A time-series production simulation method and system based on energy interconnection

Abstract

A time-series production simulation method based on energy interconnection includes the following steps: Step 1, define the scope and objectives of the energy system to be simulated, and establish a mathematical model of the energy system; Step 2, collect various data required for the operation of the energy system; clean and process the data; evaluate the quality and reliability of the collected data; manage the data documents and metadata; Step 3, implement simulation based on the established mathematical model and prepared data; Step 4, introduce a multi-objective optimization algorithm for multi-objective optimization problems in the energy system; optimize and solve the multi-objective optimization problem according to the multi-objective optimization algorithm, select the optimal solution or provide a solution for decision support; Step 5, optimize the operation effect of the energy system by adjusting the parameters and constraints in the mathematical model, and verify it; Step 6, evaluate the operation of the energy system through indicators. In this design, the energy system has high accuracy and high data validity.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid planning, and in particular to a time-series production simulation method and system based on energy interconnection. Background Art

[0002] Time-sequence production simulation of energy interconnection is a technology used to simulate and optimize the operation of energy systems. It simulates the operation of energy systems by establishing a mathematical model of the energy system, considering the characteristics of different energy sources, consumption and conversion, and the time-sequence relationship between supply and demand. It simulates the production, transfer, storage and consumption of energy in the system by considering the energy flow and conversion process between different energy nodes and the interaction with the external environment.

[0003] Although the existing time-series production simulation of energy interconnection provides valuable tools and methods to optimize the operation of energy systems, it lacks in-depth reliability analysis of power systems and has difficulties in data collection, resulting in poor accuracy of energy interconnection systems and low data validity. Summary of the invention

[0004] The purpose of the present invention is to overcome the defects and problems of poor accuracy and low data validity in the time series production simulation in the prior art, and to provide a time series production simulation method and system based on energy interconnection with high accuracy and high data validity.

[0005] To achieve the above objectives, the technical solution of the present invention is: a time-series production simulation method based on energy interconnection, comprising the following steps:

[0006] Step 1: Define the scope and objectives of the energy system to be simulated and establish a mathematical model of the energy system;

[0007] Step 2: Collect various data required for the operation of the energy system, and pre-process and evaluate the collected data;

[0008] Step 3: Conduct simulation experiments based on the energy system mathematical model and energy system operation data;

[0009] Step 4: Introduce a multi-objective optimization algorithm for the multi-objective optimization problem in the energy system, optimize and solve the multi-objective optimization problem according to the multi-objective optimization algorithm, and select the optimal solution or the solution that provides decision support;

[0010] Step 5: By adjusting the parameters and constraints in the mathematical model of the energy system, different energy scheduling strategies and technical solutions are selected to optimize the operation effect of the energy system, and the optimized mathematical model of the energy system is verified and corrected;

[0011] Step 6: Compare the simulation results of the energy system mathematical model under different energy scheduling strategies and technical solutions, and evaluate the stability and reliability of the simulation results;

[0012] Step 7: Based on the evaluation results, select the best energy scheduling strategy and technical solution and establish a visualization model.

[0013] The specific steps of step one are:

[0014] Select an integrated energy network system, which includes multiple energy systems, select the energy system that needs to be simulated from the integrated energy network system, list the problems that need to be solved in the selected energy system, and establish a mathematical model of the energy system for the problems that need to be solved.

[0015] In step 2, the specific steps of preprocessing the collected data are:

[0016] Before collecting data, analyze the data requirements to determine the required data type, format, and range, then use equipment to collect the data and store it in the corresponding files;

[0017] The collected data are merged, and the missing values ​​and outliers in the data are processed. After processing, the data format is converted into a format acceptable to the mathematical model of the energy system.

[0018] In step 2, the specific steps for evaluating the collected data are:

[0019] Assess the quality and reliability of the data collected by reviewing the technical specifications of the equipment;

[0020] Compare data collected by different devices and evaluate the consistency of the collected data;

[0021] The evaluated data is distributed by drawing a graph.

