CN111639303B - Ordered charging game method and device for electric automobile - Google Patents
Ordered charging game method and device for electric automobile Download PDFInfo
- Publication number
- CN111639303B CN111639303B CN202010475429.1A CN202010475429A CN111639303B CN 111639303 B CN111639303 B CN 111639303B CN 202010475429 A CN202010475429 A CN 202010475429A CN 111639303 B CN111639303 B CN 111639303B
- Authority
- CN
- China
- Prior art keywords
- charging
- electric vehicle
- electric automobile
- time
- phase
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 62
- 230000005611 electricity Effects 0.000 claims abstract description 78
- 238000009826 distribution Methods 0.000 claims abstract description 73
- 230000007246 mechanism Effects 0.000 claims abstract description 24
- 230000005284 excitation Effects 0.000 claims description 24
- 238000005457 optimization Methods 0.000 claims description 16
- 238000005315 distribution function Methods 0.000 claims description 11
- 230000008569 process Effects 0.000 claims description 9
- 238000000342 Monte Carlo simulation Methods 0.000 claims description 4
- 230000001172 regenerating effect Effects 0.000 claims description 4
- 238000010276 construction Methods 0.000 abstract description 3
- 230000006870 function Effects 0.000 description 4
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 230000002411 adverse Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression 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
- 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
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
-
- 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
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
-
- 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
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Physics & Mathematics (AREA)
- Economics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Tourism & Hospitality (AREA)
- Data Mining & Analysis (AREA)
- Operations Research (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Mathematical Analysis (AREA)
- Educational Administration (AREA)
- Health & Medical Sciences (AREA)
- Quality & Reliability (AREA)
- Computational Mathematics (AREA)
- Game Theory and Decision Science (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Physics (AREA)
- Development Economics (AREA)
- Mathematical Optimization (AREA)
- Evolutionary Biology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Algebra (AREA)
- General Engineering & Computer Science (AREA)
- Bioinformatics & Computational Biology (AREA)
- Software Systems (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Probability & Statistics with Applications (AREA)
- Databases & Information Systems (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The application discloses an ordered charging game method and device for an electric automobile, wherein the method comprises the following steps: establishing an electric vehicle charging demand model according to probability distribution of the electric vehicle access power grid time, the electric vehicle departure power grid time and the daily driving mileage; the method comprises the steps of establishing a real-time electricity price mechanism of a single-phase user of the power distribution network by considering load three-phase unbalance and load peak-valley difference; according to a real-time electricity price mechanism of a single-phase user and an electric vehicle charging demand model, an electric vehicle ordered charging dynamic game model participated by the single-phase user of the power distribution network is established; and solving the electric vehicle ordered charging dynamic game model by adopting an algorithm based on CPLEX to obtain an electric vehicle ordered charging scheme. The application can improve the power quality of the power distribution network and the consumption experience of users, reduce the running loss of the power distribution network, delay the construction investment of the power distribution network and improve the economy of the power distribution network.
Description
Technical Field
The application relates to the technical field of electric vehicle charging management, in particular to an ordered charging game method and device for electric vehicles.
Background
The large-scale electric automobile is connected with the power grid, and adverse effects on the aspects of load increase, control difficulty increase, network loss increase, power quality reduction and the like are brought to the power system. The household electric automobile is charged in a single-phase 220V slow charging mode at night, and when the large-scale household electric automobile is connected into a district power distribution network for charging, if the management and control are not performed, the load three-phase imbalance degree and peak-valley difference of the power distribution network can be increased, the electric energy quality is reduced, the power grid loss is increased, and even the safe operation of the power grid is jeopardized when the power grid is serious.
Disclosure of Invention
The application provides an ordered charging game method and device for an electric automobile, which solve the problems of unbalanced load three phases and increased peak-valley difference caused by electric automobile access.
In view of this, the first aspect of the present application provides an ordered charging game method for an electric vehicle, the method comprising:
establishing an electric vehicle charging demand model according to probability distribution of the electric vehicle access power grid time, the electric vehicle departure power grid time and the daily driving mileage;
the method comprises the steps of establishing a real-time electricity price mechanism of a single-phase user of the power distribution network by considering load three-phase unbalance and load peak-valley difference;
establishing an electric vehicle ordered charging dynamic game model with single-phase users participating in a power distribution network according to the real-time electricity price mechanism of the single-phase users and the electric vehicle charging demand model;
and solving the electric vehicle ordered charging dynamic game model by adopting an algorithm based on CPLEX to obtain an electric vehicle ordered charging scheme.
