A microgrid control method and system

Abstract

The application provides a micro-grid control method and system, and relates to the technical field of micro-grid control. The method comprises the following steps: obtaining multi-source data of equipment, and calculating a trigger function value according to the multi-source data; dynamically judging whether the trigger function value is greater than zero according to multi-physical quantity coupling trigger function to judge whether a control action is triggered; when the trigger function value is greater than zero, the control action is triggered, then the external characteristic parameters and the load shedding instruction are optimized through an MPC algorithm, and the external characteristic parameters of the MPC algorithm are iteratively corrected according to a closed-loop mechanism to generate a target control strategy, so that the micro-grid is cooperatively controlled based on the target control strategy. Through deep fusion of dynamic event triggering and virtual external characteristic adaptation, efficient, stable and cooperative control of the micro-grid in a complex dynamic scene is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of micro-grid control, and particularly relates to a micro-grid control method and system. BACKGROUND

[0002] As a core carrier for high penetration of renewable energy consumption, micro-grid needs to realize multi-source coordinated control and stability guarantee under dynamic scenarios (such as load mutation, new energy fluctuation, fault isolation). However, the existing technology still has significant limitations in the field of multi-source coordinated control, mainly embodied in the following aspects:

[0003] At present, the existing micro-grid control method generally relies on fixed parameter design, such as droop control and virtual inertia control. These methods perform well in stable working conditions, but are prone to power oscillation and frequency instability problems in complex dynamic scenarios. Secondly, the existing control method usually relies on periodic sampling or fixed threshold triggering mechanism, which leads to event response lag, and is difficult to meet the rapid dynamic adjustment demand of micro-grid. Thirdly, the design of external characteristic parameters (such as droop rate and virtual inertia) in the existing technology is usually isolated from real-time working conditions, and cannot realize multi-dimensional collaborative optimization. Furthermore, the existing technology is mainly optimized for single device or specific scenario, and lacks overall design for multi-source collaboration of micro-grid. SUMMARY

[0004] Therefore, the purpose of the present application is to provide a micro-grid control method and system, which dynamically adjusts the external characteristic parameters of each device to realize the collaborative optimization of multi-source, multi-load and energy storage system, and ensures the stable operation of micro-grid.

[0005] In one aspect, the present application provides a micro-grid control method, comprising:

[0006] Obtaining multi-source data of a device, the device comprising a photovoltaic inverter, an energy storage BMS, a load controller and a bus PMU, the multi-source data comprising photovoltaic output power, load power change rate, bus voltage deviation, frequency change rate, energy storage SOC and temperature change rate, obtaining a trigger function value according to the multi-source data;

[0007] According to the multi-physical quantity coupling trigger function, it is judged whether the trigger function value is greater than zero to judge whether the control action is triggered or not;

[0008] When the trigger function value is greater than zero, a control action is triggered, and then an external characteristic parameter and a load shedding instruction are optimized by an MPC algorithm, and a target control strategy is generated according to a closed-loop mechanism to iteratively correct the external characteristic parameter of the MPC algorithm, so as to cooperatively control the micro-grid based on the target control strategy, wherein the external characteristic parameter includes a frequency deviation weight coefficient, a THD weight coefficient and a control parameter, and the control parameter includes a dynamic droop rate, a virtual impedance and a virtual inertia.

[0009] The micro-grid control method realizes efficient, stable and cooperative control of the micro-grid in a complex dynamic scenario by deeply fusing the dynamic event trigger and the virtual external characteristic adaptation. Specifically, the photovoltaic inverter and the load controller are simultaneously involved in data acquisition and control execution to realize integrated perception control. The photovoltaic penetration rate is obtained according to the photovoltaic output power and the load power, and the photovoltaic penetration rate is dynamically associated with the dynamic droop rate, the virtual inertia, the energy storage SOC and the temperature change rate for dynamic parameter adaptation. In addition, the photovoltaic inverter injects frequency domain impedance to specifically solve the harmonic problem of new energy grid connection. The external characteristic parameters of each device are dynamically adjusted to realize cooperative optimization of multiple power sources, multiple loads and energy storage systems, and ensure stable operation of the micro-grid.

[0010] In addition, the micro-grid control method according to the present application can have the following additional technical features.

[0011] Further, the expression of the multi-physical quantity coupling trigger function is:

[0012] ;

[0013] In the formula, E E represents a trigger function value, which is used to determine whether a control action is triggered, and the control action is triggered when E>0; represents a load power change rate; represents a photovoltaic power change rate; V represents a voltage deviation; represents a temperature change rate of an energy storage battery; α 1, α 2, β , gamma are weight coefficients, respectively representing a photovoltaic power weight factor, a load power change rate weight factor, a voltage deviation weight factor and a dynamic temperature weight factor; represents a temperature influence coefficient.

[0014] Further, gamma The dynamic adjustment formula of E is:

[0015] gamma new = gamma 0﹒(1+ kSOC (1-SOC / 100) k priority P load

[0016] wherein, gamma new represents the temperature weight factor trigger threshold after dynamic adjustment; gamma 0 represents the reference trigger threshold; k SOC represents the energy storage state of charge influence coefficient; the energy storage SOC represents the energy storage state of charge; k priority represents the load priority influence coefficient; P load represents the total system load power at the current moment.

