Frequency-support-oriented parameter optimization and cooperative control method for virtual synchronous machine of rural island micro-grid

Abstract

The invention discloses a village island micro-grid virtual synchronous machine parameter optimization and cooperative control method oriented to frequency support, and relates to the technical field of electrical automation, and the method comprises the steps: obtaining village power distribution network parameters and load side parameters, and basic technical parameters of each virtual synchronous machine unit; constructing a standard dynamic model of the virtual synchronous machine based on the obtained parameters; introducing uncertainty integrated design on the basis of the standard dynamic model of the virtual synchronous machine, and performing equivalent aggregation on all the virtual synchronous machines to obtain a virtual synchronous machine uncertainty model; based on the uncertainty model of the virtual synchronous machine, establishing a cooperative control model by taking system frequency fluctuation minimization as a target; and solving the cooperative control model by using a commercial solver, and solving the power of each device. According to the invention, by constructing an optimization and cooperative control mechanism oriented to frequency support, the problem of over-limit of frequency deviation and frequency change rate is effectively suppressed, and the dynamic response capability of the island micro-grid under the condition of sudden load change is improved.

Description

Technical Field

[0001] This invention relates to the field of electrical automation technology, and in particular to a method for optimizing and coordinating the parameters of a virtual synchronous machine for frequency-supported rural island microgrids. Background Technology

[0002] With the increasing penetration of renewable energy in rural areas, isolated microgrids have become a key solution for power supply in remote areas. Virtual synchronous generator (VSG) technology provides crucial frequency support capabilities for rural isolated microgrids by simulating the inertia and damping characteristics of traditional synchronous generators. However, operating multiple VSGs in parallel faces severe challenges: in rural environments with weak communication, existing collaborative control strategies heavily rely on high-speed data interaction, resulting in system response delays exceeding 300ms, and significantly amplifying power distribution errors when communication is interrupted; more seriously, when facing impact loads such as irrigation pumps, fixed parameter configurations can cause frequency overshoot, with peak frequency change rates exceeding the relay protection setting of 0.5Hz / s. If the actual output of small-capacity VSG units is less than the rated value, it will further exacerbate system oscillations.

[0003] Current industry solutions all have limitations: while centralized optimization architectures can improve stability, a single point of failure in the central controller often leads to the risk of global loss of control; adaptive control schemes (such as reinforcement learning algorithms) require more than 500 GOPS of computing power, which often far exceeds the carrying capacity of rural terminal equipment; traditional coordination strategies (QV droop control, etc.) ignore frequency change rate constraints and are incompatible with power grid protection standards. These shortcomings make it difficult for existing technologies to meet the three rigid requirements of rural scenarios—distributed architecture, real-time response, and strict protection compatibility.

[0004] Especially in rural isolated microgrids with a high proportion of renewable energy integration, the weak communication infrastructure and dynamic load fluctuations create a sharp contradiction. Existing technologies cannot eliminate cross-VSG communication dependence (increasing deployment costs by 60%), nor can they achieve rapid coordination under protection constraints, leading to frequent frequency instability incidents. Therefore, a virtual synchronous machine parameter optimization and collaborative control method for rural isolated microgrids is needed to meet frequency support requirements, achieve robust frequency support, and ensure hardware security. Summary of the Invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the problem that this invention aims to solve is: in rural island microgrids with a high proportion of renewable energy access, the weak communication infrastructure and the dynamic changes in load create a sharp contradiction. Existing technologies cannot eliminate cross-VSG communication dependence, nor can they achieve rapid coordination under protection constraints, resulting in frequent frequency instability accidents.

