Optimized operation method of off-grid direct-current micro-grid

Abstract

The invention discloses an optimized operation method of an off-grid direct-current micro-grid. The off-grid direct-current micro-grid comprises a photovoltaic power generation system, a battery energy storage system, a fuel cell and an electrolytic cell which are connected through a direct-current bus. The optimized operation method comprises the following steps: constructing a system mathematical model of an off-grid direct current micro-grid, and defining a power balance relationship between a direct current load and each energy component; the system mathematical model comprises mathematical models of a photovoltaic power generation system, a battery energy storage system, a fuel cell, an electrolytic cell and a direct current load; based on the system mathematical model, establishing a linear optimization model by taking minimization of hydrogen fuel consumption or maximization of electrolytic cell hydrogen energy production as a target; the constraint condition of the linear optimization model comprises a power balance relation; and solving the linear optimization model by adopting a linear programming algorithm so as to dynamically distribute power output of each energy component, and controlling the operation of the micro-grid based on a solving result. The method can solve the problems that in the prior art, an off-grid direct-current micro-grid is high in operation cost, and an optimization algorithm is complex in calculation and difficult to apply in real time.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of new energy micro-grid, and particularly relates to an optimization operation method of off-grid direct-current micro-grid. BACKGROUND

[0002] With the promotion of global energy transformation, off-grid direct-current micro-grid technology using renewable energy such as photovoltaic is increasingly widely used in remote areas and unpowered areas. The introduction of green hydrogen energy (hydrogen production through electrolytic cell, fuel cell power generation) as an energy storage and regulation means can effectively improve the power supply reliability and energy self-sufficiency rate of such micro-grid.

[0003] At present, there are various optimization operation methods of off-grid direct-current micro-grid. However, these existing methods still have the following defects: first, the economy of system operation is poor, the hydrogen fuel consumption cost is high, and the dynamic balance and collaborative optimization of hydrogen energy production and consumption cannot be effectively realized; second, the calculation of the optimization algorithm is complex, and it is difficult to realize fast real-time solution and power distribution, which cannot meet the demand of off-grid system for real-time energy management. SUMMARY

[0004] The present application provides an optimization operation method of off-grid direct-current micro-grid to solve the problems of high operation cost of off-grid direct-current micro-grid and complex calculation of optimization algorithm in the prior art.

[0005] The present application provides an optimization operation method of off-grid direct-current micro-grid, characterized in that the off-grid direct-current micro-grid comprises a photovoltaic power generation system, a battery energy storage system, a fuel cell and an electrolytic cell connected through a direct-current bus; the optimization operation method comprises: constructing a system mathematical model of the off-grid direct-current micro-grid, and defining a power balance relationship between the direct-current load and each energy component; the system mathematical model comprises mathematical models of the photovoltaic power generation system, the battery energy storage system, the fuel cell and the electrolytic cell; a linear optimization model is established based on the system mathematical model, with the goal of minimizing hydrogen fuel consumption or maximizing hydrogen energy production of the electrolytic cell; the constraint conditions of the linear optimization model include the power balance relationship; a linear programming algorithm is used to solve the linear optimization model to dynamically allocate the power output of each energy component, and the micro-grid operation is controlled based on the solution result.

[0006] Optionally, the power balance relationship is defined by the following formula:

[0007] ;

[0008] wherein, , , are the power of the photovoltaic power generation system, the battery energy storage system and the energy management system at time ; The power of the DC load; , , The efficiency of the power converter corresponding to each energy component;

[0009] Power of the energy management system Defined as:

[0010] ;

[0011] in, Indicates the power of the fuel cell, when At this time, the fuel cell is in power generation mode, outputting power to the outside; Indicates the power of the electrolytic cell, when At that time, the electrolyzer is in a state of consuming power and producing hydrogen.

[0012] Optionally, the mathematical model of a photovoltaic power generation system is represented by the following formula:

[0013] ;

[0014] in, Indicates the photovoltaic system at time power, Indicates the photovoltaic system at time voltage, Indicates the photovoltaic system at time The current; It is a nonlinear continuous function used to calculate a given current. Solar irradiance and temperature The instantaneous voltage under the condition.

[0015] Optionally, the mathematical model of the battery energy storage system is represented by the following formula:

[0016] ;

[0017] in, Indicates the battery at time The state of charge, Indicates the instantaneous energy of the battery. Indicates the rated energy.

