Control method, device and equipment of transformer area energy storage equipment and medium

Abstract

The embodiment of the invention discloses a control method and device of transformer area energy storage equipment, equipment and a medium. Comprising the following steps: acquiring historical power quality information and multiple types of historical power load information of a target transformer area; inputting the historical electric energy quality information and the multiple types of historical electrical load information into a preset information prediction model to obtain first predicted electric energy quality information and multiple types of first predicted electrical load information; based on the first predicted power quality information and the multiple types of first predicted electrical load information, determining a charging and discharging strategy of energy storage equipment in the target transformer area; and controlling the energy storage device to charge and discharge based on the charge and discharge strategy. According to the technical scheme, the preset information prediction model is adopted to predict the electric energy quality information and the electrical load information in the future time period, the accuracy of the prediction result can be improved, the charging and discharging strategy of the energy storage equipment in the target transformer area can be accurately determined according to the prediction result, and therefore stable operation of a transformer area power system is guaranteed.

Description

Technical Field

[0001] Embodiments of the present invention relate to the technical field of power distribution networks, and in particular to a control method, device, equipment and medium for a substation energy storage device. Background Art

[0002] At present, after the introduction of energy storage equipment in the substation, the two work together to provide power to users. Among them, energy storage equipment can both charge and discharge. For example, during the low electricity consumption period in the substation, the energy storage equipment can absorb and store electricity from the substation grid, and during the peak electricity consumption period, the energy storage equipment will release the stored electricity to the substation grid. The charging and discharging strategy of energy storage equipment affects the stable operation of the power system.

[0003] In the prior art, for complex and changeable power load environments, the charging and discharging strategies of traditional substation energy storage equipment are difficult to dynamically adjust according to changes in actual loads. Lacking sufficient intelligent means, they can often only be adjusted based on simple empirical formulas or rules, and are unable to accurately determine the charging and discharging strategies of energy storage equipment. Summary of the invention

[0004] The embodiments of the present invention provide a control method, device, equipment and medium for a substation energy storage device, which can accurately determine the charging and discharging strategy of the energy storage device.

[0005] In a first aspect, an embodiment of the present invention provides a control method for a substation energy storage device, comprising:

[0006] Acquire historical power quality information of the target substation and multiple types of historical power load information; wherein the historical power quality information includes at least one of harmonics, voltage fluctuations, and frequency deviations;

[0007] Inputting the historical power quality information and the multiple types of historical power load information into a preset information prediction model to obtain first predicted power quality information and multiple types of first predicted power load information;

[0008] Determine a charging and discharging strategy of the energy storage device in the target substation based on the first predicted power quality information and the first predicted power load information of the plurality of types; wherein the charging and discharging strategy includes charging or discharging;

[0009] The energy storage device is controlled to charge and discharge based on the charge and discharge strategy.

[0010] In a second aspect, an embodiment of the present invention further provides a control device for a substation energy storage device, the device comprising:

[0011] An information acquisition module, used to acquire historical power quality information of a target substation and multiple types of historical power load information; wherein the historical power quality information includes at least one of harmonics, voltage fluctuations, and frequency deviations;

[0012] An information prediction module, configured to input the historical power quality information and the multiple types of historical power load information into a preset information prediction model to obtain first predicted power quality information and multiple types of first predicted power load information;

[0013] A charging and discharging strategy determination module, configured to determine a charging and discharging strategy of the energy storage device in the target substation based on the first predicted power quality information and the first predicted power load information of the plurality of types; wherein the charging and discharging strategy includes charging or discharging;

[0014] The device control module is used to control the energy storage device to charge and discharge based on the charge and discharge strategy.

[0015] In a third aspect, an embodiment of the present invention further provides an electronic device, the electronic device comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the control method of the substation energy storage device described in the embodiment of the present invention.

[0019] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the control method of the substation energy storage device described in the embodiment of the present invention when executed.

[0020] In a fifth aspect, an embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the control method of the substation energy storage device described in the embodiment of the present invention.

