CN112508306A - Self-adaptive method and system for power production configuration - Google Patents
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Abstract
The invention provides a self-adaptive method and a self-adaptive system for power production configuration, which comprise the steps of S1, acquiring historical power utilization data of a power user in a prediction scene, and industry data, economic data and climate data corresponding to the prediction scene, and establishing an industry user load prediction model base; step S2, determining an area to be evaluated, and acquiring model data corresponding to power users in the area to be evaluated; step S3, calculating a load prediction curve of each user in a certain future time period; obtaining a total load prediction result of an area to be evaluated; step S4, detecting whether the power load prediction result exceeds the total installed capacity of the power plant, if so, generating a power limit alarm and screening users suggesting power limit; and if not, planning the production configuration plan of each power plant. The invention grasps and evaluates the load states of all power operation target areas, and realizes the adjustment of the power configuration plan caused by the load fluctuation and climate change of users.
Description
Technical Field
The invention relates to the technical field of power system automation, in particular to a self-adaptive method and a self-adaptive system for power production configuration.
Background
Electric power occupies an important position in national economic resources, and the processing and analysis of electric power data play an important role in links such as power generation, power transmission, power distribution and the like. With the rapid development of society, the power consumption is increased unprecedentedly, new technologies such as a smart grid are developed along with the development of electric power, and the well-blowout type increase of electric power data is carried out; and a guarantee is provided for upgrading the traditional power production configuration and improving the power production efficiency.
At present, the configuration of power production is mainly based on the recent social production power load needs, a production plan is made in advance, and production is carried out according to the plan. The current situation of load characteristics, key factors influencing the load characteristics and future load prediction trends are analyzed and researched to a certain degree at present, results with reference values and operation guidance for power planning and power grid operation are obtained, however, the establishment of a power user model is lacked, and a demand portrait of users is not generated.
To some extent, for a specific user object (the target object may be a cell, a factory, an office building, a shopping mall, etc.), there are two reasons for affecting the power demand load: the first point is a time factor, the power loads in different time periods in the same time are different, if the target user is an enterprise or a factory building, the load value in the daytime is large, and the load value at night is small; when the target user is a cell, the load values are large in the morning and evening, and the load values are small in the daytime and at night; for different cities and regions, the power loads are greatly related to different time periods in a unified date. Second, economic development trend, temperature and weather: in summer and winter, due to overhigh and overlow temperature, loads of air conditioners and heating and ventilation equipment can be generated, so that the power load of a target user is increased or decreased suddenly; the power load of the target object may be changed due to different economic development levels. The method is limited by data processing capacity and condition limitation, and the influence of factors such as economic development situation, regional climate condition, industry tidal effect and the like on the power load is not considered in the existing system, so that the prediction is biased to the prediction with low precision, roughness and high delay. In the industry, an accurate, real-time and quick-response power production prediction and configuration method is lacked to quickly, accurately and efficiently meet the change of power load, and especially an analysis means based on a multidimensional, big data and a user model is lacked.
Disclosure of Invention
The invention aims to provide a self-adaptive method and a self-adaptive system for force generation configuration, which solve the technical problems of untimely and inaccurate adjustment of a power configuration plan caused by user load fluctuation and climate change.
In one aspect of the invention, there is provided an adaptive method of power production configuration, comprising the steps of:
step S1, acquiring historical power consumption data of power users in different scenes, user industry data corresponding to the power users, economic data corresponding to different industries and historical climate data, establishing industry user load prediction models of multiple industries in different scenes according to the historical power consumption data, the user industry data, the economic data and the historical climate data, and storing the industry user load prediction models of the multiple industries in different scenes into a model database; the economic data comprise energy efficiency indexes and industry growth rates of industries where different users are located;
step S2, determining an area to be evaluated, acquiring power user data of the area to be evaluated, and acquiring an industry user load prediction model matched with the power user data of the area to be evaluated from the model database; the method comprises the following steps that a region to be evaluated corresponds to a scene, and power user data correspond to an industry user load prediction model;
step S3, user vector data in a preset time period are obtained according to the power user data in the area to be evaluated, and a load prediction curve of each power user in the area to be evaluated in a certain future time period is calculated according to the user vector data and an industry user load prediction model corresponding to the power user; superposing the load prediction curves of each power user in the area to be evaluated in a certain time period in the future to obtain a total load prediction result of the area to be evaluated; the user vector data comprises the power consumption of a user in busy hours and the power consumption of the user in idle hours;
step S4, judging whether the total load prediction result exceeds the total installed capacity of the power plant in the area to be evaluated, and if so, outputting power limit warning information and a user suggesting power limit; and if not, determining a power plant production configuration plan in the area to be evaluated according to the total load prediction result of the area to be evaluated.
