Power Transmission Control Method, System, Equipment and Medium for Sandy, Gobi and Desert Energy Bases

Abstract

The present invention relates to the field of information technology, and discloses a power transmission control method, system, device and medium for the desert, Gobi and wasteland energy base. By constructing a hierarchical power supply side model of the desert, Gobi and wasteland energy base and a hierarchical power demand side model of the receiving area, and analyzing the impact of electricity subsidy data in different regions on their consumption capacity to evaluate the consumption potential value of each region; for the regions in peak electricity consumption, based on the hierarchical power supply side model and the hierarchical power demand side model of the region, calculate the power gap value of transmission lines at each voltage level and determine the amount of regulated power resources; according to the calculated amount of regulated power resources and the consumption potential value of the corresponding region, quantify the upper limit of regulated power under various output levels of the desert, Gobi and wasteland energy base, and combine it with the power gap value to generate a hierarchical power transmission control scheme and execute it. By means of the data-driven method, the consumption potential of each region for new energy is effectively evaluated, and the efficient utilization of power resources is realized.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and particularly to a power transmission control method, system, device and medium for a desert, gobi and waste energy base. Background Art

[0002] At present, due to the rich wind and light resources in the desert, gobi and waste areas, their role as clean energy supply bases is becoming increasingly important in the operation of the power grid. However, the new energy power generation in the desert, gobi and waste energy base is intermittent, volatile and random, and is often mismatched with the peak electricity consumption in the receiving areas. After large-scale grid connection, it poses challenges to the safe and stable operation of the power grid.

[0003] Therefore, how to effectively evaluate the consumption potential of new energy in each region to optimize the power transmission strategy of the desert, gobi and waste base has become a technical problem that needs to be urgently solved by those skilled in the art. Summary of the Invention

[0004] The present invention provides a power transmission control method, system, device and medium for a desert, gobi and waste energy base, and solves the problem of how to effectively evaluate the consumption potential of new energy in each region to optimize the power transmission strategy of the desert, gobi and waste base.

[0005] To solve the above technical problems, the first aspect of the present invention provides a power transmission control method for a desert, gobi and waste energy base, including:

[0006] Obtain the historical power supply side transmission data of the desert, gobi and waste energy base, and quantify the power output and outgoing time distribution data of transmission lines of multiple voltage levels based on the historical power supply side transmission data to construct a hierarchical power supply side model of the desert, gobi and waste energy base;

[0007] Collect the historical demand side load data and electricity subsidy data of multiple regions to analyze the correlation between the cost load differences of the transmission lines of each voltage level and the electricity subsidies of each region, and obtain a hierarchical power demand side model of each region;

[0008] Collect the power grid operation data of each region, analyze the impact of the electricity subsidy differences of each region on its consumption capacity to construct a consumption capacity evaluation model, and evaluate the consumption potential value of each region through the consumption capacity evaluation model;

[0009] When any region is in the peak electricity consumption period, calculate the power gap value of the transmission lines of each voltage level based on the hierarchical power supply side model and the hierarchical power demand side model of the corresponding region, and determine the adjusted power resource quantity of the transmission lines of each voltage level in combination with the transmission capacity limit and the electricity subsidy data of each region;

[0010] According to the adjustable power resources and the absorption potential values, quantify the upper limit of adjustable power of the Shagehuang Energy Base under various outputs, and combine it with the power gap value to generate and execute a hierarchical transmission control scheme.

[0011] The second aspect of the present invention provides a transmission control system for the Shagehuang Energy Base, including:

[0012] A first model construction module, configured to obtain the historical supply-side transmission data of the Shagehuang Energy Base, and quantify the power output and external transmission time distribution data of transmission lines of multiple voltage levels based on the historical supply-side transmission data, so as to construct a hierarchical power supply-side model of the Shagehuang Energy Base;

[0013] A second model construction module, configured to collect the historical demand-side load data and electricity subsidy data of multiple regions, analyze the correlation between the cost load differences of the transmission lines of each voltage level and the electricity subsidies of each region, and obtain a hierarchical power demand-side model of each region;

[0014] An absorption potential evaluation module, configured to collect the grid operation data of each region, analyze the impact of the electricity subsidy differences of each region on its absorption capacity, so as to construct an absorption capacity evaluation model, and evaluate the absorption potential values of each region through the absorption capacity evaluation model;

[0015] An adjustable resource quantification module, configured to calculate the power gap values of the transmission lines of each voltage level based on the hierarchical power supply-side model and the hierarchical power demand-side model of the corresponding region when any region is in the peak electricity consumption period, and combine the transmission capacity limit and the electricity subsidy data of each region to determine the adjustable power resources of the transmission lines of each voltage level;

[0016] A transmission scheme generation module, configured to quantify the upper limit of adjustable power of the Shagehuang Energy Base under various outputs according to the adjustable power resources and the absorption potential values, and combine it with the power gap value to generate and execute a hierarchical transmission control scheme.

[0017] The third aspect of the present invention provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the transmission control method for the Shagehuang Energy Base as described above.

[0018] The fourth aspect of the present invention provides a computer-readable storage medium, which includes a stored computer program. When the device where the computer-readable storage medium is located executes the computer program, it implements the transmission control method for the Shagehuang Energy Base as described above.

[0019] Compared with the prior art, the beneficial effects of the embodiments of the present invention are at least one of the following:

[0020] (1) By constructing a hierarchical power supply side and demand side model, the precise matching of power resources is achieved, avoiding the waste of power resources; the construction of the consumption capacity evaluation model provides a scientific basis for the optimal allocation of power resources in each region, calculates the power gap value in real time and determines the amount of adjusted power resources, ensuring the timeliness and effectiveness of power supply; by quantifying the upper limit of power adjustment, the reasonable allocation of power resources among different regions and different voltage levels is realized;

[0021] (2) By analyzing the relationship between electricity subsidies and cost load differences, it helps to formulate more reasonable electricity price policies and reduce the cost of power transmission. The implementation of the hierarchical power transmission control scheme can reduce unnecessary power losses and further improve the economy of power transmission; through the data-driven method, the precise matching, optimal allocation and efficient utilization of power resources in the desert, gobi and waste energy bases are realized, which is of great significance for improving the stability of the power system, reducing the cost of power transmission and promoting the sustainable development of power resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the present invention, the drawings required for implementation will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0023] Figure 1 is a flowchart of a power transmission control method for a desert, gobi and waste energy base provided by an embodiment of the present invention;

[0024] Figure 2 is a structural diagram of a power transmission control system for a desert, gobi and waste energy base provided by an embodiment of the present invention;

[0025] Figure 3 is a structural diagram of an electronic device provided by an embodiment of the present invention;

[0026] Reference numerals:

[0027] Among them, 10, the first model construction module; 20, the second model construction module; 30, the consumption potential evaluation module; 40, the adjusted resource quantification module; 50, the power transmission plan generation module; 5000, the electronic device; 5001, the processor; 5002, the bus; 5003, the memory; 5004, the transceiver. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings and embodiments. Obviously, the described embodiments are only a part rather than all of the embodiments of the present invention. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0029] In the description of this application, the terms "first", "second", "third", etc. are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first", "second", "third", etc. may explicitly or implicitly include one or more of such features. In the description of this application, unless otherwise stated, the meaning of "a plurality" is two or more.

