CN117424238A - Power grid energy optimal scheduling method, system and storage medium - Google Patents
Power grid energy optimal scheduling method, system and storage medium Download PDFInfo
- Publication number
- CN117424238A CN117424238A CN202311387420.5A CN202311387420A CN117424238A CN 117424238 A CN117424238 A CN 117424238A CN 202311387420 A CN202311387420 A CN 202311387420A CN 117424238 A CN117424238 A CN 117424238A
- Authority
- CN
- China
- Prior art keywords
- power grid
- energy
- graph
- data set
- energy data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 38
- 238000003860 storage Methods 0.000 title claims abstract description 18
- 238000004364 calculation method Methods 0.000 claims abstract description 95
- 238000004140 cleaning Methods 0.000 claims abstract description 31
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 16
- 238000007781 pre-processing Methods 0.000 claims abstract description 15
- 238000005457 optimization Methods 0.000 claims description 53
- 238000012545 processing Methods 0.000 claims description 35
- 230000002159 abnormal effect Effects 0.000 claims description 19
- 238000004590 computer program Methods 0.000 claims description 18
- 238000007689 inspection Methods 0.000 claims description 18
- 238000004088 simulation Methods 0.000 claims description 16
- 238000010586 diagram Methods 0.000 claims description 13
- 238000011156 evaluation Methods 0.000 claims description 10
- 238000006243 chemical reaction Methods 0.000 claims description 8
- 238000001514 detection method Methods 0.000 claims description 7
- 238000005259 measurement Methods 0.000 claims description 5
- 238000012216 screening Methods 0.000 claims description 5
- 238000004458 analytical method Methods 0.000 claims description 2
- 230000006870 function Effects 0.000 description 19
- 238000013461 design Methods 0.000 description 6
- 230000008569 process Effects 0.000 description 5
- 238000010276 construction Methods 0.000 description 4
- 230000000007 visual effect Effects 0.000 description 4
- 238000009499 grossing Methods 0.000 description 3
- 230000010354 integration Effects 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 238000013450 outlier detection Methods 0.000 description 2
- 230000008439 repair process Effects 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000008676 import Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
- H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
- G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/901—Indexing; Data structures therefor; Storage structures
- G06F16/9024—Graphs; Linked lists
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Economics (AREA)
- General Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- Databases & Information Systems (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- Quality & Reliability (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- Data Mining & Analysis (AREA)
- Game Theory and Decision Science (AREA)
- General Engineering & Computer Science (AREA)
- Operations Research (AREA)
- Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Power Engineering (AREA)
- Software Systems (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a power grid energy optimal scheduling method, a system and a storage medium, wherein the method comprises the following steps: collecting energy data of each node of the power grid, and generating an energy data set; preprocessing and cleaning the energy data set, and constructing a topological graph of the power grid according to the preprocessed and cleaned energy data set; analyzing and calculating a power grid topological graph by using a graph calculation algorithm to obtain a calculation result; and generating an optimal scheduling decision according to the calculation result, so as to optimally schedule the power grid energy of the power grid according to the optimal scheduling decision, thereby realizing the optimal scheduling of the power grid energy and improving the running efficiency and stability of the power grid.
Description
Technical Field
The invention relates to the technical field of power grid energy optimal scheduling, in particular to a power grid energy optimal scheduling method, a power grid energy optimal scheduling system and a storage medium.
Background
With the continuous expansion of the power grid scale and the increase of the complexity of the power system, efficient management and optimization of power grid energy operation in power grid energy optimization scheduling become an important task. The traditional power grid energy operation has low calculation efficiency, and the display is not visual due to the adoption of the display of text and digital data. Meanwhile, abnormal data are easy to occur in the power grid energy data processing process, the data processing is needed to be carried out manually, the speed of the manual processing process is low, time and labor are wasted, the optimal dispatching of the power grid energy is difficult to achieve, and further the operation efficiency and stability of the power grid cannot meet the use requirements.
Disclosure of Invention
The invention provides a power grid energy optimal scheduling method, a system and a storage medium, which are used for realizing optimal scheduling of power grid energy and improving the running efficiency and stability of the power grid.
The invention provides a power grid energy optimization scheduling method, which comprises the following steps:
collecting energy data of each node of the power grid, and generating an energy data set; preprocessing and cleaning the energy data set, and constructing a topological graph of the power grid according to the preprocessed and cleaned energy data set; analyzing and calculating a power grid topological graph by using a graph calculation algorithm to obtain a calculation result; and generating an optimal scheduling decision according to the calculation result, so as to optimally schedule the power grid energy of the power grid according to the optimal scheduling decision.
