Energy storage demand management system for power grid

Abstract

The invention relates to the technical field of power systems, in particular to an energy storage demand management system for a power grid, which comprises a central processing unit module, a control module, a storage module, a power failure data management module, a distribution module and an analysis module, the central processor module is bidirectionally connected with the control module, the storage module, the power failure management module, the distribution module and the analysis module. According to the energy storage demand management system for the power grid, the system has powerful data analysis functions including condition analysis, multi-dimensional statistical analysis, visual analysis and user demand model analysis, real-time tracking can be carried out according to numerical data changes of energy storage users, and the energy storage demand management system for the power grid is high in practicability. According to the system, accurate user development trend prediction is provided for a power supply unit, the intelligent level of power grid energy storage management is greatly improved, the energy supply and demand contradiction is effectively relieved, more convenient and efficient power utilization experience is brought to users, and meanwhile powerful support is provided for sustainable development of a power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to an energy storage demand management system for a power grid. Background Art

[0002] With the continuous expansion of the scale of power grids and the increasing complexity of power systems, efficient management and optimization of power grid energy storage has become an important task in the optimization and dispatching of power grid energy storage. In the existing power grid energy storage data processing process, abnormal data is prone to occur, which requires manual data processing. The manual processing process is slow, time-consuming and labor-intensive, and it is difficult to achieve the optimization and dispatching of power grid energy storage, which leads to the inability of the power grid's operating efficiency and stability to meet the use needs. There is also no timely and accurate prediction of the power system, and it is difficult to accurately and appropriately allocate the power consumption of users in the allocation and dispatch of power grid energy storage, which needs to be improved. Summary of the invention

[0003] The object of the present invention is to provide an energy storage demand management system for a power grid to solve the problems raised in the above background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions: an energy storage demand management system for a power grid, comprising a central processing unit module, a control module, a storage module, a power outage data management module, a distribution module and an analysis module, wherein the central processing unit module is bidirectionally connected with the control module, the storage module, the power outage management module, the distribution module and the analysis module;

[0005] The power outage data management module includes a data analysis module, a risk prediction module, and an intelligent optimization module;

[0006] The analysis module includes an acquisition module, a processing module, and a judgment module;

[0007] The allocation module includes a collection unit, a processing unit, a calculation unit, and a scheduling unit.

[0008] Preferably, the power outage data management module is used to mine and analyze influencing factors of the power outage plan based on big data.

[0009] Preferably, the data analysis module includes a historical data analysis unit, a future data analysis unit and a power outage execution data unit;

[0010] The historical data analysis unit is used to study and analyze the causes of power outages affecting equipment based on historical power outage data;

[0011] The future data analysis unit is used to study and analyze the causes of power outages of equipment based on future power outage plan data;

[0012] The power outage execution data unit is used to statistically analyze the power outage plan execution status and to discover the main key reasons that affect the power outage plan execution.

[0013] Preferably, the risk prediction module includes an evaluation unit and an execution unit;

[0014] The evaluation unit is used to perform quantitative evaluation based on the operational safety risk of the regional power grid, formulate technical specifications, and then study and establish an evaluation index calculation model for the power outage risk of the regional power grid based on the technical specifications;

[0015] The execution unit is used to study and identify weak areas and key equipment sets with greater power grid operation risks, and to execute measures to reduce power grid operation risks.

[0016] Preferably, the intelligent optimization module includes a constraint unit, an optimization unit and a solution unit;

[0017] The constraint unit is used to study and establish a power outage plan rule base based on the power outage procedures of power grid equipment and expert experience, comprehensively consider safety constraints such as equipment maintenance conflicts, sets of equipment that are shut down at the same time, sets of mutually exclusive equipment, and power protection, and provide effective boundary constraints for the analysis and verification of the power outage plan;

[0018] The optimization unit is used to comprehensively consider the power outage plan rule base based on the total power outage demand and the power grid and personnel carrying capacity, on the basis of meeting the construction time sequence requirements of the infrastructure construction milestone plan, to study and establish a power outage plan intelligent optimization model, balance the annual and monthly power outage plans, reduce power outage losses, reduce power outage risks, maintain the required power supply reliability, and optimize the implementation time and construction period of the power outage plan project;

[0019] The solution unit is used to study efficient solution technology for power outage plan optimization based on artificial intelligence assistance.

