Electrolytic water hydrogen production equipment unit energy consumption level monitoring and evaluation system

Abstract

This application relates to the field of water electrolysis hydrogen production equipment technology, and discloses a unit energy consumption level monitoring and evaluation system for water electrolysis hydrogen production equipment. The system includes data acquisition, data processing, energy consumption evaluation, and optimization control modules. The data acquisition module simultaneously acquires electrolysis chemical parameters, auxiliary system sub-parameters, and full life cycle indicators; the data processing module uses Faraday's law to perform double verification and cleaning of physical flow readings; the energy consumption evaluation module incorporates a standard adaptation mask matrix to define the calculation boundary and dynamically adjusts the weights of the DC side and auxiliary system based on the real-time load rate to generate a comprehensive index; the optimization control module constructs a two-layer closed-loop control architecture based on this index, time-of-use electricity prices, and equipment aging trends. This invention solves the problems of fuzzy evaluation boundaries, measurement distortion, and inability to adapt to wide power fluctuations in traditional methods, achieving refined energy efficiency management and full-cycle economic operation optimization of hydrogen production equipment.

Description

Technical Field

[0001] This invention relates to the field of water electrolysis hydrogen production equipment technology, specifically a monitoring and evaluation system for the unit energy consumption level of water electrolysis hydrogen production equipment. Background Technology

[0002] With the large-scale development of the hydrogen energy industry, water electrolysis for hydrogen production, as a core pathway for green hydrogen supply, directly determines the economic feasibility and market competitiveness of hydrogen production projects through its energy conversion efficiency. Accurately grasping the energy consumption per unit of hydrogen production is a prerequisite for process optimization and cost control in the daily operation and performance evaluation of hydrogen production equipment. Currently, the industry's energy consumption evaluation of hydrogen production equipment mainly focuses on the DC power consumption of the electrolyzer itself during the electrochemical reaction process, resulting in relatively singular evaluation indicators.

[0003] However, the existing monitoring and evaluation system has revealed the following significant problems in practical applications: First, the evaluation boundaries are vaguely defined and incomplete. A complete industrial-grade hydrogen production system, besides the electrolyzer itself, also includes a balance-of-work (BOP) system comprising fluid transport, thermal management, and gas purification. Currently, the industry lacks a unified standard for energy consumption calculation boundaries, and different evaluation specifications differ in the scope of energy consumption included in the auxiliary system, making it difficult to cross-reference energy efficiency data between different devices. Furthermore, existing systems often neglect the independent and refined measurement of BOP energy consumption. Under low-load conditions, the proportion of BOP energy consumption increases significantly, and monitoring only the stack efficiency cannot reflect the true overall energy consumption level of the system.

[0004] Secondly, there is a lack of reliable verification mechanisms for hydrogen production measurement. Accurate hydrogen production is the denominator in calculating unit energy consumption. In actual operating conditions, gas flow meters often experience significant measurement errors due to incomplete gas-liquid separation (with entrained droplets), pressure fluctuations, or being in the low-end blind zone of their measurement range. Existing technologies typically rely directly on physical instrument readings, lacking a theoretical value comparison and verification process based on electrochemical principles, leading to a risk of distortion in the final calculated energy consumption figures.

[0005] Furthermore, the control strategies lack adaptability to dynamic operating conditions and economic costs. Electrolyzer operation is affected by nonlinear factors such as temperature, pressure, and current density. Traditional energy consumption assessments often rely on offline calculations of static parameters, failing to identify equipment aging or parameter drift in real time. More importantly, existing systems typically pursue only optimal physical energy efficiency, neglecting to incorporate time-of-use electricity pricing signals from the external power grid for "cost-optimal" control. They cannot leverage peak-valley electricity price differences through cold storage or load shifting to reduce overall hydrogen production costs.

[0006] Therefore, this invention proposes a unit energy consumption level monitoring and evaluation system for water electrolysis hydrogen production equipment to address the shortcomings of existing technologies. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides a monitoring and evaluation system for the unit energy consumption level of water electrolysis hydrogen production equipment. This system solves the problems in existing technologies where energy consumption evaluation of hydrogen production equipment only focuses on the DC energy consumption of the electrolyzer itself while neglecting the energy consumption of the auxiliary balance system (BOP). Furthermore, the system's single evaluation index cannot adapt to wide power dynamic fluctuation conditions, resulting in inaccurate energy efficiency monitoring and lagging optimization and control.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a monitoring and evaluation system for the unit energy consumption level of water electrolysis hydrogen production equipment, comprising a data acquisition module, a data processing module, an energy consumption evaluation module, and an optimization control module; The data acquisition module is used to collect the electrochemical operating parameters of the electrolytic cell body, the operating parameters of the auxiliary balancing system components, the time-domain indicators of the entire life cycle, and the fluid state parameters of the product outlet. The data processing module is used to verify the reading of the physical flow meter using the theoretical hydrogen production based on current calculation and output valid data according to the verification result. The energy consumption evaluation module is used to calculate the unit hydrogen production energy consumption of the DC side and the auxiliary balance system based on multi-standard adaptation logic, and adjust the calculation weight according to the load conditions to generate a comprehensive energy consumption index. The optimization control module is used to generate control setpoints based on the comprehensive energy consumption index and send them to the field equipment layer to drive the actuators to move.

[0009] Preferably, the electrochemical operating parameters include operating voltage, operating current, and rectified input power; The operating parameters of the sub-equipment include the independent power, flow rate, temperature and pressure values ​​of the fluid transport subsystem, thermal management subsystem, gas purification subsystem and auxiliary supply subsystem in the auxiliary balancing system, as well as the cold standby power consumption and hot standby power consumption in the non-hydrogen production state. The fluid state parameters include the physical flow meter reading at the gas product outlet, absolute pressure, gas temperature, and dew point temperature. The full lifecycle time-domain metrics include system startup time, shutdown time, and power response latency.

[0010] Preferably, the data processing module has embedded flow dual verification logic, configured to receive the reading of the physical flow meter and calculate the theoretical hydrogen production based on Faraday's law of electrolysis and the real-time collected operating current; Calculate the relative deviation between the physical flow meter reading and the theoretical hydrogen production; When the relative deviation is less than a preset threshold, the physical flow meter reading is output as the valid data; When the relative deviation exceeds the preset threshold, the data is determined to be abnormal and the theoretical hydrogen production is forcibly switched to be the valid data.

[0011] Preferably, the energy consumption evaluation module is configured to calculate the unit energy consumption on the DC side and the unit energy consumption of the auxiliary balancing system based on the valid data. The energy consumption evaluation module has a pre-set standard adaptation mask matrix. It calls the corresponding binary mask coefficient according to the currently selected evaluation standard and uses the binary mask coefficient to define the energy consumption inclusion boundary. The unit energy consumption of the DC side and the unit energy consumption of the auxiliary balance system are the sum of the products of each sub-item power and the corresponding binary mask coefficient divided by the effective hydrogen production rate. The energy consumption evaluation module uses the design energy consumption value under rated operating conditions as a benchmark, and performs dimensionless normalization processing on the calculated DC side unit energy consumption and the auxiliary balance system unit energy consumption to obtain the normalized DC index and the normalized auxiliary balance system index.

