Energy storage node control method, central controller and storage medium

Abstract

The invention discloses an energy storage node control method, a central controller and a storage medium. The method comprises the following steps: determining a target energy storage node, wherein the target energy storage node is an energy storage node which is registered to a central controller and can participate in power dispatching; the current behavior data of the user and the current operation data of the target energy storage node are obtained, the current behavior data and the current operation data are input into a pre-trained prediction model, current demand data are obtained, and the prediction model is a model obtained through training by using a machine learning algorithm with the prediction of the total power demand as the target; according to the current demand data, a scheduling plan is generated, and the scheduling plan comprises a plurality of scheduling schemes; and optimizing the scheduling plan by taking the fluctuation of the total power demand quantity and the minimum total cost as optimization objectives, and controlling the target energy storage node to work according to the corresponding scheduling scheme in the optimized scheduling plan. According to the scheme, accurate and efficient management of the energy storage nodes can be realized, so that the energy utilization rate is improved.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a control method for an energy storage node, a central controller and a storage medium. Background Art

[0002] A distributed energy storage system refers to a system that stores and manages energy by connecting multiple small, dispersed energy storage nodes to a power grid or power system.

[0003] Existing distributed energy storage systems mainly use distributed energy storage facilities (such as battery energy storage systems) to adjust the load demand of the power system to balance the grid load, optimize energy use and manage the balance of power supply and demand. However, distributed energy storage facilities lack effective real-time monitoring methods and data transmission mechanisms for energy storage nodes, making it difficult to achieve comprehensive control and precise control of energy storage nodes, resulting in low overall system efficiency. Summary of the invention

[0004] The present invention provides a control method for an energy storage node, a central controller and a storage medium, which can achieve accurate and efficient management of the energy storage node, thereby improving energy utilization.

[0005] According to one aspect of the present invention, a control method for an energy storage node is provided, which is applied to a central controller, and the method includes:

[0006] Determine a target energy storage node, wherein the target energy storage node is an energy storage node registered with a central controller and capable of participating in power dispatching;

[0007] Obtain the user's current behavior data and the current operation data of the target energy storage node, and input the current behavior data and the current operation data into a pre-trained prediction model to obtain current demand data, wherein the prediction model is a model trained using a machine learning algorithm with the goal of predicting total electricity demand;

[0008] Generate a dispatch plan based on current demand data, where the dispatch plan includes multiple dispatch schemes, and one dispatch scheme corresponds to one target energy storage node;

[0009] Taking the fluctuation of total electricity demand and the minimum total cost as the optimization objectives, the dispatch plan is optimized, and the target energy storage node is controlled to work according to the corresponding dispatch scheme in the optimized dispatch plan.

[0010] According to another aspect of the present invention, a central controller is provided, the central controller comprising:

[0011] at least one processor; and a memory in communication with the at least one processor; wherein,

[0012] The memory stores a computer program that can be executed by at least one processor, and the computer program is executed by at least one processor so that the at least one processor can execute the control method of the energy storage node of any embodiment of the present invention.

[0013] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to implement the control method of the energy storage node of any embodiment of the present invention when executed by a processor.

[0014] The technical solution of the embodiment of the present invention first determines the target energy storage node that is registered to the central controller and can participate in power dispatching; then obtains the current behavior data of the user and the current operation data of the target energy storage node, and inputs them into the pre-trained prediction model to obtain the current demand data; then generates a dispatch plan based on the current demand data; finally, the dispatch plan is optimized with the fluctuation of the total power demand and the minimum total cost as the optimization goal, and the target energy storage node is controlled to work according to the corresponding dispatching scheme in the optimized dispatching plan. The control method of the energy storage node, on the one hand, selects a suitable target energy storage node to participate in the subsequent power dispatching, and can achieve comprehensive control and precise control of all energy storage nodes. On the other hand, by obtaining the current demand data through the prediction model, the total power demand can be accurately predicted, which provides a data basis for generating subsequent dispatching plans. On the third hand, since the dispatching plan includes multiple dispatching schemes, one dispatching scheme corresponds to one target energy storage node, which achieves the purpose of decomposing the overall dispatching problem into multiple dispatching sub-problems, thereby improving the calculation speed and dispatching efficiency. Fourthly, further optimizing the dispatch plan with the fluctuation of total electricity demand and the minimum total cost as the optimization goals can ensure that each target energy storage node works together and achieve accurate and efficient management of the target energy storage nodes, thereby improving energy utilization and ensuring the maximization of overall benefits.

[0015] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. 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 creative work.

[0017] Figure 1 is a structural schematic diagram of a distributed energy storage system provided in Embodiment 1 of the present invention;

[0018] Figure 2 It is a flow chart of a control method of an energy storage node provided in the first embodiment of the present invention;

[0019] Figure 3 It is a flow chart of a control method of an energy storage node provided in the second embodiment of the present invention;

[0020] Figure 4 It is a structural schematic diagram of a control device for an energy storage node provided in Embodiment 3 of the present invention;

[0021] Figure 5 is a schematic diagram of the structure of another control device for an energy storage node provided in Embodiment 3 of the present invention;

[0022] Figure 6 It is a structural diagram of a central controller provided in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0024] It should be noted that the terms "first", "second", "original", "candidate", "target", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] Embodiment 1

[0026] Figure 1 Schematic diagram of a distributed energy storage system provided in Embodiment 1 of the present invention. Figure 1 As shown, the distributed energy storage system includes a central controller 101 , multiple energy storage nodes 102 and a distributed energy trading platform 103 .

