Cooperative control method for hydrogen-electricity hybrid energy storage system under wind-light fluctuation

Abstract

The invention discloses a cooperative control method for a hydrogen-electricity hybrid energy storage system under wind-light fluctuation, and belongs to the technical field of wind-light-electricity hydrogen storage. Comprising the steps of predicting photovoltaic power generation power, wind power generation power, power grid load power, electricity price and hydrogen price; upper-layer optimization scheduling and switching control are carried out, the upper-layer optimization scheduling takes the minimum total operation cost of the system as a target, a scheduling optimization model is established, a Kepler optimization algorithm is adopted for solving to obtain an optimization variable, and the optimization variable is subjected to switching control; comprise power grid electricity purchasing power, wind and light abandoning power, electrochemical energy storage battery charging and discharging power, fuel cell power generation power, electrolytic cell hydrogen production power and hydrogen selling quantity; the switching control comprises electrolytic bath switching control and fuel cell switching control, the operation state and power of each subsystem are distributed according to the upper scheduling result, and power distribution and alternate management of the electrolytic bath and the fuel cell are achieved. The energy utilization efficiency can be improved, the operation cost of the system is reduced, the service life of equipment is prolonged, and optimized operation of the electro-hydrogen energy storage system is achieved.

Description

Technical Field

[0001] This invention belongs to the technical field of wind, solar, and hydrogen storage, and more specifically, relates to a collaborative control method for a hydrogen-electric hybrid energy storage system under wind and solar fluctuations. Background Technology

[0002] The construction of new power systems presents the distribution network with the dual challenges of balancing supply and demand and insufficient flexibility resources. On the one hand, the randomness and volatility of large-scale distributed energy (such as wind power and photovoltaics) exacerbate the uncertainty of the distribution network's supply side, necessitating balancing services. On the other hand, the reduction of traditional flexibility resources (such as thermal power units and pumped storage) and the widening of the load peak-valley difference make the distribution network urgently need peak shaving and valley filling services.

[0003] Distributed electric / hydrogen energy storage systems (comprising electrochemical energy storage, hydrogen generator sets, hydrogen storage devices, and electrolyzers) can simultaneously provide demand balancing and demand response services. They can absorb excess supply through electrolyzers, compensate for demand shortfalls with electrochemical storage, and utilize demand response for peak shaving and valley filling. However, existing configuration methods have the following technical drawbacks: First, while electrochemical energy storage offers fast response times, its capacity is limited, preventing it from providing long-term energy storage services; hydrogen energy storage, while capable of long-term storage, suffers from long cold start times and slow ramp-up speeds. Second, existing hybrid electric / hydrogen energy storage systems lack coordination and control, failing to fully leverage the advantages of each component. Specifically, the existing technologies mainly have the following technical problems: (1) lack of effective multi-timescale interaction mechanisms, making it impossible to achieve optimized configuration and scheduling of hydrogen-electric energy storage; (2) lack of a complete "source-load-storage" interaction mechanism, making it difficult to cope with the volatility of wind and solar power generation; (3) existing control strategies cannot simultaneously meet the needs of short-term power balance and long-term energy scheduling; (4) the economic operation level of hydrogen-electric energy storage systems needs to be improved, and the operating cost is relatively high; (5) equipment life management is imperfect, especially the life decay problem of fuel cells and electrolyzers. Therefore, it is urgent to carry out multi-timescale interaction research, analyze the "source-load-storage" interaction mechanism, conduct research on capacity configuration methods for hydrogen-electric coupling, and conduct research on coordinated control methods under hydrogen-electric coupling, so as to solve the above technical problems and improve the reliability, economy and operating efficiency of the system. Summary of the Invention

[0004] To address the aforementioned deficiencies or improvement needs of existing technologies, this invention provides a collaborative control method for hydrogen-electric hybrid energy storage systems under wind and solar fluctuations, aiming to solve the technical problem of low energy efficiency in existing hydrogen-electric hybrid energy storage systems.

[0005] To achieve the above objectives, this invention provides a collaborative control method for a hydrogen-electric hybrid energy storage system under wind-solar fluctuations. The system includes a photovoltaic power generation module, a wind power generation module, an electrochemical energy storage battery, an electrolysis hydrogen production system, and a fuel cell power generation system. The photovoltaic power generation module, electrochemical energy storage battery, and fuel cell power generation system are all connected to a DC bus. The fuel cell power generation system includes n fuel cells. The wind power generation module and the electrolysis hydrogen production system are both connected to an AC bus. The electrolysis hydrogen production system includes m electrolyzers. DC power is converted to AC power, and the AC power is connected to the power grid. The method is characterized by the following steps: Based on historical load data and weather factors, we forecast variables for the future period; these variables include photovoltaic power generation, wind power generation, grid load, electricity price, and hydrogen price. With the goal of minimizing the total system operating cost, a scheduling optimization model is established and solved using the Kepler optimization algorithm under preset constraints. The optimization variables include the power purchased from the grid, the power curtailed from wind and solar power, the charging and discharging power of energy storage batteries, the power generation of fuel cells, the hydrogen production power of electrolyzers, and the amount of hydrogen sold. Electrolyzer switching control and fuel cell switching control are performed based on the scheduling optimization results.

