Timing simulation analysis method and system for urban area electric hydrogen coupled energy network

Abstract

A timing simulation analysis method and system for an electric-hydrogen coupled energy network in an urban area is developed to establish a timing simulation model for an electric-hydrogen coupled micro-energy network; a timing simulation data set is formed using load simulation data and predicted values ​​of wind power generation, predicted values ​​of photovoltaic power generation, meteorological data of wind farms, and meteorological data of photovoltaic power stations; the timing simulation model uses the timing simulation data set as input, and after determining the supply and demand status of the power system and the supply and demand status of the hydrogen energy system, a first type of scheduling scheme for the electric-hydrogen coupled micro-energy network within a set time period is formulated with the goal of maximizing energy utilization efficiency, and a second type of scheduling scheme for the electric-hydrogen coupled micro-energy network within a set time period is formulated with the goal of minimizing operating costs; based on the first type of scheduling scheme and the second type of scheduling scheme, an optimization algorithm is used to determine the optimal scheduling scheme for the electric-hydrogen coupled micro-energy network within a set time period, thereby improving the efficiency and economy of the electric-hydrogen coupled energy network.

Description

Technical Field

[0001] The present invention belongs to the field of new energy technology, and in particular relates to a timing simulation analysis method and system for an urban area electric hydrogen coupling energy network. Background Art

[0002] In urban areas, energy demand is complex and diverse. Traditional energy supply methods often cannot effectively meet the needs of coordinated operation of multiple energy forms such as electricity, gas, and hydrogen.

[0003] Existing technologies tend to supply and optimize a single energy source. They rely on traditional grid architecture to meet the city's electricity demand. The use of hydrogen energy is often limited to small-scale attempts in specific areas and lacks deep coupling with the power system. In the application of renewable energy, although wind and solar energy have been used to a certain extent, they are usually operated independently and lack coordination with other energy systems. For the electric hydrogen coupled energy network in urban areas, existing technical solutions show obvious limitations. Some solutions use hydrogen energy as an emergency energy source when there is a problem with the power supply, but this approach is relatively passive and lacks systematic planning and coordination. In addition, there are some solutions that use part of the electricity for electrolysis of water to produce hydrogen for storage when renewable energy generation is sufficient, but this process often lacks a sophisticated control strategy and it is difficult to achieve efficient energy conversion and storage. The volatility and uncertainty of renewable energy systems, on the one hand, the power generation of wind and solar energy is affected by natural conditions and has great instability. On the other hand, the energy demand in urban areas also shows great changes in different time periods. For example, during the day, commercial and industrial electricity demand is large; while at night, residential electricity demand is relatively high. This load volatility poses a huge challenge to the balance of energy supply and demand and the stability of system operation, and fails to fully consider the complex coupling relationship between electricity and hydrogen energy systems and the volatility of renewable energy.

[0004] It is not good enough to deal with the volatility of renewable energy. It cannot adjust the supply of electricity and hydrogen energy in real time according to the power generation of renewable energy, and cannot effectively balance the supply and demand of energy. The existing technology lacks real-time simulation and scheduling optimization of energy supply and demand balance in different time periods, resulting in insufficient performance of the energy system in dealing with load fluctuations and energy uncertainty. For example, during peak load periods, if the supply of electricity and hydrogen energy cannot be adjusted in time, energy shortages may occur; and during low load periods, if excess energy cannot be reasonably stored, energy waste will occur. Summary of the invention

[0005] In order to address the deficiencies in the prior art, the present invention provides a timing simulation analysis method and system for an urban area electric-hydrogen coupled energy network, which simulates and optimizes the coordinated operation of the electric and hydrogen energy systems in real time based on actual load demand, renewable energy generation data, and meteorological conditions, thereby improving the efficiency and economy of the electric-hydrogen coupled energy network.

[0006] The present invention adopts the following technical solution.

[0007] The present invention proposes a time series simulation analysis method for an urban area electric hydrogen coupling energy network, comprising:

[0008] In a set time period, the weighted sum of the power load demand, gas load demand and hydrogen load demand at each time point is calculated, and the weighted sum of each time point constitutes the load simulation data in the set time period;

[0009] Obtain wind power generation prediction value, photovoltaic power generation prediction value, wind farm meteorological data and photovoltaic power station meteorological data;

[0010] Establish a timing simulation model of the electrical-hydrogen coupled micro-energy network;

[0011] A time series simulation data set is formed with load simulation data, wind power prediction values, photovoltaic power prediction values, meteorological data of wind farms, and meteorological data of photovoltaic power stations; the time series simulation model takes the time series simulation data set as input, and after determining the supply and demand status of the power system and the supply and demand status of the hydrogen energy system, formulates a first type of scheduling plan for the electric-hydrogen coupled micro-energy network within a set time period with the highest energy utilization efficiency as the goal, and formulates a second type of scheduling plan for the electric-hydrogen coupled micro-energy network within a set time period with the lowest operating cost as the goal; based on the first type of scheduling plan and the second type of scheduling plan, an optimization algorithm is used to determine the optimal scheduling plan for the electric-hydrogen coupled micro-energy network within the set time period.

[0012] Preferably, within a set time period, the weighted sum of the power load demand, the gas load demand and the hydrogen load demand at each time point is calculated, and the weighted sum of each time point constitutes the load simulation data within the set time period, including:

[0013] The multivariate linear regression method is used to obtain the power load demand, gas load demand and hydrogen load demand within a set time period;

[0014] Select the predicted value of electric load, gas load and hydrogen load within a set time period;

[0015] Calculate the first distance between the predicted power load value and the power load demand, the second distance between the predicted gas load value and the gas load demand, and the third distance between the predicted hydrogen load value and the hydrogen load demand at each time point; use the ratio of the first distance at each time point to the sum of the three distances as the weight of the power load demand at each time point, use the ratio of the second distance at each time point to the sum of the three distances as the weight of the gas load demand at each time point, and use the ratio of the third distance at each time point to the sum of the three distances as the weight of the hydrogen load demand at each time point;

[0016] The determined weights are used to calculate the weighted sum of the electricity load demand, gas load demand and hydrogen load demand at each time point, and the weighted sum of each time point constitutes the load simulation data within the set time period.

[0017] Preferably, the weight of the electricity load demand, the weight of the gas load demand, and the weight of the hydrogen load demand at each time point respectively satisfy the following relationship:

[0018] ,

[0019] ,

[0020] ,

[0021] In the formula, For time point The weight of the power load demand, For time point The weight of the gas load demand, For time point The weight of the hydrogen load demand, For time point The Fréchet distance between the power load forecast value and the power load demand, For time point The Fréchet distance between the gas load forecast value and the gas load demand, For time point The Fréchet distance between the predicted hydrogen load and the hydrogen load demand.

