Digital operation control method, system and equipment of energy storage power station and medium

Abstract

The invention discloses a digital operation control method, system and device of an energy storage power station and a medium, and belongs to the technical field of operation and maintenance of the energy storage power station, and the method comprises the steps: constructing a three-dimensional digital model of the energy storage power station; performing spatial positioning on energy storage equipment in the energy storage power station, and constructing a digital twinborn model; using a quantum heuristic algorithm to optimize charging and discharging scheduling of the energy storage power station; performing energy flow and thermal management simulation, simulating heat flow and distribution of energy storage equipment in the energy storage power station, and predicting temperature change of the energy storage equipment in the charging and discharging process; and charging and discharging strategies and cooling measures are adjusted in real time through a thermodynamic feedback mechanism. Through the quantum heuristic algorithm, the charging and discharging strategy can be intelligently optimized according to real-time data, the charging and discharging rate, the discharging time and the discharging period are accurately adjusted, the use efficiency of the battery pack is improved, and the service life of equipment is effectively prolonged.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy storage power station operation and maintenance, in particular to a digital operation control method, system, device and medium for energy storage power station. BACKGROUND

[0002] At present, the management of energy storage power station mainly relies on traditional manual inspection and monitoring means. By monitoring the basic parameters (such as voltage, current, temperature, etc.) of energy storage equipment, the operation and maintenance personnel regularly check and troubleshoot the equipment. However, the existing operation and maintenance means mainly rely on single sensor data, which cannot comprehensively reflect the key information such as spatial layout, relative position and form of the energy storage equipment. Especially in large-scale energy storage power station, the number of equipment is large and the structure is complex, the traditional operation and maintenance method cannot efficiently and accurately process these information, resulting in long response time of equipment failure and increasing the difficulty and risk of operation and maintenance. SUMMARY

[0003] In view of the above problems, the present application provides a digital operation control method, system, device and medium for energy storage power station.

[0004] Therefore, the technical problem solved by the present application is: how to accurately obtain the spatial geometric form and layout information of energy storage power station and equipment through oblique photography, point cloud data and orthographic image technology, construct a three-dimensional digital model, realize real-time monitoring and management of equipment state. At the same time, combined with quantum heuristic algorithm to optimize charge and discharge scheduling, adopt dynamic thermal management mechanism to ensure the efficient operation of energy storage equipment within the safe temperature range, so as to improve the operation efficiency and equipment safety of energy storage power station.

[0005] To solve the above technical problems, the present application provides the following technical scheme: a digital operation control method for energy storage power station, comprising, obtaining the spatial geometric form, size and layout of energy storage power station and energy storage equipment, using oblique photography, point cloud data and orthographic image to obtain the three-dimensional position and form information of energy storage power station and energy storage equipment, and constructing a three-dimensional digital model of energy storage power station based on the spatial geometric form, size, layout and three-dimensional position and form information of energy storage equipment; using geographic information system to spatially locate the energy storage equipment in the energy storage power station, obtaining the spatial coordinates and real-time running state of the energy storage equipment, mapping the spatial coordinates and real-time running state of the energy storage equipment to the equipment digital twin in the three-dimensional digital model, and constructing a digital twin model; based on the digital twin model, applying quantum heuristic algorithm to optimize the charge and discharge scheduling of energy storage power station, adjusting the charging rate, discharging time and discharging period according to the real-time state information of energy storage equipment, and optimizing the energy utilization rate and operation efficiency of energy storage power station; Based on the optimized charge and discharge scheduling results, energy flow and thermal management simulations are performed to simulate the heat flow and distribution of energy storage devices in the energy storage power station and predict the temperature changes of energy storage devices during the charge and discharge process. Based on the results of energy flow and thermal management simulations, the charging and discharging strategies and cooling measures are adjusted in real time through a thermodynamic feedback mechanism to regulate the load of the energy storage battery pack and inverter, ensuring that the energy storage equipment of the energy storage power station operates within a safe temperature range.

[0006] As a preferred embodiment of the digital operation control method for an energy storage power station described in this invention, the construction of a three-dimensional digital model of the energy storage power station includes: aligning the original data of oblique photography, point cloud data, and orthophotos with coordinates; unifying the data to the same coordinate system using a geographic information system; and registering the oblique photography images with the point cloud data and orthophotos using image registration technology to generate three-dimensional spatial data. Based on the generated three-dimensional spatial data, a surface reconstruction algorithm is used to construct a three-dimensional digital model of the energy storage power station.

[0007] As a preferred embodiment of the digital operation control method for an energy storage power station according to the present invention, the image registration technology includes selecting the location of the energy storage device within the energy storage power station as a control point. The data source of the energy storage power station is registered to generate an optimization objective function for the coordinate transformation matrix; The objective function for optimizing the coordinate transformation matrix is ​​expressed as follows: in, The objective function representing the coordinate transformation matrix is... Let represent the coordinate transformation matrix that minimizes the objective function of the coordinate transformation matrix. This represents the weighted weight of the i-th control point. This represents the registration error function, which measures the geometric error between each pair of control points. Represents the spatial coordinates of the i-th control point. Indicates the first Layout constraint functions for each energy storage device Indicates the first The three-dimensional coordinates of an energy storage device in the target coordinate system These represent the weighting coefficients of the layout constraints, used to balance the effects of registration errors and layout constraints. This represents the total number of energy storage devices in the energy storage power station, and i represents the index of the control point. Indicates an index for energy storage devices. Indicates the total number of control points; Solve for the objective function of the coordinate transformation matrix.

[0008] As a preferred scheme of the digital operation control method of the energy storage power station, in the scheme, the step of constructing a three-dimensional digital model of the energy storage power station by using a surface reconstruction algorithm comprises: using a Poisson reconstruction algorithm to construct a three-dimensional geometric model. The Poisson equation is converted into a linear equation set. The normal vector of each point is determined by using the normal vector of all points, the equipotential surface is found by using the gradient direction, and the surface point cloud is generated.

[0009] As a preferred scheme of the digital operation control method of the energy storage power station, in the scheme, the step of constructing a three-dimensional geometric model by using a Poisson reconstruction algorithm comprises: calculating a covariance matrix of a neighborhood control point set, representing the geometric features of a local point cloud, and representing as: wherein, Covariance matrix of the i-th control point, Coordinates of the neighborhood points, The neighborhood control point set of the i-th control point; The covariance matrix of the neighborhood control point set The eigenvalues and eigenvectors of the covariance matrix of the neighborhood control point set The normal vector corresponding to the minimum eigenvalue is obtained. The divergence is calculated according to the normal vector The Poisson equation is constructed, and is represented as: wherein, The Laplacian of the potential function The potential function, The divergence.

[0010] As a preferred scheme of the digital operation control method of the energy storage power station, in the scheme, the step of optimizing the charging and discharging scheduling of the energy storage power station by using a quantum heuristic algorithm comprises: initializing all qubits to a superposition state, and the superposition state represents the parallel existence of multiple charging and discharging strategies; A Hadamard gate is applied to the qubits to create a superposition state of the qubits, and a quantum controlled non-inversion gate is used to control the qubits to produce entanglement between the qubits; The state of the qubits is quantum interfered with the objective function, and the objective function is optimized through the interaction between the qubits; Through a quantum measurement operation, the state of the qubits is converted into the values of the charging rate, the discharging time and the period, and is decoded into a specific charging and discharging scheduling strategy.

