Network construction type micro-grid power router and control system

Abstract

The invention belongs to the technical field of energy scheduling, and discloses a network construction type micro-grid power router and a control system, and the system comprises a topology construction module which is used for constructing a topology structure diagram and a current power flow direction state diagram; the power prediction module is used for predicting a power value sequence of each power generation unit and each load unit; the optimization solving module is used for constructing a multi-period optimization problem, and evaluating system state change from four dimensions of topological structure difference, power change difference, energy flow difference and distribution mode difference by taking minimization of a difference sum between power flow direction state diagrams at adjacent time points as a target to generate a candidate scheme set; the economic evaluation module is used for selecting a scheme with the optimal economical efficiency; and the control execution module is used for generating and executing a power flow direction control instruction. The system can effectively inhibit the violent change of the internal power flow state of the micro-grid, reduces the frequent switching of the working mode of the power router, and improves the operation stability of the micro-grid.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy scheduling, and more particularly, to a network-structured microgrid power router and control system. BACKGROUND

[0002] In a microgrid system, a power router (also known as an energy router) is a critical power device that enables directional transmission and precise control of energy. Through high-frequency power electronic conversion technology, the power router converts and transmits electrical energy of different voltage levels, frequencies, and phases, enabling bidirectional flow and flexible allocation of energy between various units within the microgrid and between the microgrid and the external grid. The application of power routers greatly improves the flexibility, reliability, and operational efficiency of microgrids, providing technical support for large-scale integration and efficient utilization of renewable energy.

[0003] As the scale of microgrids expands and their topological structures become more complex, microgrid control systems face increasing challenges. In particular, in microgrids with a high proportion of renewable energy, due to the intermittent and fluctuating nature of photovoltaic and wind power, coordinating various types of power generation equipment, energy storage devices, and loads to achieve stable operation and economic optimization of the microgrid is one of the core issues in microgrid research and application. In traditional microgrid control methods, power scheduling strategies based on predictions are widely used. These methods typically predict future power generation and load demand over a period of time based on weather forecasts and historical data, and then develop corresponding power scheduling plans. However, existing scheduling methods often treat power generation and loads as a whole, mainly focusing on total power balance and control of overall power variation amplitude, such as limiting the total power variation between adjacent time periods to not exceed a certain threshold. While this approach ensures power balance at a macro level, it overlooks the complex power flow status within the microgrid.

[0004] Specifically, even if the predicted total load and total power generation change little, within a microgrid controlled based on total power balance and overall power variation amplitude, there may be a situation where the output of a single power generation unit significantly decreases while the output of other power generation units increases, or a situation where the output power of a power generation unit is transferred from one load to another. Such drastic changes in internal power flow status can lead to frequent switching of power router operating modes, increase control frequency, reduce device service life, and even cause system instability and power quality issues. SUMMARY

[0005] To overcome the above problems of the prior art, the present application proposes a network-structured microgrid power router and control system to solve the above problems.

[0006] The present application provides the following technical solutions:

[0007] A network-constructing microgrid power router control system, comprising:

[0008] a topology construction module, configured to construct a topology graph according to a physical connection state of the power router, and construct a current power flow state graph based on real-time power data and the topology graph;

[0009] a power prediction module, configured to obtain weather prediction data and time characteristics, predict a power generation value sequence of each power generation unit based on the weather prediction data and equipment characteristics of each power generation unit, and predict a load power value sequence of each load unit based on the time characteristics and load data of each load unit;

[0010] an optimization solution module, configured to construct a multi-period optimization problem based on the current power flow state graph, the predicted power generation value sequence of each power generation unit, and the predicted load power value sequence of each load unit, and generate a candidate scheme set by using an optimization algorithm;

[0011] an economic evaluation module, configured to perform economic evaluation on candidate methods in the candidate scheme set, and select an economically optimal scheme as a final power distribution scheme;

[0012] a control execution module, configured to generate and execute a power flow control instruction according to the final power distribution scheme.

[0013] Preferably, the topology graph is constructed according to the physical connection state of the power router, and the construction includes:

[0014] identifying all power generation units, energy storage units, load units and grid access points connected by the power router as nodes in the topology graph;

[0015] generating the topology graph according to allowed power transmission paths between the nodes as edges in the topology graph;

[0016] The current power flow state graph is constructed based on the real-time power data and the topology graph, and the construction includes:

[0017] mapping the real-time power data to corresponding edges of the topology graph; and using a node pair to represent a power direction to obtain the current power flow state graph.