[0030] In the description of this application, it should be noted that unless otherwise clearly defined and limited, the terms "installed", "connected", and "coupled" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two components. The terms "vertical", "horizontal", "left", "right", "up", "down", and similar expressions used herein are only for illustrative purposes and do not indicate or imply that the system or component referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be construed as a limitation on the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0031] In the description of this application, it should be noted that unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as commonly understood by those of ordinary skill in the technical field to which this invention belongs. The terms used in the specification of the present invention are only for describing specific embodiments and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0032] Although the sandy, rocky and desert areas are rich in wind and solar resources, the new energy power generation in these areas is intermittent, volatile and random. After large-scale grid connection, it poses challenges to the safe and stable operation of the power grid, resulting in serious phenomena of wind and light curtailment, making the consumption of new energy an urgent problem to be solved. In the demand side, the existing cross-regional power trading mechanisms mainly rely on medium- and long-term agreements, with long trading cycles and difficult adjustments, which are difficult to adapt to the rapid changes in new energy output, unable to give full play to the role of the market in resource allocation, and resulting in low trading efficiency. Moreover, the existing transmission price formation mechanisms mostly adopt fixed prices or government pricing, which are difficult to reflect the cost differences, supply-demand relationships and transmission service values of transmission lines at different voltage levels, unable to effectively guide the optimal allocation of power resources, and also difficult to stimulate the enthusiasm of grid enterprises to invest in the construction and maintenance of transmission lines. In addition, the existing power grid dispatching mainly relies on planned dispatching and experience dispatching, with limited prediction accuracy for the characteristics of transmission lines at different voltage levels and new energy output, making it difficult to achieve real-time and refined balance of inter-provincial power supply and demand, affecting the power grid operation efficiency and new energy consumption level. These problems restrict the efficiency and benefits of power transmission from the sandy, rocky and desert energy bases.

[0033] Based on this, in one embodiment, as Figure 1 shown, the first aspect of the present invention provides a transmission control method for a sandy, rocky and desert energy base, including:

[0034] S1. Obtain the historical supply-side transmission data of the sandy, rocky and desert energy base, and quantify the power output and outbound time distribution data of transmission lines at multiple voltage levels based on the historical supply-side transmission data, so as to construct a hierarchical power supply-side model of the sandy, rocky and desert energy base;

[0035] In one embodiment, step S1 includes:

[0036] Obtain the voltage level, transmitted power and power factor curve of the sandy, rocky and desert energy base as the historical supply-side transmission data, and classify and sort the historical supply-side transmission data to obtain historical data sets of transmission lines at multiple voltage levels;

[0037] Based on each of the historical data sets, use the least squares method to calculate the outbound time distribution data of each voltage-level transmission line, and use the time series decomposition method to calculate the power output of each voltage-level transmission line;

[0038] Take each of the historical data sets, each of the outbound time distribution data and each of the power outputs as input data, and input them into a transmission predictor constructed based on the neural network algorithm for processing, and output the transmission prediction data of each voltage-level transmission line;

[0039] Extract the actual power transmission data for the corresponding time periods according to the respective power transmission prediction data, so as to calculate the prediction accuracy of each power transmission prediction data through the root mean square error, and obtain the power transmission verification data sets for the power transmission lines of each voltage level to construct a hierarchical power supply side model.

[0040] Specifically, the present invention collects, from the historical operation database of the desert-governing energy base, the monitoring equipment of the power system, the data recording system, etc., historical supply-side power transmission data including the voltage levels of different lines, power transmission amounts, power transmission powers and power factor curves, timestamps, meteorological data, etc., and performs preprocessing operations such as missing value removal and outlier filtering on these data. Then, the preprocessed data is classified according to the voltage level, and labels are attached to each class of data (such as "UHVDC", "HVAC", etc., and also 1000 kV, 750 kV, 500 kV, 330 kV, 220 kV, 110 kV and their corresponding levels), thereby obtaining historical data sets of power transmission lines of various different voltage levels. Taking the 500-kV power transmission line as an example, its power transmission amount is mainly affected by voltage fluctuations, power factor and power transmission duration. When the voltage fluctuation range is between 475 kV and 525 kV, the power transmission amount usually fluctuates between 96.5% and 97.28% of the actual value; and the higher the power factor, the higher the power transmission amount. When the power factor remains above 0.95, the power transmission amount can be increased to 99.5% to 99.7% of the actual value. The influence of the power transmission duration on the power transmission amount is reflected in the characteristics of the load curve. The greater the peak-valley difference, the more obvious the fluctuation of the power transmission amount.

[0041] Taking time as the independent variable and the power transmission power in the historical data sets of the power transmission lines of each voltage level as the dependent variable, a piecewise linear regression model is constructed, and the coefficients of the piecewise linear regression model are solved by the least squares method to fit the power transmission time distribution law of the power transmission lines of each voltage level, obtaining the power transmission time distribution data of the power transmission lines of each voltage level. And the STL time series decomposition method is used to process the power transmission amount data in the historical data sets of the power transmission lines of each voltage level, extracting the trend term as the long-term power output baseline, the periodic term reflecting daily / seasonal fluctuations (such as high wind and light resources in summer and low in winter), and the residual term for anomaly detection, thereby obtaining the power output amounts of the power transmission lines of different voltage levels.

[0042] Taking the historical data sets (power, voltage, power factor, etc.), the external transmission time distribution data, and the power output of transmission lines at different voltage levels as input features, an LSTM (Long Short-Term Memory Network) model is used to capture the temporal dependencies in the input features, or a Transformer model is used to process long-sequence correlations, and an attention mechanism is added to enhance the feature weights at critical time periods (such as power mutations during peak load periods), thereby constructing a transmission predictor to output the transmission prediction data (power, voltage fluctuation range, etc.) for a future preset period of transmission lines at different voltage levels; in the process of constructing the transmission predictor, hourly power factor curve data, the external transmission time data, and the power output data at the same moment are selected as input features, and the number of input layer nodes corresponds to the length of the time series. For example, if 24-hour data is taken, 24 input nodes are set. The hidden layer adopts a two-layer structure, with the number of nodes in each layer being 16 and 8 respectively, and the corresponding transmission prediction data for the output layer; when training the data, 90 days of historical operation data can be selected, 80% of which is used for training and 20% for verification, and the weighted mean square error between the prediction data and the actual power output data is used as the loss function to assign a higher weight to the prediction error during peak periods; Dropout is used to prevent overfitting, Batch Normalization is used to accelerate convergence, and AdamW (Adam with weight decay) is used as the optimizer to dynamically adjust the parameters of the transmission predictor. Additionally, a BP neural network can also be used to construct a transmission predictor through the above input and output data. The BP neural network has a powerful non-linear mapping ability and self-learning and self-adaptive abilities, and is suitable for processing complex power system data.

[0043] Finally, extract the actual transmission data corresponding to the time periods of the transmission prediction data of transmission lines at different voltage levels, such as the actual transmitted power for the same time period in multiple years, and calculate the root mean square error between the weighted average of these data and the transmission prediction data to evaluate the accuracy of the prediction data. Or, directly according to the time period corresponding to the transmission prediction data, extract the actual transmission data from the power system as verification data to calculate the root mean square error with the prediction data. When the error result is less than the preset error threshold, it indicates that the prediction data passes the verification, and then a hierarchical power supply-side model of the Shagehuang Energy Base can be established based on these verified data to reflect the power output situation and external transmission time distribution characteristics of transmission lines at different voltage levels, providing strong support for power dispatching and planning; otherwise, adjust the parameters of the transmission predictor to update the transmission prediction data of transmission lines at different voltage levels, and then calculate the root mean square error of the updated prediction data until the accuracy of the prediction data meets the requirements, and output the finally constructed hierarchical power supply-side model.

[0044] By classifying and modeling data of different voltage levels, the present invention can finely analyze the power output characteristics of different transmission lines, avoid the error accumulation problem of the traditional rough model for the overlapping prediction of different voltage levels, and improve the prediction accuracy; based on the time series decomposition of the power transmission time distribution data and the power output, the bottleneck of the power transmission capacity in different periods can be identified, providing a basis for dynamic adjustment of power grid dispatching and reducing the risk of transmission line overload; by guiding the power transmission plan through the prediction model, the phenomenon of wind and light abandonment caused by the volatility of wind and light resources in the sandy and desert areas can be reduced, and the utilization rate of renewable energy can be improved; the predictor based on the neural network can adaptively learn the complex non-linear characteristics of historical data, providing high-confidence data support for power grid planning and reducing the dependence on manual experience.

