Virtual power plant frequency modulation control method and device based on fuzzy control

Abstract

The invention relates to a virtual power plant frequency modulation control method and device based on fuzzy control, and the method comprises the steps: describing the charge state, indoor temperature and frequency modulation price of an electric vehicle through employing a trapezoidal fuzzy membership function; establishing a fuzzy rule of the electric vehicle and a fuzzy rule of the air conditioner load; determining the output of the charge state, the indoor temperature and the frequency modulation price of the electric vehicle under the fuzzy rule of the electric vehicle and the fuzzy rule of the air conditioner load, and carrying out weighted average on all the outputs to obtain the frequency modulation will of each electric vehicle and the frequency modulation will of each air conditioner load; constructing an intra-day optimization scheduling model by taking the minimum signal tracking effect cost and the minimum user willingness cost as targets; and solving the intra-day optimal scheduling model to obtain a frequency modulation control strategy, and performing frequency modulation control on the virtual power plant by adopting the frequency modulation control strategy. According to the invention, the intra-day second-level control of the virtual power plant participating in the frequency modulation auxiliary service is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual power plant frequency regulation control, and in particular to a virtual power plant frequency regulation control method and device based on fuzzy control. Background Art

[0002] At present, demand-side resources mainly rely on user autonomous control. For example, the charging and discharging of electric vehicles is random and disorderly, and air conditioning loads operate independently at different user-set temperatures. However, this approach has problems such as poor perception and slow response, and it is difficult to meet the needs of real-time power balance scheduling of the power system. There is a lack of coordinated regulation among various demand-side resources, so they cannot effectively participate in the power system and fail to give full play to their resource value. Therefore, it is an important research direction to use virtual power plants (VPPs) to aggregate demand-side resources in smart buildings on a large scale to participate in power system regulation.

[0003] When a virtual power plant participates in frequency regulation auxiliary services during the day, it needs to respond to the frequency regulation signal sent by the power market dispatch center to the virtual power plant. The frequency regulation signal is calculated based on the energy power and frequency regulation capacity reported by the virtual power plant to the power market the day before. After the virtual power plant responds to the frequency regulation signal, the dispatch center will assess the accuracy of the frequency regulation of the virtual power plant. When the virtual power plant responds to the frequency regulation signal in real time during the day, while ensuring the accuracy of the frequency regulation, it still needs to ensure the user's usage needs, such as the travel power demand of electric vehicle users and the cooling demand of air conditioning load users. When controlling the virtual power plant, Xu Mengyao et al. analyzed the user's behavioral characteristics and established a two-stage optimization scheduling model for the electric vehicle cluster and the frequency regulation unit to participate in the frequency regulation auxiliary service considering the willingness of electric vehicle users to participate; Dai Jianfeng et al. proposed an optimization scheduling strategy for the temperature control load cluster to participate in the system frequency regulation under the framework of distributed communication, and considered the user willingness coefficient set by the user in the model. The above studies have the following problems: 1) They fail to quantify the response willingness of demand-side resources within virtual power plants to participate in frequency regulation ancillary services; 2) They fail to study the real-time control strategy of virtual power plants participating in frequency regulation ancillary services. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a virtual power plant frequency control method and device based on fuzzy control, which can evaluate the response willingness of the demand-side resources within the virtual power plant in frequency control, and while taking into account the user needs of the demand-side resources, achieve intra-day second-level control of the virtual power plant participating in frequency auxiliary services.

