CN117728379A - Intelligent operation scheduling method for regional power grid - Google Patents
Intelligent operation scheduling method for regional power grid Download PDFInfo
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Abstract
The invention provides an intelligent operation scheduling method for a regional power grid, which relates to the technical field of operation scheduling and comprises the following steps: the method comprises the steps of interactively obtaining power equipment information of a regional power grid, extracting power supply data of the power grid, extracting common characteristics, generating power supply prediction data, performing power supply decision to generate an initial power supply decision plan, calling equipment fault information and equipment use information, executing power supply stability evaluation of the initial power supply decision plan, generating power supply stability association, analyzing the power supply prediction data, generating power supply stability requirements, adjusting the initial power supply decision plan, generating an optimized power supply decision plan, and performing intelligent operation scheduling of the regional power grid according to the optimized power supply decision plan. The invention solves the technical problems that the power supply demand and the stability cannot be accurately predicted in a complex regional power grid environment, the stability control of a power supply decision is insufficient, the artificial participation is more, and the operation scheduling efficiency of the regional power grid is low and the response is slow in the prior art.
Description
Technical Field
The invention relates to the technical field of operation scheduling, in particular to an intelligent operation scheduling method for a regional power grid.
Background
Regional power grid operation scheduling refers to performing real-time monitoring, operation optimization and fault handling on a power system in a specific region so as to ensure the stability, reliability and economy of power supply, and along with population increase and economic development, the demand for power is continuously increased, meanwhile, traditional coal-fired power generation is gradually replaced by clean energy, such as wind energy, solar energy, electric automobiles and the like, so that the diversity of the energy sources in the power system is increased, and the diversity needs a more complex scheduling strategy, so that the power system needs to be effectively scheduled and optimized to meet the increasing load demand.
The conventional regional power grid operation scheduling method still has certain drawbacks, the power supply requirement and stability cannot be accurately predicted for the complex regional power grid environment in the prior art, and the stability of the power supply decision is not controlled enough, so that more people participate, and the operation scheduling efficiency of the regional power grid is low and the response is slow. Therefore, there is some liftable space for regional power grid operation scheduling.
Disclosure of Invention
The intelligent operation scheduling method for the regional power grid aims at solving the technical problems that in the prior art, the power supply requirement and stability cannot be accurately predicted in a complex regional power grid environment, the stability control of a power supply decision is insufficient, more artificial participation exists, and the operation scheduling efficiency of the regional power grid is low and the response is slow.
In view of the above, the present application provides an intelligent operation scheduling method for regional power grids.
In a first aspect of the disclosure, an intelligent operation scheduling method for a regional power grid is provided, the method comprising: the method comprises the steps of interactively obtaining power equipment information of the regional power grid, wherein the power equipment information comprises equipment attribute information and equipment distribution information; extracting power supply data of the regional power grid, extracting common characteristics according to a power supply data extraction result of the power grid, and generating power supply prediction data; carrying out power supply decision of the regional power grid according to the power equipment information and the power supply prediction data, and generating an initial power supply decision plan, wherein the initial power supply decision plan comprises a selection decision of a power supply node; invoking equipment fault information and equipment use information of the regional power grid, and executing power supply stability evaluation of the initial power supply decision plan according to the equipment fault information and the equipment use information to generate power supply stability association; analyzing the power supply prediction data to generate a power supply stability requirement, and adjusting the initial power supply decision plan according to the power supply stability requirement and the power supply stability association to generate an optimized power supply decision plan; and carrying out intelligent operation scheduling of the regional power grid according to the optimized power supply decision plan.
