Synthetic methanol autonomous reservation electric energy distribution management system based on big data algorithm

Abstract

The invention relates to the technical field of new energy, in particular to a synthetic methanol autonomous reservation electric energy distribution management system based on a big data algorithm, which comprises a data acquisition system and a data analysis processing system, the data analyzing and processing system integrates and analyzes the generated power of the green electricity and the time-sharing weather condition to obtain a green electricity generated power historical database related to the weather condition, and the data analyzing and processing system predicts the time-sharing generated power of the green electricity according to the weather condition of the angelica keiskei acquired by the data acquisition system; the green electricity generation power prediction value is equal to the average value of the historical green electricity generation power measured values under the same weather condition.

Description

Technical Field

[0001] This invention relates to the field of new energy technology, and in particular to the distribution of electrical energy during the synthesis of methanol. Background Technology

[0002] With technological advancements, the technology of producing hydrogen through water electrolysis using green electricity, and then synthesizing methanol from the hydrogen, has gradually matured. This technology not only helps reduce greenhouse gas emissions and alleviate climate change pressures, but also provides a new pathway for energy supply and improves energy security.

[0003] Green electricity generation is characterized by fluctuations and intermittency. Direct use in methanol synthesis presents problems such as unstable production capacity and potential repeated start-ups and shutdowns of equipment. If the power generation capacity of green electricity can be predicted and rationally allocated, these problems can be effectively mitigated. Summary of the Invention

[0004] The purpose of this invention is to provide a synthetic methanol autonomous reservation power allocation management system based on big data algorithms to solve the above-mentioned problems.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] The methanol synthesis autonomous reservation power distribution management system based on big data algorithms includes a data acquisition system and a data analysis and processing system. Its key features are: the data acquisition system collects real-time data on green electricity generation and hourly weather conditions; the data analysis and processing system integrates and analyzes the green electricity generation with the hourly weather conditions to obtain a historical database of green electricity generation related to weather conditions; and the data analysis and processing system predicts the hourly green electricity generation for tomorrow based on the weather conditions obtained from the data acquisition system. The predicted green electricity generation is equal to the average of the historical measured green electricity generation under the same conditions (month, time difference not exceeding 1 hour, temperature difference not exceeding 10 degrees Celsius, same weather conditions, and same wind force level).

[0007] Preferably, the data analysis and processing system integrates and analyzes the unit's hourly electricity consumption with the hourly weather conditions to obtain a historical database of the unit's electricity consumption related to the weather conditions. Based on the weather conditions for tomorrow obtained by the data acquisition system, the data analysis and processing system predicts the unit's hourly electricity consumption for tomorrow. The predicted hourly electricity consumption of the unit is equal to the average value of the measured hourly electricity consumption of the historical unit under the same date, same time period, temperature difference not exceeding 5 degrees Celsius, and same weather conditions.

[0008] Preferably, Function 1: Green electricity supply value = Total electricity consumption per unit tomorrow * Unit price at that electricity consumption - (Total electricity consumption per unit tomorrow - Total green electricity generation + Time-of-use consumption of green electricity for methanol synthesis) * Unit price at that electricity consumption + Time-of-use consumption of green electricity for methanol synthesis * Conversion rate * Market price of methanol; The data analysis and processing system calculates the total green electricity generation for tomorrow based on the green electricity generation power of tomorrow, and then substitutes it into Function 1 to calculate and give the green electricity allocation scheme when the green electricity supply value is maximized.

[0009] Preferably, the data analysis and processing system calculates the amount of green electricity used to synthesize methanol when the value of green electricity is maximized, and calculates the methanol production volume for tomorrow accordingly.

[0010] Preferably, when the methanol production volume given by the data analysis and processing system is greater than the minimum methanol production volume limit, the green electricity allocation scheme given by the data analysis and processing system shall be followed; when the methanol production volume given by the data analysis and processing system is less than the minimum methanol production volume limit, the data analysis and processing system shall give the green electricity allocation scheme according to the minimum methanol production volume and carry out the allocation.

