Energy storage energy management and scheduling method and system based on digitization and medium

Abstract

The invention provides an energy storage energy management and scheduling method and system based on digitization and a medium. The method comprises the steps of obtaining and processing productivity planning data of a user to obtain predicted power demand data, obtaining characteristic data of an energy storage cabinet, combining the characteristic data of the energy storage cabinet with the predicted power demand data for processing to obtain charging electric quantity data of the energy storage cabinet, and charging the energy storage cabinet according to the charging electric quantity data of the energy storage cabinet. Acquiring and processing electric equipment information of the user to obtain an electricity utilization priority list, and performing power dispatching according to the electricity utilization priority list; according to the method, the power utilization demand is evaluated according to the capacity of the user, the charging amount of the energy storage cabinet is intelligently adjusted according to the power utilization demand, the power utilization priority of the power utilization equipment is evaluated, and power dispatching is reasonably carried out according to the power utilization priority, so that intelligent operation and maintenance of the energy storage cabinet are realized, and the efficiency and accuracy of power dispatching are improved.

Description

Technical Field

[0001] The present application relates to the field of energy storage technology, and in particular to a digital-based energy storage energy management and scheduling method, system and medium. Background Art

[0002] With the rapid development of power technology and the construction of smart grids, energy storage technology is being used more and more widely in power systems. Energy storage cabinets can effectively balance the fluctuations in power load, improve the stability and reliability of the power grid, and reduce user operating costs. However, traditional energy storage management lacks intelligence and cannot reasonably implement energy storage according to user needs. Power dispatching also has problems such as low efficiency, slow response speed, and low accuracy.

[0003] In view of the above problems, effective technical solutions are urgently needed. Summary of the invention

[0004] The purpose of the present application is to provide a digital-based energy storage management and scheduling method, system and medium, which can evaluate the power demand according to the user's production capacity, intelligently adjust the charging amount of the energy storage cabinet according to the power demand, evaluate the power priority of the power-consuming equipment, and reasonably schedule the power according to the power priority, thereby realizing the intelligent operation and maintenance of the energy storage cabinet and improving the efficiency and accuracy of power scheduling.

[0005] The present application also provides a digital energy storage energy management and scheduling method, comprising the following steps:

[0006] Obtain the user's capacity planning data and process it to obtain the predicted electricity demand data;

[0007] Acquire characteristic data of the energy storage cabinet, process the characteristic data of the energy storage cabinet in combination with the predicted power demand data to obtain charging power data of the energy storage cabinet, and charge the energy storage cabinet according to the charging power data of the energy storage cabinet;

[0008] Obtain and process the user's electrical equipment information to obtain a power usage priority list;

[0009] Electricity dispatch is performed according to the electricity priority list.

[0010] Optionally, in the digital energy storage management and scheduling method described in the present application, the step of obtaining and processing the user's capacity planning data to obtain predicted electricity demand data includes:

[0011] Obtain the user's capacity planning data, including production data and average power consumption data per unit;

[0012] Obtain the user's historical no-load power consumption average data;

[0013] The production data, the average power consumption data of a single piece and the historical average power consumption data under no-load conditions are input into a preset power demand forecasting model for processing to obtain forecast power demand data.

[0014] Optionally, in the digital energy storage management and scheduling method described in the present application, the acquiring of energy storage cabinet characteristic data, processing the energy storage cabinet characteristic data in combination with the predicted power demand data to obtain energy storage cabinet charging power data, and charging the energy storage cabinet according to the energy storage cabinet charging power data, includes:

[0015] Obtain energy storage cabinet characteristic data, including charging rate data, minimum storage data and discharge efficiency data;

[0016] Obtain the average power loss data of the energy storage cabinet within a preset time period;

[0017] Input the predicted power demand data, the minimum stock data, the discharge efficiency data, and the power loss mean value data into a preset energy storage cabinet charging capacity evaluation model for processing to obtain energy storage cabinet charging capacity data;

[0018] Obtain valley power time data at the location of the energy storage cabinet;

[0019] The energy storage cabinet is charged according to the valley power time data combined with the charging power data and the charging rate data of the energy storage cabinet.

[0020] Optionally, in the digital-based energy storage management and scheduling method described in the present application, the performing a charging operation on the energy storage cabinet according to the valley power time data combined with the energy storage cabinet charging power data and charging rate data includes:

[0021] Extracting valley power start time data and valley power end time data according to the valley power time data;

[0022] Processing the power-on start time data and the valley power end time data to obtain valley power duration data;

[0023] Processing the charging power data and charging rate data of the energy storage cabinet to obtain charging requirement time data;

[0024] If the charging demand duration data is greater than or equal to the valley power duration data, the charging power data is increased.

[0025] Optionally, in the digital energy storage management and scheduling method described in the present application, the step of obtaining and processing the user's electrical equipment information to obtain the electricity priority list includes:

[0026] Obtain the user's electrical equipment information and extract electrical equipment characteristic data, including electrical equipment type data and electrical equipment energy consumption data;

[0027] According to the electrical equipment type data and the electrical equipment energy consumption data, a preset weight value list is queried to obtain the corresponding type weight value and energy supply weight value, and the weight values ​​are accumulated to obtain the electrical equipment weight value;

[0028] According to the weight values ​​of the power consuming devices, a power consumption priority list is generated from high to low.

[0029] Optionally, the digital energy storage management and scheduling method described in the present application further includes:

[0030] Obtain peak power storage data of the energy storage cabinet;

[0031] Obtain the number of times the predicted power demand data is greater than the peak power storage data within a preset time period;

[0032] Performing a threshold comparison between the number value and a preset number threshold;

[0033] If it is less than or equal to the preset number threshold, no processing will be performed;

[0034] If it is greater than the preset number threshold, the profit calculation is activated and the capacity of the energy storage cabinet is expanded according to the calculation results.

