Intelligent Thermal Power Generation Scheduling Method and System for Adaptive Load Regulation

Abstract

The present invention discloses an intelligent thermal power generation scheduling method and system for adaptive load regulation, which relates to the technical field of power generation scheduling management. The method includes: dynamically perceiving the power supply demand to establish a perceived demand; dynamically modeling the thermal power generation equipment to construct a power generation fitting model; obtaining the real-time energy storage state of the energy storage equipment, generating real-time power generation data through the power generation fitting model, performing an adaptation analysis of the perceived demand to establish an adaptation deviation; and performing an optimal thermal power generation scheduling through a multi-objective dynamic optimization scheduling network to generate an optimal thermal power generation scheduling result. It solves the technical problems in the prior art that it is difficult to accurately adapt to complex and changeable power supply demands and it is impossible to effectively utilize the real-time data of the thermal power generation energy storage equipment, thereby resulting in a decrease in power generation efficiency, poor stability of power supply, and energy waste. It realizes the balance between power supply and demand, and achieves the technical effects of improving the stability of power supply, increasing power generation efficiency, and reducing energy waste.

Description

Technical Field

[0001] This application relates to the technical field of power generation dispatching management, and particularly to an intelligent thermal power generation dispatching method and system for adaptive load regulation. Background Art

[0002] In today's power industry, the power supply demand is showing a complex and changeable trend. Traditional thermal power generation dispatching methods often have difficulty accurately adapting to the complex and changeable power supply demand. The electricity consumption behaviors of power users show diversified and dynamic characteristics, resulting in fluctuations in power supply demand at any time. On the one hand, it is difficult to accurately and smoothly fit the short-term power supply demand, leading to poor stability of power supply during peak and valley periods of electricity consumption, and frequent occurrences of power supply surplus or shortage, which not only causes energy waste but also may affect the reliable operation of the power grid. On the other hand, the prediction of the long-term total power supply lacks accuracy, making it impossible for power generation enterprises to make reasonable arrangements when planning the investment and operation of power generation equipment, and it is difficult to adapt to the long-term supply and demand changes in the power market. At the same time, during the operation of thermal power generation equipment, its performance will be affected by various factors such as fuel quality and equipment aging. If the dynamic modeling of these equipment cannot be carried out and the equipment data cannot be updated in real time, the real-time data of the equipment cannot be incorporated into the power generation dispatching decision in a timely and comprehensive manner, resulting in the power generation equipment not being able to operate in the optimal state, low power generation efficiency, and increased equipment loss. In addition, the traditional thermal power generation dispatching mode fails to effectively utilize the real-time energy storage state information of energy storage equipment, making it impossible to fully exert the advantages of energy storage equipment.

[0003] Therefore, in the current related technologies, there are technical problems such as difficulty in accurately adapting to complex and changeable power supply demands, inability to effectively utilize the real-time data of thermal power generation energy storage equipment, resulting in a decrease in power generation efficiency, poor stability of power supply, and energy waste. Summary of the Invention

[0004] This application provides an intelligent thermal power generation dispatching method and system for adaptive load regulation, solves the technical problems in the prior art such as difficulty in accurately adapting to complex and changeable power supply demands, inability to effectively utilize the real-time data of thermal power generation energy storage equipment, resulting in a decrease in power generation efficiency, poor stability of power supply, and energy waste, realizes the balance between power supply and demand, and achieves the technical effects of improving the stability of power supply, increasing power generation efficiency, and reducing energy waste.

[0005] The present application provides an intelligent thermal power generation scheduling method for adaptive load regulation. The method includes: dynamically perceiving the power supply demand to establish perceived demands, where the perceived demands include a first perceived demand and a second perceived demand. The first perceived demand is a short-term stable fitting demand, and the second perceived demand is a long-term total power supply prediction demand; dynamically modeling the thermal power generation equipment to construct a power generation fitting model, and sharing the real-time equipment data of the thermal power generation equipment to the power generation fitting model; obtaining the real-time energy storage state of the energy storage equipment, generating real-time power generation data through the power generation fitting model, and performing an adaptation analysis of the perceived demands based on the real-time power generation data and the real-time energy storage state to establish an adaptation deviation; taking the adaptation deviation as an optimization target, and performing an optimal scheduling of thermal power generation through a multi-objective dynamic optimization scheduling network to generate an optimal scheduling result of thermal power generation.

[0006] In a possible implementation, the intelligent thermal power generation scheduling method for adaptive load regulation further performs the following processing: obtaining the first perceived demand of the perceived demands, identifying the power supply deviation and the time-series response deviation of the real-time power generation data and the real-time energy storage state according to the first perceived demand, and establishing a first adaptation deviation; after obtaining the second perceived demand of the perceived demands, reading the equipment tasks of the thermal power generation equipment, and performing an adaptation analysis of the long-term demand based on the equipment tasks, the real-time energy storage state, and the second perceived demand to establish a second adaptation deviation, and establishing an adaptation deviation based on the first adaptation deviation and the second adaptation deviation.

[0007] In a possible implementation, the intelligent thermal power generation scheduling method for adaptive load regulation further performs the following processing: performing a short-term stable fitting evaluation on the first perceived demand to establish a fitting fluctuation coefficient; configuring a heating quality default penalty target by using the fitting fluctuation coefficient; obtaining the node performance of the thermal power generation equipment, and establishing a load balancing target according to the node performance; establishing an economic cost target and a carbon emission constraint target, and constructing the multi-objective dynamic optimization scheduling network by using the default penalty target, the load balancing target, the economic cost target, and the carbon emission constraint target to perform an optimal scheduling of thermal power generation.

[0008] In a possible implementation, the intelligent thermal power generation scheduling method for adaptive load regulation further performs the following processing: The multi-objective dynamic optimization scheduling network is as follows:

[0009] ;

[0010] wherein, represents a multi-objective dynamic optimization function, represents an adjustment adaptation target of the adaptation deviation, represents a default penalty target, ; is the penalty objective function for heating quality breach, representing the degree of penalty for breach caused by the deviation of heating stability adaptation at time is the fitting fluctuation coefficient, representing the first adaptation deviation, which is the deviation between the load supply and demand of the short-term heating system, representing the allowable deviation threshold, representing the load balancing objective, ; is the total number of nodes, represents any node, represents the th node at time actual power generation, represents the th node's rated power generation capacity, i.e., the node performance, is the total power generation of the thermal power generation equipment, represents the total rated capacity of all nodes, is the node weighting factor of the th node, represents the economic cost objective, ; among them, is the fuel cost of the th node at time , is the start-stop cost of the th node, represents the th node's operation and maintenance cost at time , is the carbon emission constraint objective, ; is the carbon emission penalty coefficient, is the total actual carbon dioxide emission of the thermal power generation equipment at time , is the maximum emission limit, , , , are the balance weight coefficients of the penalty objective for breach, load balancing objective, economic cost objective, and carbon emission constraint objective respectively.

