CN118144619A - Low-carbon emission charging method and device for electric automobile and storage medium - Google Patents
Low-carbon emission charging method and device for electric automobile and storage medium Download PDFInfo
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Abstract
The invention relates to a low-carbon emission charging method and device for an electric automobile and a storage medium, and relates to the field of electric automobile charging control. When the electric automobile is charged, the state and the charging requirement of the electric automobile are provided for the charging station; establishing an objective function that satisfies charging time and charging energy constraints and minimizes charging carbon emissions based on electric vehicle state and charging demand: ; predicting a hybrid grid area where a charging station is located for discrete periods of time Is of the expected carbon strength of (a); According toOptimizing in discrete time periodsCharging station pair electric automobileCharging power of (2)The objective function is minimized, and the low-carbon emission charging control of the electric automobile is realized.
Description
Technical Field
The invention relates to the technical field of electric vehicle charging control, in particular to a low-carbon emission charging method and device for an electric vehicle and a storage medium.
Background
In order to cope with the energy crisis and environmental problems caused by economic development, an electric automobile taking clean energy as a power source has the advantages of low energy consumption, low emission, low noise and the like, and becomes a main direction of development of the future automobile industry.
The carbon emission of the electric automobile is closely related to the power grid of a charging station used for charging the electric automobile. Along with the development of grid connection of green energy sources such as solar energy and wind energy, the contribution of the green energy sources to a power grid is larger and larger, and the contribution of the green energy sources is influenced by the green energy sources, such as the output of solar energy in the daytime, such as different contribution of wind energy generation in different seasons, the energy source structure of the power grid obviously fluctuates along with time and seasons; the carbon emission amount generated by the same electricity generated by different energy sources is different, namely, the carbon intensity corresponding to the electric energy is different along with the change of the energy source structure of the power grid, and the carbon emission during the use of the electric automobile is related to the carbon intensity of the power grid area where the charging station is located during the charging. Therefore, the charging strategy can be optimized to reduce the carbon emission of the electric automobile during the charging process.
Disclosure of Invention
In order to solve the above technical problems or at least partially solve the above technical problems, the present invention provides a low-carbon emission charging method, device and storage medium for an electric vehicle.
In a first aspect, the present invention provides a low-carbon emission charging method for an electric vehicle, including:
When the electric vehicle v arrives at the charging station for charging, the state and charging requirement of the electric vehicle are provided to the charging station, which is expressed as The state and the charging requirement comprise the remaining capacity/>, before charging, of the electric automobile vResidual electric quantity/>, required by electric automobile vCharging start time of electric automobile v/>Charging end time/>, of electric vehicle v；
Establishing an objective function that satisfies charging time and charging energy constraints and minimizes charging carbon emissions based on electric vehicle state and charging demand:
；
Representing an objective function, wherein/> Is the carbon emission coefficient,/>Is the SOC coefficient; /(I)Representing a discretized time interval; /(I)A collection representing electric vehicles charged at a charging station; /(I)Representing the discrete period/>, in the mixed grid area where the charging station is locatedCarbon strength of (2); /(I)Expressed in discrete time period/>Charging station pair electric automobile/>Is set to the charging power of (a); /(I)Representing electric automobile/>Current SOC after T period, by the time of the discrete period/>Charging station pair electric automobile/>Charging power of (2)Determining;
predicting a hybrid grid area where a charging station is located for discrete periods of time Is/are > the expected carbon strength ofAccording to/>To optimize the time period at discrete times/>Charging station pair electric automobile/>Charging power/>Minimizing the objective function.
