Hydrogen production-hydrogenation station optimal operation method facing hydrogenation load energy demand

Abstract

The application discloses a kind of hydrogen production-hydrogenation station optimization operation methods for hydrogenation load energy demand, and method steps are as follows: step 1, hydrogen production hydrogenation station equipment model construction;Step 2, hydrogen production optimization operation model construction;Step 3, hydrogenation station optimization operation model construction;Step 4, double-layer optimization configuration model solution, the double-layer optimization model constructed by the method focuses on power grid and hydrogen production side cooperation, with the minimum hydrogen production cost as the goal;Lower layer is based on traffic network and user side, and through hydrogen sales price optimization, balance site income and user satisfaction, two layers of model are mutually coupled with hydrogen production power and hydrogenation load, for this design iterative cooperative solution algorithm, double-layer problem is decomposed into sequentially solvable sub-problems, and global optimum is approached by cyclic iteration, the advantages of the application are that hydrogen production cost is greatly reduced after optimization, site income rate is improved, hydrogenation peak congestion duration is reduced, and user satisfaction is improved.

Description

Technical Field

[0001] This invention belongs to the technical fields of hydrogen energy supply, power system optimization operation and transportation energy coupling, and specifically relates to a two-layer optimization operation method for multi-region hydrogen production and hydrogen refueling stations that considers hydrogen refueling load response. Background Technology

[0002] Driven by the goals of "peak carbon and carbon neutrality," hydrogen energy has become a core energy source for the green transformation of the transportation sector. Integrated hydrogen production and refueling stations, as key hubs connecting hydrogen supply and transportation demand, directly determine the effectiveness of large-scale hydrogen transportation deployment through their operational efficiency. Urban hydrogen production and refueling stations generally suffer from core problems such as high hydrogen production costs, uneven spatial and temporal distribution of refueling loads, and low user satisfaction with refueling. Furthermore, the operation of hydrogen production and refueling is disconnected, making coordinated optimization difficult.

[0003] Existing technologies mainly revolve around the coordinated operation of grid-connected hydrogen production and distribution networks, focusing primarily on the economic efficiency of hydrogen production. They aim to reduce electricity costs by adjusting hydrogen production capacity through time-of-use pricing and optimizing hydrogen energy dispatch. International research, however, focuses on the coupling of off-grid renewable energy with hydrogen production and interaction with the electricity market, optimizing for profit maximization and cost minimization. However, existing technologies have significant shortcomings: First, the research focus is overly biased towards hydrogen production, failing to fully consider the spatiotemporal characteristics of hydrogen refueling load and user satisfaction, resulting in a disconnect between hydrogen production and refueling optimization. Second, dynamic hydrogen prices are only used as auxiliary variables, without establishing a collaborative optimization framework with hydrogen production costs and user satisfaction. Third, the response characteristics of intra-station and adjacent-station load shifts are not deeply explored, making it impossible to achieve peak shaving and valley filling through price signals. Fourth, a multi-regional operation model coupling transportation and power grids has not been constructed, making traditional single-objective optimization unsuitable for large-scale urban hydrogen refueling scenarios. Ultimately, this leads to unresolved issues such as high hydrogen production costs, low station revenue, refueling congestion, and poor user experience. Summary of the Invention

[0004] This invention addresses the technical pain points of existing hydrogen production and refueling stations, such as high hydrogen production costs, uneven spatial and temporal distribution of refueling loads, and low user satisfaction with refueling. It also addresses the technical shortcomings of the disconnect between hydrogen production and refueling optimization and the lack of multi-objective collaborative optimization by combining refueling load response. The invention provides a hydrogen production-refueling station optimized operation method oriented towards the energy demand of refueling loads, achieving a three-way collaborative balance of minimizing hydrogen production costs, maximizing station profitability, and maximizing user satisfaction with refueling.

[0005] The technical solution adopted in this invention is as follows:

[0006] An optimized operation method for hydrogen production and refueling stations to meet the energy demand of hydrogen refueling loads, the steps of which are as follows:

[0007] Step 1: Construction of hydrogen production and refueling station equipment model

[0008] 1.1 Construction of Alkaline Electrolyte Equipment Model

[0009] Hydrogen production efficiency is nonlinear, and the characteristic curve needs to be linearized within its operating range as shown in equation (1).

[0010] (1)

[0011] In the formula, To improve the operating efficiency of the electrolytic cell; is the operating load rate of the electrolytic cell; a1 and b1 are the fitting coefficients of the efficiency curve. This refers to the real-time power of the electrolytic cell. This is the maximum power of the electrolytic cell.

[0012] The power consumption of various auxiliary equipment, the power consumption of the hydrogen production station, and the hydrogen production capacity are shown in formula (2).

[0013] (2)

[0014] In the formula: This refers to the electrical power required for water electrolysis. Power consumption rate for pure water and circulation equipment; The electrical power consumed in hydrogen production; Hydrogen production; This refers to the calorific value of hydrogen. Electrolysis efficiency of the electrolytic cell;

[0015] 1.2 Construction of Compressed Hydrogen Storage and Refueling Model

[0016] After being cooled and purified at the hydrogen production station, the hydrogen is pressurized into a high-pressure gaseous state by a compressor, and its power model is expressed as shown in equation (3).