[0018] Optionally, the mathematical model of a fuel cell is represented by the following formula:

[0019] ;

[0020] in, Indicates the fuel cell at time power, Indicates the fuel cell at time The current, Indicates the fuel cell at time voltage, The slope This is the open-circuit voltage;

[0021] The mathematical model of an electrolytic cell is represented by the following formula:

[0022] ;

[0023] in, Indicates the time of the electrolytic cell power, Indicates the time of the electrolytic cell The current, Indicates the time of the electrolytic cell voltage, The slope of the polarization curve. This is the initial potential.

[0024] Optionally, the objective function of the linear optimization model for:

[0025] ;

[0026] in, This represents the piecewise constant coefficient for hydrogen energy production or consumption. This indicates the status of relevant equipment in the energy management system at any given time. The current;

[0027] Calculated using the following formula:

[0028] ;

[0029] in, , For efficiency coefficient, , These represent the number of battery cells in the fuel cell and the electrolyzer, respectively. The molar mass of hydrogen gas is... is Faraday's constant.

[0030] Optionally, system operating constraints include photovoltaic power generation limits, which are defined by the following formula:

[0031] ;

[0032] in, Indicates the photovoltaic power generation system at time The power output, MPP represents the maximum power generated by the photovoltaic system. Pmax, MPP represents the allowed tolerance related to the MPP, and respectively represent the minimum power and the maximum power of the photovoltaic power generation system at time .

[0033] Optionally, the system operation constraints include the constraints of the battery energy storage system, which are defined by the following formula:

[0034] ;

[0035] wherein, Soc represents the state of charge of the battery at time , and respectively represent the maximum value and the minimum value of the state of charge of the battery; I represents the current of the battery at time , I max represents the maximum charge-discharge current.

[0036] Optionally, the system operation constraints include the constraints of the electrolytic cell, which are defined by the following formula:

[0037] ;

[0038] wherein, P represents the power of the electrolytic cell at time , and respectively represent the maximum power limit value and the minimum power limit value of the electrolytic cell at time .

[0039] Optionally, the system operation constraints include the hydrogen tank capacity constraint, which is defined by the following formula:

[0040] ;

[0041] wherein, m represents the mass of hydrogen in the hydrogen tank, V max represents the maximum capacity of the hydrogen tank.

[0042] The optimization operation method of the off-grid direct-current micro-grid provided by the present application, by constructing a mathematical model of the system and establishing a linear optimization model with the target of minimizing the hydrogen fuel consumption or maximizing the hydrogen energy production of the electrolytic cell, using a linear programming algorithm with high calculation efficiency for fast solution, so as to realize the dynamic optimization and distribution of the power output of the photovoltaic, energy storage, fuel cell and electrolytic cell and other energy components, reduce the system operation cost, improve the real-time performance of the optimization decision, and enhance the adaptability of the system under different light conditions. BRIEF DESCRIPTION OF DRAWINGS

[0044] In order to make the technical solution in the embodiments of the present application clearer, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and all other drawings obtained by those skilled in the art without any creative effort should be within the protection scope of the present application.

[0045] Figure 1 is a structural schematic diagram of an off-grid direct-current micro-grid provided by an embodiment of the present application;

[0046] Figure 2 is a flow chart of an optimized operation method of an off-grid direct-current micro-grid provided by an embodiment of the present application;

[0047] Figure 3 is a power characteristic diagram of a PV, a BESS, a PEM system and a DC load without optimization;

[0048] Figure 4 is a preliminary result diagram of hydrogen consumption cost;

[0049] Figure 5 is a preliminary result diagram of energy management optimization of a BESS and a PEM system;

[0050] Figure 6 is a power characteristic diagram of an optimized PV, BESS, PEM system and DC load in a sunny day scenario;

[0051] Figure 7 is a power characteristic diagram of an optimized PV, BESS, PEM system and DC load in an overcast day scenario. DETAILED DESCRIPTION

[0053] In order to make those skilled in the art better understand the present application, the technical solution in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without any creative effort should be within the protection scope of the present application.

[0054] It should be noted that the terms "first", "second" and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion.

