CN115800346A - Electric vehicle charging and discharging control method and device and computer equipment - Google Patents
Electric vehicle charging and discharging control method and device and computer equipment Download PDFInfo
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Abstract
The application relates to a method and a device for controlling charging and discharging of an electric automobile, a computer device, a storage medium and a computer program product. The method comprises the following steps: acquiring electrical parameters of a micro-grid where an accessed electric vehicle is located; inputting the electrical parameters into a charge-discharge optimization model of the electric automobile to obtain a model output result; the model output result is used for representing the charging and discharging power of the corresponding electric automobile when the fluctuation parameter and the loss parameter of the microgrid meet the preset conditions; and controlling the charging and discharging of the electric automobile according to the model output result. By adopting the method, the charging and discharging of the electric automobile in the microgrid can be immediately regulated, the charging and discharging power of the electric automobile is adjusted, and the stable operation of the microgrid is guaranteed.
Description
Technical Field
The present application relates to the field of electric energy distribution technologies, and in particular, to a method and an apparatus for controlling charging and discharging of an electric vehicle, a computer device, a computer-readable storage medium, and a computer program product.
Background
Along with the development of modern electric energy use technology, the electric automobile that uses the electric energy to start appears in people's the field of vision gradually, and electric automobile uses the clean environmental protection of energy, fills electric pile also convenient safety, has become one of the vehicles that people extensively adopted. Usually, people do not let to fill electric pile idle for make full use of fill electric pile and electric automobile's resource, after obtaining electric automobile owner's permission, can regard as temporary power with the electric automobile who fills the electric pile and be connected.
In order to solve the contradiction between the electric vehicle as a renewable distributed power supply and a power grid, a micro power grid system is generated, namely a micro power grid. The electric automobile is connected with the microgrid, can be loaded and also can be stored, electric energy is obtained when the electric automobile is loaded, and the electric automobile is used as a power supply to replace a power station to supply power to other electric automobiles in the microgrid when the electric automobile is stored, so that the reduction of indirect carbon emission is facilitated. However, a scheme for optimally controlling charging and discharging of the electric vehicle in a microgrid is still lacked at present.
Disclosure of Invention
In view of the above, it is necessary to provide a method, an apparatus, a computer device, a computer readable storage medium, and a computer program product for controlling charging and discharging of an electric vehicle, which can adjust charging and discharging power of the electric vehicle.
In a first aspect, the present application provides a charge and discharge control method for an electric vehicle, including:
acquiring electrical parameters of a micro-grid where an accessed electric vehicle is located;
inputting the electrical parameters into a charge-discharge optimization model of the electric automobile to obtain a model output result; the model output result is used for representing the charging and discharging power of the corresponding electric automobile when the fluctuation parameter and the loss parameter of the microgrid meet preset conditions;
and controlling the charging and discharging of the electric automobile according to the model output result.
In one embodiment, the process for establishing the charge-discharge optimization model of the electric vehicle includes:
obtaining a loss function based on the structural parameters of the microgrid;
according to the power of the micro-grid in different working modes, a fluctuation function of the micro-grid is established;
and obtaining a charge-discharge optimization model of the electric automobile according to the loss function and the fluctuation function.
In one embodiment, the obtaining of the charge and discharge optimization model of the electric vehicle according to the loss function and the fluctuation function includes:
normalizing the loss function and the fluctuation function;
and performing linear weighting processing on the loss function and the fluctuation function after the normalization processing to obtain a charging and discharging optimization model of the electric automobile.
In one embodiment, the performing linear weighting processing on the loss function and the fluctuation function after the normalization processing to obtain the electric vehicle charge and discharge optimization model includes:
performing linear weighting processing on the loss function and the fluctuation function after the normalization processing to obtain an evaluation function;
and taking the expression corresponding to the minimum output value of the evaluation function as the electric vehicle charging and discharging optimization model.
In one embodiment, the inputting the electrical parameters into a charge and discharge optimization model of the electric vehicle to obtain a model output result includes:
and inputting the electrical parameters into a charge-discharge optimization model of the electric automobile, and obtaining a model output result when the electrical parameters meet a preset power grid constraint condition.
In one embodiment, the inputting the electrical parameter into the electric vehicle charge-discharge optimization model, and obtaining a model output result when the electrical parameter meets a preset grid constraint condition includes:
and inputting the electrical parameters into an electric vehicle charge-discharge optimization model, and obtaining a model output result when the electrical parameters meet a preset power grid constraint condition and the accessed electric vehicle parameters meet a vehicle constraint condition.
In a second aspect, the present application further provides a charge and discharge control device for an electric vehicle, the device including:
the data acquisition module is used for acquiring the electrical parameters of the microgrid where the accessed electric vehicle is located;
the model operation module is used for inputting the electrical parameters to a charge-discharge optimization model of the electric automobile to obtain a model output result; the model output result is used for representing the charging and discharging power of the corresponding electric automobile when the fluctuation parameter and the loss parameter of the microgrid meet preset conditions;
and the adjusting control module is used for controlling charging and discharging of the electric automobile according to the model output result.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the following steps when executing the computer program:
acquiring electrical parameters of a micro-grid where an accessed electric vehicle is located;
inputting the electrical parameters into a charge-discharge optimization model of the electric automobile to obtain a model output result; the model output result is used for representing the charging and discharging power of the corresponding electric automobile when the fluctuation parameter and the loss parameter of the microgrid meet preset conditions;
and controlling charging and discharging of the electric automobile according to the model output result.
In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of:
acquiring electrical parameters of a microgrid where an accessed electric vehicle is located;
inputting the electrical parameters into a charge-discharge optimization model of the electric automobile to obtain a model output result; the model output result is used for representing the charging and discharging power of the corresponding electric automobile when the fluctuation parameter and the loss parameter of the microgrid meet preset conditions;
and controlling charging and discharging of the electric automobile according to the model output result.
