A microgrid cluster power cooperative scheduling method and device

Abstract

This invention discloses a microgrid group power collaborative scheduling method and device, relating to the field of data encryption technology. The method includes: constructing a spatiotemporal coordinate framework on a regional master node; obtaining a configuration file by importing historical power grid operation data packets and inverter parameters; calculating impedance values ​​suitable for the current power grid state based on the power grid's frequency deviation, cost changes, and voltage fluctuations, and constructing an impedance matrix containing adjustment instructions; inputting the impedance matrix into a quantum-eagle swarm algorithm for global search to generate an optimized scheduling plan curve; formulating a preliminary scheduling plan based on the scheduling plan curve; performing local optimization adjustments based on real-time monitored power grid operation deviations; outputting a three-level scheduling instruction set; and generating an execution instruction package after all scheduling instructions have undergone feasibility verification; receiving the execution instruction package at an edge gateway and converting it into control instructions; inputting the control instructions into a multiphysics simulation model and outputting a simulation result dataset.

Description

Technical Field

[0001] This invention relates to the field of data encryption technology, and in particular to a microgrid group power collaborative scheduling method and apparatus. Background Technology

[0002] With the increasing penetration of renewable energy and the large-scale development of microgrids, existing technologies have gradually formed three major directions: centralized, distributed, and decentralized collaborative control. Centralized control achieves global optimization through a central processor, but it has the risk of single point of failure and high communication bandwidth dependence. Distributed control adopts the information interaction mechanism between adjacent nodes, which reduces the dependence on the central node, but it is limited by communication latency and algorithm convergence speed, making it difficult to cope with the heterogeneity and dynamic coupling characteristics of multiple subnets. Decentralized control achieves plug-and-play through drooping characteristics and virtual synchronizers, but its power allocation mechanism is limited to local autonomy and lacks cross-subnet collaborative capabilities. In recent years, although multi-agent algorithms based on reinforcement learning have improved dynamic response capabilities, they still need improvement in multi-timescale optimization and transient stability.

[0003] In existing technologies, existing mutual control relies heavily on preset power transmission thresholds, which makes it difficult to take into account the adaptive switching requirements of subnet operation scenarios and limits the efficient utilization of the spatiotemporal coupling characteristics of microgrids; therefore, it is necessary to design a new solution to meet actual needs. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a microgrid power collaborative scheduling method to solve the problem that existing mutual control relies heavily on preset power transmission thresholds, making it difficult to take into account the adaptive switching requirements of subgrid operation scenarios and limiting the efficient utilization of the spatiotemporal coupling characteristics of microgrids.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a microgrid group power cooperative scheduling method, comprising:

[0008] A spatiotemporal coordinate framework is constructed on the regional master control node. The configuration file is obtained by importing historical power grid operation data packages and inverter parameters, and the topology constraint matrix and initialization parameter package are output.

[0009] According to the configuration file, an adaptive algorithm is deployed in the inverter to calculate the impedance value suitable for the current grid condition based on the grid frequency deviation, cost changes and voltage fluctuations, and to construct an impedance matrix containing adjustment instructions.

[0010] The impedance matrix is ​​input into the quantum-eagle swarm algorithm for global search to generate an optimized scheduling plan curve. A preliminary scheduling plan is formulated based on the scheduling plan curve. Local optimization and adjustment are performed based on the real-time monitored power grid operation deviations. A three-level scheduling instruction set is output. After all scheduling instructions are verified for feasibility, an execution instruction package is generated.

[0011] The execution command packets are transmitted in segments through the 5G-TSN network, and the integrity of the execution command packets is verified by blockchain nodes. The edge gateway receives the execution command packets and converts them into control commands according to different device protocols.

[0012] The control commands are input into the multiphysics simulation model to simulate the power operation environment and output a simulation result dataset. A risk warning report is generated based on the simulation result dataset obtained from the test, and the three-level dispatch command set is operated in a hierarchical manner based on the risk warning report.

[0013] As a preferred embodiment of the microgrid group power collaborative scheduling method of the present invention, the historical operation data packet of the power grid includes load demand records, power generation records, meteorological parameter records, electricity price records, and power grid status records;

[0014] The inverter parameters include rated power, efficiency curve, and response time.

[0015] As a preferred embodiment of the microgrid group power collaborative scheduling method of the present invention, the specific steps for outputting the topology constraint matrix and initializing the parameter package are as follows:

[0016] The system processes load demand records to generate a maximum active load mapping table, generates a photovoltaic output mapping table based on photovoltaic output and irradiance, generates a wind power output mapping table based on wind power output and wind speed range, generates a node voltage extreme value table by statistically analyzing historical extreme values ​​of node voltage, and constructs a topology constraint matrix based on the power grid topology connection diagram and tie line capacity list.

[0017] The new energy priority ranking table, voltage threshold and inverter parameters are integrated to generate an encrypted configuration file, which is then packaged into an initialization parameter package.

[0018] As a preferred embodiment of the microgrid group power collaborative scheduling method of the present invention, the calculation of the impedance value suitable for the current grid state specifically includes:

[0019] Continuously monitor node frequency values ​​to identify frequency deviations, analyze time-period electricity price differences to obtain cost change trends, and compare voltage record data to determine voltage fluctuation ranges.

[0020] Based on frequency deviation, cost changes, and voltage fluctuation data, an adaptive algorithm is applied to calculate the optimal impedance value, and combined with the inverter's rated power and efficiency curves, an operating mode adapted to the current grid conditions is generated.