Preferably, the industry user load prediction model is represented by the following formula:
wherein p (t) represents the load estimation value of the user at the time t; t isaIndicating the start time of the busy hour, T, of the user of the industrybRepresenting the ending time of the busy hour of the user in the industry, namely the starting time of the idle hour;
indicating the average power consumption during the past T1 time, when the user is busy,
represents the average power consumption of the user at idle for the past time T1; r (t) represents the output of the user increased from the current output at the future time t, a represents the coefficient of electric energy consumption of the user per unit output increase under the scene, b2A power consumption coefficient indicating that the temperature needs to be increased every 1 degree when the user is busy in the scene, b1The electric energy consumption coefficient which indicates that the air temperature needs to be increased every 1 degree when the user is busy in the scene; s (t) represents the user's increase in temperature from the current temperature at a future time t.
Preferably, the step S3 includes: determining the total load prediction result of the area to be evaluated according to the following formula:
wherein p isi(t) represents the load prediction result of the ith user in the time t; c (t) represents the total load prediction result.
Preferably, the step S4 includes: the specific steps of screening out users who recommend electricity limiting are as follows:
and acquiring preset industry priority data, performing priority ranking on the users according to the industry priority data, and screening the users who recommend electricity limitation according to the priority ranking.
Preferably, the step S4 further includes: when the production allocation plan of each power plant is planned, the production allocation plan of the power plant does not cause the consumption of redundant capacity under the condition of meeting the power load of a target area.
The invention also provides an adaptive system of the power production configuration, which is used for realizing the adaptive method of the power production configuration and comprises the following steps:
the system comprises a data acquisition unit, a model database and a data processing unit, wherein the data acquisition unit is used for acquiring historical power consumption data of power users in different scenes, user industry data corresponding to the power users, economic data corresponding to different industries and historical climate data, establishing industry user load prediction models of multiple industries in different scenes according to the historical power consumption data, the user industry data, the economic data and the historical climate data, and storing the industry user load prediction models of the multiple industries in the different scenes into the model database; the economic data comprise energy efficiency indexes and industry growth rates of industries where different users are located;
the data collecting unit is used for determining a region to be evaluated, acquiring power user data of the region to be evaluated, and acquiring an industry user load prediction model matched with the power user data of the region to be evaluated from the model database; the method comprises the following steps that a region to be evaluated corresponds to a scene, and power user data correspond to an industry user load prediction model;
the power consumer load prediction and evaluation unit is used for acquiring user vector data in a preset time period according to the power consumer data in the area to be evaluated and calculating a load prediction curve of each power consumer in the area to be evaluated in a certain future time period according to the user vector data and an industry user load prediction model corresponding to the power consumer; superposing the load prediction curves of each power user in the area to be evaluated in a certain time period in the future to obtain a total load prediction result of the area to be evaluated; the user vector data comprises the power consumption of a user in busy hours and the power consumption of the user in idle hours; judging whether the total load prediction result exceeds the total installed capacity of the power plant in the area to be evaluated, and if so, outputting power limit warning information and a user recommending power limit; and if not, determining a power plant production configuration plan in the area to be evaluated according to the total load prediction result of the area to be evaluated.
Preferably, the data acquisition unit establishes an industry user load prediction model according to the following formula:
wherein p (t) represents the load estimation value of the user at the time t; t isaIndicating the start time of the busy hour, T, of the user of the industrybRepresenting the ending time of the busy hour of the user in the industry, namely the starting time of the idle hour;
indicating the average power consumption during the past T1 time, when the user is busy,
represents the average power consumption of the user at idle for the past time T1; r (t) represents the output of the user increased from the current output at the future time t, a represents the coefficient of electric energy consumption of the user per unit output increase under the scene, b2Is shown inIn this scenario, the user's busy hour air temperature increases by 1 degree and requires an increased power consumption coefficient, b1The electric energy consumption coefficient which indicates that the air temperature needs to be increased every 1 degree when the user is busy in the scene; s (t) represents the user's increase in temperature from the current temperature at a future time t.