[0017] (3)

[0018] In the formula: This refers to the compressor power. The mass of hydrogen flowing into the compressor; For the power consumption of the compressor; Standard atmospheric pressure; For reference compressor pressure; For compressor pressure,

[0019] The hydrogen produced by the hydrogen production equipment cannot be consumed in real time and needs to be stored through hydrogen storage equipment. The hydrogen storage model is shown in equation (4).

[0020] (4)

[0021] In the formula: The hydrogen capacity in the hydrogen storage device; The hydrogen dissipation rate from the compressor to the hydrogen storage device; The amount of hydrogen compressed into the hydrogen storage device at time t; The hydrogen load at time t,

[0022] The number of hydrogen refueling machines is set according to the industry's general configuration standard for integrated hydrogen production and refueling stations, and the calculation method is as shown in formula (5).

[0023] (5)

[0024] In the formula, This refers to the number of hydrogen refueling machines; Daily hydrogen loading capacity; Setting industry-standard configurations for integrated hydrogen production and refueling stations;

[0025] Step 2, Construction of hydrogen production optimization operation model

[0026] 2.1 Objective function for optimized operation of hydrogen production station

[0027] Electricity costs are high in hydrogen production via water electrolysis. When there are sufficient new energy power plants and sufficient power supply, direct power purchase from new energy power plants is adopted; when power supply is insufficient, the electricity price purchased by the power grid is adopted. The specific electricity price is selected as shown in formula (6).

[0028] (6)

[0029] In the formula, The electricity price for hydrogen production at hydrogen production and refueling station m at time t; For the direct purchase price of new energy electricity at site m; The fixed costs paid by site m to the power grid; The electricity price purchased by the power grid at time t; The total electricity cost for the hydrogen production station; Power consumption of various equipment in the hydrogen production station; and These represent the hydrogen production power of the electrolyzer and compressor at site m at time t, respectively.

[0030] Each site aims to minimize hydrogen production costs, as shown in equation (7).

[0031] (7)

[0032] In the formula To minimize the cost of hydrogen production; To address the fixed costs of hydrogen production,

[0033] The electricity cost of hydrogen production stations fluctuates with electricity prices. The cost of hydrogen production also includes fixed costs, which mainly include equipment costs and operation and maintenance costs. The fixed cost of hydrogen production at station m is calculated as (8).

[0034] (8)

[0035] In the formula, Cost of alkaline electrolysis cell equipment for site m; Cost of compressor equipment for site m; Cost of high-pressure hydrogen storage tank equipment for site m; Land cost for site m; Water usage fee for station m; Maintenance costs for site m;

[0036] 2.2 Constraints for Optimized Operation of Hydrogen Production Stations

[0037] At each station on the power grid node, the power supply and load power of the node are balanced, and the balance equation is as shown in equation (9).

[0038] (9)

[0039] In the formula, The power supplied by the power grid to station m at time t; and The power generation of the photovoltaic and wind farms at site m at time t is respectively represented by the power output of the site m. Let m be the total power load of the power grid at time t; Let m be the network loss of the station in the regional power grid at time t;

[0040] Step 3: Construction of an optimized operation model for hydrogen refueling stations

[0041] 3.1 Objective Function for Optimized Operation of Hydrogen Refueling Stations

[0042] The objective function for the operation of hydrogen production and refueling stations includes a net profit margin target and a user satisfaction target. User satisfaction includes satisfaction with hydrogen refueling prices and satisfaction with refueling congestion time. The multiple objectives are normalized using the membership function in equation (10).

[0043] (10)

[0044] In the formula, F1 is the net profit target evaluation score; F2 is the user satisfaction score for hydrogen refueling price; and F3 is the user satisfaction score for hydrogen refueling congestion time.

[0045] 3.2 Construction of Hydrogenation Load Response Model

[0046] (1) Hydrogen transfer load within the station

[0047] Hydrogen refueling stations have varying peak, off-peak, and flat hydrogen prices. Some users avoid peak hydrogen price periods, while during off-peak periods, users choose to refuel due to price incentives. Dynamic peak, off-peak, and flat hydrogen prices affect the intraday shift in hydrogen refueling load at each node within the station's service area. The amount of hydrogen refueling load shifted in response to changes in hydrogen prices is...

[0048] (15)

[0049] In the formula, This represents the hydrogen load response offset at station m. A positive value indicates that the hydrogen load is being transferred in during that time period, while a negative value indicates that the hydrogen load is being transferred out during that time period. The average hydrogen price at station m; The hydrogenation load response coefficient;

[0050] (2) Hydrogen loading transfer volume of adjacent stations

[0051] The average hydrogen price varies among different stations. Hydrogen refueling users will tend to refuel at lower-priced stations within an acceptable distance. The hydrogen refueling load within the overlapping service area of ​​adjacent stations will shift. The hydrogen refueling load transfer model is shown in Equation (16).

[0052] (16)

[0053] In the formula, The offset for the transfer from station n to station m; The average hydrogen price at station m; Let n be the average hydrogen price at station n. Hydrogen loading at site n during time period t Let n be the hydrogen loading response coefficient when switching from station n to station m.

[0054] The hydrogen refueling load response is the sum of the initial load, the offset of the load response within the station, and the load transfer from adjacent stations. The expression for the hydrogen refueling load response is calculated as shown in equation (17).

[0055] (17)

[0056] In the formula, Initial hydrogen loading for site m;

[0057] 3.3 Constraints for Optimized Operation of Hydrogen Refueling Stations

[0058] Further optimize costs using the hydrogen price tiers in equation (18).

