A power grid dynamic operation optimization method and system based on power technology information

Abstract

The present application relates to a kind of power grid dynamic operation optimization method and system based on electric power technology information, it is related to power grid dynamic operation optimization technical field, wherein: method includes: obtaining multidimensional electric power technology information data;Extract key feature information;Through space-time dimension and load type cross correlation generation electricity load feature optimization data;Using power grid simulation model simulates different operating conditions;Generation and test power source scheduling, network reconfiguration and load management scheme;Select optimal scheme dynamic adjustment power grid operation;System is used to realize the above-mentioned method, including data acquisition module, feature extraction module, optimization decision module and execution control module.The present application is based on the power grid dynamic operation optimization method and system of electric power technology information, through multidimensional information cross analysis and simulation verification, the forward-looking accurate optimization of power grid operating state is realized, and the perception ability of power grid to complex load change, decision comprehensiveness and scheme feasibility are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid dynamic operation optimization, in particular to a power grid dynamic operation optimization method and system based on power technology intelligence. BACKGROUND

[0002] In the field of power system operation management, with the large-scale integration of new energy and the rapid growth of new types of electricity load, power grid operation is facing unprecedented complexity and uncertainty. The traditional power grid dispatching method mainly relies on historical load data and short-term load prediction, which is difficult to cope with sudden changes in electricity consumption behavior caused by factors such as industrial policy adjustment and emerging technology application. Especially under the current energy transformation background, the time and space distribution characteristics of new loads such as electric vehicle charging, data centers and distributed energy have brought new challenges to power grid operation.

[0003] The existing technology usually uses optimization algorithms based on mathematical models to deal with power grid operation problems. Although these methods can achieve the optimal operation of the power grid to some extent, they have obvious limitations:

[0004] Firstly, the input data relied on by traditional methods is usually limited to real-time monitoring data of the power grid, lacking the ability to perceive changes in electricity consumption trends in advance;

[0005] Secondly, existing solutions usually optimize for a single problem, making it difficult to consider the coordination between power supply, power grid and load as a whole;

[0006] Most importantly, existing technologies cannot effectively utilize the vast amount of technology intelligence data accumulated in the power industry, resulting in a lack of foresight and adaptability in decision-making. SUMMARY

[0007] Therefore, the technical problem to be solved by the present application is to overcome the problems existing in the power grid optimization of the prior art, and to provide a power grid dynamic operation optimization method and system based on power technology intelligence, which realizes the forward-looking and accurate optimization of the power grid operation state through cross-correlation analysis and simulation verification of multi-dimensional power technology intelligence, significantly improving the power grid's ability to perceive complex load changes, the comprehensiveness of decision-making and the feasibility of the solution.

[0008] To solve the above technical problems, the present application provides a power grid dynamic operation optimization method based on power technology intelligence, comprising the following steps:

[0009] Real-time acquisition of multi-dimensional intelligence data in power technology intelligence containing electricity consumption trend changes and electricity distribution strategies;

[0010] Extracting key feature information affecting power grid operation from the multi-dimensional intelligence data;

[0011] The power grid operation optimization strategy is generated based on the key feature information, and specifically includes: cross-referencing the key feature information according to time dimensions, space dimensions and load type dimensions, identifying the coupling relationship between the feature information, and generating power consumption load feature optimization data with time and space characteristics; based on the power consumption load feature optimization data with time and space characteristics, simulating the power grid state changes under different operation conditions through a power grid simulation model; according to the simulation results, for each possible power grid state change, generating a corresponding power source scheduling scheme, network reconstruction scheme and load management scheme; testing the improvement effect of each scheme on the power grid operation index in the simulation environment, including voltage qualification rate, line load rate and power supply reliability; according to the test results, selecting the scheme combination with the best comprehensive improvement effect as the final optimization strategy.

[0012] According to the power grid operation optimization strategy, the power grid operation mode is dynamically adjusted.

[0013] In an embodiment of the present application, when the method is applied to long-term power consumption trend identification, the following steps are specifically included:

[0014] Obtain regional industrial development planning, major construction project approval documents and new energy facility construction progress reports from power technology intelligence as long-term power consumption trend data;

[0015] Identify the expected commissioning time, geographical location distribution and typical power consumption characteristic parameters of each industrial project;

[0016] Cross-referencing the industrial project features according to time dimensions, space dimensions and load type dimensions, a mapping relationship between project development time sequence and regional load growth is established; based on the mapping relationship, the influence of each industrial project on the regional power grid after commissioning is simulated through a power grid simulation model; for different development stages of each industrial project, an optimization strategy combination including a preliminary power source construction scheme, a power grid upgrading and reconstruction scheme and a load management contingency plan is generated; the improvement effect of each strategy combination on long-term power grid operation index is tested in a simulation environment; according to the test results, the optimal strategy combination that can balance short-term demand and long-term development is selected;

[0017] The optimization strategy is implemented in stages according to the actual construction progress of the industrial project, and the strategy parameters are dynamically adjusted according to the actual power consumption.