In a fifth aspect, the present application further provides a computer program product. The computer program product comprising a computer program which when executed by a processor performs the steps of:
acquiring electrical parameters of a microgrid where an accessed electric vehicle is located;
inputting the electrical parameters into a charge-discharge optimization model of the electric automobile to obtain a model output result; the model output result is used for representing the charging and discharging power of the corresponding electric automobile when the fluctuation parameter and the loss parameter of the microgrid meet preset conditions;
and controlling charging and discharging of the electric automobile according to the model output result.
According to the electric automobile charge and discharge control method, the electric automobile charge and discharge control device, the computer equipment, the computer readable storage medium and the computer program product, the model output result is obtained by establishing the electric automobile charge and discharge optimization model and inputting the electrical parameters of the microgrid where the electric automobile is located into the electric automobile charge and discharge optimization model. And when the fluctuation parameter and the loss parameter of the micro-grid are represented by the model output result and meet the preset conditions, the corresponding charge-discharge power of the electric automobile is obtained, so that the fluctuation parameter and the loss parameter of the micro-grid meet the preset conditions when the charge-discharge of the electric automobile is controlled subsequently according to the charge-discharge power output by the model. The charging and discharging of the electric automobile in the microgrid can be immediately adjusted, the charging and discharging power of the electric automobile can be adjusted, and the stable operation of the microgrid can be guaranteed.
Drawings
FIG. 1 is a diagram illustrating an exemplary embodiment of a charging/discharging control method for an electric vehicle;
FIG. 2 is a schematic flow chart illustrating a method for controlling charging and discharging of an electric vehicle according to an embodiment;
FIG. 3 is a schematic flow chart illustrating a process of building a charging/discharging optimization model of an electric vehicle according to an embodiment;
FIG. 4 is a schematic flow chart illustrating steps of obtaining a charge-discharge optimization model of an electric vehicle according to a loss function and a fluctuation function in one embodiment;
FIG. 5 is a schematic flow chart illustrating steps of performing linear weighting on the normalized loss function and the normalized fluctuation function to obtain an electric vehicle charge-discharge optimization model in one embodiment;
FIG. 6 is a schematic flow chart illustrating a method for controlling charging and discharging of an electric vehicle according to another embodiment;
FIG. 7 is a schematic flow chart illustrating a method for controlling charging and discharging of an electric vehicle according to yet another embodiment;
FIG. 8 is a block diagram showing the structure of a charge/discharge control device for an electric vehicle according to an embodiment;
FIG. 9 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description of the present application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.
It is to be understood that "connection" in the following embodiments is to be understood as "electrical connection", "communication connection", and the like if the connected circuits, modules, units, and the like have communication of electrical signals or data with each other.
As used herein, the singular forms "a", "an" and "the" may include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises/comprising," "includes" or "including," etc., specify the presence of stated features, integers, steps, operations, components, parts, or combinations thereof, but do not preclude the presence or addition of one or more other features, integers, steps, operations, components, parts, or combinations thereof.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.
The electric vehicle charging and discharging control method provided by the embodiment of the application can be applied to an application environment shown in fig. 1. The electric vehicle 100 is a specific electric vehicle, and in this embodiment, is a private electric vehicle. In the microgrid 102, there may be a plurality of electric vehicles connected, and the microgrid 102 and the electric Vehicle 100 may generally perform V2G interaction (short for Vehicle-to-grid, namely, vehicle grid interaction). When the electric automobile 100 is accessed to the microgrid 102 by a user, the electric automobile is connected with the charging pile, the charging pile is connected with the controller 104, the controller 104 is electrically connected with the storage battery 106 and the power station 108 in the microgrid 102, and the storage battery 106 or the power station 108 supplies power to the electric automobile 100. After obtaining the permission of the user of the electric vehicle 100, the electric vehicle 100 may serve as an energy storage terminal to supply power to other electric vehicles or devices in the micro grid.
The controller 104 stores an electric vehicle charge and discharge optimization model, and the electric vehicle charge and discharge optimization model obtains charge and discharge power of the electric vehicle 100 according to the electrical parameters of the microgrid and is used for controlling charge and discharge of the electric vehicle 100. Specifically, the controller 104 obtains an electrical parameter of the microgrid where the accessed electric vehicle 100 is located, obtains a charge and discharge power of the electric vehicle 100 according to the electrical parameter, and the controller 104 controls the charge and discharge of the electric vehicle 100 according to the charge and discharge power, wherein the charge and discharge power enables a fluctuation parameter and a loss parameter of the microgrid 102 to meet preset conditions. During charging and discharging control, the controller 104 may control the electric vehicle 100 as a load side to perform charging control, and the power station 108 or the storage battery 106 in the microgrid 100, or even another electric vehicle connected to the microgrid 100 and participating in charging and discharging control of the electric vehicle, supplies power to the electric vehicle 100. The controller 104 may also control the electric vehicle 100 to discharge as an energy storage terminal to supply power to the battery 106 or other electric vehicles connected to the microgrid 102. The controller 104 may be, but is not limited to, various personal computers, notebook computers, and tablet computers. The storage battery 106 and the power generation station 108 in the microgrid 102 may be but are not limited to one, and the storage battery 106 may be a lead-acid battery or a lithium battery, for example. The power plant 108 may be a photovoltaic power plant.
In one embodiment, as shown in fig. 2, a method for controlling charging and discharging of an electric vehicle is provided, which is described by taking the method as an example applied to the controller 104 in fig. 1, and includes the following steps:
and 202, acquiring the electrical parameters of the micro-grid where the accessed electric automobile is located.