[0021] As a preferred embodiment of the microgrid group power cooperative scheduling method of the present invention, the specific steps of constructing the impedance matrix containing adjustment instructions are as follows:

[0022] An empty matrix corresponding to the number of inverters is constructed. The optimal impedance value of each inverter is filled into the matrix according to the rule that the row corresponds to the inverter number and the column corresponds to the grid state variable. This ensures the capacity equalization of symmetrically connected lines and forms an impedance matrix that reflects multi-dimensional impedance adjustment commands.

[0023] As a preferred embodiment of the microgrid group power collaborative scheduling method of the present invention, the preliminary scheduling plan is specifically as follows:

[0024] Impedance parameter combinations are obtained through global search using the quantum-eagle swarm algorithm, and these parameter combinations are mapped to time series to generate scheduling plan curves.

[0025] The time window is divided according to natural days, and the scheduling plan curve is divided into time series instructions at the hourly granularity. The target power value, timestamp and node ID of each inverter are integrated to form a preliminary scheduling plan.

[0026] As a preferred embodiment of the microgrid group power collaborative scheduling method of the present invention, the specific steps of outputting the three-level scheduling instruction set are as follows:

[0027] The preliminary scheduling plan is divided into day-ahead layer instructions, real-time layer instructions, and emergency layer instructions according to time window markings;

[0028] Based on the multiphysics simulation model, predictive control (MPC) generates power adjustment strategies for each level;

[0029] The initially generated scheduling instruction set is verified for tie-line capacity, voltage threshold, and inverter parameters. Once the verification is successful, a three-level scheduling instruction set is generated.

[0030] Secondly, the present invention provides a microgrid group power collaborative scheduling device, comprising: a data processing module, an algorithm deployment module, a scheduling optimization module, a simulation early warning module, and an execution feedback module.

[0031] The data processing module is responsible for importing and processing historical power grid operation data and inverter parameters, and generating a spatiotemporal coordinate frame, a topological constraint matrix and an initialization parameter package.

[0032] The algorithm deployment module is responsible for deploying the adaptive algorithm in the inverter, calculating the impedance value according to the grid status, and generating an impedance matrix containing adjustment instructions.

[0033] The scheduling optimization module is responsible for using the quantum-eagle swarm algorithm to perform a global search, generate an optimized scheduling plan curve, and decompose it into a three-level scheduling instruction set to ensure that all instructions meet the system's safety and performance requirements.

[0034] The simulation early warning module is responsible for inputting control commands into the multiphysics simulation model, simulating the power operation environment, assessing risks, and generating risk early warning reports.

[0035] The execution feedback module is responsible for transmitting execution instruction packets through the 5G-TSN network, which are then converted into device control instructions by the edge gateway. It also monitors the power grid status in real time and dynamically adjusts the power grid operation based on the feedback information.

[0036] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the microgrid group power cooperative scheduling method as described in the first aspect of the present invention.

[0037] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the microgrid group power cooperative scheduling method as described in the first aspect of the present invention.

[0038] The beneficial effects of this invention are as follows: This invention integrates historical power grid data and inverter parameters by constructing a spatiotemporal coordinate framework, generating a topology constraint matrix and encrypted parameter packages, realizing standardized modeling of multi-source data, providing a precise spatiotemporal benchmark for scheduling, dynamically calculating impedance values ​​through an adaptive algorithm, constructing a multi-dimensional matrix, improving the ability to coordinate optimization of frequency, voltage, and cost, using the quantum-eagle swarm algorithm to globally generate three-layer instruction sets (day-ahead, real-time, and emergency), and combining MPC to achieve multi-timescale optimization and anti-disturbance compensation, ensuring the real-time performance and tamper-proof nature of instructions through 5G-TSN sharding transmission and blockchain verification, adapting to heterogeneous equipment execution, predicting voltage, temperature, and mechanical stress risks based on multi-physics field coupling simulation, dynamically classifying and correcting strategies, realizing safe, economical, and stable coordinated scheduling of microgrids, and improving the consumption of new energy. Attached Figure Description

[0039] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a flowchart of a microgrid group power collaborative scheduling method.

[0041] Figure 2 The flowchart for initializing the parameter package.

[0042] Figure 3 A schematic diagram for calculating impedance values.

[0043] Figure 4 A schematic diagram for the initial scheduling plan. Detailed Implementation

[0044] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0045] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0046] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0047] Reference Figures 1 to 4 This is one embodiment of the present invention, which provides a microgrid group power cooperative scheduling method, including the following steps:

[0048] S1. Construct a spatiotemporal coordinate framework on the regional master control node, obtain the configuration file by importing the historical operation data package of the power grid and the inverter parameters, and output the topology constraint matrix and the initialization parameter package.

[0049] The power grid historical operation data package includes load demand records, power generation records, meteorological parameter records, electricity price records, and power grid status records. Further load demand records refer to timestamps, node IDs, and active load values; power generation records refer to wind power output values; meteorological parameter records refer to solar intensity and wind speed; electricity price records refer to time periods and electricity prices; power grid status records refer to node frequency values; and inverter parameters include rated power, efficiency curves, and response time.

