Methanol generator set based on artificial intelligence

Abstract

The invention belongs to the technical field of methanol generators, and relates to a methanol generator set based on artificial intelligence. By analyzing the operation condition of the target methanol generator set, the abnormal condition of the target methanol generator set can be found in time, potential faults can be predicted in advance, stable operation of the target methanol generator set is ensured, fuel supply of the target methanol generator set can be ensured by predicting the load demand quantity of the target methanol generator set in the prediction time period, and the reliability of the target methanol generator set is improved. By analyzing the current fuel supply matching degree of the target methanol generator set, the power generation condition of the target methanol generator set can be dynamically adjusted, the power generation efficiency and the energy utilization rate of the target methanol generator set are improved, and the actual load demand quantity of the target methanol generator set in the current time period is obtained. And the output power and the charging and discharging strategy of the energy storage system can be quickly adjusted, the electric power fluctuation can be smoothed, and the stable operation of the electric power system can be ensured.

Description

Technical Field

[0001] The invention belongs to the technical field of methanol generators and relates to a methanol generator set based on artificial intelligence. Background Art

[0002] Methanol generator set is a device that uses methanol as fuel to generate electricity. With the continuous growth of global energy demand and the increasingly severe environmental problems, new energy power generation systems have become the focus of people's attention. Methanol, as a new energy fuel, has the advantages of high calorific value, wide source, and clean combustion. Methanol generator sets use methanol as fuel to generate electricity through chemical reactions, which has the advantages of environmental protection, high efficiency, and sustainability. In addition, the operating stability of methanol generator sets is of great significance to ensure the safe and stable operation of the power system.

[0003] The existing methanol generator sets can basically meet the use requirements, but they still have certain shortcomings: on the one hand, the existing methanol generator sets can generate electricity normally and meet the basic load requirements, but the existing methanol generator sets lack real-time detection of their operation conditions, so that they cannot detect abnormal conditions of the methanol generator sets in time, and thus cannot predict potential failures in advance, and provide maintenance personnel with sufficient time for inspection and maintenance. In addition, the existing methanol generator sets also ignore the prediction of load demand in future time periods, so that they cannot ensure sufficient fuel supply during peak power generation periods, and cannot avoid the risk of insufficient power generation or shutdown caused by fuel shortages.

[0004] On the other hand, the existing methanol generator sets are able to change their fuel supply, thereby changing their power generation. However, the existing methanol generator sets lack the detection and analysis of the compliance between fuel supply and load demand, and thus cannot dynamically adjust their fuel supply according to the power generation demand of the methanol generator sets, thereby unable to avoid waste and unnecessary cost expenditure caused by excess fuel, and are unable to understand the real-time load status of the methanol generator sets, thereby unable to smooth power fluctuations and ensure the stable operation of the power system. Summary of the invention

[0005] In view of this, in order to solve the problems raised in the above background technology, a methanol generator set based on artificial intelligence is proposed.

[0006] The purpose of the present invention can be achieved through the following technical solutions: The present invention provides a methanol generator set based on artificial intelligence, including: a generator set operation detection and analysis module, a load demand prediction module, a fuel supply matching analysis module, a fuel supply control module, a load detection and analysis module, an energy storage scheduling control module and an information storage library.

[0007] The generator set operation detection and analysis module is connected to the load demand prediction module, the load demand prediction module is connected to the fuel supply matching analysis module, the fuel supply matching analysis module is respectively connected to the fuel supply control module and the load detection and analysis module, the load detection and analysis module is connected to the energy storage scheduling control module, and the information storage library is respectively connected to the generator set operation detection and analysis module and the load demand prediction module.

[0008] The generator set operation detection and analysis module is used to perform real-time operation detection on the target methanol generator set, obtain the operation parameter information of the target methanol generator set, analyze the operation status of the target methanol generator set, and judge its maintenance needs.

[0009] The load demand prediction module is used to obtain the historical load data of the target methanol generator set and predict the load demand of the target methanol generator set in the prediction time period.