[0007] To address the aforementioned technical problems, this invention provides the following technical solution: a method for optimizing and coordinating the parameters of a virtual synchronous machine (VSM) in a frequency-supported rural isolated microgrid. This method includes: acquiring rural distribution network parameters and load-side parameters; acquiring the basic technical parameters of each VSM unit; reading load disturbance settings; and simultaneously acquiring hardware allowable parameter boundaries. Based on the acquired parameters, a standard dynamic model of the VSM is constructed to simulate the inertia and damping behavior of a synchronous generator, while simultaneously describing the frequency, voltage, active, and reactive power behavior of the VSM. Uncertainty integration design is introduced based on the standard dynamic model of the VSM, and all VSMs are equivalently aggregated and uniformly modeled to obtain a VSM uncertainty model, with hard constraints on the rate of frequency change and dynamic parameter boundaries set. Based on the VSM uncertainty model, and with the goal of minimizing frequency fluctuations, a VSM parameter optimization and coordinating control model for a frequency-supported rural isolated microgrid is established. A commercial solver is used to solve the VSM parameter optimization and coordinating control model for the rural isolated microgrid, determining the power of each device and returning the operating parameters to each device.

[0008] As a preferred embodiment of the frequency-supported virtual synchronous machine parameter optimization and collaborative control method for rural island microgrids described in this invention, the acquisition of rural distribution network parameters and load-side parameters includes: periodically collecting data on line topology, voltage level, node impedance, and branch capacity through intelligent terminal acquisition devices deployed at village-level main transformers; and real-time monitoring of active and reactive power curves based on edge acquisition modules installed at user nodes, combined with historical load data statistics to extract disturbance characteristics.

[0009] As a preferred embodiment of the frequency-supported virtual synchronous machine parameter optimization and collaborative control method for rural island microgrids described in this invention, the following steps are included: obtaining the basic technical parameters of each virtual synchronous machine unit includes importing the basic technical parameters from the default configuration list of the virtual synchronous machine controller; the load disturbance setting includes setting the load change timing corresponding to the load disturbance through a scenario preset configuration file.

[0010] As a preferred embodiment of the frequency-supported virtual synchronous machine parameter optimization and collaborative control method for rural island microgrids described in this invention, the standard dynamic model of the virtual synchronous machine includes rotor motion equations, voltage control equations, active power output equations, and reactive power output equations.

[0011] The rotor motion equation describes the mechanical-electrical energy conversion process of the virtual rotor in the virtual synchronous machine. The inertia and damping characteristics of the microgrid are controlled by adjusting the virtual inertia time constant and damping coefficient. The formula is expressed as follows: ; ;

[0012] in, For the first The virtual inertial time constant of a virtual synchronous machine simulates the rotational inertia of a synchronous generator; Angular acceleration, quantized as the rate of change of frequency; For the first The mechanical power input of a virtual synchronizer simulates the power of the prime mover. For the first The electromagnetic power output of a virtual synchronous machine represents the actual grid-connected power output. For the first The damping coefficient of a virtual synchronous machine is used to suppress rotor oscillation; The rotor angle change rate; The angular velocity deviation is defined by the voltage control equation, which defines the adjustment mechanism of the virtual synchronous machine's output voltage and describes the adaptive adjustment of reactive power and voltage. The formula is expressed as follows: ;

[0013] in, For the first The output voltage amplitude of a virtual synchronous machine; Reference voltage; This is the voltage-reactive power droop factor; For reactive power input reference value, For the first The actual reactive power of a virtual synchronous machine. This is reactive power deviation.

[0014] As a preferred embodiment of the frequency-supported virtual synchronous machine parameter optimization and coordinated control method for rural island microgrids described in this invention, the active power output equation calculates the active power injected into the grid by the virtual synchronous machine, establishing a direct mapping between active power and phase angle, expressed by the formula: ;

[0015] in, This refers to the voltage amplitude at the grid connection point. For the first The coupling reactance of a virtual synchronous machine; For the first The internal potential phase angle of a virtual synchronous machine; The voltage phase angle at the grid connection point; the reactive power output equation quantifies the reactive power output characteristics of the virtual synchronous machine, characterizing the coupling relationship between reactive power and voltage amplitude, and is expressed by the formula, ;

[0016] in, This is the power factor angle function term; This constitutes a voltage difference driving term.