[0055] To solve the prior art problems, the embodiment of the present application provides an optimal operation method of an off-grid direct-current micro-grid. Figure 1 is a structural schematic diagram of an off-grid direct-current micro-grid provided by the embodiment of the present application, as shown in Figure 1 The off-grid direct-current micro-grid includes a photovoltaic power generation system 10, a battery energy storage system 20, a fuel cell 30, an electrolytic tank 40 and a hydrogen storage system 50 (Hydrogen energy Storage System, HSS) connected through a direct-current bus 100.

[0056] The photovoltaic power generation system 10 includes a photovoltaic cell panel 101 and a first DC / DC converter 102, and the battery energy storage system 20 includes an energy storage battery 201 and a second DC / DC converter 202.

[0057] The working process of the off-grid direct-current micro-grid is as follows:

[0058] When the photovoltaic output is sufficient, the photovoltaic cell panel 101 delivers a large amount of electric energy to the direct-current bus 100 through the first DC / DC converter 102, and a part of the electric energy is directly supplied to the direct-current load 60; the excess electric energy is stored in the energy storage battery 201, and part of the electric energy is delivered to the electrolytic tank 40, the electrolytic tank 40 decomposes water to generate green hydrogen and store it in the hydrogen storage system 50; if the energy storage battery 201 has remaining electric quantity, it is discharged to the direct-current bus 100 through the corresponding DC / DC converter to meet the basic needs of the direct-current load 60 and the electrolytic tank 40; if the battery electric quantity is insufficient, the hydrogen storage system 50 supplies hydrogen to the fuel cell 30, and the fuel cell 30 converts chemical energy into electric energy to access the direct-current bus 100, to supplement the energy gap of the direct-current load 60 and the electrolytic tank 40.

[0059] Figure 2 is a flowchart of an optimal operation method of an off-grid direct-current micro-grid provided by the embodiment of the present application. The optimal operation method of the embodiment of the present application can be executed by an energy management unit of the off-grid direct-current micro-grid, which can be realized in the form of software and / or hardware, and can be integrated on the control system of the off-grid direct-current micro-grid. As shown in Figure 2 The optimal operation method includes:

[0060] S101, constructing a system mathematical model of the off-grid direct-current micro-grid, and defining a power balance relationship between the direct-current load and each energy component.

[0061] Specifically, constructing the system mathematical model refers to establishing mathematical relationship of input, output or state variable for photovoltaic (PV), battery energy storage system (BESS), fuel cell (FC), electrolyzer (Elz) and DC load respectively. That is, the system mathematical model includes mathematical models of PV, BESS, FC, Elz and DC load. These mathematical models aim to describe the operating characteristics of each energy component, for example, the functional relationship between the output power of the photovoltaic power generation system and its external environmental parameters (such as light intensity, temperature) and internal electrical parameters (such as current, voltage); the integral relationship between the state of charge (SOC) of the battery energy storage system and its charging and discharging power and time; the relationship between the port power of the fuel cell and the electrolyzer and its internal electrochemical parameters (such as current, voltage, polarization curve). These mathematical models together constitute a joint equation set describing the dynamic behavior of the entire microgrid system. Among them, the energy component refers to the unit involved in energy conversion and storage in the system, such as PV, BESS, FC and Elz.

[0062] Defining the power balance relationship is based on the law of conservation of energy, and establishes that at the common coupling point of the DC bus, all the injected power and all the absorbed power must be in instantaneous balance. Specifically, at any time, the sum of the power generated by the photovoltaic power generation system, the power released by the battery energy storage system (or the negative power absorbed), and the power supplied by the energy management system (including the power generated by the fuel cell and the power consumed by the electrolyzer) must be equal to the sum of the power consumed by the DC load and the sum of various losses in the system. This power balance relationship is the fundamental physical constraint for stable operation of the system, which couples the mathematical models of the aforementioned individual energy components into a complete system model.

[0063] This step abstracts the actual physical system into a mathematical system that can be used for quantitative analysis and optimization calculation by establishing mathematical models for each physical entity and defining the power balance relationship between them, laying a theoretical foundation for subsequent establishment and solution of the linear optimization model.

[0064] S102, based on the system mathematical model, a linear optimization model is established with the goal of minimizing hydrogen fuel consumption or maximizing hydrogen energy production of the electrolyzer.

