Wind-photovoltaic ratio system optimization configuration method for hydrogen production park

Abstract

A wind-photovoltaic ratio system optimization configuration method for a new energy hydrogen production park, the method comprising the steps of: S1) calculating an output time series of local wind power and photovoltaic power generation, and analyzing wind power and photovoltaic output characteristics and a wind-photovoltaic correlation; S2) establishing equivalent device models of a hydrogen energy storage unit and an electrochemical energy storage unit to form an electric bus power balance and hydrogen bus power balance relationship, and taking the minimization of a full life cycle cost as an objective function and taking into consideration constraint conditions such as the power balance, device operation characteristics and a land restriction to establish a system planning optimization configuration model for a new energy hydrogen production park, which model takes a wind-photovoltaic ratio into consideration; S3) integrating the objective function and the constraint conditions to obtain a system planning and optimization configuration scheme for the new energy hydrogen production park and the total system configuration cost; and S4) taking into consideration an electricity price factor and an energy storage operation characteristic constraint, performing annual hydrogen production timing operation simulation, and analyzing a park electricity purchase demand and a new energy consumption capacity under the present planning optimization configuration scheme.

Description

A wind-solar ratio system optimization configuration method for a hydrogen production park TECHNICAL FIELD

[0001] The application relates to the technical field of new energy power generation, and particularly relates to a wind-solar ratio system optimization configuration method for a hydrogen production park. BACKGROUND

[0002] In recent years, the solar energy industry has become a development hotspot. In industrial parks and ecological parks, the generation of "green hydrogen" can be realized by configuring photovoltaic units or wind power units, and obtaining clean and low-carbon hydrogen is an inevitable path for the sustainable development of hydrogen energy in the future.

[0003] Although wind-solar hydrogen production has great potential, there are still some deficiencies in the utilization process. First, from the perspective of conversion efficiency, the electrolytic water hydrogen production mode has the problems of low energy conversion efficiency and high hydrogen production cost in the energy storage process; second, at present, the development of wind power and photovoltaic power is planned separately based on wind energy and solar energy resources, and the operation characteristics of wind power and photovoltaic power generation and the coordinated optimization of the optimal ratio capacity are not fully considered. In addition, due to the great difference in the load characteristics of hydrogen energy in different application scenarios, the electric-hydrogen energy storage configuration strategy and equipment planning scheme need to be designed and optimized in a targeted manner. SUMMARY

[0004] In order to solve the above problems, the application provides a wind-solar ratio system optimization configuration method for a hydrogen production park, which optimizes the comprehensive utilization of wind-solar resources and realizes accurate simulation of the operation of energy equipment. The technical scheme adopted by the application is as follows:

[0005] A wind-solar ratio system optimization configuration method for a hydrogen production park comprises the following steps:

[0006] S1) calculating the output time sequence of local wind power and photovoltaic power generation, analyzing the wind power, photovoltaic power output characteristics and wind-solar correlation, wherein the power output model of the wind turbine generator is:

[0007] ,

[0008] In the formula, P WT,t is the power output of the wind turbine generator at time t;

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

[0010] P WT is the rated power output of the wind turbine generator;

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

[0012] v inCut-in wind speed of wind turbine;

[0013] v out Cut-out wind speed of wind turbine;

[0014] v 0 Rated wind speed of wind turbine;

[0015] The power output of photovoltaic power generation is direct current, and the steady-state power output model can be expressed as:

[0016] ,

[0017] In the formula: P PV Actual power generation of photovoltaic unit (kW);

[0018] P STC Rated output power of photovoltaic cell module under standard test condition (STC) (kW);

[0019] G ING Solar radiation intensity at time t (W / m 2 );

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

[0021] η loss Power loss of photovoltaic cell due to temperature rise;

[0022] To ensure wind power consumption and economic benefits of the park, at the low valley price of power grid, the power generated by the wind turbine or the remaining power after offsetting the park load demand can be stored in the energy storage system; at the peak price of power grid, the power generated by the wind turbine is preferentially consumed by the load side or released by the energy storage system to supply power to the load and the power grid;

