Zero-carbon park multi-energy collaborative optimization system based on electricity-carbon-hydrogen-alcohol coupling

Abstract

The invention discloses a zero-carbon park multi-energy collaborative optimization system based on electricity-carbon-hydrogen-alcohol coupling, and the system comprises a multi-energy flow coupling architecture which constructs a closed-loop system formed by connecting a renewable energy power generation module, a green hydrogen preparation module, a methanol synthesis module and a carbon capture module; the intelligent optimization model is deployed in a system control layer and comprises a multi-target collaborative optimization algorithm execution unit; the self-adaptive regulation and control system comprises a weather prediction module, a load demand analysis module and an equipment parameter regulation module; the system comprises a digital management platform, three-dimensional visualization, an integrated server and block chain nodes. Therefore, according to the system, a multi-energy-flow closed-loop architecture is constructed, energy flow efficient cooperation is achieved through intelligent optimization and self-adaptive regulation and control, the resource configuration precision is improved in combination with digital management, the utilization rate of renewable energy sources can be effectively increased, and the system flexibility is enhanced.

Description

Technical Field

[0001] The present invention relates to the field of energy and power, and specifically refers to a zero-carbon park multi-energy collaborative optimization system based on electricity-carbon-hydrogen-alcohol coupling. Background Art

[0002] The existing park energy system has problems such as limited renewable energy absorption capacity, insufficient multi-energy coupling efficiency, and imperfect load dynamic response mechanism. It also lacks a comprehensive solution that integrates energy flow optimization, equipment control and digital management, making it difficult to achieve deep coordination and closed-loop operation of electricity-carbon-hydrogen-alcohol multi-energy flows. Summary of the Invention

[0003] The present invention aims to solve the technical problems in the above-mentioned scenarios to a certain extent.

[0004] To this end, the present invention discloses a zero-carbon park multi-energy collaborative optimization system based on electricity-carbon-hydrogen-alcohol coupling, comprising:

[0005] A multi-energy flow coupling architecture builds a closed-loop system consisting of renewable energy power generation modules, green hydrogen production modules, methanol synthesis modules, and carbon capture modules. Each module achieves physical connection and data exchange through standardized interfaces.

[0006] The intelligent optimization model is deployed in the system control layer and includes a multi-objective collaborative optimization algorithm execution unit. The input port of the execution unit is connected to the real-time operation data acquisition device of each module, and the output port is connected to the device control unit of each module.

[0007] The adaptive control system includes a weather forecast module, a load demand analysis module, and an equipment parameter adjustment module. The outputs of the weather forecast module and the load demand analysis module are connected to the input of the equipment parameter adjustment module. The output of the equipment parameter adjustment module is connected to the control interface of each key device.

[0008] The digital management platform integrates a three-dimensional visualization server and a blockchain node. The three-dimensional visualization server is connected to the monitoring sensors of each module through a data bus, and the blockchain node is connected to the virtual power plant management system and the demand-side response terminal through a smart contract interface.

[0009] According to the zero-carbon park multi-energy collaborative optimization system based on electricity-carbon-hydrogen-alcohol coupling disclosed by the present invention, this system constructs a multi-energy flow closed-loop architecture, realizes efficient coordination of energy flow through intelligent optimization and adaptive regulation, and improves resource allocation accuracy through digital management, which can effectively improve the utilization rate of renewable energy and enhance system flexibility.

[0010] In addition, the zero-carbon park multi-energy collaborative optimization system based on electricity-carbon-hydrogen-alcohol coupling disclosed in the present invention may also have the following additional technical features:

[0011] In one embodiment of the present invention, in the multi-energy flow coupling architecture,

[0012] The renewable energy power generation module includes a solar power generation unit, a wind power generation unit and a lithium battery energy storage unit;

[0013] The solar power generation unit is composed of a polycrystalline silicon photovoltaic module array, an intelligent combiner box and a photovoltaic inverter with maximum power point tracking function;

[0014] The wind power generation unit is composed of a direct-drive or doubly-fed wind turbine generator and a converter;

[0015] The lithium battery energy storage unit is connected to the photovoltaic inverter output end, the converter output end and the grid access end respectively through a bidirectional converter.

[0016] In one embodiment of the present invention, the green hydrogen production module includes:

[0017] Electrolyzer group, hydrogen purification device and high-pressure hydrogen storage tank, including:

[0018] The input end of the electrolyzer group is connected to the output end of the photovoltaic inverter, the output end of the converter and the bidirectional converter of the lithium battery energy storage unit through a first DC bus, and the type of the electrolyzer group is an alkaline electrolyzer, a proton exchange membrane electrolyzer or a solid oxide electrolyzer;

[0019] The input end of the hydrogen purification device is connected to the hydrogen output end of the electrolyzer group, and the output end is connected to the air inlet of the high-pressure hydrogen storage tank. The high-pressure hydrogen storage tank is equipped with a pressure sensor and a temperature sensor.