[0018] In an embodiment of the present application, the acquisition of regional industrial development planning, major construction project approval documents and new energy facility construction progress reports as long-term power consumption trend data specifically includes:

[0019] A hierarchical collection mechanism of industrial intelligence is established, and the projects are divided into key monitoring type and conventional monitoring type according to the industrial type and power consumption scale;

[0020] For key monitoring projects, collect their detailed construction schedules, equipment power consumption parameters and capacity expansion plans as power consumption trend data;

[0021] For regular monitoring projects, collect their overall power consumption demand and regional distribution characteristics as power consumption trend data.

[0022] In an embodiment of the present application, the identification of the expected commissioning time, geographical location distribution and typical power consumption characteristic parameters of each industrial project specifically includes:

[0023] Extract the time nodes of each stage of project commissioning, including the construction start-up period, equipment commissioning period and formal operation period;

[0024] Analyze the topological relationship between the geographical location of the project and the key nodes of the power grid;

[0025] Establish a power consumption characteristic template library for different types of industries, including continuous production type, intermittent operation type and seasonal fluctuation type.

[0026] In an embodiment of the present application, the dynamic adjustment of the power grid operation mode according to the power grid operation optimization strategy specifically includes:

[0027] Establish a linkage mechanism between the construction progress of industrial projects and power grid optimization measures;

[0028] Set key node monitoring indicators to evaluate the implementation effect of the strategy in real time;

[0029] According to the actual power consumption growth, dynamically adjust the implementation intensity and timing of subsequent optimization measures.

[0030] In an embodiment of the present application, when the method is applied to a short-term power distribution strategy, it specifically includes the following steps:

[0031] Real-time acquisition of power market transaction data, demand response instructions and temporary power consumption applications from power technology intelligence as short-term power distribution strategy data;

[0032] Extract key dynamic characteristic information that affects the short-term operation of the power grid from the short-term power distribution strategy data, including power consumption demand mutation period, regional load fluctuation characteristics and emergency power consumption demand priority;

[0033] The dynamic characteristic information is three-dimensionally associated according to time period division, region division and electricity emergency degree, and a short-term load fluctuation characteristic atlas is established; based on the short-term load fluctuation characteristic atlas, possible power grid operation state changes in the next 2-4 hours are simulated through real-time power grid simulation; for each simulated power grid state change, an emergency optimization strategy set containing a generator unit rapid adjustment scheme, an energy storage system charging and discharging strategy and an interruptible load management scheme is generated; the improvement effects of each emergency strategy on power grid frequency stability, voltage quality and power supply continuity are verified in a real-time simulation environment; according to the verification results and strategy implementation cost, an optimal short-term electricity distribution strategy combination is selected;

[0034] According to the selected short-term electricity distribution strategy combination, the rapid adjustment of the power grid operation mode is implemented, and the adjustment effect is monitored in real time during the execution process.

[0035] In an embodiment of the present application, the real-time acquisition of power market transaction data, demand response instructions and temporary electricity application as short-term electricity distribution strategy data specifically includes:

[0036] A multi-source data fusion acquisition channel is established to synchronously access real-time quotation data of a power transaction platform, demand response system instruction data and important user temporary electricity application data;

[0037] The collected various types of data are subjected to time-effectiveness hierarchical processing to distinguish second-level, minute-level and hour-level update data;

[0038] A short-term electricity behavior characteristic pool is constructed to dynamically store the electricity behavior change characteristics in the last 24 hours.

[0039] In an embodiment of the present application, the extraction of key dynamic characteristic information affecting the short-term operation of the power grid from the short-term electricity distribution strategy data specifically includes:

[0040] The precursor characteristics of electricity behavior mutation are identified;

[0041] The spatiotemporal propagation law of various types of electricity events is analyzed;

[0042] The bearing capacity of power grid equipment to short-term load fluctuation is evaluated.

[0043] In an embodiment of the present application, the rapid adjustment of the power grid operation mode according to the selected short-term electricity distribution strategy combination specifically includes:

[0044] A strategy execution effect real-time feedback mechanism is established;

[0045] A dynamic adjustment threshold is set, and strategy correction is automatically triggered when the actual operation deviates from the expectation;

[0046] The strategy execution effect data are recorded for use in optimizing the subsequent decision-making process.