[0059] (18)

[0060] In the formula, To implement tiered electricity price increases; To ensure that the operating costs of hydrogen fuel cell vehicles are competitive with those of gasoline vehicles, a hydrogen price of 30 / kg is used, subject to constraint 5.4.

[0061] Step 4, Solving the two-layer optimization configuration model

[0062] The two-layer optimization model constructed by this method focuses on the coordination between the power grid and the hydrogen production side in the upper layer, with the goal of minimizing the hydrogen production cost; the lower layer focuses on the transportation network and the user side, and balances the station revenue and user satisfaction through the optimization of hydrogen sales price. The two-layer model is coupled with hydrogen production power and hydrogen refueling load. To this end, an iterative collaborative solution algorithm is designed to decompose the two-layer problem into subproblems that can be solved sequentially, and approximate the global optimum through iterative iteration.

[0063] Preferably, in step 3.1,

[0064] (1) Net return target

[0065] Each hydrogen production and refueling station tends to set hydrogen sales prices for different time periods to maximize net profit, and its profit maximization objective is shown in equation (11).

[0066] (11)

[0067] In the formula, Net revenue for hydrogen refueling station m; Let m be the revenue from hydrogen sales at hydrogen refueling station m; μ is the fitting coefficient for the profitability of the hydrogen refueling station.

[0068] The net revenue of a hydrogen production and refueling station can be defined as the sum of hydrogen refueling revenue and residual oxygen revenue, minus equipment, land use and operation and maintenance costs. Its amount is directly affected by decision variables such as hydrogen sales price and total hydrogen refueling load. The net revenue is calculated as Equation (12).

[0069] (12)

[0070] In the formula, The equipment cost for site m; The land use and operation and maintenance costs for station m; The residual oxygen revenue of station m; Price per unit of oxygen; The hydrogen loading at site m during time period t; The price of hydrogen sold at station m during time period t;

[0071] (2) User satisfaction target

[0072] The operational goals of hydrogen production and refueling stations should not only consider maximizing their own net profit margin, but also user satisfaction with hydrogen refueling prices and the hydrogen refueling congestion that occurs during peak hours in the transportation network. These two factors together constitute the user hydrogen refueling satisfaction target.

[0073] Satisfaction is highest when the hydrogen price is at its lowest value during the period and lowest when it is at its highest value during the period. The mathematical expression for this is shown in equation (13).

[0074] (13)

[0075] In the formula, and Let $t$ be the maximum and minimum hydrogen sales prices at station $m$ during time period $t$.

[0076] Congestion time is calculated by the difference between hydrogen refueling load and the station's service capacity. The calculation of hydrogen refueling congestion time and satisfaction is as shown in equation (14).

[0077] (14)

[0078] In the formula, Add hydrogen to station m to alleviate congestion; The maximum hourly hydrogen dispensing capacity of hydrogen dispenser m at station m; Total congestion time for hydrogen refueling; The number of hydrogen refueling machines at station m; The time for a single hydrogen addition is 5 minutes. Reference time for hydrogen refueling queue.

[0079] Preferably, the solution algorithm in step 4 is as follows:

[0080] First, perform initialization operations at the transportation network and power grid levels;

[0081] Next, based on formulas (6) and (7), the optimal cost of the upper layer (hydrogen production side) is obtained using commercial solution tools;

[0082] Then, the obtained upper-layer solution is substituted into the lower layer (hydrogenation side) to complete the initialization of the lower-layer model;

[0083] Next, the loop process of lower-level optimization begins: the system will bring the upper-level solution into the lower-level initialization and generate a new population based on formulas (12) to (19);

[0084] Subsequently, the algorithm will determine whether the population satisfies the constraints (20) and (21). If not, the population will be eliminated. If the constraints are satisfied, the multi-objective comprehensive score will be calculated based on the load response model and formulas (17) to (19).

[0085] After obtaining the score, the system will perform population screening to retain superior individuals;

[0086] Next, the algorithm will determine whether the set number of iterations has been reached. If the number of iterations has not been reached, the system will generate a new population through crossover and mutation, and return to the previous steps to continue the loop verification. If the preset number of iterations has been reached, the algorithm will output the global optimal solution, and the entire solution process will end here.

[0087] The beneficial effects that can be achieved by adopting the above-mentioned technical solution in this invention are:

[0088] This method establishes a two-layer optimized operation model for hydrogen production and refueling stations that considers hydrogen refueling load response. It optimizes the operation of multi-regional stations coupled with the transportation network and power grid. Due to the differences in the distribution of new energy power plants and hydrogen refueling load in different regions, the optimization results of each station are different. After optimization, the cost of hydrogen production is significantly reduced and the profitability of the stations is improved. The use of time-of-use hydrogen sales pricing can effectively regulate the hydrogen refueling load response, reduce the duration of peak refueling congestion, and improve user satisfaction.

