Power grid new energy characteristic modeling and decentralized coordination control method

Abstract

The invention discloses a power grid new energy characteristic modeling and decentralized coordination control method, and belongs to the technical field of power system operation and control, and the method comprises the steps: constructing a hierarchical control structure based on a multi-agent system, and the hierarchical control structure comprises a lower-layer unit agent and an upper-layer coordination agent; in the lower-layer unit intelligent agent, various renewable energy sources are subjected to decentralized control by configuring an inverter based on a double-feedback controller, the double-feedback controller comprises an outer-loop power controller and an inner-loop current and voltage controller, the outer-loop power controller is controlled by an active-frequency and reactive-amplitude droop controller, and the inner-loop current and voltage controller is controlled by an active-frequency and reactive-amplitude droop controller. The inner loop current and voltage controller adopts fractional order proportional-integral-differential control; in the upper-layer coordination agent, distributed coordination control based on a consistency protocol is adopted, the load can be distributed and the voltage can be adjusted in proportion, and better steady-state performance can be obtained.

Description

Technical Field

[0001] The invention relates to a method for modeling new energy characteristics of a power grid and decentralized coordinated control, and belongs to the technical field of power system operation and control. Background Art

[0002] With the increasing attention paid to low carbon emissions and fossil fuel reserves, renewable energy has become an increasingly important research topic. Recently, power supply applications using clean renewable energy have been applied to power supply systems. At present, the main renewable energy sources include photovoltaics, micro turbines, wind turbines, and fuel cells. However, after being introduced into the system, renewable energy has the characteristics of quasi-load. IEEE 1547 has established standard rules for the network connection of renewable energy, requiring each micro generator to exit operation when a system failure occurs. This greatly limits the full play of energy efficiency. In order to coordinate the contradiction between the power grid and renewable energy, fully develop the potential of clean renewable energy, and promote the widespread application of power production technology, the concept of the power grid has been advocated.

[0003] There are two modes of grid operation, including grid-connected mode and island mode. In the former mode, the grid has the possibility to absorb power from the utility grid or inject power into the utility grid, and the voltage and frequency are determined by the utility grid. If the grid fails, it will be disconnected from the grid, and the frequency and voltage need to be maintained by the grid in order to provide renewable energy to critical loads uninterruptedly. Therefore, an effective strategy must be proposed to manage renewable energy.

[0004] Most renewable energy sources are connected to the grid through inverters, so their control strategy is crucial. At present, people have paid extensive attention to the research on inverter control. The control strategies of inverters are mainly divided into centralized and decentralized. In terms of centralized control, a central controller manages all controllers, so this method relies on reliable communication. However, this method is difficult to ensure the robustness of complex systems. Decentralized control allows renewable energy to be "plug and play". Each device is controlled by a local controller without communication. Due to the poor coordination between renewable energy sources, the system voltage may not be effectively guaranteed. Therefore, the present invention proposes a method for modeling and decentralized coordinated control of new energy characteristics of power grids. Summary of the invention

[0005] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a method for modeling and decentralized coordinated control of new energy characteristics of a power grid, which can distribute loads and adjust voltage in proportion and obtain better steady-state performance.

[0006] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0007] In a first aspect, the present invention provides a method for modeling and decentralized coordinated control of new energy characteristics of a power grid, comprising: Constructing a hierarchical control structure based on a multi-agent system, wherein the hierarchical control structure includes a lower-layer unit agent and an upper-layer coordination agent; In the lower unit agent, decentralized control is implemented on various types of renewable energy sources by configuring an inverter based on a dual feedback controller, wherein the dual feedback controller includes an outer loop power controller, an inner loop voltage controller and an inner loop current controller, wherein: The outer loop power controller includes an active-frequency droop controller and a reactive-amplitude droop controller, which adjust the frequency and voltage amplitude according to the difference between the instantaneous value of active power and reactive power and the reference value respectively; The inner loop voltage controller outputs a control voltage according to the voltage difference and the control signal, and the inner loop current controller outputs a control current according to the current difference and the control signal, and the parameters of the controller are optimized and solved by a particle swarm algorithm; In the upper-level coordination agent, Kirchhoff's voltage and current laws are used to establish a system model, and global coordinated control is achieved by defining neighborhood errors and control signals.