In another aspect of the disclosure, there is provided an intelligent operation scheduling system for a regional power grid, the system being used in the above method, the system comprising: the device information acquisition module is used for interactively acquiring the power device information of the regional power grid, wherein the power device information comprises device attribute information and device distribution information; the power supply data extraction module is used for extracting power supply data of the regional power grid, extracting common characteristics according to a power supply data extraction result of the power grid and generating power supply prediction data; the power supply decision module is used for carrying out power supply decision of the regional power grid according to the power equipment information and the power supply prediction data to generate an initial power supply decision plan, wherein the initial power supply decision plan comprises a selection decision of a power supply node; the stability evaluation module is used for calling equipment fault information and equipment use information of the regional power grid, executing power supply stability evaluation of the initial power supply decision plan according to the equipment fault information and the equipment use information, and generating power supply stability association; the decision-making programming adjustment module is used for analyzing the power supply prediction data, generating a power supply stability requirement, adjusting the initial power supply decision-making programming according to the power supply stability requirement and the power supply stability association, and generating an optimized power supply decision-making programming; and the intelligent operation scheduling module is used for performing intelligent operation scheduling of the regional power grid according to the optimized power supply decision plan.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
the method comprises the steps of interactively obtaining power equipment information of a regional power grid, including equipment attribute information and equipment distribution information, extracting power supply data of the power grid, extracting common characteristics, generating power supply prediction data, carrying out power supply decision of the regional power grid to generate an initial power supply decision plan, calling equipment fault information and equipment use information, executing power supply stability evaluation of the initial power supply decision plan, generating power supply stability association, analyzing the power supply prediction data, generating power supply stability requirements, adjusting the initial power supply decision plan, generating an optimized power supply decision plan, and carrying out intelligent operation scheduling of the regional power grid according to the optimized power supply decision plan. The method solves the technical problems that in the prior art, the power supply demand and stability cannot be accurately predicted in a complex regional power grid environment, the stability of a power supply decision is not controlled, and the method is more in artificial participation, so that the operation scheduling efficiency of a regional power grid is low and the response is slow.
The foregoing description is only an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.
Drawings
Fig. 1 is a schematic flow chart of an intelligent operation scheduling method for a regional power grid according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a possible compensation for power supply stability association in an intelligent operation scheduling method for a regional power grid according to an embodiment of the present application;
fig. 3 is a schematic flow chart of a possible process of obtaining an initial power supply decision plan in the intelligent operation scheduling method for the regional power grid according to the embodiment of the present application;
fig. 4 is a schematic diagram of a possible structure of an intelligent operation scheduling system for a regional power grid according to an embodiment of the present application.
Reference numerals illustrate: the system comprises an equipment information acquisition module 10, a power supply data extraction module 20, a power supply decision module 30, a stability evaluation module 40, a decision planning adjustment module 50 and an intelligent operation scheduling module 60.
Detailed Description
According to the intelligent operation scheduling method for the regional power grid, the technical problems that in the prior art, power supply requirements and stability cannot be accurately predicted in a complex regional power grid environment, stability control of a power supply decision is insufficient, manual participation is more, operation scheduling efficiency of the regional power grid is low, and response is slow are solved, the technical effects that by extracting power supply data and common characteristics, power supply prediction data are generated, stability evaluation is performed by using equipment fault information and use conditions, initial power supply decision planning is optimized, intelligent operation scheduling is further performed, operation efficiency is improved, manual intervention is reduced, and stable operation of a power supply system is guaranteed are achieved.
Having described the basic principles of the present application, various non-limiting embodiments of the present application will now be described in detail with reference to the accompanying drawings.
Example 1
As shown in fig. 1, an embodiment of the present application provides an intelligent operation scheduling method for a regional power grid, where the method includes:
step S100: the method comprises the steps of interactively obtaining power equipment information of the regional power grid, wherein the power equipment information comprises equipment attribute information and equipment distribution information;
specifically, establishing contact with a management mechanism, an operator or related equipment management personnel of a regional power grid, and acquiring power equipment information of the regional power grid, wherein the equipment attribute information comprises equipment type, capacity, model number, serial number, production date and other detailed information; the equipment distribution information refers to the position and layout of equipment in a regional power grid, and comprises information such as geographic coordinates of the equipment, a power supply region to which the equipment belongs, a connection mode and the like.
Step S200: extracting power supply data of the regional power grid, extracting common characteristics according to a power supply data extraction result of the power grid, and generating power supply prediction data;
specifically, power supply data is collected from each node of a regional power grid and related equipment, the collected power supply data is characterized by power load, current, voltage and the like, the power supply data of the power grid is extracted by a Principal Component Analysis (PCA) method, firstly, the power supply data of the power grid is normalized so that data with different attributes have the same scale, a covariance matrix is calculated, a eigenvector and eigenvalues are obtained by decomposing the eigenvalues of the matrix, next, the eigenvector with the largest eigenvalue is selected as a principal component according to the magnitude of the eigenvalue, namely, the most remarkable direction and the most remarkable characteristic in the data are represented, and finally, the original data are projected on the selected principal component to obtain new eigenvalue representation.
