Source-load-storage intelligent integrated management system

Abstract

The invention relates to the technical field related to energy management, in particular to a source-load-storage intelligent integrated management system which comprises a power management module, a load management module and an energy storage management module. The future generating capacity of the distributed generating equipment can be predicted by arranging the power supply management module, and the future demand load can be predicted by arranging the load management module according to the collected load data. Therefore, the energy storage management module can control the charging and discharging of the energy storage in the future time according to the future generating capacity, predicted by the power management module, of the distributed generating equipment and the load, predicted by the load management module, of the load of each label in the future time, and the electric energy dispatching function is optimized. Through integrated management, power utilization coordination and control of distributed loads in future time are ensured. The problem of poor load distribution caused by emphasis on single or partial coordination control of the distributed power supply and the energy storage device in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field related to energy management, and more specifically, to an intelligent integrated management system for sources, loads and storage. Background Art

[0002] The rapid development of distributed power supply, communication and control technology has brought about the rise of research fields such as smart grid and active distribution network. Source-storage-load coordination optimization is a comprehensive management of distributed power supply, energy storage devices and loads, covering the links of power production, transmission and distribution, which can further improve the level of distributed renewable energy consumption and take into account the economy and safety of the system. The research and application of source-load-storage system is of great significance to energy development. Improving the ability to cope with major faults can shorten the emergency handling time of faults from minutes to milliseconds, providing professional means for preventing and controlling large-scale power outages. The currently emerging energy Internet is a new development direction of the energy system, aiming to create an energy interconnection circle with horizontal multi-energy body complementarity and vertical source-storage-load coordinated interaction, maximize the use of distributed renewable energy, and realize localized energy supply and large-scale energy interconnection. Source-storage-load coordination optimization management will be one of the important technologies for realizing energy Internet.

[0003] In the context of the rapid development of energy internet, the importance of coordinated optimization management of sources, storage and loads is increasing. At present, domestic and foreign research focuses on the single or partial coordinated control of distributed power sources and energy storage devices, lacks research on the integrated optimization management of sources, storage and loads, and fails to provide strong support for load distribution. Summary of the invention

[0004] The purpose of the present invention is to provide a source-load-storage intelligent integrated management system to solve the problems raised in the above background technology.

[0005] To achieve the above-mentioned purpose, the present invention provides a technical solution: a source-load-storage intelligent integrated management system, comprising a power management module, a load management module and an energy storage management module;

[0006] The power management module is used to count the power generation of the distributed power generation equipment and predict the future power generation of the distributed power generation equipment;

[0007] The load management module is used to monitor the load of the distributed load according to the set time to obtain load data. The load management module is also used to predict the future demand load according to the collected load data to obtain load prediction data; the load management module includes a label unit, a load quantity prediction unit and an online monitoring unit; the label unit is used to label the distributed load according to the usage scenario and the power consumption category, and classify the load according to the different labels; the online monitoring unit is used to monitor the load of each labeled load online according to the set time to obtain load data; the load quantity prediction unit is used to predict the load of each labeled load in the future time according to the load data;

[0008] The energy storage management module is used to store the energy of the distributed power generation equipment by charging and discharge the stored energy to the load. The energy storage management module is also used to control the charging and discharging of the energy storage in the future according to the future power generation of the distributed power generation equipment predicted by the power management module and the load of each label in the future predicted by the load management module. When the load of the load in the future time period is greater than the power generation in the future time period, the energy storage device is charged by an external power supply.

[0009] A further technical solution of the present application is: the online monitoring unit includes a monitoring sub-unit, and the monitoring sub-unit is used to monitor the load of each tag online in the local timing time and the long and short time series respectively, to obtain the local timing load data and the long and short time series load data.

[0010] A further technical solution of the present application is: the load prediction unit includes a cleaning subunit and a prediction subunit; the cleaning subunit is used to screen local time series load data and long-short time series load data within a set range; the prediction subunit is used to construct a local time series learning model and a long-short time series learning model according to the local time series load data and the long-short time series load data after cleaning by the cleaning subunit, and a dual time series learning model is constructed through the local time series learning model and the long-short time series learning model, and the set time t is input into the dual time series learning model. After analysis by the dual time series learning model, the load values ​​at time t and the time t+n to be predicted are obtained.