[0011] Preferably, when the methanol production rate given by the data analysis and processing system exceeds the maximum methanol production limit, the surplus green electricity is fed into the power grid or used to produce hydrogen, a raw material for methanol synthesis, so as to store the electrical energy through hydrogen.

[0012] Preferably, the data analysis and processing system determines the planned value of the time-of-use power generation of green electricity under the green electricity allocation scheme, compares the planned value of the time-of-use power generation of green electricity with the predicted value of the time-of-use power generation of green electricity for tomorrow, and if the predicted value of the time-of-use power generation of green electricity for tomorrow is greater than the planned value, the surplus green electricity is used to produce feedstock hydrogen or to heat the heat transfer medium in the thermal storage tank; if the predicted value of the time-of-use power generation of green electricity for tomorrow is less than the planned value, the currently produced hydrogen is supplemented with the already produced feedstock hydrogen, or the already heated heat transfer medium is mixed with the room temperature heating medium.

[0013] Preferably, when the actual power generation of green electricity differs from the predicted time-of-use power generation, the data analysis and processing system adjusts the green electricity allocation scheme for the remaining time periods in real time based on the green electricity usage status.

[0014] Preferably, the methanol synthesis equipment includes a premixing tank, a methanol synthesis reactor, and a heat storage tank. The premixing tank is equipped with a heat transfer medium circuit, and the methanol synthesis reactor is equipped with a heat transfer medium circuit. The heat storage tank is equipped with a heater and also stores a heat transfer medium. The heater heats the heat transfer medium. The heat storage tank is connected to a first mixing tank through a first pipeline. The first mixing tank is connected to the heat transfer medium circuit of the premixing tank through a pipeline. The heat storage tank is connected to a second mixing tank through a second pipeline. The second mixing tank is connected to the heat transfer medium circuit of the methanol synthesis reactor through a pipeline. The first mixing tank and the second mixing tank are respectively connected to an ambient temperature heat transfer medium storage tank through pipelines. Detailed Implementation

[0015] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0016] Green electrochemical methanol synthesis mainly includes two steps: electrolytic hydrogen production and methanol synthesis.

[0017] Regarding hydrogen production by electrolysis

[0018] The hydrogen production equipment utilizes green electricity to produce hydrogen through water electrolysis. The equipment includes an electrolyzer, preferably a PEM electrolyzer using proton exchange membrane (PEM) technology. This invention selects the type of electrolyzer, with PEM electrolyzers offering the following advantages: 1. Lower purity requirements for the raw water, significantly reducing impurity treatment costs during hydrogen production. 2. Fewer byproducts generated by PEM electrolyzers, contributing to environmental protection. Hydrogen production steps: Step 1: Pretreated pure water is electrolyzed in the electrolyzer, decomposing water molecules into hydrogen and oxygen. Step 2: Hydrogen and oxygen are separated by a separator, and after impurities are removed by a purification device, high-purity hydrogen and oxygen are obtained. Step 3: The purified hydrogen is compressed and stored. Electrolysis is the most electricity-intensive part of the green electricity-to-methanol synthesis process.