[0035] In a second aspect, the present application provides a digital energy storage management and scheduling system, the system comprising: a memory and a processor, the memory comprising a program of a digital energy storage management and scheduling method, the program of the digital energy storage management and scheduling method being executed by the processor to implement the following steps:

[0036] Obtain the user's capacity planning data and process it to obtain the predicted electricity demand data;

[0037] Acquire characteristic data of the energy storage cabinet, process the characteristic data of the energy storage cabinet in combination with the predicted power demand data to obtain charging power data of the energy storage cabinet, and charge the energy storage cabinet according to the charging power data of the energy storage cabinet;

[0038] Obtain and process the user's electrical equipment information to obtain a power usage priority list;

[0039] Electricity dispatch is performed according to the electricity priority list.

[0040] Optionally, in the digital energy storage management and scheduling system described in the present application, the step of obtaining and processing the user's capacity planning data to obtain predicted electricity demand data includes:

[0041] Obtain the user's capacity planning data, including production data and average power consumption data per unit;

[0042] Obtain the user's historical no-load power consumption average data;

[0043] The production data, the average power consumption data of a single piece and the historical average power consumption data under no-load conditions are input into a preset power demand forecasting model for processing to obtain forecast power demand data.

[0044] Optionally, in a digital energy storage management and scheduling system described in the present application, the acquiring of energy storage cabinet characteristic data, processing the energy storage cabinet characteristic data in combination with the predicted power demand data to obtain energy storage cabinet charging power data, and charging the energy storage cabinet according to the energy storage cabinet charging power data, includes:

[0045] Obtain energy storage cabinet characteristic data, including charging rate data, minimum storage data and discharge efficiency data;

[0046] Obtain the average power loss data of the energy storage cabinet within a preset time period;

[0047] Input the predicted power demand data, the minimum stock data, the discharge efficiency data, and the power loss mean value data into a preset energy storage cabinet charging capacity evaluation model for processing to obtain energy storage cabinet charging capacity data;

[0048] Obtain valley power time data at the location of the energy storage cabinet;

[0049] The energy storage cabinet is charged according to the valley power time data combined with the charging power data and the charging rate data of the energy storage cabinet.

[0050] In a third aspect, the present application also provides a computer-readable storage medium, which stores a digital-based energy storage energy management and scheduling method program. When the digital-based energy storage energy management and scheduling method program is executed by a processor, the steps of the digital-based energy storage energy management and scheduling method as described in any one of the above items are implemented.

[0051] From the above, it can be seen that the present application provides a digital-based energy storage management and scheduling method, system and medium, which evaluates the electricity demand according to the user's production capacity, intelligently adjusts the charging amount of the energy storage cabinet according to the electricity demand, and evaluates the electricity priority of the electrical equipment. According to the electricity priority, reasonable power scheduling is carried out, thereby realizing the intelligent operation and maintenance of the energy storage cabinet and improving the efficiency and accuracy of power scheduling.

[0052] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the embodiments of the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0054] Figure 1 A flowchart of a digital energy storage management and scheduling method provided in an embodiment of the present application;

[0055] Figure 2 A flow chart of obtaining predicted electricity demand data based on a digital energy storage energy management and scheduling method provided in an embodiment of the present application;

[0056] Figure 3 A flowchart of obtaining a power priority list based on a digital energy storage management and scheduling method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0057] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0058] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0059] Please refer to Figure 1 , Figure 1 The flowchart of the energy storage energy management and scheduling method based on digitalization in some embodiments of the present application. The energy storage energy management and scheduling method based on digitalization is used in terminal devices, such as computers, mobile terminals, etc. The energy storage energy management and scheduling method based on digitalization includes the following steps:

[0060] S11, obtaining the user's capacity planning data and processing it to obtain predicted electricity demand data;

[0061] S12, acquiring characteristic data of the energy storage cabinet, processing the characteristic data of the energy storage cabinet in combination with the predicted power demand data to obtain charging power data of the energy storage cabinet, and charging the energy storage cabinet according to the charging power data of the energy storage cabinet;

[0062] S13, obtaining and processing the user's electrical equipment information to obtain an electrical priority list;

[0063] S14. Perform power dispatch according to the power priority list.

[0064] It should be noted that in order to obtain the dynamic charging capacity of the energy storage cabinet and realize reasonable power dispatch, the user's capacity planning data is first obtained to predict the power demand data under the capacity, and then the characteristic data of the energy storage cabinet including the minimum inventory data and discharge efficiency data is obtained. After comprehensive processing, the charging capacity data of the energy storage cabinet is obtained, and the energy storage cabinet is charged. The energy storage cabinet is charged; by obtaining the information of each power-consuming device of the user, according to the type of power-consuming device and the energy consumption data of the power-consuming device, the power priority list is obtained, and reasonable power dispatch is carried out according to the power priority list.

[0065] Please refer to Figure 2 , Figure 2 The flowchart of the power demand data forecasting method based on digital energy storage management and scheduling in some embodiments of the present application is as follows. According to an embodiment of the present invention, the user's capacity planning data is obtained and processed to obtain the forecast power demand data, including:

[0066] S21. Obtain the user's production capacity planning data, including production data and average power consumption data of a single unit;

[0067] S22, obtaining the user's historical no-load power consumption average data;

[0068] S23, inputting the production data, the average power consumption data of a single piece and the historical average no-load power consumption data into a preset power demand prediction model for processing to obtain predicted power demand data.