[0047] To solve the above technical problems, the application further provides a power grid dynamic operation optimization system based on power technology intelligence, which is used to implement the above method and comprises:

[0048] A data acquisition module is configured to acquire multi-dimensional intelligence data including power consumption trend changes and power consumption allocation strategies in real time.

[0049] A feature extraction module is connected to the data acquisition module and configured to extract key feature information affecting power grid operation from the multi-dimensional intelligence data.

[0050] An optimization decision module is connected to the feature extraction module and comprises:

[0051] A feature correlation unit is configured to cross-correlate the key feature information according to time dimensions, space dimensions and load type dimensions, identify coupling relationships between feature information, and generate power consumption load feature optimization data with time-space characteristics.

[0052] A simulation and simulation unit is configured to simulate power grid state changes under different operation conditions based on the power consumption load feature optimization data with time-space characteristics through a power grid simulation model.

[0053] A strategy generation unit is configured to generate corresponding power source scheduling schemes, network reconstruction schemes and load management schemes for each possible power grid state change according to simulation results.

[0054] A strategy evaluation unit is configured to test the improvement effects of each scheme on power grid operation indexes, including voltage qualification rate, line load rate and power supply reliability, in a simulation environment.

[0055] A strategy selection unit is configured to select a scheme combination with the best comprehensive improvement effect as the final optimization strategy according to test results.

[0056] An execution control module is connected to the optimization decision module and is configured to dynamically adjust power grid operation modes according to power grid operation optimization strategies.

[0057] The above technical solutions of the application have the following advantages compared with the prior art:

[0058] The power grid dynamic operation optimization method based on power technology information provided by the application firstly establishes a comprehensive perception ability for power consumption trend changes through multi-dimensional information data collection and key feature extraction, and creatively adopts a cross correlation analysis method of space-time dimension and load type in the optimization strategy generation link, so that the originally discrete feature information is converted into power consumption load feature optimization data with space-time characteristics, which makes the subsequent analysis more accurately reflect the space-time distribution law of load changes, and through the simulation of different operation conditions by the power grid simulation model, various possible power grid state changes can be predicted in advance, and a comprehensive solution including power source scheduling, network reconstruction and load management is generated, the strategy optimization mechanism based on simulation test not only ensures the feasibility of the solution, but also realizes the quantitative comparison of the optimization effect through multi-index evaluation.

[0059] From the technical principle, the power grid dynamic operation optimization method provided by the application converts power technology information into an operable optimization strategy, realizes the prospective optimization of the power grid operation state through the closed loop process of "feature correlation-simulation simulation-strategy generation-effect evaluation", and has three significant advantages compared with the prior art: first, the perception ability of the system for power consumption trend changes is significantly improved through the use of multi-dimensional information data; second, the cross correlation of space-time characteristics is realized to accurately describe the characteristics of complex loads; third, the actual feasibility of the optimization solution is ensured through the strategy optimization mechanism of simulation test; these advantages enable the power grid operation personnel to discover potential problems earlier and consider optimization solutions more comprehensively, and finally realize the coordinated improvement of the safety, economy and reliability of the power grid operation. BRIEF DESCRIPTION OF DRAWINGS

[0060] In order to make the content of the application more easily understood, the application will be further described in detail below according to specific embodiments of the application and in combination with the drawings, in which:

[0061] Figure 1 is a step flow chart of the power grid dynamic operation optimization method based on power technology information provided by the application;

[0062] Figure 2 is a step flow chart of the application of the power grid dynamic operation optimization method provided by the application to long-term power consumption trend identification;

[0063] Figure 3 is a step flow chart of the application of the power grid dynamic operation optimization method provided by the application to short-term power consumption distribution strategy;

[0064] Figure 4 is a structural framework diagram of the power grid dynamic operation optimization system based on power technology information provided by the application. DETAILED DESCRIPTION

[0065] The application will be further described below in conjunction with the drawings and specific embodiments, so that those skilled in the art can better understand the application and implement it, but the embodiments are not as a limitation on the application.

[0066] Referring to Figure 1 The application discloses a power grid dynamic operation optimization method based on power technology intelligence, comprising the following steps:

[0067] Firstly, multi-dimensional intelligence data including power consumption trend changes and power consumption allocation strategies in the power technology intelligence is acquired in real time; this step breaks through the limitation of traditional power grid dispatching relying only on real-time monitoring data, so that the system can perceive in advance the changes in power consumption behavior caused by industrial policy adjustment and emerging technology application, and provide a more comprehensive data basis for subsequent optimization decisions.

[0068] Then, key feature information affecting power grid operation is extracted from the multi-dimensional intelligence data; this step converts massive intelligence data into quantifiable analysis operation features through data screening and feature extraction, effectively improving data processing efficiency.