[0045] S2. Collect historical demand-side load data and electricity subsidy data of multiple regions to analyze the correlation between the cost load difference of the transmission lines of each voltage level and the electricity subsidies of each region, and obtain a hierarchical power demand-side model for each region.

[0046] In one embodiment, step S2 includes:

[0047] Collect historical demand-side load data and electricity subsidy data of multiple regions, as well as the basic investment cost and operation and maintenance cost of the transmission lines of each voltage level in the same period.

[0048] Perform time series decomposition processing and normalization processing on each piece of historical demand-side load data to obtain a standardized load curve to quantify the peak-valley load difference and duration of each region.

[0049] Based on the peak-valley load difference, duration and electricity subsidy data of each region, use the support vector regression method to construct a peak-valley electricity price predictor to predict the peak-valley electricity price difference of each region.

[0050] According to the peak-valley electricity price difference of each region, statistically analyze the basic investment cost and operation and maintenance cost of the transmission lines of each voltage level in the same period to calculate the correlation between the unit power transmission cost and the electricity subsidy data of each region, and obtain a hierarchical transmission cost characteristic curve.

[0051] Based on the standardized load curve and the hierarchical transmission cost characteristic curve, use a recurrent neural network to construct a demand-side predictor to predict the multi-period electricity demand of each region, and construct a hierarchical power demand-side model for each region according to the multi-period electricity demand to quantify the electricity consumption prediction value of each region.

[0052] Specifically, the present invention takes provinces and states as the basis for regional division. Taking a province as an example of a region, it collects historical demand-side load data for each region (obtaining time-of-use electricity consumption at a granularity of 15 minutes / hour from smart meters and power trading platforms, covering industrial, commercial, and residential users, etc.) and electricity subsidy data (obtained from government public documents or internal records of power grid companies, including subsidy types such as new energy consumption subsidies, amounts, and payment cycles), and obtains the basic investment costs and operation and maintenance costs of transmission lines of various voltage levels during the same period as the historical demand-side load data and electricity subsidy data from the grid infrastructure ledger (such as line length, equipment depreciation rate) and operation and maintenance logs (such as inspection frequency, fault repair costs), and cleans and aligns the collected data for subsequent analysis and utilization. Among them, the power load data of the receiving province shows obvious time series characteristics, and the daily load curve reflects the periodic change law of electricity demand. By analyzing the load data of a receiving province for 24 hours a day, it is found that the peak electricity consumption periods on weekdays are concentrated from 9:00 to 12:00 and from 14:00 to 17:00, and the electricity load value reaches more than 95% of the daily maximum load. The low valley electricity consumption periods are mainly distributed from 1:00 to 5:00 in the early morning, and the electricity load drops to about 60% of the daily maximum load.

[0053] Use the STL decomposition method to perform time series decomposition on the historical demand-side load data of each region to separate the trend term (long-term growth), cycle term (daily / seasonal fluctuations), and residual term (random disturbances) of the historical load curve of each region, obtain the load characteristic indicators of each region, and normalize the load characteristic indicators at different time periods of each region to obtain a standardized load curve to calculate its peak-valley load difference and duration.

[0054] Take the historical peak-valley load difference and duration, electricity subsidy data, historical weather data, historical peak-valley electricity price difference, etc. of each region as training data to train the model constructed by the Gaussian kernel function, and perform parameter optimization using the grid search method during the training process to obtain the peak-valley electricity price predictor for each region. Taking the peak-valley load difference and duration, electricity subsidy data, and weather data of the corresponding region as inputs, the peak-valley electricity price difference of the region is output.

[0055] Based on the basic investment costs and operation and maintenance costs of transmission lines at different voltage levels during the same period, calculate the unit power transmission cost of each transmission line by voltage level grouping. For each region, fit a regression model of the unit power transmission cost, peak-valley electricity price difference, and electricity consumption subsidy data to characterize the correlation between the unit power transmission cost and the electricity consumption subsidy data of each region, and draw a cost-subsidy scatter plot, overlay the regression curve corresponding to the regression model, and then generate a hierarchical transmission cost characteristic curve to reveal the internal relationship between the cost, load, and electricity consumption subsidy of power transmission. Among them, the ratio relationship between the unit power transmission cost and the inter-provincial subsidy directly affects the power transmission benefit. For 500 kV transmission lines, when the transmission distance is within 1000 km, the unit power transmission cost is about 0.15 yuan per kWh. After considering the inter-provincial subsidy of 0.12 yuan per kWh, the actual transmission cost drops to 0.03 yuan per kWh. This cost-subsidy mechanism promotes cross-provincial and cross-regional power transactions.

[0056] Use the standardized historical load curve (time series data), historical hierarchical cost characteristic curve (static characteristics), and historical multi-period electricity demand as training samples to train the recurrent neural network to obtain a trained demand-side predictor. Let it take the standardized load curve and hierarchical cost characteristic curve as inputs, capture the long-term dependencies of these input features through the core network LSTM network layer, and splice the cost characteristic vector and the LSTM output to predict the multi-period electricity demand of each region in the future preset period through the output layer; finally, according to the prediction results, select appropriate machine learning or statistical models, such as linear regression, support vector machine, random forest, neural network, etc. to build models, and divide each region into different levels according to the electricity demand characteristics and prediction goals of each region, such as high-demand regions, medium-demand regions, low-demand regions, etc. For regions of different levels, build power demand-side models respectively to obtain the hierarchical power demand-side models of each region to quantify the predicted electricity consumption values of each region and provide a scientific basis for power planning and dispatching.

[0057] By constructing a demand-side predictor, the present invention can predict the multi-period electricity demand of each region and provide a scientific basis for power planning and dispatching; by analyzing the correlation between the cost-load differences of transmission lines at different voltage levels and electricity consumption subsidies, it helps to optimize the allocation of power resources and reduce transmission costs; by constructing a peak-valley electricity price predictor to predict the peak-valley electricity price difference of each region, it provides data support for the formulation of electricity price policies; quantifying the predicted electricity consumption values of each region helps to balance the supply and demand in the power market and discover prices, and improve market efficiency.

[0058] S3. Collect the grid operation data of each of the regions, analyze the impact of the electricity consumption subsidy differences in each of the regions on their consumption capabilities, so as to construct a consumption capacity evaluation model, and evaluate the consumption potential values of each of the regions through the consumption capacity evaluation model;

[0059] In one embodiment, step S3 includes:

[0060] Collect the power grid operation data of each of the said regions and combine it with the historical power transmission data on the supply side and the historical load data on the demand side of each of the said regions, so as to extract the new energy access ratio value and the consumption limit value therefrom;

[0061] Perform time series decomposition on the historical power transmission data on the supply side to obtain a new energy power generation feature data set, so as to extract seasonal fluctuation feature values through Fourier transform and perform correlation operations with the electricity subsidy data of each of the said regions to obtain the subsidy difference curves of each of the said regions;

[0062] Based on each of the said subsidy difference curves, the new energy access ratio value and the consumption limit value, use the gradient boosting tree method to construct a subsidy impact predictor to output the subsidy impact coefficients of each of the said regions;

[0063] According to each of the said subsidy impact coefficients, the power grid operation data and the new energy power generation feature data set, use a multi-layer perceptron to construct a consumption capacity evaluation model to evaluate the consumption potential values of each of the said regions.