Optionally, probability distributions of the power grid access time, the power grid departure time and the daily driving mileage of the electric automobile are specifically:
the probability distribution of the time of accessing the power grid is as follows:
the probability distribution of the grid departure time is as follows:
the probability distribution of the daily driving mileage is as follows:
wherein t is c The method comprises the steps of accessing the electric automobile into the power grid; mu (mu) s Sum sigma s Respectively connecting the electric automobile with the mean value and standard deviation of the power grid time normal distribution function; t is t l The time when the electric automobile leaves the power grid; mu (mu) e Sum sigma e Respectively connecting the electric automobile with the mean value and standard deviation of the power grid time normal distribution function; s is the daily driving mileage of the electric automobile; mu (mu) d Sum sigma d The average value and the standard deviation of the daily driving mileage log-normal distribution function of the electric automobile are respectively.
Optionally, the establishing the electric automobile charging demand model specifically includes:
randomly generating the power grid access time, the power grid departure time and the daily driving mileage of the electric automobile by adopting a Monte Carlo simulation method;
calculating the state of charge of the ith electric automobile when the electric automobile is connected to a power grid according to the daily driving mileage of the electric automobile:
wherein S is s,i And S is e,i The expected value of the state of charge when the ith electric automobile is connected to the power grid and the state of charge when the ith electric automobile is disconnected from the power grid is respectively obtained; e, e d The power consumption is the unit mileage of the electric automobile; e (E) e Is the battery capacity; i=1, 2, …, N;
judging whether the ith electric automobile can meet the charging requirement in the charging time, if not, modifying the expected value of the charge state when leaving the power grid, or regenerating the time when the electric automobile leaves the power grid:
(S e,i -S s,i )E e,i ≤P e η(t l,i -t c,i )
wherein t is l,i And t c,i The time when the ith electric automobile is connected to and separated from the power grid, P e For rated charge power, η is charge efficiency.
Optionally, the method establishes a real-time electricity price mechanism of the single-phase user of the power distribution network by considering the load three-phase unbalance degree and the load peak-valley difference, and specifically comprises the following steps:
and the real-time electricity price of the single-phase user is the sum of the basic electricity price and the excitation electricity price of the load unbalance degree and the excitation electricity price of the load peak clipping and valley filling.
Optionally, the real-time electricity price of the single-phase user is:
wherein p is X,t Real-time electricity price for single-phase user period t;the basic electricity price of the power distribution network; />Exciting electricity price for the load unbalance degree of the single-phase user; />Exciting electricity price for peak clipping and valley filling of the load of a single-phase user; x represents any one of the three phases;
the load unbalance degree excitation electricity price is as follows:
wherein q is A,t 、q B,t And q C,t The electricity consumption is A, B and the electricity consumption is C phase t period respectively; q av,t The three-phase average power consumption is t time periods; alpha is the electricity price excitation coefficient of the load with three-phase unbalance;
the load peak clipping and valley filling excitation electricity price is as follows:
wherein L is P And L V Respectively the expected values of peak load and valley load of the power distribution network; beta is the electricity price excitation coefficient of the load deviating from the expected value of the peak-to-valley load.
Optionally, the objective function of the electric vehicle ordered charging dynamic game model is as follows:
wherein C is X Representing the charging expense of the X-phase user; u (u) X,i,t To characterize the 0-1 variable of the state of charge of the ith electric vehicle accessing the X phase, u when in state of charge X,i,t =1; Δt is the optimization time.
Optionally, the constraint conditions of the electric vehicle ordered charging dynamic game model include:
charging demand constraint:
control time constraint:
optionally, the method for solving the electric vehicle ordered charging dynamic game model by adopting the CPLEX-based algorithm to obtain the electric vehicle ordered charging scheme comprises the following steps:
s1: initializing model parameters of the electric vehicle ordered charging dynamic game model;
s2: taking the charging scheme of the electric vehicle under the disordered charging condition as a dynamic game initial state, namely starting charging when the electric vehicle is connected to a power grid, and stopping charging when the electric vehicle reaches a desired value of the state of charge when the electric vehicle leaves the power grid;
s3: the electric automobile ordered charging scheme of the kth wheel is obtained by solving through a CPLEX solver according to the electric automobile ordered charging scheme optimized by the k-1 wheel, wherein each phase of users independently carry out the electric automobile ordered charging scheme;
s4: judging whether the ordered charging scheme of the kth-wheel electric automobile reaches an equilibrium state, and if so:
the electric automobile charging scheme reaches an equilibrium state, the electric automobile ordered charging scheme of each phase of users is output, and if the electric automobile ordered charging scheme does not meet the requirement, the step S3 is returned;
in the method, in the process of the application,and->The charging cost of A, B, C phases after k-1 round optimization is respectively; /> And->The charging cost of A, B, C phases after k rounds of optimization is respectively;epsilon is an empirical threshold.
