MAS-based virtual power plant frequency modulation control method under multiple network attacks

Abstract

The invention discloses a MAS-based virtual power plant frequency modulation control method under various network attacks. The method comprises the following steps: establishing a MAS-based virtual power plant frequency modulation control architecture; acquiring power regulation information required by frequency modulation of the power grid; based on the power regulation information, calculating the optimal reference regulation power of the virtual power plant under the DoS and FDI mixed attack through a virtual power plant cooperative power control design scheme; and based on the optimal reference regulation power of the virtual power plant, controlling an inverter through the designed dynamic power control method considering the spoofing attack to regulate the power output of the virtual power plant so as to meet the power regulation requirement required by the frequency modulation of the power grid. The method can effectively resist the influence of various network attacks on the frequency modulation of the virtual power plant, improves the operation economy of the virtual power plant, guarantees the control stability of the inverter, and improves the reliability of supporting the frequency modulation of the power grid by the virtual power plant.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system operation and control, in particular to a virtual power plant frequency regulation control method under multiple network attacks based on MAS. BACKGROUND

[0002] With the global energy structure transforming to low carbon, large-scale renewable energy such as wind and solar energy is connected to the grid, and the power system faces the challenge of frequency stability. Traditional frequency regulation means is slow in response and lacks flexibility, and it is difficult to cope with the randomness and volatility of renewable energy. The rapid development of distributed energy brings a large amount of fragmented resources, but the dispersion limits its regulation capacity. In this context, virtual power plants integrate distributed energy, controllable loads and energy storage systems to form a flexible frequency regulation resource pool, providing a new way for grid frequency regulation.

[0003] Currently, the research on virtual power plants participating in grid frequency regulation mainly focuses on designing resource optimization scheduling and bidding strategies to improve the stability of system frequency, and few studies have assisted grid frequency regulation through virtual power plant collaborative power control based on MAS. In addition, in the process of frequency regulation, virtual power plants may be affected by various network attacks such as DoS and FDI, which poses a great challenge to virtual power plant frequency regulation. However, existing research rarely considers the impact of various network attacks on virtual power plant frequency regulation. Therefore, the present application proposes a virtual power plant frequency regulation control method under multiple network attacks based on MAS. SUMMARY

[0004] The purpose of the present application is to overcome the shortcomings of the prior art, and to provide a virtual power plant frequency regulation control method under multiple network attacks based on MAS, which effectively resists the impact of multiple network attacks on virtual power plant frequency regulation, improves the economy of virtual power plant operation, ensures the stability of inverter control, and improves the reliability of virtual power plant supporting grid frequency regulation.

[0005] To solve the above technical problems, the present application provides the following technical solutions:

[0006] A virtual power plant frequency regulation control method under multiple network attacks based on MAS, the method comprising:

[0007] Step (1), building a virtual power plant frequency regulation control architecture based on MAS;

[0008] Step (2), obtaining power regulation information required for grid frequency regulation;

[0009] Step (3), based on the power regulation information, calculating the optimal reference regulation power of the virtual power plant under DoS and FDI mixed attacks through a virtual power plant collaborative power control design scheme;

[0010] Step (4), based on the optimal reference regulating power of the virtual power plant, the designed dynamic power control method considering the deception attack is used to control the inverter to adjust the power output of the virtual power plant to meet the power regulating demand required by the grid frequency modulation.

[0011] Preferably, the virtual power plant frequency modulation control architecture in step (1) comprises a physical network architecture and an information network architecture.

[0012] The physical network architecture comprises a plurality of virtual power plants composed of renewable energy sources such as wind power and photovoltaic power.

[0013] The information network architecture comprises virtual power plant control agents for implementing cooperative power control and renewable energy unit agents for performing dynamic power control of inverters.

[0014] Preferably, the virtual power plant cooperative power control design scheme in step (3) comprises power control optimization modeling and a cooperative power control strategy.

[0015] The power control optimization modeling comprises an objective function and a constraint condition.

