Cyber-physical integrated design method for frequency regulation of virtual power plants under diverse communication problems

Abstract

The present invention discloses a method for designing the information-physical integration of frequency regulation of virtual power plants under diversified communication problems, including: building a control architecture for virtual power plants to participate in frequency regulation; based on this, proposing a joint design scheme for virtual power plant power scheduling and communication resource allocation for packet loss under cloud-edge collaboration, wherein at the physical layer, a virtual power plant power scheduling considering packet loss is proposed, and at the information layer, a communication resource allocation strategy is designed to eliminate the impact of packet loss, thereby determining accurate power scheduling; further, a high-reliability information transmission method for preventing network attacks under edge-end collaboration is proposed, wherein at the physical layer, dynamic power control is implemented, and at the information layer, a digital signcryption algorithm is used to improve system information transmission to resist network attacks, thereby ensuring accurate dynamic control. The present invention can effectively reduce the impact of diversified communication problems on the frequency regulation business of virtual power plants and ensure the real-time and accuracy of frequency regulation business.

Description

Technical Field

[0001] The present invention relates to a virtual power plant frequency modulation information-physical integrated design method under diversified communication problems, and belongs to the technical field of power system operation and control. Background Art

[0002] As the penetration rate of renewable energy continues to increase, the synchronous inertia of the power system is gradually reduced. This makes the regulation capacity of traditional generators insufficient to meet the frequency regulation needs brought about by the intermittent output of renewable energy. In recent years, a large number of flexible distributed resources such as photovoltaics and wind power have been widely connected to the distribution network, which has provided new opportunities for system frequency regulation. Given the large number and limited capacity of these resources, it is very necessary to build virtual power plants to aggregate and coordinate these resources to support power system frequency regulation.

[0003] At present, the research on virtual power plants participating in frequency regulation mainly focuses on designing resource optimization scheduling and bidding and auction strategies to improve the stability of system frequency, while there are few studies on providing system frequency regulation services through coordinated control of virtual power plants. At the same time, the coordinated control process of virtual power plants requires a good communication network environment. However, the complexity of the actual communication environment has brought huge challenges to the frequency regulation of virtual power plants. In recent years, some advanced communication design methods have been disclosed in the prior art to overcome the influence of the communication environment to support the frequency control of virtual power plants. However, the impact of malicious network attacks and the communication costs generated in overcoming the uncertainty of the communication network are rarely considered. Therefore, the present invention proposes an information-physical integrated design method for virtual power plant frequency regulation under diversified communication problems. Summary of the invention

[0004] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a method for the physical integration design of virtual power plant frequency regulation information under diversified communication problems, effectively reduce the impact of diversified communication problems on the frequency regulation business of virtual power plants, and ensure the real-time and accuracy of frequency regulation business.

[0005] To achieve the above object, the present invention is implemented by adopting the following technical solution, which is a method for designing the frequency regulation information-physical integration of a virtual power plant under diversified communication problems, and specifically includes the following steps:

[0006] (1) Build a control architecture for virtual power plants to participate in frequency regulation;

[0007] (2) A joint design scheme for virtual power plant power scheduling and communication resource allocation under cloud-edge collaboration for packet loss is proposed. At the physical layer, a virtual power plant power scheduling considering packet loss is proposed, and at the information layer, a communication resource allocation strategy is designed to eliminate the impact of packet loss, thereby determining accurate power scheduling.

[0008] (3) A highly reliable information transmission method for preventing network attacks under edge collaboration is proposed. Dynamic power control is implemented at the physical layer, and a digital signcryption algorithm is used at the information layer to improve system information transmission and resist network attacks, thereby ensuring precise dynamic control.

[0009] Furthermore, the step (1) comprises:

[0010] In the control architecture built, the virtual power plant control center is responsible for responding to the main grid dispatch center and making decisions to provide frequency control services. The edge controller is responsible for receiving dispatch instructions from the control center via edge node-assisted communication transmission, and controlling and monitoring the distributed resources within the virtual power plant. The resources here include renewable energy such as photovoltaic power generation and wind power generation.