[0069] In the core optimization strategy generation link, the method first cross-correlates the key feature information according to three dimensions of time, space and load type, identifies the coupling relationship between the features, and generates power consumption load feature optimization data with time and space characteristics; this processing makes the originally dispersed feature information systematically reflect the time and space distribution law of load changes, laying a foundation for accurately predicting the change of power grid operation state;

[0070] Subsequently, based on these optimization data, the power grid state changes under different operation conditions are simulated through a power grid simulation model; this step realizes the pre-rehearsal of multiple possible operation scenarios through digital simulation means, greatly reducing the uncertainty in actual operation;

[0071] The power grid simulation model used in the application belongs to a basic tool for predicting the operation state of a power grid in the field of power systems, and its core function is to simulate and output the operation state changes of the power grid under different working conditions through the built-in power system physical law calculation engine according to the input power grid parameters and operation conditions; the inputs of the model mainly include: power grid basic parameters (such as network topology structure, equipment electrical parameters), real-time operation data (such as power generation output, load demand), the model can automatically calculate and output the operation state of the power grid under various preset or predicted conditions based on these input data, including key indicators such as node voltage, line power flow, equipment load rate; in this embodiment, the power grid simulation model mainly deals with short-term operation optimization problems, and the input is the time and space load feature data processed by three-dimensional correlation, and the output is the prediction of power grid state changes under different optimization strategies;

[0072] According to the simulation results, the corresponding power supply scheduling, network reconstruction and load management scheme are generated for each possible power grid state change, and the optimization idea of multi-strategy cooperation ensures the comprehensiveness and systematicness of the solution;

[0073] In the simulation environment, the improvement effect of each scheme on the key indicators such as voltage qualification rate, line load rate and power supply reliability is tested, and the quantitative evaluation provides an objective basis for strategy selection;

[0074] Finally, according to the test results, the scheme combination with the best comprehensive improvement effect is selected as the final optimization strategy, and this optimization mechanism ensures the actual effectiveness of the adopted strategy.

[0075] Finally, according to the optimization strategy, the step of dynamically adjusting the operation mode of the power grid converts the results of the previous analysis and decision into actual operation instructions, and realizes the closed-loop management from data perception to operation adjustment.

[0076] The method of the embodiment significantly improves the perception ability, decision comprehensiveness and scheme feasibility of the power grid in response to complex load changes through multi-dimensional intelligence cross-correlation analysis and simulation verification, and provides an innovative solution for the safe, economic and reliable operation of modern power grids.

[0077] Specifically, in order to realize the complete landing from theoretical method to practical application, with reference to the method shown in the figure, Figure 2 The application applies the above method to the important scene of long-term electricity consumption trend identification, including the following steps:

[0078] First, key data with long-term impact such as regional industrial development planning, major construction project approval documents and new energy facility construction progress reports are obtained from power technology intelligence, which can accurately reflect the future change trend of power demand.

[0079] By identifying the expected production time, geographical location distribution and typical electricity consumption characteristic parameters of each industrial project, the system can establish accurate prediction ability for long-term electricity consumption change.

[0080] In the optimization strategy generation link, the three-dimensional cross-correlation method is used to cross-correlate the predicted commissioning time, geographical location distribution and typical electricity consumption characteristic parameters obtained to generate typical electricity consumption characteristic parameters containing the predicted commissioning time and geographical location distribution. A mapping relationship between the project development time sequence and the regional load growth is established in particular for the characteristics of industrial projects. This data processing method makes long-term prediction more accurate and reliable. Based on this mapping relationship, the specific impact of each industrial project on the regional power grid after commissioning can be simulated through a power grid simulation model. This simulation not only considers the impact of a single project, but also evaluates the combined effect of multiple projects. For different development stages of each industrial project, the system generates a comprehensive optimization strategy combination containing preliminary power supply construction, power grid upgrading and load management. These strategies take into account not only current demand but also future development. After testing these strategy combinations in a simulation environment, the system selects the optimal strategy combination that balances short-term demand and long-term development, ensuring the sustainability of power grid planning.

[0081] Finally, the optimization strategies are implemented in stages according to the actual construction progress of industrial projects, and the strategy parameters are dynamically adjusted according to the actual electricity consumption, forming a closed-loop management mechanism.

[0082] In this embodiment, the power grid dynamic operation optimization method based on power technology intelligence is applied in long-term electricity consumption trend identification, significantly improving the scientificity and forward-looking nature of long-term power grid planning, enabling the power grid development to better adapt to the needs of regional economic transformation and upgrading, effectively avoiding common problems such as lack of forward-looking and resource mismatch in traditional planning methods, and providing an innovative solution for building a safe, efficient and sustainable modern power system.