Further, the power flow is tracked by utilizing a power flow tracking model of the hybrid power grid to fit the transfer process of carbon along with electricity in the hybrid power grid, and a hybrid power grid area where the charging station is located is established in a discrete periodThe relationship between the carbon intensity and the tide tracking model comprises:
In the tide tracking model, the energy supply node of the hybrid power grid is expressed as ; The energy consuming node is denoted/>，/>For transmission line losses in the power flow tracking model,Representing a power consumption node;
establishing an energy supply node output matrix according to energy supply node output Element/>, in energy supply node output matrixRepresenting nodes/>, as energy-providing nodesPair node/>Is provided with an energy supply force; building a load matrix/>, of energy consumption nodes, according to the energy consumption node load and the power transmission line loss; Creating a flow direction matrix/>, between energy consumption nodes and energy supply nodes; In the current flow matrix, if node/>And node/>Is switched on and has forward complex power flowing in，/>；
Calculating a power flow injection vector of the node according to the power flow direction matrix and the energy supply node output matrix，
Wherein,Represents other node pair node/>Complex power injection amount,/>Representing nodes/>Generating complex power injection quantity;
obtaining a power flow tracking model according to the power flow injection vector, wherein the power flow tracking model is expressed as Superscript/>Representing a transpose operation,/>Diagonal matrix of/>Generating a complex power injection amount matrix for each node, by/>Composition,/>For the complex power injection quantity matrix among all nodes, by/>Composition;
according to the power flow tracking model and the load matrix, obtaining the power provided by each energy supply node to the energy consumption node:
；
Each energy supply node pair energy consumption node Providing power to occupy the total energy supply node to the energy consumption node/>Providing the total power ratio as a contribution ratio,/>Elements in a load matrix which are energy consumption nodes;
the energy source type of each energy supply node is calculated to determine the real-time carbon intensity in the following calculation mode:
；
Wherein, Representing energy type,/>Representing energy supply nodes/>, in a hybrid power gridThe total amount of energy types used in the process of outputting; representing the time; /(I) Expressed at/>Moment of time, energy supply node/>Energy/>Power generation of electricity generation,/>Expressed at/>Moment of time, energy supply node/>The power generated by all energy sources; /(I)Expressed at/>Energy supply node/>, time of dayCarbon strength of/(I)Expressed at/>The carbon intensity of the e energy source used by the energy supply node s at the moment;
For the charging station, the carbon intensity of the power supply node for supplying power to the charging station is weighted by corresponding contribution proportion and summed to obtain the carbon intensity of the mixed power grid area where the charging station is located.
Further, when predicting the carbon intensity of the mixed power grid area where the charging station is located, obtaining the energy source structure variation expectation of the energy supply node according to the power grid operation plan, thereby predicting the carbon intensity of the energy supply node sAccording to the power grid operation planning, obtaining the power supply node output expectation, and using the expected power supply node output matrix/>A representation;
Acquiring a load expectation and obtaining a predicted load matrix based on the load expectation ；
Carbon intensity at the predictive energy supply node sExpected energy supply node output matrix/>And predictive load matrix/>Based on the above, predicting the discrete period/>, in the mixed power grid region where the charging station is located, by using the tide tracking modelCarbon strength/>。
Further, the load prediction neural network is used for predicting the load of the energy consumption node, and the load prediction neural network adopts an LSTM neural network.
Further, in the objective function, discretizedBased on electric automobile/>Residual quantity before charging/>Iteration:
，/>；
Wherein, Representing electric automobile/>Battery capacity of/>Representing each discrete interval of battery charge delta,，/>Indicating the charging efficiency.
Further, in the objective function, in discrete time periodsCharging station pair electric automobile/>Satisfies charging power of charging station for electric automobile/>Maximum charging power constraint of (2): /(I)Wherein/>Indicating charging station for electric vehicle/>Is set, the maximum charging power of (a) is set.
Further, in the objective function, the electric vehicleCurrent remaining power after T period/>Meets the requirements of electric automobiles/>Maximum remaining capacity constraint of (2): /(I)Wherein/>Is an electric automobileMaximum remaining power of (a).
Further, in the objective function, all electric vehicles charged at the charging stationAnd satisfies the charging station maximum charging power constraint: /(I)Wherein/>Maximum charging power for the charging station.
In a second aspect, the present invention provides an apparatus for implementing a low carbon emission charging method of an electric vehicle, including: the processing unit is connected with the storage unit through the bus unit, the storage unit stores a computer program, and the low-carbon emission charging method of the electric automobile is realized when the computer program is executed by the processing unit.
In a third aspect, the present invention provides a computer readable storage medium storing a computer program which, when executed by a processor, implements a low carbon emission charging method for an electric vehicle as described.
Compared with the prior art, the technical scheme provided by the embodiment of the invention has the following advantages:
establishing an objective function that satisfies charging time and charging energy constraints and minimizes charging carbon emissions based on electric vehicle state and charging demand:
；
In the objective function, discretizing Based on electric automobile/>Residual capacity before chargingIteration:
，/>；
Wherein, Representing electric automobile/>Battery capacity of/>Representing each discrete interval of battery charge delta,，/>Indicating the charging efficiency.
