Energy-Material Flow Coupled Green Electricity and Green Hydrogen Coordinated Scheduling Method and System for Industrial Parks

Abstract

This invention belongs to the field of green electricity and green hydrogen control technology in industrial parks. Specifically, it discloses an energy-mass flow coupled green electricity and green hydrogen collaborative scheduling method and system for industrial parks. The method includes: establishing a key equipment model for a green hydrogen chemical industrial park; allocating the operating power of alkaline electrolyzers and proton exchange membrane electrolyzers based on a two-layer array rotation strategy for electrolyzers; dynamically optimizing the intraday operating characteristics of electro-hydrogen energy storage and electrolyzers through model predictive control methods to obtain the optimal control sequence for electro-hydrogen energy storage and electrolyzers; quantifying the conditional risk value of uncertainties to chemical production using conditional risk value as a tool, and establishing an energy-mass flow coupled green electricity and green hydrogen collaborative optimization scheduling model for chemical industrial parks with the goal of ensuring the maximum net benefit of the green electricity hydrogen production system under the premise of stable ammonia production in the chemical industrial park; obtaining the optimal operating state of the green electricity hydrogen production system through solution; and achieving the optimal balance of hydrogen in energy and mass flow.

Description

Technical Field

[0001] This invention relates to the field of green electricity and green hydrogen control technology in industrial parks, and in particular to a method and system for coordinated scheduling of green electricity and green hydrogen in industrial parks that is coupled with energy and material flow. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] As the use of green electricity to produce hydrogen in chemical feedstocks continues to increase, the electro-hydrogen coupling production in chemical industrial parks has become an important pathway for absorbing new energy power generation. However, currently, under the fluctuation of green electricity supply, various equipment in green hydrogen chemical industrial parks often face problems such as insufficient research on real-time coordinated operation to maintain stable chemical production, crude control strategies for various types of electrolyzers, low accuracy of energy storage and hydrogen supply modeling, and difficulty in coordinating the balance of power curtailment risk.

[0004] Hydrogen production through water electrolysis is a crucial step in the green energy utilization of chemical industrial parks, with alkaline electrolyzers (AEL) and proton exchange membrane electrolyzers (PEMEL) being the most widely used. In actual hydrogen production, parallel electrolyzers can be connected in arrays to increase production scale. However, current research on the start-up and shutdown control of electrolyzers largely focuses on optimizing single equipment or local strategies, failing to address the coordinated operation of electrolyzer arrays and the overall industrial park under complex coupling conditions, and lacking sufficient research on the coupling characteristics of hydrogen energy and mass flow.

[0005] Comprehensive consideration of the complementary regulation of electric and hydrogen energy storage systems in chemical industrial parks can unlock more flexible resources and enhance operational regulation and renewable energy absorption capabilities. However, the operational issues of introducing hydrogen energy storage are more complex, requiring exploration of the differentiated dynamic response characteristics and synergistic issues of electric and hydrogen energy storage. Current technologies address the variability of time scales for electricity and hydrogen energy dispatch, the high uncertainty brought about by the high proportion of renewable energy, and the challenges of uneven power distribution across multiple time scales for electric energy storage and hydrogen storage tanks. Furthermore, they lack a series connection for the green electricity-green hydrogen ammonia production process, making it difficult to achieve systematic dynamic regulation of the energy flow of various devices within a day, and resulting in insufficient risk assessment capabilities. Summary of the Invention

[0006] To address the aforementioned issues, this invention proposes a collaborative scheduling method and system for green electricity and green hydrogen in chemical industrial parks, coupled with energy and material flow. From the perspective of energy and material flow coupling, it models the green electricity-to-hydrogen system and hydrogen energy-material flow in chemical industrial parks, establishing a collaborative optimization scheduling model for green electricity and green hydrogen in chemical industrial parks coupled with energy and material flow. This transforms the dynamic coupling relationship between hydrogen production, storage, and supply into quantifiable risk costs, achieving optimal profit for the green electricity-to-hydrogen system while ensuring stable and continuous chemical production.

[0007] In some implementations, the following technical solutions are adopted:

[0008] A method for coordinated scheduling of green electricity and green hydrogen in industrial parks, coupled with energy and material flow, includes:

[0009] Construct a green hydrogen production operation framework for chemical industrial parks that coordinates energy and material flow, and establish a key equipment model for green hydrogen chemical industrial parks;

[0010] By introducing an electrolyzer lifetime weighting factor and penalties for invalid start-up and shutdown, and based on a two-layer array rotation strategy for electrolyzers, the operating power of alkaline electrolyzers and proton exchange membrane electrolyzers is allocated.

[0011] A multi-timescale operation model for the coordinated operation of electrochemical energy storage and hydrogen storage equipment was established. The intraday operating characteristics of the electro-hydrogen energy storage and electrolyzer were dynamically optimized through model predictive control methods to obtain the optimal control sequence for the electro-hydrogen energy storage and electrolyzer.

[0012] Using conditional risk value as a tool, we quantify the conditional risk value of uncertain factors on chemical production. With the goal of maximizing the net benefit of the green electricity-to-hydrogen system under the premise of ensuring stable ammonia production in the chemical industrial park, we establish a coordinated optimization scheduling model for green electricity and green hydrogen in the chemical industrial park that couples energy and material flow.

[0013] By solving the green electricity and green hydrogen collaborative optimization scheduling model, the optimal operating state of the green electricity hydrogen production system is obtained, so as to achieve the optimal balance of hydrogen in energy and material flow.

[0014] As a further solution, the electrolytic cell dual-layer array rotation strategy is as follows:

[0015] The upper alkaline electrolytic cells are operated in a rotating order, with the first to start and the last to stop.

[0016] The lower proton exchange membrane electrolyzer group calculates the cumulative lifetime loss of the electrolyzer at time t based on the lifetime weighting factor and start-up / shutdown penalty; prioritizes scheduling equipment with high remaining lifetime to bear heavy loads, and implements load reduction operation or switches to hot standby for recovery of equipment with low lifetime.

[0017] As a further approach, based on the lifetime weighting factor and start-up / shutdown penalties, the cumulative lifetime loss of the electrolyzer at time t is calculated, specifically as follows:

[0018] ;

[0019] ;

[0020] in, and They are time t and t, respectively. 1. Cumulative lifespan loss, The operating loss coefficient represents the power-related aging rate. It is a power nonlinearity index, reflecting accelerated aging under high load; and These are the penalty coefficients for single start-up and shutdown, respectively. This refers to the adjustment time for a single electrolytic cell. A function characterizing the cost of electrolytic cell lifespan loss; Let be the input power of the electrolytic cell at time t; , These are 0-1 variables representing the start-up and shutdown of the electrolytic cell, respectively. The maximum power is designed for the electrolytic cell.

[0021] As a further solution, the operating power of the alkaline electrolyzer and the proton exchange membrane electrolyzer is allocated as follows:

[0022] Set the maximum overload power of the hybrid electrolyzer system Rated power and high efficiency ;

[0023] When the total output of wind and solar power reaches or exceeds the maximum overload power of the electrolyzer system, the alkaline electrolyzer queue is increased and operated at rated power, while the proton exchange membrane electrolyzer operates under an overload state of 1.2 times the rated power.

