Distributed methanol power generation method and device

Abstract

The invention discloses a distributed methanol power generation method and device, and relates to the technical field of fuel cell power generation, and the method comprises the steps: carrying out the power demand prediction through historical power data, power grid load information and real-time environmental factors, generating a methanol supply scheme, and dynamically controlling the methanol pumping of a target independent power generation unit; operating parameters of the reforming reactor are adjusted by using a bidirectional optimization feedback mechanism to obtain an optimal reforming scheme, and hydrogen-rich gas is prepared for power generation of the fuel cell; and dynamically adjusting the operation number of the independent power generation units according to the real-time power demand change. The technical problems that an existing distributed methanol power generation method depends on single parameter adjustment, is difficult to cope with the coupling effect of multiple factors, and is low in operation efficiency and poor in adaptability are solved, and the technical effect that the operation efficiency and adaptability of a methanol power generation system are improved by introducing a multi-index optimization algorithm and a bidirectional optimization feedback mechanism is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of fuel cell power generation, and in particular to a distributed methanol power generation method and device. Background Art

[0002] With the continuous growth of global energy demand and increasingly stringent environmental protection requirements, clean and efficient energy utilization technology has become a research hotspot. Methanol, as a clean, high-energy-density and easy-to-store fuel, has broad application prospects in the field of distributed power generation due to its wide sources and low carbon emission characteristics.

[0003] However, existing distributed methanol power generation methods usually adopt a fixed scheduling mode, that is, fuel supply and power output are carried out according to fixed parameters and preset conditions. This method lacks dynamic response capabilities when facing fluctuations in power demand or environmental changes, and is prone to problems such as fuel waste, insufficient power supply, or unbalanced load on power generation units. In addition, as the core component of methanol power generation, the performance of the reforming reactor is affected by multiple factors such as temperature, pressure, and catalyst activity. The traditional single parameter adjustment method is difficult to achieve global optimization, resulting in reduced system efficiency. Summary of the invention

[0004] The present application provides a distributed methanol power generation method and device, which are used to solve the technical problems that the existing distributed methanol power generation method relies on a single parameter adjustment, is difficult to cope with the coupling of multiple factors, has low operating efficiency and poor adaptability.

[0005] In a first aspect of the present application, a distributed methanol power generation method is provided, the method comprising: performing a phased power demand forecast based on historical power usage data, grid load information and real-time environmental factors of a target area to obtain a phased power demand forecast result; performing a methanol supply analysis based on the phased power demand forecast result to generate a methanol supply plan; controlling a corresponding number of target independent power generation units to pump methanol based on the methanol supply plan, wherein the methanol supply plan includes a methanol pumping plan for a plurality of target independent power generation units, and the plurality of target independent power generation units are a plurality of parallel independent power generation units; for the methanol reforming process of the plurality of target independent power generation units, adaptively and dynamically adjusting the reforming process of the reforming reactor using a bidirectional optimization feedback mechanism to obtain an optimal reforming plan; preparing hydrogen-rich gas based on the optimal reforming plan, delivering the generated hydrogen-rich gas to the fuel cell of each independent power generation unit for power supply, and during the power generation process, monitoring power demand changes in real time to add or remove independent power generation units.

[0006] The second aspect of the present application provides a distributed methanol power generation device, which includes: a power demand forecasting module, which is used to forecast the power demand of a certain period according to the historical power usage data of the target area, the grid load information and the real-time environmental factors, and obtain the power demand forecast result of the period; a methanol supply analysis module, which is used to analyze the methanol supply according to the power demand forecast result of the period, and generate a methanol supply plan; a methanol pumping module, which is used to control a corresponding number of target independent power generation units to pump methanol according to the methanol supply plan, wherein the methanol supply plan includes multiple target independent power generation units. a methanol pumping scheme for target independent power generation units, wherein the multiple target independent power generation units are multiple parallel independent power generation units; a reforming process optimization module, wherein the reforming process optimization module is used for adaptively and dynamically adjusting the reforming process of the reforming reactor according to the methanol reforming process of the multiple target independent power generation units by utilizing a two-way optimization feedback mechanism to obtain an optimal reforming scheme; and a power supply module, wherein the power supply module is used for preparing hydrogen-rich gas according to the optimal reforming scheme, transporting the generated hydrogen-rich gas to the fuel cell of each independent power generation unit for power supply, and during the power generation process, monitoring the change in power demand in real time, and adding or removing independent power generation units.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: The present application provides a distributed methanol power generation method and device, which relates to the field of fuel cell power generation technology. It generates a methanol supply plan through power demand prediction, dynamically controls the methanol pumping of independent power generation units, uses two-way optimization feedback to adjust the reforming reactor parameters, obtains the optimal reforming plan and prepares hydrogen-rich gas for fuel cell power generation, and monitors power demand changes in real time, flexibly adjusts the number of power generation units in operation, and achieves efficient and stable power supply. It solves the technical problems that the existing distributed methanol power generation method relies on single parameter adjustment, is difficult to cope with the coupling of multiple factors, has low operating efficiency and poor adaptability. By introducing a multi-index optimization algorithm and a two-way optimization feedback mechanism, the operating efficiency and adaptability of the methanol power generation system are comprehensively improved, and the technical effect of meeting the dynamic power supply needs in multiple scenarios is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1A schematic flow chart of a distributed methanol power generation method provided in an embodiment of the present application.