[0089] This method builds a full-process equipment model based on a transportation network-power grid coupling framework, combining hydrogen production cost optimization with hydrogen refueling revenue and user satisfaction optimization into a two-layer collaborative system, fully incorporating the spatiotemporal response characteristics of hydrogen refueling load. By guiding load peak shaving and valley filling through dynamic peak-valley hydrogen pricing, and using an iterative solution algorithm to achieve global optimization of the two-layer model, it can simultaneously reduce hydrogen production costs, balance hydrogen refueling load, and improve station revenue and user satisfaction. It is perfectly adapted to the large-scale operation of hydrogen production and refueling stations in multiple urban areas, and completely solves the industry problem of the disconnect between hydrogen production and refueling operations. Attached Figure Description

[0090] Figure 1 This is a diagram of the two-layer optimization model of the hydrogen production and refueling station in this invention;

[0091] Figure 2 This is a flowchart of the solution process for the two-layer optimization model in this invention;

[0092] Figure 3 This is the initial hydrogenation load diagram in this invention;

[0093] Figure 4 This is a comparison chart of the operation of site 1 before and after optimization in this invention;

[0094] Figure 5 This is a comparison chart of the operation of site 2 before and after optimization in this invention;

[0095] Figure 6 This is a comparison chart of the operation of site 3 before and after optimization in this invention;

[0096] Figure 7 This is a comparison chart of the operation of site 4 before and after optimization in this invention. Detailed Implementation

[0097] This method is based on a transportation network-power grid coupled operation architecture. It considers the operational characteristics of the entire hydrogen production, compression, storage, and refueling process at multi-regional integrated hydrogen production and refueling stations in urban areas, along with the comprehensive operational needs for hydrogen production cost control, operational revenue improvement, and user satisfaction assurance. It constructs a two-layer optimized operation model covering the physical model of core equipment and the coordinated operation of the upper-layer hydrogen production side and the lower-layer hydrogen refueling side. The two-layer optimization model is as follows: Figure 1As shown, the upper layer optimizes hydrogen production power with the goal of minimizing hydrogen production costs, while the lower layer optimizes time-of-use hydrogen sales prices with the dual objectives of site profitability and user hydrogen refueling satisfaction. The two-layer model is deeply coupled through response mechanisms such as intra-station time-of-use transfer of hydrogen refueling load and inter-station transfer between adjacent sites. An iterative collaborative solution algorithm is used to iteratively solve the two-layer model to obtain the globally optimal operation of hydrogen production power and hydrogen sales price. Finally, the feasibility and effectiveness of the scheme are verified through multi-dimensional simulation comparison using a coupled scenario of a 76-node power grid and a 25-node transportation network.

[0098] An optimized operation method for hydrogen production and refueling stations to meet the energy demand of hydrogen refueling loads, the steps of which are as follows:

[0099] Step 1: Construction of hydrogen production and refueling station equipment model

[0100] 1.1 Construction of Alkaline Electrolyte Equipment Model

[0101] To control the cost of electricity for hydrogen production, hydrogen production stations need to adjust the operating status and power of alkaline electrolyzers according to peak and off-peak electricity prices. Therefore, it is crucial to establish an operating characteristic model. Since the hydrogen production efficiency is nonlinear, the characteristic curve working range needs to be linearized as shown in equation (1).

[0102] (1)

[0103] In the formula, To improve the operating efficiency of the electrolytic cell; is the operating load rate of the electrolytic cell; a1 and b1 are the fitting coefficients of the efficiency curve. This refers to the real-time power of the electrolytic cell. This is the maximum power of the electrolytic cell.

[0104] In addition to the electrolyzer, the power consumption of other auxiliary equipment should not be ignored. The power consumption of various auxiliary equipment, the power consumption of the hydrogen production station and the hydrogen production capacity are shown in formula (2).

[0105] (2)

[0106] In the formula: This refers to the electrical power required for water electrolysis. Power consumption rate for pure water and circulation equipment; The electrical power consumed in hydrogen production; Hydrogen production; This refers to the calorific value of hydrogen. Electrolysis efficiency of the electrolytic cell;

[0107] 1.2 Construction of Compressed Hydrogen Storage and Refueling Model

[0108] After being cooled and purified at the hydrogen production station, the hydrogen is pressurized into a high-pressure gaseous state by a compressor, and its power model is expressed as shown in equation (3).

[0109] (3)

[0110] In the formula: This refers to the compressor power. The mass of hydrogen flowing into the compressor; For the power consumption of the compressor; Standard atmospheric pressure; For reference compressor pressure; For compressor pressure,

[0111] The hydrogen produced by the hydrogen production equipment cannot be consumed in real time and needs to be stored through hydrogen storage equipment. The hydrogen storage model is shown in equation (4).

[0112] (4)

[0113] In the formula: The hydrogen capacity in the hydrogen storage device; The hydrogen dissipation rate from the compressor to the hydrogen storage device; The amount of hydrogen compressed into the hydrogen storage device at time t; The hydrogen load at time t,

[0114] The number of hydrogen refueling machines is set according to the industry's general configuration standard for integrated hydrogen production and refueling stations, and the calculation method is as shown in formula (5).

[0115] (5)

[0116] In the formula, This refers to the number of hydrogen refueling machines; Daily hydrogen loading capacity; Setting industry-standard configurations for integrated hydrogen production and refueling stations;

[0117] Step 2, Construction of hydrogen production optimization operation model

[0118] 2.1 Objective function for optimized operation of hydrogen production station

[0119] Electricity costs are high in hydrogen production via water electrolysis. When there are sufficient new energy power plants and sufficient power supply, direct power purchase from new energy power plants is adopted; when power supply is insufficient, the electricity price purchased by the power grid is adopted. The specific electricity price is selected as shown in formula (6).

[0120] (6)

[0121] In the formula, The electricity price for hydrogen production at hydrogen production and refueling station m at time t; For the direct purchase price of new energy electricity at site m; The fixed costs paid by site m to the power grid; The electricity price purchased by the power grid at time t; The total electricity cost for the hydrogen production station; Power consumption of various equipment in the hydrogen production station; and Let m be the hydrogen production power of the electrolyzer and compressor at station m at time t.