[0064] Specifically, the present invention collects real-time data provided by the dispatching centers of each region (such as node voltage, line load rate, frequency deviation), new energy power station monitoring data (wind and light power prediction error, curtailment rate), etc. as the power grid operation data, and combines it with the historical power transmission data on the supply side (new energy power generation output curve with a 15-minute granularity, thermal / hydro power regulation capacity, etc.) and the historical load data on the demand side of each region (time-of-use electricity price data, interruptible load contract capacity, etc.), so as to extract the new energy access ratio value (the ratio of the real-time output of new energy to the total active power of the whole network) and the consumption limit value (the maximum consumption threshold under the grid security constraint calculated based on the line transmission limit of the N-1 criterion), so as to reflect the access situation of new energy power generation and the consumption capacity of the power system in each region. Among them, the new energy access power grid operation data shows obvious voltage level distribution characteristics. In the 500 kV voltage level transmission line, the new energy access ratio reaches 35%, and the consumption limit value is set at 40%. While for the 220 kV line, the new energy access ratio is 25% and the consumption limit value is 30%.

[0065] The STL decomposition method is used to perform time series decomposition on historical supply - side power transmission data to identify the daily cycle, monthly cycle, and seasonal variation patterns of new - energy power generation. Furthermore, a new - energy power generation feature dataset (including data such as daily power generation curves, output volatility, and power generation prediction accuracy) is constructed to reveal the time - series laws and characteristics of new - energy power generation. After extracting the main - frequency features from the new - energy power generation feature dataset through Fourier transform as seasonal fluctuation feature values and aligning them with the time - series data of electricity subsidies in each region, the Pearson correlation coefficient is calculated, and then the subsidy difference curve for each region is generated to reflect the relationship between electricity subsidy differences and the seasonal fluctuations of new - energy power generation. Its horizontal axis is time, and the vertical axis is the subsidy - weighted correlation.

[0066] Taking the historical subsidy difference curves, new - energy historical access ratio values, and historical consumption limit values in each region as core features, and the historical grid load factor and historical demand - side response participation rate at corresponding times as auxiliary features, and then combining the historical subsidy impact coefficient at the same time as training samples, a model is trained by the gradient - boosting tree method with the labeled consumption potential value according to the historical consumption rate as the target variable. After training is completed, the subsidy impact predictor is obtained. Using the subsidy difference curve, new - energy access ratio value, and consumption limit value in each region as inputs, the subsidy impact coefficient for each region is output (ranging from 0 to 1, representing the contribution weight of the subsidy to the consumption capacity, and can also be considered as the specific impact degree of electricity subsidy differences on the consumption capacity).

[0067] Finally, taking the historical subsidy impact coefficients, consumption values, historical new - energy power generation feature datasets (main - frequency amplitude, output volatility), and historical grid operation data (load factor, voltage deviation) in each region as training samples, a multi - layer perceptron with a 3 - layer fully - connected network (256 - 128 - 64 neurons) as the hidden layer and a single neuron as the output layer is trained. During the training process, the mean - squared error and L2 regularization are used as the loss function, and the Adam variant with Nesterov momentum is used as the optimizer. After reaching the maximum number of iterations, a consumption capacity evaluation model is obtained. Taking the subsidy impact coefficient, new - energy power generation feature dataset, and grid operation data in each region as inputs, the consumption potential value evaluated for each region is output.

[0068] Through the correlation analysis between the subsidy difference curve and the characteristics of new energy, the present invention can quantify the dynamic impact of different subsidy policies on the consumption capacity, providing data support for the government to optimize subsidy allocation; combined with the seasonal fluctuation characteristics and real-time power grid operation data, the model can dynamically evaluate the consumption potential of regions, assisting in planning the installed capacity of new energy and grid connection strategies for the power grid; integrating supply-side, demand-side and power grid operation data, breaking through the limitations of traditional single-dimensional evaluation, and improving the prediction confidence of the model; revealing the potential contradiction that high subsidy does not equal high consumption through the subsidy impact coefficient, promoting the coordinated development of policy formulation and power grid technology upgrade, quantifying the consumption potential values of each region, helping to formulate targeted new energy consumption strategies, and improving the consumption efficiency of new energy.

[0069] S4. When any region is in the peak electricity consumption period, calculate the power gap values of the transmission lines of each voltage level based on the hierarchical power supply-side model and the hierarchical power demand-side model of the corresponding region, and determine the adjusted power resource amounts of the transmission lines of each voltage level in combination with the transmission capacity limit and the electricity consumption subsidy data of each region.

[0070] In one embodiment, step S4 includes:

[0071] Take any region in the peak electricity consumption period as the target region and extract the real-time electricity load curve of the target region, and extract the real-time output values of the transmission lines of each voltage level at the same moment to obtain the total real-time output value.

[0072] Based on the hierarchical power supply-side model, obtain the predicted output values of the transmission lines of each voltage level at the corresponding moment to obtain the total predicted output value, and when the total real-time output value is less than the total predicted output value, determine that there is a power gap.

[0073] In response to the power gap, and based on the real-time output values and transmission distances of the transmission lines of each voltage level, use the support vector regression method to construct a transmission loss predictor to output the transmission loss prediction values of the transmission lines of each voltage level.

[0074] Quantify the actual arrived electricity in the target region according to the transmission loss prediction values and the real-time output values, and obtain the total predicted demand at the corresponding moment through the hierarchical power demand-side model of the target region to compare with the actual arrived electricity.

[0075] When the actual arrived electricity is less than the total predicted demand, quantify the power gap values of the transmission lines of each voltage level, and combine them with the transmission capacity limit, transmission loss prediction values of the transmission lines of each voltage level and the electricity consumption subsidy data of each target region, and use the support vector regression method to construct an adjustment amount predictor to output the adjusted power resource amounts of the transmission lines of each voltage level.

[0076] Specifically, the electricity load in the receiving province exhibits obvious time - period characteristics. By analyzing the electricity load curve, the daily maximum load usually appears in two time periods: from 9:00 to 11:00 and from 14:00 to 16:00. According to the real - time electricity load data of each region, the threshold detection method is used to judge whether the load in the corresponding time period exceeds 85% of the daily maximum load. If so, it indicates that the region is in the peak electricity consumption period and the electricity demand is tense. Then, one or more regions in the peak electricity consumption period are taken as target regions, and the real - time output values of transmission lines of different voltage levels at the same moment are obtained for summation operation to get the total real - time output value; otherwise, it indicates that the electricity demand in the region is not tense, and the original transmission plan (based on past historical electricity consumption data, historical power generation data, and their corresponding transmission schemes) can still be followed.

[0077] Call the hierarchical power supply - side model to predict the predicted output values of transmission lines of different voltage levels at the same moment and sum them to get the total predicted output value for comparison with the total real - time output value. When the total real - time output value is not less than the total predicted output value, it indicates that the supply - demand is in a balanced state, and then the base still conducts power transmission according to the original transmission plan. When the total real - time output value is less than the total predicted output value, it indicates that the supply is less than the demand, and it is determined that there is a power gap in the original transmission plan of the base.

[0078] In response to the power gap state of the base plan, and using the historical output values of transmission lines of different voltage levels, the transmission distance to the target region, the historical line load rate, the historical transmission loss, etc. as samples to train the polynomial kernel function, and using the grid search method to determine the parameters during the training process, a trained transmission loss predictor is obtained. It takes the real - time output values of transmission lines of different voltage levels, the transmission distance to the target region, and the real - time line load rate as inputs and outputs the predicted transmission loss values of transmission lines of different voltage levels.

[0079] Quantify the actual arrival electricity of transmission lines of different voltage levels reaching the target region according to the difference between the real - time output values and the predicted transmission loss values of transmission lines of different voltage levels, and call the hierarchical power demand - side model of the target region to output the total predicted demand value at the corresponding moment for comparison with the actual arrival electricity. When the actual arrival electricity is less than the total predicted demand value, use the difference between the total predicted demand value and the actual arrival electricity to determine the total power gap of the region, and allocate the gap amount according to the current load ratio of each line to obtain the power gap values of transmission lines of different voltage levels; considering the importance of transmission lines of different voltage levels, the weight coefficient of the 500 - kV line is set to 0.6, the 220 - kV line is 0.3, and the 110 - kV line is 0.1, just for example.