[0023] S2) Establish equivalent equipment models of hydrogen energy storage units and electrochemical energy storage units, form power balance relationship of the electric bus and hydrogen bus according to the operation characteristic models of related electric and hydrogen equipment in the park; take the lowest total life cycle cost as the objective function, consider the power balance and equipment operation characteristic constraints, land restrictions and other constraint conditions, and build a new energy hydrogen production park system planning optimization configuration model considering wind-solar ratio;

[0024] S3) Comprehensive above objective function, obtain new energy hydrogen production park system planning optimization configuration scheme and system configuration total cost;

[0025] S4) Further considering the electricity price factor and the energy storage operation characteristic constraint, the annual wind-solar-hydrogen timing operation simulation is carried out, and the park electricity demand and new energy consumption capacity under the planning optimization configuration scheme are analyzed,

[0026] In the above step S1, the typical day situation is obtained by using the clustering method, including the following steps:

[0027] S1.1) Randomly select a sample in the data set x as the first center point;

[0028] S1.2) Calculate the shortest distance between each sample and the existing cluster center, denoted as 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) Calculate the distance of each sample in the data set to the k cluster centers and divide it into the class corresponding to the minimum distance cluster center;

[0032] S1.5) Calculate the average for each class respectively and recalculate the cluster center;

[0033] S1.6) Repeat S1.4 and S1.5 until a certain termination condition is reached, which is generally that the objective function reaches the optimal or reaches the maximum number of iterations. For different distance metrics, the objective function is often different. When the Euclidean distance is used, the objective function is generally the sum of the squares of the distances of the objects to their cluster centers; when the cosine similarity is used, the objective function is generally the sum of the cosine similarities of the objects to their cluster centers.

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

[0035] S2.1) Power balance constraint:

[0036] ,

[0037] L E (t) is the park electricity load demand at t period;

[0038] L H (t) is the park hydrogen load demand at t period;

[0039] S2. 2) Equipment output constraint

[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 the above step S2, the park optimization objective function is the minimization of the life cycle cost:

[0059] ,

[0060] wherein: C cstr is the initial investment cost, C buy is the energy purchase cost, C OM is the operation and maintenance cost, C loss is the loss cost, and C Dis is the scrap cost;

[0061] The initial investment cost C cstr is the sum of the initial investment costs of each device in the life cycle:

[0062] ,

[0063] wherein: y represents the service life of the device;

[0064] r is the discount rate;

[0065] μ i is the unit capacity investment cost of the i-th device;

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

[0067] N represents the set of devices in the park, including wind turbines, electrolytic cells, fuel cells, batteries, and hydrogen storage tanks.

[0068] The energy purchase cost C buy is the sum of the annual park electricity purchase cost from the superior grid and the annual park electricity sale income to the superior grid:

[0069] ,

[0070] wherein: c t is the electricity purchase / sale price in period t;

[0071] P grid,t is the interactive electric power between the park and the superior grid in period t, and when P grid,t is positive, it indicates that the park purchases electricity from the superior grid; when P grid,t is negative, it indicates that the park sells electricity to the superior grid.

[0072] The operation and maintenance cost C OM is the sum of the operation and maintenance costs of each device in the system in each period of the year:

[0073] ,

[0074] wherein: λ i,tThe unit capacity operation and maintenance cost of the ith device in the t period,

[0075] P i,t represents the output power of the ith device in the t period;

[0076] N represents a set of devices in the park, including wind turbines, electrolytic cells, fuel cells, batteries, and hydrogen storage tanks.

