A method for optimizing the configuration of wind and solar power systems in hydrogen production parks

Abstract

This invention relates to the field of new energy power generation technology, specifically a method for optimizing the wind-solar power ratio system of a hydrogen production park, comprising: S1) calculating the output time series of local wind and solar power generation, and analyzing the output characteristics of wind and solar power and the wind-solar correlation; S2) establishing equivalent equipment models of hydrogen energy storage units and electrochemical energy storage units, based on the operating characteristic models of relevant electrical and hydrogen equipment in the park; S3) combining the above objective functions to obtain the system planning optimization configuration scheme and the total system configuration cost of the new energy hydrogen production park; S4) further considering electricity price factors and energy storage operation characteristic constraints, conducting a year-round wind-solar hydrogen production time series operation simulation, and analyzing the park's electricity purchase demand and new energy consumption capacity under the optimized configuration scheme of this plan.

Description

Technical Field

[0001] This invention relates to the field of new energy power generation technology, specifically a method for optimizing the configuration of wind and solar power ratio systems in hydrogen production parks. Background Technology

[0002] In recent years, the solar energy industry has become a hot development area. In industrial parks and eco-parks, "green hydrogen" can be generated by configuring photovoltaic units or wind turbine units. Obtaining clean and low-carbon hydrogen is an inevitable path for the sustainable development of hydrogen energy in the future.

[0003] Despite the enormous potential of wind and solar hydrogen production, several shortcomings remain in its current application. Firstly, from a conversion efficiency perspective, water electrolysis for hydrogen production suffers from low energy conversion efficiency and high production costs during energy storage. Secondly, current regional planning for wind and solar power development is largely based on individual, rather than fully considering the operational characteristics of wind and solar power generation and the optimal capacity ratio. Furthermore, due to the significant differences in hydrogen load characteristics across various application scenarios, the configuration strategies and equipment planning for hydrogen energy storage in each scenario require targeted design and optimization. Summary of the Invention

[0004] To address the aforementioned problems, this invention provides an optimized configuration method for a wind-solar ratio system in a hydrogen production park. This method optimizes the comprehensive utilization of wind and solar resources and achieves accurate simulation of energy equipment operation. The technical solution adopted by this invention is as follows:

[0005] A method for optimizing the configuration of a wind-solar ratio system in a hydrogen production park includes the following steps:

[0006] S1) Calculate the output time series of local wind and solar power, analyze the output characteristics of wind and solar power and the wind-solar correlation, wherein the power output model of wind turbine generators is:

[0007]

[0008] In the formula, P WT,t Let t be the power output of the wind turbine generator.

[0009] N represents the number of grid-connected wind / photovoltaic units in the wind farm;

[0010] P WT This refers to the rated power output of the wind turbine.

[0011] v t Let t be the actual wind speed at time t;

[0012] v in The cut-in wind speed for the wind turbine;

[0013] v out The cut-out wind speed for the wind turbine;

[0014] v 0 The rated wind speed of the wind turbine;

[0015] The electrical energy output by photovoltaic power generation is direct current (DC), and its steady-state power output model can be expressed as:

[0016]

[0017] In the formula: P PV This represents the actual power generation of the photovoltaic unit (kW).

[0018] P STC The rated output power (kW) of the photovoltaic module under standard test condition (STC).

[0019] G ING (t) represents the solar radiation intensity at time t (W / m²). 2 );

[0020] G STC Solar radiation intensity under standard conditions (1000 W / m²) 2 );

[0021] η loss This refers to the power loss caused by the increase in temperature in photovoltaic cells;

[0022] To ensure wind power absorption and the economic benefits of the industrial park, during off-peak electricity prices, the electricity generated by the wind turbines or the surplus electricity after offsetting the park's load demand can be stored in the energy storage system; during peak electricity prices, the electricity generated by the wind turbines can be directly absorbed by the load side, or the energy storage system can release power to supply the load and the grid.

[0023] S2) Establish equivalent equipment models for hydrogen energy storage units and electrochemical energy storage units. Based on the operating characteristic models of relevant electric and hydrogen equipment in the park, form the power balance relationship between the electric bus and the hydrogen bus. Taking the lowest life cycle cost as the objective function, and considering constraints such as power balance, equipment operating characteristics, and land restrictions, build a system planning optimization configuration model for the new energy hydrogen production park that considers the wind and solar power ratio.

[0024] S3) Combining the above objective functions, obtain the optimal configuration scheme for the new energy hydrogen production park system planning and the total system configuration cost;

[0025] S4) Further considering electricity price factors and energy storage operation characteristics constraints, a full-year simulation of wind-solar hydrogen production was conducted to analyze the park's electricity purchase demand and renewable energy absorption capacity under the optimized configuration scheme of this plan.