[0006] The present invention also provides an electronic device, comprising: a computer-readable storage medium and a processor; The computer-readable storage medium is used to store executable instructions; The processor is used to read executable instructions stored in the computer-readable storage medium and execute the above-described method.

[0007] The present invention also provides a computer-readable storage medium storing computer instructions for causing a processor to perform the above-described method.

[0008] The present invention also provides a computer program product, including a computer program or instructions that, when executed by a processor, implement the above-described method.

[0009] In summary, compared with the prior art, the technical solutions conceived in this invention aim to minimize the total system operating cost. This invention establishes a high-level scheduling optimization problem model and uses the Kepler optimization method to solve this problem, thereby achieving optimized operation of the electro-hydrogen energy storage system and reducing system operating costs. Furthermore, it proposes a rotation rule based on lifespan and efficiency to control the switching between multiple electrolyzers and multiple fuel cells, improving energy efficiency and extending the lifespan of electrolytic hydrogen production and fuel cell equipment. Attached Figure Description

[0010] Figure 1A schematic diagram of the collaborative control method for a hydrogen-electric hybrid energy storage system under wind and solar fluctuations provided in an embodiment of the present invention.

[0011] Figure 2 The flowchart of the Kepler optimization algorithm for solving upper-level scheduling optimization is provided in the embodiments of the present invention.

[0012] Figure 3 This is a flowchart of the electrolytic cell switching control provided in an embodiment of the present invention. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0014] This invention provides a collaborative control method for a hydrogen-electric hybrid energy storage system under wind and solar power fluctuations. The hydrogen-electric hybrid energy storage system includes a photovoltaic power generation module, a wind power generation module, an electrochemical energy storage battery, an electrolysis hydrogen production system, and a fuel cell power generation system. The photovoltaic power generation module, the electrochemical energy storage battery, and the fuel cell power generation system are all connected to a DC bus. The fuel cell power generation system includes n fuel cells. The wind power generation module and the electrolysis hydrogen production system are both connected to an AC bus. The electrolysis hydrogen production system includes m electrolyzers. The DC power is converted into AC power, and the AC power is connected to the power grid.

[0015] The photovoltaic power generation module is connected to the DC bus via a DC / DC converter; the electrochemical energy storage battery is connected to the DC bus via a bidirectional DC / DC converter; the fuel cell power generation system includes n fuel cells, each fuel cell being connected to the DC bus via a DC / DC converter.

[0016] The DC / AC inverter converts direct current into alternating current and connects it to the AC bus; the wind power generation module is connected to the AC bus via an AC / DC / AC converter; the electrolysis hydrogen production system includes m electrolyzers to convert excess electrical energy into hydrogen, and each electrolyzer is connected to the AC bus via an AC / DC rectifier; the AC power is connected to the power grid via a transformer.

[0017] like Figure 1 As shown, the collaborative control method for a hydrogen-electric hybrid energy storage system under wind and solar fluctuations provided by this invention includes: Based on historical load data and weather factors, we forecast variables for the future period; these variables include photovoltaic power generation, wind power generation, grid load, electricity price, and hydrogen price. With the goal of minimizing the total system operating cost, a scheduling optimization model is established and solved using the Kepler optimization algorithm under preset constraints. The optimization variables include the power purchased from the grid, the power curtailed from wind and solar power, the charging and discharging power of energy storage batteries, the power generation of fuel cells, the hydrogen production power of electrolyzers, and the amount of hydrogen sold. Electrolyzer switching control and fuel cell switching control are performed based on the scheduling optimization results.

[0018] The upper-level optimization scheduling problem of a hydrogen-electric hybrid energy storage system under wind and solar fluctuations can be written as:

[0019]

[0020]

[0021] In the formula, u To optimize variables, The power purchased from the grid is respectively Wind and solar power curtailment Energy storage battery discharge power Energy storage battery discharge power Fuel cell power generation Hydrogen production power of electrolyzer Hydrogen sales volume ; x For state variables, SOC t Let VLH be the state of charge of the energy storage battery at time t. t Let t be the volume level of the hydrogen storage tank.

[0022] In this upper-level scheduling problem, It is the objective function; The main inequality constraints are: charging power inequality constraints, discharging power inequality constraints, electrolyzer hydrogen production power inequality constraints, fuel cell power generation power inequality constraints, power purchase from the grid inequality constraints, wind and solar curtailment power inequality constraints, and SOC inequality constraints. The main equality constraints are: energy storage battery charge and discharge equality constraints, electrolyzer hydrogen production and fuel cell power generation equality constraints, system power balance equality constraints, system hydrogen mass balance equality constraints, and power purchase from the grid and curtailment of wind and solar power equality constraints.