[0077] In the step S1, the selected source-side devices include photovoltaic units and wind turbines, and the selected energy conversion devices include electrolytic cells and fuel cells, and the input-output relationship is as follows:

[0078] ,

[0079] In the formula: P EL,in (t), P EL,out (t), η EL are the input power, output power, and conversion efficiency of the electrolytic cell in the t period, respectively;

[0080] P FC,in (t), P FC,out (t), P FC,h (t), η FC,e , η FC,h are the input power, output power, and conversion efficiency of the fuel cell in the t period, respectively;

[0081] The selected energy storage devices include batteries and hydrogen storage tanks, and the mathematical model and input-output relationship are as follows:

[0082] ,

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

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

[0085] , and P are the charging and discharging power of the battery at time t, respectively;

[0086] , and η are the charging and discharging efficiency of the battery, respectively.

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

[0088] , and P are the charging and discharging power of the hydrogen storage tank, respectively;

[0089] , and η The filling and discharging efficiency of the hydrogen storage tank.

[0090] In the step S1, η loss may be expressed as:

[0091] ,

[0092] In the formula, k PV is a temperature coefficient;

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

[0094] T t is a reference temperature;

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

[0096] ,

[0097] In the formula, T a is the ambient temperature, and G ING is the light intensity.

[0098] The beneficial effects of the present application are:

[0099] 1) Optimize the comprehensive utilization of wind and light resources: not only the characteristics of wind power and photovoltaic output and their mutual relationship are accurately analyzed, but also the wind and light resources are fully utilized, and the utilization efficiency of new energy is improved.

[0100] 2) Realize the accurate simulation of the operating characteristics of energy equipment: by establishing the equivalent device model of hydrogen storage energy unit, electrochemical storage energy unit, etc., and considering the power balance relationship between the electric bus and the hydrogen bus, the present patent realizes the accurate simulation of the operating characteristics of various energy equipment in the park.

[0101] 3) Provide an economically feasible planning strategy: by performing annual wind-light hydrogen production timing operation simulation, the various devices in the park are reasonably configured, the present patent not only optimizes the power purchase demand and new energy consumption capacity of the park, but also provides an economically feasible planning strategy. BRIEF DESCRIPTION OF DRAWINGS

[0102] Fig. 1 is a schematic diagram of the overall layout of the hydrogen production park according to the embodiment of the present application;

[0103] Fig. 2 is a flowchart of the embodiment of the present application;

[0104] Fig. 3 is a clustering image according to the embodiment of the present application;

[0105] Fig. 4 is a schematic diagram of the typical daily electric energy balance of the hydrogen production park according to the embodiment of the present application;

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

[0107] Figure 6 is a schematic diagram of a double typical daily electric energy balance of the hydrogen production park according to the embodiment of the present application;

[0108] Figure 7 is a schematic diagram of a double typical daily hydrogen energy balance of the hydrogen production park according to the embodiment of the present application.

[0109] In the figure: 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 DESCRIPTION

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

[0111] The present embodiment is a wind-solar ratio system optimization configuration method for a hydrogen production park, which comprises the following four steps.

[0112] Step one

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

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

[0115] ,

[0116] In the formula, P WT,t is the power output of the wind turbine at time t;

[0117] N is the number of wind power / photovoltaic units connected to the grid in the wind farm;

[0118] P WT is the rated power output of the wind turbine;

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

[0120] v in is the cut-in wind speed of the wind turbine;

[0121] v out is the cut-out wind speed of the wind turbine;

[0122] v 0 is the rated wind speed of the wind turbine.

[0123] To ensure wind power consumption and economic benefits of the park, the power generated by wind turbines or the remaining power after offsetting the park load demand can be stored in the energy storage system during the low price of the power grid. During the peak price of the power grid, the power generated by wind turbines is directly consumed by the load side or released by the energy storage system to supply power to the load and the grid.

