CN112149882A - Power grid medium and long term load prediction management system - Google Patents
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Abstract
The invention discloses a power grid medium and long term load prediction management system, which comprises: the data preparation module is used for acquiring medium-and-long-term power generation data; the power generation plan compiling module is used for compiling a medium-term and long-term power generation plan according to the medium-term and long-term power generation data; and the medium and long term load forecasting module is used for forecasting the medium and long term electric power according to the medium and long term power generation plan. The method and the device can fully consider the constraint of the unit and the power grid, carry out unit combination and unit load distribution according to the selected optimization target, and carry out work such as power balance calculation, electric quantity distribution, unit combination and the like so as to meet the safety requirements of a normal mode and an N-1 mode of the power grid.
Description
Technical Field
The invention relates to the technical field of power load prediction, in particular to a power grid medium and long term load prediction management system.
Background
With the comprehensive development of energy-saving power generation scheduling, the refinement level of power grid scheduling operation is continuously improved, so that the requirements of the power system on the prediction capability of the power grid operation state in the future, the prediction capability of the power load under the influence of various relevant factors and the like are greatly improved, a new generation of intelligent load prediction algorithm and a corresponding system are urgently needed, and the safe and economic operation of the power system is ensured.
Short-term load prediction is of great significance to the safe and economic operation of power systems. The power failure and regional brake-pulling power limitation are still main problems influencing normal production and life of the society, and accurate load prediction can ensure that sufficient standby is reserved in a system at proper time and place, ensure the safety margin of power grid operation and ensure normal power supply of the power grid. But the short-term load prediction cannot be started from a long-term perspective, so that the problem of load prediction accuracy is fundamentally solved, and the further improvement of the load prediction accuracy is restricted.
Along with the improvement of global climate change and the living standard of people, the dependence on temperature regulation loads of an air conditioner and the like is gradually enhanced, so that the power grid load is more sensitive to the change of meteorological factors, great uncertainty is brought to load prediction, a more accurate prediction means is required to provide support for operation decision, and the safety requirements of a power grid normal mode and an N-1 mode cannot be met because the prediction strategy lacks intelligence and adaptability and can not predict the load characteristics of medium and long periods.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a power grid medium and long term load prediction management system to improve the load prediction accuracy and the load prediction precision and meet the safety requirements of a power grid normal mode and an N-1 mode.
In order to solve the above technical problem, the present invention provides a power grid medium and long term load prediction management system, including:
the data preparation module is used for acquiring medium-and-long-term power generation data;
the power generation plan compiling module is used for compiling a medium-long term power generation plan according to the medium-long term power generation data;
and the medium and long term load forecasting module is used for forecasting the medium and long term electric power according to the medium and long term power generation plan.
Further, the medium-and-long-term power generation basic data includes power generation basic data of a year, a season, a month or a week.
Further, the medium-and-long-term power generation data includes basic data and operation data, and the power grid medium-and-long-term load prediction management system further includes:
a basic parameter management module for managing the basic data;
and the operation parameter management module is used for managing the operation data.
Further, the power grid medium and long term load prediction management system further comprises a rolling compilation module, which is used for performing rolling compilation on the medium and long term power generation plan so as to regenerate the revised power generation plan.
Further, the power grid medium and long term load prediction management system further comprises: and the safety check module is used for performing safety check on the medium-long term power generation plan and the corrected power generation plan.
Further, the power grid medium and long term load prediction management system further comprises: and the checking and displaying module is used for displaying the result output by the safety checking module.
Further, the power grid medium and long term load prediction management system further comprises: and the bus load prediction module is used for predicting the load demands of different buses in each time period.
Furthermore, the bus load prediction module comprises a medium-long term bus load prediction unit and a short term bus load prediction unit, the medium-long term bus load prediction unit is used for realizing the prediction and management of the bus load, and the short term bus load prediction unit is used for acquiring the change rule of the bus load along with various factors according to the historical data, the meteorological data and the holiday data of the bus load, establishing a corresponding model, and predicting the bus load of multiple days in the future by using the model.
Further, the power grid medium and long term load prediction management system further comprises: and the result analysis module is used for counting, analyzing and evaluating various key indexes among one plan or a plurality of plans.
Further, the power grid medium and long term load prediction management system further comprises: and the resource monitoring module is used for analyzing the performance of the statistical system in a graphical representation mode.
