Energy storage resource allocation method and device based on power internet of things, medium and equipment

Abstract

The invention relates to an energy storage resource allocation method and device based on an electric power internet of things, a medium and equipment. According to the method, a reference charge state sequence of an energy storage end is obtained by predicting the charge state of the energy storage end, an electric quantity adjustment interval of the energy storage end is determined by combining a charge state limit value of the energy storage end, a predicted load sequence of a power grid is reconstructed, and a training loss function is adopted for supervision. Determining a reference correction sequence according to the predicted load sequence and the reference load sequence, evaluating the reference correction sequence according to the reference correction sequence and the electric quantity adjustment interval, and when an evaluation result meets a preset condition, indicating that the energy storage end can meet the required adjustment electric quantity. The accuracy of energy storage resource allocation is improved, it is ensured that the expected effect of peak clipping and valley filling can be achieved, and the expected effect of peak clipping and valley filling is improved as much as possible through iterative training of the load reconstruction model.

Description

Technical Field

[0001] The present invention is applicable to the field of electric power technology, and in particular relates to a method, device, medium and equipment for allocating energy storage resources based on electric power Internet of Things. Background Art

[0002] In the power application scenario, peak shaving and valley filling can refer to the process of storing electricity during the low load period through the energy storage end and releasing electricity during the peak load period, thereby adjusting the balance of power supply and demand and optimizing the operation of the power grid. Usually, the peak-valley difference can be used to characterize the effect of peak shaving and valley filling. The peak-valley difference can refer to the change in the difference between the maximum load and the minimum load of the power grid before and after peak shaving and valley filling.

[0003] Since the capacity of a single energy storage terminal is limited, in the prior art, multiple energy storage terminals are usually used to coordinate the response of peak shaving and valley filling. However, for electrochemical energy storage type energy storage terminals, when the energy storage terminal is over-discharged or over-charged, it will accelerate battery aging and even cause safety problems. Therefore, when shaving the peak and filling the valley, the energy storage terminal will be difficult to meet its estimated adjusted power due to the influence of the battery state of the energy storage terminal. That is, the accuracy of energy storage resource allocation is poor, which makes it difficult to achieve the expected effect of peak shaving and valley filling.

[0004] Therefore, how to improve the accuracy of energy storage resource allocation and maximize the expected effect of peak shaving and valley filling while ensuring that the expected effect of peak shaving and valley filling can be achieved has become an urgent problem to be solved. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides a method, device, medium and equipment for allocating energy storage resources based on the power Internet of Things to solve the problem.

[0006] In a first aspect, a method for allocating energy storage resources based on the power Internet of Things is provided, the method comprising: Obtain the historical state of charge sequences corresponding to the M energy storage terminals, where M is a positive integer; For any energy storage terminal, a reference state of charge sequence corresponding to the energy storage terminal is obtained according to the historical state of charge sequence corresponding to the energy storage terminal and the trained state of charge prediction model; Determine a dischargeable amount sequence and a chargeable amount sequence corresponding to the energy storage end according to a reference state of charge sequence corresponding to the energy storage end, a state of charge upper limit value corresponding to the energy storage end, a state of charge lower limit value corresponding to the energy storage end, and a maximum amount of electricity corresponding to the energy storage end; Determine the overall discharge capacity sequence according to the dischargeable capacity sequences corresponding to the M energy storage terminals; Determine the overall charge capacity sequence according to the dischargeable capacity sequences corresponding to the M energy storage terminals; According to the acquired historical load sequence and the trained load forecasting model, the forecast load sequence is obtained; Obtaining a reference load sequence according to the predicted load sequence, the sequence reconstruction model and the training loss function; Determine a reference load difference sequence according to the predicted load sequence and the reference load sequence, and determine a reference correction value sequence according to the reference load difference sequence; Determining an evaluation parameter of the reference correction amount sequence according to the reference correction amount sequence, the overall discharge amount sequence and the overall charge amount sequence; If the evaluation parameter satisfies a preset condition, determining the reference correction amount sequence as a target correction amount sequence; According to the target correction amount sequence, the estimated charge amounts or estimated discharge amounts corresponding to the M energy storage terminals at the preset time points are determined.

[0007] In a second aspect, a device for allocating energy storage resources based on the power Internet of Things is provided, the device comprising: The state of charge acquisition module is used to obtain the historical state of charge sequences corresponding to M energy storage terminals, where M is a positive integer; The state of charge prediction module is used to obtain a reference state of charge sequence corresponding to any energy storage end according to the historical state of charge sequence corresponding to the energy storage end and the trained state of charge prediction model; A power determination module, used to determine a dischargeable power sequence and a chargeable power sequence corresponding to the energy storage end according to a reference state of charge sequence corresponding to the energy storage end, a state of charge upper limit value corresponding to the energy storage end, a state of charge lower limit value corresponding to the energy storage end, and a maximum power corresponding to the energy storage end; A first sequence determination module is used to determine the overall discharge amount sequence according to the dischargeable amount sequences corresponding to the M energy storage terminals; A second sequence determination module is used to determine the overall charge amount sequence according to the dischargeable amount sequences corresponding to the M energy storage terminals; The load forecasting module is used to obtain the forecast load sequence based on the acquired historical load sequence and the trained load forecasting model; A sequence reconstruction module, used to obtain a reference load sequence according to the predicted load sequence, the sequence reconstruction model and the training loss function; A correction amount determination module, used to determine a reference load difference sequence according to the predicted load sequence and the reference load sequence, and determine a reference correction amount sequence according to the reference load difference sequence; A sequence evaluation module, used to determine an evaluation parameter of the reference correction amount sequence according to the reference correction amount sequence, the overall discharge amount sequence and the overall charge amount sequence; A first condition judgment module, configured to determine that the reference correction amount sequence is a target correction amount sequence if the evaluation parameter satisfies a preset condition; The power distribution module is used to determine the estimated charging amount or the estimated discharging amount corresponding to the M energy storage terminals at the preset time points according to the target correction amount sequence.