[0050] For load demand records, timestamps from different sources are uniformly converted to a standard time format and stored by node ID, generating a dataset indexed by node ID. For each node ID, a time window is divided by natural day, and the maximum active power load value within each natural day is identified. The maximum active power load value within each natural day is organized into a structured maximum active power load mapping table. Photovoltaic output data and meteorological parameter records are resampled at the same time granularity, and missing values ​​are filled by interpolation. Using the photovoltaic output timestamp as a benchmark, solar irradiance data within the same time period is matched with meteorological parameter records, allowing for slight time offsets. Valid data segments with solar irradiance exceeding the start-up threshold are selected. The start-up threshold is set according to user requirements. The average output value corresponding to the solar irradiance interval is statistically calculated. The solar irradiance interval, average output value, and number of data points are integrated to obtain the photovoltaic output. Mapping table; invalid output data with wind speeds lower than the turbine's cut-in speed or higher than its cut-out speed are removed. Wind power output and wind speed are aligned with the same time granularity. Linear interpolation is used to fill in missing time periods. The system is segmented by wind speed intervals, with each interval defined as 1 m / s. The average output value within each wind speed interval is calculated, and the standard deviation of each interval is calculated as the fluctuation range. The wind speed intervals, average output values, and fluctuation ranges are integrated to generate a wind power output mapping table. The system is grouped by node ID, and the historical maximum and minimum voltage values ​​are calculated for each node. Only voltage records within the rated frequency ± deviation tolerance are retained. Voltage records include timestamps, node IDs, instantaneous voltage values, and instantaneous frequency values. The voltage extreme values ​​of each node are stored by node ID. The node IDs, historical maximum voltage values, and historical minimum voltage values ​​are integrated to generate a node voltage extreme value table.

[0051] The power grid topology connection diagram is obtained by outputting the power grid planning and design drawings in CAD format. Based on the maximum active power load mapping table and the power grid topology connection diagram, a spatial axis is set. A fixed proportion of the historical maximum active power load value of each node is taken as the X-axis coordinate of the spatial axis. The average power factor is calculated based on the historical load data, and the reactive power corresponding to the average power factor is taken as the Y-axis coordinate of the spatial axis. The node level is defined according to the power grid topology connection depth. The formula for calculating the average power factor is as follows:

[0052]

[0053] Where P' represents the ratio of active power to apparent power of the current node, i.e., the average power factor, the value of P' ranges from 0 to 1, P represents the active power of the current node, and Q represents the reactive power of the current node.

[0054] Taking the moving average of the most recent 30 days as a typical value, the formula for calculating reactive power when the active power is known is:

[0055] Q = P × tan(arccos(P'));

[0056] Where tan represents the tangent function, and arccos(P') represents the power factor angle, which is the result of the inverse cosine function, in radians;

[0057] The timeline is set as follows: day-ahead layer, real-time layer, and emergency layer. The day-ahead layer has a fixed 24-hour cycle, with each node marked with a time stamp and time slices divided by hour. The real-time layer uses a rolling time window, updating at the minute level. The emergency layer uses an instantaneous response window with no fixed time division. The highest power consumption record of each device in the past is found, and a certain proportion is used as the X-axis coordinate. Another certain proportion of X is used as the Y-axis coordinate. Devices connected to the main power grid are used as the Z-axis coordinate. Devices directly connected to the main power grid have Z=0, secondary connected devices have Z=1, and more distant devices have Z=2. The device ID, node coordinates (X-axis coordinate, Y-axis coordinate, Z-axis coordinate), and time window mark are integrated and output to generate a spatiotemporal coordinate mapping table. The time window mark refers to the planned time, real-time time, and emergency status corresponding to the day-ahead layer, real-time layer, and emergency layer, respectively. The emergency status includes standby and working.

[0058] The tie line capacity is calculated based on the historical transmission power in the grid status record and the rated power in the inverter parameters. The maximum transmission power value of each tie line in a certain period of the past is counted and the historical maximum transmission power value is taken as the capacity benchmark. According to the rated power of the inverter, the tie line capacity is limited to not exceed the safe carrying capacity of the inverter, thus obtaining the tie line capacity list. The tie line capacity list includes line ID, starting node, ending node and maximum allowable transmission power.

[0059] The new energy priority ranking is specifically as follows: based on the photovoltaic power output mapping table and the wind power output mapping table, the historical maximum photovoltaic power output value and the historical maximum wind power output value in the two mapping tables are selected. The historical maximum photovoltaic power output value or wind power output value of a certain node is divided by the sum of the historical maximum power output values ​​of all nodes to obtain the theoretical output ratio of that node. Based on the theoretical output ratio and combined with the unit electricity subsidy policy, the comprehensive priority score of each node is calculated according to the weighted formula. The comprehensive priority scores are arranged from high to low to obtain the new energy priority ranking table. The new energy priority ranking table includes node ID, type, priority score and ranking.

[0060] The voltage threshold is set based on the node voltage extreme value table. The upper limit is taken as the highest quantile of the historical maximum voltage value in the node voltage extreme value table, and the lower limit is taken as the lowest quantile of the historical minimum voltage value. If the upper and lower voltage limits exceed the voltage fluctuation range allowed by the power grid, the voltage is forcibly truncated to the boundary value.

[0061] Based on the tie line capacity list and the power grid topology connection diagram, create a square matrix with the same number of nodes. Set the initial element values ​​to default values. Traverse the tie line capacity list and fill in the maximum allowable transmission power value at the corresponding position in the square matrix to ensure that the capacity values ​​at both ends are equal. Then, perform capacity equalization processing on the asymmetric connection lines to form a square matrix that reflects the tie line capacity relationship between each node, i.e., the topology constraint matrix, where the rows and columns correspond to the node IDs and the element values ​​are the tie line capacities.

[0062] The new energy priority ranking table, voltage threshold, and inverter parameters are integrated into a configuration file in a unified format. The configuration file is then encrypted using the AES symmetric encryption algorithm, and a data integrity check code is added. All configuration files are then packaged into a binary data file to obtain the initialization parameter package.