[0011] In a possible implementation, the intelligent thermal power generation scheduling method for adaptive load regulation further performs the following processing: analyzing the failure probability of the thermal power generation equipment to generate a failure probability evaluation result; establishing a protection energy storage scheme using the failure probability evaluation result; and compensating the optimal scheduling result of thermal power generation through the protection energy storage scheme to perform the control and management of the thermal power generation equipment.

[0012] In a possible implementation manner, the intelligent thermal power generation scheduling method for adaptive load regulation further performs the following processing: establishing mapped power generation data corresponding to the optimal thermal power generation scheduling result; establishing a time-series early warning database by using the mapped power generation data, and performing actual power generation anomaly early warning on thermal power generation equipment according to the time-series early warning database.

[0013] In a possible implementation manner, the intelligent thermal power generation scheduling method for adaptive load regulation further performs the following processing: obtaining the fluctuation rate of the load, and establishing a first duration constraint according to the fluctuation rate; obtaining the energy storage state of the energy storage device, and establishing a second duration constraint according to the energy storage state; constructing a short-term window and a long-term window by using the first duration constraint and the second duration constraint to establish a perceived demand.

[0014] This application also provides an intelligent thermal power generation scheduling system for adaptive load regulation. The system includes: a perceived demand establishment module, configured to dynamically perceive the power supply demand and establish a perceived demand, where the perceived demand includes a first perceived demand and a second perceived demand, the first perceived demand is a short-term stable fitting demand, and the second perceived demand is a long-term total power supply prediction demand; a power generation fitting model construction module, configured to dynamically model the thermal power generation equipment, construct a power generation fitting model, and share the real-time equipment data of the thermal power generation equipment to the power generation fitting model; an adaptation deviation establishment module, configured to obtain the real-time energy storage state of the energy storage device, generate real-time power generation data through the power generation fitting model, perform adaptation analysis of the perceived demand according to the real-time power generation data and the real-time energy storage state, and establish an adaptation deviation; a thermal power generation optimal scheduling module, configured to use the adaptation deviation as an optimization target, and perform thermal power generation optimal scheduling through a multi-objective dynamic optimization scheduling network to generate a thermal power generation optimal scheduling result.

[0015] It is intended to dynamically perceive the power supply demand through the intelligent thermal power generation scheduling method and system for adaptive load regulation proposed in this application, and establish a perceived demand; dynamically model the thermal power generation equipment and construct a power generation fitting model; obtain the real-time energy storage state of the energy storage device, generate real-time power generation data through the power generation fitting model, perform adaptation analysis of the perceived demand, and establish an adaptation deviation; perform thermal power generation optimal scheduling through a multi-objective dynamic optimization scheduling network to generate a thermal power generation optimal scheduling result. This solves the technical problems in the prior art that it is difficult to accurately adapt to complex and changeable power supply demands, and it is impossible to effectively utilize the real-time data of thermal power generation energy storage devices, resulting in a decrease in power generation efficiency, poor power supply stability, and energy waste, realizes the balance between power supply and demand, and achieves the technical effects of improving the stability of power supply, increasing power generation efficiency, and reducing energy waste. Description of the Drawings

[0016] To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the accompanying drawings of the embodiments of the present disclosure will be briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the systems according to the embodiments of the present application. It should be understood that the operations described above or below do not necessarily need to be executed precisely in order. On the contrary, as needed, various steps can be executed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several steps can be removed from these processes.

[0017] Figure 1 Schematic flowchart of the intelligent thermal power generation scheduling method for adaptive load regulation provided by the embodiments of the present application.

[0018] Figure 2 Schematic structural diagram of the intelligent thermal power generation scheduling system for adaptive load regulation provided by the embodiments of the present application.

[0019] Explanation of reference numerals: Perceived demand establishment module 10, power generation fitting model construction module 20, adaptation deviation establishment module 30, thermal power generation optimization scheduling module 40. Detailed implementation manners

[0020] The above description is only an overview of the technical solutions of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the following specifically gives the detailed implementation manners of the present application.

[0021] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limitations of the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present application.

[0022] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments. However, it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict. The terms "first" and "second" are only used to distinguish similar objects and do not represent a specific order for the objects. The terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or server comprising a series of steps or units need not be limited to those steps or units clearly listed, but may include other steps or modules not clearly listed or inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application.

[0023] The embodiments of this application provide an intelligent thermal power generation scheduling method for adaptive load regulation, as Figure 1 shown. The method includes:

[0024] Step S100, dynamically sense the power supply demand to establish a sensed demand, where the sensed demand includes a first sensed demand and a second sensed demand. The first sensed demand is a short-term stable fitting demand, and the second sensed demand is a long-term total power supply prediction demand.

[0025] Preferably, during the operation of the power system, due to the influence of numerous factors such as user electricity consumption behavior, industrial production changes, and weather factors, the power supply demand is constantly changing. Dynamically perceiving the power supply demand is to collect grid data information related to the power supply demand in real time through various monitoring devices and data acquisition devices. For example, electricity consumption data in different regions and at different times, and changes in electricity loads in different industries, etc., to obtain the grid power supply demand. Specifically, data collection at the user end includes widely installing smart meters at various users. The smart meters in residential users' homes can record data such as the power consumption at each moment and the daily cumulative electricity consumption in real time. For example, the power change when various household appliances are used intensively from 7 to 9 pm. In commercial places, such as shopping malls and office buildings, smart meters can collect electricity consumption data by region and floor to understand the electricity peaks during business hours in different regions. Industrial users use specialized industrial electricity monitoring devices to record in detail the startup and shutdown times of large production equipment and the power consumption of different production processes. For example, the high-power consumption periods during the blast furnace smelting process in a steel plant. Monitoring grid operation data, such as at the connections of substations and transmission lines, multiple sensors are deployed. Among them, voltage sensors continuously monitor the node voltage to ensure it remains stable within a reasonable range and avoid high or low voltage affecting electrical equipment; current sensors accurately measure the magnitude of the current passing through the node to reflect the load situation of power transmission; power sensors obtain active power and reactive power to provide a basis for analyzing the effective utilization and loss of electricity.

