Distribution network supply demand forecasting method combined with dynamic extreme environment simulation

Abstract

The present invention discloses a method for predicting the supply demand of a distribution network combined with dynamic extreme environment simulation, which relates to the technical field of power systems. The method comprises: establishing an extreme environment power database and classifying and calibrating it, obtaining a calibration database, extracting power consumption behavior based on it and constructing a power demand response model. Parameters are extracted according to extreme environment simulation information, and a simulation model is generated by combining data modeling such as distribution characteristics of the distribution network. A strategy library is constructed based on the calibration database, the simulation parameters are optimized to determine the optimization parameters, the simulation model is optimized based on the optimization parameters, a prediction model is established and predictions are performed. The method solves the technical problems of the prior art in predicting the supply demand of the distribution network in extreme environments, which are inaccurate, incomplete, inflexible and lack of a continuous optimization mechanism, and achieves the technical effect of ensuring the stable operation and continuous power supply of the distribution network under extreme conditions.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to a method for predicting supply demand of distribution networks combined with dynamic extreme environment simulation. Background Art

[0002] In the distribution network operation guarantee scenario, the supply guarantee demand in extreme environments is particularly prominent, and the contradiction between supply guarantee resource demand is relatively more prominent. Accurately predicting supply guarantee demand and rationally allocating resources have become crucial links in ensuring the stable operation of the distribution network. Traditional distribution network supply guarantee prediction methods are often extensive and one-sided, relying only on limited environmental data or simple models, insufficiently considering the dynamic changes in extreme environments, and lacking comprehensive analysis and utilization of user electricity consumption behavior and distribution network distribution characteristics. This makes it difficult to accurately and effectively predict the distribution network supply guarantee demand in extreme environments. There are also unreasonable situations in resource allocation, resulting in insufficient supply guarantee capacity in some key areas and waste of resources in other areas. The formulation of supply guarantee strategies is relatively simple and fixed, and cannot effectively cope with the actual and ever-changing operation of the distribution network in extreme environments.

[0003] At present, relevant technologies have the technical problems of inaccurate, incomplete, inflexible prediction of distribution network supply demand in extreme environments and lack of continuous optimization mechanism. Summary of the Invention

[0004] This application provides a distribution network supply demand prediction method combined with dynamic extreme environment simulation. It establishes an extreme environment power database and classifies and calibrates it to obtain a calibration database. Based on this, electricity consumption behavior is extracted to construct an electricity demand response model. Parameters are extracted according to the extreme environment simulation information, and a simulation model is generated by combining data modeling such as the distribution characteristics of the distribution network. A strategy library is constructed based on the calibration database, and the simulation parameters are optimized to determine the optimization parameters. The simulation model is optimized based on the optimization parameters, and a prediction model is established and predicted. This achieves the technical effect of ensuring the stable operation and continuous power supply of the distribution network under extreme conditions.

[0005] This application provides a distribution network supply demand forecasting method combined with dynamic extreme environment simulation, including:

[0006] Establish an extreme environment power database, classify and calibrate the extreme environment power database, and obtain an extreme environment calibrated power database, wherein the extreme environment calibrated power database includes extreme environment power data of each calibration category parameter; extract power consumption behavior based on the extreme environment calibrated power database, obtain a power demand response behavior data set, perform identification training on the extreme environment calibrated power database and the power demand response behavior data set, and construct an extreme environment power demand response model; extract parameters from the extreme environment calibrated power database according to extreme environment simulation information, determine extreme environment dynamic simulation parameters, perform simulation modeling based on the distribution characteristic data of the target distribution network, the power demand response model and the extreme environment dynamic simulation parameters, and generate a distribution network supply demand simulation model; construct a distribution network power supply reconstruction strategy library based on the extreme environment calibrated power database, perform strategy matching optimization on the extreme environment dynamic simulation parameters based on the distribution network power supply reconstruction strategy library, and determine power supply reconstruction strategy optimization parameters; optimize the distribution network supply demand simulation model based on the power supply reconstruction strategy optimization parameters, establish a distribution network supply demand prediction model, and predict the supply demand through the distribution network supply demand prediction model.

[0007] The proposed method for predicting distribution network supply demand combined with dynamic extreme environment simulation, proposed in this application, first establishes an extreme environment power database and classifies and calibrates it. A calibration database is obtained, and based on this, electricity consumption behavior is extracted to construct an electricity demand response model. Parameters are extracted based on the extreme environment simulation information, and a simulation model is generated by combining data such as the distribution characteristics of the distribution network. A strategy library is constructed based on the calibration database, and simulation parameters are optimized to determine the optimal parameters. The simulation model is optimized based on the optimized parameters, and a prediction model is established and performed. This achieves the technical effect of ensuring the stable operation and continuous power supply of the distribution network under extreme conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0009] Figure 1 A flow chart of a method for predicting distribution network supply demand combined with dynamic extreme environment simulation provided in an embodiment of the present application;

[0010] Figure 2A schematic diagram of a flow chart for obtaining a fault discriminator in a distribution network supply demand prediction method combined with dynamic extreme environment simulation provided in an embodiment of the present application. DETAILED DESCRIPTION

[0011] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0012] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0013] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or that are inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0014] The present application embodiment provides a method for predicting the supply demand of a distribution network combined with dynamic extreme environment simulation, such as Figure 1 As shown, the method includes:

[0015] Step S100: Establish an extreme environment power database. Classify and calibrate the extreme environment power database to obtain a calibrated extreme environment power database. The calibrated extreme environment power database includes extreme environment power data for each calibrated parameter category. Specifically, the extreme environment power database refers to a collection of power-related data collected under extreme environmental conditions. Data sources are identified, such as power company operating records, meteorological monitoring data, and field monitoring station data. Data collection equipment is prepared, and data collection metrics, such as power consumption fluctuations, voltage fluctuations, and equipment failure frequency, are clearly defined. Extensive data collection is conducted to obtain initial extreme environment power data. Initial extreme environment power data reflects the initial state of the power system at the onset of an extreme environment and is characterized by its initial nature. The scope and depth of data collection are continuously expanded to obtain comprehensive power data for the entire extreme environment period. This complete extreme environment power data covers the entire power system status during the entire period of the extreme environment and is characterized by its completeness. The collected data is organized and summarized to establish the extreme environment power database.

[0016] In one possible implementation, an extreme environment power database is established, and the extreme environment power database is classified and calibrated to obtain an extreme environment calibrated power database. The extreme environment calibrated power database includes extreme environment power data for various calibration category parameters. Step S100 further includes step S110 of obtaining extreme environment calibration factor information. The extreme environment calibration factor information includes extreme temperature and humidity, climate conditions, geographical topography, and altitude weather. Specifically, the obtained extreme environment calibration factor information is clarified to cover aspects such as extreme temperature and humidity, climate conditions, geographical topography, and altitude weather.

[0017] Step S120 involves populating the extreme environmental calibration factor information with content using the extreme environmental event library to obtain an extreme environmental factor entity content set. Specifically, the calibration factor information is populated with content using an existing extreme environmental event library, which contains a large number of detailed records and related data of various historical extreme environmental events. By matching and integrating this data, the originally relatively abstract calibration factor information is transformed into a concrete and rich extreme environmental factor entity content set, which contains more detailed and practical information.

[0018] Step S130, designing a physical factor content coding system based on the extreme environment calibration factor information. Specifically, after obtaining the extreme environment calibration factor information (such as extreme temperature and humidity, climate conditions, geographical terrain, altitude and weather, etc.), designing a physical factor content coding system, conducting an in-depth analysis of each extreme environment calibration factor, understanding its possible value range and variation characteristics, for example, extreme temperatures may range from dozens of degrees below zero to dozens of degrees above zero, and climate conditions may include a variety of different types, determining the basic rules and structure of the coding, and choosing to use digital coding, letter coding, or a combination of digital and letter coding, considering the length and readability of the coding, so as to cover all possible situations and be easy to understand and use. For extreme temperature and humidity, according to The specific value ranges of temperature and humidity are divided into different intervals, and a unique code is assigned to each interval. For climatic conditions, they can be classified and coded according to different climate types. For geographical terrain, they can be coded according to the main categories of terrain. For altitude weather, they can be coded according to different stages of altitude and corresponding weather characteristics. When designing the coding system, a certain amount of expansion space should be reserved to cope with new extreme environmental calibration factors or changes in value ranges that may appear in the future, and finally a complete, reasonable and applicable entity factor content coding system will be formed, which will provide clear and definite standards and basis for the subsequent coding work of extreme environmental factor entity content sets.

[0019] Step S140 encodes the extreme environment factor entity content set using the entity factor content encoding system to obtain an extreme environment entity content encoding set. Specifically, the designed encoding system encodes the obtained extreme environment factor entity content set. During the encoding process, the rules and requirements of the encoding system are strictly followed to ensure the accuracy and consistency of the encoding, thereby obtaining the extreme environment entity content encoding set.

[0020] Step S150 uses the extreme environment entity content code set as a calibration category parameter to divide and calibrate the extreme environment power database, thereby obtaining the extreme environment calibrated power database. Specifically, using the obtained extreme environment entity content code set as a calibration category parameter, the previously established extreme environment power database is carefully divided and calibrated, and the data in the database is classified and labeled according to different extreme environment characteristics, thereby obtaining a complete and accurate extreme environment calibrated power database, providing strong support for subsequent analysis and application.

[0021] Step S200, extracting electricity consumption behavior based on the extreme environment calibrated power database, obtaining an electricity demand response behavior dataset, performing identification training on the extreme environment calibrated power database and the electricity demand response behavior dataset, and constructing an extreme environment electricity demand response model. Specifically, the extreme environment electricity demand response model refers to a model constructed based on the extreme environment calibrated power database for reflecting the electricity demand response law. Obtain the extreme environment calibrated power database, prepare data processing and analysis tools, set relevant parameters for extraction and training, such as data screening conditions, number of model training rounds, etc., perform electricity consumption behavior extraction operations, and obtain an electricity demand response behavior dataset. The electricity demand response behavior dataset reflects the patterns and characteristics of user electricity consumption behaviors in extreme environments, and its characteristic is focused behavior response. Continue to perform identification training on the data to construct an extreme environment electricity demand response model. The model can predict the user's electricity demand under different extreme environmental conditions, and its characteristic is reflecting the demand law.