[0017] Further, the calculation formula of the dynamic droop rate is:

[0018] ;

[0019] K droop represents the dynamic droop rate, representing the slope of the power frequency droop relationship; K 0 represents the reference droop rate; k 1 represents the load rate influence coefficient; P load represents the total system load power at the current moment; P max represents the maximum allowed power of the system; k 2 represents the temperature change rate influence coefficient; k 3 represents the energy storage SOC influence coefficient; represents the temperature change rate of the energy storage battery.

[0020] Further, the calculation formula of the virtual inertia is:

[0021] ;

[0022] J virtual represents the virtual inertia; J 0 represents the reference virtual inertia; k 4 represents the frequency change rate influence coefficient; represents the frequency change rate; k 5 represents the photovoltaic penetration rate influence coefficient; P renew represents the photovoltaic penetration rate, and the calculation formula is:

[0023] ; ​​

[0024] P PV PV output power; P demand System total load power, real-time data reported by load controller.

[0025] Further, the calculation formula of the virtual impedance is:

[0026] ;

[0027] In the formula, Z vir ( s ) represents a virtual impedance frequency domain model; K p represents a proportional gain; K d represents a derivative gain; K i represents an integral gain; f c represents a cut-off frequency; s represents a complex frequency variable, s = sigma + j omega , wherein: sigma is a real part, representing an attenuation coefficient; omega is an imaginary part, representing an angular frequency; j represents an imaginary unit, j 2 = -1.

[0028] Further, in the MPC algorithm, multi-objective optimization is realized through a cost function, and the cost function is:

[0029] ;

[0030] In the formula, , represents a set of control input variables to be optimized, wherein, K droop represents a dynamic droop rate, J virtual represents a virtual inertia, K p represents a proportional gain, K d represents a derivative gain, K i represents an integral gain, L oad_shedding represents a load switching; represents a frequency deviation weight coefficient; represents a THD weight coefficient; a power loss weight coefficient; lambda represents a storage SOC penalty term coefficient; represents the frequency deviation prediction value at future k time instant; THD k represents the harmonic distortion rate prediction value at k time instant; k represents the discrete time index within the prediction time domain; SOC penalty represents the energy storage SOC boundary crossing penalty term; N represents the prediction time domain; P loss represents the system comprehensive power loss, including the energy storage PCS loss, the photovoltaic inverter loss and the line transmission loss; J represents the system comprehensive performance index.

[0031] Further, in the system comprehensive power loss P loss , the calculation formula of the energy storage PCS loss is:

[0032] ;

[0033] P loss,PCS represents the energy storage PCS loss; I PCS represents the PCS current; R PCS represents the PCS resistance; f SW represents the switching frequency; C SW represents the single switching loss;

[0034] The calculation formula of the photovoltaic inverter loss is:

[0035] ;

[0036] In the formula, represents the photovoltaic inverter efficiency; P PV represents the photovoltaic output power;

[0037] The calculation formula of the line transmission loss is:

[0038] ;

[0039] In the formula, I line represents the line; R line represents the line equivalent resistance;

[0040] The calculation formula of the system comprehensive power loss is:

[0041] P loss = P loss,PCS +P loss,PV + P loss,line ;

[0042] in the formula, P loss,PCS represents the energy storage PCS loss; P loss,PV represents the photovoltaic inverter loss; P loss,line represents the line transmission loss.

[0043] Another aspect of the present application provides a micro-grid control system, which comprises:

[0044] an acquisition module, configured to acquire multi-source data of devices, the devices comprising a photovoltaic inverter, an energy storage BMS, a load controller and a bus PMU, the multi-source data comprising photovoltaic output power, load power change rate, bus voltage deviation, frequency change rate, energy storage SOC and temperature change rate, and a trigger function value is calculated according to the multi-source data;

[0045] a judgment module, configured to dynamically judge whether the trigger function value is greater than zero according to the multi-physical quantity coupling trigger function to judge whether to trigger a control action;

[0046] a control module, configured to trigger a control action when the trigger function value is greater than zero, then optimize external characteristic parameters and load shedding instructions through an MPC algorithm, and generate a target control strategy according to an iterative correction of the external characteristic parameters of the MPC algorithm based on a closed-loop mechanism, so as to cooperatively control the micro-grid based on the target control strategy, the external characteristic parameters comprising a frequency deviation weight coefficient, a THD weight coefficient and a control parameter, and the control parameter comprising a dynamic droop rate, a virtual impedance and a virtual inertia.

[0047] Another aspect of the present application provides a computer readable storage medium, which stores a computer program, the program being executed by a processor to implement the micro-grid control method as described above.

[0048] Another aspect of the present application also provides a data processing device, which comprises a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the micro-grid control method as described above when executing the program. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 is a flowchart of the micro-grid control method of the present application;

[0050] The following specific embodiments will further illustrate the present application in combination with the above drawings. DETAILED DESCRIPTION

[0051] For the purpose of promoting the understanding of the present application, a more complete description of the application will be provided below with reference to the relevant drawings. Several embodiments of the present application are shown in the drawings. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terms used in the specification of the present application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application. The term "and / or" used herein includes any and all combinations of one or more of the associated listed items.

