Capacity configuration method of off-grid hydrogen-electric hybrid energy storage system

Abstract

The present application relates to the field of energy technology, and more particularly to a capacity configuration method of an off-grid hydrogen-electric hybrid energy storage system.The capacity configuration method of the system comprises the following steps: 1, establishing a mathematical function of the off-grid hydrogen-electric hybrid energy storage system; 2, establishing a target optimization function with the lowest total cost of the system as the economic target and the maximum self-generation rate and the minimum power abandonment rate as the operation target of the hydrogen-electric coupled direct-current microgrid system; 3, establishing a capacity configuration constraint function with the system power balance, thermal balance, grid-connected interactive power balance, equipment capacity upper limit and equipment power upper and lower limits as constraint conditions; 4, establishing a capacity configuration optimization model of the system based on the mathematical function of the off-grid hydrogen-electric hybrid energy storage system, the target optimization function and the capacity configuration constraint function, and obtaining the optimal capacity configuration scheme by solving the model through the NSGA-II algorithm.The present application simultaneously considers the economy, stability and efficiency of system operation, and realizes the reasonable capacity configuration of the system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy, in particular to a capacity configuration method of off-grid hydrogen-electric hybrid energy storage system. BACKGROUND

[0002] The off-grid hydrogen-electric hybrid energy storage system has significant potential by combining the advantages of electric energy storage and hydrogen energy storage, and has strong flexibility and independence. When facing extreme weather events such as typhoon and earthquake, the off-grid hydrogen-electric hybrid energy storage system can quickly disconnect from the main grid and operate independently.

[0003] The existing off-grid hydrogen-electric hybrid energy storage system has the defect of incomplete comprehensive capacity configuration. If only the economy of system operation is considered, the stability and efficiency of the system itself during off-grid operation will be ignored. If only the power supply stability and reliability of the system during off-grid operation are optimized, the economy of the whole system will be affected.

[0004] Therefore, it is necessary to design a hybrid energy storage capacity optimization configuration for the off-grid hydrogen-electric hybrid energy storage system, which can achieve the best balance of hydrogen and electric energy storage capacity, comprehensively consider the capacity ratio of battery and hydrogen energy storage, and develop a reasonable capacity configuration strategy. SUMMARY

[0005] In order to overcome the above problems, the purpose of the present application is to provide a capacity configuration method of off-grid hydrogen-electric hybrid energy storage system, which considers the economy, stability and efficiency of system operation, and realizes the reasonable capacity configuration of off-grid hydrogen-electric hybrid energy storage system.

[0006] In order to achieve the above purpose, the capacity configuration method of off-grid hydrogen-electric hybrid energy storage system designed by the present application is characterized by comprising the following steps:

[0007] (1) Establishing a mathematical function of off-grid hydrogen-electric hybrid energy storage system: the off-grid hydrogen-electric hybrid energy storage system includes three types of energy storage subsystems, namely electric energy storage system, hydrogen energy storage system and heat storage tank energy storage system. The mathematical function of off-grid hydrogen-electric hybrid energy storage system is established based on the charge and discharge characteristics of the three types of energy storage subsystems, and is used to represent the charge and discharge power, real-time power and energy storage capacity of each energy storage subsystem;

[0008] (2) Establishing a target optimization function with the lowest total cost of the system as the economic target, and the maximum self-generation rate and the minimum abandoned power rate as the operation target of hydrogen-electric coupled direct current microgrid system;

[0009] (3) taking system power balance, thermal balance, grid interactive power balance, equipment capacity upper limit and equipment power upper and lower limit as constraint conditions, a capacity configuration constraint function is established;

[0010] (4) based on the off-grid hydrogen and electricity hybrid energy storage system mathematical function, the target optimization function and the capacity configuration constraint function, an off-grid hydrogen and electricity hybrid energy storage system capacity configuration optimization model is established, the off-grid hydrogen and electricity hybrid energy storage system capacity configuration optimization model is solved by the NSGA-II algorithm to obtain a Pareto frontier solution, the obtained Pareto solution is sorted, the distance between each possible solution and the ideal optimal solution and the worst solution is compared, and finally the optimal capacity configuration scheme of the off-grid hydrogen and electricity hybrid energy storage system is obtained.

[0011] As a preferred scheme, the electric energy storage system in step (1) includes a photovoltaic power generation unit, a wind power generation unit, a battery unit and a lithium battery, the hydrogen energy storage system includes a hydrogen storage tank unit, and the heat storage water tank energy storage system includes a heat storage water tank unit.

[0012] As a preferred scheme, the establishment process of the off-grid hydrogen and electricity hybrid energy storage system mathematical function in step (1) is as follows: based on the charge and discharge characteristics of the photovoltaic power generation unit, the wind power generation unit, the battery unit, the lithium battery, the hydrogen storage tank unit and the heat storage water tank unit, a photovoltaic power generation mathematical function, a wind power generation mathematical function, a battery mathematical function, a lithium battery mathematical function, a hydrogen storage tank mathematical function and a heat storage water tank mathematical function are respectively established, and the photovoltaic power generation mathematical function, the wind power generation mathematical function, the battery mathematical function, the lithium battery mathematical function, the hydrogen storage tank mathematical function and the heat storage water tank mathematical function are respectively used to represent the charge and discharge power, the real-time power and the energy storage capacity of each energy storage unit.

