Virtual power plant decentration AGC method and system based on precise diffusion algorithm

Abstract

The invention discloses a virtual power plant decentration AGC method and system based on a precise diffusion algorithm. According to the method, an AGC instruction decomposition model with optimal operation cost and highest frequency modulation performance as double optimization targets is designed according to the adjustment upper and lower limits of each resource and the actual line capacity constraint; a Lagrange multiplier of the model is decoupled, and an accurate diffusion algorithm is adopted to realize rapid and accurate solution of an instruction; and finally, an intelligent agent communication model containing communication noise is obtained. A precise diffusion algorithm is rewritten and a gain function is introduced, so that the precise diffusion algorithm has the capability of translating noise, and the robustness of the algorithm is improved.

Description

Technical Field

[0001] The present invention belongs to the field of automatic power generation control of virtual power plants, and specifically relates to a decentralized AGC method and system for virtual power plants based on an accurate diffusion algorithm. Background Art

[0002] Virtual power plant technology is an important solution for the internal coordinated control and optimized management of flexible distributed resources. It widely promotes the utilization of internal flexible resources, helps to balance the power grid, reduces the reliance on traditional fossil fuel power generation models, and accelerates the goal of "zero carbon emissions". In order to maintain the stability of real-time frequency and interconnection line exchange power, automatic generation control AGC (Automatic Generation Control) is a necessary tool to balance input disturbances. As digitalization and decentralization become the operating trend of energy systems, the centralized system has limited ability to process massive data and it is difficult to meet the privacy requirements of units. Therefore, decentralized AGC systems have become a new trend. AGC has a short control cycle, requiring decentralized algorithms to have the advantages of fast convergence speed and high precision.

[0003] In addition, as the number of clean energy-based power generation resources such as photovoltaics and wind turbines in virtual power plants increases, their intermittency and volatility will cause stronger frequency fluctuations and power mismatches. Since the AGC control cycle is short (1-16s) and the economic dispatch ED (Economic Dispatch) control cycle is long (5-15min), when the penetration rate of renewable energy in virtual power plants is high, the line capacity may be exceeded. However, line capacity is rarely used as a major constraint in the optimization solution of AGC instruction decomposition. In addition, the AGC instruction allocation process usually only aims at the lowest frequency fluctuation, and rarely takes operating cost and frequency regulation quality as a dual-objective comprehensive design instruction allocation algorithm, ignoring the analysis of the AGC control effect of virtual power plants under the combined effect of operating cost and frequency regulation quality.

[0004] In addition, decentralized systems may encounter communication noise, which threatens the stability of AGC. Therefore, it is also necessary to discuss communication noise and suppression methods in distributed algorithms. Summary of the invention

[0005] The present invention improves upon the problems existing in the above-mentioned prior art, and provides a decentralized AGC method and system for a virtual power plant based on an accurate diffusion algorithm. A three-layer virtual power plant multi-agent AGC control system is constructed, and communication lines and electrical connections are modeled. The upper and lower limits of power regulation and the actual line capacity constraints of each frequency modulation resource are taken as important constraints, and an AGC instruction decomposition model with the optimal operating cost and the highest frequency modulation performance as dual optimization goals is designed to achieve reduced operating costs and improved frequency modulation performance. For this model, the Lagrange multipliers in the optimization model are decoupled, and an accurate diffusion algorithm is used to achieve fast and accurate solution of instructions in each control cycle. Finally, in response to the problem of communication noise in the decentralized system, a gain function is introduced in the accurate diffusion algorithm to effectively suppress the noise.

[0006] The above technical objectives of the present invention are achieved through the following technical solutions, including:

[0007] A decentralized AGC method for a virtual power plant based on an accurate diffusion algorithm, comprising:

[0008] Obtain the current operating parameters of each distributed resource in the virtual power plant, the control parameters of the previous control cycle, and the parameters related to the frequency regulation performance evaluation;

[0009] Each distributed resource is used as an AGC unit, and primary and secondary frequency regulation are performed based on the current operating parameters of each AGC unit and the parameters related to the frequency regulation performance evaluation, so as to jointly maintain the frequency stability of the virtual power plant; wherein, the secondary frequency regulation is based on the current operating parameters of each distributed resource in the virtual power plant, the control parameters of the previous control cycle, and the parameters related to the frequency regulation performance evaluation, and the AGC instruction decomposition model with the optimal operating cost and the highest frequency regulation performance as the dual optimization goals is used to solve and obtain the actual power instruction of each distributed resource in the next control cycle; then each distributed resource, i.e., the AGC unit, controls the power according to the corresponding actual power instruction;

[0010] Among them, the constraints considered when solving the AGC instruction decomposition model with the dual optimization goals of optimal operating cost and highest frequency regulation performance include power supply and demand balance constraints, upper and lower limit constraints on the power adjustment of each distributed resource, and line capacity constraints.