[0022] Preferably, obtaining the predicted value of wind power generation power and the predicted value of photovoltaic power generation power includes:

[0023] Obtain historical data of wind power generation to form a time series, calculate the autoregressive order of the autoregressive moving average model through the autocorrelation function of the time series, and calculate the moving average order of the autoregressive moving average model through the partial autocorrelation function of the time series; use the autoregressive moving average model after the autoregressive order and the moving average order are determined to determine the predicted value of wind power generation according to the historical data of wind power generation;

[0024] The historical data of photovoltaic power generation is obtained to form a time series, and the autoregressive order of the autoregressive integrated moving average model is calculated by the autocorrelation function of the time series. The moving average order of the autoregressive integrated moving average model is calculated by the partial autocorrelation function of the time series. The predicted value of photovoltaic power generation is determined according to the historical data of photovoltaic power generation by using the autoregressive integrated moving average model after the autoregressive order and the moving average order are determined.

[0025] Preferably, obtaining the meteorological data of the wind farm and the meteorological data of the photovoltaic power station includes:

[0026] Based on the geographical weighted regression model, the meteorological data of the wind farm and the meteorological data of the photovoltaic power station are determined according to the geographical location coordinates of the meteorological monitoring station and the meteorological monitoring data;

[0027] Meteorological monitoring data include: temperature, humidity, wind speed, wind direction, and solar radiation intensity.

[0028] Preferably, the timing simulation model of the electric-hydrogen coupled micro-energy network includes: an electric power system model, a hydrogen energy system model, and an electric-hydrogen coupled system model;

[0029] The power system model includes a transmission line model and a power generation facility module. The transmission line model is established based on circuit theory, and the power generation facility module is established according to the power system dynamic equation.

[0030] The hydrogen energy system model includes a water electrolysis hydrogen production device reaction rate model and a hydrogen storage facility hydrogen state change model. The water electrolysis hydrogen production device reaction rate model is established based on the principle of chemical reaction kinetics, and the hydrogen storage facility hydrogen state change model is established based on the principle of thermodynamics.

[0031] The electric-hydrogen coupling system model is established by using the electric-hydrogen coupling efficiency and the design parameters, operating temperature and operating current density of the electrolyzer, satisfying the following relationship:

[0032] ,

[0033] In the formula, is the electric-hydrogen coupling efficiency, Design parameters for the electrolyzer, is the operating temperature, is the operating current density, It is a function of the relationship between the electric-hydrogen coupling efficiency and the design parameters, operating temperature and operating current density of the electrolyzer.

[0034] Preferably, the least square method is used to determine the parameters of the timing simulation model, including:

[0035] The actual parameters and historical operation data of each device in the electric hydrogen coupled micro energy network are used as the initial parameters of the time series simulation model, and the historical data of energy supply and demand and the operation data of the equipment are used as the observation data of the time series simulation model; the historical data of energy supply and demand include: historical data of wind power generation, historical data of photovoltaic power generation, historical data of load demand, and historical data of hydrogen production; the operation data of the equipment include: operation time, number of starts and stops, and fault records;

[0036] Taking the electric-hydrogen coupling efficiency as the parameter to be identified, an error function is established based on the least squares method; the design parameters, operating temperature and operating current density of the electrolyzer when the error function is minimized are used as the optimization parameters of the electric-hydrogen coupling system model;

[0037] The parameters of the power system model and the electric-hydrogen coupling efficiency are combined to form a set of parameters to be identified, and an error function is established based on the least squares method; the parameters of the power system model when the error function is minimized are used as the optimization parameters of the power system model;

[0038] The parameters of the hydrogen energy system model and the electric-hydrogen coupling efficiency jointly constitute a set of parameters to be identified, and an error function is established based on the least squares method. The parameters of the hydrogen energy system model when the error function is minimized are used as the optimization parameters of the hydrogen energy system model.

[0039] Preferably, the time series simulation model uses the time series simulation data set as input to determine the supply and demand state of the power system and the supply and demand state of the hydrogen energy system, including:

[0040] The time series simulation data set is input into the constructed time series simulation model of the electrical hydrogen coupled micro energy network in chronological order;

[0041] In the time series simulation model, the electric load simulation value and the hydrogen load simulation value are determined according to the input load simulation data, the wind power generation power simulation value and the photovoltaic power generation power simulation value corresponding to the electric load simulation value and the hydrogen load simulation value are determined according to the wind power generation power prediction value and the photovoltaic power generation power prediction value, and the electric load simulation value, the hydrogen load simulation value, the wind power generation power simulation value and the photovoltaic power generation power simulation value are dynamically corrected according to the meteorological data of the wind farm and the meteorological data of the photovoltaic power station;

[0042] Using the corrected power load simulation value, wind power generation power simulation value and photovoltaic power generation power simulation value, the power system supply and demand status, including power surplus and power shortage, is obtained based on the power supply and demand balance simulation;

[0043] Using the corrected hydrogen load simulation value, wind power generation power simulation value and photovoltaic power generation power simulation value, the supply and demand status of the hydrogen energy system, including hydrogen energy surplus and hydrogen energy shortage, is obtained based on mass flow balance simulation.

[0044] Preferably, according to the supply and demand status of the power system and the supply and demand status of the hydrogen energy system, a first type of scheduling plan for the electric-hydrogen coupled micro-energy network within a set time period is formulated with the highest energy utilization efficiency as the goal, and a second type of scheduling plan for the electric-hydrogen coupled micro-energy network within a set time period is formulated with the lowest operating cost as the goal; based on the first type of scheduling plan and the second type of scheduling plan, an optimization algorithm is used to determine the optimal scheduling plan for the electric-hydrogen coupled micro-energy network within the set time period.

[0045] The present invention also proposes a timing simulation analysis system for an electric hydrogen coupled micro energy network suitable for urban areas, comprising:

[0046] The time series simulation data establishment module is used to calculate the weighted sum of the power load demand, gas load demand and hydrogen load demand at each time point within a set time period, and use the weighted sum of each time point to form the load simulation data within the set time period; obtain the wind power generation power prediction value, photovoltaic power generation power prediction value, wind farm meteorological data and photovoltaic power station meteorological data; use the load simulation data and wind power generation power prediction value, photovoltaic power generation power prediction value, wind farm meteorological data and photovoltaic power station meteorological data to form a time series simulation data set;

[0047] A timing simulation model building module is used to build a timing simulation model of an electrical-hydrogen coupled micro-energy network;

[0048] The timing simulation analysis module is used for the timing simulation model. It takes the timing simulation data set as input. After determining the supply and demand status of the power system and the supply and demand status of the hydrogen energy system, it formulates the first type of scheduling plan for the electric hydrogen coupled micro energy network within the set time period with the highest energy utilization efficiency as the goal, and formulates the second type of scheduling plan for the electric hydrogen coupled micro energy network within the set time period with the lowest operating cost as the goal; based on the first type of scheduling plan and the second type of scheduling plan, an optimization algorithm is used to determine the optimal scheduling plan for the electric hydrogen coupled micro energy network within the set time period.