[0122] Each site aims to minimize hydrogen production costs, as shown in equation (7).

[0123] (7)

[0124] In the formula To minimize the cost of hydrogen production; To address the fixed costs of hydrogen production,

[0125] The electricity cost of hydrogen production stations fluctuates with electricity prices. The cost of hydrogen production also includes fixed costs, which mainly include equipment costs and operation and maintenance costs. The fixed cost of hydrogen production at station m is calculated as (8).

[0126] (8)

[0127] In the formula, Cost of alkaline electrolysis cell equipment for site m; Cost of compressor equipment for site m; Cost of high-pressure hydrogen storage tank equipment for site m; Land cost for site m; Water usage fee for station m; Maintenance costs for site m;

[0128] 2.2 Constraints for Optimized Operation of Hydrogen Production Stations

[0129] At each station on the power grid node, the power supply and load power of the node are balanced, and the balance equation is as shown in equation (9).

[0130] (9)

[0131] In the formula, The power supplied by the power grid to station m at time t; and The power generation of the photovoltaic and wind farms at site m at time t is respectively represented by the power output of the site m. Let m be the total power load of the power grid at time t; Let m be the network loss of the station in the regional power grid at time t;

[0132] Step 3: Construction of an optimized operation model for hydrogen refueling stations

[0133] 3.1 Objective Function for Optimized Operation of Hydrogen Refueling Stations

[0134] The objective function for the operation of hydrogen production and refueling stations includes a net profit margin target and a user satisfaction target. User satisfaction includes satisfaction with hydrogen refueling prices and satisfaction with refueling congestion time. The multiple objectives are normalized using the membership function in equation (10).

[0135] (10)

[0136] In the formula, F1 is the net profit target evaluation score; F2 is the user satisfaction score for hydrogen refueling price; and F3 is the user satisfaction score for hydrogen refueling congestion time.

[0137] (1) Net return target

[0138] Each hydrogen production and refueling station tends to set hydrogen sales prices for different time periods to maximize net profit, and its profit maximization objective is shown in equation (11).

[0139] (11)

[0140] In the formula, Net revenue for hydrogen refueling station m; Let m be the revenue from hydrogen sales at hydrogen refueling station m; μ is the fitting coefficient for the profitability of the hydrogen refueling station.

[0141] The net revenue of a hydrogen production and refueling station can be defined as the sum of hydrogen refueling revenue and residual oxygen revenue, minus equipment, land use and operation and maintenance costs. Its amount is directly affected by decision variables such as hydrogen sales price and total hydrogen refueling load. The net revenue is calculated as Equation (12).

[0142] (12)

[0143] In the formula, The equipment cost for site m; The land use and operation and maintenance costs for station m; The residual oxygen revenue of station m; Price per unit of oxygen; The hydrogen loading at site m during time period t; The price of hydrogen sold at station m during time period t;

[0144] (2) User satisfaction target

[0145] The operational goals of hydrogen production and refueling stations should not only consider maximizing their own net profit margin, but also user satisfaction with hydrogen refueling prices and the hydrogen refueling congestion that occurs during peak hours in the transportation network. These two factors together constitute the user hydrogen refueling satisfaction target.

[0146] Satisfaction is highest when the hydrogen price is at its lowest value during the period and lowest when it is at its highest value during the period. The mathematical expression for this is shown in equation (13).

[0147] (13)

[0148] In the formula, and Let $t$ be the maximum and minimum hydrogen sales prices at station $m$ during time period $t$.

[0149] Congestion time is calculated by the difference between hydrogen refueling load and the station's service capacity. The calculation of hydrogen refueling congestion time and satisfaction is as shown in equation (14).

[0150] (14)

[0151] In the formula, Add hydrogen to station m to alleviate congestion; The maximum hourly hydrogen dispensing capacity of hydrogen dispenser m at station m; Total congestion time for hydrogen refueling; The number of hydrogen refueling machines at station m; The time for a single hydrogen addition is 5 minutes. Reference time for hydrogen refueling queue;

[0152] 3.2 Construction of Hydrogenation Load Response Model

[0153] (1) Hydrogen transfer load within the station

[0154] Hydrogen refueling stations have varying peak, off-peak, and flat hydrogen prices. Some users avoid peak hydrogen price periods, while during off-peak periods, users choose to refuel due to price incentives. Dynamic peak, off-peak, and flat hydrogen prices affect the intraday shift in hydrogen refueling load at each node within the station's service area. The amount of hydrogen refueling load shifted in response to changes in hydrogen prices is...

[0155] (15)

[0156] In the formula, This represents the hydrogen load response offset at station m. A positive value indicates that the hydrogen load is being transferred in during that time period, while a negative value indicates that the hydrogen load is being transferred out during that time period. The average hydrogen price at station m; The hydrogenation load response coefficient;

[0157] (2) Hydrogen loading transfer volume of adjacent stations

[0158] The average hydrogen price varies among different stations. Hydrogen refueling users will tend to refuel at lower-priced stations within an acceptable distance. The hydrogen refueling load within the overlapping service area of ​​adjacent stations will shift. The hydrogen refueling load transfer model is shown in Equation (16).

[0159] (16)

[0160] In the formula, The offset for the transfer from station n to station m; The average hydrogen price at station m; Let n be the average hydrogen price at station n. Hydrogen loading at site n during time period t Let n be the hydrogen loading response coefficient when switching from station n to station m.