[0010] Figure 2 A schematic structural diagram of a distributed methanol power generation device provided in an embodiment of the present application.

[0011] Explanation of the reference numerals: power demand prediction module 11 , methanol supply analysis module 12 , methanol pumping module 13 , reforming process optimization module 14 , power supply module 15 . DETAILED DESCRIPTION

[0012] The present application provides a distributed methanol power generation method and device, which are used to solve the technical problems that the existing distributed methanol power generation method relies on a single parameter adjustment, is difficult to cope with the coupling of multiple factors, has low operating efficiency and poor adaptability.

[0013] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0014] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices.

[0015] Embodiment 1, as Figure 1 As shown, the present application provides a distributed methanol power generation method, which is applied to a distributed methanol power generation device, wherein the device includes a plurality of independent power generation units, each of which includes a methanol storage tank, a methanol pump, a reforming reactor and a fuel cell, and the method includes: P10: Based on the historical electricity usage data, grid load information and real-time environmental factors of the target area, a phased electricity demand forecast is conducted to obtain the phased electricity demand forecast results.

[0016] It should be understood that through in-depth analysis and prediction of the electricity consumption characteristics of the target area, a scientific basis can be provided for subsequent power generation strategies.

[0017] First, collect historical electricity usage data in the target area, including the electricity consumption trend of users in the past period of time, the load characteristics of daily peak and trough periods, and seasonal fluctuations, etc. These data provide a basic basis for power demand modeling. Next, obtain the grid load information, that is, the current grid supply and demand situation and load change trend, such as real-time grid frequency, voltage fluctuations and spare capacity. This information can reflect the current operating pressure of the power system. Subsequently, combine real-time environmental factors, including weather conditions (such as temperature, humidity, wind speed) and social activity characteristics (such as holidays, major events, etc.), to analyze the dynamic impact of external factors on power demand.

[0018] Through the fusion processing of the above data and the integration of these multi-source data, the time series prediction model (such as ARIMA, LSTM) or machine learning algorithm (such as random forest, support vector machine) is used to predict the future power demand by time period, and generate the phased power demand forecast results, that is, the power demand by time period. The phased power demand forecast results not only include the total power demand, but also can be refined to the daily or hourly demand, providing a scientific basis for the subsequent dynamic allocation of methanol supply and power generation units. Through this step, it can be ensured that the power generation system can accurately match the actual power demand of the region, avoid resource waste and power shortage problems, and achieve efficient operation of the system.

[0019] P20: Based on the electricity demand forecast results for the said stage, a methanol supply analysis is conducted to generate a methanol supply plan.

[0020] Furthermore, step P20 of the embodiment of the present application further includes: P21: Based on the stage power demand forecast results, calculate the total power demand and the power demand for each time period; P22: Based on the power generation efficiency, methanol inventory and transmission rate limit of the multiple independent power generation units, calculate the maximum power generation capacity of each unit; P23: Based on the total power demand and the power demand for each time period, match the independent power generation units and generate a parallel power generation plan, which includes the number of independent power generation units participating in power generation and the corresponding power generation indicators; P24: Based on the parallel power generation plan, calculate the methanol demand of each independent power generation unit and generate the methanol supply plan.

[0021] Optionally, based on the electricity demand forecast results for the stage, a scientific and reasonable methanol supply plan can be formulated through comprehensive analysis of the performance of the power generation units and the fuel supply conditions to provide guarantee for efficient power generation.

[0022] First, based on the results of the phased power demand forecast, determine the total power demand of the target area, that is, the total power consumption that needs to be met within a specific time period. At the same time, the total demand is subdivided into power demand for each period, that is, the power demand is decomposed according to different periods of the day (such as peak and off-peak time periods), and the power supply capacity required in each period is clarified. This process can use the time series decomposition method to map the phased forecast results to specific time periods to ensure the timeliness and accuracy of power distribution.

[0023] Next, the performance of the independent power generation units is evaluated and the maximum power generation capacity of each unit is calculated. The independent power generation unit is a basic power generation module composed of a methanol storage tank, a methanol pump, a reforming reactor and a fuel cell, and its power generation efficiency is limited by the methanol conversion efficiency, the fuel cell performance and the operating parameters. By comprehensively considering the methanol inventory, pumping capacity and delivery rate limitations of each unit, the maximum power generation capacity of each unit is calculated to clarify its contribution potential in the parallel structure.