[0022] The specific steps of step three are:

[0023] Set initial values ​​for parameters in the energy system mathematical model and determine the required simulation time series, including time steps and simulation periods;

[0024] Select parameters in the mathematical model of the energy system as key parameters and perform simulation experiments;

[0025] Analyze the simulation results;

[0026] By introducing different scenarios, performing simulation experiments on each scenario, comparing the simulation results under different scenarios, and analyzing the simulation experiments of the energy system under different scenario conditions.

[0027] The specific steps of step 4 are:

[0028] According to multiple objectives and constraints in the energy system, clarify the goal of energy system optimization and introduce multi-objective optimization algorithms;

[0029] Determine the weight and priority of each objective, and select the algorithm suitable for solving the multi-objective optimization problem based on the performance, convergence speed and adaptability of each algorithm to the multi-objective optimization problem;

[0030] According to the characteristics and requirements of multi-objective optimization problems, optimization solutions are performed, the Pareto frontier structure is generated and analyzed and evaluated, and the optimal solution or the solution that provides decision support is selected according to different objective weights and priorities.

[0031] The specific steps of step five are:

[0032] Adjust the parameters and constraints of the mathematical model of the energy system;

[0033] In the process of optimization and improvement, explore various energy scheduling strategies and technical solutions, determine specific energy scheduling strategies and technical solutions, and optimize the operation effect of the energy system;

[0034] The optimized and improved energy system mathematical model is verified by comparing it with the actual operating data. During the verification process, if the prediction of the energy system mathematical model is inconsistent with the actual data, the parameters of the energy system mathematical model are adjusted by using the correction method by comparing the difference between the actual data and the output of the energy system mathematical model.

[0035] The specific steps of step six are:

[0036] Compare the simulation results of the energy system mathematical model under different energy scheduling strategies and technical solutions, and compare the performance of the simulation results under different indicators;

[0037] Analyze the advantages and disadvantages of different technical solutions, as well as the feasibility of technical solutions under different operating conditions;

[0038] By changing one or more key parameters, observing the changes in simulation results under different indicators, the stability and reliability of the simulation results can be evaluated.

[0039] The step seven also includes the following steps:

[0040] After building the visualization model, develop interactive features to enable users to interact with the visualization tool and obtain more information.

[0041] A time-series production simulation system based on energy interconnection, the system comprising a data acquisition module, a data processing and preprocessing module, a model building module, a parameter setting and simulation configuration module, a simulation execution module, a multi-objective setting and optimization module, a data analysis and visualization module, and an optimization and decision support module;

[0042] The data acquisition module is used to collect various data required for the operation of the simulation system;

[0043] The data processing module is used to process the data collected by the data acquisition module;

[0044] The model building module is used to build a mathematical or statistical model of the simulation system;

[0045] The parameter setting and simulation configuration module is used to set the parameters and configuration of the simulation experiment;

[0046] The simulation execution module is used to actually execute the simulation experiment, and based on the set parameters and input conditions, simulate the operation process of the system and generate corresponding simulation results;

[0047] The multi-objective setting and optimization module is used to consider multiple objectives for the simulation system and provide a selection space for more optimized decision-making;

[0048] The optimization and decision support module is used to provide optimization and decision support for the simulation results of the simulation execution module;

[0049] The data analysis and visualization module is used to analyze and visualize the simulation results of the simulation execution module.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] 1. In a time-series production simulation method and system based on energy interconnection of the present invention, a mathematical model is established according to the boundary and scope of the energy interconnection system, starting from multiple goals of the energy system, more data are collected, and after analyzing and filtering these data, valid data is obtained to improve the effectiveness and authority of the data after acquisition; by introducing a multi-objective optimization algorithm for optimization and solution, it can help decision makers choose the optimal solution or provide a series of trade-offs; by selecting different energy scheduling strategies and technical solutions, the energy system is optimized and improved again according to the simulation results, ensuring that the mathematical model of the energy system can accurately reflect the operation of the actual system, so as to further improve the accuracy of the energy system; through the evaluation results, the advantages and disadvantages of different technical solutions can be deeply understood, and the performance of the energy system under different parameter changes can be understood, and the stability and reliability of the model results can be evaluated, which can provide an important basis for the final decision, so as to ensure that the selected energy system technical solution can have good performance under various conditions. Therefore, the energy system of the present invention has high accuracy and high data validity.