The electric automobile is driven by electric energy, takes a vehicle-mounted power supply as power, and drives one of transportation vehicles with wheels driven by a motor. When the electric automobile is connected into the charging pile, the electric automobile enters the microgrid. The micro-grid is a small-sized power generation and distribution system formed by converging devices such as a distributed power supply, an energy storage device or an energy conversion device and the like, is an autonomous system capable of realizing self control, protection and management, and can be operated in a grid-connected mode with an external power grid or in an isolated mode. The micro-grid refers to an internal small power circulation network established between the charging pile, the electric automobile connected to the charging pile, and the power station and the storage battery electrically connected with the charging pile. The electrical parameters are various parameter data related to the electric energy in the microgrid.
Specifically, the controller is electrically connected with each device in the microgrid, and can acquire various data in the microgrid, including electrical parameters. When the controller needs to perform charging and discharging control on the accessed electric automobile, the controller can acquire the electrical parameters of the microgrid where the accessed electric automobile is located.
Illustratively, the electrical parameters include a power related parameter of the microgrid, a branch related voltage parameter, a power related parameter of the accessed electric vehicle, a power flow change related parameter of the microgrid caused by accessing the electric vehicle, and the like.
And 204, inputting the electrical parameters into the electric vehicle charge-discharge optimization model to obtain a model output result.
And when the fluctuation parameter and the loss parameter of the micro-grid meet the preset conditions, the output result of the model is used for representing the corresponding charge and discharge power of the electric automobile.
The fluctuation parameters are mainly expressed by numerical values of load peak-valley difference, and the composition of the load, the change of the environment and the change of the load quantity are main factors influencing the numerical values of the load peak-valley difference of the power system. In the scheme, the fluctuation of the microgrid refers to that the fluctuation is caused by load change caused by access of different electric vehicles and charging and discharging requirements of different electric vehicles in the operation process of the microgrid, and then the numerical value of the load peak-valley difference is changed. The loss parameter is mainly expressed by the size of active power loss, mainly due to the resistance loss of electric energy in the micro-grid on a transmission line in the transmission process.
Specifically, the electric vehicle charge-discharge optimization model is model data stored in the controller, and the electric vehicle charge-discharge optimization model is used for adjusting the charge-discharge power of the electric vehicle connected to the microgrid so as to enable the microgrid to operate stably and efficiently. The controller inputs the electrical parameters into the electric vehicle charging and discharging optimization model, charging and discharging power with fluctuation parameters and loss parameters meeting preset conditions can be obtained for the micro-grid and the electric vehicle under each condition, and the power represents that the electric vehicle uses the power to charge and discharge so that the micro-grid can run as stably as possible.
Optionally, the preset conditions of the fluctuation parameter and the loss parameter may be that the fluctuation parameter reaches a minimum value, and the loss parameter is a corresponding loss parameter when the fluctuation parameter is the minimum value. Or the corresponding fluctuation parameter when the loss parameter reaches the minimum value is selected as the fluctuation parameter. The corresponding fluctuation parameter and the corresponding loss parameter can also be obtained when the sum of the fluctuation parameter and the loss parameter is minimum after the processing.
And step 206, performing charge and discharge control on the electric automobile according to the model output result.
Specifically, the controller obtains a model output result of the electric vehicle charge-discharge optimization model, namely, the charge-discharge power of the electric vehicle. And the controller controls the charging and discharging of the electric automobile according to the charging and discharging power of the electric automobile.
Illustratively, when the model output result is charging power, the controller controls at least one of the storage battery, the photovoltaic power station and other electric vehicles connected to the microgrid according to the existing electric quantity and the charging power of the storage battery, and the charging power output by the model is used for charging the electric vehicles. And when the model output result is the discharge power, the controller controls the electric automobile to charge the storage battery or other electric automobiles connected with the micro-grid by using the discharge power output by the model.
In the electric vehicle charging and discharging control method, a model output result is obtained by establishing an electric vehicle charging and discharging optimization model and inputting the electrical parameters of the microgrid where the electric vehicle is located into the electric vehicle charging and discharging optimization model. And when the fluctuation parameter and the loss parameter of the micro-grid are represented by the model output result and meet the preset conditions, the corresponding charge-discharge power of the electric automobile is obtained, so that the fluctuation parameter and the loss parameter of the micro-grid meet the preset conditions when the charge-discharge of the electric automobile is controlled subsequently according to the charge-discharge power output by the model. The charging and discharging of the electric automobile in the micro-grid are adjusted in real time, the charging and discharging power of the electric automobile is adjusted, and the stable operation of the micro-grid is guaranteed.
And establishing an electric vehicle charge-discharge optimization model stored in the controller according to various data in the microgrid. In one embodiment, as shown in fig. 3, the process of establishing the charge-discharge optimization model of the electric vehicle includes:
step 302, establishing a fluctuation function of the microgrid according to the power of the microgrid in different working modes.
The fluctuation function is expressed by a related function of the load peak-valley difference, and for the fluctuation of the microgrid in the scheme, the fluctuation is caused by load change caused by access of different electric vehicles and charging and discharging requirements of the different electric vehicles in the operation process of the microgrid, so that the load power of the photovoltaic power station and the storage battery is influenced, and the numerical value of the load peak-valley difference is changed integrally.
Specifically, different working modes of the microgrid are formed by access of different electric vehicles and charging and discharging requirements of different electric vehicles, and the division of the working modes is mainly determined by load power of each device in the microgrid. For example, the load power for controlling the electric vehicle to discharge and the load power for controlling the electric vehicle to charge are different, and the two modes are not the same. The storage capacity of the storage battery influences the charge and discharge participation degree of the controller on the storage battery, and further influences the load power of the storage battery, so that the storage battery is not in the same working mode when the storage capacity of the storage battery is large and the storage capacity of the storage battery is small. Different working modes enable the micro-grid to have different loads, load peak-valley difference changes are caused by load changes, so that a day is divided into 24 hours, namely 24 time periods, and the mean square difference value f of the load curve of the micro-grid is established 1 As a function of the fluctuation parameter:
in the formula (f) 1 The mean square error value of the load curve of the micro-grid is obtained; t is the duration of a day, P s (t) is the integral equivalent load power in the microgrid at the time period of t; p av The average load power of the microgrid; n is a radical of k The total number of sampling points in one day is set to one sampling per hour, so the total number of sampling points is 24.