[0124] The output of photovoltaic power generation is direct current, and the output is mainly affected by solar radiation intensity, ambient temperature and other factors. The steady-state power output model can be expressed as:

[0125] ,

[0126] In the formula: P PV is the actual power generation of the photovoltaic unit (kW);

[0127] P STC is the rated output power of the photovoltaic cell module under standard test conditions (STC) (kW);

[0128] G ING (t) is the solar radiation intensity at time t (W / m 2 );

[0129] G STC is the solar radiation intensity under standard environment (1000 W / m 2 );

[0130] η loss is the power loss of the photovoltaic cell due to temperature rise, which can be expressed as:

[0131] ,

[0132] In the formula: k PV is the temperature coefficient (generally takes the value of-0.00485 / ℃);

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

[0134] T t is the reference temperature (generally takes the value of 25℃).

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

[0136] ,

[0137] In the formula: T a is the ambient temperature (℃), and G ING is the light intensity.

[0138] After obtaining the time series of photovoltaic and wind turbine outputs, a clustering method is used to obtain typical daily situations. During the clustering of wind and solar power, the following steps are needed:

[0139] 1) Data preprocessing: The data needs to be processed and standardized, including steps such as handling missing values, removing outliers, and data standardization.

[0140] 2) Time series feature extraction: For time series data such as wind and solar power, direct clustering may not be suitable because the original time series data is usually high-dimensional. Through feature extraction, the data dimension can be reduced while important information is retained.

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

[0142] 4) Determine the number of clusters: When choosing algorithms such as K-means that require pre-setting the number of clusters, some methods need to be used to determine the optimal number of clusters.

[0143] 5) Clustering: Use the selected clustering algorithm to cluster wind and solar power.

[0144] 6) Cluster analysis: Evaluate the clustering results and then analyze the characteristics of each cluster. For example, the season and weather conditions to which the cluster belongs.

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

[0146] Since the K-means method depends on the selection of initial cluster centers, it may lead to unstable clustering results each time the code is run. Therefore, in order to improve the stability of the clustering results, the K-means++ initialization method is used. The K-means++ algorithm selects initial cluster centers in an intelligent way, making them as far apart from each other 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 the dataset x as the first center point;

[0148] S1.2) Calculate the shortest distance between each sample and the existing cluster center, denoted as 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) Calculate the distance of each sample in the dataset to the k cluster centers and assign it to the class corresponding to the cluster center with the smallest distance;

[0152] S1.5) Recalculate the cluster centers by averaging each class separately;

[0153] S1.6) Repeat S1.4 and S1.5 until a certain termination condition, usually the objective function reaches the optimal or reaches the maximum number of iterations, can be terminated. For different distance metrics, the objective function is often different. When using Euclidean distance, the objective function is usually to minimize the sum of the squares of the distance of the object to its cluster center; when using cosine similarity, the objective function is usually to maximize the sum of the cosine similarity of the object to its cluster center.

[0154] Step two

[0155] S2) Establish equivalent equipment models of hydrogen storage units and electrochemical energy storage units, form the power balance relationship of the electric bus and the hydrogen bus according to the operation characteristic models of the related electric and hydrogen equipment in the park; take the minimum life cycle cost as the objective function, consider the power balance and equipment operation characteristic constraints, land restrictions and other constraint conditions, and build a new energy hydrogen production park system planning optimization configuration model considering the wind-solar ratio.

[0156] In this step two S2, the optimization objective function of the park is to minimize the life cycle cost:

[0157] ,

[0158] In the formula: C cstr is the initial investment cost, C buy is the energy purchase cost, C OM is the operation and maintenance cost, C loss is the loss cost, and C Dis is the scrap cost.

[0159] The initial investment cost C cstr is the sum of the initial investment costs of each device in the life cycle, and the expression is:

[0160] ,

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

[0162] r is the discount rate;

[0163] μ i Ci is the unit capacity investment cost of the i-th device;

[0164] S i Ci,max is the maximum capacity of the i-th device;

[0165] N represents a set of devices in the park, including wind turbines, electrolyzers, fuel cells, batteries, and hydrogen storage tanks.

[0166] C buy C represents the total cost of purchasing electricity from the upper grid and selling electricity to the upper grid in the park throughout the year, expressed as:

[0167] ,

[0168] c t is the electricity purchase / sale price at time t;

[0169] P grid,t is the interactive power between the park and the upper grid at time t, where P grid,t is positive, indicating that the park is purchasing electricity from the upper grid; and P grid,t is negative, indicating that the park is selling electricity to the upper grid.