[0065] Specifically, the constraint conditions of the linear optimization model include the power balance relationship;

[0066] Minimize hydrogen fuel consumption: This objective aims to reduce the operation cost of the system. Its essence is to minimize the amount of hydrogen consumed from the hydrogen tank by the fuel cell to meet the load demand through an optimization algorithm.

[0067] Maximize hydrogen energy production by electrolyzer: This objective aims to improve the energy conversion and storage capacity of the system. Its essence is to drive the algorithm to use as much as possible of the surplus renewable energy power such as photovoltaic to produce hydrogen by electrolysis, increasing hydrogen energy storage.

[0068] Based on the established system mathematical model, the variables related to the optimization objective (such as the power of the fuel cell, the power of the electrolyzer) are extracted as decision variables of the optimization model.

[0069] Subsequently, the economic objective of minimizing hydrogen fuel consumption or maximizing hydrogen energy production by electrolyzer is mathematically expressed as a linear combination (i.e. linear function) of these decision variables, forming the objective function of the optimization model.

[0070] At the same time, the power balance relationship defined in step S101, as well as the inherent operation limits of each energy component (such as photovoltaic power limit, battery charge and discharge state, and hydrogen tank capacity, etc.), are expressed as constraint conditions of the objective function.

[0071] By formulating the complex system operation optimization problem into a standard mathematical form with a linear objective function and linear constraint conditions, it lays the foundation for the next step of using a computationally efficient and stable linear programming algorithm for fast solving, thereby realizing the key conversion from physical system description to executable optimization strategy.

[0072] S103, use linear programming algorithm to solve the linear optimization model to dynamically allocate the power output of each energy component, and control the microgrid operation based on the solving result.

[0073] Specifically, this step uses a linear programming algorithm to mathematically solve the linear optimization model established in the previous step. Linear programming algorithm is a class of mathematical optimization methods with high computational efficiency and good convergence, especially suitable for solving problems with linear objective function and linear constraint conditions. The algorithm takes real-time or predicted system data (such as current light intensity, load power, battery state of charge, etc.) as input during operation, and through efficient mathematical iteration, it quickly calculates the power command values that each energy component should undertake in the future decision period, so that the objective function (minimize hydrogen consumption or maximize hydrogen production) is optimal.

[0074] The dynamic allocation of the power output of each energy component refers to the optimal solution obtained by solving the linear programming algorithm, which is converted into the specific power set points of the photovoltaic power generation system, the battery energy storage system, the fuel cell and the electrolytic cell. These set points constitute a coordinated power allocation scheme, which realizes the goal of economic operation of the system under the premise of meeting all operating constraints.

[0075] Finally, the control of the microgrid operation based on the solving result refers to that the energy management unit issues the optimal power set points obtained by the above calculation to the corresponding power electronic converters and controllers. These local controllers will drive the actual physical devices to execute instructions, for example, adjusting the operating point of the photovoltaic array, controlling the charge and discharge power of the battery, starting and stopping the fuel cell or adjusting the input current of the electrolytic cell, so as to convert the mathematical optimal solution into control actions, and finally realize the closed-loop optimization control of the microgrid operation.

[0076] The optimization operation method of the off-grid DC microgrid provided by the present application, by constructing the mathematical model of the system and establishing a linear optimization model with the goal of minimizing hydrogen fuel consumption or maximizing hydrogen energy production of the electrolytic cell, the linear programming algorithm with high calculation efficiency is used for fast solving, so as to realize the dynamic optimization and allocation of the power output of the photovoltaic, energy storage, fuel cell and electrolytic cell, reduce the system operation cost, improve the real-time performance of the optimization decision, and enhance the adaptability of the system under different light conditions.

[0077] The output power of the photovoltaic power generation system is determined by the solar irradiance R and the temperature T, and thus the mathematical model of the photovoltaic power generation system is represented by the following formula:

[0078] ;

[0079] wherein, represents the power of the photovoltaic system at time , represents the voltage of the photovoltaic system at time , represents the current of the photovoltaic system at time ; is a nonlinear continuous function for calculating the instantaneous voltage under the given current , solar irradiance and temperature .

[0080] The mathematical model of the battery energy storage system is represented by the following formula:

[0081] ;

[0082] wherein, represents the state of charge of the battery at time , represents the instantaneous energy of the battery, represents the rated energy.