[0022] In one possible implementation, electricity consumption behavior is extracted based on the extreme environment calibrated power database to obtain an electricity demand response behavior data set, identification training is performed on the extreme environment calibrated power database and the electricity demand response behavior data set, and an extreme environment electricity demand response model is constructed. Step S200 further includes step S210, performing necessary demand identification and necessary data extraction on the electricity demand response behavior data set to obtain a necessary electricity demand behavior data set. Specifically, work is carried out on the electricity demand response behavior dataset, and each data in the dataset is carefully analyzed to identify the necessary needs. For example, the dataset includes various electricity consumption behaviors such as lighting, cooling / heating, and communications. Through analysis, it is assumed that lighting electricity consumption behavior is more important in a specific scenario, while cooling / heating electricity consumption behavior is relatively less important in the scenario. Lighting-related data is taken as the necessary need to focus on, and demand-related data is extracted from the dataset. Lighting electricity consumption data is recorded according to time and electricity consumption, such as lighting electricity consumption in different time periods of a day. Through screening, only those lighting electricity consumption data with large electricity consumption or in specific time periods (such as nighttime) are retained, thereby obtaining the necessary electricity demand behavior dataset.

[0023] Step S220 maps the extreme environment calibrated power database to the necessary power demand behavior dataset according to the calibration category parameters, thereby obtaining a sample data set of power demand in extreme environments. Specifically, the extreme environment calibrated power database is categorized and organized according to its calibration category parameters. For example, it is divided according to calibration categories such as different extreme temperature ranges (assuming they are divided into low temperature, medium temperature, and high temperature) and climate types (assuming they are divided into heavy rain, strong wind, and drought). Each calibration category is mapped to the necessary power demand behavior dataset, and the power data under the same calibration category is associated with the corresponding necessary power demand data. For example, for the low temperature calibration category, the degree of match between the time and power consumption of the calibration category and the lighting power demand data is calculated. Assuming that lighting power consumption generally increases by a certain percentage under low temperature conditions, the extreme environment power demand sample dataset is obtained by comparing and statistically analyzing these changes.

[0024] Step S230 involves performing neural network training on the extreme environment electricity demand sample dataset to generate an initial electricity demand analysis model. Specifically, the neural network training is performed using the acquired extreme environment electricity demand sample dataset. The dataset is input into the neural network model, and the model parameters are adjusted to allow the model to learn the patterns and regularities in the data. During the training process, the model continuously compares the difference between predicted electricity demand and actual electricity demand, gradually reducing this difference, making the model's predictions increasingly accurate, and thus generating an initial electricity demand analysis model.

[0025] Step S240: Perform performance verification and iterative optimization on the initial electricity demand analysis model to construct the extreme environment electricity demand response model. Specifically, the performance verification of the initial electricity demand analysis model is performed, and a series of evaluation indicators and test data are used to test the accuracy, reliability, and generalization ability of the model. By comparing with the actual electricity consumption data, the average deviation between the electricity consumption predicted by the model and the actual electricity consumption is calculated. If the average deviation is large, it means that the model is not accurate enough. It is necessary to identify problems and deficiencies in the model based on the verification results, and then iteratively optimize by adjusting the model structure (such as increasing or decreasing the connection of certain neurons), parameters (such as changing the learning rate), or increasing training data, and finally construct an extreme environment electricity demand response model that can accurately reflect the electricity demand response in extreme environments.

[0026] Step S300, extract parameters from the extreme environment calibration power database according to the extreme environment simulation information, determine the extreme environment dynamic simulation parameters, perform simulation modeling based on the distribution characteristic data of the target distribution network, the power demand response model and the extreme environment dynamic simulation parameters, and generate a distribution network supply demand simulation model. Specifically, the distribution network supply demand simulation model refers to a model generated under a specific process for predicting the supply status of the distribution network under extreme environments. Obtain the preset extreme environment information to be simulated, perform parameter extraction operations on the extreme environment calibration power database, and determine the extreme environment dynamic simulation parameters. Obtain the distribution characteristic data of the target distribution network, integrate the power demand response model and the extreme environment dynamic simulation parameters, carry out simulation modeling, and generate a distribution network supply demand simulation model. This model reflects the distribution network's demand for power supply under extreme environments. Its unique feature is its ability to accurately predict power supply demand and obtain specific information about the preset extreme environment to be simulated. For example, it provides a detailed description of the extreme environment that may occur in a specific area during a specific time period, covering specific extreme high or low temperatures, the intensity and duration of severe weather types, and other information. Based on this extreme environment simulation information, it conducts an in-depth analysis of the extreme environment calibration power database. Parameters relevant to the current simulation information are selected from the database. For example, if the simulation is of an extreme high temperature environment, the performance change parameters of power equipment under past high temperature conditions are extracted, such as the overload limit of the transformer and the heat dissipation capacity of the line. The model also extracts parameters that change in user electricity demand during high temperatures, such as the increase in air conditioning usage. Through comprehensive parameter measurement and calculation, dynamic simulation parameters that accurately reflect the current extreme environmental characteristics are identified. The distributed characteristic data of the target distribution network is obtained, including the specific location, model, capacity, and connection method of various types of power equipment (such as transformers, poles, and wires) in the distribution network, as well as detailed information such as the distribution location of users in the distribution network and the distribution of power loads. The power demand response model, the determined extreme environment dynamic simulation parameters, and the distribution characteristic data of the target distribution network are integrated to perform simulation modeling. During the modeling process, a basic physical model is first constructed based on the distribution of equipment and users in the distribution network. The power demand response model is then incorporated into it, enabling it to predict changes in user electricity consumption behavior under extreme conditions based on the extreme environment dynamic simulation parameters. Next, the extreme environment dynamic simulation parameters are applied to the model to simulate the performance changes of power equipment in such extreme environments, such as increased line resistance and increased transformer losses.By continuously adjusting and optimizing the parameters and relationships of each part of the model, it can accurately reflect the actual operating conditions of the distribution network under extreme environments, and ultimately generate a simulation model that can effectively predict the supply demand of the distribution network under extreme environments. The model can output key information such as voltage changes at each node in the distribution network under specific extreme environments, line load conditions, fault points, and required power supply capacity, providing a solid decision-making basis for the planning, operation and maintenance of the distribution network.