[0012] Preferably, the energy consumption evaluation module is configured to monitor the load rate and load rate change rate of the electrolyzer in real time to identify whether the water electrolysis hydrogen production equipment is in a steady-state high load range, a steady-state low load range, or a transient variable load range. When the electrolysis hydrogen production equipment is identified as being in the steady-state low load range, the calculation weight of the DC side is reduced and the calculation weight of the auxiliary balancing system is increased, so that the comprehensive energy consumption index is sensitive to the energy efficiency fluctuations of the auxiliary balancing system. The energy consumption evaluation module generates the comprehensive energy consumption index by summing the weighted normalized DC index and the normalized auxiliary balance system index.

[0013] Preferably, the optimization control module is constructed with a two-layer control architecture in which the decision layer and the execution layer are physically separated; The decision-making layer resides in the central processing unit and is used to calculate the optimal operating setpoint vector over a long period using a multi-dimensional optimization algorithm. The execution layer resides in the programmable logic controller in the field device layer, and is used to receive the optimal operating setpoint vector within a short period of time and perform closed-loop adjustment in combination with the real-time feedback value from the field. Before issuing instructions, the optimization control module performs over-limit checks and gradient limit checks on the optimal operating setpoint vector through safety interlock logic.

[0014] Preferably, the optimization control module is configured to calculate the minimum theoretical flow rate for the fluid conveying equipment based on the measured current density of the electrolytic cell, the electrochemical heat generation model, and the temperature difference between the inlet and outlet of the electrolyte, using the similarity law of the pump, and generate the speed setting value of the frequency converter driver according to the mapping relationship between flow rate and current and the cubic relationship between power and speed. The optimized control module adopts a strategy combining dew point feedback and feedforward compensation for the thermal management equipment. It monitors the dew point temperature of the product gas in real time, dynamically increases the cooling water outlet temperature setpoint while ensuring that the gas dryness complies with regulations, and predicts the heat load based on the power increment when it detects an increase in the electrolytic cell power command, and adjusts the cooling power setpoint or fan speed setpoint in advance.

[0015] Preferably, the optimization control module is connected to an external power grid system to obtain time-of-use electricity price signals; during off-peak hours, the optimization control module removes the power limit of the electrolyzer to maximize hydrogen production and controls the refrigeration unit to operate at full load to reduce the water temperature of the buffer tank for cold energy pre-storage; During peak power periods, the optimization control module controls the refrigeration unit to stop or unload and uses the pre-stored cold energy for cooling, while simultaneously reducing the load on the electrolytic cell to a safe load.

[0016] Preferably, the optimization control module calculates the cumulative gas flow rate of the adsorption tower for the gas purification subsystem and estimates the real-time saturation by combining the product gas dew point data; If the current period is a peak power period and the adsorption tower has not reached its saturation limit, the optimization control module forcibly blocks the start signal of the regeneration heater and postpones the regeneration process to a non-peak power period. The optimization control module is also configured to dynamically reduce the regeneration temperature setpoint based on the adsorbent aging curve.

[0017] Preferably, the monitoring and evaluation system also includes a visualization display module; The visualization module is configured to display the changing trend of the comprehensive energy consumption index and the dynamic chart of the energy consumption ratio of the auxiliary balancing system in real time on the interface of the monitoring and evaluation system. When the relative deviation received from the data processing module exceeds the preset threshold and the duration of the corresponding state exceeds the anti-jitter time window, the visualization module triggers a low confidence alarm and indicates that the data source has been switched to the theoretical hydrogen production. The visualization module is also configured to track the moving average of the comprehensive energy consumption index and build a time-series-based attenuation prediction model. When the predicted decrease in unit energy consumption exceeds the maintenance threshold, it automatically generates a digital maintenance work order containing specific maintenance suggestions.

[0018] This invention provides a system for monitoring and evaluating the unit energy consumption level of water electrolysis hydrogen production equipment. It has the following beneficial effects: 1. This invention utilizes embedded flow dual verification logic based on electrochemical principles to perform real-time cleaning and correction of physical instruments using theoretical values ​​calculated by Faraday's law. When the system is in a gas-liquid mixed transport or low-range blind zone, causing unreliable readings from the physical instruments, the system forcibly switches to the theoretical data source, completely eliminating the inaccuracy of the energy consumption denominator caused by hydrogen production measurement errors, and ensuring the authenticity and reliability of the evaluation results across the entire range.

[0019] 2. This invention innovatively introduces a standard adaptation mask matrix, which can flexibly define energy consumption calculation boundaries according to different national or industry standards, solving the problems of ambiguous boundaries and difficulty in horizontal data benchmarking in the current evaluation system. Simultaneously, it constructs a dynamic weighting model based on load status, automatically increasing the calculation weight of the Balanced Operating System (BOP) in low-load intervals, accurately capturing and quantifying the drag effect of fixed-power devices on the overall system energy efficiency, providing precise quantitative basis for variable operating conditions.

[0020] 3. Unlike traditional extensive control methods, this invention incorporates specific physical optimization algorithms into the fluid transport and thermal management subsystems. Utilizing the pump's similarity law and the cubic relationship between speed and power, the minimum theoretical flow rate is precisely calculated. Combined with dew point feedback feedforward compensation of heat load, this eliminates the ineffective power consumption caused by excessive cooling and high-flow-rate circulation at the mechanistic level, achieving ultimate optimization of the auxiliary system's energy efficiency.

[0021] 4. This invention establishes a grid-source-load coordination mechanism combined with time-of-use electricity pricing. Utilizing strategies such as "valley electricity for cold storage and peak electricity for energy release" and "regenerative peak shifting," it achieves optimal hydrogen production costs and energy consumption. Furthermore, the system integrates full lifecycle time-domain indicator monitoring, enabling the construction of predictive models based on energy consumption decay trends and the automatic generation of digital maintenance work orders. This transforms traditional "post-fault maintenance" into "predictive maintenance," effectively extending equipment lifespan and reducing overall lifecycle operating costs. Attached Figure Description

[0022] Figure 1 This is a structural block diagram of the unit energy consumption level monitoring and evaluation system for water electrolysis hydrogen production equipment of the present invention; Figure 2 This is a flowchart illustrating the method for monitoring and evaluating the unit energy consumption level of the water electrolysis hydrogen production equipment of the present invention. Figure 3 This is a flowchart illustrating the logic of dual verification and cleaning of traffic data in this invention.