[0010] The fuel supply matching degree analysis module is used to obtain the current fuel supply information parameters of the target methanol generator set, analyze the current fuel supply matching degree of the target methanol generator set, and determine the fuel supply control demand of the target methanol generator set. If its control demand is a required control demand, the fuel supply control module is executed, otherwise, the load detection analysis module is executed.

[0011] The fuel supply control module is used to control the fuel supply of the target methanol generator set.

[0012] The load detection and analysis module is used to perform real-time detection on the actual load of the target methanol generator set, obtain the actual load demand of the target methanol generator set in the current time period, and judge the energy storage scheduling demand of the target methanol generator set accordingly. If it is a scheduling demand, the energy storage scheduling control module is executed.

[0013] The energy storage scheduling control module is used to control the energy storage scheduling of the target methanol power generation unit.

[0014] The information storage library is used to store the historical load data of the target methanol generator set, store the minimum combustion efficiency to ensure the normal operation of the target methanol generator set, and store the standard mixing ratio of air and methanol and the standard engine speed of the target methanol generator set.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. The present invention obtains the operating parameter information of the target methanol generator set and analyzes the operating status of the target methanol generator set, which helps to promptly discover abnormal conditions of the target methanol generator set, predict potential failures in advance, and provide maintenance personnel with sufficient time for inspection and maintenance, which is beneficial to reducing the impact of sudden failures on production and ensuring the stable operation of the target methanol generator set.

[0016] 2. The present invention helps to understand the changing trend of the power generation demand of the target methanol power generation unit in advance by predicting the load demand of the target methanol power generation unit in the predicted time period, which is beneficial to ensure that it has sufficient fuel supply during the peak power generation period and avoid the risk of insufficient power generation or shutdown caused by fuel shortage.

[0017] 3. The present invention analyzes the current fuel supply matching degree of the target methanol generator set and determines the fuel supply control demand of the target methanol generator set, which is conducive to dynamically adjusting the power generation of the target methanol generator set, improving its power generation efficiency and energy utilization, avoiding waste and unnecessary cost expenditure caused by excess fuel, and reducing power generation reduction and economic benefit loss caused by insufficient fuel.

[0018] 4. The present invention obtains the actual load demand of the target methanol generator set in the current time period, and judges the energy storage scheduling demand of the target methanol generator set based on this, which helps to understand the real-time load status of the target methanol generator set, so as to quickly adjust its output power and the charging and discharging strategy of the energy storage system, ensure its supply and demand balance, avoid power shortage or surplus, help smooth power fluctuations, and ensure the stable operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0020] Figure 1 It is a schematic diagram of system module connection of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] See also Figure 1As shown, the present invention provides a methanol generator set based on artificial intelligence, and the specific modules are distributed as follows: generator set operation detection and analysis module, load demand prediction module, fuel supply matching analysis module, fuel supply control module, load detection and analysis module, energy storage scheduling control module and information storage library. Among them, the connection mode between the modules is: the generator set operation detection and analysis module is connected with the load demand prediction module, the load demand prediction module is connected with the fuel supply matching analysis module, the fuel supply matching analysis module is respectively connected with the fuel supply control module and the load detection and analysis module, the load detection and analysis module is connected with the energy storage scheduling control module, and the information storage library is respectively connected with the generator set operation detection and analysis module and the load demand prediction module.

[0023] The generator set operation detection and analysis module is used to perform real-time operation detection on the target methanol generator set, obtain the operation parameter information of the target methanol generator set, analyze the operation status of the target methanol generator set, and judge its maintenance needs.

[0024] As a preferred feasibility example, the operating parameter information of the target methanol generator set includes combustion efficiency, output voltage fluctuation coefficient, air-methanol mixing ratio and engine speed.

[0025] It needs to be further explained that the specific method of obtaining the combustion efficiency of the target methanol generator set is: using the flow meter arranged in the target methanol generator set to detect its methanol consumption, to obtain the methanol consumption of the target methanol generator set within the set fixed detection time period, and directly extracting the power generation of the target methanol generator set within the set fixed detection time period from the target methanol generator set, and then ratioing the power generation of the target methanol generator set within the set fixed detection time period with its methanol consumption, and the quotient is recorded as the combustion efficiency of the target methanol generator set.

