A system grid-connected variable temperature optimization operation method based on the electrothermal characteristics of electrolyzers

Abstract

The present invention discloses a system grid-connected variable temperature optimization operation method based on the electrothermal characteristics of an electrolyzer. The present invention estimates the thermal capacity and thermal resistance parameters of the electrolyzer based on measured data fitting, and simultaneously fits the estimation formula of the electrolyzer power upper limit and temperature. Based on the measured voltage and current data at different temperatures, the electrolyzer voltage, current, and temperature relationship is fitted; based on the above parameter estimation results and the formula fitting results, the objective function of the economic optimization of the photovoltaic-alkaline electrolyzer-lithium battery system and the operating constraints considering the influence of temperature on the upper and lower limits of power are established; the system's day-ahead optimization scheduling operation mode is solved, and the system operates according to the optimization results. The variable temperature operation method of the present invention can reduce the negative impact of high temperature on the electrolyzer, thereby extending the system life and avoiding ineffective energy conversion and energy waste by heating to maintain electrolysis efficiency. And the photovoltaic prediction results are used to optimize the operation of the electrolyzer, reduce the abandoned light rate, and improve the system economy.

Description

Technical Field

[0001] The present invention relates to the field of economic optimization of an electric-hydrogen coupling system, and belongs to a day-ahead optimization scheduling technology of an electric-hydrogen coupling system based on equipment operation characteristics. Background Art

[0002] The proportion of renewable energy connected to the power distribution network is increasing, among which photovoltaic and wind turbines are the renewable energy with the largest installed capacity. Compared with wind power, photovoltaic power generation characteristics are stable and easier to predict, but there are still problems such as uneven power generation within the day, unstable photovoltaic output, and mismatch with the timing of power demand.

[0003] In order to solve the problem of unstable photovoltaic output, energy storage devices have received much attention in recent years. Commonly used ones include lithium battery energy storage and hydrogen energy storage. Hydrogen energy storage can not only alleviate the instability of photovoltaic system output and realize photovoltaic consumption, but also meet the industrial and residential hydrogen needs, such as hydrogen fuel cells. Among the many hydrogen production methods, the research on hydrogen production by alkaline electrolyzers is relatively mature and has gradually been commercialized. It has a relatively stable hydrogen production capacity and good environmental protection characteristics. However, the dynamic response of alkaline electrolyzers is slow, so they are often used together with lithium batteries to consume photovoltaic power in new energy systems. "Research on Optimization of Distributed Photovoltaic and Electric-Hydrogen Hybrid Energy Storage Planning for Multi-Energy Complementarity" proposes that the hydrogen energy storage system is combined with the electric-hydrogen hybrid energy storage of the battery energy storage system to construct a regional comprehensive energy system to absorb and regulate the unstable output of photovoltaics.

[0004] However, due to the low efficiency of hydrogen production by electrolysis, the photovoltaic-electrolyzer-lithium battery system needs to be economically optimized and scheduled to reduce costs or increase revenue to achieve sustainable operation. "Low-carbon Optimal Scheduling of Park Electric-Hydrogen-Heat System Based on Alternating Direction Multiplier Method" proposes an optimization scheduling algorithm with the goal of minimizing the total cost of park operation, and "Long-term Optimal Operation of Hydrogen-containing Regional Integrated Energy System Considering the Physical Properties of Hydrogen Storage" proposes detailed modeling of high-pressure hydrogen storage tanks and compressors to achieve optimized operation of the integrated energy system.

[0005] However, current research often ignores the energy loss caused by the thermal characteristics of the electrolyzer itself. Although maintaining the rated operating temperature of the electrolyzer can improve the electrolysis efficiency and increase the hydrogen production, it also causes more heat dissipation. In order to keep the electrolyzer at the rated power, the heating device needs to provide more heat, which increases the power capacity demand of the heating device, that is, the investment cost is increased. In addition, maintaining the rated operating temperature for a long time will also cause the electrolyzer equipment to age. In summary, how to achieve the optimal day-ahead scheduling of the photovoltaic-electrolyzer-lithium battery system with the best economy, the longest system life, and reduced abandonment rate by regulating the electrolyzer temperature while meeting the user needs of hydrogen energy and electricity is an urgent problem to be solved. Summary of the invention

[0006] In view of the high operating cost and low energy utilization of the existing photovoltaic-alkaline electrolyzer-lithium battery system, a variable temperature optimization operation method based on the electrothermal characteristics of the electrolyzer is proposed. The thermal capacitance and thermal resistance parameters of the electrolyzer are fitted based on the measured data, and the voltage, current and temperature relationship of the electrolyzer is fitted. At the same time, the temperature rise curve of the electrolyzer operating at maximum power is obtained, and the estimation formula related to the power upper limit and temperature of the electrolyzer is fitted. The objective function of system economic optimization considering the operation and investment costs and the system operation constraints considering the influence of temperature on the upper and lower limits of power are established. Matlab is used to solve the proposed nonlinear multivariable problem to obtain the optimal day-ahead operation plan for the system economy.

[0007] The technical solution adopted by the present invention is as follows:

[0008] A method for optimizing the operation of a system connected to a grid and changing temperature based on the electrothermal characteristics of an electrolyzer, wherein the system is a photovoltaic-alkaline electrolyzer-lithium battery system, and the system is connected to the grid to provide hydrogen energy load and electric energy load to users; the method comprises the following steps:

[0009] Step 1: Obtain PV forecast data, power load forecast data, hydrogen load forecast data, grid electricity price, grid electricity price, and the configuration, unit cost, upper and lower limits of the PV-alkaline electrolyzer-lithium battery system, as well as the electrolyzer thermal capacity, thermal resistance parameters, the electrolyzer voltage-current-temperature relationship, and the electrolyzer power upper limit-temperature relationship p max (T) = k·T+b, where k and b are parameters obtained by data fitting;