[0023] The scheduling optimization model aims to achieve economical operation by minimizing the total operating cost. At time point k, for a given scheduling time domain... N p This goal can be expressed as

[0024] It consists of three components, including variable operating costs. J op Cost of electrochemical energy storage batteries and the cost of hydrogen energy systems .

[0025] Variable operating costs J op for:

[0026] The first two terms of the formula represent electricity costs, including the cost of purchasing electricity from the external grid and the penalty costs incurred due to wind and solar power curtailment. The third and fourth terms represent revenue generated from power generation through fuel cells and the sale of hydrogen produced by alkaline electrolyzers (AELs). This indicates the electricity price (yuan / megawatt-hour). This represents the penalty cost (RMB / MWh) incurred from wind and solar power curtailment. This represents the revenue generated from fuel cell power generation (in yuan per megawatt-hour). This represents the revenue generated from hydrogen production (yuan / kg). This indicates the amount of electricity purchased from the external power grid (in megawatts). This indicates the amount of wind and solar power that has been curtailed (in megawatts). This indicates the amount of hydrogen produced and sold by the electrolyzer (in kilograms). T s Indicates the sampling time.

[0027] Energy storage battery cost It can be represented as:

[0028] in, This indicates the capital cost of energy storage batteries (RMB / MWh). This indicates the number of cycles of the energy storage battery. Indicates the charging power of the energy storage battery. Indicates the discharge power of the energy storage battery. T s Indicates the sampling time.

[0029] Cost of hydrogen energy systems It can be represented as:

[0030] in, This indicates the capital cost of the electrolytic cell (in yuan). Indicates the lifespan of the electrolytic cell (in hours). This indicates the operating and maintenance cost of the electrolytic cell (yuan / hour). This represents the capital cost of fuel cells (in yuan). Indicates fuel cell lifespan (in hours). This indicates the operating and maintenance cost of the fuel cell (RMB / hour).

[0031] Constraints include energy storage battery constraints, electrolyzer constraints, fuel cell constraints, power balance, and hydrogen balance.

[0032] The constraints on energy storage batteries include: charging power inequality constraints, discharging power inequality constraints, energy storage battery charging and discharging equality constraints, and SOC inequality constraints.

[0033] Energy storage battery charging power Inequality constraints are expressed as:

[0034] in Indicates the minimum charging power. This indicates the maximum charging power.

[0035] Energy storage battery discharge power Inequality constraints are expressed as:

[0036] in Indicates the minimum discharge power. This represents the discharge power at time t. This indicates the maximum discharge power.

[0037] Energy storage battery charge / discharge equation constraint: Charging and discharging of an energy storage battery cannot occur simultaneously, i.e.

[0038] The state equation for the battery's state of charge (SOC) can be expressed as follows:

[0039] The SOC inequality constraint is expressed as:

[0040] The voltage of the electrolytic cell is:

[0041] in, This refers to the number of cells per cell in the electrolyzer. Let be the reversible voltage (volts) of a single cell in the electrolytic cell at time t. Let t be the overpotential (in volts) of a single cell in the electrolyzer at time t.

[0042] The reversible voltage of a single cell in the electrolytic cell is:

[0043] In the formula, The temperature of the electrolytic cell. is a coefficient.

[0044] The overpotential of a single cell in an electrolytic cell is

[0045] In the formula, Where A is the current in the electrolytic cell (amperes), and A is the electrode area. All are coefficients.

[0046] The efficiency of the electrolytic cell is:

[0047] in, The current and voltage (in volts) of a single cell in the electrolytic cell at time t. is a coefficient.

[0048] The expression for the temperature of the electrolytic cell is:

[0049] in, This refers to the number of electrolytic cells in the electrolysis system. The heat capacity of the electrolytic cell stack (megawatt-hours per degree Celsius). For ambient temperature, The thermal resistance of the electrolytic cell stack, The heat recovery from the electrolytic cell stack (megawatts).

[0050] The hydrogen production capacity of the electrolyzer is:

[0051] in, Hydrogen production capacity (kg) of the electrolyzer. The high calorific value of hydrogen (megawatt-hours per kilogram). Hydrogen production capacity (megawatts) of the electrolyzer.