[0026] In step S1 above, a clustering method is used to obtain typical daily conditions, including the following steps:

[0027] S1.1) Randomly select a sample from dataset x as the first center point;

[0028] S1.2) Calculate the shortest distance between each sample and the existing cluster center, denoted by D(x); then calculate the probability P(x) of each sample point being selected as the next cluster center, and finally select the sample point corresponding to the maximum probability value (or probability distribution) as the next cluster center;

[0029]

[0030] S1.3) Repeat S1.2 until k cluster centers are found;

[0031] S1.4) For each sample in the dataset, calculate its distance to the k cluster centers and assign it to the class corresponding to the cluster center with the smallest distance;

[0032] S1.5) Calculate the average for each category and recalculate the cluster centers;

[0033] S1.6) Repeat steps S1.4 and S1.5 until a termination condition is met, typically when the objective function reaches its optimum or the maximum number of iterations is reached. The objective function often differs depending on the distance metric. When using Euclidean distance, the objective function is generally to minimize the sum of squared distances from an object to its cluster centroid; when using cosine similarity, the objective function is generally to maximize the sum of cosine similarities from an object to its cluster centroid.

[0034] In step S2 above, the following constraints are set for the multi-objective optimization configuration model:

[0035] S2.1) Power balance constraint:

[0036]

[0037] In the formula: L E (t) represents the park's electricity load demand during time period t;

[0038] L H (t) represents the hydrogen load demand of the park during time period t;

[0039] S2.2) Equipment output constraints

[0040] Equipment output constraints include electrolyzer output power constraints, fuel cell output power constraints, battery output and capacity constraints, and hydrogen storage tank output and capacity constraints.

[0041]

[0042] In the formula: P EL_max P FC_max These are the maximum output values ​​of the electrolyzer and fuel cell, respectively.

[0043] S E_max S H_max These are the maximum capacities of the battery and the hydrogen storage tank, respectively.

[0044] S2.3) Constraints on energy storage devices:

[0045]

[0046] In the formula: SOC(0) is the initial state of charge of the battery;

[0047] SOC(24) represents the state of charge of the battery at the end of the charging process.

[0048] S H (0) represents the initial capacity of the hydrogen storage tank;

[0049] S H (24) is the final capacity of the hydrogen storage tank;

[0050] S2.4) Constraints on the purchase and sale of electricity:

[0051]

[0052] In the formula: P grid_max and P grid_min These are the upper and lower limits of the power exchanged between the park and the upper-level power grid, respectively.

[0053] S2.5) Land area constraint:

[0054]

[0055] In the formula: P PV_max P WT_max This refers to the total installed capacity of photovoltaic units and wind turbine units.

[0056] λ PV , λ WT The floor area per unit capacity of photovoltaic units and wind turbine units;

[0057] S max This represents the largest construction area for new energy generating units in the park.

[0058] In step S2 above, the objective function for park optimization is to minimize the total life cycle cost:

[0059]

[0060] In the formula: Ccstr For a one-time investment cost, C buy For energy purchase costs, C OM For maintenance costs, C loss For the cost of loss, C Dis For scrapping costs;

[0061] One-time investment cost C cstr This refers to the sum of the initial investment costs of all equipment throughout its entire life cycle.

[0062]

[0063] In the formula: y represents the service life of the equipment;

[0064] r is the discount rate;

[0065] μ i Let be the unit capacity investment cost of the i-th type of equipment;

[0066] S i This represents the maximum capacity of the i-th type of device;

[0067] N represents the collection of equipment within the park, including wind turbines, electrolyzers, fuel cells, batteries, and hydrogen storage tanks.

[0068] Energy purchase cost C buy This refers to the total cost of electricity purchased from the upper-level power grid and the revenue from selling electricity to the upper-level power grid throughout the year.

[0069]

[0070] In the formula: c t The electricity purchase / sale price for time period t;

[0071] P grid,t Let P be the power exchanged between the park and the upper-level power grid during time period t. grid,t A positive value indicates that the park purchases electricity from the upper-level power grid; when P... grid,t A negative value indicates that the park sells electricity to the higher-level power grid.

[0072] Operation and maintenance cost C OM This refers to the total maintenance cost of all devices in the system throughout the year, across all time periods.

[0073]

[0074] In the formula: λ i,t Let be the unit capacity operation and maintenance cost of the i-th type of equipment during time period t.

[0075] P i,t This represents the output power of the i-th device during time period t;

[0076] N represents the collection of equipment within the park, including wind turbines, electrolyzers, fuel cells, batteries, and hydrogen storage tanks.

[0077] In step S1, the selected source-side equipment includes photovoltaic units and wind turbine units, and the selected energy conversion equipment includes electrolyzers and fuel cells. Their input-output relationships are as follows:

[0078]

[0079] In the formula: P EL,in (t), P EL,out (t), η EL These represent the input power, output power, and conversion efficiency of the electrolyzer during time period t.

