Micro-grid group power co-scheduling method and micro-grid group power co-scheduling device

Abstract

The invention discloses a micro-grid group power cooperative scheduling method and device, and relates to the technical field of data encryption, and the method comprises the steps: constructing a space-time coordinate frame on a regional master control node, and obtaining a configuration file through importing a power grid historical operation data package and an inverter parameter; calculating an impedance value suitable for the current power grid state according to the frequency deviation, the cost change and the voltage fluctuation of the power grid, and constructing an impedance matrix containing an adjustment instruction; inputting the impedance matrix into a quantum-eagle swarm algorithm to perform global search to generate an optimized scheduling plan curve, making a preliminary scheduling plan according to the scheduling plan curve, performing local optimization adjustment based on the power grid operation deviation monitored in real time, outputting a three-level scheduling instruction set, and performing scheduling on the power grid operation deviation. Performing feasibility verification and confirmation on all the scheduling instructions to generate an execution instruction packet; the edge gateway receives the execution instruction packet and converts the execution instruction packet into a control instruction; and inputting the control instruction into the multi-physics field simulation model, and outputting a simulation result data set.

Description

Technical Field

[0001] The present invention relates to the field of data encryption technology, and in particular to a method and device for coordinating power scheduling of a microgrid group. Background Art

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

[0003] In the existing technology, the existing mutual aid control mostly relies on preset power transmission thresholds, which makes it difficult to take into account the adaptive switching requirements of subgrid operation scenarios, limiting the efficient utilization of the spatiotemporal coupling characteristics of microgrid groups; therefore, it is necessary to design a new solution to meet actual needs. Summary of the Invention

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

[0005] Therefore, the present invention provides a microgrid group power collaborative scheduling method to solve the problem that the existing mutual aid control relies heavily on preset power transmission thresholds, is difficult to take into account the adaptive switching requirements of subnet operation scenarios, and limits the efficient utilization of the spatiotemporal coupling characteristics of the microgrid group.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

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

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

[0009] Deploy an adaptive algorithm in the inverter based on the configuration file, calculate the impedance value suitable for the current grid state based on the grid's frequency deviation, cost changes, and voltage fluctuations, and 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 dispatch plan curve. A preliminary dispatch plan is formulated based on the dispatch plan curve. Local optimization adjustments are made based on the real-time monitored grid operation deviations. A three-level dispatch instruction set is output. After all dispatch instructions are verified for feasibility, an execution instruction package is generated.

[0011] The execution instruction packet is transmitted through 5G-TSN network slices, and the integrity of the execution instruction packet is verified by the blockchain node. The edge gateway receives the execution instruction packet and converts it into control instructions according to different device protocols;

[0012] The control instructions are input into the multi-physics field simulation model to simulate the power operation environment, and the simulation result data set is output. A risk warning report is generated based on the simulation result data set obtained from the test, and the three-level dispatch instruction set is graded according to the risk warning report.

[0013] As a preferred solution of the microgrid power coordinated scheduling method of the present invention, wherein: the power grid historical operation data packet 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 solution of the microgrid power coordinated scheduling method of the present invention, wherein: the output topology constraint matrix and the initialization parameter package are specifically performed as follows:

[0016] Process load demand records to generate a maximum active load mapping table, generate a photovoltaic output mapping table based on photovoltaic output and sunlight intensity, generate a wind power output mapping table based on wind power output and wind speed range, generate a node voltage extreme value table based on historical node voltage extreme values, and construct a topology constraint matrix based on the grid topology connection diagram and the tie line capacity list;

[0017] Integrate the new energy priority table, voltage thresholds, and inverter parameters to generate an encrypted configuration file, which is finally packaged into an initialization parameter package.

[0018] As a preferred solution of the microgrid power coordinated scheduling method of the present invention, the impedance value suitable for the current grid state is calculated as follows:

[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 the voltage fluctuation range;

[0020] Based on frequency deviation, cost change and voltage fluctuation data, an adaptive algorithm is applied to calculate the optimal impedance value, and combined with the inverter rated power and efficiency curve to generate an operating mode adapted to the current grid status.