[0008] Further, the outer loop power controller includes an active-frequency droop controller and a reactive-amplitude droop controller, which adjust the frequency and voltage amplitude according to the difference between the instantaneous value of active power and reactive power and the reference value, respectively, as follows: Get the instantaneous value of active power output by the grid new energy interface inverter , expressed as: (1); Get the instantaneous value of reactive power output by the grid new energy interface inverter , expressed as: (2); in, , , , They are the d-axis and q-axis components of the inverter output voltage and current respectively; The calculation of active-frequency and reactive-amplitude droop controllers are: (3); (4) in, and are the droop coefficients of the active-frequency and reactive-amplitude droop controllers, f is the inverter output frequency, v is the inverter output voltage amplitude, is the inverter output frequency reference value, is the initial value of the inverter output voltage amplitude, is the instantaneous value of active power, Indicates the initial value of the inverter output active power, is the instantaneous value of the inverter output reactive power, Indicates the initial value of the inverter output reactive power.

[0009] Furthermore, the inner loop voltage controller outputs a control voltage according to the voltage difference and the control signal, and the inner loop current controller outputs a control current according to the current difference and the control signal, as follows: The formula for designing the inner loop voltage controller is as follows: (5); in, is the voltage difference of the voltage controller at time t, is the control signal of the voltage controller at time t, and are the voltage output of the voltage controller and its reference, represents the reference current of the current controller, are the integral and differential orders of the voltage controller, are the three parameters of the voltage controller, and D is a complex variable; The formula for designing the inner loop current controller is as follows: (6); in, is the current difference of the current controller at time t, is the control signal of the current controller at time t, and are the current output of the current controller and its reference, is the voltage output of the current controller, , are the integral and differential orders of the current controller, are the three parameters of the current controller; The parameters of the voltage controller and the current controller are as follows: (7); (8); (9); (10); (11); (12); (13); (14); (15); in, , is the reference value of the d and q axis components of the controller’s output current, , is the instantaneous value of the d and q axis components of the controller’s output current, , is the reference value of the d and q axis components of the controller input voltage, , is the instantaneous value of the d-axis and q-axis components of the controller’s input voltage, , is the reference value of the d and q axis components of the controller’s output voltage, , is the disturbance, S is a complex variable, is the angular frequency, is the q-axis component of the controller’s output voltage, , is the output of the voltage controller, , is the output of the current controller, is the feedforward parameter, is the filter capacitor of the resonant circuit, is the filter inductance of the resonant circuit.

[0010] Furthermore, the parameters of the controller are optimized and solved by a particle swarm algorithm, and the specific process is as follows: First, initialize the parameters of the particle swarm algorithm and initialize the particles; Then, the fitness function of the particle swarm algorithm is determined. Specifically, the integral performance of the absolute error is used as an indicator of controller design, and then the fitness function of the particle swarm algorithm is determined as: ,in: (16); Among them, J represents the optimal index of the designed controller, F represents the fitness function of the particle swarm algorithm, represents the absolute error at time t, represents the control input of the designed controller at time t, is the rise time, is the weight coefficient of the optimal indicator; Next, the fitness value of each particle is calculated, and the individual and global best positions are updated. Specifically, the fitness value of each particle is calculated based on the fitness function. By comparing the current fitness value with the best fitness value found previously, if the current one is the best, the individual best fitness value and its corresponding position are updated, and the global optimal solution and its corresponding position are found among the individual best fitness values ​​of all particles. Secondly, dynamically update the speed and position of each particle, which is achieved through the following speed and position update equations: (17); (18); in, , is the velocity of particle i at t+1, generation t, , represents the position of particle i at generation t+1 and t, Expressed as inertia weight, and represents the learning rate, , represents the historical best position and global best position of particle i in generation t, and Represents a random number; Finally, when the algorithm reaches the preset maximum number of iterations, the algorithm terminates and outputs the global optimal solution of the algorithm, thereby obtaining the optimal values ​​of each parameter in the designed controller.