[0027] The central controller 101 is a device capable of scheduling and management. It is the core of the distributed energy storage system and can execute the control method of the energy storage node provided by the present invention. Exemplarily, the central controller 101 can be a centralized computer system or a cloud platform. The central controller 101 communicates with each energy storage node 102 through the Internet of Things (IoT). A database can be configured in the central controller 101, or the database can exist independently of the central controller 101. The database is used to store information related to each energy storage node 102.

[0028] The number of energy storage nodes 102 is M. For ease of understanding, Figure 1 In the figure, M=3 is used as an example, and the three energy storage nodes 102 are marked as energy storage node A, energy storage node B, and energy storage node C. The energy storage node 102 can be registered with the central controller 101 so that an IoT connection is established between the central controller 101 and the energy storage node 102, thereby enabling the central controller 101 to monitor, dispatch, and control the energy storage node 102. In the present invention, the energy storage node 102 (such as Figure 1 The energy storage nodes B and C in the energy storage node are called candidate energy storage nodes, and the energy storage nodes 102 (such as Figure 1 The energy storage node A) in is not a candidate energy storage node.

[0029] The distributed energy trading platform 103 provides users with energy trading functions. The distributed energy trading platform 103 can be constructed using blockchain technology to ensure the security and transparency of energy trading. The distributed energy trading platform 103 can display the operating status and revenue of the distributed energy storage system to users through a smartphone application or a web interface. Optionally, a user incentive mechanism can be designed to encourage users to adjust their electricity consumption behavior and participate in the demand regulation process through dynamic electricity prices and reward policies.

[0030] Figure 2 is a flow chart of a control method for an energy storage node provided in the first embodiment of the present invention. This embodiment can be applied to Figure 1 The central controller shown controls the energy storage node. The method can be executed by a control device of the energy storage node. The control device of the energy storage node can be implemented in the form of hardware and / or software. The control device of the energy storage node can be configured at Figure 1 In the central controller shown. Figure 2 As shown, the method includes:

[0031] S210. Determine a target energy storage node, wherein the target energy storage node is an energy storage node registered with a central controller and capable of participating in power dispatching.

[0032] Referring to the above description, it can be seen that since the distributed energy storage system includes multiple energy storage nodes, these energy storage nodes are not necessarily registered with the central controller, and not all energy storage nodes registered with the central controller need to participate in this power dispatch. Therefore, the central controller first needs to determine the target energy storage node, which is the energy storage node registered with the central controller and can participate in power dispatch.

[0033] Specifically, the target energy storage node can be a preset energy storage node; it can also be an energy storage node selected by the user; it can also be determined in the following way: first determine the candidate energy storage nodes registered to the central controller, and then select the energy storage nodes that can participate in power dispatching from the candidate energy storage nodes as the target energy storage nodes.

[0034] In an optional implementation, the central controller may consider that all candidate energy storage nodes are able to participate in power dispatching, that is, all candidate energy storage nodes are taken as target energy storage nodes.

[0035] In another optional implementation, the central controller may determine the candidate energy storage nodes registered with the central controller, and then determine whether each candidate energy storage node can participate in power dispatching, thereby determining the target energy storage node based on the determination result.

[0036] Exemplarily, after multiple candidate energy storage nodes are registered with the central controller, the central controller obtains the node parameters and monitoring data of each candidate energy storage node; determines the current state of each candidate energy storage node based on the node parameters and monitoring data, wherein the current state includes an available state and an unavailable state; and uses the candidate energy storage node in the available state as the target energy storage node.

[0037] Node parameters are used to reflect the properties of candidate energy storage nodes. Node parameters may include but are not limited to: geographic location, energy storage capacity, charge and discharge rate, and health status. Monitoring data is used to reflect the operation of candidate energy storage nodes, and can usually be obtained through the acquisition module based on IoT technology. Monitoring data may include but are not limited to: current power, load conditions, grid electricity prices, and environmental conditions.

[0038] The current status of the candidate energy storage node includes available status and unavailable status. Available status means that the candidate energy storage node is in a state where it can participate in power dispatching; unavailable status means that the candidate energy storage node is in a state where it cannot participate in power dispatching, such as when there is excessive load or poor health status.

[0039] In this way, selecting suitable target energy storage nodes to participate in subsequent power dispatching can achieve comprehensive control and precise control of all energy storage nodes.

[0040] Optionally, when multiple candidate energy storage nodes are registered with the central controller, the central control node may assign a unique identifier to each candidate energy storage node to achieve unified and rapid management of the candidate energy storage nodes.