[0052] 2. In a method and system for simulating sequential production based on energy interconnection of the present invention, a suitable modeling method is selected according to the specific requirements of the problem and the characteristics of the data, and a module is established according to the model; data is collected through a data acquisition module; the collected raw data is processed and preprocessed through a data processing module to make it suitable for simulation modeling; the initial state of the production equipment, the requirements of the production task, the energy supply strategy and other parameters can be set through the parameter setting and simulation configuration module, so as to facilitate the simulation of the operation of the production system under different conditions; the operation process of the system can be simulated through the simulation execution module, and the corresponding simulation results can be generated; by introducing a multi-objective setting and optimization module, the sequential production simulation system of energy interconnection can consider multiple objectives more comprehensively and comprehensively, provide a more optimized decision-making selection space, and promote the sustainability of the system; the simulation results are statistically analyzed, trend analyzed, and energy utilization rate evaluated through a data analysis and visualization module, so that an in-depth insight into the production process can be obtained, which can help users understand the simulation results more intuitively, identify problems and optimization opportunities; optimization and decision support are provided through the optimization and decision support module, which can help users find the optimization plan for the production process, improve production efficiency, energy utilization rate, etc., and provide data support for decision making. Therefore, the invented energy system has higher accuracy and higher data validity. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a flow chart of a method for simulating sequential production based on energy interconnection in the present invention.

[0054] Figure 2 It is a structural block diagram of a time-series production simulation system based on energy interconnection in the present invention.

[0055] In the figure: data acquisition module 1, data processing module 2, model building module 3, parameter setting and simulation setting module 4, simulation execution module 5, multi-objective setting and optimization module 6, data analysis and visualization module 7, optimization and decision support module 8. DETAILED DESCRIPTION

[0056] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0057] See also Figure 1 , a time-series production simulation method based on energy interconnection, comprising the following steps:

[0058] Step 1: Define the scope and objectives of the energy system to be simulated and establish a mathematical model of the energy system;

[0059] Step 2: Collect various data required for the operation of the energy system, and pre-process and evaluate the collected data;

[0060] Step 3: Conduct simulation experiments based on the energy system mathematical model and energy system operation data;

[0061] Step 4: Introduce a multi-objective optimization algorithm for the multi-objective optimization problem in the energy system, optimize and solve the multi-objective optimization problem according to the multi-objective optimization algorithm, and select the optimal solution or the solution that provides decision support;

[0062] Step 5: By adjusting the parameters and constraints in the mathematical model of the energy system, different energy scheduling strategies and technical solutions are selected to optimize the operation effect of the energy system, and the optimized mathematical model of the energy system is verified and corrected;

[0063] Step 6: Compare the simulation results of the energy system mathematical model under different energy scheduling strategies and technical solutions, and evaluate the stability and reliability of the simulation results;

[0064] Step 7: Based on the evaluation results, select the best energy scheduling strategy and technical solution and establish a visualization model.

[0065] The specific steps of step one are:

[0066] Select an integrated energy network system, which includes multiple energy systems, select the energy system that needs to be simulated from the integrated energy network system, list the problems that need to be solved in the selected energy system, and establish a mathematical model of the energy system for the problems that need to be solved.

[0067] In step 2, the specific steps of preprocessing the collected data are:

[0068] Before collecting data, analyze the data requirements to determine the required data type, format, and range, then use equipment to collect the data and store it in the corresponding files;

[0069] The collected data are merged, and the missing values ​​and outliers in the data are processed. After processing, the data format is converted into a format acceptable to the mathematical model of the energy system.

