Energy storage scheduling optimization system, method and equipment of power distribution network and medium

Abstract

The invention discloses an energy storage scheduling optimization system, method and device for a power distribution network and a medium, and belongs to the technical field of power system energy storage scheduling, and the system comprises a data collection module which is used for monitoring the power distribution network, obtaining monitoring index data, and transmitting the monitoring index data to a cloud management platform; the scheduling strategy making module is used for analyzing the monitoring index data according to the management platform, obtaining a calculation result and making a scheduling strategy according to the calculation result; the instruction execution module is used for controlling the user side equipment to perform charging and discharging operation according to the scheduling strategy; and the closed-loop feedback module is used for monitoring the power distribution network after the charging and discharging operation is executed, acquiring feedback data and adjusting a scheduling strategy according to the feedback data. According to the invention, the problems of fragmentation of data acquisition and insufficient adaptability of a scheduling strategy and an actual operation state of a power distribution network energy storage scheduling system are solved.

Description

A power distribution network energy storage dispatch optimization system, method, equipment and medium Technical Field

[0001] This invention relates to the field of power system energy storage dispatching technology, specifically to an energy storage dispatching optimization system, method, equipment, and medium for distribution networks. Background Technology

[0002] With the increasing proportion of new energy access, the diversification of load types, and the large-scale deployment of distributed energy storage, power quality problems in distribution networks are becoming increasingly prominent, mainly manifested as voltage fluctuations, frequency drift, and harmonic pollution. In particular, in distribution networks, how to accurately adjust power quality has become an important issue to ensure grid security and the quality of power supply to users.

[0003] User-side energy storage devices offer advantages such as rapid response and flexible deployment, effectively compensating for voltage dips and mitigating grid fluctuations. However, most energy storage systems currently operate in isolation, lacking a scheduling mechanism coupled with power quality control, resulting in delayed energy storage response, numerous control blind spots, and low resource utilization efficiency. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by this invention is: how to achieve power dispatch and dynamic matching on the user side through a power distribution network energy storage dispatch optimization system, optimize the charging and discharging sequence and power output of energy storage devices while ensuring the stability of the power distribution network operation, and at the same time correct dispatch deviations in real time through a closed-loop feedback mechanism to improve the power quality of the power distribution network and the utilization efficiency of energy storage resources.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: an energy storage dispatch optimization system for a distribution network, comprising the following modules: a data acquisition module for monitoring the distribution network, acquiring monitoring indicator data, and transmitting the monitoring indicator data to a cloud management platform; a dispatch strategy formulation module for analyzing the monitoring indicator data based on the management platform, acquiring calculation results, and formulating a dispatch strategy based on the calculation results; an instruction execution module for controlling user-side equipment to perform charging and discharging operations using the dispatch strategy; and a closed-loop feedback module for monitoring the distribution network after the charging and discharging operations, acquiring feedback data, and adjusting the dispatch strategy based on the feedback data.

[0007] As a preferred embodiment of the energy storage dispatch optimization system for a power distribution network according to the present invention, the operation steps of the data acquisition module include: collecting power distribution network operation data by deploying monitoring devices in the power distribution network; preprocessing the power distribution network operation data to obtain the monitoring index data; and transmitting the monitoring index data to the cloud management platform for storage through a preset communication network. The beneficial effects of this preferred embodiment are: by deploying monitoring devices in the power distribution network, ensuring no omission of voltage, current, and harmonic data; by preprocessing the collected operation data to remove interference data and standardize the data format, improving the accuracy and effectiveness of the monitoring index data; and by transmitting the data to the cloud management platform for storage through a preset communication network, achieving centralized management and secure retention of data, ensuring both the stability and timeliness of data transmission, and providing support for historical data tracing and trend analysis.

[0008] As a preferred embodiment of the energy storage dispatch optimization system for a distribution network according to the present invention, the operation steps of the dispatch strategy formulation module include: retrieving the monitoring index data from the cloud management platform, classifying the monitoring index data, and obtaining the classification results; identifying abnormal items of parameters in the distribution network based on the classification results, and assessing the impact range of the abnormal items; calculating the charging and discharging related parameters of the user-side equipment based on the abnormal items, the impact range, and historical monitoring index data; and formulating the dispatch strategy based on the charging and discharging related parameters.