[0011] Furthermore, in the upper-layer coordination agent, a system model is established using Kirchhoff's voltage and current laws, and global coordination control is achieved by defining neighborhood errors and control signals, including: The i-th renewable energy source is set to be connected to other renewable energy sources through power line impedance. The single-phase electrical scheme of the power grid system adopts Kirchhoff's voltage and current laws, which are as follows: (19); Among them, d represents the differential symbol, t represents time, is the current of the i-th renewable energy source, , is the inductance and resistance of the i-th renewable energy source, , is the output voltage of the i-th renewable energy source and the voltage on its resistor, , , is the grounding capacitance of the i-th, m-th, and n-th renewable energy sides, and are the resistance and inductance in parallel with the grounding capacitor, The resistor limits the current. , are the currents injected into m and n renewable energy sources respectively for the i-th renewable energy source, and are the resistance and inductance of the ith and mth renewable energy tie lines, respectively, is the output voltage of the mth renewable energy source, , are the current of the mth renewable energy source, and are the resistance and inductance of the mth renewable energy side, and are the resistance and inductance of the nth renewable energy source, , is the output voltage of the mth renewable energy source and the voltage on its capacitor, , is the output voltage of the nth renewable energy source and the voltage on its capacitor; The model of the power grid system is expressed as: (20); in, Indicates the system status. is the state matrix, represents the system input, is the input matrix, Represents the system output, is the output matrix, and the correlation matrix in equation (20) is given by the following matrix: , , ; The neighborhood error of the i-th renewable energy source is expressed as: (twenty one); in, are the connection weights of the i-th and j-th renewable energy sources, , , is the unit value; Then, the total neighborhood error is equal to ,in, is the Laplace matrix, is the Kronecker product, is the identity matrix, , , is the unit value, T represents the matrix transpose, , , As unit value, let input , is the coefficient, is the feedback gain matrix, then the global form is: (twenty two); in, is the control signal; Therefore, the dynamic characteristics of the entire closed-loop power grid system are: (twenty three); in, , is a unit value, if the system (23) is asymptotically stable if and only if there exists A matrix that satisfies the following conditions: (twenty four); (25); (26); in, represents the smallest non-zero singular value, represents a symmetric matrix, is the largest singular value.

[0012] Furthermore, the lower-level unit agents and upper-level coordination agents in the hierarchical control structure of the multi-agent system exchange information and coordinate control through a communication network to achieve intelligent management and optimized operation of the power grid.

[0013] In a second aspect, the present invention provides a power grid new energy characteristic modeling and decentralized coordinated control system, comprising: A hierarchical control structure based on a multi-agent system, the hierarchical control structure comprising lower-level unit agents and upper-level coordination agents; The lower-layer unit intelligent agent implements decentralized control on various types of renewable energy by configuring an inverter based on a dual feedback controller, wherein the dual feedback controller includes an outer loop power controller, an inner loop voltage controller and an inner loop current controller, wherein: the outer loop power controller includes an active-frequency droop controller and a reactive-amplitude droop controller, which respectively adjust the frequency and voltage amplitude according to the difference between the instantaneous value of active power and reactive power and the reference value; the inner loop voltage controller outputs a control voltage according to the voltage difference and the control signal, and the inner loop current controller outputs a control current according to the current difference and the control signal, and the parameters of the controller are optimized and solved by a particle swarm algorithm; The upper-layer coordination agent uses Kirchhoff's voltage and current laws to establish a system model and implements global coordination control by defining neighborhood errors and control signals.

[0014] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the aforementioned methods.

[0015] In a fourth aspect, the present invention provides a computer device, comprising: Memory, for storing computer programs / instructions; A processor is used to execute the computer program / instructions to implement the steps of any of the aforementioned methods.

[0016] In a fifth aspect, the present invention provides a computer program product, comprising a computer program / instruction, which implements the steps of any one of the aforementioned methods when executed by a processor.

