Dynamic self-adaptive coordinated operation control method and system for rural greenhouse micro-grid

Abstract

The invention discloses a dynamic adaptive coordinated operation control method and system for a rural greenhouse micro-grid, and the method comprises the steps: obtaining an output frequency and an output voltage in a droop control link of an inverter, and calculating a symmetric error between the output frequency and the output voltage; establishing a dynamic output frequency gain and an output voltage gain by adopting a generalized Sigmoid function; determining upper bound constraints of the dynamic output frequency gain and the output voltage gain; determining an adjusting value of an output frequency droop coefficient and an adjusting value of an output voltage droop coefficient; a recursive convex combination first-order filter is adopted to obtain a dynamic droop coefficient of the output frequency and a dynamic droop coefficient of the output voltage; when the convex combination first-order filter in the recursion form, the dynamic droop coefficient of the output frequency and the micro-grid control system all meet stability constraints, the dynamic droop coefficient of the output frequency and the dynamic droop coefficient of the output voltage are utilized to update a droop control link model; the problems of wind and light storage coordination and voltage frequency control in the rural greenhouse micro-grid are solved.

Description

Technical Field

[0001] This invention relates to the field of rural isolated microgrid control, and in particular to a dynamic adaptive coordinated operation control method and system for a rural greenhouse microgrid containing wind, solar and energy storage systems. Background Technology

[0002] With the increasing penetration of distributed energy in the power grid, microgrids (MGs) have been widely studied as an energy utilization solution that enables on-site energy production, transmission, and consumption, effectively improving the integration and local management performance of distributed energy in remote areas. Especially in rural greenhouse microgrid applications, MGs can achieve more efficient integration of wind, solar, and energy storage, supporting precise environmental control and energy self-sufficiency in greenhouses. However, micro-source systems such as wind and solar power face challenges such as intermittent and uncertain power output, and dynamic changes in charging and discharging can lead to voltage and frequency deviations. Traditional static droop coefficients are difficult to adapt to the dynamic characteristics of multi-source heterogeneous systems of wind, solar, and energy storage: when wind and solar power output changes abruptly, the fixed coefficient can lead to increased power sharing deviations, and overload or undercharging / discharging of the energy storage system; simultaneously, the randomness and volatility of renewable energy cause voltage fluctuations and load frequency disturbances in the power system, extending the system frequency and voltage recovery time. Therefore, researching and proposing reasonable and effective dynamic droop control strategies for rural greenhouse MGs is of great significance for green electricity consumption and agricultural development.

[0003] Currently, coordinated control strategies for microgrids often employ improved droop control schemes at the primary control layer. Most studies utilize adaptive droop control methods, achieving good control results, but lack comprehensive theoretical stability proofs for adaptive droop systems. Furthermore, existing studies in simulation verification often simply equate micro-sources such as wind, solar, and energy storage to a stable DC source, which deviates significantly from actual operating conditions. In summary, existing dynamic droop optimization control methods generally suffer from complex optimization algorithms, numerous assumptions requiring verification, and insufficient stability proofs, failing to adequately and effectively guarantee the coordinated operation of isolated wind, solar, and energy storage microgrids, especially in practical scenarios such as rural greenhouse microgrids where their adaptability to dynamic environmental load changes is insufficient. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a dynamic adaptive coordinated operation control method and system for rural greenhouse microgrids. It solves the problems of wind, solar, and energy storage coordination and voltage / frequency control in rural greenhouse microgrids. Using the droop coefficient in the primary control layer of the microgrid as the control target, the monotonically increasing and saturating characteristics of the generalized Sigmoid function are introduced to dynamically solve for the droop coefficient. This achieves fine-tuning when the deviation is small and rapid compensation when the deviation is large, thereby realizing coordinated control of wind, solar, and energy storage output power. This provides an effective control scheme for the coordinated operation of distributed power sources in isolated rural greenhouse microgrids.

[0005] The present invention adopts the following technical solution.