[0020] In one embodiment of the present invention, the methanol synthesis module comprises:

[0021] Methanol synthesis reactor, gas circulation pump and methanol distillation unit, including:

[0022] The methanol synthesis reactor has an air inlet connected to the air outlet of the high-pressure hydrogen storage tank and a carbon dioxide delivery pipeline of the carbon capture module, respectively. A copper-based or zinc-based catalyst filling layer is provided inside the methanol synthesis reactor, and a temperature control jacket and a pressure sensor are provided outside.

[0023] The gas circulation pump air inlet is connected to the tail gas outlet of the methanol synthesis reactor, and the gas outlet is connected to the gas inlet of the methanol synthesis reactor;

[0024] The feed port of the methanol distillation device is connected to the discharge port of the methanol synthesis reactor, and a condensing reflux and collecting device is provided on the top of the tower.

[0025] In one embodiment of the present invention, the carbon capture module comprises:

[0026] The carbon dioxide capture device, carbon dioxide compressor and carbon dioxide drying tower installed at the emission source of the park, including

[0027] The carbon dioxide capture device adopts a chemical absorption tower, a physical adsorption tank or a membrane separation component;

[0028] The air inlet of the carbon dioxide compressor is connected to the air outlet of the capture device, the air outlet is connected to the air inlet of the carbon dioxide drying tower, and the air outlet of the drying tower is connected to the air inlet of the methanol synthesis reactor through the carbon dioxide delivery pipeline;

[0029] An online concentration sensor is provided inside the capture device, and a signal output end of the sensor is connected to the digital management platform.

[0030] In one embodiment of the present invention, the multi-objective collaborative optimization algorithm execution unit has a built-in random algorithm coupling calculation module, and the input parameters of the calculation module include: renewable energy power generation power, electrolyzer operating current, methanol synthesis reactor temperature, and carbon capture device operating pressure; the output parameters include but are not limited to: energy storage unit charging and discharging power instructions, electrolyzer start and stop number instructions, and synthesis reactor catalyst bed temperature adjustment instructions.

[0031] In one embodiment of the present invention, the meteorological forecast module includes a time series forecast model based on a machine learning model, the input data of which is the historical 720 hours of light intensity, wind speed, wind direction and temperature data, and the output is a forecast value of meteorological parameters for no less than 24 hours in the future and with a frequency of no less than 15 minutes; the load demand analysis module includes a support vector machine load forecast model, the input data of which is the historical electricity, hydrogen and methanol flow data of each user end in the park, and the output is an hourly load demand forecast value for no less than 24 hours in the future.

[0032] In one embodiment of the present invention, the three-dimensional visualization server constructs a digital twin model of the park energy system, which includes a three-dimensional geometric model of each device and a dynamic operation status display interface driven by real-time data. The interface integrates an equipment failure warning module, and the input signal of the warning module is the abnormal parameter threshold trigger signal of each device sensor;

[0033] The blockchain node adopts a consortium chain architecture, including a carbon footprint traceability smart contract, a virtual power plant resource registration smart contract and a demand-side response incentive smart contract. Each smart contract is connected to the corresponding business system through an API interface.

[0034] In one embodiment of the present invention, the energy flow path of the multi-energy flow coupling architecture is:

[0035] The output power of the renewable energy power generation module is preferentially supplied to the green hydrogen production module through the first DC bus, and the remaining power is stored in the lithium battery energy storage unit or integrated into the power grid through the bidirectional converter;

[0036] The hydrogen produced by the green hydrogen production module is processed by the hydrogen purification device and stored in a high-pressure hydrogen storage tank, and then transported to the methanol synthesis module through a pipeline;

[0037] The carbon dioxide captured by the carbon capture module is compressed and dried and then transported to the methanol synthesis module to synthesize methanol with hydrogen in the methanol synthesis reactor;

[0038] The material flow path of the closed-loop system realizes material transmission between modules through pipelines, valves and standardized interfaces.

[0039] In one embodiment of the present invention, the digital management platform is connected to the PLC control system of each module via an OPC UA data interface to achieve real-time data acquisition at a frequency of not less than 100 Hz;

[0040] The platform has built-in relational database and time series database to store equipment ledger data and operation history data respectively, and supports user management, data query and report generation functions;

[0041] The platform realizes data interaction with external power grid dispatching system and energy trading platform through power-specific communication protocol.