[0053] In the operation of the micro-grid, due to the occurrence of complex dynamic scenarios such as load fluctuation, photovoltaic power generation mutation, energy storage system failure, etc., the power balance and stability of the system may be seriously affected. In order to cope with these challenges, the present application proposes a micro-grid control method and system, which collects photovoltaic inverter output power, energy storage battery state, load power and bus electrical quantity (voltage, frequency, harmonic) in real time through the perception layer. The decision layer adopts the MPC algorithm, and dynamically determines whether the control strategy needs to be adjusted according to the multi-physical quantity coupling trigger function. When the system detects events such as load mutation, photovoltaic power drop or energy storage failure, the MPC algorithm is triggered to optimize the power-frequency droop rate of the energy storage converter, the virtual inertia and frequency domain impedance parameters of the photovoltaic inverter, and the load shedding instructions of the load controller. In the optimization process, the MPC algorithm weighs the system frequency stability, harmonic suppression effect, operation efficiency and energy storage health status through the multi-objective cost function, and generates the optimal control strategy. After adjusting the equipment parameters according to the strategy, the execution layer continuously monitors the actual operating state (such as frequency deviation, harmonic content, power balance error) through the feedback closed-loop mechanism, dynamically corrects the external characteristic parameters of the MPC algorithm, and until the system recovers to stable and meets the preset indicators (frequency deviation less than 0.05 Hz, total harmonic distortion rate less than 3%, power balance error less than 5kW). This method solves the problems of rigid control parameters, response lag and lack of multi-source cooperation in traditional control, and significantly improves the stability and efficiency of the micro-grid in complex dynamic scenarios.

[0054] For the purpose of promoting the understanding of the present application, a more complete description of the application will be provided below with reference to the relevant drawings. Several embodiments of the present application are shown in the drawings. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive.

[0055] Embodiment one

[0056] Please refer to Figure 1, as shown in the micro-grid control method in the first embodiment of the application, the method comprises steps S101 to S104:

[0057] S101, acquire multi-source data of the equipment, the equipment including a photovoltaic inverter, a storage BMS, a load controller and a bus PMU, the multi-source data including photovoltaic output power, load power change rate, bus voltage deviation, frequency change rate, storage SOC and temperature change rate, and a trigger function value is calculated according to the multi-source data.

[0058] In the embodiment, the system adopts a three-layer architecture of a perception layer, a decision layer and an execution layer, realizes multi-source data fusion and closed-loop control, specifically, the perception layer is used to collect electrical quantities, equipment states and load priority data, the core equipment thereof including a photovoltaic inverter, a storage BMS (Battery Management System, BMS for short), a load controller and a bus PMU (Phasor Measurement Unit, PMU for short). The decision layer is used to execute dynamic event trigger judgment and model predictive control and instruction generation, the core equipment thereof including an edge controller, wherein, in the embodiment, the model predictive control adopts an MPC (Model Predictive Control, MPC for short) algorithm. The execution layer is used to adjust equipment external characteristic parameters and execute load shedding or power limiting operation instructions, wherein the equipment external characteristic parameters include dynamic droop rate and virtual inertia; the core equipment of the execution layer including a storage PCS (Power Conversion System), a photovoltaic inverter and a load controller.

[0059] Further, the storage SOC (State of Charge, SOC for short) of the storage BMS, the temperature T and the temperature change rate of the storage battery of the storage BMS are acquired . The bus PMU mainly collects data including three-phase voltage, three-phase current, frequency and total harmonic distortion (THD for short); wherein the three-phase voltage is used to calculate voltage deviation ΔV and subsequently extract harmonic features through an FFT algorithm; the three-phase current is used to calculate power and harmonic content; the frequency is used to monitor system frequency change rate ; the total harmonic distortion is used to evaluate power quality. The photovoltaic output power P PV , power change rate and DC side voltage V are acquired. The current system total load power P load , power change rate , priority label and switch state are acquired.

[0060] After the data is adjusted by the decision layer, the adjusted data is output by the execution layer to control the equipment. Specifically, the virtual impedance Z vir and the power limit percentage are adjusted for the photovoltaic inverter K droop and the virtual inertia is injected J virtual The load shedding instruction is adjusted for the load controller. The equipment performs closed-loop control according to the parameters after feedback of the execution layer to optimize the equipment data until the optimal control strategy is obtained. When performing feedback closed loop, data verification and iterative optimization need to be performed. Specifically, the data verification is to compare the instruction value and the actual value by the decision layer and calculate the deviation; the iterative optimization is to adjust the weight coefficient of the MPC algorithm according to the deviation.

[0061] S102, whether the trigger function value is greater than zero is determined according to the multi-physical quantity coupling trigger function to determine whether the control action is triggered.

[0062] When the trigger function value is greater than zero, the control action is triggered, and step S103 is executed;

[0063] When the trigger function value is not greater than zero, the control action is not triggered, and step S104 is executed.

[0064] Specifically, the expression of the multi-physical quantity coupling trigger function is:

[0065] ;

[0066] In the formula, E E represents the trigger function value, dimensionless, used to determine whether the control action is triggered, when E>0, the control action is triggered; represents the load power change rate, unit: kW / s, representing the power mutation reported by the load controller, such as large load switching; represents the photovoltaic power change rate, representing the sudden drop of the photovoltaic inverter output, such as cloud shading;△ V represents the voltage deviation, unit: v, representing the difference between the voltage amplitude U rms monitored by the bus PMU and the rated value U n In this embodiment, U rms is calculated by the root mean square value of the three-phase voltage, U n =380v; represents the temperature change rate of the energy storage battery, unit: ℃ / s, representing the converter junction temperature change rate reported by the energy storage BMS, used for protecting the equipment from overheating; α 1, α 2, β , gammaare weight coefficients, respectively representing photovoltaic power weight factor, load power change rate weight factor, voltage deviation weight factor, dynamic temperature weight factor, and the typical value is α 1=0.8, α 2=0.7, β =0.5, gamma =0.2, represents a temperature influence coefficient, and the typical value is 0.15, which is used to amplify the influence of temperature change on triggering sensitivity.