[0024] When the total output of wind and solar power is between the rated power and the maximum overload power of the system, the alkaline electrolyzer is set to the rated power first, and the remaining output is absorbed by the proton exchange membrane electrolyzer.

[0025] When the total output of wind and solar power is between the system's high-efficiency power and rated power, the input power of the two types of electrolyzers is allocated according to the ratio of their rated capacity.

[0026] When the total output of wind and solar power is lower than the system's high-efficiency power, reduce the number of alkaline electrolyzers and operate them at high-efficiency power, while retaining one proton exchange membrane electrolyzer to handle high-frequency power fluctuations.

[0027] As a further approach, the intraday operating characteristics of the electro-hydrogen energy storage and electrolyzer are dynamically optimized using model predictive control methods, specifically:

[0028] Based on the system state, control input, and external disturbance, establish the system's state transition equation in discrete time.

[0029] Model predictive control is adopted based on the current system state. and future Perturbation prediction sequence of the step By minimizing the cost function, the control sequence is optimized to obtain the optimal control sequence for the hydrogen energy storage and electrolyzer.

[0030] As a further approach, conditional value-at-risk (VAT) is used as a tool to quantify the conditional risk value of uncertainty factors in chemical production, specifically:

[0031] ;

[0032] in, This represents the conditional risk value cost in chemical production, used to quantify the risks arising from uncertainty. Value at risk (VaR) represents the value at a confidence level. The maximum loss; Let E(t) represent the basic revenue of the Green Hydrogen Chemical Industrial Park, and let E(t) represent the expected profit without considering risks. E(t) represents the mathematical expectation.

[0033] As a further solution, with the goal of maximizing the net profit of the green electricity-to-hydrogen system under the premise of ensuring stable ammonia production in the chemical industrial park, an energy-material flow coupled green electricity-green hydrogen collaborative optimization scheduling model for the chemical industrial park is established. Specifically, it is the sum of the basic profit of the green hydrogen chemical industrial park and the product of the conditional risk value cost and risk weight factor of chemical production; considering the impact of the life decay of the electrolyzer and the operational risks of insufficient energy storage on the stable operation of the green electricity-green hydrogen chemical industrial park, the most economical operating result of the park is achieved, realizing the collaborative scheduling of green electricity-green hydrogen in the chemical industrial park.

[0034] In other embodiments, the following technical solutions are adopted:

[0035] An energy-material flow coupled green electricity and green hydrogen coordinated scheduling system for industrial parks includes:

[0036] The park equipment model building module is used to construct a green electricity-to-hydrogen production operation framework for chemical industrial parks with coordinated energy-material flow, and to establish key equipment models for green hydrogen chemical industrial parks.

[0037] The electrolyzer power allocation module is used to introduce an electrolyzer lifetime weighting factor and penalties for invalid start-up and shutdown. Based on the electrolyzer dual-layer array rotation strategy, it allocates the operating power of alkaline electrolyzers and proton exchange membrane electrolyzers.

[0038] The control optimization module is used to establish a multi-timescale operation model for the coordinated operation of electrochemical energy storage and hydrogen storage equipment. Through model predictive control methods, the intraday operating characteristics of the electro-hydrogen energy storage and electrolyzer are dynamically optimized to obtain the optimal control sequence for the electro-hydrogen energy storage and electrolyzer.

[0039] The optimized scheduling module is used to quantify the conditional risk value of uncertain factors on chemical production using conditional risk value as a tool. With the goal of maximizing the net benefit of the green electricity-to-hydrogen system under the premise of ensuring stable ammonia production in the chemical industrial park, a collaborative optimization scheduling model for green electricity and green hydrogen in the chemical industrial park coupled with energy and material flow is established to achieve the optimal balance of hydrogen in energy and material flow.

[0040] In other embodiments, the following technical solutions are adopted:

[0041] A terminal device includes a processor and a memory, wherein the processor is used to implement instructions; and the memory is used to store multiple instructions, which are adapted to be loaded and executed by the processor to perform the above-described energy-material flow coupled green electricity and green hydrogen coordinated scheduling method for industrial parks.

[0042] In other embodiments, the following technical solutions are adopted:

[0043] A computer-readable storage medium storing a plurality of instructions adapted for loading and execution by a processor of a terminal device of the above-described energy-material flow coupled green electricity and green hydrogen coordinated scheduling method for industrial parks.

[0044] Compared with the prior art, the beneficial effects of the present invention are:

[0045] (1) This invention addresses the rotation strategy for a dual-layer array of electrolyzers. By introducing a lifetime weighting factor and a start-stop penalty mechanism, the power allocation between the AEL and PEMEL is optimized, balancing the hydrogen production of each electrolyzer and reducing its ineffective start-stop operations. Simulation verification shows that this strategy can synchronize the lifetime decay process of the electrolyzers and improve the overall efficiency of the hydrogen production system.

[0046] (2) This invention constructs a multi-timescale operation model based on model predictive control. Through rolling optimization and real-time correction mechanisms, the chemical production system can quickly respond to fluctuations in wind and solar power output. Simulation results show that the MPC method effectively constrains the fluctuation range of energy storage SOC and SOHC, and improves the overall power supply capacity of the hybrid energy storage system.

[0047] (3) The introduction of conditional risk value quantifies uncertainties such as electrolyzer response delay and limited energy storage capacity, transforming dynamic coupling relationships into quantifiable risk costs. By adjusting the risk weighting factor, the system achieves a balance between economic benefits and operational safety.

[0048] Other features and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0049] Figure 1 This is a flowchart of the energy-material flow coupled green electricity and green hydrogen coordinated scheduling method in the park according to an embodiment of the present invention;

[0050] Figure 2 This is a system architecture diagram of an energy-material flow coupled chemical industrial park in an embodiment of the present invention;

[0051] Figure 3 This is a schematic diagram illustrating the switching relationship of the electrolytic cell's operating state in an embodiment of the present invention;

[0052] Figure 4 This is a graph showing the relationship between AEL hydrogen production efficiency and rate in an embodiment of the present invention;

[0053] Figure 5 This is a graph showing the relationship between PEMEL hydrogen production efficiency and rate in an embodiment of the present invention;

[0054] Figure 6 This is a schematic diagram of a two-layer array rotation strategy considering the operating characteristics of an electrolyzer in an embodiment of the present invention;

[0055] Figure 7 This is a typical solar power output curve in an embodiment of the present invention;

[0056] Figure 8 This is a schematic diagram of the operating state of the PEMEL electrolytic cell in one scenario of this invention.