[0080] Taking the power gap values, transmission capacity limits (transmission capacity margins), predicted transmission losses of transmission lines at different voltage levels, and electricity subsidy data of each target area as input data, with the constraint that the adjusted power resources of the transmission lines at the corresponding voltage levels are less than or equal to their transmission capacity margins and the total adjustment amount meets the power gap, the adjustment amount prediction is transformed into a constrained SVR problem and solved using the Lagrange multiplier method to obtain an adjustment amount predictor, so as to output the adjusted power resources of transmission lines at different voltage levels, providing a scientific basis for power dispatching; at the same time, the multi-level voltage transmission method reflects the hierarchy and reliability of power grid operation, jointly ensuring the electricity demand of the receiving province.

[0081] Through the combination of real-time monitoring and prediction models, the present invention accurately identifies power gaps, avoids misjudgments caused by traditional static threshold methods (such as load instantaneous fluctuations), and improves the response speed of the power grid; combines transmission loss and electricity subsidy data to optimize the adjustment strategy, reduces transmission costs, and improves the utilization efficiency of subsidy funds; calculates power gaps and adjustment amounts by level, avoids the coexistence of overloading of high-voltage lines and idleness of low-voltage lines, improves the overall transmission efficiency, realizes real-time monitoring and prediction of power supply and demand balance, provides strong support for power dispatching and resource optimization allocation; helps reduce transmission losses, improve power utilization efficiency, and guides the formulation and adjustment of electricity subsidy policies.

[0082] S5. According to each of the adjusted power resources and each of the consumption potential values, quantify the upper limit of the adjusted power of the Shagohuang Energy Base under various output levels, and combine it with the power gap value to generate a hierarchical transmission control plan and execute it;

[0083] In one embodiment, the quantifying the upper limit of the adjusted power of the Shagohuang Energy Base under various output levels according to each of the adjusted power resources and each of the consumption potential values includes:

[0084] Based on each of the adjusted power resources, obtain the adjusted standby capacity of the Shagohuang Energy Base under three output conditions of full load, medium load, and low load, and combine it with the total predicted demand value of the target area, the consumption potential value of the target area, and the power grid operation constraints, and use an adaptive neural network to construct a power consumption predictor to output the predicted value of the consumption capacity of the target area;

[0085] [[ID=##ID=15]]Perform weighted summation on the predicted value of the consumption capacity of the target area and the consumption potential value of the target area to obtain the consumption amount of the target area, and combine it with the remaining capacity data of each voltage level transmission line, and calculate the adjusted power distribution plan of each voltage level transmission line through the mathematical programming method;

[0086] According to each of the adjustable power allocation schemes, the adjustable resources of the Shagohuang Energy Base under three output conditions of full load, medium load, and low load are counted, and the statistical results are used as the upper limit of adjustable power of the Shagohuang Energy Base under multiple outputs.

[0087] Specifically, based on the adjustable power resources of transmission lines at each voltage level, the present invention obtains the adjustable reserve capacity of the Shagohuang Energy Base under three output conditions of full load, medium load, and low load. These reserve capacities are additional power resources that the Shagohuang Energy Base can provide under different output conditions, and it is necessary to ensure that the final adjustable reserve capacity meets the adjustable power resources. Among them, under the full load condition, the total output of the base reaches more than 95% of the designed capacity, and the adjustable reserve capacity is only 5% at this time. The medium load condition corresponds to an output level of 75% to 85%, and the adjustable reserve capacity reaches 15% to 20%. Under the low load condition, the output drops below 60%, and the adjustable reserve capacity exceeds 35%. Then, the output condition of the base, the corresponding adjustable reserve capacity, the total historical predicted demand value of the target area, the historical consumption potential value of the target area, and the power grid operation constraints (such as transmission line capacity limits, voltage stability limits, etc.) are used as training data to train a neural network that dynamically adjusts the number of hidden layer neurons based on the input feature complexity adaptive mechanism, and the weighted cross entropy (assigning a higher weight to the low consumption scenario) is used as the loss function to obtain a trained power consumption predictor. It takes each output condition, adjustable reserve capacity, predicted demand total value of the target area, consumption potential value of the target area, and power grid operation constraints as inputs and outputs the predicted value of the consumption capacity of the target area. Power consumption prediction needs to comprehensively consider multiple influencing factors. When the energy base is in the medium load condition and the predicted load value of the receiving province is 10 million kWh, considering the N-1 verification required by the power grid safety constraints, the consumption capacity values of each province show significant differences. The consumption capacity of the load center province can reach 40% of the total load, while that of the peripheral provinces is between 25% and 30%.

[0088] The predicted value of the consumption capacity of the target area and the consumption potential value of the target area are weighted and summed to obtain the consumption volume of the target area, which represents the amount of electric power that the target area can actually consume in a future period of time. The weight can be determined according to the historical error minimization method or the variance reciprocal weighting method, etc.; and the consumption volume of the target area is combined with the remaining capacity data of transmission lines at each voltage level for regulating resource allocation; among them, with the remaining capacity data of transmission lines at each voltage level meeting the consumption volume of the target area as a constraint and the lowest cost required for regulating resource allocation as the goal, a target function is constructed, and the linear programming method or the branch and bound method is used to solve the target function, and then the regulating power allocation plan for transmission lines at each voltage level is obtained. The regulating resource allocation plan reflects the hierarchical characteristics of the voltage level. For example, the remaining capacity of the 500 kV line is 4.5 million kW, and the allocable regulating power is 3.5 million kW, which is mainly used for large-capacity power transmission across provinces and regions. The remaining capacity of the 220 kV line is 2.5 million kW, and the allocable regulating power is 2 million kW, which focuses on ensuring power supply for key users within the province.

[0089] Finally, according to the regulating power allocation plan for transmission lines at each voltage level, the adjustable resources of the Shagehuang Energy Base under the three output conditions of full load, medium load, and low load are counted, and the statistical results are used as the upper limit of the adjustable power of the Shagehuang Energy Base under multiple outputs to represent the maximum adjustable power resources that the Shagehuang Energy Base can provide under different output conditions; there are obvious differences in the adjustable capacity of the energy base under different output conditions. When operating at full load, the upper limit value of the hierarchical adjustable power of the 500 kV line is 1.5 million kW, which is limited by the thermal stability constraint of the line. Under medium load conditions, this value is increased to 3 million kW, and under low load conditions, it can reach 4.5 million kW. The use of dynamic adjustment also provides flexibility for power grid dispatching.

[0090] The present invention constructs a refined model for the three conditions of full load, medium load, and low load to quantify the upper limit of the adjustable capacity under different new energy output fluctuations, avoid the conservatism of traditional single-condition planning, and improve resource utilization rate; combines the consumption potential value (long-term evaluation) with the predicted value of the consumption capacity (short-term dynamic) to construct a weighted decision-making model to balance the contradiction between long-term planning and short-term dispatching; when performing multi-objective optimal dispatching, the mathematical programming method is used to comprehensively consider the transmission capacity, consumption demand, and subsidy economy, realizing the balance of technical feasibility and economic optimality; it helps to improve the flexibility of power dispatching, optimize the allocation of power resources, ensure the stable operation of the power grid, and provide guidance for the construction and operation of the Shagehuang Energy Base.