Thus, the objective function is in discrete time periodsCharging station pair electric automobile/>Charging power/>And the mixed power grid area where the charging station is located in discrete time period/>Is a function of carbon strength. The application aims at minimizing carbon emission and achieving the objective function/>, under the condition of meeting the charging requirementThe difference between the charging results and the charging demands of the charging station on all electric vehicles is represented, the smaller the difference is, the more the charging station meets the charging demands of users, and the target function is usedRepresenting the product of the carbon intensity and the charging energy, i.e. the carbon emission, when the charging station charges all electric vehicles, then, with the aim of minimizing the objective function, in discrete periods/>, according to the predicted hybrid grid region in which the charging station is locatedThe charging power of the charging station to all electric vehicles is controlled by the expected carbon intensity of the charging station, and the optimal carbon emission target can be achieved.
The application predicts the mixed power grid area where the charging station is located in a discrete periodWhen the expected carbon intensity of the charging station is calculated, a tide tracing model is introduced to track the transmission of carbon emission related electric energy in the power grid, so that the mixed power grid area where the charging station is predicted to be positioned is accurately predicted to be in a discrete period/>Is a combination of the carbon strength and the expected carbon strength of the steel.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, and it will be obvious to a person skilled in the art that other drawings can be obtained from these drawings without inventive effort.
Fig. 1 is a flowchart of a low-carbon emission charging method of an electric vehicle according to an embodiment of the present invention;
Fig. 2 illustrates a hybrid power grid area in which a charging station is established in a discrete period according to an embodiment of the present invention A flow chart of the carbon intensity and trend tracking model relationship;
fig. 3 illustrates a predicted charging station location hybrid grid area in discrete time periods according to an embodiment of the present invention A flow chart of carbon intensity of (c);
Fig. 4 is a schematic diagram of a device for implementing a low-carbon emission charging method of an electric vehicle according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Example 1
As shown in fig. 1, the technology of the present invention realizes a low-carbon emission charging method for an electric vehicle, including:
When the electric vehicle v arrives at the charging station for charging, the state and charging requirement of the electric vehicle are provided to the charging station, which is expressed as The states and charging requirements include: residual electric quantity/>, before charging of electric automobile vResidual electric quantity/>, required by electric automobile vCharging start time of electric automobile v/>Charging end time/>, of electric vehicle v. In the implementation process, a user specifies the remaining capacity/>, required by the electric vehicle v, in the charging APP of the charging stationDesignating charge start time/>, of electric vehicle vSpecify the charge end time/>, of electric vehicle vCharging APP obtains residual electric quantity/> of electric automobile v before charging from vehicleAnd providing the state of the electric vehicle and the charging demand to the charging station through the server. Residual electric quantity/>, which can meet the v requirement of the electric automobile, for ensuring the charging timeThe charging APP indicates a reference time length according to the lowest charging power of the charging station to the electric automobile v and is used for designating charging start time/> of the electric automobile v according to the reference time lengthSpecify the charge end time/>, of electric vehicle v。
The server or the upper computer controlling the charging station to operate establishes an objective function which meets the charging time and the charging energy constraint and minimizes the charging carbon emission based on the state of the electric automobile and the charging demand:
；
representing an objective function expressed in terms of discretized carbon intensity, charge power, current SOC, and demand SOC, wherein/> Is the carbon emission coefficient,/>Is the SOC coefficient; /(I)Representing a discretized time interval; /(I)A collection representing electric vehicles charged at a charging station; /(I)Representing the discrete period/>, in the mixed grid area where the charging station is locatedCarbon strength of (2); /(I)Expressed in discrete time period/>Charging station pair electric automobile/>Is set to the charging power of (a); /(I)Representing electric automobile/>Current SOC after T period, by the time of the discrete period/>Charging station pair electric automobile/>Charging power/>Determining;
In the objective function, discretizing Based on electric automobile/>/>, Before chargingIteration:
，/>；
Wherein, Representing electric automobile/>Battery capacity of/>Representing each discrete interval of battery charge delta,，/>Indicating the charging efficiency.