[0080] P FC,in (t), P FC,e (t), P FC,h (t), η FC,e η FC,h These represent the input power, output electrical and thermal power, and conversion efficiency of the fuel cell during time period t.

[0081] The selected energy storage devices include batteries and hydrogen storage tanks. Their mathematical models and input-output relationships are as follows:

[0082]

[0083] In the formula: S E_max This is the maximum capacity of the battery;

[0084] SOC(t) is the charge level of the battery at time t;

[0085] , These represent the charging and discharging power of the battery at time t, respectively.

[0086] , These are the charging and discharging efficiencies of the battery, respectively.

[0087] S Hy Let be the capacity of the hydrogen storage tank at time t;

[0088] , These are the charging and discharging capacities of the hydrogen storage tank, respectively.

[0089] , These represent the filling and discharging efficiencies of the hydrogen storage tank, respectively.

[0090] In step S1 above, η loss It can be represented as:

[0091]

[0092] In the formula: k PV Temperature coefficient;

[0093] T c (t) represents the surface temperature of the photovoltaic panel;

[0094] T r For reference temperature;

[0095] The surface temperature T of the photovoltaic cell c (t) can be estimated from ambient temperature and light intensity:

[0096]

[0097] In the formula: T a For ambient temperature, G ING Light intensity.

[0098] The beneficial effects of this invention are:

[0099] 1) Optimized the comprehensive utilization of wind and solar resources: Not only did it accurately analyze the characteristics of wind power and photovoltaic output and their interrelationship, but it also made full use of wind and solar resources and improved the utilization efficiency of new energy.

[0100] 2) Accurate simulation of the operating characteristics of energy equipment: By establishing equivalent equipment models of hydrogen energy storage units, electrochemical energy storage units, etc., and considering the power balance relationship between the electric bus and the hydrogen bus, this patent achieves accurate simulation of the operating characteristics of various energy equipment in the park.

[0101] 3) Provides an economically feasible planning strategy: By conducting year-round simulation of wind and solar hydrogen production operation, and making reasonable configurations of various equipment in the park, this patent not only optimizes the park's electricity purchase demand and new energy consumption capacity, but also provides an economically feasible planning strategy. Attached Figure Description

[0102] Figure 1 This is a schematic diagram of the overall layout of the hydrogen production park according to an embodiment of the present invention;

[0103] Figure 2 This is a flowchart illustrating an embodiment of the present invention;

[0104] Figure 3 This is a clustering image from an embodiment of the present invention;

[0105] Figure 4 This is a schematic diagram of a typical daily power balance in a hydrogen production park according to an embodiment of the present invention;

[0106] Figure 5 This is a schematic diagram of a typical daily hydrogen energy balance in a hydrogen production park according to an embodiment of the present invention;

[0107] Figure 6 This is a schematic diagram of the dual typical daily power balance of a hydrogen production park according to an embodiment of the present invention;

[0108] Figure 7 This is a schematic diagram of the hydrogen energy balance of a hydrogen production park on two typical days, according to an embodiment of the present invention.

[0109] In the diagram: 1 is a wind turbine, 2 is a photovoltaic unit, 3 is a battery, 4 is an electrolyzer, 5 is a hydrogen storage tank, and 6 is a fuel cell. Detailed Implementation

[0110] The technical content of the present invention will now be described in detail with reference to the accompanying drawings.

[0111] This embodiment is a method for optimizing the configuration of a wind-solar ratio system in a hydrogen production park, which includes the following four steps.

[0112] Step 1

[0113] S1) By analyzing key data such as historical wind speed and sunshine in the planned site area in detail, the output time series of local wind power and photovoltaic power generation are calculated, and the output characteristics of wind power and photovoltaic power and the wind-solar correlation are analyzed.

[0114] The power output of wind turbine generators is mainly affected by factors such as wind speed, and its power output model is as follows:

[0115]

[0116] In the formula, P WT,t Let t be the power output of the wind turbine generator.

[0117] N represents the number of grid-connected wind / photovoltaic units in the wind farm;

[0118] P WT This refers to the rated power output of the wind turbine.

[0119] v t Let t be the actual wind speed at time t;

[0120] v in The cut-in wind speed for the wind turbine;

[0121] v out The cut-out wind speed for the wind turbine;

[0122] v 0 This refers to the rated wind speed of the wind turbine.

[0123] To ensure wind power absorption and the economic benefits of the industrial park, during off-peak electricity prices, the electricity generated by the wind turbines or the surplus electricity after offsetting the park's load demand can be stored in the energy storage system; during peak electricity prices, the electricity generated by the wind turbines can be directly absorbed by the load side, or the energy storage system can release power to supply the load and the grid.