[0010] Step 2: Establish the objective function and system operation constraints for economic optimization of the photovoltaic-alkaline electrolyzer-lithium battery system; wherein the objective function is to minimize the total cost of system operation; the system operation constraints include the power constraint of the electrolyzer, the maximum hydrogen production output flow rate constraint of the electrolyzer, the power constraint of the compressor, the pressure constraint of the hydrogen storage tank, the hydrogen storage capacity constraint of the hydrogen storage tank, the power constraint of the lithium battery, the capacity constraint of the lithium battery, the power range constraint of the heating equipment and the cooling equipment, the power constraint of the photovoltaic equipment abandoned light, the system power balance constraint, the power constraint of the power exchanged with the power grid, and the system thermal energy balance constraint; wherein the power constraint of the electrolyzer is expressed as: p Elz (t) represents the electrolytic cell power at time t, is the set of time periods divided in a day, η is the proportional coefficient, p max (T) represents the upper limit of the electrolytic cell power at temperature T;

[0011] Step three: Take the system operating parameters as the variables to be optimized, solve the objective function in step two based on the data obtained in step one, obtain the optimized operating parameters, and the system operates according to the optimized operating parameters; the operating parameters include electrolyzer power, lithium battery charging and discharging power, heating power of heating equipment, cooling power of cooling equipment, power exchanged with the power grid, electrolyzer hydrogen production rate, compressor power, electrolyzer temperature, hydrogen storage tank pressure and electrolyzer input current.

[0012] Furthermore, the configuration of the photovoltaic-alkaline electrolyzer-lithium battery system includes the rated capacity of the photovoltaic equipment, lithium batteries, compressors, hydrogen storage tanks, electrolyzers, heating equipment, and cooling equipment; the unit cost of the photovoltaic-alkaline electrolyzer-lithium battery system includes investment cost, operating cost, and depreciation cost.

[0013] Furthermore, the objective function is to minimize the total daily operating cost of the system, specifically:

[0014]

[0015] Where M is the net present value of the system construction investment cost in one day, OP(t) is the operating cost at time t, DE(t) is the depreciation cost at time t, and E Grd (t) is the cost of drawing electricity from the grid at time t, A PV (t) is the photovoltaic curtailment cost at time t; Δt is the time interval.

[0016] Furthermore, the construction investment cost is specifically:

[0017]

[0018] Among them, C PV ,C BS ,C Cpr ,C HS , C Elz , C Heat , C Cool They are the unit construction costs of photovoltaic equipment, lithium batteries, compressors, hydrogen storage tanks, electrolyzers, heating equipment, and cooling equipment; are the rated capacities of photovoltaic equipment, lithium batteries, compressors, hydrogen storage tanks, electrolyzers, heating equipment, and cooling equipment, respectively. PV ,y BS ,y Cpr ,y HS ,y Elz ,y Heat ,y Cool are the expected life spans of photovoltaic equipment, lithium batteries, compressors, hydrogen storage tanks, electrolyzers, heating equipment, and cooling equipment, respectively; r is the annual interest rate, and U(*) is the annuity factor function.

[0019] Furthermore, the operating cost of the system at time t is:

[0020]

[0021] Among them, O HS The cost of storing one unit of hydrogen in the hydrogen storage tank, O Elz , O Cpr , O PV , O Ht , O Cl M is the cost of inputting or outputting one unit of power for the electrolyzer, compressor, photovoltaic equipment, heating equipment, and cooling equipment. Elz (t), p Elz (t), p Cpr (t), p PV (t), p Ht (t), p Cl (t) are the hydrogen production rate, electrolyzer power, compressor power, photovoltaic power, heating power, and cooling power at time t, respectively;

[0022] The depreciation cost at time t is:

[0023]

[0024] Among them, D BS ,D Elz ,D Ht ,D Cl are the unit power depreciation costs of lithium batteries, electrolyzers, heating equipment, and cooling equipment, respectively. They are respectively the charging and discharging power of the lithium battery at time t;

[0025] The specific cost of taking electricity from the grid at time t is:

[0026]

[0027] e Pr (t) represents the electricity price of the power grid at time t, p Grd (t) represents the power exchanged between the system and the grid at time t;

[0028] The specific cost of photovoltaic abandonment at time t is:

[0029]

[0030] λ PV It represents the penalty cost per unit of abandoned light. Indicates the discarded optical power at time t.

[0031] Furthermore, the electrolytic cell heat capacity, thermal resistance parameters, the electrolytic cell voltage-current-temperature relationship and the electrolytic cell power upper limit-temperature relationship are obtained by the following method:

[0032] Collect the electrolytic cell from room temperature T a Temperature and power data for stable operation at rated voltage;

[0033] Based on the collected temperature and power data, the heat capacity C of the electrolytic cell is obtained by solving the following formula Elz :

[0034]

[0035] T Elz (t0) = T a

[0036] Where: t0 is the electrolytic cell temperature and room temperature T a At the same time, T Elz (t0) is the temperature of the electrolytic cell at time t0, u(t0) and i(t0) are the input voltage and current of the electrolytic cell at time t0, respectively; This is the derivative of the temperature gradient over time; u th is the thermal neutral voltage of the electrolytic cell, N is the number of electrolytic cell chambers;

[0037] Based on the collected temperature and power data, the thermal resistance R of the electrolytic cell is obtained by fitting the following formula t :

[0038]

[0039] ΔT is the temperature change;

[0040] Based on the collected temperature and power data, the power upper limit-temperature relationship of the electrolyzer is obtained by fitting;

[0041] The voltage values ​​of the electrolytic cell at different temperatures and currents were collected; based on the collected data, the voltage-current-temperature relationship of the electrolytic cell was obtained by fitting the following formula:

[0042]

[0043] u rev is the reversible voltage, S is the electrolytic cell plate area, r1, r2, s1, t1, t2, t3 are the fitting parameters, u(t) and i(t) are the input voltage and current of the electrolytic cell at time t, respectively.