According to the new feature data representation obtained, the first few most important principal components are selected as common features, and in general, the first few principal components are selected to explain the variance of most of the original data, so that the principal components are representative and can retain key information in the data. And predicting the power supply condition in a future period of time by using the extracted commonality characteristics to generate power supply prediction data.
Step S300: carrying out power supply decision of the regional power grid according to the power equipment information and the power supply prediction data, and generating an initial power supply decision plan, wherein the initial power supply decision plan comprises a selection decision of a power supply node;
further, as shown in fig. 3, step S300 of the present application includes:
step S310: performing regional grid division association of the regional power grid according to the equipment distribution information to obtain a regional grid division association result of the power supply node;
step S320: carrying out data prediction analysis on the power supply prediction data to generate a prediction analysis result;
step S330: carrying out power supply economy evaluation of the power supply node according to the prediction analysis result and the area grid division association result of the power supply node;
step S340: and screening according to the power supply economy evaluation result to obtain the initial power supply decision plan.
Specifically, the device distribution information includes information such as geographic positions of related devices, so that a method of selecting geographic positions is used for realizing regional grid division, and the regional grid is divided into a plurality of regional grids based on longitude and latitude or a coordinate system, wherein the division can be performed according to factors such as geographic distance, regional characteristics, load requirements and the like, and power supply nodes which are close in distance and have similar characteristics and load requirements are divided into the same regional grid. After the division is completed, each power supply node is associated with the area grid to which the power supply node belongs, which can be achieved by judging the relation between the geographical position of the power supply node and the area grid, for example, the area grid to which each power supply node belongs can be determined according to the longitude and latitude range or the geographical boundary, and the area grid division association result of the power supply node is obtained.
According to actual requirements, extracting characteristics related to power supply stability and economy from power supply prediction data, including seasonal change, time sequence mode, power consumption load trend and the like, performing time sequence analysis on the extracted characteristics, and building an autoregressive moving average model (ARMA), training according to existing historical data by using the model, improving prediction accuracy and stability through cross verification and model optimization, predicting power supply prediction data in a future period by using the trained model, analyzing the prediction result, and generating prediction analysis results related to power supply requirements, including information such as power load prediction and energy price required by each power supply node.
And according to the region meshing association result of the power supply nodes, associating the region meshing association result with the prediction analysis result, so that the region and related information of each power supply node can be determined. And determining an index system for evaluating the power supply economy according to actual requirements and targets, wherein the index system comprises distance length, loss condition and the like. And carrying out power supply economy evaluation on each power supply node according to an index system, for example, carrying out weight distribution on the index system according to actual demands, carrying out index calculation and weighted summation according to weight distribution results, generating power supply economy evaluation results of each power supply node based on the evaluation values obtained by calculation, and comparing the results to be used for economy among different power supply nodes and providing reference for subsequent power supply decisions.
And according to the service requirements and targets, making requirements and constraint conditions of an initial power supply decision plan, including distance length, loss condition, power supply capacity requirements, reliability indexes and the like. Setting an economy threshold, only selecting nodes with evaluation values higher than the threshold, matching the power supply economy evaluation result with a screening criterion, screening nodes which do not meet the requirement according to the economy threshold, and reserving nodes with better economy or excluding nodes with poorer economy. And obtaining a final initial power supply decision plan according to the result of the screening operation, wherein the plan comprises information of the selected nodes, power supply capacity allocation, network structure and the like.
Step S400: invoking equipment fault information and equipment use information of the regional power grid, and executing power supply stability evaluation of the initial power supply decision plan according to the equipment fault information and the equipment use information to generate power supply stability association;
further, step S400 of the present application includes:
step S410: calling the equipment use information to generate continuous working time length and equipment load data of the power equipment;
step S420: performing stability influence evaluation on the power equipment according to the power equipment information, the continuous working time and the equipment load data to generate a steady-state initial value;
step S430: and generating the power supply stability association according to the steady state initial value.
