A virtual power plant frequency modulation control method and device based on fuzzy control

Abstract

The application relates to a virtual power plant frequency modulation control method and device based on fuzzy control, wherein the method comprises the following steps: adopting a trapezoidal fuzzy membership function to describe the state of charge of an electric vehicle, indoor temperature and frequency modulation price; establishing fuzzy rules of the electric vehicle and fuzzy rules of air conditioner load; determining the output of the state of charge of the electric vehicle, indoor temperature and frequency modulation price under the fuzzy rules of the electric vehicle and the fuzzy rules of the air conditioner load, weighting and averaging all the outputs to obtain the frequency modulation willingness of each electric vehicle and the frequency modulation willingness of each air conditioner load; taking the minimum signal tracking effect cost and user willingness cost as a target, constructing an intra-day optimization scheduling model; solving the intra-day optimization scheduling model to obtain a frequency modulation control strategy, and adopting the frequency modulation control strategy to perform frequency modulation control on the virtual power plant. The application realizes intra-day second-level control of frequency modulation auxiliary services participated in by the virtual power plant.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of virtual power plant frequency modulation control, in particular to a virtual power plant frequency modulation control method and device based on fuzzy control. BACKGROUND

[0002] Currently, demand side resources mainly rely on user self-control, for example, the charging and discharging of electric vehicles is random and disordered, and air conditioning loads independently run at different user set temperatures. However, this approach has problems such as poor perception and slow response, and cannot meet the needs of real-time power balance scheduling of the power system. Various demand side resources lack coordinated regulation and control, so they cannot effectively participate in the power system and fail to fully realize their resource value. Therefore, it is an important research direction to use virtual power plants (VPP) to aggregate demand side resources in intelligent buildings on a large scale to participate in power system regulation.

[0003] When participating in frequency modulation auxiliary services during the day, the virtual power plant needs to respond to the frequency modulation signal issued by the power market dispatch center to the virtual power plant, which is calculated based on the energy and power reported by the virtual power plant to the power market in the day ahead. After the virtual power plant responds to the frequency modulation signal, the dispatch center will assess the accuracy of the virtual power plant's frequency modulation. When responding to the frequency modulation signal in real time during the day, the virtual power plant needs to ensure the accuracy of the frequency modulation while ensuring the user's usage needs, such as the electric vehicle user's travel power demand and the air conditioning load user's cooling demand. When controlling the virtual power plant, the existing research has the following problems: 1) the response willingness of the demand side resources within the virtual power plant participating in the frequency modulation auxiliary service is not quantified; 2) the real-time control strategy of the virtual power plant participating in the frequency modulation auxiliary service is not studied. SUMMARY

[0004] The technical problem to be solved by the present application is to provide a virtual power plant frequency modulation control method and device based on fuzzy control, which can evaluate the response willingness of demand side resources within the virtual power plant in frequency modulation control, and realize the real-time control of the virtual power plant participating in the frequency modulation auxiliary service while taking into account the user demand of the demand side resources.

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

[0006] The state of charge of the electric vehicle, indoor temperature and frequency modulation price are described by using trapezoidal fuzzy membership functions.

[0007] The fuzzy rules of the electric vehicle and the fuzzy rules of the air conditioning load are established.

[0008] The output of the state of charge of the electric vehicle and the frequency modulation price under the fuzzy rules of the electric vehicle is determined, and all the outputs are weighted and averaged to obtain the frequency modulation willingness of each electric vehicle.

[0009] The output of the indoor temperature and the frequency modulation price under the fuzzy rules of the air conditioning load is determined, and all the outputs are weighted and averaged to obtain the frequency modulation willingness of each air conditioning load.

[0010] Based on the frequency modulation willingness of each electric vehicle and each air conditioning load, an intra-day optimal scheduling model is constructed with the minimum signal tracking effect cost and user willingness cost as the target.

[0011] The intra-day optimal scheduling model is solved to obtain a frequency modulation control strategy, and the virtual power plant is controlled by using the frequency modulation control strategy.

[0012] When the state of charge of the electric vehicle, indoor temperature and frequency modulation price are described by using trapezoidal fuzzy membership functions, the three fuzzy subsets of the state of charge of the electric vehicle are defined as "insufficient power", "moderate power" and "high power", the three fuzzy subsets of the indoor temperature are defined as "cold", "suitable" and "hot", and the three fuzzy subsets of the frequency modulation price are defined as "cheap", "moderate" and "expensive".

