CN113675859A - Frequency modulation control method and device - Google Patents
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Abstract
The application provides a frequency modulation control method and a frequency modulation control device, which are applied to a controller, wherein the controller is used for controlling a battery to perform electric energy interaction with a power grid so as to perform frequency modulation, and the method comprises the following steps: when the battery and a power grid perform electric energy interactive operation, acquiring electric quantity parameters of the battery; calculating a first frequency modulation factor of the battery according to the electric quantity parameter; determining a primary frequency modulation power reference value of the battery according to the first frequency modulation factor; and controlling the electric energy interactive operation between the battery and the power grid according to the primary frequency modulation power reference value. Through the frequency modulation control method and the frequency modulation control device, personalized electric energy interaction can be carried out on the electric energy and the power grid according to the electric quantity parameter condition of the battery so as to carry out frequency modulation, and the frequency modulation potential of the electric automobile with different working conditions is fully excavated.
Description
Technical Field
The application relates to the technical field of Vehicle-to-grid (V2G), in particular to a frequency modulation control method and device.
Background
V2G is a short for Vehicle-to-grid, and means that when the hybrid electric Vehicle or the pure electric Vehicle is not running, the energy of the battery can be transmitted to the power grid, and conversely, when the battery of the electric Vehicle needs to be fully charged, the battery can obtain the electric energy from the power grid. The electric vehicle can charge or discharge the power grid to adjust the frequency of the power grid. Batteries of electric vehicles have a fast response characteristic, so that the batteries are particularly suitable for improving the dynamic frequency response of a power system, and the large-scale development of the electric vehicles enables the system to have huge electric energy storage resources.
In the prior art, a controller in an electric vehicle or a controller in a charging pile sends a frequency response coefficient K according to a dispatching center of a power gridEVAnd determining the variable quantity delta P of the capacity of the corresponding power grid, and controlling a bidirectional charger to charge the battery by the power grid or discharge the battery to the power grid, so that the individualized frequency modulation control cannot be realized, and the actual application requirements cannot be met.
Disclosure of Invention
In view of this, an object of the present application is to provide a frequency modulation control method and apparatus, which improve the degree of personalized frequency modulation control and meet the requirements of practical applications.
In order to achieve the purpose, the technical scheme adopted by the application is as follows:
in a first aspect, an embodiment of the present application provides a frequency modulation control method, which is applied to a controller, where the controller is configured to control a battery to perform power interaction with a power grid to perform frequency modulation, and the method includes: when the battery and a power grid perform electric energy interactive operation, acquiring electric quantity parameters of the battery; calculating a first frequency modulation factor of the battery according to the electric quantity parameter; determining a primary frequency modulation power reference value of the battery according to the first frequency modulation factor; and controlling the electric energy interactive operation between the battery and the power grid according to the primary frequency modulation power reference value.
With reference to the first aspect, an embodiment of the present application provides a first possible implementation manner of the first aspect, where: the method further comprises the following steps: calculating a second frequency modulation factor by using the frequency variation of the power grid and the first frequency modulation factor; determining a secondary frequency modulation power reference value according to the second frequency modulation factor; and controlling the electric energy interactive operation between the battery and the power grid according to the secondary frequency modulation power reference value.
With reference to the first aspect, an embodiment of the present application provides a second possible implementation manner of the first aspect, where: the step of calculating the first frequency modulation factor of the battery according to the electric quantity parameter comprises the following steps: calculating a first frequency modulation factor α of the battery by the following formula:
wherein, SOC is the percentage value of the current electric vehicle residual capacity, SOCsetTarget percentage of remaining charge, SOC, set for the user0Is the initial value of the percentage value of the residual electric quantity when the electric automobile is connected into the power grid, TplAnd t is the current charging time.
With reference to the first aspect, an embodiment of the present application provides a third possible implementation manner of the first aspect, where: the step of determining the primary frequency modulation power reference value of the battery according to the first frequency modulation factor comprises the following steps: calculating the primary frequency modulation power reference value delta P of the battery by the following formula1：
KEVIs a frequency response coefficient;
and delta f is the power grid frequency variation.