[0042] Additional contents and advantages of the present invention will be given in the following description or can be understood through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The technical solutions and beneficial effects of the present invention will become apparent and easily understood from the following contents in conjunction with the accompanying drawings, in which:

[0044] Figure 1 This is a system overall architecture block diagram of the zero-carbon park multi-energy collaborative optimization system based on electricity-carbon-hydrogen-alcohol coupling of the present invention;

[0045] Figure 2 This is a process flow chart of the zero-carbon park multi-energy collaborative optimization system based on electricity-carbon-hydrogen-alcohol coupling of the present invention. DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0047] The following will refer to the attached Figure 1 and attached Figure 2The invention discloses a zero-carbon park multi-energy collaborative optimization system based on electricity-carbon-hydrogen-alcohol coupling.

[0048] A zero-carbon park multi-energy collaborative optimization system based on electricity-carbon-hydrogen-alcohol coupling, including:

[0049] A multi-energy flow coupling architecture builds a closed-loop system consisting of renewable energy power generation modules, green hydrogen production modules, methanol synthesis modules, and carbon capture modules. Each module achieves physical connection and data exchange through standardized interfaces.

[0050] The intelligent optimization model is deployed in the system control layer and includes a multi-objective collaborative optimization algorithm execution unit. The input port of the execution unit is connected to the real-time operation data acquisition device of each module, and the output port is connected to the device control unit of each module.

[0051] The adaptive control system includes a weather forecast module, a load demand analysis module, and an equipment parameter adjustment module. The outputs of the weather forecast module and the load demand analysis module are connected to the input of the equipment parameter adjustment module. The output of the equipment parameter adjustment module is connected to the control interface of each key device.

[0052] The digital management platform integrates a three-dimensional visualization server and blockchain nodes. The three-dimensional visualization server connects to the monitoring sensors of each module through a data bus, and the blockchain nodes connect to the virtual power plant management system and the demand-side response terminal through a smart contract interface.

[0053] Specifically, the multi-energy flow coupling architecture constructs an electricity-carbon-hydrogen-alcohol closed-loop collaborative system, and each module realizes bidirectional interaction through standardized physical interfaces (such as flange connection, DC bus interface) and data interfaces (Modbus, OPC UA protocol). The electric energy output by the renewable energy power generation module is preferentially supplied to the green hydrogen preparation module. The hydrogen produced by the green hydrogen production is purified and stored, and then reacts with the carbon dioxide captured by the carbon capture module in the methanol synthesis module to produce methanol. Methanol serves as the end product or energy storage medium, forming a closed-loop flow of "electricity-hydrogen-alcohol-carbon" materials and energy. The standardized interface design supports plug-and-play modules, facilitates system expansion and equipment replacement, and the data interaction protocol realizes real-time synchronization of the operating status of each module, providing full-link data support for the intelligent optimization model.

[0054] It should be noted that in the multi-energy flow coupling architecture,

[0055] The renewable energy power generation module includes a solar power generation unit, a wind power generation unit and a lithium battery energy storage unit;

[0056] The solar power generation unit consists of a polycrystalline silicon photovoltaic module array, an intelligent combiner box, and a photovoltaic inverter with maximum power point tracking function;

[0057] The wind power generation unit consists of a direct-drive or doubly-fed wind turbine generator and a converter;

[0058] The lithium battery energy storage unit is connected to the photovoltaic inverter output end, the converter output end and the grid access end through a bidirectional converter.

[0059] Specifically, regarding renewable energy generation modules:

[0060] The solar power generation unit uses a polycrystalline silicon photovoltaic module array (conversion efficiency ≥18%), collects electrical energy through an intelligent junction box (with anti-reverse diode and overvoltage protection), and converts it into AC power through a maximum power point tracking (MPPT) photovoltaic inverter (efficiency ≥98%). It supports DC / AC dual-mode output and meets the DC bus requirements of the green hydrogen preparation module.

[0061] The wind power generation unit is equipped with a direct-drive (suitable for low wind speed scenarios, high reliability) or doubly-fed (suitable for variable wind speed scenarios, high power generation efficiency) wind turbine. The variable-frequency AC power is converted into industrial-frequency AC power through a converter (with reactive power compensation function), and then merged with photovoltaic power on the DC bus side or the AC side.

[0062] The lithium battery energy storage unit is connected to the photovoltaic inverter, wind converter and power grid through a bidirectional converter (supporting four-quadrant operation). The capacity is designed according to the peak-to-valley difference of the park load and has fast charging and discharging capabilities (charging and discharging rate ≥1C). It is used to smooth out fluctuations in renewable energy and ensure stable power supply for the green hydrogen preparation module.

[0063] It should be noted that the green hydrogen production module includes:

[0064] Electrolyzer group, hydrogen purification device and high-pressure hydrogen storage tank, including:

[0065] The input end of the electrolyzer group is connected to the output end of the photovoltaic inverter, the output end of the converter and the bidirectional converter of the lithium battery energy storage unit through the first DC bus. The electrolyzer group type is an alkaline electrolyzer, a proton exchange membrane electrolyzer or a solid oxide electrolyzer;

[0066] The input end of the hydrogen purification device is connected to the hydrogen output end of the electrolyzer group, and the output end is connected to the air inlet of the high-pressure hydrogen storage tank. The high-pressure hydrogen storage tank is equipped with a pressure sensor and a temperature sensor.