In formula (1), P av And P s The relationship of (t) is:
p in the formulae (1) and (2) s (t) the calculation formula is:
P s (t)＝P(t)+P ev (t)+P pv (t)+P bat (t) (3)
in the formula, P (t) is the load power of the microgrid per se in a period of t; p ev (t) electric vehicle charging and discharging power of microgrid at t time period；P pv (t) is the output power of the photovoltaic power station in a period of t; p is bat And (t) is the charging and discharging power of the storage battery pack in a period of t.
Therefore, a relation function of the mean square deviation value of the load curve and the charge and discharge power of the electric automobile in the microgrid is established, and the relation function is expressed as the formula (1).
And 304, obtaining a loss function based on the structural parameters of the microgrid.
Wherein the loss function is expressed as a function related to the network loss of active power in the microgrid. The structural parameter of the microgrid is one of the electrical parameters of the microgrid, and comprises data such as a branch set in the microgrid, a voltage amplitude value corresponding to each branch, a voltage phase angle difference of each branch and the like.
Specifically, the network loss of active power in the microgrid can be influenced by the structures of different microgrids, and meanwhile, the charge and discharge power of the electric automobile can be influenced by the voltage amplitude of a branch in the microgrid. Establishing network loss f based on active power in the microgrid according to structural parameters of the microgrid 2 As a function of the loss parameter:
in the formula (f) 2 Network loss which is active power in the microgrid; s. the L Collecting all branches in the microgrid; g ab Is the conductance between the branch head end and the branch tail end; u shape a,j The voltage amplitude of the branch circuit head end in the period j; u shape b,j The voltage amplitude of the branch circuit at the end of the j time period;
for branch first terminal voltage phase angle of j period
Branch end voltage phase angle minus j period
The voltage angle difference between the head and the tail ends of the branch circuit in the period j; at is a time interval, hereΔ t =1 is set.
In this formula, through U a,j And U b,j The charging and discharging power P related to the electric automobile can be calculated ev And (t) in the microgrid, the currents of all direct currents are constant and fixed, the amplitude difference obtained by subtracting the amplitude of the head end voltage from the amplitude of the tail end voltage is used as the voltage amplitude of the electric automobile, and the charging and discharging power of the electric automobile can be obtained by multiplying the voltage amplitude and the currents.
Therefore, a relation function of the network loss of the active power and the charge and discharge power of the electric automobile in the micro-grid is established, and the relation function is expressed as a formula (4).
And step 306, obtaining a charge and discharge optimization model of the electric automobile according to the loss function and the fluctuation function.
Specifically, according to a loss function expressed by a network loss function of active power in the microgrid and a fluctuation function expressed by a mean square error function of a load curve, the loss function and the fluctuation function are subjected to correlation processing, and then the electric vehicle charge-discharge optimization model can be obtained. For example, when the loss function and the fluctuation function satisfy a certain condition, a charge-discharge optimization model of the electric vehicle is obtained.
In the embodiment, the stability in the microgrid is evaluated by establishing a fluctuation function and a loss function, and the charging and discharging power of the electric vehicle is obtained according to the fluctuation function and the loss function, so that the overall stability of the microgrid is ensured when different charging and discharging tasks of the electric vehicle are executed.
In one embodiment, as shown in FIG. 4, step 306 includes step 402 and step 404.
Step 402, performing normalization processing on the loss function and the fluctuation function.
The normalization processing is to map the data to a specified range and is used for removing dimensions and dimension units of different dimensional data. Common mapping ranges are [0,1] and [ -1,1], and the most common normalization method is Min-Max (Max-Min) normalization.
Specifically, the electric vehicle charge-discharge optimization model has a loss function and a fluctuation function, which are used for optimizing a dual objective function, and the problem of solving the dual objective function is complex. The loss function and the fluctuation function need to be combined to meet the preset conditions of the loss function and the fluctuation function at the same time, and data addition and subtraction cannot be directly performed because the unit magnitudes of the loss function and the fluctuation function are different. Therefore, the loss function and the fluctuation function are normalized, and the loss function and the fluctuation function in the electric vehicle charging and discharging optimization model are converted into a target function, so that the subsequent optimization processing is facilitated.
And step 404, performing linear weighting processing on the loss function and the fluctuation function after the normalization processing to obtain a charge and discharge optimization model of the electric automobile.
The linear weighting processing is a method for solving the multi-target programming problem by giving corresponding weight coefficients to the targets according to the importance of the targets and optimizing the linear combination of the weights.
Specifically, the loss function and the ripple function are respectively set with weights, and a corresponding weight coefficient is added in front of the loss function and the ripple function. The loss function and the fluctuation function are mutually connected and interacted, and the internal variable parameters are also connected. In order to enable the loss function and the fluctuation function to meet the preset conditions, the loss function and the fluctuation function are processed by adopting a linear weighting method.
The function after normalization and linear weighting processing of equations (1) and (4) is:
F＝μ 1 (f 1 /f 1max )+μ 2 (f 2 /f 2max ) (5)
in the formula, F is a function of a loss function and a fluctuation function which are combined after normalization and linear weighting processing; mu.s 1 Is f 1 The weight coefficient of (a); f. of 1max The maximum value of the mean square error of the load curve in 24 hours in the microgrid is obtained; mu.s 2 Is f 2 The weight coefficient of (a); f. of 2max The maximum value of the network loss of active power in 24 hours in the microgrid.