[0018] Preferably, the power generation value sequence of each power generation unit is predicted, and the prediction includes: obtaining weather prediction data and equipment data of each power generation unit, inputting the weather prediction data and the equipment data of each power generation unit into a pre-established power generation prediction model to generate power prediction values of each power generation unit at a plurality of future time points, and composing the power generation value sequence.

[0019] The method for predicting the load power value sequence of each load unit comprises: obtaining time characteristics and load data of each load unit, inputting the time characteristics and the load data of each load unit into a pre-established load prediction model, generating power prediction values of each load unit at multiple future time points, and composing the load power value sequence.

[0020] Preferably, the multi-period optimization problem comprises:

[0021] The decision variables of the optimization problem are defined as: power distribution values and directions of all power transmission paths in the topology at each time point;

[0022] The optimization objective is set as: minimizing the total difference between the power flow state diagrams of adjacent time points;

[0023] The constraint conditions are constructed, and the constraint conditions comprise: at each time point, the total output power of each power generation unit is equal to the predicted power generation power thereof; the total input power of each load unit is equal to the predicted load power thereof; the charge and discharge power of each energy storage unit is limited; and the grid exchange power is limited by the grid-connected capacity;

[0024] The above objective and constraint are integrated to form a complete multi-period optimization problem model.

[0025] Preferably, the calculation of the total difference between the power flow state diagrams of adjacent time points comprises:

[0026] The power flow state diagram of the first time point is compared with the current power flow state diagram;

[0027] The power flow state diagram of each subsequent time point is compared with the power flow state diagram of the previous time point thereof;

[0028] For the power flow state diagrams of each pair of adjacent time points, the following difference calculation is performed:

[0029] The number of power transmission paths that exist only in one diagram and do not exist in the other diagram is counted as a topology difference;

[0030] The difference in power values of the common power transmission paths in the two diagrams is calculated as a power change difference;

[0031] The number of paths in which the power flow direction is changed in the two diagrams is counted as an energy flow difference;

[0032] The proportional change in the power distribution from each source node to each target node is compared as a distribution mode difference;

[0033] A single-period difference measurement function is constructed, and the topology difference, the power change difference, the energy flow difference and the distribution mode difference are normalized and weightedly summed according to weights;

[0034] The difference summation is obtained by accumulating the difference measure function values of all adjacent time points.

[0035] Preferably, the generating the candidate scheme set by the optimization algorithm comprises:

[0036] A chromosome coding rule is created to encode the power values and directions of each power transmission path at each time point into a real gene sequence, and an initial population is generated by individuals satisfying all constraint conditions;

[0037] The fitness value is generated based on the total difference value corresponding to each individual in the initial population, and the individual is selected according to the fitness value, and the selected individual is executed by the crossover and mutation operation to generate a new multi-period power allocation scheme;

[0038] It is verified whether the new power allocation scheme satisfies all constraint conditions at each time point, and the power allocation scheme that does not satisfy the constraint is deleted;

[0039] The execution is repeated until a preset iteration number is reached, and a final population is obtained;

[0040] A plurality of different individuals are extracted from the final population according to the fitness value to form a candidate scheme set.

[0041] Preferably, the economic evaluation of the candidate schemes in the candidate scheme set and the selection of the most economical scheme as the final power allocation scheme comprises:

[0042] Obtain the electricity price data of each time point in the future;

[0043] For each candidate scheme: according to the power inflow value of each time point of the power grid node and the corresponding electricity price, the power grid purchase cost is accumulated; according to the power outflow value of each time point of the power grid node and the corresponding electricity price, the power grid electricity selling income is accumulated; according to the charging and discharging power value of each time point of the energy storage node, the energy storage use cost is accumulated;

[0044] Select the scheme with the highest income after subtracting the energy storage use cost and the purchase cost from the electricity selling income as the final power allocation scheme.

[0045] Preferably, the generating and executing the power flow control instruction according to the final power allocation scheme comprises:

[0046] Extract the power flow state diagram of the first time point in the final power allocation scheme, convert the power flow state diagram into power control instructions for each interface of the power router, and assign power set values and flow direction identifiers to each power router interface;

[0047] Design a power smoothing transition curve from the current state to the target state to generate a control instruction sequence containing power values, flow directions and execution timing;

[0048] The control instruction is sent to the corresponding power router control unit for execution.