[0019] Regarding methanol synthesis

[0020] The methanol synthesis equipment converts hydrogen and carbon dioxide into methanol, with the chemical reaction formula CO2 + 3H2 → CH3OH + H2O. The methanol synthesis steps are as follows: Step a: Stored hydrogen and captured carbon dioxide are mixed in a specific ratio and fed into the reactor. Step b: Under the action of a catalyst, the mixed gas reacts in a high-temperature environment above 180 degrees Celsius to produce methanol and water. Step c: The methanol is collected and the water is removed through a condensation and separation device to obtain high-purity methanol. To improve the reaction rate, in step a, the hydrogen and carbon dioxide can be premixed and preheated to approximately 100 degrees Celsius before being fed into the reactor. The preheating process effectively improves reaction efficiency, shortens reaction time, and reduces energy consumption. A premixing tank can be installed, equipped with a heat transfer medium loop, to more uniformly heat the hydrogen and carbon dioxide within the premixing tank. The methanol synthesis reactor also has a heat transfer medium loop to maintain the reactor temperature. A heat storage tank can be provided, containing a heater and storing a heat transfer medium. The heater heats the heat transfer medium. The heat storage tank is connected to a first mixing tank via a first pipeline, and the first mixing tank is connected to the heat transfer medium circuit of a premixing tank via a pipeline. The heat storage tank is connected to a second mixing tank via a second pipeline, and the second mixing tank is connected to the heat transfer medium circuit of the methanol synthesis reactor via a pipeline. The first and second mixing tanks are each connected to a room-temperature heat transfer medium storage tank via pipelines. This changes the direct heating of the room-temperature heat transfer medium to the required temperature, instead mixing the high-temperature and room-temperature heat transfer media to the required temperature, resulting in higher efficiency and better stability. More importantly, it changes from on-demand heating to preheating, thus allowing the use of low-cost electricity for heating, effectively reducing the unit cost of methanol synthesis.

[0021] The synthetic methanol autonomous reservation power distribution management system, based on big data algorithms, includes a data acquisition system and a data analysis and processing system. The data acquisition system collects real-time data on green electricity generation and hourly weather conditions (temperature, weather, wind speed). The data analysis and processing system integrates and analyzes the green electricity generation with the hourly weather conditions to obtain a historical database of green electricity generation related to weather conditions. Based on tomorrow's weather conditions obtained from the data acquisition system, the data analysis and processing system predicts tomorrow's hourly green electricity generation. The predicted green electricity generation is equal to the average of historical measured green electricity generation under the same weather conditions. Here, "same weather conditions" refers to the same month, time difference not exceeding 1 hour, temperature difference not exceeding 10 degrees Celsius, same weather conditions, and same wind speed; it does not require all parameters in the weather conditions to be exactly the same.

[0022] The data acquisition system also collects the unit's hourly electricity consumption in real time. The data analysis and processing system integrates and analyzes the unit's hourly electricity consumption with the hourly weather conditions to obtain a historical database of the unit's electricity consumption related to the weather conditions. Based on the weather conditions for tomorrow obtained by the data acquisition system, the data analysis and processing system predicts the unit's hourly electricity consumption for tomorrow. The predicted hourly electricity consumption value for a unit is equal to the average of the measured hourly electricity consumption values ​​of historical units under the same weather conditions. Here, "same weather conditions" refers to the same date, the same time period, a temperature difference of no more than 5 degrees Celsius, and the same weather conditions (sunny or rainy), but does not require all parameters in the weather conditions to be exactly the same. The unit here can be an office building, a room, a park, etc.

[0023] In areas where there are total limits on electricity consumption and power is cut off once the quota is exhausted, the methanol synthesis autonomous reservation power allocation management system based on big data algorithms prioritizes the electricity needs of individual units, that is, it prioritizes the direct supply of green electricity to these units. Once the unit's electricity demand is met, any surplus green electricity will be connected to the grid or utilized.