[0024] Subsequently, independent power generation units are matched according to the total power demand and the power demand in each period, and power tasks are allocated in sequence from high-efficiency units to low-efficiency units according to the maximum power generation capacity of each unit, giving priority to the power generation potential of high-efficiency units, ensuring the overall optimal energy consumption, and generating a parallel power generation plan. The parallel power generation plan is a scheduling strategy that determines which power generation units need to be started, when to start, and the target power generation indicators of each unit. The plan uses optimization algorithms (such as linear programming or heuristic algorithms) to achieve the optimal combination of the number of power generation units and power generation efficiency to ensure that both dynamic demand can be met and resource consumption can be minimized.

[0025] Finally, according to the parallel power generation scheme, combined with the power generation indicators of each unit, the methanol demand required to complete the power generation task is calculated. Then, according to the methanol demand and the delivery rate limit, a methanol supply plan is generated. The plan includes the methanol delivery rate, pumping time and replenishment schedule of each unit to ensure that each unit has an uninterrupted supply of methanol during the task execution. Finally, the methanol supply plan provides fuel guarantee for the power generation execution of the entire system, and optimizes resource utilization through intelligent allocation to avoid oversupply or fuel shortage.

[0026] Through the above steps, the methanol supply solution can accurately match electricity demand and power generation capacity, which not only optimizes the methanol utilization efficiency, but also ensures the stability and flexibility of power supply, providing a solid foundation for the efficient operation of distributed power generation systems.

[0027] P30: According to the methanol supply plan, control a corresponding number of target independent power generation units to pump methanol, wherein the methanol supply plan includes methanol pumping plans for multiple target independent power generation units, and the multiple target independent power generation units are multiple parallel independent power generation units.

[0028] Specifically, according to the methanol supply plan, a corresponding number of target independent power generation units are controlled to pump methanol to ensure accurate supply and efficient use of methanol fuel. This step achieves dynamic allocation and stable operation of system resources by coordinating multiple target independent power generation units (parallel configuration).

[0029] First, according to the generated methanol supply plan, determine the number of target independent power generation units that need to be operated and the specific fuel requirements of each unit. The target independent power generation unit refers to the power generation module that needs to participate in the power generation task in the current period, and its operating status is dynamically adjusted according to the power demand and methanol supply plan. Units with high power generation efficiency and sufficient methanol reserves are given priority to participate in power generation to ensure the overall optimal energy consumption.

[0030] Next, according to the methanol pumping plan of each unit, the methanol pump is controlled to perform the pumping operation. The methanol pumping plan includes information such as the fuel demand, pumping rate, and pumping duration of each power generation unit. The pumping rate is adjusted by a real-time flow control system to match the actual consumption rate of the unit, ensuring that the fuel supply is synchronized with the power generation load to avoid fuel surplus or shortage. In addition, the pumping operation is limited by the maximum delivery rate of the methanol pump, and the pumping timing needs to be optimized through time-sharing scheduling.

[0031] During the pumping process, the real-time monitoring system is used to dynamically monitor the fuel delivery status of each power generation unit, including fuel flow, remaining inventory and pumping equipment operation status. If the pumping rate is found to be inconsistent with the actual needs of the unit, the system will adjust the pumping parameters in real time through the feedback adjustment mechanism to ensure the accuracy and continuity of the supply process.

[0032] Finally, all the target independent power generation units are operated in parallel to jointly complete the phased power generation task. In the parallel mode, each power generation unit operates independently but supplies power in coordination, which can effectively share the load and improve the stability and flexibility of the system. At the same time, the backup unit is on standby to respond to sudden power demand or operation failure.

[0033] Through the above steps, this process achieves efficient allocation and supply of methanol fuel, ensures the continuous and stable operation of the target power generation unit in the power generation task, and provides support for subsequent real-time optimization and dynamic adjustment. The entire process relies on intelligent control and real-time monitoring technology to ensure the optimal utilization of system resources and operating efficiency.

[0034] P40: With respect to the methanol reforming process of the multiple target independent power generation units, a two-way optimization feedback mechanism is utilized to adaptively and dynamically adjust the reforming process of the reforming reactor to obtain an optimal reforming scheme.