[0020] Preferably, the acquisition module is used to acquire load data of each load borrowing point of the distribution network;

[0021] The processing module is used to process the data obtained by the acquisition module. The processing module defines the node load value at the current time point as the current load value, and defines the remaining node load values ​​obtained in the detection interval as historical load values, and defines the time point when the historical load value determined by the judgment module is consistent with the current load value as a similar point; the processing module establishes a standard interval according to a preset fixed time at the current time point, and establishes a comparison interval according to a fixed time at the similar point; the processing module performs difference calculation according to the historical load values ​​at the corresponding time points in the comparison interval and the standard interval to determine the deviation load value, and determines the deviation time according to the time point corresponding to the deviation load value; the processing module determines the weight parameter corresponding to the deviation time according to the preset weight matching relationship, and calculates to determine the deviation parameter according to all the deviation load values ​​and the corresponding weight parameters; the processing module determines the deviation parameter with the smallest value according to the preset sorting rule, and defines the similar point corresponding to the deviation parameter as a reference point, and establishes a reference interval according to the reference point and the preset reference time; the processing module determines the predicted load value of the current load node at each time point within the reference time after the current time point according to the historical load values ​​in the reference interval;

[0022] The judgment module is used for judging information.

[0023] Preferably, the acquisition unit is used to collect energy storage data of each node of the power grid to generate an energy storage data set;

[0024] The processing unit is used to preprocess and clean the energy storage data set, and construct a topological map of the power grid according to the preprocessed and cleaned energy storage data set;

[0025] The calculation unit is used to analyze and calculate the power grid topology map using a graph calculation algorithm to obtain a calculation result;

[0026] The dispatching unit is used to generate an optimized dispatching decision according to the calculation result, so as to optimize the energy storage of the power grid according to the optimized dispatching decision.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] The present invention proposes an energy storage demand management system for a power grid. The system has powerful data analysis functions, including condition analysis, multidimensional statistical analysis, visualization analysis and user demand model analysis. It can also track the changes in the numerical data of energy storage users in real time, and provide accurate user development trend predictions for power supply units. The implementation of this system will greatly enhance the intelligence level of power grid energy storage management, effectively alleviate the contradiction between energy supply and demand, and bring users a more convenient and efficient electricity experience. At the same time, it also provides strong support for the sustainable development of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 Schematic diagram of the system structure of the present invention.

[0030] Figure 2 It is a schematic diagram of the structure of the power outage data management module of the present invention.

[0031] Figure 3 It is a schematic diagram of the structure of the analysis module of the present invention.

[0032] Figure 4 This is a schematic diagram of the allocation structure of the present invention. DETAILED DESCRIPTION

[0033] 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.

[0034] See also Figures 1 to 4 The present invention provides a technical solution: an energy storage demand management system for a power grid, comprising a central processing unit module, a control module, a storage module, a power outage data management module, a distribution module and an analysis module, wherein the central processing unit module is bidirectionally connected with the control module, the storage module, the power outage management module, the distribution module and the analysis module. The control module is connected to a control panel for operators to control the operation of the system, and the storage module is used to store system data.

[0035] The power outage data management module is used to mine and analyze the influencing factors of the power outage plan based on big data. The power outage data management module includes a data analysis module, a risk prediction module, and an intelligent optimization module.

[0036] The data analysis module includes a historical data analysis unit, a future data analysis unit and a power outage execution data unit. The historical data analysis unit is used to study and analyze the causes of power outages on equipment based on historical power outage data. The future data analysis unit is used to study and analyze the causes of power outages on equipment based on future power outage plan data. The power outage execution data unit is used to statistically analyze the execution status of the power outage plan and to explore the main key reasons affecting the execution of the power outage plan.

[0037] The risk prediction module includes an evaluation unit and an execution unit. The evaluation unit is used to conduct a quantitative evaluation based on the operational safety risk of the regional power grid, formulate technical specifications, and then study and establish an evaluation index calculation model for the regional power grid outage risk based on the technical specifications. The execution unit is used to study and identify weak areas and key equipment sets with greater operational risks of the power grid, and implement measures to reduce the operational risks of the power grid.