[0011] Furthermore, the step (2) comprises:

[0012] The specific steps of virtual power plant power scheduling considering packet loss proposed in (2.1) are:

[0013] First, calculate the total regulation capacity of the virtual power plant. renewable energy, which Adjust power up and down at any time , They are calculated as:

[0014] (1)

[0015] in, and express Moment The maximum and minimum output power of renewable energy sources, express Moment The renewable energy dispatch power, In a virtual power plant, it Upward and downward aggregation adjustment power at all times , They are calculated as:

[0016] (2)

[0017] in, For the The number of renewable energy sources in a virtual power plant;

[0018] Based on the aggregated regulation power of a single virtual power plant, all virtual power plants Upward and downward aggregation adjustment capabilities at all times , They are calculated as:

[0019] (3)

[0020] in, is the number of virtual power plants; then, based on the regulation capacity of virtual power plants, The regulatory tasks they undertake at all times Calculated as:

[0021] (4)

[0022] in, express The regulation power required by the main power grid dispatching center at all times;

[0023] Finally, the regulation power required by each virtual power plant is calculated proportionally based on their regulation capabilities:

[0024] (5)

[0025] in, express Moment The regulation power undertaken by each virtual power plant;

[0026] Considering the impact of packet loss, Moment The regulated power received by the virtual power plant It is expressed as:

[0027] (6)

[0028] in, Indicates The packet loss rate of data transmission of a virtual power plant, is a random variable;

[0029] (2.2) The specific steps of the communication resource allocation strategy proposed are:

[0030] First, The packet loss rate of the power data transmitted from a virtual power plant to the edge controller is defined as:

[0031] (7)

[0032] in, To adjust the arrival rate of instructions, For the The acceptance rate of edge controllers, To adjust the probability of correct transmission of power data;

[0033] Use computing power to establish a bandwidth release model:

[0034] (8)

[0035] in, and Respectively The bandwidth and computing power required by each edge controller , , and are model parameters;

[0036] Under the cooperative relay transmission of edge nodes, The acceptance rate of edge controllers is calculated as

[0037] (9)

[0038] in, is the transmission bandwidth, represents the weight of bandwidth allocation, is the information bit, Represents the packet size, represents the probability of receiving a data packet correctly, is the binary bit error rate, is the signal-to-noise ratio of the transmission link from the virtual power plant control center to the edge controller, is the signal-to-noise ratio under the cooperative relay strategy, and They are the signal-to-noise ratios between the virtual power plant control center and edge nodes, and between the edge controller and edge nodes, respectively;

[0039] Substituting (9) into (7), the packet loss rate of data transmission can be re-expressed as:

[0040] (10)

[0041] Then, the optimization problem objective of communication resource allocation is considered as:

[0042] (11)

[0043] in, and is the weight factor, and is the unit price of bandwidth and computing power;

[0044] Substituting (8) and (10) into (11), the objective can be re-described as:

[0045] (12)

[0046] make

[0047] (13)

[0048] (14)

[0049] (15)

[0050] The optimization problem of communication resource allocation is as follows:

[0051] (16)

[0052] in, Represents the total bandwidth of the edge node;

[0053] Finally, the Lagrange dual decomposition method is used to solve the above problem. The main solution process is as follows:

[0054] The Lagrangian function is defined as:

[0055] (17)

[0056] in, represents the Lagrange multiplier;

[0057] (18)

[0058] The Lagrangian function can be further written as:

[0059] (19)

[0060] make , the dual optimization problem of (P1) is written as:

[0061] (20)

[0062] The optimization problem (P2) is decomposed into the computing power allocation problem and the multiplier update problem. For the computing power allocation problem, the optimal computing power allocation value is It is derived as follows:

[0063] (twenty one)

[0064] Then, for the multiplier update problem, the optimal multiplier value is calculated as:

[0065] (twenty two)

[0066] in, represents the step length, when , the iterative algorithm stops, where Indicates the algorithm end threshold;

[0067] Based on computing power allocation value , the optimal bandwidth allocation value is calculated as:

[0068] (twenty three).

[0069] Furthermore, the step (3) comprises:

[0070] The specific steps of the proposed virtual power plant aggregate power dynamic control design are as follows:

[0071] The power dynamic control process of renewable energy can be expressed as:

[0072] (twenty four)

[0073] in, and Respectively power output and set points of renewable energy sources, It is The time constant of a renewable energy source;

[0074] The above dynamic process is expressed by the state space model as follows:

[0075] (25)

[0076] Among them, input Indicates Power setpoints for renewable energy sources, status and output Indicates The power output of each renewable energy source dynamically controls the power setpoint;

[0077] Located in Among the virtual power plants renewable energy, then The power output of a virtual power plant is defined as:

[0078] (26)

[0079] in , , , ,

[0080] , , where the status Indicates The power output of each renewable energy source in the virtual power plant, input Indicates The power setpoint of each renewable energy source in the virtual power plant, output For the Aggregate power output of virtual power plants ;