[0021] The embodiments of the present invention disclose a control method, device, equipment and medium for a substation energy storage device, including: obtaining historical power quality information of a target substation and multiple types of historical power load information; wherein the historical power quality information includes at least one of harmonics, voltage fluctuations and frequency deviations; inputting the historical power quality information and multiple types of historical power load information into a preset information prediction model to obtain first predicted power quality information and multiple types of first predicted power load information; determining a charging and discharging strategy for an energy storage device in the target substation based on the first predicted power quality information and multiple types of first predicted power load information; wherein the charging and discharging strategy includes charging or discharging; and controlling the energy storage device to charge and discharge based on the charging and discharging strategy. The above technical solution, based on the historical power quality information of the target substation and multiple types of historical power load information, adopts a preset information prediction model to predict the power quality information and power load information of the future time period, that is, the first predicted power quality information and the first predicted power load information, which can improve the accuracy of the prediction results; at the same time, the first predicted power quality information and multiple types of first predicted power load information obtained according to the above prediction can accurately determine the charging and discharging strategy of the energy storage device in the target substation; finally, the energy storage device is controlled to charge and discharge according to the charging and discharging strategy to ensure the stable operation of the power system in the substation. Compared with the prior art, the technical solution provided in this embodiment adopts a preset information prediction model to predict the power quality information and power load information of the future time period, which can improve the accuracy of the prediction results, and can accurately determine the charging and discharging strategy of the energy storage device in the target substation according to the prediction results, thereby ensuring the stable operation of the power system in the substation. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a flow chart of a control method of a substation energy storage device in Embodiment 1 of the present invention;

[0023] Figure 2 It is a flow chart of a control method of a substation energy storage device in Embodiment 2 of the present invention;

[0024] Figure 3 It is a structural schematic diagram of a control device for a substation energy storage device in Embodiment 3 of the present invention;

[0025] Figure 4 It is a structural schematic diagram of an electronic device in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0026] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0027] Embodiment 1

[0028] Figure 1 This is a flow chart of a control method for a substation energy storage device provided in Embodiment 1 of the present invention. This embodiment is applicable to the case of controlling the charging and discharging of substation energy storage devices. The method can be executed by a control device for substation energy storage devices, which can be implemented in the form of software and / or hardware. Optionally, it can be implemented by an electronic device, which can be a mobile terminal, a PC or a server, etc. Specifically, it includes the following steps:

[0029] S110: Obtain historical power quality information and multiple types of historical power load information of the target substation.

[0030] In this embodiment, the substation can be understood as the power supply range of a transformer, and the target substation is the substation studied in this embodiment. The power quality information is used to reflect the power quality status in the power system, and the historical power quality information can be understood as the power quality information in the historical time period. Optionally, the historical power quality information may include at least one of harmonics, voltage fluctuations and frequency deviations, wherein the harmonics can be understood as the total harmonic distortion rate, that is, the ratio of the effective value of the harmonic voltage (or current) to the effective value of the fundamental voltage (or current); the voltage fluctuation can be understood as the change or continuous change of the effective value of the voltage; the frequency deviation can be understood as the difference between the actual frequency of the power system and the rated frequency.

[0031] In this embodiment, the power load information can be understood as the power consumption, and the historical power load information can be understood as the power load information in the historical time period. The multiple types of historical power load information may include: historical power load information of residential users, historical power load information of commercial users, and historical power load information of industrial users, that is, the multiple types may include residential users, commercial users, and industrial users.

[0032] Furthermore, the historical power quality information of the target substation and multiple types of historical power load information are pre-stored in a setting database, and the setting database stores the historical power quality information and multiple types of historical power load information corresponding to each substation in the distribution network, and each substation is provided with a substation index number in the setting database. In this embodiment, the method of obtaining the historical power quality information and multiple types of historical power load information of the target substation can be: obtaining the index number of the target substation, and obtaining the historical power quality information and multiple types of historical power load information of the target substation from the setting database according to the index number.