[0026] Preferably, based on the grid data information obtained through dynamic perception, trend analysis is carried out to determine the long-term change trend, and feature extraction is carried out to determine the power supply demand characteristics and change rules, and then a perceived demand is constructed, including the first perceived demand and the second perceived demand. Among them, the first perceived demand is the short-term stable fitting demand, which mainly reflects the fluctuation of the power supply demand in the short term (such as different times of a day, peak and trough periods within a week, etc.). Specifically, due to the living schedules of users and industrial production arrangements, the power demand will have large fluctuations in a short period. For example, the peak periods of residential electricity consumption in the morning and evening, and the startup and shutdown periods of factories. The short-term stable fitting demand aims to analyze and process these short-term data to accurately predict and fit the power demand curve in a short time, enabling the power generation side to adjust the power generation volume in a timely manner according to the prediction results, maintain the stability of power supply, avoid power supply surplus or shortage, and ensure the stable operation of the power grid. The second perceived demand is the long-term total power supply prediction demand, which focuses on predicting the total power supply volume in a relatively long future period (such as a month, a year, or even several years). Specifically, comprehensively considering the economic development trend of the region, population growth, construction and commissioning of new large-scale electricity-consuming projects, etc., to accurately predict the long-term total power supply volume to adapt to the long-term supply and demand changes in the power market and avoid resource losses caused by insufficient or excessive power generation capacity.

[0027] Further, step S100 further includes step S110 of obtaining the fluctuation rate of the load and establishing a first duration constraint according to the fluctuation rate; step S120 of obtaining the energy storage state of the energy storage device and establishing a second duration constraint according to the energy storage state; and step S130 of constructing a short-term window and a long-term window by using the first duration constraint and the second duration constraint to establish a perceived demand.

[0028] Preferably, by analyzing the real-time data collected by devices such as smart meters and sensors in the power grid, the increase and decrease of the power load per unit time are calculated, and then the load fluctuation rate is determined. The load fluctuation rate refers to the change speed of the power load within a certain period of time. For example, within a certain time period, the power load increases from 100 MW to 120 MW in 1 hour, then the load fluctuation rate is 20 MW / hour. Then, the first duration constraint is determined according to the obtained load fluctuation rate. That is, if the load fluctuation rate is large, it means that the power demand changes rapidly. To ensure the stability and reliability of power supply, a shorter duration is required to adjust the output of the power generation equipment to adapt to the change of the load. For example, when the load fluctuation rate reaches a certain threshold, it may be required to complete the output adjustment of the power generation equipment within 15 minutes, and 15 minutes is the first duration constraint. On the contrary, if the load fluctuation rate is small, the first duration constraint can be relatively long.

[0029] Preferably, by monitoring parameters such as the power quantity and charge-discharge state of the energy storage device, the energy storage situation of the energy storage device is understood in real time. For example, the total capacity of the energy storage device is 1000 kWh, the current remaining power quantity is 500 kWh, and it is in the charging state, which is a kind of energy storage state of the energy storage device. Then, the second duration constraint is established according to the energy storage state of the energy storage device. That is, if the power quantity of the energy storage device is sufficient, there is a relatively long time for scheduling arrangements when dealing with the change of the power load. Among them, the energy storage device has sufficient ability to balance the power supply and demand within a certain period of time. For example, when the power quantity of the energy storage device is sufficient, it may be allowed to decide whether to use the energy storage device for discharging or charging operations according to the power load situation within 1 hour, and 1 hour is the second duration constraint. On the contrary, if the power quantity of the energy storage device is low, to ensure that it can play a role at a critical time, the second duration constraint will be shorter, and it may be required to complete relevant scheduling decisions within 30 minutes.

[0030] Preferably, a short-term window is determined by comprehensively considering the first duration constraint and the second duration constraint. The short-term window is mainly used to cope with the short-term fluctuations of the power load and ensure the stability of power supply. Specifically, the duration of the short-term window is determined according to the comprehensive situation of the load fluctuation rate and the energy storage state of the energy storage device. That is, if the load fluctuation rate is large and the energy storage device has sufficient power, the short-term window may be relatively short, such as 30 minutes to 1 hour, so as to quickly respond to the load change and use the energy storage device for adjustment; if the load fluctuation rate is small and the energy storage device does not have particularly sufficient power, the short-term window may be appropriately extended to 1-2 hours. Then, a long-term window is determined by comprehensively considering the first duration constraint and the second duration constraint. The duration of the long-term window is usually in units of days, weeks or months. For example, if the load fluctuation rate shows a certain pattern over a long period of time and the energy storage state of the energy storage device is relatively stable, the long-term window may be set to one week. Within this time window, the power system can formulate a power generation plan, arrange equipment maintenance, etc. according to the predicted load change trend and the available capacity of the energy storage device. At the same time, the long-term window also takes into account the supply and demand changes in the power market to ensure the long-term stable operation of the power system and thus achieve the optimal allocation of power resources. By using the first duration constraint and the second duration constraint to construct the short-term window and the long-term window, it is possible to establish a more comprehensive and detailed perception of demand, enabling the power system to flexibly adjust its operation strategy according to the demand changes on different time scales and improve the stability and reliability of power supply.

[0031] Step S200: Dynamically model the thermal power generation equipment, construct a power generation fitting model, and share the real-time equipment data of the thermal power generation equipment to the power generation fitting model.

[0032] Preferably, a dynamic model of the thermal power generation equipment is established. The thermal power generation equipment may include a boiler, a steam turbine, a generator, etc. The dynamic modeling refers to establishing a mathematical model that can describe the dynamic behavior of the thermal power generation equipment under different operating conditions. Specifically, the physical processes of the equipment (such as combustion, heat exchange, steam flow, and mechanical motion, etc.) are comprehensively considered. For example, a combustion model is established to describe the combustion situation of the fuel in the boiler, including the combustion rate, heat release, etc.; a heat exchange model is established to calculate the heat absorption process of the steam in the boiler and the heat release process in the steam turbine; a mechanical motion model is established to describe the rotation of the steam turbine rotor and the power generation process of the generator, etc. And various uncertainty factors during the equipment operation process are considered, such as the change of fuel quality, the influence of environmental temperature and humidity, etc., so that the model can more accurately reflect the actual operation of the equipment. Then, by analyzing and processing a large amount of historical operation data, a relationship model between the power generation and various operation parameters is established. Specifically, the operation data of the thermal power generation equipment under different working conditions are collected, including parameters such as fuel flow, steam pressure, temperature, rotation speed, etc. and the corresponding power generation. Data fitting techniques, such as the least squares method, neural network algorithm, etc., are used to find the best fitting function of various operation parameters and power generation, and then a power generation fitting model is established, which can quickly and accurately predict the power generation according to the current operation parameters, so as to optimize the control and scheduling of the operation of the thermal power generation equipment; finally, the real-time data of the thermal power generation equipment are transmitted to the power generation fitting model to reflect the operation state of the thermal power generation equipment. After receiving the real-time data, the power generation fitting model will update the parameters in the model according to these data, so as to adjust the prediction result of the power generation in real time, make the equipment always in the best operation state, and improve the power generation efficiency and the stability and reliability of the power grid operation.