[0005] The technical solution adopted by the present invention to solve the technical problem is: to provide a virtual power plant frequency modulation control method based on fuzzy control, comprising the following steps:

[0006] Trapezoidal fuzzy membership function is used to describe the state of charge of electric vehicles, indoor temperature and frequency modulation price;

[0007] Establish fuzzy rules for electric vehicles and fuzzy rules for air conditioning loads;

[0008] Determine the output of the state of charge of electric vehicles and the frequency regulation price under the fuzzy rules of electric vehicles, take the weighted average of all outputs, and obtain the frequency regulation willingness of each electric vehicle;

[0009] Determine the output of indoor temperature and frequency modulation price under the fuzzy rule of air conditioning load, take weighted average of all outputs, and get the frequency modulation willingness of each air conditioning load;

[0010] Based on the frequency adjustment willingness of each electric vehicle and each air-conditioning load, an intraday optimization scheduling model is constructed with the goal of minimizing the signal tracking effect cost and user willingness cost;

[0011] The intraday optimization scheduling model is solved to obtain a frequency regulation control strategy, and the frequency regulation control strategy is used to perform frequency regulation control on the virtual power plant.

[0012] When the trapezoidal fuzzy membership function is used to describe the state of charge of the electric vehicle, the indoor temperature and the frequency modulation price, the three fuzzy subsets of the state of charge of the electric vehicle are defined as "low battery", "medium battery" and "high battery"; the three fuzzy subsets of the indoor temperature are defined as "cold", "suitable" and "hot"; the three fuzzy subsets of the frequency modulation price are defined as "cheap", "medium" and "expensive".

[0013] The fuzzy rule of the electric vehicle is expressed as: Among them, f i EV (SOC,π cp ) is the fuzzy rule of electric vehicles, SOC is the state of charge of electric vehicles, π cp is the FM price, and are all constants of fuzzy rules for electric vehicles and are greater than 0, P EV is the charging and discharging power of the electric vehicle.

[0014] The fuzzy rule of the air conditioning load is expressed as: Among them, f i AC (T in ,π cp ) is the fuzzy rule of air conditioning load, T in is the indoor temperature, π cp is the FM price, and are all constants of the fuzzy rules of air conditioning load and are greater than 0, PAC is the power of the air conditioning load.

[0015] The objective function f of the intraday optimization scheduling model is expressed as: minf = ωC1 + ξC2, where C1 is the signal tracking effect cost, expressed as: C1 = ε|P t RR -P t FR |, ε is the error cost conversion coefficient, P t RR represents the actual frequency modulation capacity during period t, P t FR represents the bidding for frequency regulation capacity during period t, and C2 is the user's willingness cost, which can be expressed as: ε EV is the electric vehicle willingness cost conversion coefficient, is the frequency modulation willingness of the kth electric vehicle, ε AC is the conversion coefficient of air conditioning load willingness cost, represents the frequency regulation intention of the lth air conditioning load, represents the state of charge of the kth electric car at time t, and They represent the minimum state of charge and maximum state of charge of the electric vehicle respectively. is the indoor temperature at the location of the lth air conditioning load at time t, T min and T max They represent the lower and upper temperature limits of the temperature comfort zone, P FR represents the bidding for frequency modulation capacity, ω and ξ are the weight factors of signal tracking effect cost and user willingness cost respectively.

[0016] The constraints of the intraday optimization scheduling model are expressed as: in, represents the power of the kth electric car at time t. Please explain, P EVmax represents the maximum power of the electric vehicle, represents the power of the lth air conditioning load at time t, P ACmin and P ACmax Respectively represent the minimum and maximum power of air conditioning load.

[0017] The technical solution adopted by the present invention to solve the technical problem is: to provide a virtual power plant frequency regulation control device based on fuzzy control, comprising:

[0018] A description module is used to describe the state of charge of the electric vehicle, the indoor temperature and the frequency modulation price using a trapezoidal fuzzy membership function;

[0019] Establishing modules for establishing fuzzy rules for electric vehicles and fuzzy rules for air conditioning loads;

[0020] The electric vehicle frequency modulation willingness module is used to determine the output of the electric vehicle's state of charge and frequency modulation price under the fuzzy rules of the electric vehicle, and to perform weighted average of all outputs to obtain the frequency modulation willingness of each electric vehicle;

[0021] The air conditioning load frequency modulation willingness module is used to determine the output of indoor temperature and frequency modulation price under the fuzzy rule of air conditioning load, and to perform weighted average of all outputs to obtain the frequency modulation willingness of each air conditioning load;

[0022] A construction module is used to construct an intraday optimization scheduling model based on the frequency adjustment willingness of each electric vehicle and each air-conditioning load, with the goal of minimizing the signal tracking effect cost and the user willingness cost;

[0023] The control module is used to solve the intraday optimization scheduling model, obtain the frequency regulation control strategy, and use the frequency regulation control strategy to perform frequency regulation control on the virtual power plant.

