Simulation research and optimal matching analysis method and device for multi-energy system
By using simulation research and optimization matching analysis methods in multi-energy systems, the problem of inefficient energy utilization in traditional energy systems is solved, and the reliability and stability of the system are improved, as well as the optimization of energy utilization efficiency and economy are achieved.
Patent Information
- Application Number
- CN202411642954.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-18
- Publication Date
- 2025-03-11
AI Technical Summary
In traditional energy systems that combine electricity with new energy, energy utilization efficiency is inefficient, mainly due to the coordination and optimization problems between power and hydrogen energy production, storage and consumption, resulting in the failure of energy conversion, distribution and storage processes to accurately match energy supply and demand.
It provides a simulation research and optimization matching analysis method for multi-energy systems. By establishing a system simulation model based on Simulink, using Monte Carlo method to perform random simulation, reliability evaluation and optimization matching analysis, we obtain relevant operation and optimization results of the wind and light hydrogen system.
The reliability and stability of the system are improved, and the adaptability and efficiency of the system are improved through sensitivity analysis and system configuration suggestions, and the improvement of energy utilization efficiency and economic optimization are achieved.
Smart Images
Figure CN119598845A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of new energy technology, and more specifically, to a method and device for simulation research and optimal matching analysis of a multi-energy system. Background Art
[0002] In the energy system that combines traditional electricity and new energy, a major technical problem is the low energy utilization efficiency. This problem stems from the coordination and optimization difficulties between electricity and hydrogen production, storage and consumption. The energy conversion, distribution and storage processes in these systems often fail to accurately match energy supply and demand, resulting in losses in renewable energy power generation, insufficient utilization of energy storage equipment, and inconsistencies between new energy production and demand.
[0003] In the prior art, energy allocation is usually carried out based on experience, which is not accurate enough.
[0004] Therefore, there is an urgent need in the art for a method for optimizing matching of energy systems with multiple power generation modes. Summary of the invention
[0005] In order to solve the problem of low energy efficiency in energy systems that combine traditional electricity and new energy, the present application provides a simulation study and optimization matching analysis method and device for a multi-energy system.
[0006] The embodiment of the present application is implemented as follows:
[0007] In a first aspect, the present application provides a simulation research and optimization matching analysis method for a multi-energy system, including:
[0008] A system simulation model was established based on Simlink, and the performance and sensitivity comparison of the wind-solar-hydrogen system were carried out according to the monthly average meteorological data to obtain the influence of meteorological factors.
[0009] Monte Carlo method is used for random simulation. The operating status of wind and photovoltaic power generation systems is analyzed by system level, weather conditions and equipment failures. At the same time, hourly meteorological data is used to obtain hourly operating data of wind, solar and hydrogen energy systems.
[0010] Conduct reliability evaluation and obtain the influence characteristics of each factor on reliability and the system reliability level by changing the equipment capacity and load size;
[0011] Conduct optimization matching analysis to obtain relevant operation and optimization results of wind-solar-hydrogen systems.
[0012] In a possible implementation, establishing a system simulation model based on simlink further includes:
[0013] The mathematical model of wind turbine is established according to Betz's law, and the wind energy utilization coefficient model and wind turbine model are established based on Simulink. The wind and photovoltaic power generation systems as well as the electrolyzer and hydrogen storage tank models are packaged and connected to construct a Simulink simulation model of the wind-solar-hydrogen energy system.
[0014] In a possible implementation, the Monte Carlo method is used to perform random simulation to analyze the operating status of the wind and photovoltaic power generation systems using system levels, weather conditions and equipment failures, and hourly meteorological data is used to obtain hourly operating data of the wind-solar-hydrogen energy system, further comprising:
[0015] By using the discrete hourly wind speed data, the sequential Monte Carlo method and Markov chain calculation method are used to obtain the transition rate between the normal, derated and shutdown states of the wind turbine.