[0081] The discrete time state space model of dynamic control of aggregated power of virtual power plant is derived as follows:

[0082] (27)

[0083] in, , Indicates the sampling time and is the identity matrix;

[0084] make and To predict and control the time domain, it is derived from (27):

[0085] (28)

[0086] (29)

[0087] in, ,

[0088] ,

[0089]

[0090] ,

[0091] ,

[0092] Defining the distributed model predictive control The objective function of a virtual power plant is:

[0093] (30)

[0094] (31)

[0095] (32)

[0096] in, and represents a positive definite weight matrix, Indicates that from A reference value obtained from the aggregated power control setting values ​​received by each edge controller;

[0097] (30)-(32) can be written as:

[0098] (33)

[0099] (34)

[0100] (35)

[0101] in, , ,

[0102] ,

[0103]

[0104] ,

[0105] , ,

[0106] ,in and Respectively represent Virtual power plant The lower and upper bound power control inputs for renewable energy sources, and Respectively Virtual power plant The lower and upper bounds of the power control input variable difference of the renewable energy sources;

[0107] Minimizing the objective function (30) and the constraints (31) and (32) yields k The optimal control sequence at each moment is:

[0108] (36)

[0109] Among them, the first element As the first A distributed model predictive control law for the dynamic characteristics of aggregated power control in virtual power plants;

[0110] The specific steps of implementing the proposed digital signcryption algorithm are as follows:

[0111] Taking the communication process from the model predictive controller to the virtual power plant as an example, let the control signal provided by the model predictive controller be in the range of [0, 99] kW, and record the plaintext as PT , specifically:

[0112] (37)

[0113] in, is the digital value of each bit of the control signal, For example, and Respectively represent its integer and decimal places;

[0114] First, choose two different large prime numbers and ,make , calculated according to the Euler function , then select a large integer that satisfies (38) , by solving (39) we get another integer , specifically:

[0115] (38)

[0116] (39)

[0117] in, is the greatest common divisor, is any integer, and ,for PT Each of , use (40) and (41) to generate the public key , the private key of the model prediction controller and digital signature :

[0118] (40)

[0119] (41)

[0120] in, is the modulo operation;

[0121] After the digital signature is implemented, PT and Uses Goldreich-Goldwasser-Halevi encryption. Keys for virtual power plants is an ideal basis of the lattice, which consists of a set of linearly independent vectors, denoted by , satisfying the following constraints:

[0122] (42)

[0123] Among them, det represents the determinant, express The dimension of represents the Hadamard ratio of these vectors, where The closer to 1, the more orthogonal these vectors are, and the public key of the virtual power plant is regarded as an imperfect basis of the lattice, and its Hadamard ratio is very small, calculated as

[0124] (43)

[0125] in, express The integer matrix, PT The encryption process is described as follows:

[0126] (44)

[0127] in, Represents ciphertext, Represents random disturbance, received After that, the virtual power plant decrypts it into its original PT and signature, expressed as:

[0128] (45)

[0129] in, is a rounding operation. In addition, and The encryption and decryption processes are exactly the same. and , the signature verification of the virtual power plant is as follows:

[0130] (46)

[0131] in, is the status of the signature, 1 and 0 represent acceptance and rejection respectively.

[0132] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method for designing the information-physical integration of frequency regulation of a virtual power plant under the diversified communication problems is implemented.

[0133] A computer-readable storage medium stores computer instructions, which, when executed by a processor, implement the information-physical integrated design method for frequency regulation of a virtual power plant under the diversified communication problem.

[0134] Compared with the prior art, the beneficial effects achieved by the method for designing frequency regulation information and physics of a virtual power plant under diversified communication problems provided by the embodiment of the present invention include:

[0135] (1) The control architecture of the virtual power plant participating in frequency regulation proposed in the present invention can effectively tap the regulation potential of the massive renewable distributed resources aggregated by the virtual power plant on the distribution network side to support the frequency regulation needs of the power system;

[0136] (2) In view of the fact that current research has not considered overcoming the communication costs caused by the uncertainty of the communication network, this invention proposes a joint design scheme for virtual power plant power scheduling and communication resource allocation under cloud-edge collaboration for packet loss. This scheme can not only effectively reduce the impact of packet loss on the power scheduling of virtual power plants, but also ensure the reliability of frequency regulation communication of virtual power plants while taking into account the communication costs generated.