Preferably, the power consumer load prediction and evaluation unit determines a total load prediction result of the area to be evaluated according to the following formula:
wherein p isi(t) represents the load prediction result of the ith user in the time t; c (t) represents the total load prediction result.
Preferably, the power consumer load prediction and evaluation unit acquires preset industry priority data, performs priority ranking on the users according to the industry priority data, and screens the users who recommend power limitation according to the priority ranking;
preferably, when the power consumer load prediction and evaluation unit plans each power plant production configuration plan, the power plant production configuration plan does not cause the consumption of excess capacity when meeting the power load of the target area.
In summary, the embodiment of the invention has the following beneficial effects:
according to the self-adaptive method and system for power production configuration, the power user models under different scenes are established, the prediction of the power load is more specific and more specific, the load prediction deviation caused by different user properties and industries is effectively reduced, and the prediction precision and reliability are improved.
The target area is selected through the current scene and various modes, and the strategy selection is flexible; the current power communication network does not need to be changed, the data acquisition and calculation are realized only by adding simple software and calculation hardware, the realization cost is low, and the method is suitable for batch deployment.
The method is suitable for online automatic power load prediction and automatic power production configuration, and can also be used for assisting the traditional manual power production operation plan making. The load states of all power operation target areas are mastered and evaluated, and the most economic configuration of power, efficient operation of a power grid and accurate production of a power plant are realized.
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 introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is within the scope of the present invention for those skilled in the art to obtain other drawings based on the drawings without inventive exercise.
Fig. 1 is a schematic main flow diagram of an adaptive method for power generation configuration according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of an adaptive system of an electric power generation configuration according to an embodiment of the present invention.
FIG. 3 is a diagram illustrating compliance with over-run prediction in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings.
Fig. 1 is a schematic diagram of an embodiment of an adaptive method for power generation configuration according to the present invention. In this embodiment, the method comprises the steps of:
step S1, acquiring historical power consumption data of power users in different scenes, user industry data corresponding to the power users, economic data corresponding to different industries and historical climate data, establishing industry user load prediction models of multiple industries in different scenes according to the historical power consumption data, the user industry data, the economic data and the historical climate data, and storing the industry user load prediction models of the multiple industries in different scenes into a model database; the economic data comprise energy efficiency indexes and industry growth rates of industries where different users are located; it can be understood that as much historical data as possible is used for establishing the consumption of electric energy by different industries and types of electric power users, the relation between the electric energy consumption of the users and production, atmosphere and busy and idle periods, and a user load prediction model with time T, temperature T and capacity increment p as parameters is established.
In the specific embodiment, it is ensured that the power consumption of any power user has an obvious busy-idle status on every day, for example, a factory has production and production stop, a cell resident has daytime and nighttime, and an office building has difference between work attendance and work attendance, so that different users are divided into two status times 24 hours a day, namely busy time TbusyAnd idle affair Tidle(ii) a Wherein, Tbusy+Tidle＝24。
Specifically, an industry user load prediction model is established according to the following formula:
wherein p (t) represents the load estimated value of the predicted user at the time t; t isaIndicating the start time of the busy hour, T, of the user of the industrybRepresenting the ending time of the busy hour of the user in the industry, namely the starting time of the idle hour;
the user electricity consumption data comprises:
which indicates that the average power consumption of the user in busy hours is predicted in the past T1 time,
representing the average power consumption of the user when the user is predicted to be idle in the past T1 time;
the economic data includes: r (t) represents the predicted increased output of the user compared with the current output at the future time t, a represents the coefficient of electric energy consumption of the user per unit of increased output under the scene, b2A power consumption coefficient indicating that the temperature needs to be increased every 1 degree when the user is busy in the scene, b1Indicating that the air temperature increases every time the user is busy in this scenario1 degree of the electric energy consumption coefficient needing to be increased;
the historical climate data includes: s (t) represents the predicted user's increase in temperature at time t in the future compared to the current temperature.
Step S2, determining an area to be evaluated, acquiring power user data of the area to be evaluated, and acquiring an industry user load prediction model matched with the power user data of the area to be evaluated from the model database; the method comprises the following steps that a region to be evaluated corresponds to a scene, and power user data correspond to an industry user load prediction model; specifically, the selection can be performed according to administrative regions, according to substation coverage regions, or in a frame selection on a map; it can be understood that, on the basis of the user load prediction model being established, in each prediction, the region to be evaluated and predicted is selected first, and the selection mode can be according to the administrative region, the substation coverage region or the map. After the prediction evaluation target area is determined, the power users of the area can be obtained, the number of the selected users is set to be N, then a model library is further inquired, and model data of the users are called; the scene at least comprises city type information, industrial park information, center city and district information and county and city information.