[0049] The present invention is also a terminal, comprising a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method.

[0050] The present invention is also a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method when executed by a processor.

[0051] The beneficial effects of the present invention are that, compared with the prior art, it at least includes comprehensive consideration of the coupling relationship between electricity and hydrogen energy, and adopts data collection, processing and prediction technology, as well as a dynamically optimized scheduling strategy, which improves the accuracy of energy forecasting, optimizes the balance of energy supply and demand, reduces energy waste, and improves the operating efficiency and economy of the energy network. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a flow chart of a timing simulation analysis method for an urban area electrical hydrogen coupled energy network proposed by the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only embodiments of a part of the present invention, rather than all embodiments. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the protection scope of the present invention.

[0054] The present invention proposes a time series simulation analysis method for an urban area electric hydrogen coupled energy network. Figure 1 As shown, including:

[0055] Step 1: Calculate the weighted sum of the electricity load demand, gas load demand and hydrogen load demand at each time point within a set time period, and use the weighted sum of each time point to form the load simulation data within the set time period.

[0056] Specifically, step 1 includes:

[0057] Step 1.1, using a multiple linear regression method to obtain the power load demand, gas load demand, and hydrogen load demand within a set time period;

[0058] Specifically, within a set time period, the temperature and humidity are obtained, and the power load demand is determined using the following relationship:

[0059] ,

[0060] In the formula, is the power load demand, , , , is the fitting coefficient of power load demand, which is determined by historical data regression analysis. To set the time period, is the temperature, For humidity, is the error correction constant for power load demand;

[0061] In the embodiment, the time period is set to one week;

[0062] In the set time period, the heating status quantity, building heating energy consumption index, and personnel behavior impact index are obtained, and the gas load demand is determined by the following relationship:

[0063] ,

[0064] In the formula, is the gas load demand, , , , is the fitting coefficient of gas load demand, which is determined by historical data regression analysis. is the heating state quantity, the heating state quantity during heating 1, heating state quantity when no heating is 0, It is the energy consumption index for building heating specified by national standards. is the personnel behavior impact indicator, is the error correction constant for gas load demand;

[0065] In the embodiment, the expectation of the probability distribution of the human behavior in the building within a week is used as the human behavior impact index.

[0066] In the set time period, the hydrogen fuel cell vehicle flow and industrial hydrogen planned volume are obtained, and the hydrogen load demand is established according to the following relationship:

[0067] ,

[0068] In the formula, is the hydrogen load demand, , , is the fitting coefficient of hydrogen load demand, which is determined by historical data regression analysis. is the flow rate of hydrogen fuel cell vehicles, is the planned amount of industrial hydrogen, is the personnel behavior impact indicator, is the error correction constant for hydrogen load demand.

[0069] The present invention adopts a multiple linear regression method to obtain the load demand within a set time period, reduce the impact of load demand volatility, and thus obtain a reference value of the load demand to meet the simulation requirements of various working conditions such as steady state.

[0070] Step 1.2, select the power load forecast value, gas load forecast value and hydrogen load forecast value within the set time period;

[0071] Step 1.3, calculate the first distance between the predicted power load value and the power load demand, the second distance between the predicted gas load value and the gas load demand, and the third distance between the predicted hydrogen load value and the hydrogen load demand at each time point; use the ratio of the first distance at each time point to the sum of the three distances as the weight of the power load demand at each time point, use the ratio of the second distance at each time point to the sum of the three distances as the weight of the gas load demand at each time point, and use the ratio of the third distance at each time point to the sum of the three distances as the weight of the hydrogen load demand at each time point; satisfy the following relationship:

[0072] ,

[0073] ,

[0074] ,

[0075] In the formula, For time point The weight of the power load demand, For time point The weight of the gas load demand, For time point The weight of the hydrogen load demand, For time point The Fréchet distance between the power load forecast value and the power load demand, For time point The Fréchet distance between the gas load forecast value and the gas load demand, For time point The Fréchet distance between the predicted hydrogen load and the hydrogen load demand;

[0076] In the embodiment, using the Fréchet distance method to calculate the distance between each predicted value and each demand quantity is a non-limiting and preferred choice.

[0077] Step 1.4, using the determined weights, calculate the weighted sum of the electricity load demand, gas load demand and hydrogen load demand at each time point, and use the weighted sum of each time point to form the load simulation data within the set time period.

[0078] The load simulation data is a fusion of power load data, gas load data, and hydrogen load data. When the data is fused, the weight of each data is calculated based on the same time point. Therefore, the weighted sum of each data realizes a time-series-based fusion. During time-series simulation, only the interval between each time point needs to be adjusted to adjust the amount of fused data to meet the data volume requirements of different simulations. In addition, the time point The larger the weight, the stronger the corresponding load demand volatility, in order to meet the simulation requirements of various working conditions such as transient conditions. Moreover, the weight is equivalent to amplifying the volatility of the load, making the impact of load volatility more prominent and significant during the simulation process.

[0079] Step 1.5, normalize the load simulation data.

[0080] The normalization process maps the load simulation data to the [0,1] interval as part of the complete time series data set.

[0081] Step 2: Obtain the predicted value of wind power generation, the predicted value of photovoltaic power generation, the meteorological data of the wind farm, and the meteorological data of the photovoltaic power station.

[0082] Specifically, based on the autoregressive moving average model, the predicted value of wind power generation is determined according to the historical data of wind power generation; based on the autoregressive integral moving average model, the predicted value of photovoltaic power generation is determined according to the historical data of photovoltaic power generation; based on the geographically weighted regression model, the meteorological data of the wind farm and the meteorological data of the photovoltaic power station are determined respectively according to the geographical location coordinates of the meteorological monitoring station and the meteorological monitoring data.

[0083] Specifically, step 2 includes:

[0084] Step 2.1, obtaining the historical data of wind power generation to form a time series, calculating the autoregressive order of the autoregressive moving average model through the autocorrelation function of the time series, and calculating the moving average order of the autoregressive moving average model through the partial autocorrelation function of the time series; using the autoregressive moving average model after the autoregressive order and the moving average order are determined, the predicted value of wind power generation is determined according to the historical data of wind power generation;

[0085] According to the wind power processing characteristics, the present invention proposes to use the autoregressive moving average (ARMA) model to predict the wind power generation power;

[0086] Step 2.2, obtaining the photovoltaic power generation history data to form a time series, calculating the autoregressive order of the autoregressive integrated moving average model through the autocorrelation function of the time series, and calculating the moving average order of the autoregressive integrated moving average model through the partial autocorrelation function of the time series; using the autoregressive integrated moving average model after the autoregressive order and the moving average order are determined, the photovoltaic power generation prediction value is determined according to the photovoltaic power generation history data;

[0087] For photovoltaic power generation, considering the daily periodicity of solar radiation intensity, seasonal changes and temperature influence, the present invention proposes to use the autoregressive integrated moving average (ARIMA) model to predict photovoltaic power generation.

