Urban distributed energy storage configuration method, system and platform

Abstract

The present application relates to the technical field of energy storage configuration, and discloses a kind of urban distributed energy storage configuration method, system and platform, its method includes: real-time acquisition target city area in distributed energy storage system Multi-source data and target city area operation data and carry out data fusion processing, obtain city operation state data set;Determine energy storage configuration period and dynamically evaluate power grid demand characteristics, define the core function role and auxiliary function role of current energy storage configuration period to energy storage unit, build energy storage function role set;Preset configuration target, establish energy storage configuration optimization model, input corresponding data to obtain the energy storage configuration strategy of energy storage unit and carry out summary processing, generate comprehensive energy storage configuration strategy;Execute comprehensive energy storage configuration strategy and real-time feedback operation data, iteratively update configuration target, energy storage configuration optimization model and function role;Effectively optimize the configuration strategy of urban distributed energy storage.

Description

Technical Field

[0001] This invention relates to the field of energy storage configuration technology, and in particular to a method, system and platform for configuring distributed energy storage in cities. Background Technology

[0002] With the integration of new energy sources and the increase in urban power load, distributed energy storage is widely used in urban power grids. Existing distributed energy storage configuration methods mostly focus on static configuration during the planning stage or single configuration during the operation stage. However, static configuration is difficult to adapt to the complex and ever-changing configuration requirements of the current power grid. In addition, the functions of energy storage units are different in different time periods and scenarios, and the required energy storage configurations are also different. A single configuration cannot fully utilize the functions of energy storage units.

[0003] Therefore, the present invention provides a method, system and platform for configuring distributed energy storage in cities. Summary of the Invention

[0004] This invention provides a method, system, and platform for configuring distributed energy storage in cities. It acquires a city operational status dataset by real-time collection of multi-source data from distributed energy storage systems within a target city area, along with operational data from the target city area, and performs data fusion processing. The invention then determines the energy storage configuration cycle and dynamically assesses grid demand characteristics. It defines core and auxiliary functional roles for each energy storage unit within the current configuration cycle, constructing a set of energy storage functional roles. It presets configuration targets, establishes an energy storage configuration optimization model, inputs relevant data to obtain energy storage configuration strategies for each energy storage unit, and performs aggregated processing to generate a comprehensive energy storage configuration strategy. The invention executes the comprehensive energy storage configuration strategy and provides real-time feedback on operational data, iteratively updating the configuration targets, energy storage configuration optimization model, and functional roles. This effectively optimizes the configuration strategy for distributed energy storage in cities.

[0005] This invention provides a method for configuring distributed energy storage in urban areas, comprising: Real-time collection of multi-source data from distributed energy storage systems within the target city area, as well as operational data from the target city area, and data fusion processing are performed to obtain a city operational status dataset. Based on the city operation status dataset, the energy storage configuration cycle is determined and the grid demand characteristics are dynamically evaluated. The core functional roles and auxiliary functional roles of each energy storage unit in the target city area are defined for the current energy storage configuration cycle, and a set of energy storage functional roles is constructed. The system sets up configuration targets for distributed energy storage, establishes an energy storage configuration optimization model, inputs relevant data based on the energy storage role set, obtains the energy storage configuration strategies of each energy storage unit in the current energy storage configuration cycle and future energy storage configuration cycles, and summarizes and processes these strategies to generate a comprehensive energy storage configuration strategy. Implement integrated energy storage configuration strategies and provide real-time feedback on operational data, iteratively updating configuration targets, energy storage configuration optimization models, and functional roles.

[0006] According to the present invention, a method for configuring distributed energy storage in a city involves real-time collection of multi-source data from distributed energy storage systems within a target city area, as well as operational data from the target city area, and performing data fusion processing to obtain a city operational status dataset, including: A target city area is defined, and multi-source data of distributed energy storage systems within the target city area are collected in real time. The multi-source data includes: energy storage unit data, sub-control unit data, main control unit data, central control unit data, and other unit data. Real-time collection of operational data within the target city area, including: power grid operation data, environmental data, geographic data, and social relational data; Based on time synchronization processing and spatial mapping processing, multi-source data and operational data are fused to obtain a city operational status dataset.