[0021] As a preferred solution of the microgrid power coordinated scheduling method of the present invention, the step of constructing an impedance matrix including adjustment instructions is as follows:

[0022] An empty matrix of corresponding dimensions is constructed according to the number of inverters, and the optimal impedance value of each inverter is filled into the matrix according to the rule that rows correspond to inverter numbers and columns correspond to grid state variables, ensuring the capacity balancing of symmetrically connected lines and forming an impedance matrix that reflects multi-dimensional impedance adjustment instructions.

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

[0024] The impedance parameter combination is obtained through global search of the quantum-eagle swarm algorithm, and the parameter combination is mapped to the time series to generate the scheduling plan curve;

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

[0026] As a preferred solution of the microgrid power coordinated scheduling method of the present invention, the output of the three-level scheduling instruction set comprises the following specific steps:

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

[0028] Generate power adjustment strategies at all levels based on multi-physics simulation model predictive control (MPC);

[0029] The initially generated dispatch instruction set is subjected to interconnection line capacity, voltage threshold and inverter parameter verification, and a three-level dispatch instruction set is generated after verification.

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

[0031] The data processing module is responsible for importing and processing historical grid operation data and inverter parameters, generating a spatiotemporal coordinate framework, a topology 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 state, and generating an impedance matrix containing adjustment instructions;

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

[0034] The simulation warning module is responsible for inputting control instructions into the multi-physics field simulation model, simulating the power operation environment, assessing risks and generating risk warning reports;

[0035] The execution feedback module is responsible for transmitting execution instruction packets through the 5G-TSN network, converting them into device control instructions by the edge gateway, and monitoring the power grid status in real time, dynamically adjusting power grid operations based on feedback information.

[0036] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the microgrid power collaborative scheduling method as described in the first aspect of the present invention is implemented.

[0037] In a fourth aspect, 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, any step of the microgrid power collaborative scheduling method as described in the first aspect of the present invention is implemented.

[0038] The beneficial effects of the present invention are as follows: the present invention integrates historical power grid data and inverter parameters by constructing a space-time coordinate framework, generates a topology constraint matrix and an encrypted parameter package, realizes standardized modeling of multi-source data, provides accurate space-time benchmarks for scheduling, dynamically calculates impedance values through adaptive algorithms, constructs a multi-dimensional matrix, improves the frequency, voltage and cost collaborative optimization capabilities, uses the quantum-eagle swarm algorithm to globally generate three-layer instruction sets of day-ahead, real-time and emergency, combines MPC to achieve multi-time scale optimization and anti-disturbance compensation, ensures real-time and tamper-proof instructions through 5G-TSN shard transmission and blockchain verification, adapts to the execution of heterogeneous devices, predicts voltage, temperature and mechanical stress risks based on multi-physical field coupling simulation, and dynamically implements hierarchical correction strategies to achieve safe, economical and stable collaborative scheduling of microgrid groups, thereby improving the absorption of new energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0040] Figure 1 Flowchart of the microgrid power collaborative scheduling method.

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

[0042] Figure 3 Schematic diagram for impedance calculation.

[0043] Figure 4 Schematic diagram prepared for preliminary dispatch planning. DETAILED DESCRIPTION

[0044] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0045] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0046] Secondly, the term "one embodiment" or "embodiment" 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 various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0047] Reference Figures 1 to 4 , is an embodiment of the present invention, which provides a method for coordinating power scheduling of a microgrid group, comprising the following steps:

[0048] S1. Build a spatiotemporal coordinate framework on the regional master node, import the historical grid operation data package and inverter parameters to obtain the configuration file, and output the topology constraint matrix and initialization parameter package;

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

[0050] For the load demand records, the timestamps from different sources are uniformly converted into a standard time format, and stored in categories according to the node ID. A data set indexed by the node ID is generated. For each node ID, the time window is divided by natural day, the maximum active load value in each natural day is identified, and the maximum active load value in each natural day is organized into a structured maximum active load mapping table; the photovoltaic output data and meteorological parameter records are resampled at the same time granularity, and missing values are filled by interpolation. The photovoltaic output timestamp is used as the benchmark, and the light intensity data in the same time period is matched in the meteorological parameter records. A slight time offset is allowed, and the valid data segments with light intensity higher than the starting threshold are screened. The starting threshold is set according to user needs, and the corresponding average output value is counted according to the light intensity interval. The light intensity interval, average output value and number of data points are integrated to obtain the photovoltaic output Mapping table; eliminate invalid output data with wind speed lower than the wind turbine cut-in speed or higher than the cut-out speed, align wind power output and wind speed at the same time granularity, use linear interpolation to fill in missing periods, segment by wind speed interval, set each 1 meter / second as a wind speed interval, calculate the average output value in each wind speed interval, calculate the standard deviation of each interval as the fluctuation range, and integrate the wind speed interval, average output value and fluctuation range to generate a wind power output mapping table; group by node ID, calculate the historical maximum voltage and historical minimum voltage of each node respectively, and only retain voltage records with grid frequency within the rated frequency ± deviation tolerance. The voltage record includes timestamp, node ID, instantaneous voltage value and instantaneous frequency value. The voltage extreme value of each node is stored according to the 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;