[0069] It should be noted that the energy storage cabinet has the function of realizing peak load shaving and valley filling of electricity, which can reasonably reduce the user's operating cost. The user's production capacity determines the power demand. Therefore, firstly, the production capacity planning data including the production data and the average power consumption data of a single unit are obtained, and then the historical no-load power consumption data of the user is obtained. The historical no-load power consumption data refers to the average of the necessary power consumption data in the historical time period when the user is not performing production operations. The production data, the average power consumption data of a single unit and the historical no-load power consumption data are input into the preset power demand prediction model for processing to obtain the predicted power demand data;

[0070] The calculation formula for predicting power demand data in the power demand prediction model is:

[0071] n e =α×p r e a +β×h e ;

[0072] Among them, n e To predict electricity demand data, p r 、e a 、h e They are production data, average power consumption data of a single unit and historical average power consumption data at no-load, and α and β are preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset energy storage energy management and scheduling platform).

[0073] According to an embodiment of the present invention, the step of acquiring characteristic data of an energy storage cabinet, processing the characteristic data of the energy storage cabinet in combination with the predicted power demand data to obtain charging power data of the energy storage cabinet, and charging the energy storage cabinet according to the charging power data of the energy storage cabinet includes:

[0074] Obtain energy storage cabinet characteristic data, including charging rate data, minimum storage data and discharge efficiency data;

[0075] Obtain the average power loss data of the energy storage cabinet within a preset time period;

[0076] Input the predicted power demand data, the minimum stock data, the discharge efficiency data, and the power loss mean value data into a preset energy storage cabinet charging capacity evaluation model for processing to obtain energy storage cabinet charging capacity data;

[0077] Obtain valley power time data at the location of the energy storage cabinet;

[0078] The energy storage cabinet is charged according to the valley power time data combined with the charging power data and the charging rate data of the energy storage cabinet.

[0079] It should be noted that before charging the energy storage cabinet, the characteristics of the energy storage cabinet should be taken into consideration, such as the minimum storage capacity requirement, the discharge efficiency, etc. Therefore, the characteristic data of the energy storage cabinet including the charging rate data, the minimum storage capacity data and the discharge efficiency data are first obtained, wherein the minimum storage capacity data refers to the minimum battery capacity set to ensure the battery life, and then the average power loss data of the energy storage cabinet within the preset time period is obtained. The average power loss data refers to the average power of the energy storage cabinet that is inevitably automatically discharged during the operation of the energy storage cabinet within the preset time period. The predicted power demand data, the minimum storage capacity data, the discharge efficiency data and the average power loss data are input into the preset energy storage cabinet charging capacity evaluation model for processing to obtain the energy storage cabinet charging capacity data;

[0080] The calculation formula for the energy storage cabinet charging capacity data in the energy storage cabinet charging capacity evaluation model is:

[0081]

[0082] Among them, c e The charging power data of the energy storage cabinet, n e 、s e d e 、p l are respectively the predicted electricity demand data, the minimum stock data, the discharge efficiency data and the power loss mean data, and χ is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset energy storage energy management and dispatching platform);

[0083] The off-peak time of the power department is queried according to the geographical location of the energy storage cabinet. After the off-peak start time is triggered, the energy storage cabinet is charged according to the obtained energy storage cabinet charging power data combined with the charging rate data.

[0084] According to an embodiment of the present invention, performing a charging operation on the energy storage cabinet according to the valley power time data combined with the charging power data and the charging rate data of the energy storage cabinet includes:

[0085] Extracting valley power start time data and valley power end time data according to the valley power time data;

[0086] Processing the power-on start time data and the valley power end time data to obtain valley power duration data;

[0087] Processing the charging power data and charging rate data of the energy storage cabinet to obtain charging requirement time data;

[0088] If the charging demand duration data is greater than or equal to the valley power duration data, the charging power data is increased.

[0089] It should be noted that, since the valley power time has a fixed start and end time, in order to determine whether the energy storage cabinet can be charged within the effective time, it is necessary to extract the valley power start time data and the valley power end time data according to the valley power time data, and process to obtain the valley power duration data. For example, if the valley power start time is 20:00 and the end time is 06:00, the valley power duration data is 600 minutes. The factors that determine the charging time of the energy storage cabinet include the charging power and the charging rate. Therefore, the charging power data of the energy storage cabinet is divided by the charging rate data to obtain the charging requirement duration data. Finally, the obtained charging requirement duration data is compared with the queried valley power duration data. If it is less than the valley power duration data, there is no need to increase the charging rate. If it is greater than or equal to the valley power duration data, the charging power is increased to ensure that the energy storage cabinet is charged within the valley power time.

[0090] Please refer to Figure 3 , Figure 3 The flowchart of obtaining the power priority list of the method for digital energy storage energy management and scheduling in some embodiments of the present application is as follows. According to an embodiment of the present invention, the method of obtaining and processing the user's power equipment information to obtain the power priority list includes:

[0091] S31, obtaining the user's electrical equipment information, and extracting electrical equipment characteristic data, including electrical equipment type data and electrical equipment energy consumption data;

[0092] S32, querying a preset weight value list according to the electrical equipment type data and the electrical equipment energy consumption data, obtaining a corresponding type weight value and energy supply weight value, and accumulating them to obtain an electrical equipment weight value;

[0093] S33: Generate a power consumption priority list from high to low according to the weight values ​​of the power consumption devices.

[0094] It should be noted that the energy storage cabinet will correspond to multiple types of electrical equipment, such as fire-fighting equipment, mixing equipment, transportation equipment, tightening tools and lighting equipment, and the importance of electrical equipment is also different. Therefore, it is necessary to obtain the user's electrical equipment information, extract the electrical equipment feature data including the electrical equipment type data and the electrical equipment energy consumption data, query the preset weight value list according to the electrical equipment type data and the electrical equipment energy consumption data, and obtain the corresponding type weight value and energy supply weight value. The preset weight value list is set in advance by the user according to the enterprise situation and is corrected in real time based on the operation situation. The obtained type weight value is added to the energy supply weight value to obtain the electrical equipment weight value corresponding to the type of electrical equipment. For example, the type weight value of the fire-fighting equipment is 10 points and the energy supply weight value is 8 points, then 10+8=18 is the electrical equipment weight value of the fire-fighting equipment. According to the electrical equipment weight values ​​corresponding to multiple types of electrical equipment, a power priority list is generated from high to low. The user reasonably implements power dispatch based on the power of the energy storage cabinet to ensure priority power supply to important equipment.