[0033] Step S300, obtain the real-time energy storage state of the energy storage device, generate real-time power generation data through the power generation fitting model, perform an adaptation analysis of the sensed demand according to the real-time power generation data and the real-time energy storage state, and establish an adaptation deviation.

[0034] Preferably, through various sensors and monitoring devices installed on the energy storage device, relevant information of the energy storage device is collected in real time, such as the current power level, charge and discharge status, battery health status, etc., to comprehensively understand the actual energy storage situation of the energy storage device. For example, if the energy storage device is a lithium battery pack, obtain the percentage of remaining power, whether it is currently in the charging or discharging process, and the degree of battery aging, etc., to accurately reflect the real-time energy storage status of the energy storage device; input the current real-time operating parameters of the thermal power generation device, such as fuel flow, steam temperature, pressure, etc., into the power generation fitting model, calculate and output real-time power generation data such as the predicted power generation power and power generation amount of the thermal power generation device at the current moment. For example, according to real-time parameters such as the current steam pressure of 10 MPa and fuel flow of 50 kg / h, the power generation fitting model calculates that the current power generation power is 500 MW, that is, the real-time power generation data, which reflects the power generation capacity of the thermal power generation device in the current state; then combine the real-time power generation data and the real-time energy storage status and compare and analyze them with the established perception requirements, that is, analyze whether the current power generation and energy storage situations can meet the perception requirements, and determine the matching degree between them, including determining whether the current real-time power generation power can meet the power range specified in the short-term stable fitting requirements, and whether the current energy storage status can cooperate with the thermal power generation device to meet the long-term total power supply prediction requirements within a certain period of time in the future. For example, if the real-time power generation power is 500 MW, and the short-term stable fitting requirements require the power generation power to be stable between 450 - 550 MW within the next 1 hour, and the real-time energy storage status of the energy storage device shows that the battery is fully charged and can provide a certain amount of power support to maintain the stability of the power generation power when necessary, it is considered that the current situation meets the perception requirements to a certain extent. If the real-time power generation power is too high or too low, or the battery of the energy storage device is insufficient or excessive, it means a mismatch.

[0035] Preferably, then quantify the differences between the actual real-time power generation data and the real-time energy storage status and the perception requirements to form an adaptation deviation. For example, the difference between the real-time power generation power and the power range specified in the short-term stable fitting requirements, or the difference between the current power of the energy storage device and the power required to meet the long-term total power supply prediction requirements, etc., to intuitively reflect the gap between the actual operation of the current power supply system and the expected perception requirements. Furthermore, corresponding measures can be taken according to the magnitude and direction of the adaptation deviation, such as adjusting the operating parameters of the thermal power generation device to change the power generation power, or adjusting the charge and discharge strategy of the energy storage device, to reduce the adaptation deviation, make the power supply system better meet the perception requirements, realize the stable supply and optimal allocation of electric power, and thus improve the stability and reliability of power supply.

[0036] Further, step S300 further includes step S310 of obtaining a first perception requirement of the perception requirement, identifying real-time power generation data, a power supply deviation of the real-time energy storage state, and a timing response deviation according to the first perception requirement, and establishing a first adaptation deviation; step S320 of obtaining a second perception requirement of the perception requirement, reading a device task of a thermal power generation device, and performing an adaptation analysis of a long-term requirement according to the device task, the real-time energy storage state, and the second perception requirement, establishing a second adaptation deviation, and establishing an adaptation deviation based on the first adaptation deviation and the second adaptation deviation.

[0037] Preferably, a first perception requirement of the perception requirement (i.e., a short-term steady fitting requirement) is obtained, and then the real-time power generation data (such as data of the current actual power generation power generated by a power generation fitting model) is compared with the corresponding power requirement in the first perception requirement. If the real-time power generation power is higher than the power expected by the first perception requirement, there is a positive power supply deviation; conversely, if the real-time power generation power is lower than the expected power, there is a negative power supply deviation. For example, the first perception requirement requires the power generation power to be stable at 400 MW in the next 1 hour, and the real-time power generation data shows that the current power generation power is 420 MW, then the power supply deviation is +20 MW. The first perception requirement also has requirements for the response time of the power generation and energy storage systems. The real-time energy storage state reflects the available power, charge and discharge state, etc. of the energy storage device at the current moment. The change conditions of the real-time energy storage state and the real-time power generation data are compared and identified with the response time specified in the first perception requirement to obtain a timing response deviation. For example, when the power load suddenly increases, the first perception requirement may require the energy storage device to start discharging within 10 minutes to supplement the power gap. If the actual time for the energy storage device to start discharging exceeds 10 minutes, a timing response deviation is generated. Based on the power supply deviation and the timing response deviation, a first adaptation deviation is established. For example, weighted summation is used to convert different types of deviations into a value, indicating the overall deviation degree between the current real-time power generation data and the real-time energy storage state and the first perception requirement.

[0038] Preferably, obtain the second perceived demand (i.e., the long-term total power supply prediction demand), and then read the equipment tasks of the thermal power generation equipment, which may include the equipment maintenance plan, the scheduled power generation time period, the power generation power limit at different time periods, etc. These equipment tasks can affect the power generation capacity and operation status of the thermal power generation equipment in the long term. For example, if a certain thermal power generation equipment is planned to have a 5-day maintenance next month, it will not be able to generate power normally during these 5 days, which is part of the equipment tasks; then, based on the equipment tasks, the real-time energy storage status, and the second perceived demand, perform an adaptation analysis of the long-term demand. Specifically, consider the impact of the equipment tasks on the power generation capacity, and combine the real-time energy storage status (such as the long-term energy storage trend of the energy storage equipment, the expected charge and discharge conditions, etc.) to determine whether the current equipment operation and energy storage situation can meet the long-term total power supply requirement in the second perceived demand. For example, if the second perceived demand predicts that 10 million kWh of power needs to be generated in the next month, and according to the analysis of the equipment tasks and the real-time energy storage status, the thermal power generation equipment and the energy storage system are expected to generate only 9 million kWh of power, there is a non-meeting of the demand; furthermore, quantify the differences among the equipment tasks, the real-time energy storage status, and the second perceived demand to form a second adaptation deviation, such as the power difference, the power generation time difference, etc., to measure the gap between the current situation and the long-term demand. Finally, integrate the first adaptation deviation and the second adaptation deviation to form an adaptation deviation to comprehensively reflect the overall deviation situation between the current power system (including power generation equipment and energy storage equipment) and the perceived demand in the short term and the long term, which helps to perform dynamic optimal scheduling of thermal power generation, so as to take measures to adjust the power generation and energy storage strategies, reduce the deviation, and make the power system better meet the actual demand.