[0057] Figure 9 This is a schematic diagram of the operating status of the PEMEL electrolytic cell under scenario two in this embodiment of the invention;

[0058] Figure 10 This is a schematic diagram of the operating status of the PEMEL electrolytic cell in scenario three of this embodiment of the invention;

[0059] Figure 11 This is the power distribution curve of the electrolytic cell in an embodiment of the present invention;

[0060] Figure 12 This is a schematic diagram of the electrical load of wind, solar, energy storage, and energy-consuming equipment in the park according to an embodiment of the present invention;

[0061] Figure 13 This is a schematic diagram of the operation status of the electric hydrogen energy storage in scenario four of this embodiment of the invention;

[0062] Figure 14 This is a schematic diagram of the operating state of the electric hydrogen energy storage in scenario five of this embodiment of the invention;

[0063] Figure 15 This is a schematic diagram illustrating the expected profit under different risk weighting values ​​in an embodiment of the present invention. Detailed Implementation

[0064] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0065] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments of the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0066] Example 1

[0067] In one or more embodiments, a collaborative scheduling method for green electricity and green hydrogen in industrial parks that couples energy and matter flow is disclosed, combining... Figure 1 Specifically, it includes the following processes:

[0068] S101: Construct a green hydrogen production operation framework for chemical industrial parks with coordinated energy and material flow, and establish a key equipment model for green hydrogen chemical industrial parks.

[0069] In this embodiment, considering the stochastic fluctuations in hydrogen production and supply during chemical manufacturing processes, a green electricity and green hydrogen chemical industrial park is constructed as the research object. An energy-mass flow coordinated green electricity hydrogen production operation framework is established within the park to investigate the economical and stable operation of hydrogen production and supply equipment under fluctuating green electricity levels. The system framework consists of renewable energy power generation, green electricity hydrogen production, and chemical ammonia production. The production framework dynamically allocates hydrogen production power through an electrolyzer array rotation strategy to coordinate electrical energy input and hydrogen mass flow output. It also utilizes a multi-timescale operation strategy of an electro-hydrogen hybrid energy storage system to smooth power fluctuations. Simultaneously, model predictive control methods are used to optimize the coupling interaction between the electro-hydrogen energy flow and the mass flow in real time, achieving optimal profitability for the green electricity hydrogen production system while ensuring stable and continuous chemical production. Figure 2 The study presented an optimized operational framework for the green hydrogen chemical industrial park.

[0070] The key equipment of the Green Hydrogen Chemical Industrial Park is modeled as follows:

[0071] (1) Hydrogen storage tank model:

[0072] Among existing hydrogen storage technologies, high-pressure gaseous hydrogen storage is more suitable for chemical industrial parks with frequent hydrogen supply demands. Hydrogen produced by electrolysis is pressurized and then transported to hydrogen storage tanks for storage. The flow direction and real-time capacity of hydrogen in the storage tank can be determined by the following formulas (1)-(3):

[0073] (1)

[0074] (2)

[0075] (3)

[0076] In the formula: The remaining hydrogen storage capacity is t (unit: t). The amount of hydrogen stored in hydrogen energy storage at the previous moment; Let be the input power of the electrolytic cell at time t; This refers to the discharge power of the fuel cell. The electrolytic efficiency of the electrolytic cell; Efficiency during hydrogen charging for hydrogen energy storage; Efficiency during hydrogen storage and hydrogen release; For fuel cell discharge efficiency; Hydrogen input mass from the electrolyzer to the hydrogen storage system; This refers to the mass outflow of hydrogen from the hydrogen storage tank to the fuel cell and ammonia production equipment. Calorific value of hydrogen; Let be the mass of hydrogen consumed by the ammonia production system at time t. This indicates the time required for a single adjustment of the hydrogen storage tank.

[0077] (2) Energy storage model:

[0078] Compared with hydrogen energy storage, electric energy storage has the advantages of fast response speed and high construction cost per unit capacity. It can be used to quickly make up for the shortage of renewable energy power generation, ensure the stable production of chemical equipment, and reduce the number of ineffective start-ups and shutdowns of electrolytic cell equipment. It can be determined by the following formula (4):

[0079] (4)

[0080] In the formula: This represents the real-time reserve of electrical energy storage. This is the remaining capacity of the electrical energy storage at the previous moment. Power for charging electrical energy storage, To improve the charging efficiency of electric energy storage, For electrical energy storage discharge power, The discharge efficiency of the electrical energy storage.

[0081] (3) Fuel cell model:

[0082] Fuel cells are devices used in chemical industrial parks to convert hydrogen energy into electricity. They burn hydrogen to ensure the power supply to the load when wind and solar power output and electric energy storage are insufficient. The model is as follows:

[0083] (5)

[0084] (6)

[0085] In the formula: Let t be the amount of hydrogen used to generate electricity from the hydrogen fuel cell. Let t be the mass input of hydrogen to the fuel cell at time t; The hydrogen transport efficiency is set to 0.98, which is used to characterize the loss during the transport process from the hydrogen storage tank to the fuel cell.

[0086] (4) Electro-ammonia conversion model:

[0087] Ammonia is produced using the Haber-Bosch process, in which hydrogen and nitrogen are mixed in a 3:1 ratio and reacted with an iron-based catalyst at a high temperature of 400-500℃ and a pressure of 25 MPa to generate ammonia. The reaction formula is as follows:

[0088] (7)

[0089] In the formula: Let be the ammonia production at time t; , These represent the reactant masses of hydrogen and nitrogen, respectively. , These are the mass conversion coefficients for hydrogen and nitrogen, respectively, used for converting molar ratio to mass ratio; The efficiency of the ammonia synthesis reactor is 0.85-0.95, which covers catalytic reaction losses.

[0090] The electro-hydrogen conversion equipment receives the electro-hydrogen energy flow and the hydrogen material flow. The power consumption in its production process mainly comes from two stages: air separation of nitrogen and compression of ammonia. Its power demand model is shown in equations (8)-(10):

[0091] (8)

[0092] (9)

[0093] (10)

[0094] In the formula: Total power consumption of the green hydrogen to ammonia production system at time t; The power consumption of the air separation nitrogen generator at time t; This is the compression power used in the synthesis process to maintain high pressure in the reactor; The energy consumption coefficient of the nitrogen separation equipment; Let be the nitrogen production at time t; , The high and low pressures for operating nitrogen separation equipment are typically 10 bar and 200 bar, and the pressure difference affects the separation energy consumption. The compressibility factor is 0.02 kW·h / kg.

[0095] S102: Introducing an electrolyzer lifetime weighting factor and penalties for invalid start-up and shutdown, the operating power of alkaline electrolyzers and proton exchange membrane electrolyzers is allocated based on a dual-layer array rotation strategy.

[0096] Specifically, due to the intermittent nature of wind and solar power output, some electrolyzers need to be frequently shut down to adapt to power fluctuations, shortening their lifespan. Furthermore, AEL (Automatic Electrolyte Array) has a long cold start time, and in power fluctuation scenarios, there may be ineffective start-ups and shutdowns due to insufficient power after initial startup. Therefore, the operating states of the two types of electrolyzers are divided into three categories: shutdown, hot standby, and hydrogen production operation, and their conversion relationships are as follows: Figure 3 As shown. In hydrogen production mode, the electrolyzer operates continuously, with voltage and current maintained within allowable ranges, and is considered a variable load. In hot standby mode, the electrolyzer stops producing hydrogen and is in hot standby mode to facilitate rapid restart. In this state, its power consumption is essentially constant, and it is considered a fixed load. In shutdown mode, the cell completely stops operating, and power consumption is zero.