[0044] Furthermore, the maximum hydrogen production output flow rate constraint of the electrolyzer is specifically:

[0045]

[0046]

[0047]

[0048] is the maximum hydrogen production flow rate of the electrolyzer, M Elz (t) is the hydrogen production rate at time t; is the electrolytic cell operating efficiency at time t; η e ,η F are voltage efficiency and current efficiency respectively, u th is the thermal neutral voltage of the electrolytic cell, u(t) and i(t) are the input voltage and current of the electrolytic cell at time t, respectively, and u(t) is obtained through the voltage-current-temperature relationship of the electrolytic cell;

[0049] The specific pressure constraints of the hydrogen storage tank are:

[0050]

[0051]

[0052] in, are the minimum and maximum hydrogen storage pressure limits of the hydrogen storage tank respectively; π HS (t) is the hydrogen storage tank pressure of the electrolyzer at time t;

[0053] T HS ,Q HS are the hydrogen storage tank temperature and the maximum hydrogen storage capacity of the hydrogen storage tank, M Ld (t) is the hydrogen energy demand at time t, Δt is the time interval, M Elz (t) is the hydrogen production rate at time t, R is the ideal gas constant, and z is a parameter related to the hydrogen storage tank;

[0054] The compressor power constraints are as follows:

[0055]

[0056]

[0057] g is the compressor parameter; p Cpr (t) is the compressor power at time t, are the minimum and maximum power of the compressor, π HS (t) are the hydrogen outlet pressure of the electrolyzer and the pressure of the hydrogen storage tank at time t, R is the ideal gas constant, and g is the compressor parameter; T Elz (t) represents the electrolytic cell temperature at time t, η Cpr is the compressor efficiency;

[0058] The specific constraints on the hydrogen storage capacity of the hydrogen storage tank are:

[0059]

[0060]

[0061] SoH(t) is the hydrogen storage capacity of the hydrogen storage tank at time t, is the hydrogen storage tank parameter, indicating hydrogen energy dissipation; SoH min ,SoH max They are the minimum and maximum hydrogen storage capacity limits of the hydrogen storage tank respectively.

[0062] Furthermore, the lithium battery power constraints are specifically:

[0063]

[0064]

[0065]

[0066] They are the minimum and maximum charging power of lithium batteries, They are the minimum and maximum discharge power of lithium batteries respectively; They are respectively the charging and discharging power of the lithium battery at time t;

[0067] The specific capacity constraints of lithium batteries are:

[0068]

[0069]

[0070] SoC(t) is the capacity of the lithium battery at time t, They are respectively the charging and discharging efficiency of lithium batteries. is the power dissipation rate of the lithium battery.

[0071] Furthermore, the power range constraints of the heating device and the cooling device are specifically:

[0072]

[0073]

[0074] p Cl (t)·p Ht (t) = 0

[0075] in, are the minimum and maximum power of the heating equipment respectively; are the minimum and maximum power of the cooling equipment respectively; p Cl(t),p Ht (t) are the power of the lithium battery of the cooling device and the heating device at time t;

[0076] The specific constraints on abandoned photovoltaic power are:

[0077]

[0078] In the formula, represents the photovoltaic power abandonment at time t, It represents the predicted maximum photovoltaic power at time t, ω represents the upper limit of the allowable abandonment rate, which is set to 5% in the present invention. By adjusting the size of ω, the abandonment power constraint of the system can be adjusted. The system can achieve optimized operation under the premise that the abandonment rate is less than ω.

[0079] Furthermore, the power constraints exchanged with the grid are specifically:

[0080]

[0081] are the minimum and maximum power of the tie line, p Grd (t) is the power exchanged with the grid at time t;

[0082] The specific system power balance constraints are:

[0083] p Ld (t) is the load power consumption at time t; represents the predicted maximum photovoltaic power at time t, p Elz (t), p Cpr (t), p Ht (t), p Cl (t) represents the lithium battery discharge power, charging power, electrolytic cell power, compressor power, heating power, and cooling power at time t respectively;

[0084] The system thermal energy balance constraints are as follows:

[0085]

[0086]

[0087]

[0088] T Elz (t) represents the electrolytic cell temperature at time t, are the electric-to-heat conversion efficiencies of the heating device and the cooling device, C Elz ,R t are the thermal capacity and thermal resistance of the electrolytic cell respectively; is the thermal efficiency of the electrolytic cell for self-heating, and T represents the room temperature.

[0089] The advantages of the present invention are:

[0090] (1) The present invention can estimate the thermal capacity and thermal resistance of the electrolytic cell based on measured data.

[0091] (2) The present invention can fit the upper and lower limits of the power of the electrolytic cell at different temperatures based on measured data. The proposed approximate relationship between the operating temperature and power range of the electrolytic cell can be used for the optimization of many other models related to the operation of the electrolytic cell.

[0092] (3) The present invention proposes a day-ahead temperature-variable operation strategy for the photovoltaic-electrolyzer-lithium battery system to achieve economically optimal scheduling.

[0093] (4) The variable temperature operation method proposed in the present invention can reduce the negative impact of high temperature on the electrolyzer, reduce the system operating temperature and thus extend the system life, avoiding ineffective energy conversion and energy waste by heating to maintain electrolysis efficiency. The photovoltaic prediction results are used to optimize the operation of the electrolyzer, effectively maintaining the abandoned light rate within the limit while improving the system economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] Attached Figure 1 Flow chart of photovoltaic-electrolyzer-lithium battery variable temperature day-ahead optimization operation strategy of the present invention

[0095] Attached Figure 2 Relationship diagram between maximum power and operating temperature of electrolytic cell in the embodiment of the present invention

[0096] Attached Figure 3 Temperature variation curve of the electrolytic cell in the embodiment of the present invention

[0097] Attached Figure 4 The cell voltage-current density curves at different temperatures of the electrolytic cell in the embodiment of the present invention

[0098] Attached Figure 5 Scheduling result diagram of a specific embodiment of the implementation scheme of the present invention

[0099] Attached Figure 6 Temperature variation curve of the electrolytic cell in a specific embodiment of the present invention DETAILED DESCRIPTION