[0006] This invention proposes a dynamic adaptive coordinated operation control method for rural greenhouse microgrids, comprising: Establish a model for the droop control link of distributed energy inverters in rural greenhouse microgrids; Obtain the output frequency and output voltage of the inverter droop control loop, and calculate the symmetry error of the output frequency and the symmetry error of the output voltage. Based on the difference between the symmetry error of the output frequency and the frequency threshold, and the difference between the symmetry error of the output voltage and the voltage threshold, establish the dynamic output frequency gain and output voltage gain using the generalized Sigmoid function. Determine the upper bound constraints of the dynamic output frequency gain and output voltage gain based on the minimum and maximum values ​​of the static droop gain coefficients of the output frequency and output voltage of the inverter droop control loop, respectively. Based on the symmetry error of the output frequency and the symmetry error of the output voltage, and the dynamic output frequency gain and output voltage gain that satisfy the upper bound constraint, the adjustment values ​​of the output frequency droop coefficient and the output voltage droop coefficient are determined. The adjustment values ​​of the output frequency droop coefficient and the output voltage droop coefficient are filtered by a recursive convex combination first-order filter to obtain the dynamic droop coefficient of the output frequency and the dynamic droop coefficient of the output voltage. Based on real-time operation data of rural greenhouse microgrids, when the recursive convex combination first-order filter, the dynamic droop coefficient of the output frequency, and the microgrid control system all satisfy stability constraints, the established droop control link model is updated using the dynamic droop coefficient of the output frequency and the dynamic droop coefficient of the output voltage to perform dynamic adaptive coordinated operation control of the rural greenhouse microgrid.

[0007] Preferably, the droop control mechanism model is as follows:

[0008] in, , These are the instantaneous angular frequency and voltage amplitude of the inverter output, respectively. , These are the nominal reference values ​​for angular frequency and voltage, respectively. , These represent the instantaneous active power and reactive power output by the inverter, respectively. , These are the static droop gain coefficients for the inverter's output frequency and output voltage, respectively.

[0009] Preferably, the symmetry error of the output frequency and the symmetry error of the output voltage are as shown in the following formula:

[0010] In the formula, For at any time Symmetrical error of inverter output frequency; For at any time Symmetry error of inverter output voltage; This serves as the reference frequency for rural greenhouse microgrids. This serves as the reference voltage for the rural greenhouse microgrid. For the phase-locked loop at time The real-time frequency of data acquisition; For voltage transformers at time Real-time voltage data collected; It is the absolute norm.

[0011] Preferably, the dynamic output frequency gain and output voltage gain are as follows:

[0012] In the formula, Symmetrical error based on output voltage With voltage threshold The difference 'output voltage gain' ; Symmetrical error based on output frequency With frequency threshold The difference Dynamic output frequency gain, ; This refers to the voltage steepness parameter; This is the frequency steepness parameter.

[0013] Preferably, the upper bound constraints for the dynamic output frequency gain and output voltage gain are as follows:

[0014] In the formula, , These are the minimum and maximum values ​​of the static droop gain coefficient at the output frequency, respectively. , These are the minimum and maximum values ​​of the static droop gain coefficient of the output voltage, respectively. It is an L2 norm.

[0015] Preferably, the adjustment values ​​for the output frequency droop coefficient and the output voltage droop coefficient are as shown in the following formulas:

[0016] In the formula, For a moment The adjustment value of the output frequency droop coefficient; For a moment The adjustment value of the output voltage droop coefficient.

[0017] Preferably, the dynamic droop coefficient and the dynamic droop coefficient of the output voltage are as follows:

[0018] In the formula, , The first The dynamic droop coefficient of the output frequency and the adjustment value of the output frequency droop coefficient during the next iteration; , The first The adjustment values ​​of the dynamic droop coefficient and the output voltage droop coefficient during the next iteration; , All are preset step size parameters. , .

[0019] Preferably, the stability constraint of the convex combined first-order filter in recursive form is shown in the following equation:

[0020] In the formula, Let be the infinite norm of the dynamic droop coefficient of the output frequency. = , This is the droop factor for the output frequency reference. It is a bounded positive real number.

[0021] Preferably, the asymptotic convergence constraint of the dynamic droop coefficient of the output frequency is as follows:

[0022] In the formula, the error sequence satisfy .

[0023] Preferably, a composite Lyapunov function is constructed as shown in the following equation:

[0024] In the formula, For composite Lyapunov functions, For a moment The frequency of the microgrid This is the reference frequency for the microgrid. For a moment The dynamic droop factor of the output frequency. This is the droop factor for the output frequency reference. For a moment The state vector of a microgrid This is the reference state vector of the microgrid. Represented as The weighted Euclidean norm is used to calculate The function, It is a positive definite matrix; satisfy = , ; The Lyapunov asymptotic stability constraints for microgrid control systems are shown in the following equation: And it is positive definite and radially unbounded, that is when ; In the formula, For matrix The smallest eigenvalue.