[0097] Among the electrolyzers currently in production applications, AEL (Alternating Electrolyte) has the characteristics of large capacity, low cost, and slow response, while PEMEL (Polymer Electrolyte) has a fast response speed, small capacity, and high operating cost. Under production operation, the hydrogen production efficiency and rate of AEL and PEMEL exhibit nonlinear characteristics with input power. In this embodiment, a piecewise linearization method is used to model the hydrogen production efficiency characteristics of the electrolyzer, as shown in formula (11).

[0098] (11)

[0099] In the formula: This indicates the total electrical power consumed by the AEL; and These are its operating voltage and current, respectively; It is the number of electrolytic units connected in series; Represents the theoretically reversible voltage; For comprehensive dynamic parameters; It is the effective electrode area; For exchange current density; This indicates the equivalent ohmic internal resistance, including the electrolyte, diaphragm, etc.

[0100] After the AEL (Automatic Energy Utilization) is started, the input power starts from zero. Its initial operating efficiency and hydrogen production rate increase rapidly with increasing power. However, after reaching its peak, the hydrogen production efficiency decreases with further increases in power. This is because the AEL current density increases with power, causing an increase in overpotential, which leads to more electrical energy being converted into heat energy, thus reducing hydrogen production efficiency. Its efficiency curve is shown below. Figure 4 As shown.

[0101] The AEL power range is from 0 to rated power, and the hydrogen production efficiency is... Reaching its peak value, the overall characteristic is that it first increases and then decreases with the input power, and the hydrogen production rate is at... The maximum time.

[0102] The power model of PEMEL is affected by voltage and current, as shown in equation (12):

[0103] (12)

[0104] In the formula: This refers to the operating power of PEMEL. Its operating current, This is the total operating voltage; Here, is the temperature-dependent thermal neutral voltage, and b is the comprehensive polarization parameter. It is the effective electrochemical active area. For reference current density, This represents the total equivalent resistance of the membrane electrode assembly.

[0105] Compared to AEL, PEMEL's hydrogen production efficiency increases more rapidly with input power in the initial stage, and its power adjustment range is wider (0-1.2). Its hydrogen production efficiency is... At its maximum, as the input power increases, its trend is first upward and then downward, as shown in the following relationship: Figure 5 As shown.

[0106] As the hub for converting electrical energy into hydrogen, the operation strategy of the electrolyzer directly affects the synergistic efficiency of these two flows. The electrolyzer array rotation strategy refers to the means of optimizing scheduling in a parallel-operating electrolyzer group by adjusting the load of multiple electrolyzers or controlling their start-up and shutdown. In a single-layer rotation, the electrolyzers are rotated sequentially, with the first to start and the last to stop.

[0107] The strategy involves using AELs (Automatic Electrolytes) with low unit hydrogen production cost and large capacity, which take turns serving as the upper-level rotating cells to handle the low-frequency regular components of wind and solar power. The entire set of electrolyzers is defined. At time t, the system has The Taiwanese electrolyzer is in hydrogen production mode and is defined as the initial operating set according to its number. ;in addition The TECH solvent cell is currently shut down for collection purposes. .like The power output of wind and solar power fluctuates during the operating period, and the selection is based on the relationship between the electrolyzer capacity and the fluctuation of wind and solar power. Start during time period Taiwan Electrolytic Cells, Regulations Add r electrolytic cells to the set to be started Tail Each electrolytic cell follows a queue principle, serving as a shutdown set. The specific array rotation process for the first r electrolytic cells to shut down is as follows:

[0108] (13)

[0109] (14)

[0110] (15)

[0111] (16)

[0112] in, The unit adjustment time refers to the single adjustment time of the electrolytic cell. for The former A queue consisting of 10 elements.

[0113] The sequential rotation of single-layer electrolytic cells can achieve coordinated operation of electrolytic cells, making their service life consistent, but it cannot effectively utilize the operating characteristics of the two types of electrolytic cells, and the adjustment response speed is slow.

[0114] Therefore, this embodiment combines the efficiency-power relationship between AEL and PEMEL and the rapid start-up and shutdown capability of PEMEL, and uses PEMEL as the lower-level tank to bear the minute-level high-frequency fluctuations of wind and solar power. It introduces a lifetime weighting factor and a start-up and shutdown penalty to represent the cumulative lifetime loss of the electrolyzer at time t. It prioritizes scheduling equipment with higher remaining lifetime to bear heavier loads, while implementing load reduction or switching to hot standby for recovery for equipment with lower lifetime, thereby causing the lifetime of all electrolyzers in the array to decay synchronously. Its lifetime update formula (17) is shown:

[0115] (17)

[0116] (18)

[0117] In the formula: and They are time t and t, respectively. 1. Cumulative lifetime loss, initial value ; The operating loss coefficient represents the power-related aging rate. It is a power nonlinearity index, reflecting accelerated aging under high load; and These are the penalty coefficients for single start-up and shutdown, respectively.

[0118] The lifetime update formula in this embodiment includes parameters related to the lifetime factor, such as power-related aging rate and power nonlinearity index, which can more accurately characterize the actual physicochemical degradation process of the electrolyzer.

[0119] Set minimum startup power High efficiency and high power and rated power Using the segmentation point as the dividing point, the hydrogen production power is divided into 3 segments, and the dual-layer array rotation strategy is as follows: Figure 6 As shown in the figure. Here, high-efficiency power, or peak efficiency power, refers to the power at the point of maximum efficiency in the operating curves of the two types of electrolyzers.

[0120] Taking into account the power-efficiency characteristics of AEL and PEMEL and the wide load characteristics of PEM electrolyzers, the maximum overload power of the hybrid electrolyzer system is set to [value missing]. System rated power High-efficiency power system .

[0121] Based on the actual output levels of wind and solar power, the system allocates power to AEL and PEMEL according to the following rules:

[0122] (1) The total output of wind and solar power reaches or exceeds the maximum overload power of the electrolytic cell system. At this time, the AEL queue is increased and operated at rated power, while the PEMEL operates under overload conditions at 1.2 times the rated power.

[0123] The alkaline electrolyzer (AEL) array operates on a rotating basis, adjusting the number of cells in the queue to be started or de-started in real time based on the wind and solar power absorbed. The proton exchange membrane electrolyzer (PEMEL) is fully operational, distributing wind and solar power fluctuations with a resolution of approximately five minutes.

[0124] (2) The total output power is between the rated power and the maximum overload power of the system. When the power output is at its rated power, the AEL should be set first, and the remaining output power should be absorbed by the PEMEL.

[0125] (3) The total output power is between the system's high-efficiency power and rated power. At that time, the input power of the two types of electrolytic cells is distributed according to the ratio of their rated capacity.

[0126] (4) The total output is lower than the capacity at the system's highest efficiency point. At this time, reduce the AEL queue and operate at high power, while reserving one PEMEL to handle high-frequency fluctuation power.

[0127] S103: Establish a multi-timescale operation model for the coordinated operation of electrochemical energy storage and hydrogen storage equipment. Dynamically optimize the intraday operating characteristics of electro-hydrogen energy storage and electrolyzer through model predictive control methods to obtain the optimal control sequence for electro-hydrogen energy storage and electrolyzer.

