Energy storage device installation method and apparatus, electronic device, and computer readable medium

Abstract

Embodiments of the present disclosure disclose an energy storage device installation method, device, electronic device and computer readable medium. A specific embodiment of the method comprises: obtaining node topology data, various line parameter data, new energy output matrix, active load matrix and reactive load matrix; generating active net injection power matrix and reactive net injection power matrix; generating voltage deviation average data, out-of-limit node number set, node out-of-limit voltage sensitivity matrix and power sensitivity matrix; generating time-series out-of-limit voltage sensitivity matrix; generating time-series power sensitivity matrix; determining energy storage access point data sequence of the energy storage device; and controlling a mechanical arm to install at least one energy storage device to at least one corresponding installation point. The embodiment can reduce installation resource waste when installing the energy storage device.

Description

Technical Field

[0001] The embodiments of this disclosure relate to the field of computer technology, and more specifically to energy storage device installation methods, apparatus, electronic devices, and computer-readable media. Background Technology

[0002] Energy storage device installation refers to determining the optimal installation location and capacity of energy storage devices in the power distribution network, and then installing the corresponding energy storage devices at the optimal location. Currently, the common approach for energy storage device installation is as follows: first, determine the installation point of the energy storage device by analyzing the voltage at each circuit node. Then, determine the energy storage capacity of the energy storage device to be installed. Finally, using the installation point and energy storage capacity, control a robotic arm to install the energy storage device.

[0003] However, when installing energy storage devices using the above methods, the following technical problems often arise:

[0004] Determining the installation location of energy storage devices solely based on the voltage regulation capability of circuit nodes can easily lead to poor performance of the installed energy storage devices in mitigating power fluctuations in the power distribution network. This necessitates the use of robotic arms to reinstall the energy storage devices in a suitable location, resulting in a waste of resources during installation. Summary of the Invention

[0005] The summary portion of this disclosure is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description portion. This summary portion is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0006] Some embodiments of this disclosure provide methods, apparatuses, electronic devices, and computer-readable media for installing energy storage devices in power distribution networks to address one or more of the technical problems mentioned in the background section above.

[0007] In a first aspect, some embodiments of this disclosure provide a method for installing energy storage devices in a power distribution network. The method includes: acquiring node topology data, line parameter data, renewable energy output matrix, active power load matrix, and reactive power load matrix of each circuit node in the power distribution network from a target server; generating a net active power injection matrix and a net reactive power injection matrix based on the aforementioned renewable energy output matrix, active power load matrix, and reactive power load matrix; and generating, for each preset time point, a method based on the aforementioned node topology data, line parameter data, net active power injection matrix, net reactive power injection matrix, and preset reference data. The system generates the following data sets: average voltage deviation data, set of out-of-limit node numbers, node out-of-limit voltage sensitivity matrix, and power sensitivity matrix. Based on these data, a time-series out-of-limit voltage sensitivity matrix is ​​generated. A time-series power sensitivity matrix is ​​also generated based on the generated power sensitivity matrices. The system then determines the energy storage access point data sequence for each energy storage device based on the aforementioned time-series and power sensitivity matrices. Finally, based on the energy storage access point data sequence and the initial number of energy storage devices connected, the system controls a robotic arm to install at least one energy storage device at at least one corresponding installation point.

[0008] Secondly, some embodiments of this disclosure provide an energy storage device installation apparatus for a power distribution network. The apparatus includes: an acquisition unit configured to acquire node topology data, line parameter data, renewable energy output matrix, active power load matrix, and reactive power load matrix of each circuit node in the power distribution network from a target server; a first generation unit configured to generate a net active power injection matrix and a net reactive power injection matrix based on the aforementioned renewable energy output matrix, active power load matrix, and reactive power load matrix; and a second generation unit configured to generate a voltage deviation level for each preset time point based on the aforementioned node topology data, line parameter data, net active power injection matrix, net reactive power injection matrix, and preset reference data. The system comprises: a first generation unit, a second generation unit, and a third generation unit, configured to generate a time-series over-limit voltage sensitivity matrix based on the generated average voltage deviation data, the generated set of over-limit node numbers, and the generated over-limit voltage sensitivity matrix of each node; a third generation unit, configured to generate a time-series power sensitivity matrix based on the generated power sensitivity matrices; a fourth generation unit, configured to generate a time-series power sensitivity matrix based on the generated power sensitivity matrices; a fifth determination unit, configured to determine the energy storage access point data sequence of the energy storage device based on the aforementioned time-series over-limit voltage sensitivity matrix and the aforementioned time-series power sensitivity matrix; and a sixth control unit, configured to control a robotic arm to install at least one energy storage device to at least one corresponding installation point based on the aforementioned energy storage access point data sequence and the initial number of energy storage access points.

[0009] Thirdly, some embodiments of this disclosure provide an electronic device, including: one or more processors; and a storage device having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0010] Fourthly, some embodiments of this disclosure provide a computer-readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.

[0011] The above embodiments of this disclosure have the following beneficial effects: the energy storage device installation method for power distribution networks according to some embodiments of this disclosure can reduce installation resource waste. Specifically, the reason for installation resource waste is that determining the installation point of the energy storage device solely based on the voltage regulation capability of the circuit node easily leads to poor smoothing effect of the installed energy storage device on power fluctuations in the power distribution network, thus requiring the control of a robotic arm to reinstall the energy storage device in a suitable position, resulting in resource waste during installation. Based on this, the energy storage device installation method for power distribution networks according to some embodiments of this disclosure first obtains the node topology data, line parameter data, new energy output matrix, active load matrix, and reactive load matrix of each circuit node in the power distribution network from the target server. This yields the raw data. Second, based on the aforementioned new energy output matrix, active load matrix, and reactive load matrix, a net active power injection matrix and a net reactive power injection matrix are generated. This allows the generation of the net active power injection and net reactive power injection of each node at each time point. Then, for each of the preset time points, based on the aforementioned node topology data, the aforementioned line parameter data, the aforementioned net active power injection matrix, the aforementioned net reactive power injection matrix, and preset baseline data, average voltage deviation data, a set of over-limit node numbers, a node over-limit voltage sensitivity matrix, and a power sensitivity matrix are generated. This allows the generation of the node over-limit voltage sensitivity matrix and the power sensitivity matrix. Then, based on the generated average voltage deviation data, the generated set of over-limit node numbers, and the generated node over-limit voltage sensitivity matrices, a time-series over-limit voltage sensitivity matrix is ​​generated. This allows the determination of the impact of power changes at circuit nodes on the voltage of over-limit circuit nodes. Next, based on the generated power sensitivity matrices, a time-series power sensitivity matrix is ​​generated. This allows the determination of the impact of injected power at each circuit node on the main grid interface power through the generated time-series power sensitivity matrix. Finally, based on the aforementioned time-series over-limit voltage sensitivity matrix and the aforementioned time-series power sensitivity matrix, the energy storage access point data sequence for the energy storage device is determined. Therefore, by combining the aforementioned timing-series over-limit voltage sensitivity matrix and timing-series power sensitivity matrix, the access points can be sorted to obtain an energy storage access point data sequence. Finally, based on the aforementioned energy storage access point data sequence and the initial number of energy storage access points, the robotic arm is controlled to install at least one energy storage device to at least one corresponding installation point. This allows for energy storage device configuration.Because the installation point of the energy storage device can be determined by combining the timing over-limit voltage sensitivity matrix and the timing power sensitivity matrix, rather than by determining the installation point of the energy storage device solely by the voltage of each circuit node, it can not only improve the effect of the installed energy storage device on power fluctuations in the distribution network, but also take into account the voltage regulation capability of the energy storage device on the distribution network. Therefore, it can improve the utilization rate of the energy storage device and reduce the waste of resources when installing the energy storage device. Attached Figure Description

[0012] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and elements are not necessarily drawn to scale.

[0013] Figure 1 This is a flowchart of some embodiments of the energy storage device installation method for power distribution networks according to the present disclosure;

[0014] Figure 2 These are schematic diagrams of some embodiments of the energy storage device installation apparatus for power distribution networks according to the present disclosure;

[0015] Figure 3 This is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. Detailed Implementation

[0016] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0017] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0018] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0019] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0020] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0021] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0022] Figure 1 A flow 100 of some embodiments of the energy storage device installation method for a distribution network according to the present disclosure is shown. The energy storage device installation method for a distribution network includes the following steps:

[0023] Step 101: Obtain the node topology data, line parameter data, new energy output matrix, active load matrix, and reactive load matrix of each circuit node in the distribution network from the target server.

[0024] In some embodiments, the execution entity (e.g., a computing device) of the energy storage device installation method for a distribution network can obtain node topology data, line parameter data, new energy output matrix, active load matrix, and reactive load matrix of each circuit node in the distribution network from a target server. The execution entity can be a server. The node topology data can be a table where each row corresponds to two interconnected circuit nodes and each column corresponds to a circuit parameter. The circuit nodes can be PQ nodes in the distribution network. Each circuit node corresponds to a node number and an installation point. The node number can be a label used to identify the circuit node number. For example, the node number can be "Node 1". The installation point can be the location of the circuit node. The two interconnected circuit nodes can be two circuit nodes connected by a line. The line can be used to connect the two circuit nodes. For example, the line can be a power wire. The circuit parameters can be parameters corresponding to the line between the two interconnected circuit nodes. The circuit parameters can be, but are not limited to, resistance data, reactance data, and current data. The resistance data can be the resistance of the line between the two interconnected circuit nodes. The unit corresponding to the resistance data can be ohms. The aforementioned reactance data can represent the reactance of a line between two interconnected circuit nodes. The unit for this reactance data is ohms. The aforementioned current data can represent the current in a line between two interconnected circuit nodes. The unit for this current data is amperes. Each line parameter in the aforementioned line parameter data represents a parameter corresponding to a line between two interconnected circuit nodes. Each line parameter in the aforementioned line parameter data can include resistance and reactance data. Each line parameter in the aforementioned line parameter data corresponds to two interconnected circuit nodes. The aforementioned renewable energy output matrix can be a matrix where each row corresponds to a circuit node, each column corresponds to a time point, and each element represents the renewable energy output data of the circuit node at the corresponding time point. The aforementioned renewable energy output data can represent the active power output of renewable energy power generation devices connected to the circuit nodes. The unit for this renewable energy output data is megawatts. The aforementioned renewable energy power generation device can be a device capable of generating electricity from renewable energy sources. For example, the aforementioned renewable energy power generation device can be a wind turbine.

[0025] The active power load matrix described above can be a matrix where each row corresponds to a circuit node, each column corresponds to a time point, and each element represents the active power consumed by the circuit node at the corresponding time point. The unit for each element in the active power load matrix can be megawatts.

[0026] The reactive load matrix described above can be a matrix where each row corresponds to a circuit node, each column corresponds to a time point, and each element represents the reactive power consumed by the circuit node at the corresponding time point. The unit for each element in the reactive load matrix can be megawatts.

[0027] The target server mentioned above can be a server that stores node topology data, parameter data of each line, new energy output matrix, active load matrix and reactive load matrix.

[0028] In practice, the aforementioned executing entity can send preset data acquisition information to the aforementioned target server. This data acquisition information may include prompting the target server to send the aforementioned node topology data, the aforementioned line parameter data, the aforementioned renewable energy output matrix, the aforementioned active power load matrix, and the aforementioned reactive power load matrix. Then, the entity receives the node topology data, the aforementioned line parameter data, the renewable energy output matrix, the active power load matrix, and the reactive power load matrix sent by the target server.

[0029] Step 102: Based on the new energy output matrix, active load matrix, and reactive load matrix, generate the net active power injection matrix and the net reactive power injection matrix.

[0030] In some embodiments, the executing entity can generate a net active power injection matrix and a net reactive power injection matrix based on the aforementioned renewable energy output matrix, the aforementioned active power load matrix, and the aforementioned reactive power load matrix. The net active power injection matrix can be obtained by subtracting the aforementioned renewable energy output matrix from the aforementioned active power load matrix. The net reactive power injection matrix can be the negative matrix of the aforementioned reactive power load matrix.

[0031] In some optional implementations of certain embodiments, the aforementioned execution entity can generate a net active power injection matrix and a net reactive power injection matrix based on the aforementioned new energy output matrix, the aforementioned active power load matrix, and the aforementioned reactive power load matrix through the following steps:

[0032] The first step is to determine the difference between the above-mentioned new energy output matrix and the above-mentioned active power load matrix as the net active power injection matrix.

[0033] The second step is to determine the net reactive power injection matrix by the difference between the preset initial matrix and the aforementioned reactive load matrix. The initial matrix can be a matrix of the same size as the aforementioned reactive load matrix, with all elements being 0.

[0034] Step 103: For each of the preset time points, based on the node topology data, each line parameter data, the net active power injection matrix, the net reactive power injection matrix, and the preset reference data, generate the average voltage deviation data, the set of over-limit node numbers, the node over-limit voltage sensitivity matrix, and the power sensitivity matrix.

[0035] In some embodiments, the execution entity can, for each preset time point, generate average voltage deviation data, a set of over-limit node numbers, a node over-limit voltage sensitivity matrix, and a power sensitivity matrix based on the node topology data, the line parameter data, the net active power injection matrix, the net reactive power injection matrix, and preset reference data. The reference data can be preset voltage and power. The reference data can include reference voltage data and reference power data. The reference voltage data can be a preset voltage. For example, the reference voltage data can be 10.5 kV. The reference power data can be a preset power. For example, the reference power data can be 100 kW.

[0036] In some optional implementations of certain embodiments, the execution entity can generate average voltage deviation data, a set of over-limit node numbers, a node over-limit voltage sensitivity matrix, and a power sensitivity matrix based on the node topology data, the line parameter data, the net active power injection matrix, the net reactive power injection matrix, and preset reference data through the following steps:

[0037] The first step is to determine the net injected active power data and net injected reactive power data corresponding to the aforementioned time points, based on the aforementioned net active power injection matrix and net injected reactive power matrix. The net injected active power data can be the column vectors corresponding to the aforementioned time points in each column vector of the aforementioned net active power injection matrix. For example, when there are a total of 3 circuit nodes, the net injected active power data can be the transpose of [+50, -70, -30].

[0038] The aforementioned net injected reactive power data can be the column vectors corresponding to the aforementioned time points in each column vector of the aforementioned net injected reactive power matrix. For example, when there are a total of 3 circuit nodes, the net injected reactive power data can be the transpose of [-20, -30, -35].

[0039] In practice, firstly, the executing entity can determine the column vectors in the aforementioned net active power injection matrix corresponding to the aforementioned time points as the net injected active power data. Secondly, it can determine the column vectors in the aforementioned net reactive power injection matrix corresponding to the aforementioned time points as the net injected reactive power data.

[0040] The second step involves generating a node power matrix and a node voltage array based on the aforementioned node topology data, line parameter data, net injected active power data, net injected reactive power data, and preset reference data. The node voltage array can be an array obtained by combining the voltage data of each node. Each node voltage data point can correspond to a circuit node. Each node voltage data point can include voltage amplitude data and phase angle data. The voltage amplitude data can be the maximum voltage of the circuit node. The phase angle data can be the voltage phase. Each node voltage data point corresponds to one circuit node.

[0041] The node power matrix can be the Jacobian matrix obtained by performing power flow calculations on the node topology data, the line parameter data, the net injected active power data, the net injected reactive power data, and the reference data.

[0042] The node power matrix described above can include four submatrices. These four submatrices are of the same size. They can be located at the top-left, top-right, bottom-left, and bottom-right positions of the node power matrix, respectively.

[0043] The four sub-matrices included in the above node power matrix can be respectively used as the phase angle active power matrix, the phase angle reactive power matrix, the voltage active power matrix, and the voltage reactive power matrix.

[0044] The aforementioned phase angle active power matrix can be a matrix where each row corresponds to a circuit node, each column corresponds to a circuit node, and each element represents the degree of influence of the phase angle data of the row circuit node on the active power of the column circuit node. Specifically, the row circuit nodes can be the circuit nodes corresponding to the rows in the matrix, and the column circuit nodes can be the circuit nodes corresponding to the columns in the matrix.

[0045] For example, when the element in the second row and third column of the phase angle active power matrix is ​​12, and the node number of the circuit node corresponding to the second row is "node 2" and the node number of the circuit node corresponding to the third row is "node 3", the element 12 in the second row and third column can represent that for every 1 radian increase in the phase angle data of node 2, the active power of node 3 increases by 12 kilowatts.

[0046] The aforementioned phase angle reactive power matrix can be a matrix in which each row corresponds to a circuit node, each column corresponds to a circuit node, and each element can characterize the degree of influence of the phase angle data of the row circuit node on the reactive power of the column circuit node.

[0047] The voltage active power matrix mentioned above can be a matrix in which each row corresponds to a circuit node, each column corresponds to a circuit node, and each element can characterize the degree of influence of the voltage amplitude data of the row circuit node on the active power of the column circuit node.

[0048] The voltage reactive power matrix mentioned above can be a matrix in which each row corresponds to a circuit node, each column corresponds to a circuit node, and each element can characterize the degree of influence of the voltage amplitude data of the row circuit node on the reactive power of the column circuit node.

[0049] In practice, the aforementioned execution entity can input the aforementioned node topology data, the aforementioned line parameter data, the aforementioned net injected active power data, the aforementioned net injected reactive power data, and the aforementioned reference data into a power flow function to obtain a node voltage array. The aforementioned power flow function can be a function capable of power flow calculation. For example, the aforementioned power flow function can be the `runpf()` function in `MATPOWER`. Then, the aforementioned node voltage array can be input into a Jacobian matrix generation function to obtain the matrix output by the Jacobian matrix generation function as the node power matrix. The aforementioned Jacobian matrix generation function can be a function capable of generating a Jacobian matrix from the node voltage array. For example, the aforementioned Jacobian matrix generation function can be the `makeJac()` function in `MATPOWER`.

[0050] In practice, the aforementioned executing entity can also perform power flow calculations on the aforementioned node topology data, line parameter data, net injected active power data, net injected reactive power data, and baseline data using power flow calculation methods to obtain node voltage arrays and node power matrices. The aforementioned power flow calculation method can be any method capable of performing power flow calculations. For example, the aforementioned power flow calculation method can be the Newton-Raphson method. In this case, the node power matrix can be the Jacobian matrix used by the aforementioned power flow calculation method when generating the aforementioned node voltage arrays.

[0051] In practice, the aforementioned executing entity can also send the aforementioned node topology data, the aforementioned line parameter data, the aforementioned net injected active power data, the aforementioned net injected reactive power data, the aforementioned baseline data, and preset data prompt information to the target terminal. The aforementioned data prompt information can be used to prompt technicians to send node voltage arrays and node power matrices. For example, the aforementioned data prompt information could be "Please send node voltage arrays and node power matrices." The aforementioned target terminal can be the terminal corresponding to the technician. Then, the entity can receive the node voltage array and node power matrix information sent by the target terminal.

[0052] The third step is to determine the inverse matrix of the aforementioned node power matrix. This inverse matrix can include four sub-matrices of equal size. These four sub-matrices can be located at the top left, top right, bottom left, and bottom right positions of the node power inverse matrix, respectively. The four sub-matrices of the node power inverse matrix serve as the phase angle active power inverse matrix, the phase angle reactive power inverse matrix, the voltage active power inverse matrix, and the voltage reactive power inverse matrix, respectively. The phase angle active power inverse matrix can be a matrix where each row corresponds to one circuit node, each column corresponds to one circuit node, and each element represents the influence of the phase angle data of the column circuit node on the active power of the row circuit node. For example, when the element in the second row and third column of the phase angle active power inverse matrix is ​​12, and the node number of the circuit node corresponding to the second row is "Node 2" and the node number of the circuit node corresponding to the third row is "Node 3", the element 12 in the second row and third column can represent that for every 1 radian increase in the phase angle data of node 3, the active power of node 2 increases by 12 kilowatts.

[0053] The aforementioned phase angle reactive power inverse matrix can be a matrix in which each row corresponds to a circuit node, each column corresponds to a circuit node, and each element can characterize the degree of influence of the phase angle data of the column circuit node on the reactive power of the row circuit node.

[0054] The above voltage active power inverse matrix can be a matrix in which each row corresponds to a circuit node, each column corresponds to a circuit node, and each element can characterize the degree of influence of the voltage amplitude data of the column circuit node on the active power of the row circuit node.

[0055] The above voltage-reactive-power inverse matrix can be a matrix in which each row corresponds to a circuit node, each column corresponds to a circuit node, and each element can characterize the degree of influence of the voltage amplitude data of the column circuit node on the reactive power of the row circuit node.

[0056] The fourth step involves generating average voltage deviation data, a set of out-of-limit node numbers, and a node out-of-limit voltage sensitivity matrix based on preset voltage range data, preset standard voltage data, the above-mentioned node power inverse matrix, and the voltage amplitude data included in the above-mentioned node voltage array.

[0057] The voltage range data can be an interval used to characterize the voltage range. For example, the voltage range data can be [0.95, 1.05]. The unit corresponding to the voltage range data can be kilovolts. The standard voltage data can be a preset voltage. For example, the standard voltage data can be 1 kilovolt. The average voltage deviation data can be a value used to characterize the average deviation of the voltage amplitude data of the circuit node relative to the standard voltage data.

[0058] Fifth, based on the voltage amplitude data and phase angle data included in the above-mentioned node voltage array, the above-mentioned node power inverse matrix, the above-mentioned line parameter data, the above-mentioned voltage range data, and the above-mentioned standard voltage data, a power sensitivity matrix is ​​generated. The power sensitivity matrix can be a matrix generated from the voltage amplitude data and phase angle data included in the above-mentioned node voltage array, the above-mentioned node power inverse matrix, the above-mentioned line parameter data, the above-mentioned voltage range data, and the above-mentioned standard voltage data.

[0059] In some optional implementations of certain embodiments, the execution entity can generate average voltage deviation data, a set of out-of-limit node numbers, and a node out-of-limit voltage sensitivity matrix based on preset voltage range data, preset standard voltage data, the node power inverse matrix, and the voltage amplitude data included in the node voltage array through the following steps:

[0060] The first step is to determine the node voltage sensitivity matrix based on the aforementioned node power inverse matrix. This node voltage sensitivity matrix can be the voltage active power inverse matrix included in the aforementioned node power inverse matrix. In practice, the executing entity can determine the voltage active power inverse matrix included in the aforementioned node power inverse matrix as the node voltage sensitivity matrix.

[0061] The second step involves performing the following steps for each voltage amplitude data point within the aforementioned node voltage array:

[0062] The first sub-step involves processing the voltage amplitude data based on the aforementioned voltage range data to obtain a voltage detection result. This voltage detection result can serve as a label indicating whether the voltage amplitude data belongs to the aforementioned voltage range data. For example, the voltage detection result can be "belongs to" or "does not belong to".

[0063] In practice, in response to determining that the voltage amplitude data belongs to the aforementioned voltage range, "belongs to" can be defined as the voltage detection result. In response to determining that the voltage amplitude data does not belong to the aforementioned voltage range, "does not belong to" can be defined as the voltage detection result.

[0064] The second sub-step involves determining the circuit node corresponding to the voltage amplitude data as the target circuit node in response to the determination that the voltage detection result meets the preset voltage detection conditions. The voltage detection conditions can be that the voltage detection result is "does not belong".

[0065] The third sub-step involves adding the node number corresponding to the target circuit node to the over-limit node set to update the over-limit node set. Here, the over-limit nodes in the over-limit node set can be the node numbers corresponding to the target circuit node. The initial over-limit node set can be empty. In practice, the execution entity can use an add function to add the node number corresponding to the target circuit node to the over-limit node set to update the over-limit node set. This add function can be a function capable of adding elements to a set. For example, the add function could be the add() function.

[0066] The third step is to determine the updated set of out-of-limit nodes as the set of out-of-limit node numbers.

[0067] Fourth, for each out-of-limit node number in the aforementioned out-of-limit node number set, voltage deviation data is generated based on the aforementioned standard voltage data and the voltage amplitude data corresponding to the out-of-limit node number. The voltage deviation data can be a numerical value used to characterize the degree of deviation between the voltage amplitude data of the target circuit node and the aforementioned standard voltage data.

[0068] In practice, for each out-of-limit node number in the aforementioned set of out-of-limit node numbers, the executing entity can determine the voltage difference data as the difference between the standard voltage data and the voltage amplitude data corresponding to the out-of-limit node number. Then, the absolute value of the voltage difference data can be determined as the voltage deviation data.

[0069] The fifth step is to determine the average value of the generated voltage deviation data as the average voltage deviation data.

[0070] Step 6: For each of the preset circuit nodes, perform the following steps:

[0071] The first sub-step involves performing the following steps for each out-of-limit node number in the aforementioned set of out-of-limit node numbers:

[0072] Sub-step one: Determine the voltage deviation data corresponding to the above-mentioned out-of-limit node numbers as the voltage deviation data to be processed.

[0073] Sub-step two: Based on the aforementioned node voltage sensitivity matrix, determine the voltage sensitivity data corresponding to the aforementioned over-limit node number and the aforementioned circuit node. The voltage sensitivity data can be the element values ​​in the aforementioned node voltage sensitivity matrix. In practice, the executing entity can determine the row in each row of the aforementioned node voltage sensitivity matrix where the node number of the circuit node is the aforementioned over-limit node number as the target row. Secondly, it can determine the column in each column of the aforementioned node voltage sensitivity matrix where the corresponding circuit node is the aforementioned circuit node as the target column. Then, the element values ​​corresponding to the target row and the target column can be determined as the voltage sensitivity data corresponding to the aforementioned over-limit node number and the aforementioned circuit node. As an example, when the over-limit node number is "Node 2", the node number of the aforementioned circuit node is "Node 3", the row in the aforementioned node voltage sensitivity matrix where the node number of the circuit node is "Node 2" is the second row, and the column in each column where the node number of the circuit node is "Node 3" is the third column, then the element values ​​of the second row and third column of the aforementioned node voltage sensitivity matrix can be determined as the voltage sensitivity data.

[0074] Sub-step three: The product of the above voltage sensitivity data and the above voltage deviation data to be processed is determined as the node voltage sensitivity data.

[0075] The second sub-step involves summing the determined node voltage sensitivity data for each node.

[0076] Step 7: Based on the determined sensitivity data of each node, generate a node over-limit voltage sensitivity matrix. This matrix can be a row matrix obtained by combining the sensitivity data of each node. In practice, the executing entity can combine the sensitivity data of each node into a row matrix as the node over-limit voltage sensitivity matrix according to the order of the circuit nodes corresponding to the sensitivity data. The order of the circuit nodes can be from smallest to largest according to their corresponding node numbers. For example, when there are three circuit nodes, numbered "Node 1", "Node 2", and "Node 3", the sensitivity data of the three circuit nodes can be combined from left to right into a row matrix as the node over-limit voltage sensitivity matrix, following the order of 1, 2, 3 from smallest to largest.

[0077] In some optional implementations of certain embodiments, the execution entity may generate a power sensitivity matrix based on the voltage amplitude data and phase angle data included in the node voltage array, the node power inverse matrix, the line parameter data, the voltage range data, and the standard voltage data through the following steps:

[0078] The first step is to obtain the main grid connection line data. This main grid connection line data can be the data of two circuit nodes corresponding to a critical line in the distribution network. The critical line can be a line designated by technical personnel. The main grid connection line data can include the node numbers and voltage data of the two circuit nodes. Each node voltage data point can include phase angle data and voltage amplitude data. The main grid connection line data corresponds to two circuit nodes.

[0079] In practice, the aforementioned executing entity can send preset line acquisition information to the aforementioned target terminal. This line acquisition information can be information used to acquire main network connection line data. Then, it can receive the main network connection line data sent by the aforementioned target terminal.

[0080] The second step is to perform the following steps for each of the preset circuit nodes:

[0081] The first sub-step is to determine the above-mentioned circuit nodes as standard circuit nodes.

[0082] The second sub-step involves identifying the two circuit nodes corresponding to the main network connection line data mentioned above as the two circuit nodes to be processed.

[0083] The third sub-step involves determining the two node voltage data corresponding to the two circuit nodes to be processed as the two node voltage data to be processed.

[0084] The fourth sub-step involves determining the difference between the two phase angle data included in the voltage data of the two nodes to be processed as the phase angle difference value data.

[0085] The fifth sub-step involves determining the two voltage amplitude data included in the voltage data of the two nodes to be processed as the two voltage amplitude data to be processed.

[0086] The sixth sub-step generates, based on the two voltage amplitude data to be processed and the phase angle difference data, the first circuit node to be processed, the second circuit node to be processed, the first voltage deflection data, the second voltage deflection data, the first phase angle deflection data, and the second phase angle deflection data.

[0087] The first voltage bias derivative data can be a value used to characterize the influence of the voltage data of the two nodes to be processed on the voltage amplitude data of the first circuit node to be processed. The first circuit node to be processed can be any one of the two circuit nodes to be processed. The second voltage bias derivative data can be a value used to characterize the influence of the voltage data of the two nodes to be processed on the voltage amplitude data of the second circuit node to be processed. The second circuit node to be processed can be any one of the two circuit nodes to be processed other than the first circuit node to be processed.

[0088] The aforementioned first phase angle partial derivative data can be a value used to characterize the influence of the voltage data of the two nodes to be processed on the phase angle data of the first circuit node to be processed. The aforementioned second phase angle partial derivative data can be a value used to characterize the influence of the voltage data of the two nodes to be processed on the phase angle data of the second circuit node to be processed.

[0089] In practice, firstly, the executing entity can determine the cosine value corresponding to the phase angle difference data as the phase angle cosine data. Secondly, it can determine the sine value corresponding to the phase angle difference data as the phase angle sine data.

[0090] Then, either of the two circuit nodes to be processed can be designated as the first circuit node to be processed. The other circuit node from the two circuit nodes to be processed, excluding the first circuit node, can be designated as the second circuit node to be processed.

[0091] Then, the voltage amplitude data corresponding to the first circuit node to be processed can be determined as the first voltage amplitude data. The voltage amplitude data corresponding to the second circuit node to be processed can be determined as the second voltage amplitude data.

[0092] Then, the product of the second voltage amplitude data and the phase angle cosine data can be used to determine the second voltage cosine data. Then, the difference between the first voltage amplitude data and the second voltage cosine data can be used to determine the first voltage difference data. Next, the product of the first voltage difference data and a preset coefficient data can be used to determine the first voltage coefficient data. The preset coefficient data can be a pre-set value. For example, the preset coefficient data can be 2. Then, the ratio of the first voltage coefficient data to a preset ratio data can be used to determine the first voltage partial derivative data. The preset ratio data can be a pre-set value. For example, the preset ratio data can be 0.01.

[0093] Next, the product of the first voltage amplitude data and the phase angle cosine data can be determined as the first voltage cosine data. Then, the difference between the second voltage amplitude data and the first voltage cosine data can be determined as the second voltage difference data. The product of the second voltage difference data and the preset coefficient data can be determined as the second voltage coefficient data. The ratio of the second voltage coefficient data to the preset ratio data can be determined as the second voltage partial derivative data.

[0094] Then, the product of the first voltage amplitude data, the second voltage amplitude data, and the phase angle sine data can be determined as the voltage sine data. The negative of the preset coefficient data can be determined as the negative coefficient data. The product of the voltage sine data and the negative coefficient data can be determined as the first voltage sine coefficient data. The ratio of the first voltage sine coefficient data to the preset ratio data can be determined as the first phase angle partial derivative data.

[0095] Then, the product of the aforementioned voltage sinusoidal data and the aforementioned preset coefficient data can be determined as the second voltage sinusoidal coefficient data. The ratio of the aforementioned second voltage sinusoidal coefficient data to the aforementioned preset proportional data can be determined as the second phase angle partial derivative data.

[0096] The seventh sub-step involves determining the first voltage element data, the second voltage element data, the first phase angle element data, and the second phase angle element data based on the aforementioned node power inverse matrix, the first circuit node to be processed, and the second circuit node to be processed. Specifically, the first voltage element data can be element values ​​from the voltage active power inverse matrix included in the node power inverse matrix. The second voltage element data can be element values ​​from the voltage reactive power inverse matrix included in the node power inverse matrix. The first phase angle element data can be element values ​​from the phase angle active power inverse matrix included in the node power inverse matrix. The second phase angle element data can be element values ​​from the phase angle reactive power inverse matrix included in the node power inverse matrix.

[0097] In practice, firstly, the row circuit nodes corresponding to the voltage active power inverse matrix included in the above node power inverse matrix can be used as the above standard circuit nodes, and the corresponding column circuit nodes can be used as the element values ​​of the above first circuit node to be processed, which are then determined as the first voltage element data.

[0098] Then, the element values ​​of the corresponding row circuit nodes in the voltage reactive power inverse matrix included in the above node power inverse matrix can be determined as the above standard circuit nodes, and the corresponding column circuit nodes can be determined as the element values ​​of the above second circuit nodes to be processed, as the second voltage element data.

[0099] Then, the element values ​​of the corresponding row circuit nodes in the phase angle active power inverse matrix included in the above node power inverse matrix can be determined as the first phase angle element data.

[0100] Then, the element values ​​of the corresponding row circuit nodes in the phase angle reactive power inverse matrix included in the above node power inverse matrix can be determined as the above standard circuit nodes, and the corresponding column circuit nodes can be determined as the element values ​​of the above second circuit nodes to be processed, as the second phase angle element data.

[0101] The eighth sub-step generates line sensitivity data corresponding to the standard circuit nodes based on the first voltage bias derivative data, the second voltage bias derivative data, the first phase angle bias derivative data, the second phase angle bias derivative data, the first voltage element data, the second voltage element data, the first phase angle element data, and the second phase angle element data.

[0102] The aforementioned line sensitivity data can be a numerical value used to characterize the degree of influence of the power change of the aforementioned standard circuit node on the power of the aforementioned two circuit nodes to be processed.

[0103] In practice, firstly, the executing entity can determine the first voltage product data by multiplying the first voltage partial derivative data and the first voltage element data. Then, it can determine the second voltage product data by multiplying the second voltage partial derivative data and the second voltage element data. Next, it can determine the first phase angle product data by multiplying the first phase angle partial derivative data and the first phase angle element data. Then, it can determine the second phase angle product data by multiplying the second phase angle partial derivative data and the second phase angle element data. Finally, the sum of the first voltage product data, the second voltage product data, the first phase angle product data, and the second phase angle product data can be determined as the line sensitivity data.

[0104] The third step is to generate a power sensitivity matrix based on the generated line sensitivity data. This power sensitivity matrix can be a row matrix obtained by combining the sensitivity data from each of the aforementioned lines.

[0105] In practice, the aforementioned executing entity can arrange the sensitivity data of each circuit node into a row matrix as a power sensitivity matrix according to the order of the node numbers corresponding to each circuit node. For example, when there are three node numbers "Node 1", "Node 2", and "Node 3", the order of the node numbers can be {Node 1, Node 2, Node 3}.

[0106] Step 104: Based on the generated average voltage deviation data, the generated set of out-of-limit node numbers, and the generated out-of-limit voltage sensitivity matrix of each node, generate the time-series out-of-limit voltage sensitivity matrix.

[0107] In some embodiments, the execution entity can generate a timing-based over-limit voltage sensitivity matrix based on the generated average voltage deviation data, the generated set of over-limit node numbers, and the generated over-limit voltage sensitivity matrices for each node. The timing-based over-limit voltage sensitivity matrix can be a matrix generated from the average voltage deviation data, the set of over-limit node numbers, and the over-limit voltage sensitivity matrices for each node.

[0108] In some optional implementations of certain embodiments, the aforementioned execution entity can generate a timing-based over-limit voltage sensitivity matrix based on the generated average voltage deviation data, the generated set of over-limit node numbers, and the generated over-limit voltage sensitivity matrix of each node through the following steps:

[0109] The first step is to perform the following steps for each of the above time points:

[0110] The first sub-step involves determining the average voltage deviation data corresponding to the aforementioned time points from the above-mentioned average voltage deviation data as the average voltage deviation data to be processed.

[0111] The second sub-step involves determining the set of out-of-limit node numbers corresponding to the aforementioned time points from each of the above-mentioned out-of-limit node number sets as the set of out-of-limit node numbers to be processed.

[0112] The third sub-step involves determining the number of each out-of-limit node number included in the aforementioned set of out-of-limit node numbers to be processed as the out-of-limit quantity data.

[0113] The fourth sub-step involves determining the sensitivity correction factor data by multiplying the average data of the voltage deviation to be processed by the data of the number of out-of-limit transactions.

[0114] The fifth sub-step involves determining the node over-limit voltage sensitivity matrix corresponding to the aforementioned time point from the over-limit voltage sensitivity matrices of each node as the over-limit sensitivity matrix to be processed.

[0115] The sixth sub-step involves determining the product of the above-mentioned unprocessed sensitivity over-limit matrix and the above-mentioned sensitivity correction factor data as the unprocessed sensitivity correction matrix.

[0116] The second step is to sum the determined over-limit sensitivity correction matrices to form the timing over-limit voltage sensitivity matrix. Each element in the timing over-limit voltage sensitivity matrix corresponds to a circuit node.

[0117] Step 105: Generate a time-series power sensitivity matrix based on the generated power sensitivity matrices.

[0118] In some embodiments, the aforementioned execution entity may generate a timing power sensitivity matrix based on the generated power sensitivity matrices.

[0119] In the process of adopting technical solutions to address the aforementioned technical problems, the following issues often arise:

[0120] Determining installation points solely based on voltage data from circuit nodes can lead to low sensitivity of energy storage devices to power fluctuations, resulting in poor responsiveness to power fluctuations in the distribution network, low utilization of energy storage devices, and wasted installation resources.

[0121] In response to the aforementioned technical problems, the following solution was adopted:

[0122] In some optional implementations of certain embodiments, the aforementioned execution entity may generate a timing power sensitivity matrix based on the generated power sensitivity matrices through the following steps:

[0123] The first step is to perform the following steps for each of the above time points:

[0124] The first sub-step is to determine the power sensitivity matrix corresponding to the above time points in each of the above power sensitivity matrices as the power sensitivity matrix to be processed.

[0125] The second sub-step involves determining each element value in the aforementioned power sensitivity matrix to be processed as a separate power sensitivity data point. Each power sensitivity data point corresponds to a circuit node.

[0126] The third sub-step involves determining the average value of the aforementioned power sensitivity data to be processed as the average power sensitivity data.

[0127] The fourth sub-step involves determining the power sensitivity difference between each of the aforementioned power sensitivity data and the average power sensitivity data as a power sensitivity difference value. Each power sensitivity difference value corresponds to a circuit node.

[0128] The fifth sub-step involves combining the determined power sensitivity difference data into a power sensitivity difference data set.

[0129] The second step is to perform the following steps for each of the preset circuit nodes:

[0130] The first sub-step involves determining the power sensitivity difference data corresponding to the aforementioned circuit nodes in each of the obtained power sensitivity difference data groups as the target power sensitivity difference data.

[0131] The second sub-step involves summing the above target power sensitivity difference data to determine the timing power sensitivity data corresponding to the above circuit nodes.

[0132] The third step is to generate a timing power sensitivity matrix based on the determined timing power sensitivity data. This timing power sensitivity matrix can be a row matrix obtained by combining the various timing power sensitivity data. In practice, the execution entity can arrange the timing power sensitivity data into a row matrix according to the order of the node numbers corresponding to each circuit node.

[0133] The above technical solution and its related content, combined with step 107, serve as an inventive point of this disclosure, solving the problem of "wasteful installation resources." Factors leading to wasted installation resources often include: relying solely on voltage data of circuit nodes to determine installation points easily results in low sensitivity of energy storage devices installed at those points to power fluctuations, leading to poor response capabilities of the energy storage devices to power fluctuations in the distribution network, resulting in low utilization rates and wasted installation resources. Solving these factors can reduce wasted installation resources. To achieve this effect, this disclosure firstly performs the following steps for each of the aforementioned time points: Secondly, the power sensitivity matrix corresponding to the aforementioned time point in each of the aforementioned power sensitivity matrices is determined as the power sensitivity matrix to be processed. This generates the power sensitivity matrix to be processed for the circuit node at the aforementioned time point. Then, each element value included in the aforementioned power sensitivity matrix to be processed is determined as a power sensitivity data to be processed, wherein each power sensitivity data to be processed corresponds to a circuit node. This generates the power sensitivity data to be processed. Then, the average value of each of the aforementioned power sensitivity data to be processed is determined as the average power sensitivity data. Thus, average power sensitivity data can be generated. Then, for each of the aforementioned power sensitivity data to be processed, the difference between the aforementioned power sensitivity data to be processed and the aforementioned average power sensitivity data is determined as the power sensitivity difference data, where the power sensitivity difference data corresponds to a circuit node. Thus, the power sensitivity difference data at each time point can be determined. Then, the determined power sensitivity difference data are combined into a power sensitivity difference data group. Thus, the power sensitivity difference data group corresponding to each time point can be generated. Then, for each of the preset circuit nodes, the following steps are performed: Then, the power sensitivity difference data corresponding to the aforementioned circuit node in the obtained power sensitivity difference data groups are determined as each target power sensitivity difference data. Thus, the power sensitivity difference data of a circuit node at all time points can be filtered out. Then, the sum of the aforementioned target power sensitivity difference data is determined as the time-series power sensitivity data corresponding to the aforementioned circuit node. Thus, the time-series power sensitivity data can reflect the comprehensive power sensitivity of a circuit node at all time points. Finally, based on the determined time-series power sensitivity data, a time-series power sensitivity matrix is ​​generated. Thus, the time-series power sensitivity matrix can be obtained.Because the installation point of the energy storage device can be determined by generating the power sensitivity of each circuit node over the entire time period, the sensitivity of the installed energy storage device to power fluctuations can be improved, the response capability of the energy storage device to power fluctuations in the distribution network can be improved, and the utilization rate of the energy storage device can be improved, reducing the waste of installation resources when installing energy storage devices.

[0134] Step 106: Based on the timing over-limit voltage sensitivity matrix and the timing power sensitivity matrix, determine the energy storage access point data sequence of the energy storage device.

[0135] In some embodiments, the execution entity may determine the energy storage access point data sequence of the energy storage device based on the aforementioned timing over-limit voltage sensitivity matrix and the aforementioned timing power sensitivity matrix. Each energy storage access point data in the aforementioned energy storage access point data sequence may be a node number corresponding to a circuit node.

[0136] In practice, firstly, the aforementioned executing entity can normalize the aforementioned timing over-limit voltage sensitivity matrix using a normalization method to map each element value in the matrix to the range [0,1], obtaining a normalized timing over-limit voltage sensitivity matrix as the normalized timing over-limit voltage matrix. The normalization method can be any method capable of mapping each element value in the matrix to the range [0,1]. For example, the normalization method can be minimum-maximum normalization. Secondly, the aforementioned timing power sensitivity matrix can be normalized using the same method to map each element value to the range [0,1], obtaining a normalized timing power sensitivity matrix as the normalized timing power sensitivity matrix. Then, the product of a preset first parameter data and the aforementioned normalized timing over-limit voltage matrix can be used to determine the first matrix. The first parameter data can be a pre-set value. For example, the first parameter data can be 0.7. Finally, the difference between the preset sum data and the first preset parameter data can be used to determine the second parameter data. The aforementioned preset values ​​and data can be pre-defined values. For example, the preset values ​​and data can be 1. Then, the product of the second parameter data and the normalized time-series power sensitivity matrix can be used to determine the second matrix. Then, the sum of the first and second matrices can be used to determine the target matrix. Each element value in the target matrix corresponds to a circuit node and a node number. Then, the element values ​​in the target matrix can be arranged in descending order to form an element value sequence. Finally, the node numbers corresponding to each element value can be arranged in the order of the element value sequence to form a node number sequence, which serves as the energy storage access point data sequence.

[0137] Step 107: Based on the energy storage access point data sequence and the initial number of energy storage access points, control the robotic arm to install at least one energy storage device to the corresponding at least one installation point.

[0138] In some embodiments, the executing entity may, based on the energy storage access point data sequence and the initial energy storage access quantity, control a robotic arm to install at least one energy storage device to at least one corresponding installation point. The energy storage device may be a device capable of storing electrical energy. For example, the energy storage device may be a flow battery. The initial energy storage access quantity may be a value representing the number of circuit nodes where energy storage devices need to be installed.

[0139] In the process of adopting technical solutions to address the aforementioned technical problems, the following issues often arise:

[0140] Determining the capacity of an energy storage device solely based on the voltage of each circuit node at a single moment can easily lead to an overly large or undersized energy storage capacity, resulting in insufficient power supply or wasted capacity resources. This, in turn, necessitates the use of robotic arms to reinstall suitable energy storage devices, causing a waste of installation resources.

[0141] In response to the aforementioned technical problems, the following solution was adopted:

[0142] In some optional implementations of certain embodiments, the execution entity may control a robotic arm to install at least one energy storage device to at least one corresponding installation point based on the energy storage access point data sequence and the initial number of energy storage access points through the following steps:

[0143] The first step is to determine the quantity of each of the above time points as time quantity data.

[0144] The second step involves performing the following iterative steps based on the initial number of energy storage units connected:

[0145] The first sub-step generates voltage power data sequences and voltage capacity data based on the initial energy storage access quantity and random coefficient data. The random coefficient data can be randomly generated values. The initial random coefficient data can be a pre-set value between 0 and 1. Each voltage power data sequence represents the electrical power generated based on the random coefficient data. Each voltage capacity data sequence represents the energy storage capacity generated based on the random coefficient data. For example, the voltage capacity data could be 1000 kWh. Each voltage power data sequence corresponds to one voltage capacity data. The initial energy storage access quantity can be a value representing the number of circuit nodes where energy storage devices need to be installed. The initial energy storage access quantity can be 1.

[0146] In practice, the aforementioned executing entity can perform the following cyclical steps based on random coefficient data: First, in response to determining that the random coefficient data belongs to a first coefficient interval, the ratio of the random coefficient data to a preset interval median data can be used to update the random coefficient data. The first coefficient interval can be a pre-defined interval. For example, the first coefficient interval can be [0, interval median data). The interval median data can be a value between 0 and 1. As an example, when the interval median data is 0.3, the corresponding first coefficient interval can be [0, 0.3). Then, in response to determining that the random coefficient data belongs to a second coefficient interval, first, the difference between a preset value and the random coefficient data can be used to determine the random difference data. Then, the difference between the preset value and the interval median data can be used to determine the interval difference data. Then, the ratio of the random difference data to the interval difference data can be used to update the random coefficient data. The second coefficient interval can be a pre-defined interval. For example, the second coefficient interval can be [interval median data, 1]. The preset value can be 1.

[0147] Then, the updated random coefficient data can be added to the random number sequence to update the random number sequence. Each random number in the random number sequence can be one of the added random coefficient data. The initial random number sequence can be empty.

[0148] Then, in response to the determination that the number of each random number in the updated random number sequence is not equal to the preset quantity value, the above cyclic steps are executed again using the updated random coefficient data and the random number sequence. Here, the preset quantity value can be the product of the initial energy storage access quantity and the preset access value. The preset access value can be the sum of the circuit node quantity data and the preset capacity value. The circuit node quantity data can be the number of each circuit node. The preset capacity value can be 1.

[0149] Then, in response to determining that the number of each random number in the updated random number sequence is equal to the aforementioned preset value, the updated random number sequence can be divided into a number of subsequences of the same length corresponding to the initial number of energy storage connections. For example, when there are 300 random numbers in the random number sequence and the initial number of energy storage connections is 3, the 1st to 100th random numbers in the random number sequence can be determined as the first random number subsequence, the 101st to 200th random numbers in the random number sequence can be determined as the second random number subsequence, and the 201st to 300th random numbers in the random number sequence can be determined as the third random number subsequence.

[0150] Then, for each of the above random number subsequences, firstly, the first random number in the subsequence can be determined as the capacity random number. Then, the difference between the preset maximum capacity data and the preset minimum capacity data can be determined as the intermediate capacity data. Then, the product of the intermediate capacity data and the random capacity number can be determined as the first capacity data. Then, the sum of the first capacity data and the minimum capacity data can be determined as the voltage capacity data. Wherein, the maximum capacity data can be a preset maximum energy storage capacity corresponding to the energy storage device. The minimum capacity data can be a preset minimum energy storage capacity corresponding to the energy storage device.

[0151] Then, the subsequences other than the capacity random numbers mentioned above can be determined as power random number sequences.

[0152] Then, for each power random number in the above power random number sequence, firstly, the difference between the preset maximum power data and the preset minimum power data can be determined as the power difference data. Then, the product of the power difference data and the above power random number can be determined as the power product data. Then, the sum of the above power product data and the minimum power data can be determined as the target power data. Here, the above maximum power data can be a preset maximum electrical power. The above minimum power data can be a preset minimum electrical power. Finally, the determined target power data can be arranged into a sequence as a voltage power data sequence according to the order of the above power random number sequence.

[0153] The second sub-step involves performing the following optimization steps based on the various voltage power data sequences and the aforementioned voltage capacity data:

[0154] Sub-step one: Based on the preset capacity correspondence table, the preset power correspondence table, the above-mentioned voltage capacity data and voltage power data sequences, generate each capacity adaptation data.

[0155] The capacity correspondence table mentioned above can be a table used to characterize the correspondence between voltage capacity data and capacity value. The capacity correspondence table can include various voltage capacity data. Each voltage capacity data point corresponds to a capacity value. The capacity value can be a pre-set value by technicians to characterize the value of energy storage capacity. For example, the capacity value can be 10000. The power correspondence table mentioned above can be a table used to characterize the correspondence between voltage power data sequences and power values. The power correspondence table can include various voltage power data sequences. Each voltage power data sequence corresponds to a power value. The power value can be a pre-set value by technicians to characterize the value of electrical power. For example, the power value can be 10000. Each capacity adaptation data point mentioned above can be a value corresponding to a voltage power data sequence and a voltage capacity data.

[0156] In practice, for each voltage capacity data point in the aforementioned voltage capacity data sets, firstly, the aforementioned voltage capacity data can be identified as the voltage capacity data to be compared. Secondly, the voltage capacity data points in the capacity correspondence table that are identical to the aforementioned voltage capacity data to be compared can be identified as the voltage capacity data to be processed. Then, the capacity value corresponding to the aforementioned voltage capacity data to be processed can be identified as the capacity value data to be processed. Then, the voltage power data sequence corresponding to the aforementioned voltage capacity data to be compared can be identified as the voltage power data sequence to be compared. Then, the cosine similarity between the aforementioned voltage power data sequence to be compared and the voltage power data sequences in the power correspondence table can be identified as each power similarity data point. Then, the power similarity data point with the largest corresponding value among the aforementioned power similarity data points can be identified as the power similarity data to be processed. The voltage power data sequence corresponding to the aforementioned power similarity data to be processed can be identified as the voltage power data sequence to be processed. Then, the power value corresponding to the aforementioned voltage power data sequence to be processed can be identified as the power value data to be processed. Finally, the sum of the above-mentioned unprocessed capacity value data and the above-mentioned unprocessed power value data can be determined as the capacity adaptation data.

[0157] Sub-step two: Based on the above voltage capacity data, capacity adaptation data, and voltage power data sequences, generate optimized voltage power data sequences and optimized voltage capacity data.

[0158] In this context, each optimized voltage power data point in the aforementioned optimized voltage power data sequence can be an optimized voltage power data point. Similarly, each optimized voltage capacity data point in the aforementioned optimized voltage capacity data sequence can be an optimized voltage capacity data point.

[0159] In practice, firstly, the executing entity can input a preset mean and a first preset standard deviation into a random number generation function, and obtain the return value of the random number generation function as the first random data. The preset mean can be a pre-defined value representing the mean of a normal distribution. For example, the preset mean can be 0. The first preset standard deviation can be a pre-defined value representing the standard deviation of a normal distribution. For example, the first preset standard deviation can be 0.5. The random number generation function can be a function capable of randomly generating a value that follows a normal distribution. For example, the random number generation function can be the `random.normal()` function from the NumPy library.

[0160] Then, the aforementioned preset mean and second preset standard deviation can be input into the aforementioned random number generation function to obtain the return value of the random number generation function as the second random data. The aforementioned second preset standard deviation can be a pre-set value used to characterize the standard deviation of a normal distribution. For example, the aforementioned second preset standard deviation can be 1.

[0161] Then, the preset power of the second random data can be determined as the random power data. The preset data can be a pre-defined value. For example, when the second random data is 3 and the preset data is 1.5, the random power data can be 3 to the power of 1.5. Then, the ratio of the first random data to the random power data can be determined as the perturbation data.

[0162] Then, the capacity adaptation data with the smallest corresponding value among the above capacity adaptation data can be determined as the target capacity adaptation data. The voltage capacity data and voltage power data sequences corresponding to the above target capacity adaptation data can be determined as the target optimized voltage capacity data and the target optimized voltage power data sequences.

[0163] Then, for each of the aforementioned voltage capacity data points, the product of the voltage capacity data and a preset factor data can be determined as the capacity factor data. The preset factor data can be a pre-defined value. For example, the preset factor data can be 0.5. Next, the difference between the target optimized voltage capacity data and the capacity factor data can be determined as the capacity difference data. Then, the sum of the voltage capacity data and the capacity difference data can be determined as the optimized voltage capacity data.

[0164] Then, for each voltage power data sequence in the aforementioned voltage power data sequences, and for each voltage power data within each voltage power data included in the aforementioned voltage power data sequence, firstly, the product of the voltage power data and the aforementioned preset factor data can be determined as voltage power factor data. Secondly, the position of the voltage power data in the corresponding voltage power data sequence can be determined as the target position. Then, the target optimized voltage power data in the aforementioned target optimized voltage power data sequence whose corresponding position is the same as the target position can be determined as voltage power reference data. Then, the difference between the aforementioned voltage power reference data and the aforementioned voltage power factor data can be determined as voltage power difference data. Then, the sum of the aforementioned voltage power difference data and the aforementioned voltage power data can be determined as optimized voltage power data. Finally, the optimized voltage power data corresponding to the aforementioned voltage power data sequences can be arranged into optimized voltage power data sequences according to the chronological order of the voltage power data in the aforementioned voltage power data sequences. Thus, each optimized voltage power data sequence can be obtained.

[0165] Sub-step three: In response to determining that the number of executions corresponding to the above optimization steps meets the preset optimization number condition, at least one target voltage power data sequence and at least one target voltage capacity data are generated based on the generated optimized voltage power data sequences and the generated optimized voltage capacity data.

[0166] The optimization count condition mentioned above can be that the number of executions corresponding to the optimization steps is equal to a preset optimization count. The optimization count can be a pre-set value. Here, the specific setting of the optimization count is not limited. Each target voltage power data sequence in the at least one target voltage power data sequence can be an optimized voltage power data sequence obtained after filtering. Each target voltage capacity data in the at least one target voltage capacity data can be optimized voltage capacity data obtained after filtering.

[0167] In practice, firstly, the implementing entity can determine at least one target voltage and power data sequence as the top three optimized voltage and power data sequences with the largest corresponding capacity adaptation data from the aforementioned optimized voltage and power data sequences, based on the initial number of energy storage connections. For example, when the initial energy storage connection number is 3, the top three optimized voltage and power data sequences with the largest corresponding capacity adaptation data from the aforementioned optimized voltage and power data sequences can be determined as at least one target voltage and power data sequence. Secondly, the top three optimized voltage and capacity data sequences with the largest corresponding capacity adaptation data from the aforementioned optimized voltage and capacity data sequences, based on the initial number of energy storage connections, can be determined as at least one target voltage and capacity data sequence.

[0168] Optionally, in practice, after generating at least one target voltage power data sequence and at least one target voltage capacity data based on the generated optimized voltage power data sequences and the generated optimized voltage capacity data in response to determining that the number of executions corresponding to the above optimization steps meets the preset optimization number condition, the above execution entity may also, in response to determining that the number of executions corresponding to the above optimization steps does not meet the above optimization number condition, use the above optimized voltage power data sequences as the respective voltage power data sequences and execute the above optimization steps again.

[0169] The third sub-step involves determining the over-limit node detection data based on at least one target voltage and power data sequence mentioned above.

[0170] The aforementioned over-limit node detection data can be a label used to characterize whether the target voltage and power data in the at least one target voltage and power data sequence is included outside a preset power range interval. For example, the aforementioned over-limit node detection data can be "included" or "not included". The aforementioned power range interval can be the interval corresponding to the voltage and power data. For example, the aforementioned power range interval can be [10 kW, 20 kW].

[0171] In practice, in response to determining that at least one target voltage and power data sequence meets a preset data range condition, "not including" can be defined as out-of-limit node detection data. In response to determining that at least one target voltage and power data sequence does not meet the above data range condition, "including" can be defined as out-of-limit node detection data. The above data range condition can be that each target voltage and power data in the at least one target voltage and power data sequence is within the above power range interval.

[0172] The fourth sub-step, in response to determining that the above-mentioned over-limit node detection data meets the preset over-limit node detection conditions, determines the updated initial energy storage access quantity as the target energy storage access quantity, and determines at least one energy storage configuration data and at least one target energy storage access point data based on the above-mentioned energy storage access point data sequence, the above-mentioned at least one target voltage capacity data and the updated initial energy storage access quantity.

[0173] Specifically, the above-mentioned over-limit node detection condition can be that the over-limit node detection data is "not included". The energy storage configuration data in the above-mentioned at least one energy storage configuration data can be target voltage and capacity data. Each target energy storage access point data in the above-mentioned at least one target energy storage access point data can be filtered energy storage access point data.

[0174] In practice, the aforementioned executing entity can determine at least one target voltage capacity data as at least one energy storage configuration data. Secondly, it can determine at least one energy storage access point data in the aforementioned energy storage access point data sequence that satisfies preset access point conditions as at least one target energy storage access point data. The preset access point conditions can be the energy storage access point data being arranged in the first preset number of positions in the aforementioned energy storage access point data sequence. The preset number of arrangements can be the initial energy storage access quantity corresponding to the last execution of the aforementioned iterative steps. For example, when the preset number of arrangements is 3, the corresponding preset access point condition can be that the energy storage access point data is arranged in the first 3 positions in the aforementioned energy storage access point data sequence.

[0175] Optionally, in response to determining that the above-mentioned out-of-limit node detection data does not meet the above-mentioned out-of-limit node detection conditions, the execution entity may further determine the sum of the initial energy storage access quantity and the preset energy storage access value as the initial energy storage access quantity, so as to update the initial energy storage access quantity, and use the updated initial energy storage access quantity to execute the above-mentioned iterative steps again. The above-mentioned energy storage access value can be a preset value. For example, the above-mentioned energy storage access value can be 1.

[0176] The third step involves controlling a robotic arm to install at least one energy storage device to a corresponding installation point, based on the aforementioned at least one energy storage configuration data and at least one target energy storage access point data. The robotic arm can be a device capable of installing energy storage devices according to instructions.

[0177] In practice, firstly, for each of the at least one energy storage configuration data mentioned above, the executing entity can determine any one of the preset energy storage devices whose energy storage capacity is the same as that in the energy storage configuration data as the energy storage device to be installed. Then, the target energy storage access point data corresponding to the energy storage configuration data can be determined as the access point data to be installed. Next, the installation point corresponding to the access point data to be installed can be determined as the installation point for the energy storage device to be installed. Finally, for each of the at least one energy storage devices to be installed, a preset installation command and the installation point corresponding to the energy storage device can be sent to the robotic arm to control the robotic arm to install the energy storage device to be installed at the corresponding installation point. The installation command can be a command to instruct the robotic arm to install the energy storage device.

[0178] The above-described technical solution and its related content, as an inventive point of this disclosure, solve the problem of "wasteful installation resources." Factors leading to wasted installation resources often include: determining the capacity of an energy storage device solely based on the voltage of each circuit node at a single moment can easily result in an excessively large or small generated energy storage capacity, leading to insufficient power supply or wasted capacity resources. This necessitates the use of robotic arms to reinstall suitable energy storage devices, further wasting installation resources. Solving these factors can reduce wasted installation resources. To achieve this effect, this disclosure first determines the quantity of each time point as time quantity data. Second, based on the initial energy storage access quantity, the following iterative steps are performed: First, based on the initial energy storage access quantity and random coefficient data, each voltage power data sequence and each voltage capacity data are generated. This allows for the random generation of each voltage power data sequence and each voltage capacity data. Then, based on each voltage power data sequence and the aforementioned voltage capacity data, the following optimization steps are performed: First, based on a preset capacity correspondence table, a preset power correspondence table, the aforementioned voltage capacity data, and each voltage power data sequence, each capacity adaptation data is generated. This allows for the determination of each capacity adaptation data. Secondly, based on the aforementioned voltage capacity data, capacity adaptation data, and voltage power data sequences, optimized voltage power data sequences and optimized voltage capacity data are generated. This allows for optimization of the capacity adaptation data and voltage power data sequences. Then, in response to determining that the execution count corresponding to the above optimization steps meets a preset optimization count condition, at least one target voltage power data sequence and at least one target voltage capacity data are generated based on the generated optimized voltage power data sequences and generated optimized voltage capacity data. This allows for the determination of at least one target voltage power data sequence and at least one target voltage capacity data. Then, based on the at least one target voltage power data sequence, over-limit node detection data is determined. Then, in response to determining that the over-limit node detection data meets a preset over-limit node detection condition, the updated initial energy storage access quantity is determined as the target energy storage access quantity, and based on the aforementioned energy storage access point data sequence, the at least one target voltage capacity data, and the updated initial energy storage access quantity, at least one energy storage configuration data and at least one target energy storage access point data are determined. This allows for the determination of at least one energy storage configuration data and at least one target energy storage access point data. Finally, based on the above-mentioned at least one energy storage configuration data and the above-mentioned at least one target energy storage access point data, the robotic arm is controlled to install at least one energy storage device to the corresponding at least one installation point.Because the energy storage capacity can be continuously iterated and optimized through capacity adaptation data, the accuracy of the generated energy storage capacity can be improved. This can reduce the number of times that the robotic arm needs to be called back to install the appropriate energy storage equipment due to the energy storage capacity being too large or too small, thereby reducing the waste of resources during installation.

[0179] Optionally, the aforementioned implementing entity may also perform the following steps:

[0180] The first step involves controlling the testing equipment to perform installation testing on at least one installed energy storage device, obtaining at least one set of installation testing data. This testing equipment can be any device capable of testing energy storage devices. For example, the testing equipment could be a drone equipped with a high-definition camera. Each of the at least one set of installation testing data can be video data obtained after filming the installed energy storage device.

[0181] In practice, for each of the at least one installed energy storage devices, the aforementioned executing entity can determine the installation point corresponding to the energy storage device among the at least one installation points as the target installation point. Secondly, the target installation point and a preset detection command can be sent to the aforementioned detection device to control the detection device to capture images of the energy storage device, thereby performing installation detection on the energy storage device and obtaining the video data captured by the detection device as the installation detection data corresponding to the energy storage device. The aforementioned detection command can be a command to prompt the detection device to capture images of the energy storage device.

[0182] The second step is to send at least one of the aforementioned installation and testing data to the target terminal. The target terminal can be the terminal corresponding to the technician.

[0183] The above embodiments of this disclosure have the following beneficial effects: the energy storage device installation method for power distribution networks according to some embodiments of this disclosure can reduce installation resource waste. Specifically, the reason for installation resource waste is that determining the installation point of the energy storage device solely based on the voltage regulation capability of the circuit node easily leads to poor smoothing effect of the installed energy storage device on power fluctuations in the power distribution network, thus requiring the control of a robotic arm to reinstall the energy storage device in a suitable position, resulting in resource waste during installation. Based on this, the energy storage device installation method for power distribution networks according to some embodiments of this disclosure first obtains the node topology data, line parameter data, new energy output matrix, active load matrix, and reactive load matrix of each circuit node in the power distribution network from the target server. This yields the raw data. Second, based on the aforementioned new energy output matrix, active load matrix, and reactive load matrix, a net active power injection matrix and a net reactive power injection matrix are generated. This allows the generation of the net active power injection and net reactive power injection of each node at each time point. Then, for each of the preset time points, based on the aforementioned node topology data, the aforementioned line parameter data, the aforementioned net active power injection matrix, the aforementioned net reactive power injection matrix, and preset baseline data, average voltage deviation data, a set of over-limit node numbers, a node over-limit voltage sensitivity matrix, and a power sensitivity matrix are generated. This allows the generation of the node over-limit voltage sensitivity matrix and the power sensitivity matrix. Then, based on the generated average voltage deviation data, the generated set of over-limit node numbers, and the generated node over-limit voltage sensitivity matrices, a time-series over-limit voltage sensitivity matrix is ​​generated. This allows the determination of the impact of power changes at circuit nodes on the voltage of over-limit circuit nodes. Next, based on the generated power sensitivity matrices, a time-series power sensitivity matrix is ​​generated. This allows the determination of the impact of injected power at each circuit node on the main grid interface power through the generated time-series power sensitivity matrix. Finally, based on the aforementioned time-series over-limit voltage sensitivity matrix and the aforementioned time-series power sensitivity matrix, the energy storage access point data sequence for the energy storage device is determined. Therefore, by combining the aforementioned timing-series over-limit voltage sensitivity matrix and timing-series power sensitivity matrix, the access points can be sorted to obtain an energy storage access point data sequence. Finally, based on the aforementioned energy storage access point data sequence and the initial number of energy storage access points, the robotic arm is controlled to install at least one energy storage device to at least one corresponding installation point. This allows for energy storage device configuration.Because the installation point of the energy storage device can be determined by combining the timing over-limit voltage sensitivity matrix and the timing power sensitivity matrix, rather than by determining the installation point of the energy storage device solely by the voltage of each circuit node, it can not only improve the effect of the installed energy storage device on power fluctuations in the distribution network, but also take into account the voltage regulation capability of the energy storage device on the distribution network. Therefore, it can improve the utilization rate of the energy storage device and reduce the waste of resources when installing the energy storage device.

[0184] Further reference Figure 2 As an implementation of the methods shown in the above figures, this disclosure provides some embodiments of an energy storage device installation apparatus for a power distribution network. These apparatus embodiments are similar to... Figure 1 Corresponding to the method embodiments shown, the device can be specifically applied to various electronic devices.

[0185] like Figure 2 As shown, an energy storage device installation apparatus 200 for a power distribution network in some embodiments includes: an acquisition unit 201, a first generation unit 202, a second generation unit 203, a third generation unit 204, a fourth generation unit 205, a determination unit 206, and a control unit 207. The acquisition unit 201 is configured to acquire node topology data, line parameter data, new energy output matrix, active load matrix, and reactive load matrix of each circuit node in the power distribution network from a target server. The first generation unit 202 is configured to generate a net active power injection matrix and a net reactive power injection matrix based on the aforementioned new energy output matrix, active load matrix, and reactive load matrix. The second generation unit 203 is configured to generate, for each preset time point, average voltage deviation data, a set of over-limit node numbers, and a node over-limit voltage sensitivity, based on the aforementioned node topology data, line parameter data, net active power injection matrix, net reactive power injection matrix, and preset reference data. The system comprises a sensitivity matrix and a power sensitivity matrix; a third generation unit 204 configured to generate a time-series over-limit voltage sensitivity matrix based on the generated average voltage deviation data, the generated set of over-limit node numbers, and the generated over-limit voltage sensitivity matrix of each node; a fourth generation unit 205 configured to generate a time-series power sensitivity matrix based on the generated power sensitivity matrices; a determination unit 206 configured to determine the energy storage access point data sequence of the energy storage device based on the aforementioned time-series over-limit voltage sensitivity matrix and the aforementioned time-series power sensitivity matrix; and a control unit 207 configured to control a robotic arm to install at least one energy storage device to at least one corresponding installation point based on the aforementioned energy storage access point data sequence and the initial number of energy storage access points.

[0186] It is understandable that the units described in the device 200 are related to the reference. Figure 1The steps in the described method correspond to each other. Therefore, the operations, features, and beneficial effects described above for the method also apply to the device 200 and the units contained therein, and will not be repeated here.

[0187] The following is for reference. Figure 3 It shows a schematic diagram of the structure of an electronic device (such as a computing device) 300 suitable for implementing some embodiments of the present disclosure. Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this disclosure.

[0188] like Figure 3 As shown, the electronic device 300 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the operation of the electronic device 300. The processing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0189] Typically, the following devices can be connected to I / O interface 305: input devices 306 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 307 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 308 including, for example, magnetic tapes, hard disks, etc.; and communication devices 309. Communication device 309 allows electronic device 300 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 An electronic device 300 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 3 Each box shown can represent a device or multiple devices as needed.

[0190] In particular, according to some embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 309, or installed from storage device 308, or installed from ROM 302. When the computer program is executed by processing device 301, it performs the functions defined in the methods of some embodiments of this disclosure.

[0191] It should be noted that, in some embodiments of this disclosure, the computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In some embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0192] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0193] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: obtain node topology data, line parameter data, renewable energy output matrix, active power load matrix, and reactive power load matrix of each circuit node in the distribution network from a target server; generate a net active power injection matrix and a net reactive power injection matrix based on the aforementioned renewable energy output matrix, active power load matrix, and reactive power load matrix; and for each preset time point, based on the aforementioned node topology data, line parameter data, net active power injection matrix, net reactive power injection matrix, and a preset reference... The system generates average voltage deviation data, a set of out-of-limit node numbers, a node out-of-limit voltage sensitivity matrix, and a power sensitivity matrix. Based on the generated average voltage deviation data, the generated set of out-of-limit node numbers, and the generated node out-of-limit voltage sensitivity matrices, a time-series out-of-limit voltage sensitivity matrix is ​​generated. Based on the generated power sensitivity matrices, a time-series power sensitivity matrix is ​​generated. Based on the aforementioned time-series out-of-limit voltage sensitivity matrix and the aforementioned time-series power sensitivity matrix, the energy storage access point data sequence of the energy storage device is determined. Based on the aforementioned energy storage access point data sequence and the initial number of energy storage access points, the system controls a robotic arm to install at least one energy storage device to at least one corresponding installation point.

[0194] Computer program code for performing operations of some embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0195] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0196] The units described in some embodiments of this disclosure can be implemented in software or hardware. The described units can also be housed in a processor; for example, a processor may be described as including an acquisition unit, a first generation unit, a second generation unit, a third generation unit, a fourth generation unit, a determination unit, and a control unit. The names of these units do not necessarily limit the specific unit itself; for example, the acquisition unit may also be described as "a unit that acquires node topology data, line parameter data, new energy output matrix, active load matrix, and reactive load matrix of each circuit node in the distribution network from a target server."

[0197] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0198] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A method for installing an energy storage device in a power distribution network, comprising: Obtain node topology data, line parameter data, new energy output matrix, active load matrix, and reactive load matrix of each circuit node in the distribution network from the target server; Based on the new energy output matrix, the active load matrix, and the reactive load matrix, a net active power injection matrix and a net reactive power injection matrix are generated. For each of the preset time points, based on the node topology data, the line parameter data, the net active power injection matrix, the net reactive power injection matrix, and the preset reference data, average voltage deviation data, a set of over-limit node numbers, a node over-limit voltage sensitivity matrix, and a power sensitivity matrix are generated. Based on the generated average voltage deviation data, the generated set of out-of-limit node numbers, and the generated out-of-limit voltage sensitivity matrix of each node, a time-series out-of-limit voltage sensitivity matrix is ​​generated. Based on the generated power sensitivity matrices, a time-series power sensitivity matrix is ​​generated; Based on the time-series over-limit voltage sensitivity matrix and the time-series power sensitivity matrix, the energy storage access point data sequence of the energy storage device is determined. Based on the energy storage access point data sequence and the initial number of energy storage access points, the robotic arm is controlled to install at least one energy storage device to at least one corresponding installation point.

2. The method according to claim 1, wherein, The method further includes: The control and testing equipment performs installation testing on at least one installed energy storage device and obtains at least one installation testing data. Send the at least one installation detection data to the target terminal.

3. The method according to claim 1, wherein, The process of generating a net active power injection matrix and a net reactive power injection matrix based on the new energy output matrix, the active power load matrix, and the reactive power load matrix includes: The difference between the new energy output matrix and the active load matrix is ​​determined as the net active power injection matrix; The difference between the preset initial matrix and the reactive load matrix is ​​determined as the net reactive power injection matrix.

4. The method according to claim 1, wherein, The process of generating average voltage deviation data, a set of over-limit node numbers, a node over-limit voltage sensitivity matrix, and a power sensitivity matrix based on the node topology data, the line parameter data, the net active power injection matrix, the net reactive power injection matrix, and preset reference data includes: Based on the net active power injection matrix and the net reactive power injection matrix, determine the net injected active power data and net injected reactive power data corresponding to the time point. Based on the node topology data, the line parameter data, the net injected active power data, the net injected reactive power data, and the preset reference data, a node power matrix and a node voltage array are generated. The node voltage array includes the voltage data of each node, and each node voltage data includes voltage amplitude data and phase angle data. The inverse of the node power matrix is ​​determined as the node power inverse matrix; Based on preset voltage range data, preset standard voltage data, the node power inverse matrix, and the voltage amplitude data included in the node voltage array, average voltage deviation data, a set of out-of-limit node numbers, and a node out-of-limit voltage sensitivity matrix are generated. A power sensitivity matrix is ​​generated based on the voltage amplitude data and phase angle data included in the node voltage array, the node power inverse matrix, the line parameter data, the voltage range data, and the standard voltage data.

5. The method according to claim 4, wherein, Each node voltage data in the node voltage array corresponds to a circuit node, and each circuit node has a corresponding node number; and the generation of average voltage deviation data, a set of out-of-limit node numbers, and a node out-of-limit voltage sensitivity matrix based on preset voltage range data, preset standard voltage data, the node power inverse matrix, and the voltage amplitude data included in the node voltage array includes: Based on the node power inverse matrix, determine the node voltage sensitivity matrix; For each voltage amplitude data in the node voltage array, perform the following steps: Based on the voltage range data, the voltage amplitude data is processed to obtain the voltage detection result; In response to determining that the voltage detection result meets the preset voltage detection conditions, the circuit node corresponding to the voltage amplitude data is determined as the target circuit node; Add the node number corresponding to the target circuit node to the over-limit node set to update the over-limit node set; The updated set of out-of-limit nodes is determined as the set of out-of-limit node numbers; For each out-of-limit node number in the set of out-of-limit node numbers, voltage deviation data is generated based on the standard voltage data and the voltage amplitude data corresponding to the out-of-limit node number; The average value of each generated voltage deviation data is determined as the average voltage deviation data; For each of the preset circuit nodes, perform the following steps: For each out-of-limit node number in the set of out-of-limit node numbers, perform the following steps: The voltage deviation data corresponding to the out-of-limit node number is determined as the voltage deviation data to be processed; Based on the node voltage sensitivity matrix, determine the corresponding over-limit node number and the voltage sensitivity data of the circuit node; The product of the voltage sensitivity data and the voltage deviation data to be processed is determined as the node voltage sensitivity data; The sum of the determined node voltage sensitivity data is used to determine the node sensitivity data. Based on the determined sensitivity data of each node, a node over-limit voltage sensitivity matrix is ​​generated.

6. The method according to claim 4, wherein, The node voltage array includes node voltage data, each node voltage data corresponds to a circuit node, and the circuit node corresponds to a node number. The line parameter data includes line parameter data, each line parameter data corresponds to two circuit nodes, and each line parameter data includes resistance data and reactance data. The generation of a power sensitivity matrix based on the voltage amplitude data and phase angle data included in the node voltage array, the node power inverse matrix, the line parameter data, the voltage range data, and the standard voltage data includes: Obtain main network connection line data, wherein the main network connection line data corresponds to two circuit nodes, and the main network connection line data includes two node numbers and two node voltage data corresponding to the two circuit nodes, and each node voltage data in the two node voltage data includes phase angle data and voltage amplitude data; For each of the preset circuit nodes, perform the following steps: The circuit node is defined as a standard circuit node; The two circuit nodes corresponding to the main network connection line data are identified as two circuit nodes to be processed. The two node voltage data corresponding to the two circuit nodes to be processed are determined as the two node voltage data to be processed. The difference between the two phase angle data included in the voltage data of the two nodes to be processed is determined as the phase angle difference value data; The two voltage amplitude data included in the voltage data of the two nodes to be processed are determined as two voltage amplitude data to be processed; Based on the two voltage amplitude data to be processed and the phase angle difference data, a first circuit node to be processed, a second circuit node to be processed, first voltage deflection data, second voltage deflection data, first phase angle deflection data, and second phase angle deflection data are generated. Based on the node power inverse matrix, the first circuit node to be processed, and the second circuit node to be processed, determine the first voltage element data, the second voltage element data, the first phase angle element data, and the second phase angle element data. Based on the first voltage bias derivative data, the second voltage bias derivative data, the first phase angle bias derivative data, the second phase angle bias derivative data, the first voltage element data, the second voltage element data, the first phase angle element data, and the second phase angle element data, line sensitivity data corresponding to the standard circuit node is generated; Based on the generated sensitivity data of each line, a power sensitivity matrix is ​​generated.

7. The method according to claim 1, wherein, The generation of a time-series over-limit voltage sensitivity matrix based on the generated average voltage deviation data, the generated set of over-limit node numbers, and the generated over-limit voltage sensitivity matrix for each node includes: For each of the aforementioned time points, perform the following steps: The average voltage deviation data corresponding to the time point in each of the average voltage deviation data is determined as the average voltage deviation data to be processed. The set of out-of-limit node numbers corresponding to the time point in each set of out-of-limit node numbers is determined as the set of out-of-limit node numbers to be processed. The number of each out-of-limit node number included in the set of out-of-limit nodes to be processed is determined as the out-of-limit quantity data; The product of the average voltage deviation data to be processed and the number of out-of-limit data is determined as the sensitivity correction factor data; The node over-limit voltage sensitivity matrix corresponding to the time point in each node over-limit voltage sensitivity matrix is ​​determined as the over-limit sensitivity matrix to be processed. The product of the unprocessed over-limit sensitivity matrix and the sensitivity correction factor data is determined as the over-limit sensitivity correction matrix; The sum of the determined over-limit sensitivity correction matrices is used to determine the timing over-limit voltage sensitivity matrix.

8. An energy storage device installation apparatus for a power distribution network, comprising: The acquisition unit is configured to acquire node topology data, line parameter data, new energy output matrix, active load matrix and reactive load matrix of each circuit node in the distribution network from the target server. The first generation unit is configured to generate a net active power injection matrix and a net reactive power injection matrix based on the new energy output matrix, the active load matrix, and the reactive load matrix. The second generation unit is configured to generate, for each preset time point, average voltage deviation data, a set of over-limit node numbers, a node over-limit voltage sensitivity matrix, and a power sensitivity matrix based on the node topology data, the line parameter data, the net active power injection matrix, the net reactive power injection matrix, and preset reference data. The third generation unit is configured to generate a time-series over-limit voltage sensitivity matrix based on the generated average data of each voltage deviation, the generated set of each over-limit node number, and the generated over-limit voltage sensitivity matrix of each node. The fourth generation unit is configured to generate a time-series power sensitivity matrix based on the generated power sensitivity matrices. The determining unit is configured to determine the energy storage access point data sequence of the energy storage device based on the time-series over-limit voltage sensitivity matrix and the time-series power sensitivity matrix. The control unit is configured to control the robotic arm to install at least one energy storage device to at least one corresponding installation point based on the energy storage access point data sequence and the initial number of energy storage access points.

9. An electronic device, comprising: One or more processors; A storage device on which one or more programs are stored; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1 to 7.

10. A computer-readable medium having a computer program stored thereon, wherein, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 7.

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