[0083] The fuel cell converts the generated hydrogen into electrical energy through an electrochemical reaction, which can provide the hydrogen production required energy for the electrolyzer and meet the power supply demand of the direct current load. In the present application, the operating characteristics of the fuel cell are represented as a dynamic load dependent on the voltage source, which is the direct current bus jointly powered by the photovoltaic power generation system and the battery energy storage system. Based on the principle of electrochemical kinetics, the working characteristics of the fuel cell can be characterized by the following mathematical model:

[0084] ;

[0085] wherein, represents the power of the fuel cell at time , represents the current of the fuel cell at time , represents the voltage of the fuel cell at time , is the slope, is the open circuit voltage; obtained by linear approximation of the polarization curve. The power flux of the fuel cell is positive (consumes hydrogen to generate electricity) when it is working.

[0086] The electrolyzer drives the water electrolysis process by consuming the energy provided by the power generation system to decompose water molecules into hydrogen and oxygen. Its operating performance is significantly affected by the working temperature parameter, which directly determines the variation characteristics of the hydrogen production rate. Therefore, in order to maintain stable and continuous hydrogen production, it is necessary to ensure that the operating voltage of the electrolyzer remains constant, i.e. the initial potential. It is this characteristic that makes it necessary to use an energy management system with time-dependent constraints. Based on the polarization curve characteristics of the electrolyzer, the mathematical model of the electrolyzer is represented by the following formula:

[0087] ;

[0088] wherein, represents the power of the electrolyzer at time , represents the current of the electrolyzer at time , represents the voltage of the electrolyzer at time , is the slope of the polarization curve, is the initial potential.

[0089] The power balance relationship is defined by the following formula:

[0090] ;

[0091] wherein, , , These are the photovoltaic power generation system, battery energy storage system, and energy management system at different times. Power; The power of the DC load; , , The efficiency of the power converter corresponding to each energy component;

[0092] Power of the energy management system Defined as:

[0093] ;

[0094] in, Indicates the power of the fuel cell, when At this time, the fuel cell is in power generation mode, outputting power to the outside; Indicates the power of the electrolytic cell, when At that time, the electrolyzer is in a state of consuming power and producing hydrogen.

[0095] To minimize hydrogen fuel consumption or maximize hydrogen production from the electrolyzer, a linear optimization model is established, with system operating constraints such as photovoltaic power generation limits, battery charge / discharge states, and hydrogen tank capacity. In this invention, considering the ability to measure component power in real-time, a static power management strategy is formulated to minimize operating costs, i.e., minimizing hydrogen fuel consumption and maximizing electrolyzer utilization. Therefore, appropriate energy exchange can be selected among available energy sources, thereby optimizing power distribution among different components of the microgrid. The objective function of the linear optimization model is... for:

[0096] ;

[0097] in, This represents the piecewise constant coefficient for hydrogen energy production or consumption. This indicates the status of relevant equipment in the energy management system at any given time. The current;

[0098] Calculated using the following formula:

[0099] ;

[0100] in, , For efficiency coefficient, , These represent the number of battery cells in the fuel cell and the electrolyzer, respectively. The molar mass of hydrogen gas is... is Faraday's constant.

[0101] System operating constraints include photovoltaic power generation limits, which are defined by the following formula:

[0102] ;

[0103] in, Indicates the photovoltaic power generation system at time The power output, MPP represents the maximum power generated by the photovoltaic system. Indicates the allowable tolerances associated with MPP. and These represent the time intervals of the photovoltaic power generation system. The minimum and maximum power.

[0104] System operating constraints include those for the battery energy storage system, which are defined by the following formula:

[0105] ;

[0106] in, Indicates the battery at time The state of charge, and These represent the maximum and minimum values ​​of the battery's state of charge, respectively. For the battery at all times The current, This is the maximum charging and discharging current.

[0107] System operating constraints include constraints on the electrolytic cell, which are defined by the following formula:

[0108] ;

[0109] in, Indicates the time of the electrolytic cell power, and These represent the times of the electrolytic cell. Maximum power limit and minimum power limit.

[0110] System operating constraints include hydrogen tank capacity constraints, which are defined by the following formula:

[0111] ;

[0112] in, The mass of hydrogen in the hydrogen tank. This is the maximum capacity of the hydrogen tank.

[0113] The linear programming algorithm is used to solve the problem, and the power output of each energy component is dynamically allocated. The microgrid operation is simulated by PLECS software to verify the effectiveness of the linear optimization model and analyze the performance under different scenarios.