[0041] S220. Obtain the user's current behavior data and the current operation data of the target energy storage node, and input the current behavior data and the current operation data into a pre-trained prediction model to obtain current demand data, wherein the prediction model is a model trained using a machine learning algorithm with the goal of predicting total electricity demand.

[0042] The user's current behavior data refers to the user's behavior data in the current scheduling cycle, and the behavior data is used to reflect the user's electricity consumption behavior (such as electricity consumption patterns, seasonal data, weather characteristics data, etc.). The current operation data of the target energy storage node refers to the operation data of the target energy storage node in the current scheduling cycle, such as current power, load conditions, health status, etc.

[0043] Since the prediction model is a model trained by machine learning algorithms to predict the total power demand, the current demand data can be obtained by inputting the current behavior data and the current operation data into the pre-trained prediction model. The machine learning algorithm can be a series of mathematical and statistical methods used in the field of machine learning for data analysis, pattern recognition and prediction.

[0044] In one embodiment, the method for training a prediction model may include the following steps a1-a4:

[0045] Step a1: Obtain a training data set, wherein the training data set includes a plurality of training samples, and one training sample includes historical behavior data, historical operation data, and historical demand data.

[0046] Step a2: Input the historical behavior data and historical operation data of the current training sample into the original model to obtain the predicted demand data corresponding to the current training sample, wherein the current training sample is any training sample in the training data set.

[0047] For example, the original model may be a Long Short-Term Memory (LSTM) model.

[0048] Optionally, before inputting the historical behavior data and historical operation data of the current training sample into the original model, the current training sample can be preprocessed (such as cleaning and standardizing data, denoising, filling missing values, normalization, etc.), and then time series data is formed based on the historical behavior data and historical operation data to input the time series data into the original model. Time series data can reflect the relationship between data and time features, thereby improving the accuracy of model training.

[0049] Step a3: If the difference between the predicted demand data and the historical demand data of the current training sample is less than or equal to the preset threshold, the original model is used as the preset model.

[0050] Step a4: If the difference between the predicted demand data and the historical demand data of the current training sample is greater than the preset threshold, the parameters of the original model are adjusted, and the next training sample in the training data set is used as the current training sample, and the process returns to step a2.

[0051] By obtaining current demand data through the prediction model, we can achieve accurate prediction of total electricity demand and provide a data basis for generating subsequent dispatch plans.

[0052] S230. Generate a scheduling plan based on current demand data, wherein the scheduling plan includes multiple scheduling schemes, and one scheduling scheme corresponds to one target energy storage node.

[0053] In a possible implementation, the central controller may generate a scheduling plan for each target energy storage node based on the node parameters and monitoring data of each target energy storage node.

[0054] In another possible implementation, the central controller can construct a total dispatching function based on current demand data; decompose the total dispatching function to obtain multiple sub-dispatching functions, where one sub-dispatching function corresponds to a target energy storage node; solve the sub-dispatching functions in parallel based on the first optimization algorithm, and use the solution results as a dispatching plan.

[0055] Since the scheduling plan includes multiple scheduling schemes, one scheduling scheme corresponds to one target energy storage node, which achieves the purpose of decomposing the overall scheduling problem into multiple scheduling sub-problems, thereby improving the calculation speed and scheduling efficiency.

[0056] S240, taking the fluctuation of total power demand and the minimum total cost as optimization objectives, optimizing the dispatching plan, and controlling the target energy storage node to work according to the corresponding dispatching scheme in the optimized dispatching plan.

[0057] After the dispatch plan is generated, it is further optimized with the fluctuation of total electricity demand and the minimum total cost as the optimization goals. This can ensure that each target energy storage node works together and achieve accurate and efficient management of the target energy storage nodes, thereby improving energy utilization and ensuring the maximization of overall benefits.

[0058] Specifically, the central controller can construct an optimization objective function according to the scheduling plan and current demand data, and perform iterative optimization calculation on the optimization objective function based on the second optimization algorithm to determine the optimized scheduling plan.

[0059] In one embodiment, the first optimization algorithm and the second optimization algorithm may be the same algorithm or different algorithms. For example, the first optimization algorithm and the second optimization algorithm may both be distributed optimization algorithms.

[0060] Optionally, based on the above embodiment, the central controller can obtain the scheduling scheme execution data and health data of the target energy storage node, and dynamically adjust the algorithm parameters of the first optimization algorithm and / or the second optimization algorithm according to the scheduling scheme execution data and health data, so as to achieve continuous and efficient operation of the distributed energy storage system.

[0061] Embodiment 2

[0062] Figure 3 1 is a flow chart of a control method for an energy storage node provided in the second embodiment of the present invention. Based on the above-mentioned first embodiment, this embodiment provides a specific implementation method for generating and optimizing a scheduling plan. Figure 3 As shown, the method includes:

[0063] S310. Determine a target energy storage node, wherein the target energy storage node is an energy storage node registered with a central controller and capable of participating in power dispatching.