[0024] The technical solution adopted by the present invention to solve its technical problem is: to provide an electronic device, including a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the above-mentioned virtual power plant frequency control method based on fuzzy control when executing the computer program.

[0025] The technical solution adopted by the present invention to solve its technical problem is: providing a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned virtual power plant frequency control method based on fuzzy control are implemented.

[0026] Beneficial Effects

[0027] Due to the adoption of the above technical solution, the present invention has the following advantages and positive effects compared with the prior art: the present invention adopts a fuzzy model to quantitatively analyze the user's frequency modulation willingness, and establishes a power allocation model that takes into account the user's willingness, so as to realize the intra-day real-time control of the demand-side resources participating in the frequency modulation auxiliary service. The in-building frequency modulation power allocation algorithm based on fuzzy control proposed in the present invention can fully consider the user's usage needs, and can ensure the travel needs of electric vehicle users and the cooling needs of air-conditioning load users while the virtual power plant participates in the frequency modulation auxiliary service. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a flow chart of a virtual power plant frequency regulation control method based on fuzzy control according to a first embodiment of the present invention;

[0029] Figure 2 is a schematic diagram of the membership function of the state of charge of an electric vehicle in the first embodiment of the present invention;

[0030] Figure 3 is a schematic diagram of the membership function of the indoor temperature in the first embodiment of the present invention;

[0031] Figure 4 It is a schematic diagram of the membership function of the frequency modulation price in the first embodiment of the present invention. DETAILED DESCRIPTION

[0032] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the appended claims of the application equally.

[0033] The first embodiment of the present invention relates to a virtual power plant frequency control method based on fuzzy control. The method adopts the TSK fuzzy model to quantitatively analyze the user's frequency regulation willingness, and establishes an intraday optimization scheduling model that takes user willingness into consideration, so as to realize intraday real-time control of demand-side resources participating in frequency regulation auxiliary services.

[0034] The process of fuzzy reasoning can be divided into three steps: fuzzification, fuzzy reasoning and defuzzification. Fuzzification is to convert the precise value of the input variable into the corresponding fuzzy set, fuzzy reasoning is to calculate the fuzzy set of the output variable according to fuzzy rules and fuzzy operations, and defuzzification is to convert the fuzzy set of the output variable into one or more precise values. Fuzzy reasoning has the following advantages: it can handle incomplete and imprecise information, it can use human experience and intuition, and it can simplify the modeling and control of complex systems.

[0035] The TSK fuzzy model has excellent nonlinear approximation performance and is widely used in many fields such as system identification, pattern recognition and data processing. The uniqueness of the TSK model is that the output of its fuzzy rules is no longer a fuzzy set, but a constant or linear polynomial, so the defuzzification process of the fuzzy reasoning result can be achieved by simple digital calculations.

[0036] The process of TSK fuzzy reasoning model is:

[0037] (1) Fuzzify the input.

[0038] (2) Based on the fuzzy rules, determine the output of the input quantity under each rule.

[0039] Fuzzy rules can be defined as:

[0040] IF(x is X i )and(y is Y i ),THENλ i =fi (x,y)

[0041] f i (x,y)=a i x+b i y+c i

[0042] Where: f i (x,y) is the i-th rule; a i 、b i 、c i is a constant coefficient; x and y are input quantities.

[0043] The outputs of all rules are weighted averaged to perform defuzzification operations, as follows:

[0044]

[0045] Where: and is the membership function of the input quantity; M is the number of fuzzy rules.