[0114] In the present invention, the DC microgrid is simulated by PLECS software, including PV energy array, BESS, FC, Elz, DC load (typical daily load curve), power converter and control module, to obtain the required value after power distribution optimization. First, the parameter initialization is carried out, and the device parameters of the system are set as shown in Table 1. Considering the continuous generation of hydrogen by the electrolytic cell, when the system needs energy to meet the demand of the DC load, the fuel cell absorbs hydrogen, thereby minimizing the consumption of hydrogen.

[0115] Table 1 Parameters of DC microgrid energy

[0116] Device name Parameter value Quantity Power Panasonic photovoltaic panel 380W 24 9000W BESS 150 Ah 1 7200W Fuel cell 200W 40 8000W Electrolyser 2400W 4 8000W

[0117] The initial simulation results of the unoptimized system model are shown in Figure 3 , and the optimized parameters in the constraints are obtained: the range of BESS is , ; the power converter efficiency is , ; the polarization curve parameters of fuel cell and electrolyzer are , , , .

[0118] On this basis, the energy linprog toolbox of energy Matlab is used to solve the linear optimization problem, and the objective function is to minimize , and the constraint condition is the defined power balance equation and component operation limit. The solving time is about 0.075 s, and 387 iterations are needed to obtain the optimal solution. Based on the initial simulation results, Figure 4 , the hydrogen energy consumption cost of the microgrid system is calculated. The cost depends on the load demand and the operation cost of the related energy system, including photovoltaic power generation system, battery energy storage system and fuel cell, etc. The preliminary results of BESS and energy management (PEM) system energy management optimization problem are shown in Figure 5 , respectively, and the behavior of BESS and PEM system energy management optimization problem is visualized by introducing variable price of hydrogen.

[0119] To verify the energy efficiency of the linear optimization model, two scenarios are considered: Scenario 1 is a sunny day (cloudy), in which the photovoltaic system maximizes power generation, and the simulation results are as follows. Figure 6 As shown; Scenario 2 is a cloudy day, under which photovoltaic power generation fluctuates greatly, and the simulation results are as follows. Figure 7 As shown, the optimized results differ significantly from the initial simulation results. These results indicate that the power flow between the battery storage system and the PEM system exhibits a high correlation when the photovoltaic system is unavailable. This interaction is attributed to the establishment of a linear optimization model. The optimization algorithm identifies time intervals with lower hydrogen consumption costs, thereby minimizing system operating costs while meeting DC load demands. By dynamically optimizing the power allocation of the photovoltaic power generation system, battery storage system, fuel cell, and electrolyzer using a linear programming algorithm, this invention reduces hydrogen fuel consumption costs by 40.68% and 40.45% in sunny and cloudy scenarios, respectively. After optimization in scenario 1, hydrogen consumption decreased from 28.478 USD / kg to 16.877 USD / kg; after optimization in scenario 2, hydrogen consumption decreased from 41.203 USD / kg to 24.530 USD / kg. The linear optimization model significantly reduced operating costs. This embodiment verifies through PLECS simulation that the optimized model can effectively coordinate the power interaction between the BESS and PEM systems in both sunny (maximum photovoltaic power generation) and cloudy (large photovoltaic fluctuations) scenarios, significantly reducing dependence on fossil fuel backup power and improving the energy self-sufficiency of off-grid systems.

[0120] Based on direct DC bus connection to the PEM system (including FC and Elz), multi-stage power conversion losses are reduced (conversion efficiency reaches 91%). At the same time, through real-time power balance constraints and dynamic SOC management, the stable operation of the microgrid under renewable energy fluctuations is ensured.

[0121] This invention combines linear programming algorithms with green hydrogen production, proposing an optimization framework aimed at minimizing hydrogen consumption or maximizing electrolyzer utilization. The optimization model is compatible with PV arrays, energy storage devices, and hydrogen tank capacities of varying sizes, making it suitable for off-grid scenarios such as remote areas and islands. By optimizing the coordinated operation of renewable energy hydrogen production and energy storage, carbon emissions are reduced, promoting the implementation of zero-carbon energy systems and aligning with global carbon neutrality goals.

[0122] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description.