[0016] In a possible implementation, the reliability evaluation is performed, and by changing the equipment capacity and load size, the influence characteristics of each factor on the reliability and the system reliability level are obtained, further comprising:
[0017] Based on the established stochastic model of the wind-solar-hydrogen energy system, the total wind-solar capacity, photovoltaic ratio, capacity of each device and load size are changed on the basis of the commonly used configuration to evaluate the system reliability.
[0018] In a possible implementation, the optimization matching analysis is performed to obtain the operation and optimization results related to the wind-solar-hydrogen system, further including:
[0019] HOMER optimization simulation provides the correct optimization direction for the optimization calculation of SA-PSO, which can accelerate the convergence speed of the SA-PSO algorithm. The HOMER+SA-PSO algorithm is used to optimize the microgrid system to determine the optimal number of micro-sources, and finally achieve the optimization goal based on HOMER with the total net current cost of the entire life cycle.
[0020] In one possible implementation, the lowest total net present cost of the optimal configuration scheme simulated by HOMER can determine the constraint range of the economic conditions in the SA-PSO optimization process and obtain an optimal matching result that is both economical and reliable.
[0021] In a possible implementation, the system simulation model is established based on simlink, and the performance analysis and sensitivity comparison of the wind-solar hydrogen system are performed according to the monthly average meteorological data to obtain the influence of meteorological factors, which further includes:
[0022] Wind power generation system modeling, establish a mathematical model of the wind turbine based on Betz's law, and use Simulink to establish a wind energy utilization coefficient model and a wind turbine model. Use the variable step-size disturbance observation method to ensure that the system output is maintained at the maximum power point.
[0023] Photovoltaic power generation system modeling, based on the photovoltaic effect to establish a photovoltaic cell model, through the change of ambient temperature to adjust the parameters; photovoltaic array is affected by external factors, the maximum power tracking control model similar to the wind turbine is established, the difference is that the disturbance parameters are the output voltage and current of the photovoltaic array;
[0024] The wind power and photovoltaic power generation systems and hydrogen storage tank models are packaged and connected to construct a Simulink simulation model of the wind-solar-hydrogen energy system.
[0025] In a second aspect, the present application provides a simulation research and optimization matching analysis device for a multi-energy system, comprising:
[0026] The modeling module is used to establish a system simulation model based on Simlink, conduct performance analysis and sensitivity comparison of the wind-solar-hydrogen system based on the monthly average meteorological data, and obtain the influence of meteorological factors;
[0027] The simulation module is used to perform random simulation using the Monte Carlo method to analyze the operating status of wind and photovoltaic power generation systems using system levels, weather conditions and equipment failures, and to obtain hourly operating data of wind, solar and hydrogen energy systems using hourly meteorological data;
[0028] Evaluation module, used to evaluate reliability and obtain the influence characteristics of various factors on reliability and the system reliability level by changing the equipment capacity and load size;
[0029] The analysis module is used to perform optimization matching analysis and obtain relevant operation and optimization results of the wind-solar-hydrogen system.
[0030] The technical solution provided by this application can at least achieve the following beneficial effects:
[0031] The simulation research and optimal matching analysis method and device of the multi-energy system provided in this application make comprehensive use of multiple tools and data sources, such as automatic weather stations, HOMER software, NASA online data, and meteorological websites, and make full use of modern scientific and technological means for implementation and analysis. Through simulation and other methods, the influence of different external factors on the system is explored, providing a basis for feasibility study of system design and operation, and improving the reliability and stability of the system.
[0032] The simulation research and optimization matching analysis method and device of the multi-energy system provided in this application also put forward sensitivity analysis and system configuration suggestions. Through sensitivity analysis, the influence of different meteorological factors on the power generation rate is determined, which provides a scientific basis and suggestions for system configuration and improves the adaptability and efficiency of the system.
[0033] The simulation research and optimization matching analysis method and device of the multi-energy system provided in this application adopt system optimization matching and cost-benefit analysis, use the HOMER algorithm to perform system optimization matching analysis, and determine the optimal micro-source quantity configuration by enumerating different configuration combinations and using intelligent algorithms to perform optimization matching analysis, thereby improving the energy utilization efficiency and economy of the system and providing effective guidance for the operation and management of the system.