[0161] The hydrogen refueling load response is the sum of the initial load, the offset of the load response within the station, and the load transfer from adjacent stations. The expression for the hydrogen refueling load response is calculated as shown in equation (17).

[0162] (17)

[0163] In the formula, Initial hydrogen loading for site m;

[0164] 3.3 Constraints for Optimized Operation of Hydrogen Refueling Stations

[0165] The price of hydrogen is crucial to the development of hydrogen fuel cell vehicles. Excessively high prices will suppress demand and limit industry development; while competitive prices will incentivize consumption and promote widespread adoption. Research shows that when hydrogen prices fall to a competitive level, the cost advantage of hydrogen fuel cell vehicles will become apparent, thereby promoting the coordinated development of the entire industry chain. Further cost optimization can be achieved using the tiered hydrogen pricing mechanism (18).

[0166] (18)

[0167] In the formula, To implement tiered electricity price increases; To ensure that the operating costs of hydrogen fuel cell vehicles are competitive with those of gasoline vehicles, a hydrogen price of 30 / kg is used, subject to constraint 5.4.

[0168] Step 4, Solving the two-layer optimization configuration model

[0169] The proposed two-layer optimization model focuses on the coordination between the power grid and hydrogen production in the upper layer, aiming to minimize hydrogen production costs. The lower layer considers the transportation network and the user side, optimizing hydrogen sales prices to balance station revenue and user satisfaction. The two layers are coupled by hydrogen production capacity and hydrogen refueling load. Therefore, an iterative collaborative solution algorithm is designed to decompose the two-layer problem into sequentially solvable subproblems, approximating the global optimum through iterative iteration. The algorithm steps are as follows: Figure 2 As shown,

[0170] The solution algorithm steps are as follows:

[0171] First, perform initialization operations at the transportation network and power grid levels;

[0172] Next, based on formulas (6) and (7), the optimal cost of the upper layer (hydrogen production side) is obtained using commercial solution tools;

[0173] Then, the obtained upper-layer solution is substituted into the lower layer (hydrogenation side) to complete the initialization of the lower-layer model;

[0174] Next, the loop process of lower-level optimization begins: the system will bring the upper-level solution into the lower-level initialization and generate a new population based on formulas (12) to (19);

[0175] Subsequently, the algorithm will determine whether the population satisfies the constraints (20) and (21). If not, the population will be eliminated. If the constraints are satisfied, the multi-objective comprehensive score will be calculated based on the load response model and formulas (17) to (19).

[0176] After obtaining the score, the system will perform population screening to retain superior individuals;

[0177] Next, the algorithm will determine whether the set number of iterations has been reached. If the number of iterations has not been reached, the system will generate a new population through crossover and mutation, and return to the previous steps to continue the loop verification. If the preset number of iterations has been reached, the algorithm will output the global optimal solution, and the entire solution process will end here.

[0178] Case Analysis

[0179] Parameter settings

[0180] To verify the rationality and effectiveness of the model, a case study is first conducted using a 76-node 330kV-110kV-35kV power grid architecture and a 25-node transportation network. The planning area is divided into industrial, commercial, and residential zones. Each power grid node in the industrial zone is connected to both a photovoltaic (PV) power station and a wind power station; the power grid nodes in the residential zone are connected to PV power stations. Based on the spatiotemporal distribution of hydrogen refueling load in the transportation network, the initial hydrogen refueling load within the service area of ​​each station is as follows: Figure 3 As shown,

[0181] Case Analysis

[0182] Hydrogen production and refueling station 1 corresponds to a node where the transportation network and power grid are coupled, with supporting wind and solar renewable energy installations in the region. Before optimization, hydrogen production was only carried out at rated power during off-peak hours, with hydrogen replenishment during peak hours to meet load demand. After optimization, the hydrogen production time is extended, and high-efficiency and medium-efficiency operations are adopted at different load rates during different time periods. This fully utilizes surplus wind and solar power in the region, combined with direct power purchase from renewable energy sources and multi-condition operation strategies, effectively reducing hydrogen production costs and unit electricity costs. A comparison of the operation of station 1 before and after optimization is provided below. Figure 4 As shown.

[0183] Before optimization, hydrogen production and refueling station 2 only produced hydrogen at full power during off-peak electricity periods, resulting in a mismatch between wind power output and the electricity consumption for hydrogen production, leading to high hydrogen production costs. After optimization, the hydrogen production time was adjusted to utilize the rated power of wind power during nighttime surplus periods, improving wind power utilization and effectively reducing hydrogen production costs. A comparison of the operation of station 2 before and after optimization is provided. Figure 5 As shown.

[0184] Hydrogen production and refueling station 3 is located within a large-scale regional photovoltaic power station and has signed a direct power purchase agreement for new energy. It utilizes surplus photovoltaic power for hydrogen production. Before optimization, full-power hydrogen production during fixed periods was sufficient to meet the daily load demand. After optimization, the hydrogen production duration was slightly extended, the operating load rate was reduced, and the hydrogen production efficiency of the electrolyzer was slightly improved. However, the overall optimization effect was not significant. A comparison of the operation of station 3 before and after optimization is provided below. Figure 6 As shown.

[0185] Site 4 has no adjacent renewable energy power plants, and its distance from the grid of renewable energy power plants in the system is too far to use direct power purchase. The price of hydrogen production electricity is based on the grid purchase price. Site 4 has not changed after optimization. Figure 7 As shown.