Further, after collecting the energy data set of each node of the power grid, the method further comprises:
inputting the energy data set into Sql database software for quality inspection; the Sql database software performs quality inspection on the energy data set according to a preset data quality screening rule; the quality inspection includes: one or more of null check, duplicate check, surge check, outlier check, and integrity check; and if the quality check of the energy data set does not meet the preset condition, sending out a data quality alarm.
Further, the energy data set is preprocessed and cleaned, specifically:
the energy data set is imported into a Power BI, so that the Power BI performs first data cleaning operation on the energy data set by using Power Query to obtain a preprocessed energy data set; the first data cleansing operation includes: performing format conversion, denoising and missing value processing on the energy data set, and unifying measurement units of the energy data set;
performing a second data cleaning operation on the preprocessed energy data set to obtain a cleaned energy data set; the second data cleansing operation includes: removing non-canonical numerical units according to preset maximum and minimum values of the unit values in the energy data set; and carrying out abnormal value detection and abnormal data restoration on the energy data set.
Further, according to the preprocessed and cleaned energy data set, a topological graph of the power grid is constructed, specifically:
integrating the cleaned energy data set with a power grid topological structure to construct a topological graph of the power grid; the topological graph of the power grid comprises node and side information and connection relations among the nodes.
Further, the graph calculation algorithm is utilized to analyze and calculate the power grid topological graph, and a calculation result is obtained, specifically:
building a billboard by using the energy data set, and converting chart information of each node in the power grid topological graph into a line graph; calculating the power grid energy supply and consumption increase rate by using the graph broken line curvature in the broken line graph to obtain a calculation result; and outputting the calculation result in a form or a graph mode.
Further, chart information of each node in the power grid topological graph is converted into a line graph, specifically:
converting the chart information into a line chart to serve as a chart calculation task, dividing the chart calculation task into a plurality of subtasks, and carrying out parallel calculation on the subtasks by utilizing a multi-core processor and a distributed calculation environment so as to calculate the chart information and the graph curvature of each node in each power grid topological graph.
Further, generating an optimal scheduling decision according to the calculation result, specifically:
comparing graph information and graph curvature of each Power grid topological graph node by using a Power BI program to obtain a comparison result; according to the comparison result, formulating an adjustment strategy of the power grid energy; the adjustment strategy comprises the following steps: transporting the electric energy with preset capacity at the node position with the electric power consumption lower than the first threshold value to the node position with the electric power consumption lower than the first threshold value;
performing simulation prediction on the power grid topological graph according to the adjustment strategy, and calculating graph information of new simulation power grid topological graph nodes; and if the chart information of the new simulated power grid topological graph nodes meets the optimization target, visually outputting the adjustment strategy.
As a preferred scheme, the method processes the collected energy data through the data preprocessing and data cleaning functions, improves the data processing efficiency, detects and repairs the abnormal data through the data cleaning functions, and improves the data processing accuracy. According to the method, energy data and a power grid topological structure are integrated through a data integration and conversion function, and parallel calculation is carried out on the power grid topological graph. And a graph calculation algorithm is adopted to perform parallel calculation on the power grid topological graph, digital text data information is converted into picture information for display, display is visual, and calculation efficiency is improved. According to the method, the power grid topological graph is subjected to parallel calculation, so that the optimal dispatching of the power grid energy is realized, and the running efficiency and stability of the power grid are improved.
Additional features and advantages of the invention will be set forth in the detailed description which follows.
Correspondingly, the invention also provides a power grid energy optimization scheduling system, which comprises the following steps: the system comprises a data acquisition module, a data processing module, a graph calculation module and an optimization scheduling module;
the data acquisition module is used for acquiring energy data of each node of the power grid and generating an energy data set;
the data processing module is used for preprocessing and cleaning the energy data set and constructing a topological graph of the power grid according to the preprocessed and cleaned energy data set;
the diagram calculation module is used for analyzing and calculating a power grid topological diagram by using a diagram calculation algorithm to obtain a calculation result;
and the optimal scheduling module is used for generating an optimal scheduling decision according to the calculation result so as to optimally schedule the power grid energy of the power grid according to the optimal scheduling decision.