[0011] Further, the current operating parameters include the current power level of the AGC unit, the upper and lower limits of the AGC power adjustment at that moment, etc.;

[0012] Among them, the frequency modulation performance evaluation indicators include response climbing rate, response time, and response accuracy evaluation indicators, which are expressed as follows:

[0013] The frequency modulation quality formula is:

[0014]

[0015]

[0016] In the formula, Represents the response ramp rate evaluation index of the i-th AGC unit, is the actual ramp rate of the ith AGC unit, is the actual power output adjustment of the i-th AGC unit, Calculate the starting time for the ramp rate, The end time of the climbing rate calculation is The average standard regulation rate of the frequency regulation resources of the virtual power plant;

[0017]

[0018]

[0019] In the formula, represents the evaluation index of the response time of the i-th AGC unit, is the actual delay error of the i-th AGC unit, is the time when the i-th AGC unit responds to the action, is the time when the adjustment command is issued to the i-th AGC unit, The delay error allowed for the frequency modulation resource to respond to the modulation command;

[0020]

[0021]

[0022] In the formula, Represents the adjustment accuracy index, is the actual adjustment error of the i-th distributed resource, is the moment when the i-th distributed resource enters the target dead zone, The calculation end time of the i-th distributed resource adjustment accuracy is: It is the allowable adjustment error of the frequency modulation resource in response to the AGC controller adjustment instruction.

[0023] comprehensive , , Among these three evaluation indicators, the comprehensive evaluation index of frequency regulation quality of the i-th distributed resource of the virtual power plant is , The specific formula is as follows:

[0024]

[0025] Furthermore, the objective function of the AGC instruction decomposition model is expressed as follows:

[0026] ;

[0027] Where C(i) represents the objective function of the i-th AGC unit, is the implementation period of each control interval, t represents the current time point, , N is the total number of control cycles; is the operating cost coefficient of each AGC unit, represents the actual power command of the i-th AGC unit at time t, , represents the initial power of the i-th AGC unit at time t; is the adjustment mileage command of the i-th AGC unit at time t; For the specified mileage price, Represents performance deficiency indicator.

[0028] Furthermore, the AGC instruction decomposition model with the dual optimization objectives of optimal operating cost and highest frequency modulation performance is solved in each control cycle by using an accurate diffusion algorithm, specifically including:

[0029] Rewrite the objective function of the AGC instruction decomposition model with the dual optimization objectives of optimal operating cost and highest frequency regulation performance, power supply and demand balance constraint and line capacity constraint into the form of Lagrangian dual function, and decouple the Lagrangian multipliers therein;

[0030] Initialize Lagrange multiplier, iteration step and number of iterations;

[0031] Based on the current operating parameters of each distributed resource and the parameters related to frequency modulation performance evaluation, the Lagrange multiplier is updated using an accurate diffusion algorithm;

[0032] According to the Lagrange multiplier update result, the actual power instruction of the i-th frequency modulation AGC unit at time t corresponding to the optimal iteration round k+1 is calculated. The intermediate variable ; Based on intermediate variables Combined with the upper and lower limits of each distributed resource power adjustment constraints, the actual power instruction of the i-th frequency modulation AGC unit at time t corresponding to the iteration round k+1 is corrected;

[0033] Repeated iterations are performed according to the Lagrange multiplier update result until the set number of iterations is reached, and the final optimal actual power instruction of the i-th frequency modulation AGC unit at time t is obtained.

[0034] Furthermore, the objective function of the AGC instruction decomposition model with the dual optimization objectives of optimal operating cost and highest frequency modulation performance, power supply and demand balance constraint and line capacity constraint are rewritten into the form of Lagrangian dual function, and the Lagrangian multipliers therein are decoupled, which is expressed as follows:

[0035] ;

[0036] ;

[0037] ;

[0038] ;

[0039] ;

[0040] In the formula, is the Lagrange multiplier of the decoupled equality constraint, They represent the Lagrange multipliers of the decoupled inequality constraints, C(i) represents the objective function of the i-th AGC unit, is the adjustment mileage command of the i-th AGC unit; They represent the Lagrange multipliers of equality constraints and inequality constraints corresponding to the i-th AGC unit respectively; represents the power contribution of the ith AGC unit to line l. If the ith AGC unit contributes to the power of line l, then ,on the contrary ; is the regional control demand value of the AGC control system; n is the number of AGC units, and m is the total number of lines.

[0041] Furthermore, the Lagrange multiplier is updated using an accurate diffusion algorithm based on the current operating parameters of each AGC unit and the frequency modulation performance evaluation related parameters. The updated formula is expressed as follows:

[0042] ;

[0043] ;

[0044] ;

[0045] Where k is the number of iterations of the exact diffusion algorithm;

[0046] in, , and its calculation method is as follows:

[0047] ;

[0048] in, is the identity matrix, represents the line weight set, if ,but , which means that the i-th AGC unit and the j-th AGC unit are neighbors; if ,but , Represents a set of communication lines; the specific weights are defined as follows:

[0049] ;

[0050] in, Represent the number of neighbor nodes of AGC unit i and AGC unit j respectively.