[0035] Furthermore, step P40 of the embodiment of the present application also includes: P41: Real-time monitoring of the key operating indicators of the reforming reactor of each target independent power generation unit, wherein the key operating indicators include positive indicators and negative indicators; P42: Based on the key operating indicators, a reforming decision matrix is ​​constructed: ;in, represents the value of the i-th power generation unit on the j-th indicator; P43: weighted normalization is performed on the reorganization decision matrix to assign weights to each key operating indicator , and get the standard decision matrix: V , * , ; Where V is the standard decision matrix, is the standardized value, is the weight of the key indicator; P44: calculate and obtain the ideal solution and negative ideal solution of each key operating indicator, and according to the ideal solution and negative ideal solution, combine the standard decision matrix to optimize the reorganization plan and obtain the optimal reorganization plan.

[0036] It should be understood that, through a two-way optimization feedback mechanism, the methanol reforming process of the independent power generation unit is adaptively and dynamically adjusted to achieve optimal methanol conversion efficiency and stable fuel cell power generation performance.

[0037] First, the system uses an intelligent sensor network to monitor the operating status of the reforming reactor of each target independent power generation unit in real time and extract key operating indicators. Key operating indicators are core parameters that describe the performance of the reforming reactor, which are divided into positive indicators (such as hydrogen production and temperature stability, the larger the value, the better) and negative indicators (such as energy consumption and by-product emissions, the smaller the value, the better). By monitoring the operating status of the equipment in real time and collecting these indicators, data support is provided for subsequent optimization and adjustment. The data of key operating indicators can be collected through sensors or intelligent monitoring systems and transmitted to the control center in real time to ensure the accuracy and timeliness of the system operation data.

[0038] Furthermore, based on the key operating indicators monitored in real time, a reorganization decision matrix is ​​constructed, which is recorded as ;in, To represent the value of the i-th power generation unit on the j-th indicator, this matrix serves as the basis for subsequent optimization calculations and comprehensively describes the performance of each power generation unit on different key indicators.

[0039] In order to improve the comparability of data and the scientific nature of weight distribution, the system performs weighted normalization on the reorganization decision matrix. Convert to normalized value , the formula is: After this processing, the data values ​​of all indicators are scaled to a relative scale, eliminating the dimensional differences. Then, the importance weights of the indicators can be determined by empirical, hierarchical analysis method (AHP) or entropy weight method. , ensuring that the optimization process is more in line with actual needs. For example, the weight of hydrogen output rate may be higher, while the weight of temperature fluctuation rate is relatively lower. Finally, the standard decision matrix V is obtained ,in, * , this matrix is ​​used in subsequent optimization analysis and represents the weighted standardized performance of each power generation unit on key operating indicators.

[0040] Next, the standard decision matrix is ​​analyzed to calculate the ideal solution and negative ideal solution, where the ideal solution refers to the best value combination of key operating indicators (for example, maximum hydrogen production rate, minimum carbon accumulation rate). The negative ideal solution refers to the worst value combination of key operating indicators. Based on these solutions, the distance between each power generation unit and the ideal solution and negative ideal solution is calculated through an optimization algorithm (such as TOPSIS or weighted sum method), and relative proximity is defined as a measurement indicator to rank the reforming schemes.

[0041] Finally, all units are sorted according to their relative proximity, and the unit operation plan with the highest relative proximity is selected as the optimal reforming plan. For units with lower relative proximity, their operating parameters (such as temperature, pressure, etc.) are adjusted to gradually bring their performance closer to the optimal plan. By accurately adjusting the reforming reaction process of multiple target independent power generation units, the efficiency and stability of the overall operation of the system are ensured.

[0042] Furthermore, step P44 of the embodiment of the present application also includes: P44-1: Based on the standard decision matrix, calculate the distance between each power generation unit and the ideal solution and the negative ideal solution to obtain the relative distance; P44-2: According to the relative distance, calculate the relative proximity, and use it to sort the power generation status, and select the reforming plan corresponding to the current optimal power generation unit as the initial optimal plan; P44-3: According to the two-way optimization feedback mechanism, correct and adjust the initial optimal plan to obtain the optimal reforming plan.

[0043] In a possible embodiment of the present application, in order to further optimize the efficiency and quality of the methanol reforming reaction, the operating status of the power generation unit is deeply analyzed using a standard decision matrix, and gradually adjusted through a two-way optimization feedback mechanism to ultimately obtain the optimal reforming solution.

[0044] First, according to the key indicator value of each power generation unit in the standard decision matrix, the distance between it and the ideal solution and the negative ideal solution is calculated. The calculation formula can be: ; ; d; where, and denote the Euclidean distance between the ith power generation unit and the ideal solution and the negative ideal solution, respectively. and are the ideal value and negative ideal value of the jth indicator, respectively. Through this calculation, the system can quantify the degree of closeness of each power generation unit to the optimal and worst performance states, that is, the relative distance.

[0045] Further, based on the relative distance, the relative proximity is calculated: ;in, It is the relative proximity of samples, which is between 0 and 1. The closer the value is to 1, the closer the operating state of the unit is to the ideal solution and the better the performance is.

