Optimized scheduling method for efficient utilization of green energy of off-grid wind and light hydrogen production array type electrolytic cell

Abstract

The invention discloses an optimal scheduling method for efficient utilization of green energy of an off-grid wind and light hydrogen production array type electrolytic cell. The method comprises the steps that S1, the structure of an off-grid wind and light hydrogen production system is determined; and S2, establishing an electrolytic cell start-stop characteristic model, and analyzing the efficiency characteristic of the alkaline electrolytic cell. And S3, establishing a storage battery mathematical model. And S4, constructing a maximum hydrogen production daily profit objective function of the wind-solar complementary hydrogen production system, and setting constraint conditions. And S5, integrating a mixed integer linear programming MILP optimization problem, generating a day-ahead output planning scheme, solving by adopting a Gurobi solver, and verifying the superiority of split scheduling. According to the method, for a new energy hydrogen production system, four working states of shutdown, cold start, working and hot standby are considered, refined modeling is carried out with 5 min as a time step length, and a certain scheme is provided for efficient cluster production operation scheduling of the alkaline electrolytic cell.

Description

Technical Field

[0001] This invention relates to the field of electrolytic water electrolysis for hydrogen production technology, specifically to an optimized scheduling method for efficient green energy utilization of off-grid wind and solar array electrolytic cells for hydrogen production. Background Technology

[0002] Compared with traditional energy sources, hydrogen energy has advantages such as high energy density, high efficiency, renewability, and no pollution, and is considered the most promising energy carrier for the future. Accelerating the development of the hydrogen energy industry is an important path to help achieve the "dual carbon" goal. Electrolysis of water to produce hydrogen can effectively achieve the efficient conversion and flexible consumption of renewable energy. The electrolyzer, as an electrical conversion device, is a key piece of equipment in renewable energy electrolysis of water to produce hydrogen. Alkaline electrolyzers are currently the only water electrolysis hydrogen production equipment that meets the requirements for large-scale engineering applications, and have advantages such as mature technology and low cost. Currently, the existing technology has the following problems:

[0003] 1. For new energy hydrogen production systems, the electrolyzer lacks operational flexibility. While considering three operating states (including standby) and variable operating temperature, enabling flexible operation of the alkaline electrolyzer, the start-up and shutdown operations and related costs are not taken into account, resulting in insufficient economic efficiency.

[0004] 2. Many studies are conducted under simple operating scenarios and do not consider the coordinated operation of the electrolyzer and other equipment. The coordinated operation of the electrolyzer and the battery needs to take into account the impact of complex and variable operating scenarios.

[0005] 3. Many studies on the optimization scheduling model of alkaline electrolyzers do not consider the cold start working state, and the calculation time and calculation time step are both taken as 1 hour. They do not conduct detailed modeling of the energy consumption process and equipment of hydrogen energy.

[0006] As can be seen from the above, the current alkaline electrolyzer water electrolysis hydrogen production technology still faces constraints in supporting the efficient, flexible, and economical operation of renewable energy hydrogen production systems. Although existing models have improved operational flexibility to adapt to the fluctuations of renewable energy by introducing multiple operating states and variable temperature models, they generally ignore the significant energy consumption, equipment wear and tear, and time delay costs brought about by the start-up and shutdown of the electrolyzer and cold start itself. They also fail to fully consider the collaborative operation strategy with hydrogen-using facilities such as batteries and hydrogen storage tanks in complex and variable operating scenarios, making it difficult to achieve optimal overall system performance. Summary of the Invention

[0007] The purpose of this invention is to provide an optimized scheduling method for efficient green energy utilization of off-grid wind and solar hydrogen production array electrolyzers. It mainly addresses the problem of hydrogen production by water electrolysis in alkaline electrolyzers. By constructing an optimized scheduling model for multiple alkaline electrolyzers, combined with a dynamic efficiency model of alkaline electrolyzer equipment, introducing a cold start state, and establishing an electrolyzer start-up and shutdown characteristic model, the electrolyzer system is optimized and scheduled to establish a daily output plan.

[0008] To achieve the above objectives, the technical solution adopted by the present invention is: an optimized scheduling method for efficient utilization of green energy in an off-grid wind-solar hydrogen production array electrolyzer, the method comprising the following steps:

[0009] 1) Determine the structure of the off-grid wind and solar hydrogen production system, which includes wind power equipment, photovoltaic equipment, alkaline electrolyzer array, storage battery, and hydrogen storage tank;

[0010] 2) Establish an electrolytic cell start-up and shutdown characteristic model, define state switching logic and operating constraints. In order to accurately describe the efficiency changes of the electrolytic cell under working conditions, establish a dynamic efficiency model of alkaline electrolytic cell equipment, and linearize the nonlinear efficiency function of the electrolytic cell to simplify the calculation.