[0088] Renewable energy generation data includes types such as wind power and photovoltaic power generation. The power generation capacity of these energy sources is dynamically monitored and incorporated into the time series data set for analysis.

[0089] Step 2.3, based on the geographical weighted regression model, according to the geographical location coordinates of the meteorological monitoring station and the meteorological monitoring data, the meteorological data of the wind farm and the meteorological data of the photovoltaic power station are determined respectively;

[0090] Meteorological monitoring data include but are not limited to: temperature, humidity, wind speed, wind direction, and solar radiation intensity.

[0091] The weight of the geographical location coordinates of the meteorological monitoring station to the geographical location coordinates of the wind farm or the geographical location coordinates of the photovoltaic power station is calculated based on the spatial weight function, so as to determine the meteorological monitoring data where no meteorological monitoring station is arranged, and at the same time ensure that the meteorological data of the wind farm matches the predicted value of wind power generation, and ensure that the meteorological data of the photovoltaic power station matches the predicted value of photovoltaic power generation.

[0092] In the embodiment, multiple linear regression and time series analysis are combined with data recorded by smart meters to more comprehensively and accurately collect and process load demand data, renewable energy generation data, and meteorological condition data. This comprehensive data collection method improves the accuracy of load forecasting and provides a solid data foundation for energy management.

[0093] By adopting the geographical weighted regression method, the present invention can obtain urban area meteorological data more accurately, calculate the weight of the monitoring site to the interpolation point through the spatial weight function, and obtain the meteorological data of the unmonitored point by weighted calculation to ensure matching with other data collection frequencies. This helps to improve the accuracy of energy supply and demand balance and optimize energy distribution.

[0094] Step 3: Establish a timing simulation model of the electrical-hydrogen coupled micro-energy network.

[0095] Specifically, step 3 includes:

[0096] Step 3.1, establish a time-series simulation model of the electric-hydrogen coupled micro-energy network, including: a power system model, a hydrogen energy system model, and an electric-hydrogen coupled system model;

[0097] Constructing an electrical hydrogen coupled micro-energy network timing simulation model includes two links: designing a model architecture and setting model parameters. The present invention proposes to construct a comprehensive model architecture based on physical principles that includes a power system, a hydrogen energy system, and the coupling relationship between the two. For the power system, the power system model includes a transmission line model and a power generation facility module. The transmission line model is constructed according to circuit theory (Ohm's law, Kirchhoff's law), and the dynamic characteristics of the power generation facility module are described according to the dynamic equation of the power system; for the hydrogen energy system, the hydrogen energy system model includes a water electrolysis hydrogen production device reaction rate model and a hydrogen storage facility hydrogen state change model. The water electrolysis hydrogen production device reaction rate model is established according to the principle of chemical reaction kinetics, and the hydrogen storage facility hydrogen state change model is established according to the principle of thermodynamics; in practical applications, a more accurate state equation can be selected according to the properties of hydrogen, considering the influence of pressure and temperature on hydrogen density and storage capacity; when describing the coupling relationship between the power system and the hydrogen energy system, according to the law of conservation of energy, the conversion efficiency model of electrical energy to hydrogen chemical energy and hydrogen chemical energy to electrical energy is set. In the process of hydrogen production by electrolysis of water, the electric-hydrogen coupling system model is established based on the correlation between the electric-hydrogen coupling efficiency and the design parameters, operating conditions and other factors of the electrolyzer. Therefore, the electric-hydrogen coupling system model satisfies the following relationship:

[0098] ,

[0099] In the formula, is the electric-hydrogen coupling efficiency, Design parameters for the electrolyzer, is the operating temperature, is the operating current density, It is a function of the relationship between the electric-hydrogen coupling efficiency and the design parameters, operating temperature and operating current density of the electrolyzer.

[0100] The present invention constructs a comprehensive model framework, which includes the power system, the hydrogen energy system and the coupling relationship between the two based on physical principles. This not only improves the applicability and flexibility of the model, but also enhances the simulation capability of complex energy networks.

[0101] Step 3.2, using the least square method to determine the parameters of the timing simulation model; including:

[0102] 1) The actual parameters and historical operation data of each device in the electric hydrogen coupled micro energy network are used as the initial parameters of the time series simulation model, and the historical data of energy supply and demand and the operation data of the equipment are used as the observation data of the time series simulation model; the historical data of energy supply and demand include but are not limited to: historical data of wind power generation, historical data of photovoltaic power generation, historical data of load demand, and historical data of hydrogen production; the operation data of the equipment include but are not limited to: operation time, number of starts and stops, and fault records;

[0103] 2) Taking the electric-hydrogen coupling efficiency as the parameter to be identified, an error function is established based on the least squares method; the parameters when the error function is minimized are used as the parameters of the electric-hydrogen coupling system model; the efficiency model parameters are determined through experimental and theoretical analysis to ensure that the model accurately reflects the energy conversion process between the two;

[0104] 3) The parameters of the power system model and the electric-hydrogen coupling efficiency are combined to form a set of parameters to be identified, and an error function is established based on the least squares method; the parameters of the power system model when the error function is minimized are used as the optimization parameters of the power system model;

[0105] 4) The parameters of the hydrogen energy system model and the electric-hydrogen coupling efficiency are combined to form a set of parameters to be identified, and an error function is established based on the least squares method; the parameters of the hydrogen energy system model when the error function is minimized are used as the optimization parameters of the hydrogen energy system model.

[0106] Step 4, using load simulation data and wind power prediction values, photovoltaic power prediction values, wind farm meteorological data, and photovoltaic power station meteorological data to form a time series simulation data set; the time series simulation model uses the time series simulation data set as input, and after determining the supply and demand status of the power system and the supply and demand status of the hydrogen energy system, formulates a first type of scheduling plan for the electric hydrogen coupled micro energy network within a set time period with the highest energy utilization efficiency as the goal, and formulates a second type of scheduling plan for the electric hydrogen coupled micro energy network within a set time period with the lowest operating cost as the goal; based on the first type of scheduling plan and the second type of scheduling plan, an optimization algorithm is used to determine the optimal scheduling plan for the electric hydrogen coupled micro energy network within the set time period.