[0067] Specifically, regarding the green hydrogen production module:

[0068] The electrolyzer group supports alkaline electrolyzers (low cost, suitable for large-scale hydrogen production), proton exchange membrane (PEM) electrolyzers (fast start and stop, adapt to fluctuations in renewable energy) or solid oxide electrolyzers (high temperature and high efficiency, comprehensive efficiency ≥ 85%). It is connected to the power generation module and energy storage unit through the first DC bus (voltage level ±375V), and the capacity of a single tank is 50-500Nm3 / h, supports dynamic power adjustment (adjustment range 20%-100%).

[0069] The hydrogen purification device uses pressure swing adsorption (PSA) or membrane separation technology to remove impurities such as moisture and oxygen in hydrogen (purity ≥ 99.97%). The purified hydrogen is stored in a high-pressure hydrogen storage tank (capacity 500-5000Nm 3 ), the storage tank is equipped with a pressure sensor (accuracy ±0.5% FS) and a temperature sensor (accuracy ±1°C) to monitor the storage status in real time.

[0070] The control logic prioritizes the consumption of surplus photovoltaic / wind power. When the generated power exceeds the load demand, the electrolyzer is automatically started to produce hydrogen. When the SOC of the energy storage unit is lower than 20%, the electrolyzer power is limited to ensure the backup capacity of the power grid.

[0071] It should be noted that the methanol synthesis module includes:

[0072] Methanol synthesis reactor, gas circulation pump and methanol distillation unit, including:

[0073] The methanol synthesis reactor's air inlet is connected to the high-pressure hydrogen storage tank's air outlet and the carbon dioxide delivery pipeline of the carbon capture module. A copper-based or zinc-based catalyst packing layer is installed inside the methanol synthesis reactor, and a temperature control jacket and pressure sensor are configured on the outside.

[0074] The gas circulation pump has an air inlet connected to the tail gas outlet of the methanol synthesis reactor, and a gas outlet connected to the air inlet of the methanol synthesis reactor;

[0075] The feed inlet of the methanol distillation unit is connected to the discharge port of the methanol synthesis reactor, and a condensing reflux and collection device is set on the top of the tower.

[0076] Specifically, regarding the methanol synthesis module:

[0077] The methanol synthesis reactor adopts a shell and tube structure and is filled with copper-based (good low-temperature activity, 200-250°C) or zinc-based (high-temperature resistant, 250-300°C) catalysts. The filling layer thickness is designed according to the processing capacity (5-10m). The external temperature control jacket circulates heat transfer oil (temperature control accuracy ±2°C) to maintain the optimal reaction temperature; the air inlet is equipped with a mass flow meter (accuracy ±1%) to monitor the H2 / CO2 ratio in real time (optimal molar ratio 2.1:1), and a pressure sensor (accuracy ±0.2%FS) to monitor the reaction pressure (5-10MPa).

[0078] The gas circulation pump uses a centrifugal compressor to circulate the reactor tail gas (unreacted H2, CO2 and inert gas) back to the air inlet with a circulation ratio of 1:3-1:5 to improve the utilization rate of raw materials; the methanol content in the tail gas is monitored by an online chromatograph and is discharged into the flare system when it is lower than 0.5%.

[0079] The methanol distillation device has a feed port connected to the reactor discharge port (methanol concentration 60-70%), and the methanol vapor at the top of the tower (purity ≥99.9%) is condensed and refluxed, collected and stored (recovery rate ≥95%), and the waste water at the bottom of the tower is recycled.

[0080] It should be noted that the carbon capture module includes:

[0081] The carbon dioxide capture device, carbon dioxide compressor and carbon dioxide drying tower installed at the emission source of the park, including

[0082] The carbon dioxide capture device uses a chemical absorption tower, physical adsorption tank or membrane separation component;

[0083] The air inlet of the carbon dioxide compressor is connected to the air outlet of the capture device, the air outlet is connected to the air inlet of the carbon dioxide drying tower, and the air outlet of the drying tower is connected to the air inlet of the methanol synthesis reactor through a carbon dioxide transmission pipeline;

[0084] An online concentration sensor is set inside the capture device, and the sensor signal output end is connected to the digital management platform.

[0085] Specifically, regarding the carbon capture module:

[0086] The carbon dioxide capture device selects the technical route according to the characteristics of the park's emission sources: chemical absorption tower (MEA solution absorption, suitable for high-concentration CO2 scenarios, capture efficiency ≥90%), physical adsorption tank (zeolite molecular sieve adsorption, suitable for low-concentration scenarios) or membrane separation component (ceramic membrane / polymer membrane, low energy consumption, fast response speed), built-in online concentration sensor (accuracy ±2%), real-time monitoring of the inlet CO2 concentration (5%-30%) and outlet concentration (≤0.1%).

