables;
(b) randomly initializing a population with the size of N;
(c) calculating the fitness value of the target function, and then performing rapid non-dominated sorting on the result;
(d) calculating the crowding degree distance of the individuals, selecting and reserving excellent individuals by using an elite strategy to form a new population, and returning to the step (c);
(e) judging the iteration times, stopping iteration when the iteration times meet the requirements, outputting a Pareto front edge, and otherwise, continuing to carry out circular iteration;
(f) and calculating the membership of each Pareto solution by using a fuzzy membership function of fuzzy comprehensive evaluation, wherein the optimization scheme with the highest membership is the final scheme.
7. The method for configuring the equipment capacity of the optical hydrogen storage integrated charging station according to claim 5, wherein the membership function of the fuzzy comprehensive evaluation is as follows:
wherein
The function value of the mth non-inferior solution of the nth objective function,
the degree of membership of the mth non-inferior solution of the nth objective function,
the maximum solution and the minimum solution of the Pareto solution are respectively.
8. A device for configuring the capacity of an optical hydrogen storage integrated charging station device is characterized by comprising:
the comprehensive charging station system model building module is used for building a comprehensive charging station system model which can provide services for an electric vehicle and a hydrogen fuel electric vehicle and comprises a photovoltaic system, an energy storage battery and a hydrogen energy storage system;
the energy flow strategy determination module is used for determining an energy flow strategy of the comprehensive charging station based on the energy flow coordination relation among all equipment of the comprehensive charging station according to the system model of the comprehensive charging station;
the capacity configuration optimization model establishing module is used for establishing an equipment capacity configuration optimization model which takes the annual operation cost and the autonomous operation capacity of the comprehensive charging station as targets on the basis of energy balance constraint, element constraint and power grid power supply constraint according to an energy flow strategy;
and the capacity allocation scheme solving module is used for solving the equipment capacity allocation optimization model by adopting an NSGA-II algorithm and fuzzy comprehensive evaluation to determine a capacity allocation scheme of the comprehensive charging station equipment.
9. A computer device, comprising:
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs when executed by the processors implement the steps of the optical hydrogen storage integrated charging station device capacity configuration method of any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the optical hydrogen storage integrated charging station device capacity configuration method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210209159.9A CN114567009A (en) | 2022-03-03 | 2022-03-03 | Equipment capacity configuration method and device for light-hydrogen storage integrated charging station |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210209159.9A CN114567009A (en) | 2022-03-03 | 2022-03-03 | Equipment capacity configuration method and device for light-hydrogen storage integrated charging station |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114567009A true CN114567009A (en) | 2022-05-31 |
Family
ID=81717604
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210209159.9A Pending CN114567009A (en) | 2022-03-03 | 2022-03-03 | Equipment capacity configuration method and device for light-hydrogen storage integrated charging station |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114567009A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114997544A (en) * | 2022-08-04 | 2022-09-02 | 北京理工大学 | Method and system for optimizing and configuring capacity of hydrogen optical storage charging station |
| CN115102153A (en) * | 2022-07-11 | 2022-09-23 | 西安交通大学 | Electro-optical hydrogen storage micro-grid for transformer substation and control method thereof |
-
2022
- 2022-03-03 CN CN202210209159.9A patent/CN114567009A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115102153A (en) * | 2022-07-11 | 2022-09-23 | 西安交通大学 | Electro-optical hydrogen storage micro-grid for transformer substation and control method thereof |
| CN115102153B (en) * | 2022-07-11 | 2024-05-24 | 西安交通大学 | Electro-optical hydrogen storage micro-grid for transformer substation and control method thereof |
| CN114997544A (en) * | 2022-08-04 | 2022-09-02 | 北京理工大学 | Method and system for optimizing and configuring capacity of hydrogen optical storage charging station |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108173283B (en) | Operation method of combined heat and power system containing wind and light renewable energy | |
| CN103793758B (en) | Multi-objective optimization scheduling method for electric vehicle charging station including photovoltaic power generation system | |
| CN105811409B (en) | A kind of microgrid multiple target traffic control method containing hybrid energy storage system of electric automobile | |
| CN111404206B (en) | Wind-solar energy storage power generation system capacity double-layer planning method considering investment return constraint | |
| CN112418605A (en) | Optimal operation method for energy storage system of optical storage type charging station | |
| CN114567009A (en) | Equipment capacity configuration method and device for light-hydrogen storage integrated charging station | |
| CN106485358A (en) | Binding sequence computing and the independent micro-capacitance sensor Optimal Configuration Method of particle cluster algorithm | |
| CN112550047B (en) | Optimal configuration method and device for light charging and storage integrated charging station | |
| CN113326467B (en) | Multi-target optimization method, storage medium and optimization system for multi-station fusion comprehensive energy system based on multiple uncertainties | |
| CN105868499B (en) | A kind of electric automobile charging station capacity ratio method containing wind-light storage | |
| CN110138006A (en) | Consider more micro electric network coordination Optimization Schedulings containing New-energy electric vehicle | |
| Wang et al. | Scenario analysis, management, and optimization of a new Vehicle-to-Micro-Grid (V2μG) network based on off-grid renewable building energy systems | |
| CN112734098A (en) | Power distribution network power dispatching method and system based on source-load-network balance | |
| CN116061742B (en) | Charging control method and system for electric automobile in time-of-use electricity price photovoltaic park | |
| CN116151486B (en) | Multi-time-scale random optimization method and device for photovoltaic charging station with energy storage system | |
| CN114243791A (en) | Multi-objective optimization configuration method, system and storage medium for wind-solar-hydrogen storage system | |
| CN111064192A (en) | Independent micro-grid capacity optimal configuration method considering source load uncertainty | |
| CN115001038A (en) | Wind-solar-diesel storage capacity ratio optimization method based on stochastic programming | |
| CN114583729A (en) | Light-storage electric vehicle charging station scheduling method considering full-life-cycle carbon emission | |
| CN114462854A (en) | Hierarchical scheduling method and system containing new energy and electric vehicle grid connection | |
| CN112260274A (en) | Panoramic theory-based virtual power plant construction method | |
| CN110334856A (en) | A kind of wind-light storage method for planning capacity based on carbon transaction mechanism | |
| CN115796533A (en) | Virtual power plant double-layer optimization scheduling method and device considering clean energy consumption | |
| CN114884133B (en) | Micro-grid economic dispatching optimization method and system considering electric automobile | |
| CN115271195A (en) | Power system multi-objective energy storage optimization method based on improved genetic algorithm |
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 |