[0007] According to the present invention, a method for configuring distributed energy storage in urban areas determines the energy storage configuration cycle based on urban operation status datasets and dynamically assesses grid demand characteristics, including: Data is selected from the urban operation status dataset to obtain load data and energy storage data from the power grid operation data and perform data processing, including data identification processing, data correction processing and data standardization processing. Extract load data features from the processed load data and plot the load curve; Plot the energy storage curve based on the processed energy storage data; Obtain the volatility and error rate of the load curve and energy storage curve for the current period, identify the energy storage configuration data of the power grid for the current period, and determine the current energy storage configuration cycle according to the preset cycle rule base; Dynamic assessments are conducted based on the energy storage curves and load curves within the current energy storage configuration cycle to obtain grid demand characteristics. These dynamic assessments include dynamic security assessment, dynamic flexibility assessment, and dynamic utilization rate assessment.

[0008] A method for configuring distributed energy storage in urban areas according to the present invention further includes: When the current energy storage configuration period is determined, energy storage configuration is performed according to the current energy storage configuration period, and the energy storage configuration period is re-determined at the beginning of the next energy storage configuration period. Obtain the grid demand characteristics of the previous energy storage configuration cycle, compare and analyze them with the grid demand characteristics of the current energy storage configuration cycle, and optimize the preset energy storage configuration for the current energy storage configuration cycle.

[0009] According to a method for configuring distributed energy storage in a city provided by the present invention, the core functional roles and auxiliary functional roles of each energy storage unit in the target city area during the current energy storage configuration cycle are defined, and a set of energy storage functional roles is constructed, including: Based on the grid security requirements characteristics within the current energy storage configuration cycle, key nodes of the distributed energy storage system within the current energy storage configuration cycle are defined. The distance and response speed between each energy storage unit and key nodes within the target city area are obtained. Combined with the real-time state of charge, energy storage health, and energy storage configuration constraints of each energy storage unit, the core functional role and auxiliary functional role of each energy storage unit in the current energy storage configuration cycle are defined through a preset role allocation strategy. Each energy storage unit is classified and aggregated according to its core functional role, and aggregated data is obtained to construct a set of energy storage functional roles within the current energy storage configuration cycle.

[0010] According to the present invention, a method for configuring distributed energy storage in urban areas involves obtaining and summarizing the energy storage configuration strategies of each energy storage unit in the current and future energy storage configuration cycles to generate a comprehensive energy storage configuration strategy, including: Obtain the current energy storage configuration requirements for the current energy storage configuration cycle, preset the configuration target of distributed energy storage configuration, set the quantitative configuration objective function and the configuration target optimization priority weight, and establish an energy storage configuration optimization model by combining the energy storage unit constraints and grid constraints. According to the energy storage role set, the corresponding data is obtained and input into the energy storage configuration optimization model. A set of optimal energy storage configuration solutions is obtained according to the multi-objective optimization algorithm. The corresponding data includes: the city current operation status dataset, the city predicted operation status dataset, and aggregated data of the energy storage units corresponding to the energy storage role set. Quantify the priority of the current energy storage configuration demand and sort the priorities. Select an optimal energy storage configuration from the set of optimal energy storage configuration solutions based on the current energy storage configuration demand corresponding to the highest priority. This is denoted as the total energy storage configuration strategy of the energy storage unit in the current energy storage configuration cycle. Obtain the subset of energy storage roles corresponding to each energy storage unit in the current energy storage configuration cycle, split the total energy storage configuration strategy for the current energy storage configuration cycle, and obtain the energy storage configuration strategy for each energy storage unit in the current energy storage configuration cycle. Based on the city's predicted operational status dataset, the future energy storage configuration requirements for the future energy storage configuration cycle are obtained and prioritized. An optimal energy storage configuration is selected from the set of optimal energy storage configuration solutions, and is denoted as the total energy storage configuration strategy for each energy storage unit in the future energy storage configuration cycle. Obtain the subset of energy storage roles corresponding to each energy storage unit in the future energy storage configuration cycle. Based on the primary energy storage configuration strategy, further decompose the total energy storage configuration strategy for the future energy storage configuration cycle to obtain the secondary energy storage configuration strategy for each energy storage unit in the future energy storage configuration cycle. Based on the configuration conflict detection of primary and secondary energy storage configuration strategies of each energy storage unit, when there is a configuration conflict between the primary and secondary energy storage configuration strategies of the same energy storage unit, the configuration conflict is adjusted according to the priority of the current energy storage configuration requirements. When there is no configuration conflict between the primary energy storage configuration strategy and the secondary energy storage configuration strategy of the same energy storage unit, the configuration is smoothly adjusted according to the operational safety of the distributed energy storage system. The results of configuration conflict adjustments and configuration smoothing adjustments are summarized to generate a comprehensive energy storage configuration strategy.