[0033] S120. Input historical power quality information and multiple types of historical power load information into a preset information prediction model to obtain first predicted power quality information and multiple types of first predicted power load information.

[0034] In this embodiment, the preset information prediction model is a pre-trained model, which is used to predict power quality information and multiple types of power load information in a future time period based on historical power quality information and multiple types of historical power load information.

[0035] Furthermore, the preset information prediction model includes a multilayer perceptron and a convolutional neural network; wherein the output of the multilayer perceptron is connected to the input of the convolutional neural network. The first predicted power quality information can be understood as the predicted power quality information within a future time period, and the multiple types of first predicted power load information can be understood as the predicted power load information within a future time period.

[0036] Optionally, historical power quality information and multiple types of historical power load information are input into a preset information prediction model to obtain first predicted power quality information and multiple types of first predicted power load information. The method can be: historical power quality information and multiple types of historical power load information are input into a multi-layer perceptron and a convolutional neural network in sequence to obtain first predicted power quality information and multiple types of first predicted power load information.

[0037] In this embodiment, the training method of the preset information prediction model can be described as the following steps:

[0038] a1) Collecting power quality information samples of the target area and multiple types of power load information samples;

[0039] In this embodiment, the power quality information samples and the power load information samples can be understood as data sets used to train a preset information prediction model, and they are all samples within a historical time period.

[0040] b1) inputting the power quality information samples and multiple types of power load information samples into a preset information prediction model to obtain second predicted power quality information and multiple types of second predicted power load information;

[0041] In this embodiment, the second predicted power quality information and the second predicted power load information are prediction results output during the training of the preset information prediction model.

[0042] c1) training a preset information prediction model based on the second predicted power quality information and the actual power quality information as well as the second predicted power load information and the actual power load information.

[0043] In this embodiment, the real power quality information can be understood as the actual power quality information in the future time period corresponding to the power quality information sample in the historical time period. For example, assuming that the power quality information sample is the power quality information sample in the time period t1 to t5, the real power quality information is the power quality information in the time period t5 to t7.

[0044] In this embodiment, the real power load information can be understood as the actual power load information in the future time period corresponding to the power load information sample in the historical time period. For example, assuming that the power load information sample is the power load information sample in the time period t1 to t5, the real power load information is the power load information in the time period t5 to t7.

[0045] In this embodiment, for training a preset information prediction model based on the second predicted power quality information and the actual power quality information as well as the second predicted power load information and the actual power load information, one implementation method can be described as: determining a first loss function based on the second predicted power quality information and the actual power quality information; determining a second loss function based on the second predicted power load information and the actual power load information; and training the preset information prediction model based on the first loss function and the second loss function.

[0046] Continuing with the above description, the first loss function is used to characterize the error between the second predicted power quality information and the actual power quality information, and the second loss function is used to characterize the error between the second predicted power load information and the actual power load information. The first loss function and the second loss function can be a mean square error loss function, a mean absolute error loss function or a Huber loss function, etc. Furthermore, the Huber loss function combines the advantages of the mean square error loss function and the mean absolute error loss function. When the prediction error is small, it behaves like a mean square error loss function, which can enable the preset information prediction model to converge quickly; when the error is large, it behaves like a mean absolute error loss function, which is less sensitive to abnormally large errors.

[0047] Optionally, the method of training the preset information prediction model based on the first loss function and the second loss function can be: fusing the first loss function and the second loss function to obtain a fused loss function; and reversely adjusting the parameters in the preset information prediction model according to the fused loss function.

[0048] The fusion may be understood as weighted summation or averaging, and the parameters in the preset information prediction model may be weight parameters and the like.

[0049] S130: Determine a charging and discharging strategy for the energy storage device in the target substation based on the first predicted power quality information and multiple types of first predicted power load information.

[0050] In this embodiment, the energy storage device can be understood as a key device for storing electric energy in the target substation. The energy storage device can be charged or discharged. For example, during the low electricity consumption period in the substation, the energy storage device can absorb electric energy from the substation power grid and store it, that is, charging; and during the peak electricity consumption period, the energy storage device releases the stored electric energy to the substation power grid, that is, discharging. Furthermore, the charging and discharging strategy can include charging or discharging.