[0011] 3) Establish a mathematical model for the battery to describe its charging and discharging characteristics and storage capacity limitations, smooth out fluctuations in wind and solar power output, and ensure the stable operation of the electrolyzer.

[0012] 4) Construct the objective function for the maximum daily profit of the wind-solar hybrid hydrogen production system, and set system constraints, including power balance constraints, curtailment rate constraints, and electrolyzer ramp-up rate constraints;

[0013] 5) Integrate into a mixed integer linear programming (MILP) optimization problem, solve it using the Gurobi solver in Matlab, optimize scheduling at fixed time intervals, and generate a daily output plan.

[0014] Preferably, step 2) includes:

[0015] S2-1: Introduce four binary 0 to 1 variables to represent it: working state W, hot standby state K, shutdown state I, and cold start state C;

[0016] When the electrolyzer is shut down, both the hydrogen production power and input power of the system electrolyzer are 0.

[0017]

[0018] In the formula, I t W represents the state variable of the electrolytic cell at time t when it is shut down. t-1 K represents the working state variable of the electrolytic cell at time t-1. t-1 The state variable for the electrolytic cell in standby mode at time t-1; Let t be the hydrogen production capacity of the electrolyzer at time t, in MW; Let be the input power of the electrolyzer at time t, in MW;

[0019] When the electrolyzer is in cold start-up mode, it does not produce hydrogen, and the hydrogen production capacity of this electrolyzer is 0.

[0020]

[0021] In the formula, C t Let I be the variable for the cold start of the electrolytic cell at time t; t-1 Let t-1 be the state variable of the electrolytic cell at shutdown; E is the rated power of the electrolytic cell, MW; β1 is the cold start loss coefficient.

[0022] When the electrolyzer is in hot standby mode, it does not produce hydrogen, but consumes power to maintain the operating temperature.

[0023]

[0024] In the formula, K t W represents the standby state variable of the electrolytic cell at time t. t-1 P represents the working state variable of the electrolytic cell at time t-1. 待机 β1 represents the standby power of the electrolytic cell, in MW; β2 is the thermal standby coefficient.

[0025] When the electrolytic cell is in operation, its input power ranges from minimum operating power to full power operating power:

[0026]

[0027] In the formula, W t C represents the working state variable of the electrolytic cell at time t. t-1 K represents the variable for cold start of the electrolytic cell at time t-1. t-1 The state variable for the electrolytic cell in standby mode at time t-1; η represents the minimum operating power of the electrolyzer; η is the efficiency of the electrolyzer at time t, which is a dynamic nonlinear function.

[0028] Two binary variables, H (warm start) and Z (shutdown), are introduced to represent state transitions, namely:

[0029] H t =W t K t-1

[0030] Z t =I t K t-1 +I t W t-1

[0031] I t +C t +K t +W t =1

[0032] H t =W t K t-1 This indicates the hot start-up process of the electrolytic cell from standby to operating state at time t; Zt =I t K t-1 +I t W t-1 This indicates the shutdown process of the electrolytic cell at time t, from operation or hot standby state to shutdown; I t +C t +K t +W t =1 indicates that the electrolytic cell can only be in one operating condition at time t;

[0033] S2-2: Linearize the nonlinear efficiency function η=f(x) by introducing nonnegative continuous variables a1,a2,…,a N Let x and η be represented as a linear combination of piecewise points:

[0034]

[0035] and satisfy

[0036] a1 + a2 + ... + a N =1

[0037] Introduce 0-1 variables z1, z2, ..., z N And add constraints:

[0038]

[0039] S2-3: In addition, it also includes state operation logic constraints and minimum duration constraints to ensure the correctness and safety of state operation;

[0040]

[0041] Among them, T RTC T min These are the shortest durations of continuous operation in cold start and shutdown states, respectively. Let be the cold start variable for the nth column of the electrolytic cell at time τ; Let x be the shutdown variable for the x-th column of the electrolytic cell at time τ.

