An industrial park hydrogen energy storage two-stage optimization method considering demand charge and hydrogen pricing

Abstract

This invention belongs to the field of hydrogen energy storage optimization technology, specifically involving a two-stage optimization method for hydrogen energy storage in industrial parks that considers demand-based pricing and hydrogen pricing. The steps include: establishing an objective function for industrial park operators with hydrogen loads to achieve optimal overall benefits in the pre-month optimization phase; establishing a probability distribution model of hydrogen load for fuel cell electric vehicles (FCEVs); establishing constraints for the safe operation of the hydrogen energy storage system, constraints for photovoltaic participation in the hydrogen energy storage system, and constraints for hydrogen balance and power balance in the hydrogen energy storage system; establishing an intraday model for upper-level players to optimize the park's maximum profit and an intraday model for lower-level FCEV users to minimize hydrogen refueling costs, and setting constraints; solving the two-level model to obtain the optimal two-stage optimization scheme for hydrogen energy storage. This invention enables industrial parks to have dual control over energy costs and hydrogen revenue, effectively reducing electricity penalties caused by exceeding demand limits and the risk of hydrogen supply and demand fluctuations.

Description

Technical Field

[0001] This invention belongs to the field of hydrogen energy storage optimization technology, specifically involving a two-stage optimization method for hydrogen energy storage in industrial parks that considers demand-based pricing and hydrogen pricing. Background Technology

[0002] In response to the trend of clean and low-carbon energy structure transformation, building a new power system dominated by new energy sources has become a core direction. Hydrogen energy, due to its clean, efficient, and storable characteristics, is regarded as a key carrier for the energy transformation of industrial parks. However, industrial parks with hydrogen loads face the challenge of high energy costs. On the one hand, as high-energy-consuming entities, their energy consumption is highly concentrated and exhibits significant peak-valley fluctuations. Under the current two-part electricity pricing mechanism, peak-hour load surges can easily trigger demand exceeding limits, making demand-based electricity charges a significant factor driving up costs. For example, in 2024, my country's electrolytic aluminum industry consumed 64.488 billion kWh of electricity annually (equivalent to three times the annual power generation of the Three Gorges Dam), highlighting the pressure on demand cost control in industrial parks. On the other hand, hydrogen energy, as an important raw material and energy carrier for industrial parks, has a high unit price due to the distance-sensitive nature of logistics costs when purchased externally. Although self-produced hydrogen through water electrolysis is the preferred solution, concentrated hydrogen production loads can easily push up instantaneous power loads, exacerbating the risk of demand exceeding limits. The economic efficiency and stability of hydrogen energy acquisition urgently need to be improved.

[0003] Meanwhile, the uncertainty of hydrogen refueling demand for fuel cell electric vehicles (FCEVs) further increases the difficulty of balancing hydrogen supply and demand. Existing energy optimization solutions mostly focus on energy storage or demand management, failing to take into account the time scale difference between minute-level demand metering and hourly hydrogen pricing, and also lacking a pricing mechanism design for the hydrogen energy market. This makes it difficult to balance the dual demands of demand cost control and improved hydrogen energy revenue. Therefore, there is an urgent need to develop energy optimization methods adapted to the characteristics of hydrogen-producing industrial parks to solve the core problems of high demand electricity costs, poor hydrogen energy economics, and difficulty in coordinating across multiple time scales. Summary of the Invention

[0004] In view of the shortcomings of the prior art, the purpose of this invention is to provide a two-stage optimization method for hydrogen energy storage in industrial parks that considers demand-based pricing and hydrogen pricing. This method can give the park dual control over energy costs and hydrogen revenue, effectively reducing the electricity cost penalties and hydrogen supply and demand fluctuation risks caused by exceeding demand limits.

[0005] To achieve the above objectives, this invention provides a two-stage optimization method for hydrogen energy storage in industrial parks, considering both demand-based pricing and hydrogen pricing, comprising the following steps: S1. For industrial parks with hydrogen load, establish an objective function for industrial park operators to achieve the optimal comprehensive benefits in the pre-month optimization phase. S2. Based on Monte Carlo simulation, establish a hydrogen load probability distribution model for fuel cell electric vehicles (FCEVs); S3. Establish constraints for the safe operation of hydrogen energy storage systems, constraints for photovoltaic participation in hydrogen energy storage systems, and constraints for hydrogen balance and power balance in hydrogen energy storage systems. S4. During the intraday optimization phase, the industrial park operator and FCEVs users form a Stackelberg game model. Based on this, an intraday phase upper-level player optimization model for maximizing park profits and an intraday phase lower-level FCEVs user hydrogen refueling cost minimization profit model are established. Hydrogen price constraints, hourly electricity purchase boundary constraints, total hydrogen refueling constraints, hydrogen refueling speed constraints, and hydrogen refueling time constraints are set. S5. Solve the intraday stage upper-level game player optimization park maximum profit model and the intraday stage lower-level FCEVs user hydrogen refueling cost minimization profit model to obtain the optimal two-stage hydrogen energy storage optimization scheme.

[0006] In a preferred embodiment of the present invention, the objective function in S1 is expressed as: ; In the formula, for industrial park operators, This indicates the total electricity cost for the month; This indicates the cost of the required quantity for the current month; This indicates the electricity consumption and cost for the current month. The calculation method is as follows: ; ; In the formula, a represents the declared demand value; b represents the actual demand. This indicates the electricity purchased by the industrial park at time t on day i. The calculation method is as follows: ; In the formula, N represents the total number of moments; This indicates the electricity purchase price for the industrial park at any given time (i day). Indicates a time scale.