The power grid energy optimization scheduling system further comprises: a data quality inspection module;
the data quality inspection module is used for inputting the energy data set into Sql database software for quality inspection; the Sql database software performs quality inspection on the energy data set according to a preset data quality screening rule; the quality inspection includes: one or more of null check, duplicate check, surge check, outlier check, and integrity check; and if the quality check of the energy data set does not meet the preset condition, sending out a data quality alarm.
The data processing module comprises: the device comprises a preprocessing unit, a cleaning unit and a graph construction unit;
the preprocessing unit is used for importing the energy data set into a Power BI so that the Power BI can perform first data cleaning operation on the energy data set by using Power Query to obtain a preprocessed energy data set; the first data cleansing operation includes: performing format conversion, denoising and missing value processing on the energy data set, and unifying measurement units of the energy data set;
the cleaning unit is used for performing second data cleaning operation on the preprocessed energy data set to obtain a cleaned energy data set; the second data cleansing operation includes: removing non-canonical numerical units according to preset maximum and minimum values of the unit values in the energy data set; and carrying out abnormal value detection and abnormal data restoration on the energy data set.
The diagram construction unit is used for integrating the cleaned energy data set with the topological structure of the power grid to construct a topological diagram of the power grid; the topological graph of the power grid comprises node and side information and connection relations among the nodes.
The graph calculation module comprises a calculation unit and an output unit;
the computing unit is used for building a billboard from the energy data set and converting chart information of each node in the power grid topological graph into a line graph; calculating the power grid energy supply and consumption increase rate by using the graph broken line curvature in the broken line graph to obtain a calculation result;
the chart information of each node in the power grid topological graph is converted into a line graph, and the line graph is specifically as follows:
converting the chart information into a line chart to serve as a chart calculation task, dividing the chart calculation task into a plurality of subtasks, and carrying out parallel calculation on the subtasks by utilizing a multi-core processor and a distributed calculation environment so as to calculate the chart information and the graph curvature of each node in each power grid topological graph.
The output unit is used for outputting the calculation result in a form or a graph.
The optimal scheduling module comprises: the system comprises an optimization calculation unit, a scheduling decision unit, a scheduling result evaluation unit and a scheduling scheme output unit;
the optimization calculation unit is used for comparing graph information and graph curvature of each Power grid topological graph node by using a Power BI program to obtain a comparison result;
the scheduling decision unit is used for making an adjustment strategy of the power grid energy according to the comparison result; the adjustment strategy comprises the following steps: transporting the electric energy with preset capacity at the node position with the electric power consumption lower than the first threshold value to the node position with the electric power consumption lower than the first threshold value;
the dispatching result evaluation unit is used for carrying out simulation prediction on the power grid topological graph according to the adjustment strategy, and calculating the chart information of the new simulation power grid topological graph nodes;
and the scheduling scheme output unit is used for visually outputting the adjustment strategy if the chart information of the new simulated power grid topological graph nodes meets the optimization target.
The optimal scheduling module comprises: the system comprises an optimization calculation unit, a scheduling decision unit, a scheduling result evaluation unit and a scheduling scheme output unit;
the optimization calculation unit is used for comparing graph information and graph curvature of each Power grid topological graph node by using a Power BI program to obtain a comparison result;
the scheduling decision unit is used for making an adjustment strategy of the power grid energy according to the comparison result; the adjustment strategy comprises the following steps: transporting the electric energy with preset capacity at the node position with the electric power consumption lower than the first threshold value to the node position with the electric power consumption lower than the first threshold value;
the dispatching result evaluation unit is used for carrying out simulation prediction on the power grid topological graph according to the adjustment strategy, and calculating the chart information of the new simulation power grid topological graph nodes;
and the scheduling scheme output unit is used for visually outputting the adjustment strategy if the chart information of the new simulated power grid topological graph nodes meets the optimization target.
Accordingly, the present invention also provides a computer-readable storage medium including a stored computer program; the computer program controls the equipment where the computer readable storage medium is located to execute the power grid energy optimization scheduling method according to the content of the invention when running.
Drawings
FIG. 1 is a schematic flow chart of one embodiment of a power grid energy optimization scheduling method provided by the present invention;
fig. 2 is a schematic structural diagram of an embodiment of the power grid energy optimization scheduling system provided by the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, a power grid energy optimization scheduling method provided by an embodiment of the present invention includes steps S101-S104:
step S101: collecting energy data of each node of the power grid, and generating an energy data set;
in this embodiment, the energy data of each node of the power grid is collected, specifically: the system is connected with sensors of all nodes of the power grid, energy data of all the nodes are obtained in real time through sensor interfaces, and the collected energy data are transmitted to a data processing module in time and stored.