In the above formula,. Mu. 1 And mu 2 Also called preference coefficients, these two values need to satisfy a certain condition: mu.s 1 Greater than or equal to 0, mu 2 Greater than or equal to 0, and μ 1 and μ 2 the sum of the additions is 1.
In the implementation, the loss function and the fluctuation function are normalized, so that the problem of double-target optimization in the electric vehicle charging and discharging optimization model is converted into the problem of single-target optimization. And then carrying out linear weighting processing on the loss function and the fluctuation function after the normalization processing, and respectively giving weights to the loss function and the fluctuation function to enable the output result of the electric vehicle charge-discharge optimization model to meet the dual preset conditions of the loss function and the fluctuation function. The stability of the micro-grid during the charge and discharge control of the electric automobile is further improved.
In one embodiment, as shown in FIG. 5, step 404 includes step 502 and step 504.
And 502, performing linear weighting processing on the loss function and the fluctuation function after the normalization processing to obtain an evaluation function.
Specifically, the loss function and the fluctuation function after the normalization processing are subjected to linear weighting processing to obtain a linearly weighted function, and the function F in the formula (5) is an evaluation function.
And step 504, taking the expression corresponding to the minimum output value of the evaluation function as the electric vehicle charging and discharging optimization model.
Specifically, from the perspective of the operation stability of the microgrid, when the loss function and the fluctuation function are both minimum values, the charging and discharging power of the electric vehicle adopted at this time can make the operation of the microgrid be the most stable. However, the loss function and the fluctuation function are related to each other and cannot be independently taken. At the moment, the evaluation function is minimum in value, the values of the loss function and the fluctuation function can be balanced, and the obtained charging and discharging power of the electric vehicle can enable the fluctuation parameter and the loss parameter of the micro-grid to meet the preset conditions.
It can be obtained that in the output structure of the electric vehicle charge-discharge optimization model, the preset conditions of the fluctuation parameters and the loss parameters of the microgrid are as follows: and under the electric vehicle charging and discharging power of the model output result, the evaluation function value obtained by processing the fluctuation parameter and the loss parameter of the microgrid is the minimum. Namely, the expression of the electric vehicle charge-discharge optimization model is as follows:
minF＝μ 1 (f 1 /f 1max )+μ 2 (f 2 /f 2max ) (6)
in the embodiment, the loss function and the fluctuation function are processed to obtain the evaluation function, and the corresponding expression when the output value of the evaluation function is minimum is used as the electric vehicle charging and discharging optimization model, so that the fluctuation parameter and the loss parameter of the microgrid can meet the preset conditions through the model output result of the electric vehicle charging and discharging optimization model. The stability of the micro-grid during charging and discharging control of the electric automobile is guaranteed.
In one embodiment, as shown in FIG. 6, step 204 includes step 602.
Step 602, inputting the electrical parameters into the electric vehicle charge-discharge optimization model, and obtaining a model output result when the electrical parameters meet a preset power grid constraint condition.
The power grid constraint condition is related electrical or equipment performance constraint which ensures that the micro-grid can normally operate, is used for limiting a value range and judging whether a numerical value is valid or not, and comprises equality constraint and inequality constraint.
Specifically, the grid constraint condition includes a supply and demand balance constraint of system power in the microgrid:
P load (t)＝P ev (t)+P pv (t)+P bat (t) (7)
in the formula, P load (t) is the total supplied load power for the period t.
When the electric vehicle is connected to the microgrid, the real-time distribution, distribution and trend conditions of electric power in the microgrid, which are called as the tidal current change of the microgrid, can be caused. The micro-grid is provided with a plurality of nodes, and the nodes should satisfy the balance equation of active power and reactive power. The grid constraints also include the node flow equation constraints of the microgrid:
in the formula,. DELTA.P a The quantity is the unbalance quantity of the active power at the node a;P a active power injected into the node a; u shape a The voltage amplitude of the node a is obtained; n is the total number of nodes in the microgrid; u shape b Is the voltage amplitude of node b; g ab Is the conductance between node a and node b; delta. For the preparation of a coating ab Is the voltage angle difference between node a and node b; b is ab Is the susceptance between node a and node b; delta Q a The amount of the reactive power at the node a is the unbalance amount; q a Is the reactive power injected in the node.
When the electric automobile is connected into the microgrid and causes the tidal current change in the microgrid, not only the nodes should satisfy the equilibrium equation of active power and reactive power, but also the active power and reactive power transmitted by each branch should be within the constraint range, that is, the power grid constraint conditions also include the branch tidal current constraint of the microgrid:
in the formula, P l min A lower limit of active power for the transmission power of branch l; p l (t) is the active power transmitted by the branch circuit l at the moment t; delta P l (t) is the variable quantity of the active power transmitted by the branch circuit l at the moment t; p l max An upper limit of active power for the transmission power of branch l; q l min A lower limit of reactive power for the transmission power of branch l; q l (t) is the reactive power transmitted by the branch circuit l at the moment t; delta Q l (t) is the variable quantity of the reactive power transmitted by the branch circuit l at the moment t; q l max An upper limit of reactive power for the transmission power of branch i.
The photovoltaic power plants in the microgrid need to take power limitations into account, so grid constraints also include power constraints for each photovoltaic generator within the photovoltaic power plant:
in the formula, P pv,i min For the ith photovoltaic generatorThe lower limit of the generated power; p pv,i The power of the ith photovoltaic generator; p pv,i max The power generation power upper limit of the ith photovoltaic generator.
The storage battery in the microgrid is used for storing energy and supplying power according to corresponding control when needed, so the grid constraint conditions also include the power constraint of the storage battery:
in the formula, C bat min Is the minimum allowable charge capacity of the battery; c bat,i The electric quantity of the storage battery; c bat max Is the maximum allowable charge capacity of the storage battery.