With reference to the first implementation manner of the first aspect, an embodiment of the present application provides a fourth possible implementation manner of the first aspect, where: calculating a second frequency modulation factor by using the frequency variation of the power grid and the first frequency modulation factor, wherein the step comprises the following steps: and taking the frequency variation of the power grid and the first frequency modulation factor as the input of a fuzzy algorithm, and calculating by adopting the fuzzy algorithm to obtain and output the second frequency modulation factor.
With reference to the first implementation manner of the first aspect, an embodiment of the present application provides a fifth possible implementation manner of the first aspect, where: the step of determining the secondary frequency modulation power reference value according to the second frequency modulation factor comprises the following steps: calculating the secondary frequency modulation power reference value delta P of the battery by the following formula2：
PsetReference power for the current moment;
Pminis the minimum value of the adjustable capacity;
Pmaxis the maximum value of the adjustable capacity.
In a second aspect, an embodiment of the present application further provides a frequency modulation control apparatus, which is applied to a controller, where the controller is configured to control a battery to perform power interaction with a power grid to perform frequency modulation, and the apparatus includes: the receiving module is used for receiving the electric quantity parameter of the battery detected by the battery management system when the battery and the power grid perform electric energy interactive operation; the calculation module is used for calculating a first frequency modulation factor of the battery according to the electric quantity parameter and determining a primary frequency modulation power reference value of the battery according to the first frequency modulation factor; and the control module is used for controlling the electric energy interactive operation between the battery and the power grid according to the primary frequency modulation power reference value.
In combination with the second aspect, the present embodiments provide a first possible implementation manner of the second aspect, where: the calculation module is further configured to: calculating a second frequency modulation factor by using the frequency variation of the power grid and the first frequency modulation factor; determining a secondary frequency modulation power reference value according to the second frequency modulation factor; and controlling the electric energy interactive operation between the battery and the power grid according to the secondary frequency modulation power reference value.
In combination with the second aspect, the present embodiments provide a second possible implementation manner of the second aspect, where: the calculation module is used for calculating a first frequency modulation factor alpha of the battery through the following formula:
wherein, SOC is the percentage value of the current electric vehicle residual capacity, SOCsetTarget percentage of remaining charge, SOC, set for the user0Is the initial value of the percentage value of the residual electric quantity when the electric automobile is connected into the power grid, TplSetting a charging time length for a user, wherein t is the current charging time;
for the above-mentioned computing moduleCalculating the primary frequency modulation power reference value delta P of the battery through the following formula1：
KEVIs a frequency response coefficient;
and delta f is the power grid frequency variation.
In combination with the first implementation manner of the second aspect, the present examples provide a third possible implementation manner of the second aspect, where: the calculation module is further configured to: the frequency variation of the power grid and the first frequency modulation factor are used as input of a fuzzy algorithm, and the second frequency modulation factor is obtained and output through the operation of the fuzzy algorithm; and the calculating module is also used for calculating the secondary frequency modulation power reference value delta P by the following formula2：
PsetReference power for the current moment;
Pminis the minimum value of the adjustable capacity;
Pmaxis the maximum value of the adjustable capacity.
According to the frequency modulation control method and the frequency modulation control device, when the battery and a power grid perform electric energy interactive operation, the electric quantity parameter of the battery is obtained, and the first frequency modulation factor of the battery is calculated according to the electric quantity parameter; then, determining a primary frequency modulation power reference value of the battery according to the first frequency modulation factor; and finally, controlling the electric energy interactive operation between the battery and the power grid according to the primary frequency modulation power reference value. In the mode, the electric quantity parameter of the battery is considered, and the frequency modulation operation can be carried out in the electric energy interaction process between the battery and the power grid, so that the frequency modulation work of the battery is controlled to be more targeted and more detailed; the frequency modulation control strategy provided by the application is suitable according to local conditions, different due to batteries and different due to the residual electric quantity of the batteries, so that the charging power of each electric automobile can be effectively balanced, the frequency modulation potential of the electric automobiles with different working conditions is fully excavated, and the frequency stability of a system is improved.