[0070] In step 2, the specific steps for evaluating the collected data are:

[0071] Assess the quality and reliability of the data collected by reviewing the technical specifications of the equipment;

[0072] Compare data collected by different devices and evaluate the consistency of the collected data;

[0073] The evaluated data is distributed by drawing a graph.

[0074] The specific steps of step three are:

[0075] Set initial values ​​for parameters in the energy system mathematical model and determine the required simulation time series, including time steps and simulation periods;

[0076] Select parameters in the mathematical model of the energy system as key parameters and perform simulation experiments;

[0077] Analyze the simulation results;

[0078] By introducing different scenarios, performing simulation experiments on each scenario, comparing the simulation results under different scenarios, and analyzing the simulation experiments of the energy system under different scenario conditions.

[0079] The specific steps of step 4 are:

[0080] According to multiple objectives and constraints in the energy system, clarify the goal of energy system optimization and introduce multi-objective optimization algorithms;

[0081] Determine the weight and priority of each objective, and select the algorithm suitable for solving the multi-objective optimization problem based on the performance, convergence speed and adaptability of each algorithm to the multi-objective optimization problem;

[0082] According to the characteristics and requirements of multi-objective optimization problems, optimization solutions are performed, the Pareto frontier structure is generated and analyzed and evaluated, and the optimal solution or the solution that provides decision support is selected according to different objective weights and priorities.

[0083] The specific steps of step five are:

[0084] Adjust the parameters and constraints of the mathematical model of the energy system;

[0085] In the process of optimization and improvement, explore various energy scheduling strategies and technical solutions, determine specific energy scheduling strategies and technical solutions, and optimize the operation effect of the energy system;

[0086] The optimized and improved energy system mathematical model is verified by comparing it with the actual operating data. During the verification process, if the prediction of the energy system mathematical model is inconsistent with the actual data, the parameters of the energy system mathematical model are adjusted by using the correction method by comparing the difference between the actual data and the output of the energy system mathematical model.

[0087] The specific steps of step six are:

[0088] Compare the simulation results of the energy system mathematical model under different energy scheduling strategies and technical solutions, and compare the performance of the simulation results under different indicators;

[0089] Analyze the advantages and disadvantages of different technical solutions, as well as the feasibility of technical solutions under different operating conditions;

[0090] By changing one or more key parameters, observing the changes in simulation results under different indicators, the stability and reliability of the simulation results can be evaluated.

[0091] The step seven also includes the following steps:

[0092] After building the visualization model, develop interactive features to enable users to interact with the visualization tool and obtain more information.

[0093] See also Figure 2 , a time-series production simulation system based on energy interconnection, the system comprising a data acquisition module 1, a data processing and preprocessing module 2, a model building module 3, a parameter setting and simulation configuration module 4, a simulation execution module 5, a multi-objective setting and optimization module 6, a data analysis and visualization module 7 and an optimization and decision support module 8;

[0094] The data acquisition module 1 is used to collect various data required for the operation of the simulation system;

[0095] The data processing module 2 is used to process the data collected by the data acquisition module 1;

[0096] The model building module 3 is used to build a mathematical or statistical model of the simulation system;

[0097] The parameter setting and simulation configuration module 4 is used to set the parameters and configuration of the simulation experiment;

[0098] The simulation execution module 5 is used to actually execute the simulation experiment, and based on the set parameters and input conditions, simulate the operation process of the system and generate corresponding simulation results;

[0099] The multi-objective setting and optimization module 6 is used to consider multiple objectives for the simulation system and provide a selection space for more optimized decision-making;

[0100] The optimization and decision support module 8 is used to provide optimization and decision support for the simulation results of the simulation execution module 5;

[0101] The data analysis and visualization module 7 is used to analyze and visualize the simulation results of the simulation execution module 5.

[0102] The principle of the present invention is described as follows:

[0103] The energy system in the present invention can be a complex integrated energy network, covering the production, transmission, storage and consumption of multiple energy sources such as electricity, heat, gas, etc. It can include power plants, power grids, wind and solar power generation facilities, energy storage systems, industrial production facilities and other aspects.

