Optimized scheduling method and system based on micro-grid, equipment and storage medium

Abstract

The invention provides an optimal scheduling method and system based on a micro-grid, equipment and a storage medium, and belongs to the technical field of energy scheduling, and the method comprises the steps: calculating a power supply reliability parameter corresponding to a distributed power supply, an energy storage health parameter corresponding to energy storage equipment and a load demand parameter corresponding to a load based on the operation monitoring data of the micro-grid, inputting the power supply reliability parameter, the energy storage health parameter and the load demand parameter into a pre-trained decision tree model to obtain an operation state of the micro-grid; the micro-grid comprises a distributed power supply, an energy storage device and a load. The operation state of the micro-grid comprises a normal operation state and a power supply abnormal state; determining a target scheduling mode based on the operation state of the micro-grid; the target scheduling mode comprises any one of a normal mode and an emergency mode; determining a scheduling strategy adjustment parameter based on the target scheduling mode; the scheduling strategy adjustment parameter is used for performing optimal scheduling on the micro-grid. According to the invention, the stability and reliability of micro-grid operation can be improved.

Description

Technical Field

[0001] This application belongs to the technical field of energy scheduling. More specifically, it relates to an optimal scheduling method, system, device, and storage medium based on a microgrid. Background Art

[0002] With the transformation of the energy structure and the development of distributed energy technologies, microgrids are of great significance in improving energy utilization efficiency, enhancing power supply reliability, and promoting the consumption of renewable energy. However, the complexity and uncertainty of its operation pose challenges to optimal scheduling.

[0003] A microgrid can consist of distributed power sources, energy storage devices, and loads, etc., and the characteristics of microgrid components are complex. The health parameters of energy storage devices change after long-term use; the electricity consumption demand of loads varies dynamically due to user behavior and time. Traditional scheduling methods are based on simple optimization models and are difficult to consider complex grid changes, unable to flexibly adjust in real-time and effectively according to the actual operating status, resulting in the inability of the microgrid to achieve efficient and stable operation. Summary of the Invention

[0004] The purpose of this application is to provide an optimal scheduling method, system, device, and storage medium based on a microgrid to improve the stability and reliability of microgrid operation.

[0005] In the first aspect of the embodiments of this application, an optimal scheduling method based on a microgrid is provided, including: Calculating the power reliability parameters corresponding to distributed power sources, the energy storage health parameters corresponding to energy storage devices, and the load demand parameters corresponding to loads based on the operation monitoring data of the microgrid, and inputting the power reliability parameters, the energy storage health parameters, and the load demand parameters into a pre-trained decision tree model to obtain the operation state of the microgrid; the microgrid includes the distributed power sources, the energy storage devices, and the loads; the operation state of the microgrid includes a normal operation state and a power supply abnormal state; Determining a target scheduling mode based on the operation state of the microgrid; the target scheduling mode includes any one of a normal mode and an emergency mode; Determining a scheduling strategy adjustment parameter based on the target scheduling mode; the scheduling strategy adjustment parameter is used to optimize the scheduling of the microgrid.

[0006] In the second aspect of the embodiments of this application, an optimal scheduling system based on a microgrid is provided, including: A microgrid status analysis module is used to calculate power reliability parameters corresponding to distributed power sources, energy storage health parameters corresponding to energy storage devices, and load demand parameters corresponding to loads based on the operation monitoring data of the microgrid, and input the power reliability parameters, the energy storage health parameters, and the load demand parameters into a pre-trained decision tree model to obtain the operation status of the microgrid; the microgrid includes the distributed power sources, the energy storage devices, and the loads; the operation status of the microgrid includes a normal operation status and a power supply abnormal status; A scheduling mode determination module is used to determine a target scheduling mode based on the operation status of the microgrid; the target scheduling mode includes any one of a normal mode and an emergency mode; An optimal scheduling module is used to determine scheduling strategy adjustment parameters based on the target scheduling mode; the scheduling strategy adjustment parameters are used to perform optimal scheduling on the microgrid.

[0007] In a third aspect of the embodiments of the present application, there is provided an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the above-mentioned optimal scheduling method based on a microgrid are implemented.

[0008] In a fourth aspect of the embodiments of the present application, there is provided a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned optimal scheduling method based on a microgrid are implemented.

[0009] The beneficial effects of the optimal scheduling method, system, device, and storage medium based on a microgrid provided by the embodiments of the present application are as follows: On the one hand, based on the operation monitoring data, the embodiments of the present application calculate power reliability parameters, energy storage health parameters, and load demand parameters, which can comprehensively monitor the real-time status of each component in the microgrid and provide a data basis for accurate scheduling. On the other hand, the embodiments of the present application introduce a pre-trained decision tree model to analyze the parameters, which can quickly identify the normal or abnormal operation status of the microgrid, and then match the corresponding target scheduling mode. When the embodiments of the present application detect a power supply abnormality, they quickly switch to the emergency mode and determine the corresponding scheduling strategy adjustment parameters to perform optimal scheduling on the microgrid, so as to preferentially ensure the power supply to critical loads and avoid large-scale power outages caused by faults; in the normal operation status, the corresponding scheduling strategy adjustment parameters are determined to optimize the energy distribution in the normal mode and improve the power generation efficiency and energy storage life. The dynamic switching mechanism of the embodiments of the present application significantly enhances the ability of the microgrid to cope with complex working conditions, ensures power supply continuity and stability, reduces system operation risks, and realizes the efficient and reliable operation of the microgrid. Description of the Drawings

[0010] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0011] Figure 1 It is a schematic flowchart of an optimization scheduling method based on a microgrid provided by an embodiment of the present application; Figure 2 It is a structural block diagram of an optimization scheduling system based on a microgrid provided by an embodiment of the present application; Figure 3 It is a schematic block diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0012] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0013] To make the purpose, technical solutions, and advantages of the present application clearer, the following will be described through specific embodiments in conjunction with the accompanying drawings.

[0014] Please refer to Figure 1 , Figure 1 It is a schematic flowchart of an optimization scheduling method based on a microgrid provided by an embodiment of the present application. This method can be executed by an electronic device. Specifically, this method may include S101 to S103.

