Alkaline electrolytic cell dynamic adjustment method based on cost optimization

Abstract

The embodiment of the invention provides an alkaline electrolytic cell dynamic adjustment method based on cost optimization. According to the method, a function model of electricity utilization power and hydrogen production rate is constructed, historical wind and light data are combined to optimize electrolytic cell array configuration, electrolytic cell monomer operation states are divided, a cost model is introduced, the lowest total operation cost is taken as a target, and the number of electrolytic cells in each state is dynamically distributed through a circular queue rotation strategy. The method comprises the following specific steps: establishing a power-hydrogen production rate mapping model to predict a power demand; determining the capacity of the electrolytic cell array based on the wind-solar power peak; and constructing a cost model and setting a state allocation priority and the number of electrolytic cells in each state to realize graded regulation and control of the hydrogen production rate working condition. The system does not need an energy storage device, the wind and light fluctuation is absorbed through the self-state combination of the electrolytic cell array, and the problems of high investment cost, non-uniform equipment loss, high operation cost and the like caused by energy storage dependence at present are solved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of renewable energy hydrogen production, and relates to a renewable energy hydrogen production system coordinated operation method, in particular to an alkaline electrolytic cell dynamic adjustment method based on cost optimization. BACKGROUND

[0002] With the acceleration of global energy transformation, hydrogen production by coupling wind power and photovoltaic power generation with alkaline electrolytic cells has become the core path for large-scale production of green hydrogen. However, the inherent intermittency and volatility of wind and light energy lead to frequent fluctuations in the input power of the electrolytic cell, which not only causes the hydrogen production efficiency to decay (such as a decrease of more than 15% under low-power working conditions), but also causes electrode polarization to intensify and equipment thermal stress to accumulate, seriously restricting the system reliability.

[0003] The traditional alkaline electrolytic water hydrogen production system has the following significant deficiencies under the above fluctuating working conditions: first, it relies on energy storage devices to smooth power fluctuations, resulting in a 20-30% increase in system investment costs, and the energy storage devices themselves have significant energy losses; second, the electrolytic cell array configuration is often based on fixed power requirements, without considering the probability distribution characteristics of wind and light power, often in a dilemma of "excess capacity leading to idling" or "insufficient power causing power abandonment"; third, the lack of fine management of individual electrolytic cells at the operation strategy level, the single rated power operation mode easily leads to uneven equipment wear and tear, with some equipment being overloaded for a long time and some being shut down for a long time, resulting in a reduction of more than 30% in the overall service life. In addition, the existing technology does not comprehensively consider the cost factors of overload, fluctuation and start-stop operation, resulting in poor system operation economy and difficulty in meeting the cost control requirements of large-scale production of green hydrogen.

[0004] The above technical defects seriously restrict the development of wind and light hydrogen production systems. How to dynamically match wind and light power with electrolytic cell hydrogen production requirements without the aid of energy storage has become a key problem in breaking through the economic and reliability bottlenecks of renewable energy hydrogen production. Therefore, it is urgent to develop an electrolytic cell operation adjustment method that does not rely on energy storage, can dynamically adapt to wind and light fluctuations, and takes into account cost optimization, to achieve the coordinated optimization of hydrogen production efficiency, equipment life and system cost. SUMMARY

[0005] In view of the above shortcomings and deficiencies of the prior art, the present application provides an alkaline electrolytic cell dynamic adjustment method based on cost optimization to solve the problems of high cost, uneven equipment wear and tear, high electrolytic cell monomer wear and tear difference and high operating cost caused by the dependence on energy storage in the prior art.

[0006] To achieve the above technical purpose, the embodiments of the present application provide an alkaline electrolytic cell dynamic adjustment method based on cost optimization, which comprises the following steps:

[0007] comprising the following steps:

[0008] S1: constructing a function model of electric power P used by an alkaline electrolyzer and a hydrogen production rate;

[0009] S2: predicting required total electric power P by the function model according to a target hydrogen production rate f target and real-time monitored electrolyte temperature T and pressure p; req

[0010] S3: determining a configuration of an electrolyzer array composed of multiple electrolyzer units based on historical wind-solar power generation data;

[0011] calculating a time-series maximum value P max of the wind-solar combined power generation;

[0012] determining a theoretical capacity P L of the electrolyzer array = P max / β, where β takes a value of 1.1-1.35;

[0013] configuring a number of electrolyzers where, is a ceiling operator, P e is a unit rated power;

[0014] S4: dividing electrolyzer unit operation into the following four operating states:

[0015] a shutdown state: house 1 = 0; a fluctuating power state: house 2 = aP e , where 0.2≤α≤1.1; a rated power state: P3=P e ; and an overload power state: P e ≤P4≤P overload , where P overload =βP e ;

[0016] S5: constructing an electrolyzer cost model, including an overload operation cost C o , a fluctuating operation cost C f , a start-stop operation cost C s , and an economic optimization strategy framework;

[0017] S6: obtaining wind-solar combined power generation P fg (t) in real time;