[0011] ​As a preferred scheme of the digital operation control method of the energy storage power station, in the prediction of the temperature change of the energy storage device during the charging and discharging process, the heat input is calculated and represented as: wherein, represents the heat input, represents the optimized charging rate, is the duration of the charging process; According to the material properties and geometric shape of the energy storage device, the heat conduction through the device surface is calculated and represented as: wherein, represents the heat conduction, is the thermal conductivity of the material, is the heat conduction surface area, is the temperature difference, is the thickness of the material; The heat is transferred through convection, and the heat exchange between the device surface and the surrounding fluid is calculated and represented as: wherein, represents the heat convection, is the convective heat transfer coefficient, is the device surface temperature, is the ambient temperature; Heat is transferred through thermal radiation, and the thermal radiation is calculated using the Stefan-Boltzmann law and represented as: wherein, represents the heat radiation, is the Stefan-Boltzmann constant, is the surface emissivity, is the fourth power of the device surface temperature, is the fourth power of the ambient temperature; Heat balance quantity is represented as: According to the principle of heat conservation, the temperature change of the device and the heat capacity of the device and the heat balance quantity have the following relationship, represented as: wherein, represents the temperature change of the energy storage device during the charging process, This is the heat balance quantity. For the heat capacity of the equipment, For the quality of energy storage equipment, This refers to the specific heat capacity of materials used in energy storage devices.

[0012] This invention provides a digital operation and control system for an energy storage power station.

[0013] To solve the above technical problems, the present invention provides the following technical solution: a digital operation control system for an energy storage power station, comprising: a data acquisition module, used to acquire the spatial geometry, size, and layout of the energy storage power station and energy storage equipment, and to acquire the three-dimensional position and shape information of the energy storage power station and energy storage equipment using oblique photography, point cloud data, and orthophotos, and to construct a three-dimensional digital model of the energy storage power station based on the spatial geometry, size, layout, and three-dimensional position and shape information of the energy storage equipment; The positioning module is used to spatially locate the energy storage equipment in the energy storage power station using a geographic information system, obtain the spatial coordinates and real-time operating status of the energy storage equipment, and map the spatial coordinates and real-time operating status of the energy storage equipment to the digital twin of the equipment in the three-dimensional digital model to construct the digital twin model. The optimization module is used to optimize the charging and discharging scheduling of energy storage power stations based on digital twin models and quantum heuristic algorithms. It adjusts the charging rate, discharging timing and discharging cycle according to the real-time status information of energy storage devices to optimize the energy utilization and operating efficiency of energy storage power stations. The simulation module is used to perform energy flow and thermal management simulations based on the optimized charge and discharge scheduling results, simulate the heat flow and distribution of energy storage devices in the energy storage power station, and predict the temperature changes of energy storage devices during the charge and discharge process. The scheduling module is used to adjust the charging and discharging strategies and cooling measures in real time through a thermodynamic feedback mechanism based on the results of energy flow and thermal management simulations, thereby regulating the load of the energy storage battery pack and inverter to ensure that the energy storage equipment of the energy storage power station operates within a safe temperature range.

[0014] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the digital operation control method for an energy storage power station.

[0015] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the digital operation control method for an energy storage power station.

[0016] The beneficial effects of this invention are as follows: By using oblique photography, point cloud data and orthophoto technology, an accurate three-dimensional digital model is constructed, which enables the accurate acquisition of the spatial geometry, size and layout of energy storage power stations and equipment.

[0017] The existing charge-discharge scheduling method is based on simple rules or historical data, and cannot be optimized in real time according to the health status of the device and environmental changes, resulting in low energy utilization and low device efficiency. Through the quantum heuristic algorithm, the present application can intelligently optimize the charge-discharge strategy according to real-time data, accurately adjust the charge-discharge rate, discharge timing and cycle, improve the use efficiency of the battery pack and effectively prolong the service life of the device.

[0018] In terms of thermal management, the traditional static temperature control measure cannot be dynamically adjusted according to the actual temperature change of the device, which is easy to cause overheating or insufficient temperature control of the device. Through the dynamic thermal management mechanism of the present application, the temperature change is monitored in real time and the charge-discharge strategy is adjusted, which effectively avoids the problem of overheating. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0020] Figure 1 The overall flowchart of a digital operation control method of an energy storage power station provided by an embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings. Obviously, the described embodiments are part of the embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0022] Embodiment 1, refer to Figure 1 For an embodiment of the present application, the embodiment provides a digital operation control method of an energy storage power station, comprising: Step 1: Obtain the spatial geometry, size and layout of the energy storage power station and the energy storage device, obtain the three-dimensional position and shape information of the energy storage power station and the energy storage device by using oblique photography, point cloud data and orthographic image, and construct a three-dimensional digital model of the energy storage power station based on the spatial geometry, size and layout, three-dimensional position and shape information of the energy storage device.

[0023] It should be noted that the spatial geometry refers to the external shape of the energy storage power station and the energy storage equipment. For the energy storage power station body, the geometry includes the shape of the building facade and each room or area (such as the energy storage battery room, machine room, control center, etc.), while for the energy storage equipment, the geometry includes the shape and structure of the battery pack, inverter, transformer and other equipment. For example, the shape of the battery pack is usually rectangular or square, the shape of the inverter is cuboid, and the shape of the transformer is similar to cuboid or cylindrical shape.

[0024] The size refers to the physical size of the energy storage power station and the energy storage equipment. The overall size of the energy storage power station includes the basic dimensions of the building, such as length, width, height, while the size of the energy storage equipment includes the size of each individual battery, module, rack, battery management system (BMS) and other equipment.

[0025] The layout refers to the spatial distribution of each energy storage equipment inside the energy storage power station and their mutual positional relationship. The layout information includes the placement position of the energy storage battery pack, inverter, transformer and other equipment inside the power station, as well as the spacing between the energy storage equipment, the relative position of the energy storage equipment and other facilities such as the ventilation system, cooling system, electrical access point. The layout data can be obtained through the building plan and installation drawing of the energy storage power station.

[0026] Through oblique photography technology, the energy storage power station and the energy storage equipment are photographed from multiple angles by a camera-equipped unmanned aerial vehicle, generating a multi-view image dataset. Unlike traditional vertical aerial photography, oblique photography technology can capture target areas from different angles (usually at an angle of 30° to 45°), providing more perspective information. After image matching and analysis, the photographed images can reflect the relative position of each energy storage equipment and building in the energy storage power station and their three-dimensional shape. Through image registration and three-dimensional reconstruction technology, oblique photography provides three-dimensional spatial layout data of the energy storage power station.

[0027] Through laser radar sensors using laser scanning technology to scan the energy storage power station and the energy storage equipment, spatial point cloud data is obtained. Laser radar measures the reflection time of laser beams to accurately calculate the distance of each measurement point, thereby generating a dense three-dimensional point cloud dataset. Each point represents a small point on the surface of the energy storage power station and its energy storage equipment, with spatial coordinates, describing the three-dimensional position, shape, appearance and size of the energy storage power station and the energy storage equipment.

[0028] Orthophoto technology uses remote sensing satellites or unmanned aerial vehicles to take high-resolution two-dimensional images. After geometric correction, the two-dimensional images eliminate the effects of shooting angles and terrain undulations, so that each point in the image corresponds to the actual ground coordinates. Orthophoto can provide ground information of the energy storage power station, obtain accurate position, appearance of the energy storage power station and the energy storage equipment and the arrangement of the energy storage equipment. The three-dimensional digital model of the energy storage power station is corrected through orthophoto.

[0029] In step 1, a three-dimensional digital model of the energy storage power station is constructed, including the following steps A1-A2: A1: Align the original data of oblique photography, point cloud data, and orthophoto in coordinates, unify the data to the same coordinate system using geographic information systems, and use image registration technology to register the oblique photography images with the point cloud data and orthophoto to generate three-dimensional spatial data.

[0030] A2: Based on the generated three-dimensional spatial data, a surface reconstruction algorithm is used to construct a three-dimensional digital model of the energy storage power station.

