Method and system for intelligent scheduling and control of micro-grid

Abstract

The invention provides a micro-grid intelligent scheduling and control method and system, and relates to the technical field of micro-grid scheduling, and the method comprises the steps: extracting battery state sensing data and timestamp sensing data; when the state belongs to the abnormal state, stopping the process, and when the state belongs to the healthy state, matching a scheduling constraint rule; performing local sample adjacency analysis to obtain production demand electric quantity; performing scheduling scheme configuration to obtain a first scheduling scheme; analyzing to obtain an economic cost evaluation value and an energy-saving parameter evaluation value; when at least one of the economic cost evaluation value greater than or equal to the economic cost evaluation threshold value and the energy-saving parameter evaluation value less than or equal to the energy-saving parameter evaluation threshold value is triggered, performing multi-constraint optimization to obtain a scheduling scheme optimization result; and micro-grid intelligent scheduling control is executed. The micro-grid scheduling method solves the technical problem that a micro-grid scheduling method in the prior art often adopts a control rule of a fixed mode and is lack of flexibility for rapidly changing market conditions and continuously changing energy supply and demand conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid dispatching, and in particular to a method and system for intelligent dispatching and controlling a microgrid. Background Art

[0002] With the rapid development of renewable energy technology and the continuous growth of electricity demand, traditional energy dispatching methods have gradually been unable to meet the complexity and dynamics of modern power grids. Specifically, the rapid development of industrialization and informatization has led to the diversification of energy consumption patterns, and demand has become highly dynamic and unpredictable, making the balance of energy supply and demand more complicated. However, traditional microgrid dispatching methods often adopt fixed-mode control rules and lack sufficient flexibility to cope with rapidly changing market conditions and changing energy supply and demand conditions. This lack of flexibility may lead to energy waste or fail to effectively meet demand during peak energy demand. Summary of the invention

[0003] The present application provides a method and system for intelligent dispatching and control of a microgrid, aiming to solve the technical problem that the microgrid dispatching method in the prior art often adopts fixed-mode control rules and lacks flexibility for rapidly changing market conditions and changing energy supply and demand conditions.

[0004] In a first aspect disclosed in the present application, a method for intelligent dispatching and control of a microgrid is provided, which is applied to a system for intelligent dispatching and control of a microgrid, wherein the system is embedded in a photovoltaic storage charging and discharging integrated station, the system includes a sensing node and a dispatching node, and the method includes: extracting battery status sensing data and timestamp sensing data according to the sensing node; when the battery status sensing data is in an abnormal state, stopping the process, and when the battery status sensing data is in a healthy state, matching the dispatching constraint rule according to the timestamp sensing data; performing local sample adjacency analysis according to the timestamp sensing data to obtain production demand power; performing a dispatching scheme configuration based on the dispatching constraint rule and the production demand power through a dispatching scheme configuration channel of a dispatching node to obtain a first dispatching scheme; analyzing the first dispatching scheme through a dispatching scheme evaluation channel of the dispatching node to obtain an economic cost evaluation value and an energy-saving parameter evaluation value; when at least one of the economic cost evaluation value being greater than or equal to an economic cost evaluation threshold and the energy-saving parameter evaluation value being less than or equal to an energy-saving parameter evaluation threshold is triggered, performing multi-constraint optimization on the first dispatching scheme to obtain a dispatching scheme optimization result; and executing intelligent dispatching and control of the microgrid according to the dispatching scheme optimization result.

[0005] The second aspect disclosed in the present application provides a system for intelligent dispatching and control of a microgrid, the system is embedded in a photovoltaic storage charging and discharging integrated station, the system includes a sensing node and a dispatching node, the system is used for the above-mentioned method for intelligent dispatching and control of a microgrid, the system includes: a sensing data extraction module, used to extract battery status sensing data and timestamp sensing data according to the sensing node; a dispatching constraint rule matching module, used to stop the process when the battery status sensing data is in an abnormal state, and match the dispatching constraint rule according to the timestamp sensing data when the battery status sensing data is in a healthy state; a local sample adjacency analysis module, used to perform local sample adjacency analysis according to the timestamp sensing data to obtain the production demand power; a dispatching module A scheme configuration module is used to configure the scheduling scheme based on the scheduling constraint rules and the production demand power through the scheduling scheme configuration channel of the scheduling node to obtain a first scheduling scheme; an evaluation value acquisition module is used to analyze the first scheduling scheme through the scheduling scheme evaluation channel of the scheduling node to obtain an economic cost evaluation value and an energy-saving parameter evaluation value; a multi-constraint optimization module is used to perform multi-constraint optimization on the first scheduling scheme when at least one of the economic cost evaluation value is greater than or equal to the economic cost evaluation threshold and the energy-saving parameter evaluation value is less than or equal to the energy-saving parameter evaluation threshold is triggered to obtain a scheduling scheme optimization result; an intelligent scheduling control module is used to perform microgrid intelligent scheduling control according to the scheduling scheme optimization result.

[0006] One or more technical solutions provided in this application have at least the following beneficial effects: By monitoring the battery status and timestamp data in real time through the sensing node, the current energy storage status and time-related data of the power station can be quickly obtained. This real-time data collection provides accurate basic data for subsequent scheduling decisions, ensuring the timeliness and accuracy of the scheduling strategy; when the battery status data shows abnormalities, the relevant processes are automatically stopped to prevent possible equipment damage or efficiency reduction. When the battery is in a healthy state, the real-time data is used to match the scheduling constraint rules to ensure the optimization of energy distribution; local sample adjacency analysis is performed based on the timestamp sensing data to predict the upcoming production demand power based on historical and real-time data, so as to allocate and plan energy in advance, optimize energy utilization efficiency, make energy supply more accurate and efficient, and avoid overproduction or insufficient supply; the scheduling plan configuration channel generates a preliminary scheduling plan based on the production demand power and the preset scheduling constraint rules. , which improves the flexibility and adaptability of the dispatching scheme and can cope with changing dispatching needs; the dispatching scheme evaluation channel conducts dual evaluation of the generated dispatching scheme in terms of economic cost and energy-saving effect. This evaluation mechanism ensures that the dispatching scheme can not only meet the actual power demand, but also achieve the goals of cost-effectiveness and energy saving, providing quantitative indicators for subsequent analysis; when the dispatching scheme does not meet the set threshold in terms of cost or energy saving, multi-constraint optimization is performed to find a solution that can achieve the optimal cost and energy-saving effect while meeting all key performance indicators. This step not only ensures power supply safety and reliability, but also emphasizes the maximization of economic and environmental benefits; according to the optimization results, the microgrid intelligent dispatching control is executed, which not only improves the overall efficiency of energy use, but also ensures that the requirements for power supply stability and sustainability are met, significantly improving the efficiency and effectiveness of energy management.

[0007] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A flow chart of a method for intelligent dispatching and control of a microgrid provided in an embodiment of the present application.

[0009] Figure 2 A schematic diagram of a multi-constraint optimization process in a method for intelligent scheduling and control of a microgrid provided in an embodiment of the present application.

[0010] Figure 3 A schematic diagram of the system structure of a microgrid intelligent scheduling and control provided in an embodiment of the present application.

[0011] Explanation of the accompanying drawings: perception data extraction module 10, scheduling constraint rule matching module 20, local sample adjacency analysis module 30, scheduling plan configuration module 40, evaluation value acquisition module 50, multi-constraint optimization module 60, intelligent scheduling control module 70. DETAILED DESCRIPTION

[0012] The embodiments of the present application provide a method and system for intelligent scheduling and control of a microgrid, thereby solving the technical problem that the microgrid scheduling methods in the prior art often adopt fixed-mode control rules and lack flexibility for rapidly changing market conditions and constantly changing energy supply and demand conditions.