[0137] (3) In view of the fact that there is still a gap in current research on taking into account and overcoming the impact of malicious network attacks, the present invention proposes a high-reliability information transmission method for preventing network attacks under edge collaboration to reduce the impact of network attacks on power dynamic control, thereby effectively improving the accuracy of frequency regulation services provided by virtual power plants. BRIEF DESCRIPTION OF THE DRAWINGS

[0138] Figure 1 It is a flow chart of a method for designing virtual power plant frequency regulation information-physical integration under diversified communication problems provided by an embodiment of the present invention;

[0139] Figure 2 This is a control architecture diagram of a virtual power plant participating in frequency regulation provided by an embodiment of the present invention;

[0140] Figure 3 is a convergence diagram of distributed optimization in the communication resource allocation strategy provided by an embodiment of the present invention;

[0141] Figure 4 is a comparison result diagram of different communication resource optimization methods provided by an embodiment of the present invention;

[0142] Figure 5 This is a diagram comparing power tracking results of different methods under network attacks provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0143] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0144] like Figure 1 As shown, this embodiment provides a method for designing virtual power plant frequency regulation information-physical integration under diversified communication problems, including:

[0145] (1) Build a control architecture for virtual power plants to participate in frequency regulation;

[0146] (2) A joint design scheme for virtual power plant power scheduling and communication resource allocation under cloud-edge collaboration for packet loss is proposed. At the physical layer, a virtual power plant power scheduling considering packet loss is proposed, and at the information layer, a communication resource allocation strategy is designed to eliminate the impact of packet loss, thereby determining accurate power scheduling.

[0147] (3) A highly reliable information transmission method for preventing network attacks under edge collaboration is proposed. Dynamic power control is implemented at the physical layer, and a digital signcryption algorithm is used at the information layer to improve system information transmission and resist network attacks, thereby ensuring precise dynamic control.

[0148] The specific steps include:

[0149] Step 1: Build a control architecture for the virtual power plant to participate in frequency regulation. In this embodiment, the Figure 2 The control architecture diagram of the virtual power plant participating in frequency regulation is shown in the figure, in which the virtual power plant control center is responsible for responding to the main power grid dispatch center and making decisions to provide frequency control services. The edge controller is responsible for receiving dispatch instructions from the control center via edge node assisted communication transmission, and controlling and monitoring the distributed resources within the virtual power plant. The resources here include renewable energy, such as photovoltaic power generation and wind power generation;

[0150] Step 2: Propose a joint design scheme for virtual power plant power scheduling and communication resource allocation under cloud-edge collaboration for packet loss. At the physical layer, a virtual power plant power scheduling considering packet loss is proposed, and at the information layer, a communication resource allocation strategy is designed to eliminate the impact of packet loss, thereby determining accurate power scheduling.

[0151] Step 2.1: The specific steps of the proposed virtual power plant power scheduling considering packet loss are:

[0152] First, calculate the total regulation capacity of the virtual power plant. renewable energy, which Adjust power up and down at any time , They are calculated as:

[0153] (1)

[0154] in, and express Moment The maximum and minimum output power of renewable energy sources, express Moment The renewable energy dispatch power, In a virtual power plant, it Upward and downward aggregation adjustment power at all times , They are calculated as:

[0155] (2)

[0156] in, For the The number of renewable energy sources in a virtual power plant;

[0157] Based on the aggregated regulation power of a single virtual power plant, all virtual power plants Upward and downward aggregation adjustment capabilities at all times , They are calculated as:

[0158] (3)

[0159] in, is the number of virtual power plants; then, based on the regulation capacity of virtual power plants, The regulatory tasks they undertake at all times Calculated as:

[0160] (4)

[0161] in, express The regulation power required by the main power grid dispatching center at all times;

[0162] Finally, the regulation power required by each virtual power plant is calculated proportionally based on their regulation capabilities:

[0163] (5)

[0164] in, express Moment The regulation power undertaken by each virtual power plant;

[0165] Considering the impact of packet loss, Moment The regulated power received by the virtual power plant It is expressed as:

[0166] (6)

[0167] in, Indicates The packet loss rate of data transmission of a virtual power plant, is a random variable;

[0168] (2.2) The specific steps of the communication resource allocation strategy proposed are:

[0169] First, The packet loss rate of the power data transmitted from a virtual power plant to the edge controller is defined as:

[0170] (7)

[0171] in, To adjust the arrival rate of instructions, For the The acceptance rate of edge controllers, To adjust the probability of correct transmission of power data;

[0172] Use computing power to establish a bandwidth release model:

[0173] (8)

[0174] in, and Respectively The bandwidth and computing power required by each edge controller , , and are model parameters;