[0039] Step S400: Take the adaptation deviation as the optimization target, and perform an optimal scheduling of thermal power generation through a multi-objective dynamic optimization scheduling network to generate an optimal scheduling result of thermal power generation.

[0040] Preferably, taking the adaptation deviation as the optimization objective means that by adjusting the operating parameters and dispatching strategies of the thermal power generation system, the adaptation deviation should be minimized as much as possible, so that the output of the power generation system can better match the actual demand in multiple dimensions such as power and time; then, the multi-objective dynamic optimization dispatching network is used to perform the optimal dispatching of thermal power generation. Among them, the objectives of the optimal dispatching of thermal power generation may include load balancing, reducing economic costs, controlling carbon emissions, and minimizing the adaptation deviation. Specifically, a large amount of real-time data is first collected, including the real-time operating data of thermal power generation equipment (such as power, temperature, pressure, etc.), the real-time energy storage state of energy storage equipment (electricity, charge and discharge rate, etc.), the real-time demand and prediction data of power load, equipment task information, etc., and input into the multi-objective dynamic optimization dispatching network. Then, with the minimization of the adaptation deviation as the core objective, while considering load balancing, reducing economic costs and controlling carbon emissions, through continuous iteration and search, a set of optimal dispatching strategies are found, such as determining the power generation power of thermal power generation equipment at different times, the charge and discharge plans of energy storage equipment, etc., so that the thermal power generation system can minimize the adaptation deviation as much as possible under the premise of meeting various constraints (such as the technical limitations of equipment, power balance requirements, etc.), and optimize other objectives; and according to the optimal dispatching strategy obtained by optimization, the dispatching network generates specific dispatching instructions as the result of the optimal dispatching of thermal power generation, which are used to guide the operation of thermal power generation equipment and energy storage equipment, including the operation plans of each device of the thermal power generation system in a future period of time (such as a few minutes or hours in the short term, several days, weeks or even longer in the long term), such as the power generation power setting value of each thermal power generation equipment at different times, the charge and discharge schedule of energy storage equipment, the start and stop time of equipment, etc., and finally realize the efficient, stable and economic operation of the thermal power generation system.

[0041] Further, step S400 further includes step S410, performing a short-term smooth fitting evaluation on the first perceived demand to establish a fitting fluctuation coefficient; step S420, configuring a heating quality default penalty target by using the fitting fluctuation coefficient; step S430, obtaining the node performance of the thermal power generation equipment, and establishing a load balancing target according to the node performance; step S440, establishing an economic cost target and a carbon emission constraint target, and constructing the multi-objective dynamic optimization dispatching network by using the default penalty target, the load balancing target, the economic cost target and the carbon emission constraint target to perform the optimal dispatching of thermal power generation.

[0042] Preferably, a short-term stable fitting evaluation is performed on the first perceived demand, that is, the matching degree between the actual first perceived demand data and a certain ideal stable demand pattern is analyzed. For example, a smooth curve that can best fit the demand data is found through the least square method to evaluate the fluctuation characteristics of the demand. The fitting fluctuation coefficient is an indicator used to quantify the degree of fluctuation of the demand data around the fitting curve, such as by calculating the statistics of the difference between the actual demand data and the fitting curve (such as standard deviation, variance, etc.). The larger the fitting fluctuation coefficient, the more violent the short-term fluctuation of the first perceived demand; conversely, the smaller the fluctuation coefficient, the more stable the demand. The fitting fluctuation coefficient is then used to configure the penalty target for the default of the heating quality, that is, the fluctuation coefficient is linked to the possible default cost or penalty. For example, when the fitting fluctuation coefficient exceeds a certain threshold, the corresponding penalty mechanism will be triggered to encourage the thermal power generation system to keep the heating quality stable as much as possible and reduce the heating quality problems caused by demand fluctuations. Among them, the heating quality usually requires a relatively stable temperature or heat supply within a certain period of time. If the first perceived demand fluctuates too much, it may cause the heating quality to be unstable and fail to meet the user's expectations for the heating quality.

[0043] Preferably, the node performance of thermal power generation equipment is obtained, including the equipment's power generation efficiency, power output capacity, reliability, maintenance cost, etc., to fully understand the operating status and capacity of each thermal power generation equipment, and establish a load balancing target based on the node performance, aiming to reasonably allocate power generation tasks so that the loads of various thermal power generation equipment are relatively balanced, avoiding excessive use of some equipment and underutilization of other equipment. For example, according to the equipment's power generation efficiency and power output capacity, the total power generation task is allocated to different equipment, so that they can operate in their respective optimal working ranges as much as possible while meeting the demand, so as to improve the operating efficiency and reliability of the entire system and reduce equipment wear and maintenance costs. Then establish economic cost targets and carbon emission constraint targets. Among them, the economic cost target mainly considers various cost factors in the thermal power generation process, such as fuel cost, equipment maintenance cost, etc., and minimizes the total of these costs by optimizing power generation scheduling to improve the economic benefits of the thermal power generation system; the thermal power generation system needs to control carbon emissions, and the carbon emission constraint target is to set a carbon emission target value. By adjusting the power generation method, optimizing fuel use and other measures, the carbon emissions in the thermal power generation process do not exceed this target value, such as using cleaner fuels, improving energy utilization efficiency, and increasing the proportion of renewable energy use to achieve energy conservation and emission reduction needs.

[0044] Preferably, by integrating the heating quality default penalty target, the load balancing target, the economic cost target, and the carbon emission constraint target, a multi-objective dynamic optimization scheduling network is constructed, which can simultaneously consider the mutual relationships and constraints between multiple objectives and find the optimal scheduling scheme to achieve equilibrium among various objectives; the multi-objective dynamic optimization scheduling network continuously adjusts and optimizes the scheduling scheme based on the real-time obtained operation data of thermal power generation equipment, the first sensed demand data, and other relevant information. For example, when the first sensed demand changes, the network will recalculate and adjust the power generation power and heating plan of each device to achieve load balancing, reduce economic costs, and control carbon emissions while meeting the heating quality requirements. Through dynamic optimization scheduling, the thermal power generation system can adapt to different operating conditions and demand changes, achieving the optimization of the overall performance and thus ensuring the stable operation of the power system.