[0025] This invention also proposes a dynamic adaptive coordinated operation control system for rural greenhouse microgrids, comprising: The model building module is used to build a model of the droop control link of the distributed energy inverter in a rural greenhouse microgrid. The gain update module is used to obtain the output frequency and output voltage of the inverter droop control loop, calculate the symmetry error of the output frequency and the symmetry error of the output voltage; based on the difference between the symmetry error of the output frequency and the frequency threshold, and the difference between the symmetry error of the output voltage and the voltage threshold, the generalized sigmoid function is used to establish the dynamic output frequency gain and output voltage gain; based on the minimum and maximum values ​​of the static droop gain coefficients of the output frequency and output voltage of the inverter droop control loop, the upper bound constraints of the dynamic output frequency gain and output voltage gain are determined respectively. The droop coefficient update module is used to determine the adjustment values ​​of the output frequency droop coefficient and the output voltage droop coefficient based on the symmetry error of the output frequency and the symmetry error of the output voltage, the dynamic output frequency gain and the output voltage gain that satisfy the upper bound constraint; and to filter the adjustment values ​​of the output frequency droop coefficient and the output voltage droop coefficient using a recursive convex combination first-order filter to obtain the dynamic droop coefficient of the output frequency and the dynamic droop coefficient of the output voltage. The model update module is used to update the established droop control link model based on the real-time operation data of the rural greenhouse microgrid. When the recursive convex combination first-order filter, the dynamic droop coefficient of the output frequency, and the microgrid control system all meet the stability constraints, the module uses the dynamic droop coefficient of the output frequency and the dynamic droop coefficient of the output voltage to perform dynamic adaptive coordinated operation control of the rural greenhouse microgrid.

[0026] The present invention is also a terminal, including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to perform operations according to the instructions to execute the steps of the method.

[0027] The present invention is also a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method.

[0028] The beneficial effects of this invention are as follows: Compared with the prior art, the method proposed in this invention addresses the coordinated control problem of rural greenhouse microgrids under high volatility and structural asymmetry. It proposes an innovative four-layer control architecture that progresses from the inside out; replaces traditional absolute deviation with symmetrical error to achieve deep perception of internal structural imbalances; employs a generalized Sigmoid function to nonlinearly map the perceived error into intelligent dynamic gain; designs a convex combination recursive filter to smoothly and stably transform the intelligent gain into a real-time dynamic droop coefficient; and introduces online multi-dimensional stability constraint verification to ensure a safe closed loop throughout the adaptive update process. This forms a complete adaptive control chain encompassing symmetrical error detection, generalized Sigmoid dynamic gain generation, convex combination recursive filtering stabilization, and closed-loop stability verification and update. This achieves a fundamental shift from static fixed droop coefficients to dynamic intelligent droop coefficients, elevating droop control from open-loop parameterization to closed-loop cognitiveization, enabling the inverter to possess self-regulation and stability maintenance capabilities. This method has high practical value for the specific application scenario of rural greenhouse microgrids. Attached Figure Description

[0029] Figure 1 This is a flowchart of a dynamic adaptive coordinated operation control method for a rural greenhouse microgrid proposed in this invention.

[0030] Figure 2 This is a graph showing the output power curves of wind, solar, and energy storage in an embodiment of the present invention.

[0031] Figure 3 This is a graph showing the real-time output DC voltage of the photoelectric storage micro-source in an embodiment of the present invention.

[0032] Figure 4 This is a graph showing the output voltage of the grid-side inverter in an embodiment of the present invention.

[0033] Figure 5 This is a graph showing the output frequency of the grid-side inverter in an embodiment of the present invention. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0035] This invention proposes a method for dynamic droop control of microgrid inverters based on the Sigmoid function. First, an initial droop baseline value is established, and a parameter adjustment mechanism is formulated, including frequency sensitivity, voltage sensitivity, frequency threshold, and voltage threshold. This parameter configuration aims to enhance the strategy's response sensitivity to small deviations while applying a saturation suppression effect to severe disturbances. Furthermore, upper and lower limit constraints are introduced to curb excessive oscillation of the coefficient, ensuring that the droop coefficient remains within a reasonable range. The proposed method uses the Sigmoid function to solve for the target droop coefficient. Utilizing its monotonically increasing and saturation characteristics, the coefficient approaches the lower limit when the deviation is small, achieving fine adjustment; when the deviation increases, the coefficient rapidly approaches the upper limit, thus achieving dynamic compensation and effectively suppressing power distribution deviations and overload and under-charge / discharge problems of the energy storage unit. Regarding stability analysis, based on the constructed control model, the bounded input-output stability of the first-order autoregressive model is verified; then, based on voltage and frequency deviations, the asymptotic convergence characteristics of the dynamic droop coefficient are verified; finally, based on Lyapunov theory, the global asymptotic stability of the closed-loop system is proved.