[0013] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced below in conjunction with the accompanying drawings of the specification. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0014] Embodiment 1, as Figure 1 As shown, an embodiment of the present application provides a method for intelligent dispatching and control of a microgrid, which is applied to a system for intelligent dispatching and control of a microgrid, wherein the system is embedded in a photovoltaic storage charging and discharging integrated station, the system includes a sensing node and a dispatching node, and the method includes: According to the sensing node, battery status sensing data and timestamp sensing data are extracted.

[0015] A method for intelligent dispatching and control of a microgrid provided in an embodiment of the present application is applied to a system for intelligent dispatching and control of a microgrid. The system for intelligent dispatching and control of a microgrid is used to optimize and manage energy distribution in a microgrid. The system can monitor battery status and energy demand in real time, and automatically adjust energy output to meet demand and improve efficiency. The system is embedded in a photovoltaic storage charging and discharging integrated station. The photovoltaic storage charging and discharging integrated station is a comprehensive facility that integrates photovoltaic power generation, battery energy storage, and electric energy charging and discharging functions. The station can not only generate electric energy, but also store electric energy and supply power to the power grid or electric equipment when needed. The system includes a sensing node and a scheduling node. The sensing node is a sensor device in the system, which is used to collect data about the state of the microgrid in real time, such as battery power, voltage, current, etc., as well as environmental data, such as light intensity, temperature, etc. The data of the sensing node is crucial for the system to make intelligent decisions. The scheduling node is the part of the system responsible for processing the data collected by the sensing node and making scheduling decisions based on it. The scheduling node uses various algorithms to optimize energy use, such as battery charging and discharging time, and the best use of photovoltaic power generation.

[0016] Specifically, the sensing nodes are installed at key locations in the microgrid, such as near the battery system and photovoltaic panels, to monitor the battery voltage, current, temperature and other parameters in real time, and record the timestamps of these data. Battery status sensing data and timestamp sensing data are extracted based on the sensing nodes. The battery status sensing data includes the battery's charging status, power level, health status, etc. These data provide information on the battery's current working condition and expected life span; the timestamp sensing data is the timestamp of each sensing data point, indicating the specific time of data collection, which is used to track changes in battery status and predict future battery behavior.

[0017] When the battery status perception data belongs to an abnormal state, the process is stopped; when the battery status perception data belongs to a healthy state, the scheduling constraint rule is matched according to the timestamp perception data.

[0018] If the battery status sensing data shows that the battery is in an abnormal state, for example, the voltage is too low or too high, the temperature is abnormal, etc., the relevant operations are stopped immediately to protect the battery from further damage. In this case, the safety protocol will be initiated, such as disconnecting the battery, sending an alarm, etc.

[0019] If the battery status sensing data shows that the battery is in a healthy state, the timestamp sensing data is used to determine the current time and battery usage demand, such as whether it is at a peak time with high demand or a valley time with low demand, and match the scheduling constraint rules, including the optimal charging and discharging time of the battery, and how to optimize energy use based on electricity prices and grid demand, for example, storing energy during the night time when electricity prices are lower and releasing energy during the day time when electricity prices are higher.

[0020] This scheduling constraint rule matching mechanism effectively improves the operating efficiency and reliability of the microgrid, while reducing maintenance costs and extending the service life of the battery. Through real-time data and intelligent analysis, the microgrid can adapt to changing energy demands and market conditions.

[0021] A local sample adjacency analysis is performed based on the timestamp sensing data to obtain the production demand electricity.

[0022] Adjacency analysis refers to analyzing the collected data samples, comparing data points that are close in time to identify patterns or trends, and analyzing data at adjacent time points based on timestamp-aware data, such as electricity usage data at the same time of the previous day or the same day of the previous week, to estimate the current production demand for electricity. This method can predict short-term changes in electricity demand based on historical data. Through this analysis, the production demand for electricity obtained is the predicted amount of electricity required within a specific time period. This predicted amount will directly affect the subsequent decisions of the scheduling node.

[0023] Through the scheduling scheme configuration channel of the scheduling node, the scheduling scheme is configured based on the scheduling constraint rules and the production demand power to obtain a first scheduling scheme.

[0024] The dispatch node is the core component of the system, responsible for generating dispatch plans based on the collected and analyzed data, including the distribution of electricity, the determination of charging and discharging timing, and the interaction with the external power grid. Through the dispatch node's dispatch plan configuration channel, which is a component of the dispatch node, it is used to configure the dispatch plan. Specifically, based on the dispatch constraint rules and the production demand, a preliminary dispatch plan is formulated. This plan specifies when to use energy storage, when to use direct photovoltaic power, and how to interact with the power grid (such as charging or selling electricity). The configured dispatch plan is used as the first dispatch plan for the next implementation stage.

[0025] The first scheduling scheme is analyzed through the scheduling scheme evaluation channel of the scheduling node to obtain an economic cost evaluation value and an energy-saving parameter evaluation value.

[0026] The scheduling scheme evaluation channel is another component of the scheduling node, which is used to comprehensively evaluate the scheduling scheme to determine the feasibility and benefits of the scheme. Specifically, the scheduling scheme evaluation channel receives the first scheduling scheme, including the expected power production, consumption, energy storage usage, etc., and converts various operations in the scheduling scheme into quantitative indicators of cost and energy saving, so as to conduct economic cost evaluation and energy saving parameter evaluation. Among them, economic cost evaluation usually involves the calculation of energy purchase cost, operating cost, maintenance cost and potential benefits. Energy saving parameter evaluation mainly considers whether the scheduling scheme can effectively reduce energy consumption and improve energy efficiency. After evaluation, the economic cost evaluation value and energy saving parameter evaluation value are obtained, which provide quantitative indicators for subsequent analysis.

[0027] When at least one of the economic cost evaluation value being greater than or equal to the economic cost evaluation threshold and the energy-saving parameter evaluation value being less than or equal to the energy-saving parameter evaluation threshold is triggered, multi-constraint optimization is performed on the first scheduling scheme to obtain a scheduling scheme optimization result.

[0028] The economic cost evaluation threshold is a threshold preset based on actual needs, which indicates the maximum acceptable value of the economic cost evaluation value. If the economic cost evaluation value is greater than or equal to the set cost threshold, it means that the cost of the plan may be too high and needs to be adjusted to reduce the cost. The energy-saving parameter evaluation threshold is a threshold preset based on actual needs, which indicates the minimum acceptable value of the energy-saving parameter evaluation value. If the energy-saving parameter evaluation value is less than or equal to the set energy-saving threshold, it indicates that the energy-saving effect does not meet the standard and the energy-saving performance needs to be improved.

[0029] If any one of the above two thresholds is triggered, or both are triggered at the same time, the first scheduling plan is used as an optimization problem, in which the objective function includes one or both of minimizing the cost and maximizing the energy-saving effect. Through the optimization algorithm, the optimal or approximately optimal scheduling plan is found under given multiple constraints to obtain the scheduling plan optimization result, which can improve the overall system performance and reliability while meeting the economic and energy-saving standards.

[0030] The microgrid intelligent dispatching control is performed according to the dispatching scheme optimization result.

[0031] The optimization results of the dispatching scheme are converted into specific operating instructions, such as when to start photovoltaic power generation, when to use energy storage equipment to discharge, and how to adjust the load, etc. The microgrid is intelligently dispatched and controlled according to the operating instructions. Furthermore, during the implementation process, the deviation between the actual operation and the expected plan is continuously monitored, and adjustments are made as needed to ensure the efficiency and safety of the operation.

[0032] Through the above steps, the microgrid system can manage energy use more accurately and respond to changes in various operating conditions while ensuring the achievement of economic benefits and energy saving goals. This approach helps to dynamically adapt to changing energy demand and environmental conditions and achieve sustainable development goals.