[0013] As a preferred scheme, the photovoltaic power generation mathematical function, the wind power generation mathematical function, the battery mathematical function, the lithium battery mathematical function, the hydrogen storage tank mathematical function and the heat storage water tank mathematical function are respectively as follows:

[0014] 1.1, the photovoltaic power generation mathematical function is as shown in formula (1):

[0015]

[0016] In formula (1), P pv is the steady-state power of the photovoltaic power generation system, P STC is the installed capacity of the photovoltaic power generation system, I G is the standard rated irradiance intensity 1000 W / m 2 ,T G is the standard rated temperature 25℃, I and T are the actual working irradiance intensity and temperature of the system, P0 is the power value under the standard rated state, and k is the power temperature coefficient, which is taken as 0.0045;

[0017] 1.2, Wind power mathematical function as shown in equation (2):

[0018]

[0019] In equation (2), P WT (v) is the wind turbine power at wind speed v, v in is the cut-in wind speed, v cut is the cut-out wind speed, v G is the rated wind speed, P WTC is the rated output power of the wind turbine, δ(v) is a function of wind speed, taking the quadratic form;

[0020] 1.3, Battery mathematical function as shown in equation (3);

[0021]

[0022] In equation (3), SOC(t) is the state of charge of the battery at time t, 0.8≥SOC≥0.2, P cha , P dis are the battery charging and discharging power, η cha , η dis are the battery charging and discharging efficiency, Q bat is the battery capacity;

[0023] 1.4, Lithium battery mathematical function as shown in equation (4);

[0024]

[0025] P t bat is the battery charging and discharging efficiency, positive for charging and negative for discharging; N cycle is the cycle life of the battery.

[0026] 1.5, Hydrogen storage tank mathematical model as shown in equations (5) and (6);

[0027] When charging, the hydrogen storage tank model is shown in equation (5):

[0028] SOC HT (t) = SOC HT (t-1) + η EL (P g (t) - P L(t) / η inv )ρ (5)

[0029] In equation (5), P g (t) is the sum of photovoltaic and wind power at time t, P L(t) is the power load at time t, η invFor the efficiency of the inverter, SOC HT (t) is the remaining hydrogen volume of the hydrogen tank at time t, p is the amount of hydrogen that can be generated per kilowatt-hour of electricity, and η EL is the charging efficiency of the hydrogen production system by electrolysis of water.

[0030] When discharging, the hydrogen storage tank model is shown in equation (6):

[0031] SOC HT (t) = SOC HT (t-1) - η FC (P L (t) / η inv -P g(t) ) / η (6)

[0032] In equation (6), SOC HT (t) is the remaining hydrogen volume of the hydrogen tank at time t, P L(t) is the power load at time t, P g (t) is the sum of photovoltaic and wind power generation at time t, and η represents the amount of electricity generated per cubic meter of hydrogen, η inv is the efficiency of the inverter, and η FC is the discharging efficiency of the fuel cell.

[0033] 1.6, the mathematical model of the hot water tank is shown in equation (7):

[0034]

[0035] In equation (3), tw t represents the water temperature of the hot water tank; V t load is the hot water consumption during the hot load period t, M ttk is the volume of the hot water tank, p t boil is the heating power of the electric boiler at time t, and a1 and a2 are the heat transfer powers of the electric boiler and the fuel cell, respectively. T is the ambient temperature, and m water is the calculation coefficient of heat power to water temperature. P t thermel is the heat power of the fuel cell.

[0036] As a preferred solution, in step (2), the multi-objective optimization function F includes a system total cost function F1, a system self-generation rate function F2, and a system curtailment rate function F3.

[0037] The multi-objective optimization function F is shown in equation (8):

[0038] F = {min(F1, F3), max(F2)} (8)

[0039] The total system cost function F1 includes a one-time investment cost function C cap , a device replacement cost function C rep , a system maintenance function C mat , a system operation cost function C co_off in off-grid mode, and a system operation cost function C co in grid-connected mode on ;

[0040] The one-time investment cost function C cap is shown in equation (9):

[0041]

[0042] In equation (9), C cap is the one-time investment cost, C cap_wt , C cap_pv , C cap_bat , C cap_bl , C cap_ele , C cap_tank , C cap_fc , and C cap_ttk are the purchase costs of wind turbines, photovoltaic panels, batteries, electric heating boilers, water electrolysis hydrogen production systems, hydrogen storage tanks, fuel cells, and thermal storage water tanks, respectively. wt , k pv , k bl , k ele , and k fc are the unit power prices of wind turbines, photovoltaic panels, electric heating boilers, water electrolysis hydrogen production systems, and fuel cells, respectively. tank , k ttk are the unit mass prices of hydrogen storage tanks and thermal storage water tanks, respectively. bat is the unit capacity cost of batteries. wt , P pv , P ele , P fc , and P bl are the installed capacities of wind turbines and photovoltaic panels, the rated powers of water electrolysis hydrogen production systems and fuel cells, and the rated heating power of electric heating boilers, respectively. bat , M tank , and M ttk are the rated capacities of electrical energy storage, hydrogen energy storage, and thermal storage water tanks, respectively.

[0043] The device replacement function C rep is shown in equation (10) and includes the replacement cost of batteries C rep_bat shown in equation (11), the replacement cost of water electrolysis hydrogen production systems C rep_el shown in equation (12), and the replacement cost of fuel cells C rep_fc shown in equation (13).

[0044]

[0045] C rep =C rep_el +C rep_bat +C rep_fc (10)

[0046] In formula (10) (11) (12) (13), C rep_bat is the replacement cost of the battery, Δsoh a is the annual soh change of the battery, soh max , soh min is the initial soh of the battery and the soh when ELO is reached; C rep_el , C rep_fc are the replacement costs of the hydrogen production system from water electrolysis and the fuel cell, respectively, ΔV ela is the annual voltage rise of the hydrogen production system from water electrolysis, ΔV fca is the annual voltage decay of the hydrogen production system from water electrolysis, V eol_el , V eol_fc are the voltage values of the hydrogen production system from water electrolysis and the fuel cell when EOL is reached;

[0047] The system maintenance function C mat is shown in formula (14):

[0048] C mat =0.1*C cap (14)

[0049] In formula (14), C cap is the initial investment cost in formula (9); C mat is the system maintenance cost;

[0050] The system operation cost function includes the system operation cost function C co_off in off-grid mode and the system operation cost function C co_on in grid-connected mode; the system operation cost function model is shown in formula (15) and formula (16):

[0051]

[0052] In formula (15) and formula (16), C co_off , C co_on represent the operation costs in off-grid and grid-connected modes, respectively, C gass , C gas are the selling and purchasing prices of hydrogen, m t gass , m t gasare the sales and purchase amounts of hydrogen at time t, c t e P represents the electricity purchase price of the upper power grid at time t, t ex represents the interaction power between the microgrid and the upper grid at time t;

[0053] The total cost function F1 of the system is as follows:

[0054] F1=C cap +C rep +C mat +C co_off +C co_on (17)

[0055] The system's self-generated rate function F2 is as shown in formula (18):

[0056]

[0057] In formula (18), P t grid_buy is the amount of electricity purchased from the upper power grid at time t, P load_t is the load required on the load side at time t;

[0058] The system's curtailment rate function F3 is as shown in formula (19):

[0059]

[0060] In formula (19), P HS,max 、P bat,max is the power output of the hydrogen storage tank and battery at time t, P WT (t), P PV (t) is the output power of wind power and photovoltaic power at time t, P L (t) is the load at time t.