Specifically, fault information of different equipment in the regional power grid is obtained, wherein the fault information comprises historical fault records, maintenance reports, equipment state data and the like; and acquiring the service condition of regional power grid equipment, wherein the service condition comprises the operation time length, load change and the like of the equipment.
And calculating the continuous working time length of each power device according to the device starting and stopping time recorded in the device use information, wherein the continuous working time length refers to the time interval of continuous operation of the device from starting to stopping, and is used for evaluating the stability of the device. The load change of the device in different time periods is analyzed by means of load data in the device usage information, which represent the current or power load accepted by the device, which data will be used to evaluate the effect of the device on the power supply stability.
And according to the power equipment information, the continuous working time length and the equipment load data, performing stability influence evaluation on each power equipment, including load change analysis, temperature effect evaluation, overload and overvoltage evaluation, failure mode analysis and the like, and determining the steady state initial values of each power equipment according to the stability influence evaluation results of the power equipment, wherein the steady state initial values are used for subsequent power supply stability association generation and power supply decision planning.
And acquiring a steady-state working parameter and a related stability index of each power equipment according to the generated steady-state initial value, combining the steady-state initial value with other factors related to power supply stability, such as load conditions, network topology and the like, and generating power supply stability association, wherein the power supply stability association is a quantitative index and an evaluation result, and reflects the stability level of a power supply system.
Further, as shown in fig. 2, step S400 of the present application further includes:
step S400-1: constructing an environmental feature set through big data, performing equipment influence fitting on the power equipment according to the environmental feature set, and constructing a mapping of an influence fitting result and environmental features;
step S400-2: carrying out power grid environment data prediction on the regional power grid to generate an environment prediction result, wherein the environment prediction result comprises a time period identifier;
step S400-3: performing mapping matching of the influence fitting result and the environmental characteristics according to the environmental prediction result, and generating a stable influence coefficient with a time period mark according to the matching result;
step S400-4: and compensating the power supply stability association through the stability influence coefficient.
Specifically, environmental characteristics related to the stability of the power equipment, including weather conditions (such as temperature, humidity, wind speed, etc.), load change information, economic indicators, etc., are selected from the large dataset. And establishing a fitting model based on the neural network, so that the relation between the environmental characteristics and the equipment stability can be described, fitting the stability of the power equipment by taking the environmental characteristics as input, and obtaining an influence fitting result corresponding to each environmental characteristic, thereby constructing a mapping relation between the influence fitting result and the environmental characteristics.
Historical grid environment data including weather data, load data, market prices and the like are collected and analyzed, and the change trend and periodicity of the historical grid environment data are obtained. And predicting the power grid environment data according to the change trend and the periodicity, and generating a mark for each period according to the prediction result so as to correlate the environment prediction result with subsequent steps such as power supply stability evaluation and the like, wherein the mark can be a date/time, a serial number or other unique marks meeting the needs.
And matching the environmental prediction result of each period with the mapping model by utilizing the previously established mapping relation model of the influence fitting result and the environmental characteristic, finding a matched stable influence coefficient according to the environmental characteristic and the corresponding influence fitting result, generating a corresponding stable influence coefficient with a period identifier for each period based on the matched result, wherein the coefficients reflect the influence degree of the environmental characteristic on the stability of the power equipment and are associated with the corresponding period.
And evaluating the stability level of the power supply system according to the stability influence coefficient, if the stability influence coefficient is higher, the power supply system is sensitive to external interference, the power supply stability can be poorer, the power supply stability association is compensated through the stability influence coefficient, for example, the stability influence coefficient is reduced, the stability of the power supply system is improved through adopting corresponding measures and technical means such as improving circuits, upgrading equipment, optimizing scheduling and the like, and the stability level of the power supply system is improved.