[0107] Specifically, step 4 includes:

[0108] Step 4.1, the timing simulation data set is input into the timing simulation model;

[0109] In the embodiment, a time series simulation data set is formed by load simulation data, wind power generation power prediction values, photovoltaic power generation power prediction values, meteorological data of wind farms, and meteorological data of photovoltaic power stations; the time series simulation data set is input into the constructed time series simulation model of the electric hydrogen coupled micro energy network in chronological order.

[0110] According to the needs of actual application scenarios of multi-energy complementarity such as urban areas and industrial parks, the present invention inputs load demand data, renewable energy generation data, and meteorological conditions in different time periods to form a complete time series data set. Among them, by constructing a time series simulation model, the energy supply and demand balance of the electric hydrogen energy network is calculated in real time, and the energy flow between electricity and hydrogen energy is dynamically adjusted to cope with load fluctuations and uncertainties of renewable energy. In the data collection and processing stage, the multivariate linear regression method, time series analysis method and geographical weighted regression method are used to obtain comprehensive and accurate load demand data, renewable energy generation data and meteorological condition data, and perform fusion and normalization processing.

[0111] Step 4.2, in the time series simulation model, the power load simulation value, the hydrogen load simulation value, the wind power generation power simulation value and the photovoltaic power generation power simulation value are corrected according to the meteorological data of the wind farm and the meteorological data of the photovoltaic power station;

[0112] In the embodiment, the timing simulation model determines the power load simulation value and the hydrogen load simulation value according to the input load simulation data in each time step according to the pre-set algorithm and parameters, determines the wind power generation power simulation value and the photovoltaic power generation power simulation value corresponding to the power load simulation value and the hydrogen load simulation value according to the wind power generation power prediction value and the photovoltaic power generation power prediction value, and dynamically corrects the power load simulation value, the hydrogen load simulation value, the wind power generation power simulation value and the photovoltaic power generation power simulation value according to the meteorological data of the wind farm and the meteorological data of the photovoltaic power station. For the input data at different time points, the timing simulation model outputs the operating status of the electric hydrogen coupled micro energy network at different time points, including the corrected power load simulation value, the hydrogen load simulation value, the wind power generation power simulation value and the photovoltaic power generation power simulation value.

[0113] Among them, when determining the power load simulation value and the hydrogen load simulation value, since the load simulation data is the weighted sum of the power load demand, the gas load demand and the hydrogen load demand, a data-driven method is used to improve the reliability and accuracy of the power load simulation value and the hydrogen load simulation value in a closed-loop manner. Although the gas load simulation value is not obtained, when the power load simulation value and the hydrogen load simulation value are determined, the gas load simulation value is also obtained.

[0114] By constructing a timing simulation model, the energy supply and demand balance of the electric hydrogen energy network is calculated in real time, and the energy flow between electricity and hydrogen energy is dynamically adjusted to cope with load fluctuations and uncertainties in renewable energy, optimize the coordinated scheduling strategy of electricity and hydrogen energy systems, achieve efficient operation of the energy network, and improve the efficiency and economy of the integrated energy network.

[0115] Step 4.3, using the corrected power load simulation value, wind power generation power simulation value and photovoltaic power generation power simulation value, based on the power supply and demand balance simulation, the power system supply and demand status, including power surplus and power shortage, is obtained;

[0116] In the embodiment, within each time step, the sum of the corrected wind power generation power simulation value and the photovoltaic power generation power simulation value is first taken as the total power generation. Considering the losses in the power transmission and distribution process, the line loss power is calculated according to the impedance, current and other parameters of the transmission line, and the loss power is subtracted from the total power generation to obtain the actual available power generation. The actual available power generation is compared with the corrected power load simulation value. If the total power generation is greater than the corrected power load simulation value, there is a power surplus; if the total power generation is less than the corrected power load simulation value, there is a power shortage;

[0117] According to the supply and demand status of the power system, the control strategy of the battery energy storage device is determined, including: for the battery energy storage device, the battery charge and discharge power is determined when there is a surplus or shortage of power according to the current power of the battery energy storage device, the charge and discharge efficiency curve, and the rated capacity and other parameters. For example, when there is a surplus of power and the battery energy storage is not full, the relationship curve between the battery's charging efficiency and power is used to calculate how much excess power can be stored in the battery with the highest efficiency; when there is a power shortage and the battery energy storage has power, the discharge characteristic curve of the battery is used to determine how much power can be released from the battery to meet the load demand and avoid the impact of excessive discharge on the battery life.

[0118] The process of adjusting energy flows in real time involves assessing and responding to fluctuations in load demand and uncertainties in renewable energy generation to ensure the stability and efficiency of the electric and hydrogen energy network.

[0119] Step 4.4, using the corrected hydrogen load simulation value, wind power generation power simulation value and photovoltaic power generation power simulation value, based on the mass flow balance simulation, the supply and demand status of the hydrogen energy system is obtained, including hydrogen energy surplus and hydrogen energy shortage;

[0120] In the embodiment, in each time step, the amount of hydrogen produced is calculated according to the surplus of renewable energy power generation and the operating parameters of the water electrolysis hydrogen production device. For the change in hydrogen storage, the mass change of the stored hydrogen is calculated according to the volume of the hydrogen storage tank, the pressure change and the state equation of hydrogen. Considering the loss of hydrogen during storage, transportation and conversion (such as hydrogen leakage, hydrogen that is not completely reacted in the fuel cell, etc.), the loss coefficient is determined according to empirical data or actual tests, and the amount of hydrogen actually available for supply is obtained by subtracting the loss from the hydrogen production. The amount of hydrogen actually available for supply is compared with the hydrogen load demand. If the hydrogen production is greater than the hydrogen load demand and the hydrogen storage facility is not full, the excess hydrogen is stored; if the hydrogen load demand is greater than the sum of the hydrogen production and hydrogen storage, it is necessary to consider obtaining hydrogen from other sources (such as an external hydrogen supply source, and the corresponding supply strategy and cost parameters need to be set in the model). Real-time monitoring and analysis of the energy flow in the electrical hydrogen energy network, based on the dynamic changes in energy supply and demand, the allocation ratio of electricity and hydrogen energy is adjusted to improve the flexibility and responsiveness of the system.

[0121] In the simulation model construction stage, the present invention constructs a comprehensive model framework including the power system, the hydrogen energy system and the coupling relationship between the two based on physical principles, and uses parameter identification methods such as the least squares method to determine the optimal model parameters. In the real-time calculation of energy supply and demand balance, a data-driven simulation method is used to input the complete time series data set into the simulation model, calculate the supply and demand balance of the power system and the hydrogen energy system respectively, and determine the strategy for dealing with power surplus or shortage, hydrogen surplus or shortage.