[0046] Next, all power generation units are sorted according to their relative proximity to select the power generation unit with the best current operating status. The reforming operation parameters (such as temperature, pressure, and methanol flow) corresponding to the optimal power generation unit are extracted as the initial optimal solution. After obtaining the initial optimal solution, it is further modified and adjusted through a two-way optimization feedback mechanism to obtain the final optimal reforming solution.

[0047] Furthermore, step P44-3 of the embodiment of the present application also includes: P44-31: The bidirectional optimization feedback mechanism is embedded with a bidirectional tuning model, which includes a value evaluation network and an advantage evaluation network; P44-32: The initial optimal solution is taken as a state vector and input into the bidirectional tuning model, and the value evaluation network performs solution value evaluation to obtain a comprehensive solution value, and the advantage evaluation network outputs the corresponding action advantage for each adjustment action; P44-33: According to the comprehensive solution value and action advantage, the action value is calculated, and the adjustment action with the maximum action value is selected as the current optimization strategy, and the initial optimal solution is corrected and adjusted to obtain the optimal reorganization solution.

[0048] Furthermore, step P44-33 of the embodiment of the present application also includes: P44-331: Calculating action value through the combination of value evaluation network and advantage evaluation network: ;in, is the action value, To adjust the action, The comprehensive value of the solution, For Action action advantage.

[0049] Specifically, the initial optimal solution is further adjusted and optimized through the two-way optimization feedback mechanism, and finally the global optimal methanol reforming operation solution is obtained. This process relies on the synergy of the two-way tuning model to comprehensively evaluate the value of the solution and the effect of the adjustment action.

[0050] The two-way optimization feedback mechanism is embedded with a two-way tuning model, which includes a value assessment network and an advantage assessment network. The value assessment network is used to globally evaluate the comprehensive performance of the entire initial solution and judge the overall value of the current solution. The advantage assessment network is used to evaluate the optimization potential and relative importance of each adjustment action in the solution (such as temperature, pressure, and catalyst activity adjustment) to guide the optimization direction of specific parameters. The design of this model ensures that the overall adaptability of the solution can be evaluated globally, and the feasibility of optimizing key parameters can be evaluated locally, providing precise support for dynamic optimization.

[0051] First, the initial optimal solution is represented as the state vector , which is input into the bidirectional tuning model. The state vector Includes the key performance indicators of the initial solution (such as current hydrogen production, energy consumption, temperature, pressure, etc.), as well as the current operating environment of the solution (such as load demand, power fluctuations and other external conditions).

[0052] Then, the value evaluation network analyzes the input state vector and outputs the comprehensive value of the solution. , which is used to quantify the overall performance of the solution. The higher the comprehensive value, the better the adaptability and efficiency of the solution under the current operating conditions. The advantage evaluation network is based on the state vector and possible regulatory actions (such as increasing temperature, reducing pressure, etc.), output the action advantage of each action The action advantage represents the potential of each adjustment action to improve the performance of the solution.

[0053] Finally, the outputs of the value evaluation network and the advantage evaluation network are combined to calculate the action value of each regulatory action. : ;in, is the action value, indicating that in the current state The total optimization potential of executing adjustment action a under the condition of , the larger the value, the more likely the action is to improve the performance of the solution. To adjust the action, It is the comprehensive value of the solution, reflecting the global adaptability of the current solution. For Action The action advantage reflects the potential for optimizing the solution after executing action a.

[0054] From all possible adjustment actions, select the one with the greatest action value Adjustment action , as the current optimization strategy, and implement the selected adjustment action , adjust the parameters of the initial optimal solution (such as increasing the reactor temperature, optimizing the catalyst distribution, adjusting the feed flow rate, etc.) to make the solution state closer to the ideal solution. The revised solution is verified by dynamic feedback to ensure that it is closer to the global optimal state in terms of multi-dimensional performance indicators.

[0055] Through the two-way optimization feedback mechanism embedded in the two-way tuning model, the system can not only globally evaluate the adaptability of the initial optimal solution, but also fine-tune the specific operating parameters. The optimal restructuring solution finally obtained is close to the ideal solution in terms of multi-dimensional performance indicators, and can dynamically adapt to changes in the external operating environment, providing a guarantee for the overall stable and efficient operation of the system.

[0056] P50: Prepare hydrogen-rich gas according to the optimal reforming scheme, transport the generated hydrogen-rich gas to the fuel cells of each independent power generation unit for power supply, and monitor the change of power demand in real time during the power generation process to add or remove independent power generation units.

[0057] Furthermore, step P50 of the embodiment of the present application also includes: P51: According to the optimal reforming scheme of the optimal power generation unit, optimize the operating parameters of the remaining target independent power generation units; P52: When the relative proximity of all target independent power generation units is close to the optimal value, stop the scheme adjustment, and prepare hydrogen-rich gas according to the adjusted scheme.