[0007] As a preferred embodiment of the present invention, the hydrogen loading probability distribution model in S2 includes: The initial hydrogen storage capacity distribution of FCEVs is modeled as follows: ; In the formula, The probability density function represents the initial hydrogen storage capacity of FCEVs; x represents the initial hydrogen storage capacity of FCEVs. The standard deviation of hydrogen storage capacity; This represents the expected amount of hydrogen storage; The mathematical model for hydrogenation time in FCEVs is as follows: ; In the formula, Indicates the hydrogen refueling time for FCEVs; This indicates the total capacity of FCEVs; Indicates the hydrogenation rate of FCEVs; The mathematical model for total hydrogen demand during the day is as follows: ; ; In the formula, This indicates the total hydrogen demand for the day; This represents the hydrogen charge of the uth FCEV; U represents the number of vehicles refueling per hour. Indicates the standardization factor; This indicates the actual demand for hydrogen. This indicates the total amount of hydrogen in the simulated vehicle.

[0008] As a preferred embodiment of the present invention, the constraints for the safe operation of the hydrogen energy storage system in S3 include: The constraints on the participation of electrolyzers in the operation of hydrogen energy storage systems are expressed as follows: ; ; ; In the formula, This represents the amount of hydrogen produced by the electrolyzer at time t on day i. Indicates the electrolysis efficiency of the electrolytic cell; , These represent the power input to the electrolytic cell at time t and t-1 on day i, respectively; This indicates that hydrogen has a low calorific value; Indicates the upper limit of the electrolytic cell power; , These represent the lower and upper limits of the ramp-up power of the electrolytic cell, respectively; The constraints for fuel cells participating in the operation of hydrogen energy storage systems are expressed as follows: ; ; ; In the formula, This represents the amount of hydrogen consumed by the fuel cell at time t on day i. , These represent the fuel cell power generation at time t and t-1 on day i, respectively; Indicates the power generation efficiency of the fuel cell; Indicates the upper limit of fuel cell power; , These represent the lower and upper limits of fuel cell ramping, respectively; The constraints on the participation of hydrogen storage tanks in the operation of hydrogen energy storage systems are expressed as follows: ; ; ; ; ; In the formula, , These represent the amount of hydrogen stored in the hydrogen storage tank on day i and at time t-1, respectively. Indicates the hydrogen intake efficiency of the hydrogen storage tank; This indicates the amount of hydrogen entering the hydrogen storage tank at time t; Indicates the hydrogen output efficiency of the hydrogen storage tank; This indicates the amount of hydrogen discharged from the hydrogen storage tank at time t; Indicates the upper limit of hydrogen capacity in the hydrogen storage tank; This indicates the initial hydrogen content in the hydrogen storage tank; This indicates the hydrogen content in the storage tank at the last moment; M indicates the maximum inflow and outflow of hydrogen. This is a binary variable; a value of 1 corresponds to hydrogen entering the hydrogen storage tank, and a value of 0 corresponds to hydrogen exiting the hydrogen storage tank.

[0009] As a preferred embodiment of the present invention, the constraints on the operation of the hydrogen storage tank in the hydrogen energy storage system are supplemented by a dynamic coordinated constraint mechanism of temperature and pressure. Specifically, this mechanism involves: real-time monitoring of temperature and pressure data within the hydrogen storage tank, and dynamically correlating temperature and pressure parameters with hydrogen storage capacity, inlet hydrogen flow rate, and outlet hydrogen flow rate; setting temperature and pressure threshold ranges for safe operation of the hydrogen storage tank; reducing the inlet hydrogen flow rate and initiating cooling auxiliary measures when the temperature within the hydrogen storage tank exceeds the upper limit threshold, while simultaneously lowering the real-time maximum allowable hydrogen storage capacity of the hydrogen storage tank; and reducing the outlet hydrogen flow rate when the pressure within the hydrogen storage tank is below the lower limit threshold to ensure hydrogen output stability.

[0010] As a preferred embodiment of the present invention, the constraint condition for photovoltaic participation in the hydrogen energy storage system in S3 is expressed as follows: ; In the formula, This represents the photovoltaic power generation at time t on day i. This indicates the upper limit of the predicted photovoltaic power generation capacity; The hydrogen balance constraint condition for a hydrogen energy storage system is expressed as follows: ; In the formula, This indicates the amount of hydrogen sold in the industrial park at any given time on day i. This represents the hydrogen load in the industrial park at time t on day i, including the inherent hydrogen load of the industrial park and the hydrogen refueling load of FCEVs. The power balance constraint condition for a hydrogen energy storage system is expressed as follows: ; In the formula, This indicates the electricity purchased by the industrial park at time t on day i. The power generation of the fuel cell at time i is represented by the value of day t. This indicates the electrolytic hydrogen production capacity of the electrolyzer; This represents the electrical load of the industrial park at time t on day i.

[0011] As a preferred embodiment of the present invention, in S4, in the Stackelberg game model, the industrial park operator plays the role of the leader in the game and is responsible for formulating the hydrogen pricing method, while the user plays the role of the follower and dynamically adjusts the hydrogen refueling behavior after obtaining price information. The intraday phase upper-level game player's optimal park maximum profit model is expressed as: ; In the formula, This indicates the profit of the industrial park during the intraday period; Indicates the hydrogen valence at time t; This represents the amount of hydrogen refueling for the j-th FCEV at time t; Indicates the price of hydrogen; Indicates the retail sales volume of hydrogen; This represents the electricity price at time t; This represents the power purchased at time t; The intraday profit model for minimizing hydrogen refueling costs for lower-level FCEVs users is expressed as follows: .