[0009] As a preferred embodiment of the energy storage dispatch optimization system for a power distribution network according to the present invention, the operation steps of the instruction execution module include: parsing the control parameters in the dispatch strategy; constructing control instructions based on the control parameters; transmitting the control instructions to the user-side equipment through the preset communication network; and the user-side equipment executing the charging and discharging operation according to the control instructions. The beneficial effects of this preferred embodiment are: by parsing the control parameters in the dispatch strategy, core charging and discharging execution information is extracted, avoiding execution deviations; standardized control instructions are constructed based on the control parameters, unifying the instruction format and transmission specifications, improving the adaptability of instructions to user-side equipment, and ensuring that different types of energy storage equipment can be accurately identified and executed; and the transmission of control instructions through the preset communication network maintains the stability and timeliness of the data transmission link, ensuring that instructions reach the equipment directly.

[0010] As a preferred embodiment of the energy storage dispatch optimization system for a distribution network according to the present invention, the operation steps of the closed-loop feedback module include: collecting real-time operating data of the distribution network using the monitoring device; preprocessing the real-time operating data to obtain feedback data and comparing it with the power quality operating threshold in the cloud management platform; if the feedback data does not reach the power quality operating threshold, transmitting the feedback data to the dispatch strategy formulation module to update the dispatch strategy. The beneficial effects of this preferred embodiment are that by comparing with the preset power quality operating threshold in the cloud management platform, the power quality compliance status of the distribution network is clarified, and the control deviation is located; when the feedback data does not meet the standard, it is promptly transmitted to the dispatch strategy formulation module to update the dispatch strategy, constructing a closed-loop control mechanism, effectively compensating for the deviation of a single dispatch, and improving the system's response sensitivity and control reliability to changes in the operating status of the distribution network.

[0011] As a preferred embodiment of the energy storage dispatch optimization system for a distribution network according to the present invention, the specific manifestations of evaluating the impact range of the anomaly include: In the formula, This represents the minimum value of the optimization variable. and Indicates the weighting coefficient. This indicates the current power quality index. This indicates a power quality reference value. This represents the system operating cost.

[0012] As a preferred embodiment of the energy storage dispatch optimization system for a distribution network according to the present invention, the specific manifestation of the dispatch strategy includes: In the formula, express The output power of the time-of-flight scheduling , , These represent the control gain coefficients for voltage, reactive power, and harmonics, respectively. Indicates voltage deviation. Indicates reactive power deviation. This indicates the deviation of the harmonic current index.

[0013] This invention provides a method for optimizing energy storage scheduling in power distribution networks.

[0014] To address the aforementioned technical problems, the present invention further provides the following technical solution: a method for optimizing energy storage scheduling in a distribution network, comprising: monitoring the distribution network, acquiring monitoring indicator data, and transmitting the monitoring indicator data to a cloud management platform; analyzing the monitoring indicator data based on the management platform, acquiring calculation results, and formulating a scheduling strategy based on the calculation results; controlling user-side equipment to perform charging and discharging operations using the scheduling strategy; monitoring the distribution network after the charging and discharging operations, acquiring feedback data, and adjusting the scheduling strategy based on the feedback data.

[0015] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the energy storage dispatch optimization system for a power distribution network.

[0016] The present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements the steps of the energy storage scheduling optimization system for a power distribution network.

[0017] The beneficial effects of this invention are as follows: The data acquisition module captures distribution network monitoring index data, providing reliable data support for dispatching decisions; the dispatching strategy formulation module, through the centralized analysis capabilities of the cloud management platform, effectively combines monitoring data with the energy storage operating status, making the formulated dispatching strategy more aligned with the actual needs of the distribution network; the instruction execution module responds to power quality control requirements through user-side equipment charging and discharging operations, ensuring the completion of the dispatching strategy; the closed-loop feedback module constructs a real-time correction mechanism to adjust the dispatching strategy to eliminate deviations. The entire system forms a complete closed loop, effectively improving the stability of power quality in the distribution network. While optimizing the utilization efficiency of energy storage resources and reducing system operating costs, it also enhances the adaptability to distributed loads and new energy access, ensuring the safe and efficient operation of the distribution network. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 is a flowchart of an energy storage dispatch optimization system for a power distribution network according to an embodiment of the present invention. Detailed Implementation

[0020] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0021] Example 1, referring to Figure 1, is the first embodiment of the present invention. This embodiment provides an energy storage scheduling optimization system for a power distribution network, including: S100: a data acquisition module, which monitors the power distribution network, acquires monitoring index data, and transmits the monitoring index data to a cloud management platform.