[0031] Step A1 specifically includes the following steps A1.1-A1.3: A1.1: Select the positions of energy storage equipment (such as battery packs, inverters, transformers, etc.) within the energy storage power station as control points. For inverters, the electrical access point and areas with high heat dissipation requirements should be selected as control points. For battery packs, the center position or key components (such as battery management systems) should be selected as control points to ensure that heat dissipation requirements are reflected in coordinate transformation. For transformers, the relative position of the electrical access point should be selected as the control point to ensure the distance between the transformer and other equipment.

[0032] Obtain control points Spatial coordinates in the original coordinate system , denoted as: wherein, represents the spatial coordinates of the i-th control point in the original coordinate system, represents the z-axis coordinates of the i-th control point in the original coordinate system, represents the y-axis coordinates of the i-th control point in the original coordinate system, represents the x-axis coordinates of the i-th control point in the original coordinate system, represents the z-axis coordinates of the i-th control point in the original coordinate system, represents the y-axis coordinates of the i-th control point in the original coordinate system, represents the x-axis coordinates of the i-th control point in the original coordinate system, represents the z-axis coordinates of the i-th control point in the original coordinate system, represents the y-axis coordinates of the i-th control point in the original coordinate system, represents the x-axis coordinates of the i-th control point in the original coordinate system. Control points in the target coordinate system

[0033] denoted as: wherein, represents the spatial coordinates of the i-th control point in the target coordinate system, represents the z-axis coordinates of the i-th control point in the target coordinate system, represents the y-axis coordinates of the i-th control point in the target coordinate system, represents the x-axis coordinates of the i-th control point in the target coordinate system, represents the z-axis coordinates of the i-th control point in the target coordinate system, represents the y-axis coordinates of the i-th control point in the target coordinate system, ​The control points in the target coordinate system Axis coordinates Indicates the first The control points in the target coordinate system Axis coordinates.

[0034] The covariance matrix between the original coordinate system and the target coordinate system is calculated and represented as follows: in, Represents the covariance matrix. This represents the total number of control points, where i represents the index of the control point, from 1 to... , It is the first The three-dimensional coordinates of each control point in the target coordinate system This indicates transpose.

[0035] By analyzing the covariance matrix Performing singular value decomposition yields a rotation matrix, represented as: in, Describes a left singular matrix. Describes a right singular matrix. Represents a singular value matrix. This represents the rotation matrix.

[0036] Calculate the translation vector to ensure that the rotated point is accurately aligned to its position in the target coordinate system, expressed as: in, This represents the translation vector.

[0037] Rotation matrix Translation vector By merging, we obtain the initial coordinate transformation matrix. , is represented as: in, This represents the initial coordinate transformation matrix.

[0038] A1.2: The objective function for registering the data source of the energy storage power station and generating the coordinate transformation matrix is ​​expressed as: in, The objective function representing the coordinate transformation matrix is... Let represent the coordinate transformation matrix that minimizes the objective function of the coordinate transformation matrix. This represents the weighted weight of the i-th control point. This represents the registration error function, which measures the geometric error between each pair of control points. Represents the spatial coordinates of the i-th control point. Indicates the first Layout constraint functions for each energy storage device Indicates the first The three-dimensional coordinates of an energy storage device in the target coordinate system These represent the weighting coefficients of the layout constraints, used to balance the effects of registration errors and layout constraints. This represents the total number of energy storage devices in the energy storage power station, and i represents the index of the control point. Indicates an index for energy storage devices. This indicates the total number of control points.

[0039] Weighted weights of control point i , is represented as: in, Indicates the first The heat dissipation capacity of the energy storage device at each control point is calculated by determining the heat dissipation area based on the device dimensions. Indicates the first The power output of the energy storage device at each control point is calculated by real-time monitoring of voltage and current. Indicates the first The temperature of the energy storage device at each control point is monitored in real time by temperature sensors to obtain the device's operating temperature. It is the first The width of the energy storage device at each control point It is the first The height of the energy storage device at each control point It is the first The depth of the energy storage equipment at each control point It is the first The voltage of the energy storage device at each control point It is the first The current of the energy storage device at each control point.

[0040] The registration error function is expressed as follows: in, This represents the Euclidean norm.

[0041] The layout constraint function is expressed as: wherein, denotes the Euclidean distance between the energy storage device at the th control point and the th energy storage device, denotes the spatial coordinates of the th energy storage device in the target coordinate system, is the minimum safety distance between devices, ensuring the heat dissipation and cooling channel requirements of the devices.

[0042] The weight coefficient of the layout constraint is denoted as: wherein, is the spatial requirement of the th energy storage device, is the width of the th energy storage device, is the height of the th energy storage device, is the depth of the th energy storage device.

[0043] A1.3: Solving the optimization objective function of the coordinate transformation matrix, specifically including: Solving the gradient of the rotation matrix is denoted as: wherein, denotes the gradient of the rotation matrix ; denotes the partial derivative of the optimization objective function of the coordinate transformation matrix with respect to the rotation matrix .

[0044] Solving the gradient of the translation vector is denoted as: wherein, denotes the gradient of the translation vector ; denotes the partial derivative of the optimization objective function of the coordinate transformation matrix with respect to the translation vector .

[0045] Calculating the Hessian matrix of the rotation matrix is denoted as: wherein, Hessian matrix of the rotation matrix ; Optimization objective function of the coordinate transformation matrix Second-order derivative of the rotation matrix .

[0046] Hessian matrix of the translation vector is expressed as: wherein, Hessian matrix of the translation vector ; Optimization objective function of the coordinate transformation matrix Second-order derivative of the translation vector .

[0047] Updating the rotation matrix using the Newton method formula is expressed as: wherein, updated rotation matrix, current rotation matrix, learning rate of the rotation matrix, controlling the step size of each update of the rotation matrix, inverse matrix of the Hessian matrix of the rotation matrix .

[0048] Updating the translation vector using the Newton method formula is expressed as: wherein, updated translation vector, current translation vector, learning rate of the translation vector, controlling the step size of each update of the translation vector, inverse matrix of the Hessian matrix of the translation vector .

[0049] By calculating the change of the optimization objective function of the coordinate transformation matrix, it is determined whether to converge, expressed as: wherein, optimization objective function of the optimized coordinate transformation matrix, optimization objective function of the current coordinate transformation matrix, convergence threshold of the coordinate transformation matrix, set to 10 -4 , optimized coordinate transformation matrix, represents the current coordinate transformation matrix.

[0050] When the change of the optimization objective function of the coordinate transformation matrix is less than a set threshold , it is considered that the optimization process has converged.

[0051] When the optimization objective function of the coordinate transformation matrix converges, output the optimized coordinate transformation matrix , which includes the final rotation matrix and the translation vector , is represented as: wherein, represents the final optimized coordinate transformation matrix, represents the final optimized rotation matrix, and represents the final optimized translation vector.

[0052] Align the original coordinate points using the optimized coordinate transformation matrix , which is represented as: wherein, represents the three-dimensional coordinates of the i points after alignment.

[0053] Step A2 specifically includes the following steps A2.1-A2.3: A2.1: Use the Poisson reconstruction algorithm to construct a three-dimensional geometric model, select a set of neighborhood control points, i.e. the nearest points with a distance of , and the neighborhood range is determined by the radius .

[0054] Calculate the covariance matrix of the set of neighborhood control points, which represents the geometric features of the local point cloud, and is represented as: wherein, represents the covariance matrix of the i-th control point, represents the coordinates of the neighborhood points, and represents the set of neighborhood control points of the i-th control point.

[0055] Calculate the eigenvalues and eigenvectors of the covariance matrix , take the eigenvector corresponding to the smallest eigenvalue to obtain the normal vector .

[0056] Calculate the divergence according to the normal vector , construct the Poisson equation, which is represented as: wherein, is the Laplacian of the potential function , which is the potential function , which is the divergence, describes the degree of change of the point cloud surface normal vector.