[0017] As a preferred embodiment of the frequency-supported virtual synchronous machine parameter optimization and collaborative control method for rural isolated microgrids described in this invention, the uncertainty integration design includes mathematical modeling of three types of uncertainties in the rural microgrid: equipment parameter drift, line impedance fluctuation, and random load mutations. This is achieved by establishing an equipment parameter uncertainty model and a mechanism model to quantify the boundaries of various uncertainties. The equipment parameter uncertainty model is expressed as follows: ;

[0018] in, , , These are the initial values ​​of the virtual inertial time constant, the initial value of the damping coefficient, and the initial value of the line impedance, respectively. , , These are the deviation values ​​of the virtual inertia time constant, damping coefficient, and line impedance, respectively. , , These are the upper bounds of the perturbation for inertia, damping, and impedance, respectively; the mechanical model is expressed as follows: ;

[0019] in, The current moment; for Power at any moment This represents the step amplitude. Standard deviation; The coefficient is 0.1 times. ; It is a step function; This is the start time of the disturbance.

[0020] As a preferred embodiment of the frequency-supported virtual synchronous machine parameter optimization and collaborative control method for rural island microgrids described in this invention, the equivalent aggregation of all virtual synchronous machines includes: constructing the dynamic coupling relationship of each virtual synchronous machine based on an energy conservation-based equivalent aggregation method; introducing an impedance coupling coefficient to quantify the impact of line differences on damping; the cluster equivalent parameter is defined as follows: ; ;

[0021] in, The virtual inertial time constant after cluster equivalent. This represents the total number of virtual synchronizers. This represents the damping coefficient after cluster equivalent. The impedance coupling coefficient is obtained through impedance difference experiments. For the first The coupling reactance of a virtual synchronous machine For the first The coupling reactance of a virtual synchronous machine; the frequency dynamic equation is expressed as: ; ;

[0022] in, For the common junction frequency, The rate of change of the frequency of the common junction over time. To correspond to the frequencies of each connection point, It represents the deviation between the local frequency and the system frequency.

[0023] As a preferred embodiment of the frequency-supported virtual synchronous machine parameter optimization and collaborative control method for rural isolated microgrids described in this invention, the establishment of the frequency-supported virtual synchronous machine parameter optimization and collaborative control model for rural isolated microgrids includes: under the constraint of minimizing frequency fluctuations, formulating a multi-objective dynamic strategy to balance transient and steady-state responses, and transforming grid protection standards into mathematical constraints to construct an objective function; based on the objective function, setting active power constraints, reactive power constraints, virtual synchronous machine capacity constraints, ramp rate constraints, dynamic equation constraints, frequency change constraints, and parameter boundary constraints to form the rural isolated microgrid virtual synchronous machine parameter optimization and collaborative control model.

[0024] The beneficial effects of this invention are as follows: By constructing an optimization and collaborative control mechanism oriented towards frequency support, this invention effectively suppresses the problem of frequency offset and rate of change of frequency (RoCoF) exceeding the limit, improves the dynamic response capability of islanded microgrids under load change conditions, and meets the relay protection's limitation requirements on the rate of change of frequency.

[0025] By introducing uncertainty modeling such as equipment parameter drift, line impedance fluctuation and load disturbance, and establishing a dynamic boundary constraint mechanism, the control strategy is made more secure when facing complex rural working conditions.

[0026] This invention constructs a distributed parameter optimization and control framework based on cluster aggregation, which avoids system instability caused by communication delays or single points of failure in centralized control architectures. At the same time, it is applicable to edge devices with limited computing power, reducing deployment costs and implementation thresholds. Attached Figure Description

[0027] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a flowchart of a frequency-supported virtual synchronous machine parameter optimization and collaborative control method for rural island microgrids in Example 1.

[0029] Figure 2 This is a diagram of a simulated rural island system using a three-VSG parallel system, illustrating a frequency-supported virtual synchronous machine parameter optimization and collaborative control method for rural island microgrids in Example 1.

[0030] Figure 3 This is a graph showing the change in system frequency after load increase in the virtual synchronous machine system of a frequency-supported rural island microgrid parameter optimization and collaborative control method in Example 1, before parameter optimization.