[0175] Under the cooperative relay transmission of edge nodes, The acceptance rate of edge controllers is calculated as

[0176] (9)

[0177] in, is the transmission bandwidth, represents the weight of bandwidth allocation, is the information bit, Represents the packet size, represents the probability of receiving a data packet correctly, is the binary bit error rate, is the signal-to-noise ratio of the transmission link from the virtual power plant control center to the edge controller, is the signal-to-noise ratio under the cooperative relay strategy, and They are the signal-to-noise ratios between the virtual power plant control center and edge nodes, and between the edge controller and edge nodes, respectively;

[0178] Substituting (9) into (7), the packet loss rate of data transmission can be re-expressed as:

[0179] (10)

[0180] Then, the optimization problem objective of communication resource allocation is considered as:

[0181] (11)

[0182] in, and is the weight factor, and is the unit price of bandwidth and computing power;

[0183] Substituting (8) and (10) into (11), the objective can be re-described as:

[0184] (12)

[0185] make

[0186] (13)

[0187] (14)

[0188] (15)

[0189] The optimization problem of communication resource allocation is as follows:

[0190] (16)

[0191] in, Represents the total bandwidth of the edge node;

[0192] Finally, the Lagrange dual decomposition method is used to solve the above problem. The main solution process is as follows:

[0193] The Lagrangian function is defined as:

[0194] (17)

[0195] in, represents the Lagrange multiplier;

[0196] make

[0197] (18)

[0198] The Lagrangian function can be further written as:

[0199] (19)

[0200] make , the dual optimization problem of (P1) is written as:

[0201] (20)

[0202] The optimization problem (P2) is decomposed into the computing power allocation problem and the multiplier update problem. For the computing power allocation problem, the optimal computing power allocation value is It is derived as follows:

[0203] (twenty one)

[0204] Then, for the multiplier update problem, the optimal multiplier value is calculated as:

[0205] (twenty two)

[0206] in, represents the step length, when , the iterative algorithm stops, where Indicates the algorithm end threshold;

[0207] Based on computing power allocation value , the optimal bandwidth allocation value is calculated as:

[0208] (twenty three).

[0209] In this embodiment, the communication resource allocation strategy code is written and run by software MATLAB. In order to test the convergence performance of the proposed strategy, Figure 3 The convergence results of the distributed optimization in the proposed communication resource allocation strategy are given. Figure 3 It can be seen that the objective function value, bandwidth and computing power can quickly converge to their respective optimal values ​​within 3 iterations, which shows the superiority of the proposed strategy in communication resource allocation applications.

[0210] In this embodiment, by Figure 4 The comparison results of different communication resource optimization methods in the paper verify the effectiveness of the proposed communication resource allocation strategy in eliminating the impact of packet loss while taking into account the communication cost. The collaborative relay strategy (comparison method) without considering edge nodes is used for comparative simulation with the proposed strategy. Figure 4 As shown in the figure, it can be seen that although the optimal values ​​of the packet loss rate of the two methods are similar, the communication cost of the proposed strategy is less than that of the comparison method. Therefore, according to the simulation results, the proposed communication resource allocation strategy can not only ensure its economy, but also minimize the packet loss rate of data transmission.

[0211] Step 3: A highly reliable information transmission method for preventing network attacks under edge collaboration is proposed, in which dynamic power control is implemented at the physical layer, and a digital signcryption algorithm is used at the information layer to improve system information transmission and resist network attacks, thereby ensuring precise dynamic control.

[0212] Step 3.1: The specific steps of the proposed virtual power plant aggregate power dynamic control design are:

[0213] The specific steps of the proposed virtual power plant aggregate power dynamic control design are as follows:

[0214] The power dynamic control process of renewable energy can be expressed as:

[0215] (twenty four)

[0216] in, and Respectively power output and set points of renewable energy sources, It is The time constant of a renewable energy source;

[0217] The above dynamic process is expressed by the state space model as follows:

[0218] (25)

[0219] Among them, input Indicates Power setpoints for renewable energy sources, status and output Indicates The power output of each renewable energy source dynamically controls the power setpoint;

[0220] Located in Among the virtual power plants renewable energy, then The power output of a virtual power plant is defined as:

[0221] (26)

[0222] in , , , ,

[0223] , , where the status Indicates The power output of each renewable energy source in the virtual power plant, input Indicates The power setpoint of each renewable energy source in the virtual power plant, output For the Aggregate power output of virtual power plants ;