[0186] A comparative analysis of dynamic hydrogen price and fixed hydrogen price was conducted on site 1. The results are shown in the table. The average hydrogen refueling price of the two is the same, and there is no significant difference in total revenue. Dynamic hydrogen price can effectively guide the shift of hydrogen refueling load from peak to valley, reduce peak load and increase valley load, significantly shorten the hydrogen refueling congestion time, and significantly improve the overall operation score.

[0187]

Claims

1. An optimized operation method for hydrogen production-refueling stations to meet the energy demand of hydrogen refueling loads, characterized in that, The steps are as follows: Step 1: Construction of hydrogen production and refueling station equipment model 1.1 Construction of Alkaline Electrolyte Equipment Model Hydrogen production efficiency is nonlinear, and the characteristic curve needs to be linearized within its operating range as shown in equation (1). (1) In the formula, To improve the operating efficiency of the electrolytic cell; is the operating load rate of the electrolytic cell; a1 and b1 are the fitting coefficients of the efficiency curve. This refers to the real-time power of the electrolytic cell. This is the maximum power of the electrolytic cell. The power consumption of various auxiliary equipment, the power consumption of the hydrogen production station, and the hydrogen production capacity are shown in formula (2). (2) In the formula: This refers to the electrical power required for water electrolysis. Power consumption rate for pure water and circulation equipment; The electrical power consumed in hydrogen production; Hydrogen production; H H2 η is the calorific value of hydrogen. AEL Electrolysis efficiency of the electrolytic cell; 1.2 Construction of Compressed Hydrogen Storage and Refueling Model After being cooled and purified at the hydrogen production station, the hydrogen is pressurized into a high-pressure gaseous state by a compressor, and its power model is expressed as shown in equation (3). (3) In the formula: This refers to the compressor power. The mass of hydrogen flowing into the compressor; For the power consumption of the compressor; Standard atmospheric pressure; For reference compressor pressure; For compressor pressure, The hydrogen produced by the hydrogen production equipment cannot be consumed in real time and needs to be stored through hydrogen storage equipment. The hydrogen storage model is shown in equation (4). (4) In the formula: The hydrogen capacity in the hydrogen storage device; The hydrogen dissipation rate from the compressor to the hydrogen storage device; The amount of hydrogen compressed into the hydrogen storage device at time t; The hydrogen load at time t, The number of hydrogen refueling machines is set according to the industry's general configuration standard for integrated hydrogen production and refueling stations, and the calculation method is as shown in formula (5). (5) In the formula, This refers to the number of hydrogen refueling machines; Daily hydrogen loading capacity; Setting industry-standard configurations for integrated hydrogen production and refueling stations; Step 2, Construction of hydrogen production optimization operation model 2.1 Objective function for optimized operation of hydrogen production station Electricity costs are high in hydrogen production via water electrolysis. When there are sufficient new energy power plants and sufficient power supply, direct power purchase from new energy power plants is adopted; when power supply is insufficient, the electricity price purchased by the power grid is adopted. The specific electricity price is selected as shown in formula (6). (6) In the formula, The electricity price for hydrogen production at hydrogen production and refueling station m at time t; For the direct purchase price of new energy electricity at site m; The fixed costs paid by site m to the power grid; The electricity price purchased by the power grid at time t; The total electricity cost for the hydrogen production station; Power consumption of various equipment in the hydrogen production station; and Let m be the hydrogen production power of the electrolyzer and compressor at station m at time t. Each site aims to minimize hydrogen production costs, as shown in equation (7). (7) In the formula To minimize the cost of hydrogen production; To address the fixed costs of hydrogen production, The electricity cost of hydrogen production stations fluctuates with electricity prices. The cost of hydrogen production also includes fixed costs, which mainly include equipment costs and operation and maintenance costs. The fixed cost of hydrogen production at station m is calculated as (8). (8) In the formula, Cost of alkaline electrolysis cell equipment for site m; Cost of compressor equipment for site m; Cost of high-pressure hydrogen storage tank equipment for site m; Land cost for site m; Water usage fee for station m; Maintenance costs for site m; 2.2 Constraints for Optimized Operation of Hydrogen Production Stations At each station on the power grid node, the power supply and load power of the node are balanced, and the balance equation is as shown in equation (9). (9) In the formula, The power supplied by the power grid to station m at time t; and The power generation of the photovoltaic and wind farms at site m at time t is respectively represented by the power output of the site m. Let m be the total power load of the power grid at time t; The network loss of station m within the regional power grid at time t; Step 3: Construction of an optimized operation model for hydrogen refueling stations 3.1 Objective Function for Optimized Operation of Hydrogen Refueling Stations The objective function for the operation of hydrogen production and refueling stations includes a net profit margin target and a user satisfaction target. User satisfaction includes satisfaction with hydrogen refueling prices and satisfaction with refueling congestion time. The multiple objectives are normalized using the membership function in equation (10). (10) In the formula, F1 is the net profit target evaluation score; F2 is the user satisfaction score for hydrogen refueling price; and F3 is the user satisfaction score for hydrogen refueling congestion time. 3.2 Construction of Hydrogenation Load Response Model (1) Hydrogen transfer load within the station Hydrogen refueling stations have varying peak, off-peak, and flat hydrogen prices. Some users avoid peak hydrogen price periods, while during off-peak periods, users choose to refuel due to price incentives. Dynamic peak, off-peak, and flat hydrogen prices affect the intraday shift in hydrogen refueling load at each node within the station's service area. The amount of hydrogen refueling load shifted in response to changes in hydrogen prices is... (15) In the formula, This represents the hydrogen load response offset at station m. A positive value indicates that the hydrogen load is being transferred in during that time period, while a negative value indicates that the hydrogen load is being transferred out during that time period. The average hydrogen price at station m; The hydrogenation load response coefficient; (2) Hydrogen loading transfer volume of adjacent stations The average hydrogen price varies among different stations. Hydrogen refueling users will tend to refuel at lower-priced stations within an acceptable distance. The hydrogen refueling load within the overlapping service area of ​​adjacent stations will shift. The hydrogen refueling load transfer model is shown in Equation (16). (16) In the formula, The offset for the transfer from station n to station m; The average hydrogen price at station m; Let n be the average hydrogen price at station n. Hydrogen loading at site n during time period t Let n be the hydrogen loading response coefficient when switching from station n to station m. The hydrogen refueling load response is the sum of the initial load, the offset of the load response within the station, and the load transfer from adjacent stations. The expression for the hydrogen refueling load response is calculated as shown in equation (17). (17) In the formula, Initial hydrogen loading for site m; 3.3 Constraints for Optimized Operation of Hydrogen Refueling Stations Further optimize costs using the hydrogen price tiers in equation (18). (18) In the formula, To implement tiered electricity price increases; To ensure that the operating costs of hydrogen fuel cell vehicles are competitive with those of gasoline vehicles, a hydrogen price of 30 / kg is used, subject to constraint 5.