Additional features and advantages of the application will be set forth in the description which follows, or in part may be learned by the practice of the technology described above.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
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In order to more clearly illustrate the detailed description of the present application or the technical solutions in the prior art, the drawings needed to be used in the detailed description of the present application or the prior art description will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a schematic diagram of a hierarchical control framework system for an electric vehicle participating in frequency modulation according to an embodiment of the present disclosure;
fig. 2 is a flowchart of a frequency modulation control method according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a controller located in an electric vehicle according to an embodiment of the present disclosure;
fig. 4 is a schematic diagram of a circuit simulation structure for calculating a primary frequency modulation power reference value according to an embodiment of the present disclosure;
fig. 5 is a schematic diagram of a circuit simulation structure for calculating a secondary frequency modulation power reference value according to an embodiment of the present disclosure;
FIG. 6 is a schematic diagram illustrating the shape and distribution of a membership function of alpha factor according to an embodiment of the present invention;
FIG. 7 is a diagram illustrating the shape and distribution of a Δ f membership function according to an embodiment of the present disclosure;
FIG. 8 is a schematic diagram illustrating the shape and distribution of a membership function of a beta factor according to an embodiment of the present invention;
fig. 9 is a block diagram of a frequency modulation control apparatus according to an embodiment of the present disclosure;
fig. 10 is a schematic structural diagram of a controller according to an embodiment of the present application.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the present application will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments obtained by a person of ordinary skill in the art without any inventive work based on the embodiments in the present application are within the scope of protection of the present application.
It should be noted that the above method embodiments are all described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments may be referred to each other.
During the operation of the power grid, the load changes, which causes the fluctuation of the frequency. The rated frequency of the power grid is 50Hz, when the load of the power grid is lower, the frequency is larger than 50Hz, and the positive fluctuation is realized; when the load of the power grid is high, the frequency is less than 50Hz and fluctuates reversely. The method includes the steps that a dispatching center of a power grid monitors the operating frequency of the power grid, if the frequency of the power grid is monitored to fluctuate, a rated frequency is kept for maintaining the frequency of the power grid, further, impact of load change on the frequency is avoided, frequency modulation measures are carried out, frequency modulation is often carried out through a power plant, and for example, fig. 1 is a schematic diagram of a hierarchical control framework system of an electric vehicle participating in frequency modulation. The power grid is in communication connection with the dispatching center, so that the dispatching center can measure the operation parameters of the power grid in real time. The dispatching center is also in communication connection with a control center of an electric vehicle operator and a frequency modulation power plant (also called as a power plant), and when the load of the power grid becomes smaller and the frequency of the power grid fluctuates forward, the dispatching center can send a power reduction instruction to the frequency modulation power plant so as to reduce the output power of the frequency modulation power plant; when the load of the power grid becomes large and the frequency of the power grid fluctuates reversely, the dispatching center can send a power increasing instruction to the frequency modulation power plant so that the frequency modulation power plant increases the output power.
To assist the fm station in modulating the frequency, the electric vehicle EV (i.e., EV1 … EV in fig. 1)n-1，EVn) After a control center of an electric vehicle operator is accessed to a power grid, information such as vehicle information, SOC, a target value and expected charging time of the SOC is uploaded to the control center of the electric vehicle operator; capacity P adjustable by dispatching center for cluster electric automobilemax-PminCalculating the frequency response coefficient K of the electric automobile every 5 minutes for the reference power of the virtual synchronous machineEVAnd will sum the real-time electricity price with KEVDownloading the K to a control center of an electric vehicle operator, and enabling the control center of the electric vehicle operator to send the K to the control center of the electric vehicle operatorEVAnd sending the data to the bidirectional chargers of the EVs.
In order to better control charging and discharging, the embodiment of the application provides a frequency modulation control method and device, so that more personalized charging and discharging can be performed according to the residual capacity condition of a battery to perform frequency modulation, and the frequency modulation potential of electric vehicles with different working conditions is fully exploited. The following is a description by way of specific embodiments.