[0091] In one embodiment, constructing and executing a hierarchical transmission control plan with the power gap value includes:

[0092] Perform a three - level classification on the transmission capacity and historical line investment data of each transmission line at the corresponding voltage level to obtain high, medium, and low - grade transmission capacity thresholds and the corresponding investment cost benchmark values. Combine these with the historical operation and maintenance costs, equipment overhaul cycles, and line load rates of each transmission line at the corresponding voltage level, and use the support vector regression method to construct an operation and maintenance cost predictor to output the operation and maintenance cost benchmark values for each transmission line at the corresponding voltage level in different grades;

[0093] Based on the operation and maintenance cost benchmark values for each transmission line at the corresponding voltage level and the historical power price data, quantify the change rate of the operation and maintenance costs of each transmission line at the corresponding voltage level, and perform a time - series decomposition on each change rate of the operation and maintenance costs to obtain the cost dynamic adjustment coefficients for each transmission line at the corresponding voltage level, so as to establish a three - level ladder rule based on cost for each transmission line at the corresponding voltage level;

[0094] Combine the power gap values of each transmission line at the corresponding voltage level with the three - level ladder rule and the upper limit of the regulated power under various output levels of the Shagohuang Energy Base, and perform cost - optimized allocation on each transmission line at the corresponding voltage level through the dynamic programming method to output the allocated values of the transmission capacity by grade, and generate and execute a transmission control plan by grade through the mathematical programming method.

[0095] Specifically, the present invention groups the transmission capacities of each transmission line by voltage level, takes the 10%, 50%, and 90% quantiles to divide into low, medium, and high grades to obtain high, medium, and low - grade transmission capacity thresholds, and calculates the mean value of the historical line investment data of each transmission line at the corresponding voltage level according to the capacity grade to obtain the corresponding investment cost benchmark value. Then, associate the historical capacity threshold, historical investment cost benchmark value with the historical operation and maintenance costs, historical equipment overhaul cycles, historical operation and maintenance cost benchmark values in different grades, and historical line load rates of each transmission line according to the voltage level to obtain an associated feature matrix as training data to train the RBF kernel function model, and use the ε - insensitive loss function and Bayesian optimization parameters to obtain a trained operation and maintenance cost predictor. It takes the capacity threshold, investment cost benchmark value, and the historical operation and maintenance costs, equipment overhaul cycles, and line load rates of each transmission line as inputs and outputs the operation and maintenance cost benchmark values for each transmission line at the corresponding voltage level in different grades. Among them, when grouping, taking the 500 - kV transmission line as an example, the high - grade capacity can be defined as more than 3 million kilowatts, with a corresponding investment cost benchmark value of 3.5 million yuan per kilometer; the medium - grade capacity is 2 - 3 million kilowatts, with a benchmark value of 3 million yuan per kilometer; the low - grade capacity is less than 2 million kilowatts, with a benchmark value of 2.5 million yuan per kilometer.

[0096] Based on the maintenance cost benchmark values for different voltage levels of transmission lines and historical electricity price data, quantify the change rate of the maintenance costs of transmission lines with different voltage levels and perform a time series decomposition on it to obtain the trend term (long-term cost increase), the periodic term (seasonal fluctuations), and the residual term (random disturbances). Then, determine the cost dynamic adjustment coefficients for transmission lines with different voltage levels by weighted summation of the trend term and the periodic term (weight ratio 1:0.5) to establish a cost-based three-level ladder rule for transmission lines with different voltage levels, including load factor intervals - cost grades - adjustment coefficient ranges. Correspondingly, the maintenance cost in the high grade of the three-level ladder rule is increased by 20% based on the benchmark value, mainly considering the additional maintenance requirements brought by large-capacity power transmission. The medium grade remains unchanged at the benchmark value as a reference standard for cost accounting. The maintenance cost in the low grade is decreased by 15%, reflecting the simplicity of maintenance for small-capacity power transmission.

[0097] Taking the power gap value of each voltage level of transmission line, the power adjustment upper limit of the desert, Gobi, and wastelands energy base under various output levels, the three-level ladder rule and its cost dynamic adjustment coefficient as state variables, and the capacity adjustment amount in the next time period as the decision variable, construct a state transition equation. Then, with the goal of minimizing the capacity adjustment amount, use value iteration to update the state value function period by period, and use the Lagrangian relaxation method to handle the capacity limit to optimize the cost allocation of each voltage level of transmission line, and output the allocated value of the transmission capacity of each voltage level of transmission line to ensure cost minimization while meeting the power demand; in the cost optimization allocation, the power gap value and the adjustment upper limit value jointly restrict the allocation of the transmission capacity. When there is a power gap of 5 million kilowatts in a certain region and the adjustment upper limit value is 8 million kilowatts, the dynamic programming method will preferentially select the medium-capacity plan, which not only meets the transmission demand but also avoids the cost premium of the high-capacity plan.

[0098] Finally, with the goal of minimizing the total cost while meeting the three-level ladder rule and the adjustment upper limit, construct an optimization model with the ladder rule matching and adjustment limit as constraints, and use a commercial solver to solve the model to obtain a graded transmission control plan for implementation; the plan should include the specific allocation of the transmission capacity, the dispatching strategy, the maintenance plan, etc. For example, for a 500 kV line, when the power gap is 3 million kilowatts, select the low-capacity plan with a 15% reduction in the maintenance cost; when the gap increases to 9 million kilowatts, adopt the medium-capacity plan to maintain the benchmark cost; when the gap exceeds 15 million kilowatts, enable the high-capacity plan and accept a 20% increase in the cost.

[0099] Through the division of three - level transmission capacity thresholds and cost benchmark values, the present invention realizes dynamic management of cost grading, avoiding resource waste caused by traditional extensive operation and maintenance; the cost dynamic adjustment coefficient combines time - series decomposition technology to accurately reflect the impact of load fluctuations on operation and maintenance costs, improving the matching degree between the ladder rule and the actual operation state of the power grid; the combined optimization of dynamic programming and mathematical programming minimizes the total operation and maintenance cost while meeting capacity allocation and ladder rules, enabling the optimization of transmission capacity allocation and improving the operation efficiency of the power grid.

[0100] In the embodiments of the present application, based on the problem of how to effectively evaluate the new - energy consumption potential of each region to optimize the transmission strategy of the desert - gobi - waste base, a transmission control method for the desert - gobi - waste energy base is designed. By considering the transmission characteristics of transmission lines with different voltage levels, the constraints of electricity subsidies between regions and new - energy consumption capabilities, a hierarchical power supply - demand model and a consumption evaluation model for each region are constructed; when there is a power gap, the adjusted power is determined by combining the transmission capacity of transmission lines with different voltage levels and the electricity subsidies and consumption potential of the corresponding regions, and a stepped hierarchical transmission plan is generated in combination with the power gap to achieve the optimal allocation of cross - desert - gobi - waste energy - base transmission; through the technical path of hierarchical modeling - dynamic evaluation - precise adjustment, the new - energy consumption potential of each region is effectively evaluated, the matching between the power output of the desert - gobi - waste energy base and the power demand of each region is optimized, and thus the effective allocation and utilization efficiency of power resources are improved.

[0101] It should be noted that although the steps in the above flowcharts are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders.