[0023] Among them, 100 is the data acquisition module; 200 is the data processing module; 300 is the energy consumption evaluation module; 400 is the optimization control module; 500 is the visualization display module; 600 is the central processing unit; and 700 is the field equipment layer. Detailed Implementation

[0024] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] See attached document Figure 1 , Figure 1 This is a structural block diagram of a unit energy consumption monitoring and evaluation system for water electrolysis hydrogen production equipment according to an embodiment of the present invention. This embodiment provides a unit energy consumption monitoring and evaluation system for water electrolysis hydrogen production equipment. The system is built on the field equipment layer 700 and includes a data acquisition module 100, a data processing module 200, an energy consumption evaluation module 300, an optimization control module 400, and a visualization display module 500. Each module resides in the central processing unit 600 and interacts with the field equipment layer 700 via an industrial communication protocol.

[0026] The data acquisition module 100 connects to the sensor network of the field equipment layer 700. This data acquisition module 100 synchronously reads the electrochemical operating parameters of the electrolyzer body, the operating parameters of the auxiliary balancing system's sub-equipment, and the fluid state parameters of the product outlet at a preset frequency. The electrochemical operating parameters include operating voltage, operating current, and rectified input power; the sub-equipment operating parameters cover the independent power, flow rate, temperature, and pressure values ​​of the fluid transport, thermal management, gas purification, and auxiliary supply subsystems; the fluid state parameters include the physical flow rate reading, absolute pressure, gas temperature, and dew point temperature of the hydrogen outlet.

[0027] The data processing module 200 receives raw data and performs cleaning and calibration. This module incorporates dual flow verification logic, calculating the theoretical hydrogen production based on Faraday's law of electrolysis and real-time current, and comparing the theoretical value with the physical flowmeter reading. When the deviation is less than a preset threshold, the module outputs the physical measurement value as valid data; when the deviation exceeds the threshold, the module marks it as abnormal and outputs the theoretically calculated value as valid data. The module also uses the ideal gas law to convert the operating flow rate into a standard volumetric flow rate.

[0028] The energy consumption evaluation module 300 quantifies energy efficiency indicators based on effective data and a unified energy consumption evaluation model. This module decouples the DC energy consumption of the electrolyzer from the AC energy consumption of the auxiliary balancing system and normalizes them into unit hydrogen production energy consumption. The dynamic weighting algorithm integrated within the energy consumption evaluation module 300 automatically adjusts the calculation weights of each sub-item of energy consumption indicators according to the current load conditions and system operating mode, generating a comprehensive energy consumption index that reflects the overall energy efficiency level of the system.

[0029] The optimization control module 400 constructs a hierarchical closed-loop control circuit. This module receives the comprehensive energy consumption index and external grid electricity price signals, and generates control setpoints for the auxiliary balancing system and the electrolyzer itself based on a preset optimization strategy. Before issuing commands, the optimization control module 400 verifies the safety of the setpoints through safety interlock logic, and then sends the commands to the programmable logic controller (PLC) at the field equipment layer 700, which drives the actuators to adjust the equipment's operating status.

[0030] The visualization module 500 is used for human-computer interaction display. This visualization module 500 presents real-time energy consumption data of the system, the trend of changes in the comprehensive energy consumption index, charts of the energy consumption ratio of each subsystem, and instrument abnormality alarm information triggered by the data processing module 200 on the operating terminal.

[0031] See attached document Figure 2 , Figure 2 This is a flowchart illustrating a method for monitoring and evaluating the unit energy consumption level of a water electrolysis hydrogen production equipment according to an embodiment of the present invention. The present invention provides a method for monitoring and evaluating the unit energy consumption level of a water electrolysis hydrogen production equipment, comprising the following steps: The S100, through a sensor network deployed on-site, simultaneously collects electrochemical data from the DC side of the electrolyzer, energy consumption data of each subsystem of the auxiliary balancing system, time-domain indicators throughout the entire life cycle, and physical state data of gaseous products.

[0032] S200 calculates the theoretical hydrogen production using Faraday's law of electrolysis, compares the deviation between the physical flowmeter reading and the theoretical value, determines the validity of the data based on the deviation results and fills in the missing values, and then converts the operating flow parameters into standard volumetric flow rates.

[0033] S300, based on standardized data, calls the standard adaptation mask matrix to define the calculation boundary, and calculates the unit energy consumption of the DC side and the auxiliary balancing system respectively; according to the operating conditions and mask coefficients, it calls the dynamic weight function to calculate the system's comprehensive energy consumption index.

[0034] The S400 calculates the optimal operating setpoint based on the comprehensive energy consumption index score and external electricity price signal. After the safety interlock logic confirms that the setpoint is correct, it sends the setpoint to the lower-level controller to regulate the circulating pump, chiller and power supply equipment.

[0035] The S500 displays energy efficiency trend curves and the proportion of energy consumption for each component in real time on the monitoring interface, and triggers audible and visual alarms when abnormal flow verification or energy efficiency index exceeding limits is detected. At the same time, it performs lifespan prediction based on energy consumption decay trends, and automatically generates a digital work order containing maintenance suggestions when the energy consumption falls below the threshold.

[0036] See attached document Figure 1The unit energy consumption level monitoring and evaluation system for water electrolysis hydrogen production equipment provided in this embodiment is constructed at the physical level as a vertical control architecture consisting of a field equipment layer 700, an edge control layer, and a central processing unit 600.

[0037] The field device layer 700 encompasses the physical entities involved in the water electrolysis hydrogen production process. The electrolyzer itself is equipped with Hall effect current sensors and voltage transmitters, which are hardwired to the analog input module of the underlying controller to acquire millisecond-level high-frequency electrochemical data. The auxiliary balancing system deploys a distributed sensor network, including physical flow meters installed in the fluid delivery network, temperature sensors installed in the heat exchange loop, and pressure transmitters installed at the gas purification outlet. These sensors output 4-20mA standard analog signals via shielded twisted-pair cables or digital signals via RS485 communication interfaces to the field programmable logic controller (PLC). In addition to the sensing devices, the field device layer 700 also includes execution units, specifically variable frequency drives (VFDs) for circulating pumps, valve positioners, and control interfaces for the power rectifier cabinet. These execution units are electrically connected to the output modules of the PLC, responding to control commands from the upper layer.

[0038] The edge control layer, located between the field device layer 700 and the central processing unit 600, mainly consists of industrial gateways with edge computing capabilities and regional programmable logic controllers. Considering that different manufacturers' equipment (such as chillers and pure water systems) in the auxiliary balancing system may use heterogeneous communication protocols, the edge control layer is configured with multi-protocol conversion logic to uniformly convert heterogeneous fieldbuses such as Modbus RTU and Profibus-DP on the field side into Ethernet-based industrial communication protocols (such as Profinet or Modbus TCP). The industrial gateway establishes a high-speed bidirectional data channel with the upper-level central processing unit 600 through a fiber optic ring network or an industrial Ethernet switch, ensuring low latency and integrity of massive monitoring data transmission. For those skilled in the art, the specific wiring and anti-interference grounding treatment of industrial fieldbuses are well-known technologies and will not be elaborated upon here.