[0015] S101: Calculate the power reliability parameters corresponding to the distributed power sources, the energy storage health parameters corresponding to the energy storage devices, and the load demand parameters corresponding to the loads based on the operation monitoring data of the microgrid, and input the power reliability parameters, the energy storage health parameters, and the load demand parameters into a pre-trained decision tree model to obtain the operation state of the microgrid; the microgrid includes distributed power sources, energy storage devices, and loads; the operation state of the microgrid includes a normal operation state and a power supply abnormal state.

[0016] In this embodiment, the operation monitoring data may include parameters such as the output power of the distributed power source, voltage / frequency fluctuations, fault frequency, environmental meteorological data, the charge and discharge current, rated capacity, charge and discharge duration, remaining capacity, and temperature of the energy storage device, the real-time power consumption of the load, and its timing characteristics. The power reliability parameters can be calculated comprehensively from the output power, voltage / frequency fluctuations, and fault frequency data of the distributed power source, and are used to evaluate the ability of the power source to supply power continuously and stably. The energy storage health parameters can be calculated comprehensively from the charge and discharge current, rated capacity, remaining charge, charge and discharge duration, etc. of the energy storage device, and reflect the degree of performance degradation of the energy storage device. The load demand parameters can be calculated from the real-time power consumption and its timing characteristics, and are used to reflect the load power consumption pattern and demand intensity. The decision tree model is a classification model trained based on the historical data of the microgrid. By dividing nodes through feature parameters, it outputs whether the microgrid is in a normal state or an abnormal state currently.

[0017] In this embodiment, by collecting the operation monitoring data, the parameters of the power source, energy storage, and load are calculated respectively and input into the pre-trained decision tree model. The decision tree model can make hierarchical judgments based on the data characteristics. For example, when the power reliability parameter is lower than the threshold and the energy storage health parameter is lower than the threshold, it is determined that the power supply is in an abnormal state; if all parameters are within the normal range, it is determined that the microgrid is in a normal operation state. This evaluation method is based on multi-dimensional data cross-validation and can more comprehensively and accurately reflect the actual operation status of the microgrid.

[0018] Exemplarily, in this embodiment, current and voltage sensors can be used to collect data such as the output power and voltage frequency fluctuations of the distributed power source, and the obtained data is used to calculate the power reliability parameters corresponding to the distributed power source; an electricity metering module is used to monitor the charge and discharge current and remaining capacity of the energy storage device, and the obtained data is used to calculate the energy storage health parameters corresponding to the energy storage device; an intelligent electricity meter is used to obtain data such as the real-time power demand and power factor of the load, and the obtained data is used to calculate the load demand parameters corresponding to the load; at the same time, the environmental data of the weather station is accessed to provide a basis for the follow-up.

[0019] In this embodiment, historical operation data can be collected, labeled with normal or power supply abnormal state labels, and decision tree models can be constructed using algorithms such as C4.5 and CART. In this embodiment, the model parameters can be adjusted through cross-validation, the node division rules of the decision tree model can be optimized, and the classification accuracy of the decision tree model for the operation state of the microgrid can be improved. In this embodiment, the parameters of the power source, energy storage, and load calculated in real time can be input into the trained decision tree model. After being judged by the feature nodes, the current operation state of the microgrid can be quickly output, providing a basis for the selection of the subsequent scheduling mode.

[0020] S102: Determine the target scheduling mode based on the operation state of the microgrid; the target scheduling mode includes any one of the normal mode and the emergency mode.

[0021] In this embodiment, the target scheduling mode is determined based on the operating state of the microgrid, which specifically includes: if the operating state of the microgrid is a normal operating state, the target scheduling mode is determined as the normal mode; if the operating state of the microgrid is a power supply abnormal state, the target scheduling mode is determined as the emergency mode.

[0022] In this embodiment, the operating state of the microgrid is divided into a normal operating state and a power supply abnormal state. The former refers to the state where distributed power sources, energy storage devices, and loads all operate stably; the latter may include working conditions such as power supply failures, insufficient energy storage capacity, and load surges. The normal mode is a microgrid optimal scheduling mode based on economy, which optimizes power generation efficiency and reduces energy storage losses. The relevant parameters of the normal mode focus on power generation cost, electricity price signal, and energy storage charge-discharge efficiency. In the emergency mode, the power supply reliability of the microgrid is prioritized, and some economy is allowed to be sacrificed. The relevant parameters of the emergency mode focus on the priority of critical loads, the lower limit of the remaining energy storage capacity, and the power supply fault recovery time.

[0023] Based on the operating state evaluation results, this embodiment automatically matches the scheduling mode through preset rules. If the parameters of the power supply, energy storage, and load are all within the normal thresholds, it is determined as the normal operating state and the normal mode is enabled; if abnormal situations such as power supply interruption and exhaustion of energy storage capacity occur, it is immediately switched to the emergency mode to ensure continuous power supply to critical loads. This embodiment balances the economic and reliability requirements and avoids resource waste or power supply accidents.

[0024] Exemplarily, this embodiment can establish a hierarchical threshold system based on the performance parameters of microgrid equipment and historical operation data. For power supply reliability, this embodiment can set a power generation volatility threshold and a failure probability threshold; for energy storage health parameters, a lower limit threshold of the remaining capacity and an internal resistance growth threshold can be set; for load demand parameters, a critical load power failure risk threshold, a power overload threshold, etc. can be set to form a complete state judgment benchmark.

[0025] This embodiment can build the mapping logic between the operating state and the scheduling mode. When the power supply reliability parameter exceeds the threshold and the energy storage health parameter is lower than the threshold, the emergency mode is automatically triggered; if all parameters are within the normal range, it is determined as the normal operating state and the normal mode is enabled.

[0026] This embodiment can input the operating state evaluation results into the rule engine in real time. The engine quickly judges according to the preset rules and automatically switches the target scheduling mode. After the mode is switched, this embodiment can immediately adjust the output power of distributed power sources, the energy storage charge-discharge strategy, and the load distribution plan to ensure the efficient operation of the microgrid in the corresponding mode.