[0008] In a third aspect, an embodiment of the present invention provides a computer device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the energy storage resource allocation method based on the power Internet of Things as described in the first aspect is implemented.

[0009] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the energy storage resource allocation method based on the power Internet of Things as described in the first aspect is implemented.

[0010] Compared with the prior art, the present invention has the following beneficial effects: By predicting the state of charge of the energy storage end, the reference state of charge sequence of the energy storage end is obtained. The power adjustment range of the energy storage end is determined in combination with the state of charge limit of the energy storage end. The predicted load sequence of the power grid is reconstructed and supervised by a training loss function to generate a reference load sequence that meets the expected peak shaving and valley filling. The reference correction sequence is determined according to the predicted load sequence and the reference load sequence. The reference correction sequence is evaluated according to the reference correction sequence and the power adjustment range. When the evaluation result meets the preset conditions, it means that the energy storage end can meet the required adjustment power, which improves the accuracy of energy storage resource allocation, ensures that the expected effect of peak shaving and valley filling can be achieved, and maximizes the expected effect of peak shaving and valley filling through iterative training of the load reconstruction model. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0012] Figure 1 This is a schematic diagram of an application environment of an energy storage resource allocation method based on the power Internet of Things provided by the first embodiment of the present invention; Figure 2 It is a flow chart of a method for allocating energy storage resources based on the power Internet of Things provided in the first embodiment of the present invention; Figure 3It is a structural schematic diagram of an energy storage resource allocation device based on the power Internet of Things provided in the second embodiment of the present invention; Figure 4 It is a structural schematic diagram of a computer device for a method for allocating energy storage resources based on the power Internet of Things provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0013] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0014] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0015] It should also be understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0016] As used in the present specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]", depending on the context.

[0017] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0018] References to "one embodiment" or "some embodiments" etc. described in the present specification mean that one or more embodiments of the present invention include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0019] It should be understood that the order of execution of the steps in the following embodiments does not imply a precedence of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0020] In order to illustrate the technical solution of the present invention, specific embodiments are provided below for illustration.

[0021] A method for allocating energy storage resources based on the power Internet of Things provided in the first embodiment of the present invention can be applied in the following aspects: Figure 1 In the application environment, the server communicates with the client. The client includes but is not limited to PDA, desktop computer, notebook computer, ultra-mobile personal computer (UMPC), netbook, cloud terminal device, personal digital assistant (PDA) and other terminal devices. The server can be implemented by an independent server or a server cluster composed of multiple servers.

[0022] See also Figure 2 , is a flow chart of a method for allocating energy storage resources based on the power Internet of Things provided by the first embodiment of the present invention. The above energy storage resource allocation method can be applied to Figure 1 The server in the example can obtain the state of charge corresponding to each energy storage terminal through the power Internet of Things, that is, the sensors deployed at each energy storage terminal. After the sensors at each energy storage terminal collect the state of charge locally, they transmit the collected state of charge to the server in a preset communication method. The server is deployed with a trained state of charge prediction model, a trained load prediction model, and a sequence reconstruction model. Figure 2 As shown, the energy storage resource allocation method may include the following steps: Step S201, obtaining the historical charge state sequences corresponding to the M energy storage terminals respectively.

[0023] Among them, M is a positive integer, the energy storage end may refer to an electrochemical energy storage type energy storage end, the historical state of charge sequence may include historical states of charge corresponding to K historical time points respectively, K is a positive integer, and the time interval between two adjacent historical time points may be a fixed value T. In this embodiment, T may be set to 5 minutes, and the implementer may adjust the value of T according to actual conditions.

[0024] Specifically, for any energy storage terminal, the historical charge state of the energy storage terminal can be collected by the charge state sensor deployed by the energy storage terminal at the corresponding historical time point. The charge state sensor of each energy storage terminal belongs to the power Internet of Things, and each energy storage terminal can communicate with the service end to transmit the collected charge state.

[0025] Step S202: for any energy storage terminal, a reference state of charge sequence corresponding to the energy storage terminal is obtained according to a historical state of charge sequence corresponding to the energy storage terminal and a trained state of charge prediction model.

[0026] Among them, the trained state of charge prediction model can adopt a time series prediction model, and the time series prediction model can use a recurrent neural network (RNN) model, a long short-term memory network (LSTM) model, a temporal convolutional network (TCN) model, etc.

[0027] Specifically, this embodiment is described by taking the TCN model as an example. The sliding window length corresponding to the TCN model is L. For any energy storage end, according to the K-L+1th historical state of charge to the Kth historical state of charge in the historical state of charge sequence corresponding to the energy storage end, the reference state of charge at the first preset time point can be predicted, and then according to the K-L+2th historical state of charge to the Kth historical state of charge in the historical state of charge sequence corresponding to the energy storage end and the reference state of charge at the first preset time point, the reference state of charge at the second preset time point can be predicted, and so on, until the reference state of charge at the Nth preset time point is obtained.

[0028] It should be noted that, in order to ensure the prediction accuracy of the trained state of charge prediction model, the time interval between the Kth historical time point and the first preset time point is T, and the time interval between two adjacent preset time points is also T.