[0067] Further, gamma The dynamic adjustment formula of is as follows:

[0068] gamma new = gamma 0﹒(1+ k SOC ﹒(1-SOC / 100)+ k priority ﹒ P load );

[0069] In the formula, gamma new represents a dynamic adjusted temperature weight factor triggering threshold, which is dimensionless; gamma 0 represents a reference triggering threshold, and the typical value is 1.2; k SOC represents an energy storage state of charge influence coefficient, and the typical value is 0.15, which improves triggering sensitivity when the energy storage SOC is low; the energy storage SOC represents an energy storage state of charge, and the unit is %, which is reported by the energy storage BMS and ranges from 0% to 100%; k priority represents a load priority influence coefficient, and the typical value is 0.2, which reduces the triggering threshold of high-priority load; P load represents a current system total load power, and the load priority label includes 1st-5th, wherein the 1st is the highest, which is reported by the load controller.

[0070] S103, optimizing the external characteristic parameters and load shedding instructions through the MPC algorithm, and generating a target control strategy according to the closed-loop mechanism iteration correction of the external characteristic parameters of the MPC algorithm, to cooperatively control the microgrid based on the target control strategy, wherein the external characteristic parameters include a frequency deviation weight coefficient, a THD weight coefficient and a control parameter, and the control parameter includes a dynamic droop rate, a virtual impedance and a virtual inertia.

[0071] Further, the calculation formula of the dynamic droop rate is as follows:

[0072] ;

[0073] K droop represents dynamic droop rate, unit Hz / kW, represents the slope of power frequency droop relationship; K 0 represents reference droop rate, typical value is 0.05 Hz / kW; k 1 represents load rate influence coefficient, typical value is 0.15, increases droop rate when high load; P load represents total load power of system at current moment, unit kW, reported by load controller; P max represents maximum allowed power of system, unit kW, determined by system design; k 2 represents temperature change rate influence coefficient, typical value is 0.1, reduces droop rate to protect equipment when temperature rises too fast; k 3 represents energy storage SOC influence coefficient, typical value is 0.1, reduces droop rate to reduce energy storage discharge when low energy storage SOC; represents temperature change rate of energy storage battery, unit ℃ / s, , wherein, T k represents temperature at current moment, T K-1 represents temperature at last moment, and △t represents sampling time interval.

[0074] Further, the calculation formula of virtual inertia is:

[0075] ;

[0076] J virtual represents virtual inertia, unit s, simulates rotational inertia of traditional generator; J 0 represents reference virtual inertia, typical value is 5 s; k 4 represents frequency change rate influence coefficient, typical value is 0.2, enhances inertia support when frequency changes violently; represents frequency change rate, unit Hz / s, monitored by bus PMU; k 5 represents photovoltaic penetration rate influence coefficient, typical value is 0.15, increases inertia to suppress frequency fluctuation when high penetration rate; P renew represents photovoltaic penetration rate, calculated from the proportion of photovoltaic inverter output power to total load, dimensionless, wherein, photovoltaic penetration rate P renew The calculation formula is:

[0077] ;

[0078] P PV represents photovoltaic output power; P demandThe total load power is the real-time data reported by the load controller.

[0079] Further, the calculation formula of the virtual impedance is:

[0080] ;

[0081] In the formula, Z vir ( s ) represents a virtual impedance frequency domain model, used to suppress wideband oscillation, with units of Ω; K p represents a proportional gain, with units of Ω, and a typical value of 0.4, which controls the impedance amplitude in the low frequency band; K d represents a differential gain, with units of Ω·s, and a typical value of 0.2, which suppresses high-frequency harmonics, such as noise near the switching frequency; K i represents an integral gain, with units of Ω / s, and a typical value of 0.1, which enhances the fundamental power distribution capability; f c represents a cut-off frequency, with units of rad / s, and is set to half of the switching frequency, for example, 2.5 kHz for a 5 kHz system; s represents a complex frequency variable, s = sigma + j omega , where: sigma is the real part, representing the attenuation coefficient; omega is the imaginary part, representing the angular frequency; j represents the imaginary unit, j 2 = -1.

[0082] Further, in the MPC algorithm, multi-objective optimization is achieved through a cost function, which is:

[0083] ;

[0084] In the formula, , represents a set of control input variables that need to be optimized, where, K droop represents a dynamic droop rate, J virtual represents a virtual inertia, K p represents a proportional gain, K d represents a differential gain, K i represents an integral gain, L oad_shedding represents load switching, by adjusting the parameters in u, so that the cost function JMinimize to achieve system multi-objective optimization, such as frequency stability, harmonic suppression, and efficiency improvement; represents the frequency deviation weight coefficient, prioritizes system frequency stability, dimensionless, typical value is 0.6, dynamically increases according to the frequency drop rate; represents the THD weight coefficient, suppresses harmonic distortion, dimensionless, typical value is 0.4, dynamically adjusts according to the harmonic content; Power loss weight coefficient, optimize system operation efficiency, dimensionless, typical value is 0.3, dynamically adjusted according to the energy storage SOC; lambda represents the energy storage SOC penalty term coefficient, protects the health of energy storage, dimensionless, typical value is 0.2, dynamically adjusted according to the energy storage SOC out-of-boundary degree; represents the frequency deviation prediction value at future k time, unit Hz; THD k represents the harmonic distortion rate prediction value at k time, unit %; k represents the discrete time index in the prediction time domain; SOC penalty represents the energy storage SOC out-of-boundary penalty term, dimensionless; N represents the prediction time domain (typical value is 5 sampling periods), dimensionless; P loss represents the system comprehensive power loss, unit kW, including energy storage PCS loss, photovoltaic inverter loss, and line transmission loss; J represents the system comprehensive performance index, by weighted calculation of frequency deviation, harmonic distortion rate, power loss and energy storage SOC out-of-boundary risk at future multiple times, generates the optimal control strategy; J The smaller the value, the better the system stability, power quality and equipment health status.