[0012] In a preferred embodiment of the present invention, the hydrogen price constraint in S4 is expressed as follows: ; ; In the formula, This indicates the lower limit of the hydrogen price; This represents the upper limit of the hydrogen price; T represents the total number of moments. This represents the average price of hydrogen. The 15-minute sliding window constraint for demand-based electricity pricing is reconstructed into an hourly-level electricity purchase boundary constraint, represented as: ; In the formula, 'a' represents the declared demand value; The total hydrogenation constraint is expressed as follows: ; In the formula, Indicates the permissible hydrogenation time; This represents the planned horizontal hydrogen storage capacity of the j-th FCEV; This represents the initial hydrogen storage capacity of the j-th FCEV; The hydrogenation rate constraint is expressed as follows: ; In the formula, Indicates the maximum hydrogenation rate; The hydrogenation time constraint is expressed as: .

[0013] As a preferred embodiment of the present invention, for By introducing a user-differentiated demand identification mechanism and multi-dimensional dynamic pricing factors, an adaptive dynamic pricing model is constructed, including the following steps: Step 1: Construct a differentiated demand classification system for FCEVs users: Based on the real-time remaining hydrogen storage capacity, historical hydrogen refueling time preferences, driving route planning data, and emergency travel indicators of FCEVs users, users are divided into emergency hydrogen refueling category and regular hydrogen refueling category using the K-means clustering algorithm; Step 2: Define a multi-dimensional dynamic pricing factor set: Pricing factors include the photovoltaic output fluctuation coefficient. Real-time electricity purchase price elasticity coefficient Hydrogen storage tank remaining capacity utilization rate Real-time supply and demand ratio for two types of users ; Step 3: Establish a differentiated dynamic pricing formula: The pricing formula for emergency hydrogen refueling users is expressed as follows: ; In the formula, This indicates the price of hydrogen for emergency hydrogen refueling users; This represents the premium factor for urgent demand; This represents the average price of hydrogen. The standard pricing formula for hydrogen refueling customers is expressed as follows: ; In the formula, This indicates the price of hydrogen for regular hydrogen refueling users; , , These are the pricing adjustment coefficients for photovoltaic output, electricity purchase price, and hydrogen storage capacity, respectively. Step 4: Collect the latest photovoltaic output forecast, real-time grid electricity price, hydrogen storage tank temperature / pressure / remaining capacity data, and user demand declaration data every hour, and update the pricing factors and pricing results through a rolling time-domain optimization method.

[0014] A two-stage optimization device for hydrogen energy storage in industrial parks, considering demand-based charging and hydrogen pricing, includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement the aforementioned method.

[0015] The beneficial effects of this invention are: This invention combines a hydrogen energy storage system for hydrogen-loaded industrial parks with a dual-timescale optimization mechanism that adapts to minute-level demand management and hour-level hydrogen pricing. This enables park operators to have dual control over energy costs (demand electricity costs and electricity costs) and hydrogen revenue (FCEV hydrogen refueling revenue and external hydrogen sales revenue), breaking the limitations of traditional single-timescale optimization that cannot take into account both demand constraints and hydrogen pricing.

[0016] This invention, through a two-stage process—pre-month and intra-day—in collaboration with the Stackelberg game model, can effectively reduce electricity cost penalties and hydrogen supply and demand fluctuation risks in industrial parks caused by exceeding demand limits. In the pre-month stage, considering photovoltaic output forecasts, electricity load characteristics, and the probability distribution of hydrogen load in FCEVs, the optimal demand declaration value (DDV) is determined with the goal of minimizing the total monthly electricity cost, locking in demand charging costs from the source. In the intra-day stage, considering real-time electricity price fluctuations, user hydrogen refueling response behavior, and the economics of hydrogen storage, dynamic hydrogen prices are set by upper-level operators (not lower than 1.2 times the cost and not higher than 3 times the cost), and lower-level FCEV users independently adjust their hydrogen refueling times. This achieves "low-cost hydrogen production and storage, and high-cost hydrogen sales and power generation," both using surplus electricity to convert into hydrogen to increase revenue and using hydrogen energy regulation to smooth out peak electricity purchases and reduce electricity costs, ultimately significantly improving the overall economic benefits of the industrial park. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the principle of this invention. Detailed Implementation

[0018] The embodiments of the present invention will be further described below with reference to the accompanying drawings: Example 1: As Figure 1 As shown, a two-stage optimization method for hydrogen energy storage in industrial parks, considering demand-based charging and hydrogen pricing, includes the following steps: S1. For industrial parks with hydrogen load, establish an objective function for industrial park operators to achieve the optimal comprehensive benefits in the pre-month optimization phase. S2. Based on Monte Carlo simulation, establish a hydrogen load probability distribution model for fuel cell electric vehicles (FCEVs); S3. Establish constraints for the safe operation of hydrogen energy storage systems, constraints for photovoltaic participation in hydrogen energy storage systems, and constraints for hydrogen balance and power balance in hydrogen energy storage systems. S4. During the intraday optimization phase, the industrial park operator and FCEVs users form a Stackelberg game model. Based on this, an intraday phase upper-level player optimization model for maximizing park profits and an intraday phase lower-level FCEVs user hydrogen refueling cost minimization profit model are established. Hydrogen price constraints, hourly electricity purchase boundary constraints, total hydrogen refueling constraints, hydrogen refueling speed constraints, and hydrogen refueling time constraints are set. S5. Solve (using Gurobi) the intraday stage upper-level game player optimization park maximum profit model and the intraday stage lower-level FCEVs user hydrogen refueling cost minimization profit model to obtain the optimal two-stage hydrogen energy storage optimization scheme.