[0057] Solving the Poisson equation obtains the potential function of the surface.

[0058] A2.2: Convert the Poisson equation into a linear equation system, realize discretization, and express it as: wherein, is the matrix of the discretized Poisson equation, is the potential function at each point, is the divergence term.

[0059] Use the conjugate gradient method to iteratively solve the linear equation system to obtain the potential function , including: Initialize the iterative calculation process. At the initial moment of controlling the operation, assume that the potential function value of the energy storage device is zero, indicating that the system internal state has not been effectively solved before any iterative correction. Based on this initial assumption, the initial residual is obtained by calculating the difference between the right end vector and the initial potential function. Under this initial condition, the residual is equal to the external input vector, reflecting the total error amount of the system at the starting moment.

[0060] The obtained initial residual is directly used as the search direction of iteration. Since there is no historical direction information at this time, the residual itself is used as the initial conjugate direction to correct the position with the largest system error.

[0061] As the iteration process unfolds, each new search direction will be generated based on the previous residual and historical direction, gradually approaching the optimal solution.

[0062] Specifically, after the initialization step is completed, the initial value of the potential function, the initial residual, and the conjugate direction of the first iteration are obtained.

[0063] In each iteration, calculate the update step size: wherein, is the update step size, is the residual at the kth iteration, is the conjugate direction at the kth iteration.

[0064] Update the potential function value: ​wherein, represents the latent function vector at the k+1th iteration, represents the latent function vector at the kth iteration.

[0065] Update the residual: wherein, represents the residual vector at the k+1th iteration.

[0066] Update the conjugate direction: wherein, represents the conjugate direction update coefficient.

[0067] The current residual is small enough, the iteration stops: wherein, is the residual convergence threshold, set to 10 -4 .

[0068] The gradient of the latent function is calculated using the central difference method to obtain the normal vector, denoted as: wherein, represents the gradient of the latent function at the th point, represents the offset in the x-axis direction, represents the offset in the y-axis direction, represents the offset in the z-axis direction, represents the latent function value of the th control point.

[0069] A2.3: Use the normal vector of all points to determine the surface direction of each point, find the equipotential surface (i.e. the area where the latent function value is zero) through the gradient direction, and generate a surface point cloud.

[0070] Apply the Poisson reconstruction algorithm to construct a surface that conforms to the shape of the point cloud. The Poisson reconstruction algorithm uses the normal vector information in the discrete point cloud data to solve the Poisson equation and obtain a continuous three-dimensional surface composed of a discrete set of three-dimensional points. Use the triangular meshing method to convert the point cloud data into a grid structure, where each triangle represents a segment of the surface.

[0071] Smooth the three-dimensional surface to remove acquisition error noise and noise generated during the reconstruction process.

[0072] The position and size of the three-dimensional surface model are calibrated by orthographic image data. Using the ground coordinates and building appearance information in the orthographic image, the three-dimensional surface data is aligned to the actual scene, ensuring that the geographical position, appearance and size information of the model are accurately corrected.

[0073] According to the appearance, position and size information of the equipment in the orthographic image, the three-dimensional surface model is further optimized. The relative positions of the energy storage equipment (battery pack, inverter, transformer) are adjusted to ensure that the spacing, heat dissipation requirements and electrical access points between equipment meet the functional requirements of the energy storage power station, meet the layout requirements in the design drawings and specifications, and finally generate a three-dimensional digital model of the energy storage power station.

[0074] Step 2: Use the geographic information system to spatially position the energy storage equipment in the energy storage power station, obtain the spatial coordinates and real-time operating state of the energy storage equipment, map the spatial coordinates and real-time operating state of the energy storage equipment to the equipment digital twin in the three-dimensional digital model, and construct a digital twin model.

[0075] In step 2, the digital twin model is constructed, including the following steps B1-B3: B1: Use the geographic information system to spatially position the energy storage equipment in the energy storage power station: Select the same coordinate system, assign a unique identification code to each energy storage equipment and calibrate the initial position.

[0076] For outdoor energy storage equipment, use differential GPS (differential GPS-DGPS, DGPS) technology for positioning. For indoor energy storage equipment, use Wi-Fi, Bluetooth or ultra-wideband positioning systems for positioning.

[0077] The position data of the energy storage equipment is transmitted in real time to the geographic information system platform through the Internet of Things.

[0078] B2: Obtain the spatial coordinates and real-time operating state of the energy storage equipment: Obtain the spatial coordinates of the energy storage equipment through the geographic information system, and obtain the real-time operating data of the energy storage equipment through the communication protocol. Real-time monitoring of the operating state of the energy storage equipment is performed through sensors, including voltage, current, temperature, power output, battery health status, external load demand, etc.

[0079] Match the real-time operating data with the equipment spatial coordinates. Associate the real-time state data of the equipment with its corresponding position data to form a mapping of spatial position and real-time state.

[0080] B3: Map to the equipment digital twin in the three-dimensional digital model to construct a digital twin model: create a digital twin for each energy storage device in the three-dimensional digital model, the digital twin storing spatial position coordinates of the energy storage device and real-time state information of the energy storage device; Combine the real-time temperature of each energy storage device with the state attribute of the digital twin. Map the temperature data to a heat color representation of the digital twin (e.g., devices with high temperature are displayed in red, and low temperature is displayed in green).

[0081] Map the current voltage or current value of the energy storage device to the digital twin display parameter.

[0082] The health status information of the battery is mapped to the device state interface through the attribute of the digital twin model. For example, a battery with low health status can be identified by color as red, and a healthy battery is displayed as green. The health degree change is updated in real time to the attribute of each energy storage device in the digital twin.

[0083] Using three-dimensional modeling software, the geometry of the energy storage device is docked with the spatial coordinates; through the nodes of each energy storage device in the model, the spatial coordinates are associated with the position of the energy storage device in the digital twin; the state of each energy storage device digital twin is displayed using a visualization engine.

[0084] Step 3: Based on the digital twin model, apply a quantum heuristic algorithm to optimize the charge and discharge scheduling of the energy storage power station, adjust the charging rate, discharge timing and discharge period according to the real-time state information of the energy storage device, and optimize the energy utilization rate and operating efficiency of the energy storage power station.

[0085] In step 3, applying a quantum heuristic algorithm to optimize the charge and discharge scheduling of the energy storage power station includes the following steps C1-C4: C1: Initialize the quantum bit, each quantum bit representing a dimension of a charge and discharge strategy, including charging rate, discharge timing and discharge period. Each quantum bit is initially in a uniform superposition state, and all charge and discharge strategies have equal probability at the initial time.

[0086] The initialization of the quantum bit is represented as: wherein, represents the initialized quantum state, represents the total number of all charge and discharge strategies, represents the th charge and discharge strategy, represents the total number of all charge and discharge strategies represents the index.

[0087] At initialization, the state of each quantum bit is in a superposition state, representing all possible configurations in the current charge and discharge strategy space.

[0088] C2: Apply Hadamard gate to create superposition state of qubits, operate on qubits to ensure each qubit is in uniform superposition state, so that each charging and discharging strategy exists with equal probability in quantum space. The role of Hadamard gate is to transform the qubit from the ground state and to a superposition state, so that the qubit is in the following state, denoted as: where, represents the Hadamard gate used to create the superposition state of the qubit, represents the ground state of the qubit (usually "0" state), represents the other ground state of the qubit (usually "1" state); so that the state of the qubit becomes a uniform superposition state, i.e. each charging and discharging strategy appears with the same probability. Each qubit represents a potential charging and discharging strategy, such as different combinations of charging rate, discharging timing or discharging period.

[0089] Introduce entanglement between qubits using controlled NOT gate to control the relationship between qubits, so that the optimization of each charging and discharging strategy can be performed cooperatively in quantum space, and the operation rule is represented as: where, represents the controlled NOT gate, represents the state of two qubits, is the control bit, is the target bit, represents the XOR operation.