[0063] S2. Deploy an adaptive algorithm in the inverter according to the configuration file, calculate the impedance value suitable for the current grid state based on the grid frequency deviation, cost changes and voltage fluctuations, and construct an impedance matrix containing adjustment instructions.

[0064] Extract inverter parameters from the configuration file. Inverter parameters include rated power, efficiency curve, and response time.

[0065] Based on the node frequency values, time periods, and electricity prices in the historical power grid operation data packets, the frequency value of each node is continuously monitored. The current frequency value is compared with the rated frequency, and the difference is the frequency deviation. When the frequency value deviates from the predetermined range (±0.2Hz in this embodiment), it is considered that there is a frequency deviation. By comparing the differences in electricity prices at different time periods, the cost change trend can be analyzed. The historical maximum and minimum voltage values ​​of each node are taken as the normal range. The normal range is compared with the current voltage value to identify voltage fluctuation data that exceeds the normal range.

[0066] The obtained power grid frequency deviation, cost change and voltage fluctuation data are used to calculate using an adaptive algorithm. The adaptive algorithm adjusts the internal model according to these input parameters to find an optimal impedance value so that under the current power grid conditions, it can maintain stability and optimize economic benefits.

[0067] By combining the optimal impedance value with the inverter's rated power and efficiency curves, the best operating mode suitable for each inverter is determined, and corresponding adjustment instructions are generated for each inverter. Each adjustment instruction contains specific impedance setting suggestions.

[0068] Construct a matrix equal to the number of inverters, integrate the adjustment commands of all inverters, and fill in the corresponding values ​​according to the adjustment command of each inverter to obtain the impedance matrix. In the impedance matrix, each row represents an inverter, and each column corresponds to a different grid state variable, which refers to frequency, cost, and voltage. The elements in the impedance matrix are the impedance adjustment commands for a specific inverter and a specific grid state variable.

[0069] S3. Input the impedance matrix into the quantum-eagle swarm algorithm to perform a global search to generate an optimized scheduling plan curve. Formulate a preliminary scheduling plan based on the scheduling plan curve. Perform local optimization and adjustment based on the real-time monitored power grid operation deviation. Output a three-level scheduling instruction set. After all scheduling instructions have been verified for feasibility, generate an execution instruction package.

[0070] The impedance matrix is ​​input into the Quantum-Eagle Swarm Optimization (QPSO) algorithm. The QPSO extracts impedance adjustment commands for all inverters from the impedance matrix. These commands include impedance values ​​corresponding to frequency, cost, and voltage. It also initializes the initial position and velocity parameters of the particle swarm. Each particle represents a combination of inverter impedance parameters. Grid constraints are set based on tie-line capacity, voltage threshold, and inverter parameter limitations in the topology constraint matrix. The particle positions are dynamically adjusted using the contraction / expansion factor of the QPSO algorithm. The particle position update formula is as follows:

[0071]

[0072] α(t)=α0+(α1-α0)`(1-t / T);

[0073] in, This represents the position of the i-th particle in dimension d at time t+1. This represents the average value of the best historical positions of all particles at time t in dimension d. Let α(t) represent the average of the best positions in the history of all particles, α(t) represent the contraction-expansion factor at time t, α0 represent the initial contraction-expansion factor value, α1 represent the final contraction-expansion factor value, T represent the total number of iterations, u(t) and r(t) represent the uniformly distributed random numbers in the interval [0,1] at time t, and ln represent the natural logarithm function.

[0074] Based on the global search of QPSO, the particle position is further optimized by combining the random search of the eagle swarm algorithm to avoid getting trapped in local optima. After each iteration, it is checked whether the particle position meets the grid constraints. If not, the iteration is restarted until the total number of iterations and the grid constraints are met. The iteration stops when the optimal particle's impedance parameter combination is output. The impedance parameter combination is mapped to the time series to generate the scheduling plan curve. The scheduling plan curve specifically describes the power output target value of each inverter at different time points.

[0075] A preliminary scheduling plan is formulated based on the scheduling plan curve. The scheduling plan curve is divided into time-series instructions by time slices. The target power values ​​of each inverter are organized into a preliminary scheduling plan by timestamp and node ID. The preliminary scheduling plan must simultaneously meet the tie-line capacity limit and voltage threshold constraint in the topology constraint matrix, as well as the rated power and response time requirements in the inverter parameters.

[0076] The system monitors the node frequency values, instantaneous voltage values, and time-period electricity price data in the real-time power grid status records and price records. It calculates the current power grid frequency deviation, voltage fluctuation amplitude, and cost change trend. Frequency deviation refers to the difference between the current node frequency value and the rated frequency. Voltage fluctuation amplitude refers to the difference between the current voltage value and the historical voltage extreme value. Cost change trend refers to the difference between the current time-period electricity price and the historical electricity price. The frequency deviation, voltage fluctuation amplitude, and cost change trend are compared with the corresponding target values ​​in the preliminary dispatch plan. The inverter output is adjusted according to the comparison error to obtain an optimized dispatch plan. The specific adjustment of the inverter output according to the comparison error is as follows: if the frequency deviation is large, the inverter output power is reduced through PID control; if the voltage is low, the voltage is increased through reactive power compensation or adjustment of inverter impedance; if the cost increases, high-priority photovoltaic nodes are prioritized.

[0077] Based on the optimized scheduling plan, Model Predictive Control (MPC) is used to generate a three-level scheduling instruction set. This three-level instruction set is then divided into day-ahead, real-time, and emergency levels according to time window markings. Specifically:

[0078] Day-ahead instructions refer to time slices divided into 24-hour cycles, which integrate the target power value, timestamp, and node ID of each inverter at each hour to form day-ahead scheduling instructions.