[0064] Since the distributed energy storage system includes multiple energy storage nodes, but these energy storage nodes are not necessarily registered with the central controller, and not all energy storage nodes registered with the central controller need to participate in the power dispatch. Therefore, the central controller first needs to determine the target energy storage node, which is the energy storage node registered with the central controller and can participate in power dispatch.

[0065] Exemplarily, after multiple candidate energy storage nodes are registered with the central controller, the central controller obtains the node parameters and monitoring data of each candidate energy storage node; determines the current state of each candidate energy storage node based on the node parameters and monitoring data, wherein the current state includes an available state and an unavailable state; and uses the candidate energy storage node in the available state as the target energy storage node.

[0066] Node parameters are used to reflect the properties of candidate energy storage nodes. Node parameters may include but are not limited to: geographic location, energy storage capacity, charge and discharge rate, and health status. Monitoring data is used to reflect the operation of candidate energy storage nodes, and can usually be obtained through the acquisition module based on IoT technology. Monitoring data may include but are not limited to: current power, load conditions, grid electricity prices, and environmental conditions.

[0067] The current status of the candidate energy storage node includes available status and unavailable status. Available status means that the candidate energy storage node is in a state where it can participate in power dispatching; unavailable status means that the candidate energy storage node is in a state where it cannot participate in power dispatching, such as when there is excessive load or poor health status.

[0068] In this way, selecting suitable target energy storage nodes to participate in subsequent power dispatching can achieve comprehensive control and precise control of all energy storage nodes.

[0069] S320, obtaining the user's current behavior data and the current operation data of the target energy storage node, and inputting the current behavior data and the current operation data into a pre-trained prediction model to obtain current demand data.

[0070] The prediction model is a model trained by using a machine learning algorithm with the goal of predicting the total power demand. The method for training the prediction model may include: obtaining a training data set, wherein the training data set includes a number of training samples, and one training sample includes historical behavior data, historical operation data, and historical demand data; inputting the historical behavior data and historical operation data of the current training sample into the original model to obtain the predicted demand data corresponding to the current training sample, wherein the current training sample is any training sample in the training data set; if the difference between the predicted demand data and the historical demand data of the current training sample is less than or equal to a preset threshold, the original model is used as the preset model; if the difference between the predicted demand data and the historical demand data of the current training sample is greater than the preset threshold, the parameters of the original model are adjusted, and the next training sample in the training data set is used as the current training sample, and the step of inputting the historical behavior data and historical operation data of the current training sample into the original model to obtain the predicted demand data corresponding to the current training sample is returned.

[0071] Optionally, before inputting the historical behavior data and historical operation data of the current training sample into the original model, the current training sample can be preprocessed (such as cleaning and standardizing data, denoising, filling missing values, normalization, etc.), and then time series data is formed based on the historical behavior data and historical operation data to input the time series data into the original model. Time series data can reflect the relationship between data and time features, thereby improving the accuracy of model training.

[0072] S330. Construct a total scheduling function based on current demand data.

[0073] When generating a dispatch plan, the central controller first constructs a total dispatch function based on the current demand data, such as maximizing the overall benefits of the system or balancing power demand and supply. The dispatch function can cover the optimization objectives of all target energy storage nodes.

[0074] Specifically, the overall scheduling function is Where N is the total number of target energy storage nodes, f i (,) is the benefit function of the i-th target energy storage node, Ei is the power of the i-th target energy storage node, P iis the charging and discharging power of the i-th target energy storage node.

[0075] S340: Decompose the total dispatch function to obtain multiple sub-dispatching functions, wherein one sub-dispatching function corresponds to one target energy storage node.

[0076] Based on the characteristics of the target energy storage node, the total scheduling function is decomposed to obtain multiple sub-scheduling functions. Each sub-scheduling function is performed in a local range (i.e., for the corresponding target energy storage node). The optimization target is usually the charging and discharging plan, and the constraints include the energy storage capacity, maximum charging and discharging rate, and health status of the target energy storage node.

[0077] Specifically, the sub-scheduling function is Affected by E i (t),P i (t) constraint; where E i (t) is the power of the i-th target energy storage node at time t, P i (t) is the charging and discharging power of the i-th target energy storage node at time t.

[0078] S350, solving the sub-scheduling functions in parallel based on the first optimization algorithm, and using the solution results as a scheduling plan.

[0079] In one embodiment, the first optimization algorithm may be a distributed optimization algorithm, such as a distributed convex optimization algorithm.

[0080] Based on the first optimization algorithm, the sub-scheduling function is solved in parallel to obtain the charging and discharging plan of the target energy storage node. In this way, the calculation speed and scheduling efficiency can be improved, and the central controller can coordinate to ensure the global consistency between the target energy storage nodes, ensuring that the target energy storage nodes will not conflict with each other or have uneven resource allocation.

[0081] Alternatively, the coordination mechanism may be implemented via an iterative algorithm.

[0082] Optionally, each sub-scheduling function can be set with constraints to limit the behavior of the target energy storage node. The target energy storage nodes can also share some necessary information, such as electricity price, load, etc.