Further, the power grid medium and long term load prediction management system further comprises: and the short-term load forecasting module is used for forecasting the system load of multiple days in the future according to the short-term data of the power grid load, wherein the short-term data comprises historical data, meteorological data and holiday data.
The embodiment of the invention has the beneficial effects that: the method comprises the steps that medium-term and long-term power generation data are obtained through a data preparation module, a power generation plan compiling module compiles a medium-term and long-term power generation plan according to the medium-term and long-term power generation data, a medium-term and long-term load forecasting module forecasts medium-term and long-term power electric quantity according to the medium-term and long-term power generation plan, constraint of a unit and a power grid is fully considered, unit combination and unit load distribution are carried out according to a selected optimization target, work such as power balance calculation, electric quantity distribution, unit combination and the like is carried out, and.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a functional structure diagram of a power grid medium-and-long-term load prediction management system according to an embodiment of the present invention.
Fig. 2 is another functional structure diagram of a power grid medium-long term load prediction management system according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments refers to the accompanying drawings, which are included to illustrate specific embodiments in which the invention may be practiced.
Fig. 1 is a functional structure diagram of a power grid medium-and-long term load prediction management system according to an embodiment of the present invention, as shown in fig. 1, the power grid medium-and-long term load prediction management system includes:
the data preparation module 101 is used for acquiring medium-and-long-term power generation data;
in some embodiments, the medium-and-long-term power generation base data includes annual, seasonal, monthly, or weekly power generation base data.
The system automatically prepares medium-and-long-term power generation data for planning according to a planning period (namely year, season and month planning) selected by planning, wherein the medium-and-long-term power generation data can be imported from other modules, and can also be automatically acquired or manually input, constraint condition options, optimization targets and corresponding algorithm options.
A power generation plan compiling module 102, configured to compile a medium-and-long-term power generation plan according to the medium-and-long-term power generation data;
after the data preparation is completed and the data verification is passed, the power generation planning module 102 invokes the corresponding calculation service of the power generation planning according to the prepared data and scheme, so as to realize the specific planning of the power generation plan. The month and week unit combination adopts a safety constraint unit combination SCUC optimization algorithm based on a mixed integer programming algorithm.
The system supports the compilation and execution of single or multiple medium-long term planning schemes, monitors the execution progress of the executing planning scheme on line, and can check the result feedback of each execution stage in real time. Meanwhile, according to management needs or calculation condition feedback, the system can immediately and manually terminate some calculated planning schemes.
(1) Year (season) planning
And according to relevant information such as system load prediction, maintenance plans, adjustable output, unit and power grid basic data and the like, the load prediction and power electric quantity balance of the whole power grid are carried out by combining with the operating characteristics of the power grid, and power electric quantity balance calculation and provincial unit electric quantity distribution are organized and developed.
(2) Monthly (weekly) planning
According to system load prediction, maintenance schedule, adjustable output, unit and power grid basic data and other related information, combining unit and power grid constraints, performing electric power and electric quantity balance calculation, determining power generation combination of units in provinces and electric quantity distribution, determining load demand of the whole network, adjustable output and electric power and electric quantity balance results, determining unit combination of a direct adjusting unit, performing bus load prediction of the whole network, and performing safety check of the whole network. The monthly (weekly) plan must pass through static security check, whether to carry out dynamic security check can be selected according to needs, the dynamic security check is realized by calling a scheduling pre-decision system, and the static security check is realized by calling in the system.
Year (season), month (week) plan, as shown in table 1 below:
TABLE 1 Medium and Long term plans
And the medium and long term load prediction module 103 is used for predicting medium and long term electric power quantity according to the medium and long term power generation plan.
The medium-long term load prediction module 103 predicts annual power and electric quantity by using various sequence prediction methods according to basic data such as medium-long term power generation plans and historical records of power and electric quantity, and provides decision-making basis for power supply and power grid planning.
The middle-term load forecast carries out monthly electric power and electric quantity forecast according to parameters such as monthly economy, meteorological factors and the like and is used as basic data for carrying out year, season and month electric power and electric quantity balance. The medium-long term load prediction module 103 further includes: the system comprises a user-defined management unit, a time scale for realizing medium and long-term load prediction and a time scale for realizing medium and long-term load prediction, wherein the user-defined management unit can perform user-defined management; the association factor maintenance and detection unit is used for carrying out special library building and maintenance on the medium and long-term load influence factors, such as economic operation data, high energy consumption industry policies and the like; and the error analysis unit is used for coordinating provincial and regional prediction structures and analyzing error causes.