[0049] The application also provides a network-structured micro-grid power router for implementing a network-structured micro-grid power router control system, comprising:

[0050] The application comprises a plurality of load unit interfaces for connecting a plurality of load lines, a plurality of power generation unit interfaces for connecting a plurality of power generation lines, a plurality of energy storage unit interfaces for connecting a plurality of energy storage lines, a power grid unit interface for connecting a plurality of power grid lines, and a master control unit for data communication, power distribution scheme generation and execution.

[0051] The application provides a network-structured micro-grid power router and control system, which has the following beneficial effects:

[0052] By constructing a topology structure diagram and a power flow state diagram, the power transmission relationship inside the micro-grid is comprehensively characterized, and the total difference between adjacent time point power flow state diagrams is minimized as the optimization target, and the system state change is comprehensively evaluated from four dimensions of topology structure difference, power change difference, energy flow difference and distribution mode difference. This optimization method based on the power flow state overcomes the limitation of the traditional method which only focuses on the total power balance, can effectively reduce the drastic change of the power flow state inside the micro-grid, reduce the frequent switching of the power router working mode, reduce the control complexity, prolong the service life of the equipment, improve the system stability and power quality.

[0053] By optimizing the algorithm to generate a plurality of candidate schemes with good stability, and then performing economic evaluation to select the final scheme, the dual optimization of stability and economy is realized. At the same time, the power smooth transition curve designed by the control execution module further ensures the smooth execution of the control instruction. Improve the operation stability of the micro-grid. BRIEF DESCRIPTION OF DRAWINGS

[0054] Figure 1 It is a module schematic diagram of a network-structured micro-grid power router control system of the application.

[0055] Figure 2 It is a structural schematic diagram of a network-structured micro-grid power router of the application. DETAILED DESCRIPTION

[0056] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0057] Embodiment 1

[0058] Referring to Figure 1 In this embodiment, a network-structured microgrid power router control system includes:

[0059] a topology construction module, configured to construct a topology graph according to a physical connection state of the power router, and construct a current power flow state graph based on real-time power data and the topology graph;

[0060] The construction of the topology graph according to the physical connection state of the power router includes:

[0061] identifying all power generation units, energy storage units, load units and grid access points connected by the power router as nodes in the topology graph;

[0062] generating the topology graph as edges in the topology graph according to allowed power transmission paths between the nodes;

[0063] The construction of the current power flow state graph based on real-time power data and the topology graph includes:

[0064] mapping the real-time power data to corresponding edges of the topology graph; and using node pairs to represent power directions to obtain the current power flow state graph.

[0065] In this embodiment, it should be noted that the physical connection state of the power router refers to the actual connection relationship of the power router with various power units, including which power generation units, energy storage units, load units and grid access points are connected, and the physical characteristics of these connections. The power generation units usually include renewable energy power generation equipment such as photovoltaic power generation and wind power generation; the energy storage units mainly refer to various battery energy storage systems; the load units refer to various electrical equipment or load clusters; the grid access point is the connection point of the microgrid and the external grid. The topology graph uses nodes and edges to represent the physical structure of the microgrid. Among them, the nodes represent various functional units in the microgrid, including power generation units, energy storage units, load units and grid access points; the edges represent the allowed power transmission paths between the nodes, i.e. the lines through which power can flow. The construction of the topology graph is to identify all functional units in the system as nodes, then determine the connection relationship (edges) between the nodes according to the connection configuration of the power router, and finally form a complete network structure graph.

[0066] It should be noted that the power flow state diagram is a dynamic diagram formed by mapping real-time power data on the basis of the topology diagram. Mapping real-time power data to the edges of the topology diagram means assigning actual power values on each power transmission path to the corresponding edges; using node pairs to represent the power direction means using ordered node pairs (source node, target node) to represent the direction of power flow from the source node to the target node. In this way, the power flow state diagram not only contains the size information of the power, but also contains the flow direction information of the power, and comprehensively reflects the energy flow state in the micro-grid.

[0067] It should be noted that the power flow state diagram provides a real-time snapshot of the current operating state of the micro-grid, enabling the system to understand the power exchange between units. In the subsequent optimization process, by comparing the power flow state diagrams at different time points, the system can analyze the trend of the micro-grid operating state and provide support for smooth transition.