[0051] yes The intermediate variable updated for the kth time, is the iteration step size, is the Lagrange multiplier The intermediate variable matrix updated using the gradient method, , is the Lagrange multiplier The intermediate variable matrix after correcting the imbalance; , They are the Lagrange multipliers and The intermediate variable matrix updated using the gradient method, They are the Lagrange multipliers and The intermediate variable matrix after correcting the imbalance.

[0052] Furthermore, for the case of communication noise, the algorithm is improved on the basis of the precise diffusion algorithm, and the gain function method is introduced to effectively translate the noise. The precise diffusion algorithm updates the Lagrange multiplier, and the updated formula is expressed as follows: ;

[0053] ;

[0054] ;

[0055] Where k is the number of iterations of the exact diffusion algorithm, , is the identity matrix, represents the line weight set, if ,but , which means that the i-th AGC unit and the j-th AGC unit are neighbors; if ,but , represents a collection of communication lines; yes The intermediate variable updated for the kth time, is the iteration step size, is the Lagrange multiplier The intermediate variable matrix updated using the gradient method, , is the Lagrange multiplier The intermediate variable matrix after correcting the imbalance; , They are the Lagrange multipliers and The intermediate variable matrix updated using the gradient method, They are the Lagrange multipliers and The intermediate variable matrix after correcting the imbalance; is a Gaussian white noise that follows a normal distribution, For Update The gain function when , needs to meet the following two necessary conditions: ; ; For Update The gain function when , needs to meet the following two necessary conditions: ; ; For Update The gain function when , needs to meet the following two necessary conditions: ; .

[0056] A decentralized AGC system for a virtual power plant based on an accurate diffusion algorithm, comprising:

[0057] Communication layer: The communication layer is composed of n intelligent agents, which correspond to each AGC unit one by one; each intelligent agent calculates the frequency modulation performance of the local AGC unit according to the power data provided by the corresponding intelligent collection device and is responsible for communication between neighbors and updates the actual power instruction of the next control cycle of the local AGC unit according to the virtual power plant decentralized AGC method based on the precise diffusion algorithm, and gives the result to the middle control layer;

[0058] Control layer: The control layer consists of n controllers, which control the device layer according to the actual power command of the next control cycle calculated by the corresponding intelligent agent in the communication layer;

[0059] Equipment layer: The equipment layer consists of AGC units and corresponding intelligent acquisition devices. The intelligent acquisition devices are used to obtain the current operating parameters of the corresponding AGC units, the control parameters of the previous control cycle, and the parameters related to the frequency modulation performance evaluation.

[0060] An electronic device comprises 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, a decentralized AGC method for a virtual power plant based on an exact diffusion algorithm is implemented.

[0061] A storage medium containing computer executable instructions, which, when executed by a computer processor, implement a decentralized AGC method for a virtual power plant based on an exact diffusion algorithm.

[0062] A computer program product includes a computer program / instruction, which, when executed by a processor, implements the steps of a decentralized AGC method for a virtual power plant based on an exact diffusion algorithm.

[0063] Compared with the prior art, the present invention has the following beneficial effects:

[0064] (1) A three-layer structure of the virtual power plant AGC control system is proposed, and the communication topology and grid structure are modeled to facilitate decentralized control.

[0065] (2) A multi-objective optimization problem considering operating cost and frequency modulation performance is constructed in the AGC instruction decomposition problem, and the line capacity constraint is used as one of the constraints of the multi-objective optimization problem to prevent the frequency modulation unit from exceeding the line capacity during power adjustment;

[0066] (3) The line capacity constraints that were originally coupled together are decoupled and solved using the precise diffusion algorithm. The precise diffusion algorithm has higher convergence accuracy and convergence speed than the consensus algorithm and the traditional diffusion algorithm. The proposed method is not prone to single point failure, has high scalability and flexibility, and protects the privacy of the AGC unit.

[0067] (4) Taking the noise that occurs during the communication process into consideration, an agent communication model with communication noise is constructed. The exact diffusion algorithm is rewritten and the gain function c(k) is introduced, so that the exact diffusion algorithm has the ability to translate noise and improve the robustness of the algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0069] Figure 1 A control scheme diagram of a decentralized AGC method for a virtual power plant based on an accurate diffusion algorithm of the present invention;

[0070] Figure 2 The flowchart of the algorithm for decentralized solution using the exact diffusion algorithm;

[0071] Figure 3 The result diagram of the power adjustment value of each AGC unit solved by the exact diffusion algorithm;

[0072] Figure 4 It is the frequency fluctuation diagram of the system under a given input disturbance, and the horizontal axis is time (s);

[0073] Figure 5 It is a graph showing the change in the operating cost of the system under a given input disturbance, and the horizontal axis is time (s). DETAILED DESCRIPTION

[0074] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0075] The present application will use the terms commonly used by those skilled in the art to describe the various aspects of the illustrative embodiments, so that the essence of their work is conveyed to other technicians in the field. However, it is obvious to those skilled in the art that some alternative embodiments can be put into practice using parts of the described aspects. For the purpose of explanation, in order to provide a thorough understanding of the illustrative embodiments, specific numbers, materials and configurations are described. However, it is obvious to those skilled in the art that alternative embodiments can be implemented without specific details. In other cases, in order not to confuse the illustrative embodiments, some well-known features are omitted or simplified.