Step S500: analyzing the power supply prediction data to generate a power supply stability requirement, and adjusting the initial power supply decision plan according to the power supply stability requirement and the power supply stability association to generate an optimized power supply decision plan;
specifically, the power supply prediction data is analyzed and analyzed, relevant information such as load demands and energy supply is extracted, based on the analyzed load demands and steady state initial values of a power supply system, power supply stability association is combined, power supply stability demands are generated, an initial power supply decision plan is adjusted according to the power supply stability demands and the power supply stability association, the stability of the power supply system is improved by adjusting a generator operation strategy, line configuration, system scheduling and the like, and an optimized power supply decision plan is generated according to the adjusted power supply decision plan result, so that the scheme for achieving higher power supply system stability is achieved by comprehensively considering the power supply stability demands, the power supply stability association and other factors.
Step S600: and carrying out intelligent operation scheduling of the regional power grid according to the optimized power supply decision plan.
Specifically, the generated optimized power supply decision plan is used as a basis to monitor the regional power grid in real time, real-time operation data of each key node such as a generator, a transformer substation and a load center are collected, the data collected in real time are analyzed and processed to obtain accurate description of the state and performance of the power system, the state and the demand of the future power system are predicted according to the data analysis result, and the operations such as real-time equipment control, load distribution and generator output are adjusted according to the prediction results so as to optimize the performance and the stability of the power supply system. Continuously monitoring the running state of the power system, timely feeding back the monitoring result to the intelligent running scheduling system, and adjusting and optimizing the running strategy according to the monitoring result so as to keep the stability and the safety of the system.
Further, step S600 of the present application further includes:
step S600-1: when intelligent operation scheduling of the regional power grid is performed through the optimized power supply decision plan, generating a continuous monitoring instruction of the regional power grid;
step S600-2: real-time data monitoring is carried out on the power equipment of the regional power grid according to the continuous monitoring instruction, and a monitoring data set is constructed;
step S600-3: carrying out power supply acquisition on actual power supply data of the regional power grid to generate a power supply data set;
step S600-4: mapping equipment state evaluation is carried out on the power supply data set and the monitoring data set, and abnormal early warning information is generated;
step S600-5: and executing the real-time power supply node switching of the regional power grid according to the abnormal early warning information.
Specifically, according to the optimized power supply decision plan, the power equipment and monitoring requirements which need to be continuously monitored are determined, and according to the actual requirements and system configuration, continuous monitoring instructions are generated, including monitoring frequency, data uploading requirements and the like of the equipment, and the continuous monitoring instructions can be sent to corresponding equipment through an equipment control system, a dispatching center or a transmission network.
According to the received continuous monitoring instructions, the equipment starts to monitor and collect relevant operation data in real time, the real-time data of the power equipment such as current, voltage, temperature, power and other parameters are obtained through means such as a sensor and monitoring equipment, the real-time data are sampled, processed and stored, a monitoring data set is constructed, and the operation condition of the power equipment can be known in time through the continuous monitoring instructions and the monitoring data set.
The actual power supply data of the regional power grid, including load data, voltage data, frequency data and the like, are collected through a data collection system or a sensor, such as a smart meter, monitoring equipment and the like, and a power supply data set is constructed.
And associating and mapping the power supply data set and the monitoring data set, and matching the corresponding power supply data with the monitoring data according to the identifier or the timestamp of the equipment and other modes. And evaluating the state of the equipment according to the related data, evaluating the running state and performance of the equipment at a specific moment by using methods such as statistical analysis and the like, detecting the state of the equipment deviating from the normal running range by comparing with the expected state of the equipment, and generating corresponding abnormal early warning information including warning in aspects such as equipment fault, overload, voltage abnormality and the like.
Analyzing and evaluating the abnormal early warning information, determining the severity and the influence range of the abnormal condition, formulating a corresponding power supply node switching strategy including node selection, switching sequence, threshold setting and the like, executing real-time power supply node switching operation by using an automatic system according to the formulated switching strategy, and continuously monitoring the running state and performance of the power supply system after the switching operation to ensure the correctness and the stability of the switching operation.
Further, the present application further includes:
step S710: analyzing and obtaining the peak power supply quantity and real-time energy storage data of the N power supply points in unit time;
step S720: generating adaptive constraints of N power supply points through the power supply prediction data, the peak power supply quantity in unit time and the real-time energy storage data;
step S730: generating the initial power supply decision plan based on the adaptation constraint.