[0022] S200: Scheduling strategy formulation module. It analyzes the monitoring indicator data based on the management platform, obtains the calculation results, and formulates scheduling strategies based on the calculation results.

[0023] S300: Instruction execution module, which controls the user-side equipment to perform charging and discharging operations according to the scheduling strategy.

[0024] S400: Closed-loop feedback module, which monitors the distribution network after performing charging and discharging operations, obtains feedback data, and adjusts the scheduling strategy based on the feedback data.

[0025] It should be noted that existing distribution network energy storage dispatching systems suffer from fragmented data acquisition and insufficient adaptability of dispatching strategies to actual operating conditions. Some systems only collect data on power quality indicators, resulting in a lack of complete data support for dispatching decisions. In addition, dispatching strategies are mostly fixed processes after they are formulated, and cannot respond in a timely manner to dynamic scenarios such as distribution network voltage fluctuations and load changes.

[0026] Therefore, this application addresses the problems of fragmented data collection and insufficient adaptability of dispatch strategies to actual operating conditions in existing power distribution networks. Through steps S100-S400 of the technical solution, a power distribution network energy storage dispatch optimization system is constructed. First, a data acquisition module monitors the power distribution network, acquires monitoring indicator data, and transmits the data to a cloud management platform. Second, a dispatch strategy formulation module analyzes the monitoring indicator data based on the management platform, obtains calculation results, and formulates a dispatch strategy accordingly. Third, an instruction execution module controls user-side equipment to perform charging and discharging operations using the dispatch strategy. Finally, a closed-loop feedback module monitors the power distribution network after the charging and discharging operations, acquires feedback data, and adjusts the dispatch strategy based on the feedback data.

[0027] Example 2, referring to Figure 1, is the second embodiment of the present invention, which provides an energy storage scheduling optimization system for a power distribution network.

[0028] In this embodiment of the invention, the data acquisition module in step S100 monitors the power distribution network, obtains monitoring index data, and transmits the monitoring index data to the cloud management platform, including the following steps A1~A3: A1: Collect power distribution network operation data by deploying monitoring devices in the power distribution network.

[0029] Specifically, power quality monitoring devices are deployed at key nodes of the distribution network. These devices are capable of collecting data on voltage, current, power factor, and harmonic content.

[0030] A2: Preprocess the power distribution network operation data to obtain monitoring indicator data.

[0031] Specifically, outliers and invalid data are removed through data cleaning algorithms, data fluctuations are smoothed through mean filtering, and the format and dimensions of different types of data are standardized according to a standardized process to form structured monitoring indicator data.

[0032] A3: The monitoring indicator data will be transmitted to the cloud management platform for storage via a pre-set communication network.

[0033] It should be noted that the architecture of the technical solution in this application consists of three parts: a cloud management platform, user-side equipment, and power quality monitoring equipment for the power distribution network.

[0034] In one possible implementation, the communication network of the cloud management platform can also be replaced by a combination of IoT wireless communication and edge gateways. IoT wireless communication enables real-time collection of distributed data; the edge gateways are deployed at nodes in the power distribution network area to preprocess the collected monitoring index data, and then upload the processed data to the cloud management platform via IoT wireless communication.

[0035] In another possible implementation, the communication network of the cloud management platform can be replaced by a combination of power line carrier communication and edge computing nodes. Power line carrier communication uses the power lines of the distribution network as the transmission carrier to monitor the stable transmission of node data. The edge computing nodes are deployed in the distribution network's substations or regional control centers to receive the data transmitted by the monitoring nodes via power line carrier communication, preprocess it, filter out the effective monitoring indicator data, and then upload it to the cloud management platform via power line carrier communication.

[0036] In this embodiment of the invention, the scheduling strategy formulation module in step S200 analyzes the monitoring indicator data according to the management platform, obtains the calculation results, and formulates a scheduling strategy based on the calculation results, including the following steps B1~B4: B1: retrieve the monitoring indicator data from the cloud management platform, classify the monitoring indicator data, and obtain the classification results.