[0018] S7: based on the goal of minimizing cost, using a rotating shift strategy to allocate operating states of each electrolyzer unit in the electrolyzer array, so that the electrolyzer units rotate in one of the four operating states:

[0019] when P fg (t)≥P req ​​At this time, by adjusting the operation state of each electrolytic cell in the electrolytic cell array, the hydrogen production rate is preferentially increased to be close to f target ; the number of electrolytic cell units in the rated power operation state N 11 , the number of electrolytic cell units in the fluctuating power operation state N 12 , the number of electrolytic cell units in the overload power operation state N 13 , and the number of electrolytic cell units in the shutdown operation state N 14 are configured to satisfy N 11 +N 12 +N 13 +N 14 =n; the demand is preferentially met by the rated power state, and the remaining power is allocated in the order of "fluctuating power" to "overload power" to avoid unnecessary overload.

[0020] When P fg (t) < P req , the operation state is adjusted according to the hydrogen production rate condition classification. The number of electrolytic cell units in the rated power operation state N 21 , the number of electrolytic cell units in the fluctuating power operation state N 22 , and the number of electrolytic cell units in the shutdown operation state N 23 are configured to satisfy N 21 +N 22 +N 23 =n.

[0021] The priority order of the four operation states is: rated power state (lowest cost) > fluctuating power state (second best) > overload power state (highest cost) > shutdown state (high start-stop cost).

[0022] As a preferred step S1, the function model of the alkaline electrolytic cell electric power P and the hydrogen production rate is:

[0023]

[0024] Wherein:

[0025]

[0026] T is the electrolyte temperature in Kelvin, unit K, p is the pressure, η F is the Faraday efficiency, taking a value of 0.95-0.99; n cis the total number of electrolytic cells in series in the electrolytic tank; ΔH is the enthalpy change, taking 286 kJ / mol, ΔS is the entropy change, taking 0.163 kJ / (mol·K), both of which remain basically unchanged in the normal working temperature range of the electrolytic tank; z is the number of electrons transferred by each hydrogen molecule, taking 2; F is the Faraday constant, taking 96485 C / mol; A is the electrode area; s is the activation overvoltage influence factor, with a unit of V.

[0027] In the normal working temperature and pressure range of the electrolytic tank, the influence of temperature T and pressure p on the electrolyte resistance r is basically linear, r1 is the initial resistance value, including the contact resistance and other components irrelevant to temperature and pressure, r2 is the linear influence factor of temperature on resistance, and r3 is the linear influence factor of pressure on resistance;

[0028] The parameter t is directly related to the activation overpotential of the electrolytic tank, and the activation overpotential reflects the electrochemical reaction kinetics resistance (such as the reaction rate on the electrode surface), which follows the nonlinear characteristics of the Arrhenius equation (k ∝ exp(-E a / RT)) in relation to temperature, wherein k is the reaction rate constant, E a is the activation energy, and R is the gas constant, indicating that the reaction rate is exponentially sensitive to the inverse of temperature 1 / T;

[0029] r1, r2, r3, s, t1, t2, and t3 are all constant coefficients, and their values are determined through historical working condition data.

[0030] As a preferred step 3, the step of calculating the time sequence maximum value P max of the wind-solar hybrid power generation power includes: arranging historical wind-solar power data in descending order to form a power sorting curve; and taking the power value corresponding to the cumulative probability α (α = 0.95-1) in the curve as P max .

[0031] As a preferred step 5, the step of constructing the electrolytic tank cost model includes:

[0032] (1) Overload operation cost model: Overload operation cost C o =k1×(P overload -P e )×t, wherein k1 is an overload cost coefficient, with a unit of yuan / (kW·h), P overload is the overload power, t is the overload time length; and the overload operation cost C o is the additional cost generated by the accelerated equipment wear under the overload power state (P>P e ) of the electrolytic tank, which is positively correlated with the overload power multiple (β-1) and the running time length.

[0033] (2) Fluctuation operation cost model: Fluctuation operation cost C f= k2·|α-1|·f·t, where k2 is a fluctuation cost coefficient with unit of yuan / (%)·time, α is a fluctuation power coefficient, f is a fluctuation frequency with unit of time / h; the fluctuation operation cost C f is caused by power fluctuation (0.2P e ≤ P ≤ 1.1P e ), which is related to the power fluctuation amplitude (the degree of α deviating from 1) and the fluctuation frequency.

[0034] (3) Start-stop operation cost model: the start-stop operation cost C s = k3·m, where k3 is a single start-stop cost (yuan / time), and m is the start-stop times; the start-stop operation cost C s is the energy loss (such as the heating energy consumption) and the electrode material wear cost in the process of starting and stopping the electrolytic cell, which is positively related to the start-stop times.

[0035] The economic optimization strategy framework includes: setting the target function as the total operation cost C = C o + C f + C s , and the optimization goal is to minimize the total operation cost C; setting the hydrogen production rate constraint as where η e is a hydrogen production guarantee coefficient, and the value is 0.9-0.95, and the power balance constraint is P fg (t) = ∑P n (t).