[0027] Exemplarily, the decision tree model learns the characteristic boundaries between the normal and abnormal states of the microgrid from historical data. For example, when the power generation volatility parameter corresponding to the power supply reliability parameter is greater than 15%, and the remaining capacity parameter corresponding to the energy storage health parameter is less than 20%, the historical annotation data shows that the system is in an abnormal power supply state. The decision tree model will classify such a feature combination into the same node and output an abnormal determination.

[0028] In specific implementation, the inputs of the decision tree model are the normalized power supply reliability parameter, energy storage health parameter, and load demand parameter. The decision tree model calculates the feature importance through the C4.5 algorithm or Gini index, and preferentially uses parameters such as the power failure probability that are most significant for state differentiation as the root node, and splits layer by layer until the leaf node outputs the state category (normal / abnormal).

[0029] For example, when the real-time collected power generation volatility is 20% (exceeding the preset threshold of 15%), the decision tree model continues to judge along the branch where the power generation volatility is greater than 15%. If it is simultaneously detected that the remaining capacity of the energy storage is 15% (less than the preset threshold of 20%), an abnormal power supply state output is triggered to drive the system to switch to the emergency mode; if other parameters are normal, it is determined to be in the normal operation state and the normal mode is maintained. This process realizes the cross-verification of multi-dimensional parameters through tree-like hierarchical judgment, avoiding misjudgment by a single indicator.

[0030] S103: Determine the scheduling strategy adjustment parameter based on the target scheduling mode; the scheduling strategy adjustment parameter is used to optimize the scheduling of the microgrid.

[0031] In this embodiment, the scheduling strategy adjustment parameter includes core indicators for guiding energy distribution and equipment regulation in the microgrid. For example, it can include the upper / lower limits of the power generation power of distributed power sources, the charge / discharge power thresholds of energy storage devices, the priority weights of loads, the load power, the start / stop states of each power source and energy storage, etc.

[0032] This embodiment can generate adapted scheduling strategy adjustment parameters through a preset function or algorithm according to the characteristics of the target scheduling mode. In the normal mode, with the goal of economy, combined with the real-time electricity price and power generation cost, the power generation power of the power source and the energy storage charge / discharge plan are adjusted; in the emergency mode, priority is given to ensuring the power supply of critical loads, and by restricting the power consumption of non-critical loads, maximizing the energy storage discharge, etc., the parameters are adjusted to ensure power supply reliability. This embodiment can realize the differential and precise scheduling of the microgrid under different working conditions, balancing economic and safety requirements.

[0033] Exemplarily, in this embodiment, parameter mapping rule tables can be established for the normal mode and the emergency mode respectively. For example, in the normal mode, the weight coefficient of the power generation cost is set to 0.6, and the real-time electricity price fluctuation threshold is ±10%; in the emergency mode, the priority of key load guarantee is set to 100%, and the upper limit of the energy storage discharge depth is relaxed to 90%.

[0034] This embodiment can construct a multi-objective function including economy, reliability, and equipment life, and use a genetic algorithm or a particle swarm algorithm to solve the optimal parameter combination to meet constraint conditions such as power balance and equipment capacity. This embodiment can also adjust the power of the power supply, the charge and discharge rate of the energy storage, and the load power in real time through a distributed control system. In addition, this embodiment needs to design a parameter buffer mechanism for the transition stage. For example, when switching from emergency to normal, a 15-minute linear regression threshold for the energy storage discharge power is set to avoid frequent start and stop of equipment. This embodiment also needs to establish a safety boundary for key parameters to ensure the system stability during the mode switching process.

[0035] As can be seen from the above, on the one hand, this embodiment provides a multi-dimensional data fusion and precise evaluation mechanism. By collecting the parameters of the power supply, energy storage, and load in real time and combining with the decision tree model to cross-verify the operating status, this embodiment avoids misjudgment of a single index. For example, by simultaneously monitoring the power generation volatility and the health status of the energy storage, potential power supply risks can be identified in advance and the failure probability can be reduced.

[0036] On the other hand, this embodiment provides a dual-mode dynamic scheduling strategy. This embodiment can automatically switch between the normal mode and the emergency mode according to the system status. The normal mode gives priority to optimizing the power generation cost and the energy storage life, and the emergency mode forcibly guarantees the power supply of key loads, realizing the dynamic balance between economy and reliability and reducing resource waste and power supply accidents.

[0037] On yet another hand, this embodiment provides a parameter adaptive adjustment technology. Based on the target mode, this embodiment customizes a scheduling strategy, solves the optimal parameters through a multi-objective function and an intelligent algorithm, and combines with a safety boundary and a buffer mechanism to ensure that the energy distribution accurately adapts to the working conditions. For example, during the transition from emergency to normal, it smoothly transitions to avoid equipment impact, and finally improves the operation efficiency and robustness of the microgrid in all scenarios.

[0038] In an embodiment of the present application, the operation monitoring data of the microgrid includes the operation monitoring data corresponding to the distributed power supply, the energy storage device, and the load respectively; the operation monitoring data corresponding to the distributed power supply includes: the power generation power and the fault frequency; the operation monitoring data corresponding to the energy storage device includes: the rated capacity, the remaining power, the charge and discharge duration, and the charge and discharge efficiency; the operation monitoring data corresponding to the load includes: the real-time power consumption and the time series characteristics of the power consumption demand; Among them, calculating the power supply reliability parameters corresponding to the distributed power supply, the energy storage health parameters corresponding to the energy storage device, and the load demand parameters corresponding to the load based on the operation monitoring data of the microgrid includes: Calculate the power supply reliability parameters based on the power generation power and the fault frequency; Calculate the energy storage health parameters based on the rated capacity, remaining power, charge and discharge duration, and charge and discharge efficiency; Calculate the load demand parameters based on the real-time power consumption power and the time series characteristics of the power consumption demand.

[0039] In this embodiment, the power generation power reflects the actual production capacity of the power supply; the fault frequency reflects the stability of the power supply; the rated capacity refers to the maximum storage capacity of the energy storage device, the remaining power reflects the current available energy storage, and the charge and discharge duration and efficiency reflect the working intensity and performance loss of the energy storage device; the real-time power consumption power reflects the immediate demand of the load, and the time series characteristics of the power consumption demand are used to predict the future power consumption trend.