[0011] A further technical solution of the present application: the power management module includes a power generation statistics unit and a power generation prediction unit;

[0012] The power generation statistical unit is used to count the power generation of the distributed power generation equipment to obtain power generation data;

[0013] The power generation prediction unit is used to predict the power generation of the distributed power generation equipment in the future according to the power generation data.

[0014] A further technical solution of the present application is as follows: the power management module also includes a power generation analysis unit, and the power generation analysis unit is used to analyze the power generation data according to the classification of weather, date and season to obtain power generation analysis data.

[0015] A further technical solution of the present application is as follows: the power generation prediction unit is used to construct a power generation prediction model based on the power generation analysis data, and to predict the power generation for the specified weather, date and / or season based on the input weather, date and / or season.

[0016] A further technical solution of the present application: the energy storage management module includes a switching unit, an energy storage unit and an energy storage analysis unit;

[0017] The energy storage unit is used to charge the energy of the distributed power generation equipment and the external power supply to the energy storage device, and the energy storage unit is also used to discharge the energy stored in the energy storage device to the load;

[0018] The energy storage analysis unit is used to analyze whether the energy stored in the energy storage device is sufficient to meet the power demand of the load in the future time period according to the future power generation of the distributed power generation equipment predicted by the power management module and the load of each tag in the future time predicted by the load management module;

[0019] The switching unit is used to control the charging and discharging of the energy storage in the future time period. When the load in the future time period is greater than the power generation in the future time period, the external power supply is controlled to charge the energy storage device.

[0020] Compared with the prior art, the technical solution provided by the embodiment of the present invention has the following beneficial effects:

[0021] 1. The present invention can predict the future power generation of distributed power generation equipment by setting a power management module, and the set load management module can predict the future demand load according to the collected load data, so that the energy storage management module can control the charging and discharging of the energy storage in the future according to the future power generation of the distributed power generation equipment predicted by the power management module and the load of each label in the future predicted by the load management module, optimize the power scheduling function, and thus can cope with the power demand in the future. When the load of the load in the future time period is greater than the power generation in the future time period, the energy storage device is charged by an external power supply, which can ensure the power demand of the distributed load under the premise of reasonable use of clean energy, and can ensure the power coordination and control of the distributed load in the future through the integrated management of the power management module, the load management module and the energy storage management module; it solves the problem that the existing technology focuses on the single or partial coordinated control of distributed power sources and energy storage devices, thereby lacking research on the integrated optimization management of sources, storage and loads, and thus leading to poor load distribution.

[0022] 2. The load prediction unit provided in the present invention can clean the load data that is greatly different from the usual time through the cleaning subunit, and obtain the local time series load data and the long-short time series load data after cleaning, and then transmit the local time series load data and the long-short time series load data after cleaning to the prediction subunit, thereby saving the storage space used for data transmission; in addition, the prediction subunit can construct a local time series learning model and a long-short time series learning model according to the local time series load data and the long-short time series load data after cleaning by the cleaning subunit, and construct a dual time series learning model through the local time series learning model and the long-short time series learning model, so that when the set time t is input into the dual time series learning model, under the analysis of the dual time series learning model, the load values ​​at time t and the time t+n to be predicted can be obtained, thereby improving the accuracy of load prediction for future time.

[0023] 3. The power analysis unit provided in the present invention can obtain the predicted weather data in real time and future time online, improve the accuracy of predicted power generation, and thus can predict the power generation in the future time according to the predicted weather data based on the power generation in the past time and weather.

[0024] 4. By setting up an energy storage management module, the present invention can perform data statistics on the charging amount of distributed power generation according to time periods and time points, so that the energy storage analysis unit can analyze whether the energy stored in the energy storage device is sufficient to meet the electricity demand of the load in the future time period based on the future power generation of the distributed power generation equipment predicted by the power management module and the load of each label in the future time predicted by the load management module; and then, when the load of the load in the future time period is greater than the power generation in the future time period, the external power supply can be controlled in advance to charge the energy storage device to ensure that the energy storage device can store enough electricity to meet the demand of the distributed load in the future time period. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A schematic diagram of the structure of a source-load-storage intelligent integrated management system provided by an embodiment of the present invention;

[0026] Figure 2 A schematic diagram of the structure of a power management module provided in an embodiment of the present invention;

[0027] Figure 3 A schematic diagram of the structure of a load management module provided in an embodiment of the present invention;

[0028] Figure 4 It is a schematic diagram of the structure of the energy storage management module provided in an embodiment of the present invention.