[0076] The present invention provides a decentralized AGC method for a virtual power plant based on an accurate diffusion algorithm, wherein the distributed resources in the virtual power plant are used as AGC units, and each AGC unit is provided with a one-to-one corresponding intelligent agent, controller and intelligent collection device, and the intelligent collection device is used to obtain the current operating parameters of the corresponding AGC unit, the control parameters of the previous control cycle and the frequency modulation performance evaluation related parameters; the intelligent agent calculates the frequency modulation performance of the corresponding AGC unit according to the power data provided by the corresponding intelligent collection device and is responsible for the communication between neighbors and the virtual power plant decentralized AGC method based on an accurate diffusion algorithm described in the present invention. The actual power instruction of the next control cycle of the local corresponding AGC unit is updated, and the result is given to the intermediate controller; the controller controls the device layer according to the actual power instruction of the next control cycle calculated by the corresponding intelligent agent of the communication layer. Specifically, the decentralized AGC method for a virtual power plant based on an accurate diffusion algorithm of the present invention takes the upper and lower limits of the power regulation of each distributed resource and the actual line capacity constraints as important constraints, and designs an AGC instruction decomposition model with the optimal operating cost and the highest frequency modulation performance as the dual optimization goals, so as to reduce the operating cost and improve the frequency modulation performance. For this model, the Lagrange multipliers in the optimization model are decoupled, and the precise diffusion algorithm is used to achieve fast and accurate solution of instructions in each control cycle. Finally, in order to solve the problem of communication noise in decentralized systems, a gain function is introduced in the precise diffusion algorithm to effectively suppress the noise.

[0077] This embodiment takes a virtual power plant composed of n distributed resources as an example. A decentralized AGC method for a virtual power plant based on an accurate diffusion algorithm, such as Figure 1 As shown, including:

[0078] S1, obtaining the current operating parameters of each distributed resource in the virtual power plant, the control parameters of the previous control cycle, and the parameters related to the frequency regulation performance evaluation;

[0079] The distributed resources in the virtual power plant are used as AGC units, and the AGC units can communicate and connect lines according to demand to achieve the utilization of internal flexible resources and control the balance of the power grid. In a specific implementation scheme, the communication topology model can be represented by an undirected graph. Indicates. , represents the set of intelligent agent nodes in the virtual power plant. At the same time, since the number of intelligent agents and AGC units corresponds one to one, n also represents the number of AGC units; represents a collection of communication lines, Indicates that nodes i and j can communicate; represents the line weight set, if ,but , which means they are neighbors; if ,but , the specific weights are defined as follows:

[0080]

[0081] in, Represent the number of neighbor nodes of node i and node j respectively.

[0082] Furthermore, the grid structure formed by the line connection can be used express, , m represents the total number of lines in the virtual power plant, represents the power contribution matrix of the distributed resource, i.e., AGC unit i to line l. If AGC unit i contributes to the power of line l, then ,on the contrary .

[0083] The parameter data obtained in the present invention is mainly used for primary frequency modulation and secondary frequency modulation solution calculation. In general, the current operating parameters of each distributed resource include the current power level of the AGC unit, the upper and lower limits of the AGC power adjustment at this moment, and the control parameters of the previous control cycle include the actual power command and the adjustment mileage command of the previous control cycle. The parameters related to the frequency modulation performance evaluation include the response climbing rate, the response time, the adjustment accuracy evaluation index, etc., which are respectively expressed as follows:

[0084]

[0085]

[0086] In the formula, Represents the response ramp rate evaluation index of the i-th AGC unit, is the actual ramp rate of the ith AGC unit, is the actual power output adjustment of the i-th AGC unit, Calculate the starting time for the ramp rate of the i-th AGC unit, Calculate the end time of the ramp rate of the i-th AGC unit, The average standard regulation rate of the frequency regulation resources of the virtual power plant;

[0087]

[0088]

[0089] In the formula, represents the evaluation index of the response time of the i-th AGC unit, is the actual delay error of the i-th AGC unit, is the time when the i-th AGC unit responds to the action, is the time when the adjustment command is issued to the i-th AGC unit, The delay error allowed for the frequency modulation resource to respond to the modulation command;

[0090]

[0091]

[0092] In the formula, Represents the adjustment accuracy index, is the actual adjustment error of the i-th AGC unit, is the moment when the i-th AGC unit enters the target dead zone, The calculation end time of the adjustment accuracy of the i-th AGC unit is: It is the allowable adjustment error of the frequency modulation resource in response to the AGC controller adjustment instruction.