Further, after collecting the energy data set of each node of the power grid, the method further comprises:
inputting the energy data set into Sql database software for quality inspection; the Sql database software performs quality inspection on the energy data set according to a preset data quality screening rule; the quality inspection includes: one or more of null check, duplicate check, surge check, outlier check, and integrity check; and if the quality check of the energy data set does not meet the preset condition, sending out a data quality alarm.
In the embodiment, the energy data in the energy data set is monitored and analyzed in real time, the accuracy and the integrity of the imported data are ensured, abnormal conditions are detected, and an alarm is sent out in time.
Step S102: preprocessing and cleaning the energy data set, and constructing a topological graph of the power grid according to the preprocessed and cleaned energy data set;
further, the energy data set is preprocessed and cleaned, specifically:
the energy data set is imported into a Power BI, so that the Power BI performs first data cleaning operation on the energy data set by using Power Query to obtain a preprocessed energy data set; the first data cleansing operation includes: performing format conversion, denoising and missing value processing on the energy data set, and unifying measurement units of the energy data set;
performing a second data cleaning operation on the preprocessed energy data set to obtain a cleaned energy data set; the second data cleansing operation includes: removing non-canonical numerical units according to preset maximum and minimum values of the unit values in the energy data set; and carrying out abnormal value detection and abnormal data restoration on the energy data set.
In this embodiment, the second data cleansing operation further includes:
abnormal value detection, namely performing abnormal value detection on the collected energy data set, setting abnormal value filtering of the energy data, and identifying abnormal data points;
processing the missing value, namely processing the missing value in the acquired energy data, and filling or repairing the missing data;
data smoothing processing is carried out on the collected energy data, noise and abrupt change points are removed, and the stability and reliability of the data are improved;
and checking the consistency of the data, namely checking the acquired energy data, and ensuring the consistency and the integrity of the data.
In this embodiment, the data cleaning function improves the efficiency of data cleaning by using the functions of outlier detection, outlier missing processing and data smoothing processing, reduces the workload of manual processing, improves the accuracy of data cleaning by using the functions of outlier detection, outlier missing processing and data smoothing processing, removes outlier and noise, improves the reliability of data, and verifies the acquired energy data by using the data consistency verification function to ensure the consistency and integrity of the data.
In this embodiment, the pre-processed and cleaned energy data set is stored and a data query and management interface is provided. And acquiring required data through a data query and management interface, and constructing a topological graph of the power grid.
Further, according to the preprocessed and cleaned energy data set, a topological graph of the power grid is constructed, specifically:
integrating the cleaned energy data set with a power grid topological structure to construct a topological graph of the power grid; the topological graph of the power grid comprises node and side information and connection relations among the nodes.
In this embodiment, the design diagram data structure imports the cleaned energy data set into the Power Bi tool, and uses the data modeling function to communicate data, so as to represent the topology diagram of the Power grid, including the information of nodes and edges, and the connection relationship between the nodes.
In the embodiment, the collected energy data is processed through the data preprocessing and data cleaning functions, the data processing efficiency is improved, the abnormal data is detected and repaired through the data cleaning functions, the data processing accuracy is improved, and the energy data and the power grid topological structure are integrated through the data integration and conversion functions, so that a data basis is provided for subsequent graph calculation.
Step S103: and analyzing and calculating the power grid topological graph by using a graph calculation algorithm to obtain a calculation result.
Further, the graph calculation algorithm is utilized to analyze and calculate the power grid topological graph, and a calculation result is obtained, specifically:
building a billboard by using the energy data set, and converting chart information of each node in the power grid topological graph into a line graph; calculating the power grid energy supply and consumption increase rate by using the graph broken line curvature in the broken line graph to obtain a calculation result; and outputting the calculation result in a form or a graph mode.
Further, chart information of each node in the power grid topological graph is converted into a line graph, specifically:
converting the chart information into a line chart to serve as a chart calculation task, dividing the chart calculation task into a plurality of subtasks, and carrying out parallel calculation on the subtasks by utilizing a multi-core processor and a distributed calculation environment so as to calculate the chart information and the graph curvature of each node in each power grid topological graph.