[0029] Explanation of the symbols in the schematic diagram:

[0030] 1. Power management module; 11. Power generation statistics unit; 12. Power generation analysis unit; 13. Power generation prediction unit; 2. Load management module; 21. Label unit; 22. Load prediction unit; 221. Cleaning subunit; 222. Prediction subunit; 23. Online monitoring unit; 231. Monitoring subunit; 3. Energy storage management module; 31. Switching unit; 32. Energy storage unit; 33. Energy storage analysis unit. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. The present invention is further described below in combination with the embodiments.

[0032] See also Figure 1 , In one embodiment of the present application, a source-load-storage intelligent integrated management system includes a power management module 1, a load management module 2 and an energy storage management module 3;

[0033] The power management module 1 is used to count the power generation of distributed power generation equipment and predict the future power generation of distributed power generation equipment;

[0034] The load management module 2 is used to monitor the load of the distributed load according to the set time to obtain load data. The load management module 2 is also used to predict the future demand load according to the collected load data to obtain load prediction data; the load management module 2 includes a label unit 21, a load quantity prediction unit 22 and an online monitoring unit 23; the label unit 21 is used to label the distributed load according to the usage scenario and the power consumption category, and classify the load according to the different labels; the online monitoring unit 23 is used to monitor the load of each labeled load online according to the set time to obtain load data; the load quantity prediction unit 22 is used to predict the load of each labeled load in the future time according to the load data;

[0035] The energy storage management module 3 is used to store the energy of the distributed power generation equipment by charging and discharge the stored energy to the load. The energy storage management module 3 is also used to control the charging and discharging of the energy storage in the future according to the future power generation of the distributed power generation equipment predicted by the power management module 1 and the load of each label in the future predicted by the load management module 2. When the load in the future time period is greater than the power generation in the future time period, the energy storage device is charged by an external power supply.

[0036] In one case of this embodiment, the distributed power generation equipment includes photovoltaic power generation, wind power generation and other equipment that utilizes clean energy to generate electricity and are distributed at various points.

[0037] In actual applications, distributed loads can include ordinary household appliances, electric vehicle charging, smart sockets, industrial production equipment and other loads that meet the electricity demand in residential areas, industrial areas, and commercial areas.

[0038] The present invention can predict the future power generation of distributed power generation equipment by setting a power management module, and the set load management module can predict the future demand load according to the collected load data, so that the energy storage management module can control the charging and discharging of the energy storage in the future according to the future power generation of the distributed power generation equipment predicted by the power management module and the load of each label in the future predicted by the load management module, optimize the power scheduling function, and thus can cope with the power demand in the future. When the load of the load in the future time period is greater than the power generation in the future time period, the energy storage device is charged by an external power supply, and the power demand of the distributed load is guaranteed under the premise of reasonable use of clean energy. The integrated management of the power management module, the load management module and the energy storage management module can ensure the coordination and control of the power consumption of the distributed load in the future. The problem that the existing technology focuses on the single or partial coordinated control of distributed power sources and energy storage devices, thereby lacks research on the integrated optimization management of sources, storage and loads, and thus leads to poor load distribution is solved.

[0039] See also Figure 3 As a preferred embodiment of the present application, the online monitoring unit 23 includes a monitoring subunit 231, and the monitoring subunit 231 is used to monitor the load of each tag online in the local timing time and the long and short time sequence respectively, to obtain the local timing load data and the long and short time sequence load data.

[0040] In one case of the present embodiment, the load prediction unit 22 includes a cleaning subunit 221 and a prediction subunit 222; the cleaning subunit 221 is used to screen local time series load data and long-short time series load data within a set range; the prediction subunit 222 is used to construct a local time series learning model and a long-short time series learning model based on the local time series load data and the long-short time series load data after cleaning by the cleaning subunit 221, and a dual time series learning model is constructed through the local time series learning model and the long-short time series learning model, and the set time t is input into the dual time series learning model. After analysis by the dual time series learning model, the load values ​​at time t and the time t+n to be predicted are obtained.