[0013] The fuzzy rules of the electric vehicle are represented as: Wherein, f i EV (SOC,π cp ) is the fuzzy rule of the electric vehicle, SOC is the state of charge of the electric vehicle, π cp is the frequency modulation price, and are constants of the fuzzy rule of the electric vehicle and are greater than 0, P EV is the charging and discharging power of the electric vehicle.

[0014] The fuzzy rules of the air conditioning load are represented as: Wherein, f i AC (T in ,π cp ) is the fuzzy rule of the air conditioning load, T in is the indoor temperature, π cp is the frequency modulation price, and are constants of fuzzy rules of air conditioning load and are greater than 0, P AC is power of air conditioning load.

[0015] The objective function f of the intra-day optimization scheduling model is represented as: min f = ωC1 + ξC2, wherein C1 is a signal tracking effect cost, represented as: C1 = ε|P t RR -P t FR |, ε is an error cost conversion coefficient, P t RR represents actual frequency modulation capacity at t period, P t FR represents bid of frequency modulation capacity at t period, C2 is a user willingness cost, represented as: ε EV is an electric vehicle willingness cost conversion coefficient, is frequency modulation willingness of the kth electric vehicle, ε AC is an air conditioning load willingness cost conversion coefficient, represents frequency modulation willingness of the lth air conditioning load, represents state of charge of the kth electric vehicle at t moment, and respectively represent minimum state of charge and maximum state of charge of the electric vehicle, is indoor temperature of a location where the lth air conditioning load is located at t moment, T min and T max respectively represent temperature lower limit and temperature upper limit of a temperature comfort zone, P FR represents bid of frequency modulation capacity, ω and ξ are respectively weight factors of the signal tracking effect cost and the user willingness cost.

[0016] The constraint condition of the intra-day optimization scheduling model is represented as: wherein, represents power of the kth electric vehicle at t moment, P EVmax represents maximum power of the electric vehicle, represents power of the lth air conditioning load at t moment, P ACmin and P ACmax respectively represent minimum and maximum power of the air conditioning load.

[0017] The technical scheme adopted by the present application to solve its technical problems is: to provide a virtual power plant frequency modulation control device based on fuzzy control, comprising:

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

[0019] The establishing module is configured to establish fuzzy rules of the electric vehicles and fuzzy rules of the air conditioning loads;

[0020] The electric vehicle frequency modulation willingness module is configured to determine outputs of the state of charge of the electric vehicles and the frequency modulation price under the fuzzy rules of the electric vehicles, and to obtain the frequency modulation willingness of each electric vehicle by weightedly averaging all the outputs.

[0021] The air conditioning load frequency modulation willingness module is configured to determine outputs of the indoor temperature and the frequency modulation price under the fuzzy rules of the air conditioning loads, and to obtain the frequency modulation willingness of each air conditioning load by weightedly averaging all the outputs.

[0022] The constructing module is configured to construct an intra-day optimization scheduling model based on the frequency modulation willingness of each electric vehicle and each air conditioning load, with the minimum signal tracking effect cost and user willingness cost as the target.

[0023] The control module is configured to solve the intra-day optimization scheduling model to obtain a frequency modulation control strategy, and to perform frequency modulation control on the virtual power plant by using the frequency modulation control strategy.

[0024] The technical scheme adopted by the present application to solve the technical problem is to provide 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 fuzzy control-based virtual power plant frequency modulation control method when executing the computer program.

[0025] The technical scheme adopted by the present application to solve the technical problem is to provide a computer readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the fuzzy control-based virtual power plant frequency modulation control method when executed by a processor.

[0026] Advantages

[0027] Compared with the prior art, the present application has the following advantages and positive effects: the present application uses a fuzzy model to quantitatively analyze the frequency modulation willingness of users, and establishes a power distribution model considering user willingness, thereby realizing intra-day real-time control of demand-side resources participating in frequency modulation auxiliary services. The fuzzy control-based building intra-frequency power distribution algorithm proposed in the present application can fully consider the use demand of the user side, and can guarantee the travel demand of electric vehicle users and the refrigeration demand of air conditioning load users while the virtual power plant participates in frequency modulation auxiliary services. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 is a flowchart of the fuzzy control-based virtual power plant frequency modulation control method of the first embodiment of the present application;

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

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

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

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

[0033] The first embodiment of the application relates to a virtual power plant frequency modulation control method based on fuzzy control, which quantitatively analyzes the frequency modulation willingness of users by using a TSK fuzzy model, and establishes an intra-day optimization scheduling model considering the willingness of users, so as to realize the intra-day real-time control of the demand side resources participating in the frequency modulation auxiliary service.