[0017] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a method for modeling the characteristics of new energy in a power grid and for decentralized coordinated control. The proposed hierarchical control structure based on a multi-agent system can effectively improve the coordination between renewable energy sources and ensure the robustness of complex systems. The present invention proposes a distributed control based on a dual feedback controller at the lower layer, which can obtain better steady-state performance. The present invention proposes a distributed coordinated control method based on a consistency protocol at the upper layer, which can effectively distribute loads and adjust voltage in proportion to ensure the voltage safety of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flow chart of a method for modeling and decentralized coordinated control of new energy characteristics of a power grid provided by an embodiment of the present invention;

[0019] Figure 2 is a hierarchical control structure diagram based on a multi-agent system provided by an embodiment of the present invention;

[0020] Figure 3 It is a diagram of a power grid structure based on a multi-agent system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0021] 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.

[0022] Embodiment 1: This embodiment introduces a method for modeling and decentralized coordinated control of new energy characteristics of a power grid, including: Constructing a hierarchical control structure based on a multi-agent system, wherein the hierarchical control structure includes a lower-layer unit agent and an upper-layer coordination agent; In the lower unit agent, decentralized control is implemented on various types of renewable energy sources by configuring an inverter based on a dual feedback controller, wherein the dual feedback controller includes an outer loop power controller, an inner loop voltage controller and an inner loop current controller, wherein: The outer loop power controller includes an active-frequency droop controller and a reactive-amplitude droop controller, which adjust the frequency and voltage amplitude according to the difference between the instantaneous value of active power and reactive power and the reference value respectively; The inner loop voltage controller outputs a control voltage according to the voltage difference and the control signal, and the inner loop current controller outputs a control current according to the current difference and the control signal, and the parameters of the controller are optimized and solved by a particle swarm algorithm; In the upper-level coordination agent, Kirchhoff's voltage and current laws are used to establish a system model, and global coordinated control is achieved by defining neighborhood errors and control signals.

[0023] like Figure 1 As shown, the application process of the power grid new energy characteristic modeling and decentralized coordinated control method provided in this embodiment specifically involves the following steps: (1) Build a hierarchical control structure based on a multi-agent system; (2) At the lower level, decentralized control of each renewable energy source is performed through an inverter based on a dual feedback controller, where the outer loop power controller adopts droop control and the inner loop current and voltage controllers adopt fractional-order proportional-integral-derivative control; (3) At the upper level, a distributed coordinated control method based on a consensus protocol is proposed to achieve proportional power sharing and voltage consistency among all renewable energy sources.

[0024] The specific steps include: Step 1: Build a hierarchical control structure based on a multi-agent system. In this embodiment, Figure 2 The hierarchical control structure diagram based on the multi-agent system shown in the figure and Figure 3 The structure diagram of the power grid based on the multi-agent system shown in the figure, the control architecture involves the lower-level unit agent and the upper-level coordination agent, where the lower-level unit agent is composed of a reaction layer and a negotiation layer. The reaction layer is defined as "perception and action", which gives priority to rapid response to emergencies in the environment, and the negotiation layer is defined as "belief, desire and intention", which has a higher level of intelligence and can control and plan the behavior of the agent to achieve its desire or intention. The upper-level coordination agent is designed as a deliberative agent, which mainly performs secondary control, including distributed voltage regulation, balancing and sharing. In addition, the output of the regulator is transmitted to the lower-level agent through the action module.