[0036] This invention proposes a dynamic adaptive coordinated operation control method for rural greenhouse microgrids, such as... Figure 1 As shown, it includes: Step 1: Establish a model of the droop control link of the distributed energy inverter in the rural greenhouse microgrid.

[0037] In this embodiment, a mathematical model of distributed energy sources such as wind, solar and energy storage in a rural greenhouse microgrid and a mathematical model of droop control in the primary control layer of a traditional three-phase inverter system are constructed. This invention establishes mathematical models of various distributed energy sources involved in the microgrid system in a rural greenhouse microgrid, and models the droop control in the primary control layer of the microgrid, ultimately forming a complete mathematical model of the microgrid system.

[0038] Specifically, the model for the droop control link is as follows:

[0039] in, , These are the instantaneous angular frequency and voltage amplitude of the inverter output, respectively. , These are the nominal reference values ​​for angular frequency and voltage, respectively. , These represent the instantaneous active power and reactive power output by the inverter, respectively. , These are the static droop gain coefficients for the inverter's output frequency and output voltage, respectively.

[0040] Different distributed energy sources employ different control methods: photovoltaic systems use maximum power point tracking (MPPT) to obtain the maximum output power of the photovoltaic system, specifically using a perturbation-observation method to determine the maximum power point; in wind power systems, the core lies in the wind turbine first capturing wind energy and converting it into mechanical energy, which is then output via the turbine shaft. This process involves the conversion of wind energy into mechanical energy in the wind power system, employing an optimal tip speed ratio control method; in energy storage systems, a voltage-current dual closed-loop control method is used to drive the DC / DC converter.

[0041] Step 2: Obtain the output frequency and output voltage of the inverter droop control loop, and calculate the symmetry error of the output frequency and the symmetry error of the output voltage, as shown in the following formula:

[0042] In the formula, For at any time Symmetrical error of inverter output frequency; For at any time Symmetry error of inverter output voltage; The reference frequency for the rural greenhouse microgrid is 50Hz, as used in this embodiment. The reference voltage for the rural greenhouse microgrid is 380V, which is used in this example. For the phase-locked loop at time The real-time frequency of data acquisition; For voltage transformers at time Real-time voltage data collected; It is the absolute norm; The error values ​​between the inverter output voltage and frequency and the reference values ​​are used as the quantitative index of state deviation in the form of norm. The deviation measurement uses the absolute norm to ensure symmetry, thus realizing symmetrical quantification of state deviation. Moreover, the use of the absolute norm not only ensures the non-negativity of the error signal, but also facilitates the design of monotonic mapping of subsequent nonlinear functions.

[0043] Step 3: Based on the difference between the symmetry error of the output frequency and the frequency threshold, and the difference between the symmetry error of the output voltage and the voltage threshold, the generalized Sigmoid function is used to establish the dynamic output frequency gain and output voltage gain. Generalized Sigmoid function As shown in the following formula:

[0044] In the formula, This is the steepness parameter in the Sigmoid function, which controls how steep the curve of the function is. The difference between the output frequency or output voltage and its corresponding threshold value, when As the values ​​change, the dynamic output frequency gain and output voltage gain based on the generalized Sigmoid function are obtained from the above equation.

[0045] Based on the symmetry error of the output frequency With frequency threshold The difference Symmetry error of output voltage With voltage threshold The difference The dynamic output frequency gain and output voltage gain based on the generalized Sigmoid function are shown in the following equations:

[0046] In the formula, Symmetrical error based on output voltage With voltage threshold The difference 'output voltage gain' ; Symmetrical error based on output frequency With frequency threshold The difference Dynamic output frequency gain, ; This refers to the voltage steepness parameter; This is the frequency steepness parameter; , Among them, the frequency steepness parameter Voltage steepness parameters Output frequency threshold =0.02Hz, output voltage threshold .

[0047] Step 4: Determine the upper bound constraints of the dynamic output frequency gain and output voltage gain based on the minimum and maximum values ​​of the static droop gain coefficients of the inverter droop control loop, respectively; as shown in the following formula:

[0048] In the formula, , These are the minimum and maximum values ​​of the static droop gain coefficient at the output frequency, respectively. , These are the minimum and maximum values ​​of the static droop gain coefficient of the output voltage, respectively. It is an L2 norm.

[0049] This invention proposes a theoretical tightening of the upper bound of the generalized Sigmoid function. Compared with linear mappings, this nonlinear design significantly tightens the small gain theorem. The norm gain upper bound, which limits the maximum gain sensitivity, provides a rigorous mathematical guarantee for the robustness analysis of the subsequent closed-loop system.