A second aspect of the present application provides an electric vehicle in-order charging gaming device, the device comprising:
the first model building unit is used for building an electric vehicle charging demand model according to probability distribution of the electric vehicle in-power grid time, the electric vehicle out-of-power grid time and the daily driving mileage;
the real-time electricity price mechanism building unit is used for building a real-time electricity price mechanism of a single-phase user of the power distribution network by taking the load three-phase unbalance degree and the load peak-valley difference into consideration;
the second model unit is used for establishing an ordered charging dynamic game model of the electric vehicle with single-phase users participating in the power distribution network according to the real-time electricity price mechanism of the single-phase users and the electric vehicle charging demand model;
and the first solving unit is used for solving the electric vehicle ordered charging dynamic game model by adopting a CPLEX-based algorithm to obtain an electric vehicle ordered charging scheme.
Optionally, the solving unit further includes:
the initialization unit is used for initializing model parameters of the ordered charging dynamic game model of the electric automobile;
the starting unit is used for taking the charging scheme of the electric vehicle under the disordered charging condition as a dynamic game initial state, namely, starting charging when the electric vehicle is connected to a power grid, and stopping charging when the electric vehicle reaches a desired value of the state of charge when the electric vehicle leaves the power grid;
the second solving unit is used for solving the ordered charging scheme of the electric automobile of the kth wheel, wherein the ordered charging scheme of the electric automobile of the kth wheel is obtained by solving the ordered charging scheme of the electric automobile optimized according to the k-1 wheel through a CPLEX solver, and each phase of users independently carry out the ordered charging scheme of the electric automobile;
the balance judging unit is used for judging whether the ordered charging scheme of the kth-wheel electric automobile reaches an equilibrium state or not, and if yes:
the electric automobile charging scheme is in an equilibrium state, the electric automobile ordered charging scheme of each phase of users is output, and if the electric automobile ordered charging scheme is not met, the second solving unit is returned to;
in the method, in the process of the application,and->The charging cost of A, B, C phases after k-1 round optimization is respectively; /> And->The charging cost of A, B, C phases after k rounds of optimization is respectively; epsilon is an empirical threshold.
From the above technical scheme, the application has the following advantages:
the application provides an ordered charging game method and device for an electric automobile, wherein the method comprises the following steps: establishing an electric vehicle charging demand model according to probability distribution of the electric vehicle access power grid time, the electric vehicle departure power grid time and the daily driving mileage; the method comprises the steps of establishing a real-time electricity price mechanism of a single-phase user of the power distribution network by considering load three-phase unbalance and load peak-valley difference; according to a real-time electricity price mechanism of a single-phase user and an electric vehicle charging demand model, an electric vehicle ordered charging dynamic game model participated by the single-phase user of the power distribution network is established; and solving the electric vehicle ordered charging dynamic game model by adopting an algorithm based on CPLEX to obtain an electric vehicle ordered charging scheme.
According to the application, the game theory is applied to ordered charging management of the electric automobile, and the electric price signal is utilized to guide a single-phase user of the power distribution network to carry out ordered charging control of the electric automobile, so that the three-phase unbalance of the load of the power distribution network is restrained, the peak-valley difference of the load of the power distribution network is reduced, the power quality of the power distribution network and the consumption experience of the user can be improved, the running loss of the power distribution network and the construction investment of the power distribution network are reduced, and the economical efficiency of the power distribution network is improved.
Drawings
FIG. 1 is a flow chart of a method of one embodiment of an ordered charge gaming method for an electric vehicle of the present application;
FIG. 2 is a flow chart of another embodiment of an ordered charge gaming method for an electric vehicle according to the present application;
fig. 3 is a schematic device structure diagram of an embodiment of an ordered charging game device for an electric vehicle according to the present application.
Detailed Description
In order to make the present application better understood by those skilled in the art, the following description will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Referring to fig. 1, fig. 1 is a flowchart of a method for an ordered charging game method for an electric vehicle according to an embodiment of the application, where fig. 1 includes:
101. and establishing an electric vehicle charging demand model according to probability distribution of the electric vehicle access power grid time, the electric vehicle departure power grid time and the daily driving mileage.
The probability distribution of the time of the electric automobile accessing the power grid, the time of the electric automobile leaving the power grid and the daily driving mileage all meet the normal distribution function, and the time of the electric automobile accessing the power grid, the time of the electric automobile leaving the power grid and the daily driving mileage can be randomly generated by adopting a Monte Carlo simulation method according to the normal distribution function. Calculating the state of charge of the electric automobile when the electric automobile is connected to a power grid according to the daily driving mileage of the electric automobile; and judging whether the electric automobile can meet the charging requirement in the charging time, and if not, modifying the expected value of the state of charge when the electric automobile leaves the power grid, or regenerating the time when the electric automobile leaves the power grid.
102. And (3) taking the load three-phase unbalance degree and the load peak-valley difference into consideration, and establishing a real-time electricity price mechanism of the single-phase user of the power distribution network.