[0042] Preferably, step 3) includes:

[0043] Batteries balance the fluctuations in wind and solar power generation through charging and discharging operations. When wind and solar power generation is excessive, the batteries charge to store the excess energy; when wind and solar power generation is insufficient, the batteries discharge to make up for the energy gap. The specific mathematical model of the battery is as follows:

[0044] The charging and discharging process needs to consider its state of charge (SOC), and the model expression is as follows:

[0045]

[0046] in, Let be the battery charge at times t and t-1, respectively, in MWh; σ is the battery self-loss rate. For the charging efficiency of the storage battery; The discharge efficiency of the battery; Let be the battery charge and discharge amounts at time t, respectively, in MWh;

[0047] To ensure the physical safety and operational feasibility of the battery during charging and discharging, the following constraints are imposed on the battery:

[0048]

[0049] In the formula, E e Battery capacity, in MWh; Let be the charging state variable of the battery at time t. Let be the binary variable representing the discharge state of the battery at time t; The initial charge level. The battery's charge level at the end of the cycle is recorded; the charge / discharge state variables of the battery are dynamically adjusted based on wind and solar power fluctuations. and When wind and solar power generation is in surplus, set up Start charging Store excess energy; when wind and solar power generation is insufficient, set up Start Discharge Release energy.

[0050] Preferably, in step 4):

[0051] The objective function for maximizing daily hydrogen production profit is:

[0052] maxC = C1 - C2 - C3 - C4

[0053] Where C represents hydrogen production profit; C1 represents hydrogen sales revenue; C2 represents the start-up and shutdown costs of the electrolyzer; C3 represents the operation and maintenance costs of the electrolyzer and other equipment; and C4 represents the cost of power curtailment penalties.

[0054] Electrical balance constraints:

[0055]

[0056] Where N is the number of rows of electrolytic cells; Let t be the power output of the photovoltaic power generation equipment, in MW; Let t be the power output of the wind turbine at time t, in MW; Let be the battery charge and discharge amounts at time t, respectively, in MWh; Let be the input power (MW) of the i-th column of electrolytic cells at time t; Let t be the power curtailed at time t, in MW;

[0057] Curtailment rate constraint:

[0058]

[0059] Where ε is the maximum allowable power curtailment rate of the system;

[0060] Electrolytic cell ramp rate constraint:

[0061]

[0062] Where, ΔP down ΔP up These represent the lower limit and upper limit of the ramp rate for each row of alkaline electrolyzers, respectively.

[0063] Preferably, in step 5):

[0064] All the models, objective functions, and constraints established in steps 2) to 4) are integrated to form a mixed integer linear programming (MILP) optimization problem, which is then solved using the Yalmip toolbox in Matlab by calling the Gurobi optimization software.

[0065] The split-schedule scheduling strategy divides the electrolyzer into multiple columns, each operating independently. The start-stop status and input power of each column are determined through MILP model optimization.

[0066] Compared with the prior art, the present invention, employing the above technical solution, has the following technical effects:

[0067] (1) For the new energy hydrogen production system, four working states are considered: shutdown, cold start, working and hot standby, so as to realize the flexible operation of alkaline electrolyzer and take into account the start-up and shutdown operation of electrolyzer and related costs.

[0068] (2) Consider the coordinated operation of the electrolyzer and other equipment, and the coordinated operation of the electrolyzer, battery, and hydrogen storage tank equipment under the conditions of wind power and photovoltaic fluctuations.

[0069] (3) The alkaline electrolyzer optimization scheduling model takes into account the cold start working state, and the calculation time and calculation time step are both 5 minutes. The energy consumption process and equipment of hydrogen energy are modeled in detail. Attached Figure Description

[0070] Figure 1 This is a flowchart of the optimized scheduling method for efficient utilization of green energy in an off-grid wind and solar hydrogen production array electrolyzer according to the present invention.

[0071] Figure 2 This is a diagram of an off-grid wind and solar array electrolyzer hydrogen production system.

[0072] Figure 3 This is a schematic diagram of the state switching of an alkaline electrolytic cell.

[0073] Figure 4 This is a graph showing the efficiency characteristics of the alkaline electrolyzer of this invention.

[0074] Figure 5 This is the daily scheduling cycle power balance diagram of Strategy 1 system of the present invention.

[0075] Figure 6 This is a diagram showing the operation of a single electrolytic cell in the Strategy 1 system of the present invention within one day.

[0076] Figure 7 This is the daily scheduling cycle power balance diagram of the Strategy 2 system of the present invention.

[0077] Figure 8 This is a diagram showing the operation of a single electrolytic cell in the first column of a day in the Strategy 2 system of the present invention.