[0040] This embodiment can calculate the corresponding parameters by collecting the operation data of each component of the microgrid in real time and using statistical analysis and algorithm models. For example, evaluate the power supply reliability by using the degree of power generation power fluctuation and the fault frequency; predict the life loss according to the charge and discharge duration and efficiency of the energy storage device; predict the load demand based on the historical power consumption data and time series characteristics. These parameters provide a quantitative basis for the subsequent microgrid operation state evaluation, dispatching mode selection, and strategy optimization, and realize the refined management and efficient dispatching of the microgrid.

[0041] Exemplarily, this embodiment can use a sliding window algorithm to calculate the power generation power volatility, and evaluate the power supply reliability through reliability algorithms such as the Markov model in combination with the fault frequency data; calculate the energy storage health state based on the charge and discharge cumulative amount and efficiency decay curve of the energy storage device, in combination with the ampere-hour integration method and the equivalent circuit model; use the time series analysis algorithm to model the load real-time power and historical data, and predict the peak, valley, and change trend of the power consumption demand.

[0042] The power supply reliability parameters, energy storage health parameters, and load demand parameters calculated in this embodiment provide data support for the subsequent microgrid operation state evaluation, dispatching mode selection, and optimized dispatching strategy, and realize the full-process automatic processing from data collection to parameter output.

[0043] This embodiment deeply perceives the state of microgrid equipment through multi-dimensional data collection and accurate parameter calculation, identifies risks in advance and extends the life of the energy storage; this embodiment can accurately predict the load demand and reduce the power consumption cost; the automatic processing of this embodiment can shorten the calculation cycle, ensure the rapid response of the microgrid, and realize safe, economic, and efficient operation, significantly improving the management efficiency and system reliability.

[0044] In an embodiment of the present application, determine the dispatching strategy adjustment parameters based on the target dispatching mode, including: Determine the target coefficient constraint based on the target dispatching mode; Determine the scenario adaptation factor based on the power supply reliability parameter, energy storage health parameter, and load demand parameter; the scenario adaptation factor is a comprehensive quantitative index of the characteristics of the current operating scenario of the microgrid; Determine the target weight coefficient matrix based on the scenario adaptation factor and the target coefficient constraint; the target weight coefficient matrix includes the weight coefficients corresponding to the power generation efficiency index, power supply reliability index, and energy storage life loss index respectively; the power generation efficiency index refers to the power generation capacity and efficiency index of distributed power sources in the microgrid, and the power supply reliability index refers to the power supply capacity index for the microgrid to stably supply power to the load; the energy storage life loss index refers to the performance degradation and life consumption index caused by physical and chemical changes during the charge and discharge cycles of the energy storage device; Take the power generation efficiency index, power supply reliability index, and energy storage life loss index as the scheduling optimization objectives, and construct a multi-objective scheduling optimization function using the target weight coefficient matrix; Determine the scheduling strategy adjustment parameter based on the multi-objective scheduling optimization function.

[0045] In this embodiment, the target coefficient constraint is a hard parameter boundary set for different scheduling modes. For example, in the emergency mode, the power supply reliability weight is not less than 70%, and in the normal mode, the upper limit of the energy storage life loss weight is 20%, etc., to ensure that the scheduling strategy meets the core objectives of the mode.

[0046] The scenario adaptation factor refers to the demand tendency of the current operating scenario for the scheduling objective obtained by comprehensively quantifying the power supply reliability, energy storage health, and load demand parameters. For example, the power supply reliability weight is increased during high load periods. The target weight coefficient matrix can include the weights of the power generation efficiency, power supply reliability, and energy storage life loss indexes. In this embodiment, the target weight coefficient matrix can be dynamically adjusted through the scenario adaptation factor and the target coefficient constraint. The multi-objective scheduling optimization function refers to a mathematical model constructed with the three major indexes as the objectives and combined with the weight coefficients. By solving this function, scheduling strategy adjustment parameters such as the power generation power of the power supply and the charge and discharge strategy of the energy storage are determined.

[0047] In this embodiment, first determine the basic constraint framework according to the target scheduling mode, and then quantify the current working condition requirements through the scenario adaptation factor. The two are combined to generate the target weight coefficient matrix. This target weight coefficient matrix, as the core parameter of the multi-objective scheduling optimization function, obtains the optimal scheduling strategy by solving the multi-objective scheduling optimization function, realizing the dynamic balance of economy, reliability, and equipment life of the microgrid under different scenarios.

[0048] Exemplarily, in the normal mode, the target coefficient constraint is set such that the weight of power generation efficiency is not less than 40%, and the weight of energy storage life loss does not exceed 30%; in the emergency mode, the target coefficient constraint is set such that the weight ratio of power supply reliability is not less than 70%. After normalizing the power supply reliability parameters, energy storage health parameters, and load demand parameters, they are weighted and summed to obtain a scenario adaptation factor to quantify the scenario characteristics. For example, through the formula Q = 0.4×power supply reliability + 0.3×energy storage health + 0.3×load importance, where Q is the scenario adaptation factor.

[0049] In this embodiment, the scenario adaptation factor can be combined with the target coefficient constraint to dynamically adjust the weight coefficients of power generation efficiency, power supply reliability, and energy storage life loss. For example, when the scenario adaptation factor shows an increase in load demand, within the range of the emergency mode constraint, the weight of power supply reliability is increased to 80%.

[0050] In this embodiment, with power generation efficiency, power supply reliability, and energy storage life loss as the goals, a multi-objective scheduling optimization function can be constructed using a weight coefficient matrix. The particle swarm algorithm or genetic algorithm is used to solve the function to obtain scheduling strategy adjustment parameters such as the distributed power generation power, energy storage charge and discharge strategy, and load distribution plan.

[0051] In this embodiment, the optimized parameters can be input into the microgrid control system to execute the scheduling, and the operation data is monitored in real time. If the deviation between the actual effect and the target exceeds the threshold, the scenario adaptation factor is recalculated, the weight coefficient is corrected, and the scheduling strategy is iteratively optimized.