[0045] Furthermore, the multi-objective dynamic optimization scheduling network is as follows:

[0046] ;

[0047] Among them, represents the multi-objective dynamic optimization function, represents the adjustment adaptation target for the adaptation deviation, represents the default penalty target, ; is the heating quality default penalty objective function, representing the default penalty degree caused by the heating stability adaptation deviation at time is the fitting fluctuation coefficient, represents the first adaptation deviation, and the first adaptation deviation is the deviation between the short-term heating system load supply and demand, represents the allowable deviation threshold, represents the load balancing target, ; is the total number of nodes, represents any node, represents the th node at time actual power generation power, represents the th node's rated power generation capacity, i.e., the node performance, is the total power generation power of the thermal power generation equipment, represents the total rated capacity of all nodes, is the node weighting factor of the th node, represents the economic cost target, ; Among them, is the th node at time the fuel cost is the start - stop cost of the th node representing the th node's operation and maintenance cost at time ; is the carbon emission constraint target ; is the carbon emission penalty coefficient is the total actual carbon dioxide emissions of the thermal power generation equipment at time ; is the maximum emission limit , , , are the balance weight coefficients of the default penalty target, load balancing target, economic cost target, and carbon emission constraint target respectively.

[0048] Furthermore, step S400 further includes step S450 of performing a failure probability analysis on the thermal power generation equipment to generate a failure probability evaluation result; step S460 of establishing a protection energy storage plan using the failure probability evaluation result; and step S470 of compensating the optimal dispatching result of thermal power generation through the protection energy storage plan to execute the control and management of the thermal power generation equipment.

[0049] Preferably, perform a failure probability analysis on the thermal power generation equipment (such as boilers, steam turbines, generators, etc.), that is, collect the historical operation data of the equipment, including information such as the operation time of the equipment, maintenance records, time and cause of failures, etc., and use statistical methods and fault diagnosis techniques, such as reliability analysis, fault tree analysis, etc., to judge the failure probability of the equipment at different usage stages. For example, judge the failure probability according to the failure rate curve of the equipment (such as the bathtub curve). For equipment with a long operation time, consider factors such as wear and aging of its components to evaluate the increase in its failure probability; for new equipment, analyze its potential failure risks in combination with manufacturing quality, installation and commissioning, etc. At the same time, consider the influence of external environmental factors on the failure probability of the equipment, such as temperature, humidity, fuel quality, etc. Through the failure probability analysis, generate a failure probability evaluation result, including the failure probability of each thermal power generation equipment and key components, to reflect the failure possibility of the equipment under different operating conditions.

[0050] Preferably, based on the fault probability evaluation results, analyze the possible impacts of different equipment failures on the thermal power generation system. For example, if the boiler fails, it may cause the entire power generation system to shut down, resulting in the inability to supply power and heat normally; a steam turbine failure may affect the power generation efficiency and lead to a decrease in power generation. Also, consider the degree of interruption of power and heat supply during the occurrence of a failure, as well as the time and cost required for restoration. Then, based on the fault impact analysis, determine the capacity, type, and configuration method of the protective energy storage. The energy storage device can be a battery energy storage, a heat storage device, etc. Furthermore, formulate the charge and discharge strategy of the energy storage device. According to the fault probability and operating status of the equipment, reasonably arrange the charging time and charging amount of the energy storage device to ensure that when the equipment fails, the energy storage device has sufficient energy for compensation. At the same time, consider the service life and maintenance cost of the energy storage device, optimize the charge and discharge strategy, extend the service cycle of the energy storage device, and finally obtain the protective energy storage scheme.

[0051] Preferably, when an equipment failure occurs or a high fault probability of the equipment is predicted, use the energy storage device for compensation to perform optimal dispatching of thermal power generation. For example, if a certain power generation equipment shuts down due to a failure, the energy storage device can immediately discharge to supplement the power generation gap and maintain the stability of power supply. Specifically, according to the actual status and fault situation of the energy storage device, adjust the optimal dispatching result. If the power of the energy storage device is insufficient, it may be necessary to readjust the power generation plan of other equipment to meet the power and heat demands. At the same time, consider the charge and discharge limitations of the energy storage device to avoid overusing the energy storage device and affecting its performance and life. Through the implementation of the protective energy storage scheme and the compensation of the dispatching result, realize the control and management of thermal power generation equipment, monitor the operating status of the equipment and the status of the energy storage device in real time, adjust the operating parameters of the equipment and the charge and discharge operations of the energy storage device in a timely manner according to the fault probability and dispatching result, and establish a fault warning mechanism. When the fault probability of the equipment reaches a certain threshold, take measures in advance, such as increasing the charging amount of the energy storage device, adjusting the power generation plan, etc., to prevent the occurrence of faults, and finally realize the effective control and management of the thermal power generation system and improve the reliability and stability of the system.

[0052] Furthermore, step S400 further includes step S480 of establishing mapped power generation data with the optimal dispatching result of thermal power generation; step S490 of establishing a time series warning database using the mapped power generation data, and performing actual power generation anomaly warning of thermal power generation equipment according to the time series warning database.

[0053] Preferably, clarify the correspondence between the optimal dispatching result of thermal power generation and the actual power generation data, and then generate corresponding mapped power generation data based on the determined mapping relationship, that is, convert these dispatching parameters into a data form corresponding to the actual power generation process, which is the mapped power generation data. For example, according to the relationship between power generation power and time, calculate the theoretical power generation amount at each time point to form a mapped power generation data set, which can reflect the power generation data situation that the thermal power generation equipment should generate at each moment according to the optimal dispatching result. Or, according to the power value that a certain generator should output during a specific time period in the dispatching result, map the actual power generation data that the generator should generate during this period. Or convert the dispatching power generation time into actual time series data. Organize and store the mapped power generation data according to the time series to establish a time series early warning database. In the database, each data point corresponds to a specific time point. For example, at intervals of every minute or every hour, record the mapped power generation data at each moment, including information such as the power and power generation amount of each power generation equipment. Then, according to the performance parameters and operation experience of the thermal power generation equipment, set reasonable early warning thresholds for each power generation data. When the mapped power generation data exceeds this range, an early warning may be triggered. In addition to the mapped power generation data and early warning thresholds, the time series early warning database can also include other relevant information, such as the operation state parameters of the equipment (temperature, pressure, etc.), environmental parameters (weather conditions, grid load, etc.), and the basic information of the equipment (model, rated capacity, etc.), which helps to more comprehensively analyze and judge the operation situation of the thermal power generation equipment and improve the accuracy of abnormal early warning.