[0019] S1, as the core of the pre-month phase, clarifies the core orientation of minimizing the total monthly electricity cost by establishing an objective function that optimizes overall benefits, thus defining the overall direction for subsequent optimization. S2, based on the FCEVs hydrogen load probability distribution model constructed using Monte Carlo simulation, provides crucial hydrogen load data support for optimization and solves the problem of uncertainty in hydrogen refueling demand. S3 establishes various constraints, setting insurmountable operating boundaries for the entire optimization process and ensuring the feasibility of the optimization scheme. S4, in the intraday phase, uses the Stackelberg game model as the core. Upper-level operators formulate hydrogen pricing strategies based on prior objectives, load data, and constraints, while lower-level FCEVs users dynamically adjust their hydrogen refueling behavior according to prices. At the same time, relevant constraints are added to further refine the optimization scenario. S5, by solving this game model, integrates the objective orientation, data support, constraint boundaries, and game logic of the previous four steps, ultimately forming an optimal two-stage optimization scheme for hydrogen energy storage that balances demand cost control and hydrogen energy revenue improvement.

[0020] In S1, the objective function is expressed as: ; In the formula, for industrial park operators, This indicates the total electricity cost for the month; This indicates the cost of the required quantity for the current month; This indicates the electricity consumption and cost for the current month. Based on the two-part tariff standard for large industrial users, the threshold for exceeding the limit is set at 1.05a, which is in line with the "Notice on Improving the Implementation Method of Basic Electricity Price for Two-Part Tariff Users". The calculation method is as follows: ; ; In the formula, a represents the declared demand value; b represents the actual demand. The power purchased by the industrial park at time t on day i (kW) represents the power output of the industrial park. The calculation method is as follows: ; In the formula, N represents the total number of moments; This indicates the electricity purchase price (RMB / kWh) for the industrial park at time i on day t. Indicates a time scale.

[0021] In S2, the hydrogen loading probability distribution model includes: The initial hydrogen storage capacity distribution of FCEVs is modeled as follows: ; In the formula, The probability density function represents the initial hydrogen storage capacity of FCEVs; x represents the initial hydrogen storage capacity of FCEVs. The standard deviation of hydrogen storage capacity; This represents the expected amount of hydrogen storage; The mathematical model for hydrogenation time in FCEVs is as follows: ; In the formula, Indicates the hydrogen refueling time for FCEVs; This indicates the total capacity of FCEVs; Indicates the hydrogenation rate of FCEVs; The mathematical model for total hydrogen demand during the day is as follows: ; ; In the formula, This indicates the total hydrogen demand for the day; This represents the hydrogen charge of the uth FCEV; U represents the number of vehicles refueling per hour. Indicates the standardization factor; This indicates the actual demand for hydrogen. This represents the total amount of hydrogen in the simulated vehicle, normalized to the actual total hydrogen production. The purpose of normalization is to adapt to programming.

[0022] In S3, the constraints for the safe operation of the hydrogen energy storage system include: The constraints on the participation of electrolyzers in the operation of hydrogen energy storage systems are expressed as follows: ; ; ; In the formula, This represents the amount of hydrogen produced by the electrolyzer at time t on day i (kg). Indicates the electrolysis efficiency of the electrolytic cell; , These represent the power (kW) input to the electrolytic cell at time t and t-1 on day i, respectively. This indicates the lower calorific value of hydrogen, 33.3 kWh / kg; Indicates the upper limit of the electrolytic cell power; , These represent the lower and upper limits of the ramp-up power of the electrolytic cell, respectively; The constraints for fuel cells participating in the operation of hydrogen energy storage systems are expressed as follows: ; ; ; In the formula, This represents the amount of hydrogen (kg) consumed by the fuel cell at time t on day i. , These represent the fuel cell power generation at time t and t-1 on day i, respectively; Indicates the power generation efficiency of the fuel cell; Indicates the upper limit of fuel cell power; , These represent the lower and upper limits of fuel cell ramping, respectively; The constraints on the participation of hydrogen storage tanks in the operation of hydrogen energy storage systems are expressed as follows: ; ; ; ; ; In the formula, , Let represent the amount of hydrogen stored in the hydrogen storage tank on day i and at time t-1, respectively (kg). Indicates the hydrogen intake efficiency of the hydrogen storage tank; This indicates the amount of hydrogen entering the hydrogen storage tank at time t; Indicates the hydrogen output efficiency of the hydrogen storage tank; This indicates the amount of hydrogen discharged from the hydrogen storage tank at time t; Indicates the upper limit of hydrogen capacity in the hydrogen storage tank; This indicates the initial hydrogen content in the hydrogen storage tank; This indicates the hydrogen content in the storage tank at the last moment; M indicates the maximum inflow and outflow of hydrogen. This is a binary variable. A value of 1 corresponds to hydrogen entering the hydrogen storage tank, and a value of 0 corresponds to hydrogen exiting the hydrogen storage tank. This means that hydrogen entering and exiting the hydrogen storage tank cannot happen simultaneously. At time t, the hydrogen storage tank can only enter or exit hydrogen.