[0078] Figure 9 This is a diagram showing the operation of a single electrolytic cell in the second column of a day in the Strategy 2 system of the present invention.

[0079] Figure 10 This is a diagram showing the operation of a single electrolytic cell in the third column of a day in the Strategy 2 system of this invention.

[0080] Figure 11 This is a diagram showing the operation of a single electrolytic cell in the fourth column of a day in the Strategy 2 system of this invention.

[0081] Figure 12 This is a diagram showing the operation of a single electrolytic cell in the fifth column of a day in the Strategy 2 system of this invention. Detailed Implementation

[0082] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0083] like Figure 1 An optimized scheduling method for efficient green energy utilization of off-grid wind-solar hydrogen production array electrolyzers includes the following steps:

[0084] S1, such as Figure 2 The structure of the off-grid wind and solar hydrogen production system was determined. The system mainly includes wind power equipment, photovoltaic equipment, alkaline electrolyzer array, storage battery, and hydrogen storage tank.

[0085] S1-1: Power output characteristics of wind power generation equipment The installed capacity W of wind power generation equipment WT The wind speed v is the determining factor.

[0086]

[0087] In the formula, v is the per-unit value of wind power generation per unit installed capacity at time t; ciV is the cut-in velocity of the fan, in m / s; R V is the cut-off velocity of the fan, in m / s; co The rated wind speed of the fan is ____ m / s;

[0088] S1-2: Photovoltaic power generation is affected by solar radiation intensity. Considering the relationship between photovoltaic output power and solar radiation intensity, a model of photovoltaic system equipment is constructed.

[0089]

[0090] In the formula, G represents the per-unit installed power generation of photovoltaic power generation at time t; C This represents the actual light intensity, in W / m². 2 G N Rated light intensity, W / m 2 γ is the power temperature coefficient, C -1 ;T C T represents the surface temperature of the photovoltaic cell, in °C. N The rated temperature of the battery is in °C.

[0091] Wind and solar power generation equipment model

[0092]

[0093] In the formula, Let E be the power output of the wind turbine at time t, in MW; w The installed capacity of wind power generation equipment is expressed in MW. Let E be the power output of the photovoltaic power generation equipment at time t, in MW; s The installed capacity of photovoltaic power generation equipment is expressed in MW.

[0094] S2. Establish an alkaline electrolytic cell equipment model, analyze the efficiency characteristics of the alkaline electrolytic cell, establish a dynamic efficiency model of the electrolytic cell equipment considering input power and operating temperature, introduce a cold start state, and establish an electrolytic cell start-up and shutdown characteristic model.

[0095] S2-1: In the entire hydrogen production system, the electrolyzer is equivalent to a voltage-sensitive nonlinear DC load. The electrochemical model of the electrolyzer proposed by Ulleberg is one of the most widely used models currently available.

[0096]

[0097] In the formula, U el U is the DC voltage of the electrolytic cell; rev The reversible voltage of the electrolytic cell; the reversible voltage is the minimum voltage required for the electrolytic reaction to occur, under standard conditions (1 bar and 250°C, 1 bar = 10). 5The voltage is 1.23V at Pa; T and p are the temperature and pressure of the electrolytic cell, respectively; i is the current density; and r1, r2, d1, d2, t1, t2, t3, and s are correlation coefficients.

[0098] Faraday efficiency is:

[0099]

[0100] In the formula, f 11 f 12 f 21 f 22 These are constants measured experimentally.

[0101] The efficiency η of the alkaline electrolyzer is:

[0102]

[0103] In the formula, The higher heating value of hydrogen is 286 kJ / mol. z is the number of electrons transferred in a hydrogen molecule, 2; F is the Faraday constant, 96485 C / mol.

[0104] In actual production, the hydrogen production efficiency of an electrolyzer is not constant and varies with the current density. Since the current density of an electrolyzer is difficult to measure in production, it is converted into a per-unit value of the electrolyzer's input power for representation.

[0105]

[0106] In the formula, U elN i is the rated voltage of the electrolytic cell. N This refers to the rated current density of the electrolytic cell;

[0107] Programmatically model (5)-(8) to obtain hydrogen production efficiency curves, such as Figure 4 ;

[0108] The hydrogen production efficiency curve of the electrolyzer is a nonlinear curve, and the established model is a nonlinear optimization problem, which is not conducive to solving the model. Therefore, piecewise linearization technology can be used to divide the nonlinear characteristic curve into several segments, and line segments are used to approximate the characteristic curve in each segment.