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

[0035] TSK fuzzy model has excellent nonlinear approximation performance and is widely used in system identification, pattern recognition and data processing. The unique feature of TSK model is that the output of its fuzzy rule is no longer a fuzzy set, but a constant or a linear polynomial, so that only simple digital calculation is needed to realize the defuzzification process of the fuzzy reasoning result.

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

[0037] (1) Fuzzification of the input variable.

[0038] (2) According to the fuzzy rule, determine the output of the input variable under each rule.

[0039] The fuzzy rule can be defined as:

[0040] IF(x is X i) and (y is Y i ), THEN λ i = f i (x, y)

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

[0042] wherein f i (x, y) is the ith rule; a i , b i , c i are constant coefficients; x, y are input quantities.

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

[0044]

[0045] wherein: and are membership functions of input quantities; M is the number of fuzzy rules.

[0046] The virtual power plant frequency control method based on fuzzy control of the embodiment can be applied to a smart building. A smart building central controller receives power signals issued by a smart building cluster in real time, then confirms the operating states of electric vehicles and air conditioning loads, and changes the access power of each electric vehicle and air conditioning load to meet the energy use demand and system frequency regulation demand. When distributing power, the use demand of users is maximally met while meeting the system frequency regulation demand. As shown in Figure 1 , the embodiment specifically includes the following steps:

[0047] Step 1: The state of charge of an electric vehicle, indoor temperature, and frequency regulation price are described by using a trapezoidal fuzzy membership function.

[0048] In this step, three fuzzy subsets of the state of charge are defined as "insufficient power", "moderate power", and "high power" according to the cognition of an electric vehicle owner on power; three fuzzy subsets of indoor temperature are "cold", "comfortable", and "hot"; and three fuzzy subsets of the frequency regulation price are "cheap", "moderate", and "expensive". The three input quantities are described by using a trapezoidal fuzzy membership function. The membership functions of the three input quantities are shown in Figures 2-4 . Users can define the membership functions according to their own needs.

[0049] Step 2: Fuzzy rules of electric vehicles and fuzzy rules of air conditioning loads are established.

[0050] When the grid is in need of frequency down-regulation, the lower the state of charge of the electric vehicle, the higher the frequency regulation price, and the greater the willingness to participate in frequency regulation service. Conversely, when the grid is in need of frequency up-regulation, the higher the state of charge of the electric vehicle, the higher the frequency regulation price, and the greater the willingness to participate in frequency regulation service. Therefore, the fuzzy rule of the electric vehicle in this step is defined as:

[0051]

[0052] wherein f i EV (SOC,π cp ) is the fuzzy rule of the electric vehicle, SOC is the state of charge of the electric vehicle, π cp is the frequency regulation price, and are constants of the fuzzy rule of the electric vehicle and are both greater than 0, and P EV represents the charging and discharging power of the electric vehicle.

[0053] When the grid is in need of frequency down-regulation, the higher the indoor temperature, the higher the frequency regulation price, and the greater the willingness of the air conditioning load to participate in frequency regulation service. Conversely, when the grid is in need of frequency up-regulation, the lower the indoor temperature, the higher the frequency regulation price, and the greater the willingness of the air conditioning load to participate in frequency regulation service. Therefore, the fuzzy rule of the air conditioning load in this step is defined as:

[0054]

[0055] wherein f i AC (T in ,π cp ) is the fuzzy rule of the air conditioning load, T in is the indoor temperature, π cp is the frequency regulation price, and are constants of the fuzzy rule of the air conditioning load and are both greater than 0, and P AC represents the power of the air conditioning load.

[0056] Step 3, the state of charge of the electric vehicle and the frequency regulation price are determined under the fuzzy rule of the electric vehicle, all the outputs are weighted and averaged to obtain the frequency regulation willingness of each electric vehicle. The calculation method of this step is the defuzzification operation of the TSK fuzzy model, and after completion, the frequency regulation willingness of each electric vehicle can be obtained

[0057] Step 4, the indoor temperature and the frequency regulation price are determined under the fuzzy rule of the air conditioning load, all the outputs are weighted and averaged to obtain the frequency regulation willingness of each air conditioning load. The calculation method of this step is the defuzzification operation of the TSK fuzzy model, and after completion, the frequency regulation willingness of each air conditioning load can be obtained

[0058] Step 5, based on the frequency modulation willingness of each electric vehicle and each air conditioning load, the signal tracking effect cost and the user willingness cost are minimized to build an intra-day optimization scheduling model.