In this embodiment, the data preparation module 101 obtains medium-and-long-term power generation data, the power generation plan formulation module 102 formulates a medium-and-long-term power generation plan according to the medium-and-long-term power generation data, the medium-and-long-term load prediction module 103 predicts medium-and-long-term power electric quantity according to the medium-and-long-term power generation plan, fully considers unit and grid constraints, performs unit combination and unit load distribution according to a selected optimization target, performs work such as power balance calculation, electric quantity distribution, unit combination and the like, and meets the safety requirements of a grid normal mode and an N-1 mode.
The embodiment of the present invention provides another power grid medium and long term load prediction management system, as shown in a functional structure diagram in fig. 2, based on the above embodiment, the power grid medium and long term load prediction management system further includes:
and a rolling compilation module 104, configured to perform rolling compilation on the medium-and-long-term power generation plan so as to regenerate a revised power generation plan.
With the advance of time in the planning cycle, when a certain change occurs in the basic data according to the initial, medium and long-term planning, the planning needs to be rearranged correspondingly. The rolling compilation module 104 implements rolling compilation of the plan to recreate the new revised power generation plan. After planning is completed, some manual or automatic intervention may be required for the plan according to actual needs, such as individual data adjustment or batch adjustment or optimization of some predetermined rules. The plan adjustment function supports manual or automatic adjustment of the plan and supports "version" management before and after adjustment. Plan adjustments and optimizations include at least the following types of adjustments:
the unit plan and the tie-line plan are set according to different levels of units, power plants, regions and the like, and the maintenance and startup combination can be optimized according to the load change;
modifying the unit and the tie line plan in a plurality of ways such as single-time period and multi-time period;
tables, curves, and graphs, etc. adjust the unit and tie plans.
After the plan is adjusted, the system supports selection of whether to perform safety check on the adjusted plan or not, and displays a safety check result. And if the safety check fails, the scheme needs to be corrected according to the check result, so that the safety check requirement is met finally.
In some embodiments, the medium-and-long-term power generation data includes basic data and operational data, and further includes:
a basic parameter management module 105 for managing basic data;
and the operation parameter management module 106 is used for managing operation data.
The basic parameter management module 105 manages all basic parameters for energy-saving scheduling planning, and the basic parameters can be divided into two categories from the update frequency of data: namely a base dataclass and a run dataclass. In a research state, basic data can be automatically adopted or manually set data under various possible conditions such as certain typical conditions, extreme conditions and the like, a data template is established, and plan research and analysis of various possible conditions are carried out. The various types of data in the historical state completely adopt the historical data, and the data is not allowed to be modified in any form.
The basic parameter management module 105 implements management of various types of static data. Static data is various types of data that are almost constant or vary less frequently. The basic parameter management module 105 realizes addition, deletion, modification and version management of the basic data, supports corresponding query of various historical information and version information, and supports batch import of data in file formats such as EXCEL.
The basic parameter management module 105 mainly implements management of the following data:
and (3) power grid information: the method comprises the following steps of setting the existing power grid mechanism, basic attributes, geographical position attributes, nameplate parameters and power transmission and transformation technical performance parameters of a power grid model and equipment thereof;
basic attributes, geographical location attributes, attribution relations and the like of the power generation group/power plant and the like.
Registering parameters of each unit of the power plant: the method comprises the steps of unit type, available capacity, minimum technical output, climbing rate, start-stop cost, start-stop time parameters, auxiliary service capacity, AGC (automatic gain control) regulation rate, fuel type and the like.
The operation parameter management module 106 manages various dynamic data, i.e., various data that changes at any time during daily operation, including prediction or scheduling information of the data and actual occurrence information corresponding to the data. The operation data management realizes the addition, deletion, modification and version management of the operation data, supports the query of various historical information and version information, also supports the analysis and comparison of prediction information or plan information and actual operation information, and supports the diversified display of query and analysis results in the modes of charts, tables and the like.
In the aspect of data acquisition, besides manual maintenance, batch import of data in file formats such as EXCEL and the like is also supported, for example, the data has an external system responsible for daily maintenance, the system has a corresponding external interface, and the data can be automatically acquired through the interface in a standard mode or the interface acquisition is manually started.