[0052] In this embodiment, based on the target scheduling mode, the target coefficient constraint is determined, specifically including: if the target scheduling mode is the normal mode, then the target coefficient constraint is determined as the first coefficient constraint; the first coefficient constraint includes that the weight coefficient of the power generation efficiency index in the target weight coefficient matrix is greater than or equal to the first threshold; if the target scheduling mode is the emergency mode, then the target coefficient constraint is determined as the second coefficient constraint; the second coefficient constraint includes that the weight coefficient of the power supply reliability index in the target weight coefficient matrix is greater than or equal to the second threshold; wherein, the second threshold is greater than the first threshold.

[0053] In this embodiment, based on the power supply reliability parameters, energy storage health parameters, and load demand parameters, the scenario adaptation factor is determined, specifically including: obtaining a reference scenario adaptation factor; Based on the reference scenario adaptation factor, power supply reliability parameters, energy storage health parameters, and load demand parameters, and through the first formula, the scenario adaptation factor is determined; The first formula is:

[0054] wherein, is the scenario adaptation factor, is the reference scenario adaptation factor, is the power supply reliability parameter, is the energy storage health parameter, is the load demand parameter.

[0055] In this embodiment, both the second threshold and the first threshold are preset values. In the normal mode, this embodiment ensures that the power generation efficiency weight is not lower than the threshold through the first coefficient constraint, avoiding a sharp increase in economic costs due to excessive pursuit of reliability or energy storage protection. For example, when the energy storage is in good health, it is still preferred to charge during low-price periods to reduce costs. In the emergency mode, this embodiment improves the power supply reliability weight through the second coefficient constraint. Even if the power generation efficiency or the energy storage life loss target is damaged, the power supply for critical loads needs to be guaranteed.

[0056] In the first formula, the numerator quantifies the comprehensive abnormality degree of the power supply, energy storage, and load by taking the square root of the sum of squares. The greater the abnormality, the greater the scenario adaptation factor. The denominator is a reference value used for normalization, making the scenario adaptation factor fluctuate around the benchmark value. When a certain parameter deviates from the normal range, the scenario adaptation factor changes accordingly. If the change in the scenario adaptation factor reaches the trigger condition, the weight can be adjusted to respond to the abnormality. For example, when the power supply failure probability increases, the scenario adaptation factor increases, and this embodiment can appropriately increase the reliability weight within the normal mode constraint to prevent power supply risks in advance.

[0057] In this embodiment, the target coefficient constraint is used to determine the lower limit of the weight coefficient corresponding to the core target parameter, and the scenario adaptation factor finely tunes the weight coefficient allocation within the constraint range. For example, in the emergency mode, if the energy storage health status score is low, the weight of energy storage life loss can be reduced on the basis of the lower limit of the reliability weight, allowing deeper discharge to ensure power supply, but it needs to be controlled within the safety boundary of the equipment.

[0058] Exemplarily, this embodiment can define two types of coefficient constraints in advance according to the microgrid operation mode: Normal mode: Set the first threshold of the power generation efficiency index weight, such as 40%, to ensure the basic priority of the economic target.

[0059] Emergency mode: Set the second threshold of the power supply reliability index weight, such as 70%, and this threshold is higher than that of the normal mode.

[0060] This embodiment first initializes the reference scenario adaptation factor representing the ideal working condition, which can be set specifically based on the statistical values of the stable intervals of each parameter in the historical operation data, such as taking the typical value in the normal mode.

[0061] In this embodiment, the actual values of the power supply reliability parameters, energy storage health parameters, and load demand parameters are compared with the reference values, and the scenario adaptation factor is calculated through the first formula, which reflects the adjustment requirements of the current working condition for the dispatching strategy. The greater the parameter fluctuation, the higher the value of the scenario adaptation factor.

[0062] In this embodiment, within the framework of the target coefficient constraint, according to the calculation result of the scenario adaptation factor, each target weight is finely adjusted according to the preset rules. In this embodiment, the adjusted weight coefficient is input into the multi-objective optimal dispatching function to generate specific dispatching parameters, such as the power output of the power supply and the charge and discharge power of the energy storage, and is executed through the distributed control system.

[0063] This embodiment ensures the priority of the core objectives through target coefficient constraints, dynamically adjusts the weights in combination with the scenario adaptation factor, and realizes multi-objective collaborative optimization. It reduces costs in the normal mode, ensures stable power supply for critical loads in the emergency mode, extends the life of the energy storage, and significantly improves the dynamic balance ability and complex working condition adaptability of the microgrid among economy, reliability, and equipment protection.

[0064] In an embodiment of the present application, the dispatching strategy adjustment parameters are determined based on the multi-objective dispatching optimization function, including: Taking the power generation power of the distributed power supply, the charge and discharge power of the energy storage device, and the load power of each load as the dispatching objects, aiming at maximizing the multi-objective dispatching optimization function, using the genetic algorithm to optimize the power generation power of the distributed power supply, the charge and discharge power of the energy storage device, and the load power of each load, and obtaining the target power generation power of the distributed power supply, the target charge and discharge power of the energy storage device, and the target load power of each load; Taking the target power generation power, the target charge and discharge power, and the target load power as the dispatching strategy adjustment parameters.

[0065] In this embodiment, the multi-objective dispatching optimization function is:

[0066] Among them, is the target optimization score, , and are the weight coefficients, is the normalized power generation efficiency index, is the normalized power supply reliability index, is the normalized energy storage life loss index.

[0067] In this embodiment, the scheduling object refers to the physical quantity that can be directly regulated in the microgrid and is used to execute the optimization strategy. The distributed power generation power refers to the active power output of devices such as photovoltaic and wind power, which is restricted by environmental factors such as sunlight and wind speed. The energy storage charge and discharge power refers to the charging or discharging rate of the battery pack, which is restricted by the remaining capacity, maximum charge and discharge current, etc. The load power refers to the active power demand of various types of loads, which are divided into critical loads and non-critical loads and can be adjusted through demand response. The genetic algorithm is a heuristic optimization algorithm that simulates the biological evolution process and iteratively searches for the optimal solution through operations such as selection, crossover, and mutation.