[0031] Figure 4 This is a graph showing the change in system frequency after parameter optimization and load increase in the virtual synchronous machine system of a frequency-supported rural island microgrid virtual synchronous machine parameter optimization and collaborative control method in Example 1. Detailed Implementation

[0032] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0033] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0034] Example 1, referring to Figures 1-4 This is the first embodiment of the present invention, which provides a method for optimizing and coordinating the parameters of a virtual synchronous machine in a frequency-supported rural island microgrid, including, for example... Figure 1 As shown:

[0035] Step S1: Obtain rural power distribution network parameters and load-side parameters, obtain basic technical parameters of each virtual synchronous machine unit, read load disturbance settings, and simultaneously obtain hardware allowable parameter boundaries.

[0036] Specifically, obtaining rural power distribution network parameters and load-side parameters includes periodically collecting data such as line topology, voltage level, node impedance, and branch capacity through intelligent terminal acquisition devices (such as distribution automation terminal units, DTUs) deployed on village-level main transformers.

[0037] By relying on edge acquisition modules installed at user nodes (such as irrigation pump stations, small factories, and residential loads), active and reactive power curves are monitored in real time, and disturbance characteristics are extracted by combining historical load data statistics.

[0038] Obtaining the basic technical parameters of each virtual synchronizer unit includes importing basic technical parameters from the default configuration list of the virtual synchronizer controller, such as rated capacity, initial value of simulated inertia, initial value of damping coefficient, control response time, etc.

[0039] The load disturbance setting includes setting the load change timing corresponding to load disturbances (such as pump start-up, motor impact, and concentrated operation of residential units) through a scenario preset configuration file. It supports loading predefined disturbance sequences during the simulation phase to test the impact response of different disturbances on the system frequency.

[0040] The hardware allowable parameter boundaries include technical indicators such as maximum overload capacity, minimum economic output, and maximum ramp rate given in the equipment manual; and the boundaries are updated through on-site experiments or feedback from the operation and maintenance system to form an adjustable dynamic parameter range as the optimization input boundary conditions.

[0041] Step S2: Construct a standard dynamic model of the virtual synchronous machine based on the acquired parameters to simulate the inertia and damping behavior of the synchronous generator, and describe the frequency, voltage, active and reactive behavior of the virtual synchronous machine.

[0042] Specifically, the standard dynamic model of a virtual synchronous machine includes rotor motion equations, voltage control equations, active power output equations, and reactive power output equations.

[0043] The rotor motion equation describes the mechanical-electrical energy conversion process of the virtual rotor in the virtual synchronous machine. The inertia and damping characteristics of the microgrid are controlled by adjusting the virtual inertia time constant and damping coefficient. The formula is expressed as follows: ; ;

[0044] in, For the first The virtual inertial time constant of a virtual synchronous machine simulates the rotational inertia of a synchronous generator, which determines the strength of the system's inertial response to frequency changes. Angular acceleration, quantized as the rate of change of frequency; For the first The mechanical power input of a virtual synchronizer simulates the power of the prime mover. For the first The electromagnetic power output of a virtual synchronous machine represents the actual grid-connected power output. For the first The damping coefficient of a virtual synchronous machine is used to suppress rotor oscillation; The rotor angle change rate directly determines the system frequency offset; This represents the angular velocity deviation.

[0045] The voltage control equation defines the regulation mechanism of the output voltage of the virtual synchronous machine, describing the adaptive regulation of reactive power and voltage. The formula is expressed as follows: ;

[0046] in, For the first The output voltage amplitude of a virtual synchronous machine; Reference voltage; This is the voltage-reactive power droop factor; For reactive power input reference value, For the first The actual reactive power of a virtual synchronous machine. This is reactive power deviation.

[0047] The active power output equation calculates the active power injected into the grid by the virtual synchronous machine, establishing a direct mapping between active power and phase angle, expressed by the formula: ;

[0048] in, This refers to the voltage amplitude at the grid connection point. For the first The coupling reactance of a virtual synchronous machine; For the first The internal potential phase angle of a virtual synchronous machine; This is the phase angle of the voltage at the grid connection point.

[0049] The reactive power output equation quantifies the reactive power output characteristics of the virtual synchronous machine, characterizing the coupling relationship between reactive power and voltage amplitude. The formula is expressed as follows: ;

[0050] in, This is the power factor angle function term; This constitutes a voltage difference driving term.