[0033] Furthermore, when the battery state sensing data belongs to a healthy state, matching the scheduling constraint rules according to the timestamp sensing data includes: The scheduling constraint rules include power supply constraint type, power supply type priority and power supply constraint task, and the sum of the power supply type priority is equal to 1; when the battery status perception data belongs to the healthy state, and the timestamp perception data belongs to the valley time zone, the power supply constraint type includes large power grid power supply and photovoltaic power supply, the large power grid power supply priority ∈ [0,0.5], the photovoltaic power supply priority ∈ [0.5,1], and the power supply constraint tasks include production power supply and energy storage charging; when the battery status perception data belongs to the healthy state, and the timestamp perception data belongs to the flat time zone, and the energy storage battery power is greater than the discharge power threshold, the The power supply constraint types include large power grid power supply, photovoltaic power supply and energy storage power supply. The priority of large power grid power supply plus the priority of energy storage power supply is ∈[0,0.5], and the priority of photovoltaic power supply is ∈[0.5,1]. The power supply constraint task includes production power supply. When the battery status sensing data belongs to the health state, and the timestamp sensing data belongs to the peak time zone, and the energy storage battery power is greater than the discharge power threshold, the power supply constraint types include large power grid power supply, photovoltaic power supply and energy storage power supply. The priority of large power grid power supply is equal to ∈[0,0.2], and the priority of energy storage power supply plus photovoltaic power supply is ∈(0.2,1], and the power supply constraint task includes production power supply.

[0034] The scheduling constraint rules include power supply constraint type, power supply type priority and power supply constraint task. The power supply constraint type refers to the different power supply sources that can be selected during the scheduling process, such as large power grid power supply, photovoltaic power supply, energy storage power supply, etc. The power supply type priority is the priority set for various power supply types, and the sum is equal to 1 to ensure that the weights of each power supply type are balanced in the decision-making process. The priority setting reflects the importance and cost-effectiveness of different power sources in power supply. For example, in some cases, photovoltaic power generation with lower cost may be used first. The power supply constraint task is to specify the specific tasks that each power supply mode must meet, such as only supplying production electricity, energy storage charging, or both.

[0035] When the battery status perception data belongs to a healthy state, this condition ensures that the system operates when the battery is in a good working condition, thereby avoiding taking strategies that may aggravate battery loss when the battery performance deteriorates; when the timestamp perception data belongs to the valley time zone, the valley time zone usually refers to a period of time with lower electricity prices. The cost of using electricity is low at this time, which is an ideal time for charging and large-scale electricity use.

[0036] Under this condition, the power supply constraint types include large grid power supply and photovoltaic power supply. The two power supply types are selected based on their availability and cost-effectiveness. The large grid usually provides a stable power supply, and photovoltaic power generation provides environmentally friendly and low-cost power. Among them, the large grid power supply priority ∈ [0,0.5], this range indicates that the large grid power supply has a lower weight in the overall power supply strategy. The lower priority may be due to the high grid electricity price, or the strategic desire to reduce dependence on traditional grids and support more renewable energy use; photovoltaic power supply priority ∈ [0.5,1], indicating that in the valley time zone, photovoltaic power supply has a higher priority than the large grid, reflecting the priority support for renewable energy. Photovoltaic power generation is low-cost and environmentally friendly, and is an ideal choice for optimizing energy configuration, reducing costs and supporting environmental sustainability.

[0037] Power supply constraint tasks include production power supply and energy storage charging. Among them, production power supply ensures that production activities have sufficient power support to maintain production efficiency and continuity; energy storage charging uses low-cost electricity for energy storage operations, which can not only be used to reduce peaks and fill valleys during periods of higher electricity prices, but also improves the flexibility and safety of energy use.

[0038] Through this strategy, the microgrid intelligent dispatching system can maximize the use of renewable energy during low-cost periods, while storing energy to prepare for high-demand or high-cost periods, thereby optimizing overall energy management and cost-effectiveness. Such a dispatching strategy not only helps economic savings, but also meets the goals of environmental protection and sustainable development.

[0039] When the timestamp perception data belongs to the average time zone, this is a period when the electricity price is neither the highest nor the lowest, representing a medium level of electricity cost and demand; when the energy storage battery power is greater than the discharge power threshold, this ensures that the energy storage system has sufficient power to supply power without causing power outages due to insufficient power.

[0040] Under this condition, the power supply constraint types include large grid power supply, photovoltaic power supply and energy storage power supply. These options provide flexible energy management methods to adapt to different power supply needs and cost-effectiveness. Among them, the priority of large grid power supply plus the priority of energy storage power supply ∈ [0,0.5] means that in the average time zone, the total priority of these two power supply methods is limited to a lower level, and the sum does not exceed 0.5, reflecting the need for cost control and encouraging the use of more renewable energy; photovoltaic power supply priority ∈ [0.5,1] indicates that in the average time zone with moderate electricity prices, it tends to use renewable energy, which is both environmentally friendly and cost-effective.

[0041] The power supply constraint task includes production power supply, that is, in the average time zone, the main task of the system is to ensure that production activities are supported by sufficient electricity, because the average time zone may be the main working time on weekdays and the demand is stable.

[0042] The setting of this strategy helps to ensure that the microgrid system optimizes the power supply structure when the electricity cost is moderate and the battery power is sufficient, reduces dependence on the traditional power grid, and makes full use of renewable energy. Such a power supply configuration not only ensures the continuity and stability of production, but also supports the sustainable use of energy and improves cost efficiency.

[0043] When the timestamp-aware data belongs to the peak time zone, which is the period with the highest electricity prices, it usually corresponds to the time when the demand for electricity is the greatest. In this time zone, electricity cost control is particularly critical.

[0044] Under this condition, the power supply constraint types include large grid power supply, photovoltaic power supply and energy storage power supply, among which the priority of large grid power supply is equal to ∈[0,0.2], which means that during peak hours, the priority of large grid power supply is very low, which indicates that the reliance on purchasing electricity from high-cost power grids should be minimized to reduce operating costs; energy storage power supply plus photovoltaic power supply priority ∈(0.2,1], that is, the total priority of these two power supply modes occupies the vast majority, reflecting that during costly peak hours, stored electricity or renewable energy is given priority to maximize cost-effectiveness and sustainability.

[0045] The power supply constraint task includes production power supply, which means that in the peak time zone, even if the electricity cost is high, production power supply is still the main task, which requires the system to ensure that production activities are not affected by fluctuations in electricity costs by optimizing the power supply method.

[0046] Through this power supply constraint strategy, the microgrid system can effectively utilize energy storage and photovoltaic power generation during periods when electricity demand and costs are at peak, reducing dependence on expensive large grid power supply. This not only helps reduce operating costs, but also improves the system's energy self-sufficiency and environmental sustainability.

[0047] Furthermore, it also includes: When the battery status sensing data belongs to the healthy state, and the timestamp sensing data belongs to the flat time zone, and the energy storage battery power is less than or equal to the discharge power threshold, the power supply constraint tasks include production power supply and energy storage charging; when the battery status sensing data belongs to the healthy state, and the timestamp sensing data belongs to the peak time zone, and the energy storage battery power is less than or equal to the discharge power threshold, the large power grid power supply constraint tasks include production power supply, and the photovoltaic power supply constraint tasks include production power supply and energy storage charging.

[0048] When the energy storage battery power is less than or equal to the discharge power threshold, it indicates that the energy storage system power is at a low level and needs to be carefully managed to prevent power exhaustion, and recharging needs to be considered. Under this condition, the power supply constraint tasks include production power supply and energy storage charging, which means that even in the case of power shortage, ensuring production power is still a high priority task, because production activities usually have strict requirements for the continuous supply of electricity. Given that the energy storage power is close to or reaches the discharge threshold, an important task at this time is to charge the energy storage system, in order to ensure that the energy storage battery can provide sufficient power support when needed in the future, especially in expected high demand or high cost periods.