[0090] By controlling the relationship between qubits, the states of qubits are entangled with each other, forming mutual influence between different charging and discharging strategies in quantum space.

[0091] C3: Quantum interference between the state of the qubit and the objective function. The optimization objective function is used to quantify the effect of the charging and discharging strategy, which includes the charging rate optimization objective function, the discharging timing optimization objective function and the discharging period optimization objective function. Each quantum operation will make the probability amplitude of the quantum state evolve towards the optimal strategy direction.

[0092] After each operation, the state of the qubit changes, gradually approaching the optimization objective. The optimization of the charging and discharging strategy is dynamically adjusted through the interaction between qubits. For example, the quantum state gradually converges towards the optimal charging and discharging strategy objective (such as improving energy utilization of energy storage power station, prolonging equipment life, etc.) through interference.

[0093] For the optimization of charging rate, the objective function is: wherein, represents the charging rate optimization objective function, represents the charging rate, is the maximum charging rate of the battery, is the state of health of the battery, is the maximum voltage of the battery, is the current voltage of the battery, is the internal resistance of the battery.

[0094] For the optimization of the discharge timing, the objective function is: wherein, represents the discharge timing optimization objective function, is the external load demand, is the state of charge of the battery of the energy storage device, is the battery capacity of the energy storage device.

[0095] For the optimization of the discharge period, the objective function is: wherein, represents the discharge period optimization objective function, is the external load demand, is the state of charge of the battery of the energy storage device, is the battery capacity of the energy storage device.

[0096] C4: After each quantum operation, the state of the quantum bit is adjusted by the interference optimization objective function to adjust the charging rate, discharge timing and period, ensuring its convergence to the optimal strategy.

[0097] In the optimization process of the quantum heuristic algorithm, the state of the quantum bit is constantly adjusted through quantum operations, so that the quantum bit evolves towards the direction of the optimal charging and discharging strategy. After each quantum operation, the state of the quantum bit changes, and the interaction of the quantum bits constantly optimizes the objective function, thereby optimizing the charging rate, discharge timing and discharge period.

[0098] In each optimization process, the state of the quantum bit gradually converges, and finally reaches an optimized quantum state, which represents the optimal charging and discharging scheduling strategy.

[0099] After completing the optimization of the objective function, the quantum measurement operation is performed to convert the final state of the quantum bit into the charging and discharging scheduling strategy.

[0100] The quantum measurement operation is used to convert the quantum state of the qubits into classical information. In the quantum heuristic algorithm, after multiple quantum operations, the state of the qubits is a superposition state, containing multiple possible charging and discharging strategies. To obtain a specific charging and discharging scheduling strategy, the superposition state of the qubits is collapsed into a determined state through quantum measurement.

[0101] Specifically, the quantum measurement operation is implemented by the following steps: Prepare qubits, each qubit represents a charging and discharging strategy, and the state of the qubit represents the probability amplitude of the charging and discharging strategy. In the quantum heuristic algorithm, the probability amplitude of the charging and discharging strategy changes through quantum operations on the qubits.

[0102] Measurement is performed, and at the end of the optimization process, the state of the qubits is observed through the quantum measurement operation. Quantum measurement converts the superposition state of the qubits into a classical state. For example, if the qubits are in a superposition state of and , the measurement result can be or , and the probability is determined by the amplitude of the qubit. This process is called collapse of the qubit.

[0103] Each qubit represents a different component of the charging and discharging strategy, including charging rate, discharging opportunity, and discharging period. After quantum operation, the state of the qubit contains various possibilities of these strategies. Quantum measurement converts these possibilities into a specific charging and discharging scheduling strategy.

[0104] After quantum measurement, the state of the qubit collapses to obtain a specific classical value. For each qubit, this classical value will indicate a specific charging and discharging strategy.

[0105] Specifically, in the charging rate, if the measurement result of a qubit is , a lower charging rate is preset; if the measurement result is , a higher charging rate is preset. In this way, the result obtained by quantum measurement can be directly mapped to different levels of charging rate.

[0106] In the discharging opportunity, the measurement result will indicate a specific discharging opportunity. The measured quantum state determines the selection of the discharging opportunity, which depends on the external load demand and the state of charge of the battery .

[0107] Quantum measurement also determines the discharging period, and the length of the discharging period is determined according to the ratio of external load demand to battery capacity.

[0108] The optimized charging rate, discharging time and discharging period are obtained through quantum measurement results. Here, the measurement result of each qubit corresponds to an actual control parameter for the charging and discharging scheduling of the energy storage power station, and finally a specific charging and discharging strategy is obtained. The measurement results of each qubit are combined together to form a complete charging and discharging scheduling strategy, which includes the optimal charging rate, the optimal discharging time and the optimal discharging period.

[0109] It should be noted that the optimization of the charging and discharging strategy is realized by the quantum heuristic algorithm combined with the operation of Hadamard gate and control non-gate. The Hadamard gate provides a preliminary superposition state for the qubit, and the control non-gate introduces the mutual dependence between multiple charging and discharging strategies into the state of the qubit. Through quantum interference, the objective function is optimized, and the optimized charging and discharging strategy is obtained through quantum measurement.

[0110] It should be noted that the quantum heuristic algorithm described in the present application is simulated and realized by using a classical computing device in actual application, without relying on actual quantum computing hardware.

[0111] In a classical computer, The quantum state of one qubit is represented by a dimensional complex vector. Among them, represents the total number of qubits, represents the dimension of the quantum state space. Each element in the vector is a complex amplitude, which satisfies the normalization condition.

[0112] The charging and discharging strategy parameters are realized by qubit encoding. The charging rate dimension uses qubits, wherein represents the number of qubits required for charging rate encoding, and the charging rate range is discretized into grades, represents the maximum charging rate, the discharging time dimension uses qubits, wherein represents the number of qubits required for discharging time encoding, and the control period is divided into time slices; the discharging period dimension uses qubits, wherein represents the number of qubits required for discharging period encoding, and the discharging duration is discretized into grades.

[0113] The total number of qubits is calculated by the formula: In order to ensure the feasibility of calculation, the total number of qubits is limited to The number of quantum bits is in the range of 10 to 1000. The number of quantum bits is configured according to the scale of the energy storage station: when the number of energy storage devices is not more than 30, the number of quantum bits is preferably , and the total number of states is , and the state space dimension is ; when the number of energy storage devices is more than 30, a grouping optimization strategy is adopted, and the energy storage devices are divided into multiple subgroups according to the spatial position or the type of the energy storage devices, and each subgroup is independently optimized, and then the global adjustment is performed at the coordination layer.

[0114] The quantum gate operation is realized in a classical computer through matrix operation. The matrix representation of the Hadamard gate is: wherein, represents the Hadamard gate matrix. The matrix acts on a single quantum bit to convert the ground state into a superposition state. The matrix representation of the control non gate is: wherein, CNOT represents the control non gate matrix. The matrix acts on two quantum bits to realize the entanglement between the quantum bits.

[0115] For a system of quantum bits, the quantum gate is expanded to a matrix of through tensor product. In actual calculation, the sparse matrix storage format is adopted to reduce the memory occupation, and the multiplication of the matrix and the vector is used to realize the quantum gate operation.

[0116] The quantum interference process is realized through a phase operator , wherein represents the phase Oracle operator. The phase operator applies a phase rotation to each ground state , wherein represents the ground state index, and the value range is 0 to . The phase rotation angle is determined by the objective function value, wherein represents the phase angle corresponding to the ground state.

[0117] The calculation method of the objective function value is as follows. First, the ground state index is decoded into the charging rate , the discharging time and the discharging period , and the decoding formula is shown in the strategy parameter decoding part.