Referring to fig. 2, a flow chart of a frequency modulation control method is shown, and is applied to a controller, where the controller is used to control a battery to interact with a power grid for frequency modulation, and the method includes the following steps:
step S201, when the battery and the power grid carry out electric energy interactive operation, acquiring electric quantity parameters of the battery;
the Battery Management System (BMS) monitors the Battery to obtain a Battery parameter, and sends the Battery parameter to the controller, and the controller may be located in a charging pile outside the electric vehicle or inside the electric vehicle.
In order to make the description more concrete, referring to a schematic structural diagram of fig. 4, a controller is located in an electric vehicle, a battery, a BMS and the controller are arranged in the electric vehicle, and the BMS is used for managing the battery, monitoring the operation state of the battery, monitoring a power parameter and sending the monitored power parameter to the controller. The controller can also be positioned in a charging pile outside the electric automobile, the charging pile replaces the charging machine to work, the input end of the controller is connected with the power supply, and the output end of the controller is connected with the storage battery.
Step S202, calculating a first frequency modulation factor of the battery according to the electric quantity parameter;
step S203, determining a primary frequency modulation power reference value of the battery according to the first frequency modulation factor;
and the primary frequency modulation power reference value is the electric quantity value of electric energy interaction of the battery and the power grid for primary frequency modulation.
The controller calculates a primary frequency modulation power reference value by using the first frequency modulation factor. And correcting the primary frequency modulation power reference value through the primary frequency modulation factor so as to achieve the purpose of personalized charging and discharging.
And step S204, controlling the electric energy interactive operation between the battery and the power grid according to the primary frequency modulation power reference value.
Referring to fig. 3, a schematic diagram of a controller in an electric vehicle includes a battery, a battery management system BMS, a bidirectional charger, and a controller; the bidirectional charger is connected with the storage battery to charge the storage battery or the battery discharges to the power grid through the bidirectional charger; the BMS monitors the operating parameters of the storage battery and sends the operating parameters to the controller; the controller is used for controlling the bidirectional charger; a PWM rectifier and a DC/DC conversion circuit are arranged in the bidirectional charger; one end of the PWM rectifier is connected with an external alternating current power supply, and the other end of the PWM rectifier is connected with one end of the DC/DC conversion circuit; the other end of the DC/DC conversion circuit is connected with a battery; the controller controls the work of the PWM rectifier, and when the electric energy processed by the PWM reaches a preset value, the controller controls the PWM rectifier to stop working.
According to the method and the device, when the battery and the power grid perform electric energy interactive operation, the electric quantity parameter of the battery is obtained, and the first frequency modulation factor of the battery is calculated according to the electric quantity parameter; then, determining a primary frequency modulation power reference value of the battery according to the first frequency modulation factor; and finally, controlling the electric energy interactive operation between the battery and the power grid according to the primary frequency modulation power reference value. In the mode, the electric quantity parameter of the battery is considered, and the frequency modulation operation can be carried out in the electric energy interaction process between the battery and the power grid, so that the frequency modulation work of the battery is controlled to be more targeted and more detailed; the frequency modulation control strategy of the embodiment of the application is suitable according to local conditions, different because of the battery, different because of the residual capacity of battery to can effectively equalize each electric automobile's charging power, fully excavate the electric automobile's that possesses different operating modes frequency modulation potentiality, improve system frequency stability.
In view of the secondary frequency modulation, in one possible embodiment, the method further comprises the steps of: calculating a second frequency modulation factor by using the frequency variation of the power grid and the first frequency modulation factor; determining a secondary frequency modulation power reference value according to the second frequency modulation factor; and controlling the electric energy interactive operation between the battery and the power grid according to the secondary frequency modulation power reference value.
To calculate the first frequency modulation factor α, in one embodiment, step S202 calculates the first frequency modulation factor α of the battery by the following formula:
wherein, SOC is the percentage value of the current electric vehicle residual capacity, SOCsetTarget percentage of remaining charge, SOC, set for the user0Is the initial value of the percentage value of the residual electric quantity when the electric automobile is connected into the power grid, TplAnd t is the current charging time.