[0036] As a preferred embodiment of step 7, the round-robin strategy of the electrolytic cell units includes:

[0037] Setting a round-robin period T min , and adjusting the electrolytic cell order in each period according to the circular queue, i.e., arranging the electrolytic cell units in order to form a queue, and after completing a round-robin period, moving the head unit to the tail to form a circular ordering mechanism, configuring the number of electrolytic cell units running in the four operating states according to the real-time value of the fan output power, and distributing the operating states of each electrolytic cell unit according to the current electrolytic cell unit arrangement order, and dynamically distributing the number of electrolytic cell units in each state according to the difference between P fg (t) and P req :

[0038] When the wind and light are insufficient: the number of units in the rated state (downward rounding), the number of units in the shutdown state (upward rounding).

[0039] As another preferred embodiment of step 7, the hydrogen production rate working condition classification includes:

[0040] High-speed working condition: P fg (t) > Pn (t)≥0.7P fg (t);

[0041] Medium speed operation: 0.5P fg (t)≤P n (t)<0.7P fg (t);

[0042] Low speed operation: 0.2P fg (t)≤P n (t)<0.5P fg (t);

[0043] Not allowed operation: P n (t)<0.2P fg (t); wherein:

[0044] Real-time total power of electrolytic cells P n (t) = N 21 ×P3+N 22 ×P2+N 23 ×P1.

[0045] As another preferred of step 7, the strategy of the hierarchical adjustment comprises:

[0046] (1) When in the not allowed operation, the number of electrolytic cells in shutdown operation state is still N 23 , try to make the electrolytic cells enter the medium speed hydrogen production operation, and set the power command of the electrolytic cells to promote P n (t) to the range of the medium speed hydrogen production operation;

[0047] (2) When in the low speed operation, make the electrolytic cells enter the medium speed hydrogen production operation, and set the power command of the electrolytic cells to promote P n (t) to the range of the medium speed hydrogen production operation;

[0048] (3) When in the medium speed operation, make the electrolytic cells enter the high speed hydrogen production operation, and set the power command of the electrolytic cells to promote P n (t) to the range of the high speed hydrogen production operation;

[0049] (4) When in the high speed operation, make the electrolytic cells keep the high speed hydrogen production operation, and set the power command of the electrolytic cells to keep P n (t) in the range of the high speed hydrogen production operation.

[0050] The method provided by the technical scheme of the embodiment of the application can solve the problems of high cost, uneven equipment wear and tear, high single electrolytic cell wear and tear difference and high operation cost caused by energy storage dependence in the prior art. By constructing a function model of power consumption and hydrogen production rate, combining historical wind and light data to optimize electrolytic cell array configuration, dividing electrolytic cell single operation state, introducing a cost model, taking the minimum total operation cost as the target, and dynamically allocating the number of electrolytic cells in each state through a circular queue rotation strategy, the following beneficial effects are achieved.

[0051] 1. Dynamic power-hydrogen production rate accurate mapping: by real-time correlation of hydrogen production rate and power consumption through an electrochemical model, dynamic correction combined with temperature and pressure parameters, direct conversion of hydrogen production demand to power instruction is realized.

[0052] 2. Dynamic cost model and economic optimization: a comprehensive model including overload, fluctuation and start-stop operation cost is constructed, the total operation cost (C=C o +C f +C s ) is minimized through an objective function, and dynamic cost regulation is realized in combination with hydrogen production rate and power balance constraints. For example, when the overload cost coefficient k1=0.8 yuan / kW·h, the system can automatically optimize the number of overloaded cells, so that the total operation cost is reduced by 30-40%, and the cost per degree of hydrogen is reduced by 1.2-1.8 yuan / Nm 3 .

[0053] 3. Efficient utilization of wind and light resources and cost control: based on the probability distribution of historical wind and light data, electrolytic cell array is configured, and through a circular queue rotation strategy, the state is dynamically allocated. When wind and light are sufficient, the rated power state is used first, and the remaining power is allocated in the order of "fluctuation-overload", avoiding unnecessary overload cost.

[0054] 4. Four-state rotation balancing equipment wear and tear: the electrolytic cell single is divided into four states of shutdown, fluctuation, rated and overload, and through circular queue rotation, the single wear and tear difference rate is reduced, and the overall service life of the electrolytic cell is improved.

[0055] 5. Work condition grading and cost priority regulation: according to the hydrogen production rate work condition (high speed, medium speed, low speed, not allowed), the power instruction is dynamically adjusted, and the state allocation priority order is rated> fluctuation> overload> shutdown, so that the efficiency decay under low power condition is controlled within 5%, and the overall work efficiency is improved; when in low speed condition, the system automatically increases the power to medium speed interval, avoiding the efficiency decline caused by low power;

[0056] 6. Self-balancing without energy storage and economic advantage: by combining the state of the electrolytic cell array to utilize wind and light fluctuations, the investment of energy storage (accounting for 20-30% of the system cost) and the balance of wind and light fluctuations and hydrogen production needs are saved. BRIEF DESCRIPTION OF DRAWINGS

[0057] The following drawings are included to provide a further understanding of the application and are incorporated in and constitute a part of the specification, illustrate the application and, together with the description, serve to explain the application but do not limit the application. Include:

[0058] Figure 1 A flow chart of a cost-optimized dynamic adjustment method for an alkaline electrolytic cell of the application;

[0059] Figure 2 A flow chart of a core strategy dynamic adjustment state allocation method in a cost-optimized dynamic adjustment method for an alkaline electrolytic cell of the application. DETAILED DESCRIPTION

[0060] To make the technical problems, technical solutions and advantages of the application clearer, specific embodiments will be described in detail below with reference to the drawings and specific embodiments. It should be noted that the following description of the embodiments is only substantially exemplary, and the application is not intended to limit its application or its use, and the application is not limited to the following embodiments.