[0054] Preferably, during the actual operation of the thermal power generation equipment, collect the actual power generation data of the equipment in real time and compare it with the mapped power generation data in the time series early warning database. For example, through sensors and data acquisition systems installed on the equipment, collect actual power generation power, power generation amount and other data at regular intervals (such as every minute), and then compare it with the mapped power generation data at the corresponding moment in the database. When the actual power generation data exceeds the early warning threshold range set in the time series early warning database, it is judged that the equipment has an abnormal situation and an appropriate early warning is triggered, so that relevant personnel can timely understand the abnormal situation of the equipment and take corresponding measures, thereby ensuring the stable operation of the thermal power generation system.

[0055] In the above text, with reference to Figure 1 the intelligent thermal power generation dispatching method for adaptive load regulation according to the embodiment of the present invention is described in detail. Next, with reference to Figure 2 the intelligent thermal power generation dispatching system for adaptive load regulation according to the embodiment of the present invention will be described.

[0056] An intelligent thermal power generation scheduling system with adaptive load regulation according to an embodiment of the present invention is used to solve the technical problems existing in the prior art, such as being difficult to accurately adapt to complex and changeable power supply demands, unable to effectively utilize real-time data of thermal power generation energy storage devices, resulting in a decrease in power generation efficiency, poor stability of power supply, and energy waste. It realizes the balance between power supply and demand, and achieves the technical effects of improving the stability of power supply, increasing power generation efficiency, and reducing energy waste. As Figure 2 shown, the intelligent thermal power generation scheduling system with adaptive load regulation includes: a perception demand establishment module 10, a power generation fitting model construction module 20, an adaptation deviation establishment module 30, and a thermal power generation optimization scheduling module 40.

[0057] The perception demand establishment module 10 is used to dynamically perceive the power supply demand and establish a perception demand, where the perception demand includes a first perception demand and a second perception demand. The first perception demand is a short-term stable fitting demand, and the second perception demand is a long-term total power supply prediction demand; the power generation fitting model construction module 20 is used to dynamically model the thermal power generation equipment, construct a power generation fitting model, and share the real-time equipment data of the thermal power generation equipment to the power generation fitting model; the adaptation deviation establishment module 30 is used to obtain the real-time energy storage state of the energy storage device, generate real-time power generation data through the power generation fitting model, perform adaptation analysis of the perception demand according to the real-time power generation data and the real-time energy storage state, and establish an adaptation deviation; the thermal power generation optimization scheduling module 40 is used to take the adaptation deviation as an optimization target and perform thermal power generation optimization scheduling through a multi-objective dynamic optimization scheduling network to generate a thermal power generation optimization scheduling result.

[0058] Next, the specific configuration of the adaptation deviation establishment module 30 will be described in detail. The adaptation deviation establishment module 30 further includes: obtaining the first perception demand of the perception demand, identifying the power supply deviation and the time sequence response deviation of the real-time power generation data and the real-time energy storage state according to the first perception demand, and establishing a first adaptation deviation; after obtaining the second perception demand of the perception demand, reading the equipment tasks of the thermal power generation equipment, and performing adaptation analysis of the long-term demand according to the equipment tasks, the real-time energy storage state, and the second perception demand, and establishing a second adaptation deviation, and establishing an adaptation deviation with the first adaptation deviation and the second adaptation deviation.

[0059] Next, the specific configuration of the thermal power generation optimization scheduling module 40 will be described in detail. The thermal power generation optimization scheduling module 40 further includes: performing a short-term stationary fitting evaluation on the first perceived demand to establish a fitting fluctuation coefficient; configuring a heating quality default penalty target by using the fitting fluctuation coefficient; obtaining the node performance of the thermal power generation equipment, and establishing a load balancing target according to the node performance; establishing an economic cost target and a carbon emission constraint target, and constructing the multi-objective dynamic optimization scheduling network by using the default penalty target, the load balancing target, the economic cost target, and the carbon emission constraint target to perform thermal power generation optimization scheduling.

[0060] Next, the specific configuration of the thermal power generation optimization scheduling module 40 will be further described in detail. The thermal power generation optimization scheduling module 40 further includes: The multi-objective dynamic optimization scheduling network is as follows:

[0061] ;

[0062] Among them, represents the multi-objective dynamic optimization function, represents the adjustment adaptation target for the adaptation deviation, represents the default penalty target, ; is the heating quality default penalty objective function, representing the default penalty degree caused by the heating stability adaptation deviation at time is the fitting fluctuation coefficient, represents the first adaptation deviation, and the first adaptation deviation is the deviation between the short-term heating system load supply and demand, represents the allowable deviation threshold, represents the load balancing target, ; is the total number of nodes, represents any node, represents the th node at time actual power generation, represents the th node's rated power generation capacity, that is, the node performance, is the total power generation of the thermal power generation equipment, represents the total rated capacity of all nodes, is the th node's node weighting factor, represents the economic cost target, ; Among them, is the th node's fuel cost at time , is the The start-up and shut-down cost of each node, representing the th node at time operation and maintenance cost, is the carbon emission constraint target, ; is the carbon emission penalty coefficient, is the total actual carbon dioxide emissions of the thermal power generation equipment at time , is the maximum emission limit, , , , are the balance weight coefficients of the default penalty target, load balancing target, economic cost target, and carbon emission constraint target respectively.

[0063] Next, the specific configuration of the thermal power generation optimization scheduling module 40 will be further described in detail. The thermal power generation optimization scheduling module 40 further includes: analyzing the failure probability of the thermal power generation equipment to generate a failure probability evaluation result; establishing a protection energy storage plan using the failure probability evaluation result; and compensating the thermal power generation optimization scheduling result through the protection energy storage plan to perform the control and management of the thermal power generation equipment.

[0064] Next, the specific configuration of the thermal power generation optimization scheduling module 40 will be further described in detail. The thermal power generation optimization scheduling module 40 further includes: establishing mapped power generation data corresponding to the thermal power generation optimization scheduling result; establishing a time series early warning database using the mapped power generation data, and performing actual power generation anomaly early warning of the thermal power generation equipment according to the time series early warning database.

[0065] Next, the specific configuration of the perception demand establishment module 10 will be described in detail. The perception demand establishment module 10 further includes: obtaining the fluctuation rate of the load, and establishing a first duration constraint according to the fluctuation rate; obtaining the energy storage state of the energy storage device, and establishing a second duration constraint according to the energy storage state; and constructing a short-term window and a long-term window using the first duration constraint and the second duration constraint to establish the perception demand.