α can predict how much the user's charging needs are met: assuming that the average charging power of the electric vehicle from the beginning of charging to the time t is Pav，α>1 denotes by power PavCharging may be ahead of TplWhen the time reaches the charging requirement, the more the value of the time deviates from 1, the more the user requirement can be met; α ═ 1 means that it is just possible to specify time TplThe charging requirement is met; alpha is alpha<1 means that it is impossible to set time TplThe more the charging requirement is met, the more the value of the charging requirement deviates from 1, the more difficult the user requirement is met; alpha is alpha<0 represents charging from the beginningAnd the charging quantity of the electric automobile is smaller than the discharging quantity by the time t.
The alpha factor with 1 as a boundary can represent the condition that the electric automobile meets the charging requirement of the user. The alpha factor is limited to [0, 2] in combination with practical conditions]In the range, P when α is 0av0, charge-discharge power balance, and P is represented by α -2avCharging to TplThe actual charging amount is up to SOCsetThe required electric quantity is 2 times.
How much the alpha factor deviates from 1 can reflect the difficulty of meeting the requirements of the electric automobile users. The method for increasing the charging power of the electric automobile to participate in frequency modulation is called positive participation, and the method for reducing the charging power is called negative participation. And delta f is the system frequency deviation and is obtained by the difference between the system frequency and the reference value, wherein delta f <0 represents that the system generating power is insufficient, if the alpha <1 of the electric automobile at the moment, the electric automobile does not participate in frequency modulation, and only if the alpha >1 participates in frequency modulation.
The above calculations are performed by the controller using a central processing unit. Taking the controller in the charging pile as an example, when the user charges the electric automobile, the user inputs the target percentage value SOC of the remaining electric quantity on the charging pilesetInputting the set charging time period Tpl. Charging pile and BMS of battery carry out information interaction, display SOC0The current charging time t and the current percentage value SOC of the residual electric quantity of the electric automobile.
For calculating a primary frequency modulation power reference value delta P1In one embodiment, in step S203, the primary frequency modulation power reference value Δ P of the battery is calculated by the following formula1：
KEVThe frequency response coefficient is obtained by the dispatching center of the power grid.
Capacity P adjustable by electric automobile selected by dispatching centermax-PminCalculating the frequency response coefficient K of the electric automobile every 5 minutes for the reference power of the virtual synchronous machineEVAnd issuing the data.
And delta f is the variable quantity of the power grid frequency and is obtained by detecting the frequency deviation on the spot by a bidirectional charger.
The above calculation may be implemented in the controller by software or by a hardware circuit, and the schematic diagram of the circuit simulation structure for calculating the primary frequency modulation power reference value shown in fig. 4 includes: the device comprises a selection module and a judgment module; the multiplication sign represents a multiplier; in the figure, the selection module represents that when the delta f is larger than 0, the switch 1 is selected, otherwise, the switch 2 is selected, and the judgment module represents that when the input value is larger than 1, the 1 is output, and otherwise, the output is 0.
When the primary frequency modulation does not reach the preset effect, the secondary frequency modulation can be carried out for adjustment, and in order to calculate the secondary frequency modulation, the step of determining the reference value of the secondary frequency modulation power according to the second frequency modulation factor comprises the following steps: calculating the secondary frequency modulation power reference value delta P of the battery by the following formula2：
PsetThe reference power at the current moment is obtained by BMS monitoring of the battery;
Pminthe minimum value of the adjustable capacity is obtained by issuing from a power grid dispatching center;
Pmaxthe maximum value of the adjustable capacity is obtained by issuing from a power grid dispatching center.
The above calculation may be implemented by a software program or a hardware circuit, and the schematic diagram of the circuit structure for calculating the secondary frequency modulation power reference value shown in fig. 5 includes: the selection module is used for multiplying the signs in the figure to represent multipliers and adding the signs to represent adders; the symbols in the figure have the same meaning as the symbols in the prior art simulation.