[0061] The application aims to solve the existing problems and provides a cost-optimized dynamic adjustment method for an alkaline electrolytic cell, as shown in FIGS. 1 and 2, wherein: Figure 1 and 2

[0062] The function model of the power P used by the alkaline electrolytic cell constructed in step S1 and the hydrogen production rate is as follows:

[0063]

[0064] In the above formula (1), r1, r2, r3, s, t1, t2 and t3 are all constant coefficients, and the typical values in the embodiments of the application are shown in Table 1 below:

[0065] Table 1: Empirical values of constant coefficients

[0066]

[0067] If the actual working conditions are to be matched, they need to be corrected through historical working condition data.

[0068] Example 1

[0069] A 1000kW wind farm, a function model of the power P used and the hydrogen production rate is constructed, wherein:

[0070] The electrolyte temperature T is taken as 80℃ (corresponding to the Kelvin temperature 353.15K), the pressure p is 1.5bar, the Faraday efficiency η F is taken as 0.97, and the total number of series electrolytic units in the electrolytic cell n c ​100, the overload cost coefficient k1 is 0.8 yuan / (kW·h), the fluctuation cost coefficient k2 is 0.5 yuan / (%)·time), the single start-stop cost k3 is 500 yuan / time, and the constant coefficient is shown in Table 1, which is substituted into formula (1) to obtain:

[0071] When the target hydrogen production rate f target = 5 mol / s, the theoretical power P req ≈ 850 kW.

[0072] Electrolyzer array configuration and state division:

[0073] Based on the historical data of a certain wind-solar power station, the wind-solar combined power is arranged in descending order, and the cumulative probability α is taken as 0.95, and the corresponding P max = 1200 kW, the electrolyzer overload coefficient β is 1.25, and the theoretical capacity P L = P max / β = 960 kW.

[0074] The electrolyzer with a single rated power P e = 200 kW is selected, and the number of configurations is

[0075] The running state of 5 electrolyzers is divided into:

[0076] Shutdown state: P1 = 0;

[0077] Fluctuation power state: P2 = αP e (0.2≤α≤1.1);

[0078] Rated power state: P3 = 200 kW;

[0079] Overload power state: P4≤1.25×200 = 250 kW.

[0080] Specific implementation of shift allocation strategy:

[0081] First, set the shift cycle T min = 10 min.

[0082] Scenario 1: The measured wind-solar combined power P fg (t) = 1000 kW (P req = 850 kW), at this time P fg (t)≥P req , the number of electrolyzers in the rated state is 5 electrolyzers are in the rated state, which meets the priority order.

[0083] Cost calculation: C = C o +C f +C s= 0, i.e. no overload, fluctuation, start-stop in this scenario, and the cost is the lowest.

[0084] Scenario 2: P fg (t) = 750 kW (P req = 850 kW), at this time P fg (t) < P req , the number of stopped tanks is 1 At this time, 1 tank is stopped, 3 tanks are running at rated power (200 kW), and the last tank is running at fluctuating power (150 kW). By adjusting, the total power is: P n (t) = 3 x 200 + 150 = 750 kW, which meets the high-speed working condition requirement.

[0085] Cost calculation: C = C o + C f + C s = 0 + 0.0125 + 500 = 500.0125 yuan, i.e. no overload in this scenario, but there is 1 fluctuation and 1 start-stop, and the cost is relatively the lowest.

[0086] Working condition classification and dynamic adjustment:

[0087] When the total power of the electrolytic tank P n (t) = 500 kW, the hydrogen production rate is At this time, 2 tanks are running at rated power (200 kW) and 2 tanks are running at fluctuating power (50 kW), which is in the high-efficiency medium-speed working condition (375 kW ≤ P n (t) < 525 W). According to the classification strategy of step S7, P n (t) needs to be raised to the high-speed working condition range, and is adjusted to 3 tanks running at rated power (200 kW) and 1 tank running at fluctuating power (100 kW), the total power P n (t) = 3 x 200 + 100 = 700 kW, so that the hydrogen production rate is raised to 3.8 mol / s, entering the high-speed working condition.

[0088] System verification and effect:

[0089] Through the simulation verification of 50 tanks of 200 kW electrolytic tanks matched with a 10 MW wind-solar power station, after adopting the above rotation strategy, the annual overload running time of the electrolytic tank is controlled within 15%, the single loss balance is improved by 40%, 98% of the wind-solar power can be absorbed without the need for energy storage devices, the hydrogen production efficiency is maintained at more than 75%, the equipment investment is reduced by 25% compared with the traditional fixed power operation scheme, and the overall cost is reduced.