[0079] Real-time layer instructions refer to updates on a minute-by-minute rolling time window, including the power adjustment increment value of each inverter from the current moment to the next minute;

[0080] Emergency layer instructions refer to the generation of immediate power adjustment instructions in response to emergency situations where the power grid frequency or voltage exceeds the safe range. The immediate power adjustment instructions include triggering conditions, target power correction values, and execution priorities.

[0081] Feasibility verification of the three-level dispatch instruction set is performed. Specifically, the day-ahead layer instructions, real-time layer instructions, and emergency layer instructions are verified against the topology constraint matrix, voltage threshold, and inverter parameters to ensure that all dispatch instructions meet the tie-line capacity limit, voltage fluctuation range requirements, and inverter safe operation boundary. After the verification is passed, the instruction set is encapsulated into an execution instruction package and a check code is attached to ensure data integrity.

[0082] The execution command packets are transmitted in segments via the 5G-TSN network. The integrity of the execution command packets is verified by blockchain nodes. The edge gateway receives the execution command packets and converts them into control commands according to different device protocols. Specifically:

[0083] The execution command packet is fragmented according to the time-sensitive networking standard of the 5G-TSN network. Each fragment is timestamped and marked with a transmission priority. It is transmitted to the blockchain node through a deterministic latency communication channel. The fragmented transmission of the 5G-TSN network ensures that the command packet arrives within a preset time, while guaranteeing the real-time performance and reliability of the communication.

[0084] The blockchain node performs hash verification on each received instruction packet fragment, compares the verification code with the original data, and records the verification result in the blockchain ledger after successful verification. The verified execution instruction packet is then forwarded to the edge gateway.

[0085] The edge gateway receives the execution command packet and converts it into control commands using the OPC UA protocol. The edge gateway parses the execution command packet according to the communication protocol of each device, converts the target power value and adjustment increment in the command packet into a device-compatible format using the IEC 61850 protocol. The converted control command includes the device ID, command type (command types include power setpoint and start / stop command), and execution time window. Finally, the control command packet is sent to the corresponding inverter, forming a complete three-level scheduling command set of control commands. This realizes the full-process conversion from impedance matrix to device-side control commands, meeting the requirements of microgrid power collaborative scheduling.

[0086] S4. Input control commands into the multiphysics simulation model to simulate the power operation environment, output the simulation result dataset, and generate a risk warning report based on the simulation result dataset obtained from the test.

[0087] In the simulation software COMSOL, a new model is created. The electromagnetic field interface, thermal field interface, and structural field interface are selected in sequence. The electromagnetic field interface is based on Maxwell's equations and describes the distribution of current, voltage, and magnetic field. The thermal field interface is based on the heat conduction equation and describes the temperature field distribution. The structural field interface is based on the elasticity equation and describes mechanical stress and deformation. According to the topological constraint matrix, voltage threshold, and inverter parameters, the boundary conditions and material properties of the multiphysics simulation model are configured. The material properties refer to the inverter's rated power, efficiency curve, and response time. The boundary conditions refer to setting the boundary values ​​of node voltage, tie line current, and key component temperature according to the grid topology connection diagram. The interaction between physical fields is established through the multiphysics interface. The inverter nodes and tie lines are meshed and the coupling equations are solved using the finite element method (FEM). Key components include power semiconductor devices and capacitors.

[0088] Input the control commands into the multiphysics simulation model to start the simulation process. The expression for the simulation result dataset is as follows:

[0089] E = f 电磁 (P 调度 C 拓扑 V 节点 )

[0090]

[0091]

[0092] Where E represents the electromagnetic field distribution, which is driven by the power target value of the scheduling command, the topological constraint matrix, and the node voltage. The temperature field distribution is determined by power loss and material properties; S represents mechanical stress and deformation, a result of the coupling of electromagnetic and thermal fields; P represents... 调度 C represents the power target value in the three-level scheduling instruction set. 拓扑 V represents the topological constraint matrix. 节点 P represents the node voltage. 调度 C 拓扑 V 节点 From the initialization parameter package, q represents reactive power, σ 材料 Represents material properties, ∈ 应变 f represents the nodal displacement constraint. 电磁 f 热 and f 结构 Three physical field modeling functions are provided, corresponding to the electromagnetic field modeling function, the thermal field modeling function, and the structural field modeling function, respectively; f 电磁 The physical equations and boundary conditions representing electromagnetic fields, used to describe the distribution of electromagnetic fields, f 热 The heat conduction equation and heat source term are used to describe the distribution of the temperature field, f. 结构The structural mechanics equations, used to describe mechanical stress, strain, and deformation, I 联络线 Represents the tie line current, V 节点 T represents the node voltage. 关键部件 Indicates the temperature of critical components, T 环境 The ambient temperature is represented by h, the convective heat transfer coefficient is represented by A, and the heat dissipation surface area is represented by σ. 节点 The stress represents the nodal stress, F represents the applied force, & represents the area of ​​the surface subjected to the force, and p 损耗 The power loss of key components is represented by the calculation of the power loss of key components by solving the coupling equations using the finite element method (FEM) after setting boundary conditions and material properties.