[0083] Specifically, when obtaining long-term electricity consumption trend data, an industrial intelligence hierarchical collection mechanism is established to realize differentiated data collection for key projects and regular projects. According to the type of industry and the scale of electricity consumption, the monitored projects are divided into two categories: key monitoring and regular monitoring. For key monitoring projects, the construction schedule, equipment electricity consumption parameters and capacity expansion plan are collected as electricity consumption trend data. For regular monitoring projects, the overall electricity demand and regional distribution characteristics are collected as electricity consumption trend data.

[0084] This hierarchical processing method can optimize the allocation of data collection resources, ensure more accurate and comprehensive electricity data collection for key projects, and avoid excessive data collection for regular projects. By implementing this solution, a key and hierarchical electricity consumption trend monitoring system can be established, ensuring the accuracy of electricity consumption prediction for important industrial projects and improving the overall data collection efficiency, providing a more reliable data foundation for subsequent optimization strategy development.

[0085] In this embodiment, the feature identification process for industrial projects is expanded, and specific methods for extracting project features from three dimensions: time node, spatial location, and electricity consumption characteristics are clarified:

[0086] First, the time nodes of each stage of project construction are extracted to establish a complete timeline, including the construction start-up period, equipment commissioning period, and formal operation period; then, the spatial topological relationship between the project location and key nodes of the power grid is analyzed; finally, the electricity consumption characteristics are classified by establishing an industrial electricity consumption characteristic template library, including continuous production type, intermittent operation type, and seasonal fluctuation type.

[0087] This three-dimensional feature extraction method can comprehensively depict the impact factors of industrial projects on the power grid: the time dimension ensures accurate correspondence of development stages, the spatial dimension clarifies the locational needs of power grid transformation, and the characteristic dimension predicts the changing patterns of electricity load. It can more accurately grasp the differentiated needs of different industrial projects on the power grid, providing precise feature basis for subsequent formulation of targeted power source construction, network transformation and other optimization strategies, and effectively avoiding the planning deviation problem caused by incomplete feature extraction in traditional methods.

[0088] Furthermore, specific regulations were established for the final strategy execution phase, creating a closed-loop management mechanism from strategy formulation to implementation:

[0089] First, a linkage mechanism is established between the progress of industrial project construction and power grid optimization measures to ensure that power grid optimization measures are implemented in sync with the progress of industrial construction. Then, key node monitoring indicators are set to evaluate the effectiveness of strategy implementation in real time. Finally, the intensity and timing of subsequent optimization measures are dynamically adjusted based on actual electricity consumption growth.

[0090] This dynamic adjustment mechanism addresses the common problem of "disconnect between planning and implementation" in long-term power grid planning. It ensures the timeliness of strategies through construction progress tracking, guarantees their effectiveness through performance evaluation, and maintains their adaptability through dynamic adjustments. Implementing this scheme significantly improves the actual execution effect of power grid optimization strategies, enabling power grid development to better match changes in industry demand. It avoids the waste caused by premature resource investment and prevents power supply bottlenecks caused by construction delays, achieving coordinated advancement of power grid planning and industrial development.

[0091] Specifically, in order to achieve a complete implementation from theoretical methods to practical applications, refer to Figure 3 As shown, the present invention further applies the above method to the important scenario of short-term power allocation strategy, including the following steps:

[0092] Firstly, dynamic information such as electricity market transaction data, demand response instructions, and temporary electricity applications are obtained in real time from power technology intelligence. This data can keenly capture short-term supply and demand changes faced by the power grid.

[0093] By extracting the power demand mutation period, regional load fluctuation characteristics and emergency power demand priority key dynamic characteristics, the system establishes accurate perception ability of short-term operation state of power grid.

[0094] In the optimization strategy generation link, the three-dimensional correlation analysis method is adopted to cross-correlate the obtained power demand mutation period, regional load fluctuation characteristics and emergency power demand priority, generate the characteristic parameters of emergency power demand priority containing the pre-power demand mutation period and regional load fluctuation characteristics, and especially establish the short-term load fluctuation characteristic map of three-dimensional correlation of period, region and emergency degree for short-term power consumption characteristics. This professional data processing method makes the short-term prediction more accurate. Based on this characteristic map, the possible power grid state changes in the next 2-4 hours are simulated through real-time power grid simulation. This high-frequency simulation can timely warn potential operation risks. For each simulated power grid state change, the system will generate a comprehensive emergency strategy set containing generator group rapid regulation, energy storage system charging and discharging and interruptible load management. These strategies consider both regulation effect and implementation cost. After verifying the improvement effect of each emergency strategy on power grid frequency stability, voltage quality and power supply continuity in the real-time simulation environment, the system will select the most optimal short-term power distribution strategy combination to ensure the scientificity of the decision.