[0052] The voltage of the fuel cell is:

[0053] in

[0054]

[0055]

[0056]

[0057]

[0058]

[0059]

[0060]

[0061]

[0062]

[0063] In the formula: This represents the number of individual cells in a fuel cell. Let be the open-loop circuit voltage at time t; The activation loss electromotive force at time t; Let t be the Ohmic loss electromotive force; Let t be the concentration loss electromotive force; It refers to the temperature of the fuel cell stack. It is the hydrogen pressure. It is the oxygen pressure entering the cathode. It is the saturated steam pressure. It is cathode pressure; It is the voltage drop under zero current density conditions. and c 1 is a constant; It is the conductivity of the proton exchange membrane. It is the thickness of the proton exchange membrane; It is the water capacity. , , , All are constants.

[0064] fuel cell efficiency for

[0065] In the formula: Hydrogen flow rate for fuel cells (kg / hour); It has a low calorific value, which is due to hydrogen.

[0066] Hydrogen storage tank volume level ( The state equation is:

[0067] in, The density of hydrogen gas (kilograms per cubic meter). This refers to the amount of hydrogen sold (in kilograms).

[0068] Hydrogen production power of electrolyzer The inequality constraints are:

[0069] In the formula, , These represent the maximum and minimum power of the electrolytic cell, respectively.

[0070] The fuel cell power generation inequality constraint is as follows:

[0071] The mutual exclusion equation constraint between hydrogen production by electrolyzers and power generation by fuel cells: Hydrogen production by electrolyzers and power generation by fuel cells cannot occur simultaneously, i.e.

[0072] The system power balance equation constraint is:

[0073] in, Let t be the wind turbine's generating capacity (megawatts). Let t be the photovoltaic power generation capacity (megawatts). The power purchased from the grid Let t be the load power (megawatts).

[0074] The system hydrogen mass balance equation constraint is:

[0075] In the formula, Hydrogen production capacity (kg) via electrolysis. Hydrogen flow rate (kilograms per hour) for fuel cells. Let t be the volume level of the hydrogen storage tank (cubic meters). The density of hydrogen gas (kilograms per cubic meter). This refers to the amount of hydrogen sold (in kilograms).

[0076] Power purchased by the power grid The inequality constraints are:

[0077] Wind and solar power curtailment The inequality constraints are:

[0078] The equations for power grid purchases and curtailed wind and solar power are constrained as follows:

[0079] The upper-level optimization scheduling problem is a nonlinear optimization problem, and its classic solution methods include quasi-Newton methods, conjugate gradient methods, and particle swarm optimization. This invention employs the Kepler optimization algorithm (KOA), which offers higher solution accuracy. KOA is a novel metaheuristic algorithm based on physics, inspired by Kepler's laws of planetary motion, capable of predicting the position and velocity of planets at any given time. In KOA, the position of each planet is considered a candidate solution, and the optimization process randomly updates it based on the current optimal solution (the sun). Because candidate solutions (planetary planets) exhibit different states than the sun at different times, this algorithm can more efficiently explore and utilize the search space. Figure 2 As shown, the solution steps of this algorithm are as follows: Step 1: Initialization process.

[0080] Acquire electrical and operational data of a hydrogen-electric coupled energy storage system under wind and solar power fluctuations. Provide... .

[0081] Input parameters: Set population size N, maximum number of iterations Tmax, and initial value of gravitational constant. μ 0, Attenuation coefficient γ, Period control parameters .

[0082] Initial planetary positions are generated: In this process, some planets equal to N, called the population size, will be randomly distributed in d dimensions, representing the decision variables of the upper-level optimization scheduling problem. The initial positions are given according to the following formula:

[0083] In the formula, X i Represents the first in the search space i Planets (candidate solutions); N represents the number of candidate solutions in the search space; d represents the dimension of the optimization problem; and Let represent the upper and lower bounds of the j-th decision variable; It is a number randomly generated between 0 and 1.

[0084] No. i The orbital eccentricity of each planet e i The initialization is a random number between 0 and 1.

[0085] orbital period T i Generated according to normal distribution

[0086] Step 2: Evaluate the fitness values ​​of the initial population. Determine the global optimum (X). S Let t=0, which is the sun.

[0087] The fitness value is calculated as follows: fit

[0088] In the formula, X is the optimization variable. Let be the state variables of the hydrogen-electric hybrid energy storage system at time t. The number of inequality constraints. The number of equality constraints. For the inequality constraint functions in the upper-level optimization scheduling problem; This refers to the equality constraint function in the upper-level optimization scheduling problem.

[0089] Take the best fitness value as Sun X S :

[0090] Step 3: Calculate the sun X S and various planets X i The gravitational force and Euclidean distance between them.

[0091] sun X S and any planet X i The gravitational force is given by the law of universal gravitation, which is defined as follows:

[0092] in, and express M s and M i The normalized value, M s and M i Let X and Xi represent the masses, respectively. It is a small value; μ It is the universal gravitational constant; It is the eccentricity of the planetary orbit, which is a value between 0 and 1, giving KOA a random characteristic; r 1 is a randomly generated value between 0 and 1, allowing for more variation in the gravity value during the optimization process; yes R i The normalized value represents X S andX i The Euclidean distance between them is defined as

[0093] The masses of the sun and object i at time t can be easily calculated using fitness evaluation:

[0094]

[0095] in

[0096]

[0097] in, It is a randomly generated number between 0 and 1 to represent the mass values ​​of different planets.