[0093] Voltage thresholds are set based on the maximum and minimum voltage values ​​in the initialization parameter package; current thresholds are set based on the tie-line capacity in the topology constraint matrix; temperature thresholds are set based on the safe temperature in the inverter parameters; and stress thresholds are set based on the material yield strength in the inverter parameters. Node voltage, tie-line current, critical component temperature, and node stress are used as judgment indicators. The simulated judgment indicators are compared with the voltage, current, and stress thresholds to assess risk. Specifically:

[0094]

[0095]

[0096] Among them, V max and V min I represents the historical maximum and minimum voltage values. 额定 Indicates the rated current of the connecting line, σ max Indicates the upper limit of the safe operating temperature of critical components;

[0097] The risk level is determined based on the risk signals of each physical field. If any physical field shows a high risk, it is judged as a comprehensive high risk. If there is a medium risk but no high risk, it is judged as a comprehensive medium risk. If all are normal, it is judged as a low risk.

[0098] A risk warning report is generated by integrating risk signals, risk levels, a list of high-risk nodes, and simulation result datasets.

[0099] S5. Based on the risk warning report, perform tiered operations on the three-level scheduling instruction set, specifically as follows:

[0100] In response to the recent policy directive, the start-up and shutdown plans of generator sets will be adjusted based on the risk warning reports generated by the multiphysics simulation model. Specifically, when the risk warning report indicates that a certain node is at high risk, the output of generator sets in the high-risk area will be reduced or the generator sets will be arranged to operate during off-peak hours to increase the standby power supply.

[0101] For real-time attitude commands, the power output of the inverter is dynamically adjusted based on the real-time monitored grid operation status. Specifically, the inverter output power is automatically adjusted according to the real-time frequency deviation to maintain frequency stability, and the voltage level is improved by adjusting the reactive power output of the inverter, especially when the voltage is low.

[0102] In response to emergency orders, loads will be switched on and off rapidly based on risk warnings. Specifically, when the grid frequency or voltage is detected to be outside the safe range, some critical loads will be transferred to backup power sources or relatively low-risk grid areas.

[0103] This embodiment also provides a microgrid group power collaborative scheduling device, including: a data processing module, an algorithm deployment module, a scheduling optimization module, a simulation early warning module, and an execution feedback module.

[0104] The data processing module is responsible for importing and processing historical power grid operation data and inverter parameters, and generating a spatiotemporal coordinate frame, topology constraint matrix and initialization parameter package;

[0105] The algorithm deployment module is responsible for deploying adaptive algorithms in the inverter, calculating impedance values ​​based on grid conditions, and generating an impedance matrix containing adjustment instructions.

[0106] The scheduling optimization module is responsible for using the quantum-eagle swarm algorithm to perform a global search, generate an optimized scheduling plan curve, and decompose it into a three-level scheduling instruction set to ensure that all instructions meet the system's safety and performance requirements.

[0107] The simulation early warning module is responsible for inputting control commands into the multiphysics simulation model, simulating the power operation environment, assessing risks, and generating risk early warning reports.

[0108] The execution feedback module is responsible for transmitting execution command packets through the 5G-TSN network, which are then converted into device control commands by the edge gateway. It also monitors the power grid status in real time and dynamically adjusts the power grid operation based on the feedback information.

[0109] This embodiment also provides a computer device applicable to the microgrid group power collaborative scheduling method, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the microgrid group power collaborative scheduling method proposed in the above embodiment.

[0110] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0111] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the microgrid group power collaborative scheduling method proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0112] In summary, this invention achieves standardized modeling of multi-source data by constructing a spatiotemporal coordinate framework that integrates historical power grid data and inverter parameters, generating a topology constraint matrix and encrypted parameter packages, providing a precise spatiotemporal reference for scheduling; dynamically calculating impedance values ​​through an adaptive algorithm to construct a multi-dimensional matrix, enhancing the ability to coordinate frequency, voltage, and cost optimization; globally generating day-ahead, real-time, and emergency instruction sets using the quantum-eagle swarm algorithm, and combining MPC to achieve multi-timescale optimization and disturbance rejection compensation; ensuring instruction real-time performance and tamper-proofing through 5G-TSN sharding transmission and blockchain verification, adapting to heterogeneous device execution; and predicting voltage, temperature, and mechanical stress risks based on multi-physics coupling simulation, dynamically classifying and correcting strategies to achieve safe, economical, and stable coordinated scheduling of microgrids, thereby improving the absorption of new energy.

[0113] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A microgrid group power collaborative scheduling method, characterized in that: include: A spatiotemporal coordinate framework is constructed on the regional master control node. The configuration file is obtained by importing historical power grid operation data packages and inverter parameters, and the topology constraint matrix and initialization parameter package are output. According to the configuration file, an adaptive algorithm is deployed in the inverter to calculate the impedance value suitable for the current grid condition based on the grid frequency deviation, cost changes and voltage fluctuations, and to construct an impedance matrix containing adjustment instructions. The impedance matrix is ​​input into the quantum-eagle swarm algorithm for global search to generate an optimized scheduling plan curve. A preliminary scheduling plan is formulated based on the scheduling plan curve. Local optimization and adjustment are performed based on the real-time monitored power grid operation deviations. A three-level scheduling instruction set is output. After all scheduling instructions are verified for feasibility, an execution instruction package is generated. The execution command packets are transmitted in segments through the 5G-TSN network, and the integrity of the execution command packets is verified by blockchain nodes. The edge gateway receives the execution command packets and converts them into control commands according to different device protocols. The control commands are input into the multiphysics simulation model to simulate the power operation environment, output the simulation result dataset, and generate a risk warning report based on the simulation result dataset obtained from the test. Based on the risk warning report, the three-level dispatch instruction set is operated in a tiered manner; The specific steps for constructing the impedance matrix containing adjustment instructions are as follows: Construct an empty matrix of corresponding dimensions based on the number of inverters, and fill the matrix with the optimal impedance value of each inverter according to the rule that the row corresponds to the inverter number and the column corresponds to the grid state variable, so as to ensure the capacity equalization of symmetrical connection lines and form an impedance matrix that reflects multi-dimensional impedance adjustment commands. The impedance matrix is ​​input into the Quantum-Eagle Swarm Optimization (QPSO) algorithm. The QPSO extracts impedance adjustment commands for all inverters from the impedance matrix. These commands include impedance values ​​corresponding to frequency, cost, and voltage. It also initializes the initial position and velocity parameters of the particle swarm. Each particle represents a combination of inverter impedance parameters. Grid constraints are set based on tie-line capacity, voltage threshold, and inverter parameter limitations in the topology constraint matrix. The particle positions are dynamically adjusted using the contraction / expansion factor of the QPSO algorithm. The particle position update formula is as follows: ； ； in, Indicates time Time Particles in dimension The position above, Indicates time The average of the best positions in the history of all particles in dimension The value on, This represents the average of the best positions in the history of all particles. Indicates time The contraction and expansion factor at that time This represents the initial contraction-expansion factor value. This represents the final contraction-expansion factor value. Indicates the total number of iterations. and Indicates time A random number uniformly distributed within the time interval [0,1]. Represent the natural logarithm function; Based on the global search of QPSO, the particle position is further optimized by combining the random search of the eagle swarm algorithm to avoid getting trapped in local optima. After each iteration, it is checked whether the particle position meets the power grid constraints. If not, the iteration is restarted until the total number of iterations and the power grid constraints are met. The iteration stops when the optimal particle's impedance parameter combination is output. The impedance parameter combination is then mapped to a time series to generate a scheduling plan curve.