[0046] The virtual power plant frequency control method based on fuzzy control in this embodiment can be applied to smart buildings. The smart building centralized controller receives the power signal sent by the smart building cluster in real time, then confirms the operating status of the electric vehicle and air-conditioning load, and changes the access power of each electric vehicle and air-conditioning load to meet the energy demand and the frequency regulation demand of the system. When allocating power, the user's usage needs are met to the greatest extent while meeting the system frequency regulation requirements. Figure 1 As shown, this embodiment specifically includes the following steps:

[0047] Step 1: Use trapezoidal fuzzy membership function to describe the state of charge of electric vehicles, indoor temperature and frequency modulation price.

[0048] In this step, the three fuzzy subsets of the state of charge are defined as "low power", "medium power" and "high power" according to the electric car owner's perception of power; the three fuzzy subsets of indoor temperature are "cold", "suitable" and "hot"; the three fuzzy subsets of frequency modulation price are "cheap", "medium" and "expensive". The trapezoidal fuzzy membership function is used to describe the three input quantities. The membership functions of the three input quantities are as follows: Figure 2-Figure 4 As shown, users can define the membership function according to their own needs.

[0049] Step 2: Establish fuzzy rules for electric vehicles and fuzzy rules for air conditioning loads.

[0050] When the grid has a downward frequency regulation demand, the lower the state of charge of the electric vehicle, the higher the frequency regulation price, and the greater the willingness to participate in the frequency regulation service; conversely, when the grid has an upward frequency regulation demand, the higher the state of charge of the electric vehicle, the higher the frequency regulation price, and the greater the willingness to participate in the frequency regulation service. Therefore, in this step, the fuzzy rule of the electric vehicle is defined as:

[0051]

[0052] Among them, f i EV (SOC,π cp ) is the fuzzy rule of electric vehicles, SOC is the state of charge of electric vehicles, π cp is the FM price, and are all constants of fuzzy rules for electric vehicles and are greater than 0, P EV Indicates the charging and discharging power of an electric vehicle.

[0053] When the grid has a downward frequency regulation demand, the higher the indoor temperature, the higher the frequency regulation price, and the greater the willingness of the air conditioning load to participate in the frequency regulation service; conversely, when the grid has an upward frequency regulation demand, the lower the indoor temperature, the higher the frequency regulation price, and the greater the willingness of the air conditioning load to participate in the frequency regulation service. Therefore, in this step, the fuzzy rule of the air conditioning load is defined as:

[0054]

[0055] Among them, f i AC (T in ,π cp ) is the fuzzy rule of air conditioning load, T in is the indoor temperature, π cp is the FM price, and are all constants of the fuzzy rules of air conditioning load and are greater than 0, P AC Indicates the power of the air conditioning load.

[0056] Step 3: Determine the output of the state of charge and frequency adjustment price of the electric vehicle under the fuzzy rules of the electric vehicle, and perform weighted average of all outputs to obtain the frequency adjustment willingness of each electric vehicle. The calculation method of this step is the defuzzification operation of the TSK fuzzy model. After completion, the frequency adjustment willingness of each electric vehicle can be obtained.

[0057] Step 4: Determine the output of indoor temperature and frequency adjustment price under the fuzzy rules of air conditioning load, and perform weighted average of all outputs to obtain the frequency adjustment willingness of each air conditioning load. The calculation method of this step is the defuzzification operation of the TSK fuzzy model. After completion, the frequency adjustment willingness of each air conditioning load can be obtained.

[0058] Step 5: Based on the frequency regulation willingness of each electric vehicle and each air-conditioning load, an intraday optimization scheduling model is constructed with the goal of minimizing the signal tracking effect cost and the user willingness cost.

[0059] The frequency modulation willingness of each electric vehicle is calculated and the frequency modulation willingness of each air conditioning load Finally, an intraday optimal scheduling model within the building is established with the goal of minimizing the frequency modulation error and satisfying the user's wishes. The objective function of the intraday optimal scheduling model can be divided into signal tracking effect cost and user willingness cost.