[0068] Exemplarily, in the normal mode, a certain microgrid has sufficient photovoltaic power during the day, and the load mainly consists of non-critical equipment. At this time, the scheduling goal focuses on power generation efficiency and energy storage protection. Assume the parameter values are: Power generation efficiency index: The actual photovoltaic power generation reaches 85% of the rated power. The corresponding index value indicates high-efficiency power generation. After normalization, the value is 0.85, and the full value is 1.

[0069] Power supply reliability index: All loads are normally powered, the guarantee rate of critical loads is 100%, and the power supply rate of non-critical loads is 95%. The comprehensive value is 0.98.

[0070] Energy storage life loss index: The energy storage device is in the charging state, and the charge and discharge depth is controlled within 20%, that is, the low-loss interval, with a value of 0.2. The lower this value, the smaller the loss.

[0071] Weight coefficient: In the normal mode, the power generation efficiency weight is set at 60%, the power supply reliability at 30%, and the energy storage loss at 10%.

[0072] Then substitute the values of the above parameters into the multi-objective scheduling optimization function to calculate the total score.

[0073] Exemplarily, in the emergency mode, at night, there is a sudden shortage of energy storage capacity and an increase in critical loads. At this time, the scheduling goal is to forcibly ensure power supply stability. Assume the parameter values are: Power generation efficiency index: Photovoltaic power generation stops, relying on high-cost mains power supply, and the power generation efficiency parameter drops to 0.5.

[0074] Power supply reliability index: Critical loads need to be guaranteed 100%, and all non-critical loads are cut off, with a value of 1.0.

[0075] Energy storage life loss index: The energy storage discharges to 15% of the remaining capacity, approaching the safety lower limit, and the loss increases significantly, with a value of 0.8.

[0076] Weight coefficient: In the emergency mode, the power supply reliability weight is set at 70%, the power generation efficiency at 20%, and the energy storage loss at 10%.

[0077] Substitute the above parameter values into the multi-objective scheduling optimization function to calculate the total score.

[0078] Exemplarily, in this embodiment, scheduling object parameters such as the power generation power of distributed power sources, the charge and discharge power of energy storage devices, and the load power of each load can be encoded into chromosomes. The target optimization score calculated by the multi-objective scheduling optimization function is used as the fitness. Through population evolution, a parameter combination that maximizes the target optimization score is searched for, that is, the target power generation power, the target charge and discharge power, and the target load power.

[0079] Exemplarily, in this embodiment, weight coefficients can be set according to the scheduling mode. For example, in the normal mode, the weight of power generation efficiency accounts for 60%, the weight of power supply reliability accounts for 30%, and the weight of energy storage loss accounts for 10%, giving priority to reducing the power generation cost. In the emergency mode, the weight of power supply reliability is increased to 70%, allowing some economy and energy storage life to be sacrificed to ensure power supply.

[0080] In this embodiment, each target parameter is uniformly scaled to the interval [0, 1] to ensure that indicators with different dimensions can be directly weighted and summed.

[0081] In this embodiment, continuous parameters such as power generation power, charge and discharge power, and load distribution power are discretized into gene segments with finite values. For example, the photovoltaic power is divided into 10 grades (0 - 100% rated power).

[0082] In this embodiment, multiple groups of feasible scheduling schemes are randomly generated, such as 100 individuals, ensuring that each group of schemes meets power balance constraints and device physical constraints, such as power generation + energy storage discharge = load + energy storage charge + power purchase, upper and lower limits of energy storage capacity, etc.

[0083] In this embodiment, the multi-objective function values of each group of schemes are calculated. The higher the value, the better the scheme. For example, if a certain scheme has high power generation efficiency and low energy storage loss in the normal mode, the fitness score is higher. In this embodiment, the schemes are sorted according to fitness, and the top 50% of high-quality individuals are retained, while the bottom 50% of low-quality individuals are eliminated. In this embodiment, two high-quality individuals are randomly selected, and part of their genes are exchanged, such as exchanging the energy storage charging power and the load distribution strategy, to generate new individuals. In this embodiment, some parameters of the new individuals are randomly adjusted slightly, such as ±5% of the power generation power, to increase the population diversity. This embodiment repeats the above steps until the termination condition is met, such as the fitness does not improve for 10 consecutive generations or the iteration upper limit of 200 generations is reached, and the optimal scheme is output as the scheduling parameter.

[0084] Through flexible adjustment of the weight coefficients, the microgrid in this embodiment can switch the dispatching strategy as needed among economy, reliability, and equipment protection to adapt to different working conditions. The genetic algorithm introduced in this embodiment can avoid falling into local optimal solutions through large-scale parallel search and quickly find a better solution that takes multiple objectives into account. This embodiment can optimize the dispatching strategy in real time without manual intervention and ensure the safe operation of equipment through constraint conditions, enhancing the microgrid's ability to cope with uncertainties. This embodiment is easy to integrate new objectives (such as carbon emission indicators) or constraints (such as grid interaction requirements) and adapt to the future upgrade needs of the microgrid by adjusting the multi-objective function and algorithm parameters.

[0085] Corresponding to the optimization dispatching method based on the microgrid in the above embodiment, Figure 2 This is a structural block diagram of an optimization dispatching system based on the microgrid provided by an embodiment of the present application. For ease of illustration, only parts related to the embodiments of the present application are shown. Refer to Figure 2 The optimization dispatching system 20 based on the microgrid includes: a microgrid status analysis module 21, a dispatching mode determination module 22, and an optimization dispatching module 23.

[0086] Among them, the microgrid status analysis module 21 is used to calculate the power reliability parameters corresponding to the distributed power sources, the energy storage health parameters corresponding to the energy storage devices, and the load demand parameters corresponding to the loads based on the operation monitoring data of the microgrid, and input the power reliability parameters, energy storage health parameters, and load demand parameters into a pre-trained decision tree model to obtain the operation status of the microgrid; the microgrid includes distributed power sources, energy storage devices, and loads; the operation status of the microgrid includes a normal operation status and a power supply abnormal status; The dispatching mode determination module 22 is used to determine the target dispatching mode based on the operation status of the microgrid; the target dispatching mode includes any one of a normal mode and an emergency mode; The optimization dispatching module 23 is used to determine the dispatching strategy adjustment parameters based on the target dispatching mode; the dispatching strategy adjustment parameters are used to optimize the dispatching of the microgrid.