[0068] The power prediction module is configured to obtain weather prediction data and time characteristics, predict a power generation value sequence of each power generation unit based on the weather prediction data and equipment characteristics of each power generation unit, and predict a load power value sequence of each load unit based on the time characteristics and load data of each load unit.

[0069] The power prediction module is configured to obtain weather prediction data and equipment data of each power generation unit, input the weather prediction data and the equipment data of each power generation unit into a pre-established power generation prediction model, generate power prediction values of each power generation unit at a plurality of future time points, and form a power generation value sequence.

[0070] The power prediction module is configured to obtain weather prediction data and equipment data of each power generation unit, input the weather prediction data and the equipment data of each power generation unit into a pre-established power generation prediction model, generate power prediction values of each power generation unit at a plurality of future time points, and form a power generation value sequence.

[0071] In this embodiment, it should be noted that the power prediction module can use existing mature technologies to predict the power changes of each power generation unit and load unit in the micro-grid in a future period of time, thereby providing prediction data support for the optimization decision of the system.

[0072] It should be noted that for power generation prediction, weather prediction data (such as temperature, light intensity, wind speed, etc.) and equipment data (such as equipment type, rated power, conversion efficiency, etc.) of each power generation unit are obtained, and these data are used as input features for power generation prediction. The power generation prediction model can use existing machine learning methods such as support vector regression (SVR), random forest, long short-term memory network (LSTM), etc.

[0073] It should be noted that for load power prediction, the system obtains time characteristics (such as time, date, day of the week, etc.) and historical load data of each load unit as input features for load prediction. The load prediction model can use mature prediction techniques such as autoregressive integrated moving average model (ARIMA), exponential smoothing method, artificial neural network (ANN), etc.

[0074] It should be noted that the prediction results are output in the form of power value sequences, providing basic data for subsequent optimization solving, so that the system can plan energy scheduling strategies in advance.

[0075] The optimization solving module is configured to construct a multi-period optimization problem based on the current power flow state diagram, the predicted power value sequence of each power generation unit, and the predicted power value sequence of each load unit, and generate a candidate solution set through an optimization algorithm;

[0076] The decision variables of the optimization problem are defined as: the power distribution value and direction of all power transmission paths in the topology structure at each time point;

[0077] The optimization objective is set as: minimizing the total difference between the power flow state diagrams of adjacent time points;

[0078] The constraint conditions are constructed, including: at each time point, the total output power of each power generation unit is equal to its predicted power generation; the total input power of each load unit is equal to its predicted load power; the charge and discharge power of each energy storage unit is constrained; the grid exchange power is limited by the grid-connected capacity;

[0079] The above objective and constraint are integrated to form a complete multi-period optimization problem model.

[0080] The calculation of the total difference between the power flow state diagrams of adjacent time points includes:

[0081] The power flow state diagram of the first time point is compared with the current power flow state diagram;

[0082] The power flow state diagram of each subsequent time point is compared with the power flow state diagram of the previous time point;

[0083] For each pair of adjacent time point power flow state diagrams, the following difference calculation is performed:

[0084] The number of power transmission paths that exist only in one diagram and do not exist in the other diagram is counted as the topology structure difference;

[0085] The power value difference of the common power transmission paths in the two diagrams is calculated as the power change difference;

[0086] The number of paths with changed power flow in the two diagrams is counted as the energy flow difference;

[0087] comparing the proportional change of the power allocated by each source node to each target node as the allocation mode difference;

[0088] constructing a single time period difference measurement function, and weighting and summing the topology structure difference, the power change difference, the energy flow difference and the allocation mode difference after normalization;

[0089] adding up the difference measurement function values of all adjacent time points to obtain a total difference sum.

[0090] The generating of the candidate scheme set by the optimization algorithm comprises:

[0091] creating a chromosome coding rule, encoding the power values and directions of each power transmission path at each time point into a real gene sequence, and generating an initial population composed of individuals meeting all constraint conditions;

[0092] generating a fitness value based on the total difference value corresponding to each individual in the initial population, selecting individuals according to the fitness value, and performing crossover and mutation operations on the selected individuals to generate new multi-time period power allocation schemes;

[0093] verifying whether the new power allocation schemes meet all constraint conditions at each time point, and deleting the power allocation schemes that do not meet the constraints;

[0094] repeating the execution until a preset iteration number is reached to obtain a final population;

[0095] extracting multiple different individuals from the final population according to the fitness value to form a candidate scheme set.