[0095] According to an embodiment of the present invention, it also includes:

[0096] Obtain peak power storage data of the energy storage cabinet;

[0097] Obtain the number of times the predicted power demand data is greater than the peak power storage data within a preset time period;

[0098] Performing a threshold comparison between the number value and a preset number threshold;

[0099] If it is less than or equal to the preset number threshold, no processing will be performed;

[0100] If it is greater than the preset number threshold, the profit calculation is activated and the capacity of the energy storage cabinet is expanded according to the calculation results.

[0101] It should be noted that after the installation of the energy storage cabinet is completed, in order to determine whether the stored power of the energy storage cabinet meets the current power production demand of the user; first, the peak power storage data of the energy storage cabinet is obtained, and the predicted power demand data within the preset time period is compared with the peak power storage data of the energy storage cabinet. In this embodiment, the preset time period is set to 15 days, and the number of times greater than the peak power storage data is counted, and then the threshold is compared with the preset number threshold. In this embodiment, the preset number threshold is set to 7 times. For example, if the number of times greater than the peak power storage data within 15 days is counted as 6 times, no processing is performed, that is, there is no need to expand the energy storage cabinet. If the number of times greater than the peak power storage data obtained by statistics is 9 times, the benefit calculation is activated, and the capacity of the energy storage cabinet is expanded according to the calculation result, that is, the cost saved by expanding the energy storage cabinet and the expansion cost of installing the expanded energy storage cabinet are further calculated.

[0102] It is worth mentioning that according to an embodiment of the present invention, it also includes:

[0103] Obtaining the capacity difference data of the predicted power demand average data and the peak power storage data within a preset time period;

[0104] According to the capacity difference data, obtaining energy storage cabinet expansion demand data;

[0105] Processing the energy storage cabinet expansion demand data to obtain expansion cost amortization data of the energy storage cabinet;

[0106] Obtain the energy storage cabinet power supply cost data and the power grid power supply cost data within a preset time period for processing to obtain cost saving data;

[0107] Compare the expansion cost amortization data with the cost saving data to obtain a cost reduction deviation rate;

[0108] Comparing the cost reduction deviation rate with a preset cost reduction deviation threshold;

[0109] If it is less than or equal to the preset cost reduction deviation threshold, the energy storage cabinet will not be expanded;

[0110] If it is greater than the preset cost reduction deviation threshold, and the expansion cost amortization data is less than the grid power supply cost data, the energy storage cabinet is expanded according to the capacity difference mean data;

[0111] If it is greater than the preset cost reduction deviation threshold, and the expansion cost amortization data is greater than or equal to the grid power supply cost data, the energy storage cabinet will not be expanded.

[0112] It should be noted that a plurality of predicted power demand data are obtained, and the average value is calculated to obtain the average predicted power demand data, and the peak power storage data of the energy storage cabinet is subtracted to obtain the capacity difference data. According to the capacity difference data, the expansion demand data of the energy storage cabinet is obtained. The expansion demand data is the power storage data of the expanded energy storage cabinet. The battery cost, installation cost, operation and maintenance cost, etc. can be further obtained according to the expansion demand data, and the expansion cost is divided by the preset life of the expanded energy storage cabinet to obtain the expansion cost amortization data, that is, the cost amortized to each day. The specific calculation method is implemented according to user needs and is not described in detail here. The power supply cost data of the energy storage cabinet and the power supply cost data of the power grid within a preset time period are obtained. In this embodiment, the preset time period is 1 day. The power supply cost data of the energy storage cabinet refers to the power supply cost of the expanded part of the energy storage cabinet, including the expansion cost amortization data and the valley electricity fee data. The power supply cost data of the power grid refers to the cost of the power grid directly supplying the power exceeding the peak power storage of the energy storage cabinet. The expansion cost amortization data is compared with the cost saving data to obtain the cost reduction deviation rate.

[0113] The cost reduction deviation rate calculation formula is:

[0114]

[0115] Among them, r c is the cost reduction deviation rate, c t 、c s are respectively the power supply cost data of the energy storage cabinet and the power supply cost data of the power grid; the cost reduction deviation rate is compared with the preset cost reduction deviation threshold value. In this embodiment, the cost reduction deviation threshold value is set to (0, 0.35], (0.35, 1]. For example, if the obtained cost reduction deviation rate is 0.2, the energy storage cabinet is not expanded; if the obtained cost reduction deviation rate is 0.4, and the expansion cost amortization data is less than the power supply cost data of the power grid, the energy storage cabinet is expanded according to the capacity difference mean data; if the obtained cost reduction deviation rate is 0.4, and the expansion cost amortization data is greater than or equal to the power supply cost data of the power grid, the energy storage cabinet is not expanded.

[0116] It is worth mentioning that according to an embodiment of the present invention, it also includes:

[0117] Obtaining grid operation data within a preset range of the energy storage cabinet, including voltage fluctuation mean data, current fluctuation mean data, and frequency fluctuation mean data within a preset time period;

[0118] Inputting the voltage fluctuation mean value data, the current fluctuation mean value data and the frequency fluctuation mean value data into a preset power grid fluctuation assessment model for processing to obtain a power grid fluctuation assessment index;

[0119] Performing a threshold comparison between the power grid fluctuation assessment index and a preset power grid fluctuation assessment threshold;

[0120] If it is less than the preset grid fluctuation assessment threshold, no processing will be performed;

[0121] If it is greater than or equal to the preset grid fluctuation assessment threshold, the energy storage cabinet is activated to discharge in response to improve the stability of grid operation.