The real-time electricity price of the single-phase user is the sum of the basic electricity price of the power distribution network, the excitation electricity price of the load unbalance degree and the excitation electricity price of load peak clipping and valley filling; the basic electricity price of the power distribution network is known, so that the real-time electricity price of a single-phase user can be obtained by only calculating the excitation electricity price of the unbalanced load and the excitation electricity price of the peak load shedding and valley filling.
103. And establishing an electric vehicle ordered charging dynamic game model with single-phase users participating in the power distribution network according to the real-time electricity price mechanism of the single-phase users and the electric vehicle charging demand model.
The method can determine the objective function of the electric vehicle ordered charging dynamic game model according to the real-time electricity price mechanism of the single-phase user of the power distribution network, and obtain the constraint condition of the electric vehicle ordered charging dynamic game model according to the electric vehicle charging demand model, so as to obtain the electric vehicle ordered charging dynamic game model.
104. And solving the electric vehicle ordered charging dynamic game model by adopting an algorithm based on CPLEX to obtain an electric vehicle ordered charging scheme.
It should be noted that, solving the electric automobile ordered charging dynamic game model by adopting the CPLEX-based algorithm, the obtained electric automobile ordered charging scheme is specifically as follows:
s1: initializing model parameters of an ordered charging dynamic game model of the electric automobile;
s2: taking the charging scheme of the electric vehicle under the disordered charging condition as a dynamic game initial state, namely starting charging when the electric vehicle is connected to a power grid, and stopping charging when the electric vehicle reaches a desired value of the state of charge when the electric vehicle leaves the power grid;
s3: the electric automobile ordered charging scheme of the kth wheel is obtained by solving through a CPLEX solver according to the electric automobile ordered charging scheme optimized by the k-1 wheel, wherein each phase of users independently carry out the electric automobile ordered charging scheme;
s4: and judging whether the ordered charging scheme of the kth-wheel electric automobile reaches an equilibrium state, and outputting the ordered charging scheme of the electric automobile of each phase of users if the ordered charging scheme of the electric automobile reaches the equilibrium state.
According to the application, the game theory is applied to ordered charging management of the electric automobile, and the electric price signal is utilized to guide a single-phase user of the power distribution network to carry out ordered charging control of the electric automobile, so that the three-phase unbalance of the load of the power distribution network is restrained, the peak-valley difference of the load of the power distribution network is reduced, the power quality of the power distribution network and the consumption experience of the user can be improved, the running loss of the power distribution network and the construction investment of the power distribution network are reduced, and the economical efficiency of the power distribution network is improved.
The application also provides another embodiment of an ordered charging game method of an electric automobile, as shown in fig. 2, specifically comprising:
201. and establishing an electric vehicle charging demand model according to probability distribution of the electric vehicle access power grid time, the electric vehicle departure power grid time and the daily driving mileage.
The probability distribution of the time of the electric automobile accessing the power grid, the time of the electric automobile leaving the power grid and the daily driving mileage is as follows:
the probability distribution of the time of accessing the power grid is as follows:
the probability distribution of departure time is:
the probability distribution of the daily driving mileage is:
wherein t is c The method comprises the steps of accessing the electric automobile into the power grid; mu (mu) s Sum sigma s Respectively connecting the electric automobile with the mean value and standard deviation of the power grid time normal distribution function; t is t l The time when the electric automobile leaves the power grid; mu (mu) e Sum sigma e Respectively connecting the electric automobile with the mean value and standard deviation of the power grid time normal distribution function; s is the daily driving mileage of the electric automobile; mu (mu) d Sum sigma d The average value and the standard deviation of the daily driving mileage log-normal distribution function of the electric automobile are respectively.
Specifically, the step of establishing the electric vehicle charging demand model includes:
randomly generating the time of accessing the electric power grid, the time of leaving the electric power grid and the daily driving mileage of the electric vehicle by adopting a Monte Carlo simulation method;
calculating the state of charge of the ith electric automobile when the electric automobile is connected to a power grid according to the daily driving mileage of the electric automobile:
wherein S is s,i And S is e,i The expected value of the state of charge when the ith electric automobile is connected to the power grid and the state of charge when the ith electric automobile is disconnected from the power grid is respectively obtained; e, e d The power consumption is the unit mileage of the electric automobile; e (E) e Is the battery capacity; i=1, 2, …, N;
judging whether the ith electric automobile can meet the charging requirement in the charging time, if not, modifying the expected value of the charge state when leaving the power grid, or regenerating the time when the electric automobile leaves the power grid:
(S e,i -S s,i )E e,i ≤P e η(t l,i -t c,i )
wherein t is l,i And t c,i The time when the ith electric automobile is connected to and separated from the power grid, P e For rated charge power, η is charge efficiency.
202. And (3) taking the load three-phase unbalance degree and the load peak-valley difference into consideration, and establishing a real-time electricity price mechanism of the single-phase user of the power distribution network.