[0059] After calculating the frequency modulation willingness of each electric vehicle and the frequency modulation willingness of each air conditioning load , the intra-day optimization scheduling model in the building is established to minimize the frequency modulation error and meet the user's willingness. The objective function of the intra-day optimization scheduling model can be divided into signal tracking effect cost and user willingness cost.

[0060] The signal tracking effect cost is expressed as:

[0061] C1=ε|P t RR -P t FR |

[0062] In the formula, ε is the error cost conversion coefficient, P t RR represents the actual frequency modulation capacity at t period, P t FR represents the bid of the frequency modulation capacity at t period. The main role of this cost is to minimize the power error of the smart building participating in frequency modulation.

[0063] When the total frequency modulation signal issued by the dispatching center is positive, it means that the power system needs to reduce the frequency service, the smart building needs to increase its power consumption, the electric vehicle increases its charging power, and the air conditioning load increases its refrigeration 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 the user willingness and system demand.

[0064]

[0065] In the formula, ε EV is the electric vehicle willingness cost conversion coefficient, ε AC is the air conditioning load willingness cost conversion coefficient, represents the state of charge of the kth electric vehicle at t time, and represent the minimum state of charge and the maximum state of charge of the electric vehicle, respectively, is the indoor temperature of the lth air conditioning load at t time, T min and T max represent the lower limit and upper limit of the temperature comfort zone, respectively, P FRThe bidding of frequency modulation capacity, and omega and xi are 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 of demand side and power demand. The target of the cost is to make the overall willingness of the user highest, and try to meet the demand of the user.

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

[0067] minf=ωC1+ξC2

[0068] The constraint condition of the intraday optimization scheduling model is represented as:

[0069]

[0070] Wherein, P represents the power of the kth electric vehicle at t moment, EVmax P represents the maximum power of the electric vehicle, P represents the power of the lth air conditioning load at t moment, ACmin P and P represent the minimum power and the maximum power of the air conditioning load respectively. ACmax

[0071] Step 6, solving the intraday optimization scheduling model to obtain a frequency modulation control strategy, and using the frequency modulation control strategy to control the frequency modulation of the virtual power plant.

[0072] It is not difficult to find that the fuzzy control-based building frequency modulation power distribution algorithm of the embodiment can fully consider the use demand of the user side, and can guarantee the travel demand of the electric vehicle user and the refrigeration demand of the air conditioning load user while the virtual power plant participates in the frequency modulation auxiliary service.

[0073] The second embodiment of the application relates to a fuzzy control-based virtual power plant frequency modulation control device, which comprises:

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

[0075] A building module is used for building fuzzy rules of the electric vehicle and fuzzy rules of the air conditioning load;

[0076] An electric vehicle frequency modulation willingness module is used for determining the output of the state of charge of the electric vehicle and the frequency modulation price under the fuzzy rules of the electric vehicle, weighting and averaging all the outputs to obtain the frequency modulation willingness of each electric vehicle;

[0077] ​The air conditioner load frequency modulation willingness module is configured to determine the output of indoor temperature and frequency modulation price under the fuzzy rule of the air conditioner load, and to obtain the frequency modulation willingness of each air conditioner load by weighted average of all the outputs.

[0078] The building module is configured to build an intra-day optimization scheduling model based on the frequency modulation willingness of each electric vehicle and each air conditioner load, with the objective of signal tracking effect cost and user willingness cost being minimum.

[0079] The control module is configured to solve the intra-day optimization scheduling model to obtain a frequency modulation control strategy, and to perform frequency modulation control on the virtual power plant by using the frequency modulation control strategy.

[0080] When the description module describes the state of charge of the electric vehicle, the indoor temperature and the frequency modulation price by using the trapezoidal fuzzy membership function, the three fuzzy subsets of the state of charge of the electric vehicle are defined as "low", "medium" and "high", the three fuzzy subsets of the indoor temperature are defined as "cold", "comfortable" and "hot", and the three fuzzy subsets of the frequency modulation price are defined as "cheap", "medium" and "expensive".

[0081] The fuzzy rule of the electric vehicle built by the building module is represented as: wherein, f i EV (SOC,π cp ) is the fuzzy rule of the electric vehicle, SOC is the state of charge of the electric vehicle, π cp is the frequency modulation price, and are constants of the fuzzy rule of the electric vehicle and are greater than 0, P EV represents the charging and discharging power of the electric vehicle.