[0024] In areas where there is no total limit on electricity consumption, but electricity is tiered based on total consumption, with higher total consumption resulting in higher unit prices, a data-driven methanol synthesis autonomous reservation power allocation management system calculates the total electricity consumption for the next day's unit based on the predicted time-of-use electricity consumption. Function 1: Green electricity supply value = Total electricity consumption for the next day's unit * Unit price at that electricity consumption - (Total electricity consumption for the next day's unit - Total green electricity generation + Time-of-use green electricity for methanol synthesis) * Unit price at that electricity consumption + Time-of-use green electricity for methanol synthesis * Conversion rate * Methanol market price. The data analysis and processing system calculates the total green electricity generation for the next day based on the green electricity generation capacity, then substitutes it into Function 1 to calculate the amount of green electricity used for methanol synthesis when the green electricity supply value is maximized. Based on this, the data analysis and processing system calculates the methanol production volume for the next day. Considering the potential adverse effects of repeated shutdowns of methanol synthesis equipment, the data analysis and processing system sets a minimum methanol production limit. When the methanol production volume given by the data analysis and processing system exceeds the minimum methanol production limit, the green electricity is allocated according to the green electricity allocation scheme given by the data analysis and processing system. When the methanol production rate indicated by the data analysis and processing system is less than the minimum methanol production limit, the system generates and allocates green electricity according to the minimum production limit. If all green electricity is used for methanol synthesis but is still insufficient to reach the minimum methanol production limit, the system controls the borrowing of electricity from the power grid.

[0025] In areas where there is no total limit on electricity consumption but the unit price of electricity varies at different times—that is, areas with time-of-use pricing—the processing method of the methanol synthesis autonomous reservation power allocation management system based on big data algorithms is the same as the processing scheme under the case of total tiered electricity consumption. The only difference is that under the case of total tiered electricity consumption, the values ​​in Function 1 represent the values ​​for a day; under the case of time-of-use pricing, the values ​​in Function 1 represent the values ​​for a single hour.

[0026] Methanol synthesis units are limited by equipment and usually have a maximum methanol production limit. When the methanol production given by the data analysis and processing system exceeds the maximum methanol production limit, the surplus green electricity is fed into the power grid or used to produce hydrogen, a raw material for methanol synthesis, so that the electrical energy can be stored through hydrogen.

[0027] Green electricity primarily refers to electricity generated by solar power generation equipment (e.g., photovoltaic power plants) and wind power generation equipment (e.g., wind farms). The power output of green electricity is fluctuating and intermittent. To address this issue, the data analysis and processing system determines the planned time-of-use (TOU) power output of green electricity under the green electricity allocation scheme. This planned TOU power output is compared with the predicted TOU power output for the following day. If the predicted TOU power output for the following day is greater than the planned TOU power output, the surplus green electricity is used to produce feedstock hydrogen or to heat the heat transfer medium in the thermal storage tank. If the predicted TOU power output for the following day is less than the planned TOU power output, the currently produced hydrogen is supplemented with already produced feedstock hydrogen, or the already heated heat transfer medium is mixed with a room-temperature heating medium.

[0028] The data analysis and processing system corrects the green electricity allocation scheme based on the real-time collected green electricity generation power and the unit's time-of-use electricity consumption. When the actual green electricity generation power differs from the predicted time-of-use power generation power, the data analysis and processing system adjusts the green electricity allocation scheme for the remaining time periods in real time based on the green electricity usage status.

[0029] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A synthetic methanol autonomous subscription electric energy distribution management system based on big data algorithm, comprising a data acquisition system and a data analysis and processing system, characterized in that, The data acquisition system collects the power generation of green electricity and the time-sharing weather condition in real time, the data analysis processing system integrates and analyzes the power generation of green electricity and the time-sharing weather condition, obtains a green electricity power generation history database related to the weather condition, and predicts the time-sharing power generation of green electricity tomorrow according to the weather condition tomorrow obtained by the data acquisition system; the predicted value of the power generation of green electricity is equal to the average of the measured values of the historical power generation of green electricity under the condition that the month, time period, temperature difference, sunny or rainy, and wind force level are the same.

2. The big data algorithm based synthetic methanol autonomous pre-booked electric energy distribution management system as claimed in claim 1 comprises: The data analysis processing system integrates and analyzes the time-sharing electricity consumption of a unit and the time-sharing weather condition, obtains a unit electricity consumption history database related to the weather condition, and predicts the time-sharing electricity consumption of the unit tomorrow according to the weather condition tomorrow obtained by the data acquisition system; the predicted value of the time-sharing electricity consumption of the unit is equal to the average of the measured values of the historical time-sharing electricity consumption of the unit under the condition that the date, time period, temperature difference, and sunny or rainy are the same.