The real-time electricity price of the single-phase user is the sum of the basic electricity price of the power distribution network, the excitation electricity price of the load unbalance degree and the excitation electricity price of load peak clipping and valley filling, and the formula is as follows:
wherein p is X,t Real-time electricity price for X-phase user period t;the basic electricity price of the power distribution network; />Exciting electricity price for the load unbalance degree of the X-phase user; />Exciting electricity price for peak clipping and valley filling of the load of a single-phase user; x represents any one of the three phases;
wherein, the load unbalance degree excitation electricity price is:
wherein q is A,t 、q B,t And q C,t The electricity consumption is A, B and the electricity consumption is C phase t period respectively; q av, t is the three-phase average power consumption in t time period; alpha is the electricity price excitation coefficient of the load with three-phase unbalance;
the load peak-load-shedding valley-filling excitation electricity price is as follows:
wherein L is P And L V Respectively the expected values of peak load and valley load of the power distribution network; beta is the electricity price excitation coefficient of the load deviating from the expected value of the peak-to-valley load.
203. And establishing an electric vehicle ordered charging dynamic game model with single-phase users participating in the power distribution network according to the real-time electricity price mechanism of the single-phase users and the electric vehicle charging demand model.
It should be noted that, the objective function of the ordered charging dynamic game model of the electric automobile is:
wherein C is X Representing the charging expense of the X-phase user; u (u) X,i,t To characterize the 0-1 variable of the state of charge of the ith electric vehicle accessing the X phase, u when in state of charge X,i,t =1; Δt is the optimization time.
Constraint conditions of the ordered charging dynamic game model of the electric automobile comprise:
charging demand constraint:
control time constraint:
204. and initializing model parameters of the ordered charging dynamic game model of the electric automobile.
The initialized parameters include the basic electricity price of the power distribution networkPower price excitation coefficient alpha with load having three-phase unbalance, power price excitation coefficient beta with load deviating from peak-to-valley load expected value and power distribution network peak load expected value L P Grid valley load expectation L V 。
205. And taking the charging scheme of the electric vehicle under the disordered charging condition as a dynamic game initial state, namely starting charging when the electric vehicle is connected to a power grid, and stopping charging when the electric vehicle reaches a desired value of the state of charge when the electric vehicle is separated from the power grid.
206. The electric automobile ordered charging scheme of the kth wheel is obtained by solving through a CPLEX solver according to the electric automobile ordered charging scheme optimized by the k-1 wheel, wherein each phase of users independently conduct the electric automobile ordered charging scheme.
207. Judging whether the ordered charging scheme of the kth-wheel electric vehicle reaches an equilibrium state, and outputting the ordered charging scheme of the electric vehicle of each phase of users if the ordered charging scheme of the electric vehicle reaches the equilibrium state; if not, return to step 206.
It should be noted that, the formula for judging whether the equilibrium state is reached is:
in the method, in the process of the application,and->The charging cost of A, B, C phases after k-1 round optimization is respectively; /> And->The charging cost of A, B, C phases after k rounds of optimization is respectively; epsilon is an empirical threshold.
The foregoing is an embodiment of the method of the present application, and the present application further includes an embodiment of an ordered charging gaming device for an electric vehicle, as shown in fig. 3, including:
the first model building unit 301 is configured to build an electric vehicle charging demand model according to probability distribution of an electric vehicle accessing to a power grid, an electric vehicle leaving to the power grid, and a daily driving mileage.
The real-time electricity price mechanism building unit 302 is configured to build a real-time electricity price mechanism of a single-phase user of the power distribution network in consideration of the load three-phase imbalance and the load peak-valley difference.
And the second model unit 303 is configured to establish an ordered charging dynamic game model of the electric vehicle with which the single-phase user of the power distribution network participates according to the real-time electricity price mechanism of the single-phase user and the charging demand model of the electric vehicle.
And the first solving unit 304 is configured to solve the electric vehicle ordered charging dynamic game model by adopting a CPLEX-based algorithm, so as to obtain an electric vehicle ordered charging scheme.
In a specific embodiment, the first solving unit 304 further includes:
the initializing unit 3041 is used for initializing model parameters of the ordered charging dynamic game model of the electric automobile.
The starting unit 3042 is configured to take the charging scheme of the electric vehicle under the disordered charging condition as a dynamic game initial state, that is, start charging when the electric vehicle is connected to the power grid, and stop charging when the electric vehicle reaches a desired value of the state of charge when the electric vehicle is separated from the power grid.
The second solving unit 3043 is configured to solve the k-th wheel of ordered electric vehicle charging scheme, where the k-th wheel of ordered electric vehicle charging scheme is obtained by solving the k-1 wheel of optimized ordered electric vehicle charging scheme through a CPLEX solver, where each phase of users independently performs the ordered electric vehicle charging scheme.