[0082] The fuzzy rule of the air conditioner load built by the building module is represented as: wherein, f i AC (T in ,π cp ) is the fuzzy rule of the air conditioner load, T in is the indoor temperature, π cp is the frequency modulation price, and are constants of the fuzzy rule of the air conditioner load and are greater than 0, P AC represents the power of the air conditioner load.

[0083] The objective function f of the intra-day optimization scheduling model built by the building module is represented as: min f = ωC1 + ξC2, wherein C1 is the signal tracking effect cost, represented as: C1 = ε|P tRR P t FR ε is an error cost conversion coefficient, P t RR represents the actual frequency modulation capacity at time t, P t FR represents the bid for the frequency modulation capacity at time t, C2 is the user willingness cost, which is represented as: ε EV is an electric vehicle willingness cost conversion coefficient, is the frequency modulation willingness of the kth electric vehicle, ε AC is an air conditioning load willingness cost conversion coefficient, represents the frequency modulation willingness of the lth air conditioning load, represents the state of charge of the kth electric vehicle at time t, and respectively represent the minimum state of charge and the maximum state of charge of the electric vehicle, is the indoor temperature at the location of the lth air conditioning load at time t, T min and T max respectively represent the lower limit of the temperature comfort zone and the upper limit of the temperature comfort zone, P FR represents the bid for the frequency modulation capacity, ω and ξ are respectively the weight factors of the signal tracking effect cost and the user willingness cost.

[0084] The constraint condition of the intraday optimization scheduling model constructed by the construction module is represented as: wherein, represents the power of the kth electric vehicle 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 the maximum power of the air conditioning load.

[0085] The third embodiment of the present application relates to an electronic device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to realize the steps of the virtual power plant frequency modulation control method based on fuzzy control.

[0086] The fourth embodiment of the present application relates to a computer readable storage medium, which stores a computer program, wherein the computer program is executed by a processor to realize the steps of the virtual power plant frequency modulation control method based on fuzzy control.

[0087] Those skilled in the art will appreciate that embodiments of the application can be devised for a method, a system, or a computer program product. Accordingly, the present application can be embodied in the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage etc.) embodying computer readable program code.

[0088] The present application is described in reference to the flowchart and / or block diagram of the method, apparatus (system) and computer program product according to embodiments of the application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and a combination of flows and / or blocks 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 apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more flows and / or blocks Figure 1 means for carrying out the function specified in the flowchart block or blocks.

[0089] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the flowchart and / or block diagram block or blocks. Figure 1 one or more flows and / or blocks Figure 1 means for carrying out the function specified in the flowchart block or blocks.

[0090] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flowchart and / or block diagram block or blocks. Figure 1 one or more flows and / or blocks Figure 1 means for carrying out the function specified in the flowchart block or blocks.

[0091] The above description is only specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to 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 and frequency regulation price of electric vehicles 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 perform weighted average of all outputs. Get the frequency regulation intention of each air conditioning load; 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. The signal tracking effect cost is expressed as: C1 = ε|P t RR -P t FR |, C1 is the signal tracking effect cost, ε is the error cost conversion coefficient, P t RR represents the actual frequency regulation capacity during period t, P t FR represents the bidding for frequency regulation capacity during period t; the user's willingness cost is expressed as: C2 is the user willingness cost, 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 vehicle 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 a bid for frequency regulation capacity; 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 virtual power plant frequency modulation control method 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"; and 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, characterized in that: 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 all 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, characterized in that: 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, P 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, characterized in that: The objective function f of the intraday optimization scheduling model is expressed as: minf=ωC1+ξC2, where ω and ξ are 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, 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 charge and discharge 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, indoor temperature and frequency modulation price using trapezoidal fuzzy membership functions; Establishing modules 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 perform weighted average of all outputs to obtain the frequency modulation willingness of each air conditioning load; The construction module is used to build 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; where the signal tracking effect cost is expressed as: C1 = ε|P t RR -P t FR |, C1 is the signal tracking effect cost, ε is the error cost conversion coefficient, P t RR represents the actual frequency regulation capacity during period t, P t FR represents the bidding for frequency regulation capacity during period t; the user's willingness cost is expressed as: C2 is the user willingness cost, 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 vehicle 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 a bid for frequency regulation capacity; The control module is used to solve the intraday optimization scheduling model, obtain a 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 processor implements the steps of the virtual power plant frequency regulation control method based on fuzzy control as described in any one of claims 1-6.

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

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