[0083] S360. Taking the fluctuation of total electricity demand and the minimization of total cost as optimization goals, an optimization objective function is constructed according to the dispatch plan and current demand data, wherein the optimization objective function has constraints.

[0084] Specifically, the optimization objective function can be

[0085]

[0086] Where J represents the total power demand fluctuation and total cost within a given time range T, N is the total number of target energy storage nodes, and P i (t) is the charging and discharging power of the i-th target energy storage node at time t, λ i is the electricity price coefficient of the i-th target energy storage node, α i is the charging efficiency coefficient of the i-th target energy storage node, γ is the balance coefficient used to weigh the relationship between the fluctuation of total power demand and total cost, and D(t) is the total power demand at time t.

[0087] The constraints are

[0088] in, Indicates the power limit of the target energy storage node, E i (t) is the power of the i-th target energy storage node at time t, is the maximum power of the i-th target energy storage node; Indicates the charging and discharging power limit for the target energy storage node. is the minimum charging and discharging power of the i-th target energy storage node, is the maximum charge and discharge power of the i-th target energy storage node; Indicates the dynamic change limit of power, β i is the discharge efficiency coefficient of the i-th target energy storage node; Represents a constraint on the balance of total system demand.

[0089] In one embodiment, E i The update equation for (t) is usually E i (t+1)=E i (t)+η i P i (t)Δt.

[0090] Among them, η i is the charge and discharge efficiency coefficient, which is used to describe the energy loss or efficiency of the target energy storage node during the charging and discharging process, and can reflect the actual energy utilization efficiency of the system during charging or discharging; Δt is the time interval.

[0091] E i (t) determines the charging and discharging capacity of the target energy storage node. In the charging plan, if the battery is close to full load (such as more than 90% capacity), the system will reduce the charging power of the node; conversely, if the battery power is too low, the system will prioritize allocating more power to it to restore the energy storage state; these adjustments will affect subsequent power scheduling.

[0092] S370: Perform iterative optimization calculation on the optimization objective function based on the second optimization algorithm to determine the optimized scheduling plan.

[0093] Specifically, the optimized scheduling plan can be

[0094]

[0095] in, is the optimized scheduling scheme of the i-th target energy storage node at time t, It represents the weighted sum of the power of all target energy storage nodes and their squares. In the optimization process, this part represents the contribution of the power allocation of all target energy storage nodes to the optimization objective function; It represents the square of the difference between the total power demand and the power of all target energy storage nodes, ensuring the balance between the system demand and the sum of the power outputs of all target energy storage nodes; represents the influence of all other target energy storage nodes on target energy storage node i except target energy storage node i, which is adjusted by time-dependent function h(t), and the cumulative effect of its change rate on the current node is calculated, i≠j. This part ensures that when optimizing the scheduling scheme of target energy storage node i, the state changes and power distribution of all other target energy storage nodes are taken into account; h(t) is a time-dependent function used to adjust the smoothness of power change, which represents the weight or influencing factor that depends on a certain time t; f(E j (t)) is the energy storage state function of the target energy storage node j, which represents the impact of the energy storage state of the target energy storage node j on the target energy storage node i; g(P j (t)) is the power function of the target energy storage node j, which represents the influence of the power of the target energy storage node j on the target energy storage node i.

[0096] S380, controlling the target energy storage node to work according to the corresponding scheduling scheme in the optimized scheduling plan, and obtaining the scheduling scheme execution data and health data of the target energy storage node.

[0097] S390. Dynamically adjust algorithm parameters of the first optimization algorithm and / or the second optimization algorithm according to the scheduling scheme execution data and health data.

[0098] Dynamically adjusting the algorithm parameters of the first optimization algorithm and / or the second optimization algorithm can ensure the accuracy of the scheduling solution generated in the subsequent scheduling period. Further optionally, the prediction model can also be updated periodically.

[0099] For example, it is assumed that the distributed energy storage system includes three target energy storage nodes (node ​​1, node 2, node 3), and the basic information and optimization parameters of each target energy storage node are as follows:

[0100] Node 1: Located in city A, energy storage capacity Charge and discharge power range to α1=0.9, β1=0.95, λ1=0.1;

[0101] Node 2: Located in city B, energy storage capacity Charge and discharge power range to α2=0.85, β2=0.9, λ2=0.12;

[0102] Node 3: Located in city C, energy storage capacity Charge and discharge power range to α3=0.92, β3=0.93, λ3=0.15.

[0103] The optimized scheduling plan is:

[0104] Node 1: The plan is 20kW in the first hour, 30kW in the second hour, and 40kW in the third hour;

[0105] Node 2: The plan is 25kW in the first hour, 35kW in the second hour, and 45kW in the third hour;

[0106] Node 3: The plan is 30kW in the first hour, 40kW in the second hour, and 50kW in the third hour.

[0107] The central controller monitors the scheduling execution data and health data of the three nodes in real time through the Internet of Things, and compares the deviation between the actual operation data and the optimized scheduling plan. If the actual deviation is too large (such as the actual power is 15kW and the plan is 20kW), the scheduling plan is adjusted, the optimization problem is re-solved, and a new scheduling plan is generated.