Thus, the objective function is in discrete time periodsCharging station pair electric automobile/>Charging power/>And the mixed power grid area where the charging station is located in discrete time period/>Is a function of carbon strength. The application aims at minimizing carbon emission and achieving the objective function/>, under the condition of meeting the charging requirementThe difference between the charging results and the charging demands of the charging station on all electric vehicles is represented, the smaller the difference is, the more the charging station meets the charging demands of users, and the target function is usedRepresenting the product of the carbon intensity and the charging energy, i.e. the carbon emission, when the charging station charges all electric vehicles, then, with the aim of minimizing the objective function, in discrete periods/>, according to the hybrid network area in which the charging station is locatedThe charging power of the charging station to all electric vehicles is controlled by the carbon intensity of the charging station, and the optimal carbon emission target can be achieved.
In the objective function, in discrete time periodsCharging station pair electric automobile/>Satisfies charging power of charging station for electric automobile/>Maximum charging power constraint of (2): /(I)Wherein/>Indicating charging station for electric vehicle/>Is set, the maximum charging power of (a) is set.
In the objective function, an electric automobileCurrent remaining power after T period/>Meets the requirement of an electric automobileMaximum remaining capacity constraint of (2): /(I)Wherein/>Is an electric automobile/>Maximum remaining power of (a).
In the objective function, all electric vehicles charged at the charging stationAnd satisfies the charging station maximum charging power constraint: /(I)Wherein/>Maximum charging power for the charging station.
Predicting a hybrid grid area where a charging station is located for discrete periods of timeIs/are > the expected carbon strength ofAccording to/>To optimize the time period at discrete times/>Charging station pair electric automobile/>Charging power/>Minimizing the objective function.
In order to accurately determine the hybrid power grid area in which the charging station is located in discrete time periodsIs/are > the expected carbon strength ofAccording to the method, the power flow is tracked by utilizing the power flow tracking model of the hybrid power grid to fit the transfer process of carbon along with electricity in the hybrid power grid, and the discrete period/>, of the hybrid power grid area where the charging station is located, is establishedAs shown in fig. 2, includes:
In the tide tracking model, the energy supply node of the hybrid power grid is expressed as ; The energy consuming node is denoted/>，/>For transmission line losses in the power flow tracking model,Representing a power consumption node; the tide tracking model unifies transmission line loss and electricity consumption nodes into energy consumption nodes.
Establishing an energy supply node output matrix according to energy supply node outputElement/>, in energy supply node output matrixRepresenting nodes/>, as energy-providing nodesPair node/>Is provided with an energy supply force; building a load matrix/>, of energy consumption nodes, according to the energy consumption node load and the power transmission line loss; Creating a flow direction matrix/>, between energy consumption nodes and energy supply nodesIn the flow matrix, if node/>And node/>Is switched on and has forward complex power flowing in，/>；
Calculating a power flow injection vector of the node according to the power flow direction matrix and the energy supply node output matrix，
Wherein,Represents other node pair node/>Complex power injection amount,/>Representing nodes/>Generating complex power injection quantity;
obtaining a power flow tracking model according to the power flow injection vector, wherein the power flow tracking model is expressed as Superscript/>Representing a transpose operation,/>Diagonal matrix of/>Generating a complex power injection amount matrix for each node, by/>Composition,/>For the complex power injection quantity matrix among all nodes, by/>Composition;
according to the power flow tracking model and the load matrix, obtaining the power provided by each energy supply node to the energy consumption node:
；
Each energy supply node pair energy consumption node Providing power to occupy the total energy supply node to the energy consumption node/>Providing the total power ratio as a contribution ratio,/>Elements in a load matrix which are energy consumption nodes;
and calculating the real-time carbon intensity according to the energy structure of each energy supply node, wherein the real-time carbon intensity is as follows:
；
Wherein, Representing energy type,/>Representing energy supply nodes/>, in a hybrid power gridThe total amount of energy types used in the process of outputting; representing the time; /(I) Expressed at/>Moment of time, energy supply node/>Energy/>Power generation of electricity generation,/>Expressed at/>Moment of time, energy supply node/>The power generated by all energy sources; /(I)Expressed at/>Carbon intensity of energy supply node s at moment of time,/>Expressed at/>The carbon intensity of the e energy source used by the energy supply node s at the moment;
For the charging station, the carbon intensity of the power supply node for supplying power to the charging station is weighted by corresponding contribution proportion and summed to obtain the carbon intensity of the mixed power grid area where the charging station is located.