[0102] In another embodiment, as Figure 2 shown, the second aspect of the present invention provides a transmission control system for a desert - gobi - waste energy base, including:

[0103] A first model - building module 10, configured to obtain the historical supply - side transmission data of the desert - gobi - waste energy base, and quantify the power output and outgoing time distribution data of transmission lines with multiple voltage levels based on the historical supply - side transmission data, so as to build a hierarchical power supply - side model of the desert - gobi - waste energy base;

[0104] A second model - building module 20, configured to collect the historical demand - side load data and electricity subsidy data of multiple regions, analyze the correlation between the cost - load differences of the transmission lines with each voltage level and the electricity subsidies of each region, and obtain a hierarchical power demand - side model of each region;

[0105] The consumption potential evaluation module 30 is used to collect the power grid operation data of each of the said regions, analyze the impact of the electricity subsidy differences in each of the said regions on their consumption capabilities, so as to construct a consumption capacity evaluation model, and evaluate the consumption potential values of each of the said regions through the consumption capacity evaluation model;

[0106] The power adjustment resource quantification module 40 is used to calculate the power gap values of transmission lines of each voltage level based on the hierarchical power supply side model and the hierarchical power demand side model of the corresponding region when any region is in the peak electricity consumption period, and determine the power adjustment resource amounts of transmission lines of each voltage level in combination with the transmission capacity limit and the electricity subsidy data of each of the said regions;

[0107] The transmission plan generation module 50 is used to quantify the upper limit of the adjusted power of the Shagohuang energy base under various output levels according to the power adjustment resource amounts of each of the said regions and the consumption potential values, so as to generate a hierarchical transmission control plan in combination with the power gap values and execute it.

[0108] It should be noted that each of the above modules in a transmission control system for a Shagohuang energy base can be implemented in whole or in part through software, hardware and their combination. The above modules can be embedded in the processor in the computer device in the form of hardware or be independent of the processor, or can be stored in the memory in the computer device in the form of software, so as to facilitate the processor to call and execute the operations corresponding to each of the above modules. For the specific limitations of a transmission control system for a Shagohuang energy base, refer to the limitations of a transmission control method for a Shagohuang energy base in the above text. The two have the same functions and effects and will not be elaborated here.

[0109] The third aspect of the present invention provides an electronic device, and this electronic device includes:

[0110] A processor, a memory and a bus;

[0111] The bus is used to connect the processor and the memory;

[0112] The memory is used to store operation instructions;

[0113] The processor is used to execute the corresponding operations of a transmission control method for a Shagohuang energy base as shown in the first aspect of this application by calling the operation instructions and executing the instructions.

[0114] In an optional embodiment, an electronic device is provided, as Figure 3 shown Figure 3The electronic device 5000 shown includes: a processor 5001 and a memory 5003. Among them, the processor 5001 and the memory 5003 are connected, such as connected through a bus 5002. Optionally, the electronic device 5000 may further include a transceiver 5004. It should be noted that in practical applications, the transceiver 5004 is not limited to one, and the structure of the electronic device 5000 does not constitute a limitation on the embodiments of the present application.

[0115] The processor 5001 may be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It can implement or execute various exemplary logic blocks, modules and circuits described in connection with the disclosure of the present application. The processor 5001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0116] The bus 5002 may include a path for transmitting information between the above components. The bus 5002 may be a PCI bus or an EISA bus, etc. The bus 5002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 only a thick line is used to represent it in the figure, but it does not mean that there is only one bus or one type of bus.

[0117] The memory 5003 may be a ROM or other types of static storage devices that can store static information and instructions, a RAM or other types of dynamic storage devices that can store information and instructions, or it may also be an EEPROM, a CD-ROM or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0118] The memory 5003 is used to store the application program code for executing the solution of the present application, and is controlled by the processor 5001 to execute. The processor 5001 is used to execute the application program code stored in the memory 5003 to implement the content shown in any of the foregoing method embodiments.

[0119] Among them, the electronic device includes but is not limited to: mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle terminals (such as vehicle navigation terminals), etc. and fixed terminals such as digital TVs, desktop computers, etc.

[0120] In a fourth aspect of the present invention, there is provided a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements a power transmission control method for a desert, gobi and waste energy base as shown in the first aspect of the present application.

[0121] Another embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, and when it runs on a computer, it enables the computer to execute the corresponding content in the foregoing method embodiments.

[0122] In addition, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the steps of the above method.

[0123] In summary, the present invention relates to the field of information technology, and discloses a power transmission control method, system, device and medium for a desert, gobi and waste energy base. By constructing a hierarchical power supply-side model of the desert, gobi and waste energy base and a hierarchical power demand-side model of the receiving area, and analyzing the impact of electricity subsidy data in different regions on their consumption capacity to evaluate the consumption potential value of each region; for regions in peak electricity consumption, based on the hierarchical power supply-side model and the hierarchical power demand-side model of the region, calculate the power gap value of transmission lines at each voltage level and determine the amount of regulated power resources; according to the calculated amount of regulated power resources and the consumption potential value of the corresponding region, quantify the upper limit of regulated power for the desert, gobi and waste energy base under various power outputs, and combine it with the power gap value to generate a hierarchical power transmission control plan and execute it. By using the data-driven method, the consumption potential of each region for new energy is effectively evaluated, and the efficient utilization of power resources is realized.

[0124] Each embodiment in this specification is described in a progressive manner. For parts that are the same or similar in each embodiment, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0125] The above-described embodiments merely represent several preferred embodiments of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art of this technology, without departing from the technical principle of the present invention, several improvements and substitutions can be made, and these improvements and substitutions should also be regarded as the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the protection scope of the claims described above.

Claims

1. A power transmission control method for a desert, gobi and wasteland energy base, characterized in that Including: Obtain the historical supply - side power transmission data of the Shagohuang Energy Base, and quantify the power output and the external transmission time distribution data of transmission lines of multiple voltage levels based on the historical supply - side power transmission data, so as to construct a hierarchical power supply - side model of the Shagohuang Energy Base; Collect the historical demand - side load data and electricity subsidy data of multiple regions to analyze the correlation between the cost - load differences of transmission lines of each voltage level and the electricity subsidies of each region, and obtain a hierarchical power demand - side model for each region; Collect the grid operation data of each region, analyze the impact of the electricity subsidy differences in each region on its consumption capacity, so as to construct a consumption capacity evaluation model, and evaluate the consumption potential value of each region through the consumption capacity evaluation model; When any region is in the peak electricity consumption period, calculate the power gap value of transmission lines of each voltage level based on the hierarchical power supply - side model and the hierarchical power demand - side model of the corresponding region, and combine the transmission capacity limit and the electricity subsidy data of each region to determine the adjusted power resource quantity of transmission lines of each voltage level; According to each adjusted power resource quantity and each consumption potential value, quantify the adjusted power upper limit of the Shagohuang Energy Base under various output levels, and combine it with the power gap value to generate a hierarchical power transmission control scheme and execute it.

2. The transmission control method for a desert, gobi and waste land energy base according to claim 1, wherein The obtaining of the historical supply - side power transmission data of the Shagohuang Energy Base, and quantifying the power output and the external transmission time distribution data of transmission lines of multiple voltage levels based on the historical supply - side power transmission data, so as to construct a hierarchical power supply - side model of the Shagohuang Energy Base, includes: Obtain the voltage level, power transmission volume and power factor curve of the Shagohuang Energy Base as historical supply - side power transmission data, and classify the historical supply - side power transmission data to obtain historical data sets of transmission lines of multiple voltage levels; Based on each historical data set, use the least - squares method to calculate the external transmission time distribution data of transmission lines of each voltage level, and use the time - series decomposition method to calculate the power output of transmission lines of each voltage level; Use each historical data set, each external transmission time distribution data and each power output as input data, and input them into a transmission predictor constructed based on the neural network algorithm for processing, and output the transmission prediction data of transmission lines of each voltage level; Extract the actual transmission data of the corresponding period according to each transmission prediction data, and calculate the prediction accuracy of each transmission prediction data through the root - mean - square error to obtain a transmission verification data set of transmission lines of each voltage level to construct a hierarchical power supply - side model.