[0085] Further, in the system comprehensive power loss P loss The calculation formula of energy storage PCS loss is:

[0086] ;

[0087] P loss,PCS represents the energy storage PCS loss; I PCS represents the PCS current, unit A; R PCS represents the PCS resistance, unit Ω; f SW represents the switching frequency, unit Hz; C SW represents the single switching loss, unit W / Hz;

[0088] The calculation formula of photovoltaic inverter loss is:

[0089] ;

[0090] In the formula, represents the photovoltaic inverter efficiency; P PV represents the photovoltaic output power;

[0091] The calculation formula of line transmission loss is:

[0092] ;

[0093] In the formula, I line represents the line, unit A; R line represents the equivalent resistance of the line, unit Ω;

[0094] The calculation formula of system comprehensive power loss is:

[0095] P loss P loss,PCS + P loss,PV + P loss,line ;

[0096] In the formula, P loss,PCS represents the energy storage PCS loss; P loss,PV represents the photovoltaic inverter loss; P loss,line represents the line transmission loss.

[0097] S104, the system maintains the current state or the normal operation mode, and does not execute the MPC algorithm optimization.

[0098] The MPC algorithm is a model predictive control algorithm. In the model predictive control algorithm, a plurality of control targets are dynamically weighed by a cost function to generate a target control strategy. As a specific example, the core optimization logic of the cost function is as follows:

[0099] (1) Frequency stability priority: the weight coefficient of frequency deviation is initially set to 0.6, and when the system frequency drop rate exceeds the preset threshold (for example: -0.5 Hz / s), the weight coefficient will be dynamically increased to ensure that the frequency recovery speed is better than the traditional method.

[0100] ​(2) Harmonic Distortion Suppression: The weight coefficient of Total Harmonic Distortion (THD) is initially 0.4. When the harmonic content (e.g., 5th harmonic) monitored by the bus exceeds 3%, the weight coefficient is automatically increased to prioritize harmonic suppression.

[0101] (3) Operational Efficiency Optimization: The weight coefficient of power loss is initially 0.3 and is dynamically adjusted based on the State of Charge (SOC) of the energy storage. For example, when the energy storage SOC is below 20%, the weight is reduced to reduce energy storage device loss.

[0102] (4) Energy Storage Health Protection: The coefficient of the energy storage SOC out-of-bound penalty term is initially 0.2. When the energy storage SOC is out of bounds (e.g., below 20% or above 90%), the coefficient is dynamically increased to force protection of the energy storage device.

[0103] (5) Prediction and Feedback Logic:

[0104] Prediction Time Domain: The decision layer predicts the system state for the next 5 sampling periods (e.g., total time domain 50ms), including frequency deviation, harmonic distortion rate, power loss, and energy storage SOC out-of-bound degree.

[0105] (6) Discrete Time Index: The control parameters are optimized on a cycle-by-cycle basis to ensure real-time response to high-frequency dynamic scenarios (e.g., photovoltaic power second-level fluctuations).

[0106] (7) Closed-loop Correction: After the actual parameters (e.g., energy storage output power error) are returned by the execution layer, the decision layer recalculates the deviation and dynamically adjusts the weight coefficient (e.g., increases the frequency weight to increase the response priority), until the system meets the steady-state conditions (trigger function value ≤ 0, frequency deviation < 0.05 Hz, THD ≤ 3%, power balance error < 5kW).

[0107] As a specific example, the application scenarios of the microgrid control method include parameter adjustment rules when the load changes, parameter adjustment rules when photovoltaic power generation fluctuates, and parameter adjustment rules when the energy storage system fails. Specifically as follows:

[0108] First, the parameter adjustment rule when the load changes, which corresponds to the scenario includes: when the load suddenly increases (e.g., high-power equipment switching) or decreases (e.g., non-critical load shedding), the system may experience power imbalance, resulting in frequency drop or voltage deviation. The corresponding adjustment strategy is:

[0109] (1) Energy Storage PCS: Use MPC algorithm optimization to adjust the dynamic droop rate according to the load priority label K droopRelease emergency power, maintain system frequency stability. If the load suddenly increases and the energy storage SOC is high, increase the energy storage discharge power; if the load suddenly decreases and the energy storage SOC is low, reduce the energy storage charging power.

[0110] (2) Photovoltaic inverter: optimized by MPC algorithm, according to the frequency change rate and photovoltaic power limit operation or injection of virtual inertia J virtual , enhance the system frequency support ability.

[0111] (3) Load controller: optimized by MPC algorithm, according to the load priority label (1-5) and power gap, cut off non-critical load, and ensure the power supply of core equipment.

[0112] Secondly, the parameter adjustment rule when photovoltaic power generation capacity changes suddenly, its corresponding scene includes: photovoltaic output drops suddenly due to cloud cover or weather changes, for example, from 200kW to 50kW, which may cause system power shortage and rapid frequency drop. The corresponding adjustment strategy is:

[0113] (1) Energy storage PCS: optimized by MPC algorithm, according to the power change rate quickly adjust the droop rate K droop , and release emergency power to supplement the power gap.