In order to prevent deep discharge, the deep discharge prevention module is shown in the dotted line when the battery SOC value is lower than the discharge allowable value SOCdisAnd Δ P2+Pset<When 0, the electric automobile is prohibited from discharging, the discharge switching value (control word) s1 is set to 0, and delta P2 is set to-Pset。
In order to calculate the second frequency modulation factor β, in an embodiment, the frequency variation Δ f of the power grid and the first frequency modulation factor α may be used as inputs of a fuzzy algorithm, and the fuzzy algorithm may be used to calculate and output the second frequency modulation factor β. The method comprises the following specific steps:
a fuzzy control method that does not depend on a specific mathematical model is employed. Alpha factor, delta f and beta factor are selected as fuzzy variables, and a triangular fuzzy membership function with simple operation and high efficiency is adopted to define the linguistic value of each fuzzy linguistic variable.
Referring to the shape and distribution diagram of the membership function of the alpha factor shown in fig. 6, the horizontal axis is alpha, the value range of alpha is [0, 2], the vertical axis is u, the value range is [0, 1], 7 linguistic variables are assumed to be { LB, LM, LS, O, MS, MM, MB } in the domain, when the alpha factor is less than 1, the negative deviation is called, and when the alpha factor is greater than 1, the positive deviation is called. The fuzzy subset LB shows that the negative deviation degree of the alpha factor from 1 is extremely large and is close to 0, and the user requirements are difficult to meet at the moment; LM means that the negative deviation of the alpha factor from 1 is moderate; LS indicates that the α factor deviates less negatively from 1; o represents that the alpha factor is close to 1, and represents that the requirements of users can be basically met; MS indicates that the α factor deviates less positively from 1; MM means that the alpha factor deviates moderately from 1 in the positive direction; MB represents that the alpha factor deviates from 1 positively to a large extent, and the electric automobile has larger callable capacity.
Referring to the shape and distribution diagram of the Δ f membership function shown in fig. 7, the horizontal axis is Δ f, the value range is [ -0.5, +0.5], the vertical axis is u, and the value range is [0, 1 ]; the system requires to limit Δ f to the interval [ -0.5Hz, 0.5Hz ], so that 5 linguistic variables { NB, NS, Z, PS, PB } exist in the domain of the system, wherein fuzzy subsets NB and NS respectively represent that Δ f is less than-0.25 Hz and in the range of- (0-0.25) Hz, and the generated power of the system is insufficient; the fuzzy subset Z indicates that the deviation of the system frequency is substantially 0, and the system frequency is near the reference value; the fuzzy subsets PS and PB respectively represent that the Δ f is in the range of (0-0.25) Hz and is greater than 0.25Hz, and the system load power is insufficient.
Referring to the shape and distribution diagram of the membership function of the β factor shown in fig. 8, the horizontal axis is β, the value range is [ -1, +1], the vertical axis is u, and the value range is [0, 1 ]; the domain of discourse has 7 language variables { NB, NM, NS, Z, PS, PM and PB } which respectively represent different participation degrees, wherein NB, NM and NS respectively represent larger, moderate and smaller negative participation degrees; z represents that the electric automobile does not participate in secondary frequency modulation; PS, PM, PB are respectively less, moderate and greater in forward engagement.
An embodiment of the present application further provides a frequency modulation control device, which is applied to a controller, where the controller is configured to control a battery to perform electric energy interaction with a power grid to perform frequency modulation, see a structural block diagram of the frequency modulation control device shown in fig. 9, where the frequency modulation control device includes:
a receiving module 91, configured to receive an electric quantity parameter of the battery detected by a battery management system when the battery performs an electric energy interactive operation with a power grid;
a calculating module 92, configured to calculate a first frequency modulation factor of the battery according to the electric quantity parameter, and determine a primary frequency modulation power reference value of the battery according to the first frequency modulation factor;
and a control module 93, configured to control an electrical energy interaction operation between the battery and the power grid according to the primary frequency modulation power reference value.
Wherein: the calculation module 92 is further configured to: calculating a second frequency modulation factor by using the frequency variation of the power grid and the first frequency modulation factor; determining a secondary frequency modulation power reference value according to the second frequency modulation factor; and controlling the electric energy interactive operation between the battery and the power grid according to the secondary frequency modulation power reference value.