Step S3, user vector data in a preset time period are obtained according to the power user data in the area to be evaluated, and a load prediction curve of each power user in the area to be evaluated in a certain future time period is calculated according to the user vector data and an industry user load prediction model corresponding to the power user; superposing the load prediction curves of each power user in the area to be evaluated in a certain time period in the future to obtain a total load prediction result of the area to be evaluated; the user vector data comprises the power consumption of a user in busy hours and the power consumption of the user in idle hours; it can be understood that user vector data in the latest T1 time of a user are obtained in real time, the user vector data are combined with a user model for parallel and rapid processing, and a future T2 load prediction curve of each user is calculated respectively by utilizing a big data high-speed parallel processing technology; and superposing all the user prediction curves of T2 to obtain a target area total load prediction curve. I.e., the total load is predicted to be c (T) at a future time T2.
In a specific embodiment, the total load prediction result of the area to be evaluated is determined according to the following formula:
wherein p isi(T) represents the load prediction result of the ith user in the time T, namely the load prediction result of the ith user in the time T2; c (t) represents the total load prediction result.
Step S4, judging whether the total load prediction result exceeds the total installed capacity of the power plant in the area to be evaluated, and if so, outputting power limit warning information and a user suggesting power limit; and if not, determining a power plant production configuration plan in the area to be evaluated according to the total load prediction result of the area to be evaluated. Specifically, the basis of the configuration plan is as follows:
E(t)＝C(t)·(1+x)
wherein x is the loss proportion generated in the power distribution process, and is set by each power plant according to specific conditions.
In specific implementation, screening users who recommend limiting electricity specifically includes: and acquiring preset industry priority data, performing priority ranking on the users according to the industry priority data, and screening the users who recommend electricity limitation according to the priority ranking. It can be understood that the target area load prediction result in the time T2 is determined, whether the prediction result exceeds the installed capacity of the power supply discovery plant is detected in time, and if the prediction result shows that the prediction result continues for T in the time T2threshAnd when the time exceeds the installed capacity, generating a power limit alarm, and automatically screening out users recommending power limit through the system. As shown in fig. 3, when the market at T2-T1 exceeds a preset threshold, it is determined that the power capacity is insufficient at a high probability of traffic in the future time period T2, and the system automatically generates a power line policy, i.e., a power limitation user list, according to a preset rule and in combination with a user priority image, so that the power company can send a notification to the user in advance. In particular, screening electricity limited users is based on user preferencesThe method is carried out in a hierarchical mode, namely in the modeling process of the industry users, priorities are set for different industries, and on the basis, different priorities are set for the power users in the same industry according to the power consumption, the GDP contribution rate and the energy consumption efficiency. On the other hand, when planning the production allocation plan of each power plant, the production allocation plan of each power plant does not cause the consumption of excess capacity when the power load of the target area is satisfied. It can be understood that, by using the target area load prediction result, the power plant power production allocation plan is dynamically planned, and specifically, after the load prediction within the time of selecting the target area T2 is completed, the power plant production operation plan can be planned according to the prediction result, so that the power plant production operation plan does not cause the consumption of excess capacity under the condition of meeting the target area power load.