[0034] The simulation research and optimization matching analysis method and device of the multi-energy system provided in this application use the sequential Monte Carlo method to compare and analyze conventional units and equivalent systems based on wind power generation, which helps to evaluate the performance of the system and provides more accurate reference indicators for the design and operation of actual microgrids. BRIEF DESCRIPTION OF THE DRAWINGS
[0035] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.
[0036] Figure 1 It is a flowchart of a simulation study and an optimization matching analysis method of a multi-energy system shown in an exemplary embodiment of the present application;
[0037] Figure 2 It is a schematic diagram of building a simulation model of a wind-solar-hydrogen energy system shown in an exemplary embodiment of the present application;
[0038] Figure 3 is a schematic diagram of power generation under different system configurations shown in an exemplary embodiment of the present application;
[0039] Figure 4 is a schematic diagram of hydrogen production under different system configurations shown in an exemplary embodiment of the present application;
[0040] Figure 5 It is a schematic diagram of a random simulation process of a wind-solar-hydrogen energy system shown in an exemplary embodiment of the present application;
[0041] Figure 6 It is a schematic diagram of a reliability evaluation system of a wind-solar-hydrogen energy system shown in an exemplary embodiment of the present application;
[0042] Figure 7 It is a reliability simulation table of a basic configuration system of a wind-solar-hydrogen energy system shown in an exemplary embodiment of the present application;
[0043] Figure 8 It is a structural schematic diagram of a simulation research and optimization matching analysis device for a multi-energy system shown in an exemplary embodiment of the present application.
[0044] Reference numerals:
[0045] 1. Modeling module; 2. Simulation module; 3. Evaluation module; 4. Analysis module. DETAILED DESCRIPTION
[0046] In order to make the purpose, implementation mode and advantages of the present application clearer, the exemplary implementation mode of the present application will be clearly and completely described below in conjunction with the drawings in the exemplary embodiments of the present application. Obviously, the described exemplary embodiments are only part of the embodiments of the present application, not all of the embodiments. It should be understood that the specific embodiments described here are only used to explain the present application and are not used to limit the present application.
[0047] It should be noted that the brief description of terms in this application is only for the convenience of understanding the embodiments described below, and is not intended to limit the embodiments of this application. Unless otherwise specified, these terms should be understood according to their common and usual meanings.
[0048] The terms "first", "second", "third", etc. in the specification and claims of this application and the above drawings are used to distinguish similar or similar objects or entities, and do not necessarily mean to limit a specific order or sequence, unless otherwise noted. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances.
[0049] The terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover but not exclude inclusion, for example, a product or device comprising a list of components is not necessarily limited to all the components expressly listed but may include other components not expressly listed or inherent to such product or device.
[0050] Before explaining the simulation research and optimization matching analysis method of the multi-energy system provided in the embodiment of the present application, the application scenario and implementation environment of the embodiment of the present application are first introduced.
[0051] In the energy system combining traditional electricity and new energy, a major technical problem is the low efficiency of energy utilization. This problem stems from the coordination and optimization problems between electricity and hydrogen production, storage and consumption. The energy conversion, distribution and storage processes in these systems often fail to accurately match energy supply and demand, resulting in the loss of renewable energy generation, the underutilization of energy storage equipment, and the inconsistency between new energy production and demand.
[0052] In the prior art, energy allocation is usually carried out based on experience, which is not accurate enough.
[0053] Therefore, there is an urgent need in the art for a method for optimizing matching of energy systems with multiple power generation modes.
[0054] Based on this, this application provides a simulation study and optimal matching analysis method and device for a multi-energy system, making comprehensive use of a variety of tools and data sources. A variety of tools and data sources such as automatic weather stations, HOMER software, NASA online data, and meteorological websites are used, making full use of modern scientific and technological means for implementation and analysis. Through simulation and other methods, the influence of different external factors on the system is explored, providing a basis for feasibility study for system design and operation, and improving the reliability and stability of the system.