[0122] It should be noted that the stability of the power grid operation has a great influence on the performance of the equipment. Therefore, the power grid operation data of the voltage fluctuation mean data, the current fluctuation mean data and the frequency fluctuation mean data within the preset range of the energy storage cabinet including the preset time period are obtained, and the voltage fluctuation mean data, the current fluctuation mean data and the frequency fluctuation mean data are input into the preset power grid fluctuation assessment model for processing to obtain the power grid fluctuation assessment index;

[0123] The calculation formula of the power grid fluctuation assessment index in the power grid fluctuation assessment model is:

[0124]

[0125] Among them, f e is the power grid fluctuation assessment index, v e 、e i 、e f are the voltage fluctuation mean data, current fluctuation mean data and frequency fluctuation mean data, respectively, 1 , 2 is a preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset energy storage energy management and scheduling platform);

[0126] The grid fluctuation assessment index is compared with the preset grid fluctuation assessment threshold. In this embodiment, the grid fluctuation assessment threshold is set to (0, 0.25), [0.25, 1]. For example, if the obtained grid fluctuation assessment index is 0.2, no processing is performed; if the obtained grid fluctuation assessment index is 0.4, the energy storage cabinet is activated for discharge response to improve the stability of grid operation.

[0127] The present invention also discloses a digital energy storage energy management and scheduling system, including a memory and a processor, wherein the memory includes a digital energy storage energy management and scheduling method program, and when the digital energy storage energy management and scheduling method program is executed by the processor, the following steps are implemented:

[0128] Obtain the user's capacity planning data and process it to obtain the predicted electricity demand data;

[0129] Acquire characteristic data of the energy storage cabinet, process the characteristic data of the energy storage cabinet in combination with the predicted power demand data to obtain charging power data of the energy storage cabinet, and charge the energy storage cabinet according to the charging power data of the energy storage cabinet;

[0130] Obtain and process the user's electrical equipment information to obtain a power usage priority list;

[0131] Electricity dispatch is performed according to the electricity priority list.

[0132] It should be noted that in order to obtain the dynamic charging capacity of the energy storage cabinet and realize reasonable power dispatch, the user's capacity planning data is first obtained to predict the power demand data under the capacity, and then the characteristic data of the energy storage cabinet including the minimum inventory data and discharge efficiency data is obtained. After comprehensive processing, the charging capacity data of the energy storage cabinet is obtained, and the energy storage cabinet is charged. The energy storage cabinet is charged; by obtaining the information of each power-consuming device of the user, according to the type of power-consuming device and the energy consumption data of the power-consuming device, the power priority list is obtained, and reasonable power dispatch is carried out according to the power priority list.

[0133] According to an embodiment of the present invention, the step of obtaining and processing the user's capacity planning data to obtain predicted electricity demand data includes:

[0134] Obtain the user's capacity planning data, including production data and average power consumption data per unit;

[0135] Obtain the user's historical no-load power consumption average data;

[0136] The production data, the average power consumption data of a single piece and the historical average power consumption data under no-load conditions are input into a preset power demand forecasting model for processing to obtain forecast power demand data.

[0137] It should be noted that the energy storage cabinet has the function of realizing peak load shaving and valley filling of electricity, which can reasonably reduce the user's operating cost. The user's production capacity determines the power demand. Therefore, firstly, the production capacity planning data including the production data and the average power consumption data of a single unit are obtained, and then the historical no-load power consumption data of the user is obtained. The historical no-load power consumption data refers to the average of the necessary power consumption data in the historical time period when the user is not performing production operations. The production data, the average power consumption data of a single unit and the historical no-load power consumption data are input into the preset power demand prediction model for processing to obtain the predicted power demand data;

[0138] The calculation formula for predicting power demand data in the power demand prediction model is:

[0139] n e =α×p r e a +β×h e ;

[0140] Among them, n e To predict electricity demand data, p r 、e a 、h eThey are production data, average power consumption data of a single unit and historical average power consumption data at no-load, and α and β are preset characteristic coefficients (the characteristic coefficients are obtained by querying the preset energy storage energy management and scheduling platform).

[0141] According to an embodiment of the present invention, the step of acquiring characteristic data of an energy storage cabinet, processing the characteristic data of the energy storage cabinet in combination with the predicted power demand data to obtain charging power data of the energy storage cabinet, and charging the energy storage cabinet according to the charging power data of the energy storage cabinet includes:

[0142] Obtain energy storage cabinet characteristic data, including charging rate data, minimum storage data and discharge efficiency data;

[0143] Obtain the average power loss data of the energy storage cabinet within a preset time period;

[0144] Input the predicted power demand data, the minimum stock data, the discharge efficiency data, and the power loss mean value data into a preset energy storage cabinet charging capacity evaluation model for processing to obtain energy storage cabinet charging capacity data;

[0145] Obtain valley power time data at the location of the energy storage cabinet;

[0146] The energy storage cabinet is charged according to the valley power time data combined with the charging power data and the charging rate data of the energy storage cabinet.