[0051] In this embodiment, the first predicted power quality information may include at least one of predicted harmonics, predicted voltage fluctuations, and predicted frequency deviations.

[0052] Furthermore, one implementation method of determining the charging and discharging strategy of the energy storage device in the target substation based on the first predicted power quality information and multiple types of first predicted power load information can be described as: fusing the predicted harmonics, predicted voltage fluctuations and predicted frequency deviations in the first predicted power quality information to obtain the fused predicted power quality information; fusing multiple types of first predicted power load information to obtain the fused predicted power load information; multiplying the fused predicted power quality information and the fused predicted power load information to obtain the multiplied result; and determining the charging and discharging strategy of the energy storage device in the target substation based on the multiplied result.

[0053] Continuing with the above description, the method of determining the charging and discharging strategy of the energy storage device in the target area according to the multiplication result can be: comparing the multiplication result with a preset threshold value, and determining the charging and discharging strategy of the energy storage device in the target area as charging or discharging according to the comparison result.

[0054] S140, controlling the energy storage device to charge and discharge based on the charge and discharge strategy.

[0055] In this embodiment, the method of controlling the energy storage device to charge and discharge based on the charge and discharge strategy may be: generating a charge and discharge instruction according to the charge and discharge strategy; and controlling the energy storage device to charge and discharge according to the charge and discharge instruction. Wherein, the charge and discharge instruction includes a charge instruction or a discharge instruction. In this embodiment, the method of controlling the energy storage device to charge and discharge based on the charge and discharge instruction may be: if the charge and discharge instruction is a charge instruction, then controlling the energy storage device to charge; if the charge and discharge instruction is a discharge instruction, then controlling the energy storage device to discharge.

[0056] The technical solution of this embodiment obtains the historical power quality information of the target substation and multiple types of historical power load information; inputs the historical power quality information and multiple types of historical power load information into the preset information prediction model to obtain the first predicted power quality information and multiple types of first predicted power load information; determines the charging and discharging strategy of the energy storage device in the target substation based on the first predicted power quality information and multiple types of first predicted power load information; and controls the energy storage device to charge and discharge based on the charging and discharging strategy. The above technical solution, based on the historical power quality information of the target substation and multiple types of historical power load information, adopts the preset information prediction model to predict the power quality information and power load information of the future time period, that is, the first predicted power quality information and the first predicted power load information, which can improve the accuracy of the prediction result; at the same time, the first predicted power quality information and multiple types of first predicted power load information obtained according to the above prediction can accurately determine the charging and discharging strategy of the energy storage device in the target substation; finally, controls the energy storage device to charge and discharge according to the charging and discharging strategy to ensure the stable operation of the power system in the substation. The technical solution provided in this embodiment adopts a preset information prediction model to predict the power quality information and power load information of the future time period, which can improve the accuracy of the prediction results, and can accurately determine the charging and discharging strategy of the energy storage equipment in the target substation based on the prediction results, thereby ensuring the stable operation of the substation power system.

[0057] Embodiment 2

[0058] Figure 2 This is a flow chart of a control method for a substation energy storage device provided in Embodiment 2 of the present invention. Based on the above embodiment, the method includes the following steps:

[0059] S210: Obtain historical power quality information and multiple types of historical power load information of the target substation.

[0060] S220. Input historical power quality information and multiple types of historical power load information into a preset information prediction model to obtain first predicted power quality information and multiple types of first predicted power load information.

[0061] In this embodiment, the first predicted power quality information includes at least one of predicted harmonics, predicted voltage fluctuations, and predicted frequency deviations.

[0062] S230, performing weighted summation on the predicted harmonics, predicted voltage fluctuations, and predicted frequency deviations to obtain target power quality information.

[0063] In this embodiment, the target power quality information may be understood as a result of weighted summation of predicted harmonics, predicted voltage fluctuations, and predicted frequency deviations.