[0087] In an embodiment of the present application, the operation monitoring data of the microgrid includes the operation monitoring data corresponding to the distributed power sources, energy storage devices, and loads respectively; the operation monitoring data corresponding to the distributed power sources includes: power generation power and fault frequency; the operation monitoring data corresponding to the energy storage devices includes: rated capacity, remaining power, charge and discharge duration, and charge and discharge efficiency; the operation monitoring data corresponding to the loads includes: real-time power consumption and power consumption demand time series characteristics; the microgrid status analysis module 21 is specifically used to calculate the power reliability parameters based on the power generation power and fault frequency; Calculate the energy storage health parameters based on the rated capacity, remaining power, charge and discharge duration, and charge and discharge efficiency; Calculate the load demand parameters based on the real-time power consumption and the time series characteristics of the power consumption demand.

[0088] In an embodiment of the present application, the optimization scheduling module 23 is specifically configured to determine the target coefficient constraint based on the target scheduling mode; Determine the scenario adaptation factor based on the power supply reliability parameter, the energy storage health parameter, and the load demand parameter; the scenario adaptation factor is a comprehensive quantization index of the current operating scenario characteristics of the microgrid; Determine the target weight coefficient matrix based on the scenario adaptation factor and the target coefficient constraint; the target weight coefficient matrix includes the weight coefficients corresponding to the power generation efficiency index, the power supply reliability index, and the energy storage life loss index respectively; the power generation efficiency index refers to the power generation capacity and efficiency index of the distributed power supply in the microgrid, and the power supply reliability index refers to the power supply stability ability index of the microgrid to the load; the energy storage life loss index refers to the performance decline and life consumption index caused by physical and chemical changes during the charge and discharge cycle of the energy storage device; Taking the power generation efficiency index, the power supply reliability index, and the energy storage life loss index as the scheduling optimization objectives, construct a multi-objective scheduling optimization function by using the target weight coefficient matrix; Determine the scheduling strategy adjustment parameter based on the multi-objective scheduling optimization function.

[0089] In an embodiment of the present application, the optimization scheduling module 23 is specifically further configured to, if the target scheduling mode is the normal mode, determine that the target coefficient constraint is the first coefficient constraint; the first coefficient constraint includes that the weight coefficient of the power generation efficiency index in the target weight coefficient matrix is greater than or equal to the first threshold; If the target scheduling mode is the emergency mode, determine that the target coefficient constraint is the second coefficient constraint; the second coefficient constraint includes that the weight coefficient of the power supply reliability index in the target weight coefficient matrix is greater than or equal to the second threshold; Wherein, the second threshold is greater than the first threshold.

[0090] In an embodiment of the present application, the optimization scheduling module 23 is specifically further configured to obtain the reference scenario adaptation factor; Determine the scenario adaptation factor based on the reference scenario adaptation factor, the power supply reliability parameter, the energy storage health parameter, and the load demand parameter, and through the first formula; The first formula is:

[0091] Wherein, is the scenario adaptation factor, is the reference scenario adaptation factor, is the power supply reliability parameter, is the energy storage health parameter, is the load demand parameter.

[0092] In one embodiment of the present application, the optimization scheduling module 23 is specifically further configured to use the power generation power of the distributed power source, the charge and discharge power of the energy storage device, and the load power of each load as the scheduling objects, and maximize the multi-objective scheduling optimization function, and use the genetic algorithm to optimize the power generation power of the distributed power source, the charge and discharge power of the energy storage device, and the load power of each load, so as to obtain the target power generation power of the distributed power source, the target charge and discharge power of the energy storage device, and the target load power of each load; Use the target power generation power, the target charge and discharge power, and the target load power as the scheduling strategy adjustment parameters.

[0093] In one embodiment of the present application, the multi-objective scheduling optimization function is:

[0094] Wherein, is the target optimization score, , and are the weight coefficients, is the normalized power generation efficiency index, is the normalized power supply reliability index, is the normalized energy storage life loss index.

[0095] Refer to Figure 3 , Figure 3 which is a schematic block diagram of an electronic device provided by an embodiment of the present application. As shown in Figure 3 the electronic device 300 in this embodiment may include: one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The above-mentioned processors 301, input devices 302, output devices 303, and memories 304 communicate with each other through the communication bus 305. The memory 304 is used to store computer programs, and the computer programs include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. Among them, the processor 301 is configured to call the program instructions to execute the functions of each module in the above-mentioned system embodiments, such as Figure 2 the functions of the microgrid status analysis module 21, the scheduling mode determination module 22, and the optimization scheduling module 23 shown in

[0096] It should be understood that in the embodiments of the present application, the so-called processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0097] The input device 302 may include a touchpad, a fingerprint acquisition sensor (for acquiring the fingerprint information and the direction information of the fingerprint of the user), a microphone, etc., and the output device 303 may include a display (such as an LCD), a speaker, etc.

[0098] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A part of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the type of energy storage device.

[0099] In a specific implementation, the processor 301, the input device 302, and the output device 303 described in the embodiments of the present application may implement the implementation manners described in the embodiments of the optimization scheduling method based on a microgrid provided by the embodiments of the present application, or may also implement the implementation manner of the electronic device 300 described in the embodiments of the present application, which will not be elaborated herein.

[0100] In another embodiment of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by a processor, all or part of the processes in the methods of the above embodiments are implemented. It can also be completed by instructing relevant hardware through the computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0101] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the foregoing embodiments, such as the hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on the electronic device. Further, the computer-readable storage medium can also include both the internal storage unit and the external storage device of the electronic device. The computer-readable storage medium is used to store the computer program and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store the data that has been output or will be output.

[0102] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0103] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described electronic devices and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0104] In several embodiments provided by the present application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed coupling or direct coupling or communication connection to each other can be an indirect coupling or communication connection through some interfaces or units, or can also be in the form of electrical, mechanical or other connections.

[0105] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or can also be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments of the present application.