[0039] The central processing unit 600, as the core decision-making carrier of the system, can be physically implemented by an industrial control computer (IPC) or a private cloud server cluster deployed in the control room. The storage medium of the central processing unit 600 contains computer-readable instructions, which are specifically instantiated into a data acquisition module 100, a data processing module 200, an energy consumption evaluation module 300, an optimization control module 400, and a visualization display module 500 when executed by the processor.

[0040] Specifically, the functional logic of the data acquisition module 100 is mapped to the I / O communication driver layer of the central processing unit 600. By periodically scanning the register addresses of the edge control layer by calling the industrial communication protocol driver, it maps field physical variables into structured data objects in system memory. The data processing module 200 and the energy consumption evaluation module 300 reside in the arithmetic logic layer of the central processing unit 600, using the processor's floating-point arithmetic unit to perform data cleaning, flow verification algorithms, and calculations of the dynamic weighted energy consumption index model. The optimization control module 400 establishes a reverse control link from the central processing unit 600 to the field device layer 700. Based on the calculation results, the module generates control setpoints, which are then sent to the intermediate register area of ​​the field programmable logic controller (FPGA) via industrial Ethernet. The FPGA runs a local PID closed-loop program, converting the received setpoints into physical voltage or pulse signals that drive the actuators, thus physically realizing closed-loop control of "sensing, decision-making, and execution."

[0041] See attached document Figure 1 In this embodiment, the data acquisition module 100 executes a multi-dimensional data acquisition strategy, implementing decoupled metering for the electrolyzer body and the balance-operated system (BOP), thereby providing high-granularity input data for subsequent energy efficiency evaluation. The specific data acquisition implementation process includes the following steps: S110, high-frequency synchronous acquisition of DC-side electrochemical parameters. The system is equipped with a high-precision Hall effect current sensor at the DC output busbar of the power rectifier cabinet to acquire the real-time total operating current of the electrolyzer at millisecond intervals. Simultaneously, a multi-channel voltage monitoring instrument is connected in parallel across the end plate electrodes of the electrolysis chamber to collect the real-time total operating voltage of the electrolytic cell. To evaluate the conversion efficiency of the power system, a three-phase smart energy meter was further installed at the AC input terminal of the rectifier cabinet to collect the input AC active power. Through the aforementioned data collection points, the system can obtain the actual power output on the DC side in real time. And combine input power monitoring to monitor rectification loss.

[0042] S120, Precision monitoring of the physical state parameters of gaseous products. Located downstream of the gas-liquid separator and on the main pipeline at the outlet of the hydrogen purification unit, the system sequentially includes a pressure transmitter, a temperature transmitter, a dew point meter, and a physical flow meter. The pressure transmitter is used to collect the absolute pressure within the gas pipeline. Temperature transmitters are used to acquire the real-time temperature of gaseous media. The physical flow meter directly reads the gas's mass flow rate under operating conditions and converts it into volumetric flow rate under operating conditions within the transmitter. The above physical quantities provide the necessary physical benchmarks for subsequent conversion of flow rates to standard conditions (0℃, 101.325kPa) and for dual verification of flow rate data based on Faraday's law.

[0043] S130, Independent Metering of Sub-item Energy Consumption and Acquisition of Time-Domain Indicators Throughout the Life Cycle of the Balanced Plant (BOP) System. This system decouples the energy consumption data of the BOP system into four independent subsystems for monitoring: fluid transport, thermal management, gas purification, and auxiliary supply, through independent division of hardware electrical circuits and parsing of communication protocols.

[0044] Simultaneously, the data acquisition module 100 executes high-frequency time-domain capture logic to record the system's startup time, shutdown time, and power response lag with a resolution of 0.1 seconds; and in non-hydrogen production states, it distinguishes between acquiring cold standby power consumption (monitoring system operation only) and hot standby power consumption (heat tracing system operation). Specific implementation details are as follows: For the fluid transport subsystem, the system reads the internal registers of the variable frequency drives (VFDs) of the alkali circulation pump and the makeup water pump via the industrial bus to directly obtain the real-time operating power of the pump set. Simultaneously, an electromagnetic flowmeter is installed on the main pipeline of the alkali solution circulation loop to collect real-time circulation flow. By establishing and With the synchronous time series, the system can monitor the energy efficiency ratio of the pump set at different speeds and identify abnormalities such as high flow resistance or pump efficiency decline.

[0045] For the thermal management subsystem, the system collects the real-time input electrical power of the chiller unit or air-cooled island fan. To evaluate the effectiveness of heat removal, temperature sensors were installed at the inlet and outlet of the cooling water heat exchanger to collect the cooling water inlet temperature. With outlet temperature Combined with the flow meter readings on the cooling water pipeline This enables quantitative monitoring of the load removed by electrolytic heat.

[0046] For the gas purification subsystem, the system connects to the power distribution circuit of the purification unit's electrical instruments to collect real-time power data during the purification and drying processes. Specifically, the system monitors the start / stop signals of the adsorption tower regeneration heater by reading the PLC status bits. (For example, 0 represents the adsorption state and 1 represents the regeneration heating state), thereby distinguishing the energy consumption data into the low-energy adsorption stage and the high-energy regeneration stage, so as to identify the periodic fluctuation characteristics of BOP energy consumption.

[0047] For the auxiliary supply subsystem, the system independently collects the operating power of intermittently operating equipment such as the pure water preparation unit and instrument air compressor. and In conjunction with the aforementioned standby power consumption data, we ensure that energy consumption statistics cover all operating modes throughout the entire lifecycle.

[0048] For the specific selection of the above-mentioned sensors (such as the sensor's range and accuracy class), installation specifications, and anti-interference shielding of the 4-20mA signal, those skilled in the art can refer to the construction and acceptance specifications for industrial automation instrument installation engineering. These are well-known technologies in the field and will not be elaborated here.

[0049] See attached document Figure 3 The data processing module 200 uses an embedded verification algorithm to standardize and verify the validity of the raw flow data collected by the field equipment layer 700, in order to eliminate measurement distortion of physical instruments under gas-liquid mixed transport or low-load conditions. The specific execution steps are as follows: S210, Standardized conversion of operating flow rate. Data processing module 200 receives the gas outlet operating volumetric flow rate from data acquisition module 100. Absolute pressure of gas and gas temperature Considering the dynamic fluctuations in gas pressure and temperature during water electrolysis for hydrogen production, to ensure a unified evaluation benchmark, the module converts the operating volumetric flow rate to the standard condition (typically defined as 0℃, 101.325kPa) volumetric flow rate based on the ideal gas law. This step eliminates the interference of changes in environmental thermodynamic parameters on flow measurement, providing a unique volumetric reference for subsequent energy consumption calculations.