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

[0052]

[0053] Where P' represents the ratio of the active power to the apparent power of the current node, that is, 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 value of the last 30 days as a typical value, when the active power is known, the calculation formula for reactive power is:

[0055] Q = P × tan(arccos(P'));

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

[0057] The time axis is set as the day-ahead layer, real-time layer, and emergency layer. The day-ahead layer has a fixed 24-hour cycle. Each node is time-stamped and time slices are divided into hourly slices. The real-time layer is a rolling time window, updated at the minute level. The emergency layer is 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 taken as the X-axis coordinate. Then a certain proportion of X is taken as the Y-axis coordinate. The device connected to the main grid is used as the Z-axis coordinate. The device directly connected to the main grid has Z=0, the device connected to the secondary grid has Z=1, and the device further away has Z=2. The device ID, node coordinates (X-axis coordinate, Y-axis coordinate, Z-axis coordinate) and time window mark are integrated to generate a time-space 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 the past period is counted and used as the capacity benchmark. Based on the rated power of the inverter, the tie line capacity is limited to not exceed the safe load capacity of the inverter. The tie line capacity list is obtained, which includes the line ID, starting node, ending node, and maximum allowable transmission power.

[0059] Renewable energy priority sorting involves selecting the historical maximum PV output value and the historical maximum wind power output value from the PV output mapping table and the wind power output mapping table, dividing the historical maximum PV output value or wind power output value of a node by the sum of the historical maximum output values of all nodes to obtain the theoretical output proportion of the node. Based on the theoretical output proportion and the unit electricity subsidy policy, a weighted formula is used to calculate the comprehensive priority score of each node. The comprehensive priority scores are sorted from high to low to obtain a new energy priority sorting table. The new energy priority sorting table includes node ID, type, priority score, and ranking;

[0060] The voltage threshold is set based on the node voltage extreme value table. The high percentile value of the historical maximum voltage in the node voltage extreme value table is used as the upper limit, and the low percentile value of the historical minimum voltage is used as the lower limit. 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 grid topology connection diagram, a square matrix with the same number of nodes is created. The initial element values are set to default values. The tie line capacity list is traversed and the maximum allowable transmission power values are filled in the corresponding positions in the square matrix to ensure that the capacity values at both ends are equal. Then, the capacity of the asymmetric connection lines is balanced to form a square matrix that reflects the tie line capacity relationship between each node, namely the topology constraint matrix, in which 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 unified format to obtain a configuration file. The configuration file is encrypted using the symmetric encryption algorithm AES, and a data integrity check code is added. All configuration files are packaged into a binary data file to obtain an initialization parameter package.

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

[0064] Extract inverter parameters from the configuration file, including 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 package, the frequency value of each node is continuously monitored. The current frequency value is compared with the rated frequency. The difference is the frequency deviation. When the frequency value deviates from the predetermined range (in this embodiment, the predetermined range is ±0.2Hz), it is considered that a frequency deviation exists. By comparing the differences in electricity prices in different time periods, the cost trend can be analyzed. The historical maximum and minimum voltage values of each node are used 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 grid frequency deviation, cost change and voltage fluctuation data are calculated using an adaptive algorithm. The adaptive algorithm adjusts the internal model based on these input parameters to find an optimal impedance value that can maintain stability and optimize economic benefits under the current grid state.

[0067] By combining the optimal impedance value with the inverter's rated power and efficiency curve, the optimal operating mode for each inverter is developed. At the same time, corresponding adjustment instructions are generated for each inverter. Each adjustment instruction includes specific impedance setting recommendations.