Fig. 2 is a schematic diagram of an embodiment of an adaptive system for power generation configuration according to the present invention. In this embodiment, the system is configured to implement the adaptive method of power generation configuration, including:
the system comprises a data acquisition unit, a model database and a data processing unit, wherein the data acquisition unit is used for acquiring historical power consumption data of power users in different scenes, user industry data corresponding to the power users, economic data corresponding to different industries and historical climate data, establishing industry user load prediction models of multiple industries in different scenes according to the historical power consumption data, the user industry data, the economic data and the historical climate data, and storing the industry user load prediction models of the multiple industries in the different scenes into the model database; the economic data comprise energy efficiency indexes and industry growth rates of industries where different users are located; in the specific embodiment, the functions of data acquisition and aggregation are realized by acquiring power consumption data of bottom-layer users, industry operation, regional meteorological data and enterprise production operation, wherein an industry user load prediction model is established according to the following formula:
wherein p (t) represents a predicted userLoad estimation at time t; t isaIndicating the start time of the busy hour, T, of the user of the industrybRepresenting the ending time of the busy hour of the user in the industry, namely the starting time of the idle hour;
the user electricity consumption data comprises:
which indicates that the average power consumption of the user in busy hours is predicted in the past T1 time,
representing the average power consumption of the user when the user is predicted to be idle in the past T1 time;
the economic data includes: r (t) represents the predicted increased output of the user compared with the current output at the future time t, a represents the coefficient of electric energy consumption of the user per unit of increased output under the scene, b2A power consumption coefficient indicating that the temperature needs to be increased every 1 degree when the user is busy in the scene, b1The electric energy consumption coefficient which indicates that the air temperature needs to be increased every 1 degree when the user is busy in the scene;
the historical climate data includes: s (t) represents the predicted user's increase in temperature at time t in the future compared to the current temperature.
The data collecting unit is used for determining a region to be evaluated, acquiring power user data of the region to be evaluated, and acquiring an industry user load prediction model matched with the power user data of the region to be evaluated from the model database; the area to be evaluated corresponds to a scene, and the power consumer data corresponds to an industry consumer load prediction model.
The power consumer load prediction and evaluation unit is used for acquiring user vector data in a preset time period according to the power consumer data in the area to be evaluated and calculating a load prediction curve of each power consumer in the area to be evaluated in a certain future time period according to the user vector data and an industry user load prediction model corresponding to the power consumer; superposing the load prediction curves of each power user in the area to be evaluated in a certain time period in the future to obtain a total load prediction result of the area to be evaluated; the user vector data comprises the power consumption of a user in busy hours and the power consumption of the user in idle hours; judging whether the total load prediction result exceeds the total installed capacity of the power plant in the area to be evaluated, and if so, outputting power limit warning information and a user recommending power limit; and if not, determining a power plant production configuration plan in the area to be evaluated according to the total load prediction result of the area to be evaluated. In a specific embodiment, the power consumer load prediction and evaluation unit determines a total load prediction result of an area to be evaluated according to the following formula:
wherein p isi(t) represents the load prediction result of the ith user in the time t; c (t) represents the total load prediction result.
Specifically, the power consumer load prediction evaluation unit acquires preset industry priority data, performs priority ranking on the users according to the industry priority data, and screens the users who recommend power limitation according to the priority ranking;
when the production allocation plan of each power plant is planned, the production allocation plan of the power plant does not cause the consumption of redundant capacity under the condition of meeting the power load of a target area.
In summary, the embodiment of the invention has the following beneficial effects:
according to the self-adaptive method and system for power production configuration, the power user models under different scenes are established, the prediction of the power load is more specific and more specific, the load prediction deviation caused by different user properties and industries is effectively reduced, and the prediction precision and reliability are improved.
The target area is selected through the current scene and various modes, and the strategy selection is flexible; the current power communication network does not need to be changed, the data acquisition and calculation are realized only by adding simple software and calculation hardware, the realization cost is low, and the method is suitable for batch deployment.
The method is suitable for online automatic power load prediction and automatic power production configuration, and can also be used for assisting the traditional manual power production operation plan making. The system for optimizing the power production configuration has superior application and economic values for mastering and evaluating the load states of all power operation target areas and realizing the most economic configuration of power, the efficient operation of a power grid and the accurate production of a power plant.
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 (10)
1. A method of adapting a power production arrangement, comprising the steps of:
step S1, acquiring historical power consumption data of power users in different scenes, user industry data corresponding to the power users, economic data corresponding to different industries and historical climate data, establishing industry user load prediction models of multiple industries in different scenes according to the historical power consumption data, the user industry data, the economic data and the historical climate data, and storing the industry user load prediction models of the multiple industries in different scenes into a model database; the economic data comprise energy efficiency indexes and industry growth rates of industries where different users are located;
step S2, determining an area to be evaluated, acquiring power user data of the area to be evaluated, and acquiring an industry user load prediction model matched with the power user data of the area to be evaluated from the model database; the method comprises the following steps that a region to be evaluated corresponds to a scene, and power user data correspond to an industry user load prediction model;
step S3, user vector data in a preset time period are obtained according to the power user data in the area to be evaluated, and a load prediction curve of each power user in the area to be evaluated in a certain future time period is calculated according to the user vector data and an industry user load prediction model corresponding to the power user; superposing the load prediction curves of each power user in the area to be evaluated in a certain time period in the future to obtain a total load prediction result of the area to be evaluated; the user vector data comprises the power consumption of a user in busy hours and the power consumption of the user in idle hours;
step S4, judging whether the total load prediction result exceeds the total installed capacity of the power plant in the area to be evaluated, and if so, outputting power limit warning information and a user suggesting power limit; and if not, determining a power plant production configuration plan in the area to be evaluated according to the total load prediction result of the area to be evaluated.