[0055] Next, the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems will be described in detail through embodiments and in combination with the accompanying drawings. The embodiments may be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. Obviously, the described embodiments are part of the embodiments of the present application, not all of them.
[0056] Figure 1 It is a flowchart of a simulation study and optimization matching analysis method of a multi-energy system shown in an exemplary embodiment of the present application.
[0057] In an exemplary embodiment, Figure 1 As shown, a simulation study and optimization matching analysis method for a multi-energy system is provided, and the method may include the following steps:
[0058] Step 100: Establish a system simulation model based on simlink, conduct performance analysis and sensitivity comparison of the wind-solar-hydrogen system according to the monthly average meteorological data, and obtain the influence of meteorological factors.
[0059] Step 200: Use the Monte Carlo method to perform random simulations and analyze the operating status of the wind and photovoltaic power generation systems using system levels, weather conditions, and equipment failures. At the same time, use hourly meteorological data to obtain hourly operating data of the wind, solar, and hydrogen energy systems.
[0060] Step 300: Conduct reliability evaluation and obtain the influence characteristics of various factors on reliability and the system reliability level by changing the equipment capacity and load size.
[0061] Step 400: Perform optimization matching analysis to obtain relevant operation and optimization results of the wind-solar-hydrogen system.
[0062] Specifically, the implementation process is as follows:
[0063] 1. Simulation research on wind, solar and hydrogen energy systems
[0064] (1) Wind power generation system modeling:
[0065] The mathematical model of wind turbine is established according to Betz's law. Simulink is used to establish the wind energy utilization coefficient model and wind turbine model. The variable step-size disturbance observation method is used to ensure that the system output is maintained at the maximum power point.
[0066] (2) Photovoltaic power generation system modeling
[0067] a. A photovoltaic cell model is established based on the photovoltaic effect, and the parameters are adjusted according to the changes in ambient temperature.
[0068] b. The photovoltaic array is affected by external factors, and a maximum power tracking control model similar to that of a wind turbine is established. The difference is that the disturbance parameters are the output voltage and current of the photovoltaic array.
[0069] (3) Encapsulate and connect the wind power and photovoltaic power generation systems and the hydrogen storage tank models to construct a Simulink simulation model of the wind-solar-hydrogen energy system.
[0070] 2. Random simulation
[0071] (1) Meteorological parameter simulation
[0072] (2) Stochastic simulation of wind power generation and photovoltaic power generation systems
[0073] By analyzing the instantaneous wind speed data obtained discretely, using the sequential Monte Carlo method and the Markov chain calculation method, the transfer rate between the normal, derated and shutdown states of the wind turbine is obtained.
[0074] (3) Wind and solar energy system simulation
[0075] 3. Reliability evaluation of wind-solar-hydrogen energy system
[0076] The reliability of the basic configuration is evaluated using existing data and formulas, and three situations are simulated according to different system ratios of wind, solar and hydrogen energy systems, namely, a wind-solar complementary power generation system without energy storage, a wind-solar complementary power generation system + battery energy storage, and a wind-solar complementary power generation system + battery + hydrogen energy storage.
[0077] 4. Optimize matching analysis
[0078] Excessive capacity configuration will also lead to increased wind and solar power abandonment and increased system costs. Therefore, the HOMER+SA-PSO algorithm should be used to optimize the microgrid system to determine the optimal number of micro-sources. HOMER optimization simulation provides the correct optimization direction for the SA-PSO optimization calculation, which can accelerate the convergence speed of the SA-PSO algorithm. In addition, the minimum total net current cost of the optimal configuration solution obtained by HOMER simulation can determine the constraint range of the economic conditions in the SA-PSO optimization process, and obtain an optimization matching result that is both economical and reliable.