[0058] It should be understood that based on the optimal reforming scheme, efficient preparation and transportation of hydrogen-rich gas is achieved, and during the power generation process, the operating status of independent power generation units is dynamically adjusted to achieve real-time optimization of power supply and maximize resource utilization.

[0059] First, based on the selection of the optimal power generation unit, the operating parameters of the remaining target independent power generation units are optimized according to their optimal reforming scheme. For example, by adjusting the temperature curve, the reaction efficiency of each unit is close to the optimal state; the pressure level is dynamically optimized to match the methanol conversion rate and hydrogen production efficiency; according to different load conditions, the catalyst activation method is adjusted to reduce reaction delay or by-product generation. The operating state of all power generation units is adjusted to be close to the ideal solution to improve the overall performance of the system. This process uses an intelligent adjustment algorithm to transfer the operating experience of the optimal unit to other units to form an overall optimization.

[0060] Furthermore, the relative proximity of all target independent power generation units is calculated in real time, and the optimization effect of each unit is evaluated by analyzing the distance between each unit and the optimal state. When the proximity deviation meets the preset threshold (such as less than 5%), the scheme adjustment is stopped, indicating that the state of each unit has reached equilibrium and stability. According to the adjusted optimal reforming scheme, the reforming reactor is started to prepare hydrogen-rich gas to ensure that the output and purity of hydrogen generation meet the requirements of fuel cells. Through the intelligent pipeline system, the hydrogen-rich gas is transported to the fuel cells of each independent power generation unit, and the chemical reaction of hydrogen and oxygen is used to achieve efficient power generation.

[0061] During the power generation process, the fluctuation of power demand is monitored in real time. If the power demand increases, the independent power generation units in operation are dynamically increased; if the demand decreases, some units are shut down in time to save resources. The adjustment mechanism relies on the power load forecasting model and real-time monitoring data to ensure system flexibility.

[0062] Under the guidance of the optimal reforming scheme, the operating parameters of all target power generation units are gradually optimized to bring the performance of each unit close to the optimal state. During the power generation process, the number and state of power generation units are flexibly adjusted by real-time monitoring of power demand to ensure that the system meets dynamic power supply needs while being efficient and stable. Ultimately, this step achieves efficient preparation and utilization of hydrogen-rich gas, while improving the flexibility and resource utilization of the entire power generation system.

[0063] In summary, the embodiments of the present application have at least the following technical effects: This application predicts electricity demand through historical electricity data, grid load information and real-time environmental factors, generates a methanol supply plan, and dynamically controls the methanol pumping of the target independent power generation unit. It uses a two-way optimization feedback mechanism to adjust the operating parameters of the reforming reactor to obtain the optimal reforming plan, prepare hydrogen-rich gas for fuel cell power generation, and dynamically adjust the operating number of independent power generation units according to real-time changes in electricity demand to ensure efficient, stable and environmentally friendly electricity supply and improve resource utilization.

[0064] The technical effect of comprehensively improving the operating efficiency and adaptability of the methanol power generation system and meeting the dynamic power supply needs in multiple scenarios has been achieved by introducing a multi-index optimization algorithm and a two-way optimization feedback mechanism.

[0065] Embodiment 2, based on the same inventive concept as a distributed methanol power generation method in the above embodiment, Figure 2 As shown, the present application provides a distributed methanol power generation device, and the device and method embodiments in the present application embodiments are based on the same inventive concept. The device includes: The power demand forecasting module 11 is used to perform phased power demand forecasting based on historical power usage data of the target area, grid load information and real-time environmental factors to obtain phased power demand forecasting results.

[0066] The methanol supply analysis module 12 is used to perform methanol supply analysis and generate a methanol supply plan based on the electricity demand forecast result of the stage.

[0067] A methanol pumping module 13 is used to control a corresponding number of target independent power generation units to pump methanol according to the methanol supply plan, wherein the methanol supply plan includes methanol pumping plans for multiple target independent power generation units, and the multiple target independent power generation units are multiple parallel independent power generation units.

[0068] The reforming process optimization module 14 is used to adaptively and dynamically adjust the reforming process of the reforming reactor according to the methanol reforming process of the multiple target independent power generation units by using a two-way optimization feedback mechanism to obtain an optimal reforming scheme.

[0069] The power supply module 15 is used to prepare hydrogen-rich gas according to the optimal reforming scheme, transport the generated hydrogen-rich gas to the fuel cells of each independent power generation unit for power supply, and monitor the change of power demand in real time during the power generation process to add or remove independent power generation units.