[0051] Among them, the common system frequency meets the following conditions: ;

[0052] in, For reference angular velocity, The power angle change rate at the grid connection point; the system frequency is determined by the combined inertial response and damping dynamics of the three virtual synchronous machines (VSGs), such as... Figure 2 As shown in the figure For the current of the first virtual synchronous machine, For the current of the second virtual synchronous machine, For the current of the third virtual synchronizer, For load current, For load reactance, For load resistance, For disturbance current, For the disturbance end reactance, This is the resistance at the disturbance end.

[0053] Step S3: Based on the standard dynamic model of the virtual synchronizer, introduce uncertainty integration design, perform equivalent aggregation on all virtual synchronizers, unify modeling, obtain the uncertainty model of the virtual synchronizer, and set hard constraints on the rate of change of frequency and dynamic parameter boundaries.

[0054] Specifically, the uncertainty integrated design includes mathematical modeling of three types of uncertainties in rural microgrids: equipment parameter drift, line impedance fluctuation, and random load changes. This is achieved by establishing an equipment parameter uncertainty model and a mechanism model to quantify the boundaries of various uncertainties. The equipment parameter uncertainty model is expressed as follows: ;

[0055] in, , , These are the initial values ​​of the virtual inertial time constant, the initial value of the damping coefficient, and the initial value of the line impedance, respectively. , , These are the deviation values ​​of the virtual inertia time constant, damping coefficient, and line impedance, respectively. , , These are the upper bounds of the disturbances for inertia, damping, and impedance, respectively.

[0056] The mechanism model is represented as follows: ;

[0057] in, The current moment; for Power at any moment This represents the step amplitude. Standard deviation; The coefficient is 0.1 times. ; It is a step function; This is the start time of the disturbance.

[0058] The equivalent aggregation of all virtual synchronous machines includes constructing the dynamic coupling relationship of each virtual synchronous machine based on the equivalent aggregation method of energy conservation, and introducing an impedance coupling coefficient to quantify the impact of line differences on damping.

[0059] The cluster equivalent parameter is defined as follows: ; ;

[0060] in, The virtual inertial time constant after cluster equivalent. This represents the total number of virtual synchronizers. This represents the damping coefficient after cluster equivalent. The impedance coupling coefficient is obtained through impedance difference experiments. For the first The coupling reactance of a virtual synchronous machine For the first The coupling reactance of a virtual synchronous machine.

[0061] This embodiment can be simplified as follows: ;

[0062] The system frequency dynamic equation is expressed as follows: ; ;

[0063] in, For the common junction frequency, The rate of change of the frequency of the common junction over time. To correspond to the frequencies of each connection point, It represents the deviation between the local frequency and the system frequency.

[0064] Furthermore, a dual-layer protection mechanism is designed to ensure strict stability, with a hard constraint on the rate of frequency change to lock the transient critical window, and dynamic boundary contraction to balance response speed and accuracy.

[0065] The hard constraint on the rate of change of frequency is expressed as follows: ;

[0066] This constraint forces the system frequency change rate (RoCoF) to not exceed the relay protection setting of 0.5 Hz / s.

[0067] The dynamic parameter boundary function is defined as: ;

[0068] in, For the first A virtual synchronizer in The minimum virtual inertial time constant at time t; For the first A virtual synchronizer in The maximum virtual inertial time constant at time t; For the first A virtual synchronizer in The minimum damping coefficient at time t; For the first A virtual synchronizer in The maximum damping coefficient at time t; Represents the time constant, controlling the boundary decay rate; exponential term To achieve the characteristic of the boundary decaying with time as the disturbance progresses, in the initial stage of the disturbance (t approaches t0), 0） Wider boundaries improve response speed, while tighter boundaries ensure accuracy in steady state (when t approaches ∞).

[0069] Step S4: Based on the uncertainty model of the virtual synchronous machine, and with the goal of minimizing frequency fluctuations, establish a parameter optimization and collaborative control model for the virtual synchronous machine of a rural island microgrid oriented towards frequency support.