[0041] In actual application, the local time series can be set to monitor the load of each tag at intervals of 15 minutes, and the long and short time series can be set to monitor the load of each tag for a total of 12 hours and 1 hour. Then, in these two time periods, the load data of the load monitoring of each tag for 1 hour is used to take the average load value every minute or every fifteen minutes; the load data of the load monitoring of each tag for 12 hours is used to take the average load value every fifteen minutes or every hour, so as to improve the load prediction for future time.

[0042] In this embodiment, after having the average load data of the load in each time period, the future moment or future time period t is input into the dual-time series learning model. After analysis by the dual-time series learning model, the load value of the moment or future time period t and the predicted moment or future time period t+n is obtained. n can be preset in advance, such as fifteen minutes or one hour, etc., which is not limited in this embodiment.

[0043] Preferably, the cleaning subunit 221 cleans the load data that differs greatly from the usual ones, obtains the cleaned local time series load data and the long-short time series load data, and then transmits the cleaned local time series load data and the long-short time series load data to the prediction subunit 222, thereby saving the storage space used for data transmission.

[0044] See also Figure 2 , as a preferred embodiment of the present application, the power management module 1 includes a power generation statistics unit 11, a data storage unit and a power generation prediction unit 13;

[0045] The power generation statistics unit 11 is used to count the power generation of the distributed power generation equipment to obtain power generation data, and the power generation data is stored in the data storage unit according to the classification of weather, date and season;

[0046] The power generation prediction unit 13 is used to predict the power generation of the distributed power generation equipment in the future according to the power generation data.

[0047] In one case of this embodiment, the power management module 1 further includes a power generation analysis unit 12, and the power generation analysis unit 12 is used to analyze the power generation data according to the classification of weather, date and season to obtain power generation analysis data.

[0048] In actual application, the power generation prediction unit 13 is used to construct a power generation prediction model according to the power generation analysis data, and predict the power generation of the specified weather, date and / or season according to the input weather, date and / or season.

[0049] In this embodiment, the power generation analysis unit 12 includes a weather data online acquisition subunit, a date acquisition subunit and a power generation analysis subunit. The weather data online acquisition subunit is used to obtain the predicted weather data for the next 7 days or 15 days. The weather data online acquisition subunit is also used to obtain the real-time weather data of the current day and the real-time weather data. The real-time weather data is used to update the obtained predicted weather data for the next 7 days or 15 days to improve the accuracy of the predicted power generation. The date acquisition subunit is used to obtain the time data of the current day or future date and the current or future quarter; the power generation of the real-time time and the real-time weather conditions is stored in the data storage unit of the power management module 1, and the power generation analysis subunit is used to predict the power generation of the future time according to the predicted weather data based on the power generation of the past time and weather stored in the data storage unit.

[0050] See also Figure 4 As a preferred embodiment of the present application, the energy storage management module 3 includes a switching unit 31, an energy storage unit 32 and an energy storage analysis unit 33;

[0051] The energy storage unit 32 is used to charge the energy of the distributed power generation equipment and the external power supply to the energy storage device, and the energy storage unit 32 is also used to discharge the energy stored in the energy storage device to the load;

[0052] The energy storage analysis unit 33 is used to analyze whether the energy stored in the energy storage device is sufficient to meet the power demand of the load in the future time period according to the future power generation of the distributed power generation equipment predicted by the power management module 1 and the load of each tag in the future time predicted by the load management module 2;

[0053] The switching unit 31 is used to control the charging and discharging of the energy storage in the future time period. When the load in the future time period is greater than the power generation in the future time period, the external power supply is controlled to charge the energy storage device.

[0054] In one case of this embodiment, the energy storage device includes a device for energy storage such as a battery, and the distributed power generation device continuously charges the energy storage device, and through the energy storage energy collection component, data statistics are performed on the charging amount of the distributed power generation according to the time period and time point, so that the energy storage analysis unit 33 can analyze whether the energy stored in the energy storage device is sufficient to meet the power demand of the load in the future time period based on the future power generation of the distributed power generation device predicted by the power management module 1 and the load of each label in the future time predicted by the load management module 2; and then, when the load of the load in the future time period is greater than the power generation in the future time period, the external power supply can be controlled in advance to charge the energy storage device to ensure that the energy storage device can store enough electricity to meet the needs of the distributed load in the future time period.