[0064] For example, it is assumed that the predicted harmonics, predicted voltage fluctuations, and predicted frequency deviations are: Q 1 , Q 2 , Q 3 , we can use the formula H = w 1 Q 1 +w 2 Q 2 +w 3 Q 3 The predicted harmonics, predicted voltage fluctuations, and predicted frequency deviations are weighted and summed to obtain the target power quality information H. 1 、w 2 、w 3 They are weights for predicting harmonics, predicting voltage fluctuations, and predicting frequency deviations, respectively, and the weights can be preset.

[0065] S240: Merge multiple types of first predicted power load information to obtain target power load information.

[0066] In this embodiment, the multiple types of first predicted power load information may include: first predicted power load information of residential users, first predicted power load information of commercial users, and first predicted power load information of industrial users. The target power load information can be understood as the predicted power load information of the target substation, that is, the power load information after the multiple types of first predicted power load information are integrated.

[0067] Exemplarily, the following formula may be used to fuse multiple types of first predicted power load information to obtain target power load information.

[0068]

[0069] Where L represents the target power load information, P i represents the first predicted power load information of the i-th type, and N represents the total number of types. i ) represents the response function corresponding to the first predicted power load information of the i-th type, and the response function depends on the time t and the load characteristic parameter θ i .

[0070] Optionally, the method of fusing multiple types of first predicted power load information to obtain target power load information may also be: summing the multiple types of first predicted power load information and using the summed result as the target power load information.

[0071] S250: Determine a charging and discharging strategy for the energy storage device in the target substation based on the target power quality information and the target power load information.

[0072] In this embodiment, for determining the charging and discharging strategy of the energy storage device in the target substation based on the target power quality information and the target power load information, one implementation method can be described as the following steps:

[0073] a2) fusing the target power quality information and the target power load information to obtain fused information;

[0074] Exemplarily, the target power quality information and the target power load information are fused to obtain the fused information by multiplying the target power quality information and the target power load information, and using the result of the multiplication as the fused information.

[0075] b2) Determine the charging and discharging strategy of the energy storage equipment in the target area based on the fused information and the set threshold.

[0076] In this embodiment, the set threshold is a preset value.

[0077] Optionally, one implementation method of determining the charging and discharging strategy of the energy storage device in the target area based on the fused information and the set threshold can be described as: if the fused information is greater than the set threshold, the charging and discharging strategy of the energy storage device in the target area is discharging; if the fused information is less than or equal to the set threshold, the charging and discharging strategy of the energy storage device in the target area is charging.

[0078] S260: Controlling the energy storage device to charge and discharge based on the charge and discharge strategy.

[0079] The technical solution of this embodiment obtains historical power quality information of the target substation and multiple types of historical power load information; inputs the historical power quality information and multiple types of historical power load information into a preset information prediction model to obtain first predicted power quality information and multiple types of first predicted power load information; performs weighted summation of predicted harmonics, predicted voltage fluctuations, and predicted frequency deviations to obtain target power quality information; fuses multiple types of first predicted power load information to obtain target power load information; determines a charging and discharging strategy for energy storage equipment in the target substation based on the target power quality information and the target power load information; and controls the energy storage equipment to charge and discharge based on the charging and discharging strategy. The above technical scheme, based on the historical power quality information of the target substation and multiple types of historical power load information, adopts a preset information prediction model to predict the power quality information and power load information of the future time period, that is, the first predicted power quality information and the first predicted power load information, which can improve the accuracy of the prediction results; at the same time, the three predicted power quality information, namely the predicted harmonics, predicted voltage fluctuations and predicted frequency deviations, are integrated into a comprehensive target power quality information by weighted summation, which can accurately reflect the overall level of power quality and avoid the one-sidedness of a single indicator; then, multiple types of first predicted power load information are integrated to accurately obtain the target power load information of the target substation; and, according to the target power quality information and the target power load information, the charging and discharging strategy of the energy storage device in the target substation can be accurately determined; finally, the energy storage device is controlled to charge and discharge based on the charging and discharging strategy, thereby ensuring the stable operation of the substation power system. The technical solution provided in this embodiment adopts a preset information prediction model to predict the power quality information and power load information of the future time period, which can improve the accuracy of the prediction results, and integrate the three predicted power quality information of predicted harmonics, predicted voltage fluctuations and predicted frequency deviations into a comprehensive target power quality information, which can accurately reflect the overall level of power quality and avoid the one-sidedness of a single indicator. It can accurately obtain the target power load information of the target substation and accurately determine the charging and discharging strategy of the energy storage equipment in the target substation, thereby ensuring the stable operation of the substation power system.