[0016] Objective function: ;

[0017] wherein, is the number of virtual power plants, , , is the cost coefficient of the virtual power plant i, is the power output of the virtual power plant i at time t;

[0018] Constraint condition:

[0019] ;

[0020] ;

[0021] wherein, is the power loss of the virtual power plant i at time t, is the power regulating demand of the upper-level grid automatic generation control system, and is the upper and lower limit of the power output of the virtual power plant i at time t;

[0022] The cooperative power control strategy comprises:

[0023] The state of the virtual power plant control agent i is updated according to the formula :

[0024] ;

[0025] wherein, is the iteration number, is the set of virtual power plant control agents i and their neighboring agents are secure, is the connection relationship between virtual power plant control agents i and j, is the set of virtual power plant control agents i that are subject to false data injection (FDI) attacks, is the corrected state of virtual power plant control agent j, which is represented as:

[0026] ;

[0027] wherein, is the state transmitted by virtual power plant control agent i, is the set of virtual power plant control agents i that cannot receive state information from virtual power plant control agent j under denial-of-service (DoS) attacks, is the feedback proportionality parameter, is the power deviation of virtual power plant control agent i, is the iteration step size, is the partial derivative of the objective function, is the injected false state information of virtual power plant control agent i subject to FDI attacks;

[0028] The power output of virtual power plant control agent i is updated according to the formula :

[0029] .

[0030] Preferably, the dynamic power control method considering spoofing attacks designed in step (4) includes:

[0031] A renewable energy state space model is constructed: ;

[0032] wherein, , is the state and output vector, is the input vector, , are the state transition matrix and input control matrix, respectively;

[0033] The model is discretized using a sampling time to obtain: ;

[0034] wherein, is the discretized state vector, is the discretized input vector, is the discretized state transition matrix, e is the base of the natural logarithm, is the input control matrix, I = I;

[0035] Let e = x - x r = x - x

[0036] ;

[0037] where A = I

[0038] Considering the deception attack, the actual state x = x + e ;

[0039] where e = x - x r = x - x f = e

[0040] Based on this, the state feedback control of the following formula is designed, and the following formula is obtained:

[0041] ;

[0042] where K = A e = x - x

[0043] The feedback control gain K is calculated by solving the inequality :

[0044] ;

[0045] ;

[0046] ;

[0047] ; ;

[0048] ; ;

[0049] where , , , A = I , , A = I , , , , , , , is a known coefficient.

[0050] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned MAS-based virtual power plant frequency regulation control method under multiple network attacks.

[0051] A computer device includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the program, it implements the steps of the above-mentioned MAS-based virtual power plant frequency control method under multiple network attacks.

[0052] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0053] This invention provides a method for controlling virtual power plant frequency regulation under various network attacks based on MAS. This method uses a collaborative power control design for the virtual power plant to calculate the optimal reference regulation power for the virtual power plant under a combined DoS and FDI attack. The method then uses a dynamic power control method designed to account for spoofing attacks to control the inverter and adjust the power output of the virtual power plant to meet the power regulation requirements for grid frequency regulation. This method can effectively resist the impact of various network attacks on virtual power plant frequency regulation, improve the economic efficiency of virtual power plant operation, ensure the stability of inverter control, and enhance the reliability of the virtual power plant in supporting grid frequency regulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0055] Figure 1 This is a flow chart of a method for controlling frequency regulation of a virtual power plant under multiple network attacks based on MAS provided by an embodiment of the present invention;

[0056] Figure 2 This is a power deviation comparison diagram provided by an embodiment of the present invention;

[0057] Figure 3 This is a comparison chart of total operating costs provided by an embodiment of the present invention;

[0058] Figure 4 This is a comparison chart of dynamic response results provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0059] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all the other embodiments obtained by those skilled in the art without creative work belong to the scope of protection of the present application.