[0029] The training process of the state of charge prediction model may include, for any energy storage end, predicting the sample state of charge at the L+1th historical time point according to the 1st historical state of charge to the Lth historical state of charge in the historical state of charge sequence corresponding to the energy storage end, and calculating the state of charge prediction model sub-loss according to the historical state of charge at the L+1th historical time point and the sample state of charge at the L+1th historical time point in combination with the mean square error loss function. Similarly, according to the 2nd historical state of charge to the L+1th historical state of charge in the historical state of charge sequence corresponding to the energy storage end, predicting the sample state of charge at the L+2th historical time point According to the historical state of charge at the L+2th historical time point and the sample state of charge at the L+2th historical time point, combined with the mean square error loss function, the state of charge prediction model sub-loss can also be calculated. By analogy, a single energy storage end can correspond to KL state of charge prediction model sub-losses, and M energy storage ends can correspond to M*(KL) state of charge prediction model sub-losses. The state of charge prediction model loss is formed by all the state of charge prediction model sub-losses. According to the state of charge prediction model loss, the gradient descent method is used to train the state of charge prediction model until the state of charge prediction model loss converges, and the trained state of charge prediction model can be obtained.

[0030] Step S203, determining a dischargeable amount sequence and a chargeable amount sequence corresponding to the energy storage end according to a reference state of charge sequence corresponding to the energy storage end, a state of charge upper limit value corresponding to the energy storage end, a state of charge lower limit value corresponding to the energy storage end, and a maximum amount of electricity corresponding to the energy storage end.

[0031] Among them, the upper limit value of the state of charge can be used to avoid overcharging of the battery, and the lower limit value of the state of charge can be used to avoid deep discharge of the battery. The basic upper limit value of the state of charge and the basic lower limit value of the state of charge can be determined locally at the energy storage end by charge and discharge test method, accelerated aging test, etc. Since this embodiment is applied to the peak shaving and valley filling scenario, the implementer can adjust the basic upper limit value of the state of charge and the basic lower limit value of the state of charge, for example, increase the basic upper limit value of the state of charge and reduce the basic lower limit value of the state of charge.

[0032] The maximum power may refer to the maximum power capacity of the energy storage end.

[0033] The dischargeable amount sequence may include the dischargeable amounts corresponding to N preset time points respectively. The dischargeable amount may refer to the maximum discharge amount of the energy storage end at the preset time point under the premise of satisfying the upper limit value and the lower limit value of the state of charge corresponding to the energy storage end.

[0034] The chargeable capacity sequence may include chargeable capacities corresponding to N preset time points respectively. The chargeable capacity may refer to the maximum charge capacity of the energy storage end at a preset time point under the premise that the charge state upper limit value and charge state lower limit value corresponding to the energy storage end are met.

[0035] Optionally, the reference state of charge sequence includes reference states of charge corresponding to N preset time points, respectively, where N is a positive integer; Determining a dischargeable amount sequence and a chargeable amount sequence corresponding to the energy storage end according to a reference state of charge sequence corresponding to the energy storage end, a state of charge upper limit value corresponding to the energy storage end, a state of charge lower limit value corresponding to the energy storage end, and a maximum amount of electricity corresponding to the energy storage end, including: For any preset time point, determine a first state of charge difference according to the state of charge upper limit value and the reference state of charge corresponding to the preset time point, and determine the dischargeable amount of the energy storage end corresponding to the preset time point according to the first state of charge difference and the maximum power; Determine a second state of charge difference according to the reference state of charge and the lower limit of the state of charge corresponding to the preset time point, and determine the chargeable amount of the energy storage end corresponding to the preset time point according to the second state of charge difference and the maximum power; Traversing N preset time points, the dischargeable amounts corresponding to the obtained N preset time points respectively form a dischargeable amount sequence corresponding to the energy storage end, and the chargeable amounts corresponding to the obtained N preset time points respectively form a chargeable amount sequence corresponding to the energy storage end.

[0036] Among them, the first state of charge difference can be obtained by subtracting the state of charge upper limit from the reference state of charge corresponding to the preset time point, and the first state of charge difference and the maximum power are multiplied to obtain the dischargeable amount of the energy storage end corresponding to the preset time point.

[0037] The second state of charge difference can be obtained by subtracting the reference state of charge corresponding to the preset time point from the lower limit of the state of charge. The second state of charge difference and the maximum power can be multiplied to obtain the chargeable capacity of the energy storage end corresponding to the preset time point.

[0038] Specifically, the dischargeable amounts corresponding to the N preset time points are sequentially formed into a dischargeable amount sequence corresponding to the energy storage end, and the chargeable amounts corresponding to the N preset time points are sequentially formed into a chargeable amount sequence corresponding to the energy storage end.

[0039] Step S204, determining an overall discharge capacity sequence according to the dischargeable capacity sequences corresponding to the M energy storage terminals.

[0040] The overall discharge amount sequence may include overall discharge amounts corresponding to N preset time points.

[0041] Specifically, for any preset time point, the dischargeable capacities corresponding to the M energy storage terminals at the preset time point are added together to obtain the overall discharge capacity corresponding to the preset time point. The N preset time points are traversed to obtain the overall discharge capacities corresponding to the N preset time points. The overall discharge capacities corresponding to the N preset time points are used to form an overall discharge capacity sequence in chronological order.

[0042] Step S205 , determining an overall charging capacity sequence according to the dischargeable capacity sequences corresponding to the M energy storage terminals.

[0043] The overall charge amount sequence may include overall charge amounts corresponding to N preset time points.

[0044] Specifically, for any preset time point, the chargeable capacities corresponding to the M energy storage terminals at the preset time point are added together to obtain the overall charge capacity corresponding to the preset time point. The N preset time points are traversed to obtain the overall charge capacities corresponding to the N preset time points. The overall charge capacities corresponding to the N preset time points are used to form an overall charge capacity sequence in chronological order.

[0045] Step S206, obtaining a predicted load sequence according to the acquired historical load sequence and the trained load prediction model.

[0046] Among them, the historical load sequence can include the power grid loads corresponding to K historical time points respectively, and the trained load forecasting model can also adopt the time series forecasting model. Therefore, the reasoning and training process of the load forecasting model will not be described in detail here.

[0047] The predicted load sequence may include predicted loads corresponding to N preset time points respectively.