[0050] Step 5: Based on the symmetry error of the output frequency and the symmetry error of the output voltage, the dynamic output frequency gain and output voltage gain that satisfy the upper bound constraint, determine the adjustment values ​​of the output frequency droop coefficient and the output voltage droop coefficient.

[0051] The adjustment values ​​for the output frequency droop coefficient and the output voltage droop coefficient are shown in the following formulas:

[0052] In the formula, For a moment The adjustment value of the output frequency droop coefficient; For a moment The adjustment value of the output voltage droop coefficient; , These are the minimum and maximum values ​​of the static droop gain coefficient at the output frequency, respectively. , These are the minimum and maximum values ​​of the static droop gain coefficient of the output voltage, respectively. This is the output frequency threshold; Output voltage threshold; This is the dynamic output frequency gain function; It is the output voltage gain function; In this embodiment, based on the inverter capacity and line impedance characteristics of the rural greenhouse microgrid, the output frequency reference droop factor is set to... The output voltage reference droop factor is Minimum value of output frequency droop factor and maximum value Minimum value of output voltage droop factor and maximum value Frequency steepness parameter Voltage steepness parameters Output frequency threshold =0.02Hz, output voltage threshold .

[0053] This invention proposes a dynamic adjustment mechanism for the output frequency droop coefficient and the output voltage droop coefficient. The gain based on the generalized Sigmoid function has the following characteristics; 1) For small deviation areas << It exhibits noise-resistant dead-zone characteristics; when the system is near steady state, the deviation... Much smaller than the threshold When the generalized sigmoid function is in the flat region on the left, the absolute value of the first derivative of the gain based on the generalized sigmoid function is small and close to linear. This low sensitivity characteristic enables the controller to effectively filter out high-frequency interference caused by measurement noise, switching harmonics or small fluctuations in greenhouse load, and avoid system oscillation caused by over-control.

[0054] 2) For areas with large deviations >> It exhibits fast response and soft limiting characteristics; when a sudden load change or islanding switch causes a sharp increase in deviation, the generalized sigmoid function quickly crosses the linear region and enters the saturation region. At this time, the value of the generalized sigmoid function rapidly approaches... or It provides strong regulation support to suppress voltage / frequency fluctuations; more importantly, the asymptotic saturation behavior of the Sigmoid function constitutes a natural soft limiting mechanism. Unlike hard cutoff, it smoothly constrains the potential overflow of gain and prevents the small gain theorem from being violated due to excessive droop coefficient under extreme fault conditions.

[0055] Step 6: Use a recursive convex combination first-order filter to filter the adjusted values ​​of the output frequency droop coefficient and the output voltage droop coefficient to obtain the dynamic droop coefficient of the output frequency and the dynamic droop coefficient of the output voltage.

[0056] To ensure the smoothness of the dynamic droop coefficient, this framework uses a recursive convex combination first-order filter for filtering, resulting in the following equations for the dynamic droop coefficients of the output frequency and the output voltage:

[0057] In the formula, , The first The dynamic droop coefficient of the output frequency and the adjustment value of the output frequency droop coefficient during the next iteration; , The first The adjustment values ​​of the dynamic droop coefficient and the output voltage droop coefficient during the next iteration; , All are preset step size parameters. , ; The preset step size parameter determines the system's rate of accepting new target values ​​and its degree of memorization of historical values. = =0.05 is the nominal value; initial value = , = The smaller step size parameter means that the final coefficient at the current moment only contains 5% of the current target value and 95% of the historical value. From the frequency domain perspective, this is equivalent to a low-pass filter with an extremely low cutoff frequency. Although this design sacrifices a little parameter response speed, it greatly enhances the smoothness of parameter changes, effectively suppresses droop coefficient jitter caused by measurement noise fluctuations, and ensures the quality of the inverter output voltage waveform.

[0058] This invention first performs symmetrical quantization on the error values ​​between the inverter output voltage and frequency and the reference values. Then, it constructs a state-dependent dynamic control architecture for the droop coefficient, and introduces a generalized sigmoid function and a recursive convex combination first-order filter to achieve bounded adjustment of the dynamic droop coefficient and recursive smooth evolution to obtain a dynamically changing droop coefficient to replace the fixed droop coefficient.