[0060] Please refer to Figures 1-4 , the present application provides technical solutions:

[0061] Embodiment 1: A MAS-based virtual power plant frequency regulation control method under multiple network attacks, comprising:

[0062] Building a MAS-based virtual power plant frequency regulation control architecture;

[0063] Obtaining power regulation information required for grid frequency regulation;

[0064] Based on the power regulation information, the optimal reference regulation power of the virtual power plant under the DoS and FDI hybrid attack is calculated through the virtual power plant cooperative power control design scheme;

[0065] Based on the optimal reference regulation power of the virtual power plant, the inverter is controlled by the designed dynamic power control method considering the deception attack to adjust the power output of the virtual power plant to meet the power regulation demand required for grid frequency regulation.

[0066] As Figure 1 shown, the MAS-based virtual power plant frequency regulation control method under multiple network attacks provided by the present embodiment specifically involves the following steps in the application process:

[0067] Step 1: Building a MAS-based virtual power plant frequency regulation control architecture.

[0068] The MAS-based virtual power plant frequency regulation control architecture involved includes a physical network architecture and an information network architecture. The physical network architecture includes a plurality of virtual power plants composed of renewable energy sources such as wind power and photovoltaic power. The information network architecture includes virtual power plant control agents for implementing cooperative power control and renewable energy unit agents for performing inverter dynamic power control.

[0069] Step 2: Obtaining power regulation information required for grid frequency regulation.

[0070] Step 3: Based on the power regulation information, the optimal reference regulation power of the virtual power plant under the DoS and FDI hybrid attack is calculated through the virtual power plant cooperative power control design scheme.

[0071] Power control optimization modeling includes: objective function and constraint condition;

[0072] Objective function: ;

[0073] wherein, is the number of virtual power plants, , , is the cost coefficient of virtual power plant i, is the power output of virtual power plant i at time t;

[0074] Constraints: ; ;

[0075] wherein, is the power loss of virtual power plant i at time t, is the power regulation requirement of the upper-level grid automatic generation control system, and is the upper and lower limits of the power output of virtual power plant i at time t;

[0076] The cooperative power control strategy includes:

[0077] The state of virtual power plant control agent i is updated according to the following formula :

[0078] ;

[0079] wherein, is the number of iterations, is the set of virtual power plant control agents i and its adjacent agents that are safe, is the connection relationship between virtual power plant control agent i and j, is the set of virtual power plant control agent i that is attacked by false data injection (FDI), is the corrected state of virtual power plant control agent j, which is represented as:

[0080] ;

[0081] wherein, is the state transmitted by virtual power plant control agent i, is the set of virtual power plant control agents i that cannot receive the state information of virtual power plant control agent j under denial of service (DoS) attack, is the feedback proportionality parameter, is the power deviation of virtual power plant control agent i, is the iteration step size, is the partial derivative of the objective function, is the injected false state information of virtual power plant control agent i that is attacked by FDI;

[0082] The power output of the virtual power plant control agent i is updated according to the following formula :

[0083] ;

[0084] In this embodiment, the code writing and running of the proposed virtual power plant cooperative power control design scheme are completed by software MATLAB. In order to test the convergence performance of the proposed method, Figure 2 and Figure 3 respectively give the comparison results of power deviation and total operation cost under different methods, wherein comparison method 1 adopts the consensus-based control method under network attack, and comparison method 2 is the consensus-based control method without considering network attack. It can be seen from Figure 2 that comparison method 1 has significant power deviation due to FDI and DoS attacks, which highlights the characteristics of FDI covert attack. However, the proposed method can effectively eliminate the power deviation by taking defensive measures against attacks. It can be seen from Figure 3 that after reducing the influence of network attacks, the final convergence results of the proposed method and comparison method 2 are close to the optimal value, while the final convergence results of comparison method 1 have significant difference from the optimal value. Therefore, according to the simulation results, it is shown that the proposed method can effectively resist the influence of multiple network attacks on virtual power plant frequency regulation, and improve the economy of virtual power plant operation.

[0085] Step 4: Based on the optimal reference regulation power of the virtual power plant, the designed dynamic power control method considering the deception attack is used to control the inverter to adjust the power output of the virtual power plant to meet the power regulation demand required by the grid frequency regulation.