[0070] Specifically, establishing a frequency-supported virtual synchronous machine parameter optimization and collaborative control model for rural island microgrids includes: formulating a multi-objective dynamic strategy under the constraint of minimizing frequency fluctuations, balancing transient and steady-state responses, and transforming the grid protection standard (RoCoF≤0.5Hz / s) into mathematical constraints to construct the objective function, expressed by the formula: ;

[0071] in, To minimize The value is the target. , , , , The corresponding penalty coefficient is set. for The difference in frequency change over time for The voltage difference at time t, The rate of change of frequency, This represents the difference in frequency variation.

[0072] To further explain, the frequency deviation term penalizes sustained frequency offsets that deviate from the rated frequency, using a quadratic form, prioritizing the suppression of large deviations rather than small ones; the rate of change term specifically limits the acceleration / deceleration of the frequency, thereby reducing the mechanical stress on the connected equipment.

[0073] Based on the objective function, active power constraints, reactive power constraints, virtual synchronous machine capacity constraints, ramp rate constraints, dynamic equation constraints, frequency variation constraints, and parameter boundary constraints are set to form a virtual synchronous machine parameter optimization and collaborative control model for rural island microgrids.

[0074] Specifically, the active power constraint equation is as follows: the power output essentially combines primary frequency regulation and virtual inertial response through a composite expression, which is expressed as follows: ;

[0075] in, For the first The active power output of a virtual synchronous machine. For active power input reference value, This is the droop coefficient.

[0076] Reactive power constraint equation: Reactive power and voltage dynamics follow similar formulas, but with weaker coupling. The formula is expressed as follows: ;

[0077] in, For the first The reactive power output of a virtual synchronous machine The rate of change of voltage. The derivative coefficient (1 / 10) represents the voltage variation difference, reflecting the natural decoupling between voltage and frequency dynamics.

[0078] VSG capacity constraint: This constraint addresses the underload problem of small-capacity VSG units, and is expressed by the formula: ;

[0079] in, The rated power is used; the lower limit of the capacity constraint ensures that the unit is not lower than the economic output point of 0.7P, and the upper limit considers the short-time overload capacity of power devices (105% of the rated value) of 1.05P to avoid the power distribution imbalance common in rural scenarios.

[0080] The formula for the gradeability constraint is expressed as follows: ;

[0081] in, To limit the ramp rate, the VSG power change rate is restricted, meaning the change per second cannot exceed 20% of the rated power. This simulates the physical response limit of a real inverter. This constraint prevents sudden power changes caused by impact loads such as irrigation pumps and reduces mechanical stress on the equipment.

[0082] Dynamic equality constraints: The discretized oscillation equations employ the first-order Euler approximation method, expressed as follows: ;

[0083] in, for The difference in power change at time t, for The difference in frequency change over time.

[0084] Frequency variation constraints: Relevant national standards are transformed into a mathematical constraint matrix, forming a two-dimensional security protection boundary, expressed by the formula, ;

[0085] Frequency constraints and frequency change rate constraints transform the requirements for protection relays into mathematical forms.

[0086] Parameter boundary constraints: Inheriting the dynamic parameter boundary function from step S3, the boundary width is ±15% in the initial stage of the disturbance to improve the response speed. It then shrinks to ±3% within 1 second as the exponential decay function progresses, balancing transient and steady-state performance. The formula is expressed as follows: ; ;

[0087] in, For the first The minimum virtual inertial time constant of a virtual synchronizer; For the first The maximum virtual inertial time constant of a virtual synchronizer; For the first The minimum damping coefficient of a virtual synchronous machine; For the first The maximum damping coefficient of a virtual synchronizer.

[0088] Step S5: Use a commercial solver to solve the virtual synchronous machine parameter optimization and collaborative control model of the rural isolated microgrid, solve for the power of each device, and return the operating parameters to each device.

[0089] Specifically, the parameter optimization and cooperative control model structure of the virtual synchronous machine in rural isolated microgrids can be efficiently solved using the interior point optimizer (IPOPT) because:

[0090] Sparsity utilization: More than 85% of the elements in the Jacobian matrix are zero, which is due to the decoupling of the time step.