2. The microgrid group power collaborative scheduling method as described in claim 1, characterized in that: The power grid historical operation data package includes load demand records, power generation records, meteorological parameter records, electricity price records, and power grid status records; The inverter parameters include rated power, efficiency curve, and response time.

3. The microgrid group power collaborative scheduling method as described in claim 2, characterized in that: The specific steps for outputting the topological constraint matrix and initialization parameter package are as follows: The system processes load demand records to generate a maximum active load mapping table, generates a photovoltaic output mapping table based on photovoltaic output and irradiance, generates a wind power output mapping table based on wind power output and wind speed range, generates a node voltage extreme value table by statistically analyzing historical extreme values ​​of node voltage, and constructs a topology constraint matrix based on the power grid topology connection diagram and tie line capacity list. The new energy priority ranking table, voltage threshold and inverter parameters are integrated to generate an encrypted configuration file, which is then packaged into an initialization parameter package.

4. The microgrid group power collaborative scheduling method as described in claim 3, characterized in that: The calculation of the impedance value suitable for the current power grid condition is specifically as follows: Continuously monitor node frequency values ​​to identify frequency deviations, analyze time-period electricity price differences to obtain cost change trends, and compare voltage record data to determine voltage fluctuation ranges. Based on frequency deviation, cost changes, and voltage fluctuation data, an adaptive algorithm is applied to calculate the optimal impedance value, and combined with the inverter's rated power and efficiency curves, an operating mode adapted to the current grid conditions is generated.

5. The microgrid group power collaborative scheduling method as described in claim 1, characterized in that: The preliminary scheduling plan is as follows: Impedance parameter combinations are obtained through global search using the quantum-eagle swarm algorithm, and these parameter combinations are mapped to time series to generate scheduling plan curves. The time window is divided according to natural days, and the scheduling plan curve is divided into time series instructions at the hourly granularity. The target power value, timestamp and node ID of each inverter are integrated to form a preliminary scheduling plan.

6. The microgrid group power collaborative scheduling method as described in claim 5, characterized in that: The specific steps for outputting the three-level scheduling instruction set are as follows: The preliminary scheduling plan is divided into day-ahead layer instructions, real-time layer instructions, and emergency layer instructions according to time window markings; Based on the multiphysics simulation model, predictive control (MPC) generates power adjustment strategies for each level; The initially generated scheduling instruction set is verified for tie-line capacity, voltage threshold, and inverter parameters. Once the verification is successful, a three-level scheduling instruction set is generated.