[0098] μ (t) is a function that controls the search precision, which decreases exponentially with time, and is defined as follows:

[0099] in, It is a constant; μ 0 is the initial value. t and These are the current iteration number and the maximum iteration number, respectively.

[0100] Step 4: Calculate the values ​​of each planet X i At a speed of [unclear], generate random numbers r1 and r.

[0101] Calculate the speed using the following formula:

[0102] in,

[0103]

[0104]

[0105]

[0106]

[0107]

[0108]

[0109]

[0110]

[0111] in, This represents the velocity of the object at point X at time t; Represents objects i ; and It is a value randomly generated in the interval [0,1]. and It is a vector containing two random values ​​between 0 and 1. and This represents a solution randomly selected from the population; and Represent X S and X i The quality; μ (t) represents the gravitational constant; It is a small value to prevent division by zero error; Indicates the best solution at time t X S With objects X i The distance between them; a i Let represent the semi-major axis of the elliptical orbit of object i at time t. The specific calculation formula is as follows:

[0112] in, T i Let represent the orbital period of object i. Assume that the semi-major axis of the elliptical orbit of object i gradually decreases with the increase of generations, where the solution moves towards the promising region where the global optimum may be found.

[0113] Indicates to X S and X i The Euclidean distance between them is normalized and defined as follows:

[0114] if If the object moves closer to the sun, it will increase its speed to prevent itself from drifting towards the sun due to its immense gravity. Otherwise, the object will slow down.

[0115] Escaping local optima. In the solar system, most celestial bodies revolve around the Sun counterclockwise and rotate on their own axes; however, some revolve clockwise. This algorithm utilizes this property to escape the constraints of local optima. The algorithm uses a flag... To simulate this behavior, the flag changes the search direction, giving the agent a good chance to accurately scan the search space.

[0116]

[0117] Generate random numbers r1 and r.

[0118] Check if r is greater than or equal to r1. If not, proceed to step 5 to update the sun. X S and various planets X i The position between; if true, proceed to step 6 to update the sun. X S and various planets X i The distance between them.

[0119] Step 5: Update the sun X S and various planets X i The positions between.

[0120] The algorithm simulates the exploration and development phases through two main stages. KOA explores objects farther from the sun to find new solutions, while simultaneously using solutions closer to the sun to more accurately find new solutions near the optimal solution. Based on the previous steps, the new position of each object farther from the sun is updated using an equation:

[0121] in, For objects i At the new position at time t+1; For objects i The speed required to reach the new location; X S ( t This is the best position of the sun discovered to date; Used as a marker to change the search direction.

[0122] Step 6: Update the sun X S and various planets X i The distance between them.

[0123] Update the distance to the sun according to the following formula:

[0124] in, h It is an adaptive factor that controls the distance between the Sun and the current planet, defined as follows:

[0125] Where r is a number randomly generated based on a normal distribution, and η It is a factor that decreases linearly from 1 to -2, and is specifically defined as follows:

[0126] in a 2 is a loop control parameter that will be adjusted throughout the optimization process. Each period is a cycle that gradually decreases from -1 to -2, as defined below:

[0127] Step 7: Adopt an elitist strategy, calculate fitness, and update the global optimization (i.e., Sun XS). This step ensures that planets and the Sun are in optimal positions by implementing an elitist strategy. This process can be represented as:

[0128] Step 8: Iteration: Check if t has reached the maximum number of iterations Tmax. If not, set t = t + 1. If it has, stop the iteration and output the current solution. X ( t This serves as the optimal solution for upper-level scheduling.

[0129] The switching includes electrolyzer switching control and fuel cell switching control.

[0130] See the electrolytic cell switching control process. Figure 3 .

[0131] Step 1: Obtain the hydrogen production power allocated to the electrolyzer. rated power of electrolytic cell The number of electrolytic cells is m.

[0132] Based on the hydrogen production power allocated to the electrolyzer To determine whether the electrolytic cell system is operating under heavy, medium, or light load, the following criteria are used: if If so, the electrolytic cell system will operate under heavy load; if Then the electrolytic cell system operates at medium load; if If so, the electrolytic cell system will operate under light load.

[0133] in, and For example, load determination coefficient =0.7, =0.4.

[0134] Step 2: Heavy-load workflow. In the heavy-load operating area, all m electrolytic cells are allocated the same power, i.e.:

[0135] Step 3: Intermediate Load Workflow. In the intermediate load work area, allocate approximately [amount missing] to the working electrolytic cell. When the power is reduced, other electrolytic cells stop working. This is the working factor for medium load, for example, 0.9.