[0090] Example 2

[0091] A 5 MW wind-solar power station (3 MW wind power and 2 MW solar power) is equipped with an alkaline electrolyzer hydrogen production system. The target hydrogen production rate f target = 8 mol / s, electrolyte temperature T = 75℃ (corresponding to Kelvin temperature 348.15 K), pressure p = 2 bar, Faraday efficiency η F = 0.96, total number of series electrolysis units in the electrolyzer n c = 120, electrode area A = 10 m 2 , overload cost coefficient k1 is 1.2 yuan / (kW·h), fluctuation cost coefficient k2 is 0.8 yuan / (%)·time), and single start-stop cost k3 is 800 yuan / time.

[0092] The function model of the power P of the alkaline electrolyzer and the hydrogen production rate f is constructed as formula (1), wherein:

[0093] ΔH = 286 kJ / mol, z = 2, F = 96485 C / mol, and the constant coefficient is as shown in Table 1. The target power is calculated as: The final calculation gives P req ≈ 1020 kW.

[0094] Electrolyzer array configuration:

[0095] The wind-solar power data of the power station in 2023 is arranged in descending order, and the cumulative probability α is taken as 0.98. The corresponding P max is 1500 kW, the electrolyzer overload coefficient β is 1.35, and the theoretical capacity P L = P max / β = 1111 kW is calculated.

[0096] The electrolyzer with a single rated power P e of 150 kW is selected, and the number of configurations is The actual configuration capacity P eL of the array is 8 × 150 = 1200 kW.

[0097] Shift strategy and state allocation:

[0098] Scenario 1: wind and solar are sufficient (P fg (t) = 1300 kW ≥ P req ).

[0099] The number of rated state tanks 6 electrolyzers are in the rated state, with a power of 900 kW, and the remaining power is 1300-900 = 400 kW. According to the “fluctuation→overload” allocation:

[0100] Fluctuation power (α = 1.1, P2 = 165 kW), 2 units are running with a total power of 330 kW.

[0101] Overload power: remaining 70kW, But because N 11 +N 12 +N 13 =10>8, adjust to N 13 =0, N 12 =2, the remaining 70kW is borne by 1 overload.

[0102] Final configuration: 6 rated, 2 fluctuation (α = 1.1), 1 overload, shutdown N 14 =-1. Correct to N 12 =1, N 13 =2, N 14 =0.

[0103] Cost calculation:

[0104] Fluctuation cost: C f =0.8×|1.1-1|×0.2 times / h×10min / 60≈0.0267 yuan.

[0105] Overload cost: C o =1.2×(202.5-150)×10min / 60≈10.5 yuan.

[0106] Total operating cost C = 10.5 + 0.0267 + 0 = 10.5267 yuan, the cost is the lowest.

[0107] Scenario 2: insufficient wind and light (P fg (t) = 950kW < P req ).

[0108] Electrolytic cell array configuration:

[0109] 6 electrolytic cells are in rated state, power is 900kW; N 22 =8-6-1=1; total power P n (t) = 6×150+1×50 = 950kW, in high-speed efficient working condition.

[0110] Cost calculation:

[0111] Fluctuation cost: C f =0.8×|0.33-1|×0.1 times / h×10min / 60≈0.0089 yuan.

[0112] Start-stop cost: C s =800×1 = 800 yuan.

[0113] Total operating cost C = 0 + 0.0089 + 800 = 800.0089 yuan, the lowest cost.

[0114] Operating condition classification and dynamic adjustment:

[0115] When the total power P n (t) = 600 kW, corresponding to 475 kW ≤ P n (t) < 665 kW, at this time 2 units of rated power (150 kW) run, 5 units of fluctuating power (60 kW) run, in the medium-speed operating condition. According to the classification strategy of step S7, P n (t) needs to be raised to the high-speed operating condition range, adjusted to 5 units of rated power (150 kW) running, 2 units of fluctuating power (100 kW) running, the total power P n (t) = 5 × 150 + 2 × 100 = 950 kW, entering the high-speed operating condition. After calculation, the overall cost is reduced after adjusting the operating condition.

[0116] System verification and effect:

[0117] After the system runs for 1 year, compared with the traditional fixed power scheme, the difference rate of electrolyzer single unit loss is reduced from 45% to 12%, the longest / shortest service life ratio is optimized from 3:1 to 1.2:1; the wind and light consumption rate is increased from 82% to 97%, the annual hydrogen production is increased by 18%; without energy storage device, the equipment investment is reduced by 30%.

[0118] Example 3

[0119] A 2 MW wind and light power station (wind power 1.2 MW, photovoltaic 0.8 MW) is matched with an alkaline electrolyzer hydrogen production system. The target hydrogen production rate f target = 3 mol / s, the electrolyte temperature T = 65℃ (corresponding to the Kelvin temperature 338.15 K), the pressure p = 1 bar, the Faraday efficiency η F = 0.95, the total number of series electrolysis units in the electrolyzer n c = 80, the electrode area A = 8 m 2 , the overload cost coefficient k1 is 0.6 yuan / (kW·h), the fluctuation cost coefficient k2 is 0.3 yuan / (%)·time), and the single start-stop cost k3 is 300 yuan / time.