[0118] Then, the decoded parameters are substituted into the objective function. The charging rate objective function is: wherein, denotes the charging rate target function value corresponding to the th base state, is the maximum charging rate of the battery, is the state of health of the battery, is the maximum voltage of the battery, is the current voltage of the battery, is the internal resistance of the battery.

[0119] The discharge timing target function is: wherein, denotes the discharge timing target function value corresponding to the th base state, is the external load demand, is the state of charge of the battery of the energy storage device, is the capacity of the battery of the energy storage device.

[0120] The discharge cycle target function is: wherein, denotes the discharge cycle target function value corresponding to the th base state.

[0121] The comprehensive target function is constructed as: wherein, denotes the comprehensive target function value corresponding to the th base state, denotes the weight coefficient of the charging rate target function, denotes the weight coefficient of the discharge timing target function, denotes the weight coefficient of the discharge cycle target function, and the sum of the three is 1. The weight coefficients are set according to the operation priority of the energy storage power station, for example, the value of is increased when energy utilization rate is prioritized, and the value of is increased when external load demand is prioritized.

[0122] The phase rotation angle is calculated by target function value normalization: wherein, denotes the minimum value of the comprehensive target function in all base states, denotes the maximum value of the comprehensive target function in all base states. By phase setting, the base state with a smaller target function value, i.e., a better charging and discharging strategy, obtains a larger negative phase, and its probability amplitude is enhanced in the amplitude amplification process.

[0123] Amplitude amplification is achieved by iteratively applying Grover's operator. Grover's operator is denoted as , which amplifies the probability amplitude of the states with smaller objective function values by repeatedly acting on the quantum state. The number of iterations is adaptively adjusted according to the problem size.

[0124] The quantum measurement process is achieved by calculating the probability distribution. The measurement probability of each ground state is calculated by the modulus square of the probability amplitude, where represents the probability of measuring the th ground state. By repeating the measurement and counting the frequency, the state with the highest frequency of occurrence is selected as the optimal solution.

[0125] The optimal state index obtained by measurement is decoded into specific charging and discharging strategy parameters, where represents the state index corresponding to the optimal solution. The decoding process is as follows: The charging rate index calculation formula is: where represents the discrete index of the charging rate.

[0126] The actual value of the charging rate is calculated by the formula: where represents the decoded charging rate value.

[0127] The discharge timing index calculation formula is: where represents the discrete index of the discharge timing, represents the floor function.

[0128] The actual value of the discharge timing is calculated by the formula: where represents the decoded discharge start time, represents the total duration of the control period. The discharge period index calculation formula is: where represents the discrete index of the discharge period.

[0129] The actual value of the discharge period is calculated by the formula: where represents the decoded discharge duration, represents the maximum allowed discharge duration.

[0130] For large-scale energy storage power stations, when the number of energy storage devices exceeds 30, a grouping and parallel optimization strategy is adopted. The energy storage devices are divided into subgroups, wherein represents the total number of subgroups, and each subgroup contains no more than 20 energy storage devices, wherein represents the number of energy storage devices in the th subgroup, represents the subgroup index, and the value range is 1 to .

[0131] The computational complexity of the quantum heuristic algorithm on a classical computer is related to the number of qubits n. The state space dimension is , and the time complexity of the algorithm is , where k represents the number of iterations. To meet the real-time control requirements, the control period needs to consider the computational complexity, wherein represents the time required for the energy storage power station to perform a complete optimization-scheduling-feedback cycle.

[0132] When , the state space dimension is , and the time for completing a complete optimization calculation on an industrial control computer is about 5 to 10 minutes, so the control period set to 10 to 15 minutes can meet the real-time requirements.

[0133] When , the state space dimension is , and the optimization calculation time is about 2 to 5 minutes, and the control period can be set to 5 to 10 minutes.

[0134] For large-scale energy storage power stations, through the grouping and parallel optimization strategy, the calculation task is distributed to multiple processing units for parallel execution, which can effectively reduce the equivalent calculation time of single optimization. The optimization calculation of each subgroup can be performed in parallel, and the overall optimization time depends on the calculation time of a single subgroup and the global adjustment time of the coordination layer.

[0135] The setting principle of the control period is: wherein represents the time required for single optimization calculation, and the coefficient 1.5 is a safety margin to ensure that optimization calculation, strategy update, and execution instruction issuance can be completed within one control period.

[0136] Step 4: Based on the optimized charging and discharging scheduling results, energy flow and thermal management simulation is performed to simulate the heat flow and distribution of the energy storage devices in the energy storage power station, and to predict the temperature change of the energy storage devices during the charging and discharging process.

[0137] In step 4, energy flow and thermal management simulation is performed, including the following steps D1-D2: D1: Calculate the heat input, represented as: wherein, represents the heat input, represents the optimized charging rate, is the duration of the charging process.

[0138] According to the material properties and geometric shape of the energy storage device, the heat conduction through the device surface is calculated, represented as: wherein, represents the heat conduction, is the thermal conductivity of the material, is the heat conduction surface area, is the temperature difference, is the thickness of the material.

[0139] Heat is transferred through convection, calculating the heat exchange between the device surface and the surrounding fluid, represented as: wherein, represents the heat convection, is the convective heat transfer coefficient, is the device surface temperature, is the ambient temperature.

[0140] Heat is transferred through thermal radiation, using the Stefan-Boltzmann law to calculate the thermal radiation, represented as: wherein, represents the heat radiation, is the Stefan-Boltzmann constant, is the surface emissivity, is the fourth power of the device surface temperature, is the fourth power of the ambient temperature.

[0141] D2: Heat balance is represented as: wherein, represents the heat balance.

[0142] According to the principle of heat conservation, the temperature change of the device is related to the heat capacity of the device and the heat balance, which is expressed as: wherein, represents the temperature change of the energy storage device during charging, is the heat capacity of the device, which describes the device's ability to absorb heat.

[0143] The heat capacity of the device can be determined by the material and geometry of the device, which is expressed as: wherein, is the mass of the energy storage device, is the specific heat capacity of the energy storage device material.

[0144] It should be noted that during the charging and discharging process of the energy storage device, the calculation of the heat input parameter is determined by the internal heat generation and heat dissipation during the charging process. When the heat generated by the internal resistance is not enough to offset the heat dissipated through conduction, convection and radiation, the calculation result is negative. At this time, the negative value corresponds to the net heat flow direction of the energy storage device in this period, which is that the dissipation is greater than the generation, reflecting that the temperature of the device tends to decrease.

[0145] In the case of large cooling system power or low external environment temperature, the heat dissipated by the device may exceed the heat generated, which is represented by less than zero. This result directly reflects the temperature decrease in the subsequent calculation, simulating the thermodynamic process of the energy storage device under different working conditions.

[0146] Step 5: According to the results of energy flow and thermal management simulation, real-time adjustment of charging and discharging strategy and cooling measures is carried out through thermodynamic feedback mechanism to adjust the load of energy storage battery pack and inverter, ensuring that the energy storage equipment of the energy storage power station operates within the safe temperature range.

[0147] Specifically, after the energy flow and thermal management simulation obtains the temperature change of the energy storage device during charging, the actual temperature of the energy storage device in this period is determined as the algebraic sum of the initial temperature of the last period and the temperature change of the energy storage device during charging. If the temperature change of the energy storage device during charging is positive, the energy storage device is in the process of heating; if the temperature change of the energy storage device during charging is negative, the energy storage device is in the process of cooling; if the temperature change of the energy storage device during charging is close to zero, the temperature of the energy storage device remains basically stable. ​

[0148] The safe temperature range for energy storage devices is set to 15℃ to 45℃. 15℃ is the minimum safe temperature at which the energy storage device can maintain normal operation under low-temperature conditions, and 45℃ is the maximum safe temperature at which the energy storage device can avoid thermal runaway under high-temperature conditions. The convergence threshold is set as follows: when the deviation between the actual temperature and the target temperature of the energy storage device does not exceed 0.5℃, and the absolute value of the temperature change of the energy storage device during charging does not exceed 0.2℃, the system is considered to have reached convergence. In this invention, the control cycle of the energy storage power station is dynamically determined based on the system scale and computational complexity, rather than a fixed value. The control cycle setting must ensure that the optimization algorithm can complete the calculation within one cycle and leave sufficient margin for strategy execution. A typical control cycle range is 5 to 20 minutes, with the specific value determined based on the number of energy storage devices, qubit encoding accuracy, and available computing resources.