[0128] In this embodiment, to enhance the dynamic adjustment of electric energy storage to real-time wind and solar power and the real-time adaptability of hydrogen storage equipment to the hydrogen energy material flow produced by the electrolyzer, model predictive control (MPC) is adopted for intraday scheduling of electric-hydrogen hybrid energy storage. MPC uses a three-step day-ahead prediction model, rolling optimization and feedback correction as its core mechanism to finely match green electricity fluctuations with the load demand of chemical production.

[0129] Model predictive control is based on a discrete state-space model of the controlled system. In this study, the system state... Control input and external disturbances Defined as:

[0130] (19)

[0131] In the formula: To store energy in the battery; This refers to the amount of hydrogen stored in the hydrogen storage system. The state vector characterizing the lifespan loss of the electrolyzer; The charging and discharging power of the storage battery; Hydrogen charging and discharging power of the hydrogen storage system; For each electrolyzer unit, the hydrogen production power vector is defined. , To optimize the output of new energy power generation within the rolling optimization cycle, , Real-time power of the hydrogen load during the rolling optimization cycle.

[0132] The system's state transition equations specifically describe the dynamic processes of the electro-hydrogen hybrid energy storage and electrolyzer, and form the basis for MPC's multi-step forward prediction:

[0133] (20)

[0134] Within a specified sampling time t, MPC is based on the current state. and future Perturbation prediction sequence of the step The goal is to find the optimal control method within an optimization cycle. Its mathematical description lies in finding the optimal control sequence. The optimization problem-solving process is as follows:

[0135] (twenty one)

[0136] (twenty two)

[0137] In the formula, The control sequence to be optimized; State transition constraints; middle , and The update status is shown in formulas (1), (4) and (17). , , System status Control input and external disturbances Predict the system state vector, control input vector, and external disturbance vector at time t.

[0138] After solving the optimization problem, the first element of the obtained optimal control sequence is... Apply to the actual system.

[0139] (twenty three)

[0140] At the next time step t+1, the system will be based on the new state measurements. Repeat the prediction-optimization-execution process described above. Periodically reset the prediction starting point using real-time measurements; this is known as MPC closed-loop feedback correction, the core expression of which is:

[0141] (twenty four)

[0142] In the formula: To optimize the implicitly defined nonlinear feedback control law for online rolling.

[0143] S104: Using conditional risk value as a tool to quantify the conditional risk value of uncertain factors on chemical production, with the goal of maximizing the net profit of the green electricity-to-hydrogen system under the premise of ensuring stable ammonia production in the chemical industrial park, a green electricity-to-hydrogen collaborative optimization scheduling model coupled with energy and material flow in the chemical industrial park is established to achieve the optimal balance of hydrogen in energy and material flow.

[0144] In this embodiment, a risk management mechanism is introduced into the green electricity-to-hydrogen operation framework of a chemical industrial park that coordinates energy and material flow. To ensure stable chemical production, the park balances economic benefits with operational risks. This can be achieved by reducing wind and solar power output or adjusting the operating status of the electrolyzer array, avoiding frequent start-ups and shutdowns of electrolyzers due to an excessive pursuit of full wind and solar energy utilization. Conditional Value at Risk (CVaR) is used as a risk measurement tool. CVaR effectively reflects, at a specified confidence level, the conditional expectation that losses caused by uncertainties such as wind and solar fluctuations, electrolyzer response delays, and limited energy storage capacity exceed the value at risk.

[0145] The objective is to maximize the net profit of the green electricity-to-hydrogen system while ensuring stable ammonia production in the chemical industrial park. This includes the park's basic revenue and risk costs. The objective function is:

[0146] (25)

[0147] In the formula, The basic revenue of the Green Hydrogen Chemical Industrial Park represents the expected profit without considering risks. It is the conditional risk value cost in chemical production, used to quantify the risks arising from uncertainty. This is a risk weighting factor, representing risk preference.

[0148] Conditional risk value cost of chemical production Specifically:

[0149] (26)

[0150] in, Value at risk (VaR) represents the value at a confidence level. The greatest loss. The operator for mathematical expectation is the result of a weighted average based on the probability of occurrence, reflecting the average level of the random variable.

[0151] Basic benefits of the Green Hydrogen Chemical Industrial Park Specifically:

[0152] (27)

[0153] in, Revenue from hydrogen sales in the chemical industrial park; Cost of hydrogen production via electrolyzer; For energy storage operating costs; The operating cost of the electro-ammonia conversion equipment (since the daily ammonia production is fixed, the operating cost of the electro-ammonia conversion equipment is also a fixed value). The cost of penalties for abandoning wind and solar power.

[0154] Cost of hydrogen production by electrolyzer Specifically:

[0155] (28)

[0156] (29)

[0157] (30)

[0158] (31)

[0159] in, The operating costs of the two types of electrolyzers; The cost of starting up the electrolytic cell; Costs associated with the shutdown of electrolytic cells; Costs related to the lifespan degradation of the electrolytic cell; , These are 0-1 variables for starting and stopping, where 1 represents a start or stop action and 0 represents no action. The operating cost of the alkaline electrolyzer (AEL); The operating cost of the proton exchange membrane electrolyzer (PEMEL); The single start-up cost of the alkaline electrolyzer (AEL); The single start-up cost of the PEMEL proton exchange membrane electrolyzer; The cost of a single shutdown of an alkaline electrolyzer (AEL); The cost of a single downtime for the PEMEL proton exchange membrane electrolyzer; For the operating cost of electric energy storage; For the operating cost of hydrogen energy storage;

[0160] Energy storage operating costs Specifically:

[0161] (32)

[0162] In addition to meeting the hydrogen demand for ammonia production, the hydrogen produced in the hydrogen energy chemical industrial park generates revenue by selling surplus hydrogen. The specific revenue streams from hydrogen sales in the chemical industrial park are as follows:

[0163] (33)

[0164] in, For hydrogen production from green electricity; This refers to the price of hydrogen sold.

[0165] Curtailment of wind and solar power penalties Specifically:

[0166] (34)

[0167] (35)

[0168] in, The penalty cost per unit of power abandoned; This represents the amount of electricity wasted per unit time in the chemical industrial park. The unit of adjustment time refers to the unit of time for power wastage. This is the power function for curtailed wind power; This represents the power function of abandoned photovoltaic power.

[0169] The specific constraints on the above objective function are as follows:

[0170] (1) Power output constraints of wind and solar power generation:

[0171] (36)

[0172] In the formula: , The output power of photovoltaic and wind power at time t are respectively; , These represent the minimum and maximum wind power output at time t, respectively. , These represent the minimum and maximum output of the photovoltaic system at time t, respectively.

[0173] (2) System power balance constraints:

[0174] The power balance constraint is:

[0175] (37)

[0176] Hydrogen energy balance constraints:

[0177] (38)

[0178] In the formula: This represents the amount of electricity wasted per unit time in the chemical industrial park. The total power consumption of the green hydrogen to ammonia production system at time t. The input power of PEMEL at time t; , The mass of hydrogen produced by the two types of electrolyzers; Let be the mass of hydrogen consumed by the ammonia production system at time t. This refers to the sales volume of hydrogen. Let be the power of the alkaline electrolyzer AEL at time t; Let t be the mass of hydrogen consumed by the fuel cell at time t.