[0048] Step S207, obtaining a reference load sequence according to the predicted load sequence, the sequence reconstruction model and the training loss function.

[0049] Among them, the sequence reconstruction model can be used to reconstruct the predicted load sequence to output the expected load sequence after peak shaving and valley filling, that is, the reference load sequence.

[0050] Specifically, the sequence reconstruction model adopts an online learning method. During the use of the sequence reconstruction model, the sequence reconstruction model is trained in real time according to the training loss function.

[0051] Optionally, a reference load sequence is obtained according to the predicted load sequence, the sequence reconstruction model and the training loss, including: Input the predicted load sequence into the sequence reconstruction model to obtain the reconstructed load sequence; The training loss is calculated based on the reconstructed load sequence, the predicted load sequence and the training loss function; The parameters of the sequence reconstruction model are updated according to the training loss until the training loss converges to obtain a trained sequence reconstruction model; The predicted load sequence is input into the trained sequence reconstruction model to obtain the reference load sequence.

[0052] The reconstructed load sequence may refer to an intermediate output result of a sequence reconstruction model, and the training loss may be used for training the sequence reconstruction model.

[0053] Specifically, after the sequence reconstruction model is trained according to the training loss and a trained sequence reconstruction model is obtained, the output of the trained sequence reconstruction model after the predicted load sequence is inputted is used as a reference load sequence.

[0054] Optionally, the training loss is calculated based on the reconstructed load sequence, the predicted load sequence and the training loss function, including: Accumulating the predicted load sequence to obtain a first load amount; Accumulating the reconstructed load sequence to obtain a second load amount; determining a first sub-loss according to a difference between the first load amount and the second load amount; The variance of the reconstructed load sequence is used as the second sub-loss; Multiplying the first sub-loss by the preset first weight to obtain a first multiplication result; Multiply the second sub-loss by the preset second weight to obtain a second multiplication result; The sum of the first multiplication result and the second multiplication result is taken as the training loss.

[0055] The first load amount may be used to characterize the predicted total load amount within a time period formed by N preset time points, and the second load amount may be used to characterize the reconstructed total load amount within a time period formed by N preset time points.

[0056] Specifically, the absolute value of the difference between the first load and the second load is used as the first sub-loss, and the first sub-loss can be used to supervise that the total load of the power grid remains as consistent as possible before and after peak shaving and valley filling.

[0057] The variance of the reconstructed load sequence can characterize the volatility of the reconstructed load sequence, and can also characterize the effect after peak shaving and valley filling. The smaller the variance of the reconstructed load sequence, the smaller the peak-to-valley difference, and the better the effect after peak shaving and valley filling.

[0058] The preset first weight can be used to control the influence of the first sub-loss on the training loss. In order to ensure that the total load of the power grid is stable before and after peak shaving and valley filling, and thus provide stable power supply to the power consumption side, the implementer can set the preset first weight to a larger value so that the first sub-loss is prioritized to be smaller during the training process.

[0059] The preset second weight can be used to control the influence of the second sub-loss on the training loss, and the initial value of the preset second weight can be 1.

[0060] Step S208, determining a reference load difference sequence based on the predicted load sequence and the reference load sequence, and determining a reference correction value sequence based on the reference load difference sequence.

[0061] Among them, for any preset time point, the predicted load corresponding to the preset time point in the predicted load sequence is subtracted from the reference load corresponding to the preset time point in the reference load sequence, and the subtraction result is used as the reference load difference corresponding to the preset time point. N preset time points are traversed to obtain the reference load difference values ​​corresponding to the N preset time points respectively. The reference load difference values ​​corresponding to the N preset time points are used to form a reference load difference sequence in chronological order. It can be known that the reference load difference can be a negative value.

[0062] Specifically, the accumulated value of the reference load difference corresponding to the first preset time point to the i-th preset time point is used as the reference correction value for the i-th preset time point, where i is an integer in the range of [1, N]. By traversing the values ​​of i, the reference correction values ​​corresponding to the N preset time points can be obtained, and the reference correction values ​​corresponding to the N preset time points form a reference correction value sequence in chronological order.

[0063] Step S209, determining evaluation parameters of the reference correction amount sequence according to the reference correction amount sequence, the overall discharge amount sequence and the overall charge amount sequence.

[0064] The evaluation parameter can be used to characterize the adaptability of the reference correction sequence to the state of the energy storage end.

[0065] Optionally, the reference correction amount sequence includes reference correction amounts corresponding to N preset time points respectively, the overall discharge amount sequence includes overall discharge amounts corresponding to N preset time points respectively, and the overall charge amount sequence includes overall charge amounts corresponding to N preset time points respectively, wherein N is a positive integer; According to the reference correction amount sequence, the overall discharge amount sequence and the overall charge amount sequence, the evaluation parameters of the reference correction amount sequence are determined, including: For any preset time point, if the reference correction amount corresponding to the preset time point is less than the overall discharge amount corresponding to the preset time point and the reference correction amount corresponding to the preset time point is greater than the overall charge amount corresponding to the preset time point, determine that the sub-evaluation value corresponding to the preset time point is the first preset value; Otherwise, calculating a first difference between the overall discharge amount corresponding to the preset time point and the reference correction amount corresponding to the preset time point; Calculating a second difference between the reference correction amount corresponding to the preset time point and the overall charging amount corresponding to the preset time point; The smaller value of the first difference and the second difference is used as the target value, and the target value is mapped according to the reference correction amount corresponding to the preset time point, and the mapping result is used as the sub-evaluation value corresponding to the preset time point; Traverse N preset time points, add up the sub-evaluation values ​​corresponding to the N preset time points, and use the added result as the evaluation parameter of the reference correction value sequence.

[0066] The first preset value may be 0, and both the first difference and the second difference are processed by taking absolute values.

[0067] Specifically, the target value is compared with the reference correction amount corresponding to the preset time point, and the ratio calculation result is used as the mapping processing result.