[0147] It should be noted that before charging the energy storage cabinet, the characteristics of the energy storage cabinet should be taken into consideration, such as the minimum storage capacity requirement, the discharge efficiency, etc. Therefore, the characteristic data of the energy storage cabinet including the charging rate data, the minimum storage capacity data and the discharge efficiency data are first obtained, wherein the minimum storage capacity data refers to the minimum battery capacity set to ensure the battery life, and then the average power loss data of the energy storage cabinet within the preset time period is obtained. The average power loss data refers to the average power of the energy storage cabinet that is inevitably automatically discharged during the operation of the energy storage cabinet within the preset time period. The predicted power demand data, the minimum storage capacity data, the discharge efficiency data and the average power loss data are input into the preset energy storage cabinet charging capacity evaluation model for processing to obtain the energy storage cabinet charging capacity data;

[0148] The calculation formula for the energy storage cabinet charging capacity data in the energy storage cabinet charging capacity evaluation model is:

[0149]

[0150] Among them, c e The charging power data of the energy storage cabinet, n e 、s e d e 、p lare respectively the predicted electricity demand data, the minimum stock data, the discharge efficiency data and the power loss mean data, and χ is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset energy storage energy management and dispatching platform);

[0151] The off-peak time of the power department is queried according to the geographical location of the energy storage cabinet. After the off-peak start time is triggered, the energy storage cabinet is charged according to the obtained energy storage cabinet charging power data combined with the charging rate data.

[0152] According to an embodiment of the present invention, performing a charging operation on the energy storage cabinet according to the valley power time data combined with the charging power data and the charging rate data of the energy storage cabinet includes:

[0153] Extracting valley power start time data and valley power end time data according to the valley power time data;

[0154] Processing the power-on start time data and the valley power end time data to obtain valley power duration data;

[0155] Processing the charging power data and charging rate data of the energy storage cabinet to obtain charging requirement time data;

[0156] If the charging demand duration data is greater than or equal to the valley power duration data, the charging power data is increased.

[0157] It should be noted that, since the valley power time has a fixed start and end time, in order to determine whether the energy storage cabinet can be charged within the effective time, it is necessary to extract the valley power start time data and the valley power end time data according to the valley power time data, and process to obtain the valley power duration data. For example, if the valley power start time is 20:00 and the end time is 06:00, the valley power duration data is 600 minutes. The factors that determine the charging time of the energy storage cabinet include the charging power and the charging rate. Therefore, the charging power data of the energy storage cabinet is divided by the charging rate data to obtain the charging requirement duration data. Finally, the obtained charging requirement duration data is compared with the queried valley power duration data. If it is less than the valley power duration data, there is no need to increase the charging rate. If it is greater than or equal to the valley power duration data, the charging power is increased to ensure that the energy storage cabinet is charged within the valley power time.

[0158] According to an embodiment of the present invention, the step of obtaining and processing the user's power consumption device information to obtain a power consumption priority list includes:

[0159] Obtain the user's electrical equipment information and extract electrical equipment characteristic data, including electrical equipment type data and electrical equipment energy consumption data;

[0160] According to the electrical equipment type data and the electrical equipment energy consumption data, a preset weight value list is queried to obtain the corresponding type weight value and energy supply weight value, and the weight values ​​are accumulated to obtain the electrical equipment weight value;

[0161] According to the weight values ​​of the power consuming devices, a power consumption priority list is generated from high to low.

[0162] It should be noted that the energy storage cabinet will correspond to multiple types of electrical equipment, such as fire-fighting equipment, mixing equipment, transportation equipment, tightening tools and lighting equipment, and the importance of electrical equipment is also different. Therefore, it is necessary to obtain the user's electrical equipment information, extract the electrical equipment feature data including the electrical equipment type data and the electrical equipment energy consumption data, query the preset weight value list according to the electrical equipment type data and the electrical equipment energy consumption data, and obtain the corresponding type weight value and energy supply weight value. The preset weight value list is set in advance by the user according to the enterprise situation and is corrected in real time based on the operation situation. The obtained type weight value is added to the energy supply weight value to obtain the electrical equipment weight value corresponding to the type of electrical equipment. For example, the type weight value of the fire-fighting equipment is 10 points and the energy supply weight value is 8 points, then 10+8=18 is the electrical equipment weight value of the fire-fighting equipment. According to the electrical equipment weight values ​​corresponding to multiple types of electrical equipment, a power priority list is generated from high to low. The user reasonably implements power dispatch based on the power of the energy storage cabinet to ensure priority power supply to important equipment.

[0163] According to an embodiment of the present invention, it also includes:

[0164] Obtain peak power storage data of the energy storage cabinet;

[0165] Obtain the number of times the predicted power demand data is greater than the peak power storage data within a preset time period;

[0166] Performing a threshold comparison between the number value and a preset number threshold;

[0167] If it is less than or equal to the preset number threshold, no processing will be performed;

[0168] If it is greater than the preset number threshold, the profit calculation is activated and the capacity of the energy storage cabinet is expanded according to the calculation results.

[0169] It should be noted that after the installation of the energy storage cabinet is completed, in order to determine whether the stored power of the energy storage cabinet meets the current power production demand of the user; first, the peak power storage data of the energy storage cabinet is obtained, and the predicted power demand data within the preset time period is compared with the peak power storage data of the energy storage cabinet. In this embodiment, the preset time period is set to 15 days, and the number of times greater than the peak power storage data is counted, and then the threshold is compared with the preset number threshold. In this embodiment, the preset number threshold is set to 7 times. For example, if the number of times greater than the peak power storage data within 15 days is counted as 6 times, no processing is performed, that is, there is no need to expand the energy storage cabinet. If the number of times greater than the peak power storage data obtained by statistics is 9 times, the benefit calculation is activated, and the capacity of the energy storage cabinet is expanded according to the calculation result, that is, the cost saved by expanding the energy storage cabinet and the expansion cost of installing the expanded energy storage cabinet are further calculated.