[0179] (3) Constraints on the operation of equipment in the park:

[0180] (39)

[0181] in, , These represent the rated power of the alkaline electrolyzer (AEL) and the proton exchange membrane electrolyzer (PEMEL), respectively.

[0182] (4) Constraints of hydrogen storage tanks:

[0183] (40)

[0184] (41)

[0185] (42)

[0186] In the formula: The input power of PEMEL at time t; The maximum capacity value is designed for hydrogen energy storage; The amount of hydrogen stored at midnight on each natural day; This represents the amount of hydrogen stored at 24:00 on the day of hydrogen energy storage. Let t be the power of the electrolytic cell. Let t be the capacity of the hydrogen storage tank at time t.

[0187] (5) Constraints of fuel cells:

[0188] (43)

[0189] In the formula: and These represent the maximum and minimum amounts of hydrogen that can be consumed when a hydrogen fuel cell generates electricity; For the 0-1 state variables of the fuel cell; Let t be the power of the fuel cell at time t.

[0190] (6) Energy storage constraints:

[0191] (44)

[0192] (45)

[0193] (46)

[0194] (47)

[0195] (48)

[0196] In the formula: The remaining electricity stored at midnight on the same day; This refers to the remaining electricity stored at 24:00 on the same day. This represents the maximum design capacity of the electrical energy storage. Let t be the battery capacity. The charging power of the energy storage at time t; Let t be the energy storage discharge power at time t; Let t be the charging state of the energy storage, which is either 0 or 1. Let t be the discharge state of the stored energy, which is a variable between 0 and 1. This is the maximum power of the electrical energy storage.

[0197] Based on the power allocation strategy for the alkaline electrolyzer and proton exchange membrane electrolyzer obtained in step S102 and the optimal control sequence for the electro-hydrogen storage and electrolyzer obtained in step S103, the objective function is solved to obtain the most economical operating result of the green electricity and green hydrogen chemical park on the basis of ensuring stable ammonia production. That is, the power allocation of the electrolyzer array rotation and the charging and discharging energy allocation strategy of the electro-hydrogen mixed energy storage.

[0198] The method of this embodiment will be verified by example below.

[0199] This embodiment verifies the effectiveness of the proposed optimized operation method based on equipment conditions and load-based hydrogen production data of a green hydrogen chemical industrial park. The system is equipped with 10 AEL units (5MW rated power), 5 PEMEL units (1MW rated power), 120MW of wind and solar power generation equipment, as well as hydrogen-electric hybrid energy storage and electro-ammonia conversion equipment. The hydrogen production efficiency constant is 4.5 kWh / N. 3 The optimization model was solved using the Matlab R2022b-Cplex commercial solver, with the hardware configuration being Windows 10, an AMD Ryzen 74800H processor (2.90 GHz), and 16GB of memory.

[0200] The wind and solar power output data is extracted from a typical day of a certain year as the day-ahead scheduling plan. The annual wind and solar power output range and the typical daily output curve are as follows: Figure 7 As shown in Table 1, the operating parameters of the electrolytic cell are as follows.

[0201] Table 1 Electrolyte Cell Operating Parameters

[0202] Relevant parameters AEL PEMEL Power per unit (MW) 5 1 Power modulation range / % 20-100 5-120 Warm start time / min 10 1 Maximum electrolysis efficiency 68% 76% Startup cost (RMB / time) 9 9 Downtime cost (RMB / time) 6 6 Electrolyzer operating cost (RMB / MW.h) 100 300

[0203] In this embodiment, the proposed dual-layer array rotation strategy for electrolytic cells was used to randomly select 12 hours of wind and solar load operation data from the park, and three different scenarios were designed for comparison and verification, as shown in Table 2.

[0204] Table 2. Calculation settings for optimized operation of electrolytic cells

[0205] Sequential rotation Dual-layer operation Lifetime factor Start-stop penalty Scene 1 √ √ Scene 2 √ √ √ Scene 3 √ √ √ √

[0206] Scenario 1: AELs rotate sequentially, and PEMELs start and stop according to their number priority. Scenario 2: Upper-level AELs rotate sequentially, while all lower-level PEMELs are in hot standby mode, responding immediately to system power fluctuations. Scenario 3: Upper-level AELs rotate sequentially, and lower-level PEMELs incorporate the start / stop penalty and lifetime weighting factor proposed in this embodiment, alternating shifts to absorb wind and solar power fluctuations. The operation and shutdown results of the PEMEL electrolyzers in the three scenarios are as follows: Figure 8-10 As shown, the power distribution curve of the electrolyzer is as follows: Figure 11 As shown.

[0207] In Scenario 1, under the priority-based start-up and shutdown strategy for electrolyzers by cell number, electrolyzers 4 and 5 have lower start-up and shutdown priorities, resulting in lower operating time and hydrogen production compared to electrolyzers 1-3. This uneven operating time leads to differences in the lifespan values ​​of different electrolyzers. Scenario 2 and Scenario 3 introduce a lifespan weighting factor, which makes the operating time and hydrogen production of the PEMEL array more balanced, effectively mitigating the problem of lifespan differences among electrolyzers.

[0208] The operating time allocation of each electrolyzer in three scenarios is compared. In Scenario 1, electrolyzer 1 is in continuous operation, while the operating time of other electrolyzers is shorter and more dispersed, reflecting the limitations of the priority start-stop strategy. In Scenario 2, the total operating time of the electrolyzers is balanced, but the operating time of each electrolyzer is discontinuous, with intermittent blank periods resulting from frequent ineffective start-stop operations, reducing operating efficiency and increasing lifespan loss. Therefore, Scenario 3 further introduces a start-stop penalty mechanism to optimize the operating time allocation of each electrolyzer and ensure a more continuous operating mode. The hydrogen production benefits of electrolyzers in the three scenarios are shown in Table 3.

[0209] Table 3 Comparison of Hydrogen Production Benefits from Electrolyzers

[0210]

[0211] The electrical load of wind, solar, energy storage, and energy-consuming equipment in the park is as follows: Figure 12 As shown, the electrical load is the electro-ammonia conversion equipment, which ensures power supply during periods of insufficient wind and solar power through electrical energy storage discharge and hydrogen energy storage and hydrogen-to-electricity conversion with fuel cells. The electrolyzer is used to absorb excess wind and solar power, reducing the penalty for curtailment. To avoid waste in the electro-hydrogen conversion, only one of the two electro-hydrogen conversion devices, the electrolyzer and the fuel cell, is allowed to operate at any given time.

[0212] To verify the effectiveness of the model predictive control method in dynamically optimizing the intraday operating characteristics of hydrogen energy storage and electrolyzers, scenarios four and five were established. Figures 13-14 As shown.