In the embodiment, the calculation efficiency of graph calculation is improved through parallel calculation and optimization algorithm design, the analysis and calculation speed of the power grid topological graph are accelerated, the calculation accuracy of the graph calculation is improved through accurate graph data structure design and algorithm implementation, the reliability of a calculation result is ensured, the graph calculation task is divided into a plurality of subtasks through parallel calculation framework design, and the calculation efficiency is improved through parallel calculation by utilizing a multi-core processor or a distributed calculation environment.
Step S104: and generating an optimal scheduling decision according to the calculation result, so as to optimally schedule the power grid energy of the power grid according to the optimal scheduling decision.
Further, generating an optimal scheduling decision according to the calculation result, specifically:
comparing graph information and graph curvature of each Power grid topological graph node by using a Power BI program to obtain a comparison result; according to the comparison result, formulating an adjustment strategy of the power grid energy; the adjustment strategy comprises the following steps: transporting the electric energy with preset capacity at the node position with the electric power consumption lower than the first threshold value to the node position with the electric power consumption lower than the first threshold value;
performing simulation prediction on the power grid topological graph according to the adjustment strategy, and calculating graph information of new simulation power grid topological graph nodes; and if the chart information of the new simulated power grid topological graph nodes meets the optimization target, visually outputting the adjustment strategy.
In the embodiment, a hardware optimization function of the Power BI is started, the calculation efficiency is improved, the number of page visual objects is reduced, a plurality of groups of generated charts of each Power grid topological graph node are converted into picture information, the program calculation time in the user interaction process is shortened, the optimization scheduling is performed on icon display of Power grid energy, and the optimization algorithm design is realized. And (3) comparing graph information and graph curvature of each Power grid topological graph node by using a Power BI program according to the graph calculation result and the output of an optimization algorithm, formulating a Power grid energy adjustment strategy, transporting the Power at the node position with low Power consumption to other areas with insufficient Power supply, and keeping Power and load balance.
The invention has the function of evaluating the design scheduling result, utilizes the Power BI program to simulate and evaluate and analyze the result of optimizing scheduling, calculates the chart information of the new simulated Power grid topological graph nodes, and evaluates the scheduling effect and the achievement degree of the optimizing target; and (3) visually outputting the scheduling scheme icon obtained by the optimal scheduling module by using a Power BI program, and providing an operation and maintenance personnel to implement scheduling decisions.
The implementation of the embodiment of the invention has the following effects:
the invention processes the collected energy data through the data preprocessing and data cleaning functions, improves the data processing efficiency, detects and repairs the abnormal data through the data cleaning functions, and improves the data processing accuracy. According to the method, energy data and a power grid topological structure are integrated through a data integration and conversion function, and parallel calculation is carried out on the power grid topological graph. And a graph calculation algorithm is adopted to perform parallel calculation on the power grid topological graph, digital text data information is converted into picture information for display, display is visual, and calculation efficiency is improved. According to the method, the power grid topological graph is subjected to parallel calculation, so that the optimal dispatching of the power grid energy is realized, and the running efficiency and stability of the power grid are improved.
Example two
Referring to fig. 2, a power grid energy optimization scheduling system provided by an embodiment of the present invention includes: a data acquisition module 201, a data processing module 202, a graph calculation module 203 and an optimization scheduling module 204;
the data acquisition module 201 is configured to acquire energy data of each node of the power grid, and generate an energy data set;
the data processing module 202 is configured to perform preprocessing and cleaning on the energy data set, and construct a topology map of the power grid according to the preprocessed and cleaned energy data set;
the graph calculation module 203 is configured to analyze and calculate a power grid topological graph by using a graph calculation algorithm, so as to obtain a calculation result;
the optimization scheduling module 204 is configured to generate an optimization scheduling decision according to the calculation result, so as to perform optimization scheduling on the grid energy of the grid according to the optimization scheduling decision.
The power grid energy optimization scheduling system further comprises: a data processing module and a data quality checking module;
the data processing module is used for storing energy data of each node;
the data quality inspection module is used for inputting the energy data set into Sql database software for quality inspection; the Sql database software performs quality inspection on the energy data set according to a preset data quality screening rule; the quality inspection includes: one or more of null check, duplicate check, surge check, outlier check, and integrity check; and if the quality check of the energy data set does not meet the preset condition, sending out a data quality alarm.