[0079] Figure 2 is a schematic diagram of building a simulation model of a wind-solar-hydrogen energy system shown in an exemplary embodiment of the present application. Figure 3 is a schematic diagram of power generation under different system configurations shown in an exemplary embodiment of the present application, Figure 4 is a schematic diagram of hydrogen production under different system configurations shown in an exemplary embodiment of the present application, Figure 5 is a schematic diagram of a random simulation process of a wind-solar-hydrogen energy system shown in an exemplary embodiment of the present application, Figure 6 is a schematic diagram of a reliability evaluation system of a wind-solar-hydrogen energy system shown in an exemplary embodiment of the present application, Figure 7 It is a reliability simulation table of a basic configuration system of a wind-solar-hydrogen energy system shown in an exemplary embodiment of the present application.
[0080] In one possible implementation, Figure 2 As shown, the detailed implementation method and specific operation process are as follows:
[0081] S1, firstly, take the off-grid wind-solar-hydrogen system in a certain place as a reference, measure the hourly wind speed, radiation intensity and ambient temperature data from 9:00 to 16:00 on that day through the automatic weather station, and monitor the hourly system power generation rate, as shown in the attached figure. Figure 3 As shown, the meteorological data are then input into the above simulation model one by one, and the corresponding power generation rate is simulated and compared with the experimental results.
[0082] The model has a certain idealization component, and the power generation rate value obtained by simulation is slightly larger than the experimental data. Overall, it has a certain degree of reliability.
[0083] S2, analyze the performance of the wind-solar-hydrogen system, obtain the monthly average radiation intensity and wind speed data of the area through HOMER software and NASA online data, query the monthly average ambient temperature on the meteorological website and input it into the simulink model for simulation, calculate the power generation and hydrogen production of wind turbines, photovoltaic arrays and wind-solar complementary power generation systems, such as Figure 4 As shown, the photovoltaic array power generation efficiency is finally obtained.
[0084] The results show that in terms of power generation efficiency, wind turbines are higher than photovoltaic arrays. Affected by photovoltaic arrays, the power generation efficiency of the wind-solar complementary system is also low. In terms of hydrogen production and power generation, except for January and December, wind turbines are lower than photovoltaic arrays in other periods, indicating that compared with wind energy, the region's solar energy resources are more abundant and more suitable for the development of photovoltaic power generation systems.
[0085] S3, according to the local monthly average meteorological parameters, based on the simulation model of the wind-solar-hydrogen energy system, a sensitivity analysis was conducted with wind speed, radiation intensity and ambient temperature as independent variables and power generation rate as dependent variable.
[0086] The results show that the sensitivity of power generation rate to different meteorological factors at the average annual level is ranked as follows: radiation intensity > wind speed > ambient temperature. Taking all factors into consideration, under the meteorological conditions, for this wind-solar-hydrogen energy system, the system configuration should be based on photovoltaic arrays and supplemented by wind turbines, and the effective utilization of solar energy resources can be achieved by increasing the capacity of photovoltaic arrays.
[0087] according to Figure 5 After inputting the operation parameters and operation strategy in the way of , the simulation period is set, and the random numbers representing the equipment status are produced cyclically within the period, the duration of various states is calculated, and the load point indicators are counted. Figure 6 As shown, the reliability index of the system is obtained according to the obtained parameters such as battery capacity, hydrogen storage capacity, load, etc., and the system reliability is evaluated.
[0088] The reliability of the basic configuration is evaluated using existing data and formulas. Three situations are simulated according to different system ratios of wind-solar-hydrogen energy systems, namely, wind-solar complementary power generation system without energy storage, wind-solar complementary power generation system + battery energy storage, and wind-solar complementary power generation system + battery + hydrogen energy storage. The various evaluation indicators for the three situations are obtained, such as Figure 7 shown.
[0089] S4, optimizes the matching analysis of wind-solar-hydrogen system. First, the HOMER algorithm is used to establish the topological structure of the micro-element system, input equipment, meteorological and load data, design the optimized capacity of each component, enumerate it, and finally select the configuration combination with the best cost-effectiveness.
[0090] There are four situations in total, namely: wind-solar hybrid + battery + hydrogen storage system, wind-solar hybrid + battery system, photovoltaic + battery + hydrogen storage system, photovoltaic + battery system. Through data calculation, it is found that the system power shortage rate of wind-solar hybrid + battery + hydrogen storage system is only 0.016, energy utilization is more sufficient, and it is more resistant to unstable meteorological factors. It is the optimal configuration solution.