[0026] The specific method for obtaining the output voltage fluctuation coefficient of the target methanol generator set is: using a deployed voltmeter to detect the output voltage of the target methanol generator set, and obtaining the output voltage fluctuation coefficient of the target methanol generator set at each detection. ,in , is the number corresponding to each test, is the number of detections, according to the analytical formula ,in It is the allowable fluctuation value of the set output voltage.

[0027] The specific method for obtaining the mixing ratio of air and methanol of the target methanol generator set is: extracting the mixing ratio of air and methanol of the target methanol generator set from the maintenance record sheet of the target methanol generator set.

[0028] The specific method for obtaining the engine speed of the target methanol generator set is: directly measuring the engine speed of the target methanol generator set using a deployed speed sensor.

[0029] As a preferred feasibility example, the operation evaluation coefficient of the target methanol generator set needs to be constructed during the analysis of the operation status of the target methanol generator set. The specific analysis method includes: extracting the combustion efficiency, output voltage fluctuation coefficient, air-methanol mixing ratio and engine speed of the target methanol generator set, which are recorded as , analyze the operation evaluation coefficient of the target methanol power generation unit ,in The minimum combustion efficiency to ensure the normal operation of the target methanol power generation unit is extracted from the information reserve. is the output voltage fluctuation coefficient threshold of the target methanol generator set, The standard mixing ratio of air and methanol and the standard engine speed of the target methanol generator set extracted from the information storage library, It is the permissible difference between the set air-methanol mixture ratio and the standard air-methanol mixture ratio, and the permissible difference between the engine speed and its standard speed.

[0030] The operation evaluation coefficient of the target methanol generator set is compared with the set operation evaluation coefficient threshold. If the operation evaluation coefficient of the target methanol generator set is less than the operation evaluation coefficient threshold, the maintenance demand of the target methanol generator set is recorded as maintenance demand; otherwise, the maintenance demand of the target methanol generator set is recorded as maintenance-free demand.

[0031] It needs to be further explained that when the maintenance demand of the target methanol power generation unit is maintenance-required, maintenance processing is performed on it.

[0032] Specifically, (1) Regularly inspect and clean burner components such as oil nozzles, elastic couplings, flame detectors, etc.

[0033] (2) Keep the fuel system clean and clean the fuel tank and oil filter regularly.

[0034] (3) Check the tightness of the electrical connections to ensure that they are not loose or corroded.

[0035] (4) Regularly clean the dust and oil on the surface and inside of the generator set.

[0036] The present invention obtains the operating parameter information of the target methanol generator set and analyzes the operating status of the target methanol generator set, which helps to timely discover abnormal conditions of the target methanol generator set, predict potential failures in advance, and provide maintenance personnel with sufficient time for inspection and maintenance, which is beneficial to reducing the impact of sudden failures on production and ensuring the stable operation of the target methanol generator set.

[0037] The load demand prediction module is used to obtain the historical load data of the target methanol generator set and predict the load demand of the target methanol generator set in the prediction time period.

[0038] As a preferred feasibility example, the historical load data of the target methanol power generation unit includes the load demand corresponding to each time period on each historical date.

[0039] As a preferred feasibility example, the specific method of obtaining the load demand of the target methanol generator set in the predicted time period includes: extracting the load demand of each historical date corresponding to each time period of the target methanol generator set, and determining the load demand of each time period corresponding to each historical date corresponding to the current date in the target methanol generator set, and recording it as the load demand of each time period corresponding to each historical reference date of the target methanol generator set.

[0040] The current time point is recorded as the reference starting time point, and the continuous time periods corresponding to the historical reference dates of the target methanol power generation group with the same load demand as the reference starting time point are further determined from the load demand of each time period corresponding to each historical reference date of the target methanol power generation group, and recorded as the reference continuous time periods corresponding to each historical reference date of the target methanol power generation group.