[0100] The present invention fits the thermal capacitance and thermal resistance parameters of the electrolytic cell based on measured data, fits the voltage-current-temperature relationship of the electrolytic cell, and simultaneously obtains the temperature rise curve of the electrolytic cell running at maximum power, and fits the estimated formula related to the upper limit of the electrolytic cell power and temperature. Fits the estimated formula related to the upper limit of the electrolytic cell power and temperature. Based on the above parameters and formulas, an objective function for economic optimization of the photovoltaic-alkaline electrolytic cell-lithium battery system grid connection considering the operating and investment costs and system operating constraints considering the influence of temperature on the upper and lower limits of power are established, and then the operating plan with the best system economy is solved. According to the operating plan, the variable temperature optimized operation of the system grid connection is realized, which can reduce the negative impact of high temperature on the electrolytic cell, thereby extending the life of the system. Specifically, the present invention includes the following steps:

[0101] Step 1: Obtain photovoltaic forecast data, power load forecast data, hydrogen energy load forecast data, grid electricity price, grid electricity price, and the configuration, unit cost, upper and lower limits of the photovoltaic-alkaline electrolyzer-lithium battery system, as well as the electrolyzer thermal capacity, thermal resistance parameters, the electrolyzer voltage-current-temperature relationship, and the electrolyzer power upper limit-temperature relationship;

[0102] Among them, photovoltaic prediction data, power load prediction data, hydrogen energy load prediction data, etc. can be obtained based on predictions such as light intensity, user electricity consumption behavior, and hydrogen energy usage behavior.

[0103] The configuration of the photovoltaic-alkaline electrolyzer-lithium battery system includes the rated capacity of photovoltaic equipment, lithium batteries, compressors, hydrogen storage tanks, electrolyzers, heating equipment, and cooling equipment. The unit cost includes investment cost, operating cost, and depreciation cost. The upper and lower limits of operation are obtained by collecting the number of system configurations and the relevant parameters of the collection equipment.

[0104] The electrolytic cell thermal capacity, thermal resistance parameters, the electrolytic cell voltage-current-temperature relationship and the electrolytic cell power upper limit-temperature relationship are obtained by the following method:

[0105] (1) Set the electrolytic cell to room temperature T a Start running, and control the program to stabilize the electrolytic cell at the rated voltage U max , obtain the electrolytic cell temperature and power data at equal intervals of time t'. To ensure the accuracy of the curve, t' is generally less than 10 minutes. The experiment is carried out until the electrolytic cell temperature reaches T max End. The experimental results are recorded as: u(t), i(t), p max (t)=u(t)·i(t),T(t). Among them, variables u(t),i(t),p max (t), T(t) have the same dimensions.

[0106] When no external heat source is added to the electrolytic cell, the temperature change formula is shown in (1).

[0107]

[0108] Among them, Δt is the interval time, ΔT is the temperature change, p Elz (t) is the input power of the electrolyzer at time t, is the electrolyzer operating efficiency, which is related to both temperature and input power, p Loss (T) is the heat dissipation power of the electrolytic cell, which is related to the ambient temperature and the electrolytic cell temperature. Substituting the electrolytic cell temperature at time t, as shown in formula (2), T Elz (t) is the electrolytic cell temperature at time t, C Elz is the heat capacity of the electrolytic cell, R t is the thermal resistance of the electrolytic cell, all of which are parameters to be determined.

[0109]

[0110] Thermal efficiency of the electrolyzer for self-heating It can be expressed as formula (3), where u(t) is the input voltage of the electrolytic cell at time t, N is the number of electrolytic cell chambers, and u th The thermal neutral voltage of the electrolytic cell is 1.48V in the present invention.

[0111]

[0112] The electrolytic cell power is equal to the product of input voltage and input current, that is, P Elz (t) = u(t)·i(t), then formula (1) can be rewritten as:

[0113]

[0114] Therefore, when the cell temperature is equal to the ambient temperature and the time interval is small enough, the cell capacitance can be estimated according to formula (5), where t0 is the moment when the cell temperature is equal to the room temperature, This is the derivative of the temperature gradient changing with time at this time.

[0115]

[0116] According to formula (4) and the experimentally obtained u(t),i(t),p max (t),T Elz (t) record, (4) can be transformed into:

[0117]

[0118] make Then we can get formula (7).

[0119] R t ·τ(t)+T a =TElz (t) (7)

[0120] Since τ(t) and T Elz (t) is an array of the same dimension, which is related to the data collection time. Therefore, the least square method is used to fit formula (7) to obtain R t The estimated value of .

[0121] (2) In order to obtain the maximum power of the electrolyzer at different temperatures, according to the experimentally obtained u(t), i(t), p max (t),T Elz (t) Record and fit the function:

[0122] p max (T) = p_max(T) (8)

[0123] Because p max (t),T Elz (t) is taken from the same time group, so it can be used to fit function (8). The least squares method is used to fit formula (8) as a linear function, as shown in formula (9), which is the fitting expression related to the upper limit of the electrolytic cell power and temperature.

[0124] p max (T) = k·T + b (9)

[0125] k and b are parameters obtained by data fitting.

[0126] (3) The voltage values ​​of the electrolytic cell at different currents are measured at different temperatures (three temperatures T1, T2, T3) to obtain three electrolytic cell voltage-current curves. According to the electrolytic cell voltage-current-temperature correlation formula shown in (10), the six parameters r1, r2, s1, t1, t2, t3 are estimated using the least squares method to obtain parameter estimation values.

[0127]

[0128] Where N is the number of electrolytic cell chambers, u rev is the reversible voltage, taken as 1.23V, and S is the area of ​​the electrolytic cell plate.

[0129] Step 2: Establish the objective function and system operation constraints for the economic optimization of the photovoltaic-alkaline electrolyzer-lithium battery system; the objective function is to minimize the total cost of system operation; taking the optimization objective function as the daily operating cost as an example, the specific formula is as follows:

[0130]

[0131] Among them, M is the net present value of the construction investment cost in one day, OP(t) is the operating cost at time t, DE(t) is the depreciation cost at time t, and E Grd (t) is the cost of drawing electricity from the grid at time t, A PV (t) is the photovoltaic power abandonment cost at time t; the present invention takes 15 minutes as a time interval, so the operating cost of one day is the sum of the operating costs of 96 time intervals.