[0091] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the instructions, these steps are not necessarily executed in sequence according to the order of the instructions. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the above-mentioned embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the steps or stages in other steps.
[0092] Corresponding to the aforementioned embodiments of the simulation study and optimization matching analysis method of the multi-energy system, the present application also provides embodiments of the simulation study and optimization matching analysis device of the multi-energy system using the same technical concept.
[0093] Figure 8 It is a structural schematic diagram of a simulation research and optimization matching analysis device for a multi-energy system shown in an exemplary embodiment of the present application.
[0094] In an exemplary embodiment, Figure 8 As shown, the simulation research and optimization matching analysis device of the multi-energy system includes:
[0095] Modeling module 1 is used to establish a system simulation model based on simlink, conduct performance analysis and sensitivity comparison of the wind-solar-hydrogen system based on the monthly average meteorological data, and obtain the influence of meteorological factors;
[0096] Simulation module 2 is used to perform random simulation using the Monte Carlo method to analyze the operating status of wind and photovoltaic power generation systems using system levels, weather conditions and equipment failures, and to obtain hourly operating data of wind, solar and hydrogen energy systems using hourly meteorological data;
[0097] Evaluation module 3 is used to evaluate reliability and obtain the influence characteristics of various factors on reliability and the system reliability level by changing the equipment capacity and load size;
[0098] Analysis module 4 is used to perform optimization matching analysis and obtain relevant operation and optimization results of the wind-solar-hydrogen system.
[0099] It can be seen that some implementation cases of this application have the following advantages:
[0100] 1. Comprehensive use of multiple tools and data sources. We used multiple tools and data sources such as automatic weather stations, HOMER software, NASA online data, and meteorological websites, and made full use of modern scientific and technological means for implementation and analysis. Through simulation and other methods, we explored the influence of different external factors on the system, provided a basis for feasibility study of system design and operation, and improved the reliability and stability of the system.
[0101] 2. Proposed sensitivity analysis and system configuration suggestions. Through sensitivity analysis, the influence of different meteorological factors on power generation rate was determined, which provided scientific basis and suggestions for system configuration and improved the adaptability and efficiency of the system.
[0102] 3. Adopt system optimization matching and cost-benefit analysis. Use the HOMER algorithm to perform system optimization matching analysis. By enumerating different configuration combinations and using intelligent algorithms to perform optimization matching analysis, the optimal micro-source quantity configuration is determined, which improves the energy utilization efficiency and economy of the system and provides effective guidance for the operation and management of the system.
[0103] 4. The sequential Monte Carlo method is used to compare and analyze conventional units and equivalent systems based on wind power generation, which helps to evaluate the performance of the system and provides more accurate reference indicators for the design and operation of actual microgrids.
[0104] For the specific definition of the simulation research and optimization matching analysis device for multi-energy systems, please refer to the definition of the simulation research and optimization matching analysis method for multi-energy systems in the above text, which will not be repeated here. Each module in the above-mentioned simulation research and optimization matching analysis device for multi-energy systems can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.
[0105] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0106] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.
Claims
1. A simulation study and optimization matching analysis method for a multi-energy system, characterized in that: include: A system simulation model was established based on Simlink, and the performance and sensitivity comparison of the wind-solar-hydrogen system were carried out according to the monthly average meteorological data to obtain the influence of meteorological factors. Monte Carlo method is used for random simulation. The operating status of wind and photovoltaic power generation systems is analyzed by system level, weather conditions and equipment failures. At the same time, hourly meteorological data is used to obtain hourly operating data of wind, solar and hydrogen energy systems. Conduct reliability evaluation and obtain the influence characteristics of each factor on reliability and the system reliability level by changing the equipment capacity and load size; Conduct optimization matching analysis to obtain relevant operation and optimization results of wind-solar-hydrogen systems.