[0061] As a preferred solution, in step (3), the function of the constraint condition is expressed as:

[0062] In the grid-connected mode, the system power balance constraint function is as shown in formula (20):

[0063]

[0064] In off-grid mode, the system power balance constraint function is as shown in formula (21):

[0065]

[0066] In formula (20) and formula (21), is the output power of the photovoltaic system, is the output power of the wind system, Pbat is the output power of the battery, Pfc is the output power of the fuel cell, Pload is the load power at this moment, t el Phydrogen is the output power of the water electrolysis hydrogen production system, t boiler Pboiler is the output power of the electric boiler;

[0067] The system thermal balance constraint function is as shown in formula (22):

[0068]

[0069] In formula (22), a1 and a1 are the electric heating efficiency of the electric boiler and the heat recovery rate of the fuel cell, P t tload Pheat is the system heat load demand at t moment;

[0070] The water temperature constraint function of the heat storage tank is as shown in formula (23): 50≤tw t ≤70 (23)

[0071] The interactive power constraint function in the grid-connected mode is as shown in formula (24):

[0072]

[0073] In formula (24), Pmax is the upper limit of the power exchange with the upper-level power grid when the system is grid-connected; Pgrid is the transmission power with the upper-level power grid at t moment when the system is grid-connected;

[0074] The equipment capacity upper and lower limit constraint function is as shown in formula (25):

[0075]

[0076] The equipment power upper and lower limit constraint function is as shown in formula (26) and (27):

[0077]

[0078]

[0079] In formula (26), P bat_rated Pbat is the rated power of the battery, el_rated Pele is the rated power of the electrolyzer, fc_rated Pfc is the rated power of the fuel cell, boli_rated Pboiler is the rated power of the electric boiler, and β is the electrolyzer overload coefficient; in formula (27), when the excess photovoltaic output power cannot be consumed, the output power will be reduced through modes such as cutting or exiting mppt to achieve power balance, P tpvi is the ideal output of the photovoltaic module at time t in the MPPT mode.

[0080] As a preferred solution, in step (4), the optimal capacity configuration scheme of the off-grid hydrogen and electricity hybrid energy storage system includes wind and light power, battery power, electrolytic tank power, fuel cell power, and hydrogen mass in the hydrogen storage tank.

[0081] As a preferred solution, in step (3), the process of the NSGA-II algorithm is as follows: first, input the control parameters of the NSGA-II algorithm, including population size (N) and maximum number of iterations, Gen max Then, select the capacity allocation parameter Ri as the initial population of the NSGA-II algorithm, the capacity allocation parameter Ri includes wind and light power generation scale, heat storage tank capacity, electric heating boiler power, electrolytic tank capacity, hydrogen energy storage capacity, and fuel cell capacity, input the capacity allocation parameter Ri into the capacity configuration optimization model of the off-grid hydrogen and electricity hybrid energy storage system, solve the model, and obtain the Pareto front solution.

[0082] Compared with the traditional one, the present application has the following advantages: firstly, the present application establishes a mathematical function of the off-grid hydrogen and electricity hybrid energy storage system based on the charge and discharge characteristics of the energy storage subsystem of the off-grid hydrogen and electricity hybrid energy storage system, and the mathematical function of the off-grid hydrogen and electricity hybrid energy storage system is used to represent the charge and discharge power, real-time power, and energy storage capacity of each energy storage subsystem; then, based on the operation strategy of the system, a multi-dimensional target optimization function is established under two different scenarios of grid connection and off-grid, with the lowest total cost as the economic target, and the maximum self-generation rate and the minimum power abandonment rate as the operation targets of the hydrogen and electricity coupled direct current micro-grid system; then, a capacity configuration constraint function is established with the system power balance, thermal balance, grid interaction power balance, and upper limit of equipment capacity as the constraint conditions; finally, a capacity configuration optimization model of the off-grid hydrogen and electricity hybrid energy storage system is established based on the mathematical function of the off-grid hydrogen and electricity hybrid energy storage system, the target optimization function, and the capacity configuration constraint function, and the optimal capacity configuration scheme is obtained by solving the capacity configuration optimization model. Compared with the traditional surplus electricity hydrogen production mode, the capacity configuration method of the present application can effectively balance the consumption and energy efficiency, and improve the comprehensive efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0083] Figure 1 It is the operation topology diagram of the off-grid hydrogen and electricity hybrid energy storage system;

[0084] Figure 2 It is the operation strategy diagram of the off-grid hydrogen and electricity hybrid energy storage system in the grid-connected mode;

[0085] Figure 3 It is the operation strategy diagram of the off-grid hydrogen and electricity hybrid energy storage system in the off-grid mode;

[0086] Figure 4 Flow chart for capacity configuration of off-grid hydrogen and electricity hybrid energy storage system

[0087] Figure 5 Data chart of light intensity in Cixi, Ningbo in normal weather condition in Example 1

[0088] Figure 6 Data chart of wind speed in Cixi, Ningbo in normal weather condition in Example 1

[0089] Figure 7 Data chart of electric load in Cixi, Ningbo in normal weather condition in Example 1

[0090] Figure 8 Data chart of electricity price in Cixi, Ningbo in normal weather condition in Example 1

[0091] Figure 9 Data chart of light intensity in Cixi, Ningbo in extreme weather condition in Example 2 DETAILED DESCRIPTION

[0092] In order to better understand the present application, the application will be described in detail below in combination with the drawings and specific examples.