The data processing module comprises: the device comprises a preprocessing unit, a cleaning unit and a graph construction unit;
the preprocessing unit is used for importing the energy data set into a Power BI so that the Power BI can perform first data cleaning operation on the energy data set by using Power Query to obtain a preprocessed energy data set; the first data cleansing operation includes: performing format conversion, denoising and missing value processing on the energy data set, and unifying measurement units of the energy data set;
the cleaning unit is used for performing second data cleaning operation on the preprocessed energy data set to obtain a cleaned energy data set; the second data cleansing operation includes: removing non-canonical numerical units according to preset maximum and minimum values of the unit values in the energy data set; and carrying out abnormal value detection and abnormal data restoration on the energy data set.
The diagram construction unit is used for integrating the cleaned energy data set with the topological structure of the power grid to construct a topological diagram of the power grid; the topological graph of the power grid comprises node and side information and connection relations among the nodes.
The graph calculation module comprises a calculation unit and an output unit;
the computing unit is used for building a billboard from the energy data set and converting chart information of each node in the power grid topological graph into a line graph; calculating the power grid energy supply and consumption increase rate by using the graph broken line curvature in the broken line graph to obtain a calculation result;
the chart information of each node in the power grid topological graph is converted into a line graph, and the line graph is specifically as follows:
converting the chart information into a line chart to serve as a chart calculation task, dividing the chart calculation task into a plurality of subtasks, and carrying out parallel calculation on the subtasks by utilizing a multi-core processor and a distributed calculation environment so as to calculate the chart information and the graph curvature of each node in each power grid topological graph.
The output unit is used for outputting the calculation result in a form or a graph.
The optimal scheduling module comprises: the system comprises an optimization calculation unit, a scheduling decision unit, a scheduling result evaluation unit and a scheduling scheme output unit;
the optimization calculation unit is used for comparing graph information and graph curvature of each Power grid topological graph node by using a Power BI program to obtain a comparison result;
the scheduling decision unit is used for making an adjustment strategy of the power grid energy according to the comparison result; the adjustment strategy comprises the following steps: transporting the electric energy with preset capacity at the node position with the electric power consumption lower than the first threshold value to the node position with the electric power consumption lower than the first threshold value;
the dispatching result evaluation unit is used for carrying out simulation prediction on the power grid topological graph according to the adjustment strategy, and calculating the chart information of the new simulation power grid topological graph nodes;
and the scheduling scheme output unit is used for visually outputting the adjustment strategy if the chart information of the new simulated power grid topological graph nodes meets the optimization target.
The optimal scheduling module comprises: the system comprises an optimization calculation unit, a scheduling decision unit, a scheduling result evaluation unit and a scheduling scheme output unit;
the optimization calculation unit is used for comparing graph information and graph curvature of each Power grid topological graph node by using a Power BI program to obtain a comparison result;
the scheduling decision unit is used for making an adjustment strategy of the power grid energy according to the comparison result; the adjustment strategy comprises the following steps: transporting the electric energy with preset capacity at the node position with the electric power consumption lower than the first threshold value to the node position with the electric power consumption lower than the first threshold value;
the dispatching result evaluation unit is used for carrying out simulation prediction on the power grid topological graph according to the adjustment strategy, and calculating the chart information of the new simulation power grid topological graph nodes;
and the scheduling scheme output unit is used for visually outputting the adjustment strategy if the chart information of the new simulated power grid topological graph nodes meets the optimization target.
The power grid energy optimization scheduling system can implement the power grid energy optimization scheduling method of the method embodiment. The options in the method embodiments described above are also applicable to this embodiment and will not be described in detail here. The rest of the embodiments of the present application may refer to the content of the method embodiments described above, and in this embodiment, no further description is given.
Example III
Correspondingly, the invention further provides a computer readable storage medium, which comprises a stored computer program, wherein the computer program is used for controlling equipment where the computer readable storage medium is located to execute the power grid energy optimization scheduling method according to any embodiment.
The computer program may be divided into one or more modules/units, which are stored in the memory and executed by the processor to accomplish the present invention, for example. The one or more modules/units may be a series of computer program instruction segments capable of performing the specified functions, which instruction segments are used for describing the execution of the computer program in the terminal device.
The terminal equipment can be computing equipment such as a desktop computer, a notebook computer, a palm computer, a cloud server and the like. The terminal device may include, but is not limited to, a processor, a memory.