[0027] In one possible implementation, parameters are extracted from the extreme environment calibration power database based on the extreme environment simulation information to determine extreme environment dynamic simulation parameters. Simulation modeling is then performed based on the distribution characteristic data of the target distribution network, the power demand response model, and the extreme environment dynamic simulation parameters to generate a distribution network supply demand simulation model. Step S300 further includes step S310, where the distribution characteristic data of the target distribution network is spatially modeled using three-dimensional modeling technology to generate a distribution network distribution architecture spatial model. Specifically, three-dimensional modeling technology is used to process the distribution characteristic data of the target distribution network. The distribution characteristic data includes detailed information such as the precise location, height, orientation, and interconnection relationships of each power device (such as transformers, utility poles, and power lines) in the distribution network. Professional three-dimensional modeling software and tools are used to convert the data into an intuitive three-dimensional model. During the modeling process, the three-dimensional shape and position of each power device are accurately constructed based on the actual physical size and spatial layout, thereby generating a distribution network distribution architecture spatial model. The model can clearly display the structure and layout of the distribution network in space.

[0028] Step S320, using the electricity demand response model and the extreme environment dynamic simulation parameters, the distribution network distribution architecture space model is subjected to power supply simulation modeling to obtain a basic supply guarantee demand simulation model. Specifically, the electricity demand response model and the extreme environment dynamic simulation parameters are introduced into the distribution network distribution architecture space model. The electricity demand response model can predict the changes in user demand under different electricity consumption conditions, and the extreme environment dynamic simulation parameters reflect the performance changes of power equipment under extreme environments. The distribution network distribution architecture space model is subjected to power supply simulation modeling. Through complex calculations and simulations, factors such as the flow of electricity, voltage changes, and power distribution are taken into account to simulate the power supply conditions of the distribution network under different electricity demands and extreme environments, thereby obtaining a basic supply guarantee demand simulation model.

[0029] Step S330, compare and verify the basic supply and demand simulation model to obtain the model reliability coefficient, and optimize the parameters of the basic supply and demand simulation model based on the model reliability coefficient to obtain the distribution network supply and demand simulation model. Specifically, compare and verify the obtained basic supply and demand simulation model, compare the output results of the model with the actual monitoring data, historical experience data or other reliable reference data, analyze the degree of difference between the model prediction results and the actual situation through a series of evaluation indicators and calculation methods, and calculate the model reliability coefficient based on the comparison results. The coefficient can be a value between 0 and 1. The closer the value is to 1, the more reliable the model is. Based on the calculated model reliability coefficient, adjust and optimize the parameters of the basic supply and demand simulation model. It is necessary to adjust the parameters of the power equipment, the coefficient of the power demand, the influencing factors of the extreme environment, etc. Through continuous experiments and improvements, the prediction results of the model are closer to the actual situation, and finally obtain an accurate and reliable distribution network supply and demand simulation model.

[0030] Step S400, based on the extreme environment calibrated power database, construct a distribution network power supply reconstruction strategy library, perform strategy matching optimization on the extreme environment dynamic simulation parameters based on the distribution network power supply reconstruction strategy library, and determine the power supply reconstruction strategy optimization parameters. Specifically, the distribution network power supply reconstruction strategy optimization parameters refer to the key indicators for optimizing the distribution network power supply reconstruction strategy determined according to specific processes and conditions. Based on the extreme environment calibrated power database, construct a distribution network power supply reconstruction strategy library. Based on this strategy library, perform strategy matching optimization on the extreme environment dynamic simulation parameters, and finally determine the power supply reconstruction strategy optimization parameters based on the extreme environment calibrated power database. Carry out in-depth analysis and research on the database to extract the characteristics and laws of the impact of different extreme environments on the operation of the distribution network. Based on this, construct a distribution network power supply reconstruction strategy library. During construction, specific and detailed reconstruction strategies are carefully formulated for each possible extreme weather scenario, such as typhoons, heavy rain, heavy snow, and high temperatures. Taking typhoon weather as an example, a carefully formulated reconstruction strategy may include: identifying key loads such as hospitals and emergency command centers, and prioritizing their stable power supply; optimizing the structure of the distribution network, which may include adjusting line connection methods, adding backup power sources, or enhancing the carrying capacity of certain lines; and adjusting transformer configurations to adapt to changes in power demand in different regions. Through specific reconstruction strategies, the distribution network's wind-resistant power supply capabilities are enhanced during typhoon weather. Relying on the established distribution network power supply reconstruction strategy library, strategy matching optimization is carried out for dynamic simulation parameters of extreme environments. The extreme environment dynamic simulation parameters are input into the strategy library. Through a series of calculations and analyses, the reconstruction strategy that best matches these parameters is found. During the matching process, a variety of factors are comprehensively considered, such as current power demand, equipment operating status, possible failure risks, etc., and the initially matched strategy is further optimized and adjusted to ensure that it can adapt to the specific extreme environment dynamic simulation parameters to the greatest extent. Finally, the power supply reconstruction strategy optimization parameters are finalized. The parameters clarify the specific details and key indicators of the optimal reconstruction strategy that the distribution network should implement under specific extreme environments, providing precise guidance for the actual operation and management of the distribution network.