[0037] Specifically, the stored monitoring indicator data is retrieved in batches from the cloud management platform, classified according to the electrical parameter type corresponding to the data, and dimension identifiers are added to each type of data by combining the data collection timestamp and monitoring node number.

[0038] B2: Identify abnormal parameters in the distribution network based on the classification results, and assess the scope of their impact.

[0039] Specifically, based on the classification results of step B1, the data of various monitoring indicators are compared with the preset normal operation parameter thresholds of the distribution network in the cloud management platform, and the parameter data that exceeds the threshold range are identified as abnormal parameters in the distribution network.

[0040] Among them, the normal operation parameter thresholds include the charging and discharging limits of small energy storage devices and the safety operation constraints of the power distribution network.

[0041] Specifically, the charging and discharging limitations of small energy storage devices include both energy limits and power limits.

[0042] The specific manifestations of power limitation are as follows: In the formula, This indicates the lower limit of the electrical capacity corresponding to the minimum state of charge of the energy storage device. Indicates usually The amount of electricity corresponding to the actual state of charge of the energy storage device at any given time. This indicates the maximum amount of electricity that corresponds to the maximum state of charge of the energy storage device.

[0043] The specific manifestations of power limitation are as follows: In the formula, This indicates the lower limit of the minimum power constraint for the device. It usually indicates the first The device in the Actual operating power during the period This indicates the upper limit of the device's maximum power constraint.

[0044] Furthermore, the specific manifestations of the constraints on the safe operation of the distribution network are as follows: In the formula, Indicates the time period The actual operating power inside, Indicates the distribution network during the time period Maximum safe power within.

[0045] Furthermore, the specific manifestations of assessing the scope of impact of outliers are as follows: In the formula, This represents the minimum value of the optimization variable. and Indicates the weighting coefficient. This indicates the current power quality index. This indicates a power quality reference value. This represents the system operating cost.

[0046] B3: Calculate the charging and discharging related parameters of user-side equipment based on anomalies, the scope of impact, and historical monitoring data.

[0047] In one possible implementation, the objective function optimization for assessing the impact range of anomalies can also be replaced by a genetic algorithm. The genetic algorithm encodes the charging and discharging power and duration parameters of the user-side equipment into chromosomes, uses the objective function as the fitness function, incorporates the state of charge of the energy storage equipment, power constraints, and power distribution network safety operation constraints, and then iteratively evolves the population through genetic operations to select the parameter combination corresponding to the chromosome with the best fitness as the charging and discharging parameters of the user-side equipment.

[0048] In another possible implementation, the objective function for evaluating the impact range of anomalies can be replaced by the particle swarm optimization algorithm. The particle swarm optimization algorithm takes the achievement of power quality standards in the distribution network and the minimum system operating cost as the optimization objectives. It takes anomalies, impact range and historical monitoring index data as input variables, and iteratively calculates parameters such as charging and discharging power and duration of user-side equipment by simulating the optimization process of the particle swarm. At the same time, it combines boundary conditions to ensure that the calculation results meet the control requirements of the distribution network.

[0049] It should be noted that power quality operating thresholds need to be included as constraints when calculating charging and discharging related parameters.

[0050] B4: Develop a scheduling strategy based on charging and discharging parameters.

[0051] Specifically, the scheduling strategy is manifested in the following ways: In the formula, express The output power of the time-of-flight scheduling , , These represent the control gain coefficients for voltage, reactive power, and harmonics, respectively. Indicates voltage deviation. Indicates reactive power deviation. This indicates the deviation of the harmonic current index.

[0052] It should be noted that power quality operating thresholds need to be included as constraints when formulating scheduling strategies.

[0053] In this embodiment of the invention, the instruction execution module in step S300 controls the user-side equipment to perform charging and discharging operations according to the scheduling strategy, including the following steps C1~C3: C1: Parse the control parameters in the scheduling strategy.

[0054] Specifically, the core control parameters clearly defined in the scheduling strategy are precisely broken down and extracted, with a focus on analyzing the charging and discharging power amplitude, charging and discharging mode, start-up time, and duration of user-side energy storage devices, while simultaneously extracting the power quality operation threshold.

[0055] C2: Construct control commands based on control parameters and transmit the control commands to the user-side equipment through a preset communication network.

[0056] Specifically, the cloud platform sends control commands to energy storage devices distributed on the user side through a remote communication protocol, providing specific guidance on how each device should perform charging or discharging operations according to the scheduling plan.

[0057] C3: The user-side equipment performs charging and discharging operations according to the control commands.

[0058] Specifically, after receiving instructions from the cloud platform, the user-side energy storage device responds in real time and strictly follows the charging and discharging strategy issued by the platform to perform the corresponding actions.

[0059] In one possible implementation, when a user-side energy storage device receives a command from the cloud platform, it can also replace the command through a combination of secondary verification and hierarchical response mechanism by the local edge controller. The local edge controller pre-stores the safe operating parameters of the user-side energy storage device and the operating constraints of the distribution network area. After receiving the command from the cloud platform, the edge controller first performs secondary verification between the charging and discharging parameters in the command and the locally stored safety parameters. If the parameters exceed the device's safety range or conflict with the regional power grid constraints, they are automatically adjusted to the optimal parameters within the safety threshold.

[0060] In another possible implementation, when user-side energy storage devices receive instructions from the cloud platform, they can also use a combination of a multi-device collaborative scheduler and dynamic power allocation. The user-side deploys a device collaborative scheduler. When multiple energy storage devices simultaneously receive charging and discharging instructions from the cloud platform, the scheduler first parses the total control power demand and control target in the instructions, and then, combined with the real-time status of each device, allocates the total control power to each device through power allocation.

[0061] In this embodiment of the invention, the closed-loop feedback module in step S400 monitors the distribution network after the charging and discharging operation, obtains feedback data, and adjusts the scheduling strategy according to the feedback data, including the following steps D1 and D2: D1: Using a monitoring device, collect real-time operating data of the distribution network.

[0062] Specifically, after the energy storage device executes the charging and discharging strategy, the power quality monitoring equipment of the distribution network continues to monitor in real time and feeds back the new power quality data to the cloud platform.

[0063] In one possible implementation, the monitoring device in the closed-loop feedback module can also be replaced by a combination of user-side smart meters and distributed energy controllers. The user-side smart meters collect user electricity consumption data in real time, and the distributed energy controller collects power data. The two types of devices transmit data to the closed-loop feedback module through a preset communication network and execute subsequent loop processes. This implementation reduces system deployment costs by utilizing existing user-side equipment to achieve data collection, while covering key data nodes for interaction between the user side and the distribution network.

[0064] In another possible implementation, the monitoring device in the closed-loop feedback module can be replaced by a combination of a distribution network area intelligent fusion terminal and a distributed fault indicator. The distribution network area intelligent fusion terminal is deployed on the high-voltage side and low-voltage side of the distribution area to collect operating data; the distributed fault indicator is used to monitor line current changes and ground fault abnormal signals; the two types of devices transmit the collected real-time data and abnormal signals to the closed-loop feedback module through a preset communication network.

[0065] D2: Preprocess real-time operating data, obtain feedback data, and compare it with the power quality operating threshold in the cloud management platform.

[0066] Specifically, if the feedback data does not reach the power quality operating threshold, the feedback data is transmitted to the scheduling strategy formulation module to update the scheduling strategy.

[0067] It should be noted that the platform reassesses and adjusts its strategies based on feedback data, forming a closed-loop regulation and control system to continuously optimize the power quality of the power grid.

[0068] Specifically, the feedback data is used as the acquisition parameters to re-execute steps S200 to S400.

[0069] In summary, the data acquisition module captures distribution network monitoring data, providing reliable data support for dispatching decisions; the dispatching strategy formulation module, through the centralized analysis capabilities of the cloud management platform, effectively combines monitoring data with the energy storage operation status, making the formulated dispatching strategies more aligned with the actual needs of the distribution network; the command execution module responds to power quality control requirements through user-side equipment charging and discharging operations, ensuring the implementation of the dispatching strategy; and the closed-loop feedback module constructs a real-time correction mechanism to adjust the dispatching strategy to eliminate deviations. The entire system forms a complete closed loop, effectively improving the stability of power quality in the distribution network. While optimizing the utilization efficiency of energy storage resources and reducing system operating costs, it also enhances the adaptability to distributed loads and new energy access, ensuring the safe and efficient operation of the distribution network.