[0224] The discrete time state space model of dynamic control of aggregated power of virtual power plant is derived as follows:

[0225] (27)

[0226] in, , Indicates the sampling time and is the identity matrix;

[0227] make and To predict and control the time domain, it is derived from (27):

[0228] (28)

[0229] (29)

[0230] in, ,

[0231] ,

[0232]

[0233] ,

[0234] ,

[0235] Defining the distributed model predictive control The objective function of a virtual power plant is:

[0236] (30)

[0237] (31)

[0238] (32)

[0239] in, and represents a positive definite weight matrix, Indicates that from A reference value obtained from the aggregated power control setting values ​​received by each edge controller;

[0240] (30)-(32) can be written as:

[0241] (33)

[0242] (34)

[0243] (35)

[0244] in, , ,

[0245] ,

[0246]

[0247] ,

[0248] , ,

[0249] ,in and Respectively represent Virtual power plant The lower and upper bound power control inputs for renewable energy sources, and Respectively Virtual power plant The lower and upper bounds of the power control input variable difference of the renewable energy sources;

[0250] Minimizing the objective function (30) and the constraints (31) and (32) yields k The optimal control sequence at each moment is:

[0251] (36)

[0252] Among them, the first element As the first A distributed model predictive control law for the dynamic characteristics of aggregated power control in virtual power plants;

[0253] Step 3.2: The specific steps for implementing the proposed digital signcryption algorithm are:

[0254] Taking the communication process from the model predictive controller to the virtual power plant as an example, let the control signal provided by the model predictive controller be in the range of [0, 99] kW, and record the plaintext as PT , specifically:

[0255] (37)

[0256] in, is the digital value of each bit of the control signal, For example, and Respectively represent its integer and decimal places;

[0257] First, choose two different large prime numbers and ,make , calculated according to the Euler function , then select a large integer that satisfies (38) , by solving (39) we get another integer , specifically:

[0258] (38)

[0259] (39)

[0260] in, is the greatest common divisor, is any integer, and ,for PT Each of , use (40) and (41) to generate the public key , the private key of the model prediction controller and digital signature :

[0261] (40)

[0262] (41)

[0263] in, is the modulo operation;

[0264] After the digital signature is implemented, PT and Uses Goldreich-Goldwasser-Halevi encryption. Keys for virtual power plants is an ideal basis of the lattice, which consists of a set of linearly independent vectors, denoted by , satisfying the following constraints:

[0265] (42)

[0266] Among them, det represents the determinant, express The dimension of represents the Hadamard ratio of these vectors, where The closer to 1, the more orthogonal these vectors are, and the public key of the virtual power plant is regarded as an imperfect basis of the lattice, and its Hadamard ratio is very small, calculated as

[0267] (43)

[0268] in, express The integer matrix, PT The encryption process is described as follows:

[0269] (44)

[0270] in, Represents ciphertext, Represents random disturbance, received After that, the virtual power plant decrypts it into its original PT and the signature, expressed as:

[0271] (45)

[0272] in, is a rounding operation. In addition, and The encryption and decryption processes are exactly the same. and , the signature verification of the virtual power plant is as follows:

[0273] (46)

[0274] in, is the status of the signature, 1 and 0 represent acceptance and rejection respectively.

[0275] In this embodiment, by Figure 5 The power dynamic tracking comparison results of different methods under network attacks verify the effectiveness of the proposed high-reliability information transmission method for preventing network attacks under edge collaboration. The tracking method without considering the high-reliability information transmission scheme (comparison method 1) and the tracking method considering the encryption scheme based on Goldreich-Goldwasser-Halevi (comparison method 2) are used for comparative simulation with the proposed power dynamic control method. Figure 5 As shown in the figure, it can be seen that the proposed method can quickly track the received power set point within a small number of iterations, just like the method without network attack. However, both of the two comparison methods cannot effectively track the received target power value. Therefore, the proposed high-reliability information transmission method for preventing network attacks under edge collaboration is verified to be effective in overcoming the precise tracking under the influence of network attacks.