[0124] The electrical energy output by photovoltaic power generation is direct current (DC). Its output is mainly affected by factors such as solar irradiance and ambient temperature. Its steady-state power output model can be expressed as:

[0125]

[0126] In the formula: P PV This represents the actual power generation of the photovoltaic unit (kW).

[0127] P STC The rated output power (kW) of the photovoltaic module under standard test condition (STC).

[0128] G ING (t) represents the solar radiation intensity at time t (W / m²). 2 );

[0129] G STC Solar radiation intensity under standard conditions (1000 W / m²) 2 );

[0130] η loss The power loss of photovoltaic cells due to increased temperature can be expressed as:

[0131]

[0132] In the formula: k PV This is the temperature coefficient (typically taken as -0.00485 / ℃).

[0133] T c (t) represents the surface temperature of the photovoltaic panel;

[0134] T r This is a reference temperature (generally taken as 25℃).

[0135] The surface temperature T of the photovoltaic cell c (t) can be estimated from ambient temperature and light intensity:

[0136]

[0137] In the formula: T a For ambient temperature (°C), G ING Light intensity.

[0138] After obtaining the time series of photovoltaic and wind turbine outputs, a clustering method is used to obtain typical daily conditions. The following steps are required in the process of clustering wind and solar outputs:

[0139] 1) Data preprocessing: This involves processing and standardizing the data, including handling missing values, removing outliers, and performing data standardization.

[0140] 2) Time Series Feature Extraction: For time series data such as wind and solar power output, direct clustering may not be suitable because the original time series data is usually high-dimensional. Feature extraction can reduce the data dimensionality while retaining important information.

[0141] 3) Choosing a clustering algorithm: There are many different clustering algorithms to choose from, including K-means, spectral clustering, DBSCAN, etc. Choose the most suitable clustering algorithm based on the data and the problem.

[0142] 4) Determine the number of clusters: If you choose an algorithm such as K-means that requires a pre-set number of clusters, you need to use some methods to determine the optimal number of clusters.

[0143] 5) Clustering: Cluster the wind and solar power output using the selected clustering algorithm.

[0144] 6) Cluster analysis: Evaluate the clustering results and then analyze the characteristics of each cluster, such as the seasonal climate and weather conditions to which the cluster belongs.

[0145] 7) Practical application: Apply the clustering results to practical problems, model and simulate the clusters corresponding to different scenarios, and output the equipment configuration and operation status of the integrated energy system of source, grid, load and storage under different scenarios.

[0146] Since the K-means method's results depend on the choice of initial cluster centers, the number of clusters obtained in each run of the code may be unstable. Therefore, to improve the stability of the clustering results, the K-means++ initialization method is used. The K-means++ algorithm intelligently selects initial cluster centers so that they are as far apart as possible. This can significantly reduce the instability of K-means clustering results. The specific implementation steps are as follows:

[0147] S1.1) Randomly select a sample from dataset x as the first center point;

[0148] S1.2) Calculate the shortest distance between each sample and the existing cluster center, denoted by D(x); then calculate the probability P(x) of each sample point being selected as the next cluster center, and finally select the sample point corresponding to the maximum probability value (or probability distribution) as the next cluster center;

[0149]

[0150] S1.3) Repeat S1.2 until k cluster centers are found;

[0151] S1.4) For each sample in the dataset, calculate its distance to the k cluster centers and assign it to the class corresponding to the cluster center with the smallest distance;

[0152] S1.5) Calculate the average for each category and recalculate the cluster centers;

[0153] S1.6) Repeat steps S1.4 and S1.5 until a termination condition is met, typically when the objective function reaches its optimum or the maximum number of iterations is reached. The objective function often differs depending on the distance metric. When using Euclidean distance, the objective function is generally to minimize the sum of squared distances from an object to its cluster centroid; when using cosine similarity, the objective function is generally to maximize the sum of cosine similarities from an object to its cluster centroid.

[0154] Step Two

[0155] S2) Establish equivalent equipment models for hydrogen energy storage units and electrochemical energy storage units. Based on the operating characteristic models of relevant electric and hydrogen equipment in the park, form the power balance relationship between the electric bus and the hydrogen bus. Taking the lowest life cycle cost as the objective function, and considering constraints such as power balance, equipment operating characteristics, and land restrictions, build a system planning optimization configuration model for the new energy hydrogen production park that considers the wind and solar power ratio.

[0156] In step S2, the objective function for park optimization is to minimize the total lifecycle cost:

[0157]

[0158] In the formula: C cstr For a one-time investment cost, C buy For energy purchase costs, C OM For maintenance costs, C loss For the cost of loss, C Dis Cost of scrapping.