[0170] C OM represents the total operating cost of all devices in the system throughout the year, expressed as:

[0171] ,

[0172] λ i,t is the unit capacity operating cost of the i-th device at time t,

[0173] P i,t is the output power of the i-th device at time t;

[0174] N represents a set of devices in the park, including wind turbines, electrolyzers, fuel cells, batteries, and hydrogen storage tanks.

[0175] C loss is the cost of energy loss during transmission, usually about 5% of the energy purchase cost C buy C Dis is the sum of the demolition cost and the residual value of the device after being abandoned, usually about 3% of the initial investment cost C cstr .

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

[0177] S2.1) Power balance constraint

[0178] The system capacity configuration of the wind-solar-hydrogen park needs to meet the power balance constraints of the electric bus and the hydrogen bus. The power balance relationship of each day throughout the year or the power balance relationship under the typical day set is respectively expressed as follows:

[0179] ,

[0180] In the formula: L E (t) is the park electric load demand at t period;

[0181] L H (t) is the park hydrogen load demand at t period.

[0182] S2. 2) Equipment output constraints

[0183] The equipment output constraints include electrolyzer output power constraints, fuel cell output power constraints, battery output and capacity constraints, hydrogen storage tank output and capacity constraints, etc. The specific expression is as follows:

[0184] ,

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

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

[0187] S2.3) Energy storage device constraints

[0188] In order to realize the sustainability of the energy storage device, the energy storage device initial and final capacity equal constraints need to be met:

[0189] ,

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

[0191] SOC(24) is the final state of charge of the battery;

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

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

[0194] S2.4) Power purchase and sale constraints

[0195] ,

[0196] In the formula: Pgrid_max and P grid_min Pmax, Pmin are the upper and lower limits of power exchange between the park and the upper grid

[0197] S2.5) land area constraint

[0198] Since the construction area of new energy units in the wind-solar hydrogen production park is limited, the total construction area of wind turbines and photovoltaic units is limited by the maximum construction area. Under the condition of not considering the terrain restriction, the land area constraint is expressed as follows:

[0199] ,

[0200] In the formula: P PV_max , P WT_max are the total installed capacity of photovoltaic units and wind turbines;

[0201] λ PV , λ WT are the unit capacity land area of photovoltaic units and wind turbines;

[0202] S max is the maximum construction area of new energy units in the park.

[0203] Step three

[0204] S3) Integrate the above objective function and constraint conditions to obtain the optimal configuration scheme of new energy hydrogen production park system planning and the total cost of system configuration.

[0205] In the step one S1, the selected source side equipment includes photovoltaic units, wind turbines.

[0206] The selected energy conversion equipment includes electrolytic cell, fuel cell, and its mathematical model and input-output relationship are as follows:

[0207] ,

[0208] In the formula: P EL,in (t), P EL,out (t), η EL are the input power, output power and conversion efficiency of the electrolytic cell at t period;

[0209] P FC,in (t), P FC,out (t), P FC,h (t), η FC,e , η FC,h are the input power and output power, heat power, conversion efficiency of the fuel cell at t period.

[0210] The selected energy storage equipment includes battery, hydrogen storage tank, and its mathematical model and input-output relationship are as follows:

[0211] ,

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

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

[0214] , P(t) and P(t) are the charging and discharging power of the battery at time t, respectively;

[0215] , ηc and ηd are the charging and discharging efficiency of the battery, respectively.

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

[0217] , P(t) and P(t) are the charging and discharging power of the hydrogen storage tank, respectively;

[0218] , ηc and ηd are the charging and discharging efficiency of the hydrogen storage tank, respectively.