3. The big data algorithm based synthetic methanol autonomous pre-booked electric energy distribution management system as claimed in claim 2 comprises: Function 1: green electricity power supply value = total electricity consumption of the unit tomorrow * unit price of the electricity consumption - (total electricity consumption of the unit tomorrow - total power generation of green electricity + time-sharing methanol consumption of green electricity synthesis) * unit price of the electricity consumption + time-sharing methanol consumption of green electricity synthesis * conversion rate * methanol market price; the data analysis processing system calculates the total power generation of green electricity tomorrow according to the power generation of green electricity tomorrow, and then calculates the green electricity distribution scheme when the green electricity power supply value is maximum.

4. The big data algorithm based synthetic methanol autonomous pre-booked electric energy distribution management system as claimed in claim 3 comprises: The data analysis processing system calculates the methanol production amount tomorrow according to the green electricity distribution scheme.

5. The big data algorithm based synthetic methanol autonomous pre-booked electric energy distribution management system as claimed in claim 4 comprises: When the methanol production amount given by the data analysis processing system is greater than the minimum methanol production limit, the green electricity is distributed according to the green electricity distribution scheme given by the data analysis processing system; when the methanol production amount given by the data analysis processing system is less than the minimum methanol production limit, the data analysis processing system gives a green electricity distribution scheme according to the minimum methanol production, and distributes the green electricity.

6. The big data algorithm based synthetic methanol autonomous pre-booked electric energy distribution management system as claimed in claim 5 comprises: When the methanol production amount given by the data analysis processing system is greater than the maximum methanol production limit, the excess green electricity is connected to the power grid or used to produce raw material hydrogen for methanol synthesis to store the electric energy by hydrogen.

7. The big data algorithm based synthetic methanol autonomous pre-booked electric energy distribution management system as claimed in claim 5 comprises: The data analysis processing system determines the time-sharing power generation plan value of green electricity under the green electricity distribution scheme according to the green electricity distribution scheme, compares the time-sharing power generation plan value of green electricity with the predicted value of the time-sharing power generation of green electricity tomorrow, uses the excess green electricity to produce raw material hydrogen or heat the heat transfer medium in the heat storage tank when the predicted value of the time-sharing power generation of green electricity tomorrow is greater than the time-sharing power generation plan value of green electricity; uses the raw material hydrogen produced to supplement the hydrogen produced or mixes the heated heat transfer medium with the normal temperature heat transfer medium when the predicted value of the time-sharing power generation of green electricity tomorrow is less than the time-sharing power generation plan value of green electricity.

8. The big data algorithm based synthetic methanol autonomous pre-booked electric energy distribution management system as claimed in claim 5 comprises: When the actual power generation of green electricity is different from the predicted time-sharing power generation, the data analysis processing system adjusts the green electricity distribution scheme of the remaining time period in real time according to the use of green electricity.

9. The big data algorithm based synthetic methanol autonomous pre-booked electric energy distribution management system as claimed in claim 6 or 7 comprises: The methanol synthesis device comprises a premixing tank, a methanol synthesis reactor and a heat storage tank, the premixing tank is provided with a heat transfer medium loop, and the methanol synthesis reactor is provided with a heat transfer medium loop; the heat storage tank is provided with a heater, and the heat storage tank also stores heat transfer medium, the heater heats the heat transfer medium, the heat storage tank is communicated with a first mixing tank through a first pipeline, and the first mixing tank is connected with the heat transfer medium loop of the premixing tank through a pipeline; the heat storage tank is communicated with a second mixing tank through a second pipeline, and the second mixing tank is connected with the heat transfer medium loop of the methanol synthesis reactor through a pipeline; the first mixing tank and the second mixing tank are connected with a normal-temperature heat transfer medium storage tank through pipelines respectively.