[0060] Among them, the signal tracking effect cost is expressed as:

[0061] C1=ε|P t RR -P t FR |

[0062] Where ε is the error cost conversion coefficient, P t RR represents the actual frequency modulation capacity during period t, P t FR It represents the bidding of frequency regulation capacity in period t. The main function of this cost is to minimize the power error of smart buildings participating in frequency regulation.

[0063] When the total frequency modulation signal sent by the dispatch center is positive, it means that the power system needs to lower the frequency modulation service, the smart building needs to increase its power consumption, the electric vehicle increases its charging power, and the air conditioning load increases its cooling power. At the same time, users with greater frequency modulation willingness should participate more in frequency modulation; vice versa. Therefore, the following user willingness cost function is set, which comprehensively considers user willingness and system requirements.

[0064]

[0065] In the formula, ε EV is the electric vehicle willingness cost conversion coefficient, ε AC is the conversion coefficient of air conditioning load willingness cost, represents the state of charge of the kth electric car at time t, and They represent the minimum state of charge and maximum state of charge of the electric vehicle respectively. is the indoor temperature at the location of the lth air conditioning load at time t, T min and T max They represent the lower and upper temperature limits of the temperature comfort zone, P FRrepresents the bidding for frequency regulation capacity, ω and ξ are the weight factors of signal tracking effect cost and user willingness cost respectively. In real-time scheduling, the proportion of the two costs can be adjusted according to actual demand to cope with the uncertainty and electricity demand on the demand side. The goal of this cost is to maximize the overall willingness of users and meet their needs as much as possible.

[0066] Therefore, the objective function f of the intraday optimization scheduling model in this step is expressed as:

[0067] minf=ωC1+ξC2

[0068] The constraints of the intraday optimization scheduling model are expressed as:

[0069]

[0070] in, represents the power of the kth electric car at time t. Please explain, P EVmax represents the maximum power of the electric vehicle, represents the power of the lth air conditioning load at time t, P ACmin and P ACmax Respectively represent the minimum power and maximum power of air conditioning load.

[0071] Step 6: Solve the intraday optimization scheduling model to obtain a frequency control strategy, and use the frequency control strategy to perform frequency control on the virtual power plant.

[0072] It is not difficult to find that the in-building frequency regulation power allocation algorithm based on fuzzy control in this implementation mode can fully take into account the usage needs on the user side, and can ensure the travel needs of electric vehicle users and the cooling needs of air-conditioning load users while the virtual power plant participates in the frequency regulation auxiliary service.

[0073] The second embodiment of the present invention relates to a virtual power plant frequency regulation control device based on fuzzy control, comprising:

[0074] A description module is used to describe the state of charge of the electric vehicle, the indoor temperature and the frequency modulation price using a trapezoidal fuzzy membership function;

[0075] Establishing modules for establishing fuzzy rules for electric vehicles and fuzzy rules for air conditioning loads;

[0076] The electric vehicle frequency modulation willingness module is used to determine the output of the electric vehicle's state of charge and frequency modulation price under the fuzzy rules of the electric vehicle, and to perform weighted average of all outputs to obtain the frequency modulation willingness of each electric vehicle;

[0077] The air conditioning load frequency modulation willingness module is used to determine the output of indoor temperature and frequency modulation price under the fuzzy rule of air conditioning load, and to perform weighted average of all outputs to obtain the frequency modulation willingness of each air conditioning load;

[0078] A construction module is used to construct an intraday optimization scheduling model based on the frequency adjustment willingness of each electric vehicle and each air-conditioning load, with the goal of minimizing the signal tracking effect cost and the user willingness cost;

[0079] The control module is used to solve the intraday optimization scheduling model, obtain the frequency regulation control strategy, and use the frequency regulation control strategy to perform frequency regulation control on the virtual power plant.