Specifically, collecting, by a monitoring device, a sensor, or other data source, relevant data of N power supply points, including peak power supply amount per unit time and real-time energy storage data, where the peak power supply amount per unit time represents the maximum power supply amount that the power supply point can provide in a given time period, and is expressed in power units, such as kilowatts (kW) or Megawatts (MW); the real-time energy storage data refers to the current energy storage level or available energy storage energy of each power supply point, and comprises information such as battery states, available energy storage energy of the energy storage device and the like.
And generating constraint conditions adapted to each power supply point through the power supply prediction data, the peak power supply amount in unit time and the real-time energy storage data, wherein the constraint conditions match load demands, power supply capacity and energy storage states to ensure reliable operation of the power supply points. The adaptation constraint includes minimum and maximum power supply limits, for example, ensuring that the power supply to a certain power supply point is not below a certain threshold, or not above a peak power supply per unit time.
According to the adaptive constraint and the expected load demand, optimizing and distributing the resources between the power supply and the load to ensure reliable power supply of each node of the power supply system, and integrating the priority and importance of different power supply points, so that the key load demand can be better met, the system performance can be optimized, and initial power supply decision plans are generated, and relate to the power output adjustment of the generator of each power supply point, the operation mode of the energy storage equipment, the load control strategy and the like.
Further, the present application further includes:
step S810: performing abnormal recording of power supply to the regional power grid, wherein an abnormal recording result comprises abnormal characteristics;
step S820: and updating the abnormal characteristics to the equipment fault information to finish accurate power supply stability analysis of the regional power grid.
Specifically, according to the abnormality early warning information, the abnormal situation in the regional power grid is recorded, and relevant abnormal characteristics are extracted, wherein the abnormal recording result comprises the time, place, relevant equipment and abnormal characteristics of occurrence of the abnormality, and the abnormal characteristics comprise abnormal current, frequency change and the like.
And (3) associating and updating the abnormal characteristics of the step power supply system with the existing equipment fault information, analyzing and evaluating the power supply stability of the regional power grid by utilizing the updated equipment fault information, including statistics of abnormal events, fault tree analysis, reliability evaluation and the like, and performing power supply stability analysis based on the equipment fault information and the abnormal characteristics so as to reveal potential problems in the power supply system, such as bottleneck equipment, fragile components and the like.
In summary, the intelligent operation scheduling method for the regional power grid provided by the embodiment of the application has the following technical effects:
the method comprises the steps of interactively obtaining power equipment information of a regional power grid, including equipment attribute information and equipment distribution information, extracting power supply data of the power grid, extracting common characteristics, generating power supply prediction data, carrying out power supply decision of the regional power grid to generate an initial power supply decision plan, calling equipment fault information and equipment use information, executing power supply stability evaluation of the initial power supply decision plan, generating power supply stability association, analyzing the power supply prediction data, generating power supply stability requirements, adjusting the initial power supply decision plan, generating an optimized power supply decision plan, and carrying out intelligent operation scheduling of the regional power grid according to the optimized power supply decision plan.
The method solves the technical problems that in the prior art, the power supply demand and stability cannot be accurately predicted in a complex regional power grid environment, the stability of a power supply decision is not controlled, and the method is more in artificial participation, so that the operation scheduling efficiency of a regional power grid is low and the response is slow.
Example two
Based on the same inventive concept as the intelligent operation scheduling method for regional power grids in the foregoing embodiments, as shown in fig. 4, the present application provides an intelligent operation scheduling system for regional power grids, the system including:
the device information acquisition module 10 is configured to interactively obtain power device information of the regional power grid, where the power device information includes device attribute information and device distribution information;
the power supply data extraction module 20 is used for extracting power supply data of the regional power grid, extracting common characteristics according to a power supply data extraction result of the power grid, and generating power supply prediction data;
the power supply decision module 30 is configured to perform a power supply decision of the regional power grid according to the power equipment information and the power supply prediction data, and generate an initial power supply decision plan, where the initial power supply decision plan includes a selection decision of a power supply node;
the stability evaluation module 40 is configured to invoke equipment fault information and equipment usage information of the regional power grid, execute power supply stability evaluation of the initial power supply decision plan according to the equipment fault information and the equipment usage information, and generate a power supply stability association;
the decision-making plan adjusting module 50 is configured to parse the power supply prediction data, generate a power supply stability requirement, and adjust the initial power supply decision-making plan according to the power supply stability requirement and the power supply stability association, so as to generate an optimized power supply decision-making plan;
the intelligent operation scheduling module 60 is configured to perform intelligent operation scheduling of the regional power grid according to the optimized power supply decision plan.