[0132] Each type of equipment in the system has a construction investment cost, and the specific calculation method is shown in (12).

[0133]

[0134] Among them, C PV ,C BS ,C Cpr ,C HS , C Elz , C Heat , C Cool They are the unit construction costs of photovoltaic equipment, lithium batteries, compressors, hydrogen storage tanks, electrolyzers, heating equipment, and cooling equipment; The rated capacities of photovoltaic equipment, lithium batteries, compressors, hydrogen storage tanks, electrolyzers, heating equipment, and cooling equipment are respectively. The product of the unit cost and the rated capacity is its initial investment cost. In order to calculate the daily net present value of the investment cost within the day to be optimized, the present invention adopts the annuity factor function formula (13).

[0135]

[0136] Among them, r is the annual interest rate, which is set to 5%, and y is the expected life of the equipment. PV ,y BS ,y Cpr ,y HS ,y Elz ,y Heat ,y Cool They are the expected lifespans of photovoltaic equipment, lithium batteries, compressors, hydrogen storage tanks, electrolyzers, heating equipment, and cooling equipment.

[0137] The operating cost of the system at time t is shown in formula (14).

[0138]

[0139] Among them, O HS The cost of storing one unit of hydrogen in the hydrogen storage tank ($ / kg), O Elz , O Cpr , O PV , O Ht , O ClThe cost ($ / MWh) required to input (output) one unit of power for the electrolyzer, compressor, photovoltaic equipment, heating equipment, and cooling equipment, respectively. Elz (t), p Elz (t), p Cpr (t), p PV (t), p Ht (t), p Cl (t) are the hydrogen production rate, electrolyzer power, compressor power, photovoltaic power, heating power, and cooling power at time t, respectively. It is a set of time periods divided into 15 minutes in a day, and Δt is the time interval of 15 minutes.

[0140] The depreciation cost of the system at time t is shown in formula (15).

[0141]

[0142] Among them, D BS ,D Elz ,D Ht ,D Cl The unit power depreciation costs of lithium batteries, electrolyzers, heating equipment, and cooling equipment are respectively, are the charging and discharging power of the lithium battery at time t respectively.

[0143] The grid electricity cost of the system at time t is shown in formula (16).

[0144]

[0145] e Pr (t) represents the electricity price of the power grid at time t, p Grd (t) represents the power exchanged with the grid at time t.

[0146] The photovoltaic penalty cost of the system at time t is shown in formula (17).

[0147]

[0148] λ PV It represents the penalty cost per unit of abandoned light. Indicates the discarded optical power at time t.

[0149] The present invention proposes the operating constraints of photovoltaic-electrolyzer-battery based on the electrothermal characteristics of the electrolyzer. In the operating mode proposed by the present invention, the upper and lower limits of the electrolyzer operating power are affected by temperature. When the temperature is T, according to the maximum operating power in step 1, the power constraint of the electrolyzer can be obtained as shown in formula (18).

[0150]

[0151] η is the proportionality coefficient, which is generally taken as 20%.

[0152] The maximum hydrogen production output flow rate constraint of the electrolyzer is shown in formula (19).

[0153]

[0154] in, is the maximum hydrogen production flow rate of the electrolyzer. The hydrogen production efficiency of the electrolyzer at time t can be calculated by formula (20).

[0155]

[0156] where η e ,η F are the voltage efficiency and current efficiency respectively. The hydrogen production rate of the electrolyzer at time t can be calculated by formula (21).

[0157]

[0158] The temperature and power of the electrolytic cell determine its voltage and current, that is, the electrolysis efficiency of the electrolytic cell. The relationship between the voltage and current of the electrolytic cell is shown in formula (10).

[0159] The power consumed by the compressor is related to the hydrogen production, electrolyzer temperature and hydrogen storage tank pressure, as shown in formula (22). At the same time, the compression efficiency of the compressor is set to η Cpr , the compressor power limitation is shown in formula (23).

[0160]

[0161]

[0162] in, π HS (t) are the hydrogen outlet pressure of the electrolyzer and the hydrogen storage tank pressure at time t, R is the ideal gas constant, and g is the compressor parameter. Elz (t) represents the electrolytic cell temperature at time t, η Cpr is the compressor efficiency, are the minimum and maximum power of the compressor respectively. The pressure of the hydrogen storage tank is related to the hydrogen storage capacity of the hydrogen storage tank, as shown in formula (24).

[0163]

[0164] Among them, T HS ,Q HS are the hydrogen storage tank temperature and the maximum hydrogen storage capacity of the hydrogen storage tank, M Ld(t) is the hydrogen energy demand at time t, that is, the hydrogen output of the hydrogen storage tank. z is a parameter related to the hydrogen storage tank. The pressure limit of the hydrogen storage tank is shown in formula (25).

[0165]

[0166] in, They are the minimum and maximum hydrogen storage pressure limits of the hydrogen storage tank respectively.

[0167] The dynamic hydrogen storage capacity of the hydrogen storage tank is shown in formula (26).

[0168]

[0169] Among them, SoH(t) is the hydrogen storage capacity of the hydrogen storage tank at time t, is the hydrogen storage tank parameter, which represents the hydrogen energy dissipation. The hydrogen energy storage capacity limit of the hydrogen storage tank is shown in formula (27).

[0170]

[0171] Among them, SoH min ,SoH max They are the minimum and maximum hydrogen storage capacity limits of the hydrogen storage tank respectively.

[0172] The battery constraints in the system are mainly battery charging and discharging power constraints and battery capacity constraints, as shown in formulas (28), (29), and (30).