[0025] Step 2: In the lower layer, decentralized control of each renewable energy source is performed through an inverter based on a dual feedback controller, where the outer loop power controller adopts droop control and the inner loop current and voltage controllers adopt fractional-order proportional-integral-derivative control; First, the power controller is designed as follows: The instantaneous power output by the grid new energy interface inverter and It can be expressed as: (1); (2); in, , , , They are the d-axis and q-axis components of the inverter output voltage and current respectively; The active-frequency and reactive-amplitude droop controllers are: (3); (4); in, and are the droop coefficients of the active-frequency and reactive-amplitude droop controllers, f is the inverter output frequency, v is the inverter output voltage amplitude, is the inverter output frequency reference value, is the initial value of the inverter output voltage amplitude, is the instantaneous value of the inverter output active power, Indicates the initial value of the inverter output active power, is the instantaneous value of the inverter output reactive power, Indicates the initial value of the inverter output reactive power; Then, the voltage and current controller is designed as follows: The inner loop voltage controller is designed as follows: (5); in, is the voltage difference of the voltage controller at time t, is the control signal of the voltage controller at time t, and are the voltage output and reference of the voltage controller, respectively, represents the reference current of the current controller, are the integral and differential orders of the voltage controller, are the three parameters of the voltage controller, and D is a complex variable; The inner loop current controller is designed as follows: (6); in, is the current difference of the current controller at time t, is the control signal of the current controller at time t, and are the current output of the current controller and its reference, is the voltage output of the current controller, , are the integral and differential orders of the current controller, are the three parameters of the current controller; The output voltage and current of the controller are described as: (7); (8); (9); (10); (11); (12); (13); (14); (15); in, , is the reference value of the d and q axis components of the controller’s output current, , is the instantaneous value of the d and q axis components of the controller’s output current, , is the reference value of the d and q axis components of the controller input voltage, , is the instantaneous value of the d-axis and q-axis components of the controller’s input voltage, , is the reference value of the d and q axis components of the controller’s output voltage, , is the disturbance, S is a complex variable, is the angular frequency, is the q-axis component of the controller’s output voltage, , is the output of the voltage controller, , is the output of the current controller, is the feedforward parameter, is the filter capacitor of the resonant circuit, is the filter inductance of the resonant circuit; In order to determine the above controller parameters, the particle swarm algorithm is used for optimization and solution. The specific process is as follows: (1) Parameter initialization, defining the number of particles, search space dimension, weight coefficient, parameter range, and learning factor; (2) Particle initialization; (3) Determine the fitness function of the particle swarm algorithm. Specifically, the integral performance of the absolute error is used as the control target. In order to obtain excellent dynamic performance and prevent the control input from being too large, this paper determines the fitness function of the particle swarm algorithm as: , (16); Among them, J represents the optimal index of the designed controller, F represents the fitness function of the particle swarm algorithm, represents the absolute error at time t, represents the control input of the designed controller at time t, To control the rise time of the process, is the weight coefficient of the optimal indicator; (4) Next, the fitness value of each particle is calculated, and the individual and global optimal positions are updated. Specifically, the fitness value of each particle is calculated based on the fitness function. By comparing the current fitness value with the previously found optimal fitness value, if the current one is optimal, the individual optimal fitness value and its corresponding position are updated, and the global optimal solution and its corresponding position are found among the individual optimal fitness values ​​of all particles. (5) Dynamically update the velocity and position of each particle, which is achieved through the following velocity and position update equations: (17); (18); in, , is the velocity of particle i at t+1, generation t, , represents the position of particle i at generation t+1 and t, Expressed as inertia weight, and represents the learning rate, , represents the historical best position and global best position of particle i in generation t, and Represents a random number; (6) When the algorithm reaches the preset maximum number of iterations, the algorithm terminates and outputs the global optimal solution of the algorithm, thereby obtaining the optimal values ​​of each parameter in the designed controller.

[0026] Step 3: At the upper level, a distributed coordination control method based on a consistency protocol is proposed to achieve proportional power sharing and voltage consistency among all renewable energy sources; Assume that the i-th renewable energy source is connected to other renewable energy sources through power line impedance. The single-phase electrical scheme of the power grid system adopts Kirchhoff's voltage and current laws as follows: (19); Among them, d represents the differential symbol, t represents time, is the current of the i-th renewable energy source, , is the inductance and resistance of the i-th renewable energy source, , is the output voltage of the i-th renewable energy source and the voltage on its resistor, , , is the grounding capacitance of the i-th, m-th, and n-th renewable energy sides, and are the resistance and inductance in parallel with the grounding capacitor, The resistor limits the current. , are the currents injected into m and n renewable energy sources respectively for the i-th renewable energy source, and are the resistance and inductance of the ith and mth renewable energy tie lines, respectively, is the output voltage of the mth renewable energy source, , are the current of the mth renewable energy source, and are the resistance and inductance of the mth renewable energy side, and are the resistance and inductance of the nth renewable energy source, , is the output voltage of the mth renewable energy source and the voltage on its capacitor, , is the output voltage of the nth renewable energy source and the voltage on its capacitor; The system model is expressed as: (20); in, Indicates the system status. is the state matrix, represents the system input, is the input matrix, Represents the system output, is the output matrix, and the correlation matrix in equation (20) is given by the following matrix: , , , The neighborhood error of the i-th renewable energy source is described as: (twenty one); in, are the connection weights of the i-th and j-th renewable energy sources, , , is the unit value; Then, the total neighborhood error is equal to ,in, is the Laplace matrix, is the Kronecker product, is the identity matrix, , , is the unit value, T represents the matrix transpose, , , As unit value, let input , is the coefficient, is the feedback gain matrix, then the global form is: (twenty two); in, is the control signal; Therefore, the dynamic characteristics of the entire closed-loop system are: (twenty three); in, , is a unit value, if the system (23) is asymptotically stable if and only if there exists A matrix that satisfies the following conditions: (twenty four); (25); (26); in, represents the smallest non-zero singular value, represents a symmetric matrix, is the largest singular value.