[0050] S220, based on the theoretical hydrogen production calculation of electrochemical principles. To establish a verification benchmark for the flow rate data, the data processing module 200 utilizes Faraday's law of electrolysis, based on the real-time collected total DC current of the electrolyzer. Calculate the theoretical hydrogen production rate at the current moment. This calculation process does not rely on a physical flow meter; it is only related to the amount of charge passing through the electrolysis chamber. The calculation model is as follows: ; In the formula, This represents the number of electrolytic cells connected in series within the electrolytic cell; Represents real-time direct current, measured in amperes (A). This represents the molar volume of the gas under standard conditions, with a value of 22.414 L / mol. This represents the Faraday constant, with a value of 96485 C / mol; The coefficient represents the current efficiency, which is usually taken as an empirical value or a correction factor obtained from a table based on temperature and pressure; the coefficient 2 represents the number of electrons transferred to generate 1 mole of hydrogen gas. Matching used to convert units to hours and other time units.

[0051] S230, flow deviation calculation and anomaly pattern identification. Data processing module 200 compares the standardized physical measurement values ​​in real time. Compared with theoretical calculation values Calculate the relative deviation rate between the two. This step aims to utilize the stability of electrochemical theoretical values ​​to detect measurement anomalies in physical flow meters. For example, under conditions of incomplete gas-liquid separation, physical flow meters (such as vortex or mass flow meters) often exhibit inflated readings due to entrainment of liquid droplets; while at extremely low loads or during initial startup, physical instruments may experience distorted readings due to being in a dead zone. Relative deviation rate The calculation logic is as follows: ; S240, Automatic data source switching and cleaning strategy. The data processing module 200 has preset allowable deviation thresholds. (For example, set to 5%). Data processing module 200 is based on... and The comparison results are used to perform data cleaning and output decisions. when When the physical flow meter is deemed to be working properly and the gas-liquid separation effect is good, the system outputs the physical measurement value. This data on effective hydrogen production will be used in subsequent energy consumption assessments. when If the system determines that the physical measurement data is at risk of distortion, the module will automatically disable the physical measurement values, force a switch, and output the theoretically calculated values. As a substitute for effective hydrogen production data, it simultaneously triggers status flags indicating abnormal flow meters or abnormal liquid levels in gas-liquid separators.

[0052] The energy consumption evaluation module 300, based on the effective data cleaned in the preceding steps, constructs a dynamic evaluation system that adapts to varying operating conditions. This solves the problem of inaccurate evaluation of traditional static indicators under wide power fluctuations in the electrolyzer. The specific execution steps are as follows: S310 features decoupling and boundary reconstruction of unit hydrogen production energy consumption based on multi-standard adaptation. The energy consumption evaluation module 300 retrieves the standard-condition effective hydrogen production rate from the database after double verification. (Unit: Nm) 3 / h), and simultaneously acquire the real-time power of the DC side. And the power vector of the auxiliary balancing system. To address the inconsistency in the definition of energy consumption inclusion boundaries in different evaluation standards (such as GB / T-32311, T / CECA-G-0188, etc.), the energy consumption evaluation module 300 has a pre-built standard adaptation mask matrix. .

[0053] The energy consumption evaluation module 300 calls the corresponding binary mask coefficients based on the currently selected evaluation standard. and (Values ​​are 0 or 1, indicating whether the energy consumption item is included), calculate the unit energy consumption on the DC side respectively. Unit energy consumption of auxiliary systems The calculation model is as follows: ; ; In the formula, and The units are all kWh / Nm 3 ; This represents DC-side ancillary losses such as power conversion losses and line losses. , and The boundary mask coefficients are determined by the current evaluation criteria; Indicates the first Real-time power of each auxiliary subsystem (covering fluid transport, thermal management, gas purification, and auxiliary supply).

[0054] At the same time, the system automatically reads the benchmark parameters required by the current standard (such as current density, temperature, and pressure) and corrects the measured energy consumption data to the benchmark conditions, thereby achieving horizontal comparability of energy consumption data across standards and operating conditions.

[0055] S320, Normalization of Evaluation Indicators. Given the significant difference in magnitude between DC-side energy consumption and BOP energy consumption (the former is typically several times the latter), to avoid the large number swallowing the small number effect, the module introduces a design baseline value for dimensionless normalization. Setting... and The normalized DC index is calculated based on the design energy consumption of the electrolytic cell under rated operating conditions. With normalized BOP index The calculation formula is as follows: This process makes different energy-consuming components mathematically comparable, with lower values ​​indicating better energy efficiency.

[0056] S330, real-time identification of the system operating state vector. The energy consumption evaluation module 300 does not use fixed weights for comprehensive evaluation; instead, it first determines the system state based on current operating parameters. The energy consumption evaluation module 300 monitors the load rate of the electrolyzer in real time. (i.e., the ratio of real-time current to rated current) and its rate of change System status It is divided into steady-state high load range, steady-state low load range, and transient variable load range. For example, when and ( When the threshold for stability is reached, the region is determined to be in a steady-state low-load range. This step is the logical basis for dynamic weight drift.

[0057] S340, Calculation of Integrated Energy Consumption Index (ECI) based on dynamic weights. Addressing the characteristic that the energy consumption of auxiliary systems in water electrolysis hydrogen production equipment increases significantly under low load (due to the fixed base power consumption of pumps and chillers), this module constructs an integrated energy consumption index model with variable weight coefficients. This model introduces a state dependency function... The relative importance of the DC side and the BOP side in the comprehensive evaluation is dynamically adjusted. (Comprehensive Energy Consumption Index) The calculation model is as follows: ; st ; In the formula, and These are the dynamic weighting coefficients for the DC side and the BOP side, respectively.

[0058] The specific implementation of the weight shifting logic mainly includes: In the steady-state high load range (such as...) The system primarily focuses on electrochemical conversion efficiency, and the weighting function is set accordingly. The dominant value (e.g., 0.8-0.9). As an auxiliary value, the ECI index at this time mainly reflects the performance of the electrolyzer itself; In the steady-state low-load range (e.g.) The decrease in hydrogen production leads to a sharp, non-linear increase in the energy consumption per unit of BOP (Body-Operate-Place), becoming a bottleneck in system energy efficiency. At this point, the weighting function automatically adjusts to reduce... And significantly improve The value of the index (e.g., increasing it to 0.4-0.5) makes the ECI index more sensitive to fluctuations in the energy efficiency of auxiliary systems, thereby guiding the control system to prioritize optimizing the BOP operation strategy. In the transient load range, the system introduces a smoothing filter factor to dampen the weight changes, so as to avoid the evaluation index from jumping due to instantaneous load fluctuations.