[0106] In addition, each functional unit in various embodiments of the present application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0107] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. An optimized scheduling method based on a microgrid, characterized in that, Including: Calculating power reliability parameters corresponding to distributed power sources, energy storage health parameters corresponding to energy storage devices, and load demand parameters corresponding to loads based on the operation monitoring data of the microgrid, and inputting the power reliability parameters, the energy storage health parameters, and the load demand parameters into a pre-trained decision tree model to obtain the operation state of the microgrid; the microgrid includes the distributed power sources, the energy storage devices, and the loads; the operation state of the microgrid includes a normal operation state and a power supply abnormal state; Determining a target scheduling mode based on the operation state of the microgrid; the target scheduling mode includes any one of a normal mode and an emergency mode; Determining a scheduling strategy adjustment parameter based on the target scheduling mode; the scheduling strategy adjustment parameter is used for optimizing the scheduling of the microgrid.

2. The optimization scheduling method based on a microgrid according to claim 1, wherein The operation monitoring data of the microgrid includes the operation monitoring data corresponding to the distributed power sources, the energy storage devices, and the loads respectively; The operation monitoring data corresponding to the distributed power sources includes: power generation power and fault frequency; the operation monitoring data corresponding to the energy storage devices includes: rated capacity, remaining power, charge and discharge duration, and charge and discharge efficiency; the operation monitoring data corresponding to the loads includes: real-time power consumption and power demand time series characteristics; Wherein, calculating the power reliability parameters corresponding to the distributed power sources, the energy storage health parameters corresponding to the energy storage devices, and the load demand parameters corresponding to the loads based on the operation monitoring data of the microgrid includes: Calculating the power reliability parameters based on the power generation power and the fault frequency; Calculating the energy storage health parameters based on the rated capacity, the remaining power, the charge and discharge duration, and the charge and discharge efficiency; Calculating the load demand parameters based on the real-time power consumption and the power demand time series characteristics.

3. The optimization scheduling method based on a microgrid according to claim 1, characterized in that Determining the scheduling strategy adjustment parameter based on the target scheduling mode includes: Determining a target coefficient constraint based on the target scheduling mode; Determining a scenario adaptation factor based on the power reliability parameters, the energy storage health parameters, and the load demand parameters; the scenario adaptation factor is a comprehensive quantification index of the current operation scenario characteristics of the microgrid; Determining a target weight coefficient matrix based on the scenario adaptation factor and the target coefficient constraint; the target weight coefficient matrix includes the weight coefficients corresponding to the power generation efficiency index, the power supply reliability index, and the energy storage life loss index respectively; the power generation efficiency index refers to the power generation capacity and efficiency index of the distributed power sources in the microgrid, and the power supply reliability index refers to the power supply stability ability index of the microgrid to the loads; the energy storage life loss index refers to the performance decline and life consumption index of the energy storage devices caused by physical and chemical changes during the charge and discharge cycles; Taking the power generation efficiency index, the power supply reliability index, and the energy storage life loss index as the scheduling optimization objectives, and constructing a multi-objective scheduling optimization function by using the target weight coefficient matrix; Determining the scheduling strategy adjustment parameter based on the multi-objective scheduling optimization function.

4. The optimization scheduling method based on a microgrid according to claim 3, wherein Determining the target coefficient constraint based on the target scheduling mode includes: If the target scheduling mode is the normal mode, determine that the target coefficient constraint is the first coefficient constraint; the first coefficient constraint includes that the weight coefficient of the power generation efficiency index in the target weight coefficient matrix is greater than or equal to the first threshold; If the target scheduling mode is the emergency mode, determine that the target coefficient constraint is the second coefficient constraint; the second coefficient constraint includes that the weight coefficient of the power supply reliability index in the target weight coefficient matrix is greater than or equal to the second threshold; Wherein, the second threshold is greater than the first threshold.

5. The optimization scheduling method based on a microgrid according to claim 3, characterized in that The determining the scenario adaptation factor based on the power supply reliability parameter, the energy storage health parameter, and the load demand parameter includes: Obtain a reference scenario adaptation factor; Based on the reference scenario adaptation factor, the power supply reliability parameter, the energy storage health parameter, and the load demand parameter, and through a first formula, determine the scenario adaptation factor; The first formula is: ； wherein, is the scene adaptation factor, is the reference scene adaptation factor, is the power supply reliability parameter, is the energy storage health parameter, is the load demand parameter.

6. The optimization scheduling method based on a microgrid according to claim 3, wherein The determining the scheduling strategy adjustment parameter based on the multi-objective scheduling optimization function includes: Taking the power generation power of the distributed power source, the charge and discharge power of the energy storage device, and the load power of each load as the scheduling objects, aiming at maximizing the multi-objective scheduling optimization function, using the genetic algorithm to optimize the power generation power of the distributed power source, the charge and discharge power of the energy storage device, and the load power of each load, and obtaining the target power generation power of the distributed power source, the target charge and discharge power of the energy storage device, and the target load power of each load; Use the target power generation power, the target charge and discharge power, and the target load power as the scheduling strategy adjustment parameters.

7. The optimization scheduling method based on a microgrid according to claim 3, wherein The multi-objective scheduling optimization function is: Among them, is the target optimization score, , and are the weight coefficients, is the normalized power generation efficiency index, is the normalized power supply reliability index, is the normalized energy storage life loss index.

8. An optimized scheduling system based on a microgrid, characterized in that Including: A microgrid status analysis module, configured to calculate the power supply reliability parameter corresponding to the distributed power source, the energy storage health parameter corresponding to the energy storage device, and the load demand parameter corresponding to the load based on the operation monitoring data of the microgrid, and input the power supply reliability parameter, the energy storage health parameter, and the load demand parameter into a pre-trained decision tree model to obtain the operation status of the microgrid; the microgrid includes the distributed power source, the energy storage device, and the load; the operation status of the microgrid includes a normal operation status and a power supply abnormal status; A scheduling mode determination module, configured to determine a target scheduling mode based on the operation status of the microgrid; the target scheduling mode includes any one of a normal mode and an emergency mode; An optimized scheduling module, configured to determine a scheduling strategy adjustment parameter based on the target scheduling mode; the scheduling strategy adjustment parameter is used to perform optimized scheduling on the microgrid.

9. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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