[0276] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for the integrated design of frequency regulation information and physics of virtual power plants under diversified communication problems, characterized in that: The following steps are involved: (1) Build a control architecture for virtual power plants to participate in frequency regulation; (2) Design a joint design scheme for virtual power plant power scheduling and communication resource allocation under cloud-edge collaboration to address packet loss. At the physical layer, a virtual power plant power scheduling considering packet loss is proposed. At the information layer, a communication resource allocation strategy is designed to eliminate the impact of packet loss, thereby determining accurate power scheduling. (3) Design a highly reliable information transmission method to prevent network attacks under edge collaboration, in which dynamic power control is implemented at the physical layer, and a digital signcryption algorithm is used at the information layer to improve system information transmission to resist network attacks and ensure accurate dynamic control; In step (1), in the control architecture constructed, the virtual power plant control center is responsible for responding to the main power grid dispatch center and making decisions on providing frequency control services, and the edge controller is responsible for receiving dispatch instructions from the control center via edge node assisted communication transmission, and implementing control and monitoring of distributed resources within the virtual power plant, where the resources include renewable energy, including photovoltaic power generation and wind power generation; Step (2) includes the following steps: The specific steps of the virtual power plant power dispatch considering packet loss proposed in (2.1) are: First, the total regulation capacity of the virtual power plant is calculated. For the i-th renewable energy, its upward and downward regulation power at time t is They are calculated as: in, and represents the maximum and minimum output power of the i-th renewable energy source at time t, represents the dispatch power of the i-th renewable energy source at time t. In the j-th virtual power plant, its upward and downward aggregated adjustment power at time t They are calculated as: Where m is the number of renewable energy sources in the jth virtual power plant; Based on the aggregated regulation power of a single virtual power plant, the upward and downward aggregated regulation capabilities of all virtual power plants at time t They are calculated as: Where n is the number of virtual power plants; then, based on the regulation capacity of virtual power plants, the regulation tasks they undertake at time t Calculated as: in, It represents the regulation power required by the main power grid dispatching center at time t; Finally, the regulation power required by each virtual power plant is calculated proportionally based on their regulation capabilities: in, represents the regulation power undertaken by the jth virtual power plant at time t; Taking into account the impact of packet loss, the regulated power received by the jth virtual power plant at time t is It is expressed as: Among them, l j represents the packet loss rate of data transmission of the jth virtual power plant, e j is a random variable; (2.2) The specific steps of the communication resource allocation strategy proposed are: First, the packet loss rate of the power data transmission sent by the jth virtual power plant to the edge controller is defined as: Among them, R ar To adjust the arrival rate of instructions, is the acceptance rate of the jth edge controller, g is the probability of correct transmission of the adjusted power data; Use computing power to establish a bandwidth release model: Among them, B j With C j are the bandwidth and computing power required by the jth edge controller, respectively, and M, α, β, and δ are model parameters; Under the cooperative relay transmission of edge nodes, the acceptance rate of the jth edge controller is calculated as Among them, B e is the transmission bandwidth, η is the weight of bandwidth allocation, I is the information bit, K represents the packet size, h(s ce )=[1-2E(s ce )] K represents the probability of correctly receiving a data packet, E is the binary bit error rate, s ce is the signal-to-noise ratio of the transmission link from the virtual power plant control center to the edge controller, s′=s ce +[s cn s ne / (1+s cn +s ne )] is the signal-to-noise ratio under the cooperative relay strategy, s cn and ne They are the signal-to-noise ratios between the virtual power plant control center and edge nodes, and between the edge controller and edge nodes, respectively; Substituting (9) into (7), the packet loss rate of data transmission can be re-expressed as: Then, the optimization problem objective of communication resource allocation is considered as: Among them, λ j With v j is the weight factor, a and b are the unit prices of bandwidth and computing power; Substituting (8) and (10) into (11), the objective can be re-described as: make from j =volume j M (15) The optimization problem of communication resource allocation is as follows: Among them, B max Represents the total bandwidth of the edge node; Finally, the Lagrange dual decomposition method is used to solve the above problem. The solution process is as follows: The Lagrangian function is defined as: Where μ represents the Lagrange multiplier; make The Lagrangian function can be further written as: make The dual optimization problem of (P1) is written as: (P2)maxE(μ) The optimization problem (P2) is decomposed into the computing power allocation problem and the multiplier update problem. For the computing power allocation problem, the optimal computing power allocation value is It is derived as follows: Then, for the multiplier update