As shown in fig. 3, when predicting the carbon intensity of the hybrid power grid area where the charging station is located, the energy source structure variation expectation of the energy supply node is obtained according to the power grid operation plan, so as to predict the carbon intensity of the energy supply node sAccording to the power grid operation planning, obtaining the power supply node output expectation, and using the expected power supply node output matrix/>And (3) representing.
Acquiring a load expectation and obtaining a predicted load matrix based on the load expectation; In particular implementations, one example employs a load prediction neural network that employs an LSTM neural network to predict node loads.
Carbon intensity at the predictive energy supply node sExpected energy supply node output matrix/>And predictive load matrix/>Based on the above, predicting the discrete period/>, in the mixed power grid region where the charging station is located, by using the tide tracking modelCarbon strength/>。
Example 2
Referring to fig. 4, an embodiment of the present invention provides an apparatus for implementing a low carbon emission charging method of an electric vehicle, including: the processing unit is connected with the storage unit through the bus unit, and the storage unit is used as a computer readable storage medium and can be used for storing software programs, computer executable programs and modules, such as the software programs, the computer executable programs and the modules corresponding to the low-carbon emission charging method of the electric automobile. The processing unit executes a software program, a computer executable program and a module stored in the storage unit, so as to realize the low-carbon emission charging method of the electric automobile, and the method comprises the following steps:
When the electric vehicle v arrives at the charging station for charging, the state and charging requirement of the electric vehicle are provided to the charging station, which is expressed as The state and the charging requirement comprise the remaining capacity/>, before charging, of the electric automobile vResidual electric quantity/>, required by electric automobile vCharging start time of electric automobile v/>Charging end time/>, of electric vehicle v；
Establishing an objective function that satisfies charging time and charging energy constraints and minimizes charging carbon emissions based on electric vehicle state and charging demand:
；
representing an objective function by discretized carbon intensity, charging power, current residual capacity and required residual capacity, wherein/> Is the carbon emission coefficient,/>Is the residual electric quantity coefficient; /(I)Representing a discretized time interval; /(I)A collection representing electric vehicles charged at a charging station; /(I)Representing the discrete period/>, in the mixed grid area where the charging station is locatedCarbon strength of (2); Expressed in discrete time period/> Charging station pair electric automobile/>Is set to the charging power of (a); /(I)Representing electric automobile/>The current residual capacity after the T period is calculated by the time interval/>Charging station pair electric automobile/>Charging power/>Determining, T is the charging start time/>, of the electric vehicle vCharging end time/>, of electric vehicle vIs a constraint of (2);
predicting a hybrid grid area where a charging station is located for discrete periods of time Is/are > the expected carbon strength ofAccording to/>Optimizing at discrete time period/>Charging station pair electric automobile/>Charging power/>Minimizing the objective function.
Of course, the storage unit in the device for implementing the low-carbon emission charging method of the electric vehicle according to the embodiment of the present invention is not limited to the above-mentioned method operation, and the related operations in the low-carbon emission charging method of the electric vehicle according to any embodiment of the present invention may also be performed.
Example 3
An embodiment of the present invention provides a computer readable storage medium storing a computer program, which when executed, implements the low-carbon emission charging method of an electric vehicle, including:
When the electric vehicle v arrives at the charging station for charging, the state and charging requirement of the electric vehicle are provided to the charging station, which is expressed as The state and the charging requirement comprise the remaining capacity/>, before charging, of the electric automobile vResidual electric quantity/>, required by electric automobile vCharging start time of electric automobile v/>Charging end time/>, of electric vehicle v；
Establishing an objective function that satisfies charging time and charging energy constraints and minimizes charging carbon emissions based on electric vehicle state and charging demand:
；
representing an objective function by discretized carbon intensity, charging power, current residual capacity and required residual capacity, wherein/> Is the carbon emission coefficient,/>Is the residual electric quantity coefficient; /(I)Representing a discretized time interval; /(I)A collection representing electric vehicles charged at a charging station; /(I)Representing the discrete period/>, in the mixed grid area where the charging station is locatedCarbon strength of (2); Expressed in discrete time period/> Charging station pair electric automobile/>Is set to the charging power of (a); /(I)Representing electric automobile/>The current residual capacity after the T period is calculated by the time interval/>Charging station pair electric automobile/>Charging power/>Determining, T is the charging start time/>, of the electric vehicle vCharging end time/>, of electric vehicle vIs a constraint of (2);
predicting a hybrid grid area where a charging station is located for discrete periods of time Is/are > the expected carbon strength ofAccording to/>Optimizing at discrete time period/>Charging station pair electric automobile/>Charging power/>Minimizing the objective function.