The operation parameter management module 106 mainly implements management of the following data:
unit sequencing information: government assigned unit sequencing list
Primary energy information: the primary energy information comprises prediction data and actual data of primary energy such as coal conditions, water conditions, wind conditions, sun and sunshine conditions and the like.
Power generation consumption, emission and heat supply information: coal consumption information, equal micro coal consumption information, sulfide emission information and heat supply distribution information of the hot spot unit for power generation of the thermal power unit.
And (3) generating and transmitting equipment maintenance and commissioning and decommissioning plan: and (4) maintenance plans and commissioning and decommissioning plan information of all equipment related to the tidal current operation of all the main dispatching units and the power grid network in the whole network.
Purchase (sale) of electricity plan: a plan for buying (selling) electricity for a medium-long period such as year and month and a plan for actually buying electricity. The system supports detailed viewing and analysis of the power purchase and sale plan, such as each gateway, each tie line historical curve, actual curve, plan curve contrast chart.
Standby planning: the system power generation side standby, the rotary standby and the AGC adjustment standby are set according to the proportion or the absolute value, and can be classified and managed according to the power generation types and used for standby analysis and monitoring and early warning calculation of the power generation plan in the day ahead.
And (4) connecting line planning: the system supports the link plan query in a graph and table form and can query according to the general plan, the grouping plan and the branch plan. On the basis of automatically acquiring the latest tie plan, the total tie plan, the grouped tie plan, and each branch plan may be manually adjusted. The system provides the function of setting form single data and batch data.
A monitoring element: the monitoring elements currently set comprise a power transmission section, a transformer and a branch circuit, and the system can inquire power transmission limit values of the monitoring elements, provide table and graphic limit value display, provide table limit value modification, support time-interval element limit value setting, and provide list monitoring element addition and deletion.
And (3) unit fixed output plan: the system provides a table and graph mode to display the fixed plan of the unit.
And (3) partition standby management: the standby of the generating side, the rotating standby and the AGC adjustment standby are set according to the unit partition, classified management can be performed according to the generating type, a system supports table and graphic display, and a table single data and batch data partition standby setting function is provided.
Meteorological data: the meteorological data are important factors of load prediction, and the system supports the viewing and analysis of the meteorological data, such as historical curves, actual curves and prediction curve comparison graphs of meteorological temperature, sunlight and other data.
And the safety check module 107 is used for performing safety check on the medium-and-long-term power generation plan and the corrected power generation plan.
The security check module 107 further includes a security check calculation control unit and a static security check unit. The safety check calculation control unit is used for setting and managing the safety check related parameters, supporting a graphical interface to inquire the control parameters of the current safety check calculation, and supporting the graphical interface to set various control parameters used in the safety check calculation. The control parameters of the safety check mainly comprise: whether to carry out N-1 check; whether ground state power flow check is carried out or not; participating in a check period; a balancing machine mode. The latest check calculation control parameters are automatically used in the safety check calculation.
The static safety check unit supports manual selection of various plans which are not checked at present, or selects an external plan file on an interface, specifically realizes safety check of the plans according to the set current control parameters, and has the functions of progress of the safety check process and graphical monitoring of results of corresponding stages.
The static security check can select one or more of the following security check functions according to different control parameter settings so as to meet the security check service requirements of different time periods: automatically generating check section tidal current data according to the equipment state, bus load prediction, unit power generation plan, network model and normal position setting, wherein the calculation result of the tidal current data is accurate and credible, and the node voltage is basically consistent with the actual historical similar section; performing ground state static security check, and performing check analysis on the transmission section and the branch; performing N-1 static safety check, and performing check analysis on the line, the transformer and the direct current transmission element; correcting the optimized calculation result according to the checking result; controlling and displaying a computing process, and displaying a computing progress and a computing state in each time period; and the expected fault selection supports the definition and selection of the expected faults participating in the safety check, and supports the manual selection of the fault combination participating in the safety check.
In some embodiments, the system for predicting and managing long-term load in a power grid according to the present invention further includes: and the checking and displaying module is used for displaying the result output by the safety checking module.