[0031] In a possible implementation, a distribution network power supply reconstruction strategy library is constructed based on the extreme environment calibration power database, and the extreme environment dynamic simulation parameters are optimized based on the distribution network power supply reconstruction strategy library to determine the power supply reconstruction strategy optimization parameters, such as Figure 2Said step S400 further includes step S410, performing strategy matching on the extreme environment dynamic simulation parameters based on the distribution network power supply reconstruction strategy library, and determining a target power supply reconstruction strategy. Specifically, based on the constructed distribution network power supply reconstruction strategy library, the extreme environment dynamic simulation parameters are input therein. The strategy library contains a variety of power supply reconstruction strategies formulated for various extreme environmental conditions. For example, the extreme environment dynamic simulation parameters may show that under certain extreme weather conditions, the power load will rise sharply, and certain areas may experience large voltage fluctuations. Carefully compare the various strategies in the strategy library, different line switching arrangements, transformer adjustment strategies, etc., to determine which strategy can more effectively cope with the current situation of such a sharp increase in load and voltage fluctuations, thereby determining the target power supply reconstruction strategy.

[0032] Step S420: Optimize and analyze the parameters of the target distribution network based on the target power supply reconstruction strategy to obtain a threshold for optimizing the reconstruction strategy parameters. Specifically, based on the determined target power supply reconstruction strategy, perform in-depth optimization and analysis on the relevant parameters of the target distribution network, comprehensively considering factors such as the equipment performance, load distribution, and line capacity of the distribution network. For example, if the target power supply reconstruction strategy is to adjust the output voltage of a certain transformer, it is necessary to calculate whether the power supply capacity of the area after the adjustment can meet the load demand. Through such analysis and calculation, the parameter range that can achieve the optimal reconstruction effect is determined, and then the threshold for optimizing the reconstruction strategy parameters is obtained.

[0033] Step S430: Obtain a reconstruction optimization objective, evaluate and fit the reconstruction optimization objective, and obtain a fitness function for the distribution network reconstruction effect. Specifically, obtain clear reconstruction optimization objectives, such as improving power supply capacity, ensuring voltage stability, and improving power quality. For improving power supply capacity, it is necessary to calculate whether the maximum power that can be supplied under different reconstruction schemes can meet the expected load growth; for voltage stability, it is necessary to calculate whether the voltage fluctuation range of each node is within the allowable range; for power quality, it is necessary to evaluate indicators such as harmonic content and voltage deviation. The specific optimization objectives are converted into a quantifiable and calculable form, thereby obtaining a fitness function that can measure the distribution network reconstruction effect.

[0034] Step S440: Parameter optimization is performed on the reconstruction strategy parameter optimization threshold based on the distribution network reconstruction effect fitness function to determine the power supply reconstruction strategy optimization parameters. Specifically, based on the obtained distribution network reconstruction effect fitness function, a detailed optimization operation is performed on the parameters within the reconstruction strategy parameter optimization threshold, trying different parameter combinations, such as changing the resistance of certain lines, adjusting the gear of the transformer, etc., and calculating their corresponding fitness values. This is like selecting the one that best meets the optimization goal from many solutions. After repeated exploration and comparison, the most ideal power supply reconstruction strategy optimization parameters are finally determined, providing a strong guarantee for the stable operation and efficient power supply of the distribution network in extreme environments.

[0035] In one possible implementation, based on the distribution network reconstruction effect fitness function, parameter optimization is performed on the reconstruction strategy parameter optimization threshold to determine the power supply reconstruction strategy optimization parameters. Step S440 further includes step S441 of randomly obtaining multiple reconstruction strategy parameters within the reconstruction strategy parameter optimization threshold. Specifically, within the range defined by the reconstruction strategy parameter optimization threshold, multiple reconstruction strategy parameters are obtained through random generation. For example, the reconstruction strategy parameters include line switching methods, transformer adjustment ranges, and the like. Multiple different schemes are randomly combined from various possibilities allowed by the threshold, and each scheme is a set of reconstruction strategy parameters.

[0036] Step S442: Simulate the multiple reconstruction strategy parameters based on the distribution network supply demand simulation model to obtain multiple distribution network reconstruction simulation parameters. Specifically, the multiple randomly obtained reconstruction strategy parameters are input into the distribution network supply demand simulation model for simulation. During the simulation, the model calculates various possible extreme environments and power demand conditions based on the parameters, such as calculating the current flowing through a certain line under the input parameters; or calculating whether the voltage at a certain node increases or decreases under the parameter settings, and the amount of change, thereby obtaining multiple distribution network reconstruction simulation parameters.