[0136] The number of working electrolytic cells for:

[0137] In the formula, Indicates rounding down.

[0138] but The power of each electrolytic cell is:

[0139] Therefore, during medium-load operation, there are One electrolytic cell stopped working.

[0140] Step 4: Light-load workflow. In the light-load working area, allocate approximately [amount missing] to the working electrolytic cell. When the power is reduced, other electrolytic cells stop working. This is the light-load working factor, for example, 0.6.

[0141] The number of working electrolytic cells for:

[0142] but The power of each electrolytic cell is:

[0143] Therefore, during light-load operation, there is One electrolytic cell stopped working.

[0144] Step 5: Rotate the workflow. The rotation workflow will begin when the following conditions are met: 1) When switching from heavy load to medium load operation, there are One electrolytic cell stopped working; 2) When switching from heavy load to light load operation, there is One electrolytic cell stopped working; 3) When switching from medium load to light load or vice versa, there are The electrolytic cell stops or starts working; 4) When a certain electrolytic cell is operating continuously Hour, This is the continuous operation factor, for example, 5.

[0145] When rotation is required, a selection is made from an established working rotation sequence or a stopped rotation sequence. The working rotation sequence sorts the electrolytic cells that have stopped operating, and the sorting is based on the following calculation, with the larger working sequence listed first:

[0146] in This indicates the time when the i-th electrolytic cell stops operating. Indicates the first i The cumulative effective hydrogen production power of each electrolyzer; Indicates the first i The number of times an electrolytic cell is started and stopped. These are the weighting coefficients.

[0147] The stop rotation sequence is performed on the currently operating electrolytic cells, and the order is calculated according to the following formula, with the larger one listed first:

[0148] in This represents the continuous operating time of the i-th electrolytic cell. These are the weighting coefficients.

[0149] The switching control process for fuel cells is similar to that for electrolyzers. However, the lifespan of a fuel cell is shorter than that of an electrolyzer, requiring the incorporation of n control objectives to converge the lifespans of the fuel cell systems into the power allocation of the switching control process. The specific process is as follows: Step 1: Obtain the power allocated to the fuel cell Fuel cell rated power The number of fuel cells n, and the voltage of n fuel cells at rated power. The average voltage of a fuel cell system at rated power , .

[0150] Based on the power generated by the fuel cell To determine whether a fuel cell system is operating under heavy, medium, or light load, the following criteria are used: if If so, the fuel cell system will operate under heavy load; if Then the fuel cell system operates at medium load; if If so, the fuel cell system will operate under light load.

[0151] in, and For example, the fuel cell load determination coefficient. =0.7, =0.4.

[0152] Step 2: Heavy-load Workflow. In the heavy-load operating region, the power of the n fuel cells is distributed according to the following formula:

[0153] Step 3: Mid-load Workflow. In the mid-load work area, allocate approximately [amount missing] to the working fuel cell. When the power output is reduced, other fuel cells stop working. This is the load factor in the fuel cell system, for example, 0.8.

[0154] The number of working fuel cells for:

[0155] In the formula, Indicates rounding down.

[0156] but The power of each fuel cell is:

[0157] Therefore, during medium-load operation, there are One fuel cell stopped working.

[0158] Step 4: Light-load workflow. In the light-load work area, approximately [amount missing] is allocated to the working fuel cell. When the power output is reduced, other fuel cells stop working. This is the light-load working factor, for example, 0.5.

[0159] The number of working fuel cells for:

[0160] but The power of each fuel cell is:

[0161] Therefore, during light-load operation, there is One fuel cell stopped working.

[0162] Step 5: Rotate the workflow. The rotation workflow will begin when the following conditions are met: 1) When switching from heavy load to medium load operation, there are One fuel cell system stopped working; 2) When switching from heavy load to light load operation, there is One fuel cell system stopped working; 3) When switching from medium load to light load or vice versa, there are The fuel cell stops or starts working; 4) When a certain fuel cell operates continuously Hour, This is the continuous operation factor, for example, 5.

[0163] When rotation is required, a selection is made from an established working rotation sequence or a stopped rotation sequence. The working rotation sequence prioritizes fuel cells that have stopped operating, based on the following calculation: the longer-running fuel cells are listed first.

[0164] in Indicates the first i The cumulative operating power of each fuel cell; Indicates the first i The number of times a fuel cell is started and stopped. These are the weighting coefficients.

[0165] The shutdown sequence is performed on the currently operating fuel cells, and the order is calculated according to the following formula, with the larger one listed first:

[0166] in, These are the weighting coefficients.

[0167] Example 2 The present invention also relates to an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0168] The electronic device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The memory can be used to store computer programs and / or modules. The processor performs various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory.