[0170] It is worth mentioning that according to an embodiment of the present invention, it also includes:

[0171] Obtaining the capacity difference data of the predicted power demand average data and the peak power storage data within a preset time period;

[0172] According to the capacity difference data, obtaining energy storage cabinet expansion demand data;

[0173] Processing the energy storage cabinet expansion demand data to obtain expansion cost amortization data of the energy storage cabinet;

[0174] Obtain the energy storage cabinet power supply cost data and the power grid power supply cost data within a preset time period for processing to obtain cost saving data;

[0175] Compare the expansion cost amortization data with the cost saving data to obtain a cost reduction deviation rate;

[0176] Comparing the cost reduction deviation rate with a preset cost reduction deviation threshold;

[0177] If it is less than or equal to the preset cost reduction deviation threshold, the energy storage cabinet will not be expanded;

[0178] If it is greater than the preset cost reduction deviation threshold, and the expansion cost amortization data is less than the grid power supply cost data, the energy storage cabinet is expanded according to the capacity difference mean data;

[0179] If it is greater than the preset cost reduction deviation threshold, and the expansion cost amortization data is greater than or equal to the grid power supply cost data, the energy storage cabinet will not be expanded.

[0180] It should be noted that a plurality of predicted power demand data are obtained, and the average value is calculated to obtain the average predicted power demand data, and the peak power storage data of the energy storage cabinet is subtracted to obtain the capacity difference data. According to the capacity difference data, the expansion demand data of the energy storage cabinet is obtained. The expansion demand data is the power storage data of the expanded energy storage cabinet. The battery cost, installation cost, operation and maintenance cost, etc. can be further obtained according to the expansion demand data, and the expansion cost is divided by the preset life of the expanded energy storage cabinet to obtain the expansion cost amortization data, that is, the cost amortized to each day. The specific calculation method is implemented according to user needs and is not described in detail here. The power supply cost data of the energy storage cabinet and the power supply cost data of the power grid within a preset time period are obtained. In this embodiment, the preset time period is 1 day. The power supply cost data of the energy storage cabinet refers to the power supply cost of the expanded part of the energy storage cabinet, including the expansion cost amortization data and the valley electricity fee data. The power supply cost data of the power grid refers to the cost of the power grid directly supplying the power exceeding the peak power storage of the energy storage cabinet. The expansion cost amortization data is compared with the cost saving data to obtain the cost reduction deviation rate.

[0181] The cost reduction deviation rate calculation formula is:

[0182]

[0183] Among them, r c is the cost reduction deviation rate, c t 、c s are respectively the power supply cost data of the energy storage cabinet and the power supply cost data of the power grid; the cost reduction deviation rate is compared with the preset cost reduction deviation threshold value. In this embodiment, the cost reduction deviation threshold value is set to (0, 0.35], (0.35, 1]. For example, if the obtained cost reduction deviation rate is 0.2, the energy storage cabinet is not expanded; if the obtained cost reduction deviation rate is 0.4, and the expansion cost amortization data is less than the power supply cost data of the power grid, the energy storage cabinet is expanded according to the capacity difference mean data; if the obtained cost reduction deviation rate is 0.4, and the expansion cost amortization data is greater than or equal to the power supply cost data of the power grid, the energy storage cabinet is not expanded.

[0184] It is worth mentioning that according to an embodiment of the present invention, it also includes:

[0185] Obtaining grid operation data within a preset range of the energy storage cabinet, including voltage fluctuation mean data, current fluctuation mean data, and frequency fluctuation mean data within a preset time period;

[0186] Inputting the voltage fluctuation mean value data, the current fluctuation mean value data and the frequency fluctuation mean value data into a preset power grid fluctuation assessment model for processing to obtain a power grid fluctuation assessment index;

[0187] Performing a threshold comparison between the power grid fluctuation assessment index and a preset power grid fluctuation assessment threshold;

[0188] If it is less than the preset grid fluctuation assessment threshold, no processing will be performed;

[0189] If it is greater than or equal to the preset grid fluctuation assessment threshold, the energy storage cabinet is activated to discharge in response to improve the stability of grid operation.

[0190] It should be noted that the stability of the power grid operation has a great influence on the performance of the equipment. Therefore, the power grid operation data of the voltage fluctuation mean data, the current fluctuation mean data and the frequency fluctuation mean data within the preset range of the energy storage cabinet including the preset time period are obtained, and the voltage fluctuation mean data, the current fluctuation mean data and the frequency fluctuation mean data are input into the preset power grid fluctuation assessment model for processing to obtain the power grid fluctuation assessment index;

[0191] The calculation formula of the power grid fluctuation assessment index in the power grid fluctuation assessment model is:

[0192]

[0193] Among them, f e is the power grid fluctuation assessment index, v e 、e i 、e f are the voltage fluctuation mean data, current fluctuation mean data and frequency fluctuation mean data, respectively, 1 , 2 is a preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset energy storage energy management and scheduling platform);

[0194] The grid fluctuation assessment index is compared with the preset grid fluctuation assessment threshold. In this embodiment, the grid fluctuation assessment threshold is set to (0, 0.25), [0.25, 1]. For example, if the obtained grid fluctuation assessment index is 0.2, no processing is performed; if the obtained grid fluctuation assessment index is 0.4, the energy storage cabinet is activated for discharge response to improve the stability of grid operation.

[0195] A third aspect of the present invention provides a readable storage medium, which stores a digital-based energy storage energy management and scheduling method program. When the digital-based energy storage energy management and scheduling method program is executed by a processor, the steps of the digital-based energy storage energy management and scheduling method as described in any one of the above items are implemented.

[0196] The present invention discloses a digital-based energy storage management and scheduling method, system and medium, which evaluates the power demand according to the user's production capacity, intelligently adjusts the charging amount of the energy storage cabinet according to the power demand, evaluates the power priority of the power-consuming equipment, and reasonably schedules the power according to the power priority, thereby realizing the intelligent operation and maintenance of the energy storage cabinet and improving the efficiency and accuracy of power scheduling.