[0068] A matrix equal to the number of inverters is constructed based on the number of inverters. The adjustment instructions of all inverters are integrated, and the values at the corresponding positions are filled in according to the adjustment instructions 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. The grid state variables refer to frequency, cost, and voltage. The elements in the impedance matrix are the impedance adjustment instructions for specific inverters and specific grid state variables.

[0069] S3. Input the impedance matrix into the quantum-eagle swarm algorithm for global search to generate an optimized dispatch plan curve. A preliminary dispatch plan is formulated based on the dispatch plan curve. Local optimization adjustments are made based on the real-time monitored grid operation deviations. A three-level dispatch instruction set is output. All dispatch instructions are verified for feasibility and then generated into an execution instruction package.

[0070] The impedance matrix is input into the quantum-Eagle Swarm algorithm, which extracts the impedance adjustment instructions of all inverters from the impedance matrix. The impedance adjustment instructions include the impedance values corresponding to frequency, cost, and voltage, and initializes the initial position and speed parameters of the particle swarm. Each particle represents a set of inverter impedance parameter combinations. The grid constraints are set according to the tie line capacity, voltage threshold, and inverter parameter restrictions in the topology constraint matrix. The particle positions are dynamically adjusted using the contraction and expansion factors of the quantum particle swarm algorithm QPSO. The particle position update formula is:

[0071]

[0072] α(t)=α0+(α1-α0)`(1-t / T);

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

[0074] Based on the global search of QPSO, the random search of the Eagle Swarm algorithm is combined to further optimize the particle position to avoid falling into the local optimum. After each iteration, the particle position is checked to see if it meets the grid constraints. If not, the iteration is repeated until the total number of iterations is reached and the grid constraints are met. The impedance parameter combination corresponding to the optimal particle is output and mapped to a time series to generate a scheduling plan curve. The scheduling plan curve specifically describes the power output target value of each inverter at different time points.

[0075] Formulate a preliminary scheduling plan based on the scheduling plan curve. Divide the scheduling plan curve into time series instructions by time slices. Organize the target power value of each inverter 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] Real-time monitoring of node frequency values, instantaneous voltage values in grid status records, and time period electricity price data in electricity price records. Calculate the frequency deviation, voltage fluctuation amplitude, and cost change trend of the current grid. 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 period electricity price and the historical electricity price. Compare the frequency deviation, voltage fluctuation amplitude, and cost change trend with the corresponding target values in the preliminary scheduling plan. Adjust the inverter output based on the comparison error to obtain an optimized scheduling plan. Specifically, adjust the inverter output based on the comparison error as follows: If the frequency deviation is large, reduce the inverter output power through PID control. If the voltage is low, increase the voltage through reactive power compensation or adjust the inverter impedance. If the cost increases, prioritize the high-priority photovoltaic nodes.

[0077] According to the optimized scheduling plan, the model predictive control (MPC) is used to generate a three-level scheduling instruction set. The three-level scheduling instruction set is divided into day-ahead layer instructions, real-time layer instructions, and emergency layer instructions according to the time window mark. Specifically:

[0078] The day-ahead layer instruction refers to the time slice divided based on the 24-hour cycle, which integrates the target power value, timestamp and node ID of each inverter per hour to form the day-ahead scheduling instruction.

[0079] Real-time layer instructions are updated in minute-level rolling time windows, 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 conditions where the grid frequency or voltage exceeds the safe range. The immediate power adjustment instructions include trigger conditions, target power correction values, and execution priorities.

[0081] The feasibility of the three-level dispatching instruction set is verified. Specifically, the day-ahead layer instructions, real-time layer instructions and emergency layer instructions are verified with the topology constraint matrix, voltage threshold and inverter parameters respectively to ensure that all dispatching instructions meet the interconnection line capacity limit, voltage fluctuation range requirements and inverter safe operation boundaries. After the verification is passed, the instruction set is encapsulated into an execution instruction package and a checksum is attached to ensure data integrity.

[0082] The execution instruction packet is transmitted through the 5G-TSN network slice, and the integrity of the execution instruction packet is verified by the blockchain node. The edge gateway receives the execution instruction packet and converts it into control instructions according to different device protocols. Specifically:

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

[0084] The blockchain node performs hash verification on each instruction packet fragment received, compares the verification code with the original data to see if it is consistent. After verification, the verification result is recorded in the blockchain ledger and the verified execution instruction packet is forwarded to the edge gateway.