[0066] The adaptive load regulation intelligent thermal power generation scheduling system provided by the embodiments of the present invention can execute the adaptive load regulation intelligent thermal power generation scheduling method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0067] Although this application makes various references to certain modules in the system according to the embodiments of this application, however, any number of different modules can be used and run on the user terminal and / or the server. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of mutual distinction and are not used to limit the protection scope of the present invention.

[0068] The above specific embodiments do not constitute a limitation to the protection scope of this application. Those skilled in the art should understand that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of this application shall be included within the protection scope of this application.

Claims

1. An intelligent thermal power generation scheduling method with adaptive load regulation, characterized in that: The method comprises: Dynamically sense the power supply demand and establish the sensed demand, wherein the sensed demand includes a first sensed demand and a second sensed demand, wherein the first sensed demand is a short-term stable fitting demand and the second sensed demand is a long-term total power supply forecast demand; Dynamically modeling the thermal power generation equipment, constructing a power generation fitting model, and sharing the real-time equipment data of the thermal power generation equipment to the power generation fitting model; Acquire the real-time energy storage status of the energy storage device, generate real-time power generation data through the power generation fitting model, perform adaptation analysis of the sensed demand according to the real-time power generation data and the real-time energy storage status, and establish an adaptation deviation; Taking the adaptation deviation as the optimization target, executing thermal power generation optimization scheduling through a multi-objective dynamic optimization scheduling network to generate a thermal power generation optimization scheduling result; The method of taking the adaptation deviation as the optimization target and performing thermal power generation optimization scheduling through a multi-objective dynamic optimization scheduling network includes: Performing a short-term stable fitting evaluation on the first perceived demand and establishing a fitting fluctuation coefficient; Using the fitted fluctuation coefficient, configuring a penalty target for heat quality breach; Obtaining node performance of thermal power generation equipment, and establishing a load balancing target according to the node performance; Establishing an economic cost target and a carbon emission constraint target, and using the default penalty target, the load balancing target, the economic cost target and the carbon emission constraint target to construct the multi-objective dynamic optimization scheduling network to perform thermal power generation optimal scheduling; The multi-objective dynamic optimization scheduling network is as follows: ; in, Characterize the multi-objective dynamic optimization function, Characterize the adaptation deviation of the regulation adaptation target, Characterize the default penalty target, ; is the penalty objective function for heat quality breach, representing The degree of penalty for breach of contract caused by the deviation of heat supply stability adaptation at all times, is the fitted volatility coefficient, Characterizes the first adaptation deviation, which is the deviation between the short-term heating system load supply and demand. Characterizes the allowable deviation threshold, Characterize the load balancing target, ; is the total number of nodes, Representing any node, Characterization The time of the node Actual power generation, Characterization The rated power generation capacity of each node, i.e., node performance, is the total power generation capacity of thermal power generation equipment, Characterizes the total rated capacity of all nodes, For the The node weight factor of the node, Characterize economic cost targets, ;in, For the Nodes at time The fuel cost, For the The start-up and stop costs of each node, Characterization Nodes at time Operation and maintenance costs, To meet carbon emission constraints, ; is the carbon emission penalty coefficient, For thermal power generation equipment at all times The actual total carbon dioxide emissions, is the maximum emission limit, , , , They are the balance weight coefficients of the default penalty target, load balancing target, economic cost target, and carbon emission constraint target respectively.

2. The intelligent thermal power generation scheduling method with adaptive load regulation according to claim 1, characterized in that: The performing adaptation analysis of the perceived demand according to the real-time power generation data and the real-time energy storage state and establishing the adaptation deviation includes: Acquire a first sensed demand of the sensed demand, identify the real-time power generation data, the power supply deviation of the real-time energy storage state, and the timing response deviation according to the first sensed demand, and establish a first adaptation deviation; After obtaining the second perceived demand of the perceived demand, the equipment task of the thermal power generation equipment is read, and an adaptation analysis of the long-term demand is performed based on the equipment task, the real-time energy storage status, and the second perceived demand, and a second adaptation deviation is established, and an adaptation deviation is established with the first adaptation deviation and the second adaptation deviation.

3. The intelligent thermal power generation scheduling method with adaptive load regulation according to claim 1, characterized in that: After generating the thermal power generation optimal dispatch result, the method includes: Performing a failure probability analysis on the thermal power generation equipment to generate a failure probability evaluation result; Establishing a protection energy storage scheme using the fault probability evaluation result; The protection energy storage scheme is used to compensate for the optimal dispatch result of thermal power generation to execute control management of thermal power generation equipment.

4. The intelligent thermal power generation scheduling method with adaptive load regulation according to claim 1, characterized in that: After generating the thermal power generation optimal dispatch result, the method further includes: Establishing power generation data mapped with thermal power generation optimal dispatch results; A time series warning database is established by using the mapped power generation data, and an actual power generation abnormality warning of the thermal power generation equipment is performed based on the time series warning database.

5. The intelligent thermal power generation scheduling method with adaptive load regulation according to claim 1, characterized in that: The dynamically sensing the power supply demand and establishing the sensing demand includes: Acquire a load fluctuation rate, and establish a first duration constraint according to the fluctuation rate; Acquire an energy storage state of the energy storage device, and establish a second duration constraint according to the energy storage state; The first duration constraint and the second duration constraint are used to construct a short-term window and a long-term window to establish a perception requirement.

6. Intelligent thermal power generation dispatching system with adaptive load regulation, characterized in that: The system is used to implement the intelligent thermal power generation scheduling method with adaptive load regulation according to any one of claims 1 to 5, and the system comprises: A sensing demand establishment module is used to dynamically sense the power supply demand and establish the sensing demand, wherein the sensing demand includes a first sensing demand and a second sensing demand, wherein the first sensing demand is a short-term stable fitting demand and the second sensing demand is a long-term total power supply forecast demand; A power generation fitting model building module is used to dynamically model the thermal power generation equipment, build a power generation fitting model, and share the real-time equipment data of the thermal power generation equipment to the power generation fitting model; An adaptation deviation establishment module is used to obtain the real-time energy storage state of the energy storage device, generate real-time power generation data through the power generation fitting model, perform adaptation analysis of the perceived demand according to the real-time power generation data and the real-time energy storage state, and establish an adaptation deviation; The thermal power generation optimal scheduling module is used to take the adaptation deviation as the optimization target, perform thermal power generation optimal scheduling through a multi-objective dynamic optimization scheduling network, and generate a thermal power generation optimal scheduling result.

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