The balance judging unit 3044 is configured to judge whether the orderly charging scheme of the kth-wheel electric vehicle reaches an equilibrium state, if yes:
the electric automobile charging scheme is in an equilibrium state, the electric automobile ordered charging scheme of each phase of users is output, and if the electric automobile ordered charging scheme is not met, the step of returning to the second solving unit 3043 is performed;
in the method, in the process of the application,and->The charging cost of A, B, C phases after k-1 round optimization is respectively; /> And->The charging cost of A, B, C phases after k rounds of optimization is respectively; epsilon is an empirical threshold.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
The terms "first," "second," "third," "fourth," and the like in the description of the application and in the above figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented, for example, in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in the present application, "at least one (item)" means one or more, and "a plurality" means two or more. "and/or" for describing the association relationship of the association object, the representation may have three relationships, for example, "a and/or B" may represent: only a, only B and both a and B are present, wherein a, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (one) of a, b or c may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: u disk, mobile hard disk, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk, etc.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims (9)
1. An ordered charging game method for an electric automobile is characterized by comprising the following steps:
establishing an electric vehicle charging demand model according to probability distribution of the electric vehicle access power grid time, the electric vehicle departure power grid time and the daily driving mileage;
the method comprises the steps of establishing a real-time electricity price mechanism of a single-phase user of the power distribution network by considering load three-phase unbalance and load peak-valley difference;
according to the real-time electricity price mechanism of the single-phase user and the electric vehicle charging demand model, an electric vehicle ordered charging dynamic game model participated by the single-phase user of the power distribution network is established, and an objective function of the electric vehicle ordered charging dynamic game model is as follows:
wherein C is X Representing the charging expense of the X-phase user; p is p X,t Real-time electricity price for single-phase user period t; u (u) X,i,t To characterize the 0-1 variable of the state of charge of the ith electric vehicle accessing the X phase, u when in state of charge X,i,t ＝1；P e The rated charging power, eta is the charging efficiency; Δt is the optimization time;
and solving the electric vehicle ordered charging dynamic game model by adopting an algorithm based on CPLEX to obtain an electric vehicle ordered charging scheme.
2. The ordered charging game method of electric vehicles according to claim 1, wherein probability distributions of the power grid access time, the power grid departure time and the daily driving mileage of the electric vehicles are specifically as follows:
the probability distribution of the time of accessing the power grid is as follows:
the probability distribution of the grid departure time is as follows:
the probability distribution of the daily driving mileage is as follows:
wherein t is c The method comprises the steps of accessing the electric automobile into the power grid; mu (mu) s Sum sigma s Respectively connecting the electric automobile with the mean value and standard deviation of the power grid time normal distribution function; t is t l The time when the electric automobile leaves the power grid; mu (mu) e Sum sigma e Respectively connecting the electric automobile with the mean value and standard deviation of the power grid time normal distribution function; s is the daily driving mileage of the electric automobile; mu (mu) d Sum sigma d The average value and the standard deviation of the daily driving mileage log-normal distribution function of the electric automobile are respectively.
3. The ordered charging game method of an electric vehicle according to claim 1, wherein the establishing an electric vehicle charging demand model is specifically as follows:
randomly generating the power grid access time, the power grid departure time and the daily driving mileage of the electric automobile by adopting a Monte Carlo simulation method;
calculating the state of charge of the ith electric automobile when the electric automobile is connected to a power grid according to the daily driving mileage of the electric automobile:
wherein S is s,i And S is e,i The expected value of the state of charge when the ith electric automobile is connected to the power grid and the state of charge when the ith electric automobile is disconnected from the power grid is respectively obtained; e, e d The power consumption is the unit mileage of the electric automobile; e (E) e Is the battery capacity; i=1, 2, …, N;
judging whether the ith electric automobile can meet the charging requirement in the charging time, if not, modifying the expected value of the charge state when leaving the power grid, or regenerating the time when the electric automobile leaves the power grid:
(S e,i -S s,i )E e,i ≤P e η(t l,i -t c,i )
wherein t is l,i And t c,i And (5) respectively accessing and leaving the power grid by the ith electric automobile.
4. The ordered charging game method of the electric automobile according to claim 1, wherein the method is characterized in that the real-time electricity price mechanism of the single-phase user of the power distribution network is established by considering the load three-phase unbalance degree and the load peak-valley difference, and specifically comprises the following steps:
and the real-time electricity price of the single-phase user is the sum of the basic electricity price and the excitation electricity price of the load unbalance degree and the excitation electricity price of the load peak clipping and valley filling.