[0096] In the embodiment, it should be noted that the multi-time period power allocation optimization problem is constructed and solved, and the purpose is to determine the optimal power allocation scheme at each time point in the microgrid. Among them, the decision variable of the optimization problem is defined as the power allocation value and direction of all power transmission paths in the topology structure at each time point, which is to determine the energy flow state of the microgrid at each future time point.

[0097] It should be noted that the optimization objective of the optimization problem is to minimize the total difference sum between the power flow state diagrams of adjacent time points. The smaller the total sum is, the smaller the change of the operation state of the microgrid between adjacent time points is, and the smaller the adjustment amplitude is, thereby being able to ensure the smoothness and stability of the system operation. In actual microgrid operation, frequent and large-scale power adjustment may lead to power quality problems, increased equipment wear and tear, and reduced system stability. By minimizing the state change difference, the system can realize smooth transition and improve the operation stability of the microgrid under the premise of meeting the power generation and load demand. At the same time, by considering the energy supply and demand balance, the physical limitations of energy storage devices and the grid-connected capacity limitations, etc. Set constraint conditions to ensure the feasibility and rationality of the scheme.

[0098] It should be noted that the calculation of the difference takes into account four aspects: the topological structure difference reflects the change of the power transmission path; the power change difference reflects the change of the transmission power size; the energy flow difference reflects the change of the power flow direction; and the allocation mode difference reflects the change of the energy allocation ratio. For the four types of differences, the original values are calculated, and then normalized by methods such as the maximum and minimum normalization method. Finally, according to the influence degree of each type of difference on the stability of the system, a weight coefficient is set, and a weighted sum is calculated to obtain the comprehensive difference measurement value of a single time period. For example, the transmission path change will have a greater impact on stability, and a higher weight value can be set for it. The difference measurement values of all adjacent time points are added to form a total difference, and through this multi-dimensional and hierarchical difference calculation method, the changes in each aspect of the microgrid state are accurately reflected, and the optimization result is more in line with the actual operation requirements.

[0099] It should be noted that the optimization solving method based on genetic algorithm is used to encode the multi-period power distribution problem as a chromosome, and through selection, crossover, mutation and other operations, the population evolution is carried out, and finally a set of candidate schemes is obtained. Genetic algorithm can effectively handle large-scale decision variables and complex constraint conditions. The system extracts multiple different individuals from the final population to form a candidate scheme set, rather than selecting only the optimal individual, which takes into account the multi-objective characteristics of microgrid operation. Although this optimization stage mainly focuses on the stability target, the economic target also needs to be considered in the subsequent economic evaluation. By retaining multiple candidate schemes, the system can find a better balance point between stability and economy.

[0100] The economic evaluation module is used to evaluate the economic performance of the candidate methods in the candidate scheme set, and select the economically optimal scheme as the final power distribution scheme.

[0101] The economic evaluation of the candidate methods in the candidate scheme set includes:

[0102] Obtain the electricity price data of each time point in the future;

[0103] For each candidate scheme: according to the power inflow value of each time point of the grid node and the corresponding electricity price, the grid power purchase cost is accumulated; according to the power outflow value of each time point of the grid node and the corresponding electricity price, the grid power selling income is accumulated; according to the charge and discharge power value of each time point of the energy storage node, the energy storage use cost is accumulated;

[0104] Select the scheme with the highest income after subtracting the energy storage use cost and the power purchase cost from the power selling income as the final power distribution scheme.

[0105] In the embodiment, it is to be noted that the core of the economic evaluation is to calculate the economic benefits of each candidate scheme, which mainly includes three aspects of economic factors: power grid power purchase cost, power grid power selling benefit and energy storage use cost. Among them, the power purchase cost is calculated according to the power flowing into the microgrid from the power grid and the electricity price of the corresponding period; the power selling benefit is calculated according to the power flowing out from the microgrid to the power grid and the electricity price of the corresponding period; and the energy storage use cost is calculated according to the loss of the energy storage device in the charging and discharging process combined with the price of the energy storage device. The scheme with the highest net benefit (power selling benefit minus power purchase cost and energy storage use cost) is selected as the final power distribution scheme through the calculation of the net benefit, so as to optimize the economy of the microgrid operation.