Claims

1. An optimized operation method for an off-grid DC microgrid, characterized in that, The off-grid DC microgrid includes a photovoltaic power generation system, a battery energy storage system, a fuel cell, and an electrolyzer connected via a DC bus. The optimized operation method includes: A system mathematical model for an off-grid DC microgrid is constructed, and the power balance relationship between the DC load and each energy component is defined; the system mathematical model includes mathematical models of the photovoltaic power generation system, the battery energy storage system, the fuel cell, and the electrolyzer; Based on the system mathematical model, a linear optimization model is established with the goal of minimizing hydrogen fuel consumption or maximizing hydrogen production from the electrolyzer; the constraints of the linear optimization model include the power balance relationship. The linear optimization model is solved using a linear programming algorithm to dynamically allocate the power output of each energy component and control the operation of the microgrid based on the solution results.

2. The optimized operation method for off-grid DC microgrids according to claim 1, characterized in that, The power balance relationship is defined by the following formula: ； in, , , These are the photovoltaic power generation system, battery energy storage system, and energy management system at different times. Power; The power of the DC load; , , The efficiency of the power converter corresponding to each energy component; The power of the energy management system Defined as: ； in, Indicates the power of the fuel cell, when At this time, the fuel cell is in power generation mode, outputting power to the outside; Indicates the power of the electrolytic cell, when At that time, the electrolyzer is in a state of consuming power and producing hydrogen.

3. The optimized operation method for off-grid DC microgrids according to claim 1, characterized in that, The mathematical model of the photovoltaic power generation system is represented by the following formula: ； in, Indicates the photovoltaic system at time power, Indicates the photovoltaic system at time voltage, Indicates the photovoltaic system at time The current; It is a nonlinear continuous function used to calculate a given current. Solar irradiance and temperature The instantaneous voltage under the condition.

4. The optimized operation method for off-grid DC microgrids according to claim 1, characterized in that, The mathematical model of the battery energy storage system is expressed by the following formula: ； in, Indicates the battery at time The state of charge, Indicates the instantaneous energy of the battery. Indicates the rated energy.

5. The optimized operation method for off-grid DC microgrids according to claim 1, characterized in that, The mathematical model of the fuel cell is represented by the following formula: ； in, Indicates the fuel cell at time power, Indicates the fuel cell at time The current, Indicates the fuel cell at time voltage, The slope This is the open-circuit voltage; The mathematical model of the electrolytic cell is represented by the following formula: ； in, Indicates the time of the electrolytic cell power, Indicates the time of the electrolytic cell The current, Indicates the time of the electrolytic cell voltage, The slope of the polarization curve. This is the initial potential.

6. The optimized operation method for off-grid DC microgrids according to claim 1, characterized in that, The objective function of the linear optimization model for: ； in, This represents the piecewise constant coefficient for hydrogen energy production or consumption. This indicates the status of relevant equipment in the energy management system at any given time. The current; The Calculated using the following formula: ； in, , For efficiency coefficient, , These represent the number of battery cells in the fuel cell and the electrolyzer, respectively. The molar mass of hydrogen gas is... is Faraday's constant.

7. The optimized operation method for off-grid DC microgrids according to claim 1, characterized in that, The system operating constraints include photovoltaic power generation limits, which are defined by the following formula: ； in, Indicates the photovoltaic power generation system at time The power output, MPP represents the maximum power generated by the photovoltaic system. Indicates the allowable tolerances associated with MPP. and These represent the time intervals of the photovoltaic power generation system. The minimum and maximum power.

8. The optimized operation method for off-grid DC microgrids according to claim 1, characterized in that, The system operating constraints include constraints on the battery energy storage system, which are defined by the following formula: ； in, Indicates the battery at time The state of charge, and These represent the maximum and minimum values ​​of the battery's state of charge, respectively. For the battery at all times The current, This is the maximum charging and discharging current.

9. The optimized operation method for off-grid DC microgrids according to claim 1, characterized in that, The system operating constraints include constraints on the electrolytic cell, which are defined by the following formula: ； in, Indicates the time of the electrolytic cell power, and These represent the times of the electrolytic cell. Maximum power limit and minimum power limit.

10. The optimized operation method for off-grid DC microgrids according to claim 1, characterized in that, The system operating constraints include hydrogen tank capacity constraints, which are defined by the following formula: ； in, The mass of hydrogen in the hydrogen tank. This is the maximum capacity of the hydrogen tank.

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