3. A transmission control method for a desert, gobi and wasteland energy base according to claim 1, characterized in that, The collecting of the historical demand - side load data and electricity subsidy data of multiple regions to analyze the correlation between the cost - load differences of transmission lines of each voltage level and the electricity subsidies of each region, and obtaining a hierarchical power demand - side model for each region, includes: Collect the historical demand - side load data and electricity subsidy data of multiple regions, as well as the basic investment cost and operation and maintenance cost of transmission lines of each voltage level in the same period; Perform time series decomposition and normalization processing on each of the historical demand-side load data to obtain a standardized load curve to quantify the peak-valley load difference and duration of each of the regions; Based on the peak-valley load difference, duration, and electricity subsidy data of each of the regions, use the support vector regression method to construct a peak-valley electricity price predictor to predict the peak-valley electricity price difference of each of the regions; Statistically analyze the basic investment cost and operation and maintenance cost of each voltage level transmission line in the same period according to the peak-valley electricity price difference of each of the regions to calculate the correlation between the unit power transmission cost and the electricity subsidy data of each of the regions, and obtain a hierarchical transmission cost characteristic curve; Based on the standardized load curve and the hierarchical transmission cost characteristic curve, use a recurrent neural network to construct a demand-side predictor to predict the multi-period electricity demand of each of the regions, and construct a hierarchical power demand-side model for each of the regions according to the multi-period electricity demand to quantify the electricity consumption prediction value of each of the regions.

4. A transmission control method for a desert, Gobi and wasteland energy base according to claim 1, characterized in that, Collect the grid operation data of each of the regions, analyze the impact of the electricity subsidy difference in each of the regions on its consumption capacity, construct a consumption capacity evaluation model, and evaluate the consumption potential value of each of the regions through the consumption capacity evaluation model, including: Collect the grid operation data of each of the regions and combine it with the historical supply-side power transmission data and each of the historical demand-side load data to extract the new energy access ratio value and consumption limit value; Perform time series decomposition on the historical supply-side power transmission data to obtain a new energy power generation characteristic data set, extract seasonal fluctuation characteristic values through Fourier transform and perform correlation operations with the electricity subsidy data of each of the regions to obtain the subsidy difference curve of each of the regions; Based on each of the subsidy difference curves, the new energy access ratio value, and the consumption limit value, use the gradient boosting tree method to construct a subsidy impact predictor to output the subsidy impact coefficient of each of the regions; According to each of the subsidy impact coefficients, the grid operation data, and the new energy power generation characteristic data set, use a multi-layer perceptron to construct a consumption capacity evaluation model to evaluate the consumption potential value of each of the regions.

5. A transmission control method for a desert, gobi and wasteland energy base according to claim 1, characterized in that When any region is in the peak electricity consumption period, calculate the power gap value of each voltage level transmission line based on the hierarchical power supply-side model and the hierarchical power demand-side model of the corresponding region, and combine the transmission capacity limit and the electricity subsidy data of each of the regions to determine the adjusted power resource amount of each voltage level transmission line, including: Take any region in the peak electricity consumption period as the target region and extract the real-time electricity load curve of the target region, and extract the real-time output value of each voltage level transmission line at the same moment to obtain the total real-time output value; Based on the hierarchical power supply-side model, obtain the predicted output value of each voltage level transmission line at the corresponding moment to obtain the total predicted output value, and when the total real-time output value is less than the total predicted output value, determine that there is a power gap; In response to the power gap and based on the real-time output values and transmission distances of transmission lines at each voltage level, a transmission loss predictor is constructed using the support vector regression method to output the predicted transmission loss values of transmission lines at each voltage level; Quantify the actual received power of the target area according to the predicted transmission loss values and the real-time output values at each voltage level, and obtain the total predicted demand value at the corresponding moment through the hierarchical power demand-side model of the target area for comparison with the actual received power; When the actual received power is less than the total predicted demand value, quantify the power gap values of transmission lines at each voltage level, and combine them with the transmission capacity limits, predicted transmission loss values of transmission lines at each voltage level, and electricity subsidy data of each target area, and use the support vector regression method to construct a regulation amount predictor to output the regulated power resource amounts of transmission lines at each voltage level.

6. The transmission control method for a desert, gobi and arid land energy base according to claim 5, wherein, Quantify the upper limit of regulated power of the Shagohuang Energy Base under various output levels according to the regulated power resource amounts and the absorption potential values at each voltage level, including: Based on the regulated power resource amounts at each voltage level, obtain the regulated reserve capacities of the Shagohuang Energy Base under three output conditions of full load, medium load, and low load, and combine them with the total predicted demand value of the target area, the absorption potential value of the target area, and the power grid operation constraints, and use an adaptive neural network to construct a power absorption predictor to output the predicted absorption capacity value of the target area; Perform a weighted sum of the predicted absorption capacity value of the target area and the absorption potential value of the target area to obtain the absorption amount of the target area, combine it with the remaining capacity data of transmission lines at each voltage level, and calculate the regulated power allocation plan for transmission lines at each voltage level through the mathematical programming method; According to the regulated power allocation plans at each voltage level, count the adjustable resources of the Shagohuang Energy Base under three output conditions of full load, medium load, and low load, and use the statistical results as the upper limit of regulated power of the Shagohuang Energy Base under various output levels.

7. A power transmission control method for a desert, Gobi and arid land energy base according to claim 6, characterized in that, Construct and execute a hierarchical transmission control plan in combination with the power gap value, including: Perform a three-level division on the transmission capacities and historical line investment data of transmission lines at each voltage level to obtain high, medium, and low transmission capacity thresholds and corresponding investment cost benchmark values, combine them with the historical operation and maintenance costs, equipment overhaul cycles, and line load rates of transmission lines at each voltage level, and use the support vector regression method to construct an operation and maintenance cost predictor to output the operation and maintenance cost benchmark values at each grade of transmission lines at each voltage level; Quantify the operation and maintenance cost change rates of transmission lines at each voltage level based on the operation and maintenance cost benchmark values at each grade of transmission lines at each voltage level and the historical power price data, and perform a time series decomposition on the operation and maintenance cost change rates to obtain the cost dynamic adjustment coefficients of transmission lines at each voltage level, so as to establish a three-level ladder rule based on cost for transmission lines at each voltage level. Combine the power gap values of transmission lines at each voltage level with the three - level ladder rule and the upper limit of adjustable power of the Shagohuang Energy Base under various outputs, optimize the cost allocation of transmission lines at each voltage level through the dynamic programming method, and generate and execute a hierarchical transmission control plan through the mathematical programming method.

8. A power transmission control system for a desert, Gobi, and arid land energy base, characterized in that, It includes: The first model construction module is used to obtain the historical supply - side transmission data of the Shagohuang Energy Base, and quantify the power output and outgoing time distribution data of transmission lines at various voltage levels based on the historical supply - side transmission data, so as to construct a hierarchical power supply - side model of the Shagohuang Energy Base; The second model construction module is used to collect the historical demand - side load data and electricity subsidy data of multiple regions, analyze the correlation between the cost - load differences of transmission lines at each voltage level and the electricity subsidies of each region, and obtain a hierarchical power demand - side model of each region; The consumption potential evaluation module is used to collect the grid operation data of each region, analyze the impact of the electricity subsidy differences of each region on its consumption capacity, construct a consumption capacity evaluation model, and evaluate the consumption potential values of each region through the consumption capacity evaluation model; The adjustable resource quantification module is used to calculate the power gap values of transmission lines at each voltage level based on the hierarchical power supply - side model and the hierarchical power demand - side model of the corresponding region when any region is in the peak power consumption period, and determine the amount of adjustable power resources of transmission lines at each voltage level in combination with the transmission capacity limit and the electricity subsidy data of each region; The transmission plan generation module is used to quantify the upper limit of adjustable power of the Shagohuang Energy Base under various outputs according to the amounts of adjustable power resources of each and the consumption potential values of each, combine with the power gap values to generate and execute a hierarchical transmission control plan.

9. An electronic device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the transmission control method for the Shagohuang Energy Base as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer - readable storage medium includes a stored computer program. When the device where the computer - readable storage medium is located executes the computer program, it implements the transmission control method for the Shagohuang Energy Base as described in any one of claims 1 to 7.

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