[0122] Step 4.5, based on the supply and demand status of the power system and the supply and demand status of the hydrogen energy system, formulate a first type of scheduling plan for the electric-hydrogen coupled micro-energy network within a set time period with the goal of maximizing energy utilization efficiency, and formulate a second type of scheduling plan for the electric-hydrogen coupled micro-energy network within a set time period with the goal of minimizing operating costs; based on the first type of scheduling plan and the second type of scheduling plan, use an optimization algorithm to determine the optimal scheduling plan for the electric-hydrogen coupled micro-energy network within the set time period.

[0123] Based on the energy balance results and system operating status, the coordinated dispatching strategy of electricity and hydrogen energy systems is designed and optimized to achieve efficient operation of the energy network and improve the overall economic efficiency.

[0124] In the embodiment, the dynamic adjustment of energy flow is divided into two parts: adjusting energy flow according to the balance of electricity supply and demand and adjusting energy flow according to the balance of hydrogen supply and demand. The rule-based energy management strategy adjusts the flow of electric energy, and formulates detailed rules based on the operating status and efficiency of each component of the power system (power generation equipment, energy storage equipment, load, etc.). When there is a surplus of electricity in the power system, if the battery energy storage device is full or the charging efficiency is lower than the set threshold, and the water electrolysis hydrogen production device has the ability to produce hydrogen and the hydrogen production efficiency is If it is higher than the set value, the surplus energy will be According to a certain proportion, it is directed to the water electrolysis hydrogen production device, converting the electrical energy into hydrogen and storing it. The remaining electrical energy The allocation is based on the rules for maintaining grid stability.

[0125] ,

[0126] In the formula, For time point of surplus electricity, For time point of available electrical energy, For time point The corrected power load simulation value, For time point The hydrogen production power, is the hydrogen production efficiency, For time point of remaining electrical energy.

[0127] When there is a power shortage in the power system, if the battery energy storage has power (Q(t)>0), the power is released from the battery first to supplement the power demand. The power released is based on the remaining power of the battery and the discharge efficiency. and the extent of power shortage is determined;

[0128] ,

[0129] In the formula, For time point The release power of the battery energy storage device, For time point The power of the battery energy storage device, is the discharge efficiency, is the discharge time.

[0130] If the battery energy storage is insufficient and the fuel cell has the ability to generate electricity, the fuel cell will be started to use the stored hydrogen to generate electricity to supplement the power supply. The power generation power of the fuel cell is calculated and determined according to pre-set rules based on factors such as the hydrogen storage amount, the fuel cell power generation efficiency, and the power shortage, ensuring that energy utilization efficiency is optimized while meeting power demand.

[0131] The dynamic optimization control method is used to balance the supply and demand of hydrogen energy. First, the objective function is to minimize the sum of hydrogen storage cost and supply cost. At the same time, constraints are set, including capacity limits of hydrogen storage facilities. Based on real-time data such as hydrogen load demand, hydrogen storage facility status (current storage volume, pressure, etc.) and external hydrogen supply, the flow of hydrogen storage and supply to external users or external hydrogen is determined through classical planning algorithms. When there is a hydrogen surplus in the hydrogen energy system, if the hydrogen storage facility is not full, the excess hydrogen will be stored; if the hydrogen storage facility is full and there is external hydrogen demand, the hydrogen flow supplied to external users is determined based on the optimization calculation results to maximize economic benefits. When there is a shortage of hydrogen in the hydrogen energy system, if there is a surplus of renewable energy power generation and the water electrolysis hydrogen production device has the capacity to produce hydrogen, the power of water electrolysis hydrogen production is increased to increase the hydrogen output. The increased hydrogen production power is determined through optimization calculation based on the degree of hydrogen shortage, the surplus power of renewable energy power generation and the operating characteristics of the water electrolysis hydrogen production device. If there is no surplus of renewable energy power generation and there is an external hydrogen supply source, hydrogen is obtained from the outside to supplement the supply. The amount obtained is determined through optimization calculation based on factors such as the shortage, supply cost and supply reliability, so as to balance the supply and demand of hydrogen energy.

[0132] The optimization and coordinated scheduling strategy includes three parts: optimizing the scheduling strategy based on the energy balance results, optimizing the scheduling strategy considering the system operation status, and using the optimization algorithm to determine the best scheduling plan. In the stage of dynamically adjusting the energy flow, the present invention uses a rule-based energy management strategy and a dynamic optimization control method to adjust the energy flow according to the balance of electricity supply and demand and the balance of hydrogen supply and demand, respectively. In the stage of optimizing the coordinated scheduling strategy, a predictive control method, a state feedback control method, and a mixed integer linear programming algorithm are used to comprehensively consider the energy balance results and the system operation status to determine the best scheduling plan. Through these steps, the present invention can effectively cope with load fluctuations and uncertainties of renewable energy, realize the efficient operation of the electric hydrogen coupled micro-energy network, and improve the efficiency and economy of the integrated energy network.

[0133] In the embodiment, the predictive control method is used to optimize the scheduling strategy, providing a forward-looking information basis for the entire optimization and coordinated scheduling strategy. First, the energy supply and demand situation in the next 1-24 hours is predicted by combining the ARIMA model with the meteorological forecast data and the load demand forecast data. At the beginning of each hour, based on the operating status of each device at the current moment, the energy storage capacity, etc., the optimal control sequence such as the power generation power setting value of the power generation equipment, the charging and discharging power setting value of the energy storage equipment, and the hydrogen production power setting value of the hydrogen production device in the next few control cycles is calculated. By repeatedly predicting and calculating the optimal control sequence, the dynamic optimization scheduling of the energy network is realized, so that the performance indicators of the system in the future prediction time domain are optimized.

[0134] Use the state feedback control method to adjust the scheduling strategy and supplement and correct the above links in real time. Monitor the equipment operating status of the electric hydrogen coupled micro-energy network in real time, including the operating efficiency of power generation equipment such as wind turbines, solar panels, fuel cells, the remaining capacity of batteries and hydrogen storage tanks, the internal resistance changes of batteries, capacity attenuation, the pressure stability of hydrogen storage tanks and other health conditions, and the transmission losses of transmission lines and pipelines. Feedback this operating status information into the scheduling strategy and adjust the control strategy according to the current actual state of the system. Establish a priority list based on the operating status of the equipment, increase the priority of equipment with high operating efficiency and good health, and give priority to it in scheduling; reduce the priority of equipment with poor operating status, reduce its use or arrange maintenance. When the operating status of the equipment changes, update the priority list in real time, dynamically adjust the distribution of energy supply, and ensure the stable and efficient operation of the energy network.