[0104] Embodiment 1:

[0105] See also Figure 1 , a time-series production simulation method based on energy interconnection, comprising the following steps:

[0106] Step 1: Select an integrated energy network system, which includes power plants, power grids, wind and solar power generation facilities, energy storage systems, industrial production facilities and other energy systems. Select the energy system to be simulated from the integrated energy network system and list the problems that need to be solved in the selected energy system, including the following problems:

[0107] Optimizing energy production efficiency: ensuring that the production process of the energy system is carried out under efficient conditions, converting input energy into useful output energy to the greatest extent possible, thereby reducing energy loss;

[0108] Carbon Emission Reduction: Reducing the system’s carbon footprint by optimizing energy production and consumption to support environmental sustainability and climate change mitigation;

[0109] Maximizing economic benefits: Taking into account energy supply and demand, maximize the economic benefits of the energy system and reduce energy costs through reasonable energy allocation and scheduling strategies;

[0110] Improved reliability of energy supply: Optimize energy production and transmission to ensure the stability and reliability of energy supply to meet the needs of different users and sectors;

[0111] Renewable energy integration: If the system includes renewable energy (such as wind power, solar power, etc.), the goal may be to maximize the utilization of renewable energy and reduce dependence on non-renewable energy;

[0112] Enhanced energy system resilience: Taking into account energy market fluctuations and emergency situations, optimizing energy system dispatch strategies and improving adaptability to external disturbances;

[0113] Balance between effectiveness and efficiency: balance the overall effectiveness of the energy system and the efficiency of each subsystem to ensure optimal overall performance;

[0114] Social acceptability: taking social and policy factors into account to ensure that the optimization of the energy system is feasible in terms of law, policy and social acceptability;

[0115] In view of the problems to be solved, the mathematical model of the energy system is formed and established according to the following formula:

[0116] Energy conservation equations: For different components in an energy system, such as power plants, transmission lines, energy storage devices, etc., the energy conservation equations are the basic starting point:

[0117] Ein=Eout+Loss

[0118] Among them, Ein represents input energy, Eout represents output energy, and Loss represents loss energy;

[0119] Power generation model: Different types of power generation equipment (coal, wind, solar, etc.) will have different power generation models to describe the relationship between their power output and input parameters:

[0120] P=C*V3

[0121] Among them, P represents power output and V represents wind speed;

[0122] Energy storage model: For energy storage systems, such as batteries and water pump energy storage, it is necessary to establish the relationship between parameters such as charging and discharging efficiency, capacity, and charging and discharging rate to describe the energy storage and release process under different states:

[0123] dE / dt=Eff*Pin-(1 / Eff)*Pout

[0124] Among them, E represents the stored energy, Eff represents the charge and discharge efficiency, Pin represents the charging power, and Pout represents the discharging power;

[0125] Transmission loss model: There will be a certain amount of transmission loss during power transmission, and its magnitude is related to the transmission distance, wire material, etc. The resistivity and wire length can be used to calculate the power transmission loss:

[0126] Ploss=I2*R=(V / R)2*R=V2 / R

[0127] Among them, Ploss represents power loss, R represents resistance, I represents current, and L represents wire length;

[0128] Energy market model: If the simulation involves energy market transactions, it may be necessary to establish models such as supply and demand relationships and price changes to consider the impact of market factors on the energy system:

[0129] Pdemand=Psupply, Price=f(Pdemand,Psupply)

[0130] Among them, Pdemand represents the demand power, Psupply represents the supply power, and Price represents the price;

[0131] Scheduling strategy model: Different energy scheduling strategies may be based on optimization algorithms, rules, etc., which can be expressed by mathematical functions, such as the objective function or constraints of the optimization problem;

[0132] Environmental factor model: If we consider the impact of environmental factors on energy, such as the impact of temperature on solar power generation, we can introduce relevant physical relationships and formulas:

[0133] Efficiency = f(T)

[0134] Wherein, T represents temperature and Efficiency represents efficiency;

[0135] Economic cost model: When considering cost factors, a cost model can be established to relate different energy costs (production, transmission, storage, etc.) to system operation:

[0136] Cost=Cproduction+Ctransmission

[0137] Among them, Cost represents cost, Cproduction represents production cost, and Ctransmission represents transmission cost.