[0219] Step four

[0220] S4) Further considering the electricity price factor and the energy storage operation characteristic constraint, the annual wind-solar-hydrogen timing operation simulation is carried out, and the park electricity demand and new energy consumption capacity under the planning optimization configuration scheme are analyzed,

[0221] Example one

[0222] According to the established optimal planning model, the investment economic situation of wind turbine and photovoltaic turbine under different construction areas is shown in Table 1.

[0223] Table 1 Investment economic situation of wind turbine and photovoltaic turbine under different construction areas

[0224] ,

[0225] Taking the total construction area of 190 million m 2 As an example, the following three energy storage collaborative configuration constraints are 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 minimum life cycle cost is taken as the objective function.

[0227] Collaborative constraint 2: The capacity configuration of hydrogen energy storage equipment in the park is limited by the upper limit, and the maximum design of the hydrogen storage tank capacity is 4000MWh, and the minimum life cycle cost is taken as the objective function.

[0228] Synergistic constraint 3: The capacity configuration of the hydrogen storage energy equipment in the park is subject to an upper limit constraint, and the capacity of the hydrogen storage tank is designed to be 3000 MWh, with the lowest life cycle cost as the objective function

[0229] Based on the above objective function and constraint conditions, the optimization configuration model of the present application is solved by using a mixed integer linear programming method, and the planning capacity, rated output and cost details of each part of the equipment in the park are obtained, as shown in Tables 2 and 3.

[0230] Table 2: Wind-solar-hydrogen system planning optimization configuration results

[0231]

[0232] Table 3: Cost of each part (unit: billion yuan)

[0233]

[0234] On the basis of the above device configuration scheme, further considering the factors of electricity price and energy storage operation characteristics constraint, the annual wind-solar-hydrogen time sequence operation simulation is carried out, and the output of each device in the electric-hydrogen park and the power balance of the electric and hydrogen bus are obtained. The single-day wind-solar-hydrogen park system electric energy balance schematic diagram and electric energy balance schematic diagram are shown in Figures 4 and 5, respectively.

[0235] From the optimization configuration results, it can be seen that:

[0236] (1) With the decrease of the configuration capacity of the hydrogen storage tank, the output power and corresponding hours of the hydrogen storage tank configured by the corresponding optimization model will also decrease. At the same time, the output power, capacity and corresponding hours of the configured battery will increase, and the configuration power of the electrolytic tank will also increase.

[0237] (2) When the configuration capacity of the hydrogen storage tank is sufficient, the capacity and power of the battery can be small, i.e. the hydrogen storage tank can compensate for the insufficient regulation capacity of the battery. When the configuration capacity of the hydrogen storage tank decreases, the regulation capacity of the hydrogen storage tank decreases and cannot meet the hydrogen load demand of the park. Therefore, the capacity, output power and corresponding hours of the configured battery are increased to enhance the energy storage regulation capacity of the PIES. At the same time, it is also necessary to speed up the rate of converting electric energy into hydrogen energy by the electrolytic tank, so the output power of the configured electrolytic tank will also increase.

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

[0239] Example Two ​​

[0240] The equipment optimization configuration method of the electric hydrogen park under the double typical days is considered, the wind and light unit output curve is simulated, the objective function and the constraint condition are the same as in example one, the mixed integer linear programming method is adopted to solve the above optimization configuration model, and the planning capacity, rated output and cost details of each part of the equipment in the park are obtained, as shown in table 4 and table 5.

[0241] Table 4: Wind and light hydrogen production system planning optimization configuration results

[0242]

[0243] Table 5: Cost of each part (unit: billion yuan)

[0244]

[0245] On the basis of the above equipment configuration scheme, the electricity price factor and the energy storage operation characteristic constraint are further considered, the annual wind and light hydrogen production time sequence operation simulation is carried out, the output of each equipment in the electric hydrogen park and the power balance of the electric and hydrogen bus are obtained. The electric energy balance schematic diagram and the electric energy balance schematic diagram of the single day wind and light hydrogen production park system are shown in figures 6 and 7 respectively.