[0041] The overlapping time periods in the reference continuous time periods corresponding to the historical reference dates of the target methanol power generation group are recorded as the prediction time periods of the target methanol power generation group.

[0042] As a preferred feasibility example, the load demand of the target methanol generator set in the forecast time period is specifically forecasted by extracting the load demand of each historical date corresponding to each time period of the target methanol generator set, and extracting the load demand of each historical reference date corresponding to the forecast time period of the target methanol generator set. ,in , is the number corresponding to each historical reference date, is the quantity corresponding to the historical reference date, according to the analysis formula Get the load demand of the target methanol generator unit in the forecast period ,in It is the compensation value corresponding to the load demand of the target methanol power generation unit.

[0043] As a preferred feasibility example, the specific method for obtaining the compensation value corresponding to the load demand of the target methanol power generation group includes: extracting the load demand of each historical date corresponding to each time period of the target methanol power generation group, taking the load demand of each historical reference date corresponding to the predicted time period of the target methanol power generation group as the reference date, extracting the load demand of each historical date corresponding to the predicted time period of the target methanol power generation group for a set number of dates forward and backward, and recording it as the load demand of each historical compensation date corresponding to the predicted time period of the target methanol power generation group ,in , is the number of each historical compensation date, is the number of historical compensation dates, according to the analytical formula Get the corresponding compensation value of the load demand of the target methanol generator set .

[0044] By predicting the load demand of the target methanol generator set in the predicted time period, the present invention helps to understand the changing trend of the power generation demand of the target methanol generator set in advance, which is beneficial to ensure that it has sufficient fuel supply during the peak power generation period and avoid the risk of insufficient power generation or shutdown caused by fuel shortage.

[0045] The fuel supply matching degree analysis module is used to obtain the current fuel supply information parameters of the target methanol generator set, analyze the current fuel supply matching degree of the target methanol generator set, and determine the fuel supply control demand of the target methanol generator set. If its control demand is a required control demand, the fuel supply control module is executed, otherwise, the load detection analysis module is executed.

[0046] As a preferred feasible example, the fuel supply information parameters of the target methanol power generation set include the methanol supply amount and the temperature and pressure of the combustion chamber.

[0047] It needs to be further explained that the specific method of obtaining the methanol supply amount of the target methanol generator set and the temperature and pressure of the combustion chamber is: directly detecting the methanol supply amount of the target methanol generator set and the temperature and pressure of the combustion chamber from the flow meter of the target methanol generator set and the temperature sensor and pressure gauge arranged in the combustion chamber.

[0048] As a preferred feasibility example, the fuel supply control demand of the target methanol power generation unit is specifically determined by matching the load demand of the target methanol power generation unit in the predicted time period with the reference methanol supply corresponding to each load demand in the information storage library and the reference temperature and reference pressure of the combustion chamber, and obtaining the reference methanol supply of the target methanol power generation unit and the reference temperature and reference pressure of the combustion chamber, which are respectively recorded as .

[0049] Extract the methanol supply of the target methanol generator set and the temperature and pressure of the combustion chamber, which are recorded as , analyze the current fuel supply matching degree of the target methanol generator set ,in They are respectively the allowable difference between the set methanol supply amount and the reference methanol supply amount, the allowable difference between the temperature of the combustion chamber and its reference temperature, and the allowable difference between the pressure of the combustion chamber and its reference pressure.

[0050] The current fuel supply matching degree of the target methanol generator set is compared with the set fuel supply matching degree threshold. If the current fuel supply matching degree of the target methanol generator set is less than the fuel supply matching degree threshold, the fuel supply control demand of the target methanol generator set is a control demand; otherwise, the fuel supply control demand of the target methanol generator set is a non-control demand.

[0051] The fuel supply control module is used to control the fuel supply of the target methanol generator set.

[0052] As a preferred feasibility example, the control of the fuel supply of the target methanol generator set includes the following specific operations: obtaining the current load demand of the target methanol generator set, subtracting it from the load demand of the target methanol generator set in the predicted time period, and obtaining the load demand difference between the current and predicted time periods of the target methanol generator set. .