[0093] comprehensive , , Among these three evaluation indicators, the comprehensive evaluation index of frequency regulation quality of the i-th distributed resource of the virtual power plant is , The specific formula is as follows:

[0094]

[0095] S2, taking each distributed resource as an AGC unit, and performing primary and secondary frequency regulation based on the current operating parameters of each distributed resource and the parameters related to the frequency regulation performance evaluation, so as to jointly maintain the frequency stability of the virtual power plant; wherein, the secondary frequency regulation is based on the current operating parameters of each distributed resource in the virtual power plant, the control parameters of the previous control cycle, and the parameters related to the frequency regulation performance evaluation, using the AGC instruction decomposition model with the dual optimization objectives of optimal operating cost and highest frequency regulation performance to obtain the actual power instruction of each distributed resource in the next control cycle;

[0096] The principle diagram of the virtual power plant participating in the AGC control method of the present invention is as follows: Figure 1 As shown, is the total adjustment of the actual power output of all AGC units, s represents the complex variable in Laplace transform, represents the frequency deviation of the AGC control system, M represents the equivalent inertia constant, D represents the equivalent damping coefficient, ACE represents the area control error, Represents the load disturbance input to the system, B represents the frequency deviation coefficient. The AGC control system maintains the frequency stability of the virtual power plant under the joint action of primary frequency regulation and secondary frequency regulation. Primary frequency regulation is when the frequency of the system deviates from the rated value. The unit that can be frequency regulated once will automatically control the increase or decrease of the unit's active power according to the frequency deviation of the AGC control system to limit the change in grid frequency. It is differential regulation. As for secondary frequency regulation, after receiving the load disturbance input to the system, ACE is calculated, and then the regional control demand ARR is obtained through the action of the PI controller. The present invention uses the communication and calculation of the communication layer agent to decentralizedly solve the optimal solution of the optimization problem, calculate the AGC instructions of each AGC unit, and then guide the distributed resources of the equipment layer to adjust to the specified power through the control layer. In each control cycle, the AGC instruction decomposition model with dual-objective optimization is designed with the lowest operating cost and the highest frequency regulation performance:

[0097] The proposed AGC instruction decomposition model considers the lowest operating cost and the highest frequency modulation performance, where the operating cost objective function C1 is as follows:

[0098]

[0099] in, represents the operating cost objective function of the i-th AGC unit, It is the implementation period of each control interval, generally 1-16s, t represents the current time point, , N is the total number of control cycles. is the operating cost coefficient of each AGC unit, represents the actual power command of the i-th AGC unit at time t, , represents the initial power of the i-th AGC unit at time t; is the adjustment mileage command of the i-th AGC unit at time t.

[0100] Furthermore, in order to motivate resources with high frequency modulation performance to make greater resource adjustments, it is necessary to allow them to have a larger adjustment instruction input. The frequency modulation performance objective function C2 is specifically as follows:

[0101]

[0102] in, represents the frequency modulation performance objective function of the i-th AGC unit, For the specified mileage price, is the frequency modulation performance score of each AGC unit, which is obtained by linearly weighting the three indicators of response climbing rate, response time and response accuracy after real-time evaluation, as shown in formula (8).

[0103] In order to ensure that C2 is convenient for subsequent gradient-based solution, formula (10) is written as follows:

[0104]

[0105] in, ,because ,therefore , represents the Performance Insufficiency Index, which measures the part of the FM performance score that does not reach the maximum value. In addition, the squared absolute value function maintains the properties of a convex function and is differentiable everywhere.

[0106] The objective function of the AGC instruction decomposition model proposed in the present invention can be specifically written as:

[0107]

[0108] Among them, C(i)=C1(i)+C2(i).

[0109] Furthermore, a constraint function group is constructed, and the construction of the constraint function group takes into account the power supply and demand balance constraint, the upper and lower limit constraints of the power adjustment of each AGC unit, and the line capacity constraint.

[0110] Furthermore, the power supply and demand balance constraint is:

[0111]

[0112] in, is the regional control demand value of the AGC control system at time t.

[0113] Furthermore, the upper and lower limits of resource adjustment are constrained as follows:

[0114]

[0115] in, and They represent the lower limit and upper limit of the power adjustment of the i-th AGC unit at time t respectively.

[0116] Furthermore, the line capacity constraint is:

[0117]

[0118] in, and are the lower and upper limits of the power flow of the lth line respectively, and m is the total number of lines in the virtual power plant.