[0085] The edge gateway receives the execution instruction packet and converts it into a control instruction using the OPC UA protocol. The edge gateway parses the execution instruction packet according to the communication protocol of each device, and converts the target power value and adjustment increment in the instruction packet into a format compatible with the device using the IEC 61850 protocol. The converted control instruction contains the device ID, instruction type (the instruction type includes power setting value and start and stop instructions) and execution time window. Finally, the control instruction packet is sent to the corresponding inverter, and the control instruction of the complete three-level scheduling instruction set is finally formed, realizing the full process conversion from the impedance matrix to the device-side control instruction, meeting the needs of microgrid power coordinated scheduling.

[0086] S4. Input the control instructions into the multi-physics field simulation model, simulate the power operation environment, output the simulation result data set, and generate a risk warning report based on the simulation result data set obtained from the test;

[0087] Create a new model in the simulation software COMSOL, and select the electromagnetic field interface, thermal field interface, and structural field interface in turn. 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 elastic mechanics equation and describes the mechanical stress and deformation. According to the topological constraint matrix, voltage threshold, and inverter parameters, configure the boundary conditions and material properties of the multi-physics simulation model. Material properties refer to the inverter rated power, efficiency curve, and response time. 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 multi-physics field interface, and the mesh of the inverter nodes and tie lines is refined. The finite element method (FEM) is used to solve the coupling equations. Key components include power semiconductor devices and capacitors.

[0088] Input the control command into the multi-physics simulation model to start the simulation process, and obtain the simulation result data set expression 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 instruction, the topology constraint matrix and the node voltage. represents the temperature field distribution, which is determined by power loss and material properties, S represents mechanical stress and deformation, the result of coupling electromagnetic field and thermal field, and P 调度 Represents the power target value in the three-level scheduling instruction set, C 拓扑 represents the topological constraint matrix, V 节点 represents the node voltage, P 调度 ,C 拓扑 ,V 节点 From the initialization parameter package, q represents reactive power, σ 材料 represents the material properties, ∈ 应变 represents the node displacement constraint, f 电磁 、f 热 and f 结构 are three physical field modeling functions, corresponding to the electromagnetic field modeling function, thermal field modeling function and structural field modeling function respectively; f 电磁 The physical equations and boundary conditions that represent the electromagnetic field are used for the distribution of the electromagnetic field, f 热 Represents the heat conduction equation and heat source term, which is used to describe the distribution of temperature field, f 结构Represents the structural mechanics equations used to describe mechanical stress, strain and deformation, I 联络线 Represents the tie line current, V 节点 represents the node voltage, T 关键部件 Indicates the temperature of key components, T 环境 represents the ambient temperature, h represents the convection heat transfer coefficient, A represents the heat dissipation surface area, σ 节点 represents the node stress, F represents the applied force, & represents the area of the force-bearing surface, and p 损耗 Represent the power loss of key components (by setting boundary conditions and material properties, and solving the coupled equations using the finite element method (FEM), the power loss of key components can be calculated);

[0093] The voltage threshold is set according to the maximum and minimum voltage values in the initialization parameter package. The current threshold is set according to the tie line capacity in the topology constraint matrix. The temperature threshold is set according to the safety temperature in the inverter parameters. The stress threshold is set according to the material yield strength in the inverter parameters. The node voltage, tie point current, key component temperature, and node stress are used as judgment indicators. The judgment indicators after simulation are compared with the voltage threshold, current threshold, and stress threshold to determine the risk. Specifically:

[0094]

[0095]

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

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

[0098] A risk warning report is formed based on the integration of risk signals, risk levels, high-risk node lists and simulation result data sets.

[0099] S5. Perform hierarchical operations on the three-level scheduling instruction set based on the risk warning report, specifically:

[0100] For day-ahead attitude instructions, the start and stop plans of generator sets are adjusted based on the risk warning reports generated by the multi-physics field simulation model. Specifically, when the risk warning report indicates a high risk at a certain node, the generator output in the high-risk area is reduced or the generator set is arranged to operate during off-peak hours to increase the readiness of the backup power supply.

[0101] For real-time attitude instructions, 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 inverter's reactive power output, especially when the voltage is low.