The checking and displaying module is used for displaying the static safety checking result, and displaying the safety checking result in various ways such as a table, a curve, a station diagram, a super-flow diagram, a geographical wiring diagram and the like from various angles of time dimension, monitoring element dimension and expected fault dimension. And displaying the time intervals of heavy load, ground state out-of-limit and expected fault out-of-limit in the safety check calculation in the time dimension, and displaying the load flow and the check result in a plan mode at any time interval. The number of elements with overload and out-of-limit in each time period can be displayed. And displaying the number of time intervals of overloading, out-of-limit and anticipated fault out-of-limit in the safety check calculation from the monitoring element dimension, and displaying the planned power flow of each principle in each time interval. And displaying the number of the time intervals when the overload and the out-of-limit occur in a certain expected fault situation and the names of the elements with the most serious out-of-limit in a certain time interval from an expected fault dimension.
The static safety check result display support is in various display modes such as tables, curves and graphs, various display visual angles such as station diagrams, tidal current diagrams and geographic wiring diagrams, and various display means such as tidal current section display in a single time period and tidal current rolling play in multiple time periods.
And the bus load prediction module 108 is used for predicting the load demands of different buses in each time period.
The bus load prediction module 108 comprises a medium-long term bus load prediction unit and a short term bus load prediction unit, the medium-long term bus load prediction unit realizes prediction and management of bus load, improves bus load prediction accuracy, and improves management level of load prediction work, so that operating personnel can more accurately analyze safe operation level and stability margin of a power grid under the predicted load level, and powerful technical support is provided for day-ahead safety check and energy-saving power generation scheduling work of the power grid. The medium-long term bus load prediction unit requires bus load prediction to distribute load prediction or system load or externally input regional load so as to complete prediction of bus load within a certain period of time (adjustable within 5 minutes to 1 hour) on a specified day; the bus load prediction can be carried out on the basis of a common load data model and a date model to forecast the region and a single bus; the bus load prediction has the functions of load data model creation and management; the bus load prediction has the functions of load date model creation and management; the provincial and local integrated bus load prediction function can be realized; the network parameter correction and identification function is provided; the method has the function of statistical analysis of load prediction errors.
The short-term bus load prediction unit obtains the change rule of the bus load along with various factors according to the historical data, the meteorological data and the holiday data of the bus load, establishes a corresponding model, and predicts the bus load for multiple days in the future by using the model. The forecasting method mainly comprises a proportion distribution method, a time sequence method, an artificial neuron network method, a similar daily method and the like.
The short-term bus load prediction result is as follows: time-sharing load of each bus and corrected bus load distribution coefficient. The short-term bus load forecasting function mainly comprises the following steps: and (3) load comprehensive query: inquiring and comparing multi-calendar history loads and predicted load curves of each bus in different areas and different voltage grades; weather data management: query and input management of historical measured weather information and predicted weather information are shared with a system load prediction function; festival and holiday definition management: the system is used for defining the start and end date of the holiday and the holiday type, is used for load prediction classification and sample organization, and is shared with a system load prediction function; processing historical load pseudo data: automatically checking historical load data, identifying phenomena of loss of sampling points, abnormal sampling of partial measuring points and the like in the historical load, and correcting invalid data in the historical load to form a usable prediction sample; and (3) prediction sample management: providing various prediction sample query and manual correction means such as graphs, curves and tables, and maintaining the prediction samples; setting prediction parameters: the method allows manual setting of prediction days, prediction time intervals, multi-prediction algorithm weight coefficients, prediction algorithm control parameters, automatic prediction starting time and the like; and (3) load prediction: the load forecasting calculation is allowed to be started manually, can be started automatically according to the setting, and the load forecasting result is displayed in various forms such as graphs, curves and tables; manual intervention: providing various data display means and data modification means such as graphs, curves and tables, assisting load forecasting personnel to flexibly adjust a forecasting result, wherein the adjustment means comprises single data modification, specified range absolute quantity adjustment, specified range relative quantity adjustment, specified range reference historical load change trend, mouse load curve drawing, integral point load correction and automatic linear interpolation, specified range historical load taking, specified range historical forecasting load taking and the like; and (4) analyzing a prediction result: analyzing the daily electric quantity, the maximum electric power, the minimum electric power, the maximum electric power occurrence period, the minimum electric power occurrence period, the average electric power and the like of the predicted load of each bus, and analyzing the change of the electric quantity and the maximum and minimum predicted load with the previous day-to-day ring ratio, the change of the electric quantity and the maximum and minimum predicted load with the previous week-to-day ring ratio, the change of the electric quantity and the maximum and minimum predicted load with the previous month-to-day ring ratio and the change of; and (3) error analysis: analyzing the maximum error, the minimum error, the arithmetic mean error and the mean square error of the predicted load of each bus, including absolute error and relative error, counting the bus predicted qualification rate, the maximum bus point of the mean predicted error and the minimum bus point of the mean predicted error, and counting the predicted errors according to multiple strategies such as regions, voltage grades and the like; the prediction algorithm self-learns: and (4) automatically adjusting the weight coefficient of each prediction algorithm according to the analysis of the recent load prediction result, and improving the load prediction precision.