[0087] The carbon dioxide compressor is a multi-stage centrifugal compressor that compresses the captured CO2 from atmospheric pressure to 5-8MPa. A cooler is configured between stages (outlet temperature ≤ 40°C). The compressed CO2 passes through a drying tower (silica gel / molecular sieve adsorption) to remove moisture (dew point ≤ -40°C) and is transported to the methanol synthesis reactor through a stainless steel pipeline (pressure rating 10MPa). The pipeline is equipped with a flow meter (accuracy ±1.5%) and a safety valve (opening pressure 11MPa).

[0088] Data interaction: the operating parameters of the capture device (such as absorbent circulation volume and membrane module pressure difference) are uploaded to the digital management platform in real time to support remote monitoring and fault diagnosis.

[0089] It should be noted that the multi-objective collaborative optimization algorithm execution unit has a built-in random algorithm coupling calculation module. The input parameters of the calculation module include: renewable energy power generation, electrolyzer operating current, methanol synthesis reactor temperature, and carbon capture device operating pressure; the output parameters include but are not limited to: energy storage unit charging and discharging power instructions, electrolyzer start and stop quantity instructions, and synthesis reactor catalyst bed temperature adjustment instructions.

[0090] Specifically, regarding the intelligent optimization model:

[0091] The multi-objective collaborative optimization algorithm execution unit uses a genetic algorithm (GA) and particle swarm optimization (PSO) coupling strategy to establish a multi-objective function for renewable energy utilization, methanol production, and system operating costs:

[0092] As for input parameters, real-time collection is carried out on renewable energy power generation (accuracy ±0.5%), electrolyzer operating current (reflecting hydrogen production load), methanol synthesis reactor temperature (affecting catalytic efficiency), carbon capture device operating pressure (affecting capture energy consumption), as well as energy storage unit SOC and grid electricity price signals (distinguishing between peak and valley periods).

[0093] The output instructions are specifically reflected as follows:

[0094] The energy storage unit generates charging and discharging power instructions (resolution 1kW) based on load forecasts and electricity price signals, giving priority to charging during off-peak hours and discharging during peak hours.

[0095] The electrolyzer group dynamically adjusts the number of starts and stops (the minimum adjustment unit is 1) and the operating power to absorb surplus electricity and avoid power abandonment;

[0096] The synthesis reactor optimizes the methanol synthesis efficiency by adjusting the flow of thermal oil in the jacket to precisely control the catalyst bed temperature (adjustment accuracy ±1°C).

[0097] The algorithm optimization cycle is 15 minutes, and it supports online rolling optimization to adapt to rapid changes in meteorological conditions and loads.

[0098] It should be noted that the meteorological forecast module includes a time series forecast model based on a machine learning model. The input data is the historical 720 hours of light intensity, wind speed, wind direction and temperature data, and the output is the forecast value of meteorological parameters for no less than 24 hours in the future and with a frequency of no less than 15 minutes; the load demand analysis module includes a support vector machine load forecast model. The input data is the historical electricity, hydrogen and methanol flow data of each user end in the park, and the output is the hourly load demand forecast value for no less than 24 hours in the future.

[0099] Specifically, regarding the adaptive control system:

[0100] The weather forecast module builds a time series forecast model based on the LSTM neural network, inputs the historical 720 hours (30 days) of light intensity (accuracy ±5W / m 2 ), wind speed (accuracy ±0.1m / s), wind direction (accuracy ±5°), and temperature (accuracy ±0.5℃) data, captures spatiotemporal correlations through the attention mechanism, and outputs meteorological parameter forecasts for no less than 24 hours in the future and with a frequency of no less than 15 minutes. The prediction error is ≤10% (light) / 5% (wind speed), providing basic data for power generation prediction.

[0101] The load demand analysis module uses the support vector machine (SVM) model to input the historical electricity consumption (kWh) and hydrogen consumption (Nm 3 ), using methanol (kg) flow data, combined with weekdays / holidays and seasonal factors, output hourly load demand forecast values ​​for no less than 24 hours in the future (accuracy ≥ 92%), distinguish between industrial load (accounting for 60%) and civil load (accounting for 40%), and support a stepped demand response strategy.

[0102] The equipment parameter adjustment module integrates meteorological forecasts and load demand data, and generates equipment adjustment instructions through fuzzy control algorithms. For example, it adjusts the pitch angle of wind turbines according to wind speed forecasts, and dynamically allocates energy storage and electrolyzer power according to power load forecasts, thus realizing "prediction-decision-execution" closed-loop control.