[0120] The function model of the power P of the alkaline electrolyzer and the hydrogen production rate is constructed as formula (1), wherein:

[0121] ΔH = 286 kJ / mol, z = 2, F = 96485 C / mol, and the constant coefficient is as shown in Table 1. Calculation gives: Theoretical power P req ≈ 345 kW.

[0122] Electrolyzer array configuration:

[0123] Based on the historical data of the power station, take the cumulative probability α as 0.95, and the corresponding P max = 450kW, the overload coefficient β of the electrolyzer is 1.3, and the theoretical capacity P L = P max / β = 346kW. Select the electrolyzer with a single rated power P e = 100kW, and the configuration number

[0124] Shift strategy and state allocation:

[0125] Scenario 1: wind and light are sufficient (P fg (t) = 400kW > P req ).

[0126] The rated number of states N 11 = 3, and the power of the three electrolyzers is 300kW, and the remaining power is 100kW. The priority fluctuation power (α = 1.0, P2 = 100kW), N 12 = 1.

[0127] Overload power: remaining 70kW, But since N 11 +N 12 +N 13 = 10 > 8, adjust N 13 = 0, N 12 = 2, and the remaining 70kW is borne by one overload.

[0128] Final configuration: 3 rated, 1 fluctuation (α = 1.0), no overload and shutdown.

[0129] Cost calculation:

[0130] Fluctuation cost: C f = 0.3 × |1.0-1| × 0.5 times / h × 10min / 60 = 0 yuan.

[0131] Total operating cost C = 0 + 0 + 0 = 0 yuan, the cost is the lowest.

[0132] Scenario 2: wind and light are insufficient (P fg (t) = 250kW < P req ).

[0133] Two electrolyzers are in rated state, with a power of 200kW; N 22 = 4-2-1 = 1 (α = 0.5, P2 = 50kW).

[0134] Final configuration: 2 rated, 1 fluctuation, 1 shutdown, P n (t) = 2 x 100 + 1 x 50 = 250 kW.

[0135] Cost calculation:

[0136] Fluctuation cost: C f = 0.3 x |0.5 - 1| x 0.2 times / h x 10 min / 60 ≈ 0.005 yuan.

[0137] Start-stop cost: C s = 300 x 1 = 300 yuan.

[0138] Total operating cost C = 0 + 0.005 + 300 = 300.005 yuan, the lowest cost.

[0139] Working condition classification and dynamic adjustment:

[0140] When the total power of the electrolytic cell P n (t) = 170 kW, corresponding to 125 kW ≤ P n (t) < 175 kW, at this time 1 rated power (100 kW) runs, 2 fluctuation powers (35 kW) run, in the medium speed working condition. According to the classification strategy of step S7, P n (t) needs to be raised to the high speed working condition range, adjusted to 2 rated (100 kW) running, 1 fluctuation (50 kW) running, the total power P n (t) = 2 x 100 + 1 x 50 = 250 kW, enter the high speed working condition. After calculation, the overall cost is reduced after adjusting the working condition.

[0141] System verification and effect:

[0142] After the system runs for 6 months, compared with the traditional fixed power scheme, the electrolytic cell single unit loss difference rate is reduced to 15%, the wind and light consumption rate is increased from 78% to 95%, the equipment investment is reduced by 22%, and the overall operating cost is reduced.

[0143] Example 4

[0144] A 10 MW wind and light power station (6 MW of wind power and 4 MW of photovoltaic power) is matched with an alkaline electrolytic cell hydrogen production system. The target hydrogen production rate f target = 12 mol / s, the electrolyte temperature T = 90℃ (corresponding to the Kelvin temperature 363.15 K), the pressure p = 3 bar, the Faraday efficiency η F = 0.98, the total number of series electrolytic units in the electrolytic cell nc = 150, the electrode area A = 15 m 2The overload cost coefficient k1 is 1.5 yuan / (kW·h), the fluctuation cost coefficient k2 is 1.0 yuan / (%·time), and the single start-stop cost k3 is 1000 yuan / time.

[0145] Power P required for constructing an alkaline electrolyzer and hydrogen production rate The function model is shown in formula (1), where:

[0146] ΔH = 286 kJ / mol, z = 2, F = 96485 C / mol, and constant coefficients are shown in Table 1. The calculated values ​​are: I s ≈15772A, theoretical power P req =1620kW.

[0147] Electrolytic cell array configuration:

[0148] Arrange the 2023 wind and solar power data of this power station in descending order, and take the cumulative probability α as 0.99, corresponding to P max With a power output of 2200kW and an overload factor β of 1.15 for the electrolytic cell, the theoretical capacity P is calculated. L =P max / β=1913kW. Select a single unit with a rated power P. e For a 300kW electrolytic cell, the number of cells configured is as follows: Actual array configuration capacity P eL =7×300=2100kW.