[0059] Step 7: Based on the real-time operation data of the rural greenhouse microgrid, when the recursive convex combination first-order filter, the dynamic droop coefficient of the output frequency, and the microgrid control system all satisfy the stability constraints, the established droop control link model is updated using the dynamic droop coefficient of the output frequency and the dynamic droop coefficient of the output voltage to perform dynamic adaptive coordinated operation control of the rural greenhouse microgrid.

[0060] Based on the constructed dynamic control model, stability constraints are applied to the introduced recursive convex combined first-order filter, the dynamic droop coefficient, and the overall microgrid control system, as follows: 1) Stability constraints of convex combined first-order filters based on the recursive form of the AR(1) model; The recursive process of a convex combination first-order filter in recursive form is equivalent to the AR(1) model, let... , Denotes the set of bounded sequences; bounded sequences satisfy... , It is an infinite norm; Given bounded positive real numbers, iterative expansion of the update equation yields:

[0061] Then when When bounded, the recursive form of the convex combined first-order filter is stable. Therefore, the stability constraint of the recursive form of the convex combined first-order filter is as follows:

[0062] 2) Asymptotic convergence constraint on the dynamic droop coefficient of the output frequency; Establish error sequence satisfy The exponential curve asymptotically converges to zero, and the error is recursively given by:

[0063] Slowly changing, quasi-steady state approximately satisfies This applies to Sigmoid smoothness; therefore, the asymptotic convergence constraint of the dynamic droop coefficient of the output frequency is as follows:

[0064] 3) Lyapunov asymptotic stability constraints for microgrid control systems; To prove global asymptotic stability, a composite Lyapunov function is constructed, as shown in the following equation:

[0065] In the formula, For composite Lyapunov functions, For a moment The frequency of the microgrid This is the reference frequency for the microgrid. For a moment The dynamic droop factor of the output frequency. This is the droop factor for the output frequency reference. For a moment The state vector of a microgrid This is the reference state vector of the microgrid. Represented as The weighted Euclidean norm is used to calculate The function, It is a positive definite matrix; satisfy = , ; The Lyapunov asymptotic stability constraints for microgrid control systems are shown in the following equation: And it is positive definite and radially unbounded, that is when ; In the formula, For matrix The smallest eigenvalue.

[0066] Furthermore, the dynamic adaptive coordinated operation control method applied to rural greenhouse microgrids uses Similink as a modeling tool to verify the effectiveness and practicality of the proposed control method.

[0067] The dynamic droop coefficient control system for a distributed power generation microgrid with wind, solar and energy storage, designed based on a dynamic adaptive coordinated operation control method for rural greenhouse microgrids, incorporates modules such as voltage and current dual closed-loop control, Parker transformation, and power calculation found in the primary control layer of traditional islanded microgrids.

[0068] To verify the effectiveness and practicality of the dynamic adaptive coordinated operation control method applied to rural greenhouse microgrids, a simulation model was built based on the Simulink platform to verify the operation status of the constructed rural greenhouse wind-solar-storage MG system. The system structure diagram is shown below. Figure 2 As shown in Table 1, the wind turbine system in the wind power unit uses a PMSG (Permanent Magnet Synchronous Generator) which is first connected to the DC bus via a converter, and then connected to the AC side via a DC-AC grid-side inverter and a parallel RLC (Resistor-Inductor-Capacitor) filter circuit. The photovoltaic unit is connected to the DC side of the grid-connected inverter via a DC-DC (DC-DC) conversion through an MPPT (Maximum Power Point Tracking) strategy. The energy storage unit uses dual closed-loop control to complete the DC-DC (DC-DC) conversion. Both the photovoltaic and energy storage modules are connected to the DC bus, which is connected to a DC load. The inverter circuit adopts a control strategy combining dynamic droop control and event-triggered control to convert DC power to AC power. Some simulation parameters are shown in Table 1.

[0069] Table 1 System Simulation Parameters

[0070] The microgrid control operation was simulated using MATLAB software. The active power output curves of each micro-source (wind, solar, and energy storage) were obtained using the method proposed in this embodiment. Different operating conditions were considered in the simulation: ① The AC load was simulated to be connected and disconnected at 2s and 4s, respectively; ② A partial failure of the photovoltaic panel was simulated at 2.7s.

[0071] Figure 2 The output power curves for wind, solar, and energy storage are presented, where Pw represents the output power curve of the wind power system, Ppv represents the output power curve of the photovoltaic system, and Pc represents the real-time output curve of the energy storage system. Although there were brief oscillations at the beginning of operation, the system quickly returned to stable operation. Figure 3 This refers to the real-time DC voltage Udc output by the photovoltaic-storage micro-source; this process does not involve a wind turbine. Figure 3 It can be seen that when the wind-solar-storage system is connected to the DC side of the grid-side inverter, the voltage fluctuates significantly at the beginning of the dynamic droop coefficient adjustment process, but it stabilizes at the set voltage value of 750V within a short period of time. Figure 4 and Figure 5The figures show the output voltage and frequency of the grid-side inverter. As can be seen from the two figures, the system only failed to reach the set voltage and frequency values ​​for a short period at the beginning of operation; otherwise, it remained in a stable operating state for almost the entire time.