[0109] Special Sequence Sets (SOS-2) is an ordered piecewise linearization technique that approximates a nonlinear function curve as a piecewise linear function by introducing segmentation points and auxiliary variables. Linearization is applied to the nonlinear efficiency function η=f(x) by introducing nonnegative continuous variables a1, a2, …, a N Let x and η be represented as a linear combination of piecewise points:

[0110]

[0111] and satisfy

[0112] a1 + a2 + ... + a N =1

[0113] Introduce 0-1 variables z1, z2, ..., z N And add constraints:

[0114]

[0115] By using SOS-2 piecewise linearization, the complex mixed-integer nonlinear programming (MINLP) problem is transformed into a mixed-integer linear programming (MILP) problem, which significantly improves the solution efficiency and stability.

[0116] S2-2: As Figure 3 To ensure sufficient accuracy in scheduling calculations, and considering technical and economic indicators, the electrolytic cell model is incorporated into production, standby, and shutdown states, and cold start and hot start states are introduced. Since the hot start time is very short, it is not considered in this optimization model. Four binary 0-1 variables are introduced to represent it: production state W, hot standby state K, shutdown state I, and cold start state C.

[0117] When the electrolyzer is shut down, both the hydrogen production power and input power of the system electrolyzer are 0.

[0118]

[0119] In the formula, I t W represents the state variable of the electrolytic cell at time t when it is shut down. t-1 K represents the working state variable of the electrolytic cell at time t-1. t-1 The state variable for the electrolytic cell in standby mode at time t-1; Let t be the hydrogen production capacity of the electrolyzer at time t, in MW; Let be the input power of the electrolyzer at time t, in MW;

[0120] When the electrolyzer is in cold start-up mode, it does not produce hydrogen, and the hydrogen production capacity of this electrolyzer is 0.

[0121]

[0122] In the formula, C t Let I be the variable for the cold start of the electrolytic cell at time t; t-1 Let t-1 be the state variable of the electrolytic cell at shutdown; E is the rated power of the electrolytic cell, MW; β1 is the cold start loss coefficient.

[0123] When the electrolyzer is in hot standby mode, it does not produce hydrogen, but consumes power to maintain the operating temperature.

[0124]

[0125] In the formula, K t W represents the standby state variable of the electrolytic cell at time t. t-1 P represents the working state variable of the electrolytic cell at time t-1. 待机 β1 represents the standby power of the electrolytic cell, in MW; β2 is the thermal standby coefficient.

[0126] When the electrolytic cell is in operation, its input power ranges from minimum operating power to full power operating power:

[0127]

[0128] In the formula, W t C represents the working state variable of the electrolytic cell at time t. t-1 K represents the variable for cold start of the electrolytic cell at time t-1. t-1 The state variable for the electrolytic cell in standby mode at time t-1; η represents the minimum operating power of the electrolyzer; η is the efficiency of the electrolyzer at time t, which is a dynamic nonlinear function.

[0129] S2-3: In order to accurately describe the state transitions of the electrolytic cell during operation and ensure the correctness and safety of the transitions, strict constraints must be imposed on the switching behavior of the electrolytic cell to ensure the continuous, efficient and safe operation of the production process and meet the strict process requirements.

[0130] The electrolytic cell can only be switched from the shutdown state to the cold start state;

[0131] I t-1 +W T ≤1

[0132] I t-1 +K t ≤1

[0133] The electrolytic cell can only be switched from cold start to working state;

[0134] C t-1 +I t ≤1

[0135] C t-1 +K t ≤1

[0136] The electrolytic cell cannot switch from hot standby mode to cold start mode;

[0137] K t-1 +C t ≤1

[0138] The electrolytic cell cannot be switched from the working state to the cold start state;

[0139] W t-1+C t ≤1

[0140] In addition, two binary variables are introduced to represent the hot start and shutdown of the electrolytic cell: hot start H and shutdown Z;

[0141] H t =W t K t-1

[0142] Z t =I t K t-1 +I t W t-1

[0143] I t +C t +K t +W t =1

[0144] Formula H t =W t K t-1 This indicates the hot-start state variables from standby to operating state; Equation Z t =I t K t-1 +I t W t-1 Indicates the power-off state variable; Equation I t +C t +K t +W t =1 indicates that the electrolytic cell can only be in one operating condition at any given time;

[0145] S2-4: In addition, it also includes state operation logic constraints and minimum duration constraints to ensure the correctness and safety of state operation;

[0146]

[0147] Among them, T RTC T min These are the shortest durations of continuous operation in cold start and shutdown states, respectively. Let be the cold start variable for the nth column of the electrolytic cell at time τ; Let be the shutdown variable for the nth column of electrolytic cells at time τ.