5. The ordered charging gaming method of an electric vehicle of claim 4, wherein the real-time electricity prices of the single-phase users are:
in the method, in the process of the application,for distributing powerNetwork base electricity price; />Exciting electricity price for the load unbalance degree of the single-phase user; />Exciting electricity price for peak clipping and valley filling of the load of a single-phase user; x represents any one of the three phases;
the load unbalance degree excitation electricity price is as follows:
wherein q is A,t 、q B,t And q C,t The electricity consumption is A, B and the electricity consumption is C phase t period respectively; q av,t The three-phase average power consumption is t time periods; alpha is the electricity price excitation coefficient of the load with three-phase unbalance;
the load peak clipping and valley filling excitation electricity price is as follows:
wherein L is P And L V Respectively the expected values of peak load and valley load of the power distribution network; beta is the electricity price excitation coefficient of the load deviating from the expected value of the peak-to-valley load.
6. The method of in-order charging and gaming for an electric vehicle of claim 5, wherein the constraints of the in-order charging and dynamic gaming model for an electric vehicle comprise:
charging demand constraint:
control time constraint:
7. the method for ordered charging and gaming of electric vehicles according to claim 1, wherein the step of solving the dynamic ordered charging and gaming model of electric vehicles by using a CPLEX-based algorithm to obtain an ordered charging scheme of electric vehicles comprises:
s1: initializing model parameters of the electric vehicle ordered charging dynamic game model;
s2: taking the charging scheme of the electric vehicle under the disordered charging condition as a dynamic game initial state, namely starting charging when the electric vehicle is connected to a power grid, and stopping charging when the electric vehicle reaches a desired value of the state of charge when the electric vehicle leaves the power grid;
s3: the electric automobile ordered charging scheme of the kth wheel is obtained by solving through a CPLEX solver according to the electric automobile ordered charging scheme optimized by the k-1 wheel, wherein each phase of users independently carry out the electric automobile ordered charging scheme;
s4: judging whether the ordered charging scheme of the kth-wheel electric automobile reaches an equilibrium state, and if so:
the electric automobile charging scheme reaches an equilibrium state, the electric automobile ordered charging scheme of each phase of users is output, and if the electric automobile ordered charging scheme does not meet the requirement, the step S3 is returned;
in the method, in the process of the application,and->The charging cost of A, B, C phases after k-1 round optimization is respectively; /> And->The charging cost of A, B, C phases after k rounds of optimization is respectively; epsilon is an empirical threshold.
8. An electric automobile ordered charging gaming device, characterized by comprising:
the first model building unit is used for building an electric vehicle charging demand model according to probability distribution of the electric vehicle in-power grid time, the electric vehicle out-of-power grid time and the daily driving mileage;
the real-time electricity price mechanism building unit is used for building a real-time electricity price mechanism of a single-phase user of the power distribution network by taking the load three-phase unbalance degree and the load peak-valley difference into consideration;
the second model unit is used for establishing an electric vehicle ordered charging dynamic game model with single-phase users participating in the power distribution network according to the real-time electricity price mechanism of the single-phase users and the electric vehicle charging demand model, and the objective function of the electric vehicle ordered charging dynamic game model is as follows:
wherein C is X Representing the charging expense of the X-phase user; p is p X,t Real-time electricity price for single-phase user period t; u (u) X,i,t To characterize the 0-1 variable of the state of charge of the ith electric vehicle accessing the X phase, u when in state of charge X,i,t ＝1；P e The rated charging power, eta is the charging efficiency; Δt is the optimization time;
and the first solving unit is used for solving the electric vehicle ordered charging dynamic game model by adopting a CPLEX-based algorithm to obtain an electric vehicle ordered charging scheme.