[0038] The intelligent optimization module includes a constraint unit, an optimization unit and a solution unit. The constraint unit is used to comprehensively consider safety constraints such as equipment maintenance conflicts, sets of equipment that stop at the same time, sets of mutually exclusive equipment, and power supply protection based on the power grid equipment power outage procedures and expert experience, and to study and establish a power outage plan rule base to provide effective boundary constraints for the analysis and verification of the power outage plan. The optimization unit is used to comprehensively consider the power outage plan rule base based on the total power outage demand and the power grid and personnel carrying capacity, on the basis of meeting the construction timing requirements of the infrastructure construction milestone plan, and to study and establish an intelligent optimization model for the power outage plan, balance the annual and monthly power outage plans, reduce power outage losses, reduce power outage risks, maintain the required power supply reliability, and optimize the implementation time and construction period of the power outage plan project. The solution unit is used to study efficient solution technology for power outage plan optimization assisted by artificial intelligence.

[0039] The power outage data management module in the present invention evaluates the risk of power outage in the power system and calculates indicators, and then analyzes the causes of the risks and takes precautions against the causes of the risks. It optimizes the power outage plan through power grid safety constraints and performs real-time revision processing on the power outage plan, thereby providing the application of artificial intelligence algorithms in the process of load analysis and prediction of the power grid, achieving the effect of facilitating accurate and real-time load prediction of the main grid and realizing the goal of facilitating load prediction of substations.

[0040] The analysis module includes an acquisition module, a processing module, and a judgment module; the acquisition module is used to acquire the load data of each load borrowing point of the distribution network; the processing module is used to process the data acquired by the acquisition module, the processing module defines the node load value at the current time point as the current load value, and defines the remaining node load values ​​acquired within the detection interval as the historical load value, and defines the time point when the historical load value determined by the judgment module is consistent with the current load value as the similarity point; the processing module establishes a standard interval at the current time point according to a preset fixed time length, and establishes a comparison interval at the similarity point according to a fixed time length; the processing module compares the historical load values ​​at the corresponding time points in the comparison interval and the standard interval. The difference calculation is performed on the values ​​to determine the deviation load value, and the deviation duration is determined according to the time point corresponding to the deviation load value; the processing module determines the weight parameter corresponding to the deviation duration according to the preset weight matching relationship, and calculates according to all the deviation load values ​​and the corresponding weight parameters to determine the deviation parameter; the processing module determines the deviation parameter with the smallest value according to the preset sorting rule, and defines the similar point corresponding to the deviation parameter as the reference point, and establishes a reference interval according to the reference point and the preset reference duration; the processing module determines the predicted load value of the current load node at each time point within the reference duration after the current time point according to the historical load value in the reference interval; the judgment module is used for information judgment.

[0041] The analysis module can analyze the historical load conditions of the load nodes of the distribution transformer to determine the load values ​​that may appear at the load nodes in the future, so that the equipment can make corresponding processing in advance to reduce the overload of the equipment; determine the time period similar to the current one according to the load change trend of the load nodes under historical conditions, so as to improve the accuracy of the predicted load value determination; use the specific conditions of each time point within the determined time interval to determine the predicted load value, so as to further improve the accuracy of the predicted load value determination.

[0042] The allocation module includes an acquisition unit, a processing unit, a calculation unit, and a scheduling unit. The acquisition unit is used to collect energy storage data from each node of the power grid and generate an energy storage data set; the processing unit is used to preprocess and clean the energy storage data set, and construct a topological map of the power grid based on the preprocessed and cleaned energy storage data set; the calculation unit is used to analyze and calculate the power grid topological map using a graph calculation algorithm to obtain a calculation result; the scheduling unit is used to generate an optimized scheduling decision based on the calculation result, so as to optimize the scheduling of the energy storage of the power grid based on the optimized scheduling decision.

[0043] The allocation module has the functions of preprocessing and cleaning the power grid data, processing the collected power grid energy storage data, improving the efficiency of data processing, detecting and repairing abnormal data through the data cleaning function, improving the accuracy of data processing, integrating the energy storage data with the power grid topology structure, and performing parallel calculations on the power grid topology map. Using the graph calculation algorithm, the power grid topology map is calculated in parallel, and the digital text data information is converted into picture information for display, which is intuitive and improves the calculation efficiency. The results of parallel calculations on the power grid topology map are used to achieve optimal scheduling of power grid energy storage and improve the operation efficiency and stability of the power grid.