[0169] Example 3 The present invention also relates to a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0170] Specifically, the memory may include high-speed random access memory, as well as non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital (SD) cards, flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0171] Example 4 This invention provides a computer program product or computer program that includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the steps of the method described in the above embodiments of this invention.

[0172] The technical features of the embodiments described above can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification. It should be noted that the terms "in one embodiment," "for example," and "again" in this invention are intended to illustrate the invention and are not intended to limit the invention.

[0173] The embodiments described above are merely examples of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

Claims

1. A method for coordinated control of a hydrogen-electric hybrid energy storage system under wind-solar fluctuations, wherein the hydrogen-electric hybrid energy storage system includes a photovoltaic power generation module, a wind power generation module, an energy storage battery, an electrolytic hydrogen production system, and a fuel cell power generation system; the photovoltaic power generation module, the electrochemical energy storage battery, and the fuel cell power generation system are all connected to a DC bus; the fuel cell power generation system includes n fuel cells; the wind power generation module and the electrolytic hydrogen production system are both connected to an AC bus; the electrolytic hydrogen production system includes m electrolyzers; DC power is converted into AC power, and the AC power is connected to the power grid; characterized in that… Includes the following steps: Based on historical load data and weather factors, we forecast variables for the future period; these variables include photovoltaic power generation, wind power generation, grid load, electricity price, and hydrogen price. With the goal of minimizing the total operating cost of the system, a scheduling optimization model is established and solved under preset constraints. The optimization variables include the grid power purchase, wind and solar power curtailment, energy storage battery charging and discharging power, fuel cell power generation, electrolyzer hydrogen production power, and hydrogen sales volume. Electrolyzer switching control and fuel cell switching control are performed based on the scheduling optimization results.

2. The method for coordinated control of a hydrogen-electric hybrid energy storage system under wind and solar fluctuations as described in claim 1, characterized in that, The objective function is: Among them, J op Variable operating costs include electricity purchase costs, wind and solar curtailment penalties, fuel cell power generation revenue, and hydrogen sales revenue; J b The cost of electrochemical energy storage batteries is related to the number of battery cycles and charge / discharge power; J hes The cost of a hydrogen energy system includes the capital costs of the electrolyzer and fuel cell, as well as the operating and maintenance costs.

3. The method for coordinated control of a hydrogen-electric hybrid energy storage system under wind and solar fluctuations as described in claim 1, characterized in that, The preset constraints include inequality constraints and equality constraints; Inequality constraints include: Energy storage battery charging power inequality constraint: in Indicates the minimum charging power. This represents the charging power at time t. Indicates the maximum charging power; Energy storage battery discharge power inequality constraint: in Indicates the minimum discharge power. This represents the discharge power at time t. Indicates the maximum discharge power; Electrolyzer hydrogen production power inequality constraint: in Indicates the minimum power of the electrolytic cell. This represents the hydrogen production power of the electrolyzer at time t. Indicates the maximum power of the electrolytic cell; Fuel cell power generation inequality constraints: in Indicates the minimum power of the fuel cell. This represents the power generation of the fuel cell at time t. Indicates the maximum power of the fuel cell; Power purchase power inequality constraint: in Indicates the minimum power required for grid purchase. This represents the power purchased by the power grid at time t. Indicates the maximum power purchased by the power grid; Wind and solar power curtailment power inequality constraint: in This indicates the minimum power level that can be curtailed from wind and solar power. This represents the power of wind and solar power curtailment at time t. This indicates the maximum power output that can be curtailed from wind and solar power. SOC inequality constraints: in Represents the minimum SOC. Let SOC be the value at time t. Indicates the maximum SOC; Equality constraints include: Energy storage battery charge / discharge equation constraints: Mutually exclusive equality constraints between hydrogen production via electrolyzers and power generation via fuel cells: System power balance equation constraints: In the formula, Let t be the power generation capacity of the wind turbine. Let be the photovoltaic power generation at time t. Let be the discharge power of the energy storage battery at time t. Let t be the fuel cell power generation at time t. The power purchased from the grid, Let t be the charging power of the energy storage battery. The hydrogen production capacity of the electrolyzer. For the curtailment of wind and solar power, Let be the load power at time t; System hydrogen mass balance equation constraints: In the formula, For the amount of hydrogen produced by electrolysis, For the hydrogen flow rate of fuel cells, Let t be the volume level of the hydrogen storage tank. The density of hydrogen gas, This refers to the volume of hydrogen sold. Equation constraints for grid power purchase and wind / solar power curtailment; .

