Energy storage optimization configuration method for improving power distribution network source load bearing capacity

Abstract

The invention discloses an energy storage optimization configuration method for improving the power distribution network source load bearing capacity, and belongs to the technical field of power distribution networks, and the method comprises the steps: collecting the operation multi-source data of a power distribution network, and carrying out the preprocessing and integration, and forming the operation feature data of the power distribution network; constructing a power distribution network energy storage optimization configuration model to analyze the operation characteristic data of the power distribution network, optimizing an energy storage operation mode, and solving an optimal energy storage configuration scheme; and verifying and evaluating the energy storage configuration scheme, generating an energy storage configuration verification and evaluation report, and performing energy storage configuration of the power distribution network according to the energy storage configuration verification and evaluation report, thereby maximally utilizing the resources of the power distribution network. According to the method, the problems that energy storage optimization configuration cannot be well carried out, the power distribution network source load bearing capacity cannot be improved, and the stability and economical efficiency of the power distribution network are reduced in the prior art are solved. According to the method, energy storage optimization configuration can be well carried out, the power distribution network source load bearing capacity can be improved, challenges brought by new energy access can be effectively relieved, and the stability and economical efficiency of the power distribution network are improved.

Description

Technical Field

[0001] This invention relates to the field of power distribution network technology, specifically to an energy storage optimization configuration method for improving the load-carrying capacity of power distribution networks. Background Technology

[0002] A distribution network is a power grid that receives electrical energy from the transmission network or regional power plants and distributes it locally or in stages according to voltage to various users through distribution facilities. It is a network composed of overhead lines, cables, poles, distribution transformers, disconnect switches, reactive power compensators and some auxiliary facilities, and plays an important role in distributing electrical energy in the power grid.

[0003] In particular, due to the mismatch between distributed generation and load in time and space, the distribution network cannot support more distributed generation and load without upgrading or with minimal upgrades to grid equipment. Specifically, this manifests as follows: 1) Photovoltaic power generation is high during the midday peak, but the load may not be high, leading to reverse power transmission and voltage exceeding limits. At the same time, photovoltaic power generation is not available during the evening peak, and power supply is required from the upstream grid, which may cause line overload. 2) A certain region has abundant photovoltaic resources, but the local consumption capacity is insufficient, and the electricity needs to be transmitted to a distant place, which leads to overload of certain lines or transformers; 3) The intermittency and volatility of distributed power sources can cause voltage fluctuations, flicker, and even frequency deviations.

[0004] Therefore, optimizing the configuration of energy storage to improve the load-carrying capacity of the distribution network is particularly important.

[0005] Existing technologies cannot effectively optimize energy storage configuration and improve the load-carrying capacity of the distribution network, nor can they effectively alleviate the challenges brought by the access of new energy sources, thus reducing the stability and economy of the distribution network. Summary of the Invention

[0006] The purpose of this invention is to provide an energy storage optimization configuration method to improve the load-carrying capacity of the distribution network. This method can optimize energy storage configuration and improve the load-carrying capacity of the distribution network, effectively alleviate the challenges brought by the access of new energy sources, improve the stability and economy of the distribution network, and solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: Methods for optimizing energy storage configuration to improve the load-carrying capacity of distribution networks include: The system collects multi-source data on power distribution network operation based on intelligent acquisition terminals, and preprocesses and integrates the collected multi-source data to form characteristic data of power distribution network operation. An optimal energy storage configuration model for the distribution network is constructed to analyze the operation characteristics data of the distribution network, optimize the operation mode of energy storage, and solve for the optimal energy storage configuration scheme. Verify and evaluate the energy storage configuration scheme, generate an energy storage configuration verification and evaluation report, and configure energy storage in the distribution network according to the energy storage configuration verification and evaluation report to enable the coordinated operation of source, grid, load and storage, maximize the utilization of distribution network resources and enable it to carry more distributed power sources and loads.

[0008] Preferably, based on the intelligent acquisition terminal, multi-source data of the power distribution network operation are collected, and the following operations are performed: Based on the distributed power source monitoring equipment, the real-time output of distributed photovoltaic and wind power is sensed, and power data during the operation of the distribution network is obtained. Based on the SCADA system, real-time voltage, current and power flow of substations, switching stations and lines are sensed to obtain power grid data during the operation of the distribution network; Based on the electricity consumption information collection equipment, the user side's detailed load data is sensed to obtain load data during the operation of the distribution network; Based on the energy storage information acquisition equipment, the power and health status of energy storage devices are sensed to obtain energy storage data during the operation of the distribution network; Based on power source data, grid data, load data, and energy storage data during the operation of the distribution network, multi-source data for distribution network operation is generated.

[0009] Preferably, the collected multi-source data on power distribution network operation is preprocessed, and the following operations are performed: Based on Python, the system cleans multi-source data of power distribution network operation and identifies missing and outlier values ​​in the multi-source data. Missing values ​​are data gaps caused by equipment failure or communication interruption during the data acquisition process, while outlier values ​​are data points that deviate significantly from the normal range or pattern, including load data with negative power values ​​and power distribution network data with voltage as high as 100kV. The missing and outlier values ​​in the multi-source data of the distribution network operation are evaluated to determine whether the missing and outlier values ​​are valuable for the energy storage optimization configuration to improve the source-load carrying capacity of the distribution network. When missing values ​​and outliers are valuable for optimizing the energy storage configuration to improve the load-carrying capacity of the distribution network, the mean, median or mode are used to fill the missing values, and the average or median of the data before and after the time interval is used to replace the outliers. If missing values ​​and outliers are of no value to the optimized configuration of energy storage to improve the load-carrying capacity of the distribution network, then missing values ​​and outliers in the multi-source data of the distribution network operation will be removed.

[0010] Preferably, the preprocessed multi-source data of the distribution network operation are integrated, and the following operations are performed: Aligning multi-source data of distribution network operation based on timestamps unifies the timestamps of data from different sources in the multi-source data of distribution network operation to the same time zone and the same format, removes the dimensional differences between multi-source data of distribution network operation, and forms standardized multi-source data of distribution network operation. ETL tools are used to integrate multi-source data of distribution network operation, making the multi-source data of distribution network operation interconnected and integrated into a unified data view, forming a unified and interconnected data view. Feature extraction is performed on the multi-source data of distribution network operation, and feature vectors related to energy storage optimization configuration are extracted from the multi-source data of distribution network operation to determine the characteristic data of distribution network operation, and the storage management of the characteristic data of distribution network operation is performed.

[0011] Preferably, an optimal configuration model for energy storage in the distribution network is constructed, and the following operations are performed: Collect historical data on the operation of the distribution network and divide the collected historical data into training and test sets in an 8:2 ratio. The mathematical model is trained using a training set, enabling it to learn the optimal configuration behavior of energy storage in the distribution network autonomously. By optimizing the operation mode of energy storage, an energy storage configuration scheme is formulated, and the optimal configuration model of energy storage in the distribution network is determined. The distribution network energy storage optimization configuration model is tested using a test set to evaluate its generalization performance and determine whether it can optimize the energy storage operation mode and formulate an energy storage configuration scheme. The model test evaluation results are then determined. Based on the model test and evaluation results, the parameters of the distribution network energy storage optimization configuration model are adjusted, and after continuous iterative optimization, the optimal distribution network energy storage optimization configuration model is formed.