[0119] In a specific implementation, the Lagrange multipliers in the optimization model can be decoupled, and an accurate diffusion algorithm can be used to achieve fast and accurate solution of instructions in each control cycle, including:

[0120] First, formula (12), formula (13) and formula (14) in step S2 are written in the form of Lagrangian dual functions, and then the Lagrangian multipliers are updated through distributed algorithms to achieve optimization solutions. This is a common method of distributed algorithm solution. Rewriting into Lagrangian functions is as follows:

[0121]

[0122] in, is the stipulated mileage price, and serves as the Lagrange multiplier for the equality constraint, and is the Lagrange multiplier of the inequality constraint. However, since the power supply and demand balance constraint and the line capacity constraint in the optimization problem are coupled constraints, that is, the constraint needs to be combined through the power of several AGC units, it is impossible to solve this optimization problem in a decentralized manner. Therefore, the constraint is decoupled:

[0123]

[0124] Decompose into Similarly, and Decompose into , The above decoupling method can be used to obtain the model of distributed AGC instruction decomposition:

[0125]

[0126]

[0127] like is the optimal solution to the original problem. When the power constraint (14) is not considered, The following optimal solution conditions must be met:

[0128]

[0129] Furthermore, the algorithm flow chart for decentralized solution using the exact diffusion algorithm is as follows: Figure 2 As shown, the details are as follows:

[0130] Obtain relevant parameters, including introducing power measurement values ​​at the device layer to calculate and update the frequency modulation performance score; update the communication line set according to the current communication topology, and update the line weight set according to the grid structure;

[0131] Initialize Lagrange multiplier, iteration step u, and number of iterations k;

[0132] Based on the current operating parameters of each distributed resource and the frequency modulation performance evaluation related parameters, the Lagrange multiplier is updated using the precise diffusion algorithm. The updating method of the Lagrange multiplier using the precise diffusion algorithm is as follows:

[0133]

[0134] Where k is the number of iterations of the exact diffusion algorithm, is the intermediate variable, , , is the identity matrix, yes The intermediate variable updated for the kth time, is the iteration step size, is the Lagrange multiplier The intermediate variable matrix updated using the gradient method, is the Lagrange multiplier The intermediate variable matrix after correcting the imbalance needs to be updated The intermediate variable is always greater than 0. and In each update round and Similarly, it is expressed as follows:

[0135]

[0136]

[0137] Similarly, , They are the Lagrange multipliers and The intermediate variable matrix updated using the gradient method, They are the Lagrange multipliers and The intermediate variable matrix after correcting the imbalance. It is also necessary to ensure that and The intermediate variable is always greater than 0.

[0138] In the algorithm flow chart, after each round of Lagrange multiplier update, it is necessary to calculate the actual power command of the i-th frequency modulation AGC unit at time t corresponding to the corresponding round k+1 according to formula (24): The intermediate variable , and according to The upper and lower limit constraints are modified to obtain the final , the specific formula is as follows:

[0139]

[0140] Furthermore, the proposed power correction calculation method is:

[0141]

[0142] Repeated iterations are performed according to the Lagrange multiplier update result until the set number of iterations is reached, and the final optimal actual power command of the i-th frequency modulation AGC unit at time t is obtained.

[0143] In a preferred embodiment, based on the precise diffusion algorithm of step S3, a method of introducing a gain function into the precise diffusion algorithm is proposed to effectively suppress noise:

[0144] By introducing noise into the proposed exact diffusion algorithm, the third equation of equation (21) can be rewritten as follows:

[0145]

[0146] Furthermore, the gain function is introduced into the exact diffusion algorithm To suppress the influence of communication noise, the expression is as follows:

[0147]

[0148] is a Gaussian white noise that follows a normal distribution, The following two necessary conditions must be met: ; In this embodiment, . The noise suppression method is similar and is expressed as follows:

[0149]

[0150]

[0151] The effect of the present invention is further described below in conjunction with a specific embodiment:

[0152] This embodiment verifies the effectiveness of the method and system by using an AGC control system of a virtual power plant consisting of five distributed resources, namely, one photovoltaic unit, one wind turbine, one small hydropower unit, one energy storage unit, and one adjustable load. The model is built on the MATLAB / Simulink platform, and a 30MW disturbance is input during the 200s of system simulation. Figure 3The command distribution of the five AGC units in the AGC control system shows that the frequency modulation performance and operating cost of different AGC units are different, which will affect the power output. In addition, the centralized fixed ratio allocation method and the traditional diffusion algorithm are compared with the method proposed in this invention. Figure 4 The frequency deviations calculated by the three methods are shown in Figure 2. It can be seen that the centralized fixed ratio allocation method cannot improve the frequency modulation performance because there is no dynamic optimization process, so the frequency deviation is the largest. The accuracy of the traditional diffusion algorithm is lower than that of the precise diffusion algorithm, and there is no way to calculate the optimal solution for the frequency modulation performance. In comparison, the proposed method has the smallest frequency deviation for the system, which proves that the proposed method improves the frequency modulation performance. Figure 5 The operating costs of the three methods were compared, and it was found that the method proposed in the present invention also had the lowest operating cost, which proved that the proposed method can reduce the operating cost.