[0011] According to the present invention, a method for configuring distributed energy storage in urban areas is provided, which executes a comprehensive energy storage configuration strategy and provides real-time feedback on operational data, and iteratively updates the configuration objectives, energy storage configuration optimization model, and functional roles, including: The integrated energy storage configuration strategy is decomposed into energy storage configuration control commands that can be executed by each energy storage unit and sent to the distributed energy storage system in the target city area to execute the integrated energy storage configuration strategy. The distributed energy storage system is monitored in real time to obtain the actual operating data of each energy storage unit and provide real-time feedback. The real-time feedback results are compared and analyzed with the preset configuration targets. Combined with the latest urban operation status dataset, the preset configuration targets, energy storage configuration optimization model and functional roles are iteratively updated.

[0012] A method for configuring distributed energy storage in urban areas according to the present invention further includes: The system simulates extreme operating conditions for the distributed energy storage system within a target city area. During the energy storage configuration cycle, it periodically simulates these extreme operating conditions to evaluate the matching degree between the energy storage function role set and the comprehensive energy storage configuration strategy. Based on the matching degree, it expands the energy storage function role set.

[0013] This invention provides an urban distributed energy storage configuration system, comprising: The data acquisition and processing module is used to collect multi-source data of distributed energy storage systems and operational data of the target city area in real time, and perform data fusion processing to obtain a city operation status dataset. The energy storage function role module is used to determine the energy storage configuration cycle based on the city operation status dataset and dynamically evaluate the grid demand characteristics. It defines the core functional roles and auxiliary functional roles of each energy storage unit in the target city area for the current energy storage configuration cycle and constructs a set of energy storage function roles. The integrated energy storage configuration module is used to preset the configuration target of distributed energy storage configuration, establish an energy storage configuration optimization model, input relevant data according to the energy storage role set, obtain the energy storage configuration strategy of each energy storage unit in the current energy storage configuration cycle and the future energy storage configuration cycle, and perform summary processing to generate an integrated energy storage configuration strategy. The energy storage configuration feedback module is used to execute comprehensive energy storage configuration strategies and provide real-time feedback on operational data, and to iteratively update configuration targets, energy storage configuration optimization models, and functional roles.

[0014] This invention provides an urban distributed energy storage configuration platform, comprising: Distributed energy storage systems are used to store and visualize energy within a target urban area. A distributed energy storage configuration system is used to configure and visualize the energy storage of a distributed energy storage system.

[0015] The beneficial effects of this invention compared to the prior art are as follows: By collecting multi-source data from distributed energy storage systems within the target city area in real time, as well as operational data from the target city area, and performing data fusion processing, a city operational status dataset is obtained. The energy storage configuration cycle is determined, and the grid demand characteristics are dynamically assessed. Core functional roles and auxiliary functional roles are defined for each energy storage unit within the current energy storage configuration cycle, constructing a set of energy storage functional roles. Configuration targets are preset, an energy storage configuration optimization model is established, and corresponding data is input to obtain the energy storage configuration strategies for each energy storage unit. These strategies are then aggregated and processed to generate a comprehensive energy storage configuration strategy. The comprehensive energy storage configuration strategy is executed, and operational data is fed back in real time. The configuration targets, energy storage configuration optimization model, and functional roles are iteratively updated. This effectively optimizes the configuration strategy for urban distributed energy storage.