[0159] One-time investment cost C cstr It refers to the sum of the initial investment costs of all equipment throughout its entire life cycle, expressed as:

[0160]

[0161] In the formula: y represents the service life of the equipment;

[0162] r is the discount rate;

[0163] μ i Let be the unit capacity investment cost of the i-th type of equipment;

[0164] S i This represents the maximum capacity of the i-th type of device;

[0165] N represents the collection of equipment within the park, including wind turbines, electrolyzers, fuel cells, batteries, and hydrogen storage tanks.

[0166] Energy purchase cost C buy This refers to the total cost of electricity purchased from the upper-level power grid and the revenue from selling electricity to the upper-level power grid throughout the year, expressed as:

[0167]

[0168] In the formula: c t The electricity purchase / sale price for time period t;

[0169] P grid,t Let P be the power exchanged between the park and the upper-level power grid during time period t. grid,t A positive value indicates that the park purchases electricity from the upper-level power grid; when P... grid,t A negative value indicates that the park sells electricity to the higher-level power grid.

[0170] Operation and maintenance cost C OM This refers to the total maintenance cost of all devices in the system throughout the year, across all time periods. The expression is:

[0171]

[0172] In the formula: λ i,t Let be the unit capacity operation and maintenance cost of the i-th type of equipment during time period t.

[0173] P i,t This represents the output power of the i-th device during time period t;

[0174] N represents the collection of equipment within the park, including wind turbines, electrolyzers, fuel cells, batteries, and hydrogen storage tanks.

[0175] Loss cost C loss This refers to the cost incurred due to energy loss during power transmission, typically expressed as the energy purchase cost C. buy Calculated at approximately 5%. Scrapping cost C Dis This refers to the sum of the dismantling cost and the residual value of the equipment after it is abandoned, usually expressed as the initial investment cost C. cstr It is calculated to be approximately 3%.

[0176] In step S2, the following constraints are set for the multi-objective optimization configuration model:

[0177] S2.1) Power balance constraint

[0178] The capacity configuration of the wind-solar-hydrogen production park system needs to meet the power balance constraints of both the electric bus and the hydrogen bus. The power balance relationships for each day of the year or under a typical daily set are expressed as follows:

[0179]

[0180] In the formula: L E (t) represents the park's electricity load demand during time period t;

[0181] L H (t) represents the hydrogen load demand of the park during time period t.

[0182] S2.2) Equipment output constraints

[0183] Equipment output constraints include electrolyzer output power constraints, fuel cell output power constraints, battery output and capacity constraints, and hydrogen storage tank output and capacity constraints. These are specifically outlined below:

[0184]

[0185] In the formula: P EL_max P FC_max These are the maximum output values ​​of the electrolyzer and fuel cell, respectively.

[0186] S E_max S H_max These are the maximum capacities of the battery and the hydrogen storage tank, respectively.

[0187] S2.3) Constraints on energy storage devices

[0188] To ensure the sustainability of energy storage devices, the constraint that the initial and final capacities of the energy storage devices must be equal must be met:

[0189]

[0190] In the formula: SOC(0) is the initial state of charge of the battery;

[0191] SOC(24) represents the state of charge of the battery at the end of the charging process.

[0192] S H (0) represents the initial capacity of the hydrogen storage tank;

[0193] S H (24) is the final capacity of the hydrogen storage tank.

[0194] S2.4) Constraints on Purchase and Sale of Electricity

[0195]

[0196] In the formula: Pgrid_max and P grid_min These are the upper and lower limits of the power exchanged between the park and the upper-level power grid.

[0197] S2.5) Land area constraint

[0198] Because the construction area for new energy units in wind-solar-hydrogen production parks is limited, the total construction area for wind turbines and photovoltaic units is restricted by the maximum construction area. Without considering terrain limitations, the land area constraint is as follows:

[0199]

[0200] In the formula: P PV_max P WT_max This refers to the total installed capacity of photovoltaic units and wind turbine units.

[0201] λ PV , λ WT The floor area per unit capacity of photovoltaic units and wind turbine units;

[0202] S max This represents the largest construction area for new energy generating units in the park.

[0203] Step 3

[0204] S3) Combining the above objective function and constraints, obtain the optimal configuration scheme for the new energy hydrogen production park system planning and the total system configuration cost.

[0205] In step S1, the selected source-side equipment includes photovoltaic units and wind turbine units.

[0206] The selected energy conversion equipment includes electrolyzers and fuel cells, and their mathematical models and input-output relationships are as follows:

[0207]

[0208] In the formula: P EL,in (t), P EL,out (t), η EL These represent the input power, output power, and conversion efficiency of the electrolyzer during time period t.

[0209] P FC,in (t), P FC,e (t), P FC,h (t), η FC,e η FC,h These represent the input power, output electrical and thermal power, and conversion efficiency of the fuel cell during time period t.