If too many electric vehicles are charged at the same node in the microgrid, the voltage of the node is reduced to some extent, and the stable operation of the microgrid is influenced. The grid constraints also include voltage magnitude constraints for the same node:
in the formula of U a min Is the lower voltage amplitude limit of node a; u shape a (t) is the voltage amplitude of the node a at time t; u shape a max The upper voltage amplitude limit of the node a.
The tie lines between each equipment connection of the microgrid also have power limits, and the grid constraint conditions further include power constraints on the tie lines:
in the formula, P grid min The lower power limit of each tie line in the micro-grid is set; p is grid (t) is the power of each tie line in the microgrid; p is grid max The upper power limit of each tie line in the microgrid.
In the above formula, when P grid max When the current is greater than 0, the power flow direction on the connecting line is used for controlling the charging of the electric automobile; when P is present grid min If 0 or less, it means that the power flow direction on the link is the electric vehicle discharge control.
In the embodiment, a power grid constraint condition is set for the electrical parameters in the electric vehicle charge and discharge optimization model, so that the electric vehicle charge and discharge optimization model reasonably obtains a model output result under the power grid constraint condition, and the stability of the micro-grid during the charge and discharge control of the electric vehicle is further ensured.
In one embodiment, as shown in FIG. 7, step 602 includes step 702.
And 702, inputting the electrical parameters into the electric vehicle charge-discharge optimization model, and obtaining a model output result when the electrical parameters meet the preset power grid constraint conditions and the accessed electric vehicle parameters meet the vehicle constraint conditions.
The automobile constraint condition is a limit range brought by parameters of the electric automobile, and is set up according to parameters of different dimensions such as the model, the electric quantity and the service time of the electric automobile.
Specifically, the vehicle constraint conditions include the state of charge constraint of the electric vehicle:
C b ·SOC i (t+1)＝C b ·SOC i (t)+P c (t)·Δt·η c -P d (t)·Δt/η d (14)
in the formula, C b The battery capacity of the electric automobile; SOC (system on chip) i (t) is the state of charge of the electric vehicle i at time t; p is c (t) is the charging power of the electric automobile at the moment t; p d (t) the discharge power of the electric vehicle at the time t; eta c is the charging efficiency of the battery of the electric automobile; eta d Is the discharge efficiency of the battery of the electric automobile.
Electric vehicles receivable in the microgrid at the same time are limited, so that the charging and discharging quantity of the electric vehicles in each period is restricted:
in the formula, N c (t) the number of electric vehicles in a charging state at the moment t; n is a radical of max (t) is the total number of electric vehicles which can be accommodated by the microgrid at the moment t; n is a radical of hydrogen d And (t) is the number of the electric automobiles in the discharging state at the moment t.
In order to keep the battery of the electric vehicle to have good performance and avoid excessive charging and discharging during charging and discharging control, the vehicle constraint conditions include the battery state of charge constraint of the electric vehicle:
SOC min ≤SOC i (t)≤SOC max (16)
in the formula, SOC min The upper limit and the lower limit of the battery state of charge value of the electric automobile; SOC max Is the upper limit of the battery state of charge value of the electric automobile.
Meanwhile, the charge and discharge power limit of the electric vehicle is also one of the vehicle constraint conditions, which is as follows:
in the formula, P ev,i (t) the charging and discharging power of the electric vehicle at time t, P (t) ev,i And P ev,i (t) have the same meaning; p (t) c,max The maximum charging power of the electric automobile; p (t) d,max The maximum discharge power of the electric automobile.
When considering the constraint conditions of the automobile, the requirements of users of the electric automobile on traveling need to be considered. The automotive constraints also include time of use constraints. Taking the travel demand of a working day as an example, the electric automobile is not connected to the power grid in the traveling process of the electric automobile in the morning and the evening at the time periods of 8-9. In order to ensure the requirement of the user for going to and from work, when the electric automobile leaves the microgrid at 8.
In the embodiment, the electric vehicle charging and discharging optimization model can reasonably obtain the model output result under the electric network constraint condition and the automobile constraint condition by establishing the automobile constraint condition on the basis of establishing the electric network constraint condition on the electric parameters in the electric vehicle charging and discharging optimization model. The normal use requirement of the electric automobile can be guaranteed, and the micro-grid can be stably operated.
The method takes the minimum mean square error of the load curve of the micro-grid and the minimum network loss of active power in the micro-grid in one day as objective functions. The method comprises the steps of establishing an optimization model taking the charge and discharge power of the electric automobile as a variable by taking the power grid constraint condition and the automobile constraint condition in the micro-grid as constraints and taking the private automobile of the electric automobile which is subjected to conventional charge and discharge as a research object, and controlling the charge and discharge of the electric automobile according to the output result of the model, so that the reasonable distribution and planning of the charge and discharge power of the electric automobile can be realized. The travel requirement of the electric automobile is met, and the stable operation of the micro-grid is ensured. Meanwhile, the distributed energy storage function of the electric automobile with a certain travel rule is utilized, the burden of traditional energy storage equipment such as a storage battery is reduced, the capacity requirement of the energy storage equipment is reduced, the receiving and absorbing capacity of the micro-grid on a photovoltaic power station is improved, the load power fluctuation of the micro-grid is reduced, the load peak-valley difference value is reduced, and the operation reliability of the micro-grid is improved.
It should be understood that, although the steps in the flowcharts related to the embodiments as described above are sequentially displayed as indicated by arrows, the steps are not necessarily performed sequentially as indicated by the arrows. The steps are not limited to being performed in the exact order illustrated and, unless explicitly stated herein, may be performed in other orders. Moreover, at least a part of the steps in the flowcharts related to the embodiments described above may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the execution order of the steps or stages is not necessarily sequential, but may be performed alternately or alternately with other steps or at least a part of the steps or stages in other steps.