The computer readable storage medium according to the embodiment of the present invention stores a computer program not limited to the method operations described above, but also can perform the related operations in the low-carbon emission charging method of an electric vehicle according to any embodiment of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed structures and methods may be implemented in other manners. For example, the structural embodiments described above are merely illustrative, and for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, for example, 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 interfaces, structures or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A low carbon emission charging method for an electric vehicle, comprising:
when the electric automobile v arrives at the charging station for charging, the state and the charging requirement of the electric automobile are provided for the charging station, and the state and the charging requirement comprise the residual electric quantity before the electric automobile v is charged Residual electric quantity of electric automobile v demandCharging start time of electric automobile v/>Charging end time/>, of electric vehicle v；
Establishing an objective function that satisfies charging time and charging energy constraints and minimizes charging carbon emissions based on electric vehicle state and charging demand:
；
Representing an objective function, wherein/> Is the carbon emission coefficient,/>Is the residual electric quantity coefficient; /(I)Representing a discretized time interval; /(I)A collection representing electric vehicles charged at a charging station; /(I)Representing the discrete period/>, in the mixed grid area where the charging station is locatedCarbon strength of (2); /(I)Expressed in discrete time period/>Charging station pair electric automobile/>Is set to the charging power of (a); Representing electric automobile/> The current residual capacity after the T period is calculated by the time interval/>Charging station pair electric automobile/>Charging power/>Determining;
predicting a hybrid grid area where a charging station is located for discrete periods of time Is/are > the expected carbon strength ofAccording to/>To optimize the time period at discrete times/>Charging station pair electric automobile/>Charging power/>Minimizing the objective function.
2. The method for charging electric vehicle with low carbon emission according to claim 1, wherein the power flow is tracked by using a power flow tracking model of the hybrid power grid to fit the transfer process of carbon with electricity in the hybrid power grid, and the hybrid power grid area where the charging station is located is established in a discrete periodThe relationship between the carbon intensity and the tide tracking model comprises:
In the tide tracking model, the energy supply node of the hybrid power grid is expressed as ; The energy consumption node is expressed as，/>For transmission line losses in the power flow tracking model,Representing a power consumption node;
establishing an energy supply node output matrix according to energy supply node output Element in energy supply node output matrixRepresenting nodes/>, as energy-providing nodesPair node/>Is provided with an energy supply force; building a load matrix/>, of energy consumption nodes, according to the energy consumption node load and the power transmission line loss; Creating a flow direction matrix/>, between energy consumption nodes and energy supply nodesIn the flow matrix, if node/>And node/>Is switched on and has forward complex power flowing in，/>；
Calculating a power flow injection vector of the node according to the power flow direction matrix and the energy supply node output matrix，
Wherein,Represents other node pair node/>Complex power injection amount,/>Representing nodes/>Generating complex power injection quantity;
obtaining a power flow tracking model according to the power flow injection vector, wherein the power flow tracking model is expressed as Superscript/>Representing a transpose operation,/>Diagonal matrix of/>Generating a complex power injection amount matrix for each node, by/>Composition,/>For the complex power injection quantity matrix among all nodes, by/>Composition;
according to the power flow tracking model and the load matrix, obtaining the power provided by each energy supply node to the energy consumption node:
；
Each energy supply node pair energy consumption node Providing power to occupy the total energy supply node to the energy consumption node/>Providing the total power ratio as a contribution ratio,/>Elements in a load matrix which are energy consumption nodes;
and calculating the real-time carbon intensity according to the energy structure of each energy supply node, wherein the real-time carbon intensity is as follows:
；
Wherein, Representing energy type,/>Representing energy supply nodes/>, in a hybrid power gridThe total amount of energy types used in the process of outputting; /(I)Representing the time; /(I)Expressed at/>Moment of time, energy supply node/>Energy/>Power generation of electricity generation,/>Is shown inMoment of time, energy supply node/>The power generated by all energy sources; /(I)Expressed at/>Carbon intensity of energy supply node s at moment of time,/>Expressed at/>The carbon intensity of the e energy source used by the energy supply node s at the moment;
For the charging station, the carbon intensity of the power supply node for supplying power to the charging station is weighted by corresponding contribution proportion and summed to obtain the carbon intensity of the mixed power grid area where the charging station is located.