[0093] Capacity configuration of off-grid hydrogen and electricity hybrid energy storage system in Cixi, Ningbo in grid-connected mode in Example 1

[0094] In grid-connected mode, the off-grid hydrogen and electricity hybrid energy storage system extracts the historical data of one year of normal weather condition in Cixi, Ningbo. The average annual sunshine hours in Cixi are 2038 hours, and the annual sunshine percentage is 47%. The average annual temperature is 16.0℃, the highest in July, the average is 28.2℃, and the lowest in January, the average is 3.8℃. The whole year is dominated by east wind, the average annual wind speed is 3 meters / second, and the average annual gale day is 9.6 days. There are many tropical storms in summer and autumn. The wind, light and load data are shown in Figure 5 、 Figure 6 、 Figure 7 and Figure 8 .

[0095] In combination with Figure 4 , the method for capacity configuration of off-grid hydrogen and electricity hybrid energy storage system in Cixi, Ningbo in grid-connected mode includes the following steps:

[0096] (1) Establishing mathematical function of off-grid hydrogen and electricity hybrid energy storage system

[0097] In combination with Figure 1As shown, the off-grid hydrogen and electricity hybrid energy storage system includes three types of energy storage subsystems, namely, an electric energy storage system, a hydrogen energy storage system, and a hot water tank energy storage system. More specifically, the off-grid hydrogen and electricity hybrid energy storage system includes a photovoltaic power generation unit, a wind power generation unit, a battery unit, a lithium battery, a hydrogen storage tank unit, and a hot water storage tank unit. The present embodiment provides detailed mathematical functions for each unit. By establishing mathematical functions for each unit, the operating characteristics, energy conversion process, and power output characteristics of the system can be accurately described. These mathematical functions provide basic data and scientific basis for the overall design and capacity configuration of the system.

[0098] 1.1, the photovoltaic power generation mathematical function is shown in formula (1): the steady-state power of the photovoltaic power generation mathematical function is related to natural light irradiance, environmental temperature and other factors under the condition that the inclination angle of the photovoltaic panel is certain.

[0099]

[0100] In formula (1), P pv is the steady-state power of the photovoltaic power generation system, P STC is the installed capacity of the photovoltaic power generation system, I G is the standard rated irradiance 1000 W / m 2 , T G is the standard rated temperature 25℃, I and T are the actual working irradiance and temperature of the system, P0 is the power value under the standard rated state, and k is the power temperature coefficient, which is 0.0045;

[0101] 1.2, the wind power generation mathematical function is shown in formula (2): the wind power generation adopts a segmented function output model, that is, four working states are divided according to the actual wind speed.

[0102]

[0103] In formula (2), P WT (v) is the power generation power of the fan at wind speed v, v in is the cut-in wind speed, v cut is the cut-out wind speed, v G is the rated wind speed, P WTC is the rated output power of the wind turbine, and δ(v) is a function of wind speed, which is quadratic.

[0104] 1.3, the battery mathematical function is shown in formula (3); the battery mathematical function uses the state of charge SOC to represent the battery, and the thermal load is the actual water load, which is the mass of water, so the heat flow can select water temperature as the measurement standard.

[0105]

[0106] In formula (3), SOC(t) is the battery state of charge at time t, 0.8≥SOC≥0.2, P cha , P dis are the battery charging and discharging power, η cha , η dis are the battery charging and discharging efficiency, and Q bat is the battery capacity.

[0107] 1.4, the mathematical function of the lithium battery is shown in formula (4); the lithium battery uses the battery health state soh to evaluate and manage the life of the lithium battery.

[0108]

[0109] P t bat is the battery charging and discharging efficiency, positive for charging and negative for discharging; N cycle is the cycle life of the battery.

[0110] 1.5, the mathematical model of the hydrogen storage tank is shown in formula (5) and formula (6); the mathematical model of the hydrogen storage tank is that the real-time hydrogen mass in the hydrogen storage tank should be the sum of the hydrogen generation amount of the electrolytic water hydrogen production system and the hydrogen consumption amount of the fuel cell.

[0111] When charging, the hydrogen storage tank model is shown in formula (5):

[0112] SOC HT (t)=SOC HT (t-1)+η EL (P g (t)-P L(t) / η inv )ρ (5)

[0113] In formula (5), P g (t) is the sum of the photovoltaic and wind power at time t, P L(t) is the power load at time t, η inv is the efficiency of the inverter, SOC HT (t) is the remaining hydrogen volume of the hydrogen tank at time t, ρ is the hydrogen amount that can be generated per kilowatt-hour of power, and η EL is the charging efficiency of the electrolytic water hydrogen production system.

[0114] When discharging, the hydrogen storage tank model is shown in formula (6):

[0115] SOC HT (t)=SOC HT (t-1)-η FC (P L (t) / η inv -P g(t) ) / η(6)

[0116] SOC (t) = V (t) - V (t-1) (6) HT (t) is the remaining hydrogen volume of the hydrogen tank at time t, P L(t) is the power load at time t, P g (t) is the sum of photovoltaic and wind power generation at time t, η represents the power generated per cubic meter of hydrogen, η inv is the efficiency of the inverter, η FC is the discharge efficiency of the fuel cell;

[0117] 1.6, the mathematical model of the heat storage water tank is shown in equation (7): the thermal load of the heat storage water tank is the actual water load, with the unit of water mass, so the heat flow can be selected as the water temperature twt as the measurement standard.

[0118]

[0119] In equation (7), tw t represents the water temperature of the hot water tank; V t load is the hot water volume at time t, M ttk is the volume of the hot water tank, p t boil is the heating power of the electric boiler at time t, a1 and a2 are the heat transfer power of the electric boiler and the fuel cell respectively. T is the ambient temperature, m water is the calculation coefficient of heat power to water temperature. P t thermel is the heat power of the fuel cell.

[0120] (2) Based on the operation strategy analysis of the off-grid hydrogen and electricity hybrid energy storage system, the minimum total cost is taken as the economic target, the maximum self-generation rate and the minimum abandoned power rate are taken as the operation targets of the hydrogen and electricity coupled direct current microgrid system, and the target optimization function is established.