[0246] From the optimization configuration results, it can be seen that:

[0247] (1) Compared with the high utilization hours, the wind and light hydrogen production park selects photovoltaic units with larger unit construction area power to replace wind turbines due to the reduction of utilization hours of photovoltaic units and wind turbines, so the installed capacity of photovoltaic units increases and the installed capacity of wind turbines decreases, and the configuration power and capacity of energy storage equipment in the park increase to enhance the flexible regulation and control of energy. The above related measures make the configuration cost, operation cost and other part costs of the wind and light hydrogen production park system increase.

[0248] (2) When considering the high load double typical day, the configuration optimization result of the wind and light hydrogen production park is more in line with the actual situation. At this time, the system considers the various typical days that may occur in a year, the utilization hours of the wind and light unit output of the system is close to the average level of the whole year, so the configuration capacity of the photovoltaic unit is higher than that in the high utilization hours, and the configuration capacity of the wind turbine is lower.​​

Claims

1. A method for optimizing configuration of a wind-solar ratio system in a hydrogen production park, characterized in that, Comprising the following steps: S1) Calculate the output time series of local wind power and photovoltaic power generation, analyze the wind power, photovoltaic output characteristics and wind-light correlation, wherein the power output model of the wind turbine generator is: ， In the formula, P WT,t is the power generated by the wind turbine at time t; N is the number of wind power / photovoltaic units connected to the grid in the wind farm; P WT Pn is the rated power of the wind turbine; v t V is the actual wind speed at time t; v in Cut-in wind speed for wind turbine; v out cut-out wind speed for the wind turbine; v 0 V is the rated wind speed of the wind turbine; The output power of photovoltaic power generation is direct current, and its steady-state power output model can be represented as: ， where: P PV is the actual power generated by the photovoltaic unit (kW); P STC Pmax is the rated output power (kW) of the photovoltaic cell module under standard test conditions (STC); G ING (t) is the solar radiation intensity (W / m 2 ) at time t; G STC For the standard solar radiation intensity (1000 W / m 2 ) in the standard environment; η loss η is the power loss due to the temperature increase of the photovoltaic cell; In order to ensure wind power consumption and economic benefits of the park, the power generated by the wind turbine or the remaining power after offsetting the park load demand can be stored in the energy storage system during the low valley electricity price of the power grid; during the peak electricity price of the power grid, the power generated by the wind turbine is preferentially directly consumed by the load side, or the power released by the energy storage system is supplied to the load and the power grid; S2) Establish equivalent equipment models of hydrogen energy storage units and electrochemical energy storage units, form the power balance relationship of the electric bus and the hydrogen bus according to the operating characteristic models of the related electric and hydrogen equipment in the park; take the minimum life cycle cost as the objective function, consider the power balance and constraint conditions, and build a new energy hydrogen production park system planning optimization configuration model considering wind-light ratio; S3) Comprehensive the above objective function, obtain the new energy hydrogen production park system planning optimization configuration scheme and the total cost of system configuration; S4) Further consider the factors of electricity price and the constraint of energy storage operation characteristics, perform annual wind-light hydrogen production timing operation simulation, and analyze the park power purchase demand and new energy consumption capacity under the planning optimization configuration scheme, 2. The method of claim 1, wherein the method further comprises: In the above step S1, the clustering method is used to obtain the typical day situation, comprising the following steps: S1.1) randomly select a sample in the data set x as the first center point; S1.2) calculate the shortest distance between each sample and the existing cluster center, represented 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) calculate the distance of each sample in the data set to the k cluster centers and divide it into the class corresponding to the minimum distance cluster center; S1.5) calculate the average for each class respectively, and recalculate the cluster center; S1.6) repeat S1.4 and S1.5 until the termination condition.