[0102] For emergency attitude instructions, loads are quickly switched on and off based on risk warnings. Specifically, when it is detected that the grid frequency or voltage exceeds the safe range, some critical loads are transferred to the backup power supply or relatively low-risk grid area.

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

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

[0105] 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 the impedance matrix containing the adjustment instructions;

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

[0107] The simulation warning module is responsible for inputting control instructions into the multi-physics field simulation model, simulating the power operation environment, assessing risks and generating risk warning reports;

[0108] The execution feedback module is responsible for transmitting execution instruction packets through the 5G-TSN network, converting them into device control instructions by the edge gateway, and monitoring the power grid status in real time, dynamically adjusting power grid operations based on feedback information.

[0109] This embodiment also provides a computer device, which is applicable to the microgrid 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 computer-executable instructions to implement the microgrid power collaborative scheduling method proposed in the above embodiment.

[0110] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0111] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for realizing the power coordinated scheduling of a microgrid group as proposed in the above embodiment; 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 read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or optical disk.

[0112] In summary, the present invention achieves standardized modeling of multi-source data by constructing a spatiotemporal coordinate framework to integrate historical grid data and inverter parameters, generates a topology constraint matrix and an encrypted parameter package, and provides precise spatiotemporal benchmarks for scheduling. It dynamically calculates impedance values through an adaptive algorithm, constructs a multi-dimensional matrix, and improves the collaborative optimization capabilities of frequency, voltage, and cost. It uses the quantum-eagle swarm algorithm to globally generate a three-layer instruction set of day-ahead, real-time, and emergency instructions, combines MPC to achieve multi-time scale optimization and anti-disturbance compensation, ensures the real-time and tamper-proof nature of instructions through 5G-TSN sharding transmission and blockchain verification, adapts to the execution of heterogeneous devices, predicts voltage, temperature, and mechanical stress risks based on multi-physical field coupling simulation, and dynamically implements hierarchical correction strategies to achieve safe, economical, and stable collaborative scheduling of microgrid groups, 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 the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for coordinated power scheduling of a microgrid group, characterized by: include: A spatiotemporal coordinate framework is constructed on the regional master node. The configuration file is obtained by importing the historical operation data packet of the power grid and the inverter parameters, and the topology constraint matrix and initialization parameter package are output. Deploy an adaptive algorithm in the inverter based on the configuration file, calculate the impedance value suitable for the current grid state based on the grid's frequency deviation, cost changes, and voltage fluctuations, and 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 dispatch plan curve. A preliminary dispatch plan is formulated based on the dispatch plan curve. Local optimization adjustments are made based on the real-time monitored grid operation deviations. A three-level dispatch instruction set is output. After all dispatch instructions are verified for feasibility, an execution instruction package is generated. The execution instruction packet is transmitted through 5G-TSN network slices, and the integrity of the execution instruction packet is verified by the blockchain node. The edge gateway receives the execution instruction packet and converts it into control instructions according to different device protocols; Input control instructions into the multi-physics field simulation model, simulate the power operation environment, output the simulation result data set, and generate a risk warning report based on the simulation result data set obtained from the test; The three-level scheduling instruction set is graded and operated according to the risk warning report.

2. The microgrid power coordinated scheduling method according to claim 1, wherein: 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 power coordinated scheduling method according to claim 2, wherein: The output topology constraint matrix and the initialization parameter package are specifically as follows: Process load demand records to generate a maximum active load mapping table, generate a photovoltaic output mapping table based on photovoltaic output and sunlight intensity, generate a wind power output mapping table based on wind power output and wind speed range, generate a node voltage extreme value table based on historical node voltage extreme values, and construct a topology constraint matrix based on the grid topology connection diagram and the tie line capacity list; Integrate the new energy priority table, voltage thresholds, and inverter parameters to generate an encrypted configuration file, which is finally packaged into an initialization parameter package.

4. The microgrid power coordinated scheduling method according to claim 3, wherein: The impedance value suitable for the current grid state is calculated 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 the voltage fluctuation range; Based on frequency deviation, cost change and voltage fluctuation data, an adaptive algorithm is applied to calculate the optimal impedance value, and combined with the inverter rated power and efficiency curve to generate an operating mode adapted to the current grid status.