[0121] The operation strategy analysis of the off-grid hydrogen and electricity hybrid energy storage system is combined with Figure 2 and Figure 3The time-of-use price of the grid is considered when the system is in grid-connected mode. When there is excess energy, hydrogen is preferentially produced to meet the hydrogen load demand and ensure the hydrogen storage level of the system. According to the current electricity price level, when there is insufficient energy and the electricity price is at a low point, electricity is preferentially purchased from the grid and the battery is discharged to meet the load demand. This is because there is a large energy loss in the "electricity-gas-electricity" closed loop process. In this way, the operating efficiency of the system is reduced, and the fuel cell can only start working under limited conditions; but when the electricity price is not at a low point, the energy storage system is preferentially considered to supply power, which can ensure the economy of grid-connected operation. When the system is in off-grid mode, the power supply stability of the system is considered. When the power generated by photovoltaic and wind power exceeds the power demand, the electrolytic device will convert the excess power into hydrogen by electrolyzing water, and charge the battery. Conversely, when the net load power of the system is less than 0, the battery unit and the fuel cell unit start to discharge to meet the load demand of the system. If the load demand difference is too large and cannot be completely met, a certain amount of low-priority load is considered to be cut off, and the input power of wind and light is increased.

[0122] The multi-objective optimization function F includes a total cost function Fl of the system, a self-generation rate function F2 of the system, and an abandoned power rate function F3 of the system;

[0123] The multi-objective optimization function F is shown in formula (8):

[0124] F = {min (Fl, F3), max (F2)} (8)

[0125] 2.1, the total cost function Fl of the system includes a one-time investment cost function C cap , an equipment replacement cost function C rep , a system maintenance function C mat , a system operation cost function C co_off in off-grid mode, and a system operation cost function C co_on in grid-connected mode; the total cost model Fl of the system is shown in formula (17):

[0126] Fl = C cap + C rep + C mat + C co_off + C co_on (17)

[0127] wherein the one-time investment cost function C cap is shown in formula (9):

[0128]

[0129] In formula (9), C cap is the one-time investment cost function, C cap_wt , C cap_pv , Ccap_bat , C cap_bl , C cap_ele , C cap_tank , C cap_fc , C cap_ttk , C wt , k pv , k bl , k ele , k fc , k tank , k ttk , k bat , P wt , P pv , P ele , P fc , P bl , P bat , M tank , M ttk , M

[0130] Equipment replacement function C rep As shown in equation (10): including the replacement cost of the battery C rep_bat , as shown in equation (12) the replacement cost of the water electrolysis hydrogen production system C rep_el , as shown in equation (13) the replacement cost of the fuel cell C rep_fc :

[0131]

[0132] C rep =C rep_el +C rep_bat +C rep_fc (10)

[0133] In equation (10) (11) (12) (13), C rep_bat is the replacement cost of the battery, Δsoh a is the annual soh change of the battery, soh max , soh min is the initial soh of the battery and the soh when reaching ELO; C rep_el , C rep_fc is the replacement cost of the water electrolysis hydrogen production system and the fuel cell, ΔVela is the annual voltage rise of the water electrolysis hydrogen production system, ΔV fca is the annual voltage attenuation of the water electrolysis hydrogen production system, V eol_el 、V eol_fc This is the voltage value when the water electrolysis hydrogen production system and fuel cell reach EOL;

[0134] System maintenance function C mat As shown in formula (14):

[0135] C mat =0.1*C cap (14)

[0136] In formula (14), C cap is the one-time investment cost in formula (8); C mat System maintenance costs;

[0137] The system operation cost function includes the system operation cost function C of the off-grid mode co_off and the system operation cost function C of the grid-connected mode co_on The system operation cost function model is shown in formula (15) and formula (16):

[0138]

[0139] In formula (14) and formula (15), C co_off 、C co_on Represent the operating costs of off-grid and grid-connected operation, C gass 、C gas are the selling price and purchase price of hydrogen respectively, m t gass 、m t gas are the sales and purchase amounts of hydrogen at time t, c t e P represents the electricity purchase price of the upper power grid at time t, t ex It represents the interaction power between the microgrid and the upper grid at time t.

[0140] 2.2 The system's self-generated rate function F2 is as shown in formula (18):

[0141]

[0142] In formula (18), P t grid_buy is the amount of electricity purchased from the upper power grid at time t, P load_t is the load required on the load side at time t;

[0143] 2.3 The system curtailment rate function F3 is as shown in formula (19):

[0144]

[0145] In formula (19), P HS,max 、P bat,max is the power output of the hydrogen storage tank and battery at time t, P WT (t), P PV (t) is the output power of wind power and photovoltaic power at time t, P L (t) is the load at time t.

[0146] (3) With the system power balance, thermal balance, grid-connected interactive power balance, and equipment capacity upper limit as constraints, a capacity configuration constraint function is established:

[0147] In the grid-connected mode, the system power balance constraint function is as shown in formula (20):

[0148]

[0149] In off-grid mode, the system power balance constraint function is as shown in formula (21):

[0150]

[0151] In formula (20) and formula (21), is the output power of the photovoltaic system, is the output power of the wind system, is the battery output power, Output power for fuel cells This is the load power at this moment, P t el is the output power of the water electrolysis hydrogen production system, P t boiler is the output power of the electric boiler;

[0152] The system thermodynamic balance constraint function is as shown in formula (22):

[0153]

[0154] In formula (22), a1 and a1 are the electric heating efficiency of the electric boiler and the heat recovery rate of the fuel cell, P t tload is the system heat load demand at time t;

[0155] The water temperature constraint function of the hot water storage tank is as shown in formula (23):

[0156] 50≤tw t ≤70 (23)

[0157] The interactive power constraint function in grid-connected mode is as formula (24):

[0158]

[0159] is the upper limit of power exchange with the upper-level power grid when the system is connected to the grid; is the transmission power of the system with the upper-level power grid at time t after the system is connected to the grid;

[0160] The device capacity upper and lower limit constraint function is as formula (25):

[0161]

[0162] The device should run within the respective rated operating interval, in particular, when the photovoltaic re-output is excessive and cannot be absorbed, the output will be reduced through modes such as cutting off or exiting mppt to achieve power balance, therefore, the device power upper and lower limit constraint function is set, as formula (26) and formula (27):

[0163]

[0164]

[0165] In formula (26), P bat_rated is the rated power of the battery, P el_rated is the rated power of the electrolytic cell, P fc_rated is the rated power of the fuel cell, P boli_rated is the rated power of the electric heating boiler, and β is the electrolytic cell overload coefficient; in formula (27), when the photovoltaic re-output is excessive and cannot be absorbed, the output will be reduced through modes such as cutting off or exiting mppt to achieve power balance, P t pvi is the ideal output of the photovoltaic module at time t in mppt mode.