[0044] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An energy storage demand management system for a power grid, characterized in that: It includes a central processing unit module, a control module, a storage module, a power outage data management module, a distribution module and an analysis module, wherein the central processing unit module is bidirectionally connected with the control module, the storage module, the power outage management module, the distribution module and the analysis module; The power outage data management module includes a data analysis module, a risk prediction module, and an intelligent optimization module; The analysis module includes an acquisition module, a processing module, and a judgment module; The allocation module includes a collection unit, a processing unit, a calculation unit, and a scheduling unit.

2. The energy storage demand management system for a power grid according to claim 1, characterized in that: The power outage data management module is used to mine and analyze the influencing factors of the power outage plan based on big data.

3. The energy storage demand management system for a power grid according to claim 1, characterized in that: The data analysis module includes a historical data analysis unit, a future data analysis unit and a power outage execution data unit; The historical data analysis unit is used to study and analyze the causes of power outages affecting equipment based on historical power outage data; The future data analysis unit is used to study and analyze the causes of power outages of equipment based on future power outage plan data; The power outage execution data unit is used to statistically analyze the power outage plan execution status and to discover the main key reasons that affect the power outage plan execution.

4. The energy storage demand management system for a power grid according to claim 1, characterized in that: The risk prediction module includes an evaluation unit and an execution unit; The evaluation unit is used to perform quantitative evaluation based on the operational safety risk of the regional power grid, formulate technical specifications, and then study and establish an evaluation index calculation model for the power outage risk of the regional power grid based on the technical specifications; The execution unit is used to study and identify weak areas and key equipment sets with greater power grid operation risks, and to execute measures to reduce power grid operation risks.

5. The energy storage demand management system for a power grid according to claim 1, characterized in that: The intelligent optimization module includes a constraint unit, an optimization unit and a solution unit; The constraint unit is used to study and establish a power outage plan rule base based on the power outage procedures of power grid equipment and expert experience, comprehensively consider safety constraints such as equipment maintenance conflicts, sets of equipment that are shut down at the same time, sets of mutually exclusive equipment, and power protection, and provide effective boundary constraints for the analysis and verification of the power outage plan; The optimization unit is used to comprehensively consider the power outage plan rule base based on the total power outage demand and the power grid and personnel carrying capacity, on the basis of meeting the construction time sequence requirements of the infrastructure construction milestone plan, to study and establish a power outage plan intelligent optimization model, balance the annual and monthly power outage plans, reduce power outage losses, reduce power outage risks, maintain the required power supply reliability, and optimize the implementation time and construction period of the power outage plan project; The solution unit is used to study efficient solution technology for power outage plan optimization based on artificial intelligence assistance.

6. The energy storage demand management system for a power grid according to claim 1, characterized in that: The acquisition module is used to acquire load data of each load borrowing point in the distribution network; The processing module is used to process the data obtained by the acquisition module, the processing module defines the node load value at the current time point as the current load value, and defines the remaining node load values ​​obtained within the detection interval as the historical load values, and defines the time point when the historical load value determined by the judgment module is consistent with the current load value as the similarity point; the processing module establishes a standard interval at the current time point according to a preset fixed time length, and establishes a comparison interval at the similarity point according to a fixed time length; The processing module performs difference calculation according to the historical load values ​​at the corresponding time points in the comparison interval and the standard interval to determine the deviation load value, and determines the deviation duration according to the time point corresponding to the deviation load value; the processing module determines the weight parameter corresponding to the deviation duration according to the preset weight matching relationship, and performs calculation according to all the deviation load values ​​and the corresponding weight parameters to determine the deviation parameter; the processing module determines the deviation parameter with the smallest value according to the preset sorting rule, and defines the similar point corresponding to the deviation parameter as the reference point, and establishes a reference interval according to the reference point and the preset reference duration; the processing module determines the predicted load value of the current load node at each time point within the reference duration after the current time point according to the historical load values ​​in the reference interval; The judgment module is used for judging information.

7. The energy storage demand management system for a power grid according to claim 1, characterized in that: The acquisition unit is used to collect energy storage data of each node of the power grid and generate an energy storage data set; The processing unit is used to preprocess and clean the energy storage data set, and construct a topological map of the power grid according to the preprocessed and cleaned energy storage data set; The calculation unit is used to analyze and calculate the power grid topology map using a graph calculation algorithm to obtain a calculation result; The dispatching unit is used to generate an optimized dispatching decision according to the calculation result, so as to optimize the energy storage of the power grid according to the optimized dispatching decision.

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