[0148] S3. In off-grid wind and solar hydrogen production systems, batteries are responsible for energy storage, conversion, and allocation to achieve stable hydrogen production and supply. Precise mathematical models are established to describe the operating characteristics of the components.

[0149] Batteries balance the fluctuations in wind and solar power generation through charging and discharging. When wind and solar power generation is excessive, the batteries charge to store the excess energy; when wind and solar power generation is insufficient, the batteries discharge to supplement the energy gap. As a key component between the wind and solar power generation equipment and the electrolyzer, the batteries play a crucial role in mitigating the volatility of wind and solar power generation and ensuring the stable operation of the electrolyzer. Since all batteries exhibit self-discharge, energy conversion during charging and discharging incurs corresponding losses; therefore, both self-discharge and energy conversion losses of the batteries must be considered.

[0150]

[0151] In the formula, Let t be the battery charge at time t, in MWh; Let be the battery charge at time t-1, in MWh; σ be the battery self-loss rate, which is 0.01. For the charging efficiency of the storage battery; The discharge efficiency of the battery is 0.95, and the charging efficiency is 0.95. Let t be the amount of battery charge at time t, in MWh; Let t be the battery discharge amount at time t, in MWh;

[0152] To ensure the physical safety and operational feasibility of the battery during charging and discharging, the following constraints are imposed on the battery:

[0153]

[0154] In the formula, E e Battery capacity, in MWh; Let be the charging state variable of the battery at time t. Let be the binary variable representing the discharge state of the battery at time t; The initial charge level. The battery's charge level at the end of the cycle is recorded; the charge / discharge state variables of the battery are dynamically adjusted based on wind and solar power fluctuations. and When wind and solar power generation is in surplus, set up Start charging Store excess energy; when wind and solar power generation is insufficient, set up Start Discharge Release energy.

[0155] S4. Construct the objective function for the maximum daily profit of the wind-solar hybrid hydrogen production system, and set system constraints, including power balance constraints, power curtailment rate constraints, and electrolyzer ramp-up rate constraints.

[0156] S4-1: When the electrolyzer improves its absorption capacity of wind and solar power, the input electrical energy will be converted into hydrogen energy through electrolysis. The hydrogen production per unit time of the electrolyzer is directly related to the input power, electrolysis efficiency, and operating time. The higher the absorbed wind and solar power, the more hydrogen will be produced. Therefore, the objective is transformed into maximizing the hydrogen sales revenue function of the electrolyzer combination. The objective function is:

[0157] maxC = C1 - C2 - C3 - C4

[0158]

[0159] In the formula: C is the profit from hydrogen production; C1 is the revenue from hydrogen sales; C2 is the start-up and shutdown cost of the electrolyzer; and C3 is the operation and maintenance cost of the electrolyzer. The selling price per unit of hydrogen; M t The total hydrogen production at time t is expressed in kg; C cold C hot C off These are the cost coefficients for cold start, hot start, and shutdown of the electrolytic cell, respectively. These represent the cold start, shutdown, and hot start variables for the nth electrolytic cell at time t; C ele b is the system's operation and maintenance cost coefficient. t With c t The price of the electrolytic cell and other equipment is calculated over a time period. Let be the total input power of the system at time t, in MW; Let be the power curtailed by the system at time t, in MW; Let t be the penalty cost coefficient for wind and solar power curtailment;

[0160] S4-2: Constraints include power balance constraints, power abandonment rate constraints, and electrolytic cell ramp rate constraints.

[0161] Electrical balance constraints:

[0162]

[0163] Where N is the number of rows of electrolytic cells; Let t be the power output of the photovoltaic power generation equipment, in MW; Let t be the power output of the wind turbine at time t, in MW; Let be the battery charge and discharge amounts at time t, respectively, in MWh; Let be the input power (MW) of the i-th column of electrolytic cells at time t; Let t be the power curtailed at time t, in MW;

[0164] Curtailment rate constraint:

[0165]

[0166] Where ε is the maximum allowable power curtailment rate of the system;

[0167] Electrolytic cell ramp rate constraint:

[0168]

[0169] Where, ΔP down ΔP up These represent the lower limit and upper limit of the ramp rate for each row of alkaline electrolyzers, respectively.