[0149] Rotation strategy and state allocation:

[0150] Scene 1: Abundant Scenery (P) fg (t)=1800kW≥P req ).

[0151] Number of slots under rated conditions All 5 electrolytic cells are operating at their rated power of 1500kW; the remaining power is 300kW, with priority power fluctuation (α=0.5, P2=150kW), N 12 =2.

[0152] Final configuration: 5 rated units, 2 fluctuating units (α=0.5), no overload or shutdown.

[0153] Cost calculation:

[0154] Fluctuation cost: C f =1.0×|0.5-1|×0.1 times / h×10min / 60≈0.0085 yuan.

[0155] The total operating cost C = 0 + 2 × 0.0085 + 0 = 0.017 yuan, which is the lowest cost.

[0156] Scenario 2: Insufficient wind and light (P fg (t) = 1400 kW < P req ).

[0157] 4 electrolyzers are in the rated state, with a power of 1200 kW; N 22 = 7-4-2 = 1 (a = 0.33, P2= 100 kW).

[0158] Final configuration: 4 rated, 2 fluctuating, P n (t) = 4x300 + 2x100 = 1400 kW, in high-speed working condition.

[0159] Cost calculation:

[0160] Fluctuation cost: C f = 1.0x|0.33-1|x0.1 / hx10 / 60 = 0.011 yuan.

[0161] Start-stop cost: C s = 1000x1 = 1000 yuan.

[0162] Total operating cost C = 0 + 0.011 + 1000 = 1000.005 yuan, with the lowest cost.

[0163] Working condition classification and dynamic adjustment:

[0164] When the total power of electrolyzers P n (t) = 900 kW, corresponding to 700 kW < Pn(t) < 980 kW, at this time 2 rated power (300 kW) runs, 4 fluctuating power (75 kW) runs, in medium-speed working condition. According to the classification strategy of step S7, P n (t) needs to be raised to the high-speed working condition range, adjusted to 4 rated (300 kW) running, 2 fluctuating (100 kW) running, total power P n (t) = 4x300 + 2x100 = 1400 kW, into high-speed working condition. After calculation, the overall cost is reduced after adjusting the working condition.

[0165] System verification and effect:

[0166] The electrolyzer overload time ratio is controlled at 12%, the wind and light consumption rate reaches 99%, the annual hydrogen production is increased by 25%, the equipment investment is reduced by 28%, and the overall operating cost is reduced.

[0167] The above-described embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented by software, the above-described embodiments can be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art can be aware that units and algorithm steps of each example described in connection with the embodiments disclosed herein can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are performed by hardware or software depends on the specific application and design constraints of the technical solutions.

[0168] For the above-mentioned embodiments of the present application, the well-known specific structures and characteristics in the scheme and the common knowledge are not described in detail; each embodiment is described in a progressive manner, and the technical features involved in each embodiment can be combined with each other on the premise that they do not conflict with each other, and the same or similar parts between each embodiment can be referred to each other. It should be pointed out that for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can also be made, which should be considered to fall within the scope of protection of the present application.

Claims

1. A method for dynamic adjustment of an alkaline electrolyzer based on cost optimization, characterized in that, Includes the following steps: Step 1: Power P and hydrogen production rate required for constructing an alkaline electrolyzer Functional model: Step 2: Based on the target hydrogen production rate f target Based on real-time monitoring of electrolyte temperature T and pressure p, the required total power P is predicted using the aforementioned function model. req ; Step 3: Determine the configuration of the electrolyzer array, consisting of multiple individual electrolyzers, based on historical wind and solar power generation data: Calculate the time-series maximum value P of combined wind and solar power generation. max ; Determine the theoretical capacity P of the electrolytic cell array L =P max / β, where β takes values ​​from 1.1 to 1.35; Number of electrolytic cells configured Among them, P e This refers to the rated power of a single unit; Step 4: Divide the operation of the electrolytic cell into the following four operating states: Shutdown state: P1 = 0; Fluctuating power state: P2 = αP e Where 0.2≤α≤1.1; Rated power state: P3=P e Overload power condition: P e ≤P4≤P overload , where P overload =βP e ; Step 5: Construct an electrolyzer cost model, including overload operating costs C. o Fluctuating operating costs C f Start-up and shutdown operating costs C s and an economic optimization strategy framework; Step 6: Real-time acquisition of combined wind and solar power generation P fg (t); Step 7: Based on the goal of minimizing costs, a rotation strategy is adopted to allocate the operating state of each electrolytic cell in the electrolytic cell array, so that the electrolytic cell cells take turns operating in one of the four operating states. The priority of the four operating states from high to low is as follows: rated power state, fluctuating power state, overload power state, and shutdown state. When P fg (t)≥P req At the same time, by adjusting the operating status of each individual electrolyzer in the electrolyzer array, the hydrogen production rate is preferentially increased to near f. target Prioritize configuring the number N of individual electrolytic cells operating at rated power. 11 The number N of electrolytic cells configured for fluctuating power operation. 12 The number N of electrolytic cells configured for overload power operation. 13 And the number N of individual electrolytic cells configured for shutdown operation. 14 Make it satisfy N 11 +N 12 +N 13 +N 14 =n; priority is given to meeting the demand using the rated power state, and the remaining power is allocated in the order of "fluctuating power" to "overload power" to avoid unnecessary overload; When P fg (t)<P req At that time, the operating status is adjusted according to the hydrogen production rate and operating conditions, and the number N of electrolyzers configured to operate at rated power is determined according to the priority order. 21 The number N of electrolytic cells configured for fluctuating power operation. 22 And the number N of the electrolytic cells configured for shutdown operation. 23 Make it satisfy N 21 +N 22 +N 2a =n.