[0210] The selected energy storage devices include batteries and hydrogen storage tanks. Their mathematical models and input-output relationships are as follows:

[0211]

[0212] In the formula: S E_max This is the maximum capacity of the battery;

[0213] SOC(t) is the charge level of the battery at time t;

[0214] , These represent the charging and discharging power of the battery at time t, respectively.

[0215] , These are the charging and discharging efficiencies of the battery, respectively.

[0216] S Hy Let be the capacity of the hydrogen storage tank at time t;

[0217] , These are the charging and discharging capacities of the hydrogen storage tank, respectively.

[0218] , These represent the filling and discharging efficiencies of the hydrogen storage tank, respectively.

[0219] Step Four

[0220] S4) Further considering electricity price factors and energy storage operation characteristics constraints, a full-year simulation of wind-solar hydrogen production was conducted to analyze the park's electricity purchase demand and renewable energy absorption capacity under the optimized configuration scheme of this plan.

[0221] Example 1

[0222] Based on the established optimization planning model, the investment economics of wind turbines and photovoltaic units under different construction areas are shown in Table 1.

[0223] Table 1. Investment efficiency of wind turbines and photovoltaic units under different construction areas.

[0224]

[0225] With a total construction area of ​​19 million square meters 2 For example, the following three energy storage collaborative configuration constraints were considered:

[0226] Collaborative Constraint 1: There is no upper limit constraint on the capacity configuration of hydrogen energy storage equipment in the park, and the objective function is to minimize the total life cycle cost.

[0227] Collaborative Constraint 2: The capacity configuration of hydrogen energy storage equipment in the park is subject to an upper limit. The maximum design capacity of hydrogen storage tanks is 4000MWh, with the objective function being the lowest total life cycle cost.

[0228] Cooperative Constraint 3: The capacity configuration of hydrogen energy storage equipment in the park is subject to an upper limit. The maximum designed capacity of hydrogen storage tanks is 3000MWh, with the objective function being the lowest total life cycle cost.

[0229] Based on the above objective function and constraints, the optimal configuration model of this application is solved using the mixed integer linear programming method, and the planned capacity, rated output and cost details of each piece of equipment in the park are obtained, as shown in Tables 2 and 3.

[0230] Table 2 Optimization and Configuration Results of Wind-Solar Hydrogen Production System

[0231]

[0232] Table 3. Cost breakdown by component (unit: RMB 100 million)

[0233]

[0234] Based on the above equipment configuration scheme, and further considering electricity price factors and energy storage operation characteristics constraints, a year-round simulation of wind-solar hydrogen production was conducted to obtain the output of each device in the hydrogen production park and the power balance of the electricity and hydrogen bus. Daily power balance diagrams of the wind-solar hydrogen production park system are attached. Figure 4 Appendix Figure 5 As shown.

[0235] The optimization configuration results show that:

[0236] (1) As the capacity of the hydrogen storage tank decreases, the output power and corresponding hours of the hydrogen storage tank configured in the corresponding optimization model will also decrease. At the same time, the output power, capacity and corresponding hours of the required battery will increase, and the configuration power of the electrolyzer will also increase.

[0237] (2) When the hydrogen storage tank has sufficient capacity, the battery capacity-to-power ratio corresponding to the number of hours can be relatively small, meaning the hydrogen storage tank can compensate for the insufficient regulation capability of the battery. When the hydrogen storage tank capacity decreases, the regulation capability of the hydrogen storage tank decreases, failing to meet the hydrogen load demand of the park. Therefore, the configured battery capacity, output power, and corresponding number of hours are increased to enhance the energy storage regulation capability of the PIES. At the same time, it is also necessary to accelerate the rate at which the electrolyzer converts electrical energy into hydrogen energy, thus the output power of the configured electrolyzer will also increase.

[0238] (3) The capacity configuration result of the fuel cell is always 0 under different scenarios. This is because the electricity load demand of the park is relatively small, and the power generation of the wind turbine is sufficient to meet the electricity load demand of the park. At the same time, since the hydrogen-to-electricity conversion efficiency of the fuel cell is only 60%, it is not recommended to configure fuel cells in wind and solar hydrogen production parks.

[0239] Example 2

[0240] Considering the equipment optimization configuration method of the hydrogen power park under two typical days, the simulation is carried out in combination with the output curves of wind and solar turbines under two typical days. The objective function and constraints are the same as those in Example 1. The above optimization configuration model is solved by the mixed integer linear programming method to obtain the planned capacity, rated output and cost details of each equipment in the park, as shown in Tables 4 and 5.

[0241] Table 4. Optimization and Configuration Results of Wind-Solar Hydrogen Production Systems

[0242]

[0243] Table 5. Cost breakdown by component (unit: RMB 100 million)

[0244]

[0245] Based on the above equipment configuration scheme, and further considering electricity price factors and energy storage operation characteristics constraints, a year-round simulation of wind-solar hydrogen production was conducted to obtain the output of each device in the hydrogen production park and the power balance of the electricity and hydrogen bus. Daily power balance diagrams of the wind-solar hydrogen production park system are attached. Figure 6 , Figure 7 As shown.