[0106] The control execution module is configured to generate and execute the power flow control instruction according to the final power distribution scheme.

[0107] The generating and executing the power flow control instruction according to the final power distribution scheme comprises:

[0108] extracting the power flow state diagram of the first time point in the final power distribution scheme, converting the power flow state diagram into the power control instruction of each interface of the power router, and assigning the power set value and the flow direction identifier to each power router interface;

[0109] designing a power smooth transition curve from the current state to the target state, and generating a control instruction sequence containing power value, flow direction and execution timing;

[0110] issuing the control instruction to the corresponding power router control unit for execution.

[0111] In the embodiment, it is to be noted that the role of the control execution module is to convert the optimal power distribution scheme selected after the economic evaluation into the actual control instruction of the power router, and to ensure the smooth execution of the control instruction.

[0112] It is to be noted that the control execution module first extracts the power flow state diagram of the first time point in the final power distribution scheme. This state diagram is converted into specific power router control instructions, and each power router interface is assigned a specific power set value and flow direction identifier. This conversion process uses existing power electronic control technology to map the abstract power flow information to specific power electronic device control parameters.

[0113] It should be noted that in order to ensure the stability of the system operation, the control execution module designs a power smooth transition curve from the current state to the target state. This smooth transition is a common technique in microgrid control, which usually uses methods such as ramp function or S-shaped curve to avoid power surges that may cause system impact. Based on the transition curve, the system generates a detailed control instruction sequence containing power values, flow directions and execution timing, and sends these instructions to the corresponding power router control unit for execution according to the preset timing. This smooth transition control method can effectively reduce power fluctuations, protect equipment safety, and improve the reliability of microgrid operation.

[0114] Embodiment 2

[0115] Please refer to Figure 2 The application provides a network-structured microgrid power router for implementing a network-structured microgrid power router control system, which comprises:

[0116] A plurality of load unit interfaces for connecting a plurality of load lines; a plurality of power generation unit interfaces for connecting a plurality of power generation lines; a plurality of energy storage unit interfaces for connecting a plurality of energy storage lines; a power grid unit interface for connecting a plurality of power grid lines; and a master control unit for data communication, power distribution scheme generation and execution.

[0117] In several embodiments provided by the application, it should be understood that the disclosed system, device and method can be implemented by other ways. For example, the device embodiments described above are only illustrative, for example, the division of the units is only one, and actual implementation can have another division way, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0118] The above description is only a specific implementation of the application, but the protection scope of the application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the application, which should be included in the protection scope of the application.

[0119] Finally: the above description is only the preferred embodiment of the application and is not used to limit the application, any modification, equivalent replacement, improvement, etc. within the spirit and principle of the application should be included in the protection scope of the application.

Claims

1. A power router control system for a grid-connected microgrid, characterized in that, include: The topology building module is used to build a topology diagram based on the physical connection status of the power routers, and to build a current power flow state diagram based on real-time power data and the topology diagram. The power prediction module is used to acquire weather forecast data and time characteristics, and based on the weather forecast data and the equipment characteristics of each power generation unit, predict the power generation value sequence of each power generation unit; based on the time characteristics and the load data of each load unit, predict the load power value sequence of each load unit. The optimization solution module is used to construct multi-period optimization problems based on the current power flow state diagram, the predicted power generation value sequence and the load power value sequence, and generate a set of candidate solutions through optimization algorithms; The economic evaluation module is used to evaluate the economic efficiency of candidate methods in the candidate solution set and select the most economical solution as the final power allocation scheme. The control execution module is used to generate and execute power flow control commands based on the final power allocation scheme.

2. The microgrid power router control system according to claim 1, characterized in that, The process of constructing the topology diagram based on the physical connection status of the power routers includes: Identify all power generation units, energy storage units, load units, and grid access points connected by the power router, and treat them as nodes in the topology graph; Based on the allowed power transmission paths between nodes, a topology graph is generated as edges in the topology graph. The construction of the current power flow state diagram based on real-time power data and topology diagram includes: Real-time power data is mapped to the corresponding edges in the topology graph; and node pairs are used to represent the power direction to obtain the current power flow state graph.