[0072] The analysis shows that the dynamic droop control method, which improves upon the original fixed droop control method, has a better control effect on the system.

[0073] This invention proposes a dynamic droop control method for microgrid inverters based on the Sigmoid function, applicable to real-world scenarios with dynamically changing environmental loads, particularly in rural greenhouse microgrids and wind-solar-storage islanded microgrids. This method achieves precise and sensitive response to small deviations, saturation suppression of severe disturbances, and effective compensation for power distribution deviations through the establishment of an initial droop base value, parameter adjustment mechanisms, and the monotonically increasing saturation characteristics of the Sigmoid function. This significantly reduces the risk of overload and under-charge / discharge of energy storage units, ensuring the global asymptotic stability and robustness of the closed-loop system. Compared to existing methods, its advantages lie in simplified optimization algorithms, reduced reliance on hypothesis verification, and the provision of complete stability constraints, including first-order autoregressive bounded input-output, asymptotic convergence of dynamic coefficients, and Lyapunov theoretical analysis. This improves the reliability and adaptability of coordinated microgrid operation and reduces the complexity of actual deployment.

[0074] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0075] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0076] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0077] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A dynamic adaptive coordinated operation control method for rural greenhouse microgrids, characterized in that, include: Establish a model for the droop control link of distributed energy inverters in rural greenhouse microgrids; Obtain the output frequency and output voltage of the inverter droop control loop, and calculate the symmetry error of the output frequency and the symmetry error of the output voltage. Based on the difference between the symmetry error of the output frequency and the frequency threshold, and the difference between the symmetry error of the output voltage and the voltage threshold, establish the dynamic output frequency gain and output voltage gain using the generalized Sigmoid function. Determine the upper bound constraints of the dynamic output frequency gain and output voltage gain based on the minimum and maximum values ​​of the static droop gain coefficients of the output frequency and output voltage of the inverter droop control loop, respectively. Based on the symmetry error of the output frequency and the symmetry error of the output voltage, and the dynamic output frequency gain and output voltage gain that satisfy the upper bound constraint, the adjustment values ​​of the output frequency droop coefficient and the output voltage droop coefficient are determined. The adjustment values ​​of the output frequency droop coefficient and the output voltage droop coefficient are filtered by a recursive convex combination first-order filter to obtain the dynamic droop coefficient of the output frequency and the dynamic droop coefficient of the output voltage. Based on real-time operation data of rural greenhouse microgrids, when the recursive convex combination first-order filter, the dynamic droop coefficient of the output frequency, and the microgrid control system all satisfy stability constraints, the established droop control link model is updated using the dynamic droop coefficient of the output frequency and the dynamic droop coefficient of the output voltage to perform dynamic adaptive coordinated operation control of the rural greenhouse microgrid.

2. The dynamic adaptive coordinated operation control method for rural greenhouse microgrids according to claim 1, characterized in that, The model for the droop control mechanism is as follows: in, , These are the instantaneous angular frequency and voltage amplitude of the inverter output, respectively. , These are the nominal reference values ​​for angular frequency and voltage, respectively. , These represent the instantaneous active power and reactive power output by the inverter, respectively. , These are the static droop gain coefficients for the inverter's output frequency and output voltage, respectively.

3. The dynamic adaptive coordinated operation control method for rural greenhouse microgrids according to claim 1, characterized in that, The symmetry errors of the output frequency and output voltage are shown in the following formulas: In the formula, For at any time Symmetrical error of inverter output frequency; For at any time Symmetry error of inverter output voltage; This serves as the reference frequency for rural greenhouse microgrids. This serves as the reference voltage for the rural greenhouse microgrid. For the phase-locked loop at time The real-time frequency of data acquisition; For voltage transformers at time Real-time voltage data collected; It is the absolute norm.

4. The dynamic adaptive coordinated operation control method for rural greenhouse microgrids according to claim 3, characterized in that, The dynamic output frequency gain and output voltage gain are shown in the following formulas: In the formula, Symmetrical error based on output voltage With voltage threshold The difference 'output voltage gain' ; Symmetrical error based on output frequency With frequency threshold The difference Dynamic output frequency gain, ; This refers to the voltage steepness parameter; This is the frequency steepness parameter.