5. The microgrid power coordinated scheduling method according to claim 4, characterized in that: The specific steps of constructing the impedance matrix including the adjustment instructions are as follows: An empty matrix of corresponding dimensions is constructed according to the number of inverters, and the optimal impedance value of each inverter is filled into the matrix according to the rule that rows correspond to inverter numbers and columns correspond to grid state variables, ensuring the capacity balancing of symmetrically connected lines and forming an impedance matrix that reflects multi-dimensional impedance adjustment instructions.

6. The microgrid power coordinated scheduling method according to claim 5, characterized in that: The preliminary scheduling plan is specifically as follows: The impedance parameter combination is obtained through global search of the quantum-eagle swarm algorithm, and the parameter combination is mapped to the time series to generate the scheduling plan curve; The time window is divided into natural days, the scheduling plan curve is split into time series instructions at hourly granularity, and the target power value, timestamp and node ID of each inverter are integrated to form a preliminary scheduling plan.

7. The microgrid power coordinated scheduling method according to claim 6, characterized in that: The specific steps of outputting the three-level scheduling instruction set are as follows: The preliminary dispatch plan is divided into day-ahead layer instructions, real-time layer instructions and emergency layer instructions according to time window marks; Generate power adjustment strategies at all levels based on multi-physics simulation model predictive control (MPC); The initially generated dispatch instruction set is subjected to interconnection line capacity, voltage threshold and inverter parameter verification, and a three-level dispatch instruction set is generated after verification.

8. The microgrid power coordinated scheduling method according to claim 2, wherein: For load demand records, timestamps from different sources are converted to a standard time format, stored by node ID, and a data set indexed by node ID is generated. For each node ID, the time window is divided by natural day, and the maximum active load value within each natural day is identified. The maximum active load value within each natural day is organized into a structured maximum active load mapping table; The photovoltaic output data and meteorological parameter records are resampled at the same time granularity, and missing values are filled by interpolation. The photovoltaic output timestamp is used as the benchmark, and the light intensity data within the same time period is matched in the meteorological parameter records. A small time offset is allowed, and the valid data segments with light intensity higher than the starting threshold are screened. The starting threshold is set according to user needs, and the corresponding average output value is counted according to the light intensity interval. The light intensity interval, average output value and number of data points are integrated to obtain the photovoltaic output mapping table; the invalid output data with wind speed lower than the wind turbine cut-in speed or higher than the cut-out speed are eliminated, and the wind power output and wind speed are aligned at the same time granularity. Linear interpolation is used to fill the missing period. The wind speed interval is segmented, with each 1 meter / second being a wind speed interval. 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 interval, average output value, and fluctuation range are integrated to generate a wind power output mapping table. The data is grouped by node ID, and the historical maximum voltage and historical minimum voltage values of each node are calculated separately. Only voltage records where the grid frequency is within the rated frequency ± deviation tolerance are retained. The voltage records include timestamp, node ID, instantaneous voltage value, and instantaneous frequency value. The voltage extreme value of each node is 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 grid topology connection diagram is obtained by outputting the grid planning and design drawings in CAD format. Based on the maximum active load mapping table and the grid topology connection diagram, a point space axis is set. A fixed ratio of the historical maximum active load value of each node is taken as the X-axis coordinate of the space axis. The average power factor is calculated based on the historical load data. The reactive power corresponding to the average power factor is used as the Y-axis coordinate of the space axis. The node level is defined according to the grid topology connection depth. The average power factor calculation formula is: Where P' represents the ratio of the active power to the apparent power of the current node, that is, 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. Taking the moving average value of the last 30 days as a typical value, when the active power is known, the calculation formula for reactive power is: Q = P × tan(arccos(P')); Where tan represents the tangent function, arccos(P') represents the power factor angle, which is the result of the inverse cosine function and is expressed in radians. The time axis is set as the day-ahead layer, real-time layer, and emergency layer. The day-ahead layer has a fixed 24-hour cycle. Each node is time-stamped and time slices are divided into hourly slices. The real-time layer is a rolling time window, updated at the minute level. The emergency layer is 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 taken as the X-axis coordinate. Then a certain proportion of X is taken as the Y-axis coordinate. The device connected to the main grid is used as the Z-axis coordinate. The device directly connected to the main grid has Z=0, the device connected to the secondary grid has Z=1, and the device further away has Z=2. The device ID, node coordinates, and time window mark are integrated and output to generate a time-space 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. 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 the past period is counted and used as the capacity benchmark. Based on the rated power of the inverter, the tie line capacity is limited to not exceed the safe load capacity of the inverter. The tie line capacity list is obtained, which includes the line ID, starting node, ending node, and maximum allowable transmission power. Renewable energy priority sorting involves selecting the historical maximum PV output value and the historical maximum wind power output value from the PV output mapping table and the wind power output mapping table, dividing the historical maximum PV output value or wind power output value of a node by the sum of the historical maximum output values of all nodes to obtain the theoretical output proportion of the node. Based on the theoretical output proportion and the unit electricity subsidy policy, a weighted formula is used to calculate the comprehensive priority score of each node. The comprehensive priority scores are sorted from high to low to obtain a new energy priority sorting table. The new energy priority sorting table includes node ID, type, priority score, and ranking; The voltage threshold is set based on the node voltage extreme value table. The high percentile value of the historical maximum voltage in the node voltage extreme value table is used as the upper limit, and the low percentile value of the historical minimum voltage is used as the lower limit. 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 grid topology connection diagram, a square matrix with the same number of nodes is created. The initial element values are set to default values. The tie line capacity list is traversed and the maximum allowable transmission power values are filled in the corresponding positions in the square matrix to ensure that the capacity values at both ends are equal. Then, the capacity of the asymmetric connection lines is balanced to form a square matrix that reflects the tie line capacity relationship between each node, namely the topology constraint matrix, in which 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 unified format to obtain a configuration file. The configuration file is encrypted using the symmetric encryption algorithm AES, and a data integrity check code is added. All configuration files are packaged into a binary data file to obtain an initialization parameter package.