Further, the system further comprises:
the equipment influence fitting module is used for constructing an environment feature set through big data, performing equipment influence fitting on the power equipment according to the environment feature set, and constructing a mapping of an influence fitting result and the environment feature;
the environment data prediction module is used for predicting the power grid environment data of the regional power grid and generating an environment prediction result, wherein the environment prediction result comprises a time period identifier;
the mapping matching module is used for executing mapping matching of the influence fitting result and the environmental characteristics according to the environmental prediction result, and generating a stable influence coefficient with a time period identifier according to the matching result;
and the compensation module is used for compensating the power supply stability association through the stability influence coefficient.
Further, the system further comprises:
the grid division association module is used for carrying out area grid division association of the area power grid according to the equipment distribution information and obtaining an area grid division association result of the power supply node;
the data prediction analysis module is used for carrying out data prediction analysis on the power supply prediction data to generate a prediction analysis result;
the economic evaluation module is used for evaluating the power supply economic performance of the power supply node through the prediction analysis result and the area grid division association result of the power supply node;
and the decision plan acquisition module is used for screening and acquiring the initial power supply decision plan according to the power supply economy evaluation result.
Further, the system further comprises:
the instruction generation module is used for generating continuous monitoring instructions of the regional power grid when intelligent operation scheduling of the regional power grid is performed through the optimized power supply decision plan;
the data monitoring module is used for carrying out real-time data monitoring on the power equipment of the regional power grid according to the continuous monitoring instruction, and constructing a monitoring data set;
the power supply acquisition module is used for carrying out power supply acquisition on actual power supply data of the regional power grid to generate a power supply data set;
the state evaluation module is used for carrying out mapping equipment state evaluation on the power supply data set and the monitoring data set and generating abnormal early warning information;
and the node switching module is used for executing the real-time power supply node switching of the regional power grid according to the abnormal early warning information.
Further, the system further comprises:
the analysis module is used for analyzing and obtaining the peak power supply quantity in unit time and the real-time energy storage data of the N power supply points;
the adaptive constraint generation module is used for generating adaptive constraints of N power supply points through the power supply prediction data, the peak power supply quantity in unit time and the real-time energy storage data;
and the initial decision plan generation module is used for generating the initial power supply decision plan based on the adaptation constraint.
Further, the system further comprises:
the continuous working time length generation module is used for calling the equipment use information to generate continuous working time length and equipment load data of the power equipment;
the stability influence evaluation module is used for evaluating the stability influence of the power equipment according to the power equipment information, the continuous working time length and the equipment load data, and generating a steady state initial value;
and the power supply stability association generating module is used for generating the power supply stability association according to the steady state initial value.
Further, the system further comprises:
the abnormal recording module is used for performing abnormal recording of power supply on the regional power grid, wherein an abnormal recording result comprises abnormal characteristics;
and the abnormal characteristic updating module is used for updating the abnormal characteristic to the equipment fault information so as to finish accurate power supply stability analysis of the regional power grid.
The foregoing detailed description of the intelligent operation scheduling method for the regional power grid will clearly be known to those skilled in the art, and the device disclosed in the embodiment is relatively simple to describe, and the relevant places refer to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. An intelligent operation scheduling method for a regional power grid, which is characterized by comprising the following steps:
the method comprises the steps of interactively obtaining power equipment information of the regional power grid, wherein the power equipment information comprises equipment attribute information and equipment distribution information;
extracting power supply data of the regional power grid, extracting common characteristics according to a power supply data extraction result of the power grid, and generating power supply prediction data;
carrying out power supply decision of the regional power grid according to the power equipment information and the power supply prediction data, and generating an initial power supply decision plan, wherein the initial power supply decision plan comprises a selection decision of a power supply node;
invoking equipment fault information and equipment use information of the regional power grid, and executing power supply stability evaluation of the initial power supply decision plan according to the equipment fault information and the equipment use information to generate power supply stability association;
analyzing the power supply prediction data to generate a power supply stability requirement, and adjusting the initial power supply decision plan according to the power supply stability requirement and the power supply stability association to generate an optimized power supply decision plan;
and carrying out intelligent operation scheduling of the regional power grid according to the optimized power supply decision plan.