[0053] when When the methanol supply to the target methanol generator set is reduced, the temperature and pressure of the combustion chamber of the target methanol generator set are lowered.

[0054] It needs to be further explained that the specific operation of reducing the methanol supply of the target methanol generator set or lowering the temperature and pressure of the combustion chamber of the target methanol generator set is: reducing the methanol supply of the target methanol generator set to its minimum methanol supply, calculating the difference between the load demand of the target methanol generator set between the current and predicted time periods at this time, and if it is still greater than zero, further reducing the temperature and pressure of the combustion chamber of the target methanol generator set until the difference between the load demand of the target methanol generator set between the current and predicted time periods is equal to zero.

[0055] when When the methanol supply to the target methanol power generation unit and the temperature and pressure of the combustion chamber are maintained unchanged.

[0056] when When the methanol supply to the target methanol generator set is increased, the temperature and pressure of the combustion chamber of the target methanol generator set are increased.

[0057] It needs to be further explained that the specific operation of increasing the methanol supply of the target methanol generator set or increasing the temperature and pressure of the combustion chamber of the target methanol generator set is: increasing the methanol supply of the target methanol generator set to its maximum methanol supply, calculating the difference between the load demand of the target methanol generator set between the current and predicted time periods at this time, and if it is still less than zero, further increasing the temperature and pressure of the combustion chamber of the target methanol generator set until the difference between the load demand of the target methanol generator set between the current and predicted time periods is equal to zero.

[0058] The present invention analyzes the current fuel supply matching degree of the target methanol generator set and determines the fuel supply control demand of the target methanol generator set, which is beneficial to dynamically adjust the power generation situation of the target methanol generator set, improve its power generation efficiency and energy utilization rate, avoid waste and unnecessary cost expenditure caused by excess fuel, and reduce power generation reduction and economic benefit loss caused by insufficient fuel.

[0059] The load detection and analysis module is used to perform real-time detection on the actual load of the target methanol generator set, obtain the actual load demand of the target methanol generator set in the current time period, and judge the energy storage scheduling demand of the target methanol generator set accordingly. If it is a scheduling demand, the energy storage scheduling control module is executed.

[0060] As a preferred feasibility example, the specific method for determining the energy storage scheduling demand of the target methanol power generation unit includes: extracting the actual load demand of the target methanol power generation unit in the current time period, subtracting it from the load demand of the target methanol power generation unit in the predicted time period, and obtaining the load demand difference between the current time period and the predicted time period of the target methanol power generation unit. .

[0061] If the load demand difference between the current time period and the predicted time period of the target methanol power generation unit is 0, the energy storage scheduling demand of the target methanol power generation unit is recorded as no scheduling demand. Otherwise, the energy storage scheduling demand of the target methanol power generation unit is recorded as a scheduling demand.

[0062] The energy storage scheduling control module is used to control the energy storage scheduling of the target methanol power generation unit.

[0063] As a preferred feasibility example, the energy storage scheduling of the control target methanol power generation unit includes: When the target methanol power generation unit is dispatched from the energy storage system of load.

[0064] when When the target methanol generator set is stored in the energy storage system of load.

[0065] The information storage library is used to store historical load data of the target methanol generator set, store the minimum combustion efficiency to ensure the normal operation of the target methanol generator set, and store the standard mixing ratio of air and methanol and the standard engine speed of the target methanol generator set.

[0066] The present invention obtains the actual load demand of the target methanol generator set in the current time period, and judges the energy storage scheduling demand of the target methanol generator set based on this, which helps to understand the real-time load status of the target methanol generator set, so as to quickly adjust its output power and the charging and discharging strategy of the energy storage system, ensure its supply and demand balance, avoid power shortage or surplus, help smooth power fluctuations, and ensure the stable operation of the power system.