[0049] Through such a power supply strategy, the microgrid system can effectively cope with the challenge of insufficient power while ensuring the stability of production activities and timely charging of the energy storage system. This strategy helps to improve the energy efficiency and reliability of the entire system and ensure normal operation under various power conditions.

[0050] When the timestamp sensing data belongs to the peak time zone, and the energy storage battery power is less than or equal to the discharge power threshold, it indicates that the electricity cost is high and the energy storage power is insufficient, and the energy storage power supply cannot be fully relied on to meet the power demand during peak hours. Under this condition, the power supply constraint task of the large power grid includes production power supply, that is, in the peak time zone, although the power cost of the power grid is high, considering the insufficient energy storage power, the system needs to rely on the large power grid to ensure the stable operation of the production facilities, in order to ensure that production is not interrupted due to power shortages; the photovoltaic power supply constraint task includes production power supply and energy storage charging, that is, the photovoltaic system can provide direct power supply during the day. If conditions permit, photovoltaic power should be used to support production first, reducing the need to purchase high-priced electricity from the power grid. When the power generated by the photovoltaic system exceeds the production demand, the additional power should be used to charge the energy storage equipment. This is to maximize the use of renewable resources when there is sufficient sunshine and prepare for possible power shortages.

[0051] Through such a power supply strategy, the microgrid system can maximize the economy and sustainability of energy use while ensuring production continuity. This flexible power management strategy helps maintain the efficiency and stability of the system under various operating conditions.

[0052] Furthermore, local sample adjacency analysis is performed based on the timestamp sensing data to obtain the production demand power, including: According to the timestamp sensing data, adjacent local production logs are collected, wherein the local production logs include a power consumption record data set; mode analysis is performed on the power consumption record data set to obtain the production demand power.

[0053] Timestamp-aware data provides accurate time tags to identify power usage during a specific period of time. Based on the timestamp-aware data, local production logs at adjacent time points are automatically collected. These logs include power consumption record data sets that record actual power consumption in production activities. With these data, a comprehensive view of energy consumption can be obtained, providing a basis for further analysis.

[0054] Mode analysis is used to find the value that appears most frequently in a set of data. In power consumption records, the mode represents the most common or typical power consumption. Specifically, mode analysis is performed on the collected power consumption record data set to determine the most common power consumption patterns in different production cycles or time periods, which helps to predict the possible maximum power demand under similar production conditions. The analysis results point out the main trends in power use in the production process and determine the production power demand under normal production conditions. This enables the dispatching system to plan the allocation of power resources more effectively, especially during periods when power demand may reach peak values.

[0055] Furthermore, the first scheduling scheme is analyzed through the scheduling scheme evaluation channel of the scheduling node to obtain an economic cost evaluation value and an energy-saving parameter evaluation value, including: According to the scheduling constraint rules, the associated scheduling scheme evaluation node is activated; the first scheduling scheme is preprocessed through the coding layer to obtain a dimensionless input vector; the dimensionless input vector is fit through the economic cost evaluation layer of the associated scheduling scheme evaluation node, and the economic cost evaluation value is output; the dimensionless input vector is fit through the energy-saving evaluation layer of the associated scheduling scheme evaluation node, and the energy-saving parameter evaluation value is output.

[0056] According to the scheduling constraint rules, the associated scheduling scheme evaluation nodes are activated. The associated scheduling scheme evaluation nodes are components in the system responsible for analyzing and evaluating the performance of scheduling schemes. They use various algorithms and models to evaluate the economy, reliability and efficiency of scheduling schemes.

[0057] The encoding layer is a key part of data preprocessing, which is responsible for converting various parameters in the scheduling scheme (such as power supply, time, cost, etc.) into dimensionless input vectors. The preprocessing step aims to convert the data of the first scheduling scheme into a format suitable for model analysis, which usually includes standardization and dimension removal so that different data types can be compared and analyzed under the same standard. After preprocessing, the dimensionless input vector is obtained, which eliminates the unit and scale differences in the original data. The dimensionless input vector enables the model to evaluate the scheduling scheme based only on the relative importance and relevance of the data, without being affected by the original value.

[0058] The economic cost evaluation layer is used to evaluate the economic feasibility of the scheduling scheme, including cost-benefit analysis and financial sustainability assessment. It converts the data in the dimensionless input vector into quantitative indicators of economic cost through a model fitting process. Specifically, a predetermined economic model is used to combine the data in the input vector, such as electricity usage, power supply type, time distribution and other cost-related factors, to calculate the expected total cost or unit cost. This fitting process includes regression analysis, cost function calculation or other statistical methods to ensure the accuracy and reliability of the evaluation value. The output economic cost evaluation value provides a quantitative indicator of the financial performance of the scheduling scheme, including direct costs, operating costs and potential economic benefits.

[0059] The energy-saving evaluation layer is used to evaluate the energy efficiency and energy-saving potential of the scheduling scheme, and examine the performance of the scheme in reducing energy consumption and improving energy efficiency. Similar to the economic cost evaluation, the energy-saving evaluation layer also uses dimensionless input vectors for model fitting, aiming to identify and calculate the effects of energy-saving measures, such as reducing energy consumption and the proportion of renewable energy use. Environmental protection indicators, energy consumption data and efficiency standards can be used to evaluate the energy-saving effect of the scheduling scheme. The final output energy-saving parameter evaluation value provides quantitative information about the performance of the scheduling scheme in energy saving, including estimated energy savings, reduced emissions and improved energy efficiency.

[0060] Furthermore, if Figure 2 As shown, when at least one of the economic cost evaluation value is greater than or equal to the economic cost evaluation threshold, and the energy-saving parameter evaluation value is less than or equal to the energy-saving parameter evaluation threshold, a multi-constraint optimization is performed on the first scheduling scheme to obtain a scheduling scheme optimization result, including: A first weight is configured for the economic cost evaluation value, and a second weight is configured for the energy-saving parameter evaluation value, wherein the sum of the first weight and the second weight is equal to 1; based on the first weight and the second weight, the economic cost evaluation value and the energy-saving parameter evaluation value are merged to obtain a first fitness evaluation value; the first scheduling scheme and the first fitness evaluation value are associated and stored, and added to a scheduling scheme group; when the number of schemes in the scheduling scheme group is greater than or equal to a quantity threshold, a multi-constraint optimization is performed based on the scheduling scheme group to obtain the scheduling scheme optimization result.

[0061] The weight configuration is designed to determine the relative importance of the economic cost evaluation value and the energy saving parameter evaluation value in the overall evaluation. This configuration reflects the strategic focus of the organization, for example, whether it attaches more importance to cost savings or environmental sustainability. According to actual needs, the first weight is assigned to the economic cost evaluation value, indicating the importance of the cost factor in the decision-making process; the second weight is assigned to the energy saving parameter evaluation value, indicating the importance of the energy saving effect in the decision-making process. The sum of these two weights is equal to 1, ensuring the consistency and balance of the evaluation system.

[0062] Using the first weight and the second weight, the economic cost evaluation value and the energy-saving parameter evaluation value are weightedly fused. This fusion process takes into account their respective weights and synthesizes a comprehensive evaluation index as the first fitness evaluation value. The first fitness evaluation value reflects the overall performance of the first scheduling scheme under the given weights. A high fitness evaluation value indicates that the scheme performs well in terms of economy and environmental protection and is a choice with better comprehensive performance.

[0063] The first scheduling scheme and the first fitness evaluation value are stored in association with each other to facilitate subsequent comparison and selection. This can clearly show the performance of each scheme in the comprehensive evaluation and help compare the advantages and disadvantages of different schemes. After the associated storage, they are added to the scheduling scheme group. This scheduling scheme group contains multiple potential scheduling schemes. Each scheme has a specific fitness evaluation value, reflecting its performance under the current set conditions.