2. The method of claim 1, wherein the method further comprises:
constructing an environmental feature set through big data, performing equipment influence fitting on the power equipment according to the environmental feature set, and constructing a mapping of an influence fitting result and environmental features;
carrying out power grid environment data prediction on the regional power grid to generate an environment prediction result, wherein the environment prediction result comprises a time period identifier;
performing mapping matching of the influence fitting result and the environmental characteristics according to the environmental prediction result, and generating a stable influence coefficient with a time period mark according to the matching result;
and compensating the power supply stability association through the stability influence coefficient.
3. The method of claim 1, wherein the method further comprises:
performing regional grid division association of the regional power grid according to the equipment distribution information to obtain a regional grid division association result of the power supply node;
carrying out data prediction analysis on the power supply prediction data to generate a prediction analysis result;
carrying out power supply economy evaluation of the power supply node according to the prediction analysis result and the area grid division association result of the power supply node;
and screening according to the power supply economy evaluation result to obtain the initial power supply decision plan.
4. The method of claim 1, wherein the method further comprises:
when intelligent operation scheduling of the regional power grid is performed through the optimized power supply decision plan, generating a continuous monitoring instruction of the regional power grid;
real-time data monitoring is carried out on the power equipment of the regional power grid according to the continuous monitoring instruction, and a monitoring data set is constructed;
carrying out power supply acquisition on actual power supply data of the regional power grid to generate a power supply data set;
mapping equipment state evaluation is carried out on the power supply data set and the monitoring data set, and abnormal early warning information is generated;
and executing the real-time power supply node switching of the regional power grid according to the abnormal early warning information.
5. The method of claim 1, wherein the method further comprises:
analyzing and obtaining the peak power supply quantity and real-time energy storage data of the N power supply points in unit time;
generating adaptive constraints of N power supply points through the power supply prediction data, the peak power supply quantity in unit time and the real-time energy storage data;
generating the initial power supply decision plan based on the adaptation constraint.
6. The method of claim 1, wherein the method further comprises:
calling the equipment use information to generate continuous working time length and equipment load data of the power equipment;
performing stability influence evaluation on the power equipment according to the power equipment information, the continuous working time and the equipment load data to generate a steady-state initial value;
and generating the power supply stability association according to the steady state initial value.
7. The method of claim 1, wherein the method further comprises:
performing abnormal recording of power supply to the regional power grid, wherein an abnormal recording result comprises abnormal characteristics;
and updating the abnormal characteristics to the equipment fault information to finish accurate power supply stability analysis of the regional power grid.
8. An intelligent operation scheduling system for a regional power grid, for implementing the intelligent operation scheduling method for a regional power grid according to any one of claims 1 to 7, comprising:
the device information acquisition module is used for interactively acquiring the power device information of the regional power grid, wherein the power device information comprises device attribute information and device distribution information;
the power supply data extraction module is used for extracting power supply data of the regional power grid, extracting common characteristics according to a power supply data extraction result of the power grid and generating power supply prediction data;
the power supply decision module is used for carrying out power supply decision of the regional power grid according to the power equipment information and the power supply prediction data to generate an initial power supply decision plan, wherein the initial power supply decision plan comprises a selection decision of a power supply node;
the stability evaluation module is used for calling equipment fault information and equipment use information of the regional power grid, executing power supply stability evaluation of the initial power supply decision plan according to the equipment fault information and the equipment use information, and generating power supply stability association;
the decision-making programming adjustment module is used for analyzing the power supply prediction data, generating a power supply stability requirement, adjusting the initial power supply decision-making programming according to the power supply stability requirement and the power supply stability association, and generating an optimized power supply decision-making programming;
and the intelligent operation scheduling module is used for performing intelligent operation scheduling of the regional power grid according to the optimized power supply decision plan.
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