[0114] (2) Photovoltaic inverter: optimized by MPC algorithm, dynamically adjust the output power upper limit, and inject virtual inertia J virtual , delay the frequency drop rate.

[0115] (3) Load controller: optimized by MPC algorithm, if the power gap is large, cut off part of the non-critical load according to the priority to maintain power balance.

[0116] Thirdly, the parameter adjustment rule when the energy storage system fails, its corresponding scene includes: when the energy storage system fails due to overheating, low energy storage SOC or hardware failure, the system may lose important power regulation means. The adjustment strategy is:

[0117] (1) Photovoltaic inverter: optimized by MPC algorithm, according to the remaining new energy output and load demand, dynamically adjust the photovoltaic power limit percentage and virtual inertia J virtual .

[0118] (2) Load controller: optimized by MPC algorithm, according to the load priority label, cut off part of the non-critical load to reduce the system power demand.

[0119] (3) Backup power supply: MPC algorithm is used to evaluate the power gap, and the backup power supply (such as diesel generator) is started to fill the gap.

[0120] The technical solution of the present application is illustrated below with a specific example:

[0121] Scenario background: The initial operating state of the microgrid system is: the output power of the photovoltaic inverter is 200kW, the DC side voltage is 600V (the DC voltage before the input inverter); the load power suddenly increases from 100kW to 250kW; the bus PMU monitors a voltage deviation of 6V (rated voltage 380V, current voltage 386V), and the frequency drop rate is 0.5Hz / s; the energy storage BMS reports that the energy storage SOC is 80% (after Kalman filtering), and the temperature change rate is 1.5℃ / s.

[0122] I. Trigger function calculation decision layer calculates the trigger function value according to the following parameters: load power change rate weight factor 0.7; voltage deviation weight factor 0.5; temperature influence weight factor 0.2; temperature influence coefficient 0.15. The calculation result is:

[0123] E=0.7×150+0.5×6-0.2×(1+0.15×1.5)=107.755>0

[0124] Determine the trigger control action, and start the MPC algorithm for multi-objective optimization. The multi-objective optimization process of the MPC algorithm is as follows:

[0125] (1) Frequency stability optimization: the goal is to control the frequency deviation within ±0.05Hz. Parameter adjustment: the dynamic droop rate of the energy storage PCS is calculated as 0.04975Hz / kW, and the emergency power of 150kW is released to offset the load surge; the photovoltaic inverter injects virtual inertia of 5.1s (baseline 5s + frequency change rate correction), and the frequency drop rate is slowed down to 0.2Hz / s (original 0.5Hz / s).

[0126] (2) Harmonic suppression optimization: the goal is to suppress the total harmonic distortion (THD) to below 3%. Parameter adjustment: the photovoltaic inverter adjusts the virtual impedance model (proportional gain 0.4Ω, differential gain 0.2Ω·s), and the 5th harmonic content is reduced from 5.8% to 2.1%.

[0127] (3) Operation efficiency optimization: the goal is to reduce the system's comprehensive power loss (including energy storage PCS, photovoltaic inverter, and line loss). Parameter adjustment: the photovoltaic inverter limits the power to 160kW (original 200kW), reducing the inverter switching loss; the power loss weight coefficient of the energy storage PCS is dynamically adjusted to 0.3 (original 0.2), optimizing the charging and discharging efficiency.

[0128] (4) Energy storage health protection: The target is to maintain the energy storage SOC in the safe interval of 20%-90%. Parameter adjustment: Introduce the energy storage SOC influence coefficient (0.1) in the dynamic droop rate formula to avoid over-discharge of the energy storage (current energy storage SOC=80%, the energy storage SOC drops to 75% after releasing power).

[0129] II. Execution layer control action:

[0130] (1) Energy storage PCS: According to the optimized droop rate 0.04975 Hz / kW, release emergency power 150 kW, and the system frequency returns to 50.02 Hz (deviation 0.02 Hz).

[0131] (2) Photovoltaic inverter: limit power to 160 kW, inject virtual inertia 5.1 s, and adjust the virtual impedance parameters (0.4 Ω + 0.2 Ω·s), and the 5th harmonic content drops from 5.8% to 2.1%.

[0132] (3) Load controller: cut off 5-level non-critical load 50 kW, and the system power supply-demand error is reduced from 50 kW to 2 kW (energy storage actual output 148 kW vs. instruction 150 kW).

[0133] (4) Feedback closed loop and dynamic correction: the decision layer continuously optimizes,

[0134] Deviation analysis: The deviation between the actual output power of the energy storage and the instruction is 2 kW, which determines that the priority of the load response needs to be improved.

[0135] Weight correction: increase the load power change rate weight factor in the MPC algorithm (from 0.7 to 0.75), strengthen the response ability of subsequent control to load mutation; adjust the power loss weight coefficient (from 0.3 to 0.25), balance efficiency and frequency stability.

[0136] In summary, the microgrid control method in the above embodiments of the application realizes efficient, stable and collaborative control of the microgrid in complex dynamic scenarios through deep integration of dynamic event triggering and virtual external characteristic adaptation. Specifically, the photovoltaic inverter and the load controller participate in data acquisition and control execution at the same time, realizing integrated sensing control; the photovoltaic penetration rate is obtained according to the photovoltaic output power and the load power, and the photovoltaic penetration rate is dynamically related to the dynamic droop rate, virtual inertia, energy storage SOC, and temperature change rate, which is used for dynamic parameter adaptation; further, the photovoltaic inverter injects frequency domain impedance to solve the harmonic problem of new energy grid connection; by dynamically adjusting the external characteristic parameters of each device, the collaborative optimization of multi-power, multi-load and energy storage system is realized, ensuring the stable operation of the microgrid.