[0153] The present invention also provides a decentralized AGC system for a virtual power plant based on an accurate diffusion algorithm. The system has a three-layer structure, which is specifically as follows from top to bottom:

[0154] Communication layer: The communication layer is composed of intelligent agents (multi-agent system) and has three main tasks: 1) calculate the frequency modulation performance of the AGC unit based on the power data provided by the intelligent collection device, 2) be responsible for communication between neighbors, and 3) update local variables through the precise diffusion algorithm mentioned above and give the power adjustment results to the middle control layer.

[0155] Control layer: includes n controllers. Each AGC unit end-side controller controls the device layer according to the power adjustment value calculated by the communication layer. At the same time, primary control is also completed at the control layer.

[0156] Device layer: The device layer mainly consists of AGC units and intelligent acquisition devices. The intelligent experience of the communication layer will extract power measurement values ​​from the device layer.

[0157] Corresponding to the aforementioned embodiment of a decentralized AGC method for a virtual power plant based on an exact diffusion algorithm, the present invention also provides an electronic device.

[0158] An electronic device provided by an embodiment of the present invention includes one or more processors, which are used to implement a decentralized AGC method of a virtual power plant based on an exact diffusion algorithm in the above embodiment.

[0159] The electronic device of the present invention can be applied to any device with data processing capability, and the device with data processing capability can be a device or apparatus such as a computer.

[0160] The device embodiment can be implemented by software, hardware or a combination of software and hardware. Taking software implementation as an example, as a device in a logical sense, it is formed by the processor of any device with data processing capability in which it is located to read the corresponding computer program instructions in the non-volatile memory into the memory for execution. From the hardware level, it includes a processor, memory, network interface, and non-volatile memory. In addition, any device with data processing capability in which the device in the embodiment is located can also include other hardware according to the actual function of the device with data processing capability, which will not be described in detail.

[0161] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0162] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of the present invention. Ordinary technicians in this field can understand and implement it without paying creative work.

[0163] An embodiment of the present invention also provides a computer-readable storage medium having a program stored thereon. When the program is executed by a processor, a decentralized AGC method of a virtual power plant based on an exact diffusion algorithm in the above embodiment is implemented.

[0164] The computer-readable storage medium may be an internal storage unit of any device with data processing capability described in any of the aforementioned embodiments, such as a hard disk or a memory. The computer-readable storage medium may also be any device with data processing capability, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), an SD card, a flash card (Flash Card), etc. equipped on the device. Furthermore, the computer-readable storage medium may also include both an internal storage unit of any device with data processing capability and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by any device with data processing capability, and may also be used to temporarily store data that has been output or is to be output.

[0165] The above are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should be regarded as the protection scope of the present invention.

Claims

1. A decentralized AGC method for virtual power plants based on an exact diffusion algorithm, characterized in that: include: Obtain the current operating parameters of each distributed resource in the virtual power plant, the control parameters of the previous control cycle, and the parameters related to the frequency regulation performance evaluation; Each distributed resource is used as an AGC unit, and primary and secondary frequency regulation are performed based on the current operating parameters of each AGC unit and the parameters related to the frequency regulation performance evaluation, so as to jointly maintain the frequency stability of the virtual power plant; wherein, the secondary frequency regulation is based on the current operating parameters of each distributed resource in the virtual power plant, the control parameters of the previous control cycle, and the parameters related to the frequency regulation performance evaluation, and the AGC instruction decomposition model with the optimal operating cost and the highest frequency regulation performance as the dual optimization goals is used to solve and obtain the actual power instruction of each distributed resource in the next control cycle; then each distributed resource, i.e., the AGC unit, controls the power according to the corresponding actual power instruction; Among them, the constraints considered when solving the AGC instruction decomposition model with the dual optimization goals of optimal operating cost and highest frequency regulation performance include power supply and demand balance constraints, upper and lower limit constraints on the power adjustment of each distributed resource, and line capacity constraints.

2. The method according to claim 1, characterized in that The objective function of the AGC instruction decomposition model is expressed as follows: ； Where C(i) represents the objective function of the i-th AGC unit, is the implementation period of each control interval, t represents the current time point, , N is the total number of control cycles; is the operating cost coefficient of each AGC unit, represents the actual power command of the i-th AGC unit at time t, , represents the initial power of the i-th AGC unit at time t; is the adjustment mileage command of the i-th AGC unit at time t; For the specified mileage price, Represents performance deficiency indicator.

3. The method according to claim 2, characterized in that The AGC instruction decomposition model with the dual optimization objectives of optimal operating cost and highest frequency modulation performance is solved in each control cycle by using an accurate diffusion algorithm, specifically including: Rewrite the objective function of the AGC instruction decomposition model with the dual optimization objectives of optimal operating cost and highest frequency regulation performance, power supply and demand balance constraint and line capacity constraint into the form of Lagrangian dual function, and decouple the Lagrangian multipliers therein; Initialize Lagrange multiplier, iteration step and number of iterations; Based on the current operating parameters of each distributed resource and the parameters related to frequency modulation performance evaluation, the Lagrange multiplier is updated using an accurate diffusion algorithm; According to the Lagrange multiplier update result, the actual power instruction of the i-th frequency modulation AGC unit at time t corresponding to the optimal iteration round k+1 is calculated. The intermediate variable ; Based on intermediate variables Combined with the upper and lower limits of each distributed resource power adjustment constraints, the actual power instruction of the i-th frequency modulation AGC unit at time t corresponding to the iteration round k+1 is corrected; Repeated iterations are performed according to the Lagrange multiplier update result until the set number of iterations is reached, and the final optimal actual power instruction of the i-th frequency modulation AGC unit at time t is obtained.