[0027] Embodiment 2: This embodiment provides a power grid new energy characteristic modeling and decentralized coordinated control system, including: A hierarchical control structure based on a multi-agent system, the hierarchical control structure comprising lower-level unit agents and upper-level coordination agents; The lower-layer unit intelligent agent implements decentralized control on various types of renewable energy by configuring an inverter based on a dual feedback controller, wherein the dual feedback controller includes an outer loop power controller, an inner loop voltage controller and an inner loop current controller, wherein: the outer loop power controller includes an active-frequency droop controller and a reactive-amplitude droop controller, which respectively adjust the frequency and voltage amplitude according to the difference between the instantaneous value of active power and reactive power and the reference value; the inner loop voltage controller outputs a control voltage according to the voltage difference and the control signal, and the inner loop current controller outputs a control current according to the current difference and the control signal, and the parameters of the controller are optimized and solved by a particle swarm algorithm; The upper-layer coordination agent uses Kirchhoff's voltage and current laws to establish a system model and implements global coordination control by defining neighborhood errors and control signals.

[0028] The specific functional implementation of each of the above modules can be found in the relevant contents of the method in Example 1 and will not be elaborated here.

[0029] Embodiment 3, this embodiment provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of any one of the methods described in Embodiment 1 are implemented.

[0030] Embodiment 4: This embodiment provides a computer device, including: Memory, for storing computer programs / instructions; A processor, configured to execute the computer program / instructions to implement the steps of any one of the methods described in Example 1.

[0031] Embodiment 5: This embodiment provides a computer program product, including a computer program / instruction, which implements the steps of any method described in Embodiment 1 when executed by a processor.

[0032] 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.

[0033] It should be understood by those skilled in the art that the embodiments of the present disclosure may be provided as methods, systems or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present disclosure may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0034] The present disclosure is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram and the combination of processes and / or blocks in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0035] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0036] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0037] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present disclosure rather than to limit its protection scope. Although the present disclosure has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that after reading the present disclosure, those skilled in the art can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the invention, but these changes, modifications or equivalent substitutions are all within the protection scope of the disclosed claims to be approved.

Claims

1. A method for modeling and decentralized coordinated control of new energy characteristics of power grid, characterized in that: include: Constructing a hierarchical control structure based on a multi-agent system, wherein the hierarchical control structure includes a lower-layer unit agent and an upper-layer coordination agent; In the lower unit agent, decentralized control is implemented on various types of renewable energy sources by configuring an inverter based on a dual feedback controller, wherein the dual feedback controller includes an outer loop power controller, an inner loop voltage controller and an inner loop current controller, wherein: The outer loop power controller includes an active-frequency droop controller and a reactive-amplitude droop controller, which adjust the frequency and voltage amplitude according to the difference between the instantaneous value of active power and reactive power and the reference value respectively; The inner loop voltage controller outputs a control voltage according to the voltage difference and the control signal, and the inner loop current controller outputs a control current according to the current difference and the control signal, and the parameters of the controller are optimized and solved by a particle swarm algorithm; In the upper-level coordination agent, Kirchhoff's voltage and current laws are used to establish a system model, and global coordinated control is achieved by defining neighborhood errors and control signals.