[0064] A quantity threshold is set in advance, which is the minimum number of solutions that need to be reached before starting to perform multi-constraint optimization. This threshold ensures that the system has enough data for effective comparison and selection. When the number of solutions in the scheduling solution group is greater than or equal to the quantity threshold, it means that the number of solutions is sufficient for optimization analysis. Multi-constraint optimization is an optimization process that considers multiple constraints, such as cost, energy efficiency, reliability, etc. The goal is to find the best comprehensive performance solution. In this step, all solutions in the scheduling solution group are analyzed and the optimization algorithm is used to determine which solutions can best meet all constraints. After the optimization process is completed, the optimal scheduling solution is output as the scheduling solution optimization result. The optimal scheduling solution provides the highest comprehensive benefit while meeting all key performance indicators and constraints.

[0065] Furthermore, according to the first weight and the second weight, the economic cost evaluation value and the energy-saving parameter evaluation value are integrated to obtain a first fitness evaluation value, including: When the economic cost evaluation value is less than the economic cost evaluation threshold, and the energy-saving parameter evaluation value is less than or equal to the energy-saving parameter evaluation threshold, the first fitness evaluation value = the second weight * the normalized value of the energy-saving parameter evaluation value; when the economic cost evaluation value is greater than or equal to the economic cost evaluation threshold, and the energy-saving parameter evaluation value is less than or equal to the energy-saving parameter evaluation threshold, the first fitness evaluation value = the second weight * the normalized value of the energy-saving parameter evaluation value - the first weight * the normalized value of the economic cost evaluation value; when the economic cost evaluation value is greater than or equal to the economic cost evaluation threshold, and the energy-saving parameter evaluation value is greater than the energy-saving parameter evaluation threshold, the first fitness evaluation = - the first weight * the normalized value of the economic cost evaluation value.

[0066] When the economic cost evaluation value is less than the economic cost evaluation threshold, it indicates that the cost efficiency of the scheduling scheme is high and lower than the preset cost threshold, so the cost consideration can be relatively reduced; when the energy-saving parameter evaluation value is less than or equal to the energy-saving parameter evaluation threshold, it indicates that the scheduling scheme is not performing well in terms of energy saving and does not meet the preset energy-saving target. In this case, since the cost is already at a qualified level, energy-saving performance becomes a more critical evaluation indicator. In order to ensure the fairness and consistency of the evaluation, the energy-saving parameter evaluation value is normalized, and the original evaluation value is converted into a standardized proportional value, usually ranging from 0 to 1, to obtain the normalized value of the energy-saving parameter evaluation value. The calculation formula for the fitness evaluation value is: the first fitness evaluation value = the second weight * the normalized value of the energy-saving parameter evaluation value. This calculation method ensures that when cost is no longer the main limiting factor, the evaluation of the scheduling scheme depends more on its energy-saving effect.

[0067] When the economic cost evaluation value is greater than or equal to the economic cost evaluation threshold, it indicates that the scheduling scheme performs poorly in terms of cost control, and the cost exceeds the preset acceptable range; when the energy-saving parameter evaluation value is less than or equal to the energy-saving parameter evaluation threshold, it indicates that the scheduling scheme also fails to reach the expected level in terms of energy saving, that is, the energy consumption is relatively high. In this case, both the economic cost evaluation value and the energy-saving parameter evaluation value are normalized, and the fitness evaluation value is calculated as follows: first fitness evaluation value = second weight * normalized value of energy-saving parameter evaluation value - first weight * normalized value of economic cost evaluation value. This calculation method emphasizes the importance of balancing different performance indicators in the comprehensive evaluation, ensuring that the scheduling scheme can meet certain standards in terms of both cost and energy saving.

[0068] When the economic cost evaluation value is greater than or equal to the economic cost evaluation threshold, it indicates that the scheduling scheme performs poorly in cost control and the cost exceeds the preset acceptable range, so it needs to be analyzed in detail; when the energy-saving parameter evaluation value is greater than the energy-saving parameter evaluation threshold, it indicates that the scheduling scheme performs well in energy saving and meets the preset energy saving target. In this case, since the energy-saving performance is already at a qualified level, the economic cost becomes a more critical evaluation indicator, so the calculation formula of the fitness evaluation value is: the first fitness evaluation value = - the first weight * the normalized value of the economic cost evaluation value. This calculation method ensures that when energy saving is no longer the main limiting factor, the evaluation of the scheduling scheme depends more on its economic cost effect.

[0069] Furthermore, performing multi-constraint optimization based on the scheduling scheme group to obtain the scheduling scheme optimization result includes: Sort the scheduling scheme group from large to small based on the fitness evaluation value to obtain the scheduling scheme sorting result; extract the first number of head fitness schemes and the second number of tail fitness schemes from the scheduling scheme sorting result; take the first number of head fitness schemes as the target, perform the same-dimensional parameter mutation on the second number of tail fitness schemes to obtain a set of scheduling update schemes; when the set of scheduling update schemes has a scheduling update scheme that satisfies both the conditions that the economic cost evaluation value is less than the economic cost evaluation threshold and the energy-saving parameter evaluation value is greater than the energy-saving parameter evaluation threshold, output the scheduling scheme optimization result; otherwise, perform a cyclic analysis.

[0070] By sorting the scheduling scheme group from high to low according to the fitness evaluation value, we can quickly identify which schemes are better than other schemes in comprehensive performance. This sorting is to ensure that the optimal scheduling scheme is ranked first. After sorting, the scheduling scheme ranking results are obtained. Among them, the scheme with a high fitness evaluation value shows better economy and higher energy saving effect, and is therefore regarded as a better scheme.

[0071] The first number of fitness schemes extracted from the scheduling scheme sorting results are the head fitness schemes, which are the schemes with the highest fitness evaluation value in the sorting results, that is, the schemes with the best performance. Selecting this scheme can provide the best expected results for implementation; the second number of fitness tail schemes extracted from the scheduling scheme sorting results are the schemes with the lowest fitness evaluation value in the sorting results, that is, the schemes with the worst performance. Identifying this scheme helps analyze which factors lead to poor scheduling performance and thus make improvements.

[0072] Same-dimensional parameter mutation is an optimization technique in which parameter mutation is performed on the same parameter dimension as the optimal solution. This means that the mutation operation does not introduce new variables or change the parameter structure, but adjusts the values ​​of existing parameters to explore potential better solutions. Specifically, the first quantity head fitness solution is used as the target to guide the mutation process, and the second quantity fitness tail solution is subjected to same-dimensional parameter mutation. The purpose of the mutation is to adjust these parameters on the same parameter dimension as the optimal solution to make them closer to or better than the performance of the optimal solution. Mutation includes increasing or decreasing certain parameter values, adjusting schedules, changing resource allocation, etc., depending on the nature and needs of the scheduling solution. After performing same-dimensional parameter mutation on the parameters of the worst solution, a set of new scheduling solutions are generated to form a set of scheduling enhancement solutions for further evaluation and selection.

[0073] Check whether there is at least one scheme in the newly generated scheduling scheme set whose economic cost evaluation value is less than the economic cost evaluation threshold and whose energy-saving parameter evaluation value is greater than the energy-saving parameter evaluation threshold. These two conditions ensure that the selected scheme is not only acceptable in terms of cost, but also meets the target standard in terms of energy saving. If there are schemes that meet the above conditions, these schemes are judged as optimized successfully, and these schemes are output as recommended scheduling strategies and as scheduling scheme optimization results.