[0091] Constraint scaling: All equations are normalized to per-unit values ​​for calculation.

[0092] Hot start strategy: Select the optimal RoCoF solution as the initial point of IPOPT, and initialize the value close to the optimal solution.

[0093] The resulting nonlinear optimization problem has 600 variables and converges in less than 20 iterations on standard hardware, making it suitable for offline planning applications. The multi-objective formulation provides Pareto optimal solutions, balancing stability and control effort, and the weight adjustment allows operators to adjust according to their preferences.

[0094] Furthermore, to verify the effectiveness and superiority of the proposed frequency-supported virtual synchronous machine parameter optimization and cooperative control strategy for rural island microgrids, tests were conducted on the Matrix Laboratory (MATLAB) system. In this test, three parallel VSG systems were set up, each designed to respond independently to grid changes while working collaboratively to maintain the frequency stability of the entire system.

[0095] like Figure 3 and Figure 4 As shown, when the load changes, these three VSG systems adjust their output power in real time through their built-in optimized control strategies to smoothly adapt to the new load demands. These adjustments include, but are not limited to, frequency regulation, voltage stabilization, and the distribution of active and reactive power.

[0096] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for parameter optimization and coordinated control of virtual synchronous machines in frequency-supported rural island microgrids, characterized in that: include, Obtain rural power distribution network parameters and load-side parameters, obtain basic technical parameters of each virtual synchronous machine unit, read load disturbance settings, and obtain hardware allowable parameter boundaries; Based on the acquired parameters, a standard dynamic model of the virtual synchronous machine is constructed to simulate the inertial and damping behavior of the synchronous generator, while describing the frequency, voltage, active and reactive behavior of the virtual synchronous machine. Based on the standard dynamic model of virtual synchronizers, uncertainty integration design is introduced to perform equivalent aggregation of all virtual synchronizers, unify modeling, obtain the uncertainty model of virtual synchronizers, and set hard constraints on the rate of change of frequency and dynamic parameter boundaries. Based on the uncertainty model of the virtual synchronizer, and with the goal of minimizing frequency fluctuations, a parameter optimization and collaborative control model for the virtual synchronizer of a rural island microgrid oriented towards frequency support is established. A commercial solver was used to solve the parameter optimization and collaborative control model of a virtual synchronous machine for a rural isolated microgrid, and the power of each device was calculated. The operating parameters were then returned to each device.

2. The method for parameter optimization and cooperative control of virtual synchronous machines in frequency-supported rural island microgrids as described in claim 1, characterized in that: The acquisition of rural power distribution network parameters and load-side parameters includes periodically collecting data on line topology, voltage level, node impedance, and branch capacity through intelligent terminal acquisition devices deployed on village-level main transformers; By relying on the edge acquisition module installed at the user node, the active and reactive power curves are monitored in real time, and disturbance characteristics are extracted by combining historical load data statistics.

3. The method for parameter optimization and coordinated control of a virtual synchronous machine in a frequency-supported rural island microgrid as described in claim 2, characterized in that: The process of obtaining the basic technical parameters of each virtual synchronization machine unit includes importing the basic technical parameters from the default configuration list of the virtual synchronization machine controller. The load disturbance setting includes setting the load change timing corresponding to the load disturbance through a scenario preset configuration file.

4. The method for parameter optimization and coordinated control of virtual synchronous machines in frequency-supported rural island microgrids as described in claim 3, characterized in that: The standard dynamic model of the virtual synchronous machine includes rotor motion equations, voltage control equations, active power output equations, and reactive power output equations. The rotor motion equation describes the mechanical-electrical energy conversion process of the virtual rotor in the virtual synchronous machine. The inertia and damping characteristics of the microgrid are controlled by adjusting the virtual inertia time constant and damping coefficient. The formula is expressed as follows: ； ； in, For the first The virtual inertial time constant of a virtual synchronous machine simulates the rotational inertia of a synchronous generator; Angular acceleration, quantized as the rate of change of frequency; For the first The mechanical power input of a virtual synchronizer simulates the power of the prime mover. For the first The electromagnetic power output of a virtual synchronous machine represents the actual grid-connected power output. For the first The damping coefficient of a virtual synchronous machine is used to suppress rotor oscillation; The rotor angle change rate; This refers to the angular velocity deviation. The voltage control equation defines the regulation mechanism of the virtual synchronous machine's output voltage, describing the adaptive regulation of reactive power and voltage. The formula is expressed as follows: ； in, For the first The output voltage amplitude of a virtual synchronous machine; Reference voltage; This is the voltage-reactive power droop factor; For reactive power input reference value, For the first The actual reactive power of a virtual synchronous machine. This is reactive power deviation.