[0068] It can be known that the larger the evaluation parameter is, the worse the adaptability of the reference correction value sequence to the energy storage end state is, and the smaller the evaluation parameter is, the better the adaptability of the reference correction value sequence to the energy storage end state is, and the minimum evaluation parameter is 0.

[0069] Step S210: If the evaluation parameter meets the preset condition, the reference correction amount sequence is determined as the target correction amount sequence.

[0070] The preset conditions can be used to evaluate whether the power adjustment of the reference correction amount sequence can be achieved without overcharging or overdischarging at each energy storage end.

[0071] Specifically, when the evaluation parameters meet the preset conditions, it can be considered that the power adjustment corresponding to the reference correction amount sequence can be achieved without overcharging or overdischarging at each energy storage end, and the reference correction amount sequence is determined to be the target correction amount sequence.

[0072] Optionally, after determining the evaluation parameter of the reference correction amount sequence, the method further includes: If the evaluation parameter does not meet the preset conditions, the second weight is updated with a preset step size, and the process returns to the step of obtaining a reference load sequence based on the predicted load sequence, the sequence reconstruction model and the training loss function.

[0073] Among them, when the evaluation parameters do not meet the preset conditions, it can be considered that the power adjustment corresponding to the reference correction sequence cannot be achieved without overcharging and over-discharging at each energy storage end. At this time, it means that the peak shaving and valley filling effect of the reference load sequence is difficult to meet, so the reference load sequence needs to be updated.

[0074] Specifically, in this embodiment, the preset step size can be -0.05, that is, when the evaluation parameter does not meet the preset condition, the second weight is reduced, so that the supervision of the second sub-loss is reduced when the reference load sequence is generated. In other words, the peak shaving and valley filling effect represented by the reference load sequence output by the sequence reconstruction model can be appropriately reduced to adapt to the battery status of the energy storage end.

[0075] Step S211, determining the estimated charge amount or the estimated discharge amount corresponding to each of the M energy storage terminals at a preset time point according to the target correction amount sequence.

[0076] Among them, the estimated charging amount and the estimated discharging amount can be used to assist in formulating peak shaving and valley filling plans for each preset time point. The peak shaving and valley filling plan can be used to transmit to each energy storage terminal, so that the energy storage terminal can perform charging and discharging control at the preset time point based on its own situation, or provide feedback to the service end in advance when the control requirements cannot be met.

[0077] Optionally, the target correction amount sequence includes target correction amounts corresponding to N preset time points, the dischargeable amount sequence includes dischargeable amounts corresponding to N preset time points, and the chargeable amount sequence includes chargeable amounts corresponding to N preset time points, wherein N is a positive integer; According to the target correction amount sequence, the estimated charge amount or estimated discharge amount corresponding to each of the M energy storage terminals at a preset time point is determined, including: For any preset time point, if the target correction amount corresponding to the preset time point is less than the second preset value, then according to the target correction amount corresponding to the preset time point and the chargeable amounts corresponding to the M energy storage ends at the preset time point, the estimated charge amounts corresponding to the M energy storage ends are determined respectively; If the target correction amount corresponding to the preset time point is greater than or equal to the second preset value, the estimated discharge amounts corresponding to the M energy storage ends are determined according to the target correction amount corresponding to the preset time point and the dischargeable amounts corresponding to the M energy storage ends at the preset time points.

[0078] Among them, for any energy storage end, the chargeable amount of the energy storage end at the preset time point is compared with the overall charge amount corresponding to the preset time point to obtain the charge amount ratio of the energy storage end at the preset time point, and the target correction amount corresponding to the preset time point and the charge amount ratio of the energy storage end at the preset time point are multiplied to obtain the estimated charge amount of the energy storage end at the preset time point.

[0079] Similarly, for any energy storage end, the dischargeable amount of the energy storage end at the preset time point is compared with the overall discharge amount corresponding to the preset time point to obtain the discharge amount ratio of the energy storage end at the preset time point, and the target correction amount corresponding to the preset time point and the discharge amount ratio of the energy storage end at the preset time point are multiplied to obtain the estimated discharge amount of the energy storage end at the preset time point.

[0080] In this embodiment, the state of charge of the energy storage terminal is predicted to obtain a reference state of charge sequence of the energy storage terminal, and the power adjustment interval of the energy storage terminal is determined in combination with the state of charge limit of the energy storage terminal. The predicted load sequence of the power grid is reconstructed, and a training loss function is used for supervision to generate a reference load sequence that meets the expected peak shaving and valley filling. A reference correction amount sequence is determined according to the predicted load sequence and the reference load sequence, and the reference correction amount sequence is evaluated according to the reference correction amount sequence and the power adjustment interval. When the evaluation result meets the preset conditions, it indicates that the energy storage terminal can meet the required adjustment power, thereby improving the accuracy of energy storage resource allocation, ensuring that the expected effect of peak shaving and valley filling can be achieved, and the expected effect of peak shaving and valley filling is improved as much as possible through iterative training of the load reconstruction model.

[0081] Corresponding to the method of the above embodiment, Figure 3 The schematic diagram of the structure of a power storage resource allocation device based on the power Internet of Things provided by the second embodiment of the present invention is shown. The above-mentioned power storage resource allocation device is applied to the server. The server can obtain the charge state corresponding to each energy storage terminal through the power Internet of Things, that is, the sensors deployed at each energy storage terminal. After the sensors of each energy storage terminal collect the charge state locally, they transmit the collected charge state to the server in a preset communication mode. The server is deployed with a trained charge state prediction model, a trained load prediction model and a sequence reconstruction model. For the convenience of explanation, only the part related to the embodiment of the present invention is shown.