[0173]

[0174]

[0175]

[0176] in, They are the minimum and maximum charging power of lithium batteries, are the minimum and maximum discharge powers of the lithium battery respectively. SoC(t) is the capacity of the lithium battery at time t, and its value range is shown in formula (31). They are respectively the charging and discharging efficiency of lithium batteries. is the power dissipation rate of the lithium battery.

[0177]

[0178] In order to improve the system power utilization, the battery cannot be charged and discharged at the same time, so the battery power meets the constraint (32).

[0179]

[0180] The power range constraints of the heating equipment and the cooling equipment are shown in formulas (33) and (34).

[0181]

[0182]

[0183] in, are the minimum and maximum power of the heating equipment respectively; are the minimum and maximum power of the cooling equipment respectively.

[0184] In order to improve the thermal energy utilization rate of the system, the cooling equipment and the heating equipment cannot be operated at the same time, so the operating power of the two meets the constraint (35).

[0185]

[0186] In the optimization method proposed in the present invention, the photovoltaic power in the optimization period is a predicted value based on historical data, assuming that the photovoltaic power works at the maximum power point, that is: Therefore, the photovoltaic equipment needs to constrain the abandoned power to be less than the photovoltaic power prediction value, as shown in formula (36).

[0187]

[0188] In the formula, represents the photovoltaic abandoned power at time t, It represents the predicted maximum photovoltaic power at time t, ω represents the upper limit of the allowable abandonment rate, which is set to 5% in the present invention. By adjusting the size of ω, the abandonment power constraint of the system can be adjusted. The system can achieve optimized operation under the premise that the abandonment rate is less than ω.

[0189] In the grid-connected state, the system needs to exchange electric energy with the large power grid to maintain the system's electric energy balance. The constraint formula is shown in (37).

[0190]

[0191] Among them, p Grd (t), p Ld (t) are the power exchanged with the grid at time t and the load power consumption at time t. The power exchanged between the system and the grid passes through the tie line. The tie line power constraint is shown in formula (38).

[0192]

[0193] in, are the minimum and maximum power of the tie line respectively.

[0194] The system not only has electrical energy balance constraints, but also thermal energy balance constraints, as shown in formula (39).

[0195]

[0196] in:

[0197]

[0198] in, are the electric-thermal conversion efficiency of the heating device and the cooling device, C Elz ,R t The estimated value of is obtained from step 1.

[0199] Step 3: Take the system operating parameters as the variables to be optimized. The variable groups to be optimized are They correspond to: electrolyzer power, battery charging power, battery discharging power, heating device heating power, cooling device cooling power, power exchanged with the power grid, electrolyzer hydrogen production rate, compressor compression power, electrolyzer temperature, hydrogen storage tank pressure, electrolyzer current. Each variable group has 96 variables, a total of 11*96 variables. Based on the data obtained in step 1, the objective function in step 2 is solved using the nonlinear optimization function of matlab to obtain the system's day-ahead optimization scheduling results that consider the change in electrolyzer temperature when the configuration mode and the photovoltaic, hydrogen load, and electric load prediction results are certain. The system performs variable temperature operation based on the optimized operating parameters, which improves the scientific nature of the optimized operation mode, can reduce the negative impact of high temperature on the electrolyzer, and thus prolong the system life. The optimal day-ahead optimization scheduling of the photovoltaic-electrolyzer-lithium battery system with the best economic efficiency is achieved, which can effectively reduce energy waste and improve the economic benefits of the system.

[0200] The effect of the present invention is further described below in conjunction with a specific embodiment.

[0201] First, start the electrolytic cell at room temperature, and set the operating voltage to the rated operating voltage U max =42V, record the voltage, current, power and temperature of the electrolytic cell every few minutes, and record the data of the electrolytic cell temperature rising from room temperature (10°C) to the rated operating temperature (80°C). The number of chambers in the electrolytic cell is N=48. Use formula (5) to calculate the heat capacity C of the electrolytic cell Elz The maximum power and temperature data are input into Matlab as an array, and the least squares method is used to fit the formula (9). The fitting formula of the temperature and the upper limit of the electrolytic cell power is as follows. The comparison between the fitting function and the measured data is shown in the attached figure. Figure 2 shown.

[0202] p max (T) = 376.2 T + 2115

[0203] Process the voltage, current, power and temperature data of the electrolytic cell, calculate the ΔT array, and then calculate the array Using Matlab software and the least square method to fit formula (7), the thermal resistance R of the electrolytic cell is obtained. t , which is 7.6℃ / kJ.

[0204] Using the estimated thermal capacitance and thermal resistance parameters, the temperature change of the electrolytic cell when the power curve with the same measured power is input into the electrolytic cell is shown in the attached figure. Figure 3 As shown, the actual temperature values ​​are evenly distributed on both sides of the estimated curve.

[0205] When the temperature is 40℃, 60℃ and 80℃, 40 sets of voltage and current data are measured quickly, and the set current is changed step by step to obtain the corresponding voltage value. It is necessary to measure 40 sets of voltage and current data at a certain temperature within 5 minutes to prevent the temperature change from affecting the fitting accuracy. Matlab software is used to fit formula (10) using the least squares method to obtain the unknown parameter values ​​in formula (10). The cell voltage fitting formula is as follows: the voltage value in formula (10) is divided by the number of cells 48. The comparison between the fitting function and the measured data is shown in the attached figure. Figure 4 As shown, but its abscissa is the current density, that is,

[0206]