[0059] S350, adaptive weight correction. To address characteristic shifts caused by equipment aging, the system incorporates a self-learning mechanism based on historical data. The energy consumption evaluation module 300 periodically statistically analyzes specific states. Below and The module tracks the historical distribution characteristics of an indicator. If an irreversible shift in the mean of a certain indicator is detected during long-term operation, the module will fine-tune the weight function using a gradient descent algorithm. The parameters ensure that the ECI index can always objectively reflect the relative energy efficiency level of the current equipment health status, rather than a static evaluation based solely on factory data.

[0060] The optimized control module 400 adopts a two-layer control architecture with a physical separation of the decision-making layer and the execution layer. This architecture aims to resolve the contradiction between the time delay caused by the high computational load of the complex energy efficiency optimization algorithm at the upper layer and the millisecond-level real-time response requirements of the underlying industrial control. The specific implementation process of this architecture is as follows: S410 constructs a non-synchronous cycle decision-execution hierarchical control link. The system divides the control task into a long-cycle intelligent decision domain and a short-cycle instant execution domain. The intelligent decision domain resides in the central processing unit 600 (IPC), and its operating cycle is set to the second or minute level (e.g., 10s-60s). In this domain, based on the comprehensive energy consumption index (ECI) calculated in previous steps and the current operating parameters, the system uses a multi-dimensional optimization algorithm to calculate the operating setpoint vector that optimizes the overall energy efficiency of the system. The immediate execution domain resides in the field-programmable logic controller (PLC), and its execution cycle is in the millisecond range (e.g., 10ms-50ms). The PLC asynchronously receives commands from the IPC via Industrial Ethernet. This value is used as the target value for the local closed-loop control loop, and deviation adjustment is performed in conjunction with real-time feedback values ​​from field sensors. This architecture ensures that complex energy consumption optimization calculations do not obstruct the rapid protection and adjustment functions of the underlying devices.

[0061] S420 is a physical model-based adaptive optimization system for Balanced Operating Plant (BOP). For the fluid transport and thermal management subsystems, which account for a significant portion of energy consumption in the Balanced Operating Plant (BOP), the decision-making layer does not use fixed operating parameters but dynamically generates setpoints based on real-time load demands and fluid / thermodynamic characteristics.

[0062] For the alkali circulation pump and the water replenishment pump, the system abandons the traditional power frequency constant current mode. The decision-making level bases the decision on the measured current density of the electrolyzer. Electrochemical heat generation model and temperature difference between electrolyte inlet and outlet The minimum theoretical flow rate required to satisfy mass transfer and heat removal is calculated using the similarity law of pumps. The setpoint calculation logic follows a nonlinear mapping relationship between flow rate and current, utilizing the cubic relationship between power and rotational speed (…). To minimize electrolyte flow rate and temperature difference constraints, the operating frequency with the lowest energy consumption is determined, avoiding unnecessary work. Its flow rate setpoint... The calculation logic is as follows: ; In the formula, The flow coefficient is based on the mass transport rate; The thermal balance coefficient is based on heat dissipation requirements; This refers to the real-time heat generation power of the electrolytic cell; To ensure sufficient traffic margin for safe system operation.

[0063] For the thermal management subsystem, the decision-making level implements a load matching strategy based on a combination of dew point feedback and feedforward compensation. The system monitors the dew point temperature of the product gas in real time and dynamically increases the cooling water outlet temperature setpoint while ensuring gas dryness compliance. Utilizing the characteristic that the chiller's COP increases with evaporation temperature, the unit operates within its high-efficiency range. Simultaneously, when the system detects an increase in the electrolyzer power command, before the electrolyte temperature rises significantly, the decision-making level predicts the future heat load based on the power increment and adjusts the cooling system's cooling power setpoint or the air-cooled fan speed setpoint in advance. This feedforward mechanism reduces the lag in temperature control and allows the chiller to operate within its high-efficiency range at partial load, avoiding full-power rapid cooling operation after temperature overshoot.

[0064] S430, Safety Interlocking and Limit Exceedance Verification of Control Commands. Before the optimized setpoints generated at the decision layer are sent to the execution layer, the system must pass a safety gate verification at the logic layer to prevent equipment damage caused by algorithm anomalies. This verification process includes absolute value limit exceedance checks and gradient limit checks. The absolute value limit exceedance check will check the setpoints... Physical boundaries allowed by the device The system performs a comparison; if the value exceeds the boundary, it forcibly clamps to the boundary value. Gradient limit checks are used to calculate the rate of change of the setpoint. If the rate of change exceeds a preset safety step size (e.g., heating rate ≤ 2℃ / min), the system will perform smoothing filtering on the instruction. Simultaneously, the system has hard-interlock logic at the PLC level, independent of the upper-level algorithm. When temperature, pressure, or liquid level is detected to reach the trip threshold, the upper-level optimization instruction is unconditionally blocked, and emergency shutdown or pressure relief procedures are executed first to ensure the inherent safety of the physical system.

[0065] This embodiment introduces the external grid electricity price signal as a key control variable for the system, breaking away from the traditional single control mode of hydrogen production systems that only focuses on energy conversion efficiency (efficiency optimization). It constructs a "cost-optimal" control closed loop with the goal of minimizing the total cost of hydrogen production (LCOH). The specific execution steps are as follows: S510 establishes a time-of-use electricity price response model based on the time dimension. The optimization control module 400 establishes a data connection with the plant's energy management system (EMS) or the upstream grid dispatch terminal via Modbus-TCP or OPC-UA communication protocols to obtain real-time electricity price data. Alternatively, a preset time-of-use (TOU) electricity price table for the next 24 hours can be used. The system divides the day into off-peak hours (low-price zone), flat-peak hours, and peak hours (high-price zone), and incorporates the electricity price factor as a weighting term into the system's objective function to generate a dynamically changing power setpoint benchmark.

[0066] The S520 implements a strategy for maximizing production capacity and storing cold energy during off-peak electricity hours. When the system clock enters an off-peak period, the controller automatically removes power restrictions, increasing the DC current setpoint of the electrolyzer to 100%-110% of its rated value, maximizing hydrogen production using low-cost electricity. Simultaneously, the optimized control module 400 monitors the pressure limit of the downstream hydrogen storage facility and the real-time limits of downstream absorption capacity. If the hydrogen storage pressure reaches a preset safety threshold or absorption capacity is limited, the system automatically lowers the hydrogen production power setpoint, achieving a dynamic decoupling balance between hydrogen production rate and downstream storage and transportation capacity. Furthermore, for the thermal management subsystem in the auxiliary balancing system, the system implements a "cold energy pre-storage" strategy. If the system is equipped with a chilled water buffer tank, the controller forces the chiller unit to operate at full load during off-peak hours, lowering the water temperature in the buffer tank to the lower limit allowed by the process (e.g., 5°C), thereby storing cold energy in the buffer tank as sensible heat. At this point, the operation of the refrigeration unit no longer simply follows the real-time heat load, but instead converts electrical energy into cold energy storage by performing work in advance, thus providing a cold source that does not require energy consumption for equipment shutdown during subsequent peak power periods.