[0170] The S5 and electrolytic cell systems were compared using both unified scheduling and separate scheduling methods. With the goal of maximizing daily revenue, the electrolytic cell system was optimized and a daily output plan was established.

[0171] S5-1: Set the unit time interval to 5 minutes. The photovoltaic installed capacity and wind power installed capacity are 20MW and 100MW respectively. Select 20 electrolytic cells of the same specifications with a rated operating power of 5MW. Form a row of 4 electrolytic cells, for a total of 5 rows. Each row of electrolytic cells shares a set of electrolyte to form an electrolytic cell combination with a total rated operating power of 20MW. The total capacity of the electrolytic cells is 100MW. For off-grid systems, configure 20MWh battery energy storage.

[0172] S5-2: When calculating hydrogen production and hydrogen production profits, two schemes are compared and analyzed.

[0173] Strategy 1: Treat the hydrogen production system as a unified hydrogen production unit, with 20 electrolyzers starting and stopping simultaneously, and distribute the power evenly among the electrolyzers;

[0174] Strategy 2: Divide the electrolyzers into 5 groups, with 4 electrolyzers in each group. Each group shares the same electrolyte solution, and optimize the scheduling of the electrolyzers.

[0175] The electrolytic cell system is optimized and scheduled to maximize daily profits.

[0176] S5-3: Substitute the model into the Gurobi solver for solving. Gurobi can solve large-scale linear problems, quadratic problems, and mixed-integer linear and quadratic problems, supporting multi-objective optimization. The Gurobi solver converges quickly when solving optimization problems, is easy to tune parameters, and has a wider range of applications, but its global search capability is slightly weaker. The scheduling results of Strategy 1 are as follows: Figure 5 and Figure 6 The scheduling result of strategy 2 is as follows: Figures 7 to 12 .

[0177] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the above embodiments do not limit the scope of protection of the present invention in any way, and all technical solutions obtained by equivalent substitution or other means fall within the scope of protection of the present invention. Parts not covered in this invention are the same as or can be implemented using existing technology.

Claims

1. A method for optimizing and scheduling the efficient utilization of green energy in an off-grid wind-solar hydrogen production array electrolyzer, characterized in that... The method includes the following steps: 1) Determine the structure of the off-grid wind and solar hydrogen production system, which includes wind power equipment, photovoltaic equipment, alkaline electrolyzer array, storage battery, and hydrogen storage tank; 2) Establish an electrolytic cell start-up and shutdown characteristic model, define state switching logic and operating constraints. In order to accurately describe the efficiency changes of the electrolytic cell under working conditions, establish a dynamic efficiency model of alkaline electrolytic cell equipment, and linearize the nonlinear efficiency function of the electrolytic cell to simplify the calculation. 3) Establish a mathematical model for the battery to describe its charging and discharging characteristics and storage capacity limitations, smooth out fluctuations in wind and solar power output, and ensure the stable operation of the electrolyzer. 4) Construct the objective function for the maximum daily profit of the wind-solar hybrid hydrogen production system, and set system constraints, including power balance constraints, curtailment rate constraints, and electrolyzer ramp-up rate constraints; 5) Integrate into a mixed integer linear programming (MILP) optimization problem, solve it using the Gurobi solver in Matlab, optimize scheduling at fixed time intervals, and generate a daily output plan.