[0016] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in this application.

[0017] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0018] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 A flowchart illustrating a method for configuring distributed energy storage in urban areas, as provided in an embodiment of the present invention; Figure 2This is a schematic diagram of the structure of a distributed energy storage system provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an urban distributed energy storage configuration system provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of an urban distributed energy storage configuration platform provided in an embodiment of the present invention. Detailed Implementation

[0019] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0020] Example 1: This invention provides a method for configuring distributed energy storage in urban areas, referring to... Figure 1 ,include: Real-time collection of multi-source data from distributed energy storage systems within the target city area, as well as operational data from the target city area, and data fusion processing are performed to obtain a city operational status dataset. Based on the city operation status dataset, the energy storage configuration cycle is determined and the grid demand characteristics are dynamically evaluated. The core functional roles and auxiliary functional roles of each energy storage unit in the target city area are defined for the current energy storage configuration cycle, and a set of energy storage functional roles is constructed. The system sets up configuration targets for distributed energy storage, establishes an energy storage configuration optimization model, inputs relevant data based on the energy storage role set, obtains the energy storage configuration strategies of each energy storage unit in the current energy storage configuration cycle and future energy storage configuration cycles, and summarizes and processes these strategies to generate a comprehensive energy storage configuration strategy. Implement integrated energy storage configuration strategies and provide real-time feedback on operational data, iteratively updating configuration targets, energy storage configuration optimization models, and functional roles.

[0021] In this embodiment, the distributed energy storage system refers to a system that is geographically dispersed and deployed within the target urban area.

[0022] In this embodiment, multi-source data refers to data obtained from the sources of each unit in the distributed energy storage system.

[0023] In this embodiment, the target city area operation data refers to the data related to energy storage operation within the target city area.

[0024] In this embodiment, the city operation status dataset refers to the dataset representing the city operation status of the target city obtained by performing data feature fusion processing based on multi-source data and operation data.

[0025] In this embodiment, the energy storage configuration period refers to the time period during which energy storage configuration is performed.

[0026] In this embodiment, grid demand characteristics refer to the characteristics of grid demand determined based on dynamic assessment results.

[0027] In this embodiment, the core functional role refers to the role corresponding to the main energy storage configuration performed by the energy storage unit during the current energy storage configuration cycle.

[0028] In this embodiment, the auxiliary functional role refers to the role that the energy storage unit temporarily or auxiliaryly plays in addition to its core functional role.

[0029] In this embodiment, the energy storage function role set refers to the set of roles obtained by classifying and aggregating core function roles.

[0030] In this embodiment, the configuration target refers to the preset target for energy storage configuration in the distributed energy storage configuration, i.e., the preset configuration target.

[0031] In this embodiment, the energy storage configuration optimization model refers to a model used to optimize the energy storage configuration of a distributed energy storage system.

[0032] In this embodiment, the corresponding data refers to data that is associated with the energy storage role set.

[0033] In this embodiment, the comprehensive energy storage configuration strategy refers to the overall energy storage configuration strategy that is ultimately optimized and determined by the energy storage units.

[0034] The beneficial effects of the above technical solution are as follows: By collecting multi-source data from distributed energy storage systems and operational data of the target city area in real time and performing data fusion processing, a city operation status dataset is obtained; the energy storage configuration cycle is determined and the grid demand characteristics are dynamically evaluated; the core functional roles and auxiliary functional roles of the energy storage unit in the current energy storage configuration cycle are defined, and a set of energy storage functional roles is constructed; configuration targets are preset, an energy storage configuration optimization model is established, corresponding data is input to obtain the energy storage configuration strategy of the energy storage unit, and the data is summarized and processed to generate a comprehensive energy storage configuration strategy; the comprehensive energy storage configuration strategy is executed and operational data is fed back in real time, and the configuration targets, energy storage configuration optimization model, and functional roles are iteratively updated; the configuration strategy of urban distributed energy storage is effectively optimized.