[0137] Example two

[0138] The second embodiment of the present application provides a micro-grid control system, comprising:

[0139] An acquisition module is configured to acquire multi-source data of a device, the device comprising a photovoltaic inverter, an energy storage BMS, a load controller and a bus PMU, the multi-source data comprising photovoltaic output power, load power change rate, bus voltage deviation, frequency change rate, energy storage SOC and temperature change rate, and a trigger function value is calculated according to the multi-source data;

[0140] A judgment module is configured to dynamically judge whether the trigger function value is greater than zero according to the multi-physical quantity coupling trigger function to judge whether to trigger a control action;

[0141] A control module is configured to trigger a control action when the trigger function value is greater than zero, then optimize external characteristic parameters and load shedding instructions through an MPC algorithm, and generate a target control strategy according to an iterative correction of the external characteristic parameters of the MPC algorithm according to a closed-loop mechanism, so as to cooperatively control the micro-grid based on the target control strategy, the external characteristic parameters comprising a frequency deviation weight coefficient, a THD weight coefficient and a control parameter, and the control parameter comprising a dynamic droop rate, a virtual impedance and a virtual inertia.

[0142] In addition, an embodiment of the present application further provides a computer readable storage medium, which has a computer program stored thereon, and the program is executed by a processor to realize the steps of the method in the above embodiment.

[0143] In addition, an embodiment of the present application further provides a data processing device, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor realizes the steps of the method in the above embodiment when executing the program.

[0144] The logic and / or steps represented in the flowchart and / or otherwise described herein, for example, can be considered a list of executable instructions for implementing logic functions, and can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor- based system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions, or a combination of both. For the purposes of this specification, a "computer-readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

[0145] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can also be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example, via an optical scanner, then compiled, interpreted, or otherwise processed, using suitable methods, before being stored in a computer memory.

[0146] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the embodiments described above, various steps or methods can be implemented, for example, in software or firmware that is stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, and in another embodiment, any of the following techniques, which are well known in the art, can be used to implement the application: a hybrid of the techniques mentioned above; a combination of one or more of the techniques mentioned above; or one or more other techniques suitable for use in the computer-based systems described above.

[0147] In the description of the specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" and the like means that the particular feature, structure, material or characteristic being described is included in at least one embodiment or example of the application. The illustrative descriptions of such terms in this specification are not necessarily referring to the same embodiment or example. Furthermore, the described particular features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0148] Although embodiments of the application have been shown and described, it is to be understood that various modifications, substitutions, combinations and variations can be made to these embodiments by those skilled in the art without departing from the spirit and scope of the application, which is defined by the appended claims and their equivalents.

Claims

1. A microgrid control method, characterized by, The method comprises the following steps: acquiring multi-source data of a device, the device comprising a photovoltaic inverter, a storage BMS, a load controller and a bus PMU, the multi-source data comprising photovoltaic output power, load power change rate, bus voltage deviation, frequency change rate, storage SOC and temperature change rate, and calculating a trigger function value according to the multi-source data; dynamically judging whether the trigger function value is greater than zero according to a multi-physical quantity coupling trigger function to judge whether to trigger a control action; when the trigger function value is greater than zero, triggering the control action, then optimizing external characteristic parameters and load shedding instructions through an MPC algorithm, and generating a target control strategy according to a closed-loop mechanism to iteratively correct the external characteristic parameters of the MPC algorithm to cooperatively control a microgrid based on the target control strategy, the external characteristic parameters comprising a frequency deviation weight coefficient, a THD weight coefficient and a control parameter, the control parameter comprising a dynamic droop rate, a virtual impedance and a virtual inertia; wherein an expression of the multi-physical quantity coupling trigger function is: ； Where, E Indicates the trigger function value, which is used to determine whether to trigger the control action. When E>0, the control action is triggered; Indicates the load power change rate; Indicates the photovoltaic power change rate; V Indicates voltage deviation; Indicates the temperature change rate of the energy storage battery; α 1, α 2, β , γ are weight coefficients, representing the photovoltaic power weight factor, load power change rate weight factor, voltage deviation weight factor, and dynamic temperature weight factor respectively; represents the temperature influence coefficient; In the MPC algorithm, multi-objective optimization is realized through a cost function, and the cost function is: ； In the formula, , represents the set of control input variables that need to be optimized, wherein, K droop represents the dynamic droop rate, J virtual represents the virtual inertia, K p represents the proportional gain, K d represents the derivative gain, K i represents the integral gain, L oad_shedding represents the load switching; represents the frequency deviation weight coefficient; represents the THD weight coefficient; power loss weight coefficient; λ represents the energy storage SOC penalty term coefficient; represents the future k-time frequency deviation prediction value; THD k represents the k-time harmonic distortion rate prediction value; k represents the discrete time index within the prediction time domain; SOC penalty represents the energy storage SOC out-of-range penalty term; N represents the prediction time domain; P loss represents the system comprehensive power loss, including the energy storage PCS loss, photovoltaic inverter loss, and line transmission loss; J represents the system comprehensive performance index.