[0086] Step 4.1: Construct a renewable energy state space model, including: ;

[0087] wherein, , is a state and output vector, is an input vector, , are state transition matrix and input control matrix respectively;

[0088] Discretize the model using sampling time to obtain: ;

[0089] wherein, is a discretized state vector, is a discretized input vector, is a discretized state transition matrix, e is the base of natural logarithm, is an input control matrix, I = I;

[0090] Step 4.2: Let be the error state, be the reference state, we have

[0091] ;

[0092] where is a constant matrix;

[0093] Considering the spoofing attack, the actual state is: ;

[0094] where is a random variable, is the latest transmission state data, is a spoofing attack function;

[0095] To this end, the state feedback control of the following formula is designed, and the following formula is obtained:

[0096] ;

[0097] where is the feedback control gain, is the state deviation;

[0098] Step 4.3: The feedback control gain is calculated by solving the following inequality :

[0099] ;

[0100] ;

[0101] ;

[0102] ; ;

[0103] ; ;

[0104] where , , , are auxiliary matrices, , , are constant matrices, , , , , , , , is a known coefficient.

[0105] In this embodiment, by Figure 4 The dynamic response comparison results in the figure verify the effectiveness of the proposed dynamic power control method considering deception attacks, among which the comparison method 3 adopts the traditional power tracking method based on proportional integral differential control. Figure 4 As shown in the figure, it can be seen that during the power tracking control process, the comparative method 4 exhibits significant fluctuations when subjected to a spoofing attack. In contrast, the proposed method significantly reduces the number of fluctuations and is able to quickly track the power reference value within 0.15 seconds. Therefore, the simulation results show that the proposed method can ensure the stability of inverter control under the influence of spoofing attacks and improve the reliability of virtual power plant frequency regulation.

[0106] Example 2: The computer-readable storage medium of this embodiment stores a computer program thereon, which, when executed by a processor, implements the steps of the virtual power plant frequency control method under multiple network attacks based on MAS in Example 1.

[0107] The computer-readable storage medium of this embodiment may be an internal storage unit of the terminal, such as a hard disk or memory of the terminal; the computer-readable storage medium of this embodiment may also be an external storage device of the terminal, such as a plug-in hard disk, a smart memory card, a secure digital card, a flash memory card, etc. equipped on the terminal; further, the computer-readable storage medium may also include both an internal storage unit of the terminal and an external storage device.

[0108] The computer-readable storage medium of this embodiment is used to store computer programs and other programs and data required by the terminal. The computer-readable storage medium can also be used to temporarily store data that has been output or is to be output.

[0109] Example 3: The computer device of this embodiment includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the virtual power plant frequency control method under multiple network attacks based on MAS in Example 1 are implemented.

[0110] In this embodiment, the processor can be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The memory can include read-only memory and random access memory, and provide instructions and data to the processor. A part of the memory can also include non-volatile random access memory. For example, the memory can also store information about the device type.

[0111] Those skilled in the art will appreciate that embodiments disclosed herein can be provided as methods, systems, or computer program products. Accordingly, the present application can be embodied in the form of hardware embodiments, software embodiments, or embodiments combining software and hardware aspects. Furthermore, the present application can be embodied in the form of a computer program product embodied on one or more computer readable storage media (including, but not limited to, disk memory and optical memory) having computer usable program code embodied thereon.

[0112] The present application is described in reference to the flowchart illustrations and / or block diagrams according to embodiments of the application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, 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 illustrations and / or block diagrams block or blocks. Figure 1 The 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 functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 The 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 functions specified in the flowchart illustrations and / or block diagrams block or blocks.

[0113] 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 function specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 The 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 functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 The 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 functions specified in the flowchart illustrations and / or block diagrams block or blocks.

[0114] 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 functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 The 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 functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 The 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 functions specified in the flowchart illustrations and / or block diagrams block or blocks.

[0115] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing relevant hardware, and the program can be stored in a computer readable storage medium. When the program is executed, the program can include the processes of the above-mentioned embodiment methods. The storage medium can be a magnetic disc, an optical disc, a read-only memory (ROM) or a random access memory (RAM), etc.