[0012] Preferably, the operation characteristics data of the distribution network are analyzed to optimize the energy storage operation mode, solve for the optimal energy storage configuration scheme, and perform the following operations: The distribution network operation characteristic data is input into the distribution network energy storage optimization configuration model. The distribution network operation characteristic data is analyzed based on the distribution network energy storage optimization configuration model. Multi-objective optimization is carried out with economic and technical objectives, and the energy storage operation mode is optimized under the constraints of grid safety operation and energy storage status itself. The optimal energy storage configuration scheme is solved, including energy storage location, power, capacity and operation mode. Among them, energy storage location refers to the installation location of the energy storage device in the distribution network node; energy storage power refers to the rated charging and discharging power of the energy storage device; energy storage capacity refers to the rated energy storage capacity of the energy storage device; and energy storage operation mode refers to the charging and discharging scheme of the energy storage device at different time periods.

[0013] Preferably, the energy storage configuration scheme is verified and evaluated, an energy storage configuration verification and evaluation report is generated, and the following operations are performed: Digital simulation verification of the energy storage configuration scheme is carried out to verify its technical feasibility, simulate its operation in the actual power grid, and conduct a comprehensive benefit assessment of the energy storage configuration scheme to evaluate its economic rationality and determine the return on investment of the project. The comprehensive benefit assessment indicators include: technical benefits, economic benefits and social benefits. During digital simulation verification, steady-state simulation verification is performed on the energy storage configuration scheme. Power system analysis software is used to verify whether the energy storage configuration scheme can solve the problem of improving the source load carrying capacity of the distribution network under normal and long-term operation. After configuring energy storage, it is verified whether the voltage of each node of the grid is within the qualified range under all typical operating scenarios, whether the overload problem is solved, whether the equipment load rate is restored to within the safe limit, and the impact of the energy storage configuration scheme on network losses is evaluated. It is also calculated whether the photovoltaic and wind power power reduction is significantly reduced after configuring energy storage, and whether the absorption rate reaches the target. After the energy storage configuration scheme is assessed for technical feasibility and comprehensive benefits, an energy storage configuration verification and evaluation report is generated and presented in a visual format.

[0014] Preferably, the distribution network energy storage configuration is carried out according to the energy storage configuration verification and evaluation report, and the following operations are performed: After the energy storage configuration verification and evaluation report shows that the energy storage configuration scheme can meet the needs of improving the source load carrying capacity of the distribution network, the energy storage configuration of the distribution network is carried out according to the energy storage configuration scheme, and the daily and hourly charging and discharging plans of the energy storage equipment are formulated. Among them, peak shaving and valley filling are based on electricity prices. When distributed power generation occurs at noon, the energy storage device is charged, and when the load peak occurs in the evening, the energy storage device is discharged, transferring energy from the redundant period to the scarce period and smoothing the net load curve. Among them, energy storage devices are configured in areas rich in distributed power sources. These devices can absorb excess power locally, reduce the impact of through power on the main power grid, and automatically absorb or generate reactive or active power to support the grid when voltage exceeds the limit. This maximizes the utilization of distribution network resources and enables it to carry more distributed power sources and loads.

[0015] Preferred, the energy storage optimization configuration method for improving the source-load carrying capacity of the distribution network also includes real-time evaluation of the source-load coordination status and dynamic adjustment of the energy storage operation strategy during the operation of the distribution network, in order to cope with the random fluctuations in the output of distributed power sources and load demand, and to ensure the stability and carrying capacity of the distribution network are optimized. Specifically, it includes the following steps: Data verification algorithms are used to verify power supply data, grid data, load data, and energy storage data to form a real-time data stream. Among them, power supply data refers to the real-time output value of distributed photovoltaic and wind power, grid data refers to the real-time voltage, current, power flow and system frequency values ​​of substations, switching stations and lines, load data refers to the detailed load power value on the user side, and energy storage data refers to the real-time power, available energy and health status value of energy storage devices. Based on real-time data streams, a dynamic source-load coordination index for the distribution network is calculated to quantify the source-load balance and operational stability of the distribution network at time t. The calculation formula is as follows: DSCI(t)=[ωV×VI(t)+ωF×FI(t)+ωL×LI(t)+ωE×EI(t)+ωG×GI(t)] / (ωV+ωF+ωL+ωE+ωG) Wherein, DSCI(t) represents the dynamic source-load coordination index at time t, a dimensionless parameter ranging from 0 to 1. A higher value indicates better source-load coordination, stability, and carrying capacity of the distribution network. ωV, ωF, ωL, ωE, and ωG are the weighting coefficients of the voltage index, frequency index, load index, energy storage index, and power output index, respectively. These weighting coefficients are set through historical data training or expert experience, ranging from 0 to 1, and satisfying ωV+ωF+ωL+ωE+ωG>0. VI(t) represents the voltage index at time t, calculated as VI(t)=1-|Vavg(t)-V nom| / Vnom, where Vavg(t) is the average node voltage of the distribution network at time t, in kilovolts. This data comes directly from voltage measurements in the power grid data. Vnom is the rated voltage of the distribution network, in kilovolts, and is a preset constant. VI(t) is a dimensionless parameter reflecting the degree of voltage deviation from the rated value. FI(t) represents the frequency exponent at time t, calculated as FI(t) = 1 - |f(t) - fnom| / fnom, where f(t) is the system frequency of the distribution network at time t, in Hertz. This data comes directly from frequency measurements in the power grid data. m is the rated frequency of the distribution network, measured in Hertz, and is a preset constant. FI(t) is a dimensionless parameter reflecting the degree to which the frequency deviates from the rated value. LI(t) represents the load index at time t, calculated as LI(t) = 1 - Lratio(t), where Lratio(t) is the overall load factor of the distribution network at time t, calculated as Lratio(t) = Pload(t) / Pcapacity, where Pload(t) is the total load power of the distribution network at time t, measured in kilowatts, which is directly derived from the load power value in the load data. Pcapacity... The total capacity of the distribution network is expressed in kilowatts (kW) and is a preset constant. Lratio(t) and LI(t) are both dimensionless parameters that reflect the load level relative to the capacity. EI(t) represents the energy storage index at time t, calculated as EI(t) = Eavail(t) / Erated. Eavail(t) is the available energy value of energy storage at time t, expressed in kilowatt-hours (kWh). This data comes directly from the available energy value in the energy storage data. Erated is the rated capacity value of energy storage, expressed in kilowatt-hours (kWh) and is a preset constant. EI(t) is a dimensionless parameter that reflects the energy reserve level of energy storage.GI(t) represents the power output index at time t, and is calculated by the formula GI(t) = Pdg(t) / Pdgmax, where Pdg(t) is the total output value of distributed power sources in the distribution network at time t, in kilowatts. This data comes directly from the distributed photovoltaic and wind power output values ​​in the power source data. Pdgmax is the maximum possible output value of distributed power sources, in kilowatts, which is a preset constant. GI(t) is a dimensionless parameter that reflects the output and utilization of distributed power sources. After calculating DSCI(t), it is compared with the preset source-load coordination threshold DSCIthreshold; where the source-load coordination threshold is set according to the distribution network safety operation standard, and the value is between 0.75 and 0.95. When DSCI(t) is lower than DSCIthreshold, it indicates insufficient coordination between the power supply and load in the distribution network, posing risks of overload, voltage exceeding limits, or power waste. At this time, the dynamic power supply and load coordination assessment and energy storage adjustment module automatically triggers the adjustment of the energy storage operation strategy: First, based on the deviation of DSCI(t), the energy storage power adjustment amount ΔPess(t) = k × (DSCIthreshold - DSCI(t)) × Pessrated is calculated, where k is the adjustment coefficient, a dimensionless constant determined through simulation optimization, Pessrated is the rated power of the energy storage in kilowatts, and ΔPess(t) is the energy storage power adjustment value in kilowatts. A positive value indicates an increase in discharge power to support the load, while a negative value indicates an increase in charging power to absorb excess power. At the same time, the charging and discharging time window of the energy storage is optimized, with intervention prioritized during periods when DSCI(t) is lower. When DSCI(t) is higher than or equal to DSCIthreshold, it indicates that the distribution network is operating well and the existing energy storage operation strategy remains unchanged. The adjusted energy storage operation strategy is sent to the energy storage device in real time through the distribution network energy management system, and the operation mode parameters in the energy storage configuration plan are updated.