[0023] The constraints for photovoltaic (PV) participation in hydrogen energy storage systems are expressed as follows: ; In the formula, This represents the photovoltaic power generation (kW) at time t on day i. This indicates the upper limit of the predicted photovoltaic power generation capacity; The hydrogen balance constraint condition for a hydrogen energy storage system is expressed as follows: ; In the formula, This indicates the amount of hydrogen sold (kg) in the industrial park at time t on day i. The hydrogen load (kg) in the industrial park at time t on day i includes the inherent hydrogen load of the industrial park and the hydrogen loading load of FCEVs. The power balance constraint condition for a hydrogen energy storage system is expressed as follows: ; In the formula, This indicates the electricity purchased by the industrial park at time t on day i. The power generation of the fuel cell at time i is represented by the value of day t. This indicates the electrolytic hydrogen production capacity of the electrolyzer; This represents the electrical load of the industrial park at time t on day i.

[0024] In S4, in the Stackelberg game model, the industrial park operator plays the role of the leader, responsible for setting the hydrogen pricing method, while the user plays the role of the follower, dynamically adjusting the hydrogen refueling behavior after obtaining price information. The intraday phase upper-level game player's optimal park maximum profit model is expressed as: ; In the formula, This indicates the profit of the industrial park during the intraday period; Indicates the hydrogen valence at time t; This represents the amount of hydrogen refueling for the j-th FCEV at time t; Indicates the price of hydrogen; Indicates the retail sales volume of hydrogen; This represents the electricity price at time t; This represents the power purchased at time t; The intraday profit model for minimizing hydrogen refueling costs for lower-level FCEVs users is expressed as follows: .

[0025] The hydrogen price constraint is expressed as follows: ; ; In the formula, This indicates the lower limit of the hydrogen price; This represents the upper limit of the hydrogen price; T represents the total number of moments. This represents the average price of hydrogen. The 15-minute sliding window constraint for demand-based electricity pricing is reconstructed into an hourly-level electricity purchase boundary constraint, represented as: ; In the formula, 'a' represents the declared demand value; The total hydrogenation constraint is expressed as follows: ; In the formula, Indicates the permissible hydrogenation time; This represents the planned horizontal hydrogen storage capacity of the j-th FCEV; This represents the initial hydrogen storage capacity of the j-th FCEV; The hydrogenation rate constraint is expressed as follows: ; In the formula, Indicates the maximum hydrogenation rate; The hydrogenation time constraint is expressed as: .

[0026] This embodiment combines the hydrogen energy storage system of hydrogen-loaded industrial parks with a dual-timescale optimization mechanism adapted to "minute-level demand management and hour-level pricing decisions." This empowers the parks with dual control over energy costs and hydrogen revenue. By coordinating energy market operations with a Stackelberg game model in two phases—pre-monthly and intra-monthly—it effectively reduces electricity penalties for exceeding demand limits and mitigates the risk of hydrogen supply and demand fluctuations. In the pre-monthly phase, considering photovoltaic output forecasts, electricity load characteristics, and the probability distribution of hydrogen load for fuel cell electric vehicles (FCEVs), the optimal demand declaration value (DDV) is determined with the goal of minimizing the total monthly electricity cost (demand cost and electricity cost), locking in demand charging costs. Alternatively, in the intra-day phase, considering real-time electricity price fluctuations, user hydrogen refueling response behavior, and the economics of hydrogen storage and sales, a game model is implemented where upper-level park operators set dynamic hydrogen prices, and lower-level FCEV users independently adjust refueling times. This reduces the threat to the safe and economical operation of the hydrogen energy storage system posed by disorderly FCEV refueling. To increase the benefits of hydrogen energy utilization and ultimately improve the overall economic efficiency of the park.

[0027] Example 2: Based on Example 1, a dynamic coordinated constraint mechanism for temperature and pressure is added to the constraints on the operation of the hydrogen storage tank in the hydrogen energy storage system. Specifically, this involves: real-time monitoring of temperature and pressure data within the hydrogen storage tank, dynamically correlating temperature and pressure parameters with hydrogen storage capacity, inlet hydrogen flow rate, and outlet hydrogen flow rate; setting safe operating temperature and pressure threshold ranges for the hydrogen storage tank; when the temperature inside the hydrogen storage tank exceeds the upper threshold, reducing the inlet hydrogen flow rate and initiating cooling auxiliary measures (e.g., introducing coolant or cold air through built-in / external cooling pipes for heat exchange, or activating a heat dissipation device to enhance heat dissipation from the tank surface), while simultaneously lowering the real-time maximum allowable hydrogen storage capacity of the hydrogen storage tank; and when the pressure inside the hydrogen storage tank is below the lower threshold, reducing the outlet hydrogen flow rate to ensure that the pressure inside the hydrogen storage tank remains within the effective operating range and to guarantee the stability of hydrogen output.

[0028] When the temperature or pressure approaches the threshold boundary, the hydrogen supply rhythm from the electrolyzer to the hydrogen storage tank or the hydrogen consumption rhythm of the fuel cell is adjusted in advance to achieve coordinated matching of the temperature, pressure, hydrogen storage capacity, and hydrogen inlet and outlet flow rates of the hydrogen storage tank.

[0029] By introducing coordinated constraints on temperature, pressure, hydrogen storage capacity, and flow rate, the operating constraints of the hydrogen storage tank are made more closely aligned with actual working conditions. This not only fills the gap in the dynamic adaptation of the original constraints to the environment and operating status, but also improves the actual energy storage efficiency and hydrogen output stability of the hydrogen storage system under the premise of ensuring operational safety through early warning and dynamic adjustment, thus avoiding hydrogen refueling interruptions or energy waste caused by temperature and pressure runaway.