2. The simulation research and optimization matching analysis method of the multi-energy system according to claim 1, characterized in that: The system simulation model is established based on simlink, and further includes: The mathematical model of wind turbine is established according to Betz's law, and the wind energy utilization coefficient model and wind turbine model are established based on Simulink. The wind and photovoltaic power generation systems as well as the electrolyzer and hydrogen storage tank models are packaged and connected to construct a Simulink simulation model of the wind-solar-hydrogen energy system.
3. The simulation research and optimization matching analysis method of the multi-energy system according to claim 1, characterized in that: The Monte Carlo method is used to perform random simulation, and the system level, weather conditions and equipment failures are used to analyze the operating status of the wind and photovoltaic power generation systems, and hourly meteorological data are used to obtain hourly operating data of the wind, solar and hydrogen energy systems, further comprising: By using the discrete hourly wind speed data, the sequential Monte Carlo method and Markov chain calculation method are used to obtain the transition rate between the normal, derated and shutdown states of the wind turbine.
4. The simulation research and optimization matching analysis method of the multi-energy system as claimed in claim 3, characterized in that: The reliability evaluation is performed, and by changing the equipment capacity and load size, the influence characteristics of each factor on the reliability and the system reliability level are obtained, further including: Based on the established stochastic model of the wind-solar-hydrogen energy system, the total wind-solar capacity, photovoltaic ratio, capacity of each device and load size are changed on the basis of the commonly used configuration to evaluate the system reliability.
5. The simulation research and optimization matching analysis method of the multi-energy system according to claim 1, characterized in that: The optimization matching analysis is performed to obtain the operation and optimization results of the wind-solar-hydrogen system, further including: HOMER optimization simulation provides the correct optimization direction for the optimization calculation of SA-PSO, which can accelerate the convergence speed of the SA-PSO algorithm. The HOMER+SA-PSO algorithm is used to optimize the microgrid system to determine the optimal number of micro-sources, and finally achieve the optimization goal based on HOMER with the total net current cost of the entire life cycle.
6. The simulation research and optimization matching analysis method of the multi-energy system according to claim 5, characterized in that: The lowest total net present cost of the optimal configuration scheme simulated by HOMER can determine the constraint range of the economic conditions in the SA-PSO optimization process and obtain an optimal matching result that is both economical and reliable.
7. The simulation research and optimization matching analysis method of the multi-energy system according to claim 1, characterized in that: The system simulation model is established based on simlink, and the performance analysis and sensitivity comparison of the wind-solar hydrogen system are performed according to the monthly average meteorological data to obtain the influence of meteorological factors, which further includes: Wind power generation system modeling, establish a mathematical model of the wind turbine based on Betz's law, and use Simulink to establish a wind energy utilization coefficient model and a wind turbine model. Use the variable step-size disturbance observation method to ensure that the system output is maintained at the maximum power point. Photovoltaic power generation system modeling, based on the photovoltaic effect to establish a photovoltaic cell model, through the change of ambient temperature to adjust the parameters; photovoltaic array is affected by external factors, the maximum power tracking control model similar to the wind turbine is established, the difference is that the disturbance parameters are the output voltage and current of the photovoltaic array; The wind power and photovoltaic power generation systems and hydrogen storage tank models are packaged and connected to construct a Simulink simulation model of the wind-solar-hydrogen energy system.
8. A simulation research and optimization matching analysis device for a multi-energy system, characterized in that: include: The modeling module is used to establish a system simulation model based on Simlink, conduct performance analysis and sensitivity comparison of the wind-solar-hydrogen system based on the monthly average meteorological data, and obtain the influence of meteorological factors; The simulation module is used to perform random simulation using the Monte Carlo method to analyze the operating status of wind and photovoltaic power generation systems using system levels, weather conditions and equipment failures, and to obtain hourly operating data of wind, solar and hydrogen energy systems using hourly meteorological data; Evaluation module, used to evaluate reliability and obtain the influence characteristics of various factors on reliability and the system reliability level by changing the equipment capacity and load size; The analysis module is used to perform optimization matching analysis and obtain relevant operation and optimization results of the wind-solar-hydrogen system.