Wherein: the calculating module 92 is configured to calculate the first frequency modulation factor α of the battery according to the following formula:
wherein, SOC is the percentage value of the current electric vehicle residual capacity, SOCsetTarget percentage of remaining charge, SOC, set for the user0Is the initial value of the percentage value of the residual electric quantity when the electric automobile is connected into the power grid, TplFor the userSetting a charging time length, wherein t is the current charging time;
the calculating module 92 is used for calculating the primary frequency modulation power reference value Δ P of the battery according to the following formula1：
KEVIs a frequency response coefficient;
and delta f is the power grid frequency variation.
Wherein: the calculation module 92 is further configured to: the frequency variation of the power grid and the first frequency modulation factor are used as input of a fuzzy algorithm, and the second frequency modulation factor is obtained and output through the operation of the fuzzy algorithm; and the calculating module is also used for calculating the secondary frequency modulation power reference value delta P by the following formula2：
PsetReference power for the current moment;
Pminis the minimum value of the adjustable capacity;
Pmaxis the maximum value of the adjustable capacity.
According to the device, the first frequency modulation factor obtained according to the electric quantity parameter of the battery is utilized when the primary frequency modulation power reference value of the interaction between the battery and the power grid is calculated, so that the work of controlling the battery to carry out frequency modulation is more targeted and more detailed; the frequency modulation control strategy provided by the application is suitable according to local conditions, different due to batteries and different due to the residual electric quantity of the batteries, so that the charging power of each electric automobile can be effectively balanced, the frequency modulation potential of the electric automobiles with different working conditions is fully excavated, and the frequency stability of a system is improved.
The present application further discloses a controller, as shown in fig. 10, which is a schematic structural diagram of the controller provided in the present application, and includes: a processor 101, a memory 102, and a bus 103;
the memory 102 stores machine-readable instructions executable by the processor 101, and the processor 101 and the memory 102 communicate via a bus 103. In addition, the controller may further include a communication interface 104, and the communication interface 104 and the memory 102 are connected by a bus 103.
The Memory 102 may include a high-speed Random Access Memory (RAM) and may also include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 104 (which may be wired or wireless), the internet, a wide area network, a local area network, a metropolitan area network, etc. may be used, and the controller may obtain the initial model of the neural network through the communication interface 104.
Bus 103 may be an ISA bus, PCI bus, EISA bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 10, but this does not indicate only one bus or one type of bus.
The memory 102 is used for storing a program, and the processor 101 executes the program after receiving an execution instruction, and the method executed by the method or the apparatus disclosed in any of the foregoing embodiments of the present application may be applied to the processor 101, or implemented by the processor 101.
The processor 101 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 101. The processor 101 may be a general-purpose processor, and includes a Central Processing Unit (CPU), a Network processor (Network)
Processor, NP for short), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 102, and the processor 101 reads the information in the memory 102 and completes the steps of the method in combination with the hardware thereof.
The embodiment of the invention has the following beneficial effects:
1. the alpha factor introduced by the invention can reflect whether the electric automobile can meet the charging requirement of a user, and can be used as a general weighing factor to be applied to the charge and discharge of the electric automobile and the technical research of V2G.
2. The beta factor introduced by the invention can represent the strength of the electric automobile participating in secondary frequency modulation under different running conditions, the beta factor of each electric automobile is calculated by utilizing fuzzy control and participates in system secondary frequency modulation, the charging power of each electric automobile can be effectively balanced, the frequency modulation potential of the electric automobiles with different working conditions is fully excavated, and the frequency stability of the system is improved.
3. The electric automobile frequency modulation control strategy based on the alpha factor and the beta factor introduced by the invention can not only improve the frequency stability of a power system and reduce the construction investment of a power grid, but also adjust the charging power of the electric automobile in the frequency modulation process, balance the charging power of each electric automobile, meet the charging requirement of a user as much as possible, and has good effect.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The apparatus embodiments described above are merely illustrative, and the flowcharts and block diagrams in the figures, for example, illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present application, and are used for illustrating the technical solutions of the present application, but not for limiting the same, and the scope of the present application is not limited thereto, and although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope disclosed in the present application; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present application, and should be construed as being included therein. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A frequency modulation control method is applied to a controller, wherein the controller is used for controlling a battery to interact with electric energy of a power grid so as to perform frequency modulation, and the method comprises the following steps:
when the battery and a power grid perform electric energy interactive operation, acquiring electric quantity parameters of the battery;
calculating a first frequency modulation factor of the battery according to the electric quantity parameter;
determining a primary frequency modulation power reference value of the battery according to the first frequency modulation factor;
and controlling the electric energy interactive operation between the battery and the power grid according to the primary frequency modulation power reference value.