[0030] Example 3: Based on Example 1, for By introducing a user-differentiated demand identification mechanism and multi-dimensional dynamic pricing factors, an adaptive dynamic pricing model is constructed, including the following steps: Step 1: Construct a differentiated demand classification system for FCEVs users: Based on FCEVs users' real-time remaining hydrogen storage, historical hydrogen refueling time preferences, driving route planning data, and emergency travel indicators, users are divided into emergency hydrogen refueling category (remaining hydrogen storage ≤ 20% of the safe hydrogen storage threshold or marked as emergency travel) and regular hydrogen refueling category (remaining hydrogen storage > 20% of the safe hydrogen storage threshold and no emergency travel indicator) using the K-means clustering algorithm; the safe hydrogen storage threshold (20%) is dynamically adjusted based on the average driving range of FCEVs and real-time road condition predictions. Step 2: Define a multi-dimensional dynamic pricing factor set: Pricing factors include the photovoltaic output fluctuation coefficient. Real-time electricity purchase price elasticity coefficient Hydrogen storage tank remaining capacity utilization rate Real-time supply and demand ratio for two types of users ; The pricing factors can be obtained in the following ways: for First, minute-level power output data and corresponding weather data of photovoltaic power plants over the past year are collected. Then, continuous sunny day data are selected to fit an ideal sunny day power output curve to isolate the natural diurnal variation pattern. Next, the absolute value of the deviation between the measured photovoltaic power output at the current moment and the ideal curve is calculated. This value is then divided by the maximum power output deviation value under historical extreme weather conditions to complete the normalization. Finally, a photovoltaic power output fluctuation coefficient that only reflects the real fluctuation caused by weather is obtained. This method can avoid confusing the effects of natural changes and sudden weather changes.

[0031] for We collected hourly electricity purchase price and volume data for nearly six months, combined with the day-ahead market supply and demand curve of the power grid, and used monotonic spline regression to fit a nonlinear demand curve that can depict the sharp drop in demand when electricity prices are high and the gradual smoothness of demand when electricity prices are low. Based on this curve, we calculated the dynamic elasticity of electricity purchase to price at the current moment (the ratio of the rate of change of electricity purchase to the rate of change of price), took its absolute value and divided it by the absolute value of the historical maximum elasticity to complete the normalization, thus replacing the traditional fixed elasticity value and more closely reflecting the actual sensitivity differences in the electricity market.

[0032] for The method collects real-time temperature and pressure data of the hydrogen storage tank, combines the tank capacity parameters, and calculates the current remaining hydrogen mass using the corrected ideal gas equation of state with the introduction of a compressibility factor. If the hydrogen storage tank is connected to a fuel cell, the amount of hydrogen consumed by the fuel cell in the past hour needs to be subtracted. Then, the corrected remaining hydrogen mass is divided by the maximum hydrogen storage mass of the hydrogen storage tank under rated operating conditions to obtain an accurate remaining capacity utilization rate. This method reduces the error of traditional pressure estimation from more than ±10% to within ±3% through temperature and pressure correction.

[0033] for First, the real-time supply capacity is quantified by taking the smaller value between the maximum hydrogen refueling speed of the hydrogen refueling station equipment and the safe availability of the remaining hydrogen storage. Then, the total real-time hydrogen refueling applications from emergency users (with remaining hydrogen storage ≤ 20%) and regular users are counted as the demand. The supply capacity is divided by the total demand to obtain the supply-demand ratio. At the same time, boundary constraints are applied to cases with no demand or excessively high / low supply-demand ratios (e.g., a value of 2 when there is no demand). This replaces manual experience-based judgment and achieves a quantitative characterization of the hydrogen refueling supply-demand relationship.

[0034] Step 3: Establish a differentiated dynamic pricing formula: The pricing formula for emergency hydrogen refueling users is expressed as follows: ; In the formula, This indicates the price of hydrogen for emergency hydrogen refueling users; This represents the emergency demand premium coefficient (range: 0.1-0.3). This represents the average price of hydrogen. The standard pricing formula for hydrogen refueling customers is expressed as follows: ; In the formula, This indicates the price of hydrogen for regular hydrogen refueling users; , , These are the pricing adjustment coefficients for photovoltaic output, electricity purchase price, and hydrogen storage capacity (each ranging from 0.05 to 0.2). Step 4: Collect the latest photovoltaic output forecast, real-time grid electricity price, hydrogen storage tank temperature / pressure / remaining capacity data, and user demand declaration data every hour, and update the pricing factors and pricing results through the rolling time-domain optimization (RTO) method.

[0035] This embodiment solves the problem that the existing single pricing model cannot simultaneously meet urgent needs and optimize supply and demand balance by combining differentiated demand identification with multi-dimensional dynamic pricing. It can improve the profit stability of industrial park operators under the scenarios of photovoltaic output fluctuations and electricity price fluctuations, reduce the hydrogen refueling cost for regular users, protect the hydrogen refueling rights of emergency users, and significantly improve the operational flexibility and user satisfaction of hydrogen energy storage systems.

[0036] Example 4: A two-stage optimization device for hydrogen energy storage in industrial parks, considering demand-based charging and hydrogen pricing, comprising: One or more processors; Memory, used to store one or more computer programs; When one or more programs are executed by one or more processors, the one or more processors perform the methods in Embodiment 1, Embodiment 2, or Embodiment 3.