[0138] Step 2: Select a simplified wind power generation system, build a wind power generation system simulation, collect time series data of wind speed and power generation within one year by using wind speed sensors and generator monitoring systems, and store the time series data in a CSV file;

[0139] The wind speed and power generation data are merged by timestamp and missing values ​​are filled using linear interpolation. Based on the normal range of wind speed and power generation, outliers that are out of range are excluded. The timestamp format is converted to a time series format acceptable to the model.

[0140] Check the technical specifications of the sensors to ensure data quality and reliability, compare wind speed data measured by different sensors to ensure consistency, and draw distribution histograms of wind speed and power generation;

[0141] Mark possible anomalies, such as power generation changes under extreme wind speeds, using anomaly detection algorithms to automatically mark possible anomalies.

[0142] Record the information of wind speed sensors and generator monitoring systems; record the steps of data merging, missing value processing and outlier processing; record the physical meaning and units of wind speed and power generation.

[0143] Step 3: Set initial values ​​for the parameters in the mathematical model of the wind power generation system (assuming that there is no wind in the wind turbine and the energy storage device is in an unloaded state at the beginning), set the simulation time step to 1 hour, and define the total time range of the simulation to be one year (365 days);

[0144] The rated wind speed of the wind turbine is selected as the key parameter, and a sensitivity analysis is performed on the rated wind speed of the wind turbine. The rated wind speed is set to 10m / s, 12m / s, and 14m / s respectively. For each rated wind speed, a simulation run is performed, the power generation and storage status in each time step are recorded, and indicators such as power generation and storage status under different rated wind speeds are compared;

[0145] Introduce different wind speed scenarios, such as low wind speed, stable wind speed, and high wind speed; perform simulation runs for each wind speed scenario, and record the energy generation, transfer, storage, and consumption processes within the simulation cycle; compare the simulation results under different wind speed scenarios, and analyze the operation of the system under different wind speed conditions;

[0146] Step 4: Based on the goals of minimizing energy costs, maximizing energy sustainability, and minimizing environmental impact in the energy system, multi-objective optimization algorithms are introduced through constraints such as power generation requirements, energy storage capacity limitations, wind speed range limitations, and environmental impact limitations, including genetic algorithms, particle swarm algorithms, and multi-objective evolutionary algorithms;

[0147] Determine the weight and priority of each objective, and select the algorithm suitable for solving the multi-objective optimization problem based on the performance, convergence speed and adaptability of each algorithm to the multi-objective optimization problem;

[0148] According to the characteristics and requirements of multi-objective optimization problems, optimization solutions are performed, the Pareto frontier structure is generated and analyzed and evaluated, and the optimal solution or the solution that provides decision support is selected according to different objective weights and priorities.

[0149] Step 5: Adjust the wind turbine efficiency, capacity, starting wind speed and other parameters to better reflect the actual generator performance, set the maximum charging and discharging rate of the energy storage device, and the energy storage efficiency to accurately simulate the energy storage behavior, consider the constraints of the grid connection, and ensure a stable connection between the power generation system and the grid;

[0150] Try different energy scheduling strategies and technical solutions:

[0151] 1. Adjust the start and stop strategy of wind turbines, such as starting and stopping the generators in different wind speed ranges to optimize power generation efficiency;

[0152] 2. Introduce energy storage systems and explore different energy storage charging and discharging strategies to balance grid load fluctuations;

[0153] 3. Try intelligent control strategies to achieve more accurate energy scheduling based on weather forecasts and grid demand;