The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is a control center of the terminal device, and which connects various parts of the entire terminal device using various interfaces and lines.
The memory may be used to store the computer program and/or the module, and the processor may implement various functions of the terminal device by running or executing the computer program and/or the module stored in the memory and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the mobile terminal, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
Wherein the terminal device integrated modules/units may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as stand alone products. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
The foregoing embodiments have been provided for the purpose of illustrating the general principles of the present invention, and are not to be construed as limiting the scope of the invention. It should be noted that any modifications, equivalent substitutions, improvements, etc. made by those skilled in the art without departing from the spirit and principles of the present invention are intended to be included in the scope of the present invention.
Claims (10)
1. The power grid energy optimization scheduling method is characterized by comprising the following steps of:
collecting energy data of each node of the power grid, and generating an energy data set; preprocessing and cleaning the energy data set, and constructing a topological graph of the power grid according to the preprocessed and cleaned energy data set; analyzing and calculating a power grid topological graph by using a graph calculation algorithm to obtain a calculation result; and generating an optimal scheduling decision according to the calculation result, so as to optimally schedule the power grid energy of the power grid according to the optimal scheduling decision.
2. The power grid energy optimization scheduling method according to claim 1, wherein after the collecting the energy data sets of the nodes of the power grid, the method further comprises:
inputting the energy data set into Sql database software for quality inspection; the Sql database software performs quality inspection on the energy data set according to a preset data quality screening rule; the quality inspection includes: one or more of null check, duplicate check, surge check, outlier check, and integrity check; and if the quality check of the energy data set does not meet the preset condition, sending out a data quality alarm.
3. The power grid energy optimization scheduling method according to claim 1, wherein the preprocessing and cleaning of the energy data set is specifically as follows:
the energy data set is imported into a Power BI, so that the Power BI performs first data cleaning operation on the energy data set by using Power Query to obtain a preprocessed energy data set; the first data cleansing operation includes: performing format conversion, denoising and missing value processing on the energy data set, and unifying measurement units of the energy data set;
performing a second data cleaning operation on the preprocessed energy data set to obtain a cleaned energy data set; the second data cleansing operation includes: removing non-canonical numerical units according to preset maximum and minimum values of the unit values in the energy data set; and carrying out abnormal value detection and abnormal data restoration on the energy data set.
4. The power grid energy optimization scheduling method according to claim 3, wherein the building of the power grid topology map according to the preprocessed and cleaned energy data set is specifically as follows:
integrating the cleaned energy data set with a power grid topological structure to construct a topological graph of the power grid; the topological graph of the power grid comprises node and side information and connection relations among the nodes.
5. The power grid energy optimization scheduling method according to claim 1, wherein the analysis and calculation are performed on the power grid topological graph by using a graph calculation algorithm to obtain a calculation result, specifically:
building a billboard by using the energy data set, and converting chart information of each node in the power grid topological graph into a line graph; calculating the power grid energy supply and consumption increase rate by using the graph broken line curvature in the broken line graph to obtain a calculation result; and outputting the calculation result in a form or a graph mode.
6. The power grid energy optimization scheduling method according to claim 5, wherein the converting the chart information of each node in the power grid topological graph into a line graph specifically comprises:
converting the chart information into a line chart to serve as a chart calculation task, dividing the chart calculation task into a plurality of subtasks, and carrying out parallel calculation on the subtasks by utilizing a multi-core processor and a distributed calculation environment so as to calculate the chart information and the graph curvature of each node in each power grid topological graph.
7. The power grid energy optimization scheduling method according to claim 6, wherein the generating an optimization scheduling decision according to the calculation result is specifically:
comparing graph information and graph curvature of each Power grid topological graph node by using a Power BI program to obtain a comparison result; according to the comparison result, formulating an adjustment strategy of the power grid energy; the adjustment strategy comprises the following steps: transporting the electric energy with preset capacity at the node position with the electric power consumption lower than the first threshold value to the node position with the electric power consumption lower than the first threshold value;
performing simulation prediction on the power grid topological graph according to the adjustment strategy, and calculating graph information of new simulation power grid topological graph nodes; and if the chart information of the new simulated power grid topological graph nodes meets the optimization target, visually outputting the adjustment strategy.