[0166] (4) Based on the mathematical function, the objective optimization function, and the capacity configuration constraint function of the off-grid hydrogen-electric hybrid energy storage system, a capacity configuration optimization model of the off-grid hydrogen-electric hybrid energy storage system is established, the capacity configuration optimization model of the off-grid hydrogen-electric hybrid energy storage system is solved by NSGA-II algorithm to obtain the Pareto front solution, the obtained Pareto solution is sorted, by comparing the distance of each possible solution with the ideal optimal solution and the worst solution, the optimal capacity configuration scheme of the off-grid hydrogen-electric hybrid energy storage system is finally obtained.

[0167] The process of the NSGA-II algorithm is as follows: first, input the control parameters of the NSGA-II algorithm, including the population size (N) and the maximum number of iterations, Gen maxThen, the capacity allocation parameters Ri, including the wind and solar power scale, the thermal storage tank capacity, the electric heat boiler power, the electrolyzer capacity, the hydrogen storage capacity, and the fuel cell capacity, are selected as the initial population of the NSGA-II algorithm. The capacity allocation parameters Ri are input into the capacity configuration optimization model of the off-grid hydrogen and electricity hybrid energy storage system, the model is solved, and the Pareto frontier solution is obtained. Specifically, the NSGA-II algorithm process is as follows:

[0168] Step 1: Input the control parameters of the NSGA-II algorithm, including the population size (N) and the maximum number of iterations (Gen max .

[0169] Step 2: Initialize the population and set Gen = 1. Randomly select N sets of integer arrays (Ri) as the initial population of the NSGA-II algorithm. Each array represents a set of upper-level decision variables, such as the wind and solar power scale (x1), the thermal storage tank capacity (x2), the electric heat boiler power (x3), the electrolyzer capacity (x4), the hydrogen storage capacity (x5), and the fuel cell capacity (x6).

[0170] Step 3: Input the capacity allocation parameters (Ri) of the individuals in the population into the simplified optimization model. By combining meteorological data, load data, and wind power data, the model is solved.

[0171] Step 4: Calculate the optimization objectives of each individual in the population. Perform non-dominated ranking and crowding calculation to obtain the ranking values and crowding degrees of all individuals.

[0172] Step 5: Determine the current individual size. If the population size is equal to N, go to step 6. Otherwise, go to step 7.

[0173] Step 6: Create a size of N / 2 offspring population using selection, crossover, and mutation operations. Merge the offspring population with the parent population to form a new combined population with a size equal to N+N / 2. Return to step 3.

[0174] Step 7: Select N individuals from the population based on the rank value and crowding degree. These individuals constitute the new population. The optimization process continues, and if the number of iterations (Gen) exceeds the maximum allowed number of iterations (Gen max ), the optimization method ends. Output the Pareto frontier, and increment Gen by 1. The process returns to step 6.

[0175] Through this solving process, the proposed off-grid optimization model can be effectively solved. After performing the above optimization calculation, a set of Pareto frontier solutions is obtained, each representing a different trade-off between system performance criteria. Through further multi-criteria objective selection, the most suitable solution is determined. The obtained optimization results are shown in Table 1.

[0176] Table 1 Capacity configuration of the off-grid hydrogen and electricity hybrid energy storage system in Ningbo Cixi in grid-connected mode

[0177] Wind power / kW 400 / 4058.05 Battery / kWh 5670.31 Electrolyser / kW 396.35 Fuel cell / kW 239.80 Hydrogen storage / Kg 390.00

[0178] Example 2 Capacity configuration of the off-grid hydrogen and electricity hybrid energy storage system in Ningbo Cixi in off-grid mode

[0179] Under extreme weather operation, the output proportion of photovoltaic and wind power is low, and the planning time period and the constant time are different, which needs to be considered in different cases. That is, through the configuration under extreme weather, the device capacity mainly participating in off-grid energy support is obtained, and in the normal condition model, the optimization configuration results of the remaining devices are obtained. In this example, the requirement of 168-hour off-grid operation under extreme weather is proposed. Under off-grid mode, the off-grid hydrogen and electricity hybrid energy storage system extracts the extreme weather historical data of Ningbo Cixi for one year. The extreme weather extraction is the case that the wind and light resources are low for 7 consecutive days, resulting in insufficient system energy. The extreme weather extraction result is shown in Table 2. Figure 9

[0180] The capacity configuration method of the off-grid hydrogen and electricity hybrid energy storage system in Ningbo Cixi in off-grid mode is basically the same as that in Example 1, and the difference lies in that the system total cost function F1 adopts the system operation cost function C co_off under off-grid mode, and the system power balance constraint function adopts the system power balance constraint function under off-grid mode. Finally, the capacity configuration of the off-grid hydrogen and electricity hybrid energy storage system in Ningbo Cixi in off-grid mode is shown in Table 2.

[0181] Table 2 Capacity configuration of the off-grid hydrogen and electricity hybrid energy storage system in Ningbo Cixi in off-grid mode

[0182] Wind power / kW 400 / 4058.05 Battery / kWh 5887.11 Electrolyser / kW 396.35 Fuel cell / kW 239.80 Hydrogen storage / Kg 390.00

[0183] ​It can be known from the capacity configuration results of the off-grid hydrogen and electricity hybrid energy storage system of the comparative example 1 and the example 2 that the capacity configuration results in the two cases have great similarity, which shows that the capacity configuration method of the off-grid hydrogen and electricity hybrid energy storage system has good robustness and stability, and can maintain the stability and reliability of the system under normal operation and extreme weather. The capacity configuration method of the off-grid hydrogen and electricity hybrid energy storage system has good adaptability, and can cope with the challenges under different operation modes such as power grid failure, maintenance and extreme weather, and fully considers the economy and self-sufficiency of the off-grid hydrogen and electricity hybrid energy storage system. In the grid-connected state, the model realizes long-term economic benefits through the minimum cost, and in the off-grid state, the self-sufficiency and balance of the system are ensured by maximizing the self-generation rate and minimizing the power abandonment rate. The similarity of the results further proves the comprehensiveness of the optimization strategy, and no matter in the grid-connected or off-grid condition, the capacity configuration method of the off-grid hydrogen and electricity hybrid energy storage system can effectively ensure the system performance, and shows its universality and practical value.

Claims

1. A capacity configuration method for an off-grid hydrogen-electric hybrid energy storage system, characterized in that: Including steps: (1) Establishing a mathematical function of an off-grid hydrogen-electric hybrid energy storage system: The off-grid hydrogen-electric hybrid energy storage system includes three types of energy storage subsystems, namely, an electric energy storage system, a hydrogen energy storage system, and a hot water tank energy storage system. Based on the charge and discharge characteristics of the three types of energy storage subsystems, a mathematical function of the off-grid hydrogen-electric hybrid energy storage system is established, and the mathematical function of the off-grid hydrogen-electric hybrid energy storage system is used to characterize the charge and discharge power, real-time power, and energy storage capacity of each energy storage subsystem; (2) Taking the lowest total system cost as the economic goal and the maximum self-generation rate and the minimum power abandonment rate as the operation goals of the hydrogen-electricity coupled DC microgrid system, a multi-objective optimization function is established; (3) Establish a capacity configuration constraint function based on the system power balance, thermal balance, grid-connected interactive power balance, upper and lower limits of equipment capacity, and upper and lower limits of equipment power as constraints; (4) Based on the mathematical function, multi-objective optimization function and capacity configuration constraint function of the off-grid hydrogen-electric hybrid energy storage system, a capacity configuration optimization model of the off-grid hydrogen-electric hybrid energy storage system is established. The capacity configuration optimization model of the off-grid hydrogen-electric hybrid energy storage system is solved by the NSGA-II algorithm to obtain the Pareto frontier solution. The obtained Pareto solutions are sorted, and the distance between each possible solution and the ideal optimal solution and the worst solution is compared to finally obtain the optimal capacity configuration scheme of the off-grid hydrogen-electric hybrid energy storage system. Wherein, in said step (2), the multi-objective optimization function F includes the system total cost function F1, the system self-generation rate function F2, and the system power abandonment rate function F3; The multi-objective optimization function F is shown in formula (8): F={min(F1,F3),max(F2)} (8) The total cost function F1 of the system includes the one-time investment cost function C cap , equipment replacement cost function C rep , system maintenance function C mat , the system operation cost function C in off-grid mode co_off and the system operation cost function C of the grid-connected mode co_on ; One-time investment cost function C cap As shown in formula (9): In formula (9), C cap is the one-time investment cost, C cap_wt 、C cap_pv 、C cap_bat 、C cap_bl 、C cap_ele 、C cap_tank 、C cap_fc 、C cap_ttk are the purchase costs of fans, photovoltaic panels, batteries, electric boilers, water electrolysis hydrogen production systems, hydrogen storage tanks, fuel cells, and hot water storage tanks; k wt 、k pv 、k bl 、k ele 、k fc are the unit power prices of wind turbine, photovoltaic panel, electric boiler, water electrolysis hydrogen production system and fuel cell, respectively. tank 、k ttk are the unit mass prices of hydrogen storage tank and hot water storage tank respectively, k bat is the unit capacity cost of the battery; P wt 、P pv 、P ele 、P fc 、P bl They are the installed capacity of the wind turbine, the installed capacity of the photovoltaic panel, the rated power of the water electrolysis hydrogen production system, the rated power of the fuel cell, and the rated heating power of the electric boiler; E bat 、M tank 、M ttk are the rated capacities of the battery, hydrogen storage tank, and hot water storage tank respectively; Device replacement function C rep As shown in formula (10): including the battery replacement cost C shown in formula (11) rep_bat , as shown in formula (12) the replacement cost C of the water electrolysis hydrogen production system rep_el , the replacement cost of the fuel cell C as shown in formula (13) rep_fc : C rep =C rep_el +C rep_bat +C rep_fc (10) In formulas (10)(11)(12)(13), C rep_bat is the replacement cost of the battery, Δsoh a is the annual change in soh of the battery, where soh max is the initial soh value of the battery, soh min is the soh value when reaching EOL; C rep_el 、C rep_fc are the replacement costs of the water electrolysis hydrogen production system and the fuel cell respectively; ΔV ela is the annual voltage rise of the water electrolysis hydrogen production system, ΔV fca is the annual fuel cell voltage attenuation, V eol_el 、V eol_fc This is the voltage value when the water electrolysis hydrogen production system and fuel cell reach EOL; System maintenance function C mat As shown in formula (14): C mat =0.1*C cap (14) In formula (14), C cap is the one-time investment cost in formula (9); C mat System maintenance costs; The system operation cost function includes the system operation cost function C of the off-grid mode co_off and the system operation cost function C of the grid-connected mode co_on The system operation cost function model is shown in formula (15) and formula (16): In formula (15) and formula (16), C co_off 、C co_on Represent the operating costs of off-grid and grid-connected operation, C gass 、C gas are the selling price and purchase price of hydrogen respectively, m t gass 、m t gas are the sales and purchase amounts of hydrogen at time t, c t e P represents the electricity purchase price of the upper power grid at time t, t ex represents the interaction power between the microgrid and the upper grid at time t; The total cost function F1 of the system is as follows: F1=C cap +C rep +C mat +C co_off +C co_on (17) The system's self-generated rate function F2 is as shown in formula (18): In formula (18), P t grid_buy is the amount of electricity purchased from the upper power grid at time t, P load_t is the load required on the load side at time t; The system's curtailment rate function F3 is as shown in formula (19): In formula (19), P HS,max 、P bat,max is the power output of the hydrogen storage tank and battery at time t, P WT (t), P PV (t) are the output power of wind turbine and photovoltaic panel at time t, P L (t) is the power load at time t.