[0037] Step S443: Evaluate the effects of the multiple distribution network reconstruction simulation parameters using the distribution network reconstruction effect fitness function to obtain multiple pieces of reconstruction effect fitness information. Specifically, the distribution network reconstruction effect fitness function is used to evaluate the effects of the multiple distribution network reconstruction simulation parameters. The fitness function measures the effect of each simulation parameter according to a pre-set reconstruction optimization goal. For example, it calculates how much the maximum power that can be supplied under the simulation parameter has increased compared to before; to ensure voltage stability, it checks whether the voltage fluctuations at each node have become smaller and by how much; to improve power quality, it checks whether the harmonic content has been reduced and to what extent. Through comparison and measurement, a score or evaluation is given to each simulation parameter, and ultimately multiple pieces of reconstruction effect fitness information are obtained.

[0038] Step S444: Based on the multiple pieces of reconstruction effect fitness information, the multiple reconstruction strategy parameters are compared and optimized to determine the power supply reconstruction strategy optimization parameters. Specifically, based on the obtained multiple pieces of reconstruction effect fitness information, the multiple reconstruction strategy parameters are carefully compared and optimized. Each reconstruction strategy parameter and its corresponding fitness information are compared with each other to find the set of reconstruction strategy parameters with the highest fitness and the best effect. These are determined as the final power supply reconstruction strategy optimization parameters. The power supply reconstruction strategy optimization parameters can provide guidance for the reconstruction and optimization of the distribution network in extreme environments, achieving more stable and efficient power supply.

[0039] In one possible implementation, the multiple reconstruction strategy parameters are compared and optimized based on the multiple reconstruction effect fitness information to determine the power supply reconstruction strategy optimization parameters. Step S444 further includes step S4441, which proportionally screens the multiple distribution network reconstruction simulation parameters based on the multiple reconstruction effect fitness information to obtain a set of superior reconstruction strategy parameters. Specifically, based on the multiple reconstruction effect fitness information obtained, the multiple distribution network reconstruction simulation parameters are proportionally screened, the reconstruction effect fitness is arranged in descending order, a screening ratio is set, and from the sorted parameters, the parameters that are ranked first and correspond to the set screening ratio are selected. The selected parameters constitute the set of superior reconstruction strategy parameters.

[0040] Step S4442 sets a parameter step length. Based on this step length, the optimal reconstruction strategy parameter set is mutated and expanded to obtain a reconstruction strategy parameter variant population. Specifically, the parameter step length is set. For example, for a particular parameter, an initial value is set, and the possible values ​​after the parameter mutation are between the initial value minus the step length and the initial value plus the step length. Based on the set step length, each parameter in the optimal reconstruction strategy parameter set is mutated and expanded to obtain a series of new parameter combinations, forming a reconstruction strategy parameter variant population.

[0041] Step S4443, iterative optimization is performed within the reconstruction strategy parameter variation population until the preset number of iterations is reached, and the power supply reconstruction strategy optimization parameters are determined. Specifically, the iterative optimization process is started within the reconstruction strategy parameter variation population. In each iteration, the parameters in the population are evaluated and compared. For example, if the fitness of a group of parameters is low, then in the next round, a larger variation is made to it; if the fitness is high, a smaller adjustment is made. The above process is repeated until the preset number of iterations is reached. When the preset number of iterations is reached, the group with the highest fitness is found from the parameter combination obtained in the last round of iterations, and it is determined as the final power supply reconstruction strategy optimization parameter. The parameters are the optimal solutions obtained after multiple screening, adjustment and optimization, which can provide the best strategic guidance for the reconstruction of the distribution network and ensure the stable and efficient operation of the distribution network in extreme environments.

[0042] Step S500, based on the power supply reconstruction strategy optimization parameters, the distribution network supply demand simulation model is optimized for supply demand, a distribution network supply demand prediction model is established, and supply demand prediction is performed through the distribution network supply demand prediction model. Specifically, the distribution network supply demand prediction scheme refers to a process for predicting the distribution network supply demand based on specific parameters and models, obtaining the power supply reconstruction strategy optimization parameters obtained through rigorous calculation and screening, introducing them into the distribution network supply demand simulation model, optimizing the model for supply demand, and then constructing a distribution network supply demand prediction model, and implementing supply demand prediction through the model, obtaining the obtained power supply reconstruction strategy optimization parameters, and integrating the parameters into the distribution network supply demand simulation model, as if injecting new vitality and accuracy into the model. For example, if the optimization parameters involve the re-planning of certain key lines, then the connection of these lines will be adjusted accordingly in the model. Connection mode and carrying capacity setting; if it is related to the optimization of specific equipment, such as the upgrade or replacement of transformers, the performance parameters of the equipment in the model will be updated. By incorporating the optimized parameters into the simulation model, a full range of supply demand optimization is implemented to enable the model to more realistically and accurately reflect the operating conditions and supply capacity of the distribution network under various extreme environments. On this basis, a distribution network supply demand prediction model is further constructed. The model focuses on reducing the adverse effects of extreme environments on the power supply of the distribution network. During the construction process, the advantages and characteristics of the optimized simulation model are fully integrated, and a large amount of historical data and actual operation experience are combined. For example, The problems and response measures presented by the distribution network under extreme weather conditions, transforming experience into part of the model, and performing meticulous preprocessing on the collected data, including removing outliers and standardizing data, to ensure data quality and consistency. In terms of model architecture design, algorithms and structures that can effectively capture time series characteristics and complex relationships are carefully selected to better predict future supply and demand. Once the distribution network supply and demand forecasting model is successfully constructed, it can be put into actual supply and demand forecasting operations, and the real-time operating data of the current distribution network, such as the current, voltage, power, etc. of each node, as well as the latest extreme weather forecast information and changes in power load, can be used to forecast the supply and demand of the power grid. When the model is fed with information such as trends in electricity supply, the model will use its complex internal calculation and reasoning logic to comprehensively analyze and process the information, and ultimately output a detailed forecast of the distribution network's supply demand in the future, covering important content such as emerging power shortage areas and time periods, the stress conditions of key equipment under extreme weather conditions, and the reliability assessment of the overall power supply. The forecast results can provide the power sector with a valuable basis for decision-making, helping it to formulate reasonable power allocation plans, arrange equipment maintenance plans, and prepare emergency power supplies in advance, thereby greatly improving the distribution network's supply capacity and power supply reliability under extreme weather conditions, and ensuring that users' normal electricity needs are met.