[0016] Preferred energy storage optimization configuration method for improving the load-carrying capacity of distribution networks also includes optimizing the operating efficiency of distribution networks through multi-timescale energy storage scheduling, solving the problems of insufficient utilization of energy storage resources and high operating costs of distribution networks caused by single-timescale scheduling, specifically including the following steps: Based on distribution network operation characteristic data, a multi-timescale energy storage dispatch framework is constructed, including long-cycle dispatch, medium-cycle dispatch, and short-cycle dispatch. In long-cycle dispatch, daily charging and discharging plans for energy storage devices are formulated based on predicted daily load curves and renewable energy output curves, optimizing energy allocation during peak and off-peak periods and reducing distribution network operating costs. In medium-cycle dispatch, the charging and discharging power of energy storage devices is adjusted according to hourly load and renewable energy output fluctuations, smoothing the hourly net load curve and reducing the risk of distribution network overload. In short-cycle dispatch, the power output of energy storage devices is rapidly adjusted to address minute-level load mutations or voltage fluctuations, providing real-time support for the stable operation of the distribution network. By employing a multi-timescale energy storage dispatch framework, long-, medium-, and short-cycle dispatch strategies are coordinated to ensure that the operating status of energy storage devices at different time scales is highly matched with the actual needs of the distribution network. A priority mechanism is used to determine the execution order of dispatch at each time scale: short-cycle dispatch prioritizes responding to emergency loads or voltage fluctuations, medium-cycle dispatch prioritizes optimizing hourly operating efficiency, and long-cycle dispatch prioritizes overall economic efficiency. By coordinating dispatch objectives at different time scales, the utilization efficiency of energy storage resources is maximized, thereby enhancing the load-carrying capacity of the distribution network. During multi-timescale scheduling, the operating parameters of energy storage devices, including charging and discharging power, operating time, and scheduling priority, are dynamically adjusted based on real-time updates of multi-source data on distribution network operation. The power distribution network dispatching system distributes multi-timescale dispatching strategies to energy storage devices and monitors the operating status of the energy storage devices in real time to ensure the effectiveness of the dispatching strategies.

[0017] Compared with the prior art, the beneficial effects of the present invention are: This invention collects multi-source data on distribution network operation through intelligent acquisition terminals, preprocesses and integrates the collected data to form distribution network operation characteristic data, analyzes the distribution network operation characteristic data through a distribution network energy storage optimization configuration model, performs multi-objective optimization with economic and technical objectives, and optimizes the energy storage operation mode under the constraints of grid safety operation and the energy storage's own state, solves the optimal energy storage configuration scheme, verifies and evaluates the energy storage configuration scheme, generates an energy storage configuration verification and evaluation report, and configures distribution network energy storage according to the energy storage configuration verification and evaluation report, so as to achieve coordinated operation of source, grid, load and storage, maximize the utilization of distribution network resources, enable it to carry more distributed power sources and loads, can better optimize energy storage configuration and improve the source and load carrying capacity of distribution network, effectively alleviate the challenges brought by new energy access, and improve the stability and economy of distribution network. Attached Figure Description

[0018] Figure 1This is a flowchart of the energy storage optimization configuration method for improving the load-carrying capacity of the distribution network according to the present invention. Detailed Implementation

[0019] 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.

[0020] To address the current limitations in optimizing energy storage configurations and improving the load-carrying capacity of distribution networks, which hinders the effective mitigation of challenges posed by renewable energy integration and reduces the stability and economic efficiency of distribution networks, please refer to [link to relevant documentation]. Figure 1 This embodiment provides the following technical solution: Methods for optimizing energy storage configuration to improve the load-carrying capacity of distribution networks include: The system collects multi-source data on power distribution network operation based on intelligent acquisition terminals, and preprocesses and integrates the collected multi-source data to form characteristic data of power distribution network operation.

[0021] Specifically, multi-source data on power distribution network operation are collected based on intelligent data acquisition terminals, including: Based on the distributed power source monitoring equipment, the real-time output of distributed photovoltaic and wind power is sensed, and power data during the operation of the distribution network is obtained. Based on the SCADA system, real-time voltage, current and power flow of substations, switching stations and lines are sensed to obtain power grid data during the operation of the distribution network; Based on the electricity consumption information collection equipment, the user side's detailed load data is sensed to obtain load data during the operation of the distribution network; Based on the energy storage information acquisition equipment, the power and health status of energy storage devices are sensed to obtain energy storage data during the operation of the distribution network; Based on power source data, grid data, load data, and energy storage data during the operation of the distribution network, multi-source data for distribution network operation is generated.

[0022] Specifically, the collected multi-source data on power distribution network operation is preprocessed, including: Based on Python, the system cleans multi-source data of power distribution network operation and identifies missing and outlier values ​​in the multi-source data. Missing values ​​are data gaps caused by equipment failure or communication interruption during the data acquisition process, while outlier values ​​are data points that deviate significantly from the normal range or pattern, including load data with negative power and power distribution network data with voltage as high as 100kV. The missing and outlier values ​​in the multi-source data of the distribution network operation are evaluated to determine whether the missing and outlier values ​​are valuable for the energy storage optimization configuration to improve the source-load carrying capacity of the distribution network. When missing values ​​and outliers are valuable for optimizing the energy storage configuration to improve the load-carrying capacity of the distribution network, the mean, median or mode are used to fill the missing values, and the average or median of the data before and after the time point is used to replace the outliers. If missing values ​​and outliers are of no value to the optimized configuration of energy storage to improve the load-carrying capacity of the distribution network, then missing values ​​and outliers in the multi-source data of the distribution network operation will be removed.

[0023] Specifically, the preprocessed multi-source data on distribution network operation is integrated, including: Aligning multi-source data of distribution network operation based on timestamps unifies the timestamps of data from different sources in the multi-source data of distribution network operation to the same time zone and the same format, removes the dimensional differences between multi-source data of distribution network operation, and forms standardized multi-source data of distribution network operation. ETL tools are used to integrate multi-source data of distribution network operation, making the multi-source data of distribution network operation interconnected and integrated into a unified data view, forming a unified and interconnected data view. Feature extraction is performed on the multi-source data of distribution network operation, and feature vectors related to energy storage optimization configuration are extracted from the multi-source data of distribution network operation to determine the characteristic data of distribution network operation, and the storage management of the characteristic data of distribution network operation is performed.

[0024] An optimal energy storage configuration model for the distribution network is constructed to analyze the operation characteristics data of the distribution network, optimize the operation mode of energy storage, and solve for the optimal energy storage configuration scheme.