[0135] On the basis of the above two links, the mixed integer linear programming (MILP) algorithm is used for comprehensive decision-making. The objective function is to minimize the operating cost of the energy network and maximize the energy utilization efficiency. At the same time, set constraints, including equipment operation parameter constraints such as the maximum power and minimum power limit of power generation equipment, the maximum capacity and minimum capacity limit of energy storage equipment, etc., and energy supply and demand balance constraints, including power balance and mass flow balance constraints of the power system and hydrogen energy system in each time step, and equipment start and stop state constraints. In each time step, according to the current load demand, renewable energy power generation, equipment operation status and other real-time data and energy supply and demand balance, equipment remaining capacity and other model calculation results, these data are substituted into the MILP model, and the CPLEX solver in MATLAB is used to solve the optimal scheduling decision, including the production, storage, conversion and distribution of electricity and hydrogen energy. By repeatedly solving the solution process, dynamic optimization scheduling is achieved, and the scheduling plan is adjusted in time according to the real-time changes of the energy network to improve the efficiency and economy of the integrated energy network.

[0136] The present invention also proposes a timing simulation analysis system for an electric hydrogen coupled micro energy network suitable for urban areas, comprising:

[0137] The time series simulation data establishment module is used to calculate the weighted sum of the power load demand, gas load demand and hydrogen load demand at each time point within a set time period, and use the weighted sum of each time point to form the load simulation data within the set time period; obtain the wind power generation power prediction value, photovoltaic power generation power prediction value, wind farm meteorological data and photovoltaic power station meteorological data; use the load simulation data and wind power generation power prediction value, photovoltaic power generation power prediction value, wind farm meteorological data and photovoltaic power station meteorological data to form a time series simulation data set;

[0138] A timing simulation model building module is used to build a timing simulation model of an electrical-hydrogen coupled micro-energy network;

[0139] The timing simulation analysis module is used for the timing simulation model. It takes the timing simulation data set as input. After determining the supply and demand status of the power system and the supply and demand status of the hydrogen energy system, it formulates the first type of scheduling plan for the electric hydrogen coupled micro energy network within the set time period with the highest energy utilization efficiency as the goal, and formulates the second type of scheduling plan for the electric hydrogen coupled micro energy network within the set time period with the lowest operating cost as the goal; based on the first type of scheduling plan and the second type of scheduling plan, an optimization algorithm is used to determine the optimal scheduling plan for the electric hydrogen coupled micro energy network within the set time period.

[0140] By calculating the energy supply and demand balance in real time and dynamically adjusting the energy flow, the present invention can more effectively manage the supply and demand of electricity and hydrogen energy, reduce energy waste, and improve energy utilization efficiency. In addition, the collaborative scheduling strategy is optimized. Using the predictive control method and the state feedback control method, the present invention can optimize the scheduling strategy, realize the dynamic optimization scheduling of the energy network, and optimize the performance indicators of the system in the future prediction time domain. Finally, the energy utilization efficiency is improved while reducing the operating cost. Using the mixed integer linear programming algorithm for comprehensive decision-making, with the lowest operating cost of the energy network and the highest energy utilization efficiency as the objective function, the present invention can effectively reduce the operating cost and improve the energy utilization efficiency.

[0141] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0142] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the above. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the above. The computer-readable storage medium used herein is not to be interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0143] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0144] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect through the Internet). In some embodiments, by using the state information of the computer-readable program instructions to personalize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit may execute the computer-readable program instructions, thereby implementing various aspects of the present disclosure.

[0145] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A timing simulation analysis method for an urban area electric hydrogen coupling energy network, characterized in that: include: In a set time period, the weighted sum of the power load demand, gas load demand and hydrogen load demand at each time point is calculated, and the weighted sum of each time point constitutes the load simulation data in the set time period; Obtain wind power generation prediction value, photovoltaic power generation prediction value, wind farm meteorological data and photovoltaic power station meteorological data; Establish a timing simulation model of the electrical-hydrogen coupled micro-energy network; A time series simulation data set is formed by load simulation data and wind power prediction values, photovoltaic power prediction values, meteorological data of wind farms and meteorological data of photovoltaic power stations; the time series simulation model takes the time series simulation data set as input, and takes the power load simulation value, hydrogen load simulation value, wind power simulation value and photovoltaic power simulation value corrected according to the meteorological data of wind farms and the meteorological data of photovoltaic power stations as output; after determining the supply and demand status of the power system and the supply and demand status of the hydrogen energy system by using the corrected power load simulation value, hydrogen load simulation value, wind power simulation value and photovoltaic power simulation value, a first type of scheduling plan for the electric hydrogen coupled micro energy network within a set time period is formulated with the highest energy utilization efficiency as the goal, and a second type of scheduling plan for the electric hydrogen coupled micro energy network within a set time period is formulated with the lowest operating cost as the goal; based on the first type of scheduling plan and the second type of scheduling plan, an optimization algorithm is used to determine the optimal scheduling plan for the electric hydrogen coupled micro energy network within the set time period.

2. The timing simulation analysis method of the urban area electric hydrogen coupling energy network according to claim 1 is characterized in that: In a set time period, the weighted sum of the power load demand, gas load demand and hydrogen load demand at each time point is calculated, and the weighted sum of each time point constitutes the load simulation data in the set time period, including: The multivariate linear regression method is used to obtain the power load demand, gas load demand and hydrogen load demand within a set time period; Select the predicted value of electric load, gas load and hydrogen load within a set time period; Calculate the first distance between the predicted power load value and the power load demand, the second distance between the predicted gas load value and the gas load demand, and the third distance between the predicted hydrogen load value and the hydrogen load demand at each time point; use the ratio of the first distance at each time point to the sum of the three distances as the weight of the power load demand at each time point, use the ratio of the second distance at each time point to the sum of the three distances as the weight of the gas load demand at each time point, and use the ratio of the third distance at each time point to the sum of the three distances as the weight of the hydrogen load demand at each time point; The determined weights are used to calculate the weighted sum of the electricity load demand, gas load demand and hydrogen load demand at each time point, and the weighted sum of each time point constitutes the load simulation data within the set time period.

3. The timing simulation analysis method of the urban area electric hydrogen coupling energy network according to claim 2 is characterized in that: The weight of the electricity load demand, the weight of the gas load demand, and the weight of the hydrogen load demand at each time point satisfy the following relationships: In the formula, For time point The weight of the power load demand, For time point The weight of the gas load demand, For time point The weight of the hydrogen load demand, For time point The power load forecast value and power load demand The Fréchet distance between For time point Gas load forecast value Gas load demand The Fréchet distance between For time point Hydrogen load prediction value Hydrogen load demand The Fréchet distance between .