4. The method according to claim 3, characterized in that The objective function of the AGC instruction decomposition model with the dual optimization objectives of optimal operating cost and highest frequency modulation performance, power supply and demand balance constraint and line capacity constraint are rewritten into the form of Lagrangian dual function, and the Lagrangian multipliers therein are decoupled, which is expressed as follows: ； ； ； ； ； In the formula, is the Lagrange multiplier of the decoupled equality constraint, They represent the Lagrange multipliers of the decoupled inequality constraints, C(i) represents the objective function of the i-th AGC unit, is the adjustment mileage command of the i-th AGC unit; They represent the Lagrange multipliers of equality constraints and inequality constraints corresponding to the i-th AGC unit respectively; represents the power contribution of the ith AGC unit to line l. If the ith AGC unit contributes to the power of line l, then ,on the contrary ; is the regional control demand value of the AGC control system; n is the number of AGC units, and m is the total number of lines.

5. The method according to claim 4, characterized in that Based on the current operating parameters of each AGC unit and the parameters related to the frequency modulation performance evaluation, the Lagrange multiplier is updated using the accurate diffusion algorithm. The updated formula is as follows: ； ； ； Where k is the number of iterations of the exact diffusion algorithm; in, , and its calculation method is as follows: ； in, is the identity matrix, represents the line weight set, if ,but , which means that the i-th AGC unit and the j-th AGC unit are neighbors; if ,but , Represents a set of communication lines; the specific weights are defined as follows: ； in, Respectively represent the number of neighbor nodes of AGC unit i and AGC unit j; yes The intermediate variable updated for the kth time, is the iteration step size, is the Lagrange multiplier The intermediate variable matrix updated using the gradient method, , is the Lagrange multiplier The intermediate variable matrix after correcting the imbalance; , They are the Lagrange multipliers and The intermediate variable matrix updated using the gradient method, They are the Lagrange multipliers and The intermediate variable matrix after correcting the imbalance.

6. The method according to claim 4, characterized in that The exact diffusion algorithm updates the Lagrange multiplier, and the update formula is expressed as follows: ； ； ； Where k is the number of iterations of the exact diffusion algorithm, , is the identity matrix, represents the line weight set, if ,but , which means that the i-th AGC unit and the j-th AGC unit are neighbors; if ,but , represents a collection of communication lines; yes The intermediate variable updated for the kth time, is the iteration step size, is the Lagrange multiplier The intermediate variable matrix updated using the gradient method, , is the Lagrange multiplier The intermediate variable matrix after correcting the imbalance; , They are the Lagrange multipliers and The intermediate variable matrix updated using the gradient method, They are the Lagrange multipliers and The intermediate variable matrix after correcting the imbalance; is a Gaussian white noise that follows a normal distribution, For Update The gain function when , needs to meet the following two necessary conditions: ; ; For Update The gain function when , needs to meet the following two necessary conditions: ; ; For Update The gain function when , needs to meet the following two necessary conditions: ; .

7. A decentralized AGC system for a virtual power plant based on an accurate diffusion algorithm, characterized in that: include: Communication layer: The communication layer is composed of n intelligent agents, which correspond to each AGC unit one by one; each intelligent agent calculates the frequency modulation performance of the local AGC unit according to the power data provided by the corresponding intelligent collection device and is responsible for communication between neighbors and updating the actual power instruction of the next control cycle of the local AGC unit according to the virtual power plant decentralized AGC method based on the precise diffusion algorithm according to any one of claims 1 to 6, and gives the result to the middle control layer; Control layer: The control layer consists of n controllers, which control the device layer according to the actual power command of the next control cycle calculated by the corresponding intelligent agent in the communication layer; Equipment layer: The equipment layer consists of AGC units and corresponding intelligent acquisition devices. The intelligent acquisition devices are used to obtain the current operating parameters of the corresponding AGC units, the control parameters of the previous control cycle, and the parameters related to the frequency modulation performance evaluation.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it implements a decentralized AGC method for a virtual power plant based on an exact diffusion algorithm as described in any one of claims 1-6.

9. A storage medium comprising computer executable instructions, which, when executed by a computer processor, implement a decentralized AGC method for a virtual power plant based on an exact diffusion algorithm as described in any one of claims 1 to 6.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of a decentralized AGC method for a virtual power plant based on an exact diffusion algorithm as described in any one of claims 1 to 6 are implemented.

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