2. The method for modeling and decentralized coordinated control of new energy characteristics of power grid according to claim 1 is characterized in that: The outer loop power controller includes an active-frequency droop controller and a reactive-amplitude droop controller, which adjust the frequency and voltage amplitude according to the difference between the instantaneous value of active power and reactive power and the reference value, respectively, as follows: Get the instantaneous value of active power output by the grid new energy interface inverter , expressed as: （1）； Get the instantaneous value of reactive power output by the grid new energy interface inverter , expressed as: （2）； in, , , , They are the d-axis and q-axis components of the inverter output voltage and current respectively; The calculation of active-frequency and reactive-amplitude droop controllers are: （3）； （4）； in, and are the droop coefficients of the active-frequency and reactive-amplitude droop controllers, f is the inverter output frequency, v is the inverter output voltage amplitude, is the frequency reference value, is the initial value of the voltage amplitude, is the instantaneous value of active power, represents the initial value of active power, is the instantaneous value of reactive power, Indicates the initial value of reactive power.

3. The method for modeling and decentralized coordinated control of new energy characteristics of power grid according to claim 1, characterized in that: The inner loop voltage controller outputs a control voltage according to the voltage difference and the control signal, and the inner loop current controller outputs a control current according to the current difference and the control signal, as follows: The formula for designing the inner loop voltage controller is as follows: （5）； in, is the voltage difference of the voltage controller at time t, is the control signal of the voltage controller at time t, and are the voltage output of the voltage controller and its reference, represents the reference current of the current controller, are the integral and differential orders of the voltage controller, are the three parameters of the voltage controller, and D is a complex variable; The formula for designing the inner loop current controller is as follows: （6）； in, is the current difference of the current controller at time t, is the control signal of the current controller at time t, and are the current output of the current controller and its reference, is the voltage output of the current controller, , are the integral and differential orders of the current controller, are the three parameters of the current controller; The parameters of the voltage controller and the current controller are as follows: （7）； （8）； （9）； （10）； （11）； （12）； （13）； （14）； （15）； in, , is the reference value of the d and q axis components of the controller’s output current, , is the instantaneous value of the d and q axis components of the controller’s output current, , is the reference value of the d and q axis components of the controller input voltage, , is the instantaneous value of the d and q axis components of the controller’s input voltage, , is the reference value of the d and q axis components of the controller’s output voltage, , is the disturbance, S is a complex variable, is the reference angular frequency, is the q-axis component of the controller’s output voltage, , is the output of the voltage controller, , is the output of the current controller, is the feedforward parameter, is the filter capacitor of the resonant circuit, is the filter inductance of the resonant circuit.

4. The method for modeling and decentralized coordinated control of new energy characteristics of power grid according to claim 1, characterized in that: The parameters of the controller are optimized and solved by the particle swarm algorithm. The specific process is as follows: First, initialize the parameters of the particle swarm algorithm and initialize the particles; Then, the fitness function of the particle swarm algorithm is determined. Specifically, the integral performance of the absolute error is used as an indicator of controller design, and then the fitness function of the particle swarm algorithm is determined as: ,in: （16） Among them, J represents the optimal index of the designed controller, F represents the fitness function of the particle swarm algorithm, represents the absolute error at time t, represents the control output of the designed controller at time t, To control the rise time of the process, is the weight coefficient of the optimal indicator; Next, the fitness value of each particle is calculated, and the individual and global best positions are updated. Specifically, the fitness value of each particle is calculated based on the fitness function. By comparing the current fitness value with the best fitness value found previously, if the current one is the best, the individual best fitness value and its corresponding position are updated, and the global optimal solution and its corresponding position are found among the individual best fitness values ​​of all particles. Secondly, dynamically update the speed and position of each particle, which is achieved through the following speed and position update equations: （17）； （18）； in, , is the velocity of particle i at t+1, generation t, , represents the position of particle i at generation t+1 and t, Expressed as inertia weight, and represents the learning rate, , represents the historical best position and global best position of particle i in generation t, and Represents a random number; Finally, when the algorithm reaches the preset maximum number of iterations, the algorithm terminates and outputs the global optimal solution of the algorithm, thereby obtaining the optimal values ​​of each parameter in the designed controller.