[0074] On the contrary, if no plan meets the set thresholds of economic cost and energy saving at the same time, analysis and adjustment will continue, including readjusting parameters, re-running optimization algorithms or exploring other potential adjustment strategies, using feedback loops to make subtle adjustments to the scheduling strategy based on the performance of previous plans, generating new scheduling plans again, and re-evaluating whether these plans meet the set standards. This cyclic analysis process helps to continuously improve the quality of scheduling plans and ensure that the final strategy is optimized in both cost and energy saving. The cyclic mechanism ensures that it can flexibly adapt and provide the best solution in the face of complex operating environments and changing needs.

[0075] In summary, the method for intelligent dispatching and controlling of a microgrid provided in the embodiment of the present application has the following technical effects: By monitoring the battery status and timestamp data in real time through the sensing node, the current energy storage status and time-related data of the power station can be quickly obtained. This real-time data collection provides accurate basic data for subsequent scheduling decisions, ensuring the timeliness and accuracy of the scheduling strategy; when the battery status data shows abnormalities, the relevant processes are automatically stopped to prevent possible equipment damage or efficiency reduction. When the battery is in a healthy state, the real-time data is used to match the scheduling constraint rules to ensure the optimization of energy distribution; local sample adjacency analysis is performed based on the timestamp sensing data to predict the upcoming production demand power based on historical and real-time data, so as to allocate and plan energy in advance, optimize energy utilization efficiency, make energy supply more accurate and efficient, and avoid overproduction or insufficient supply; the scheduling plan configuration channel generates a preliminary scheduling plan based on the production demand power and the preset scheduling constraint rules. , which improves the flexibility and adaptability of the dispatching scheme and can cope with changing dispatching needs; the dispatching scheme evaluation channel conducts dual evaluation of the generated dispatching scheme in terms of economic cost and energy-saving effect. This evaluation mechanism ensures that the dispatching scheme can not only meet the actual power demand, but also achieve the goals of cost-effectiveness and energy saving, providing quantitative indicators for subsequent analysis; when the dispatching scheme does not meet the set threshold in terms of cost or energy saving, multi-constraint optimization is performed to find a solution that can achieve the optimal cost and energy-saving effect while meeting all key performance indicators. This step not only ensures power supply safety and reliability, but also emphasizes the maximization of economic and environmental benefits; according to the optimization results, the microgrid intelligent dispatching control is executed, which not only improves the overall efficiency of energy use, but also ensures that the requirements for power supply stability and sustainability are met, significantly improving the efficiency and effectiveness of energy management.

[0076] Embodiment 2, based on the same inventive concept as the method for intelligent dispatching and controlling a microgrid in the above embodiment, Figure 3 As shown, an embodiment of the present application provides a system for intelligent dispatching and control of a microgrid, the system is embedded in a photovoltaic storage charging and discharging integrated station, the system includes a sensing node and a dispatching node, and the system includes: The sensing data extraction module 10 is used to extract the battery state sensing data and the timestamp sensing data according to the sensing node; the scheduling constraint rule matching module 20 is used to stop the process when the battery state sensing data belongs to an abnormal state, and when the battery state sensing data belongs to a healthy state, match the scheduling constraint rule according to the timestamp sensing data; the local sample adjacency analysis module 30 is used to perform local sample adjacency analysis according to the timestamp sensing data to obtain the production demand power; the scheduling scheme configuration module 40 is used to configure the scheduling scheme channel of the scheduling node based on the scheduling constraint rule and the production demand power. A scheduling scheme configuration is performed to obtain a first scheduling scheme; an evaluation value acquisition module 50 is used to analyze the first scheduling scheme through the scheduling scheme evaluation channel of the scheduling node to obtain an economic cost evaluation value and an energy-saving parameter evaluation value; a multi-constraint optimization module 60 is used to perform multi-constraint optimization on the first scheduling scheme when at least one of the economic cost evaluation value is greater than or equal to the economic cost evaluation threshold and the energy-saving parameter evaluation value is less than or equal to the energy-saving parameter evaluation threshold is triggered to obtain a scheduling scheme optimization result; an intelligent scheduling control module 70 is used to perform microgrid intelligent scheduling control according to the scheduling scheme optimization result.

[0077] Furthermore, the scheduling constraint rule matching module 20 includes: A scheduling constraint rule description unit is used to describe that the scheduling constraint rule includes power supply constraint type, power supply type priority and power supply constraint task, and the sum of the power supply type priority is equal to 1; a first rule description unit is used to describe that when the battery state perception data belongs to the healthy state, and the timestamp perception data belongs to the valley time zone, the power supply constraint type includes large power grid power supply and photovoltaic power supply, the large power grid power supply priority ∈ [0,0.5], the photovoltaic power supply priority ∈ [0.5,1], and the power supply constraint tasks include production power supply and energy storage charging; a second rule description unit is used to describe that when the battery state perception data belongs to the healthy state, and the timestamp perception data belongs to the flat time zone, and the energy storage The battery power is greater than the discharge power threshold, the power supply constraint types include large power grid power supply, photovoltaic power supply and energy storage power supply, the large power grid power supply priority plus the energy storage power supply priority ∈ [0,0.5], the photovoltaic power supply priority ∈ [0.5,1], and the power supply constraint task includes production power supply; a third rule description unit is used to describe that when the battery status perception data belongs to the health state, and the timestamp perception data belongs to the peak time zone, and the energy storage battery power is greater than the discharge power threshold, the power supply constraint types include large power grid power supply, photovoltaic power supply and energy storage power supply, the large power grid power supply priority is equal to ∈ [0,0.2], the energy storage power supply plus the photovoltaic power supply priority ∈ (0.2,1], and the power supply constraint task includes production power supply.

[0078] Furthermore, the scheduling constraint rule matching module 20 includes: The fourth rule description unit is used to describe that when the battery status perception data belongs to the healthy state, and the timestamp perception data belongs to the flat time zone, and the energy storage battery power is less than or equal to the discharge power threshold, the power supply constraint tasks include production power supply and energy storage charging; the fifth rule description unit is used to describe that when the battery status perception data belongs to the healthy state, and the timestamp perception data belongs to the peak time zone, and the energy storage battery power is less than or equal to the discharge power threshold, the large power grid power supply constraint tasks include production power supply, and the photovoltaic power supply constraint tasks include production power supply and energy storage charging.

[0079] Furthermore, the local sample adjacency analysis module 30 includes: A local production log collection unit is used to collect adjacent local production logs according to the timestamp sensing data, wherein the local production logs include a power consumption record data set; a mode analysis unit is used to perform mode analysis on the power consumption record data set to obtain the production demand power.

[0080] Furthermore, the evaluation value acquisition module 50 includes: An evaluation node activation unit is used to activate the associated scheduling scheme evaluation node according to the scheduling constraint rule; a preprocessing unit is used to preprocess the first scheduling scheme through the coding layer to obtain a dimensionless input vector; a first fitting unit is used to fit the dimensionless input vector through the economic cost evaluation layer of the associated scheduling scheme evaluation node, and output the economic cost evaluation value; a second fitting unit is used to fit the dimensionless input vector through the energy-saving evaluation layer of the associated scheduling scheme evaluation node, and output the energy-saving parameter evaluation value.

[0081] Furthermore, the multi-constraint optimization module 60 includes: A weight configuration unit, used to configure a first weight for the economic cost evaluation value, and a second weight for the energy-saving parameter evaluation value, wherein the sum of the first weight and the second weight is equal to 1; a fusion unit, used to fuse the economic cost evaluation value and the energy-saving parameter evaluation value according to the first weight and the second weight, to obtain a first fitness evaluation value; an associated storage unit, used to associate and store the first scheduling scheme and the first fitness evaluation value, and add them to a scheduling scheme group; a multi-constraint optimization unit, used to perform multi-constraint optimization based on the scheduling scheme group when the number of schemes in the scheduling scheme group is greater than or equal to a quantity threshold, to obtain the scheduling scheme optimization result.