[0095] Finally, the selected strategy combination is used to quickly adjust the power grid operation mode, and the adjustment effect is monitored in real time during the execution process to form a closed-loop control mechanism.

[0096] In this embodiment, the power grid dynamic operation optimization method based on power technology intelligence is applied in the short-term power distribution strategy, which significantly improves the response speed and decision accuracy of short-term power grid scheduling, makes the power grid operation better adapt to the rapidly changing demands such as market transactions and demand response, effectively avoids the problems such as response lag and extensive regulation in traditional scheduling methods, and provides an innovative solution for building a flexible, reliable and economic modern power system.

[0097] In this embodiment, when acquiring the data of short-term power distribution strategy, a multi-source data fusion acquisition channel is established to realize the systematic acquisition of three types of key information: power market transaction data, demand response instructions and temporary power application.

[0098] Specifically, a three-layer data processing mechanism is adopted: first, a multi-source data fusion collection channel is established to synchronously access real-time bidding data from the power trading platform, demand response system instruction data, and temporary power consumption application data from important users, solving the problem of data islands; second, the collected data is processed in a time-sensitive manner, with second-level, minute-level, and hour-level update data being distinguished to ensure real-time management of the data; and finally, a short-term power consumption behavior feature pool is constructed to dynamically store the power consumption behavior change features in the last 24 hours, providing a data foundation for subsequent analysis. This structured collection method can effectively solve the problem of scattered data and varying time sensitivity in traditional short-term scheduling, ensuring real-time acquisition of key data, and through the construction of a feature pool, continuously tracking power consumption behavior features, providing high-quality data support for short-term load forecasting, and significantly improving the accuracy of subsequent optimization strategies.

[0099] In this embodiment, the extraction method of key dynamic feature information applied in the short-term power consumption allocation strategy is further refined, and a complete analysis chain from power consumption behavior mutation early warning to power grid bearing capacity evaluation is established.

[0100] Specifically, first, the early warning of potential risks is realized by identifying the precursor features of power consumption behavior mutation (such as sudden increase in load change rate, abnormal power factor, etc.); then the propagation law of various power consumption events in the time and space dimensions is analyzed to predict the impact range; finally, the tolerance of key power grid equipment (such as transformers, lines, etc.) to load fluctuations is evaluated. This progressive feature analysis method can systematically reveal the influence mechanism of short-term power consumption changes on the power grid, considering both the characteristics of power consumption behavior and the actual operating state of power grid equipment, providing a scientific basis for subsequent development of targeted emergency strategies, effectively avoiding the strategy misalignment problem caused by one-sided feature analysis in traditional methods.

[0101] In this embodiment, the execution link of the short-term power consumption allocation strategy is further improved, and a closed-loop control mechanism including real-time feedback, dynamic adjustment, and experience learning is constructed.

[0102] Specifically, first, a real-time monitoring and feedback channel for strategy execution effectiveness is established to ensure the observability of the operating state; then dynamic adjustment thresholds (such as voltage deviation exceeding limits, frequency fluctuation exceeding standards, etc.) are set to realize automatic identification and strategy correction of abnormal situations; finally, strategy execution effect data is recorded to form a knowledge base. This closed-loop execution mechanism solves the general problem of "emphasizing decision-making and neglecting execution" in short-term scheduling, ensuring the landing effect of the strategy through real-time feedback, responding to unexpected situations through dynamic adjustment, and continuously optimizing the decision-making algorithm through execution records, which can significantly improve the adaptability and robustness of the short-term scheduling strategy, making the power grid more agile and reliable in responding to uncertainties, and continuously improving the overall decision-making level of the system through the accumulation of operating experience.

[0103] Referring to Figure 4 As shown in the above method, the application also discloses a power grid dynamic operation optimization system based on power technology information, which is used to implement the above method and comprises:

[0104] A data acquisition module is configured to acquire multi-dimensional information data including power consumption trend changes and power consumption allocation strategies in real time.

[0105] A feature extraction module is connected to the data acquisition module and configured to extract key feature information affecting power grid operation from the multi-dimensional information data.

[0106] An optimization decision module is connected to the feature extraction module and comprises:

[0107] A feature correlation unit is configured to cross-correlate the key feature information according to time dimensions, space dimensions and load type dimensions, identify coupling relationships between the feature information, and generate power consumption load feature optimization data with time and space characteristics.

[0108] A simulation unit is configured to simulate power grid state changes under different operation conditions based on the power consumption load feature optimization data with time and space characteristics.

[0109] A strategy generation unit is configured to generate corresponding power source scheduling schemes, network reconstruction schemes and load management schemes for each possible power grid state change according to simulation results.