3. The method for charging electric vehicle with low carbon emissions according to claim 2, wherein when predicting the carbon intensity of the hybrid grid region where the charging station is located, the energy source structure variation expectation of the energy supply node is obtained according to the grid operation plan, so as to predict the carbon intensity of the energy supply node sAccording to the power grid operation planning, obtaining the power supply node output expectation, and using the expected power supply node output matrix/>A representation;
Acquiring a load expectation and obtaining a predicted load matrix based on the load expectation ；
Carbon intensity at the predictive energy supply node sExpected energy supply node output matrix/>And predictive load matrix/>Based on the above, predicting the discrete period/>, in the mixed power grid region where the charging station is located, by using the tide tracking modelIs/are > the expected carbon strength of。
4. The method for charging low-carbon emissions of an electric vehicle according to claim 3, wherein the node load is predicted using a load prediction neural network, the load prediction neural network employing an LSTM neural network.
5. The method for low-carbon emission charging of an electric vehicle according to claim 1, wherein in the objective function, discretization is performedBased on electric automobile/>Residual quantity before charging/>Iteration:
，/>；
Wherein, Representing electric automobile/>Battery capacity of/>Representing each discrete interval of battery charge delta,，/>Indicating the charging efficiency.
6. The method of claim 1, wherein in the objective function, at discrete time intervalsCharging station pair electric automobile/>Satisfies charging power of charging station for electric automobile/>Maximum charging power constraint of (2): /(I)Wherein/>Indicating charging station for electric vehicle/>Is set, the maximum charging power of (a) is set.
7. The method for charging low-carbon emissions of an electric vehicle according to claim 1, wherein, in the objective function, the electric vehicleCurrent remaining power after T period/>Meets the requirements of electric automobiles/>Maximum remaining capacity constraint of (2): Wherein/> Is an electric automobile/>Maximum remaining power of (a).
8. The method for low carbon emission charging of electric vehicles according to claim 1, wherein in the objective function, all electric vehicles charged at a charging stationAnd satisfies the charging station maximum charging power constraint: Wherein/> Maximum charging power for the charging station.
9. An apparatus for implementing a low carbon emission charging method of an electric vehicle, comprising: at least one processing unit, the processing unit is connected with the storage unit through the bus unit, the storage unit stores a computer program, and when the computer program is executed by the processing unit, the low-carbon emission charging method of the electric automobile according to any one of claims 1-8 is realized.
10. A computer readable storage medium storing a computer program, which when executed by a processor, implements the low carbon emission charging method of an electric vehicle according to any one of claims 1-8.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111682538A (en) * | 2020-06-29 | 2020-09-18 | 华北电力大学 | Charging demand management method and system considering space-time characteristics |
| CN115511685A (en) * | 2022-10-26 | 2022-12-23 | 国网浙江电动汽车服务有限公司 | Carbon dioxide emission reduction determination method, device, equipment and medium |
| CN116505510A (en) * | 2023-03-24 | 2023-07-28 | 国网四川省电力公司 | Space-time pricing strategy-based cluster charging load double-layer optimal scheduling method |
| CN117254529A (en) * | 2023-09-19 | 2023-12-19 | 国网河北省电力有限公司石家庄供电分公司 | Power distribution network real-time scheduling method and system considering carbon emission and uncertainty |
| WO2024055545A1 (en) * | 2022-09-15 | 2024-03-21 | 广东邦普循环科技有限公司 | Automobile power battery management and control method and system |
-
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111682538A (en) * | 2020-06-29 | 2020-09-18 | 华北电力大学 | Charging demand management method and system considering space-time characteristics |
| WO2024055545A1 (en) * | 2022-09-15 | 2024-03-21 | 广东邦普循环科技有限公司 | Automobile power battery management and control method and system |
| CN115511685A (en) * | 2022-10-26 | 2022-12-23 | 国网浙江电动汽车服务有限公司 | Carbon dioxide emission reduction determination method, device, equipment and medium |
| CN116505510A (en) * | 2023-03-24 | 2023-07-28 | 国网四川省电力公司 | Space-time pricing strategy-based cluster charging load double-layer optimal scheduling method |
| CN117254529A (en) * | 2023-09-19 | 2023-12-19 | 国网河北省电力有限公司石家庄供电分公司 | Power distribution network real-time scheduling method and system considering carbon emission and uncertainty |
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