3. The method of claim 1, wherein the method further comprises: In the above step S2, the following constraint conditions are set for the multi-objective optimization configuration model: S2.1) power balance constraint: ， In the formula, L E (t) is the park electrical load demand at time t; L H (t) is the hydrogen load demand for the park at time t; S2. 2) equipment output constraint The equipment output constraint includes electrolyzer output power constraint, fuel cell output power constraint, battery output and capacity constraint, and hydrogen storage tank output and capacity constraint: ， wherein: P EL_max , P FC_max are the maximum power output of the electrolyzer, respectively fuel cell S E_max , S H_max respectively the maximum capacity of the battery and the hydrogen storage tank S2.3) energy storage device constraint: ， Wherein: SOC(0) is the initial state of charge of the battery; SOC(24) is the final state of charge of the battery; S H (0) is the initial capacity of the hydrogen storage tank; S H (24) is the end-of-life capacity of the hydrogen storage tank; S2.4) power purchase and sale power constraint: ， In the formula, P grid_max and P grid_min 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: ， where: P PV_max , P WT_max is the total installed capacity of photovoltaic and wind power plants; λ PV , λ WT Aland Awindare the unit land areas of photovoltaic and wind power units, respectively. S max The maximum construction area of new energy units in the park.

4. The method of claim 3, wherein the method further comprises: In the above step S2, the park optimization objective function is the minimization of the life cycle cost: ， In the formula: C cstr is the initial investment cost, C buy is the purchase cost, C OM is the operation and maintenance cost, C loss is the loss cost, C Dis is the scrap cost; Initial investment cost C cstr Total initial investment cost of all equipment in the life cycle ， Wherein: y represents the service life of the equipment; r is the discount rate; μ i Ci investment cost per unit capacity of the ith equipment; S i Ci represents the maximum capacity of the i-th device; N represents the set of equipment in the park, including wind turbine, electrolyzer, fuel cell, battery and hydrogen storage tank. Cost of energy C buy denotes the sum of the cost of energy purchased from the superior grid and the revenue from selling energy to the superior grid during the whole year. ， In the formula: c t The electricity purchase / sale price for the period t; P grid,t P is the power exchanged between the park and the upper grid for the time period t, when P grid,t is positive, it means that the park buys power from the upper grid; when P grid,t is negative, it means that the park sells power to the upper grid. Operation and maintenance cost C OM denotes the total operation and maintenance cost of each device in the system in each time period throughout the year: ， where λ i,t is the unit capacity operation and maintenance cost of the ith device in period t, P i,t Pti(t) represents the output power of the i-th device at time t; N represents a set of devices in the park, including wind turbines, electrolytic cells, fuel cells, batteries and hydrogen storage tanks.

5. The method of claim 1, wherein the method further comprises: In the step S1, the selected source-side devices include photovoltaic units and wind turbines, and the selected energy conversion devices include electrolytic cells and fuel cells, and the input-output relationship is as follows: ， where P EL,in (t), P EL,out (t), η EL are the input power, output power, and conversion efficiency of the electrolyzer for a period t, respectively. P FC,in (t), P FC,out (t), P FC,h (t), η FC,e , η FC,h are the input power and the electrical, thermal power output, the conversion efficiency of the fuel cell, respectively, for a time period t. The selected energy storage devices include batteries and hydrogen storage tanks, and the mathematical model and input-output relationship are as follows: ， In the formula: S E_max is the maximum capacity of the battery; SOC(t) is the charge level of the battery at time t; 、 respectively, are the charging and discharging power of the battery at time t; 、 respectively, are the charging and discharging efficiency of the battery. S Hy Vt is the hydrogen storage tank capacity at time t; 、 respectively, are the charging and discharging power of the hydrogen storage tank; 、 respectively, are the charging and discharging efficiency of the hydrogen storage tank.

6. The method of claim 1, wherein the method further comprises: In the step S1, η loss may be expressed as: ， wherein: k PV is the temperature coefficient; T c (t) is the photovoltaic panel surface temperature; T t for reference temperature; The photovoltaic cell surface temperature T c (t) can be estimated from the ambient temperature and the light intensity: ， wherein: T a is the ambient temperature, G ING is the light intensity.

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