2. The method of claim 1, wherein the industry user load prediction model is expressed by the following equation:
wherein p (t) represents the load estimation value of the user at the time t; t isaIndicating the start time of the busy hour, T, of the user of the industrybRepresenting the ending time of the busy hour of the user in the industry, namely the starting time of the idle hour;
indicating the average power consumption during the past T1 time, when the user is busy,
represents the average power consumption of the user at idle for the past time T1; r (t) represents the output of the user increased from the current output at the future time t, a represents the coefficient of electric energy consumption of the user per unit output increase under the scene, b2A power consumption coefficient indicating that the temperature needs to be increased every 1 degree when the user is busy in the scene, b1The electric energy consumption coefficient which indicates that the air temperature needs to be increased every 1 degree when the user is busy in the scene; s (t) represents the user's increase in temperature from the current temperature at a future time t.
3. The method of claim 2, wherein the step S3 includes:
determining the total load prediction result of the area to be evaluated according to the following formula:
wherein p isi(t) represents the load prediction result of the ith user in the time t; c (t) represents the total load prediction result.
4. The method of claim 3, wherein the step S4 includes:
and acquiring preset industry priority data, performing priority sequencing on the users according to the industry priority data, and determining the users for recommending power limitation according to the priority sequencing.
5. The method of claim 4, wherein the step S4 further comprises:
when the power plant production configuration plan in the area to be evaluated is determined, the power plant production configuration plan does not cause the consumption of redundant capacity under the condition that the power load of the target area is met.
6. An adaptive system of power production configurations to implement the method of any of claims 1-5, comprising:
the system comprises a data acquisition unit, a model database and a data processing unit, wherein the data acquisition unit is used for acquiring historical power consumption data of power users in different scenes, user industry data corresponding to the power users, economic data corresponding to different industries and historical climate data, establishing industry user load prediction models of multiple industries in different scenes according to the historical power consumption data, the user industry data, the economic data and the historical climate data, and storing the industry user load prediction models of the multiple industries in the different scenes into the model database; the economic data comprise energy efficiency indexes and industry growth rates of industries where different users are located;
the data collecting unit is used for determining a region to be evaluated, acquiring power user data of the region to be evaluated, and acquiring an industry user load prediction model matched with the power user data of the region to be evaluated from the model database; the method comprises the following steps that a region to be evaluated corresponds to a scene, and power user data correspond to an industry user load prediction model;
the power consumer load prediction evaluation unit is used for acquiring user vector data in a preset time period according to the power consumer data in the area to be evaluated and calculating a load prediction curve of each user in a certain future time period according to the user vector data and the model data corresponding to the power consumers; superposing all user prediction curves in a certain future time period to obtain a total load prediction result of the area to be evaluated; the user vector data comprises the power consumption of a user in busy hours and the power consumption of the user in idle hours; judging whether the total load prediction result exceeds the total installed capacity of the power plant in the area to be evaluated, and if so, outputting power limit warning information and a user recommending power limit; and if not, determining a power plant production configuration plan in the area to be evaluated according to the total load prediction result of the area to be evaluated.
7. The method of claim 6, wherein the data collection unit builds an industry user load prediction model according to the following formula:
wherein p (t) represents the load estimated value of the predicted user at the time t; t isaIndicating the start time of the busy hour, T, of the user of the industrybRepresenting the ending time of the busy hour of the user in the industry, namely the starting time of the idle hour;
the user electricity consumption data comprises:
indicating the time at T1 in the pastIn addition, the average power consumption of the user in busy hours is predicted,
representing the average power consumption of the user when the user is predicted to be idle in the past T1 time;
the economic data includes: r (t) represents the predicted increased output of the user compared with the current output at the future time t, a represents the coefficient of electric energy consumption of the user per unit of increased output under the scene, b2A power consumption coefficient indicating that the temperature needs to be increased every 1 degree when the user is busy in the scene, b1The electric energy consumption coefficient which indicates that the air temperature needs to be increased every 1 degree when the user is busy in the scene;
the historical climate data includes: s (t) represents the predicted user's increase in temperature at time t in the future compared to the current temperature.