And the result analysis module 109 is used for counting, analyzing and evaluating various key indexes between one plan or a plurality of plans.
The result analysis module 109 realizes statistics, analysis and evaluation of various key indexes of a certain plan or different plan plans, and specifically includes: the system can be used for classifying and inquiring the plan compiled by the system or manually adjusted, and the inquiry of a unit plan and a connecting line plan according to different levels of units, power plants, regions and the like is supported. Performing index statistics and comparative analysis of a single plan or multiple plans, such as: the system power generation cost and the power generation coal consumption, the generated energy of each power plant (unit), the power generation plan completion rate, the power generation cost, the power generation coal consumption and other information, so that the difference of different schemes is known, and the influence of different factors on the plan is quantitatively evaluated. The system has the function of exporting the plan, various query results and analysis result files and automatically generating reports according to file formats such as EXCEL/WORD and the like.
The resource monitoring module 110 is configured to analyze the performance of the statistical system in a graphical representation, including a table, a pie chart, and other graphical representations, such as: CPU load, network resources, process resources, memory, disk occupation, etc.; monitoring the equipment state, identifying the faults of computer equipment and network equipment in the system and corresponding fault types and giving an alarm, such as detectable equipment faults of switch faults, computer faults (such as overhigh CPU load rate, hard disk errors or capacity overrun, non-fatal errors of an operating system and the like) and the like; all planned processes can be monitored and controlled; the alarm information can be sent to the related personnel through mails and the like according to the requirements.
And the short-term load forecasting module 111 is used for forecasting the system load of multiple days in the future according to the short-term data of the power grid load, wherein the short-term data comprises historical data, meteorological data and holiday data.
The short-term load prediction module 111 finds out the change rule of the load along with various factors according to the historical data, the meteorological data and the holiday data of the power grid load, establishes a proper model, and predicts the system load of multiple days in the future by using the model. The forecasting method mainly comprises a time sequence method, an artificial neuron network method, a similar day method and the like.
The short-term system load prediction result is as follows: the time-interval load of the whole system and the time-interval load of each division are different. The short-term system load prediction function mainly comprises the following functions: and (3) load comprehensive query: inquiring and comparing multiple calendar history loads and predicted load curves; weather data management: inquiring and inputting management of historical measured weather information and predicted weather information; festival and holiday definition management: the system is used for defining the start and end date of the holiday and the holiday type, and is used for load prediction classification and sample organization; processing historical load pseudo data: automatically checking historical load data, identifying phenomena of loss of sampling points, abnormal sampling of partial measuring points and the like in the historical load, and correcting invalid data in the historical load to form a usable prediction sample; and (3) prediction sample management: providing various prediction sample query and manual correction means such as graphs, curves and tables, and maintaining the prediction samples; setting prediction parameters: the method comprises the following steps of allowing manual setting of prediction days, load partitioning, prediction time intervals, multi-prediction algorithm weight coefficients, prediction algorithm control parameters, automatic prediction starting time and the like; and (3) load prediction: the load forecasting calculation is allowed to be started manually, can be started automatically according to the setting, and the load forecasting result is displayed in various forms such as graphs, curves and tables; manual intervention: providing various data display means and data modification means such as graphs, curves and tables, assisting load forecasting personnel to flexibly adjust a forecasting result, wherein the adjustment means comprises single data modification, specified range absolute quantity adjustment, specified range relative quantity adjustment, specified range reference historical load change trend, mouse load curve drawing, integral point load correction and automatic linear interpolation, specified range historical load taking, specified range historical forecasting load taking and the like; and (4) analyzing a prediction result: analyzing and predicting daily electric quantity of the load, maximum electric power, minimum electric power, a maximum electric power occurrence period, a minimum electric power occurrence period, average electric power and the like, and analyzing changes of the electric quantity and the maximum and minimum predicted load and previous day-to-day cycle ratio, previous week-to-day cycle ratio, previous month-to-day cycle ratio and last year-to-day cycle ratio; and (3) error analysis: analyzing the maximum error, the minimum error, the arithmetic mean error and the mean square error of the predicted load, wherein the maximum error, the minimum error, the arithmetic mean error and the mean square error comprise absolute errors and relative errors; the prediction algorithm self-learns: and (4) automatically adjusting the weight coefficient of each prediction algorithm according to the analysis of the recent load prediction result, and improving the load prediction precision.