[0070] Furthermore, the methanol supply analysis module 12 is further configured to perform the following steps: Calculate the total power demand and power demand for each period based on the stage power demand forecast results; According to the power generation efficiency, methanol inventory and transmission rate limit of the multiple independent power generation units, the maximum power generation capacity of each unit is calculated; based on the total power demand and the power demand in each time period, the independent power generation units are matched to generate a parallel power generation plan, which includes the number of independent power generation units participating in power generation and the corresponding power generation indicators; according to the parallel power generation plan, the methanol demand of each independent power generation unit is calculated to generate the methanol supply plan.

[0071] Furthermore, the reforming process optimization module 14 is also used to perform the following steps: Real-time monitoring of key operating indicators of the reforming reactor of each target independent power generation unit, wherein the key operating indicators include positive indicators and negative indicators; based on the key operating indicators, a reforming decision matrix is ​​constructed: ;in, represents the value of the i-th power generation unit on the j-th indicator; the reorganization decision matrix is ​​weighted and normalized to assign weights to each key operating indicator , and get the standard decision matrix: V , * , ; Where V is the standard decision matrix, is the standardized value, is the weight of the key indicator; the ideal solution and the negative ideal solution of each key operating indicator are calculated, and the reorganization scheme is optimized according to the ideal solution and the negative ideal solution in combination with the standard decision matrix to obtain the optimal reorganization scheme.

[0072] Furthermore, the reforming process optimization module 14 is also used to perform the following steps: Based on the standard decision matrix, the distance between each power generation unit and the ideal solution and the negative ideal solution is calculated to obtain the relative distance; based on the relative distance, the relative proximity is calculated, and the power generation status is sorted based on this, and the reforming scheme corresponding to the current optimal power generation unit is selected as the initial optimal scheme; according to the two-way optimization feedback mechanism, the initial optimal scheme is corrected and adjusted to obtain the optimal reforming scheme.

[0073] Furthermore, the reforming process optimization module 14 is also used to perform the following steps: The bidirectional optimization feedback mechanism is embedded with a bidirectional tuning model, which includes a value evaluation network and an advantage evaluation network; the initial optimal solution is taken as a state vector and input into the bidirectional tuning model, and the value evaluation network performs solution value evaluation to obtain a comprehensive solution value, and the advantage evaluation network outputs the corresponding action advantage for each adjustment action; the action value is calculated based on the comprehensive solution value and the action advantage, and the adjustment action with the maximum action value is selected as the current optimization strategy, and the initial optimal solution is corrected and adjusted to obtain the optimal reorganization solution.

[0074] Furthermore, the reforming process optimization module 14 is also used to perform the following steps: The action value is calculated by combining the value evaluation network and the advantage evaluation network: ;in, is the action value, To adjust the action, The comprehensive value of the solution, For Action action advantage.

[0075] Furthermore, the power supply module 15 is also used to perform the following steps: According to the optimal reforming scheme of the optimal power generation unit, the operating parameters of the remaining target independent power generation units are optimized; when the relative proximity of all target independent power generation units is close to the optimal value, the scheme adjustment is stopped, and hydrogen-rich gas is prepared according to the adjusted scheme.

[0076] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0077] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0078] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. A distributed methanol power generation method, characterized in that: The method is applied to a distributed methanol power generation device, the device comprising a plurality of independent power generation units, each of which comprises a methanol storage tank, a methanol pump, a reforming reactor and a fuel cell, and the method comprises: According to the historical power usage data, grid load information and real-time environmental factors of the target area, the power demand forecast is carried out to obtain the power demand forecast results; Conduct methanol supply analysis and generate a methanol supply plan based on the electricity demand forecast results for the stage; According to the methanol supply scheme, a corresponding number of target independent power generation units are controlled to perform methanol pumping, wherein the methanol supply scheme includes methanol pumping schemes for multiple target independent power generation units, and the multiple target independent power generation units are multiple parallel independent power generation units; For the methanol reforming process of the multiple target independent power generation units, a two-way optimization feedback mechanism is used to adaptively and dynamically adjust the reforming process of the reforming reactor to obtain an optimal reforming scheme; Hydrogen-rich gas is prepared according to the optimal reforming scheme, and the generated hydrogen-rich gas is transported to the fuel cells of each independent power generation unit for power supply. During the power generation process, changes in power demand are monitored in real time to increase or remove independent power generation units.

2. A distributed methanol power generation method according to claim 1, characterized in that: According to the electricity demand forecast results of the said stage, methanol supply analysis is carried out to generate a methanol supply plan, including: Calculate the total power demand and power demand for each period based on the stage power demand forecast results; Calculating the maximum power generation capacity of each unit based on the power generation efficiency, methanol inventory and delivery rate limit of the plurality of independent power generation units; Based on the total power demand and the power demand in each time period, the independent power generation units are matched to generate a parallel power generation plan, wherein the parallel power generation plan includes the number of independent power generation units participating in power generation and corresponding power generation indicators; According to the parallel power generation scheme, the methanol demand of each independent power generation unit is calculated to generate the methanol supply scheme.