[0149] When the actual temperature of the energy storage device is higher than At that time, cooling feedback adjustment is performed. The charging current of this cycle relative to the previous cycle is adjusted according to the principle of "every time exceeding..." Lower The proportion of "" decreased, with the maximum decrease in a single cycle not exceeding At the same time, ensure that the charging current is not lower than the minimum allowable charging current of the energy storage device; the start time of the discharge in this cycle is postponed relative to the previous cycle, and each time it exceeds... Postponed ,in This represents the delay in the discharge time, and its value is the control period. of The single-cycle delay time shall not exceed the control cycle. of Furthermore, the start time of discharge must not cross the boundary of this control cycle; the duration of discharge in this cycle relative to the previous cycle is calculated according to the rule of "every time exceeding..." shorten The proportion of "" is shortened, and the shortening rate in a single cycle does not exceed At the same time, ensure that the discharge duration is not less than the minimum allowable discharge pulse duration of the energy storage device; the cooling power of this cycle relative to the previous cycle is calculated according to "every time exceeding..." improve The proportion of "increases, with the maximum increase in a single cycle not exceeding" At the same time, ensure that the cooling power does not exceed the maximum continuous power of the cooling device. After adjustment, perform boundary trimming on all commands to ensure that the charging current, discharge start time, discharge duration, and cooling power are all within the allowable operating range of the energy storage device.

[0150] When the actual temperature of the energy storage device is lower than At that time, temperature rise feedback adjustment is performed. The charging current of this cycle relative to the previous cycle is adjusted according to the principle of "every time it is below..." Upward The proportion of "increases, with the maximum increase in a single cycle not exceeding" At the same time, ensure that the charging current does not exceed the rated charging current of the energy storage device; the discharge start time of this cycle is earlier than that of the previous cycle, and each time it is lower than the rated charging current of the energy storage device... in advance ,in This represents the advance amount of the discharge timing, and its value is the control period. of The advance time of a single cycle shall not exceed the control cycle. of The duration of discharge in this cycle relative to the previous cycle is calculated according to the principle of "every time below..." extend The proportion of "" is extended, and the extension range of a single cycle does not exceed At the same time, ensure that the discharge duration does not exceed the maximum allowable discharge pulse duration of the energy storage device; the cooling power of this cycle relative to the previous cycle is calculated according to "every time it is lower than..." reduce The proportion of "reduction" will decrease, with the maximum reduction in a single cycle not exceeding At the same time, ensure that the cooling power is not lower than the minimum sustaining power of the cooling system. After adjustment, perform boundary trimming on all commands to ensure that the charging current, discharge start time, discharge duration, and cooling power are all within the operating range of the energy storage device and cooling system.

[0151] When the actual temperature of the energy storage device is at to When the temperature is within the safe temperature range, fine-grained tuning is performed. If the temperature change of the energy storage device during charging is positive, and the distance is... Insufficient temperature difference In this cycle, the charging current will be reduced. Increased cooling power The discharge start time is delayed by the control cycle. of The discharge duration is shortened. If the temperature change of the energy storage device during the charging process is negative, and the distance is... Insufficient temperature difference In this cycle, the charging current will be increased. Reduced cooling power The discharge start time is controlled in advance. of The discharge duration is prolonged. If the temperature change of the energy storage device during the charging process is close to zero, and the actual temperature of the energy storage device is located at... to If the interval is within the range, the charging current, cooling power, discharge start time and discharge duration are kept unchanged, and the next cycle calculation is directly entered.

[0152] At the end of each control cycle, a smoothing constraint is imposed on the instruction rate of change. Between the adjacent two cycles, the change amplitude of the charging current does not exceed the charging current of the last cycle , the change amplitude of the cooling power does not exceed the cooling power of the last cycle , the cumulative adjustment amount of the discharge start time does not exceed the control cycle , and the cumulative adjustment amount of the discharge duration does not exceed After completing the smoothing constraint, the updated instruction is taken as the input of the next cycle, and the energy flow and thermal management simulation is re-performed to obtain the temperature change of the energy storage device during the charging process and the actual temperature of the energy storage device, and the closed-loop operation is entered. The closed-loop operation continues until the threshold is met, and each instruction is kept constant, and only temperature monitoring and protection are performed. Among them, represents the control cycle, that is, the time required for the energy storage power station to perform a complete optimization-scheduling-feedback cycle; represents the delay amount of the discharge time; represents the advance amount of the discharge time.

[0153] Embodiment 2 is an embodiment of the present application, which provides a digital operation control system of an energy storage power station, comprising: An acquisition module is configured to obtain the spatial geometric shape, size and layout of the energy storage power station and the energy storage device, obtain the three-dimensional position and shape information of the energy storage power station and the energy storage device by using oblique photography, point cloud data and orthographic image, and construct a three-dimensional digital model of the energy storage power station based on the spatial geometric shape, size, layout, three-dimensional position and shape information of the energy storage device. A positioning module is configured to use a geographic information system to spatially position the energy storage device in the energy storage power station, obtain the spatial coordinates and real-time running state of the energy storage device, map the spatial coordinates and real-time running state of the energy storage device to a digital twin of the device in the three-dimensional digital model, and construct a digital twin model. An optimization module is configured to optimize the charging and discharging scheduling of the energy storage power station by applying a quantum heuristic algorithm based on the digital twin model, adjust the charging rate, discharging time and discharging period according to the real-time state information of the energy storage device, and optimize the energy utilization rate and operation efficiency of the energy storage power station. A simulation module is configured to perform energy flow and thermal management simulation based on the optimized charging and discharging scheduling result, simulate the heat flow and distribution of the energy storage device in the energy storage power station, and predict the temperature change of the energy storage device during the charging and discharging process. ​The scheduling module is configured to adjust the charging and discharging strategy and the cooling measure in real time through a thermodynamic feedback mechanism according to the result of the energy flow and thermal management simulation, regulate the load of the energy storage battery pack and the inverter, and ensure that the energy storage equipment of the energy storage power station operates within a safe temperature range.

[0154] The embodiment also provides an electronic device suitable for the case of the digital operation control method of the energy storage power station, which comprises a memory and a processor.

[0155] The embodiment also provides a storage medium having a computer program stored thereon, and the computer program is executed by a processor to implement the digital operation control method of the energy storage power station.

[0156] The storage medium provided by the embodiment and the digital operation control method of the energy storage power station provided by the above embodiment belong to the same inventive concept, and the technical details not described in the embodiment can be referred to the above embodiment, and the embodiment has the same beneficial effects as the above embodiment.

[0157] From the above description of the embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better embodiment. Based on this understanding, the technical solutions of the present application or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a floppy disk, a read-only memory (ROM), a random access memory (RAM), a FLASH, a hard disk or an optical disk, etc., including a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the method of each embodiment of the present application.

[0158] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, and all of them should be covered in the scope of the claims of the present application.