[0082] Furthermore, the fusion unit comprises: The first evaluation value description channel is used to explain that when the economic cost evaluation value is less than the economic cost evaluation threshold, and the energy-saving parameter evaluation value is less than or equal to the energy-saving parameter evaluation threshold, the first fitness evaluation value = the second weight * the normalized value of the energy-saving parameter evaluation value; the second evaluation value description channel is used to explain that when the economic cost evaluation value is greater than or equal to the economic cost evaluation threshold, and the energy-saving parameter evaluation value is less than or equal to the energy-saving parameter evaluation threshold, the first fitness evaluation value = the second weight * the normalized value of the energy-saving parameter evaluation value - the first weight * the normalized value of the economic cost evaluation value; the third evaluation value description channel is used to explain that when the economic cost evaluation value is greater than or equal to the economic cost evaluation threshold, and the energy-saving parameter evaluation value is greater than the energy-saving parameter evaluation threshold, the first fitness evaluation = - the first weight * the normalized value of the economic cost evaluation value.

[0083] Furthermore, the multi-constraint optimization unit includes: An evaluation value sorting channel is used to sort the scheduling scheme group from large to small based on the fitness evaluation value to obtain a scheduling scheme sorting result; a scheme extraction channel is used to extract the first number of head fitness schemes and the second number of tail fitness schemes from the scheduling scheme sorting result; a same-dimensional parameter mutation channel is used to perform same-dimensional parameter mutation on the second number of tail fitness schemes with the first number of head fitness schemes as the target to obtain a set of scheduling scheme additions; an optimization result output channel is used to output the scheduling scheme optimization result when the set of scheduling scheme additions has a scheduling scheme that satisfies both conditions that the economic cost evaluation value is less than the economic cost evaluation threshold and the energy-saving parameter evaluation value is greater than the energy-saving parameter evaluation threshold; a loop analysis channel is used for loop analysis otherwise.

[0084] Through the above detailed description of a method for intelligent dispatching and control of a microgrid, those skilled in the art can clearly understand a system for intelligent dispatching and control of a microgrid in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0085] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for intelligent dispatching and control of a microgrid, characterized in that: A system for intelligent dispatching and control of a microgrid, wherein the system is embedded in a photovoltaic storage charging and discharging integrated station, the system includes a sensing node and a dispatching node, and the method includes: According to the sensing node, extract the battery status sensing data and timestamp sensing data; When the battery status sensing data belongs to an abnormal state, the process is stopped; when the battery status sensing data belongs to a healthy state, the scheduling constraint rule is matched according to the timestamp sensing data; Performing local sample adjacency analysis based on the timestamp sensing data to obtain production demand power; Through the scheduling scheme configuration channel of the scheduling node, a scheduling scheme is configured based on the scheduling constraint rule and the production demand power to obtain a first scheduling scheme; Analyze the first scheduling scheme through the scheduling scheme evaluation channel of the scheduling node to obtain an economic cost evaluation value and an energy-saving parameter evaluation value; When at least one of the economic cost evaluation value is greater than or equal to the economic cost evaluation threshold and the energy-saving parameter evaluation value is less than or equal to the energy-saving parameter evaluation threshold is triggered, multi-constraint optimization is performed on the first scheduling scheme to obtain a scheduling scheme optimization result; Execute microgrid intelligent dispatching control according to the dispatching scheme optimization result; Among them, the optimization results of the scheduling scheme are obtained, including: Assigning a first weight to the economic cost evaluation value and a second weight to the energy-saving parameter evaluation value, wherein the sum of the first weight and the second weight is equal to 1; According to the first weight and the second weight, the economic cost evaluation value and the energy-saving parameter evaluation value are integrated to obtain a first fitness evaluation value; The first scheduling scheme and the first fitness evaluation value are associated and stored, and added into a scheduling scheme group; When the number of solutions in the scheduling solution group is greater than or equal to a quantity threshold, a multi-constraint optimization search is performed based on the scheduling solution group to obtain the scheduling solution optimization result.

2. A method for intelligent dispatching and control of a microgrid as claimed in claim 1, characterized in that: When the battery state sensing data belongs to a healthy state, matching the scheduling constraint rule according to the timestamp sensing data includes: The scheduling constraint rule includes a power supply constraint type, a power supply type priority, and a power supply constraint task, and the sum of the power supply type priority is equal to 1; When the battery state sensing data belongs to the healthy state, and the timestamp sensing data belongs to the valley time zone, the power supply constraint types include large power grid power supply and photovoltaic power supply, the large power grid power supply priority ∈ [0, 0.5], the photovoltaic power supply priority ∈ [0.5, 1], and the power supply constraint tasks include production power supply and energy storage charging; When the battery state sensing data belongs to the healthy state, and the timestamp sensing data belongs to the average time zone, and the energy storage battery power is greater than the discharge power threshold, the power supply constraint types include large grid power supply, photovoltaic power supply and energy storage power supply, the large grid power supply priority plus the energy storage power supply priority ∈ [0, 0.5], the photovoltaic power supply priority ∈ [0.5, 1], and the power supply constraint task includes production power supply; When the battery status sensing data belongs to the healthy state, and the timestamp sensing data belongs to the peak time zone, and the energy storage battery power is greater than the discharge power threshold, the power supply constraint types include large power grid power supply, photovoltaic power supply and energy storage power supply, the large power grid power supply priority is equal to ∈[0,0.2], the energy storage power supply plus the photovoltaic power supply priority ∈(0.2,1], and the power supply constraint task includes production power supply.

3. A method for intelligent dispatching and control of a microgrid as claimed in claim 2, characterized in that: Also includes: When the battery status sensing data belongs to the healthy state, and the timestamp sensing data belongs to the average time zone, and the energy storage battery power is less than or equal to the discharge power threshold, the power supply constraint tasks include production power supply and energy storage charging; When the battery status sensing data belongs to the healthy state, and the timestamp sensing data belongs to the peak time zone, and the energy storage battery power is less than or equal to the discharge power threshold, the large power grid power supply constraint task includes production power supply, and the photovoltaic power supply constraint task includes production power supply and energy storage charging.

4. A method for intelligent dispatching and controlling a microgrid according to claim 1, characterized in that: Perform local sample adjacency analysis based on the timestamp sensing data to obtain the production demand power, including: Collecting adjacent local production logs according to the timestamp sensing data, wherein the local production logs include a power consumption record data set; Performing mode analysis on the power consumption record data set to obtain the production demand power.

5. A method for intelligent dispatching and controlling a microgrid according to claim 1, characterized in that: The first scheduling scheme is analyzed through the scheduling scheme evaluation channel of the scheduling node to obtain an economic cost evaluation value and an energy-saving parameter evaluation value, including: According to the scheduling constraint rule, activating the associated scheduling scheme evaluation node; Preprocessing the first scheduling scheme through a coding layer to obtain a dimensionless input vector; Fitting the dimensionless input vector through the economic cost evaluation layer of the associated scheduling scheme evaluation node and outputting the economic cost evaluation value; The dimensionless input vector is fitted through the energy-saving evaluation layer of the associated scheduling scheme evaluation node, and the energy-saving parameter evaluation value is output.

6. A method for intelligent dispatching and controlling a microgrid according to claim 1, characterized in that: According to the first weight and the second weight, the economic cost evaluation value and the energy-saving parameter evaluation value are integrated to obtain a first fitness evaluation value, including: When the economic cost evaluation value is less than the economic cost evaluation threshold, and the energy-saving parameter evaluation value is less than or equal to the energy-saving parameter evaluation threshold, the first fitness evaluation value = the second weight * the normalized value of the energy-saving parameter evaluation value; When the economic cost evaluation value is greater than or equal to the economic cost evaluation threshold, and the energy-saving parameter evaluation value is less than or equal to the energy-saving parameter evaluation threshold, the first fitness evaluation value = the second weight * the normalized value of the energy-saving parameter evaluation value - the first weight * the normalized value of the economic cost evaluation value; When the economic cost evaluation value is greater than or equal to the economic cost evaluation threshold, and the energy-saving parameter evaluation value is greater than the energy-saving parameter evaluation threshold, the first fitness evaluation=-the first weight*the normalized value of the economic cost evaluation value.