[0035] Example 2: Based on Example 1, this invention provides a method for configuring distributed energy storage in cities. This method involves real-time collection of multi-source data from distributed energy storage systems within a target city area, as well as operational data from the target city area, followed by data fusion processing to obtain a city operational status dataset, including: A target city area is defined, and multi-source data of distributed energy storage systems within the target city area are collected in real time. The multi-source data includes: energy storage unit data, sub-control unit data, main control unit data, central control unit data, and other unit data. Real-time collection of operational data within the target city area, including: power grid operation data, environmental data, geographic data, and social relational data; Based on time synchronization processing and spatial mapping processing, multi-source data and operational data are fused to obtain a city operational status dataset.

[0036] In this embodiment, the target urban area refers to the urban area where distributed energy storage is planned to be configured.

[0037] In this embodiment, such as Figure 2 As shown, a distributed energy storage system refers to a geographically dispersed system deployed within a target urban area to regulate and balance the power grid's energy supply.

[0038] In this embodiment, such as Figure 2 As shown, the distributed energy storage system includes: BMU sub-control unit, BCU main control unit, BAU central control management unit and other units, including: cloud platform, wireless communication gateway, smart air conditioner, PCS energy storage converter, touch screen display unit and metering meter.

[0039] In this embodiment, each battery cell, i.e., energy storage unit, integrates a BMU sub-control unit, which is used to collect battery voltage and battery temperature and upload them to the BCU main control unit, and at the same time receive control commands issued by the BCU main control unit.

[0040] In this embodiment, the central control management unit communicates bidirectionally with the PCS energy storage converter, the BCU main control unit, and the BCU main control unit and the BMU sub-control unit via a CAN bus.

[0041] In this embodiment, such as Figure 4 As shown, 232 is used for data exchange; 485 indicates that it is used for communication connection and data transmission.

[0042] In this embodiment, the BCU main control unit is used to collect the total voltage of the battery cells.

[0043] In this embodiment, multi-source data refers to data obtained from the sources of each unit in the distributed energy storage system, such as energy storage unit data, sub-control unit data, main control unit data, central control unit data, and other unit data.

[0044] In this embodiment, the operational data refers to data related to energy storage operation within the target city area, including grid operation data, environmental data, geographic data, and socially relevant data. Among these, the socially relevant data includes traffic flow data, public activity data, and population flow data based on the target city area.

[0045] In this embodiment, the social connection data is sourced from a publicly available data platform.

[0046] In this embodiment, time synchronization processing refers to synchronizing data with consistent time series to facilitate subsequent data processing.

[0047] In this embodiment, spatial mapping processing refers to the process of synchronously mapping data with consistent spatial sequences, which facilitates the subsequent processing of data monitored together.

[0048] In this embodiment, data feature fusion processing refers to extracting data features from multi-source data and running data respectively, and performing feature fusion processing based on time synchronization processing and spatial mapping processing.

[0049] In this embodiment, the city operation status dataset refers to the dataset representing the city operation status of the target city obtained by performing data feature fusion processing based on multi-source data and operation data, which is used to simplify the data and ensure data accuracy.

[0050] The beneficial effects of the above technical solution are as follows: by collecting multi-source data of distributed energy storage systems and operational data of the target city area in real time and performing data fusion processing, a city operation status dataset is obtained, which lays a data foundation for subsequent configuration of distributed energy storage in the city.

[0051] Example 3: Based on Example 1, this invention provides a method for configuring distributed energy storage in cities, which determines the energy storage configuration cycle based on a city operation status dataset and dynamically assesses grid demand characteristics, including: Data is selected from the urban operation status dataset to obtain load data and energy storage data from the power grid operation data and perform data processing, including data identification processing, data correction processing and data standardization processing. Extract load data features from the processed load data and plot the load curve; Plot the energy storage curve based on the processed energy storage data; Obtain the volatility and error rate of the load curve and energy storage curve for the current period, identify the energy storage configuration data of the power grid for the current period, and determine the current energy storage configuration cycle according to the preset cycle rule base; Dynamic assessments are conducted based on the energy storage curves and load curves within the current energy storage configuration cycle to obtain grid demand characteristics. These dynamic assessments include dynamic security assessment, dynamic flexibility assessment, and dynamic utilization rate assessment.