[0037] Example 5: A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, cause the processor to perform the method of Example 1, Example 2 or Example 3.

Claims

1. A two-stage optimization method for hydrogen energy storage in industrial parks, considering demand-based pricing and hydrogen pricing, characterized in that... Includes the following steps: S1. For industrial parks with hydrogen load, establish an objective function for industrial park operators to achieve the optimal comprehensive benefits in the pre-month optimization phase. S2. Based on Monte Carlo simulation, establish a hydrogen load probability distribution model for fuel cell electric vehicles (FCEVs); S3. Establish constraints for the safe operation of hydrogen energy storage systems, constraints for photovoltaic participation in hydrogen energy storage systems, and constraints for hydrogen balance and power balance in hydrogen energy storage systems. S4. During the intraday optimization phase, the industrial park operator and FCEVs users form a Stackelberg game model. Based on this, an intraday phase upper-level player optimization model for maximizing park profits and an intraday phase lower-level FCEVs user hydrogen refueling cost minimization profit model are established. Hydrogen price constraints, hourly electricity purchase boundary constraints, total hydrogen refueling constraints, hydrogen refueling speed constraints, and hydrogen refueling time constraints are set. S5. Solve the intraday stage upper-level game player optimization park maximum profit model and the intraday stage lower-level FCEVs user hydrogen refueling cost minimization profit model to obtain the optimal two-stage hydrogen energy storage optimization scheme.

2. The two-stage optimization method for hydrogen energy storage in industrial parks, considering demand-based pricing and hydrogen pricing, as described in claim 1, is characterized in that... In S1, the objective function is expressed as: ； In the formula, for industrial park operators, This indicates the total electricity cost for the month; This indicates the cost of the required quantity for the current month; This indicates the electricity consumption and cost for the current month. The calculation method is as follows: ； ； In the formula, a represents the declared demand value; b represents the actual demand. This indicates the electricity purchased by the industrial park at time t on day i. The calculation method is as follows: ； In the formula, N represents the total number of moments; This indicates the electricity purchase price for the industrial park at any given time (i day). Indicates a time scale.

3. The two-stage optimization method for hydrogen energy storage in industrial parks, considering demand-based pricing and hydrogen pricing, as described in claim 1, is characterized in that... In S2, the hydrogen loading probability distribution model includes: The initial hydrogen storage capacity distribution of FCEVs is modeled as follows: ； In the formula, The probability density function represents the initial hydrogen storage capacity of FCEVs; x represents the initial hydrogen storage capacity of FCEVs. The standard deviation of hydrogen storage capacity; This represents the expected amount of hydrogen storage; The mathematical model for hydrogenation time in FCEVs is as follows: ； In the formula, Indicates the hydrogen refueling time for FCEVs; This indicates the total capacity of FCEVs; Indicates the hydrogenation rate of FCEVs; The mathematical model for total hydrogen demand during the day is as follows: ； ； In the formula, This indicates the total hydrogen demand for the day; This represents the hydrogen charge of the uth FCEV; U represents the number of vehicles refueling per hour. Indicates the standardization factor; This indicates the actual demand for hydrogen. This indicates the total amount of hydrogen in the simulated vehicle.

4. The two-stage optimization method for hydrogen energy storage in industrial parks, considering demand-based pricing and hydrogen pricing, as described in claim 1, is characterized in that... In S3, the constraints for the safe operation of the hydrogen energy storage system include: The constraints on the participation of electrolyzers in the operation of hydrogen energy storage systems are expressed as follows: ； ； ； In the formula, This represents the amount of hydrogen produced by the electrolyzer at time t on day i. Indicates the electrolysis efficiency of the electrolytic cell; , These represent the power input to the electrolytic cell at time t and t-1 on day i, respectively; This indicates that hydrogen has a low calorific value; Indicates the upper limit of the electrolytic cell power; , These represent the lower and upper limits of the ramp-up power of the electrolytic cell, respectively; The constraints for fuel cells participating in the operation of hydrogen energy storage systems are expressed as follows: ； ； ； In the formula, This represents the amount of hydrogen consumed by the fuel cell at time t on day i. , These represent the fuel cell power generation at time t and t-1 on day i, respectively; Indicates the power generation efficiency of the fuel cell; Indicates the upper limit of fuel cell power; , These represent the lower and upper limits of fuel cell ramping, respectively; The constraints on the participation of hydrogen storage tanks in the operation of hydrogen energy storage systems are expressed as follows: ； ； ； ； ； In the formula, , These represent the amount of hydrogen stored in the hydrogen storage tank on day i and at time t-1, respectively. Indicates the hydrogen intake efficiency of the hydrogen storage tank; This indicates the amount of hydrogen entering the hydrogen storage tank at time t; Indicates the hydrogen output efficiency of the hydrogen storage tank; This indicates the amount of hydrogen discharged from the hydrogen storage tank at time t; Indicates the upper limit of hydrogen capacity in the hydrogen storage tank; This indicates the initial hydrogen content in the hydrogen storage tank; This indicates the hydrogen content in the storage tank at the last moment; M indicates the maximum inflow and outflow of hydrogen. This is a binary variable; a value of 1 corresponds to hydrogen entering the hydrogen storage tank, and a value of 0 corresponds to hydrogen exiting the hydrogen storage tank.