[0108] Table 1 is a comparison table of the predicted load, actual load, charging and discharging conditions, and grid prices of the three target energy storage nodes.

[0109] Table 1 Comparison of predicted load, actual load, charging and discharging conditions and grid prices

[0110]

[0111] As can be seen from Table 1, it can be seen that the control method of the energy storage node provided by the embodiment of the present invention can keep the state of the node within a reasonable range. For example, the battery charging state of node 1 gradually decreases from 80% to 68% within 6 hours, the battery charging state of node 2 decreases from 70% to 52% within 6 hours, and the battery charging state of node 3 decreases from 90% to 66% within 6 hours, all of which remain within a safe range. This avoids the situation where a single node is over-discharged or over-charged, further improving the stability of the system and reducing equipment damage.

[0112] An embodiment of the present invention provides a control method for an energy storage node, comprising: determining a target energy storage node, wherein the target energy storage node is an energy storage node registered to a central controller and capable of participating in power dispatching; obtaining a user's current behavior data and current operating data of the target energy storage node, and inputting the current behavior data and the current operating data into a pre-trained prediction model to obtain current demand data, wherein the prediction model is a model trained using a machine learning algorithm with the goal of predicting total power demand; generating a scheduling plan based on the current demand data, wherein the scheduling plan includes multiple scheduling schemes, and one scheduling scheme corresponds to one target energy storage node; optimizing the scheduling plan with the total power demand fluctuation and the minimum total cost as optimization goals, and controlling the target energy storage node to work according to the corresponding scheduling scheme in the optimized scheduling plan. The technical solution of the embodiment of the present invention first determines the target energy storage node that is registered to the central controller and can participate in power dispatching; then obtains the current behavior data of the user and the current operation data of the target energy storage node, and inputs them into the pre-trained prediction model to obtain the current demand data; then generates a dispatch plan based on the current demand data; finally, the dispatch plan is optimized with the fluctuation of the total power demand and the minimum total cost as the optimization goal, and the target energy storage node is controlled to work according to the corresponding dispatching scheme in the optimized dispatching plan. The control method of the energy storage node, on the one hand, selects a suitable target energy storage node to participate in the subsequent power dispatching, and can achieve comprehensive control and precise control of all energy storage nodes. On the other hand, by obtaining the current demand data through the prediction model, the total power demand can be accurately predicted, which provides a data basis for generating subsequent dispatching plans. On the third hand, since the dispatching plan includes multiple dispatching schemes, one dispatching scheme corresponds to one target energy storage node, which achieves the purpose of decomposing the overall dispatching problem into multiple dispatching sub-problems, thereby improving the calculation speed and dispatching efficiency. Fourthly, further optimizing the dispatch plan with the fluctuation of total electricity demand and the minimum total cost as the optimization goals can ensure that each target energy storage node works together and achieve accurate and efficient management of the target energy storage nodes, thereby improving energy utilization and ensuring the maximization of overall benefits.

[0113] Embodiment 3

[0114] Figure 4 Schematic diagram of the structure of a control device for an energy storage node provided in Embodiment 3 of the present invention. Figure 4 As shown, the device includes: a node determination module 401, a demand prediction module 402, a plan generation module 403, a plan optimization module 404 and a plan execution module 405.

[0115] The node determination module 401 is used to determine the target energy storage node, wherein the target energy storage node is an energy storage node registered with the central controller and capable of participating in power dispatching;

[0116] The demand prediction module 402 is used to obtain the current behavior data of the user and the current operation data of the target energy storage node, and input the current behavior data and the current operation data into a pre-trained prediction model to obtain the current demand data, wherein the prediction model is a model trained by using a machine learning algorithm with the goal of predicting the total power demand;

[0117] The plan generation module 403 is used to generate a scheduling plan according to the current demand data, wherein the scheduling plan includes multiple scheduling schemes, and one scheduling scheme corresponds to one target energy storage node;

[0118] The plan optimization module 404 is used to optimize the dispatch plan by taking the fluctuation of total power demand and the minimum total cost as the optimization objectives;

[0119] The plan execution module 405 is used to control the target energy storage node to work according to the corresponding scheduling scheme in the optimized scheduling plan.

[0120] Optionally, the node determination module 401 is specifically used to obtain node parameters and monitoring data of each candidate energy storage node after multiple candidate energy storage nodes are registered with the central controller, wherein the node parameters are used to reflect the own properties of the candidate energy storage node, and the monitoring data are used to reflect the operating conditions of the candidate energy storage node; after multiple candidate energy storage nodes are registered with the central controller, obtain node parameters and monitoring data of each candidate energy storage node, wherein the node parameters are used to reflect the own properties of the candidate energy storage node, and the monitoring data are used to reflect the operating conditions of the candidate energy storage node; and use the candidate energy storage node in an available state as the target energy storage node.

[0121] Optional, combined Figure 4 , Figure 5 Schematic diagram of another control device for energy storage nodes provided in Embodiment 3 of the present invention. Figure 5 As shown, the device also includes: a model training module 406.