7. The microgrid group power collaborative scheduling method as described in claim 2, characterized in that: For load demand records, timestamps from different sources are uniformly converted into a standard time format, stored by node ID, and a data set indexed by node ID is generated. For each node ID, a time window is divided by natural day, the maximum active load value within each natural day is identified, and the maximum active load value within each natural day is organized into a structured maximum active load mapping table. Photovoltaic output data and meteorological parameter records are resampled at the same time granularity. Missing values ​​are filled in by interpolation. Using the photovoltaic output timestamp as a benchmark, solar irradiance data within the same time period is matched with meteorological parameter records, allowing for slight time offsets. Valid data segments with solar irradiance higher than a trigger threshold are selected. The trigger threshold is set according to user needs. The average output value corresponding to each solar irradiance range is statistically calculated. The solar irradiance range, average output value, and number of data points are integrated to obtain a photovoltaic output mapping table. Invalid output data with wind speeds lower than the turbine's cut-in speed or higher than its cut-out speed are removed. Wind power output and wind speed are aligned at the same time granularity, and missing time periods are filled in using linear interpolation. Wind speed intervals are segmented, with each interval defined as 1 m / s. The average power output within each interval is calculated, and the standard deviation of each interval is used as the fluctuation range. The wind speed intervals, average power output, and fluctuation range are integrated to generate a wind power output mapping table. Wind power is grouped by node ID, and the historical maximum and minimum voltage values ​​are calculated for each node. Only voltage records within the rated frequency ± deviation tolerance are retained. These voltage records include a timestamp, node ID, instantaneous voltage value, and instantaneous frequency value. The voltage extremes of each node are stored by node ID, and the node ID, historical maximum voltage value, and historical minimum voltage value are integrated to generate a node voltage extreme value table. The power grid topology connection diagram is obtained by outputting the power grid planning and design drawings in CAD format. Based on the maximum active power load mapping table and the power grid topology connection diagram, a spatial axis is set. A fixed proportion of the historical maximum active power load value of each node is taken as the X-axis coordinate of the spatial axis. The average power factor is calculated based on the historical load data, and the reactive power corresponding to the average power factor is taken as the Y-axis coordinate of the spatial axis. The node level is defined according to the power grid topology connection depth. The formula for calculating the average power factor is: ； in, The average power factor represents the ratio of active power to apparent power at the current node. The value range is 0~1. This represents the active power of the current node. This represents the reactive power of the current node; Taking the moving average of the most recent 30 days as a typical value, the formula for calculating reactive power when active power is known is: ； in, Represents the tangent function. The power factor angle is the result of the inverse cosine function, and its unit is radians. The timeline is set as a day-ahead layer, a real-time layer, and an emergency layer. The day-ahead layer has a fixed 24-hour cycle, with each node marked with a time stamp and time slices divided by hours. The real-time layer uses a rolling time window, updating on a minute-by-minute basis. The emergency layer uses an instantaneous response window with no fixed time division. The highest power consumption record for each device is found, and a certain percentage is taken as the X-axis coordinate. Another certain percentage of the X-axis coordinate is taken as the Y-axis coordinate. Devices connected to the main power grid are used as the Z-axis coordinate. Devices directly connected to the main power grid have Z=0, secondary connected devices have Z=1, and more distant devices have Z=2. The device ID, node coordinates, and time window markers are integrated to generate a spatiotemporal coordinate mapping table. The time window markers refer to the planned time, real-time time, and emergency status corresponding to the day-ahead layer, real-time layer, and emergency layer, respectively. The emergency status includes standby and working. The tie line capacity is calculated based on the historical transmission power in the grid status record and the rated power in the inverter parameters. The maximum transmission power value of each tie line in a certain period of the past is counted and the historical maximum transmission power value is taken as the capacity benchmark. According to the rated power of the inverter, the tie line capacity is limited to not exceed the safe carrying capacity of the inverter, thus obtaining the tie line capacity list. The tie line capacity list includes line ID, starting node, ending node and maximum allowable transmission power. The new energy priority ranking is specifically as follows: based on the photovoltaic power output mapping table and the wind power output mapping table, the historical maximum photovoltaic power output value and the historical maximum wind power output value in the two mapping tables are selected. The historical maximum photovoltaic power output value or the historical maximum wind power output value of a certain node is divided by the sum of the historical maximum power output values ​​of all nodes to obtain the theoretical output ratio of that node. Based on the theoretical output ratio and combined with the unit electricity subsidy policy, the comprehensive priority score of each node is calculated according to the weighted formula. The comprehensive priority scores are arranged from high to low to obtain the new energy priority ranking table. The new energy priority ranking table includes node ID, type, priority score and ranking. The voltage threshold is set based on the node voltage extreme value table. The upper limit is taken as the highest quantile of the historical maximum voltage value in the node voltage extreme value table, and the lower limit is taken as the lowest quantile of the historical minimum voltage value. If the upper and lower voltage limits exceed the voltage fluctuation range allowed by the power grid, the voltage is forcibly truncated to the boundary value. Based on the tie line capacity list and the power grid topology connection diagram, create a square matrix with the same number of nodes. Set the initial element values ​​to default values. Traverse the tie line capacity list and fill in the maximum allowable transmission power value at the corresponding position in the square matrix to ensure that the capacity values ​​at both ends are equal. Then, perform capacity equalization processing on the asymmetric connection lines to form a square matrix that reflects the tie line capacity relationship between each node, i.e., the topology constraint matrix, where the rows and columns correspond to the node IDs and the element values ​​are the tie line capacities. The new energy priority ranking table, voltage threshold, and inverter parameters are integrated into a configuration file in a unified format. The configuration file is then encrypted using the AES symmetric encryption algorithm, and a data integrity check code is added. All configuration files are then packaged into a binary data file to obtain the initialization parameter package.

8. A microgrid group power collaborative scheduling device, based on the microgrid group power collaborative scheduling method according to any one of claims 1 to 7, characterized in that: include: Data processing module, algorithm deployment module, scheduling optimization module, simulation early warning module, and execution feedback module: The data processing module is responsible for importing and processing historical power grid operation data and inverter parameters, and generating a spatiotemporal coordinate frame, a topological constraint matrix and an initialization parameter package. The algorithm deployment module is responsible for deploying the adaptive algorithm in the inverter, calculating the impedance value according to the grid status, and generating an impedance matrix containing adjustment instructions. The scheduling optimization module is responsible for using the quantum-eagle swarm algorithm to perform a global search, generate an optimized scheduling plan curve, and decompose it into a three-level scheduling instruction set to ensure that all instructions meet the system's safety and performance requirements. The simulation early warning module is responsible for inputting control commands into the multiphysics simulation model, simulating the power operation environment, assessing risks, and generating risk early warning reports. The execution feedback module is responsible for transmitting execution instruction packets through the 5G-TSN network, which are then converted into device control instructions by the edge gateway. It also monitors the power grid status in real time and dynamically adjusts the power grid operation based on the feedback information.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the microgrid group power collaborative scheduling method according to any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the microgrid group power collaborative scheduling method according to any one of claims 1 to 6.

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