[0043] In one possible implementation, the distribution network supply demand simulation model is optimized based on the power supply reconstruction strategy optimization parameters, a distribution network supply demand prediction model is established, and supply demand prediction is performed through the distribution network supply demand prediction model. Step S500 further includes step S510, performing supply demand prediction and actual comparison feedback based on the distribution network supply demand prediction model to obtain prediction accuracy feedback parameters. Specifically, the distribution network supply demand forecasting model is used to forecast the supply demand. Based on the input data such as the current distribution network operating status, weather conditions, power load, etc., the forecast results of the distribution network supply demand in the future period are given, and the forecast results are compared with the actual distribution network supply demand situation. By collecting and organizing the actual operating data, including the actual current, voltage, power of each node, the actual power load changes, and the actual supply situation, etc., they are compared one by one with the forecast results. In the process of comparison, the difference between the forecast result and the actual situation is calculated. For example, the predicted power gap is compared with the actual power gap, the predicted voltage fluctuation range is compared with the actual voltage fluctuation range, etc. Through comparison and calculation, the forecast accuracy feedback parameter is obtained. This parameter can quantitatively reflect the accuracy of the forecast model.

[0044] Step S520: Perform model optimization analysis on the prediction accuracy feedback parameters to obtain model parameter optimization rules. Specifically, the obtained prediction accuracy feedback parameters are subjected to in-depth model optimization analysis to analyze the deviations and deficiencies in the model reflected by the parameters, such as underestimation of the impact of extreme weather or inaccurate understanding of the changing trends of electricity load. Through analysis, the direction of model improvement and optimization is identified, thereby obtaining model parameter optimization rules. The rules may include adjusting the weights of certain parameters, increasing or decreasing the consideration of certain factors, or improving the model algorithm and structure.

[0045] Step S530 iteratively updates the parameters of the distribution network supply demand forecasting model based on the model parameter optimization rules to obtain an optimized distribution network supply demand forecasting model. Specifically, based on the obtained model parameter optimization rules, the parameters of the distribution network supply demand forecasting model are iteratively updated, and various parameters in the model are modified and adjusted according to the rules. Predictions and comparisons are performed, and the above process is repeated until the model's prediction results achieve the expected accuracy and reliability, ultimately obtaining an optimized distribution network supply demand forecasting model.

[0046] The present embodiment establishes an extreme environment power database and classifies and calibrates it, obtaining a calibration database from which electricity consumption behavior is extracted and a power demand response model is constructed. Parameters are extracted based on extreme environment simulation information, and a simulation model is generated by combining data such as the distribution characteristics of the distribution network. A strategy library is constructed based on the calibration database, and simulation parameters are optimized to determine the optimal parameters. The simulation model is optimized based on the optimized parameters, and a prediction model is established and performed. This achieves the technical effect of ensuring stable operation and continuous power supply of the distribution network under extreme conditions.