[0080] When the description module uses a trapezoidal fuzzy membership function to describe the state of charge of the electric vehicle, the indoor temperature and the frequency modulation price, the three fuzzy subsets of the state of charge of the electric vehicle are defined as "low battery", "medium battery" and "high battery"; the three fuzzy subsets of the indoor temperature are defined as "cold", "suitable" and "hot"; the three fuzzy subsets of the frequency modulation price are defined as "cheap", "medium" and "expensive".

[0081] The fuzzy rules of the electric vehicle established by the establishment module are expressed as: Among them, f i EV (SOC,π cp ) is the fuzzy rule of electric vehicles, SOC is the state of charge of electric vehicles, π cp is the FM price, and are all constants of fuzzy rules for electric vehicles and are greater than 0, P EV Indicates the charging and discharging power of an electric vehicle.

[0082] The fuzzy rule of the air conditioning load established by the establishment module is expressed as: Among them, f i AC (T in ,π cp ) is the fuzzy rule of air conditioning load, T in is the indoor temperature, π cp is the FM price, and are all constants of the fuzzy rules of air conditioning load and are greater than 0, P AC Indicates the power of the air conditioning load.

[0083] The objective function f of the intraday optimization scheduling model constructed by the building module is expressed as: minf = ωC1 + ξC2, where C1 is the signal tracking effect cost, expressed as: C1 = ε|P t RR -Pt FR |, ε is the error cost conversion coefficient, P t RR represents the actual frequency modulation capacity during period t, P t FR represents the bidding of frequency regulation capacity in period t, C2 is the user's willingness cost, which is expressed as: ε EV is the conversion coefficient of electric vehicle willingness cost, is the frequency modulation willingness of the kth electric vehicle, ε AC is the conversion coefficient of air conditioning load willingness cost, represents the frequency regulation intention of the lth air conditioning load, represents the state of charge of the kth electric car at time t, and They represent the minimum state of charge and maximum state of charge of the electric vehicle respectively. is the indoor temperature at the location of the lth air conditioning load at time t, T min and T max They represent the lower and upper temperature limits of the temperature comfort zone, P FR represents the bidding for frequency modulation capacity, ω and ξ are the weight factors of signal tracking effect cost and user willingness cost respectively.

[0084] The constraints of the intraday optimization scheduling model constructed by the building module are expressed as: in, represents the power of the kth electric car at time t. Please explain, P EVmax represents the maximum power of the electric vehicle, represents the power of the lth air conditioning load at time t, P ACmin and P ACmax Respectively represent the minimum power and maximum power of air conditioning load.

[0085] The third embodiment of the present invention relates to an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned virtual power plant frequency control method based on fuzzy control when executing the computer program.

[0086] The fourth embodiment of the present invention relates to a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned virtual power plant frequency control method based on fuzzy control are implemented.

[0087] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) that contain computer-usable program code.

[0088] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0089] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction method, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0090] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0091] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A virtual power plant frequency control method based on fuzzy control, characterized in that: The following steps are involved: Trapezoidal fuzzy membership function is used to describe the state of charge of electric vehicles, indoor temperature and frequency modulation price; Establish fuzzy rules for electric vehicles and fuzzy rules for air conditioning loads; Determine the output of the state of charge of electric vehicles and the frequency regulation price under the fuzzy rules of electric vehicles, take the weighted average of all outputs, and obtain the frequency regulation willingness of each electric vehicle; Determine the output of indoor temperature and frequency modulation price under the fuzzy rule of air conditioning load, and take weighted average of all outputs. Get the frequency regulation intention of each air conditioning load; Based on the frequency adjustment willingness of each electric vehicle and each air-conditioning load, an intraday optimization scheduling model is constructed with the goal of minimizing the signal tracking effect cost and user willingness cost; The intraday optimization scheduling model is solved to obtain a frequency regulation control strategy, and the frequency regulation control strategy is used to perform frequency regulation control on the virtual power plant.