5. The method for modeling and decentralized coordinated control of new energy characteristics of power grid according to claim 1, characterized in that: In the upper-layer coordination agent, a system model is established using Kirchhoff's voltage and current laws, and global coordination control is achieved by defining neighborhood errors and control signals, including: The i-th renewable energy source is set to be connected to other renewable energy sources through power line impedance. The single-phase electrical scheme of the power grid system adopts Kirchhoff's voltage and current laws, which are as follows: （19）； Where d represents the differential symbol, t represents time, is the current of the i-th renewable energy source, , is the inductance and resistance of the i-th renewable energy source, , is the output voltage of the i-th renewable energy source and the voltage on its resistor, , , is the grounding capacitance of the i-th, m-th, and n-th renewable energy sides, and are the resistance and inductance in parallel with the grounding capacitor, The resistor limits the current. , are the currents injected into m and n renewable energy sources respectively for the i-th renewable energy source, and are the resistance and inductance of the ith and mth renewable energy tie lines, respectively, is the output voltage of the mth renewable energy source, , are the current of the mth renewable energy source, and are the resistance and inductance of the mth renewable energy side, and are the resistance and inductance of the nth renewable energy source, , is the output voltage of the mth renewable energy source and the voltage on its capacitor, , is the output voltage of the nth renewable energy source and the voltage on its capacitor; The model of the power grid system is expressed as: （20）； in, Indicates the system status. is the state matrix, represents the system input, is the input matrix, Represents the system output, is the output matrix, and the correlation matrix in equation (20) is given by the following matrix: ， ， ； The neighborhood error of the i-th renewable energy source is expressed as: （21）； in, are the connection weights of the i-th and j-th renewable energy sources, , , is the unit value; Then, the total neighborhood error is equal to ,in, is the Laplace matrix, is the Kronecker product, is the identity matrix, , , is the unit value, T represents the matrix transpose, , , is the unit value, let input , is the coefficient, is the feedback gain matrix, then the global form is: （22）； in, is the control signal; Therefore, the dynamic characteristics of the entire closed-loop power grid system are: （23）； in, , is a unit value, if the system (23) is asymptotically stable if and only if there exists A matrix that satisfies the following conditions: （24）； （25）； （26）； in, represents the smallest non-zero singular value, represents a symmetric matrix, is the largest singular value.

6. The method for modeling and decentralized coordinated control of new energy characteristics of power grid according to claim 1, characterized in that: The lower-level unit agents and the upper-level coordination agents in the hierarchical control structure of the multi-agent system exchange information and coordinate control through a communication network to achieve intelligent management and optimized operation of the power grid.

7. A new energy characteristic modeling and decentralized coordinated control system for a power grid, characterized in that: include: A hierarchical control structure based on a multi-agent system, the hierarchical control structure comprising lower-level unit agents and upper-level coordination agents; The lower-layer unit intelligent agent implements decentralized control on various types of renewable energy by configuring an inverter based on a dual feedback controller, wherein the dual feedback controller includes an outer loop power controller, an inner loop voltage controller and an inner loop current controller, wherein: the outer loop power controller includes an active-frequency droop controller and a reactive-amplitude droop controller, which respectively adjust the frequency and voltage amplitude according to the difference between the instantaneous value of active power and reactive power and the reference value; the inner loop voltage controller outputs a control voltage according to the voltage difference and the control signal, and the inner loop current controller outputs a control current according to the current difference and the control signal, and the parameters of the controller are optimized and solved by a particle swarm algorithm; The upper-layer coordination agent uses Kirchhoff's voltage and current laws to establish a system model and implements global coordination control by defining neighborhood errors and control signals.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method described in any one of claims 1 to 6 are implemented.

9. A computer device, characterized in that: include: Memory, for storing computer programs / instructions; A processor, configured to execute the computer program / instructions to implement the steps of the method according to 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 the method according to any one of claims 1 to 6 are implemented.

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