[0067] S530 performs load transfer and auxiliary load reduction during peak power periods, as well as deep load shedding at the Balance of Plant (BOP). During peak power periods, the system automatically reduces the electrolyzer load to the minimum safe load (e.g., 30%) to maintain system thermal balance, minimizing the consumption of expensive electricity. On the BOP side, the system utilizes the chilled water stored in step S520 for passive circulating cooling, allowing the high-energy-consuming refrigeration unit compressor to shut down or operate at low frequency during peak power periods.

[0068] S540 implements staggered peak control for gas purification and regeneration based on adsorption capacity integration and temperature control optimization. For the energy-intensive electrically heated regeneration stage in the gas purification subsystem (drying tower), this system abandons the traditional fixed time interval (e.g., every 8 hours) triggering mode and implements a dynamic periodic extension strategy based on remaining adsorption capacity. The system calculates the cumulative gas flow rate processed by the adsorption tower since the last regeneration by integrating the data. And combined with the current adsorption pressure And product dew point feedback to estimate the real-time saturation of the adsorbent. The judgment logic is as follows: ; ; In the formula, This is the peak electricity period; This represents the real-time gas production rate. This represents the maximum designed gas throughput of the adsorption tower under the current pressure. For safety factors (e.g., 0.9).

[0069] This logic indicates that if the current power consumption period is peak and the adsorption tower has not yet reached its saturation limit, the system will forcibly block the start signal of the regeneration heater, postponing the high-energy-consuming heating and regeneration action to the off-peak or low-peak power consumption period, thus achieving time-shifting of energy consumption. Furthermore, based on the adsorbent's full life-cycle aging curve, the system dynamically reduces the regeneration temperature setpoint in the early stages of the adsorbent's lifespan, thereby reducing ineffective heat energy consumption while ensuring regeneration efficiency.

[0070] The visualization module 500 serves as the interface between the system and maintenance personnel. It not only transforms abstract energy consumption data into intuitive charts but also handles proactive anomaly push notifications based on data logic. The specific execution steps are as follows: The S610 enables real-time reconstruction and visualization of multi-dimensional energy efficiency data. The visualization module 500 extracts cleaned and calculated real-time data from the backend database via a high-speed data bus. The system constructs a hierarchical information display architecture on the human-machine interface (HMI) or large-screen display.

[0071] The overall overview layer displays the current comprehensive energy consumption per unit of hydrogen production in real time at the core of the interface. And the Dynamic Integrated Energy Index (ECI). One side of the screen features a dynamic pie chart showing the BOP energy consumption percentage. The data source for this pie chart is linked to the sub-item data output by the energy consumption evaluation module 300. Unlike traditional static charts, this pie chart refreshes in real time as the electrolyzer load rate changes, intuitively presenting the dynamic process of the expansion of the auxiliary system's energy consumption percentage under low-load conditions (for example, when the load drops from 100% to 20%, the area of ​​each sector automatically adjusts, highlighting the dominant position of BOP energy consumption).

[0072] In the trend analysis layer, the interface provides hourly, shift-by-shift, and daily energy efficiency curve views, and overlays the current operating curve with the design baseline curve on the same screen, allowing maintenance personnel to intuitively judge the degree to which the current operating status deviates from the design value.

[0073] The S620 features intelligent alarm interaction based on data verification results. The system is configured with tiered alarm logic, executing differentiated interaction strategies for different types of data anomalies.

[0074] In response to abnormal flow metering, the system invokes the dual verification results from data processing module 200. This is done when the relative deviation rate between the physical flow value and the theoretical calculated value is detected. Exceeding the preset alarm threshold (e.g., 8%), and the duration of this state. Exceeding the anti-shake time window At this time, the system triggers a yellow alert for "low confidence level of flow meter". The interface not only displays an anomaly flag in the flow display area, but also automatically pops up a message indicating that the system has been forcibly switched to the theoretical hydrogen production data source to prevent misleading maintenance decisions. The triggering logic is described below: ; In the formula: This represents the abnormal alarm status of traffic verification. A value of 1 indicates that the system is in alarm trigger state, at which time the interface displays a yellow warning icon and forcibly switches the data source to the theoretical calculation value. A value of 0 indicates that the system is in normal monitoring state. The real-time relative deviation rate between the measured physical flow rate and the calculated theoretical hydrogen production rate; This represents the upper limit of the system's preset allowable deviation alarm threshold; Represents the deviation rate Continuously exceeding the threshold The cumulative value of continuous timekeeping; This represents the anti-shake time threshold for alarm triggering (e.g., set to 30 seconds), used to shield against false alarms caused by sudden changes in operating conditions.

[0075] S630 features a closed-loop system for predicting energy efficiency degradation throughout the entire product lifecycle and managing maintenance work orders. To address long-term performance degradation, the system continuously tracks the moving average of the ECI index. A time-series-based degradation prediction model was constructed. This model analyzes the drift rate of the electrolyzer voltage-time curve (Ut) and the declining trend of BOP equipment efficiency, predicting the change in unit energy consumption within a specific future time window (e.g., 3 months). If the predicted or measured value, under the same load conditions, decreases by more than the historical baseline value, a maintenance threshold is established. (e.g., 15%), the system determines that the equipment's energy efficiency level has significantly deteriorated. At this point, the visualization module no longer stops at just a buzzer alarm, but automatically generates a digital maintenance work order containing the fault code, the time of the anomaly, the suspected fault source, and specific maintenance recommendations (such as "recommend replacing the electrolyte," "adsorbent is nearing the end of its thermal aging life," or "risk of wear on the circulating pump impeller"). This work order is directly pushed to a handheld maintenance terminal or the factory asset management system (EAM) via an industrial IoT interface, triggering the offline inspection process. After the maintenance is completed, maintenance personnel are required to fill in the processing results, thus achieving a complete closed loop from "data discovery of anomalies" to "offline elimination of hidden dangers."

[0076] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A monitoring and evaluation system for the unit energy consumption level of water electrolysis hydrogen production equipment, characterized in that, It includes a data acquisition module, a data processing module, an energy consumption evaluation module, and an optimization control module; The data acquisition module is used to collect the electrochemical operating parameters of the electrolytic cell body, the operating parameters of the auxiliary balancing system components, the time-domain indicators of the entire life cycle, and the fluid state parameters of the product outlet. The data processing module is used to verify the reading of the physical flow meter using the theoretical hydrogen production based on current calculation and output valid data according to the verification result. The energy consumption evaluation module is used to calculate the unit hydrogen production energy consumption of the DC side and the auxiliary balance system based on multi-standard adaptation logic, and adjust the calculation weight according to the load conditions to generate a comprehensive energy consumption index. The optimization control module is used to generate control setpoints based on the comprehensive energy consumption index and send them to the field equipment layer to drive the actuators to move.