3. The microgrid power router control system according to claim 2, characterized in that, The predicted power generation value sequence for each power generation unit includes: acquiring weather forecast data and equipment data of each power generation unit, inputting the weather forecast data and equipment data of each power generation unit into a pre-established power generation prediction model, generating power prediction values ​​for each power generation unit at multiple future time points, and forming a power generation value sequence. The method for predicting the load power value sequence of each load unit includes: acquiring the current time characteristics and the load data of each load unit, inputting the time characteristics and the load data of each load unit into a pre-established load prediction model, generating the power prediction values ​​of each load unit at multiple future time points, and forming a load power value sequence.

4. The microgrid power router control system according to claim 3, characterized in that, The multi-time-period optimization problem includes: The decision variables for the optimization problem are defined as: the power distribution and direction of all power transmission paths in the topology at each time point; The optimization objective is set as follows: minimize the sum of differences between the power flow state diagrams at adjacent time points; The constraints are constructed as follows: at each time point, the total output power of each power generation unit is equal to its predicted power generation; the total input power of each load unit is equal to its predicted load power; the charging and discharging power of each energy storage unit is constrained; and the grid switching power is limited by the grid connection capacity. By integrating the above objectives and constraints, a complete multi-period optimization problem model is formed.

5. A grid-type microgrid power router control system according to claim 4, characterized in that, The calculation of the sum of differences between the power flow state diagrams at adjacent time points includes: Compare the power flow state diagram at the first time point with the current power flow state diagram; Compare the power flow state diagram at each subsequent time point with the power flow state diagram at the previous time point; For each pair of adjacent time points, the following difference calculation is performed: The number of power transmission paths that exist only in one graph and not in another is counted as topology differences. Calculate the power value difference of the shared power transmission path in the two graphs as the power change difference; The paths where the power flow direction changes in the two graphs are counted as differences in energy flow. Compare the changes in the proportion of power allocated by each source node to each target node as the difference in allocation mode; A single-time-period difference measurement function is constructed, and the differences in topology, power change, energy flow, and distribution patterns are normalized and then summed according to their weights. The sum of the difference measurement function values ​​for all adjacent time points is obtained by summing the differences.

6. A grid-type microgrid power router control system according to claim 5, characterized in that, The process of generating a candidate solution set through optimization algorithms includes: Create chromosome coding rules to encode the power values ​​and directions of each power transmission path at each time point into real gene sequences; and generate individuals that satisfy all constraints to form an initial population; Fitness values ​​are generated based on the total difference value of each individual in the initial population. Individuals are selected based on their fitness values, and crossover and mutation operations are performed on the selected individuals to generate new multi-time period power allocation schemes. Verify that the new power allocation scheme meets all constraints at each time point, and delete power allocation schemes that do not meet the constraints; Repeat this process until the preset number of iterations is reached, and the final population is obtained. Multiple individuals are extracted from the final population based on their fitness values ​​to form a set of candidate solutions.

7. A grid-type microgrid power router control system according to claim 6, characterized in that, The step of evaluating the economic viability of candidate methods in the candidate scheme set and selecting the most economical scheme as the final power allocation scheme includes: Obtain electricity price data at various future points in time; For each candidate scheme: based on the power inflow value and corresponding electricity price of the grid node at each time point, accumulate the grid electricity purchase cost; based on the power outflow value and corresponding electricity price of the grid node at each time point, accumulate the grid electricity sales revenue; based on the charging and discharging power value of the energy storage node at each time point, accumulate the energy storage usage cost. The scheme with the highest profit after deducting the cost of energy storage and the cost of purchasing electricity from the revenue from electricity sales will be selected as the final power allocation scheme.

8. A grid-type microgrid power router control system according to claim 7, characterized in that, The step of generating and executing power flow control commands based on the final power allocation scheme includes: Extract the power flow state diagram at the first time point in the final power allocation scheme, convert the power flow state diagram into power control instructions for each interface of the power router, and assign a power setting value and flow identifier to each power router interface; Design a smooth power transition curve from the current state to the target state, and generate a sequence of control instructions that includes power value, flow direction and execution timing; The control commands are sent to the corresponding power router control unit for execution.

9. A grid-type microgrid power router, used to implement the grid-type microgrid power router control system as described in any one of claims 1-8, characterized in that, include: Multiple load unit interfaces are provided for connecting multiple load lines. Multiple power generation unit interfaces are provided for connecting multiple power generation lines; Multiple energy storage unit interfaces for connecting multiple energy storage lines, and one grid unit interface for connecting multiple grid lines; A main control unit is used for data communication, power allocation scheme generation and execution.

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