2. The frequency modulation control method of a virtual power plant based on fuzzy control according to claim 1 is characterized in that: When the trapezoidal fuzzy membership function is used to describe the state of charge of the electric vehicle, the indoor temperature and the frequency modulation price, the three fuzzy subsets of the state of charge of the electric vehicle are defined as "low battery", "medium battery" and "high battery"; the three fuzzy subsets of the indoor temperature are defined as "cold", "suitable" and "hot"; the three fuzzy subsets of the frequency modulation price are defined as "cheap", "medium" and "expensive".

3. The frequency modulation control method of a virtual power plant based on fuzzy control according to claim 1 is characterized in that: The fuzzy rule of the electric vehicle is expressed as: in, is the fuzzy rule of electric vehicles, SOC is the state of charge of electric vehicles, π cp is the FM price, and are all constants of fuzzy rules for electric vehicles and are greater than 0, P EV Indicates the charging and discharging power of electric vehicles.

4. The frequency modulation control method of a virtual power plant based on fuzzy control according to claim 1 is characterized in that: The fuzzy rule of the air conditioning load is expressed as: in, is the fuzzy rule of air conditioning load, T in is the indoor temperature, π cp is the FM price, and are all constants of the fuzzy rules of air conditioning load and are greater than 0. AC Indicates the power of the air conditioning load.

5. The frequency modulation control method of a virtual power plant based on fuzzy control according to claim 1 is characterized in that: The objective function f of the intraday optimization scheduling model is expressed as: minf = ωC1 + ξC2, where C1 is the signal tracking effect cost, expressed as: ε is the error cost conversion coefficient, Indicates the actual frequency modulation capacity during period t. represents the bidding for frequency regulation capacity during period t, and C2 is the user's willingness cost, which can be expressed as: ε EV is the conversion coefficient of electric vehicle willingness cost, is the frequency modulation willingness of the kth electric vehicle, ε AC is the conversion coefficient of air conditioning load willingness cost, represents the frequency regulation intention of the lth air conditioning load, represents the state of charge of the kth electric car at time t, and They represent the minimum state of charge and maximum state of charge of the electric vehicle respectively. is the indoor temperature at the location of the lth air conditioning load at time t, T min and T max They represent the lower and upper temperature limits of the temperature comfort zone, P FR represents the bidding for frequency modulation capacity, ω and ξ are the weight factors of signal tracking effect cost and user willingness cost respectively.

6. The frequency modulation control method of a virtual power plant based on fuzzy control according to claim 1 is characterized in that: The constraints of the intraday optimization scheduling model are expressed as: in, represents the charging and discharging power of the kth electric vehicle at time t, P EVmax Indicates the maximum charging and discharging power of electric vehicles, represents the power of the lth air conditioning load at time t, P ACmin and P ACmax Respectively represent the minimum power and maximum power of air conditioning load.

7. A virtual power plant frequency control device based on fuzzy control, characterized in that: include: A description module is used to describe the state of charge of the electric vehicle, the indoor temperature and the frequency modulation price using a trapezoidal fuzzy membership function; Establishing a module for establishing fuzzy rules for electric vehicles and fuzzy rules for air conditioning loads; The electric vehicle frequency modulation willingness module is used to determine the output of the electric vehicle's state of charge and frequency modulation price under the fuzzy rules of the electric vehicle, and to perform weighted average of all outputs to obtain the frequency modulation willingness of each electric vehicle; The air conditioning load frequency modulation willingness module is used to determine the output of indoor temperature and frequency modulation price under the fuzzy rule of air conditioning load, and to perform weighted average of all outputs to obtain the frequency modulation willingness of each air conditioning load; A construction module is used to construct an intraday optimization scheduling model based on the frequency adjustment willingness of each electric vehicle and each air-conditioning load, with the goal of minimizing the signal tracking effect cost and the user willingness cost; The control module is used to solve the intraday optimization scheduling model, obtain the frequency regulation control strategy, and use the frequency regulation control strategy to perform frequency regulation control on the virtual power plant.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the virtual power plant frequency control method based on fuzzy control as described in any one of claims 1-6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the virtual power plant frequency control method based on fuzzy control as described in any one of claims 1-6 are implemented.

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