[0103] It should be noted that the 3D visualization server constructs a digital twin model of the campus energy system, which includes a 3D geometric model of each device and a dynamic operating status display interface driven by real-time data. The interface integrates an equipment fault warning module, and the input signal of the warning module is the abnormal parameter threshold trigger signal of each device sensor;

[0104] The blockchain node adopts a consortium chain architecture, including a carbon footprint traceability smart contract, a virtual power plant resource registration smart contract, and a demand-side response incentive smart contract. Each smart contract is connected to the corresponding business system through an API interface.

[0105] Specifically, regarding the digital management platform:

[0106] The 3D visualization server, built using Unity3D, builds digital twin models of equipment such as photovoltaic arrays, wind turbines, electrolyzers, and reactors at a 1:1 scale. It integrates a dynamic display interface (refresh rate ≥ 10Hz) driven by real-time data, displaying equipment operating parameters (such as voltage, pressure, and temperature), energy flow paths (color-coded to distinguish between electricity, hydrogen, alcohol, and carbon media), and equipment status (green for normal, yellow for warning, and red for fault). The fault warning module presets sensor anomaly thresholds (such as a temperature exceeding the upper limit by 10%), automatically triggering audible and visual alarms and pushing maintenance work orders to the operation and maintenance terminal.

[0107] Blockchain nodes adopt a consortium chain architecture (nodes include park operators, power grids, and users) and deploy three types of smart contracts:

[0108] Carbon footprint traceability contracts record data such as CO2 capture, methanol production energy consumption, and carbon emission quotas, and support blockchain evidence storage and carbon trading audits;

[0109] Virtual power plant contracts, which register renewable energy generation units and energy storage units as virtual power plant resources, automatically matching grid dispatch instructions with resource availability;

[0110] Demand response contracts send demand response incentive plans (such as flexible load adjustment subsidies) to users based on load forecasts and electricity price signals, which are automatically executed and settled through smart meters.

[0111] Each contract is connected to the business system through the RESTful API interface, and data is uploaded to the chain once per minute to ensure that the data cannot be tampered with and is transparently shared.

[0112] It should be noted that the energy flow path of the multi-energy flow coupling architecture is:

[0113] The output power of the renewable energy power generation module is first supplied to the green hydrogen production module through the first DC bus, and the remaining power is stored in the lithium battery energy storage unit or connected to the power grid through the bidirectional converter;

[0114] The hydrogen produced by the green hydrogen production module is processed by the hydrogen purification device and stored in a high-pressure hydrogen storage tank, and then transported to the methanol synthesis module through a pipeline;

[0115] The carbon dioxide captured by the carbon capture module is compressed and dried and then transported to the methanol synthesis module to synthesize methanol with hydrogen in the methanol synthesis reactor;

[0116] The material flow path of the closed-loop system realizes material transmission between modules through pipes, valves and standardized interfaces.

[0117] Specifically, the energy flow and material flow paths are as follows:

[0118] For energy flows, specifically:

[0119] After being converted by the inverter / converter, photovoltaic / wind power generation is preferentially supplied to the electrolyzer for hydrogen production through the first DC bus (voltage ±375V), with a DC power supply efficiency of ≥95%;

[0120] When the generated power exceeds the hydrogen production demand, the remaining power is stored in the lithium battery energy storage unit (SOC upper limit 80%) via a bidirectional converter (efficiency ≥ 97%), or connected to the grid (power factor ≥ 0.95) via a grid-connected inverter (with island protection).

[0121] The energy storage unit discharges during peak electricity consumption or low renewable energy periods, giving priority to ensuring the basic load of the park and secondly supplying the hydrogen production module.

[0122] For material flows, specifically:

[0123] The hydrogen produced by the electrolyzer (purity ≥99%) is purified by PSA (taking 15 minutes) and stored in a high-pressure storage tank (pressure 30MPa). It is then transported to the methanol synthesis reactor through a stainless steel pipe (diameter 50mm) with a pipeline flow rate of ≤10m / s.

[0124] The CO2 captured by the carbon capture module (purity ≥95%) is compressed and dried (pressure 6MPa) and then introduced into the reactor with hydrogen at a molar ratio of 2.1:1 to synthesize methanol under the action of a catalyst (reaction formula: CO2+3H2→CH3OH+H2O);

[0125] The synthesized crude methanol is purified by a distillation device (purity ≥ 99.9%) and transported to the end user or storage tank by a pump, and the tail gas is recycled or discharged after treatment.

[0126] The closed-loop system achieves material balance control through pressure sensors and flow control valves (accuracy ±1%), and check valves and emergency shut-off valves are configured at key nodes to ensure process safety.