[0154] Use historical wind speed data and actual power generation data for verification, compare the difference between the power generation predicted by the model and the actual power generation, and verify the prediction accuracy of the model under different weather conditions, such as the power generation capacity under low wind speed, high wind speed, etc.;

[0155] If there is a deviation between the model prediction and the actual data, correction methods such as proportional correction or error correction can be used to adjust the parameters in the model, taking into account the maintenance records and performance data of the actual generator, and correcting the parameters in the model according to the actual situation to improve accuracy;

[0156] In step six, based on the previous optimization and improvement steps, the simulation results of different energy scheduling strategies and technical solutions were obtained, and the two strategies were evaluated based on indicators such as power generation, energy cost, charging and discharging efficiency of the energy storage system, grid connection rate, and carbon emissions.

[0157] 1. Power generation (MWh / year):

[0158] Strategy A: The wind speed is predicted more accurately and the maximum power generation can be achieved when there is wind.

[0159] Strategy B: An intelligent control system was introduced to adjust the start and stop strategies of the generators according to weather forecasts and grid demand to achieve more stable power generation.

[0160] 2.Energy cost (USD / year):

[0161] Strategy A: Since wind speed prediction is accurate, the generator operation is more stable and the cost is lower.

[0162] Strategy B: Although intelligent control is introduced, the operation and maintenance cost is slightly higher due to the need to maintain the start and stop frequency of the generator.

[0163] 3. Energy storage system charging and discharging efficiency (%):

[0164] Strategy A: Without energy storage system, this indicator is not applicable.

[0165] Strategy B: Energy storage systems play a role in balancing grid load fluctuations, but charging and discharging losses will affect efficiency.

[0166] 4. Grid connection rate (%):

[0167] Strategy A: Wind speed fluctuations may cause unstable grid connection, resulting in power outages during certain periods of time.

[0168] Strategy B: Grid connection stability may be higher due to the presence of intelligent control systems.

[0169] 5. Carbon emissions (tons / year):

[0170] Strategy A: In the case of no wind or low wind speed, it may be necessary to rely on traditional energy, resulting in certain carbon emissions.

[0171] Strategy B: Due to the enhanced stability of power generation, the use of traditional energy may be reduced, thereby reducing carbon emissions.

[0172] By gradually adjusting the accuracy parameters of wind speed prediction, the changes in indicators such as power generation and energy cost are observed to evaluate the sensitivity of the model to wind speed prediction.

[0173] Step 7, data visualization, build a visualization model based on the obtained results. Develop interactive features to enable users to interact with the visualization tool and get more information. This can include selecting specific data subsets, switching different chart views, zooming, sorting, filtering, and exploring the details of the simulation results.

[0174] Embodiment 2:

[0175] See also Figure 2 , a time-series production simulation system based on energy interconnection, the system comprising a data acquisition module 1, a data processing and preprocessing module 2, a model building module 3, a parameter setting and simulation configuration module 4, a simulation execution module 5, a multi-objective setting and optimization module 6, a data analysis and visualization module 7 and an optimization and decision support module 8;

[0176] The data acquisition module 1 is used to collect various data required for the operation of the simulation system;

[0177] The data processing module 2 is used to process the data collected by the data acquisition module 1;

[0178] The model building module 3 is used to build a mathematical or statistical model of the simulation system;

[0179] The parameter setting and simulation configuration module 4 is used to set the parameters and configuration of the simulation experiment;

[0180] The simulation execution module 5 is used to actually execute the simulation experiment, and based on the set parameters and input conditions, simulate the operation process of the system and generate corresponding simulation results;

[0181] The multi-objective setting and optimization module 6 is used to consider multiple objectives for the simulation system and provide a selection space for more optimized decision-making;

[0182] The optimization and decision support module 8 is used to provide optimization and decision support for the simulation results of the simulation execution module 5;

[0183] The data analysis and visualization module 7 is used to analyze and visualize the simulation results of the simulation execution module 5.