In some embodiments, an interface module 112 is further included for performing horizontal data interaction and vertical data interaction. The horizontal data interaction comprises the following steps:
interacting with an EMS system, inputting data including a static network model, SCADA acquisition and state estimation results, expected fault set definition, line and transmission section transmission quota, and outputting data including a day-ahead power generation plan;
interacting with an AGC system, wherein input data comprise a unit AGC control state; the output data includes a day-ahead power generation schedule;
interacting with a desulfurization monitoring system, and inputting data including real-time running state of desulfurization equipment, desulfurization running qualification rate, operation time of the desulfurization equipment and desulfurization online electric quantity;
interacting with an electric energy metering system, wherein input data comprise unit daily generated energy, unit daily on-grid electric quantity, unit monthly generated energy and unit monthly on-grid electric quantity;
interacting with a maintenance management system, and inputting data including a unit, a transformer, a line annual maintenance plan, a unit, a transformer, a line monthly maintenance plan, a unit, a transformer and a line day-ahead maintenance plan;
interacting with a coal consumption monitoring system, and inputting data including real-time coal consumption of the unit;
interacting with a scheduling information management system, wherein input data comprise annual contract electric quantity, and output data comprise a unit day-ahead power generation plan.
In some embodiments, the longitudinal data interaction should meet the southern power grid longitudinal data exchange specification requirements.
In some embodiments, the system for predicting and managing medium and long term loads of a power grid further includes a plan approval module 113, which performs final approval on the planned plan, and after the approval is passed, the system sends the final approved plan to a corresponding system for circulation approval, and automatically issues the final plan to other systems after the approval is passed. If the system is not approved, the system carries out plan reprogramming according to the approval opinions or a recomputation scheme or carries out correction through plan adjustment, and then the system is sent to the corresponding system again for circulation. And the process is circulated until the plan is finally approved in the system.
In some embodiments, the system for predicting and managing medium and long term loads of a power grid further includes a plan issuing module 114, and the system obtains a plan approval result from the system in a manual or automatic manner, for example, if the plan approval passes, the system issues the approved plan. The system issues the approval plan to a factory network information communication interaction platform in a file mode or an automatic issuing mode.
In some embodiments, the system management module 115 provides a uniform system management environment for the entire system, and includes functions of implementing management system level parameter setting of each main function of the system, implementing monitoring and clustering management on main service processes of the system, and providing uniform client authority access management. The system management module 115 specifically includes:
the planning procedure control and management unit can realize the flow control and management of the planning procedure according to the process requirements of specific planning, and the basic requirements on the flow control and management are as follows: the flow control programming tool should provide a friendly human-machine interface to accomplish the following operations, including: the method comprises the steps of executing a flow cycle, starting time, executing sequence and cycle of each service and delay time of event triggering; determining the execution sequence; and (5) starting and stopping operation. The flow chart of the specific execution of each flow gives graphical display, and marks the current running service function, the end time and the like; the method has the advantages that the method carries out flow arrangement and control through a script arrangement mode; various functional services and calculation analysis service definitions can be customized into new services through any combination; the creation, calling and destruction of the service flow do not affect the functions and performances of each public service and the calculation analysis service; the process control can be started periodically, manually or by events, and the starting time and period can be defined; the flow control is abnormally interrupted before the completion due to software or hardware faults, a prompt is given in an alarm mode, and the reason for stopping is visually displayed; the service execution process after service arrangement can be monitored and recorded; the process control and management should have perfect authority management, at least supporting the following functions: defining the authority of the user for using the calculation analysis service according to the role and the user; the content of the calculation analysis service used by the user is determined by the permission setting; the service registration of the third-party system must also be authenticated and controlled by the authority.