9. The electric vehicle in-order charging gaming device of claim 8, wherein the first solving unit further comprises:
the initialization unit is used for initializing model parameters of the ordered charging dynamic game model of the electric automobile;
the starting unit is used for taking the charging scheme of the electric vehicle under the disordered charging condition as a dynamic game initial state, namely, starting charging when the electric vehicle is connected to a power grid, and stopping charging when the electric vehicle reaches a desired value of the state of charge when the electric vehicle leaves the power grid;
the second solving unit is used for solving the ordered charging scheme of the electric automobile of the kth wheel, wherein the ordered charging scheme of the electric automobile of the kth wheel is obtained by solving the ordered charging scheme of the electric automobile optimized according to the k-1 wheel through a CPLEX solver, and each phase of users independently carry out the ordered charging scheme of the electric automobile;
the balance judging unit is used for judging whether the ordered charging scheme of the kth-wheel electric automobile reaches an equilibrium state or not, and if yes:
the electric automobile charging scheme is in an equilibrium state, the electric automobile ordered charging scheme of each phase of users is output, and if the electric automobile ordered charging scheme is not met, the second solving unit is returned;
in the method, in the process of the application,and->The charging cost of A, B, C phases after k-1 round optimization is respectively; /> And->The charging cost of A, B, C phases after k rounds of optimization is respectively; epsilon is an empirical threshold.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010475429.1A CN111639303B (en) | 2020-05-29 | 2020-05-29 | Ordered charging game method and device for electric automobile |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010475429.1A CN111639303B (en) | 2020-05-29 | 2020-05-29 | Ordered charging game method and device for electric automobile |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111639303A CN111639303A (en) | 2020-09-08 |
| CN111639303B true CN111639303B (en) | 2023-09-15 |
Family
ID=72329344
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010475429.1A Active CN111639303B (en) | 2020-05-29 | 2020-05-29 | Ordered charging game method and device for electric automobile |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111639303B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114336706B (en) * | 2021-12-29 | 2023-11-14 | 杭州电子科技大学 | Ordered charge-discharge scheduling and response excitation method for electric vehicles with network master-slave games |
| CN114389345B (en) * | 2022-02-28 | 2024-08-30 | 上海交通大学 | Electric automobile energy exchange method considering battery loss cost |
| CN114649815B (en) * | 2022-04-08 | 2023-10-10 | 沈阳农业大学 | Flexible load demand response method and device based on three-phase load unbalance |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107745650A (en) * | 2017-10-26 | 2018-03-02 | 电子科技大学 | A kind of orderly charge control method of electric automobile based on Peak-valley TOU power price |
| CN110443415A (en) * | 2019-07-24 | 2019-11-12 | 三峡大学 | It is a kind of meter and dynamic electricity price strategy electric automobile charging station Multiobjective Optimal Operation method |
-
2020
- 2020-05-29 CN CN202010475429.1A patent/CN111639303B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107745650A (en) * | 2017-10-26 | 2018-03-02 | 电子科技大学 | A kind of orderly charge control method of electric automobile based on Peak-valley TOU power price |
| CN110443415A (en) * | 2019-07-24 | 2019-11-12 | 三峡大学 | It is a kind of meter and dynamic electricity price strategy electric automobile charging station Multiobjective Optimal Operation method |
Non-Patent Citations (2)
| Title |
|---|
| 基于动态博弈的配电网单相电力用户用电行为分析;石帮松 等;《电力系统自动化》;20170725;第41卷(第14期);第87-91、139页 * |
| 电动汽车充电站有序充电调度的分散式优化;程杉 等;《电力系统自动化》;20180110;第42卷(第1期);第39-46页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111639303A (en) | 2020-09-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111639303B (en) | Ordered charging game method and device for electric automobile | |
| CN108960510B (en) | Virtual power plant optimization trading strategy device based on two-stage random planning | |
| CN107294120B (en) | Active power distribution network hybrid energy storage capacity optimal configuration method and device | |
| CN113824111B (en) | Energy storage capacity configuration and scheduling method in optical energy storage scene | |
| Hossain et al. | Design a novel controller for stability analysis of microgrid by managing controllable load using load shaving and load shifting techniques; and optimizing cost analysis for energy storage system | |
| CN107492901B (en) | Distributed energy storage system real-time optimization method and device | |
| CN108512238B (en) | Two-stage optimal scheduling method for smart home based on demand side response | |
| CN114447959B (en) | Dynamic phase selection low-voltage park source storage coordination optimization control method | |
| CN110406422A (en) | A kind of meter and the electric bus battery of multi-party interests participate in V2G control method | |
| CN105896580B (en) | A kind of micro-capacitance sensor multiobjective optimization control method and device | |
| CN110932296B (en) | Energy storage control method and device and virtual power plant | |
| CN115471044A (en) | Power distribution station electric automobile scheduling method and system with light storage and storage medium | |
| CN116707016A (en) | Scene generation and reduction-based wind-solar-hydrogen storage day-ahead random optimization scheduling method | |
| CN117039954A (en) | Operation strategy making method and device for new energy station combined leasing shared energy storage | |
| CN116454920A (en) | Power distribution network frequency modulation method, device, equipment and storage medium | |
| CN112926762B (en) | Clearing method and device based on safety constraint unit combination model | |
| CN113555866A (en) | Method and device for electric vehicle mobile energy storage participation power grid optimization regulation and control | |
| CN113910962A (en) | Charging method, device and medium for charging pile | |
| CN111723993B (en) | Double-layer cooperative scheduling method, device, terminal and storage medium for power distribution network | |
| CN111934331A (en) | Electric automobile charging and discharging optimal scheduling method and device | |
| CN117154801B (en) | Method and device for determining energy storage configuration and output scheme of power grid system | |
| CN117791662B (en) | Hybrid energy storage capacity distribution method, system, electronic equipment and medium | |
| CN115848215B (en) | Electric vehicle V2G power dynamic programming method considering battery loss | |
| CN118003953B (en) | Control method and device for charging behavior in charging station | |
| CN116345477B (en) | Electric automobile load time sequence allocation method under electric quantity type demand response |
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 |