[0213] Scenario 4 represents the energy storage operation without a rolling optimization strategy. Its SOC and SOHC curves fluctuate significantly, with frequent charging and discharging during wind and solar power output fluctuations, making it difficult to smooth out power deficits and resulting in a significant difference between peak and trough values. Traditional methods lack dynamic rolling coordination, leading to lag in energy storage response and low wind and solar power absorption rates. In Scenario 5, the SOC change of the proposed method is stable, with a reduced fluctuation range. MPC dynamically adapts to wind and solar uncertainties by real-time correcting the power allocation of the hybrid energy storage, allowing the hybrid energy storage to return to its initial state at the end of a typical day.

[0214] Scenario 5 combines a conditional value-at-risk model to quantify the risk of limited energy storage capacity. During peak wind and solar power output, electrical energy storage prioritizes absorbing excess power; when wind, solar, and energy storage capacity is insufficient, hydrogen energy storage supplements the shortfall through fuel cells to ensure normal production and avoid the risk of load shedding.

[0215] The expected profit of the park under various weighted risk values ​​is as follows Figure 15 As shown, chemical industrial parks need a certain ability to withstand uncertainties in order to ensure stable production. As the risk weighting factor increases, the park's profits decrease accordingly. The highest return is achieved when the risk weighting is 0, but this requires complete tolerance of potential risks. Conversely, if the focus is on risk aversion, the strategy will become more conservative as the risk weighting increases, avoiding more risks.

[0216] Therefore, operational plans exist for different risk tolerance levels, allowing park operators to design different operational strategies based on their preferences and find a balance between profit and risk aversion. The proposed model achieves optimal results under different risk weighting values, validating the effectiveness of the solution scheme under different parameters.

[0217] In summary, this embodiment addresses the contradiction between stable production and fluctuating green electricity supply in hydrogen chemical industrial parks by proposing a production framework based on the coupling of electrohydrogen energy and material flow. Utilizing an electrolyzer array rotation strategy, a multi-timescale optimization model for hybrid energy storage, and a conditional value-at-risk (VAT) approach, the renewable energy integration rate and equipment lifespan are improved. Simulation results demonstrate that the proposed method can increase the park's operating profit, effectively mitigate electrolyzer losses, and ensure reliable production in the green hydrogen chemical industrial park.

[0218] For the dual-layer array rotation strategy of electrolyzers, this embodiment optimizes the power allocation between AEL and PEMEL by introducing a lifetime weighting factor and a start-stop penalty mechanism, thereby balancing the hydrogen production of each electrolyzer and reducing its ineffective start-stop operations. Simulation verification shows that this strategy can synchronize the lifetime decay process of the electrolyzers and improve the overall efficiency of the hydrogen production system.

[0219] This embodiment constructs a multi-timescale operation model based on model predictive control. Through rolling optimization and real-time correction mechanisms, the chemical production system can quickly respond to fluctuations in wind and solar power output. Simulation results show that the MPC method effectively constrains the fluctuation range of energy storage SOC and SOHC, improving the overall power supply capacity of the hybrid energy storage system.

[0220] This embodiment introduces conditional value of risk (VoV) to quantify uncertainties such as electrolyzer response delay and limited energy storage capacity, transforming dynamic coupling relationships into quantifiable risk costs. By adjusting risk weighting factors, the system achieves a balance between economic benefits and operational safety.

[0221] Example 2

[0222] In one or more embodiments, an energy-material flow coupled green electricity and green hydrogen coordinated scheduling system for industrial parks is disclosed, comprising:

[0223] The park equipment model building module is used to construct a green electricity-to-hydrogen production operation framework for chemical industrial parks with coordinated energy-material flow, and to establish key equipment models for green hydrogen chemical industrial parks.

[0224] The electrolyzer power allocation module is used to introduce an electrolyzer lifetime weighting factor and penalties for invalid start-up and shutdown. Based on the electrolyzer dual-layer array rotation strategy, it allocates the operating power of alkaline electrolyzers and proton exchange membrane electrolyzers.

[0225] The control optimization module is used to establish a multi-timescale operation model for the coordinated operation of electrochemical energy storage and hydrogen storage equipment. Through model predictive control methods, the intraday operating characteristics of the electro-hydrogen energy storage and electrolyzer are dynamically optimized to obtain the optimal control sequence for the electro-hydrogen energy storage and electrolyzer.

[0226] The optimized scheduling module is used to quantify the conditional risk value of uncertain factors on chemical production using conditional risk value as a tool. With the goal of maximizing the net benefit of the green electricity-to-hydrogen system under the premise of ensuring stable ammonia production in the chemical industrial park, a collaborative optimization scheduling model for green electricity and green hydrogen in the chemical industrial park coupled with energy and material flow is established to achieve the optimal balance of hydrogen in energy and material flow.

[0227] The specific implementation methods of the above modules are exactly the same as those in Example 1, and will not be described in detail again.

[0228] Example 3

[0229] In one or more embodiments, a terminal device is disclosed, comprising a processor and a memory, wherein the processor is used to implement instructions; and the memory is used to store multiple instructions adapted to be loaded by the processor and executed by the processor to perform the energy-matter flow coupled green electricity and green hydrogen coordinated scheduling method for parks as described in Embodiment 1.

[0230] It should be understood that in this embodiment, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0231] Memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of memory may also include non-volatile random access memory. For example, memory may also store information about the device type.

[0232] In the implementation process, each step of the above method can be completed by the integrated logic circuits in the processor hardware or by software instructions.

[0233] Example 4

[0234] In one or more embodiments, a computer-readable storage medium is disclosed, wherein a plurality of instructions are stored, the instructions being adapted to be loaded by a processor of a terminal device and executed by the energy-matter flow coupled green electricity and green hydrogen coordinated scheduling method for industrial parks as described in Embodiment 1.

[0235] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for coordinated scheduling of green electricity and green hydrogen in industrial parks based on energy-material flow coupling, characterized in that, include: Construct a green hydrogen production operation framework for chemical industrial parks that coordinates energy and material flow, and establish a key equipment model for green hydrogen chemical industrial parks; By introducing an electrolyzer lifetime weighting factor and penalties for invalid start-up and shutdown, and based on a two-layer array rotation strategy for electrolyzers, the operating power of alkaline electrolyzers and proton exchange membrane electrolyzers is allocated. The specific rotating strategy for the dual-layer array of electrolytic cells is as follows: The upper alkaline electrolytic cells are operated in a rotating order, with the first to start and the last to stop. The lower proton exchange membrane electrolyzer group calculates the cumulative lifetime loss of the electrolyzer at time t based on the lifetime weighting factor and start-up / shutdown penalty; prioritizes scheduling equipment with high remaining lifetime to bear heavy loads, and implements load reduction operation or switches to hot standby for recovery of equipment with low lifetime. Based on the lifetime weighting factor and start-up / shutdown penalties, the cumulative lifetime loss of the electrolyzer at time t is calculated as follows: ； ； in, and They are time t and t, respectively.