[0025] Specifically, constructing an optimal configuration model for energy storage in the distribution network includes: Historical data on the operation of the distribution network were collected and divided into training and test sets in an 8:2 ratio. The mathematical model is trained using a training set, enabling it to learn the optimal configuration behavior of energy storage in the distribution network autonomously. By optimizing the operation mode of energy storage, an energy storage configuration scheme is formulated, and the optimal configuration model of energy storage in the distribution network is determined. The distribution network energy storage optimization configuration model is tested using a test set to evaluate its generalization performance and determine whether it can optimize the energy storage operation mode and formulate an energy storage configuration scheme. The model test evaluation results are then determined. Based on the model test and evaluation results, the parameters of the distribution network energy storage optimization configuration model are adjusted, and after continuous iterative optimization, the optimal distribution network energy storage optimization configuration model is formed.

[0026] Specifically, the analysis of distribution network operation characteristic data is used to optimize energy storage operation modes and solve for the optimal energy storage configuration scheme, including: The distribution network operation characteristic data is input into the distribution network energy storage optimization configuration model. The distribution network operation characteristic data is analyzed based on the distribution network energy storage optimization configuration model. Multi-objective optimization is carried out with economic and technical objectives, and the energy storage operation mode is optimized under the constraints of grid safety operation and energy storage status itself. The optimal energy storage configuration scheme is solved, including energy storage location, power, capacity and operation mode. Among them, energy storage location refers to the installation location of the energy storage device in the distribution network node; energy storage power refers to the rated charging and discharging power of the energy storage device; energy storage capacity refers to the rated energy storage capacity of the energy storage device; and energy storage operation mode refers to the charging and discharging scheme of the energy storage device at different time periods.

[0027] Specifically, by utilizing the spatiotemporal shifting characteristics of energy storage, the problem of mismatch between distributed power sources and loads in time and space can be solved, thereby supporting more distributed power sources and loads without upgrading or with minimal upgrades to grid equipment.

[0028] Verify and evaluate the energy storage configuration scheme, generate an energy storage configuration verification and evaluation report, and configure energy storage in the distribution network according to the energy storage configuration verification and evaluation report to enable the coordinated operation of source, grid, load and storage, maximize the utilization of distribution network resources and enable it to carry more distributed power sources and loads.

[0029] Specifically, the energy storage configuration scheme is verified and evaluated, and an energy storage configuration verification and evaluation report is generated, including: Digital simulation verification of the energy storage configuration scheme is conducted to verify its technical feasibility, simulate its operation in the actual power grid, and evaluate its comprehensive benefits to assess its economic rationality and determine the project's return on investment. The comprehensive benefit evaluation indicators include: technical benefits, economic benefits, and social benefits. During digital simulation verification, steady-state simulation verification is performed on the energy storage configuration scheme. Power system analysis software is used to verify whether the energy storage configuration scheme can solve the problem of improving the source load carrying capacity of the distribution network under normal and long-term operation. After configuring energy storage, it is verified whether the voltage of each node of the grid is within the qualified range under all typical operating scenarios, whether the overload problem is solved, whether the equipment load rate is restored to within the safe limit, and the impact of the energy storage configuration scheme on network losses is evaluated. It is also calculated whether the photovoltaic and wind power power reduction is significantly reduced after configuring energy storage, and whether the absorption rate reaches the target. After the energy storage configuration scheme is assessed for technical feasibility and comprehensive benefits, an energy storage configuration verification and evaluation report is generated and presented in a visual format.

[0030] Specifically, energy storage configuration for the distribution network is carried out based on the energy storage configuration verification and evaluation report, including: After the energy storage configuration verification and evaluation report shows that the energy storage configuration scheme can meet the needs of improving the source load carrying capacity of the distribution network, the energy storage configuration of the distribution network is carried out according to the energy storage configuration scheme, and the daily and hourly charging and discharging plans of the energy storage equipment are formulated. Among them, peak shaving and valley filling are based on electricity prices. When distributed power generation occurs at noon, the energy storage device is charged, and when the load peak occurs in the evening, the energy storage device is discharged, transferring energy from the redundant period to the scarce period and smoothing the net load curve. Among them, energy storage devices are configured in areas rich in distributed power sources. These devices can absorb excess power locally, reduce the impact of through power on the main power grid, and automatically absorb or generate reactive or active power to support the grid when voltage exceeds the limit. This maximizes the use of distribution network resources and enables it to carry more distributed power sources and loads.

[0031] In summary, by analyzing the operational characteristic data of the distribution network through an energy storage optimization configuration model, multi-objective optimization is performed with economic and technical objectives as the goals. Constraints are placed on grid safety operation and the energy storage's own state to optimize energy storage operation modes and find the optimal energy storage configuration scheme. The energy storage configuration scheme is then verified and evaluated, generating an energy storage configuration verification and evaluation report. Based on this report, energy storage configuration is carried out in the distribution network, enabling coordinated operation of power generation, grid, load, and storage. This maximizes the utilization of distribution network resources, allowing it to support more distributed power sources and loads. This approach can effectively optimize energy storage configuration and improve the power distribution network's power generation and load carrying capacity, effectively mitigating the challenges brought by new energy access and improving the stability and economy of the distribution network.

[0032] Specifically, the energy storage optimization configuration method to improve the source-load carrying capacity of the distribution network also includes real-time assessment of the source-load coordination status and dynamic adjustment of energy storage operation strategies during the operation of the distribution network, in order to cope with the random fluctuations in distributed power output and load demand, and ensure the stability and carrying capacity of the distribution network are optimized. This includes the following steps: Data verification algorithms are used to verify power supply data, grid data, load data, and energy storage data to form a real-time data stream. Among them, power supply data refers to the real-time output value of distributed photovoltaic and wind power, grid data refers to the real-time voltage, current, power flow and system frequency values ​​of substations, switching stations and lines, load data refers to the detailed load power value on the user side, and energy storage data refers to the real-time power, available energy and health status value of energy storage devices. Based on real-time data streams, a dynamic source-load coordination index for the distribution network is calculated to quantify the source-load balance and operational stability of the distribution network at time t. The calculation formula is as follows: DSCI(t)=[ωV×VI(t)+ωF×FI(t)+ωL×LI(t)+ωE×EI(t)+ωG×GI(t)] / (ωV+ωF+ωL+ωE+ωG) Wherein, DSCI(t) represents the dynamic source-load coordination index at time t, a dimensionless parameter ranging from 0 to 1. A higher value indicates better source-load coordination, stability, and carrying capacity of the distribution network. ωV, ωF, ωL, ωE, and ωG are the weighting coefficients of the voltage index, frequency index, load index, energy storage index, and power output index, respectively. These weighting coefficients are set through historical data training or expert experience, ranging from 0 to 1, and satisfying ωV+ωF+ωL+ωE+ωG>0. VI(t) represents the voltage index at time t, calculated as VI(t)=1-|Vavg(t)-V nom| / Vnom, where Vavg(t) is the average node voltage of the distribution network at time t, in kilovolts. This data comes directly from voltage measurements in the power grid data. Vnom is the rated voltage of the distribution network, in kilovolts, and is a preset constant. VI(t) is a dimensionless parameter reflecting the degree of voltage deviation from the rated value. FI(t) represents the frequency exponent at time t, calculated as FI(t) = 1 - |f(t) - fnom| / fnom, where f(t) is the system frequency of the distribution network at time t, in Hertz. This data comes directly from frequency measurements in the power grid data. m is the rated frequency of the distribution network, measured in Hertz, and is a preset constant. FI(t) is a dimensionless parameter reflecting the degree to which the frequency deviates from the rated value. LI(t) represents the load index at time t, calculated as LI(t) = 1 - Lratio(t), where Lratio(t) is the overall load factor of the distribution network at time t, calculated as Lratio(t) = Pload(t) / Pcapacity, where Pload(t) is the total load power of the distribution network at time t, measured in kilowatts, which is directly derived from the load power value in the load data. Pcapacity... The total capacity of the distribution network is expressed in kilowatts (kW) and is a preset constant. Lratio(t) and LI(t) are both dimensionless parameters that reflect the load level relative to the capacity. EI(t) represents the energy storage index at time t, calculated as EI(t) = Eavail(t) / Erated. Eavail(t) is the available energy value of energy storage at time t, expressed in kilowatt-hours (kWh). This data comes directly from the available energy value in the energy storage data. Erated is the rated capacity value of energy storage, expressed in kilowatt-hours (kWh) and is a preset constant. EI(t) is a dimensionless parameter that reflects the energy reserve level of energy storage.GI(t) represents the power output index at time t, and is calculated by the formula GI(t) = Pdg(t) / Pdgmax, where Pdg(t) is the total output value of distributed power sources in the distribution network at time t, in kilowatts. This data comes directly from the distributed photovoltaic and wind power output values ​​in the power source data. Pdgmax is the maximum possible output value of distributed power sources, in kilowatts, which is a preset constant. GI(t) is a dimensionless parameter that reflects the output and utilization of distributed power sources. After calculating DSCI(t), it is compared with the preset source-load coordination threshold DSCIthreshold; where the source-load coordination threshold is set according to the distribution network safety operation standard, and the value is between 0.75 and 0.95. When DSCI(t) is lower than DSCIthreshold, it indicates insufficient coordination between the power supply and load in the distribution network, posing risks of overload, voltage exceeding limits, or power waste. At this time, the dynamic power supply and load coordination assessment and energy storage adjustment module automatically triggers the adjustment of the energy storage operation strategy: First, based on the deviation of DSCI(t), the energy storage power adjustment amount ΔPess(t) = k × (DSCIthreshold - DSCI(t)) × Pessrated is calculated, where k is the adjustment coefficient, a dimensionless constant determined through simulation optimization, Pessrated is the rated power of the energy storage in kilowatts, and ΔPess(t) is the energy storage power adjustment value in kilowatts. A positive value indicates an increase in discharge power to support the load, while a negative value indicates an increase in charging power to absorb excess power. At the same time, the charging and discharging time window of the energy storage is optimized, with intervention prioritized during periods when DSCI(t) is lower. When DSCI(t) is higher than or equal to DSCIthreshold, it indicates that the distribution network is operating well and the existing energy storage operation strategy remains unchanged. The adjusted energy storage operation strategy is sent to the energy storage device in real time through the distribution network energy management system, and the operation mode parameters in the energy storage configuration plan are updated.

[0033] This step analyzes the dynamic operating status of the distribution network through real-time data stream analysis, calculates the Dynamic Source-Load Coordination Index (DSCI), and dynamically adjusts the energy storage operation strategy based on the comparison results with preset thresholds. This addresses the random fluctuations in distributed power output and load demand, ensuring optimal stability and carrying capacity of the distribution network. Power data is collected by distributed power monitoring equipment, which records the power output of photovoltaic panels and wind turbines per second using high-precision sensors. Grid data is collected per second by the SCADA system using voltage transformers, current transformers, and frequency meters. Load data is recorded per minute by electricity consumption information collection equipment using smart meters. Energy storage data is collected per second by energy storage information collection equipment using a battery management system. These data are verified using data validation algorithms to eliminate invalid data caused by communication interruptions or equipment failures, forming a real-time data stream.

[0034] Based on real-time data streams, DSCI(t) is calculated as follows: DSCI(t) = [ωV×VI(t) + ωF×FI(t) + ωL×LI(t) + ωE×EI(t) + ωG×GI(t)] / (ωV + ωF + ωL + ωE + ωG), where DSCI(t) is the dynamic source-load coordination index at time t, reflecting the degree of source-load balance and operational stability. The weighting coefficients ωV, ωF, ωL, ωE, and ωG are set through training with historical data or expert experience to ensure their sum is greater than 0. Voltage index VI(t) = 1 - |Vavg(t) - Vnom| / Vnom, where Vavg(t) is the average node voltage collected by the SCADA system and Vnom is the rated voltage of the distribution network; Frequency index FI(t) = 1 - |f(t) - fnom| / fnom, where f(t) is the system frequency collected by the SCADA system and fnom is the rated frequency; Load index LI(t) = 1 - Lratio(t), where Lratio(t) = Pload(t) / Pcapacity, where Pload(t) is the total load power collected by the smart meter and Pcapacity is the total capacity of the distribution network; Energy storage index EI(t) = Eavail(t) / Erated, where Eavail(t) is the available energy of energy storage collected by the BMS and Erated is the rated capacity of energy storage; Power output index GI(t) = Pdg(t) / Pdgmax, where Pdg(t) is the total output collected by the distributed power monitoring equipment and Pdgmax is the maximum possible output.

[0035] DSCI(t) is compared with the preset source-load coordination threshold DSCIthreshold. If DSCI(t) < DSCIthreshold, it indicates insufficient source-load coordination. The dynamic source-load coordination evaluation and energy storage adjustment module triggers an adjustment, and calculates the energy storage power adjustment amount ΔPess(t) = k×(DSCIthreshold - DSCI(t))×Pessrated, where k is the adjustment coefficient, Pessrated is the rated power of the energy storage, and ΔPess(t) is the energy storage power adjustment value. At the same time, the charging and discharging time window is optimized, and the period with a lower DSCI(t) is preferentially intervened. If DSCI(t) ≥ DSCIthreshold, the existing strategy is maintained. The adjusted strategy is sent to the energy storage device through the distribution network energy management system, and the operation mode parameters of the energy storage configuration plan are updated.

[0036] For example, in a certain 10kV distribution network, the total installed capacity of distributed photovoltaics and wind power is 5MW, the rated capacity of the energy storage device is 2MWh, and the rated power is 500kW. The output of photovoltaics and wind power collected by the distributed power monitoring device is 3MW; the SCADA system collects the voltage of 10.2kV and the frequency of 50.1Hz; the smart meter collects the load power of 4MW; the BMS collects the energy storage power of 200kW and the available energy of 1.5MWh. The data forms a real-time data stream through verification.

[0037] Set parameters: Vnom = 10kV, fnom = 50Hz, Pcapacity = 6MW, Erated = 2MWh, Pdgmax = 5MW, and weight coefficients ωV = 0.3, ωF = 0.2, ωL = 0.2, ωE = 0.2, ωG = 0.1. Calculate each index: VI(t) = 1 - |10.2 - 10| / 10 = 0.98, FI(t) = 1 - |50.1 - 50| / 50 = 0.998, LI(t) = 1 - 4 / 6 = 0.333, EI(t) = 1.5 / 2 = 0.75, GI(t) = 3 / 5 = 0.6. Calculate DSCI(t) = (0.3×0.98 + 0.2×0.998 + 0.2×0.333 + 0.2×0.75 + 0.1×0.6) / (0.3 + 0.2 + 0.2 + 0.2 + 0.1) = 0.7702 / 0.9 = 0.8558.

[0038] Let DSCIthreshold = 0.8. DSCI(t) = 0.8558 > 0.8, indicating good source-load coordination, and the existing strategy remains unchanged. Assuming a sudden load increase to 5MW, recalculate LI(t) = 1 - 5 / 6 = 0.167, DSCI(t) = (0.3 × 0.98 + 0.2 × 0.998 + 0.2 × 0.167 + 0.2 × 0.75 + 0.1 × 0.6) / 0.9 = 0.737 / 0.9 = 0.8189 > 0.8, still meeting the requirements. Assuming the load increases to 5.8MW and the voltage drops to 9.8kV, we calculate LI(t) = 1 - 5.8 / 6 = 0.033, VI(t) = 1 - |9.8 - 10| / 10 = 0.98, and DSCI(t) = (0.3 × 0.98 + 0.2 × 0.998 + 0.2 × 0.033 + 0.2 × 0.75 + 0.1 × 0.6) / 0.9 = 0.7102 / 0.9 = 0.7891 < 0.8, thus triggering the adjustment mechanism.

[0039] Assuming k=0.5, the calculated ΔPess(t) = 0.5 × (0.8 - 0.7891) × 500 = 2.725 kW. Energy storage adds 2.725 kW of discharge power to support the load, prioritizing intervention during peak load periods. The adjustment strategy is disseminated to the energy storage device via EMS, and the BMS updates the operating parameters. This method effectively addresses fluctuations in photovoltaic output and sudden load changes, significantly improving the stability of the distribution network.

[0040] Specifically, the energy storage optimization configuration method to improve the load-carrying capacity of the distribution network also includes optimizing the operating efficiency of the distribution network through multi-timescale energy storage scheduling, solving the problems of insufficient utilization of energy storage resources and high operating costs of the distribution network caused by single-timescale scheduling. This includes the following steps: Based on distribution network operation characteristic data, a multi-timescale energy storage dispatch framework is constructed, including long-cycle dispatch, medium-cycle dispatch, and short-cycle dispatch. In long-cycle dispatch, daily charging and discharging plans for energy storage devices are formulated based on predicted daily load curves and renewable energy output curves, optimizing energy allocation during peak and off-peak periods and reducing distribution network operating costs. In medium-cycle dispatch, the charging and discharging power of energy storage devices is adjusted according to hourly load and renewable energy output fluctuations, smoothing the hourly net load curve and reducing the risk of distribution network overload. In short-cycle dispatch, the power output of energy storage devices is rapidly adjusted to address minute-level load mutations or voltage fluctuations, providing real-time support for the stable operation of the distribution network. By employing a multi-timescale energy storage dispatch framework, long-, medium-, and short-cycle dispatch strategies are coordinated to ensure that the operating status of energy storage devices at different time scales is highly matched with the actual needs of the distribution network. A priority mechanism is used to determine the execution order of dispatch at each time scale: short-cycle dispatch prioritizes responding to emergency loads or voltage fluctuations, medium-cycle dispatch prioritizes optimizing hourly operating efficiency, and long-cycle dispatch prioritizes overall economic efficiency. By coordinating dispatch objectives at different time scales, the utilization efficiency of energy storage resources is maximized, thereby enhancing the load-carrying capacity of the distribution network. During multi-timescale scheduling, the operating parameters of energy storage devices, including charging and discharging power, operating time, and scheduling priority, are dynamically adjusted based on real-time updates of multi-source data on distribution network operation. The power distribution network dispatching system distributes multi-timescale dispatching strategies to energy storage devices and monitors the operating status of the energy storage devices in real time to ensure the effectiveness of the dispatching strategies.

[0041] By constructing a multi-timescale energy storage scheduling framework and combining long-cycle, medium-cycle, and short-cycle scheduling strategies, the operating parameters of energy storage devices are optimized to address the problems of insufficient utilization of energy storage resources and high operating costs of distribution networks caused by single-timescale scheduling, thereby improving the source-load carrying capacity. Distribution network operating characteristic data are acquired through intelligent acquisition terminals, including distributed photovoltaic and wind power output, voltage, frequency, load power, energy storage power, and available energy.

[0042] Long-cycle dispatching, based on predicted daily load curves and renewable energy output curves, formulates daily charging and discharging plans to optimize energy allocation during peak and off-peak periods and reduce operating costs. Medium-cycle dispatching, based on hourly load and renewable energy output fluctuations, adjusts charging and discharging power to smooth the net load curve and reduce overload risk. Short-cycle dispatching, targeting minute-level load mutations or voltage fluctuations, rapidly adjusts power output to support the stable operation of the distribution network.

[0043] The scheduling framework employs a priority mechanism: short-cycle priority is given to responding to emergency fluctuations, medium-cycle priority is given to optimizing hourly efficiency, and long-cycle priority is given to economic efficiency. The strategies for each cycle are coordinated through a weighted objective function to ensure that energy storage operation matches the demand of the distribution network. Operating parameters are dynamically adjusted based on real-time data. The scheduling strategy is distributed to the energy storage device through the distribution network scheduling system. The BMS monitors the operating status in real time, provides feedback on deviations, and adjusts the strategy accordingly to ensure effective execution.

[0044] For example, in a certain 10kV distribution network, the energy storage device has a rated capacity of 2MWh and a rated power of 500kW, while the total installed capacity of photovoltaic and wind power is 5MW. Operational characteristic data is obtained through an intelligent data acquisition terminal: photovoltaic output 4MW, load power 5MW, and voltage 10.2kV.

[0045] Long-cycle scheduling uses an LSTM model to predict daily load and photovoltaic output curves, and formulates a plan: 12:00-14:00 charging 300kW (2 hours), charging energy 600kWh; 18:00-20:00 discharging 300kW (2 hours), discharging energy 600kWh, ensuring energy balance between charging and discharging. Considering the electricity price difference (peak 0.8 yuan / kWh, valley 0.3 yuan / kWh) and a charging / discharging efficiency of 90%, the actual discharging energy is 540kWh, and the cost saving = 540×0.8 - 600×0.3 = 252 yuan / day.

[0046] Medium-cycle dispatch detects hourly fluctuations; for example, if the photovoltaic capacity drops to 3MW at 14:00 and the load increases to 4.5MW, the charging power is adjusted to 200kW to smooth the net load and avoid line overload. Short-cycle dispatch detects minute-level voltage fluctuations; for example, if the voltage drops to 9.5kV at 10:00, a rapid discharge of 50kW is used to support the voltage.

[0047] The priority mechanism prioritizes short-cycle periods, followed by medium-cycle periods, and lastly long-cycle periods. The overall benefit is optimized through a coordinated objective function, and the EMS (Energy Management System) distributes the strategy to the energy storage devices. BMS monitoring shows that the State of Charge (SOC) decreased from 80% to 60%, with a power deviation of <3%, indicating effective strategy execution. The following day, monitoring showed a 20% improvement in the smoothness of the net load curve, a 15% reduction in network losses, and a 10% reduction in operating costs, demonstrating that multi-timescale scheduling significantly improves energy storage utilization and distribution network stability.

[0048] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0049] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing energy storage configuration to improve the load-carrying capacity of a power distribution network, characterized in that, include: The system collects multi-source data on power distribution network operation based on intelligent acquisition terminals, and preprocesses and integrates the collected multi-source data to form characteristic data of power distribution network operation. An optimal energy storage configuration model for the distribution network is constructed to analyze the operation characteristics data of the distribution network, optimize the operation mode of energy storage, and solve for the optimal energy storage configuration scheme. Verify and evaluate the energy storage configuration scheme, generate an energy storage configuration verification and evaluation report, and configure energy storage in the distribution network according to the energy storage configuration verification and evaluation report to enable the coordinated operation of source, grid, load and storage and maximize the utilization of distribution network resources. Among these measures, the source-load coordination status is evaluated in real time during the operation of the distribution network, and the energy storage operation strategy is dynamically adjusted. The dynamic source-load coordination index of the distribution network is calculated to quantify the source-load balance and operational stability of the distribution network.

2. The energy storage optimization configuration method for improving the source-load carrying capacity of the distribution network according to claim 1, characterized in that, It also includes real-time assessment of the source-load coordination status and dynamic adjustment of energy storage operation strategies during the operation of the distribution network, in order to cope with the random fluctuations in distributed power output and load demand, and to ensure the optimal stability and carrying capacity of the distribution network. Specifically, it includes the following steps: Data verification algorithms are used to verify power supply data, grid data, load data, and energy storage data to form a real-time data stream. Among them, power supply data refers to the real-time output value of distributed photovoltaic and wind power, grid data refers to the real-time voltage, current, power flow and system frequency values ​​of substations, switching stations and lines, load data refers to the detailed load power value on the user side, and energy storage data refers to the real-time power, available energy and health status value of energy storage devices. Based on real-time data streams, a dynamic source-load coordination index for the distribution network is calculated to quantify the source-load balance and operational stability of the distribution network at time t. The calculation formula is as follows: DSCI(t)=[ωV×VI(t)+ωF×FI(t)+ωL×LI(t)+ωE×EI(t)+ωG×GI(t)] / (ωV+ωF+ωL+ωE+ωG) Wherein, DSCI(t) represents the dynamic source-load coordination index at time t, a dimensionless parameter ranging from 0 to 1. A higher value indicates better source-load coordination, stability, and carrying capacity of the distribution network. ωV, ωF, ωL, ωE, and ωG are the weighting coefficients of the voltage index, frequency index, load index, energy storage index, and power output index, respectively. These weighting coefficients are set through historical data training or expert experience, ranging from 0 to 1, and satisfying ωV+ωF+ωL+ωE+ωG>0. VI(t) represents the voltage index at time t, calculated as VI(t)=1-|Vavg(t)-V nom| / Vnom, where Vavg(t) is the average node voltage of the distribution network at time t, in kilovolts. This data comes directly from voltage measurements in the power grid data. Vnom is the rated voltage of the distribution network, in kilovolts, and is a preset constant. VI(t) is a dimensionless parameter reflecting the degree of voltage deviation from the rated value. FI(t) represents the frequency exponent at time t, calculated as FI(t) = 1 - |f(t) - fnom| / fnom, where f(t) is the system frequency of the distribution network at time t, in Hertz. This data comes directly from frequency measurements in the power grid data. m is the rated frequency of the distribution network, measured in Hertz, and is a preset constant. FI(t) is a dimensionless parameter reflecting the degree to which the frequency deviates from the rated value. LI(t) represents the load index at time t, calculated as LI(t) = 1 - Lratio(t), where Lratio(t) is the overall load factor of the distribution network at time t, calculated as Lratio(t) = Pload(t) / Pcapacity, where Pload(t) is the total load power of the distribution network at time t, measured in kilowatts, which is directly derived from the load power value in the load data. Pcapacity... The total capacity of the distribution network is expressed in kilowatts (kW) and is a preset constant. Lratio(t) and LI(t) are both dimensionless parameters that reflect the load level relative to the capacity. EI(t) represents the energy storage index at time t, calculated as EI(t) = Eavail(t) / Erated. Eavail(t) is the available energy value of energy storage at time t, expressed in kilowatt-hours (kWh). This data comes directly from the available energy value in the energy storage data. Erated is the rated capacity value of energy storage, expressed in kilowatt-hours (kWh) and is a preset constant. EI(t) is a dimensionless parameter that reflects the energy reserve level of energy storage.GI(t) represents the power output index at time t, and is calculated by the formula GI(t) = Pdg(t) / Pdgmax, where Pdg(t) is the total output value of distributed power sources in the distribution network at time t, in kilowatts. This data comes directly from the distributed photovoltaic and wind power output values ​​in the power data. Pdgmax is the maximum possible output value of distributed power sources, in kilowatts, which is a preset constant. GI(t) is a dimensionless parameter that reflects the output and utilization of distributed power sources. After calculating DSCI(t), it is compared with the preset source-load coordination threshold DSCIthreshold; where the source-load coordination threshold is set according to the distribution network safety operation standard, and the value is between 0.75 and 0.

95. When DSCI(t) is lower than DSCIthreshold, it indicates insufficient coordination between the power supply and load in the distribution network, posing risks of overload, voltage exceeding limits, or power waste. At this time, the dynamic power supply and load coordination assessment and energy storage adjustment module automatically triggers the adjustment of the energy storage operation strategy: First, based on the deviation of DSCI(t), the energy storage power adjustment amount ΔPess(t) = k × (DSCIthreshold - DSCI(t)) × Pessrated is calculated, where k is the adjustment coefficient, a dimensionless constant determined through simulation optimization, Pessrated is the rated power of the energy storage in kilowatts, and ΔPess(t) is the energy storage power adjustment value in kilowatts. A positive value indicates an increase in discharge power to support the load, while a negative value indicates an increase in charging power to absorb excess power. At the same time, the charging and discharging time window of the energy storage is optimized, with intervention prioritized during periods when DSCI(t) is lower. When DSCI(t) is higher than or equal to DSCIthreshold, it indicates that the distribution network is operating well and the existing energy storage operation strategy remains unchanged. The adjusted energy storage operation strategy is sent to the energy storage device in real time through the distribution network energy management system, and the operation mode parameters in the energy storage configuration plan are updated.

3. The energy storage optimization configuration method for improving the source-load carrying capacity of the distribution network according to claim 2, characterized in that, It also includes optimizing the operating efficiency of the distribution network through multi-timescale energy storage scheduling, solving the problems of insufficient utilization of energy storage resources and high operating costs of the distribution network caused by single-timescale scheduling. Specifically, it includes the following steps: Based on distribution network operation characteristic data, a multi-timescale energy storage dispatch framework is constructed, including long-cycle dispatch, medium-cycle dispatch, and short-cycle dispatch. In long-cycle dispatch, daily charging and discharging plans for energy storage devices are formulated based on predicted daily load curves and renewable energy output curves, optimizing energy allocation during peak and off-peak periods and reducing distribution network operating costs. In medium-cycle dispatch, the charging and discharging power of energy storage devices is adjusted according to hourly load and renewable energy output fluctuations, smoothing the hourly net load curve and reducing the risk of distribution network overload. In short-cycle dispatch, the power output of energy storage devices is rapidly adjusted to address minute-level load mutations or voltage fluctuations, providing real-time support for the stable operation of the distribution network. By employing a multi-timescale energy storage dispatch framework, long-, medium-, and short-cycle dispatch strategies are coordinated to ensure that the operating status of energy storage devices at different time scales is highly matched with the actual needs of the distribution network. A priority mechanism is used to determine the execution order of dispatch at each time scale: short-cycle dispatch prioritizes responding to emergency loads or voltage fluctuations, medium-cycle dispatch prioritizes optimizing hourly operating efficiency, and long-cycle dispatch prioritizes overall economic efficiency. By coordinating dispatch objectives at different time scales, the utilization efficiency of energy storage resources is maximized, thereby enhancing the load-carrying capacity of the distribution network. During multi-timescale scheduling, the operating parameters of energy storage devices, including charging and discharging power, operating time, and scheduling priority, are dynamically adjusted based on real-time updates of multi-source data on distribution network operation. The power distribution network dispatching system distributes multi-timescale dispatching strategies to energy storage devices and monitors the operating status of the energy storage devices in real time to ensure the effectiveness of the dispatching strategies.