[0110] A strategy evaluation unit is configured to test the improvement effects of each scheme on power grid operation indexes, including voltage qualification rate, line load rate and power supply reliability, in a simulation environment.

[0111] A strategy selection unit is configured to select a scheme combination with the best comprehensive improvement effect as a final optimization strategy according to test results.

[0112] An execution control module is connected to the optimization decision module and configured to dynamically adjust power grid operation modes according to power grid operation optimization strategies.

[0113] The power grid dynamic operation optimization system based on power technology information can implement the power grid dynamic operation optimization method in the above embodiment, and the specific implementation process is described above and will not be repeated here.

[0114] Those skilled in the art will appreciate that embodiments of the application can be devised for a method, a system, or a computer program product. Accordingly, the present application can be embodied in the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer readable program code.

[0115] The present application is described in reference to the flowchart and / or block diagrams of the method, apparatus (system) and computer program product according to embodiments of the application. It will be understood that each block of the flowchart and / or block diagrams, and combinations of blocks in the flowchart and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0116] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0117] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0118] Obviously, the above-described embodiments are only examples for clarity and conciseness and do not limit the technical solutions. For those of ordinary skill in the art, other changes and modifications can be made to the above description based on the principle of the technical solutions. The changes and modifications do not depart from the scope of the technical solutions.

Claims

1. A method for optimizing the dynamic operation of a power grid based on power technology intelligence, characterized in that: The method comprises the following steps: Real-time acquisition of multi-dimensional intelligence data including power consumption trend changes and power distribution strategies from power technology intelligence; wherein: regional industrial development planning, major construction project approval documents and new energy facility construction progress reports are obtained from power technology intelligence as long-term power consumption trend data, and real-time acquisition of power market transaction data, demand response instructions and temporary power consumption applications from power technology intelligence as short-term power distribution strategy data; Extracting key feature information affecting power grid operation from the multi-dimensional intelligence data; Generating a power grid operation optimization strategy based on the key feature information, specifically including: cross-referencing the key feature information according to time dimension, space dimension and load type dimension, identifying the coupling relationship between feature information, and generating power consumption load feature optimization data with time and space characteristics; based on the power consumption load feature optimization data with time and space characteristics, simulating the state changes of the power grid under different operating conditions through a power grid simulation model; according to the simulation results, for each possible power grid state change, generating a corresponding power source scheduling scheme, network reconstruction scheme and load management scheme; testing the improvement effect of each scheme on the power grid operation indicators, including voltage qualification rate, line load rate and power supply reliability, in a simulation environment; according to the test results, selecting the scheme combination with the best comprehensive improvement effect as the final optimization strategy; According to the power grid operation optimization strategy, dynamically adjusting the power grid operation mode. 2.The power technology information based power grid dynamic operation optimization method of claim 1, wherein: When the method is applied to long-term power consumption trend identification, it specifically includes the following steps: Obtaining regional industrial development planning, major construction project approval documents and new energy facility construction progress reports as long-term power consumption trend data from power technology intelligence; Identifying the expected production time, geographical location distribution and typical power consumption characteristic parameters of each industrial project; Cross-referencing the characteristics of industrial projects according to time dimension, space dimension and load type dimension, establishing a mapping relationship between project development time sequence and regional load growth; based on the mapping relationship, simulating the impact of each industrial project on the regional power grid after it is put into production through a power grid simulation model; for each industrial project in different development stages, generating an optimization strategy combination including a preliminary power source construction scheme, a power grid upgrading and reconstruction scheme and a load management contingency plan; testing the improvement effect of each strategy combination on long-term power grid operation indicators in a simulation environment; according to the test results, selecting the optimal strategy combination that can balance short-term demand and long-term development; Implementing the optimization strategy in stages according to the actual construction progress of the industrial project, and dynamically adjusting the strategy parameters according to the actual power consumption. 3.The power technology information based power grid dynamic operation optimization method of claim 2, wherein: Obtaining regional industrial development planning, major construction project approval documents and new energy facility construction progress reports as long-term power consumption trend data specifically includes: Establishing an industrial intelligence hierarchical collection mechanism, dividing projects into key monitoring type and regular monitoring type according to industry type and power consumption scale; For key monitoring type projects, collecting their detailed construction schedule, equipment power consumption parameters and capacity expansion plan as power consumption trend data; For regular monitoring type projects, collecting their overall power consumption demand and regional distribution characteristics as power consumption trend data. 4.The power technology information based power grid dynamic operation optimization method of claim 2, wherein: Identifying the expected production time, geographical location distribution and typical power consumption characteristic parameters of each industrial project specifically includes: Extract the time nodes of each stage of the project, including the construction start-up period, equipment commissioning period, and formal operation period; Analyze the topological relationship between the project's geographical location and the key nodes of the power grid; Establish a template library of electricity consumption characteristics for different types of industries, including continuous production, intermittent operation, and seasonal fluctuations.