[0207] Preset the variable group to be optimized in Matlab, P Elz , P Ht ,P Cl ,P Grd ,M Elz ,P Cpr ,T Elz ,π HS ,i, input the 15-minute interval photovoltaic forecast data, 15-minute interval power load forecast data, 15-minute interval hydrogen energy load forecast data, 15-minute interval grid electricity price, 15-minute interval grid electricity price; then input the system configuration and unit cost settings: each equipment capacity, each equipment unit construction cost, each equipment unit maintenance cost, battery charging and discharging power upper and lower limit settings, electrolyzer hydrogen production upper and lower limit settings, heating and cooling equipment operating power constraints, etc., use matlab to solve, and obtain the results of the variable group to be optimized. The scheduling results in this example are as follows Figure 5As shown in the figure, PV, ELC, EB, CS, Load, and Grid represent the output power of photovoltaic, the operating power of electrolyzer, the operating power of heating device, the operating power of cooling device, power load, and the exchange power between system and grid respectively; BE-C and BE-D represent the charging and discharging power of lithium battery respectively; SOC and SOH represent the storage state of lithium battery and the storage state of hydrogen storage device respectively. The temperature change of electrolyzer is shown in the figure below: Figure 6 As shown, case 1 is the temperature change curve using the optimization scheduling method proposed by the present invention, and case 2 is the temperature change curve of the traditional operation at the rated temperature. It can be seen that the present invention can operate at variable temperatures according to demand, avoid the electrolytic cell from operating at a high rated operating temperature for a long time, reduce the negative impact of high temperature on the electrolytic cell, thereby extending the life of the system, and avoid ineffective energy conversion by heating to maintain electrolysis efficiency, thereby avoiding energy waste.

[0208] In this example, case 1 and case 2 are set, and the setting differences and scheduling results are shown in Table 1. Compared with the traditional scheduling method, the method proposed in the present invention reduces the average daily cost by 5.31%.

[0209] Case Number 1 2 Scheduling method settings Method of the present invention Traditional methods Average daily cost 76.14 80.41

[0210] The variable temperature optimization operation scheduling model proposed in the present invention can be solved by a variety of nonlinear optimization methods. These methods do not affect the realization of the effect of the present invention. The protection part of the present invention is the constraint of temperature on power limit in variable temperature optimization operation, that is, formula (18)-formula (21) and the above-mentioned parameter determination and formula fitting method. At the same time, the 15-minute optimization time interval determined by the present invention can be adjusted.

[0211] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the present invention.

Claims

1. A system grid-connected variable temperature optimization operation method based on the electrothermal characteristics of an electrolyzer, wherein the system is a photovoltaic-alkaline electrolyzer-lithium battery system, and the system is grid-connected to provide hydrogen energy load and electric energy load to users; characterized in that: The following steps are involved: Step 1: Obtain PV forecast data, power load forecast data, hydrogen load forecast data, grid electricity price, grid electricity price, and the configuration, unit cost, upper and lower limits of the PV-alkaline electrolyzer-lithium battery system, as well as the electrolyzer thermal capacity, thermal resistance parameters, the electrolyzer voltage-current-temperature relationship, and the electrolyzer power upper limit-temperature relationship p max (T) = k·T+b, where k and b are parameters obtained by data fitting; Step 2: Establish the objective function and system operation constraints for economic optimization of the photovoltaic-alkaline electrolyzer-lithium battery system; wherein the objective function is to minimize the total cost of system operation; the system operation constraints include the power constraint of the electrolyzer, the maximum hydrogen production output flow rate constraint of the electrolyzer, the power constraint of the compressor, the pressure constraint of the hydrogen storage tank, the hydrogen storage capacity constraint of the hydrogen storage tank, the power constraint of the lithium battery, the capacity constraint of the lithium battery, the power range constraint of the heating equipment and the cooling equipment, the power constraint of the photovoltaic equipment abandoned light, the system power balance constraint, the power constraint of the power exchange with the power grid, and the system thermal energy balance constraint; wherein the power constraint of the electrolyzer is expressed as: η·p max (T)≤p Elz (t)≤p max (T), p Elz (t) represents the electrolytic cell power at time t, is the set of time periods divided in a day, η is the proportional coefficient, p max (T) represents the upper limit of the electrolytic cell power at temperature T; Step three: Take the system operating parameters as the variables to be optimized, solve the objective function in step two based on the data obtained in step one, obtain the optimized operating parameters, and the system operates according to the optimized operating parameters; the operating parameters include electrolyzer power, lithium battery charging and discharging power, heating power of heating equipment, cooling power of cooling equipment, power exchanged with the power grid, electrolyzer hydrogen production rate, compressor power, electrolyzer temperature, hydrogen storage tank pressure and electrolyzer input current.

2. The method according to claim 1, characterized in that The configuration of the photovoltaic-alkaline electrolyzer-lithium battery system includes the rated capacity of the photovoltaic equipment, lithium batteries, compressors, hydrogen storage tanks, electrolyzers, heating equipment, and cooling equipment; the unit cost of the photovoltaic-alkaline electrolyzer-lithium battery system includes investment cost, operating cost, and depreciation cost.

3. The method according to claim 1, characterized in that The objective function is to minimize the total daily operating cost of the system, specifically: Where M is the net present value of the system construction investment cost in one day, OP(t) is the operating cost at time t, DE(t) is the depreciation cost at time t, and E Grd (t) is the cost of drawing electricity from the grid at time t, A PV (t) is the photovoltaic curtailment cost at time t; Δt is the time interval.

4. The method according to claim 3, characterized in that The construction investment costs are as follows: Among them, C PV ,C BS ,C Cpr ,C HS , C Elz , C Heat , C Cool They are the unit construction costs of photovoltaic equipment, lithium batteries, compressors, hydrogen storage tanks, electrolyzers, heating equipment, and cooling equipment; are the rated capacities of photovoltaic equipment, lithium batteries, compressors, hydrogen storage tanks, electrolyzers, heating equipment, and cooling equipment, respectively. PV ,y BS ,y Cpr ,y HS ,y Elz ,y Heat ,y Cool are the expected life spans of photovoltaic equipment, lithium batteries, compressors, hydrogen storage tanks, electrolyzers, heating equipment, and cooling equipment, respectively; r is the annual interest rate, and U(*) is the annuity factor function.