[0080] Embodiment 3

[0081] Figure 3 A schematic diagram of a control device for a substation energy storage device provided in Embodiment 3 of the present invention is shown in FIG. Figure 3 As shown, the device comprises:

[0082] The information acquisition module 310 is used to acquire historical power quality information of the target substation and multiple types of historical power load information; wherein the historical power quality information includes at least one of harmonics, voltage fluctuations and frequency deviations;

[0083] An information prediction module 320, configured to input the historical power quality information and the multiple types of historical power load information into a preset information prediction model to obtain first predicted power quality information and multiple types of first predicted power load information;

[0084] A charging and discharging strategy determination module 330 is used to determine the charging and discharging strategy of the energy storage device in the target substation based on the first predicted power quality information and the first predicted power load information of the multiple types; wherein the charging and discharging strategy includes charging or discharging;

[0085] The device control module 340 is used to control the energy storage device to charge and discharge based on the charge and discharge strategy.

[0086] Optionally, the first predicted power quality information includes at least one of predicted harmonics, predicted voltage fluctuations, and predicted frequency deviations.

[0087] Optionally, the charging and discharging strategy determination module 330 may specifically include:

[0088] a target power quality information determination unit, configured to perform weighted summation on the predicted harmonics, the predicted voltage fluctuation, and the predicted frequency deviation to obtain target power quality information;

[0089] a target power load information determination unit, configured to merge the plurality of types of first predicted power load information to obtain target power load information;

[0090] A charging and discharging strategy determining unit is used to determine the charging and discharging strategy of the energy storage device in the target substation based on the target power quality information and the target power load information.

[0091] Optionally, the charging and discharging strategy determination unit may specifically include:

[0092] An information fusion subunit, used to fuse the target power quality information and the target power load information to obtain fused information;

[0093] The strategy determination subunit is used to determine the charging and discharging strategy of the energy storage device in the target area based on the fused information and the set threshold.

[0094] Optionally, the strategy determination subunit may be specifically used for:

[0095] If the fused information is greater than a set threshold, the charging and discharging strategy of the energy storage device in the target area is discharging; if the fused information is less than or equal to the set threshold, the charging and discharging strategy of the energy storage device in the target area is charging.

[0096] Optionally, the device further includes a preset information prediction model training module, including:

[0097] An information sample collection unit, used to collect power quality information samples of the target substation and multiple types of power load information samples;

[0098] An information prediction unit, configured to input the power quality information sample and the multiple types of power load information samples into a preset information prediction model to obtain second predicted power quality information and multiple types of second predicted power load information;

[0099] A model training unit is used to train the preset information prediction model based on the second predicted power quality information and the actual power quality information and the second predicted power load information and the actual power load information.

[0100] Optionally, the model training unit may be specifically used for:

[0101] Determine a first loss function based on the second predicted power quality information and the actual power quality information;

[0102] Determine a second loss function based on the second predicted power load information and the actual power load information;

[0103] The preset information prediction model is trained based on the first loss function and the second loss function.

[0104] The above device can execute the methods provided by all the above embodiments of the present invention, and has the corresponding functional modules and beneficial effects of executing the above methods. For technical details not described in detail in this embodiment, please refer to the methods provided by all the above embodiments of the present invention.

[0105] Embodiment 4

[0106] Figure 4 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0107] like Figure 4As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0108] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0109] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 11 executes the various methods and processes described above, such as a control method for a station area energy storage device.