5. A two-stage optimization method for hydrogen energy storage in industrial parks, considering demand-based pricing and hydrogen pricing, as described in claim 4, is characterized in that... In the constraints on the participation of hydrogen storage tanks in the operation of hydrogen energy storage systems, a dynamic coordinated constraint mechanism of temperature and pressure is added. Specifically, the temperature and pressure data inside the hydrogen storage tank are monitored in real time, and the temperature and pressure parameters are dynamically correlated with the hydrogen storage capacity, hydrogen inflow rate, and hydrogen outflow rate. Set the temperature and pressure threshold ranges for safe operation of the hydrogen storage tank. When the temperature inside the hydrogen storage tank exceeds the upper limit threshold, reduce the hydrogen inlet flow rate and activate cooling auxiliary measures, while simultaneously lowering the real-time maximum allowable hydrogen storage capacity of the hydrogen storage tank. When the pressure inside the hydrogen storage tank is lower than the lower limit threshold, reduce the hydrogen outlet flow rate to ensure hydrogen outlet stability.

6. The two-stage optimization method for hydrogen energy storage in industrial parks, considering demand-based charging and hydrogen pricing, as described in claim 4, is characterized in that... In S3, the constraint condition for photovoltaic participation in the hydrogen energy storage system is expressed as follows: ； In the formula, This represents the photovoltaic power generation at time t on day i. This indicates the upper limit of the predicted photovoltaic power generation capacity; The hydrogen balance constraint condition for a hydrogen energy storage system is expressed as follows: ； In the formula, This indicates the amount of hydrogen sold in the industrial park at any given time on day i. This represents the hydrogen load in the industrial park at time t on day i, including the inherent hydrogen load of the industrial park and the hydrogen refueling load of FCEVs. The power balance constraint condition for a hydrogen energy storage system is expressed as follows: ； In the formula, This indicates the electricity purchased by the industrial park at time t on day i. The power generation of the fuel cell at time i is represented by the value of day t. This indicates the electrolytic hydrogen production capacity of the electrolyzer; This represents the electrical load of the industrial park at time t on day i.

7. A two-stage optimization method for hydrogen energy storage in industrial parks, considering demand-based pricing and hydrogen pricing, as described in claim 1, is characterized in that... In the S4 described above, in the Stackelberg game model, the industrial park operator plays the role of the leader in the game, responsible for formulating the hydrogen pricing method, while the user plays the role of the follower, dynamically adjusting the hydrogen refueling behavior after obtaining price information. The intraday phase upper-level game player's optimal park maximum profit model is expressed as: ； In the formula, This indicates the profit of the industrial park during the intraday period; Indicates the hydrogen valence at time t; This represents the amount of hydrogen refueling for the j-th FCEV at time t; Indicates the price of hydrogen; Indicates the retail sales volume of hydrogen; This represents the electricity price at time t; This represents the power purchased at time t; The intraday profit model for minimizing hydrogen refueling costs for lower-level FCEVs users is expressed as follows: 。 8. A two-stage optimization method for hydrogen energy storage in industrial parks, considering demand-based pricing and hydrogen pricing, as described in claim 7, is characterized in that... In S4, the hydrogen price constraint is expressed as follows: ； ； In the formula, This indicates the lower limit of the hydrogen price; This represents the upper limit of the hydrogen price; T represents the total number of moments. This represents the average price of hydrogen. The 15-minute sliding window constraint for demand-based electricity pricing is reconstructed into an hourly-level electricity purchase boundary constraint, represented as: ； In the formula, 'a' represents the declared demand value; The total hydrogenation constraint is expressed as follows: ； In the formula, Indicates the permissible hydrogenation time; This represents the planned horizontal hydrogen storage capacity of the j-th FCEV; This represents the initial hydrogen storage capacity of the j-th FCEV; The hydrogenation rate constraint is expressed as follows: ； In the formula, Indicates the maximum hydrogenation rate; The hydrogenation time constraint is expressed as: 。 9. A two-stage optimization method for hydrogen energy storage in industrial parks, considering demand-based pricing and hydrogen pricing, as described in claim 7, is characterized in that... for By introducing a user-differentiated demand identification mechanism and multi-dimensional dynamic pricing factors, an adaptive dynamic pricing model is constructed, including the following steps: Step 1: Construct a differentiated demand classification system for FCEVs users: Based on the real-time remaining hydrogen storage capacity, historical hydrogen refueling time preferences, driving route planning data, and emergency travel indicators of FCEVs users, users are divided into emergency hydrogen refueling category and regular hydrogen refueling category using the K-means clustering algorithm; Step 2: Define a multi-dimensional dynamic pricing factor set: Pricing factors include the photovoltaic output fluctuation coefficient. Real-time electricity purchase price elasticity coefficient Hydrogen storage tank remaining capacity utilization rate Real-time supply and demand ratio for two types of users ; Step 3: Establish a differentiated dynamic pricing formula: The pricing formula for emergency hydrogen refueling users is expressed as follows: ； In the formula, This indicates the price of hydrogen for emergency hydrogen refueling users; This represents the premium factor for urgent demand; This represents the average price of hydrogen. The standard pricing formula for hydrogen refueling customers is expressed as follows: ； In the formula, This indicates the price of hydrogen for regular hydrogen refueling users; , , These are the pricing adjustment coefficients for photovoltaic output, electricity purchase price, and hydrogen storage capacity, respectively. Step 4: Collect the latest photovoltaic output forecast, real-time grid electricity price, hydrogen storage tank temperature / pressure / remaining capacity data, and user demand declaration data every hour, and update the pricing factors and pricing results through a rolling time-domain optimization method.

10. A two-stage optimization device for hydrogen energy storage in industrial parks, considering demand-based pricing and hydrogen pricing, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in any one of claims 1-9.

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