5. The method according to claim 3, characterized in that: The operating cost of the system at time t is: Among them, O HS The cost of storing one unit of hydrogen in the hydrogen storage tank, O Elz , O Cpr , O PV , O Ht , O Cl M is the cost of inputting or outputting one unit of power for the electrolyzer, compressor, photovoltaic equipment, heating equipment, and cooling equipment. Elz (t), p Elz (t), p Cpr (t), p PV (t), p Ht (t), p Cl (t) are the hydrogen production rate, electrolyzer power, compressor power, photovoltaic power, heating power, and cooling power at time t, respectively; The depreciation cost at time t is: Among them, D BS ,D Elz ,D Ht ,D Cl are the unit power depreciation costs of lithium batteries, electrolyzers, heating equipment, and cooling equipment, respectively. They are respectively the charging and discharging power of the lithium battery at time t; The specific cost of taking electricity from the grid at time t is: e Pr (t) represents the electricity price of the power grid at time t, p Grd (t) represents the power exchanged between the system and the grid at time t; The specific cost of photovoltaic abandonment at time t is: λ PV It represents the penalty cost per unit of abandoned light. Indicates the discarded optical power at time t.

6. The method according to claim 1, characterized in that The electrolytic cell heat capacity, thermal resistance parameters, the electrolytic cell voltage-current-temperature relationship and the electrolytic cell power upper limit-temperature relationship are obtained by the following method: Collect the electrolytic cell from room temperature T a Temperature and power data for stable operation at rated voltage; Based on the collected temperature and power data, the heat capacity C of the electrolytic cell is obtained by solving the following formula Elz : T Elz (t0)=T a Where: t0 is the electrolytic cell temperature and room temperature T a At the same time, T Elz (t0) is the temperature of the electrolytic cell at time t0, u(t0) and i(t0) are the input voltage and current of the electrolytic cell at time t0, respectively; This is the derivative of the temperature gradient over time; u th is the thermal neutral voltage of the electrolytic cell, N is the number of electrolytic cell chambers; Based on the collected temperature and power data, the thermal resistance R of the electrolytic cell is obtained by fitting the following formula t : ΔT is the temperature change; Based on the collected temperature and power data, the power upper limit-temperature relationship of the electrolyzer is obtained by fitting; The voltage values ​​of the electrolytic cell at different temperatures and currents were collected; based on the collected data, the voltage-current-temperature relationship of the electrolytic cell was obtained by fitting the following formula: u rev is the reversible voltage, S is the electrolytic cell plate area, r1, r2, s1, t1, t2, t3 are the fitting parameters, u(t) and i(t) are the input voltage and current of the electrolytic cell at time t, respectively.

7. The method according to claim 1, characterized in that The maximum hydrogen production output flow rate constraints of the electrolyzer are as follows: is the maximum hydrogen production flow rate of the electrolyzer, M Elz (t) is the hydrogen production rate at time t; is the electrolytic cell operating efficiency at time t; η e ,η F are voltage efficiency and current efficiency respectively, u th is the thermal neutral voltage of the electrolytic cell, u(t) and i(t) are the input voltage and current of the electrolytic cell at time t, respectively, and u(t) is obtained through the voltage-current-temperature relationship of the electrolytic cell; The specific pressure constraints of the hydrogen storage tank are: in, are the minimum and maximum hydrogen storage pressure limits of the hydrogen storage tank respectively; π HS (t) is the pressure of the hydrogen storage tank of the electrolyzer at time t; T HS ,Q HS are the hydrogen storage tank temperature and the maximum hydrogen storage capacity of the hydrogen storage tank, M Ld (t) is the hydrogen energy demand at time t, Δt is the time interval, M Elz (t) is the hydrogen production rate at time t, R is the ideal gas constant, and z is a parameter related to the hydrogen storage tank; The compressor power constraints are as follows: g is the compressor parameter; p Cpr (t) is the compressor power at time t, are the minimum and maximum power of the compressor, are the hydrogen outlet pressure of the electrolyzer and the hydrogen storage tank pressure at time t, R is the ideal gas constant, g is the compressor parameter; T Elz (t) represents the electrolytic cell temperature at time t, η Cpr is the compressor efficiency; The specific constraints on the hydrogen storage capacity of the hydrogen storage tank are: SoH(t) is the hydrogen storage capacity of the hydrogen storage tank at time t, is the hydrogen storage tank parameter, indicating hydrogen energy dissipation; SoH min ,SoH max They are the minimum and maximum hydrogen storage capacity limits of the hydrogen storage tank respectively.

8. The method according to claim 1, characterized in that The specific power constraints of lithium batteries are: They are the minimum and maximum charging power of lithium batteries, They are the minimum and maximum discharge power of lithium batteries respectively; They are respectively the charging and discharging power of the lithium battery at time t; The specific capacity constraints of lithium batteries are: SoC(t) is the capacity of the lithium battery at time t, are the charging and discharging efficiency of lithium batteries, is the power dissipation rate of the lithium battery.

9. The method according to claim 1, characterized in that: The power range constraints of the heating equipment and cooling equipment are as follows: p Cl (t)·p Ht (t)=0 in, are the minimum and maximum power of the heating equipment respectively; are the minimum and maximum power of the cooling equipment respectively; p Cl (t),p Ht (t) are the power of the lithium battery of the cooling device and the heating device at time t; The specific constraints on abandoned photovoltaic power are: In the formula, represents the photovoltaic abandoned power at time t, represents the predicted maximum photovoltaic power at time t, and ω represents the upper limit of the allowable abandonment rate.

10. The method according to claim 1, characterized in that The power constraints exchanged with the grid are as follows: are the minimum and maximum power of the tie line, p Grd (t) is the power exchanged with the grid at time t; The specific system power balance constraints are: p Ld (t) is the load power consumption at time t; represents the predicted maximum photovoltaic power at time t, p Elz (t), p Cpr (t), p Ht (t), p Cl (t) represents the lithium battery discharge power, charging power, electrolytic cell power, compressor power, heating power, and cooling power at time t respectively; The system thermal energy balance constraints are as follows: T Elz (t) represents the electrolytic cell temperature at time t, are the electric-thermal conversion efficiency of the heating device and the cooling device, C Elz ,R t are the thermal capacity and thermal resistance of the electrolytic cell respectively; is the thermal efficiency of the electrolytic cell for self-heating, and T represents the room temperature.

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