[0246] The optimization configuration results show that:

[0247] (1) Compared with the high utilization hours scenario, since the utilization hours of photovoltaic and wind turbine units are reduced, the park has selected photovoltaic units with higher power generation per unit construction area to replace wind turbine units. Therefore, the installed capacity of photovoltaic units has increased while the installed capacity of wind turbine units has decreased. At the same time, the configured power and capacity of energy storage equipment in the park have increased to enhance the flexible control of energy. The above-mentioned measures have led to an increase in the configuration cost, operating cost, and other costs of the wind-solar hydrogen production park system.

[0248] (2) When considering the typical high-load days, the configuration optimization results of the wind-solar hydrogen production park are more in line with the actual situation. At this time, the system considers the various typical day situations that may occur within a year. At this time, the power utilization hours of the wind and solar units are close to the average level of the whole year. Therefore, the configuration capacity of the photovoltaic units is higher and the utilization hours are lower, while the utilization hours are higher and the wind turbine units are higher. The configuration capacity of the wind turbine units is the opposite.

Claims

1. A method for optimizing the configuration of a wind-solar ratio system in a hydrogen production park, characterized in that, Includes the following steps: S1) Calculate the output time series of local wind and solar power, analyze the output characteristics of wind and solar power and the wind-solar correlation, wherein the power output model of wind turbine generators is: In the formula, P WT,t Let t be the power output of the wind turbine generator. N represents the number of grid-connected wind / photovoltaic units in the wind farm; P WT This refers to the rated power output of the wind turbine. v t Let t be the actual wind speed at time t; v in The cut-in wind speed for the wind turbine; v out The cut-out wind speed for the wind turbine; v 0 The rated wind speed of the wind turbine; The electrical energy output by photovoltaic power generation is direct current (DC), and its steady-state power output model can be expressed as: In the formula: P PV This represents the actual power generation (kW) of the photovoltaic unit. P STC The rated output power (kW) of the photovoltaic module under standard test condition (STC). G ING (t) represents the solar radiation intensity at time t (W / m²). 2 ); G STC Solar radiation intensity under standard conditions (1000 W / m²) 2 ); η loss This refers to the power loss caused by the increase in temperature in photovoltaic cells; To ensure wind power absorption and the economic benefits of the industrial park, during off-peak electricity prices, the electricity generated by the wind turbines or the surplus electricity after offsetting the park's load demand can be stored in the energy storage system; during peak electricity prices, the electricity generated by the wind turbines can be directly absorbed by the load side, or the energy storage system can release power to supply the load and the grid. S2) Establish equivalent equipment models for hydrogen energy storage units and electrochemical energy storage units. Based on the operating characteristic models of relevant electric and hydrogen equipment in the park, form the power balance relationship between the electric bus and the hydrogen bus. Taking the lowest life cycle cost as the objective function, and considering power balance and constraints, build a system planning optimization configuration model for the new energy hydrogen production park that takes into account the wind and solar power ratio. The following constraints are set for the multi-objective optimization configuration model: S2.1) Power balance constraint: In the formula: L E (t) represents the park's electricity load demand during time period t; L H (t) represents the hydrogen load demand of the park during time period t; P grid (t) represents the power exchanged between the park and the upper-level power grid during time period t; P WT (t) represents the power generation of the wind turbine generators in the park during time period t; P PV (t) represents the actual power generation of the photovoltaic units in the park during time period t; P EL,in (t) represents the input power of the electrolytic cell in the park during time period t; , These represent the discharge power and charging power of the park's batteries during time period t, respectively. P FC,ou t(t) represents the output power of the fuel cell in the park during time period t; P EL,out (t) represents the output power of the electrolytic cell in the park during time period t; , These represent the charging and discharging power of the hydrogen storage tanks in the park during time period t. P FC,in (t) represents the input power of the fuel cell in the park during time period t; S2.2) Equipment output constraints Equipment output constraints include electrolyzer output power constraints, fuel cell output power constraints, battery output and capacity constraints, and hydrogen storage tank output and capacity constraints. In the formula: P EL_max P FC_max These are the maximum output values ​​of the electrolyzer and fuel cell, respectively. S E_max S H_max These are the maximum capacities of the battery and the hydrogen storage tank, respectively. S2.3) Constraints on energy storage devices: In the formula: SOC(0) is the initial state of charge of the battery; SOC(24) represents the state of charge of the battery at the end of the charging process. S H (0) represents the initial capacity of the hydrogen storage tank; S H (24) is the final capacity of the hydrogen storage tank; S2.4) Constraints on the purchase and sale of electricity: In the formula: P grid_max and P grid_min These are the upper and lower limits of the power exchanged between the park and the upper-level power grid, respectively. S2.5) Land area constraint: In the formula: P PV_max P WT_max This refers to the total installed capacity of photovoltaic units and wind turbine units. λ PV , λ WT The floor area per unit capacity of photovoltaic units and wind turbine units; S max This represents the largest construction area for new energy generating units in the park; S3) Combining the above objective functions, obtain the optimal configuration scheme for the new energy hydrogen production park system planning and the total system configuration cost; S4) Further considering electricity price factors and energy storage operation characteristics constraints, conduct a full-year wind and solar hydrogen production time-series operation simulation to analyze the park's electricity purchase demand and new energy consumption capacity under the optimized configuration scheme of this plan.