1. Cumulative lifespan loss, The operating loss coefficient represents the power-related aging rate. It is a power nonlinearity index, reflecting accelerated aging under high load; and These are the penalty coefficients for single start-up and shutdown, respectively. This refers to the adjustment time for a single electrolytic cell. A function characterizing the cost of electrolytic cell lifespan loss; Let be the input power of the electrolytic cell at time t; , These are 0-1 variables representing the start-up and shutdown of the electrolytic cell, respectively. Design the maximum power for the electrolytic cell; A multi-timescale operation model for the coordinated operation of electrochemical energy storage and hydrogen storage equipment was established. The intraday operating characteristics of the electro-hydrogen energy storage and electrolyzer were dynamically optimized through model predictive control methods to obtain the optimal control sequence for the electro-hydrogen energy storage and electrolyzer. Using conditional risk value as a tool, we quantify the conditional risk value of uncertain factors on chemical production. With the goal of maximizing the net benefit of the green electricity-to-hydrogen system under the premise of ensuring stable ammonia production in the chemical industrial park, we establish a coordinated optimization scheduling model for green electricity and green hydrogen in the chemical industrial park that couples energy and material flow. By solving the green electricity and green hydrogen collaborative optimization scheduling model, the optimal operating state of the green electricity hydrogen production system is obtained, so as to achieve the optimal balance of hydrogen in energy and material flow.

2. The energy-material flow coupled green electricity and green hydrogen coordinated scheduling method for industrial parks as described in claim 1, characterized in that, The operating power of the alkaline electrolyzer and the proton exchange membrane electrolyzer is allocated as follows: Set the maximum overload power of the hybrid electrolyzer system Rated power and high efficiency power ; When the total output of wind and solar power reaches or exceeds the maximum overload power of the electrolyzer system, the alkaline electrolyzer queue is increased and operated at rated power, while the proton exchange membrane electrolyzer operates under an overload state of 1.2 times the rated power. When the total output of wind and solar power is between the rated power and the maximum overload power of the system, the alkaline electrolyzer is set to the rated power first, and the remaining output is absorbed by the proton exchange membrane electrolyzer. When the total output of wind and solar power is between the system's high-efficiency power and rated power, the input power of the two types of electrolyzers is allocated according to the ratio of their rated capacity. When the total output of wind and solar power is lower than the system's high-efficiency power, reduce the number of alkaline electrolyzers and operate them at high-efficiency power, while retaining one proton exchange membrane electrolyzer to handle high-frequency power fluctuations.

3. The energy-material flow coupled green electricity and green hydrogen coordinated scheduling method for industrial parks as described in claim 1, characterized in that, The intraday operating characteristics of the hydrogen energy storage and electrolyzer are dynamically optimized using model predictive control methods, specifically: Based on the system state, control input, and external disturbance, establish the system's state transition equation in discrete time. Model predictive control is adopted based on the current system state. and future Perturbation prediction sequence of the step By minimizing the cost function, the control sequence is optimized to obtain the optimal control sequence for the hydrogen energy storage and electrolyzer.

4. The energy-material flow coupled green electricity and green hydrogen coordinated scheduling method for industrial parks as described in claim 1, characterized in that, Using conditional value at risk as a tool to quantify the conditional risk value of uncertainty factors in chemical production, specifically: ； in, This represents the conditional risk value cost in chemical production, used to quantify the risks arising from uncertainty. Value at risk (VaR) represents the value at a confidence level. The maximum loss; E represents the basic revenue of the Green Hydrogen Chemical Industrial Park, indicating the expected profit without considering risks; ) represents the mathematical expectation.

5. The energy-material flow coupled green electricity and green hydrogen coordinated scheduling method for industrial parks as described in claim 4, characterized in that, To maximize the net profit of the green electricity-to-hydrogen system under the premise of ensuring stable ammonia production in the chemical industrial park, an energy-material flow coupled green electricity-to-hydrogen collaborative optimization scheduling model for the chemical industrial park is established. Specifically, it is the sum of the basic profit of the green hydrogen chemical industrial park and the product of the conditional risk value cost and risk weight factor of chemical production; considering the impact of the life decay of the electrolyzer and the operational risks of insufficient energy storage on the stable operation of the green electricity-to-hydrogen chemical industrial park, the most economical operating result of the park is achieved, realizing the collaborative scheduling of green electricity-to-hydrogen in the chemical industrial park.

6. A park-based green electricity and green hydrogen coordinated scheduling system with energy-material flow coupling, characterized in that, include: The park equipment model building module is used to construct a green electricity-to-hydrogen production operation framework for chemical industrial parks with coordinated energy-material flow, and to establish key equipment models for green hydrogen chemical industrial parks. The electrolyzer power allocation module is used to introduce an electrolyzer lifetime weighting factor and penalties for invalid start-up and shutdown. Based on the electrolyzer dual-layer array rotation strategy, it allocates the operating power of alkaline electrolyzers and proton exchange membrane electrolyzers. The specific rotating strategy for the dual-layer array of electrolytic cells is as follows: The upper alkaline electrolytic cells are operated in a rotating order, with the first to start and the last to stop. The lower proton exchange membrane electrolyzer group calculates the cumulative lifetime loss of the electrolyzer at time t based on the lifetime weighting factor and start-up / shutdown penalty; prioritizes scheduling equipment with high remaining lifetime to bear heavy loads, and implements load reduction operation or switches to hot standby for recovery of equipment with low lifetime. Based on the lifetime weighting factor and start-up / shutdown penalties, the cumulative lifetime loss of the electrolyzer at time t is calculated as follows: ； ； in, and They are time t and t, respectively.

1. Cumulative lifespan loss, The operating loss coefficient represents the power-related aging rate. It is a power nonlinearity index, reflecting accelerated aging under high load; and These are the penalty coefficients for single start-up and shutdown, respectively. This refers to the adjustment time for a single electrolytic cell. A function characterizing the cost of electrolytic cell lifespan loss; Let be the input power of the electrolytic cell at time t; , These are 0-1 variables representing the start-up and shutdown of the electrolytic cell, respectively. Design the maximum power for the electrolytic cell; The control optimization module is used to establish a multi-timescale operation model for the coordinated operation of electrochemical energy storage and hydrogen storage equipment. Through model predictive control methods, the intraday operating characteristics of the electro-hydrogen energy storage and electrolyzer are dynamically optimized to obtain the optimal control sequence for the electro-hydrogen energy storage and electrolyzer. The optimized scheduling module is used to quantify the conditional risk value of uncertain factors on chemical production using conditional risk value as a tool. With the goal of maximizing the net benefit of the green electricity-to-hydrogen system under the premise of ensuring stable ammonia production in the chemical industrial park, a collaborative optimization scheduling model for green electricity and green hydrogen in the chemical industrial park coupled with energy and material flow is established to achieve the optimal balance of hydrogen in energy and material flow.

7. A terminal device comprising a processor and a memory, the processor for implementing instructions; the memory for storing multiple instructions, characterized in that, The instructions are adapted to be loaded by a processor and executed by the energy-material flow coupled green electricity and green hydrogen coordinated scheduling method for industrial parks as described in any one of claims 1-5.

8. A computer-readable storage medium storing a plurality of instructions, characterized in that, The instructions are adapted to be loaded by the processor of the terminal device and executed by the energy-material flow coupled green electricity and green hydrogen coordinated scheduling method for the park as described in any one of claims 1-5.

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