2. The method according to claim 1, characterized in that, Step 2) includes: S2-1: Introduce four binary 0 to 1 variables to represent it: working state W, hot standby state K, shutdown state I, and cold start state C; When the electrolyzer is shut down, both the hydrogen production power and input power of the system electrolyzer are 0. In the formula, I t W represents the state variable of the electrolytic cell at time t when it is shut down. t-1 K represents the working state variable of the electrolytic cell at time t-1. t-1 The state variable for the electrolytic cell in standby mode at time t-1; Let t be the hydrogen production capacity of the electrolyzer at time t, in MW; Let be the input power of the electrolyzer at time t, in MW; When the electrolyzer is in cold start-up mode, it does not produce hydrogen, and the hydrogen production capacity of this electrolyzer is 0. In the formula, C t Let I be the variable for the cold start of the electrolytic cell at time t; t-1 Let t-1 be the state variable of the electrolytic cell at shutdown; E is the rated power of the electrolytic cell, MW; β1 is the cold start loss coefficient. When the electrolyzer is in hot standby mode, it does not produce hydrogen, but consumes power to maintain the operating temperature. In the formula, K t W represents the standby state variable of the electrolytic cell at time t. t-1 P represents the working state variable of the electrolytic cell at time t-1. 待机 β1 represents the standby power of the electrolytic cell, in MW; β2 is the thermal standby coefficient. When the electrolytic cell is in operation, its input power ranges from minimum operating power to full power operating power: In the formula, W t C represents the working state variable of the electrolytic cell at time t. t-1 K represents the variable for cold start of the electrolytic cell at time t-1. t-1 The state variable for the electrolytic cell in standby mode at time t-1; η represents the minimum operating power of the electrolyzer; η is the efficiency of the electrolyzer at time t, which is a dynamic nonlinear function. Two binary variables, H (warm start) and Z (shutdown), are introduced to represent state transitions, namely: H t =W t K t-1 Z t =I t K t-1 +I t W t-1 I t +C t +K t +W t =1 H t =W t K t-1 This indicates the hot start-up process of the electrolytic cell from standby to operating state at time t; Z t =I t K t-1 +I t W t-1 This indicates the shutdown process of the electrolytic cell at time t, from operation or hot standby state to shutdown; I t +C t +K t +W t =1 indicates that the electrolytic cell can only be in one operating condition at time t; S2-2: Linearize the nonlinear efficiency function η=f(x) by introducing nonnegative continuous variables a1,a2,…,a N Let x and η be represented as a linear combination of piecewise points: and satisfy a1+a2+…+a N =1 Introduce 0-1 variables z1, z2, ..., z N And add constraints: S2-3: In addition, it also includes state operation logic constraints and minimum duration constraints to ensure the correctness and safety of state operation; Among them, T RTC T min These are the shortest durations of continuous operation in cold start and shutdown states, respectively. Let be the cold start variable for the nth column of the electrolytic cell at time τ; Let be the shutdown variable for the nth column of electrolytic cells at time τ.

3. The method according to claim 1, characterized in that, Step 3) includes: Batteries balance the fluctuations in wind and solar power generation through charging and discharging operations. When wind and solar power generation is excessive, the batteries charge to store the excess energy; when wind and solar power generation is insufficient, the batteries discharge to make up for the energy gap. The specific mathematical model of the battery is as follows: The charging and discharging process needs to consider its state of charge (SOC), and the model expression is as follows: in, Let be the battery charge at times t and t-1, respectively, in MWh; σ is the battery self-loss rate. For the charging efficiency of the storage battery; The discharge efficiency of the battery; Let be the battery charge and discharge amounts at time t, respectively, in MWh; To ensure the physical safety and operational feasibility of the battery during charging and discharging, the following constraints are imposed on the battery: In the formula, E e Battery capacity, in MWh; Let be the charging state variable of the battery at time t. Let be the binary variable representing the discharge state of the battery at time t; The initial charge level. The battery's charge level at the end of the cycle is recorded; the charge / discharge state variables of the battery are dynamically adjusted based on wind and solar power fluctuations. and When wind and solar power generation is in surplus, set up Start charging to store excess energy; when wind and solar power generation is insufficient, set... Initiate discharge and release energy.

4. The method according to claim 1, characterized in that, In step 4): The objective function for maximizing daily hydrogen production profit is: maxC = C1 - C2 - C3 - C4 Where C represents hydrogen production profit; C1 represents hydrogen sales revenue; C2 represents the start-up and shutdown costs of the electrolyzer; C3 represents the operation and maintenance costs of the electrolyzer and other equipment; and C4 represents the cost of power curtailment penalties. Electrical balance constraints: Where N is the number of rows of electrolytic cells; Let t be the power output of the photovoltaic power generation equipment, in MW; Let t be the power output of the wind turbine at time t, in MW; Let be the battery charge and discharge amounts at time t, respectively, in MWh; Let be the input power (MW) of the i-th column of electrolytic cells at time t; Let t be the power curtailed at time t, in MW; Curtailment rate constraint: Where ε is the maximum allowable power curtailment rate of the system; Electrolytic cell ramp rate constraint: Where, ΔP down ΔP up These represent the lower limit and upper limit of the ramp rate for each row of alkaline electrolyzers, respectively.

5. The method according to claim 1, characterized in that, In step 5): All the models, objective functions, and constraints established in steps 2) to 4) are integrated to form a mixed integer linear programming (MILP) optimization problem, which is then solved using the Yalmip toolbox in Matlab by calling the Gurobi optimization software. The split-schedule scheduling strategy divides the electrolyzer into multiple columns, each operating independently. The start-stop status and input power of each column are determined through MILP model optimization.

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