3. A distributed methanol power generation method as claimed in claim 2, characterized in that: For the methanol reforming process of the multiple target independent power generation units, the reforming process of the reforming reactor is adaptively and dynamically adjusted by using a two-way optimization feedback mechanism to obtain an optimal reforming scheme, including: Real-time monitoring of key operating indicators of each target independent power generation unit reforming reactor, wherein the key operating indicators include positive indicators and negative indicators; Based on the key operating indicators, a reorganization decision matrix is ​​constructed: ;in, represents the value of the i-th power generation unit on the j-th index; The reorganization decision matrix is ​​weighted and normalized to assign weights to each key operating indicator. , and get the standard decision matrix: V ， * ， ； Among them, V is the standard decision matrix, is the standardized value, is the key indicator weight; The ideal solution and negative ideal solution of each key operating indicator are calculated and obtained, and the reorganization scheme is optimized based on the ideal solution and negative ideal solution in combination with the standard decision matrix to obtain the optimal reorganization scheme.

4. A distributed methanol power generation method as claimed in claim 3, characterized in that: According to the ideal solution and the negative ideal solution, combined with the standard decision matrix, a reorganization scheme is optimized to obtain an optimal reorganization scheme, including: Based on the standard decision matrix, calculating the distance between each power generation unit and the ideal solution and the negative ideal solution to obtain a relative distance; According to the relative distance, the relative proximity is calculated, and the power generation states are sorted based on this, and the reforming scheme corresponding to the current optimal power generation unit is selected as the initial optimal scheme; According to the two-way optimization feedback mechanism, the initial optimal solution is corrected and adjusted to obtain the optimal reorganization solution.

5. A distributed methanol power generation method as claimed in claim 4, characterized in that: According to the two-way optimization feedback mechanism, the initial optimal solution is corrected and adjusted to obtain the optimal reorganization solution, including: The two-way optimization feedback mechanism is embedded with a two-way tuning model, and the two-way tuning model includes a value evaluation network and an advantage evaluation network; The initial optimal solution is used as a state vector and input into the two-way tuning model, the value evaluation network performs solution value evaluation to obtain the comprehensive value of the solution, and the advantage evaluation network outputs the corresponding action advantage for each adjustment action; According to the comprehensive value of the scheme and the action advantage, the action value is calculated, and the adjustment action with the maximum action value is selected as the current optimization strategy. The initial optimal scheme is corrected and adjusted to obtain the optimal reorganization scheme.

6. A distributed methanol power generation method as claimed in claim 5, characterized in that: According to the comprehensive value of the scheme and the advantages of the action, the action value is calculated, including: The action value is calculated by combining the value evaluation network and the advantage evaluation network: ； in, is the action value, To adjust the action, The comprehensive value of the solution, For Action action advantage.

7. A distributed methanol power generation method as claimed in claim 6, characterized in that: The method of preparing hydrogen-rich gas according to the optimal reforming scheme includes: Optimizing the operating parameters of the remaining target independent power generation units according to the optimal reforming scheme of the optimal power generation unit; When the relative proximity of all target independent power generation units approaches the optimal value, the scheme adjustment is stopped, and hydrogen-rich gas is prepared according to the adjusted scheme.

8. A distributed methanol power generation device, characterized in that: The device comprises a plurality of independent power generation units, each of which comprises a methanol storage tank, a methanol pump, a reforming reactor and a fuel cell. The device comprises: An electricity demand forecasting module, which is used to forecast the electricity demand in stages according to the historical electricity usage data, grid load information and real-time environmental factors of the target area, and obtain the forecast results of the electricity demand in stages; A methanol supply analysis module, which is used to perform methanol supply analysis and generate a methanol supply plan based on the power demand forecast result of the stage; A methanol pumping module, the methanol pumping module is used to control a corresponding number of target independent power generation units to perform methanol pumping according to the methanol supply scheme, wherein the methanol supply scheme includes methanol pumping schemes for multiple target independent power generation units, and the multiple target independent power generation units are multiple parallel independent power generation units; A reforming process optimization module, wherein the reforming process optimization module is used to adaptively and dynamically adjust the reforming process of the reforming reactor according to the methanol reforming process of the multiple target independent power generation units by using a two-way optimization feedback mechanism to obtain an optimal reforming scheme; A power supply module, wherein the power supply module is used to prepare hydrogen-rich gas according to the optimal reforming scheme, transport the generated hydrogen-rich gas to the fuel cells of each independent power generation unit for power supply, and during the power generation process, monitor the change in power demand in real time and add or remove independent power generation units.

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