5. The power technology information based power grid dynamic operation optimization method of claim 2, wherein: According to the dynamic adjustment of the power grid operation optimization strategy, the specific content includes: Establish a linkage mechanism between the construction progress of industrial projects and the optimization measures of the power grid; Set key node monitoring indicators to evaluate the effectiveness of strategy implementation in real time; According to the actual electricity growth, dynamically adjust the implementation strength and timing of subsequent optimization measures. 6.The power technology information based power grid dynamic operation optimization method of claim 1, wherein: When the method is applied to short-term electricity distribution strategy, the specific steps include: Real-time acquisition of power market transaction data, demand response instructions and temporary electricity application from power technology intelligence as short-term electricity distribution strategy data; Extract key dynamic characteristic information that affects the short-term operation of the power grid from the short-term electricity distribution strategy data, including electricity demand mutation period, regional load fluctuation characteristics and emergency electricity demand priority; Divide the dynamic characteristic information according to time period, region and electricity emergency level, and establish a short-term load fluctuation characteristic map; Based on the short-term load fluctuation characteristic map, simulate the possible changes of the power grid operation state within 2-4 hours through real-time power grid simulation; For each simulated power grid state change, generate an emergency optimization strategy set containing generator unit rapid adjustment scheme, energy storage system charging and discharging strategy and interruptible load management scheme; Verify the improvement effect of each emergency strategy on power grid frequency stability, voltage quality and power supply continuity in real-time simulation environment; According to the verification results and the cost of strategy implementation, select the optimal short-term electricity distribution strategy combination; According to the selected short-term electricity distribution strategy combination, implement the rapid adjustment of the power grid operation mode, and monitor the adjustment effect in real time during the execution process.

7. The power technology information based power grid dynamic operation optimization method of claim 6, wherein: The real-time acquisition of power market transaction data, demand response instructions and temporary electricity application as short-term electricity distribution strategy data specifically includes: Establish a multi-source data fusion collection channel to synchronously access real-time quotation data from the power trading platform, demand response system instruction data and important user temporary electricity application data; Classify and process the collected data according to their time effectiveness, distinguishing between second-level, minute-level and hour-level update data; Construct a short-term electricity behavior characteristic pool to dynamically store the electricity behavior change characteristics within the last 24 hours. 8.The power technology information based power grid dynamic operation optimization method of claim 6, wherein: The extraction of key dynamic characteristic information that affects the short-term operation of the power grid from the short-term electricity distribution strategy data specifically includes: Identify the precursor characteristics of electricity behavior mutation; Analyze the spatio-temporal propagation law of various electricity events; Evaluate the short-term load fluctuation bearing capacity of power grid equipment. 9.The power technology information based power grid dynamic operation optimization method of claim 6, wherein: The rapid adjustment of the power grid operation mode according to the selected short-term electricity distribution strategy combination specifically includes: Establish a real-time feedback mechanism for strategy implementation effect; Set dynamic adjustment threshold, automatically trigger strategy correction when actual operation deviates from expectation; Record strategy implementation effect data for optimizing subsequent decision-making process.

10. A power grid dynamic operation optimization system based on power technology information, for implementing the method of any one of claims 1-9, characterized in that: It includes: Data acquisition module for real-time acquisition of multi-dimensional intelligence data in power technology intelligence, including electricity trend changes and electricity distribution strategies; The feature extraction module is connected with the data acquisition module and is configured to extract key feature information affecting power grid operation from the multi-dimensional intelligence data; The optimization decision module is connected with the feature extraction module and comprises: A feature correlation unit is configured to cross-correlate the key feature information according to time dimension, space dimension and load type dimension, identify coupling relationships between feature information, and generate power consumption load feature optimization data with time and space characteristics; An analog simulation unit is configured to simulate power grid state changes under different operating conditions based on the power consumption load feature optimization data with time and space characteristics through a power grid simulation model; A strategy generation unit is configured to generate corresponding power source scheduling schemes, network reconstruction schemes and load management schemes for each possible power grid state change according to simulation results; A strategy evaluation unit is configured to test the improvement effects of each scheme on power grid operation indexes, including voltage qualification rate, line load rate and power supply reliability, in a simulation environment; A strategy selection unit is configured to select a scheme combination with the best comprehensive improvement effect as a final optimization strategy according to test results; An execution control module is connected with the optimization decision module and is configured to dynamically adjust power grid operation modes according to power grid operation optimization strategies.

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