[0116] Finally, it should be noted that the above only describes the preferred embodiments of the present application and is not intended to limit the present application. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing embodiments or make equivalent replacements to some technical features. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for controlling frequency regulation of a virtual power plant under multiple network attacks based on MAS, characterized by: The method comprises: Step (1), build a virtual power plant frequency control architecture based on MAS; Step (2), obtaining the power regulation information required for grid frequency regulation; Step (3): Based on the power regulation information, the optimal reference regulation power of the virtual power plant under the combined attack of DoS and FDI is calculated through the collaborative power control design scheme of the virtual power plant; Step (4) is to adjust the power output of the virtual power plant based on the optimal reference of the virtual power plant by controlling the inverter through the designed dynamic power control method considering the deception attack to meet the power regulation requirements required for grid frequency regulation.

2. The method for controlling frequency regulation of a virtual power plant under multiple network attacks based on MAS according to claim 1, characterized in that: The frequency regulation control architecture of the virtual power plant in step (1) includes: a physical grid and an information grid; The physical grid includes multiple virtual power plants composed of renewable energy sources such as wind power and photovoltaic power; The information grid includes a virtual power plant control agent for implementing coordinated power control and a renewable energy unit agent for performing inverter dynamic power control.

3. The method for controlling frequency regulation of a virtual power plant under multiple network attacks based on MAS according to claim 1, characterized in that: The virtual power plant collaborative power control design scheme in step (3) includes: power control optimization modeling and collaborative power control strategy; The power control optimization modeling includes: an objective function and constraint conditions; Objective function: ; in, is the number of virtual power plants, , , is the cost coefficient of virtual power plant i, is the power output of virtual power plant i at time t; Constraints: ； ； in, is the power loss of virtual power plant i at time t, To meet the power regulation requirements of the upper power grid automatic power generation control system, and are the upper and lower limits of the power output of virtual power plant i at time t; The collaborative power control strategy includes: Update the state of the virtual power plant control agent i according to the formula : ； in, is the number of iterations, For the virtual power plant control agent i and its neighboring agents are a safe set, is the connection relationship between virtual power plant control agents i and j, is the set of virtual power plant control agent i that is attacked by false data injection FDI, is the corrected state of the virtual power plant control agent j, which is expressed as: ； in, The state transmitted by the virtual power plant control agent i, is the set of virtual power plant control agents i that cannot receive the status information of virtual power plant control agent j under the denial of service DoS attack, is the feedback ratio parameter, is the power deviation of virtual power plant control agent i, is the iteration step length, is the partial derivative of the objective function, The false state information injected by the virtual power plant control agent i under FDI attack; Update the power output of the virtual power plant control agent i according to the formula : 。 4. The method for controlling frequency regulation of a virtual power plant under multiple network attacks based on MAS according to claim 2, characterized in that: The dynamic power control method designed in step (4) considering spoofing attacks includes: Constructing a renewable energy state space model: ; in, , are state and output vectors, is the input vector, , are the state transfer matrix and input control matrix respectively; Using sampling time Discretizing the model, we get: ; in, is the discretized state vector, is the discretized input vector, is the discretized state transfer matrix, e is the base of the natural logarithm, is the input control matrix, is the identity matrix; make is the error state, As the reference state, we can get: ； in, is a constant matrix; Considering the deception attack, the actual status for: ; in, is a random variable, For the latest transmission status data, is the deception attack function; Based on this, the following state feedback control is designed and obtained: ； in, is the feedback control gain, is the state deviation; The feedback control gain is calculated by solving the inequality : ； ； ； ； ； ； ； in, , , , is the auxiliary matrix, , , is a constant matrix, , , , , , , , is a known coefficient.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of a virtual power plant frequency regulation control method under multiple network attacks based on MAS are implemented as described in any one of claims 1-4.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the program, it implements the steps in the virtual power plant frequency control method under multiple network attacks based on MAS as described in any one of claims 1-4.

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