4. The timing simulation analysis method of the urban area electric hydrogen coupling energy network according to claim 1 is characterized in that: Obtain wind power generation prediction values ​​and photovoltaic power generation prediction values, including: Obtain historical data of wind power generation to form a time series, calculate the autoregressive order of the autoregressive moving average model through the autocorrelation function of the time series, and calculate the moving average order of the autoregressive moving average model through the partial autocorrelation function of the time series; use the autoregressive moving average model after the autoregressive order and the moving average order are determined to determine the predicted value of wind power generation according to the historical data of wind power generation; The historical data of photovoltaic power generation is obtained to form a time series, and the autoregressive order of the autoregressive integrated moving average model is calculated by the autocorrelation function of the time series. The moving average order of the autoregressive integrated moving average model is calculated by the partial autocorrelation function of the time series. The predicted value of photovoltaic power generation is determined according to the historical data of photovoltaic power generation by using the autoregressive integrated moving average model after the autoregressive order and the moving average order are determined.

5. The timing simulation analysis method for the urban area electric hydrogen coupling energy network according to claim 1 is characterized in that: Obtain meteorological data for wind farms and photovoltaic power plants, including: Based on the geographical weighted regression model, the meteorological data of the wind farm and the meteorological data of the photovoltaic power station are determined according to the geographical location coordinates of the meteorological monitoring station and the meteorological monitoring data; Meteorological monitoring data include: temperature, humidity, wind speed, wind direction, and solar radiation intensity.

6. The timing simulation analysis method of the urban area electric hydrogen coupling energy network according to claim 1 is characterized in that: The timing simulation model of the electric-hydrogen coupled micro-energy network includes: power system model, hydrogen energy system model and electric-hydrogen coupled system model; The power system model includes a transmission line model and a power generation facility module. The transmission line model is established based on circuit theory, and the power generation facility module is established according to the power system dynamic equation. The hydrogen energy system model includes a reaction rate model of a water electrolysis hydrogen production device and a hydrogen state change model of a hydrogen storage facility. The reaction rate model of a water electrolysis hydrogen production device is established based on the principle of chemical reaction kinetics, and the hydrogen state change model of a hydrogen storage facility is established based on the principle of thermodynamics. The electric-hydrogen coupling system model is established by using the electric-hydrogen coupling efficiency and the design parameters, operating temperature and operating current density of the electrolyzer, satisfying the following relationship: In the formula, is the electric-hydrogen coupling efficiency, Design parameters for the electrolyzer, is the operating temperature, is the operating current density, It is a function of the relationship between the electric-hydrogen coupling efficiency and the design parameters, operating temperature and operating current density of the electrolyzer.

7. The timing simulation analysis method for the urban area electric hydrogen coupling energy network according to claim 6 is characterized in that: The least squares method is used to determine the parameters of the timing simulation model, including: The actual parameters and historical operation data of each device in the electric hydrogen coupled micro energy network are used as the initial parameters of the time series simulation model, and the historical data of energy supply and demand and the operation data of the equipment are used as the observation data of the time series simulation model; the historical data of energy supply and demand include: historical data of wind power generation, historical data of photovoltaic power generation, historical data of load demand, and historical data of hydrogen production; the operation data of the equipment include: operation time, number of starts and stops, and fault records; Taking the electric-hydrogen coupling efficiency as the parameter to be identified, an error function is established based on the least squares method; the design parameters, operating temperature and operating current density of the electrolyzer when the error function is minimized are used as the optimization parameters of the electric-hydrogen coupling system model; The parameters of the power system model and the electric-hydrogen coupling efficiency are combined to form a set of parameters to be identified, and an error function is established based on the least squares method; the parameters of the power system model when the error function is minimized are used as the optimization parameters of the power system model; The parameters of the hydrogen energy system model and the electric-hydrogen coupling efficiency jointly constitute a set of parameters to be identified, and an error function is established based on the least squares method. The parameters of the hydrogen energy system model when the error function is minimized are used as the optimization parameters of the hydrogen energy system model.

8. The timing simulation analysis method for the urban area electric hydrogen coupling energy network according to claim 1 is characterized in that: The time series simulation model uses the time series simulation data set as input to determine the supply and demand status of the power system and the supply and demand status of the hydrogen energy system, including: The time series simulation data set is input into the constructed time series simulation model of the electrical hydrogen coupled micro energy network in chronological order; In the time series simulation model, the electric load simulation value and the hydrogen load simulation value are determined according to the input load simulation data, the wind power generation power simulation value and the photovoltaic power generation power simulation value corresponding to the electric load simulation value and the hydrogen load simulation value are determined according to the wind power generation power prediction value and the photovoltaic power generation power prediction value, and the electric load simulation value, the hydrogen load simulation value, the wind power generation power simulation value and the photovoltaic power generation power simulation value are dynamically corrected according to the meteorological data of the wind farm and the meteorological data of the photovoltaic power station; Using the corrected power load simulation value, wind power generation power simulation value and photovoltaic power generation power simulation value, the power system supply and demand status, including power surplus and power shortage, is obtained based on the power supply and demand balance simulation; Using the corrected hydrogen load simulation value, wind power generation power simulation value and photovoltaic power generation power simulation value, the supply and demand status of the hydrogen energy system, including hydrogen energy surplus and hydrogen energy shortage, is obtained based on the mass flow balance simulation.

9. A time series simulation and analysis system for an urban area electric hydrogen coupled energy network, characterized in that: include: The time series simulation data establishment module is used to calculate the weighted sum of the power load demand, gas load demand and hydrogen load demand at each time point within a set time period, and use the weighted sum of each time point to form the load simulation data within the set time period; obtain the wind power generation power prediction value, photovoltaic power generation power prediction value, wind farm meteorological data and photovoltaic power station meteorological data; use the load simulation data and wind power generation power prediction value, photovoltaic power generation power prediction value, wind farm meteorological data and photovoltaic power station meteorological data to form a time series simulation data set; A timing simulation model building module is used to build a timing simulation model of an electrical-hydrogen coupled micro-energy network; The timing simulation analysis module is used for the timing simulation model, which takes the timing simulation data set as input and takes the power load simulation value, hydrogen load simulation value, wind power generation power simulation value and photovoltaic power generation power simulation value corrected according to the meteorological data of the wind farm and the meteorological data of the photovoltaic power station as output; after determining the supply and demand status of the power system and the supply and demand status of the hydrogen energy system by using the corrected power load simulation value, hydrogen load simulation value, wind power generation power simulation value and photovoltaic power generation power simulation value, a first type of scheduling plan for the electric hydrogen coupled micro energy network within a set time period is formulated with the highest energy utilization efficiency as the goal, and a second type of scheduling plan for the electric hydrogen coupled micro energy network within a set time period is formulated with the lowest operating cost as the goal; based on the first type of scheduling plan and the second type of scheduling plan, an optimization algorithm is used to determine the optimal scheduling plan for the electric hydrogen coupled micro energy network within the set time period.

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