[0122] The model training module 406 is used to obtain a training data set, wherein the training data set includes a number of training samples, and one training sample includes historical behavior data, historical operation data and historical demand data; the historical behavior data and historical operation data of the current training sample are input into the original model to obtain the predicted demand data corresponding to the current training sample, wherein the current training sample is any training sample in the training data set; if the difference between the predicted demand data and the historical demand data of the current training sample is less than or equal to a preset threshold, the original model is used as the preset model; if the difference between the predicted demand data and the historical demand data of the current training sample is greater than the preset threshold, the parameters of the original model are adjusted, and the next training sample in the training data set is used as the current training sample, and the step of inputting the historical behavior data and historical operation data of the current training sample into the original model to obtain the predicted demand data corresponding to the current training sample is returned to be executed.

[0123] Optionally, the plan generation module 403 is used to construct a total scheduling function based on current demand data; decompose the total scheduling function to obtain multiple sub-scheduling functions, where one sub-scheduling function corresponds to a target energy storage node; solve the sub-scheduling functions in parallel based on the first optimization algorithm, and use the solution results as the scheduling plan.

[0124] Optionally, the overall scheduling function is

[0125] The sub-scheduling function is Affected by E i (t),P i (t) restraint;

[0126] Where N is the total number of target energy storage nodes, f i (,) is the benefit function of the i-th target energy storage node, E i is the power of the i-th target energy storage node, P i is the charging and discharging power of the i-th target energy storage node; E i (t) is the power of the i-th target energy storage node at time t, P i (t) is the charging and discharging power of the i-th target energy storage node at time t.

[0127] Optionally, the planning optimization module 404 is specifically used to construct an optimization objective function based on the scheduling plan and current demand data with the fluctuation of total electricity demand and the minimization of total cost as optimization goals, wherein the optimization objective function has constraints; and perform iterative optimization calculations on the optimization objective function based on the second optimization algorithm to determine the optimized scheduling plan.

[0128] Optionally, the optimization objective function is

[0129]

[0130] Where J represents the total power demand fluctuation and total cost within a given time range T, N is the total number of target energy storage nodes, and P i (t) is the charging and discharging power of the i-th target energy storage node at time t, λ i is the electricity price coefficient of the i-th target energy storage node, α i is the charging efficiency coefficient of the i-th target energy storage node, γ is the balance coefficient used to weigh the relationship between the fluctuation of total power demand and total cost, and D(t) is the total power demand at time t;

[0131] The constraints are

[0132] Among them, E i (t) is the power of the i-th target energy storage node at time t, is the maximum power of the i-th target energy storage node, is the minimum charging and discharging power of the i-th target energy storage node, is the maximum charge and discharge power of the i-th target energy storage node, β i is the discharge efficiency coefficient of the i-th target energy storage node;

[0133] The optimized scheduling plan is

[0134]

[0135] in, is the optimized scheduling scheme of the i-th target energy storage node at time t, represents the weighted sum of the power and square of all target energy storage nodes, Represents the square of the difference between the total power demand and the power of all target energy storage nodes, represents the influence of all target energy storage nodes other than target energy storage node i on target energy storage node i, h(t) is the time-dependent function used to adjust the smoothness of power change, f(E j (t)) is the energy storage state function of the target energy storage node j, g(P j (t)) is the power function of the target energy storage node j.

[0136] Optionally, the plan generation module 403 is further used to obtain the scheduling scheme execution data and health data of the target energy storage node; dynamically adjust the algorithm parameters of the first optimization algorithm according to the scheduling scheme execution data and health data;

[0137] The planning optimization module 404 is also used to obtain the scheduling scheme execution data and health data of the target energy storage node; and dynamically adjust the algorithm parameters of the second optimization algorithm according to the scheduling scheme execution data and health data.

[0138] The control device of the energy storage node provided in the embodiment of the present invention can execute the control method of the energy storage node provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0139] Embodiment 4

[0140] Figure 6 : is a structural diagram of a central controller provided in Embodiment 4 of the present invention. The central controller 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 central controller may 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.

[0141] like Figure 6 As shown, the central controller 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 central controller 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.

[0142] A number of components in the central controller 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 central controller 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0143] 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, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a control method for an energy storage node.

[0144] In some embodiments, the control method of the energy storage node 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 central controller 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 energy storage node described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the control method of the energy storage node in any other appropriate manner (e.g., by means of firmware).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] To provide interaction with a user, the systems and techniques described herein may be implemented on a central controller 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 pointing device (e.g., a mouse or trackball) through which the user can provide input to the central controller. 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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 an energy storage node, characterized in that: Applied to a central controller, the method comprises: Determine a target energy storage node, wherein the target energy storage node is an energy storage node registered with the central controller and capable of participating in power dispatching; Acquire the user's current behavior data and the current operation data of the target energy storage node, and input the current behavior data and the current operation data into a pre-trained prediction model to obtain current demand data, wherein the prediction model is a model trained using a machine learning algorithm with the goal of predicting total power demand; Generate a scheduling plan according to the current demand data, wherein the scheduling plan includes multiple scheduling schemes, and one scheduling scheme corresponds to one target energy storage node; The dispatch plan is optimized with the fluctuation of total power demand and the minimum total cost as the optimization objectives, and the target energy storage node is controlled to work according to the corresponding dispatch scheme in the optimized dispatch plan.