The system parameter setting and managing unit specifically comprises: configuration management of system operation modes, such as configuration and management of application clusters; configuration management of automatic fault switching and manual switching; setting and managing system-level application parameters, environment parameters and the like; event information should be generated in fault switching, manual switching and parameter setting; data, backup and restoration of setting parameters.
The safety management unit comprises permission setting, only a system administrator has the right to operate the authorization password, and the permission of other operators is authorized by the system administrator; the system has a perfect network login mechanism, and ensures the network security of the system; the operation authority and the use range of various operators are comprehensively set according to roles and users; the remote access automation system must be authenticated. User login: the user must log in when entering the system operation, the intranet user logs in through an account and a password, and the extranet user logs in through a USB KEY and an account password.
The system log unit performs trace management on important operation behaviors of the system, and comprises the following steps: carrying out brief log record on system management or functional operation to realize detailed record of key operation; the log files are managed and stored in a unified mode, and inquiry and statistics are carried out in a man-machine interface mode.
In the embodiment, the designated tasks are respectively completed by the modules, and the modules are matched with each other, so that the work of power balance calculation, electric quantity distribution, unit combination and the like is carried out, and the safety requirements of a normal mode and an N-1 mode of a power grid are met.
It is understood that the same or similar parts in the above embodiments may be mutually referred to, and the same or similar parts in other embodiments may be referred to for the content which is not described in detail in some embodiments.
As can be seen from the above description, the embodiments of the present invention have the following beneficial effects: the method comprises the steps that medium-term and long-term power generation data are obtained through a data preparation module, a power generation plan compiling module compiles a medium-term and long-term power generation plan according to the medium-term and long-term power generation data, a medium-term and long-term load forecasting module forecasts medium-term and long-term power electric quantity according to the medium-term and long-term power generation plan, constraint of a unit and a power grid is fully considered, unit combination and unit load distribution are carried out according to a selected optimization target, work such as power balance calculation, electric quantity distribution, unit combination and the like is carried out, and.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.
Claims (11)
1. A power grid medium and long term load prediction management system is characterized by comprising:
the data preparation module is used for acquiring medium-and-long-term power generation data;
the power generation plan compiling module is used for compiling a medium-long term power generation plan according to the medium-long term power generation data;
and the medium and long term load forecasting module is used for forecasting the medium and long term electric power according to the medium and long term power generation plan.
2. The system according to claim 1, wherein the medium-and-long-term power generation basis data comprises annual, seasonal, monthly or weekly power generation basis data.
3. The system according to claim 1, wherein the medium-and-long-term power generation data includes basic data and operational data, and further comprising:
a basic parameter management module for managing the basic data;
and the operation parameter management module is used for managing the operation data.
4. The system according to claim 1, further comprising a rolling compilation module for rolling compilation of the medium-and-long-term power generation plans to regenerate revised power generation plans.
5. The system for long term load forecast management in an electrical grid according to claim 4, further comprising: and the safety check module is used for performing safety check on the medium-long term power generation plan and the corrected power generation plan.
6. The system for long term load forecast management in an electrical grid according to claim 5, further comprising: and the checking and displaying module is used for displaying the result output by the safety checking module.
7. The system for long term load forecast management in an electrical grid according to claim 1, further comprising: and the bus load prediction module is used for predicting the load demands of different buses in each time period.
8. The system for forecasting and managing long-term loads in the power grid according to claim 7, wherein the bus load forecasting module comprises a long-term and medium-term bus load forecasting unit and a short-term bus load forecasting unit, the long-term and medium-term bus load forecasting unit is used for forecasting and managing the bus load, the short-term bus load forecasting unit is used for acquiring the change rule of the bus load along with various factors according to historical data, meteorological data and holiday data of the bus load, establishing a corresponding model, and forecasting the bus load for multiple days in the future by using the model.
9. The system for long term load forecast management in an electrical grid according to claim 1, further comprising: and the result analysis module is used for counting, analyzing and evaluating various key indexes among one plan or a plurality of plans.
10. The power grid medium-long term load prediction management system according to any one of claims 1 to 9, further comprising: and the resource monitoring module is used for analyzing the performance of the statistical system in a graphical representation mode.
11. The power grid medium-long term load prediction management system according to any one of claims 1 to 9, further comprising: and the short-term load forecasting module is used for forecasting the system load of multiple days in the future according to the short-term data of the power grid load, wherein the short-term data comprises historical data, meteorological data and holiday data.
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