8. A power grid energy optimization scheduling system, comprising: the system comprises a data acquisition module, a data processing module, a graph calculation module and an optimization scheduling module;
the data acquisition module is used for acquiring energy data of each node of the power grid and generating an energy data set;
the data processing module is used for preprocessing and cleaning the energy data set and constructing a topological graph of the power grid according to the preprocessed and cleaned energy data set;
the diagram calculation module is used for analyzing and calculating a power grid topological diagram by using a diagram calculation algorithm to obtain a calculation result;
and the optimal scheduling module is used for generating an optimal scheduling decision according to the calculation result so as to optimally schedule the power grid energy of the power grid according to the optimal scheduling decision.
9. The grid energy optimal scheduling system of claim 8, wherein the optimal scheduling module comprises: the system comprises an optimization calculation unit, a scheduling decision unit, a scheduling result evaluation unit and a scheduling scheme output unit;
the optimization calculation unit is used for comparing graph information and graph curvature of each Power grid topological graph node by using a Power BI program to obtain a comparison result;
the scheduling decision unit is used for making an adjustment strategy of the power grid energy according to the comparison result; the adjustment strategy comprises the following steps: transporting the electric energy with preset capacity at the node position with the electric power consumption lower than the first threshold value to the node position with the electric power consumption lower than the first threshold value;
the dispatching result evaluation unit is used for carrying out simulation prediction on the power grid topological graph according to the adjustment strategy, and calculating the chart information of the new simulation power grid topological graph nodes;
and the scheduling scheme output unit is used for visually outputting the adjustment strategy if the chart information of the new simulated power grid topological graph nodes meets the optimization target.
10. A computer readable storage medium, wherein the computer readable storage medium comprises a stored computer program; wherein the computer program, when running, controls a device in which the computer readable storage medium is located to perform a grid energy optimized scheduling method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311387420.5A CN117424238A (en) | 2023-10-24 | 2023-10-24 | Power grid energy optimal scheduling method, system and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311387420.5A CN117424238A (en) | 2023-10-24 | 2023-10-24 | Power grid energy optimal scheduling method, system and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117424238A true CN117424238A (en) | 2024-01-19 |
Family
ID=89532029
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311387420.5A Pending CN117424238A (en) | 2023-10-24 | 2023-10-24 | Power grid energy optimal scheduling method, system and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117424238A (en) |
-
2023
- 2023-10-24 CN CN202311387420.5A patent/CN117424238A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107169628B (en) | Power distribution network reliability assessment method based on big data mutual information attribute reduction | |
| CN109687447B (en) | Electric power energy consumption prediction method and device | |
| CN113410813A (en) | Digital twin distribution network fault-based diagnosis method and related device | |
| CN103761685A (en) | Electric power data parallelization anomaly detection method based on MapReduce | |
| CN111339072B (en) | User behavior-based change value analysis method and device, electronic equipment and medium | |
| CN111177128B (en) | Metering big data batch processing method and system based on improved outlier detection algorithm | |
| CN113268403A (en) | Time series analysis and prediction method, device, equipment and storage medium | |
| CN114239198B (en) | Power grid subgraph division method and device based on parallel optimization | |
| CN115730605A (en) | Data analysis method based on multi-dimensional information | |
| CN112948353B (en) | Data analysis method, system and storage medium applied to DAstudio | |
| CN112686773B (en) | Electric power metering all-link key business anomaly positioning model construction method based on fusion business topology | |
| CN117424238A (en) | Power grid energy optimal scheduling method, system and storage medium | |
| CN114692487B (en) | Electronic equipment maintenance spare part pre-casting method, device, equipment and storage medium | |
| CN107121616B (en) | Method and device for fault positioning of intelligent instrument | |
| WO2021237459A1 (en) | Industrial object model-based data processing method, apparatus and device | |
| CN115290139A (en) | Building outdoor environment performance detection and prediction platform based on big data | |
| CN116415385A (en) | Fan joint debugging simulation method and device, server and computer storage medium | |
| WO2019055329A1 (en) | A method of learning robust regression models from limited training data | |
| CN114154415A (en) | Equipment life prediction method and device | |
| CN113887101A (en) | Visualization method and device of network model, electronic equipment and storage medium | |
| CN115841343B (en) | Sales limit determining method and device | |
| CN117335416B (en) | Method, device, equipment and storage medium for optimizing power load | |
| CN117435870B (en) | Load data real-time filling method, system, equipment and medium | |
| CN116700148A (en) | Availability analysis method, device, equipment and medium for numerical control machine tool | |
| CN118054565A (en) | Power grid state analysis method and device, terminal equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |