A power distribution network energy storage system site selection and capacity planning method and device, electronic equipment and storage medium

Abstract

This invention discloses a method, apparatus, electronic device, and storage medium for site selection and capacity planning of energy storage systems in distribution networks, belonging to the field of distribution network planning technology. The method includes: acquiring typical daily load curves, topology data, and power supply demands of load nodes to be restored under fault scenarios in the distribution network; determining the peak-shaving capacity, charge / discharge control curves, and expected remaining capacity of the energy storage system based on the load curves, and determining the maximum emergency support power in conjunction with operational constraints; screening a set of target load nodes based on electrical distance and power constraints; constructing a line fault rate index based on the target load node set, establishing a multi-point site selection and capacity optimization model that considers both line fault rate and investment cost, and solving for the site selection and capacity planning scheme of the distribution network energy storage system. Therefore, by implementing this invention, the problem of inaccurate assessment of available emergency support power and serious misjudgment of load restoration range in existing technologies can be solved.

Description

Technical Field

[0001] This invention relates to the field of power distribution network planning technology, and specifically to a method, apparatus, electronic equipment, and storage medium for site selection and capacity planning of power distribution network energy storage systems. Background Technology

[0002] With the expansion of distribution network scale and the increasing complexity of its operation, distribution network energy storage systems play a crucial role in ensuring the safe and stable operation of the power grid. Scientific and rational site selection and capacity planning for distribution network energy storage systems can not only meet the daily operational needs of the power grid, but also quickly serve as emergency power to support loads awaiting restoration in extreme scenarios where distribution network faults lead to partial power outages. This is of great significance for improving the power supply reliability of the distribution network and controlling the overall construction and operation costs of the power grid.

[0003] However, existing methods for site selection and capacity planning of energy storage in distribution networks generally suffer from a technical flaw when dealing with emergency fault scenarios: a disconnect between reliability assessment and actual physical boundaries. This flaw arises because existing planning models often treat routine peak-shaving and emergency fault conditions in isolation. When assessing emergency support capabilities, they rely solely on static parameters, failing to consider the significant reduction in the actual expected remaining capacity of the energy storage system after participating in routine peak-shaving control, and neglecting to incorporate grid topology data to verify the objective constraints of the underlying power flow safety boundary on power output. This fragmented assessment logic leads to a severe discrepancy between the predicted available support power during the planning phase and the actual physical extreme values ​​during fault occurrence. Consequently, when expanding outwards along electrical distances to restore load, serious range misjudgments occur, ultimately resulting in a site selection and capacity planning scheme that fails to truly achieve the optimal balance between line fault resilience and overall investment cost. Summary of the Invention

[0004] This invention provides a method, apparatus, electronic device, and storage medium for site selection and capacity planning of distribution network energy storage systems. It can solve the problem that in the prior art, when site selection and capacity planning of distribution network energy storage systems, daily peak-shaving conditions and fault emergency conditions are evaluated in isolation and without objective physical constraints such as the underlying power flow safety boundary, resulting in inaccurate assessment of available emergency support power and serious misjudgment of load recovery range.

[0005] An embodiment of the present invention provides a method for site selection and capacity planning of energy storage systems in power distribution networks, comprising: Obtain typical daily load curves of the distribution network, distribution network topology data, and the node power supply requirements of at least one load node to be restored under the fault operation scenario of the distribution network; Based on the typical daily load curve of the distribution network and the preset peak-shaving index, calculate the peak-shaving capacity of the distribution network energy storage system; based on the peak-shaving capacity and the typical daily load curve of the distribution network, generate the charging and discharging control curve of the distribution network energy storage system; based on the charging and discharging control curve, calculate the expected remaining capacity of the distribution network energy storage system within the preset operating cycle. Based on the expected remaining capacity and the preset emergency support duration, calculate the first power limit; perform power flow calculation on the distribution network topology data to generate the second power limit; select the minimum value among the first power limit, the second power limit, and the preset converter rated power limit as the maximum emergency support power of the distribution network energy storage system. Using the preset alternative access nodes as the source nodes, the electrical distance from the source nodes to each load node to be restored is calculated based on the distribution network topology data; Following the order of increasing electrical distance, starting from the load node to be restored with the smallest electrical distance, the longest continuous sequence of nodes that meets the sum of the power supply requirements of the nodes less than or equal to the maximum emergency support power is selected, and the target load node set is formed by the load nodes to be restored contained in the longest continuous sequence of nodes. A line failure rate index is constructed based on the target load node set; a multi-point location and capacity optimization model is constructed with the goal of minimizing the line failure rate index and the preset comprehensive investment cost; the multi-point location and capacity optimization model is solved to output the location and capacity planning scheme of the distribution network energy storage system.

[0006] Furthermore, based on the typical daily load curve of the distribution network and the preset peak-shaving index, the peak-shaving capacity of the distribution network energy storage system is calculated, including: Determine the maximum peak-to-valley difference of the distribution network before peak shaving based on the typical daily load curve of the distribution network. The difference between the preset baseline value and the preset peak-shaving index is calculated to obtain the peak-shaving coefficient. The product of the peak-shaving coefficient and the maximum peak-valley difference is used as the peak-shaving capacity of the distribution network energy storage system.

[0007] Furthermore, based on the peak-shaving capacity and the typical daily load curve of the distribution network, a charge-discharge control curve for the distribution network energy storage system is generated; according to the charge-discharge control curve, the expected remaining capacity of the distribution network energy storage system within a preset operating cycle is calculated, including: Based on the typical daily load curve and peak-shaving capacity of the distribution network, determine the charging and discharging time periods of the distribution network energy storage system; Based on the charging and discharging time periods, the charging and discharging control curves of the distribution network energy storage system are generated; Based on the charge and discharge control curve, determine the remaining capacity of the distribution network energy storage system at each moment within the preset operating cycle; Based on the remaining capacity at each time point, the average remaining capacity of the distribution network energy storage system within a preset operating cycle is determined as the expected remaining capacity.

[0008] Furthermore, based on the expected remaining capacity and the preset emergency support duration, a first power ceiling is calculated; power flow calculations are performed on the distribution network topology data to generate a second power ceiling, including: Calculate the ratio of the expected remaining capacity to the preset emergency support duration, and use the ratio as the first power limit; Perform power flow calculations on the distribution network topology data to determine the maximum node injection power that will not cause voltage overruns at distribution network nodes or line current overloads. The maximum node injection power is used as the second power upper limit.

[0009] Furthermore, using preset alternative access nodes as source nodes, the electrical distances from the source nodes to each load node to be restored are calculated based on the distribution network topology data, including: For each load node to be restored, a preset alternative access node is used as the source node, and the connection path from the source node to the current load node to be restored is determined based on the distribution network topology data. Obtain the line impedance corresponding to the connection path, and use the line impedance as the electrical distance from the source node to the current load node to be restored.

[0010] Furthermore, following the order of increasing electrical distance, starting from the load node with the smallest electrical distance to be restored, the longest continuous sequence of nodes whose sum of power supply requirements for each node is less than or equal to the maximum emergency support power is selected. The target load node set is composed of the load nodes to be restored contained in the longest continuous sequence, including: Based on the electrical distance, each load node to be restored is sorted in ascending order to generate a basic node sequence; Starting with the first load node to be restored in the basic node sequence, node sequences of different lengths are extracted sequentially according to the sorting order to obtain multiple candidate sequences; For each of the multiple candidate sequences, calculate the sum of the node power supply requirements of all load nodes to be restored within the current candidate sequence; Among the candidate sequences where the sum of node power demand is less than or equal to the maximum emergency support power, the candidate sequence containing the largest number of nodes with loads to be restored is selected as the longest continuous node sequence. All load nodes to be restored in the longest continuous node sequence are added to the target load node set.

[0011] Furthermore, a line failure rate index is constructed based on the target load node set; a multi-point location and capacity optimization model is constructed with the goal of minimizing the line failure rate index and the preset comprehensive investment cost; the multi-point location and capacity optimization model is solved to output a location and capacity planning scheme for the distribution network energy storage system, including: The number of load nodes to be restored contained in the target load node set is counted as the total number of supported nodes; Calculate the difference between the preset total number of feeder nodes and the total number of supported nodes, and use the ratio of the difference to the total number of feeder nodes as the line failure rate indicator. Calculate the product of the line failure rate index and the preset reliability requirement coefficient, and establish a weighted objective function that includes the product and the preset comprehensive investment cost; With minimizing the weighted objective function as the optimization direction, and combined with the preset distribution network safety operation constraints, a multi-point location and capacity optimization model is constructed. The multi-point location and capacity optimization model is solved to obtain the target decision variables that minimize the weighted objective function. The access status, access power, and access capacity corresponding to the target decision variables are used as the location and capacity planning scheme for the distribution network energy storage system.

[0012] Based on the above method embodiments, the present invention provides corresponding apparatus embodiments.

[0013] One embodiment of the present invention provides a site selection and capacity planning device for a distribution network energy storage system, comprising: a data acquisition module, a supporting power determination module, a load set delineation module, and a site selection and capacity optimization module; The data acquisition module is used to acquire typical daily load curves of the distribution network, distribution network topology data, and the node power supply requirements of at least one load node to be restored under the fault operation scenario of the distribution network. The supporting power determination module is used to calculate the peak-shaving capacity of the distribution network energy storage system based on the typical daily load curve of the distribution network and the preset peak-shaving index; generate the charging and discharging control curve of the distribution network energy storage system based on the peak-shaving capacity and the typical daily load curve of the distribution network; calculate the expected remaining capacity of the distribution network energy storage system within a preset operating cycle based on the charging and discharging control curve; calculate the first power limit based on the expected remaining capacity and the preset emergency support duration; perform power flow calculation on the distribution network topology data to generate the second power limit; and select the minimum value among the first power limit, the second power limit, and the preset converter rated power limit as the maximum emergency support power of the distribution network energy storage system. The load set delineation module is used to calculate the electrical distance from the source node to each load node to be restored based on the distribution network topology data, using the preset alternative access nodes as source nodes; and to extract the longest continuous node sequence that satisfies the sum of the power supply requirements of the nodes being less than or equal to the maximum emergency support power, starting from the load node to be restored with the smallest electrical distance in ascending order, and the target load node set is composed of the load nodes to be restored contained in the longest continuous node sequence. The site selection and capacity optimization module is used to construct a line failure rate index based on the target load node set; construct a multi-point site selection and capacity optimization model with the goal of minimizing the line failure rate index and the preset comprehensive investment cost; solve the multi-point site selection and capacity optimization model, and output the site selection and capacity planning scheme of the distribution network energy storage system.

[0014] Based on the above method embodiments, the present invention provides corresponding electronic device embodiments.

[0015] An embodiment of the present invention provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the distribution network energy storage system site selection and capacity planning method described in any of the above-described method embodiments.

[0016] Based on the above method embodiments, the present invention provides corresponding storage medium embodiments.

[0017] One embodiment of the present invention provides a storage medium storing a computer program thereon, wherein, when the computer program is running, it controls the device where the storage medium is located to execute any of the above-described method embodiments of the distribution network energy storage system site selection and capacity planning method.

[0018] Compared with the prior art, the present invention has the following beneficial effects: This invention provides a method, apparatus, electronic device, and storage medium for the site selection and capacity planning of a distribution network energy storage system. The method acquires typical daily load curves, distribution network topology data, and power supply demands of load nodes to be restored under fault scenarios; calculates the peak-shaving capacity of the energy storage system based on the typical daily load curves and generates charge / discharge control curves to determine the expected remaining capacity of the energy storage system; determines the maximum emergency support power of the energy storage system by combining the expected remaining capacity, emergency support duration, power flow calculation results, and converter rated power; calculates the electrical distances from candidate access nodes to each load node to be restored based on the distribution network topology and filters a set of target load nodes that meet power constraints; constructs a line fault rate index based on the target load node set, and establishes a multi-point site selection and capacity optimization model with the goal of optimizing the line fault rate index and overall investment cost; and solves the optimization model to obtain the site selection and capacity planning scheme for the distribution network energy storage system.

[0019] This invention combines the expected remaining capacity of the energy storage system under daily peak-shaving control with the power grid flow safety boundary to extract the maximum emergency support power extreme value of the energy storage system. This overcomes the technical defects of existing technologies, which isolate the assessment of daily peak-shaving and fault emergency scenarios, leading to a discrepancy between the available support power and the actual physical extreme value. Based on this, the solution uses this accurate power extreme value to delineate the target load node set by selecting the longest continuous node sequence that meets the power constraint according to electrical distance from near to far. Then, it constructs and solves an optimization model aimed at minimizing the line fault rate index and the overall investment cost. This effectively eliminates the misjudgment of the load recovery range caused by inaccurate support capacity assessment, and achieves accurate site selection and capacity determination of distribution network energy storage that balances grid fault resilience and economy. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating a method for site selection and capacity planning of a power distribution network energy storage system according to an embodiment of the present invention.

[0021] Figure 2 This is a schematic diagram of the structure of a distribution network energy storage system location and capacity planning device provided in an embodiment of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] like Figure 1As shown, to address the problem that existing technologies for energy storage systems in distribution networks isolate and evaluate daily peak-shaving and emergency fault conditions during site selection and capacity planning, detaching them from objective physical constraints such as underlying power flow safety boundaries, leading to inaccurate assessments of available emergency support power and serious misjudgments of load recovery range, an embodiment of the present invention provides a method for site selection and capacity planning of energy storage systems in distribution networks, comprising at least the following steps: Step S1: Obtain the typical daily load curve of the distribution network, the distribution network topology data, and the node power supply demand of at least one load node to be restored under the fault operation scenario of the distribution network. Specifically, to understand the overall operational status of the distribution network, the first step is to collect typical daily load curves. These curves reflect the objective physical laws governing the change in overall load power over time within a 24-hour continuous operating cycle. Introducing typical daily load curves clarifies the peak and off-peak electricity consumption periods during normal peak-shaving operation. The specific steps for obtaining these curves involve retrieving historical load power sequences from the past year's continuous operation stored in the distribution network dispatch center database. Data cleaning is then performed on these historical load power sequences, removing outliers and missing zero-value segments caused by communication failures in measurement equipment. The cleaned continuous load sequences are then divided into multiple groups of single-day load samples based on their natural day span. An unsupervised clustering operation is then performed on these multiple groups of single-day load samples. This unsupervised clustering operation employs an alternating optimization mathematical convergence logic, iteratively updating the coordinates of the center points of each cluster until the sum of the spatial distances from all single-day load samples to their respective cluster center points reaches a minimum. The central distribution trajectories of each cluster are summarized, and the central distribution trajectory corresponding to the cluster with the largest number of samples is formally established as the typical daily load curve of the distribution network. By applying the aforementioned clustering extraction logic, the interference of extreme weather or sudden abnormal power consumption events during holidays on the normal load pattern is eliminated, ensuring that the extracted benchmark parameters can most realistically reflect the long-term normal operation of the power grid.

[0024] Distribution network topology data is collected synchronously. The specific data collection process involves extracting data using a geographic information system (GIS) and energy management system deployed within the distribution network. This ensures that the total number of feeder nodes, locations of potential access nodes, and physical connection paths between nodes are absolutely consistent with the actual operational status of the physical power grid. Distribution network topology data characterizes the spatial connectivity and electrical parameter attributes of each physical node within the distribution network. Specifically, the topology data includes the total number of feeder nodes, locations of potential access nodes, connection paths between nodes, and the corresponding line impedances. Extracting distribution network topology data provides the foundational support for subsequent calculations of electrical distances and deduction of underlying power flow safety boundaries.

[0025] After completing the collection of routine operation data, the next step is to set the boundary conditions for extreme operating scenarios. Specifically, this involves obtaining the power supply demand of at least one load node to be restored under distribution network fault scenarios. When a partial line disconnects from the distribution network, the physical nodes within the network that lose power transmission from the upstream main grid become load nodes to be restored. To formulate precise emergency power assistance strategies, it is essential to accurately determine the objective load consumption required by the load nodes to be restored during the fault, defining this load consumption as the node's power supply demand. The specific value of the node's power supply demand is estimated by retrieving the operation logs of similar loads during the same historical period, or by extracting the actual measured power at the moment before the fault occurred using an advanced measurement system for equivalent substitution, thus providing reliable numerical input for subsequent power extreme value comparisons.

[0026] By comprehensively extracting the aforementioned multi-dimensional underlying operating parameters and physical architecture information, the actual operating conditions of energy storage devices under long-term daily peak-shaving scenarios and short-term fault emergency scenarios can be visualized, thereby laying a solid and reliable data foundation for subsequent site selection and capacity planning schemes that combine physical feasibility and economic optimization.

[0027] Step S2: Calculate the peak-shaving capacity of the distribution network energy storage system based on the typical daily load curve of the distribution network and the preset peak-shaving index; generate the charging and discharging control curve of the distribution network energy storage system based on the peak-shaving capacity and the typical daily load curve of the distribution network; calculate the expected remaining capacity of the distribution network energy storage system within the preset operating cycle based on the charging and discharging control curve; calculate the first power limit based on the expected remaining capacity and the preset emergency support duration; perform power flow calculation on the distribution network topology data to generate the second power limit; select the minimum value among the first power limit, the second power limit, and the preset converter rated power limit as the maximum emergency support power of the distribution network energy storage system. In a preferred embodiment, the peak-shaving capacity of the distribution network energy storage system is calculated based on the typical daily load curve of the distribution network and a preset peak-shaving index, including: Determine the maximum peak-to-valley difference of the distribution network before peak shaving based on the typical daily load curve of the distribution network. The difference between the preset baseline value and the preset peak-shaving index is calculated to obtain the peak-shaving coefficient. The product of the peak-shaving coefficient and the maximum peak-valley difference is used as the peak-shaving capacity of the distribution network energy storage system.

[0028] In a preferred embodiment, a charge / discharge control curve for the distribution network energy storage system is generated based on the peak-shaving capacity and the typical daily load curve of the distribution network; according to the charge / discharge control curve, the expected remaining capacity of the distribution network energy storage system within a preset operating cycle is calculated, including: Based on the typical daily load curve and peak-shaving capacity of the distribution network, determine the charging and discharging time periods of the distribution network energy storage system; Based on the charging and discharging time periods, the charging and discharging control curves of the distribution network energy storage system are generated; Based on the charge and discharge control curve, determine the remaining capacity of the distribution network energy storage system at each moment within the preset operating cycle; Based on the remaining capacity at each time point, the average remaining capacity of the distribution network energy storage system within a preset operating cycle is determined as the expected remaining capacity.

[0029] In a preferred embodiment, a first power limit is calculated based on the expected remaining capacity and a preset emergency support duration; power flow calculation is performed on the distribution network topology data to generate a second power limit, including: Calculate the ratio of the expected remaining capacity to the preset emergency support duration, and use the ratio as the first power limit; Perform power flow calculations on the distribution network topology data to determine the maximum node injection power that will not cause voltage overruns at distribution network nodes or line current overloads. The maximum node injection power is used as the second power upper limit.

[0030] Specifically, to accurately assess the demand boundary of the distribution network in response to daily load fluctuations, it is necessary to first determine the maximum peak-to-valley difference faced by the distribution network before peak shaving based on the typical daily load curve of the distribution network. The specific logic for determining the maximum peak-to-valley difference is to scan the power extremes of the typical daily load curve of the distribution network during the continuous operation cycle of the whole day, extract the global maximum load power and the global minimum load power, and directly define the difference between the global maximum load power and the global minimum load power as the maximum peak-to-valley difference. For the pre-entered operation evaluation system, the mathematical difference between the preset benchmark value and the preset peak shaving index is calculated, and the calculated mathematical difference is defined as the peak shaving coefficient. The preset benchmark value is usually set to a constant of 1 in the physical calculation logic, and the preset peak shaving index is set in advance based on the historical dispatch experience of the distribution network and the reserve capacity margin of the network structure. Its value range is usually strictly configured between 0.2 and 0.5 to ensure that the issued peak shaving depth has the underlying physical feasibility. The smaller the peak shaving coefficient value, the higher the gain of peak shaving and valley filling actions on the power supply stability of the underlying power grid. Subsequently, the peak-shaving coefficient is multiplied by the maximum peak-to-valley difference of the distribution network, and the product is defined as the peak-shaving capacity of the distribution network energy storage system. The aforementioned logic for determining the peak-shaving capacity is expressed mathematically as follows: In the formula, Indicates the peak-shaving capacity of the distribution network energy storage system. This represents the preset baseline value. This indicates the preset peak-shaving target. This represents the maximum peak-to-valley difference in the distribution network before peak shaving.

[0031] After determining the basic peak-shaving boundary, based on the typical daily load curve and peak-shaving capacity of the distribution network, the start and end times for the energy storage system in the charging state and the discharge state are clearly defined. The basic charge-discharge control curve of the energy storage system is then constructed based on these charging and discharging times. The underlying logic for determining the charging and discharging times is as follows: a load baseline is pre-defined. The typical daily load curve of the distribution network is compared with the load baseline. The time intervals in the typical daily load curve where the vertical coordinate value is higher than the load baseline are defined as the discharge time intervals. The time intervals in the typical daily load curve where the vertical coordinate value is lower than the load baseline and the slope is positively increasing are defined as the charging time intervals. A constant power absorption command is issued based on the defined charging time intervals, and a constant power output command is issued based on the defined discharging time intervals. The absorption and output commands are smoothly spliced ​​together according to the time axis sequence to construct a complete basic charge-discharge control curve. To further mitigate the transient and severe fluctuations in the underlying load of the power grid, a power smoothing and gradual change function is introduced at the transition edge between the absorption and output commands. This ensures that the energy storage devices in the distribution network do not cause a sudden voltage drop on the distribution network bus during the switching between charging and discharging states. Segmented judgment and smooth splicing actions are adopted to effectively avoid ineffective and frequent charging and discharging switching actions of the energy storage devices during periods of stable load, significantly extending the physical cycle life of the underlying chemical batteries. Considering the inevitable data deviation between the actual load and the day-ahead forecast load in the objective physical power grid, a compensation feedback mechanism based on load forecast error is introduced in the actual operation to correct the basic charging and discharging control curve. The expression for the physical deviation value between the actual load and the forecast load within a preset time period is defined as: In the formula, Indicates time node The corresponding load deviation value, Indicates time node The actual physical load, Indicates time node The predicted reference load.

[0032] Further combining the penalty coefficient reflecting the health and safety status of the device's inner battery, a revised formula for calculating real-time charge and discharge power is constructed: In the formula, This indicates the corrected real-time charge / discharge power command. This indicates the basic charge-discharge control curve at time node. The reference charge and discharge power, This represents the charge / discharge power correction factor. This represents the penalty coefficient for a safe state. Indicates the ideal state of charge of the target. Indicates time node The true physical state of charge.

[0033] By tracking the corrected real-time charge and discharge control curves, the physical remaining capacity of the distribution network energy storage system at each independent discrete moment within a preset operating cycle is calculated. The physical remaining capacity at all distributed moments is integrated to deduce the time integral value of the distribution network energy storage system over the entire preset operating cycle. The sum of the integrals is divided by the total duration of the preset operating cycle to determine the average remaining capacity of the distribution network energy storage system within the preset operating cycle. This average remaining capacity is extracted as the expected remaining capacity, which characterizes the baseline energy reserve health level of the distribution network energy storage system under normal operating conditions throughout the entire timeframe. The preset operating cycle is typically set to 24 hours, strictly corresponding to a complete cycle of the aforementioned typical daily load curve of the distribution network, to ensure that the assessment of the expected remaining capacity fully covers the charging and discharging fluctuation characteristics of the power grid throughout the day.

[0034] The process then transitions to boundary scenario simulations for handling sudden power outages. The expected remaining capacity and the preset emergency support duration are extracted. A division operation is performed on the extracted parameters to calculate the mathematical ratio between the expected remaining capacity and the preset emergency support duration. The preset emergency support duration is not arbitrarily given; rather, it is determined by pre-retrieving the average physical time for line repair and restoration from the local distribution network's historical fault database, or by directly using the mandatory power supply guarantee time required for this type of load level in the State Grid Reliability Guidelines as a rigid reference. The calculated ratio represents the maximum allowable power output under the ideal energy storage capacity boundary, and this ratio is strictly set as the first power upper limit. The corresponding formula is as follows: In the formula, Indicates the first power limit. Indicates the expected remaining capacity. This indicates the preset emergency support duration.

[0035] Besides internal reserve capacity limitations, the underlying transmission physical architecture of the distribution network also presents objective obstacles and constraints to reverse power transmission. A power flow distribution simulation is performed on the distribution network topology data. Under the stringent grid safety conditions of ensuring that the voltage of any physical node in the distribution network does not exceed limits and that the current load of all transmission lines does not experience overheating or overload, the maximum node injection power that the current distribution network connection structure can tolerate is calculated. The maximum node injection power that meets both the underlying topology and power flow safety standards is directly defined as the second power upper limit. The corresponding formula is: In the formula, This indicates the second power limit. This represents the maximum node injection power that will not cause voltage overruns at distribution network nodes or line current overloads.

[0036] Once external environmental constraints are established, the hardware converters used in distribution network energy storage systems also possess insurmountable manufacturing specification limitations. The upper limit of the converter's rated power is extracted from the operational configuration parameters. This upper limit directly derives from the physical parameters on the hardware nameplates of the actual energy storage-supporting converters in the procurement list, constituting the absolute physical hardware boundary of the energy storage system's output power. A joint extreme value comparison mechanism is established, comparing the first power limit (representing the battery capacity reserve limit), the second power limit (representing the physical extreme value of the underlying power grid), and the converter's rated power limit (representing the extreme value of the hardware equipment) on the same dimension. The minimum value among these three power indicators is accurately captured, and this minimum value is taken as the maximum emergency support power of the distribution network energy storage system. The corresponding extreme value selection formula is as follows: In the formula, Indicates the maximum emergency support power. This indicates the upper limit of the rated power of the converter.

[0037] By coordinating daily peak-shaving dynamic correction parameters with multi-dimensional physical safety extreme value constraints, the uncontrollable impact of changes in time-series operating status on the underlying power reserves is eliminated. This ensures that the emergency support power output boundary in sudden power outage scenarios is strictly limited by the actual energy reserves, topology power flow safety, and hardware converter specifications, laying an absolutely rigorous physical constraint benchmark for subsequent reliable selection of load nodes to be restored and optimization of layout planning schemes.

[0038] Step S3: Using the preset alternative access nodes as the source nodes, calculate the electrical distance from the source nodes to each load node to be restored based on the distribution network topology data; in order of increasing electrical distance, starting from the load node to be restored with the smallest electrical distance, extract the longest continuous node sequence that satisfies the sum of the power supply requirements of the nodes being less than or equal to the maximum emergency support power, and form the target load node set by the load nodes to be restored contained in the longest continuous node sequence. In a preferred embodiment, using a preset candidate access node as the source node, the electrical distance from the source node to each load node to be restored is calculated based on the distribution network topology data, including: For each load node to be restored, a preset alternative access node is used as the source node, and the connection path from the source node to the current load node to be restored is determined based on the distribution network topology data. Obtain the line impedance corresponding to the connection path, and use the line impedance as the electrical distance from the source node to the current load node to be restored.

[0039] In a preferred embodiment, following the order of increasing electrical distance, starting from the load node with the smallest electrical distance to be restored, the longest continuous sequence of nodes whose sum of power supply requirements is less than or equal to the maximum emergency support power is selected. The target load node set is composed of the load nodes to be restored contained in the longest continuous node sequence, including: Based on the electrical distance, each load node to be restored is sorted in ascending order to generate a basic node sequence; Starting with the first load node to be restored in the basic node sequence, node sequences of different lengths are extracted sequentially according to the sorting order to obtain multiple candidate sequences; For each of the multiple candidate sequences, calculate the sum of the node power supply requirements of all load nodes to be restored within the current candidate sequence; Among the candidate sequences where the sum of node power demand is less than or equal to the maximum emergency support power, the candidate sequence containing the largest number of nodes with loads to be restored is selected as the longest continuous node sequence. All load nodes to be restored in the longest continuous node sequence are added to the target load node set.

[0040] Specifically, a set of pre-defined candidate access nodes are selected as source nodes. These candidate access nodes are network nodes selected through pre-screening via manual on-site surveys or by accessing geographic information system asset ledgers. These nodes possess sufficient physical area to house the energy storage battery compartment, and the connected main transformer has ample spare capacity. For each load node to be restored, using the pre-defined candidate access node as the source node, the objective physical connection path between the source node and the current load node to be restored is precisely located based on the distribution network topology data. The actual line impedance corresponding to the aforementioned physical connection path is then obtained. The distribution network node impedance correlation model maps physical line impedance to physical distance in the network space dimension; therefore, the extracted line impedance is directly used as the electrical distance from the source node to the current load node to be restored.

[0041] After determining the electrical distances of all load nodes to be restored, a global sorting of all distributed load nodes to be restored is performed according to a strict ascending order of electrical distance, thus generating a basic node sequence. Using the first load node to be restored in the established basic node sequence as a fixed starting point, segments with varying numbers of consecutive nodes are sequentially extracted along the pre-arranged ascending order, resulting in multiple candidate sequences of progressively increasing length. Priority is given to extending the extracted sequence towards the node with the smallest electrical distance, which physically represents prioritizing the coverage of load points with low line impedance and low voltage sag risk. To ensure the real connectivity of the extracted candidate sequences in the physical power grid space, a continuity topology verification mechanism is introduced when performing the sequential extraction of node sequences of different lengths according to the sorting order. The first load node to be restored in the basic node sequence is used as the initial probe anchor point to verify whether there is a direct physical feeder connecting the subsequent load nodes to be restored to the initial probe anchor point. If a direct physical feeder exists, the subsequent load nodes to be restored are directly incorporated into the current candidate sequence. If no directly connected physical feeder exists, and the current extension direction is determined to have reached the end of the physical network or encountered a line break, the interception action in the current direction is immediately stopped, and the set of nodes already absorbed is output as a separate and complete candidate sequence. The introduction of a continuity topology verification mechanism ensures that the purely mathematical sequence interception action strictly adheres to the actual physical wiring constraints of the underlying power grid, eliminating the generation of erroneous candidate schemes that provide power across physical breakpoints, and fully guaranteeing the absolute executability of subsequent site selection and capacity planning schemes.

[0042] For each independent candidate sequence among the identified multiple candidate sequences, the sum of the node power supply requirements of all load nodes to be restored within that current candidate sequence is calculated separately. The formula for summing node power supply requirements is as follows: In the formula, This represents the sum of the power supply requirements of all load nodes to be restored within a specific candidate sequence set. This represents the specific set of candidate sequences currently being measured. This refers to the independent load nodes to be restored included within a specific candidate sequence set. This represents the actual load power demand objectively required when an independent load node awaiting restoration experiences a power outage.

[0043] After calculating the sum of power supply demands for all nodes, the sum of power supply demands for each node is rigorously compared with the pre-calculated maximum emergency support power. Among all compliant candidate sequences where the sum of node power supply demands is less than or equal to the maximum emergency support power, the candidate sequence containing the largest number of load nodes to be restored is strictly selected and formally designated as the longest continuous node sequence. All load nodes to be restored contained in the longest continuous node sequence are completely incorporated into the target load node set. To address the extreme case of multiple parallel longest continuous node sequences with exactly the same number of load nodes to be restored, the sum of node power supply demands is introduced as a secondary decision criterion. For the aforementioned multiple parallel longest continuous node sequences, the sum of node power supply demands for each sequence is extracted. All extracted sums of node power supply demands are then re-sorted and compared in ascending order. A specific candidate sequence with the smallest sum of corresponding node power supply demands is selected. This specific candidate sequence is individually designated as the longest continuous node sequence with unique exclusivity. The introduction of a secondary decision-making mechanism based on the sum of node power demand not only completely eliminates the decision ambiguity caused by sequences of equal length, but also ensures at the objective physical level that, while restoring the same number of power outage nodes, the excessive consumption of the precious emergency reserve power of the distribution network energy storage system is minimized, so that the system's remaining power can redundantly cope with more unpredictable secondary grid failure events.

[0044] Relying on the rigorous screening mechanism based on static set filtering and mathematical extreme value conditions, the boundary ambiguity defects caused by dynamic cyclic judgment in the past have been completely eliminated. The operation of finding the emergency power supply range is completely transformed into a static sequence interception action with an absolutely unique solution. This effectively ensures that, under the dual constraints of the underlying energy limit and the grid topology safety boundary, the defined power outage restoration range can accurately reach the objective physical limit of the distribution network, realizing the ultimate development and utilization of the available emergency support power of the distribution network.

[0045] Step S4: Construct a line failure rate index based on the target load node set; construct a multi-point location and capacity optimization model with the optimization objective of minimizing the line failure rate index and the preset comprehensive investment cost; solve the multi-point location and capacity optimization model and output the location and capacity planning scheme of the distribution network energy storage system.

[0046] In a preferred embodiment, a line failure rate index is constructed based on the target load node set; a multi-point location and capacity optimization model is constructed with the goal of minimizing the line failure rate index and the preset comprehensive investment cost; the multi-point location and capacity optimization model is solved to output a location and capacity planning scheme for the distribution network energy storage system, including: The number of load nodes to be restored contained in the target load node set is counted as the total number of supported nodes; Calculate the difference between the preset total number of feeder nodes and the total number of supported nodes, and use the ratio of the difference to the total number of feeder nodes as the line failure rate indicator. Calculate the product of the line failure rate index and the preset reliability requirement coefficient, and establish a weighted objective function that includes the product and the preset comprehensive investment cost; With minimizing the weighted objective function as the optimization direction, and combined with the preset distribution network safety operation constraints, a multi-point location and capacity optimization model is constructed. The multi-point location and capacity optimization model is solved to obtain the target decision variables that minimize the weighted objective function. The access status, access power, and access capacity corresponding to the target decision variables are used as the location and capacity planning scheme for the distribution network energy storage system.

[0047] Specifically, a statistical count is performed on the load nodes to be restored within the defined target load node set. The integer values ​​greater than zero obtained from this count are defined as the total number of supported nodes. A predefined total number of feeder nodes is obtained, and a subtraction operation is performed to calculate the mathematical difference between this predefined total number of feeder nodes and the total number of supported nodes. This mathematical difference is then divided by the predefined total number of feeder nodes, and the resulting ratio is used as the line failure rate indicator. The calculation logic formula for the line failure rate indicator is expressed as follows: In the formula, This indicates the line failure rate. This indicates the preset total number of feeder nodes. This indicates the total number of supported nodes.

[0048] To measure the economic expenditure level of deploying multi-point distribution network energy storage devices, it is necessary to define the decision space of the multi-point location and capacity optimization model. The decision variable matrix involved in the multi-point location and capacity optimization model is defined to cover three dimensions of physical parameters. Specifically, these dimensions are represented by the Boolean matrix of whether candidate access nodes are connected to the distribution network energy storage device, the access power matrix of the distribution network energy storage device, and the access capacity matrix of the distribution network energy storage device. Based on the established decision variable matrix parameters, and combined with the preset fixed construction cost and purchase unit price, the preset comprehensive investment cost is calculated. The preset formula for measuring the comprehensive investment cost is as follows: In the formula, This represents the pre-set comprehensive investment cost. This represents the preset unit capacity cost. Represents the access capacity matrix. This represents the preset cost per unit of power. Represents the access power matrix. This represents the infrastructure cost of a single site. This represents the total number of actual constructed sites, calculated based on the access status Boolean matrix.

[0049] Establish a comprehensive weighted evaluation mechanism for the power supply reliability and economic investment expenditure of the distribution network. Obtain independently calculated line failure rate indicators and preset reliability requirement coefficients that reflect the degree of demand for power supply stability in local distribution networks. The preset reliability requirement coefficients are specifically set according to the social importance level of electricity users within the target load node set. When the supported load nodes include first-level loads such as hospitals or critical communication base stations, this coefficient will be assigned a larger weight value in advance.

[0050] The line failure rate index is multiplied by a preset reliability requirement coefficient, and the resulting product term, along with a preset comprehensive investment cost term, is used to construct a weighted objective function. The mathematical structure of the weighted objective function is defined as follows: In the formula, This represents the weighted objective function. This represents the preset reliability requirement coefficient.

[0051] In the optimization process, it is essential to strictly adhere to the underlying physical operating limits of the distribution network to avoid planning results deviating from actual operating conditions. Therefore, it is necessary to introduce pre-defined distribution network safety operation constraints. These pre-defined constraints cover both node voltage boundaries and line transmission power boundaries, and the corresponding objective constraint formulas are defined as follows: In the formula, This indicates the lower limit of the voltage at a distribution network node. This represents the actual operating voltage of any physical node in the distribution network. Indicates the upper limit of voltage at distribution network nodes. This represents the lower limit of the apparent power of a branch line in a distribution network. This indicates the actual apparent power of the distribution network branch lines. This indicates the upper limit of apparent power of a branch line in the distribution network.

[0052] After clarifying the multi-dimensional physical constraints, the optimization direction is to minimize the weighted objective function, and the pre-defined constraints on the safe operation of the distribution network are used as the feasible boundary of the solution space to construct a complete multi-point location and capacity stabilization optimization model. Since the weighted objective function exhibits highly nonlinear and discrete numerical characteristics, an intelligent optimization algorithm is used to perform a global search and solution operation on the multi-point location and capacity stabilization optimization model. Specifically, a genetic algorithm is used to mathematically encode the Boolean matrix of access state, the access power matrix, and the access capacity matrix involved in the multi-point location and capacity stabilization optimization model as chromosome genes. A fitness evaluation criterion is constructed using the weighted objective function, and continuous iteration is performed in the global solution space through selection, crossover, and mutation population evolution operations, effectively overcoming the technical bias of traditional gradient-based planning methods that are prone to getting trapped in local optima. The specific execution of the population evolution operation is as follows: an initial population containing multiple sets of random decision variables is generated based on the pre-established multi-point location and capacity stabilization optimization model. Each set of random decision variables is substituted into the weighted objective function to calculate the corresponding fitness value. The fitness calculation process strictly relies on a pre-constructed comprehensive weighted evaluation mechanism that considers the power supply reliability and economic investment expenditure of the distribution network. It comprehensively assesses the improvement in line fault defense capabilities and the overall capital consumption scale after connecting energy storage equipment to the distribution network. Decision variables with smaller fitness values ​​represent those closer to the global optimum, implying higher power outage resilience and lower equipment procurement costs. A roulette wheel selection strategy is used to select the best from the initial population, giving decision variables with smaller fitness values ​​a higher probability of retention. These high-quality decision variables are then placed in a mating pool. Within the mating pool, two sets of decision variables are randomly paired, and parameter genes at the same matrix coordinate position are exchanged according to a preset crossover probability, thus generating offspring decision variables with mixed characteristics. To prevent the algorithm from falling into local extrema traps, a mutation prevention mechanism is introduced, randomly flipping or numerically perturbing individual elements within the offspring decision variable matrix according to a preset mutation probability. After completing the selection, crossover, and mutation actions, a completely new offspring population is generated. The previous generation of the population is completely replaced by a completely new offspring population, and the fitness calculation and iterative evolution operation are restarted until the number of iterations reaches the pre-set maximum generation threshold, or the optimal fitness value within the population remains stable for several consecutive generations without showing a decreasing convergence trend. The group of individuals with the smallest fitness value within the final generation that meets the stopping condition is extracted, and this extracted group of individuals is officially recognized as the global optimal solution. Relying on the above-mentioned parallel collaborative search mechanism that highly simulates biological evolution, even in the face of a huge distribution network planning optimization space containing massive discrete variables and nonlinear physical constraints, it can still efficiently and robustly lock in the optimal site and capacity blueprint that balances grid resilience and financial budget.

[0053] After multiple rounds of iterative updates and calculations, the target decision variables that minimize the weighted objective function value globally are obtained. From the solved target decision variables, the optimal access state, optimal access power, and optimal access capacity are extracted separately, and the extracted operating parameter combinations are formally defined as the site selection and capacity planning scheme for the distribution network energy storage system.

[0054] Based on the established line fault rate quantitative calculation mechanism and the collaborative optimization logic subject to multiple physical constraints, the entire engineering planning link, from the underlying voltage line safety limit and power outage risk quantitative assessment to the economic cost accounting of power plant construction, has been completely opened up. This ensures that the final output site selection and capacity planning scheme can achieve a global optimal balance between local power outage defense effectiveness and overall construction investment expenditure under the premise of strictly ensuring the safety of the distribution network architecture and not exceeding the limits.

[0055] Based on the above method embodiments, the present invention provides corresponding apparatus embodiments.

[0056] like Figure 2 As shown, an embodiment of the present invention provides a site selection and capacity planning device for a distribution network energy storage system, including: a data acquisition module, a supporting power determination module, a load set delineation module, and a site selection and capacity optimization module; The data acquisition module is used to acquire typical daily load curves of the distribution network, distribution network topology data, and the node power supply requirements of at least one load node to be restored under the fault operation scenario of the distribution network. The supporting power determination module is used to calculate the peak-shaving capacity of the distribution network energy storage system based on the typical daily load curve of the distribution network and the preset peak-shaving index; generate the charging and discharging control curve of the distribution network energy storage system based on the peak-shaving capacity and the typical daily load curve of the distribution network; calculate the expected remaining capacity of the distribution network energy storage system within a preset operating cycle based on the charging and discharging control curve; calculate the first power limit based on the expected remaining capacity and the preset emergency support duration; perform power flow calculation on the distribution network topology data to generate the second power limit; and select the minimum value among the first power limit, the second power limit, and the preset converter rated power limit as the maximum emergency support power of the distribution network energy storage system. The load set delineation module is used to calculate the electrical distance from the source node to each load node to be restored based on the distribution network topology data, using the preset alternative access nodes as source nodes; and to extract the longest continuous node sequence that satisfies the sum of the power supply requirements of the nodes being less than or equal to the maximum emergency support power, starting from the load node to be restored with the smallest electrical distance in ascending order, and the target load node set is composed of the load nodes to be restored contained in the longest continuous node sequence. The site selection and capacity optimization module is used to construct a line failure rate index based on the target load node set; construct a multi-point site selection and capacity optimization model with the goal of minimizing the line failure rate index and the preset comprehensive investment cost; solve the multi-point site selection and capacity optimization model, and output the site selection and capacity planning scheme of the distribution network energy storage system.

[0057] In a preferred embodiment, the supporting power determination module calculates the peak-shaving capacity of the distribution network energy storage system based on the typical daily load curve of the distribution network and a preset peak-shaving index, including: Determine the maximum peak-to-valley difference of the distribution network before peak shaving based on the typical daily load curve of the distribution network. The difference between the preset baseline value and the preset peak-shaving index is calculated to obtain the peak-shaving coefficient. The product of the peak-shaving coefficient and the maximum peak-valley difference is used as the peak-shaving capacity of the distribution network energy storage system.

[0058] In a preferred embodiment, the supporting power determination module generates a charge / discharge control curve for the distribution network energy storage system based on the peak-shaving capacity and the typical daily load curve of the distribution network; and calculates the expected remaining capacity of the distribution network energy storage system within a preset operating cycle based on the charge / discharge control curve, including: Based on the typical daily load curve and peak-shaving capacity of the distribution network, determine the charging and discharging time periods of the distribution network energy storage system; Based on the charging and discharging time periods, the charging and discharging control curves of the distribution network energy storage system are generated; Based on the charge and discharge control curve, determine the remaining capacity of the distribution network energy storage system at each moment within the preset operating cycle; Based on the remaining capacity at each time point, the average remaining capacity of the distribution network energy storage system within a preset operating cycle is determined as the expected remaining capacity.

[0059] In a preferred embodiment, the support power determination module calculates a first power limit based on the expected remaining capacity and a preset emergency support duration; and performs power flow calculations on the distribution network topology data to generate a second power limit, including: Calculate the ratio of the expected remaining capacity to the preset emergency support duration, and use the ratio as the first power limit; Perform power flow calculations on the distribution network topology data to determine the maximum node injection power that will not cause voltage overruns at distribution network nodes or line current overloads. The maximum node injection power is used as the second power upper limit.

[0060] In a preferred embodiment, the load set delineation module, using preset candidate access nodes as source nodes, calculates the electrical distance from the source nodes to each load node to be restored based on the distribution network topology data, including: For each load node to be restored, a preset alternative access node is used as the source node, and the connection path from the source node to the current load node to be restored is determined based on the distribution network topology data. Obtain the line impedance corresponding to the connection path, and use the line impedance as the electrical distance from the source node to the current load node to be restored.

[0061] In a preferred embodiment, the load set delineation module, following the order of increasing electrical distance, starts from the load node with the smallest electrical distance to be restored, and extracts the longest continuous sequence of nodes whose sum of power supply requirements for the nodes is less than or equal to the maximum emergency support power. The target load node set is composed of the load nodes to be restored contained in the longest continuous node sequence, including: Based on the electrical distance, each load node to be restored is sorted in ascending order to generate a basic node sequence; Starting with the first load node to be restored in the basic node sequence, node sequences of different lengths are extracted sequentially according to the sorting order to obtain multiple candidate sequences; For each of the multiple candidate sequences, calculate the sum of the node power supply requirements of all load nodes to be restored within the current candidate sequence; Among the candidate sequences where the sum of node power demand is less than or equal to the maximum emergency support power, the candidate sequence containing the largest number of nodes with loads to be restored is selected as the longest continuous node sequence. All load nodes to be restored in the longest continuous node sequence are added to the target load node set.

[0062] In a preferred embodiment, the location and capacity optimization module constructs a line failure rate index based on the target load node set; constructs a multi-point location and capacity optimization model with the optimization objective of minimizing the line failure rate index and the preset comprehensive investment cost; solves the multi-point location and capacity optimization model, and outputs a location and capacity planning scheme for the distribution network energy storage system, including: The number of load nodes to be restored contained in the target load node set is taken as the total number of nodes supported. Calculate the difference between the preset total number of feeder nodes and the total number of supported nodes, and use the ratio of the difference to the total number of feeder nodes as the line failure rate indicator. Calculate the product of the line failure rate index and the preset reliability requirement coefficient, and establish a weighted objective function that includes the product and the preset comprehensive investment cost; With minimizing the weighted objective function as the optimization direction, and combined with the preset distribution network safety operation constraints, a multi-point location and capacity optimization model is constructed. The multi-point location and capacity optimization model is solved to obtain the target decision variables that minimize the weighted objective function. The access status, access power, and access capacity corresponding to the target decision variables are used as the location and capacity planning scheme for the distribution network energy storage system.

[0063] It should be noted that the embodiments of the device described above correspond to the embodiments of the present invention described above, and can realize the site selection and capacity planning method for distribution network energy storage systems described above in any one of the present invention. Furthermore, the embodiments of the device described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between modules indicates that they have a communication connection, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without creative effort.

[0064] Based on the above-described method embodiments of the present invention, a corresponding embodiment of an electronic device is provided.

[0065] An embodiment of the present invention provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the site selection and capacity planning method for energy storage systems in power distribution networks according to any one of the present invention, or, when the processor executes the computer program, it implements the functions of each module in the above-described device embodiments.

[0066] For example, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the terminal device.

[0067] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0068] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0069] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, applications required for at least one function, etc.; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital card (SD card), flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0070] Based on the above method embodiments, the present invention provides corresponding storage medium embodiments; Another embodiment of the present invention provides a storage medium including a stored computer program, wherein, when the computer program is running, it controls the device where the storage medium is located to execute any of the above-described distribution network energy storage system site selection and capacity planning methods of the present invention.

[0071] The aforementioned storage medium is a computer-readable storage medium, and the computer program includes computer program code, which may be in the form of source code, object code, executable file, or certain intermediate forms. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0072] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0073] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for site selection and capacity planning of energy storage systems in power distribution networks, characterized in that, include: Obtain typical daily load curves of the distribution network, distribution network topology data, and the node power supply requirements of at least one load node to be restored under the fault operation scenario of the distribution network; Based on the typical daily load curve of the distribution network and the preset peak-shaving index, calculate the peak-shaving capacity of the distribution network energy storage system; based on the peak-shaving capacity and the typical daily load curve of the distribution network, generate the charging and discharging control curve of the distribution network energy storage system; based on the charging and discharging control curve, calculate the expected remaining capacity of the distribution network energy storage system within the preset operating cycle. Calculate the first power limit based on the expected remaining capacity and the preset emergency support duration; Perform power flow calculations on the distribution network topology data to generate a second power ceiling; The minimum value among the first power limit, the second power limit, and the preset converter rated power limit is selected as the maximum emergency support power of the distribution network energy storage system. Using the preset alternative access nodes as the source nodes, the electrical distance from the source nodes to each load node to be restored is calculated based on the distribution network topology data; Following the order of increasing electrical distance, starting from the load node to be restored with the smallest electrical distance, the longest continuous sequence of nodes that meets the sum of the power supply requirements of the nodes less than or equal to the maximum emergency support power is selected, and the target load node set is formed by the load nodes to be restored contained in the longest continuous sequence of nodes. A line failure rate index is constructed based on the target load node set; a multi-point location and capacity optimization model is constructed with the goal of minimizing the line failure rate index and the preset comprehensive investment cost. Solve the multi-point location and capacity optimization model to output the location and capacity planning scheme of the distribution network energy storage system.

2. The method for site selection and capacity planning of distribution network energy storage systems as described in claim 1, characterized in that, Based on the typical daily load curve of the distribution network and the preset peak-shaving index, calculate the peak-shaving capacity of the distribution network energy storage system, including: Determine the maximum peak-to-valley difference of the distribution network before peak shaving based on the typical daily load curve of the distribution network. The difference between the preset baseline value and the preset peak-shaving index is calculated to obtain the peak-shaving coefficient. The product of the peak-shaving coefficient and the maximum peak-valley difference is used as the peak-shaving capacity of the distribution network energy storage system.

3. The method for site selection and capacity planning of distribution network energy storage systems as described in claim 2, characterized in that, Based on peak-shaving capacity and typical daily load curves of the distribution network, charge-discharge control curves for the distribution network energy storage system are generated. Based on these control curves, the expected remaining capacity of the distribution network energy storage system within a preset operating cycle is calculated, including: Based on the typical daily load curve and peak-shaving capacity of the distribution network, determine the charging and discharging time periods of the distribution network energy storage system; Based on the charging and discharging time periods, the charging and discharging control curves of the distribution network energy storage system are generated; Based on the charge and discharge control curve, determine the remaining capacity of the distribution network energy storage system at each moment within the preset operating cycle; Based on the remaining capacity at each time point, the average remaining capacity of the distribution network energy storage system within a preset operating cycle is determined as the expected remaining capacity.

4. The method for site selection and capacity planning of distribution network energy storage systems as described in claim 3, characterized in that, Calculate the first power limit based on the expected remaining capacity and the preset emergency support duration; Power flow calculations are performed on the distribution network topology data to generate a second power ceiling, including: Calculate the ratio of the expected remaining capacity to the preset emergency support duration, and use the ratio as the first power limit; Perform power flow calculations on the distribution network topology data to determine the maximum node injection power that will not cause voltage overruns at distribution network nodes or line current overloads. The maximum node injection power is used as the second power upper limit.

5. The method for site selection and capacity planning of distribution network energy storage systems as described in claim 4, characterized in that, Using preset alternative access nodes as source nodes, the electrical distances from the source nodes to each load node to be restored are calculated based on the distribution network topology data, including: For each load node to be restored, a preset alternative access node is used as the source node, and the connection path from the source node to the current load node to be restored is determined based on the distribution network topology data. Obtain the line impedance corresponding to the connection path, and use the line impedance as the electrical distance from the source node to the current load node to be restored.

6. The method for site selection and capacity planning of distribution network energy storage systems as described in claim 5, characterized in that, Following the order of increasing electrical distance, starting with the load node to be restored with the smallest electrical distance, the longest continuous sequence of nodes whose sum of power supply requirements is less than or equal to the maximum emergency support power is selected. The target load node set is composed of the load nodes to be restored contained in the longest continuous node sequence, including: Based on the electrical distance, each load node to be restored is sorted in ascending order to generate a basic node sequence; Starting with the first load node to be restored in the basic node sequence, node sequences of different lengths are extracted sequentially according to the sorting order to obtain multiple candidate sequences; For each of the multiple candidate sequences, calculate the sum of the node power supply requirements of all load nodes to be restored within the current candidate sequence; Among the candidate sequences where the sum of node power demand is less than or equal to the maximum emergency support power, the candidate sequence containing the largest number of nodes with loads to be restored is selected as the longest continuous node sequence. All load nodes to be restored in the longest continuous node sequence are added to the target load node set.

7. The method for site selection and capacity planning of distribution network energy storage systems as described in claim 6, characterized in that, A line failure rate index is constructed based on the target load node set; a multi-point location and capacity optimization model is constructed with the goal of minimizing the line failure rate index and the preset comprehensive investment cost. Solve the multi-site location and capacity optimization model to output the location and capacity planning scheme for the distribution network energy storage system, including: The number of load nodes to be restored contained in the target load node set is counted as the total number of supported nodes; Calculate the difference between the preset total number of feeder nodes and the total number of supported nodes, and use the ratio of the difference to the total number of feeder nodes as the line failure rate indicator. Calculate the product of the line failure rate index and the preset reliability requirement coefficient, and establish a weighted objective function that includes the product and the preset comprehensive investment cost; With minimizing the weighted objective function as the optimization direction, and combined with the preset distribution network safety operation constraints, a multi-point location and capacity optimization model is constructed. The multi-point location and capacity optimization model is solved to obtain the target decision variables that minimize the weighted objective function. The access status, access power, and access capacity corresponding to the target decision variables are used as the location and capacity planning scheme for the distribution network energy storage system.

8. A site selection and capacity planning device for a power distribution network energy storage system, characterized in that, include: The module includes a data acquisition module, a support power determination module, a load set delineation module, and a location and capacity optimization module. The data acquisition module is used to acquire typical daily load curves of the distribution network, distribution network topology data, and the node power supply requirements of at least one load node to be restored under the fault operation scenario of the distribution network. The supporting power determination module is used to calculate the peak-shaving capacity of the distribution network energy storage system based on the typical daily load curve of the distribution network and the preset peak-shaving index; generate the charging and discharging control curve of the distribution network energy storage system based on the peak-shaving capacity and the typical daily load curve of the distribution network; calculate the expected remaining capacity of the distribution network energy storage system within the preset operating cycle based on the charging and discharging control curve; and calculate the first power upper limit based on the expected remaining capacity and the preset emergency support duration. Perform power flow calculations on the distribution network topology data to generate a second power ceiling; The minimum value among the first power limit, the second power limit, and the preset converter rated power limit is selected as the maximum emergency support power of the distribution network energy storage system. The load set delineation module is used to calculate the electrical distance from the source node to each load node to be restored based on the distribution network topology data, using the preset alternative access nodes as source nodes; and to extract the longest continuous node sequence that satisfies the sum of the power supply requirements of the nodes being less than or equal to the maximum emergency support power, starting from the load node to be restored with the smallest electrical distance in ascending order, and the target load node set is composed of the load nodes to be restored contained in the longest continuous node sequence. The site selection and capacity optimization module is used to construct a line failure rate index based on the target load node set; construct a multi-point site selection and capacity optimization model with the goal of minimizing the line failure rate index and the preset comprehensive investment cost; solve the multi-point site selection and capacity optimization model, and output the site selection and capacity planning scheme of the distribution network energy storage system.

9. An electronic device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the distribution network energy storage system location and capacity planning method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium includes a stored computer program, wherein, when the computer program is running, it controls the device where the storage medium is located to execute the distribution network energy storage system site selection and capacity planning method as described in any one of claims 1 to 7.

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