5. The method for parameter optimization and coordinated control of a virtual synchronous machine in a frequency-supported rural island microgrid as described in claim 4, characterized in that: The active power output equation calculates the active power injected into the grid by the virtual synchronous machine, establishing a direct mapping between active power and phase angle, expressed by the formula: ； in, This refers to the voltage amplitude at the grid connection point. For the first The coupling reactance of a virtual synchronous machine; For the first The internal potential phase angle of a virtual synchronous machine; The phase angle of the voltage at the grid connection point; The reactive power output equation quantifies the reactive power output characteristics of the virtual synchronous machine, characterizing the coupling relationship between reactive power and voltage amplitude. The formula is expressed as follows: ； in, This is the power factor angle function term; This is a voltage difference driven term.

6. The method for parameter optimization and coordinated control of a virtual synchronous machine in a frequency-supported rural island microgrid as described in claim 5, characterized in that: The integrated uncertainty design includes mathematical modeling of three types of uncertainties in rural microgrids: equipment parameter drift, line impedance fluctuation, and random load changes. This is achieved by establishing an equipment parameter uncertainty model and a mechanism model to quantify the boundaries of various uncertainties. The equipment parameter uncertainty model is expressed as follows: ； in, , , These are the initial values ​​of the virtual inertial time constant, the initial value of the damping coefficient, and the initial value of the line impedance, respectively. , , These are the deviation values ​​of the virtual inertia time constant, damping coefficient, and line impedance, respectively. , , These are the upper bounds of the disturbances for inertia, damping, and impedance, respectively. The mechanism model is represented as follows: ； in, The current moment; for Power at any moment This represents the step amplitude. Standard deviation; The coefficient is 0.1 times. ; It is a step function; This is the start time of the disturbance.

7. The method for parameter optimization and coordinated control of virtual synchronous machines in frequency-supported rural island microgrids as described in claim 6, characterized in that: The equivalent aggregation of all virtual synchronous machines includes constructing the dynamic coupling relationship of each virtual synchronous machine based on the equivalent aggregation method of energy conservation, and introducing an impedance coupling coefficient to quantify the impact of line differences on damping. The cluster equivalent parameter is defined as follows: ； ； in, The virtual inertial time constant after cluster equivalent. This represents the total number of virtual synchronizers. This represents the damping coefficient after cluster equivalent. The impedance coupling coefficient is obtained through impedance difference experiments. For the first The coupling reactance of a virtual synchronous machine For the first The coupling reactance of a virtual synchronous machine; The frequency dynamic equation is expressed as: ； ； in, For the common junction frequency, The rate of change of the frequency of the common junction over time. To correspond to the frequencies of each connection point, It represents the deviation between the local frequency and the system frequency.

8. The method for parameter optimization and cooperative control of a virtual synchronous machine in a frequency-supported rural island microgrid as described in claim 7, characterized in that: The establishment of a frequency-supported virtual synchronous machine parameter optimization and collaborative control model for rural island microgrids includes, under the constraint of minimizing frequency fluctuations, formulating a multi-objective dynamic strategy, balancing transient and steady-state responses, and transforming grid protection standards into mathematical constraints to construct an objective function. Based on the objective function, active power constraints, reactive power constraints, virtual synchronous machine capacity constraints, ramp rate constraints, dynamic equation constraints, frequency variation constraints, and parameter boundary constraints are set to form a virtual synchronous machine parameter optimization and collaborative control model for rural island microgrids.

Images