[0067] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. A methanol generator set based on artificial intelligence, characterized by: include: The generator set operation detection and analysis module is used to perform real-time operation detection on the target methanol generator set, obtain the operation parameter information of the target methanol generator set, analyze the operation status of the target methanol generator set, and judge its maintenance needs; A load demand prediction module is used to obtain historical load data of a target methanol generator set and predict the load demand of the target methanol generator set in a prediction period; The fuel supply matching degree analysis module is used to obtain the current fuel supply information parameters of the target methanol generator set, analyze the current fuel supply matching degree of the target methanol generator set, and determine the fuel supply control demand of the target methanol generator set. If the control demand is a control demand, the fuel supply control module is executed, otherwise, the load detection analysis module is executed; A fuel supply control module, used to control the fuel supply of the target methanol generator set; The load detection and analysis module is used to detect the actual load of the target methanol generator set in real time, obtain the actual load demand of the target methanol generator set in the current time period, and judge the energy storage scheduling demand of the target methanol generator set accordingly. If it is a scheduling demand, the energy storage scheduling control module is executed; An energy storage dispatch control module, used to control the energy storage dispatch of the target methanol power generation unit; The information storage library is used to store the historical load data of the target methanol generator set, store the minimum combustion efficiency to ensure the normal operation of the target methanol generator set, and store the standard mixing ratio of air and methanol and the standard engine speed of the target methanol generator set.

2. The artificial intelligence-based methanol generator set according to claim 1, characterized in that: The operating parameter information of the target methanol generator set includes combustion efficiency, output voltage fluctuation coefficient, air-methanol mixing ratio and engine speed; The historical load data of the target methanol power generation unit includes the load demand corresponding to each time period on each historical date; The fuel supply information parameters of the target methanol power generation set include the methanol supply amount and the temperature and pressure of the combustion chamber.

3. The artificial intelligence-based methanol generator set according to claim 2, characterized in that: In the process of analyzing the operation status of the target methanol power generation unit, it is necessary to construct the operation evaluation coefficient of the target methanol power generation unit, and the specific analysis method includes: The combustion efficiency, output voltage fluctuation coefficient, air-methanol mixing ratio and engine speed of the target methanol generator set are extracted and recorded as , analyze the operation evaluation coefficient of the target methanol power generation unit ,in The minimum combustion efficiency to ensure the normal operation of the target methanol power generation unit is extracted from the information reserve. is the output voltage fluctuation coefficient threshold of the target methanol generator set, The standard mixing ratio of air and methanol and the standard engine speed of the target methanol generator set extracted from the information storage library, The permissible difference between the set air-methanol mixture ratio and the standard air-methanol mixture ratio and the permissible difference between the engine speed and its standard speed; The operation evaluation coefficient of the target methanol generator set is compared with the set operation evaluation coefficient threshold. If the operation evaluation coefficient of the target methanol generator set is less than the operation evaluation coefficient threshold, the maintenance demand of the target methanol generator set is recorded as maintenance demand; otherwise, the maintenance demand of the target methanol generator set is recorded as maintenance-free demand.

4. The artificial intelligence-based methanol generator set according to claim 2, characterized in that: The specific method for obtaining the load demand of the target methanol power generation unit in the forecast time period includes: Extract the load demand of each time period corresponding to each historical date of the target methanol power generation unit, determine the load demand of each time period corresponding to each historical date corresponding to the current date in the target methanol power generation unit, and record it as the load demand of each time period corresponding to each historical reference date of the target methanol power generation unit; Record the current time point as the reference starting time point, further determine the target methanol power generation group's historical reference dates corresponding to the continuous time periods with the reference starting time point as the starting time point and the load demand corresponding to each time period from the target methanol power generation group's historical reference dates, and record them as the target methanol power generation group's historical reference dates corresponding to the reference continuous time periods; The overlapping time periods in the reference continuous time periods corresponding to the historical reference dates of the target methanol power generation group are recorded as the prediction time periods of the target methanol power generation group.