9. The microgrid power coordinated scheduling method according to claim 6, characterized in that: The impedance matrix is input into the quantum-Eagle Swarm algorithm, which extracts the impedance adjustment instructions of all inverters from the impedance matrix. The impedance adjustment instructions include the impedance values corresponding to frequency, cost, and voltage, and initializes the initial position and speed parameters of the particle swarm. Each particle represents a set of inverter impedance parameter combinations. The grid constraints are set according to the tie line capacity, voltage threshold, and inverter parameter restrictions in the topology constraint matrix. The particle positions are dynamically adjusted using the contraction and expansion factors of the quantum particle swarm algorithm QPSO. The particle position update formula is: α(t)=α0+(α1-α0)·(1-t / T); in, represents the position of the i-th particle in dimension d at time t+1, represents the value of the average of the best historical positions of all particles at time t in dimension d, represents the average value of the best historical positions of all particles, α(t) represents the shrinkage and expansion factor at time t, α0 represents the initial shrinkage and expansion factor value, α1 represents the final shrinkage and expansion factor value, T represents the total number of iterations, u(t) and r(t) represent random numbers uniformly distributed in the interval [0,1] at time t, and ln represents the natural logarithm function; Based on the global search of QPSO, the random search of Eagle Swarm Algorithm is combined to further optimize the particle position to avoid falling into the local optimum. After each iteration, the particle position is checked to see if it meets the grid constraints. If not, the iteration is repeated until the total number of iterations is reached and the grid constraints are met. The impedance parameter combination corresponding to the optimal particle is output, and the impedance parameter combination is mapped to the time series to generate the scheduling plan curve.

10. A microgrid power collaborative scheduling device, based on the microgrid 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 warning module and execution feedback module: The data processing module is responsible for importing and processing historical grid operation data and inverter parameters, generating a spatiotemporal coordinate framework, a topology 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 state, and generating an impedance matrix containing adjustment instructions; The scheduling optimization module is responsible for using the quantum-eagle swarm algorithm to perform global search, generate an optimized scheduling plan curve, and decompose it into a three-level scheduling instruction set to ensure that all instructions meet system safety and performance requirements; The simulation warning module is responsible for inputting control instructions into the multi-physics field simulation model, simulating the power operation environment, assessing risks and generating risk warning reports; The execution feedback module is responsible for transmitting execution instruction packets through the 5G-TSN network, converting them into device control instructions by the edge gateway, and monitoring the power grid status in real time, dynamically adjusting power grid operations based on feedback information.

11. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the microgrid power coordinated scheduling method according to any one of claims 1 to 7 are implemented.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the microgrid power coordinated scheduling method according to any one of claims 1 to 7 are implemented.

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