2. The unit energy consumption level monitoring and evaluation system for water electrolysis hydrogen production equipment according to claim 1, characterized in that, The electrochemical operating parameters include operating voltage, operating current, and rectified input power; The operating parameters of the sub-equipment include the independent power, flow rate, temperature and pressure values ​​of the fluid transport subsystem, thermal management subsystem, gas purification subsystem and auxiliary supply subsystem in the auxiliary balancing system, as well as the cold standby power consumption and hot standby power consumption in the non-hydrogen production state. The fluid state parameters include the physical flow meter reading at the gas product outlet, absolute pressure, gas temperature, and dew point temperature. The full lifecycle time-domain metrics include system startup time, shutdown time, and power response latency.

3. The unit energy consumption level monitoring and evaluation system for water electrolysis hydrogen production equipment according to claim 1, characterized in that, The data processing module has embedded flow dual verification logic, configured to receive the reading of the physical flow meter and calculate the theoretical hydrogen production based on Faraday's law of electrolysis and the real-time collected operating current. Calculate the relative deviation between the physical flow meter reading and the theoretical hydrogen production; When the relative deviation is less than a preset threshold, the physical flow meter reading is output as the valid data; When the relative deviation exceeds the preset threshold, the data is determined to be abnormal and the theoretical hydrogen production is forcibly switched to be the valid data.

4. The unit energy consumption level monitoring and evaluation system for water electrolysis hydrogen production equipment according to claim 1, characterized in that, The energy consumption evaluation module is configured to calculate the unit energy consumption on the DC side and the unit energy consumption of the auxiliary balancing system based on the valid data. The energy consumption evaluation module has a pre-set standard adaptation mask matrix. It calls the corresponding binary mask coefficient according to the currently selected evaluation standard and uses the binary mask coefficient to define the energy consumption inclusion boundary. The unit energy consumption of the DC side and the unit energy consumption of the auxiliary balance system are the sum of the products of each sub-item power and the corresponding binary mask coefficient divided by the effective hydrogen production rate. The energy consumption evaluation module uses the design energy consumption value under rated operating conditions as a benchmark, and performs dimensionless normalization processing on the calculated DC side unit energy consumption and the auxiliary balance system unit energy consumption to obtain the normalized DC index and the normalized auxiliary balance system index.

5. The unit energy consumption level monitoring and evaluation system for water electrolysis hydrogen production equipment according to claim 4, characterized in that, The energy consumption evaluation module is configured to monitor the load rate and load rate change rate of the electrolyzer in real time to identify whether the water electrolysis hydrogen production equipment is in a steady-state high load range, a steady-state low load range, or a transient variable load range. When the electrolysis hydrogen production equipment is identified as being in the steady-state low load range, the calculation weight of the DC side is reduced and the calculation weight of the auxiliary balancing system is increased, so that the comprehensive energy consumption index is sensitive to the energy efficiency fluctuations of the auxiliary balancing system. The energy consumption evaluation module generates the comprehensive energy consumption index by summing the weighted normalized DC index and the normalized auxiliary balance system index.

6. The unit energy consumption level monitoring and evaluation system for water electrolysis hydrogen production equipment according to claim 1, characterized in that, The optimization control module is constructed with a two-layer control architecture in which the decision-making layer and the execution layer are physically separated; The decision-making layer resides in the central processing unit and is used to calculate the optimal operating setpoint vector over a long period using a multi-dimensional optimization algorithm. The execution layer resides in the programmable logic controller in the field device layer, and is used to receive the optimal operating setpoint vector within a short period of time and perform closed-loop adjustment in combination with the real-time feedback value from the field. Before issuing instructions, the optimization control module performs over-limit checks and gradient limit checks on the optimal operating setpoint vector through safety interlock logic.

7. The unit energy consumption level monitoring and evaluation system for water electrolysis hydrogen production equipment according to claim 1, characterized in that, The optimized control module is configured to calculate the minimum theoretical flow rate for the fluid conveying equipment based on the measured current density of the electrolytic cell, the electrochemical heat generation model, and the temperature difference between the inlet and outlet of the electrolyte, using the similarity law of the pump, and generate the speed setting value of the frequency converter driver according to the mapping relationship between flow rate and current and the cubic relationship between power and speed. The optimized control module adopts a strategy combining dew point feedback and feedforward compensation for the thermal management equipment. It monitors the dew point temperature of the product gas in real time, dynamically increases the cooling water outlet temperature setpoint while ensuring that the gas dryness complies with regulations, and predicts the heat load based on the power increment when it detects an increase in the electrolytic cell power command, and adjusts the cooling power setpoint or fan speed setpoint in advance.

8. The unit energy consumption monitoring and evaluation system for water electrolysis hydrogen production equipment according to claim 1, characterized in that, The optimization control module is connected to the external power grid system to obtain time-of-use electricity price signals. During off-peak hours, the optimization control module removes the power limit of the electrolyzer to maximize hydrogen production and controls the refrigeration unit to operate at full load to reduce the water temperature of the buffer tank for cold energy pre-storage. During peak power periods, the optimization control module controls the refrigeration unit to stop or unload and uses the pre-stored cold energy for cooling, while simultaneously reducing the load on the electrolytic cell to a safe load.

9. The unit energy consumption level monitoring and evaluation system for water electrolysis hydrogen production equipment according to claim 1, characterized in that, The optimization control module calculates the cumulative gas flow rate of the adsorption tower for the gas purification subsystem and estimates the real-time saturation by combining the product gas dew point data. If the current period is a peak power period and the adsorption tower has not reached its saturation limit, the optimization control module forcibly blocks the start signal of the regeneration heater and postpones the regeneration process to a non-peak power period. The optimization control module is also configured to dynamically reduce the regeneration temperature setpoint based on the adsorbent aging curve.

10. A monitoring and evaluation system for unit energy consumption level of water electrolysis hydrogen production equipment according to claim 1, characterized in that, The monitoring and evaluation system also includes a visualization display module; The visualization module is configured to display the changing trend of the comprehensive energy consumption index and the dynamic chart of the energy consumption ratio of the auxiliary balancing system in real time on the interface of the monitoring and evaluation system. When the relative deviation received from the data processing module exceeds the preset threshold and the duration of the corresponding state exceeds the anti-jitter time window, the visualization module triggers a low confidence alarm and indicates that the data source has been switched to the theoretical hydrogen production. The visualization module is also configured to track the moving average of the comprehensive energy consumption index and build a time-series-based attenuation prediction model. When the predicted decrease in unit energy consumption exceeds the maintenance threshold, it automatically generates a digital maintenance work order containing specific maintenance suggestions.

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