[0197] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0198] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0199] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0200] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0201] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. A digital energy storage management and scheduling method, characterized in that: The following steps are involved: Obtain the user's capacity planning data and process it to obtain the predicted electricity demand data; Acquire characteristic data of the energy storage cabinet, process the characteristic data of the energy storage cabinet in combination with the predicted power demand data to obtain charging power data of the energy storage cabinet, and charge the energy storage cabinet according to the charging power data of the energy storage cabinet; Obtain and process the user's electrical equipment information to obtain a power usage priority list; Electricity dispatch is performed according to the electricity priority list.

2. The digital energy storage management and scheduling method according to claim 1 is characterized in that: The obtaining and processing of the user's capacity planning data to obtain the predicted electricity demand data includes: Obtain the user's capacity planning data, including production data and average power consumption data per unit; Obtain the user's historical no-load power consumption average data; The production data, the average power consumption data of a single piece and the historical average power consumption data under no-load conditions are input into a preset power demand forecasting model for processing to obtain forecast power demand data.

3. The digital energy storage management and scheduling method according to claim 2 is characterized in that: The acquiring of energy storage cabinet characteristic data, processing the energy storage cabinet characteristic data in combination with the predicted power demand data to obtain energy storage cabinet charging power data, and charging the energy storage cabinet according to the energy storage cabinet charging power data, includes: Obtain energy storage cabinet characteristic data, including charging rate data, minimum storage data and discharge efficiency data; Obtain the average power loss data of the energy storage cabinet within a preset time period; Input the predicted power demand data, the minimum stock data, the discharge efficiency data, and the power loss mean value data into a preset energy storage cabinet charging capacity evaluation model for processing to obtain energy storage cabinet charging capacity data; Obtain valley power time data at the location of the energy storage cabinet; The energy storage cabinet is charged according to the valley power time data combined with the charging power data and the charging rate data of the energy storage cabinet.

4. The digital energy storage management and scheduling method according to claim 3 is characterized in that: The performing a charging operation on the energy storage cabinet according to the valley power time data combined with the charging power data and the charging rate data of the energy storage cabinet comprises: Extracting valley power start time data and valley power end time data according to the valley power time data; Processing the power-on start time data and the valley power end time data to obtain valley power duration data; Processing the charging power data and charging rate data of the energy storage cabinet to obtain charging requirement time data; If the charging demand duration data is greater than or equal to the valley power duration data, the charging power data is increased.

5. The digital energy storage management and scheduling method according to claim 4 is characterized in that: The step of obtaining and processing the user's electrical equipment information to obtain an electrical equipment priority list includes: Obtain the user's electrical equipment information and extract electrical equipment characteristic data, including electrical equipment type data and electrical equipment energy consumption data; According to the electrical equipment type data and the electrical equipment energy consumption data, a preset weight value list is queried to obtain the corresponding type weight value and energy supply weight value, and the weight values ​​are accumulated to obtain the electrical equipment weight value; According to the weight values ​​of the power consuming devices, a power consumption priority list is generated from high to low.

6. The digital energy storage management and scheduling method according to claim 5 is characterized in that: Also includes: Obtain peak power storage data of the energy storage cabinet; Obtain the number of times the predicted power demand data is greater than the peak power storage data within a preset time period; Performing a threshold comparison between the number value and a preset number threshold; If it is less than or equal to the preset number threshold, no processing will be performed; If it is greater than the preset number threshold, the profit calculation is activated and the capacity of the energy storage cabinet is expanded according to the calculation results.

7. A digital energy storage management and dispatching system, characterized in that: The system comprises a memory and a processor, wherein the memory comprises a program of a digital energy storage energy management and scheduling method, and the program of the digital energy storage energy management and scheduling method is executed by the processor to implement the following steps: Obtain the user's capacity planning data and process it to obtain the predicted electricity demand data; Acquire characteristic data of the energy storage cabinet, process the characteristic data of the energy storage cabinet in combination with the predicted power demand data to obtain charging power data of the energy storage cabinet, and charge the energy storage cabinet according to the charging power data of the energy storage cabinet; Obtain and process the user's electrical equipment information to obtain a power usage priority list; Electricity dispatch is performed according to the electricity priority list.

8. The digital energy storage management and dispatching system according to claim 7 is characterized in that: The obtaining and processing of the user's capacity planning data to obtain the predicted electricity demand data includes: Obtain the user's capacity planning data, including production data and average power consumption data per unit; Obtain the user's historical no-load power consumption average data; The production data, the average power consumption data of a single piece and the historical average no-load power consumption data are input into a preset power demand forecasting model for processing to obtain forecasted power demand data.

9. The digital energy storage management and dispatching system according to claim 8 is characterized in that: The acquiring of energy storage cabinet characteristic data, processing the energy storage cabinet characteristic data in combination with the predicted power demand data to obtain energy storage cabinet charging power data, and charging the energy storage cabinet according to the energy storage cabinet charging power data, includes: Obtain energy storage cabinet characteristic data, including charging rate data, minimum storage data and discharge efficiency data; Obtain the average power loss data of the energy storage cabinet within a preset time period; Input the predicted power demand data, the minimum stock data, the discharge efficiency data, and the power loss mean value data into a preset energy storage cabinet charging capacity evaluation model for processing to obtain energy storage cabinet charging capacity data; Obtain valley power time data at the location of the energy storage cabinet; The energy storage cabinet is charged according to the valley power time data combined with the charging power data and the charging rate data of the energy storage cabinet.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a digital energy storage energy management and scheduling program. When the digital energy storage energy management and scheduling program is executed by a processor, the steps of the digital energy storage energy management and scheduling method as described in any one of claims 1 to 6 are implemented.

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