Claims

1. A time-series production simulation method based on energy interconnection, characterized in that: The following steps are involved: Step 1: Define the scope and objectives of the energy system to be simulated, select a comprehensive energy network system, which includes multiple energy systems, select the energy system to be simulated from the comprehensive energy network system, list the problems to be solved in the selected energy system, and establish a mathematical model of the energy system for the problems to be solved; Step 2: Collect various data required for the operation of the energy system. Before collecting data, analyze the data requirements, determine the required data type, format and range, and then use equipment to collect data and store it in the corresponding file; merge the collected data, and process the missing values ​​and abnormal values ​​in the data. After processing, convert the data format into a format acceptable to the mathematical model of the energy system and evaluate it; evaluate the quality and reliability of the collected data by checking the technical specifications of the equipment; compare the data collected by different equipment and evaluate the consistency of the collected data; distribute the evaluated data by drawing charts; Step 3: Set initial values ​​for the parameters in the mathematical model of the energy system, determine the required simulation time series, including time step and simulation cycle; select the parameters in the mathematical model of the energy system as key parameters, and perform simulation experiments; analyze the simulation results; introduce different scenarios, perform simulation experiments for each scenario, compare the simulation results under different scenarios, and analyze the simulation experiments of the energy system under different scenario conditions; Step 4: According to multiple objectives and constraints in the energy system, clarify the goal of energy system optimization and introduce a multi-objective optimization algorithm; Determine the weight and priority of each objective, and select the algorithm suitable for solving the multi-objective optimization problem based on the performance, convergence speed and adaptability of each algorithm to the multi-objective optimization problem; According to the characteristics and requirements of multi-objective optimization problems, we can optimize and solve them, generate and analyze and evaluate the Pareto frontier structure, and select the best solution or the solution that provides decision support according to different objective weights and priorities; Step 5: Adjust the parameters and constraints of the mathematical model of the energy system; in the process of optimization and improvement, explore a variety of energy scheduling strategies and technical solutions, determine specific energy scheduling strategies and technical solutions, and optimize the operation effect of the energy system; verify the optimized and improved mathematical model of the energy system by comparing it with the actual operation data. If the prediction of the mathematical model of the energy system is inconsistent with the actual data during the verification process, use the correction method to adjust the parameters of the mathematical model of the energy system by comparing the difference between the actual data and the output of the mathematical model of the energy system; Step 6. Compare the simulation results of the energy system mathematical model under different energy scheduling strategies and technical solutions, and compare the performance of the simulation results under different indicators; analyze the advantages and disadvantages of different technical solutions, as well as the feasibility of technical solutions under different operating conditions; observe the changes in simulation results under different indicators by changing one or more key parameters, and evaluate the stability and reliability of the simulation results; Step 7. Based on the evaluation results, select the best energy scheduling strategy and technical solution, establish a visualization model, and develop interactive functions to enable users to interact with the visualization tool and obtain more information.

2. A time-series production simulation system based on energy interconnection, characterized in that: The system is based on a time-series production simulation method based on energy interconnection as described in claim 1, and the system comprises a data acquisition module (1), a data processing and preprocessing module (2), a model building module (3), a parameter setting and simulation configuration module (4), a simulation execution module (5), a multi-objective setting and optimization module (6), a data analysis and visualization module (7), and an optimization and decision support module (8); The data acquisition module (1) is used to collect various data required for the operation of the simulation system; The data processing and preprocessing module (2) is used to process the data collected by the data acquisition module (1); The model building module (3) is used to build a mathematical or statistical model of the simulation system; The parameter setting and simulation configuration module (4) is used to set the parameters and configuration of the simulation experiment; The simulation execution module (5) is used to actually execute the simulation experiment, and based on the set parameters and input conditions, simulate the operation process of the system and generate corresponding simulation results; The multi-objective setting and optimization module (6) is used to consider multiple objectives for the simulation system and provide a selection space for more optimized decision-making; The optimization and decision support module (8) is used to provide optimization and decision support for the simulation results of the simulation execution module (5); The data analysis and visualization module (7) is used to analyze and visualize the simulation results of the simulation execution module (5).

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