[0127] It should be noted that the digital management platform is connected to the PLC control system of each module through the OPC UA data interface to achieve real-time data acquisition frequency of no less than 100Hz;

[0128] The platform has built-in relational database and time series database to store equipment ledger data and operation history data respectively, supporting user management, data query and report generation functions;

[0129] The platform realizes data exchange with external power grid dispatching systems and energy trading platforms through power-specific communication protocols.

[0130] Specifically, regarding the technical details of the digital management platform:

[0131] Data acquisition is connected to the PLC control system of each module through the OPC UA data interface to achieve 100Hz real-time data acquisition (such as analog quantities such as voltage, current, temperature, and digital quantities such as switch status), with data transmission delay ≤50ms, and support for breakpoint resumption and abnormal data verification.

[0132] The database architecture uses a relational database (MySQL) to store structured data such as device records (model, manufacturer, operation and maintenance records), user information, and contract data, supporting complex queries and report generation.

[0133] The time series database (InfluxDB) stores historical operating data (timestamps, equipment parameters, energy consumption data). A single node supports writing millions of data points (rate ≥ 100,000 points / second) and provides functions such as trend analysis and fault tracing.

[0134] External interactions involve exchanging real-time power, energy storage status and other information with the power grid dispatching system through power-specific communication protocols (such as IEC 61850 and DL / T645) and receiving dispatching instructions; connecting with the energy trading platform to realize the automated trading of green electricity, green certificates and carbon quotas, and supporting API key authentication and data encryption transmission (AES-256).

[0135] The user function provides web and mobile interfaces, supports multi-role permission management (administrator, engineer, user), and has functions such as remote device control, historical data query (supports custom time intervals), energy efficiency analysis report (generated by day / month / year), etc. The operation interface response time is ≤2 seconds.

[0136] In summary, according to the zero-carbon park multi-energy collaborative optimization system based on electricity-carbon-hydrogen-alcohol coupling disclosed in the present invention, this system constructs a multi-energy flow closed-loop architecture, realizes efficient coordination of energy flow through intelligent optimization and adaptive regulation, and combines digital management to improve resource allocation accuracy, which can effectively improve the utilization rate of renewable energy and enhance system flexibility.

[0137] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A zero-carbon park multi-energy collaborative optimization system based on electricity-carbon-hydrogen-alcohol coupling, characterized by: include: A multi-energy flow coupling architecture builds a closed-loop system consisting of renewable energy power generation modules, green hydrogen production modules, methanol synthesis modules, and carbon capture modules. Each module achieves physical connection and data exchange through standardized interfaces. The intelligent optimization model is deployed in the system control layer and includes a multi-objective collaborative optimization algorithm execution unit. The input port of the execution unit is connected to the real-time operation data acquisition device of each module, and the output port is connected to the device control unit of each module. The adaptive control system includes a weather forecast module, a load demand analysis module, and an equipment parameter adjustment module. The outputs of the weather forecast module and the load demand analysis module are connected to the input of the equipment parameter adjustment module. The output of the equipment parameter adjustment module is connected to the control interface of each key device. The digital management platform integrates a three-dimensional visualization server and a blockchain node. The three-dimensional visualization server is connected to the monitoring sensors of each module through a data bus, and the blockchain node is connected to the virtual power plant management system and the demand-side response terminal through a smart contract interface.

2. The zero-carbon park multi-energy collaborative optimization system based on electricity-carbon-hydrogen-alcohol coupling according to claim 1 is characterized in that: In the multi-energy flow coupling architecture, The renewable energy power generation module includes a solar power generation unit, a wind power generation unit and a lithium battery energy storage unit; The solar power generation unit is composed of a polycrystalline silicon photovoltaic module array, an intelligent combiner box and a photovoltaic inverter with maximum power point tracking function; The wind power generation unit is composed of a direct-drive or doubly-fed wind turbine generator and a converter; The lithium battery energy storage unit is connected to the photovoltaic inverter output end, the converter output end and the grid access end respectively through a bidirectional converter.

3. The zero-carbon park multi-energy collaborative optimization system based on electricity-carbon-hydrogen-alcohol coupling according to claim 2 is characterized in that: The green hydrogen production module includes: Electrolyzer group, hydrogen purification device and high-pressure hydrogen storage tank, including: The input end of the electrolyzer group is connected to the output end of the photovoltaic inverter, the output end of the converter and the bidirectional converter of the lithium battery energy storage unit through a first DC bus, and the type of the electrolyzer group is an alkaline electrolyzer, a proton exchange membrane electrolyzer or a solid oxide electrolyzer; The input end of the hydrogen purification device is connected to the hydrogen output end of the electrolyzer group, and the output end is connected to the air inlet of the high-pressure hydrogen storage tank. The high-pressure hydrogen storage tank is equipped with a pressure sensor and a temperature sensor.