Claims

1. A digital operation control method for an energy storage power station, characterized in that: include, Acquire the spatial geometry, size, and layout of the energy storage power station and energy storage equipment. Utilize oblique photography, point cloud data, and orthophotos to obtain the three-dimensional position and shape information of the energy storage power station and energy storage equipment. Construct a three-dimensional digital model of the energy storage power station based on the spatial geometry, size, layout, and three-dimensional position and shape information of the energy storage equipment. Geographic Information System is used to spatially locate energy storage equipment in energy storage power station, obtain the spatial coordinates and real-time operating status of energy storage equipment, and map the spatial coordinates and real-time operating status of energy storage equipment to the equipment digital twin in three-dimensional digital model to construct digital twin model; Based on the digital twin model, a quantum heuristic algorithm is applied to optimize the charging and discharging scheduling of the energy storage power station. According to the real-time status information of the energy storage equipment, the charging rate, discharging timing and discharging cycle are adjusted to optimize the energy utilization and operating efficiency of the energy storage power station. Based on the optimized charge and discharge scheduling results, energy flow and thermal management simulations are performed to simulate the heat flow and distribution of energy storage devices in the energy storage power station and predict the temperature changes of energy storage devices during the charge and discharge process. Based on the results of energy flow and thermal management simulations, the charging and discharging strategies and cooling measures are adjusted in real time through a thermodynamic feedback mechanism to regulate the load of the energy storage battery pack and inverter, ensuring that the energy storage equipment of the energy storage power station operates within a safe temperature range.

2. The digital operation control method for an energy storage power station as described in claim 1, characterized in that: The construction of the three-dimensional digital model of the energy storage power station includes aligning the original data of oblique photography, point cloud data and orthophotos with coordinates, unifying the data to the same coordinate system using a geographic information system, and registering the oblique photography images with point cloud data and orthophotos using image registration technology to generate three-dimensional spatial data. Based on the generated three-dimensional spatial data, a surface reconstruction algorithm is used to construct a three-dimensional digital model of the energy storage power station.

3. The digital operation control method for an energy storage power station as described in claim 2, characterized in that: The image registration technology includes selecting the location of energy storage equipment within the energy storage power station as a control point; The data source of the energy storage power station is registered to generate an optimization objective function for the coordinate transformation matrix; The objective function for optimizing the coordinate transformation matrix is ​​expressed as follows: in, The objective function representing the coordinate transformation matrix is... Let represent the coordinate transformation matrix that minimizes the objective function of the coordinate transformation matrix. This represents the weighted weight of the i-th control point. This represents the registration error function, which measures the geometric error between each pair of control points. Represents the spatial coordinates of the i-th control point. Indicates the first Layout constraint functions for each energy storage device Indicates the first The three-dimensional coordinates of an energy storage device in the target coordinate system These represent the weighting coefficients of the layout constraints, used to balance the effects of registration errors and layout constraints. This represents the total number of energy storage devices in the energy storage power station, and i represents the index of the control point. Indicates an index for energy storage devices. Indicates the total number of control points; Solve for the objective function of the coordinate transformation matrix.

4. The digital operation control method for an energy storage power station as described in claim 3, characterized in that: The method of constructing a three-dimensional digital model of an energy storage power station using a surface reconstruction algorithm includes constructing a three-dimensional geometric model using a Poisson reconstruction algorithm. Transform the Poisson equation into a system of linear equations; The surface orientation of each point is determined by using the normal vectors of all points, and the equipotential surface is found by the gradient direction to generate a surface point cloud.

5. The digital operation control method for an energy storage power station as described in claim 4, characterized in that: The construction of the three-dimensional geometric model using the Poisson reconstruction algorithm includes, The covariance matrix is ​​calculated for the set of neighborhood control points, representing the geometric features of the local point cloud, and is expressed as: in, Let i represent the covariance matrix of the i-th control point. Represents the coordinates of neighboring points. Represents the set of neighboring control points of the i-th control point; Calculate the set of neighborhood control points covariance matrix Given the eigenvalues ​​and eigenvectors, take the eigenvector corresponding to the smallest eigenvalue to obtain the normal vector. ; Based on the normal vector Calculate the divergence, construct the Poisson equation, which is expressed as: in, It is a latent function The Laplace operator, Represent the latent function, This represents the divergence.

6. The digital operation control method for an energy storage power station as described in claim 5, characterized in that: The application of quantum heuristic algorithms to optimize the charging and discharging scheduling of energy storage power stations includes, All qubits are initialized to a superposition state, which indicates that the charging and discharging strategies exist in parallel. By applying a Hadamard gate to a qubit, a superposition state of the qubit is created. A quantum controlled NOT gate is used to control the qubit, generating entanglement between qubits. Quantum interference is performed between the state of the qubit and the objective function, and the objective function is optimized through the interaction between the qubits. Through quantum measurement operations, the state of a qubit is transformed into values ​​for charging rate, discharging timing, and period, and then decoded into a specific charging and discharging scheduling strategy.

7. The digital operation control method for an energy storage power station as described in claim 6, characterized in that: The predicted temperature changes of the energy storage device during charging and discharging include, The calculated heat input is expressed as: in, Indicates heat input, This indicates the optimized charging rate. The duration of the charging process; Based on the material properties and geometry of the energy storage device, the heat conduction through the device surface is calculated and expressed as: in, Indicates heat conduction, The thermal conductivity of the material For thermally conductive surface area, For temperature difference, For material thickness; Heat is transferred through convection. The heat exchange between the surface of the computing device and the surrounding fluid is represented as: in, Indicates heat convection. The convective heat transfer coefficient, The surface temperature of the equipment. The ambient temperature; Thermal radiation transfers heat, and the Stefan-Boltzmann law is used to calculate thermal radiation, which is expressed as: in, Indicates heat radiation. The Stefan-Boltzmann constant is... For surface emissivity, This represents the fourth power of the equipment surface temperature. The fourth power of the ambient temperature; Heat balance Represented as: According to the principle of conservation of heat, the temperature change of the equipment With the heat capacity of the equipment and heat balance The following relationship exists between them, represented as: in, This indicates the temperature change of the energy storage device during the charging process. This is the heat balance quantity. For the heat capacity of the equipment, For the quality of energy storage equipment, This refers to the specific heat capacity of materials used in energy storage devices.

8. A digital operation control system for an energy storage power station, employing the digital operation control method for an energy storage power station as described in any one of claims 1 to 7, characterized in that, include: The acquisition module is used to acquire the spatial geometry, size, and layout of the energy storage power station and energy storage equipment. It uses oblique photography, point cloud data, and orthophotos to acquire the three-dimensional position and shape information of the energy storage power station and energy storage equipment. Based on the spatial geometry, size, layout, and three-dimensional position and shape information of the energy storage equipment, a three-dimensional digital model of the energy storage power station is constructed. The positioning module is used to spatially locate the energy storage equipment in the energy storage power station using a geographic information system, obtain the spatial coordinates and real-time operating status of the energy storage equipment, and map the spatial coordinates and real-time operating status of the energy storage equipment to the digital twin of the equipment in the three-dimensional digital model to construct the digital twin model. The optimization module is used to optimize the charging and discharging scheduling of energy storage power stations based on digital twin models and quantum heuristic algorithms. It adjusts the charging rate, discharging timing and discharging cycle according to the real-time status information of energy storage devices to optimize the energy utilization and operating efficiency of energy storage power stations. The simulation module is used to perform energy flow and thermal management simulations based on the optimized charge and discharge scheduling results, simulate the heat flow and distribution of energy storage devices in the energy storage power station, and predict the temperature changes of energy storage devices during the charge and discharge process. The scheduling module is used to adjust the charging and discharging strategies and cooling measures in real time through a thermodynamic feedback mechanism based on the results of energy flow and thermal management simulations, thereby regulating the load of the energy storage battery pack and inverter to ensure that the energy storage equipment of the energy storage power station operates within a safe temperature range.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the digital operation control method for an energy storage power station according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the digital operation control method for an energy storage power station as described in any one of claims 1 to 7.

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