[0070] Example 3 is the third embodiment of the present invention. This embodiment provides a method for optimizing energy storage scheduling in a distribution network, including: monitoring the distribution network, acquiring monitoring index data, and transmitting the monitoring index data to a cloud management platform; analyzing the monitoring index data on the management platform, acquiring calculation results, and formulating a scheduling strategy based on the calculation results; controlling user-side equipment to perform charging and discharging operations using the scheduling strategy; monitoring the distribution network after the charging and discharging operations, acquiring feedback data, and adjusting the scheduling strategy based on the feedback data.

[0071] Example 4, the fourth embodiment of the present invention, differs from the previous three embodiments in that: if the function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0072] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0073] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0074] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination of all three. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0075] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An energy storage dispatch optimization system for a power distribution network, characterized in that, include: The data acquisition module monitors the power distribution network, acquires monitoring indicator data, and transmits the monitoring indicator data to the cloud management platform. The scheduling strategy formulation module analyzes the monitoring indicator data based on the management platform, obtains the calculation results, and formulates a scheduling strategy based on the calculation results. The instruction execution module controls the user-side equipment to perform charging and discharging operations according to the scheduling strategy; the closed-loop feedback module monitors the distribution network after the charging and discharging operation, obtains feedback data, and adjusts the scheduling strategy according to the feedback data.

2. The energy storage dispatch optimization system for a distribution network as described in claim 1, characterized in that... The operation steps of the data acquisition module include: collecting power distribution network operation data by deploying a monitoring device in the power distribution network; preprocessing the power distribution network operation data to obtain the monitoring indicator data; and transmitting the monitoring indicator data to the cloud management platform for storage through a preset communication network.

3. The energy storage dispatch optimization system for a distribution network as described in claim 2, characterized in that... The operation steps of the scheduling strategy formulation module include: retrieving the monitoring indicator data from the cloud management platform, classifying the monitoring indicator data, and obtaining the classification results; identifying abnormal items of parameters in the distribution network based on the classification results, and assessing the impact range of the abnormal items; calculating the charging and discharging related parameters of the user-side equipment based on the abnormal items, the impact range, and historical monitoring indicator data; and formulating the scheduling strategy based on the charging and discharging related parameters.

4. The energy storage dispatch optimization system for a distribution network as described in claim 3, characterized in that... The operation steps of the instruction execution module include: parsing the control parameters in the scheduling strategy; constructing control instructions based on the control parameters; transmitting the control instructions to the user-side device through the preset communication network; and the user-side device executing the charging and discharging operation according to the control instructions.

5. The energy storage dispatch optimization system for a distribution network as described in claim 4, characterized in that... The operation steps of the closed-loop feedback module include: using the monitoring device to collect real-time operating data of the distribution network; preprocessing the real-time operating data to obtain feedback data and comparing it with the power quality operating threshold in the cloud management platform; if the feedback data does not reach the power quality operating threshold, transmitting the feedback data to the scheduling strategy formulation module to update the scheduling strategy.

6. The energy storage dispatch optimization system for a distribution network as described in claim 3, characterized in that... The specific manifestations of assessing the scope of impact of the anomalies include: In the formula, This represents the minimum value of the optimization variable. and Indicates the weighting coefficient. This indicates the current power quality index. This indicates a power quality reference value. This represents the system operating cost.

7. The energy storage dispatch optimization system for a distribution network as described in claim 6, characterized in that... The specific manifestations of scheduling strategies include: In the formula, express The output power of the time-of-flight scheduling 、 、 These represent the control gain coefficients for voltage, reactive power, and harmonics, respectively. Indicates voltage deviation. Indicates reactive power deviation. This indicates the deviation of the harmonic current index.

8. A method for optimizing energy storage dispatch in a distribution network, using an energy storage dispatch optimization system for a distribution network as described in any one of claims 1 to 7, characterized in that... include: The power distribution network is monitored, monitoring indicator data is obtained, and the monitoring indicator data is transmitted to the cloud management platform; The management platform analyzes the monitoring indicator data, obtains calculation results, and formulates scheduling strategies based on the calculation results. The scheduling strategy controls the user-side equipment to perform charging and discharging operations; the power distribution network after the charging and discharging operations are monitored, feedback data is obtained, and the scheduling strategy is adjusted based on the feedback data.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the energy storage dispatch optimization system for a power distribution network according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the energy storage dispatch optimization system for a power distribution network according to any one of claims 1 to 7.

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