2. The method of claim 1, further comprising:
calculating a second frequency modulation factor by using the frequency variation of the power grid and the first frequency modulation factor;
determining a secondary frequency modulation power reference value according to the second frequency modulation factor;
and controlling the electric energy interactive operation between the battery and the power grid according to the secondary frequency modulation power reference value.
3. The method of claim 1, wherein calculating a first frequency modulation factor of the battery based on the charge parameter comprises: calculating a first frequency modulation factor α of the battery by the following formula:
wherein, SOC is the percentage value of the current electric vehicle residual capacity, SOCsetTarget percentage of remaining charge, SOC, set for the user0Is the initial value of the percentage value of the residual electric quantity when the electric automobile is connected into the power grid, TplAnd t is the current charging time.
4. The method of claim 1, wherein determining the primary fm power reference for the battery based on the first fm factor comprises: calculating a primary frequency modulation power reference value delta P of the battery by the following formula1：
KEVIs a frequency response coefficient;
and delta f is the power grid frequency variation.
5. The method of claim 2, wherein the step of calculating a second frequency modulation factor using the frequency variation of the power grid and the first frequency modulation factor comprises: and taking the frequency variation of the power grid and the first frequency modulation factor as the input of a fuzzy algorithm, and calculating by adopting the fuzzy algorithm to obtain and output the second frequency modulation factor.
6. The method of claim 2, wherein determining a secondary fm power reference value based on the second fm factor comprises: calculating a secondary frequency modulation power reference value delta P of the battery by the following formula2：
PsetReference power for the current moment;
Pminis the minimum value of the adjustable capacity;
Pmaxis the maximum value of the adjustable capacity.
7. A frequency modulation control device is applied to a controller, the controller is used for controlling a battery to interact with electric energy of a power grid so as to perform frequency modulation, and the device comprises:
the receiving module is used for receiving the electric quantity parameter of the battery detected by the battery management system when the battery and the power grid perform electric energy interactive operation;
the calculation module is used for calculating a first frequency modulation factor of the battery according to the electric quantity parameter and determining a primary frequency modulation power reference value of the battery according to the first frequency modulation factor;
and the control module is used for controlling the electric energy interactive operation between the battery and the power grid according to the primary frequency modulation power reference value.
8. The apparatus of claim 7, wherein the computing module is further configured to:
calculating a second frequency modulation factor by using the frequency variation of the power grid and the first frequency modulation factor;
determining a secondary frequency modulation power reference value according to the second frequency modulation factor;
and controlling the electric energy interactive operation between the battery and the power grid according to the secondary frequency modulation power reference value.
9. The apparatus of claim 7, wherein the calculating module is configured to calculate the first frequency modulation factor α of the battery by:
wherein, SOC is the percentage value of the current electric vehicle residual capacity, SOCsetTarget percentage of remaining charge, SOC, set for the user0Is the initial value of the percentage value of the residual electric quantity when the electric automobile is connected into the power grid, TplSetting a charging time length for a user, wherein t is the current charging time;
the calculation module is used for calculating a primary frequency modulation power reference value delta P of the battery through the following formula1：
KEVIs a frequency response coefficient;
and delta f is the power grid frequency variation.
10. The apparatus of claim 8, wherein the computing module is further configured to: the frequency variation of the power grid and the first frequency modulation factor are used as input of a fuzzy algorithm, and the second frequency modulation factor is obtained and output through operation of the fuzzy algorithm;
and the calculation module is also used for calculating a secondary frequency modulation power reference value delta P through the following formula2：
PsetReference power for the current moment;
Pminis the minimum value of the adjustable capacity;
Pmaxis the maximum value of the adjustable capacity.
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