Based on the same inventive concept, the embodiment of the application also provides an electric vehicle charge and discharge control device for realizing the electric vehicle charge and discharge control method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the method, so specific limitations in one or more embodiments of the electric vehicle charge and discharge control device provided below can be referred to the limitations on the electric vehicle charge and discharge control method in the above, and details are not repeated herein.
In one embodiment, as shown in fig. 8, there is provided a charge and discharge control device for an electric vehicle, including: a data acquisition module 802, a model operation module 804, and a regulation control module 806, wherein:
and the data acquisition module 802 is configured to acquire an electrical parameter of a microgrid where the accessed electric vehicle is located.
The model operation module 804 is used for inputting the electrical parameters into the electric vehicle charge-discharge optimization model to obtain a model output result; and the model output result is used for representing the charging and discharging power of the corresponding electric automobile when the fluctuation parameter and the loss parameter of the microgrid meet preset conditions.
And the adjusting control module 806 is configured to perform charge and discharge control on the electric vehicle according to the model output result.
In one embodiment, the model operation module 804 further includes a model building module, and the model building module is configured to build a fluctuation function of the microgrid according to power of the microgrid in different working modes; obtaining a loss function based on the structural parameters of the microgrid; and obtaining a charge and discharge optimization model of the electric automobile according to the loss function and the fluctuation function.
In one embodiment, the model building module is used for carrying out normalization processing on the loss function and the fluctuation function; and performing linear weighting processing on the loss function and the fluctuation function after the normalization processing to obtain a charging and discharging optimization model of the electric automobile.
In one embodiment, the model establishing module is configured to perform linear weighting processing on the normalized loss function and the normalized fluctuation function to obtain an evaluation function; and taking the expression corresponding to the minimum output value of the evaluation function as the electric vehicle charging and discharging optimization model.
In one embodiment, the model operation module 804 is configured to input the electrical parameters to the electric vehicle charge-discharge optimization model, and obtain a model output result when the electrical parameters meet a preset power grid constraint condition.
In one embodiment, the model operation module 804 is configured to input the electrical parameter to the electric vehicle charge-discharge optimization model, and obtain a model output result when the electrical parameter meets a preset power grid constraint condition and the accessed electric vehicle parameter meets a vehicle constraint condition.
All or part of the modules in the electric vehicle charging and discharging control device can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and the internal structure thereof may be as shown in fig. 9. The computer device includes a processor, a memory, an Input/Output interface (I/O for short), and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer equipment is used for instantly acquiring and storing the electrical parameters in the microgrid, the accessed data of the electric automobile and the charge-discharge optimization model data of the electric automobile. The input/output interface of the computer device is used for exchanging information between the processor and an external device. The communication interface of the computer device is used for connecting and communicating with an external terminal through a network. The computer program is executed by a processor to realize the electric vehicle charging and discharging control method.
Those skilled in the art will appreciate that the architecture shown in fig. 9 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory having a computer program stored therein and a processor that when executing the computer program performs the steps of:
acquiring electrical parameters of a micro-grid where an accessed electric vehicle is located;
inputting the electrical parameters into a charge-discharge optimization model of the electric automobile to obtain a model output result; the model output result is used for representing the charging and discharging power of the corresponding electric automobile when the fluctuation parameter and the loss parameter of the microgrid meet preset conditions;
and controlling charging and discharging of the electric automobile according to the model output result.
In one embodiment, the processor when executing the computer program further performs the steps of: according to the power of the micro-grid in different working modes, a fluctuation function of the micro-grid is established; obtaining a loss function based on the structural parameters of the microgrid; and obtaining a charge and discharge optimization model of the electric automobile according to the loss function and the fluctuation function.
In one embodiment, the processor, when executing the computer program, further performs the steps of: carrying out normalization processing on the loss function and the fluctuation function; and performing linear weighting processing on the loss function and the fluctuation function after the normalization processing to obtain a charge-discharge optimization model of the electric automobile.
In one embodiment, the processor, when executing the computer program, further performs the steps of: performing linear weighting processing on the loss function and the fluctuation function after the normalization processing to obtain an evaluation function; and taking the expression corresponding to the minimum output value of the evaluation function as the electric vehicle charging and discharging optimization model.
In one embodiment, the processor when executing the computer program further performs the steps of: and inputting the electrical parameters into the electric vehicle charge-discharge optimization model, and obtaining a model output result when the electrical parameters meet the preset power grid constraint conditions.
In one embodiment, the processor, when executing the computer program, further performs the steps of: and inputting the electrical parameters into the electric vehicle charging and discharging optimization model, and obtaining a model output result when the electrical parameters meet the preset power grid constraint conditions and the accessed electric vehicle parameters meet the vehicle constraint conditions.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring electrical parameters of a microgrid where an accessed electric vehicle is located;
inputting the electrical parameters into a charge-discharge optimization model of the electric automobile to obtain a model output result; the model output result is used for representing the charging and discharging power of the corresponding electric automobile when the fluctuation parameter and the loss parameter of the microgrid meet preset conditions;
and controlling charging and discharging of the electric automobile according to the model output result.
In one embodiment, the computer program when executed by the processor further performs the steps of: according to the power of the micro-grid in different working modes, a fluctuation function of the micro-grid is established; obtaining a loss function based on the structural parameters of the microgrid; and obtaining a charge and discharge optimization model of the electric automobile according to the loss function and the fluctuation function.
In one embodiment, the computer program when executed by the processor further performs the steps of: carrying out normalization processing on the loss function and the fluctuation function; and performing linear weighting processing on the loss function and the fluctuation function after the normalization processing to obtain a charging and discharging optimization model of the electric automobile.
In one embodiment, the computer program when executed by the processor further performs the steps of: performing linear weighting processing on the loss function and the fluctuation function after the normalization processing to obtain an evaluation function; and taking the expression corresponding to the minimum output value of the evaluation function as the electric vehicle charging and discharging optimization model.