2. The method for optimizing the wind-solar ratio system configuration of a hydrogen production park according to claim 1, characterized in that, In step S1 above, a clustering method is used to obtain typical daily conditions, including the following steps: S1.1) Randomly select a sample from dataset x as the first center point; S1.2) Calculate the shortest distance between each sample and the existing cluster center, denoted by D(x); then calculate the probability P(x) of each sample point being selected as the next cluster center, and finally select the sample point corresponding to the maximum probability value or probability distribution as the next cluster center; S1.3) Repeat S1.2 until k cluster centers are found; S1.4) For each sample in the dataset, calculate its distance to the k cluster centers and assign it to the class corresponding to the cluster center with the smallest distance; S1.5) Calculate the average for each category and recalculate the cluster centers; S1.6) Repeat operations S1.4 and S1.5 until the termination condition is met.

3. The method for optimizing the wind-solar ratio system configuration of a hydrogen production park according to claim 1, characterized in that, In step S2 above, the objective function for park optimization is to minimize the total lifecycle cost: In the formula: C cstr For a one-time investment cost, C buy For energy purchase costs, C OM For maintenance costs, C loss For the cost of loss, C Dis For scrapping costs; One-time investment cost C cstr This refers to the sum of the initial investment costs of all equipment throughout its entire life cycle. In the formula: y represents the service life of the equipment; r is the discount rate; μ i Let be the unit capacity investment cost of the i-th type of equipment; S i This represents the maximum capacity of the i-th type of device; N represents the collection of equipment within the park, including wind turbines, electrolyzers, fuel cells, batteries, and hydrogen storage tanks; Energy purchase cost C buy This refers to the total cost of electricity purchased from the upper-level power grid and the revenue from selling electricity to the upper-level power grid throughout the year. In the formula: c t The electricity purchase / sale price for time period t; P grid,t Let P be the power exchanged between the park and the upper-level power grid during time period t. grid,t A positive value indicates that the park purchases electricity from the upper-level power grid; when P... grid,t A negative value indicates that the park sells electricity to the higher-level power grid. Operation and maintenance cost C OM This refers to the total maintenance costs of all devices in the system throughout the year, across all time periods. In the formula: λ i,t Let be the unit capacity operation and maintenance cost of the i-th type of equipment during time period t. P i,t This represents the output power of the i-th device during time period t; N represents the collection of equipment within the park, including wind turbines, electrolyzers, fuel cells, batteries, and hydrogen storage tanks.

4. The method for optimizing the wind-solar ratio system configuration of a hydrogen production park according to claim 1, characterized in that, In step S1, the selected source-side equipment includes photovoltaic units and wind turbine units, and the selected energy conversion equipment includes electrolyzers and fuel cells. Their input-output relationships are as follows: In the formula: P EL,in (t), P EL,out (t), η EL These represent the input power, output power, and conversion efficiency of the electrolyzer during time period t. P FC,in (t), P FC,e (t), P FC,h (t), η FC,e η FC,h These represent the input power, output electrical and thermal power, and conversion efficiency of the fuel cell during time period t. The selected energy storage devices include batteries and hydrogen storage tanks. Their mathematical models and input-output relationships are as follows: In the formula: S E_max This is the maximum capacity of the battery; SOC(t) is the charge level of the battery at time t; , These represent the charging and discharging power of the battery at time t, respectively. , These are the charging and discharging efficiencies of the battery, respectively. S Hy Let be the capacity of the hydrogen storage tank at time t; , These are the charging and discharging capacities of the hydrogen storage tank, respectively. , These represent the filling and discharging efficiencies of the hydrogen storage tank, respectively.

5. The method for optimizing the wind-solar ratio system configuration of a hydrogen production park according to claim 1, characterized in that, In step S1 above, η loss It can be represented as: In the formula: k PV Temperature coefficient; T c (t) represents the surface temperature of the photovoltaic panel; T r For reference temperature; The surface temperature T of the photovoltaic cell c (t) can be estimated from ambient temperature and light intensity: In the formula: T a For ambient temperature, G ING Light intensity.

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