2. The method according to claim 1, characterized in that, In step 1, the electrical power P of the alkaline electrolyzer is related to the hydrogen production rate. The function model is: in: T is the Kelvin temperature of the electrolyte, p is the pressure, and η is the... F For Faraday efficiency, the value ranges from 0.95 to 0.99; n c ΔH is the total number of electrolytic units connected in series in the electrolytic cell; ΔH is the enthalpy change, taken as 286 kJ / mol, and ΔS is the entropy change, taken as 0.163 kJ / (mol·K), both of which remain basically constant within the normal operating temperature range of the electrolytic cell; z is the number of electrons transferred per hydrogen molecule, taken as 2; F is the Faraday constant, taken as 96485 C / mol; A is the electrode area; s is the activation overvoltage influence factor, in V; r1 is the initial resistance value, r2 is the linear influence factor of temperature on resistance, and r3 is the linear influence factor of pressure on resistance. The parameter t is directly related to the activation overpotential of the electrolytic cell. r1, r2, r3, s, t1, t2, and t3 are all constant coefficients, and their values ​​are determined through historical operating data.

3. The method according to claim 1, characterized in that, In step 3, the time-series maximum value P of the combined wind and solar power generation is calculated. max The steps include: Historical wind and solar power data are sorted in descending order to form a power sorting curve; Take the power value corresponding to the cumulative probability α (α = 0.95 ~ 1) in the curve as P. max .

4. The method according to claim 1, characterized in that, The cost model for constructing the electrolyzer in step 5 includes: Overload operating cost model: Overload operating cost C o =k1×(P overload -P e )×t, where k1 is the overload cost coefficient, P overload The overload power is t, and the overload duration is t. Fluctuating operating cost model: Fluctuating operating cost C f = k2·|α-1|·f·t, where k2 is the fluctuation cost coefficient, α is the fluctuation power coefficient, and f is the fluctuation frequency; Start-up and shutdown operation cost model: Start-up and shutdown operation cost C s = k3·m, where k3 is the cost of a single start-stop operation and m is the number of start-stop operations; Economic optimization strategy framework: Set the objective function as total operating cost C = C o +C f +C s Set hydrogen production rate constraint as follows: Where η e The hydrogen production guarantee factor is set at 0.9–0.95, and the power balance constraint is P. fg (t)=∑P n (t).

5. The method according to claim 1, characterized in that, The rotation strategy in step 7 includes: Set the rotation period T min Each cycle, the electrolytic cells are sorted according to the cyclic queue. The number of individual electrolytic cells operating in four different states is configured based on the real-time output power of the blower. The operating states of each individual electrolytic cell are then assigned sequentially according to the current order of their arrangement, based on P... fg (t) and P req The difference is used to dynamically allocate the number of individual electrolyzer cells in each state: When wind and light are insufficient: Number of tanks under rated conditions Number of slots in shutdown state 6. The method according to claim 1, characterized in that, The hydrogen production rate condition classification in step 7 includes: High-speed operating condition: P fg (t)>P n (t)≥0.7P fg (t); Medium-speed operation: 0.5P fg (t)≤P n (t) < 0.7P fg (t); Low-speed operation: 0.2P fg (t)≤P n (t) < 0.5P fg (t); Operating condition not permitted: P n (t) < 0.2P fg (t); where: Total power P of real-time electrolyzer n (t)=N 21 ×P3+N 22 ×P2+N 23 ×P1.

7. The method according to claim 1, characterized in that, The tiered adjustment strategy in step 7 includes: When operating under conditions where operation is not permitted, the number of individual electrolytic cells that remain in a shutdown state, while adhering to the priority order, is still N. 23 To maximize hydrogen production, the electrolyzer should be put into medium-speed hydrogen production mode, and the operating power command for the electrolyzer should be formulated to increase P. n (t) to the medium-speed hydrogen production operating range; When operating at low speed, and while prioritizing operations, the electrolyzer should be switched to medium-speed hydrogen production mode. The electrolyzer operating power command should be set to increase P. n (t) to the medium-speed hydrogen production operating range; When operating at medium speed, and while prioritizing operations, the electrolyzer is switched to high-speed hydrogen production mode. The electrolyzer operating power command is then set to increase P. n (t) to the range of high-speed hydrogen production conditions; When operating at high speed, while adhering to priority settings, the electrolyzer maintains high-speed hydrogen production. The electrolyzer operating power command is set to maintain P... n (t) within the high-speed hydrogen production operating range.

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