4. The energy storage optimization configuration method for improving the source-load carrying capacity of the distribution network according to claim 3, characterized in that, Based on the intelligent data acquisition terminal, collect multi-source data on the operation of the power distribution network and perform the following operations: Based on the distributed power source monitoring equipment, the real-time output of distributed photovoltaic and wind power is sensed, and power data during the operation of the distribution network is obtained. Based on the SCADA system, real-time voltage, current and power flow of substations, switching stations and lines are sensed to obtain power grid data during the operation of the distribution network; Based on the electricity consumption information collection equipment, the user side's detailed load data is sensed to obtain load data during the operation of the distribution network; Based on the energy storage information acquisition equipment, the power and health status of energy storage devices are sensed to obtain energy storage data during the operation of the distribution network; Based on power source data, grid data, load data, and energy storage data during the operation of the distribution network, multi-source data for distribution network operation is generated.

5. The energy storage optimization configuration method for improving the source-load carrying capacity of the distribution network according to claim 4, characterized in that, The collected multi-source data on power distribution network operation are preprocessed, and the following operations are performed: Based on Python, the system cleans multi-source data of power distribution network operation and identifies missing and outlier values ​​in the multi-source data. Missing values ​​are data gaps caused by equipment failure or communication interruption during the data acquisition process, while outlier values ​​are data points that deviate significantly from the normal range or pattern, including load data with negative power and power distribution network data with voltage as high as 100kV. The missing and outlier values ​​in the multi-source data of the distribution network operation are evaluated to determine whether the missing and outlier values ​​are valuable for the energy storage optimization configuration to improve the source-load carrying capacity of the distribution network. When missing values ​​and outliers are valuable for optimizing the energy storage configuration to improve the load-carrying capacity of the distribution network, the mean, median or mode are used to fill the missing values, and the average or median of the data before and after the time interval is used to replace the outliers. If missing values ​​and outliers are of no value to the optimized configuration of energy storage to improve the load-carrying capacity of the distribution network, then missing values ​​and outliers in the multi-source data of the distribution network operation will be removed.

6. The energy storage optimization configuration method for improving the source-load carrying capacity of a distribution network according to claim 5, characterized in that, The preprocessed multi-source data on distribution network operation are integrated, and the following operations are performed: Aligning multi-source data of distribution network operation based on timestamps unifies the timestamps of data from different sources in the multi-source data of distribution network operation to the same time zone and the same format, removes the dimensional differences between multi-source data of distribution network operation, and forms standardized multi-source data of distribution network operation. ETL tools are used to integrate multi-source data of distribution network operation, making the multi-source data of distribution network operation interconnected and integrated into a unified data view, forming a unified and interconnected data view. Feature extraction is performed on the multi-source data of distribution network operation, and feature vectors related to energy storage optimization configuration are extracted from the multi-source data of distribution network operation to determine the characteristic data of distribution network operation, and the storage management of the characteristic data of distribution network operation is performed.

7. The energy storage optimization configuration method for improving the source-load carrying capacity of a distribution network according to claim 6, characterized in that, Construct an optimal energy storage configuration model for the distribution network and perform the following operations: Collect historical data on the operation of the distribution network and divide the collected historical data into training and test sets in an 8:2 ratio. The mathematical model is trained using a training set, enabling it to learn the optimal configuration behavior of energy storage in the distribution network autonomously. By optimizing the operation mode of energy storage, an energy storage configuration scheme is formulated, and the optimal configuration model of energy storage in the distribution network is determined. The distribution network energy storage optimization configuration model is tested using a test set to evaluate its generalization performance and determine whether it can optimize the energy storage operation mode and formulate an energy storage configuration scheme. The model test evaluation results are then determined. Based on the model test and evaluation results, the parameters of the distribution network energy storage optimization configuration model are adjusted, and after continuous iterative optimization, the optimal distribution network energy storage optimization configuration model is formed.

8. The energy storage optimization configuration method for improving the source-load carrying capacity of the distribution network according to claim 7, characterized in that, Analyze the operational characteristics data of the distribution network, optimize the energy storage operation mode, solve for the optimal energy storage configuration scheme, and perform the following operations: The distribution network operation characteristic data is input into the distribution network energy storage optimization configuration model. The distribution network operation characteristic data is analyzed based on the distribution network energy storage optimization configuration model. Multi-objective optimization is carried out with economic and technical objectives, and the energy storage operation mode is optimized under the constraints of grid safety operation and energy storage status itself. The optimal energy storage configuration scheme is solved, including energy storage location, power, capacity and operation mode. Among them, energy storage location refers to the installation location of the energy storage device in the distribution network node; energy storage power refers to the rated charging and discharging power of the energy storage device; energy storage capacity refers to the rated energy storage capacity of the energy storage device; and energy storage operation mode refers to the charging and discharging scheme of the energy storage device at different time periods.

9. The energy storage optimization configuration method for improving the source-load carrying capacity of a distribution network according to claim 8, characterized in that, Verify and evaluate the energy storage configuration scheme, generate an energy storage configuration verification and evaluation report, and perform the following operations: Digital simulation verification of the energy storage configuration scheme is conducted to verify its technical feasibility, simulate its operation in the actual power grid, and evaluate its comprehensive benefits to assess its economic rationality and determine the project's return on investment. The comprehensive benefit evaluation indicators include: technical benefits, economic benefits, and social benefits. During digital simulation verification, steady-state simulation verification is performed on the energy storage configuration scheme. Power system analysis software is used to verify whether the energy storage configuration scheme can solve the problem of improving the source load carrying capacity of the distribution network under normal and long-term operation. After configuring energy storage, it is verified whether the voltage of each node of the grid is within the qualified range under all typical operating scenarios, whether the overload problem is solved, whether the equipment load rate is restored to within the safe limit, and the impact of the energy storage configuration scheme on network losses is evaluated. It is also calculated whether the photovoltaic and wind power power reduction is significantly reduced after configuring energy storage, and whether the absorption rate reaches the target. After the energy storage configuration scheme is assessed for technical feasibility and comprehensive benefits, an energy storage configuration verification and evaluation report is generated and presented in a visual format.

10. The energy storage optimization configuration method for improving the source-load carrying capacity of a distribution network according to claim 9, characterized in that, Based on the energy storage configuration verification and evaluation report, configure energy storage for the distribution network and perform the following operations: After the energy storage configuration verification and evaluation report shows that the energy storage configuration scheme can meet the needs of improving the source load carrying capacity of the distribution network, the energy storage configuration of the distribution network is carried out according to the energy storage configuration scheme, and the daily and hourly charging and discharging plans of the energy storage equipment are formulated. Among them, peak shaving and valley filling are based on electricity prices. When distributed power generation occurs at noon, the energy storage device is charged, and when the load peak occurs in the evening, the energy storage device is discharged, transferring energy from the redundant period to the scarce period and smoothing the net load curve. Among them, energy storage devices are configured in areas with abundant distributed power sources to absorb excess power locally. When the voltage exceeds the limit, the devices automatically absorb or generate reactive or active power to provide support.

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