5. The artificial intelligence-based methanol generator set according to claim 4, characterized in that: The load demand of the target methanol power generation unit in the forecast time period, and its specific forecasting method includes: Extract the load demand of each historical date corresponding to each time period of the target methanol power generation unit, and extract the load demand of each historical reference date corresponding to the forecast time period of the target methanol power generation unit ,in , is the number corresponding to each historical reference date, is the quantity corresponding to the historical reference date, according to the analysis formula Get the load demand of the target methanol generator unit in the forecast period ,in It is the compensation value corresponding to the load demand of the target methanol power generation unit.

6. The artificial intelligence-based methanol power generation unit according to claim 5, characterized in that: The specific method for obtaining the compensation value corresponding to the load demand of the target methanol power generation unit includes: Extract the load demand of each historical date corresponding to each time period of the target methanol power generation unit, taking the load demand of each historical reference date corresponding to the forecast time period of the target methanol power generation unit as the reference date, extract the load demand of each historical date corresponding to the forecast time period of the target methanol power generation unit for the set number of dates forward and backward, and record it as the load demand of each historical compensation date corresponding to the forecast time period of the target methanol power generation unit ,in , is the number of each historical compensation date, is the number of historical compensation dates, according to the analytical formula Get the corresponding compensation value of the load demand of the target methanol generator set .

7. The artificial intelligence-based methanol generator set according to claim 5, characterized in that: The specific determination method of the fuel supply control demand of the target methanol power generation unit includes: The load demand of the target methanol power generation unit in the forecast period is matched with the reference methanol supply corresponding to each load demand in the information storage library and the reference temperature and reference pressure of the combustion chamber to obtain the reference methanol supply of the target methanol power generation unit and the reference temperature and reference pressure of the combustion chamber, which are recorded as ; Extract the methanol supply of the target methanol generator set and the temperature and pressure of the combustion chamber, which are recorded as , analyze the current fuel supply matching degree of the target methanol generator set ,in They are the permissible difference between the set methanol supply amount and the reference methanol supply amount, the permissible difference between the temperature of the combustion chamber and its reference temperature, and the permissible difference between the pressure of the combustion chamber and its reference pressure; The current fuel supply matching degree of the target methanol generator set is compared with the set fuel supply matching degree threshold. If the current fuel supply matching degree of the target methanol generator set is less than the fuel supply matching degree threshold, the fuel supply control demand of the target methanol generator set is a control demand; otherwise, the fuel supply control demand of the target methanol generator set is a non-control demand.

8. The artificial intelligence-based methanol power generation unit according to claim 7, characterized in that: The specific operations of controlling the fuel supply of the target methanol power generation unit include: Obtain the current load demand of the target methanol generator set, and subtract it from the load demand of the target methanol generator set in the forecast time period to obtain the load demand difference between the current and forecast time periods of the target methanol generator set. ; when When the target methanol generator set is used, the methanol supply amount of the target methanol generator set is reduced or the temperature and pressure of the combustion chamber of the target methanol generator set are lowered; when When the methanol supply of the target methanol power generation unit and the temperature and pressure of the combustion chamber are maintained unchanged; when When the methanol supply to the target methanol generator set is increased, the temperature and pressure of the combustion chamber of the target methanol generator set are increased.

9. The artificial intelligence-based methanol power generation unit according to claim 8, characterized in that: The specific determination method of the energy storage dispatching demand of the target methanol power generation unit includes: Extract the actual load demand of the target methanol generator set in the current time period, and subtract it from the load demand of the target methanol generator set in the predicted time period to obtain the load demand difference between the current time period and the predicted time period of the target methanol generator set. ; If the load demand difference between the current time period and the predicted time period of the target methanol power generation unit is 0, the energy storage scheduling demand of the target methanol power generation unit is recorded as no scheduling demand. Otherwise, the energy storage scheduling demand of the target methanol power generation unit is recorded as a scheduling demand.

10. The artificial intelligence-based methanol power generation unit according to claim 9, characterized in that: The specific operations of controlling the energy storage scheduling of the target methanol power generation unit include: when When the target methanol power generation unit is dispatched from the energy storage system Load; when When the target methanol generator set is stored in the energy storage system of load.

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