[0052] In this embodiment, data selection refers to selecting specific data from the city operation status dataset for subsequent data analysis and evaluation processing, such as selecting load data from the city operation status dataset.

[0053] In this embodiment, load data refers to the data on energy consumption in the power grid operation data, such as user data.

[0054] In this embodiment, energy storage data refers to the data stored by the distributed energy storage system.

[0055] In this embodiment, there are problems such as data acquisition interruption and data acquisition error during the data acquisition process. Therefore, load data and energy storage data in the power grid operation data are acquired and processed to ensure the accuracy of the data.

[0056] In this embodiment, the load curve refers to the curve obtained by feature extraction of the load data, which is used to simplify and accurately represent the load data.

[0057] In this embodiment, the energy storage curve refers to the curve drawn based on the energy storage data after data processing.

[0058] In this embodiment, volatility refers to the fluctuations in the load curve and energy storage curve caused by unforeseen circumstances.

[0059] In this embodiment, the error rate refers to the error between the load curve and the energy storage curve caused by prediction.

[0060] In this embodiment, the volatility and error rate of the load curve and energy storage curve in the current time period refer to the volatility and error rate of the load curve and the volatility and error rate of the energy storage curve.

[0061] In this embodiment, the energy storage configuration data refers to the data on energy storage configuration carried out by the power grid during the current time period.

[0062] In this embodiment, the preset cycle rule base refers to a preset rule base used to determine the energy storage configuration cycle. For example, when the volatility of both the load curve and the energy storage curve is lower than a1 and the error rate is lower than b1, it is determined that the energy storage configuration cycle adopts a short energy storage configuration cycle, such as a 4-hour cycle.

[0063] In this embodiment, dynamic evaluation refers to real-time dynamic evaluation based on the energy storage curve and load curve within the current energy storage configuration cycle.

[0064] In this embodiment, grid demand characteristics refer to the characteristics of grid demand determined based on dynamic assessment results, where grid demand refers to the grid's need for energy storage configuration.

[0065] In this embodiment, dynamic security assessment, dynamic flexibility assessment, and dynamic utilization assessment refer to dynamic assessment from three dimensions, and dynamic assessment weights are allocated according to the current grid operation. Finally, the dynamic assessment results are obtained by weighted comprehensive calculation, which are then used for subsequent energy storage configuration.

[0066] The beneficial effects of the above technical solutions are: by determining the energy storage configuration cycle and dynamically assessing the grid demand characteristics through the urban operation status dataset, the urban operation status dataset is fully utilized, which facilitates accurate energy storage configuration in the future.

[0067] Example 4: Based on Example 3, the present invention provides a method for configuring distributed energy storage in cities, which further includes: When the current energy storage configuration period is determined, energy storage configuration is performed according to the current energy storage configuration period, and the energy storage configuration period is re-determined at the beginning of the next energy storage configuration period. Obtain the grid demand characteristics of the previous energy storage configuration cycle, compare and analyze them with the grid demand characteristics of the current energy storage configuration cycle, and optimize the preset energy storage configuration for the current energy storage configuration cycle.

[0068] In this embodiment, the current energy storage configuration cycle refers to the energy storage configuration cycle of the current time period.

[0069] In this embodiment, the next energy storage configuration cycle refers to the energy storage configuration cycle in the future.

[0070] In this embodiment, the energy storage configuration cycle is redefined at the start of the next energy storage configuration cycle to ensure that the energy storage configuration cycle is adapted to the energy storage configuration.

[0071] In this embodiment, the preset energy storage configuration refers to the preset energy storage configuration.

[0072] In this embodiment, comparative analysis refers to comparative analysis in three dimensions: security, flexibility, and utilization, in order to optimize the preset energy storage configuration for the current energy storage configuration cycle.

[0073] The beneficial effects of the above technical solutions are as follows: by redetermining the energy storage configuration cycle and optimizing the preset energy storage configuration, the accuracy and adaptability of the energy storage configuration cycle and the preset energy storage configuration are ensured.