2. The capacity configuration method of the off-grid hydrogen-electric hybrid energy storage system according to claim 1, characterized in that: In step (1), the electric energy storage system includes photovoltaic panels, wind turbines, batteries and lithium batteries, the hydrogen energy storage system includes a hydrogen storage tank, and the hot water tank energy storage system includes a hot water tank.

3. The capacity configuration method of the off-grid hydrogen-electric hybrid energy storage system according to claim 2, characterized in that: The process of establishing the mathematical function of the off-grid hydrogen-electric hybrid energy storage system in step (1) is as follows: based on the charge and discharge characteristics of the photovoltaic panel, wind turbine, battery, lithium battery, hydrogen storage tank, and hot water storage tank, respectively establish a photovoltaic power generation mathematical function, a wind power generation mathematical function, a battery mathematical function, a lithium battery mathematical function, a hydrogen storage tank mathematical function, and a hot water storage tank mathematical function, and the photovoltaic power generation mathematical function, the wind power generation mathematical function, the battery mathematical function, the lithium battery mathematical function, the hydrogen storage tank mathematical function, and the hot water storage tank mathematical function are used to respectively correspond to characterize the charge and discharge power, real-time power, and energy storage capacity of each energy storage unit.

4. The capacity configuration method of the off-grid hydrogen-electric hybrid energy storage system according to claim 3 is characterized in that: The mathematical functions of photovoltaic power generation, wind power generation, battery, lithium battery, hydrogen storage tank, and hot water storage tank are respectively: 1.

1. The mathematical function of photovoltaic power generation is shown in formula (1): In formula (1), P pv is the steady-state power of the photovoltaic panel, P STC is the installed capacity of photovoltaic panels, I G The standard rated irradiance is 1000W / m 2 ,T G is the standard rated temperature of 25°C, I and T are the actual working radiation intensity and temperature of the system, and k is the power temperature coefficient, which is 0.0045; 1.

2. The mathematical function of wind power generation is shown in formula (2): In formula (2), P WT (v) is the wind turbine power generation under wind speed v, v in is the cut-in wind speed, v cut is the cut-out wind speed, v G is the rated wind speed, P WTC is the rated output power of the wind turbine unit, δ(v) is a function of wind speed and takes quadratic form; 1.3, the mathematical function of the battery is shown in formula (3); In formula (3), SOC(t) is the battery state of charge at time t, 0.8≥SOC≥0.2, P cha 、P dis are the battery charging and discharging power, η cha ,η dis are the battery charge and discharge efficiency, Q bat is the battery capacity; 1.4, the mathematical function of lithium battery is shown in formula (4); P t bat N is the charge and discharge power of lithium batteries, positive for charging and negative for discharging; cycle is the cycle life of the lithium battery; Q bat ' is the capacity of lithium battery; 1.

5. The mathematical functions of hydrogen storage tanks are shown in formulas (5) and (6); When charging, the hydrogen storage tank model is shown in formula (5): SOCIETY HT (t)=SOC HT (t-1)+η EL (P g (t)-P L (t) / η inv )ρ (5) In formula (5), P g (t) is the sum of the power generated by the photovoltaic panels and wind turbines at time t, P L (t) is the power load at time t, η inv is the efficiency of the inverter, SOC HT (t) is the remaining hydrogen volume in the hydrogen storage tank at time t, ρ is the amount of hydrogen that can be generated per kilowatt-hour of electricity, and η EL The charging efficiency of the water electrolysis hydrogen production system; During discharge, the hydrogen storage tank model is shown in formula (6): SOCIETY HT (t)=SOC HT (t-1)-η FC (P L (t) / η inv -P g (t)) / η (6) In formula (6), SOC HT (t) is the remaining hydrogen volume in the hydrogen storage tank at time t, P L (t) is the power load at time t, P g (t) is the sum of the power generated by the photovoltaic panels and wind turbines at time t, η represents the amount of electricity generated per cubic meter of hydrogen, and η inv is the efficiency of the inverter, η FC is the discharge efficiency of the fuel cell; 1.

6. The mathematical function of the hot water storage tank is shown in formula (7): In formula (7), tw t Indicates the water temperature of the hot water tank; V t load is the heat water consumption during the heat load period t, M ttk is the volume of the hot water tank, p t boil is the heating power of the electric boiler at time t, a1 and a2 are the heat transfer powers of the electric boiler and the fuel cell respectively, T is the ambient temperature, m water is the calculation coefficient of thermal power to water temperature, P t thermel is the thermal power of the fuel cell.

5. The capacity configuration method of the off-grid hydrogen-electric hybrid energy storage system according to claim 1, characterized in that: In step (4), the optimal capacity configuration scheme of the off-grid hydrogen-electric hybrid energy storage system includes wind and solar power, battery power, electrolyzer power, fuel cell power, and hydrogen quality in the hydrogen storage tank.

6. The capacity configuration method of the off-grid hydrogen-electric hybrid energy storage system according to claim 1, characterized in that: In step (4), the process of the NSGA-II algorithm is as follows: first, the control parameters of the NSGA-II algorithm are input, including the population size and the maximum number of iterations Gen max Then, the capacity allocation parameter Ri is selected as the initial population of the NSGA-II algorithm. The capacity allocation parameter Ri includes the scale of wind and solar power generation, the capacity of the hot water storage tank, the power of the electric boiler, the capacity of the electrolyzer, the hydrogen energy storage capacity, and the capacity of the fuel cell. The capacity allocation parameter Ri is input into the capacity configuration optimization model of the off-grid hydrogen-electric hybrid energy storage system, and the model is solved to obtain the Pareto frontier solution.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the capacity configuration method of the off-grid hydrogen-electric hybrid energy storage system according to any one of claims 1 to 6 is implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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