4. Step 4, Solving the two-layer optimization configuration model The two-layer optimization model constructed by this method focuses on the coordination between the power grid and the hydrogen production side in the upper layer, with the goal of minimizing the hydrogen production cost; the lower layer focuses on the transportation network and the user side, and balances the station revenue and user satisfaction through the optimization of hydrogen sales price. The two-layer model is coupled with hydrogen production power and hydrogen refueling load. To this end, an iterative collaborative solution algorithm is designed to decompose the two-layer problem into subproblems that can be solved sequentially, and approximate the global optimum through iterative iteration.

2. The optimized operation method for hydrogen production-refueling stations based on the energy demand of hydrogen refueling load according to claim 1, characterized in that, In step 3.1, (1) Net return target Each hydrogen production and refueling station tends to set hydrogen sales prices for different time periods to maximize net profit, and its profit maximization objective is shown in equation (11). (11) In the formula, Net revenue for hydrogen refueling station m; Let m be the revenue from hydrogen sales at hydrogen refueling station m; μ is the fitting coefficient for the profitability of the hydrogen refueling station. The net revenue of a hydrogen production and refueling station can be defined as the sum of hydrogen refueling revenue and residual oxygen revenue, minus equipment, land use and operation and maintenance costs. Its amount is directly affected by decision variables such as hydrogen sales price and total hydrogen refueling load. The net revenue is calculated as Equation (12). (12) In the formula, The equipment cost for site m; The land use and operation and maintenance costs for station m; The residual oxygen revenue of station m; Price per unit of oxygen; The hydrogen loading at site m during time period t; The price of hydrogen sold at station m during time period t; (2) User satisfaction target The operational goals of hydrogen production and refueling stations should not only consider maximizing their own net profit margin, but also user satisfaction with hydrogen refueling prices and the hydrogen refueling congestion that occurs during peak hours in the transportation network. These two factors together constitute the user hydrogen refueling satisfaction target. Satisfaction is highest when the hydrogen price is at its lowest value during the period and lowest when it is at its highest value during the period. The mathematical expression for this is shown in equation (13). (13) In the formula, and Let $t$ be the maximum and minimum hydrogen sales prices at station $m$ during time period $t$. Congestion time is calculated by the difference between hydrogen refueling load and the station's service capacity. The calculation of hydrogen refueling congestion time and satisfaction is as shown in equation (14). (14) In the formula, Add hydrogen to station m to alleviate congestion; The maximum hourly hydrogen dispensing capacity of hydrogen dispenser m at station m; Total congestion time for hydrogen refueling; The number of hydrogen refueling machines at station m; The time for a single hydrogen addition is 5 minutes. Reference time for hydrogen refueling queue.

3. The optimized operation method for hydrogen production-refueling stations based on the energy demand of hydrogen refueling load according to claim 1, characterized in that, The solution algorithm in step 4 is as follows: First, perform initialization operations at the transportation network and power grid levels; Next, based on formulas (6) and (7), the optimal cost of the upper layer (hydrogen production side) is obtained using commercial solution tools; Then, the obtained upper-layer solution is substituted into the lower layer (hydrogenation side) to complete the initialization of the lower-layer model; Next, the loop process of lower-level optimization begins: the system will bring the upper-level solution into the lower-level initialization and generate a new population based on formulas (12) to (19); Subsequently, the algorithm will determine whether the population satisfies the constraints (20) and (21). If not, the population will be eliminated. If the constraints are satisfied, the multi-objective comprehensive score will be calculated based on the load response model and formulas (17) to (19). After obtaining the score, the system will perform population screening to retain superior individuals; Next, the algorithm will determine whether the set number of iterations has been reached. If the number of iterations has not been reached, the system will generate a new population through crossover and mutation, and return to the previous steps to continue the loop verification. If the preset number of iterations has been reached, the algorithm will output the global optimal solution, and the entire solution process will end here.

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