2. The control method of the energy storage node according to claim 1, characterized in that: The determining of the target energy storage node comprises: After multiple candidate energy storage nodes are registered with the central controller, node parameters and monitoring data of each candidate energy storage node are obtained, wherein the node parameters are used to reflect the own attributes of the candidate energy storage node, and the monitoring data are used to reflect the operation status of the candidate energy storage node; Determine the current state of each candidate energy storage node according to the node parameters and the monitoring data, wherein the current state includes an available state and an unavailable state; The candidate energy storage node in an available state is used as the target energy storage node.

3. The control method of the energy storage node according to claim 1, characterized in that: The method for training the prediction model comprises: Acquire a training data set, wherein the training data set includes a plurality of training samples, and one of the training samples includes historical behavior data, historical operation data, and historical demand data; Inputting historical behavior data and historical operation data of a current training sample into the original model to obtain predicted demand data corresponding to the current training sample, wherein the current training sample is any training sample in the training data set; If the difference between the predicted demand data and the historical demand data of the current training sample is less than or equal to a preset threshold, the original model is used as the preset model; If the difference between the predicted demand data and the historical demand data of the current training sample is greater than a preset threshold, the parameters of the original model are adjusted, and the next training sample in the training data set is used as the current training sample, and the process returns to the step of inputting the historical behavior data and historical operation data of the current training sample into the original model to obtain the predicted demand data corresponding to the current training sample.

4. The control method of the energy storage node according to claim 1, characterized in that: The step of generating a scheduling plan according to the current demand data includes: Constructing a total scheduling function according to the current demand data; Decomposing the total dispatch function to obtain a plurality of sub-dispatching functions, wherein one sub-dispatching function corresponds to one target energy storage node; The sub-scheduling functions are solved in parallel based on the first optimization algorithm, and the solution results are used as the scheduling plan.

5. The control method of the energy storage node according to claim 4, characterized in that: The overall scheduling function is The sub-scheduling function is Affected by E i (t),P i (t) restraint; Where N is the total number of target energy storage nodes, f i (,) is the benefit function of the i-th target energy storage node, E i is the power of the i-th target energy storage node, P i is the charging and discharging power of the i-th target energy storage node; E i (t) is the power of the i-th target energy storage node at time t, P i (t) is the charging and discharging power of the i-th target energy storage node at time t.

6. The control method of the energy storage node according to claim 1, characterized in that: The optimization of the dispatching plan with the fluctuation of total power demand and the minimum total cost as the optimization objectives includes: Taking the fluctuation of total power demand and the minimum total cost as optimization objectives, constructing an optimization objective function according to the dispatch plan and the current demand data, wherein the optimization objective function has constraints; An iterative optimization calculation is performed on the optimization objective function based on a second optimization algorithm to determine the optimized scheduling plan.

7. The control method of the energy storage node according to claim 6, characterized in that: The optimization objective function is Where J represents the total power demand fluctuation and total cost within a given time range T, N is the total number of target energy storage nodes, and P i (t) is the charge and discharge power of the i-th target energy storage node at time t, λi is the electricity price coefficient of the i-th target energy storage node, α i is the charging efficiency coefficient of the i-th target energy storage node, γ is the balance coefficient used to weigh the relationship between the fluctuation of total power demand and total cost, and D(t) is the total power demand at time t; The constraints are Among them, E i (t) is the power of the i-th target energy storage node at time t, is the maximum power of the i-th target energy storage node, is the minimum charging and discharging power of the i-th target energy storage node, is the maximum charge and discharge power of the i-th target energy storage node, β i is the discharge efficiency coefficient of the i-th target energy storage node; The optimized scheduling plan is: in, is the optimized scheduling scheme of the i-th target energy storage node at time t, represents the weighted sum of the power and square of all target energy storage nodes, Represents the square of the difference between the total power demand and the power of all target energy storage nodes, represents the influence of all target energy storage nodes other than target energy storage node i on target energy storage node i, h(t) is the time-dependent function used to adjust the smoothness of power change, f(E j (t)) is the energy storage state function of the target energy storage node j, g(P j (t)) is the power function of the target energy storage node j.

8. The control method of the energy storage node according to claim 6, characterized in that: Also includes: Obtaining scheduling scheme execution data and health data of the target energy storage node; Dynamically adjust algorithm parameters of the first optimization algorithm and / or the second optimization algorithm according to the scheduling scheme execution data and the health data.

9. A central controller, characterized in that: The central controller comprises: at least one processor; and a memory in communication with 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 energy storage node described in any one of claims 1-8.

10. 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 energy storage node according to any one of claims 1 to 8 when executed.

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