5. The dynamic adaptive coordinated operation control method for rural greenhouse microgrids according to claim 4, characterized in that, The upper bound constraints for dynamic output frequency gain and output voltage gain are shown in the following equations: In the formula, , These are the minimum and maximum values ​​of the static droop gain coefficient at the output frequency, respectively. , These are the minimum and maximum values ​​of the static droop gain coefficient of the output voltage, respectively. It is an L2 norm.

6. The dynamic adaptive coordinated operation control method for rural greenhouse microgrids according to claim 5, characterized in that, The adjustment values ​​for the output frequency droop coefficient and the output voltage droop coefficient are shown in the following formulas: In the formula, For a moment The adjustment value of the output frequency droop coefficient; For a moment The adjustment value of the output voltage droop coefficient.

7. The dynamic adaptive coordinated operation control method for rural greenhouse microgrids according to claim 6, characterized in that, The dynamic droop coefficient and the dynamic droop coefficient of the output voltage are shown in the following formula: In the formula, , The first The dynamic droop coefficient of the output frequency and the adjustment value of the output frequency droop coefficient during the next iteration; , The first The adjustment values ​​of the dynamic droop coefficient and the output voltage droop coefficient during the next iteration; , All are preset step size parameters. , .

8. The dynamic adaptive coordinated operation control method for rural greenhouse microgrids according to claim 7, characterized in that, The stability constraint of the convex combined first-order filter in recursive form is shown in the following equation: In the formula, Let be the infinite norm of the dynamic droop coefficient of the output frequency. = , This is the droop factor for the output frequency reference. It is a bounded positive real number.

9. The dynamic adaptive coordinated operation control method for rural greenhouse microgrids according to claim 7, characterized in that, The asymptotic convergence constraint of the dynamic droop coefficient of the output frequency is shown in the following equation: In the formula, the error sequence satisfy .

10. The dynamic adaptive coordinated operation control method for rural greenhouse microgrids according to claim 7, characterized in that, Construct a composite Lyapunov function as shown in the following equation: In the formula, For composite Lyapunov functions, For a moment The frequency of the microgrid This is the reference frequency for the microgrid. For a moment The dynamic droop factor of the output frequency. This is the droop factor for the output frequency reference. For a moment The state vector of a microgrid This is the reference state vector of the microgrid. Represented as The weighted Euclidean norm is used to calculate The function, It is a positive definite matrix; satisfy = , ; The Lyapunov asymptotic stability constraints for microgrid control systems are shown in the following equation: And it is positive definite and radially unbounded, that is when ; In the formula, For matrix The smallest eigenvalue.

11. A dynamic adaptive coordinated operation control system for a rural greenhouse microgrid, used to implement the dynamic adaptive coordinated operation control method for the rural greenhouse microgrid according to any one of claims 1 to 10, characterized in that, include: The model building module is used to build a model of the droop control link of the distributed energy inverter in a rural greenhouse microgrid. The gain update module is used to obtain the output frequency and output voltage of the inverter droop control loop, calculate the symmetry error of the output frequency and the symmetry error of the output voltage; based on the difference between the symmetry error of the output frequency and the frequency threshold, and the difference between the symmetry error of the output voltage and the voltage threshold, the generalized sigmoid function is used to establish the dynamic output frequency gain and output voltage gain; based on the minimum and maximum values ​​of the static droop gain coefficients of the output frequency and output voltage of the inverter droop control loop, the upper bound constraints of the dynamic output frequency gain and output voltage gain are determined respectively. The droop coefficient update module is used to determine the adjustment values ​​of the output frequency droop coefficient and the output voltage droop coefficient based on the symmetry error of the output frequency and the symmetry error of the output voltage, the dynamic output frequency gain and the output voltage gain that satisfy the upper bound constraint; and to filter the adjustment values ​​of the output frequency droop coefficient and the output voltage droop coefficient using a recursive convex combination first-order filter to obtain the dynamic droop coefficient of the output frequency and the dynamic droop coefficient of the output voltage. The model update module is used to update the established droop control link model based on the real-time operation data of the rural greenhouse microgrid. When the recursive convex combination first-order filter, the dynamic droop coefficient of the output frequency, and the microgrid control system all meet the stability constraints, the module uses the dynamic droop coefficient of the output frequency and the dynamic droop coefficient of the output voltage to perform dynamic adaptive coordinated operation control of the rural greenhouse microgrid.

12. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1-10.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1-10.

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