4. The zero-carbon park multi-energy collaborative optimization system based on electricity-carbon-hydrogen-alcohol coupling according to claim 3 is characterized in that: The methanol synthesis module comprises: Methanol synthesis reactor, gas circulation pump and methanol distillation unit, including: The methanol synthesis reactor has an air inlet connected to the air outlet of the high-pressure hydrogen storage tank and a carbon dioxide delivery pipeline of the carbon capture module, respectively. A copper-based or zinc-based catalyst filling layer is provided inside the methanol synthesis reactor, and a temperature control jacket and a pressure sensor are provided outside. The gas circulation pump air inlet is connected to the tail gas outlet of the methanol synthesis reactor, and the gas outlet is connected to the gas inlet of the methanol synthesis reactor; The feed port of the methanol distillation device is connected to the discharge port of the methanol synthesis reactor, and a condensing reflux and collecting device is provided on the top of the tower.

5. The zero-carbon park multi-energy collaborative optimization system based on electricity-carbon-hydrogen-alcohol coupling according to claim 4 is characterized in that: The carbon capture module comprises: The carbon dioxide capture device, carbon dioxide compressor and carbon dioxide drying tower installed at the emission source of the park, including The carbon dioxide capture device adopts a chemical absorption tower, a physical adsorption tank or a membrane separation component; The air inlet of the carbon dioxide compressor is connected to the air outlet of the capture device, the air outlet is connected to the air inlet of the carbon dioxide drying tower, and the air outlet of the drying tower is connected to the air inlet of the methanol synthesis reactor through the carbon dioxide delivery pipeline; An online concentration sensor is provided inside the capture device, and a signal output end of the sensor is connected to the digital management platform.

6. The zero-carbon park multi-energy collaborative optimization system based on electricity-carbon-hydrogen-alcohol coupling according to claim 1 is characterized in that: The multi-objective collaborative optimization algorithm execution unit has a built-in random algorithm coupling calculation module, and the input parameters of the calculation module include: renewable energy power generation, electrolyzer operating current, methanol synthesis reactor temperature, and carbon capture device operating pressure; the output parameters are not limited to: energy storage unit charging and discharging power instructions, electrolyzer start and stop number instructions, and synthesis reactor catalyst bed temperature adjustment instructions.

7. The zero-carbon park multi-energy collaborative optimization system based on electricity-carbon-hydrogen-alcohol coupling according to claim 1 is characterized in that: The meteorological forecast module includes a time series forecast model based on a machine learning model, the input data of which is the historical 720 hours of light intensity, wind speed, wind direction and temperature data, and outputs meteorological parameter forecast values ​​for the next no less than 24 hours and with a frequency of no less than 15 minutes; the load demand analysis module includes a support vector machine load forecast model, the input data of which is the historical electricity, hydrogen and methanol flow data of each user end in the park, and outputs hourly load demand forecast values ​​for the next no less than 24 hours.

8. The zero-carbon park multi-energy collaborative optimization system based on electricity-carbon-hydrogen-alcohol coupling according to claim 1 is characterized in that: The three-dimensional visualization server builds a digital twin model of the park energy system, which includes a three-dimensional geometric model of each device and a dynamic operation status display interface driven by real-time data. The interface integrates an equipment failure warning module, and the input signal of the warning module is the abnormal parameter threshold trigger signal of each device sensor; The blockchain node adopts a consortium chain architecture, including a carbon footprint traceability smart contract, a virtual power plant resource registration smart contract and a demand-side response incentive smart contract. Each smart contract is connected to the corresponding business system through an API interface.

9. The zero-carbon park multi-energy collaborative optimization system based on electricity-carbon-hydrogen-alcohol coupling according to any one of claims 1 to 8, characterized in that: The energy flow path of the multi-energy flow coupling architecture is: The output power of the renewable energy power generation module is preferentially supplied to the green hydrogen production module through the first DC bus, and the remaining power is stored in the lithium battery energy storage unit or integrated into the power grid through the bidirectional converter; The hydrogen produced by the green hydrogen production module is processed by the hydrogen purification device and stored in a high-pressure hydrogen storage tank, and then transported to the methanol synthesis module through a pipeline; The carbon dioxide captured by the carbon capture module is compressed and dried and then transported to the methanol synthesis module to synthesize methanol with hydrogen in the methanol synthesis reactor; The material flow path of the closed-loop system realizes material transmission between modules through pipelines, valves and standardized interfaces.

10. The zero-carbon park multi-energy collaborative optimization system based on electricity-carbon-hydrogen-alcohol coupling according to claim 8, characterized in that: The digital management platform is connected to the PLC control system of each module through the OPC UA data interface to achieve real-time data acquisition frequency of no less than 100Hz; The platform has built-in relational database and time series database to store equipment ledger data and operation history data respectively, and supports user management, data query and report generation functions; The platform realizes data interaction with external power grid dispatching system and energy trading platform through power-specific communication protocol.

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