7. A method for intelligent dispatching and controlling a microgrid according to claim 1, characterized in that: Performing multi-constraint optimization based on the scheduling scheme group to obtain the scheduling scheme optimization result includes: Sort the scheduling scheme group from large to small based on the fitness evaluation value to obtain a scheduling scheme sorting result; Extracting a first number of head fitness schemes and a second number of tail fitness schemes from the scheduling scheme sorting results; Taking the first number of head fitness schemes as the target, performing the same-dimensional parameter mutation on the second number of tail fitness schemes to obtain a set of scheduling enhancement schemes; When the scheduling scheme set includes a scheduling scheme that satisfies both the conditions that the economic cost evaluation value is less than the economic cost evaluation threshold and the energy-saving parameter evaluation value is greater than the energy-saving parameter evaluation threshold, outputting the scheduling scheme optimization result; Otherwise, loop the analysis.

8. A microgrid intelligent dispatching and control system, characterized in that: The system is embedded in a photovoltaic storage charging and discharging integrated station, and the system includes a sensing node and a scheduling node, and is used to implement a method for intelligent scheduling and control of a microgrid according to any one of claims 1 to 7, and the system includes: A sensing data extraction module, used for extracting battery status sensing data and timestamp sensing data according to sensing nodes; A scheduling constraint rule matching module, used to stop the process when the battery status sensing data belongs to an abnormal state, and match the scheduling constraint rule according to the timestamp sensing data when the battery status sensing data belongs to a healthy state; A local sample adjacency analysis module, used to perform local sample adjacency analysis based on the timestamp sensing data to obtain production demand power; A scheduling scheme configuration module, configured to configure a scheduling scheme based on the scheduling constraint rule and the production demand power through a scheduling scheme configuration channel of a scheduling node to obtain a first scheduling scheme; An evaluation value acquisition module, used to analyze the first scheduling scheme through the scheduling scheme evaluation channel of the scheduling node to obtain an economic cost evaluation value and an energy-saving parameter evaluation value; A multi-constraint optimization module, configured to perform multi-constraint optimization on the first scheduling scheme to obtain a scheduling scheme optimization result when at least one of the economic cost evaluation value is greater than or equal to the economic cost evaluation threshold and the energy-saving parameter evaluation value is less than or equal to the energy-saving parameter evaluation threshold is triggered; An intelligent dispatching control module is used to perform microgrid intelligent dispatching control according to the optimization result of the dispatching scheme; Wherein, the multi-constraint optimization module includes: a weight configuration unit, configured to configure a first weight for the economic cost evaluation value and a second weight for the energy-saving parameter evaluation value, wherein the sum of the first weight and the second weight is equal to 1; a fusion unit, configured to fuse the economic cost evaluation value and the energy-saving parameter evaluation value according to the first weight and the second weight to obtain a first fitness evaluation value; an associative storage unit, used to associate and store the first scheduling scheme and the first fitness evaluation value, and add them into a scheduling scheme group; The multi-constraint optimization unit is used to perform multi-constraint optimization based on the scheduling scheme group when the number of schemes in the scheduling scheme group is greater than or equal to a quantity threshold, so as to obtain the scheduling scheme optimization result.

9. A microgrid intelligent dispatching and control system as claimed in claim 8, characterized in that: The scheduling constraint rule matching module includes: A scheduling constraint rule description unit, used to describe that the scheduling constraint rule includes a power supply constraint type, a power supply type priority, and a power supply constraint task, and the sum of the power supply type priority is equal to 1; A first rule description unit is used to describe that when the battery state sensing data belongs to the healthy state, and the timestamp sensing data belongs to the valley time zone, the power supply constraint types include large power grid power supply and photovoltaic power supply, the large power grid power supply priority ∈ [0,0.5], the photovoltaic power supply priority ∈ [0.5,1], and the power supply constraint tasks include production power supply and energy storage charging; A second rule description unit is used to describe that when the battery state sensing data belongs to the healthy state, and the timestamp sensing data belongs to the average time zone, and the energy storage battery power is greater than the discharge power threshold, the power supply constraint types include large power grid power supply, photovoltaic power supply and energy storage power supply, the large power grid power supply priority plus the energy storage power supply priority ∈ [0,0.5], the photovoltaic power supply priority ∈ [0.5,1], and the power supply constraint task includes production power supply; The third rule description unit is used to describe that when the battery state sensing data belongs to the healthy state, and the timestamp sensing data belongs to the peak time zone, and the energy storage battery power is greater than the discharge power threshold, the power supply constraint types include large power grid power supply, photovoltaic power supply and energy storage power supply, the large power grid power supply priority is equal to ∈[0,0.2], the energy storage power supply plus the photovoltaic power supply priority ∈(0.2,1], and the power supply constraint task includes production power supply.

10. A microgrid intelligent dispatching and control system as claimed in claim 9, characterized in that: The scheduling constraint rule matching module includes: A fourth rule description unit, used to describe that when the battery state sensing data belongs to the healthy state, and the timestamp sensing data belongs to the average time zone, and the energy storage battery power is less than or equal to the discharge power threshold, the power supply constraint tasks include production power supply and energy storage charging; The fifth rule description unit is used to describe that when the battery status perception data belongs to the healthy state, and the timestamp perception data belongs to the peak time zone, and the energy storage battery power is less than or equal to the discharge power threshold, the large power grid power supply constraint task includes production power supply, and the photovoltaic power supply constraint task includes production power supply and energy storage charging.

11. A microgrid intelligent dispatching and control system as claimed in claim 8, characterized in that: The local sample adjacency analysis module includes: A local production log collection unit, configured to collect adjacent local production logs according to the timestamp sensing data, wherein the local production logs include a power consumption record data set; The mode analysis unit is used to perform mode analysis on the power consumption record data set to obtain the production demand power.

12. A microgrid intelligent dispatching and control system as claimed in claim 8, characterized in that: The evaluation value acquisition module includes: An evaluation node activation unit, used to activate the associated scheduling scheme evaluation node according to the scheduling constraint rule; A preprocessing unit, configured to preprocess the first scheduling scheme through a coding layer to obtain a dimensionless input vector; A first fitting unit, configured to fit the dimensionless input vector through the economic cost evaluation layer of the associated scheduling scheme evaluation node, and output the economic cost evaluation value; The second fitting unit is used to fit the dimensionless input vector through the energy-saving evaluation layer of the associated scheduling scheme evaluation node, and output the energy-saving parameter evaluation value.

13. A microgrid intelligent dispatching and control system as claimed in claim 8, characterized in that: The fusion unit comprises: A first evaluation value description channel, used to describe that when the economic cost evaluation value is less than the economic cost evaluation threshold, and the energy-saving parameter evaluation value is less than or equal to the energy-saving parameter evaluation threshold, the first fitness evaluation value = the second weight * the normalized value of the energy-saving parameter evaluation value; The second evaluation value description channel is used to describe that when the economic cost evaluation value is greater than or equal to the economic cost evaluation threshold, and the energy-saving parameter evaluation value is less than or equal to the energy-saving parameter evaluation threshold, the first fitness evaluation value = the second weight * the normalized value of the energy-saving parameter evaluation value - the first weight * the normalized value of the economic cost evaluation value; The third evaluation value description channel is used to explain that when the economic cost evaluation value is greater than or equal to the economic cost evaluation threshold, and the energy-saving parameter evaluation value is greater than the energy-saving parameter evaluation threshold, the first fitness evaluation = - the first weight * the normalized value of the economic cost evaluation value.

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