[0110] In some embodiments, the control method of the metro area energy storage device may be implemented as a computer program, which is tangibly contained in a computer readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the control method of the metro area energy storage device described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the control method of the metro area energy storage device in any other appropriate manner (e.g., by means of firmware).

[0111] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0112] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0113] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0114] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0115] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0116] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0117] An embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements a control method for a substation energy storage device as provided in any embodiment of the present application.

[0118] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0119] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0120] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A control method for a substation energy storage device, characterized in that: include: Acquire historical power quality information of the target substation and multiple types of historical power load information; wherein the historical power quality information includes at least one of harmonics, voltage fluctuations, and frequency deviations; Inputting the historical power quality information and the multiple types of historical power load information into a preset information prediction model to obtain first predicted power quality information and multiple types of first predicted power load information; Determine a charging and discharging strategy of the energy storage device in the target substation based on the first predicted power quality information and the first predicted power load information of the plurality of types; wherein the charging and discharging strategy includes charging or discharging; The energy storage device is controlled to charge and discharge based on the charge and discharge strategy.

2. The method according to claim 1, characterized in that The first predicted power quality information includes at least one of predicted harmonics, predicted voltage fluctuations, and predicted frequency deviations. Determining a charging and discharging strategy of the energy storage device in the target substation based on the first predicted power quality information and the first predicted power load information of the plurality of types includes: Performing weighted summation on the predicted harmonics, the predicted voltage fluctuation, and the predicted frequency deviation to obtain target power quality information; Merging the plurality of types of first predicted power load information to obtain target power load information; A charging and discharging strategy for the energy storage device in the target substation is determined based on the target power quality information and the target power load information.

3. The method according to claim 2, characterized in that Determining a charging and discharging strategy of the energy storage device in the target area based on the target power quality information and the target power load information includes: Fusing the target power quality information and the target power load information to obtain fused information; The charging and discharging strategy of the energy storage device in the target area is determined based on the fused information and the set threshold.

4. The method according to claim 3, characterized in that Determining a charging and discharging strategy of the energy storage device in the target area based on the fused information and the set threshold includes: If the fused information is greater than a set threshold, the charging and discharging strategy of the energy storage device in the target area is discharging; If the fused information is less than or equal to the set threshold, the charging and discharging strategy of the energy storage device in the target area is charging.

5. The method according to claim 1, characterized in that The training method of the preset information prediction model is: Collect power quality information samples of the target substation and multiple types of power load information samples; Inputting the power quality information sample and the multiple types of power load information samples into a preset information prediction model to obtain second predicted power quality information and multiple types of second predicted power load information; The preset information prediction model is trained based on the second predicted power quality information and the actual power quality information as well as the second predicted power load information and the actual power load information.

6. The method according to claim 5, characterized in that Training the preset information prediction model based on the second predicted power quality information and the actual power quality information and the second predicted power load information and the actual power load information includes: Determine a first loss function based on the second predicted power quality information and the actual power quality information; Determine a second loss function based on the second predicted power load information and the actual power load information; The preset information prediction model is trained based on the first loss function and the second loss function.

7. A control device for a substation energy storage device, characterized in that: include: An information acquisition module, used to acquire historical power quality information of a target substation and multiple types of historical power load information; wherein the historical power quality information includes at least one of harmonics, voltage fluctuations, and frequency deviations; An information prediction module, configured to input the historical power quality information and the multiple types of historical power load information into a preset information prediction model to obtain first predicted power quality information and multiple types of first predicted power load information; A charging and discharging strategy determination module, configured to determine a charging and discharging strategy for the energy storage device in the target substation based on the first predicted power quality information and the first predicted power load information of the plurality of types; wherein the charging and discharging strategy includes charging or discharging; The device control module is used to control the energy storage device to charge and discharge based on the charge and discharge strategy.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the control method of the substation energy storage device according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the control method of the substation energy storage device according to any one of claims 1 to 6 when executed.

10. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the control method of the substation energy storage device according to any one of claims 1 to 6 is implemented.

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