[0055] The present invention and its embodiments are described schematically above, and the description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. Therefore, if a person skilled in the art is inspired by it and designs a structural method and an embodiment similar to the technical solution without creativity without departing from the purpose of the invention, they shall all fall within the protection scope of the present invention.

[0056] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A source-load-storage intelligent integrated management system, characterized in that: It comprises a power management module (1), a load management module (2) and an energy storage management module (3); The power management module (1) is used to count the power generation of the distributed power generation equipment and predict the future power generation of the distributed power generation equipment; The load management module (2) is used to monitor the load of the distributed load according to a set time to obtain load data. The load management module (2) is also used to predict the future demand load according to the collected load data to obtain load prediction data. The load management module (2) includes a label unit (21), a load quantity prediction unit (22) and an online monitoring unit (23). The label unit (21) is used to label the distributed load according to the usage scenario and the power consumption category, and classify the load according to the different labels. The online monitoring unit (23) is used to monitor the load of each labeled load online according to the set time to obtain load data. The load quantity prediction unit (22) is used to predict the load of each labeled load at a future time according to the load data. The energy storage management module (3) is used to store the energy of the distributed power generation equipment by charging and discharge the stored energy to the load. The energy storage management module (3) is also used to control the charging and discharging of the energy storage in the future according to the future power generation of the distributed power generation equipment predicted by the power management module (1) and the load of each tag in the future predicted by the load management module (2). When the load of the load in the future time period is greater than the power generation in the future time period, the energy storage device is charged by an external power supply.

2. The source-load-storage intelligent integrated management system according to claim 1 is characterized in that: The online monitoring unit (23) comprises a monitoring subunit (231), and the monitoring subunit (231) is used to monitor the load of each tag online in a local time series and a long-short time series, respectively, to obtain local time series load data and long-short time series load data.

3. The source-load-storage intelligent integrated management system according to claim 2 is characterized in that: The load prediction unit (22) comprises a cleaning subunit (221) and a prediction subunit (222); the cleaning subunit (221) is used to screen local time series load data and long-short time series load data within a set range; the prediction subunit (222) is used to construct a local time series learning model and a long-short time series learning model based on the local time series load data and the long-short time series load data after cleaning by the cleaning subunit (221), and to construct a dual time series learning model through the local time series learning model and the long-short time series learning model, and to input a set time t into the dual time series learning model. After analysis by the dual time series learning model, the load values ​​at time t and the time t+n to be predicted are obtained.

4. The source-load-storage intelligent integrated management system according to claim 1, characterized in that: The power management module (1) comprises a power generation statistical unit (11) and a power generation prediction unit (13); The power generation statistical unit (11) is used to collect statistics on the power generation of the distributed power generation equipment to obtain power generation data; The power generation prediction unit (13) is used to predict the power generation of the distributed power generation equipment in the future based on the power generation data.

5. The source-load-storage intelligent integrated management system according to claim 4 is characterized in that: The power management module (1) further comprises a power generation analysis unit (12), wherein the power generation analysis unit (12) is used to analyze power generation data according to the classification of weather, date and season to obtain power generation analysis data.

6. The source-load-storage intelligent integrated management system according to claim 5, characterized in that: The power generation prediction unit (13) is used to construct a power generation prediction model based on the power generation analysis data, and to predict the power generation at the specified weather, date and / or season based on the input weather, date and / or season.

7. The source-load-storage intelligent integrated management system according to claim 1, characterized in that: The energy storage management module (3) comprises a switching unit (31), an energy storage unit (32) and an energy storage analysis unit (33); The energy storage unit (32) is used to charge the energy of the distributed power generation equipment and the external power supply to the energy storage device, and the energy storage unit (32) is also used to discharge the energy stored in the energy storage device to the load; The energy storage analysis unit (33) is used to analyze whether the energy stored in the energy storage device is sufficient to meet the power demand of the load in the future time period according to the future power generation of the distributed power generation equipment predicted by the power management module (1) and the load of each tag in the future time period predicted by the load management module (2); The switching unit (31) is used to control the charging and discharging of the energy storage in the future time period, and when the load in the future time period is greater than the power generation in the future time period, the external power supply is controlled to charge the energy storage device.

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