8. The method of claim 7, wherein the power consumer load prediction evaluation unit determines the total load prediction result of the area to be evaluated according to the following formula:
wherein p isi(t) represents the load prediction result of the ith user in the time t; c (t) represents the total load prediction result.
9. The method of claim 8, wherein the power consumer load prediction and evaluation unit obtains preset industry priority data, prioritizes the consumers according to the industry priority data, and screens the consumers with recommended electricity limit according to the prioritization.
10. The method of claim 9, wherein the power consumer load forecast evaluation unit schedules each power plant production allocation plan such that the power plant production allocation plan does not cause consumption of excess capacity if the target area power load is met.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113435923A (en) * | 2021-06-15 | 2021-09-24 | 北京百度网讯科技有限公司 | Power consumption prediction method and device and electronic equipment |
| CN116151509A (en) * | 2023-02-13 | 2023-05-23 | 国家电投集团数字科技有限公司 | Power information management method and system based on data fusion |
| CN116307295A (en) * | 2023-05-22 | 2023-06-23 | 南京宝能科技有限公司 | Intelligent energy digital management system and method applied to cloud platform |
| CN116613868A (en) * | 2023-07-20 | 2023-08-18 | 东莞市奥源电子科技有限公司 | BMS (Battery management System) system of outdoor power supply and outdoor power supply control method |
| CN117039910A (en) * | 2023-10-09 | 2023-11-10 | 国网浙江省电力有限公司宁波供电公司 | Management method and management device for electric power energy requirements based on multiple models |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104134102A (en) * | 2014-07-30 | 2014-11-05 | 国家电网公司 | LEAP-model-based method for predicating medium-and-long-term electricity demand distribution of power grid |
| CN108346009A (en) * | 2018-03-21 | 2018-07-31 | 深圳库博能源科技有限公司 | A kind of power generation configuration method and device based on user model self study |
| CN108491969A (en) * | 2018-03-16 | 2018-09-04 | 国家电网公司 | Spatial Load Forecasting model building method based on big data |
-
2020
- 2020-12-28 CN CN202011580131.3A patent/CN112508306A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104134102A (en) * | 2014-07-30 | 2014-11-05 | 国家电网公司 | LEAP-model-based method for predicating medium-and-long-term electricity demand distribution of power grid |
| CN108491969A (en) * | 2018-03-16 | 2018-09-04 | 国家电网公司 | Spatial Load Forecasting model building method based on big data |
| CN108346009A (en) * | 2018-03-21 | 2018-07-31 | 深圳库博能源科技有限公司 | A kind of power generation configuration method and device based on user model self study |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113435923A (en) * | 2021-06-15 | 2021-09-24 | 北京百度网讯科技有限公司 | Power consumption prediction method and device and electronic equipment |
| CN116151509A (en) * | 2023-02-13 | 2023-05-23 | 国家电投集团数字科技有限公司 | Power information management method and system based on data fusion |
| CN116307295A (en) * | 2023-05-22 | 2023-06-23 | 南京宝能科技有限公司 | Intelligent energy digital management system and method applied to cloud platform |
| CN116307295B (en) * | 2023-05-22 | 2023-08-04 | 南京宝能科技有限公司 | Intelligent energy digital management system and method applied to cloud platform |
| CN116613868A (en) * | 2023-07-20 | 2023-08-18 | 东莞市奥源电子科技有限公司 | BMS (Battery management System) system of outdoor power supply and outdoor power supply control method |
| CN116613868B (en) * | 2023-07-20 | 2024-01-30 | 东莞市奥源电子科技有限公司 | BMS (Battery management System) system of outdoor power supply and outdoor power supply control method |
| CN117039910A (en) * | 2023-10-09 | 2023-11-10 | 国网浙江省电力有限公司宁波供电公司 | Management method and management device for electric power energy requirements based on multiple models |
| CN117039910B (en) * | 2023-10-09 | 2024-01-05 | 国网浙江省电力有限公司宁波供电公司 | Management method and management device for electric power energy requirements based on multiple models |
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