[0082] See also Figure 3 , the energy storage resource allocation device comprises: The state of charge acquisition module 301 is used to obtain the historical state of charge sequences corresponding to M energy storage terminals, where M is a positive integer; The state of charge prediction module 302 is used to obtain a reference state of charge sequence corresponding to any energy storage terminal according to the historical state of charge sequence corresponding to the energy storage terminal and the trained state of charge prediction model; The power determination module 303 is used to determine the dischargeable power sequence and the chargeable power sequence corresponding to the energy storage end according to the reference state of charge sequence corresponding to the energy storage end, the upper limit value of the state of charge corresponding to the energy storage end, the lower limit value of the state of charge corresponding to the energy storage end and the maximum power corresponding to the energy storage end; A first sequence determination module 304 is used to determine an overall discharge amount sequence according to the dischargeable amount sequences corresponding to the M energy storage terminals; The second sequence determination module 305 is used to determine the overall charge amount sequence according to the dischargeable amount sequences corresponding to the M energy storage terminals; The load forecasting module 306 is used to obtain a forecast load sequence based on the acquired historical load sequence and the trained load forecasting model; A sequence reconstruction module 307 is used to obtain a reference load sequence according to the predicted load sequence, the sequence reconstruction model and the training loss function; The correction amount determination module 308 is used to determine a reference load difference sequence according to the predicted load sequence and the reference load sequence, and determine a reference correction amount sequence according to the reference load difference sequence; A sequence evaluation module 309, for determining an evaluation parameter of a reference correction amount sequence according to the reference correction amount sequence, the overall discharge amount sequence and the overall charge amount sequence; A first condition judgment module 310 is used to determine the reference correction amount sequence as the target correction amount sequence if the evaluation parameter meets the preset condition; The power distribution module 311 is used to determine the estimated charge amount or estimated discharge amount corresponding to the M energy storage terminals at the preset time points according to the target correction amount sequence.

[0083] Optionally, the reference state of charge sequence includes reference states of charge corresponding to N preset time points, respectively, where N is a positive integer; The power determination module 303 includes: A dischargeable capacity determination unit, for determining, for any preset time point, a first state of charge difference according to a state of charge upper limit value and a reference state of charge corresponding to the preset time point, and determining the dischargeable capacity of the energy storage end corresponding to the preset time point according to the first state of charge difference and a maximum power; A chargeable capacity determination unit, configured to determine a second state of charge difference according to a reference state of charge and a lower limit of the state of charge corresponding to the preset time point, and determine a chargeable capacity of the energy storage terminal corresponding to the preset time point according to the second state of charge difference and a maximum power; The sequence forming unit is used to traverse N preset time points, and form a dischargeable amount sequence corresponding to the energy storage end from the dischargeable amounts corresponding to the N preset time points, and form a chargeable amount sequence corresponding to the energy storage end from the chargeable amounts corresponding to the N preset time points.

[0084] Optionally, the sequence reconstruction module 307 includes: A model reconstruction unit, used for inputting the predicted load sequence into the sequence reconstruction model to obtain a reconstructed load sequence; A loss calculation unit, used for calculating the training loss according to the reconstructed load sequence, the predicted load sequence and the training loss function; A model training unit is used to update the parameters of the sequence reconstruction model according to the training loss until the training loss converges to obtain a trained sequence reconstruction model; The sequence output unit is used to input the predicted load sequence into the trained sequence reconstruction model to obtain the reference load sequence.

[0085] Optionally, the loss calculation unit includes: A first load quantity calculation subunit is used to accumulate the predicted load sequence to obtain a first load quantity; A second load amount calculation subunit is used to accumulate the reconstructed load sequence to obtain a second load amount; A first sub-loss calculation sub-unit, configured to determine a first sub-loss according to a difference between the first load amount and the second load amount; A second sub-loss calculation sub-unit, used for taking the variance of the reconstructed load sequence as the second sub-loss; A first weighted subunit, used for multiplying the first sub-loss by a preset first weight to obtain a first multiplication result; A second weighted subunit, used for multiplying the second sub-loss by a preset second weight to obtain a second multiplication result; The loss determination subunit is used to take the sum of the first multiplication result and the second multiplication result as the training loss.

[0086] Optionally, the energy storage resource allocation device further includes: The second condition judgment module is used to update the second weight with a preset step size if the evaluation parameter does not meet the preset condition, and return to execute the step of obtaining a reference load sequence based on the predicted load sequence, the sequence reconstruction model and the training loss function.

[0087] Optionally, the reference correction amount sequence includes reference correction amounts corresponding to N preset time points respectively, the overall discharge amount sequence includes overall discharge amounts corresponding to N preset time points respectively, and the overall charge amount sequence includes overall charge amounts corresponding to N preset time points respectively, wherein N is a positive integer; The above sequence evaluation module 309 includes: A first sub-evaluation value calculation unit, for any preset time point, if the reference correction amount corresponding to the preset time point is less than the overall discharge amount corresponding to the preset time point and the reference correction amount corresponding to the preset time point is greater than the overall charge amount corresponding to the preset time point, determines that the sub-evaluation value corresponding to the preset time point is the first preset value; A first difference calculation unit, used for calculating a first difference between the overall discharge amount corresponding to the preset time point and the reference correction amount corresponding to the preset time point; A second difference calculation unit, used to calculate a second difference between the reference correction amount corresponding to the preset time point and the overall charging amount corresponding to the preset time point; A second sub-evaluation value calculation unit is used to use the smaller value of the first difference and the second difference as the target value, perform mapping processing on the target value according to the reference correction amount corresponding to the preset time point, and use the mapping processing result as the sub-evaluation value corresponding to the preset time point; The evaluation parameter determination unit is used to traverse N preset time points, add the sub-evaluation values ​​corresponding to the N preset time points, and use the addition result as the evaluation parameter of the reference correction amount sequence.