problem, the optimal multiplier value is calculated as: Where θ represents the step size. When |E(μ τ+1 )-E(μ τ )|≤ε, the iterative algorithm stops, where ε represents the algorithm end threshold; Based on computing power allocation value The optimal bandwidth allocation value is calculated as: Step (3) includes the following steps: The specific steps of the proposed virtual power plant aggregate power dynamic control design are as follows: The dynamic power control process of renewable energy is expressed as: in, and are the power output and set point of the i-th renewable energy source, T i is the time constant of the i-th renewable energy source; The above dynamic process is expressed by the state space model as follows: Among them, input u i represents the power setting value of the i-th renewable energy source, state x i and output y i represents the power output of the i-th renewable energy source with dynamically controlled power setting value; Assuming that there are m renewable energy sources in the jth virtual power plant, the power output of the jth virtual power plant is defined as: in The state x j represents the power output of each renewable energy source in the jth virtual power plant, and the input u j represents the power setting value of each renewable energy source in the jth virtual power plant, and outputs y j is the aggregate power output of the jth virtual power plant The discrete time state space model of dynamic control of aggregated power of virtual power plant is derived as follows: in, T s Denotes the sampling time and I′ as the identity matrix; Let N p and N c To predict and control the time domain, it is derived from (27): X j (k)=A′ j x j (k)+B′ j U j (k) (28) Y j (k)=F j x j (k)+G j U j (k) (29) in, X j (k)=[x j (k+1|k),x j (k+2|k),...,x j (k+N p |k)] T , AND j (k)=[and i (k+1|k), and j (k+2|k),...,and j (k+N p |k)] T , The objective function of the j-th virtual power plant of the distributed model predictive control is defined as: Among them, r j and w j represents a positive definite weight matrix, represents the reference value obtained from the aggregated power control setting value received from the jth edge controller; (30)-(32) can be written as: J j (k)=U j (k) T H j U j (k)+I j (k)U j (k)+L j (k)(33) s.t.U j，min ≤U j (k)≤U j，max (34) ΔU j，min ≤ΔU j (k)≤ΔU j，max (35) in, ΔU j (k)=[Δu j (k|k),Δu j (k+1|k),...,Δu j (k+N c -1|k)] T , ， Among them, j，m and O j，m denote the lower and upper bound power control inputs of the mth renewable energy source in the jth virtual power plant, respectively, and t j，m and T′ j，m are the lower and upper bound power control input variable differences of the mth renewable energy in the jth virtual power plant, respectively; Minimizing the objective function (30) and constraints (31) and (32), the optimal control sequence at time k is obtained as: Among them, the first element u j (k|k) is taken as the distributed model predictive control law for the dynamic characteristics of the aggregated power control of the jth virtual power plant; (3.2) The specific steps for implementing the proposed digital signcryption algorithm are: The communication process from the model predictive controller to the virtual power plant, assuming that the control signal provided by the model predictive controller is in the range of [0, 99] kW, and the plain text is recorded as PT, is as follows: Among them, d l is the digital value of each bit of the control signal, d (4m-3)～(4m-2) and d (4m-1)～(4m) Respectively represent its integer and decimal places; First, randomly select two different large prime numbers p and q, let c = pq, calculate φ(c) = (p-1)(q-1) according to the Euler function, then select a large integer e′ that satisfies (38), and obtain another integer f by solving (39), which is: e′f=h′φ(c)+1 (39) Where gcd is the greatest common divisor, h′ is an arbitrary integer, and h′≥1. For each d in PT l , using (40) and (41) to generate the public key PK M , the private key SK of the model prediction controller M and digital signature S d : PK M ={c,e′},SK M ={p,q,f}( 40) Among them, mod is the modulus operation; After the digital signature is implemented, the PT and S d Using Goldreich-Goldwasser-Halevi encryption, the virtual power plant key SK V is an ideal basis of the lattice, which consists of a set of linearly independent vectors, denoted by The following constraints are met: Among them, det represents the determinant, N v represents the dimension of v, represents the Hadamard ratio of these vectors, where The closer to 1, the more orthogonal these vectors are, and the public key PK of the virtual power plant V is regarded as an imperfect basis of the lattice, and its Hadamard ratio is very small, calculated as Among them, U′ represents N v ×N v In PT, the encryption process is described as: CT=PT·PK V +r d (44) Among them, CT represents the ciphertext, r d Represents a random disturbance. After receiving CT, the virtual power plant decrypts it into the original PT and signature, which is expressed as: PT=σ[CT·(SK V ) -1 ]SK V (PK V ) -1 ( 45) Among them, σ is a rounding operation, and S d The encryption and decryption process is exactly the same as that of PT. d And PT, the signature verification of the virtual power plant is as follows: in, is the status of the signature, 1 and 0 represent acceptance and rejection respectively.

2. An electronic 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 virtual power plant frequency regulation information-physical integrated design method under diversified communication problems as described in claim 1.

3. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the computer instruction is executed by the processor, the method for designing the information-physical integration of frequency regulation of a virtual power plant under the diversified communication problems as described in claim 1 is implemented.

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