In one embodiment, the computer program when executed by the processor further performs the steps of: and inputting the electrical parameters into the electric vehicle charge-discharge optimization model, and obtaining a model output result when the electrical parameters meet the preset power grid constraint conditions.
In one embodiment, the computer program when executed by the processor further performs the steps of: and inputting the electrical parameters into the electric vehicle charge-discharge optimization model, and obtaining a model output result when the electrical parameters meet the preset power grid constraint conditions and the accessed electric vehicle parameters meet the vehicle constraint conditions.
In one embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, performs the steps of:
acquiring electrical parameters of a micro-grid where an accessed electric vehicle is located;
inputting the electrical parameters into a charge-discharge optimization model of the electric automobile to obtain a model output result; the model output result is used for representing the charging and discharging power of the corresponding electric automobile when the fluctuation parameter and the loss parameter of the microgrid meet the preset conditions;
and controlling the charging and discharging of the electric automobile according to the model output result.
In one embodiment, the computer program when executed by the processor further performs the steps of: according to the power of the micro-grid in different working modes, a fluctuation function of the micro-grid is established; obtaining a loss function based on the structural parameters of the microgrid; and obtaining a charge and discharge optimization model of the electric automobile according to the loss function and the fluctuation function.
In one embodiment, the computer program when executed by the processor further performs the steps of: carrying out normalization processing on the loss function and the fluctuation function; and performing linear weighting processing on the loss function and the fluctuation function after the normalization processing to obtain a charging and discharging optimization model of the electric automobile.
In one embodiment, the computer program when executed by the processor further performs the steps of: performing linear weighting processing on the loss function and the fluctuation function after the normalization processing to obtain an evaluation function; and taking the expression corresponding to the minimum output value of the evaluation function as the electric vehicle charging and discharging optimization model.
In one embodiment, the computer program when executed by the processor further performs the steps of: and inputting the electrical parameters into the electric vehicle charge-discharge optimization model, and obtaining a model output result when the electrical parameters meet the preset power grid constraint conditions.
In one embodiment, the computer program when executed by the processor further performs the steps of: and inputting the electrical parameters into the electric vehicle charging and discharging optimization model, and obtaining a model output result when the electrical parameters meet the preset power grid constraint conditions and the accessed electric vehicle parameters meet the vehicle constraint conditions.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, databases, or other media used in the embodiments provided herein can include at least one of non-volatile and volatile memory. The nonvolatile Memory may include a Read-Only Memory (ROM), a magnetic tape, a floppy disk, a flash Memory, an optical Memory, a high-density embedded nonvolatile Memory, a resistive Random Access Memory (ReRAM), a Magnetic Random Access Memory (MRAM), a Ferroelectric Random Access Memory (FRAM), a Phase Change Memory (PCM), a graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases referred to in various embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing based data processing logic devices, etc., without limitation.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, and these are all within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.
Claims (10)
1. A charge and discharge control method for an electric vehicle is characterized by comprising the following steps:
acquiring electrical parameters of a micro-grid where an accessed electric vehicle is located;
inputting the electrical parameters into a charge-discharge optimization model of the electric automobile to obtain a model output result; the model output result is used for representing the charging and discharging power of the corresponding electric automobile when the fluctuation parameter and the loss parameter of the microgrid meet preset conditions;
and controlling charging and discharging of the electric automobile according to the model output result.
2. The method according to claim 1, wherein the establishing process of the electric vehicle charge and discharge optimization model comprises the following steps:
according to the power of the micro-grid in different working modes, a fluctuation function of the micro-grid is established;
obtaining a loss function based on the structural parameters of the microgrid;
and obtaining a charge and discharge optimization model of the electric automobile according to the loss function and the fluctuation function.
3. The method according to claim 2, wherein the obtaining of the charge-discharge optimization model of the electric vehicle according to the loss function and the fluctuation function comprises:
normalizing the loss function and the fluctuation function;
and performing linear weighting processing on the loss function and the fluctuation function after the normalization processing to obtain a charging and discharging optimization model of the electric automobile.
4. The method according to claim 3, wherein the linear weighting processing is performed on the loss function and the fluctuation function after the normalization processing to obtain the electric vehicle charge-discharge optimization model, and the method comprises the following steps:
performing linear weighting processing on the loss function and the fluctuation function after the normalization processing to obtain an evaluation function;
and taking the expression corresponding to the minimum output value of the evaluation function as the electric vehicle charging and discharging optimization model.
5. The method of claim 1, wherein inputting the electrical parameters into a charge-discharge optimization model of the electric vehicle to obtain a model output result comprises:
and inputting the electrical parameters into an electric vehicle charge-discharge optimization model, and obtaining a model output result when the electrical parameters meet a preset power grid constraint condition.
6. The method according to claim 5, wherein the inputting the electrical parameters into the electric vehicle charge-discharge optimization model, and obtaining a model output result when the electrical parameters satisfy a preset grid constraint condition, comprises:
and inputting the electrical parameters into an electric vehicle charging and discharging optimization model, and obtaining a model output result when the electrical parameters meet a preset power grid constraint condition and the accessed electric vehicle parameters meet a vehicle constraint condition.
7. A charge and discharge control device for an electric vehicle, the device comprising:
the data acquisition module is used for acquiring the electric parameters of the micro-grid where the accessed electric automobile is located;
the model operation module is used for inputting the electrical parameters to a charge-discharge optimization model of the electric automobile to obtain a model output result; the model output result is used for representing the charging and discharging power of the corresponding electric automobile when the fluctuation parameter and the loss parameter of the microgrid meet preset conditions;
and the adjusting control module is used for controlling charging and discharging of the electric automobile according to the model output result.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 6.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program realizes the steps of the method of any one of claims 1 to 6 when executed by a processor.
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