[0088] Optionally, the target correction amount sequence includes target correction amounts corresponding to N preset time points, the dischargeable amount sequence includes dischargeable amounts corresponding to N preset time points, and the chargeable amount sequence includes chargeable amounts corresponding to N preset time points, wherein N is a positive integer; The power distribution module 311 includes: A charging capacity allocation unit is used to determine the estimated charging capacities corresponding to the M energy storage terminals respectively according to the target correction amount corresponding to the preset time point and the chargeable capacities corresponding to the M energy storage terminals respectively at the preset time point for any preset time point if the target correction amount corresponding to the preset time point is less than the second preset value; The discharge amount distribution unit is used to determine the estimated discharge amounts corresponding to the M energy storage ends respectively according to the target correction amount corresponding to the preset time point and the dischargeable amounts corresponding to the M energy storage ends respectively at the preset time point if the target correction amount corresponding to the preset time point is greater than or equal to the second preset value.

[0089] It should be noted that the information interaction, execution process, etc. between the above-mentioned modules, units, and sub-units are based on the same concept as the embodiment of the method of the present invention. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0090] Figure 4 A schematic diagram of the structure of a computer device for a method for allocating energy storage resources based on the power Internet of Things provided in Embodiment 3 of the present invention. Figure 4 As shown, the computer device of this embodiment includes: at least one processor ( Figure 4 Only one is shown), a memory, and a computer program stored in the memory and executable on at least one processor, and when the processor executes the computer program, the steps in any of the above-mentioned embodiments of the energy storage resource allocation method based on the power Internet of Things are implemented.

[0091] The computer device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that Figure 4This is merely an example of a computer device and does not constitute a limitation on the computer device. The computer device may include more or fewer components than those shown in the figure, or a combination of certain components, or different components. For example, it may also include a network interface, a display screen, and an input device.

[0092] The processor may be a CPU, or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0093] The memory includes a readable storage medium, an internal memory, etc., wherein the internal memory may be the memory of a computer device, and the internal memory provides an environment for the operation of the operating system and computer-readable instructions in the readable storage medium. The readable storage medium may be a hard disk of a computer device, and in other embodiments, it may also be an external storage device of the computer device, for example, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device. Further, the memory may also include both an internal storage unit of the computer device and an external storage device. The memory is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of a computer program, etc. The memory may also be used to temporarily store data that has been output or is to be output.

[0094] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned device can refer to the corresponding process in the above-mentioned method embodiment, which will not be repeated here. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned method embodiment can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device capable of carrying computer program code, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0095] The present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by a computer program product. When the computer program product runs on a computer device, the computer device can implement the steps in the above-mentioned method embodiment when executing.

[0096] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0097] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0098] In the embodiments provided by the present invention, it should be understood that the disclosed devices / computer equipment and methods can be implemented in other ways. For example, the device / computer equipment embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0099] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, 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 solution of this embodiment.

[0100] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A method for allocating energy storage resources based on the power Internet of Things, characterized in that: The method comprises: Obtain the historical state of charge sequences corresponding to the M energy storage terminals, where M is a positive integer; For any energy storage terminal, a reference state of charge sequence corresponding to the energy storage terminal is obtained according to the historical state of charge sequence corresponding to the energy storage terminal and the trained state of charge prediction model; Determine a dischargeable amount sequence and a chargeable amount sequence corresponding to the energy storage end according to a reference state of charge sequence corresponding to the energy storage end, a state of charge upper limit value corresponding to the energy storage end, a state of charge lower limit value corresponding to the energy storage end, and a maximum amount of electricity corresponding to the energy storage end; Determine the overall discharge capacity sequence according to the dischargeable capacity sequences corresponding to the M energy storage terminals; Determine the overall charge capacity sequence according to the dischargeable capacity sequences corresponding to the M energy storage terminals; According to the acquired historical load sequence and the trained load forecasting model, the forecast load sequence is obtained; Obtaining a reference load sequence according to the predicted load sequence, the sequence reconstruction model and the training loss function; Determine a reference load difference sequence according to the predicted load sequence and the reference load sequence, and determine a reference correction value sequence according to the reference load difference sequence; Determining an evaluation parameter of the reference correction amount sequence according to the reference correction amount sequence, the overall discharge amount sequence and the overall charge amount sequence; If the evaluation parameter satisfies a preset condition, determining the reference correction amount sequence as a target correction amount sequence; According to the target correction amount sequence, the estimated charge amounts or estimated discharge amounts corresponding to the M energy storage terminals at the preset time points are determined.

2. The energy storage resource allocation method according to claim 1, characterized in that: The reference state of charge sequence includes reference states of charge corresponding to N preset time points, where N is a positive integer; The step of determining the dischargeable amount sequence and the chargeable amount sequence corresponding to the energy storage end according to the reference state of charge sequence corresponding to the energy storage end, the state of charge upper limit value corresponding to the energy storage end, the state of charge lower limit value corresponding to the energy storage end, and the maximum amount of electricity corresponding to the energy storage end includes: For any preset time point, determine a first state of charge difference according to the state of charge upper limit value and the reference state of charge corresponding to the preset time point, and determine the dischargeable amount of the energy storage end corresponding to the preset time point according to the first state of charge difference and the maximum power; Determine a second state of charge difference according to the reference state of charge corresponding to the preset time point and the lower limit of the state of charge, and determine the chargeable amount of the energy storage end corresponding to the preset time point according to the second state of charge difference and the maximum amount of electricity; Traversing N preset time points, the dischargeable amounts corresponding to the obtained N preset time points respectively form a dischargeable amount sequence corresponding to the energy storage end, and the chargeable amounts corresponding to the obtained N preset time points respectively form a chargeable amount sequence corresponding to the energy storage end.