4. The method for coordinated control of a hydrogen-electric hybrid energy storage system under wind and solar fluctuations as described in claim 1, characterized in that, The solution under preset constraints includes using the Kepler optimization algorithm, and the execution steps of the Kepler optimization algorithm include: Step 1: Obtain electrical and operational data of the hydrogen-electric coupled energy storage system under wind and solar fluctuations, and provide... ,in The initial charge state of the electrochemical energy storage battery. This represents the initial volume level of the hydrogen storage tank. The input parameters are: population size N and maximum number of iterations T. max Initial value of gravitational constant μ 0, Attenuation coefficient γ, Period control parameters ; Initial planetary positions generated: In the formula, X i Represents the first in the search space i Planets; N represents the number of candidate solutions in the search space; d represents the number of optimization variables; and Let represent the upper and lower bounds of the j-th decision variable; It is a number randomly generated between 0 and 1; Step 2: Calculate the fitness value for each planet. The fitness value is calculated using the following formula: fit In the formula, X is the optimization variable. Let be the state variables of the hydrogen-electric hybrid energy storage system at time t. The number of inequality constraints. The number of equality constraints. This is an inequality constraint function; This is the equality constraint function; Take the best fitness value as the sun X S Let t=0; Step 3: Calculate the sun X S and various planets X i The gravitational force and Euclidean distance between them; Step 4: Calculate the values ​​of each planet X i At a certain speed, generate random numbers r1 and r, and determine whether r is greater than or equal to r1. If not, proceed to step 5; if so, proceed to step 6. Step 5: Update the sun X S and various planets X i The position between; Step 6: Update the sun X S and various planets X i The distance between them; Step 7: Adopt an elitist strategy, retain the top k planets with the best fitness during the iteration process, calculate their fitness, and update the global optimization, i.e., the Sun. X S ; Step 8: Iteration: Determine if t has reached the maximum number of iterations Tmax. If not, set t = t + 1 and calculate the system state variables. x t Return to step 3; if satisfied, stop iterating and output the current solution. X ( t This serves as the optimal solution for upper-level optimization scheduling.

5. The method for coordinated control of a hydrogen-electric hybrid energy storage system under wind and solar fluctuations as described in claim 1, characterized in that, The electrolytic cell switching control process is as follows: Obtain the hydrogen production power allocated to the electrolyzer rated power of electrolytic cell Number of electrolytic cells (m) Based on the hydrogen production power allocated to the electrolyzer Determine whether the electrolytic cell system is operating under heavy load, medium load, or light load; if If so, the electrolytic cell system will operate under heavy load; if Then the electrolytic cell system operates at medium load; if Then the electrolytic cell system operates under light load; in, and This is the load determination coefficient; In the heavy-duty operating area, all m electrolytic cells are allocated the same power, that is: In the intermediate working area, approximately [amount missing] of the working electrolytic cells are allocated. With the power reduced, other electrolytic cells stop working. This is the medium load working factor; In the light-load working area, approximately [amount missing] is allocated to the working electrolytic cell. With the power reduced, other electrolytic cells stop working. For light load working factor; Rotate workflows.

6. The method for coordinated control of a hydrogen-electric hybrid energy storage system under wind and solar fluctuations as described in claim 5, characterized in that, The conditions for rotating work include: when switching from heavy load to medium load, there are One electrolytic cell stops working; when switching from heavy load to light load, there are One electrolytic cell stops working; when switching from medium load to light load or from light load to medium load, there are An electrolytic cell stops or starts operating; when an electrolytic cell is operating continuously... Hour, This is the continuous working factor; When rotation is required, a selection is made from an established working rotation sequence or a stopped rotation sequence. The working rotation sequence sorts the electrolytic cells that have stopped operating, and the sorting is calculated according to the following formula, with the larger one listed first: in This indicates the time when the i-th electrolytic cell stops operating. Indicates the first i The cumulative effective hydrogen production power of each electrolyzer; Indicates the first i The number of times an electrolytic cell is started and stopped. These are the weighting coefficients; The shutdown sequence then sorts the currently operating electrolytic cells according to the following formula, with the largest cells listed first: in This represents the continuous operating time of the i-th electrolytic cell. These are the weighting coefficients.

7. The method for coordinated control of a hydrogen-electric hybrid energy storage system under wind and solar fluctuations as described in claim 1, characterized in that, The fuel cell switching control process is as follows: Obtain the power generated by the fuel cell Fuel cell rated power The number of fuel cells n, and the voltage of n fuel cells at rated power. The average voltage of a fuel cell system at rated power , ; Based on the power generated by the fuel cell To determine whether the fuel cell system is operating under heavy, medium, or light load conditions; In the heavy-duty operating range, the power of n fuel cells is distributed according to the following formula: In the mid-load working area, approximately [amount missing] fuel cells are allocated for operation. When the power output is reduced, other fuel cells stop working. The load factor in the fuel cell system; In the light-load operating area, approximately [amount] is allocated to the working fuel cell. When the power output is reduced, other fuel cells stop working. For light load working factor; Complete the rotation workflow.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method as described in any one of claims 1 to 7.

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