[0047] The above specific embodiments do not constitute a limitation to the scope of protection of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of this application should be included in the scope of protection of this application. In some cases, the actions or steps recorded in this application can be performed in an order different from that in the embodiments and can still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A distribution network supply demand forecasting method based on dynamic extreme environment simulation is characterized by: The method comprises: Establishing an extreme environment power database, classifying and calibrating the extreme environment power database to obtain an extreme environment calibrated power database, wherein the extreme environment calibrated power database includes extreme environment power data of various calibration category parameters; Extracting electricity consumption behavior based on the extreme environment calibration power database to obtain an electricity demand response behavior dataset, performing identification training on the extreme environment calibration power database and the electricity demand response behavior dataset, and constructing an extreme environment electricity demand response model; Extract parameters from the extreme environment calibration power database according to the extreme environment simulation information, determine the extreme environment dynamic simulation parameters, perform simulation modeling based on the distribution characteristic data of the target distribution network, the power demand response model and the extreme environment dynamic simulation parameters, and generate a distribution network supply demand simulation model; According to the extreme environment calibration power database, a distribution network power supply reconstruction strategy library is constructed, and based on the distribution network power supply reconstruction strategy library, strategy matching optimization is performed on the extreme environment dynamic simulation parameters to determine the power supply reconstruction strategy optimization parameters; Optimizing the distribution network supply demand simulation model based on the power supply reconstruction strategy optimization parameters, establishing a distribution network supply demand prediction model, and performing supply demand prediction using the distribution network supply demand prediction model; The step of obtaining the extreme environment calibration power database includes: Fill the extreme environment calibration factor information with content through the extreme environment event library to obtain the extreme environment factor entity content set; Designing an entity factor content coding system based on the extreme environment calibration factor information; Encoding the extreme environment factor entity content set by using the entity factor content coding system to obtain an extreme environment entity content coding set; Using the extreme environment entity content code set as a calibration category parameter, dividing and calibrating the extreme environment power database, and obtaining the extreme environment calibrated power database; Determining the power supply reconstruction strategy optimization parameters includes: Performing strategy matching on the extreme environment dynamic simulation parameters based on the distribution network power supply reconstruction strategy library to determine a target power supply reconstruction strategy; Performing parameter optimization analysis on the target distribution network according to the target power supply reconstruction strategy to obtain a reconstruction strategy parameter optimization threshold; Obtaining a reconstruction optimization target, performing evaluation and fitting on the reconstruction optimization target, and obtaining a distribution network reconstruction effect fitness function; Parameter optimization is performed on the reconstruction strategy parameter optimization threshold based on the distribution network reconstruction effect fitness function to determine the power supply reconstruction strategy optimization parameter.

2. The method for predicting distribution network supply demand combined with dynamic extreme environment simulation according to claim 1, characterized in that: Acquire extreme environment calibration factor information, wherein the extreme environment calibration factor information includes extreme temperature and humidity, climate conditions, geographical terrain, and altitude weather.

3. The method for predicting distribution network supply demand combined with dynamic extreme environment simulation according to claim 1, characterized in that: The construction of the extreme environment electricity demand response model includes: Identifying necessary demands and extracting necessary data from the electricity demand response behavior dataset to obtain a necessary electricity demand behavior dataset; Mapping the extreme environment calibration power database with the necessary power demand behavior dataset according to calibration category parameters to obtain an extreme environment power demand sample dataset; Performing neural network learning training on the extreme environment electricity demand sample data set to generate an initial electricity demand analysis model; The performance of the initial electricity demand analysis model is verified and iteratively optimized to construct the extreme environment electricity demand response model.

4. The method for predicting distribution network supply demand in combination with dynamic extreme environment simulation according to claim 1, characterized in that: The generating of the distribution network supply guarantee demand simulation model includes: Using the three-dimensional modeling technology to perform spatial modeling on the distribution characteristic data of the target distribution network, a distribution network distribution architecture spatial model is generated; By using the electricity demand response model and the extreme environment dynamic simulation parameters, power supply simulation modeling is performed on the distribution network distribution architecture space model to obtain a basic power supply demand simulation model; The basic supply guarantee demand simulation model is compared and verified to obtain a model reliability coefficient, and the parameters of the basic supply guarantee demand simulation model are optimized based on the model reliability coefficient to obtain the distribution network supply guarantee demand simulation model.

5. The method for predicting distribution network supply demand combined with dynamic extreme environment simulation according to claim 1, characterized in that: The determining of the power supply reconfiguration strategy optimization parameters includes: randomly obtaining a plurality of reconstruction strategy parameters within the reconstruction strategy parameter optimization threshold; Simulating the multiple reconstruction strategy parameters based on the distribution network supply guarantee demand simulation model to obtain multiple distribution network reconstruction simulation parameters; Evaluating the effects of the plurality of distribution network reconstruction simulation parameters using the distribution network reconstruction effect fitness function to obtain a plurality of reconstruction effect fitness information; The multiple reconstruction strategy parameters are compared and optimized based on the multiple reconstruction effect fitness information to determine the power supply reconstruction strategy optimization parameters.

6. The method for predicting distribution network supply demand combined with dynamic extreme environment simulation according to claim 5, characterized in that: The determining of the power supply reconfiguration strategy optimization parameters includes: Proportionally screening the plurality of distribution network reconstruction simulation parameters based on the plurality of reconstruction effect fitness information to obtain an optimal reconstruction strategy parameter set; Setting a parameter variation step length, and performing mutation expansion on the optimal reconstruction strategy parameter set based on the parameter variation step length to obtain a reconstruction strategy parameter mutation population; Iterative optimization is performed within the reconstruction strategy parameter variation population until a preset number of iterations is reached to determine the optimized parameters of the power supply reconstruction strategy.

7. The method for predicting distribution network supply demand combined with dynamic extreme environment simulation according to claim 1, characterized in that: The method comprises: Based on the distribution network supply demand forecasting model, supply demand forecasting and actual comparison feedback are performed to obtain forecast accuracy feedback parameters; Performing model optimization analysis on the prediction accuracy feedback parameters to obtain model parameter optimization rules; The parameters of the distribution network supply demand prediction model are iteratively updated based on the model parameter optimization rules to obtain the distribution network supply demand optimization prediction model.

Images