3. The energy storage resource allocation method according to claim 1, characterized in that: The step of obtaining a reference load sequence according to the predicted load sequence, the sequence reconstruction model and the training loss comprises: Inputting the predicted load sequence into the sequence reconstruction model to obtain a reconstructed load sequence; Calculating the training loss according to the reconstructed load sequence, the predicted load sequence and the training loss function; The parameters of the sequence reconstruction model are updated according to the training loss until the training loss converges to obtain a trained sequence reconstruction model; The predicted load sequence is input into the trained sequence reconstruction model to obtain the reference load sequence.

4. The energy storage resource allocation method according to claim 3, characterized in that: The calculating the training loss according to the reconstructed load sequence, the predicted load sequence and the training loss function comprises: Accumulating the predicted load sequence to obtain a first load amount; Accumulating the reconstructed load sequence to obtain a second load amount; determining a first sub-loss according to a difference between the first load amount and the second load amount; Using the variance of the reconstructed load sequence as the second sub-loss; Multiplying the first sub-loss by a preset first weight to obtain a first multiplication result; Multiplying the second sub-loss by a preset second weight to obtain a second multiplication result; The sum of the first multiplication result and the second multiplication result is taken as the training loss.

5. The energy storage resource allocation method according to claim 4, characterized in that: After determining the evaluation parameters of the reference correction sequence, the method further includes: If the evaluation parameter does not meet the preset condition, the second weight is updated with a preset step size, and the step of obtaining a reference load sequence based on the predicted load sequence, the sequence reconstruction model and the training loss function is returned to be executed.

6. The energy storage resource allocation method according to claim 1, characterized in that: The reference correction amount sequence includes reference correction amounts corresponding to N preset time points respectively, the overall discharge amount sequence includes overall discharge amounts corresponding to N preset time points respectively, and the overall charge amount sequence includes overall charge amounts corresponding to N preset time points respectively, wherein N is a positive integer; Determining the evaluation parameter of the reference correction amount sequence according to the reference correction amount sequence, the overall discharge amount sequence and the overall charge amount sequence includes: For any preset time point, if the reference correction amount corresponding to the preset time point is less than the overall discharge amount corresponding to the preset time point and the reference correction amount corresponding to the preset time point is greater than the overall charge amount corresponding to the preset time point, determine that the sub-evaluation value corresponding to the preset time point is the first preset value; Otherwise, calculating a first difference between the overall discharge amount corresponding to the preset time point and the reference correction amount corresponding to the preset time point; Calculating a second difference between the reference correction amount corresponding to the preset time point and the overall charging amount corresponding to the preset time point; Taking the smaller value of the first difference and the second difference as the target value, mapping the target value according to the reference correction amount corresponding to the preset time point, and taking the mapping result as the sub-evaluation value corresponding to the preset time point; Traversing N preset time points, adding up the sub-evaluation values ​​corresponding to the N preset time points, and taking the added result as the evaluation parameter of the reference correction value sequence.

7. The energy storage resource allocation method according to claim 1, characterized in that: The target correction amount sequence includes target correction amounts corresponding to N preset time points respectively, the dischargeable amount sequence includes dischargeable amounts corresponding to N preset time points respectively, and the chargeable amount sequence includes chargeable amounts corresponding to N preset time points respectively, wherein N is a positive integer; Determining the estimated charge amounts or estimated discharge amounts corresponding to the M energy storage terminals at preset time points according to the target correction amount sequence includes: For any preset time point, if the target correction amount corresponding to the preset time point is less than the second preset value, then according to the target correction amount corresponding to the preset time point and the chargeable amounts corresponding to the M energy storage ends at the preset time point, the estimated charge amounts corresponding to the M energy storage ends are determined respectively; If the target correction amount corresponding to the preset time point is greater than or equal to the second preset value, the estimated discharge amounts corresponding to the M energy storage ends are determined according to the target correction amount corresponding to the preset time point and the dischargeable amounts corresponding to the M energy storage ends at the preset time points.

8. An energy storage resource allocation device based on the power Internet of Things, characterized in that: The device comprises: The state of charge acquisition module is used to obtain the historical state of charge sequences corresponding to M energy storage terminals, where M is a positive integer; The state of charge prediction module is used to obtain a reference state of charge sequence corresponding to any energy storage end according to the historical state of charge sequence corresponding to the energy storage end and the trained state of charge prediction model; A power determination module, used to determine a dischargeable power sequence and a chargeable power sequence corresponding to the energy storage end according to a reference state of charge sequence corresponding to the energy storage end, a state of charge upper limit value corresponding to the energy storage end, a state of charge lower limit value corresponding to the energy storage end, and a maximum power corresponding to the energy storage end; A first sequence determination module is used to determine the overall discharge amount sequence according to the dischargeable amount sequences corresponding to the M energy storage terminals; A second sequence determination module is used to determine the overall charge amount sequence according to the dischargeable amount sequences corresponding to the M energy storage terminals; The load forecasting module is used to obtain the forecast load sequence based on the acquired historical load sequence and the trained load forecasting model; A sequence reconstruction module, used to obtain a reference load sequence according to the predicted load sequence, the sequence reconstruction model and the training loss function; A correction amount determination module, used to determine a reference load difference sequence according to the predicted load sequence and the reference load sequence, and determine a reference correction amount sequence according to the reference load difference sequence; A sequence evaluation module, used to determine an evaluation parameter of the reference correction amount sequence according to the reference correction amount sequence, the overall discharge amount sequence and the overall charge amount sequence; A first condition judgment module, configured to determine that the reference correction amount sequence is a target correction amount sequence if the evaluation parameter satisfies a preset condition; The power distribution module is used to determine the estimated charging amount or the estimated discharging amount corresponding to the M energy storage terminals at the preset time points according to the target correction amount sequence.

9. A computer device, characterized in that: The computer device includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the energy storage resource allocation method based on the power Internet of Things as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the energy storage resource allocation method based on the power Internet of Things as described in any one of claims 1 to 7 is implemented.

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