2. The microgrid control method of claim 1, wherein, γ The dynamic adjustment formula is: γ new γ 0. (1 + 0.5 * (SOC / 100)) * (1 - SOC / 100) k SOC 0. (1 + 0.5 * (SOC / 100)) * (1 - SOC / 100) k priority 0. (1 + 0.5 * (SOC / 100)) * (1 - SOC / 100) P load 0. (1 + 0.5 * (SOC / 100)) * ( In the formula, The calculation formula of the dynamic droop rate is: new represents a temperature weight factor trigger threshold after dynamic adjustment; The calculation formula of the virtual impedance is: 0 represents a reference trigger threshold; k SOC represents an energy storage state of charge influence coefficient; the energy storage SOC represents an energy storage state of charge; k priority represents a load priority influence coefficient; P load represents the total load power of the system at the current moment.

3. The microgrid control method of claim 1, wherein, The calculation formula of the virtual inertia is: ； K droop denotes a dynamic droop rate, representing the slope of the power-frequency droop relationship; K 0 denotes a reference droop rate; k 1 denotes a load rate influence coefficient; P load denotes the total load power of the system at the current moment; P max denotes the maximum allowable power of the system; k 2 denotes a temperature change rate influence coefficient; k 3 denotes a storage SOC influence coefficient; denotes the temperature change rate of the storage battery.

4. The microgrid control method of claim 1, wherein, σ ； J virtual represents a virtual inertia; J 0 represents a reference virtual inertia; k 4 represents a frequency variation rate influence coefficient; represents a frequency variation rate; k 5 represents a photovoltaic penetration rate influence coefficient; P renew represents a photovoltaic penetration rate, and the calculation formula is: ； P PV Photovoltaic output power is represented; P demand System total load power, real-time data reported by load controller.

5. The microgrid control method of claim 1, wherein, jω ； where Z vir s represents a virtual impedance frequency domain model; K p represents a proportional gain; K d represents a derivative gain; K i represents an integral gain; f c represents a cut-off frequency; s represents a complex frequency variable, s σ ω where: The calculation formula of photovoltaic inverter loss is: is a real part representing a damping coefficient; The calculation formula of line transmission loss is: is an imaginary part representing an angular frequency; j represents an imaginary unit, j 2 = -1; f represents a frequency.​​​ 6. The microgrid control method of claim 1, wherein, The overall power loss in the system P loss In the calculation formula of energy storage PCS loss, the formula is: ； P loss,PCS represents the energy storage PCS losses; I PCS represents the PCS current; R PCS represents the PCS resistance; f SW represents the switching frequency; C SW represents the single switching loss; The calculation formula of system comprehensive power loss is: ； wherein represents the photovoltaic inverter efficiency; P PV represents the photovoltaic output power; The system comprises: ； wherein I line represents a line; R line represents a line equivalent resistance; an acquiring module, configured to acquire multi-source data of a device, the device comprising a photovoltaic inverter, a storage BMS, a load controller and a bus PMU, the multi-source data comprising photovoltaic output power, load power change rate, bus voltage deviation, frequency change rate, storage SOC and temperature change rate, and calculate a trigger function value according to the multi-source data; P loss = P loss,PCS + P loss,PV + P loss,line ； wherein P loss,PCS represents the energy storage PCS losses; P loss,PV represents the photovoltaic inverter losses; P loss,line represents the line transmission losses.

7. A microgrid control system, characterized in that: a judging module, configured to dynamically judge whether the trigger function value is greater than zero according to a multi-physical quantity coupling trigger function to judge whether to trigger a control action; a control module, configured to, when the trigger function value is greater than zero, trigger the control action, then optimize external characteristic parameters and load shedding instructions through an MPC algorithm, and generate a target control strategy according to a closed-loop mechanism to iteratively correct the external characteristic parameters of the MPC algorithm to cooperatively control a microgrid based on the target control strategy, the external characteristic parameters comprising a frequency deviation weight coefficient, a THD weight coefficient and a control parameter, the control parameter comprising a dynamic droop rate, a virtual impedance and a virtual inertia; wherein an expression of the multi-physical quantity coupling trigger function is: γ In the MPC algorithm, multi-objective optimization is realized through a cost function, and the cost function is: ； In the formula, E represents the trigger function value, used to determine whether to trigger the control action, when E>0, the control action is triggered; represents the load power change rate; represents the photovoltaic power change rate; V represents the voltage deviation; represents the temperature change rate of the energy storage battery; α 1, α 2, β , λ is a weight coefficient, respectively representing the photovoltaic power weight factor, the load power change rate weight factor, the voltage deviation weight factor, and the dynamic temperature weight factor; represents the temperature influence coefficient; THD ； In the formula, , represents a set of control input variables that need to be optimized, wherein, K droop represents a dynamic droop rate, J virtual represents a virtual inertia, K p represents a proportional gain, K d represents a derivative gain, K i represents an integral gain, L oad_shedding represents a load switching; represents a frequency deviation weight coefficient; represents a THD weight coefficient; a power loss weight coefficient; SOC represents a storage SOC penalty term coefficient; represents a future k-time frequency deviation prediction value; The processor realizes the microgrid control method according to any one of claims 1-6 when executing the program. k represents a k-time harmonic distortion rate prediction value; k represents a discrete time index within a prediction time domain; ​ penalty represents a storage SOC out-of-range penalty term; N represents a prediction time domain; P loss represents a system comprehensive power loss, including a storage PCS loss, a photovoltaic inverter loss, and a line transmission loss; J represents a system comprehensive performance index.

8. A data processing device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, ​

Images