[0074] Example 5: Based on Example 4, this invention provides a method for configuring distributed energy storage in cities. It defines core functional roles and auxiliary functional roles for each energy storage unit within a target urban area during the current energy storage configuration cycle, constructing a set of energy storage functional roles, including: Based on the grid security requirements characteristics within the current energy storage configuration cycle, key nodes of the distributed energy storage system within the current energy storage configuration cycle are defined. The distance and response speed between each energy storage unit and key nodes within the target city area are obtained. Combined with the real-time state of charge, energy storage health, and energy storage configuration constraints of each energy storage unit, the core functional role and auxiliary functional role of each energy storage unit in the current energy storage configuration cycle are defined through a preset role allocation strategy. Each energy storage unit is classified and aggregated according to its core functional role, and aggregated data is obtained to construct a set of energy storage functional roles within the current energy storage configuration cycle.

[0075] In this embodiment, the key node refers to the main configuration node for energy storage configuration of the distributed energy storage system within the current energy storage configuration cycle, which is determined based on the characteristics of grid security requirements within the current energy storage configuration cycle.

[0076] In this embodiment, the preset role allocation strategy refers to a preset strategy for assigning energy storage configuration roles to energy storage units. The matching degree between the energy storage unit and the target role is calculated, and the role is assigned to the energy storage sub-unit with the high matching degree. For example, the role b1 is assigned to the energy storage unit a1 for rapid voltage adjustment.

[0077] In this embodiment, the core functional role refers to the role that the energy storage unit mainly performs in the current energy storage configuration cycle. For example, if the energy storage unit a1 mainly performs rapid voltage regulation in the current energy storage configuration cycle, then the core functional role of the energy storage unit a1 is the voltage regulation core functional role.

[0078] In this embodiment, the auxiliary function role refers to the role that the energy storage unit temporarily plays or assists in addition to its core function role. For example, in addition to quickly regulating the voltage, the energy storage unit a1 temporarily plays the auxiliary function role of fluctuation suppression.

[0079] In this embodiment, the energy storage unit is generally assigned one core functional role and one or more auxiliary functional roles.

[0080] In this embodiment, classification aggregation refers to aggregating energy storage units according to their core functional roles, so as to facilitate the energy storage configuration of the energy storage units together.

[0081] In this embodiment, aggregated data refers to data obtained by aggregation according to classification, which is used to represent the same type of energy storage unit, including data of core functional roles and data of auxiliary functional roles corresponding to all the same type of energy storage units.

[0082] In this embodiment, the energy storage function role set refers to the set of roles obtained by classifying and aggregating core function roles, which is used to comprehensively represent the energy storage unit.

[0083] The beneficial effects of the above technical solution are as follows: defining the core functional roles and auxiliary functional roles of each energy storage unit in the target city area for the current energy storage configuration cycle, constructing a set of energy storage functional roles, and ensuring the accuracy and flexibility of subsequent energy storage configuration.

[0084] Example 6: Based on Example 1, this invention provides a method for configuring distributed energy storage in cities, which obtains the energy storage configuration strategies of each energy storage unit in the current energy storage configuration cycle and future energy storage configuration cycles, and performs summary processing to generate a comprehensive energy storage configuration strategy, including: Obtain the current energy storage configuration requirements for the current energy storage configuration cycle, preset the configuration target of distributed energy storage configuration, set the quantitative configuration objective function and the configuration target optimization priority weight, and establish an energy storage configuration optimization model by combining the energy storage unit constraints and grid constraints. According to the energy storage role set, the corresponding data is obtained and input into the energy storage configuration optimization model. A set of optimal energy storage configuration solutions is obtained according to the multi-objective optimization algorithm. The corresponding data includes: the city current operation status dataset, the city predicted operation status dataset, and aggregated data of the energy storage units corresponding to the energy storage role set. Quantify the priority of the current energy storage configuration demand and sort the priorities. Select an optimal energy storage configuration from the set of optimal energy storage configuration solutions based on the current energy storage configuration demand corresponding to the highest priority. This is denoted as the total energy storage configuration strategy of the energy storage unit in the current energy storage configuration cycle. Obtain the subset of energy storage roles corresponding to each energy storage unit in the current energy storage configuration cycle, split the total energy storage configuration strategy for the current energy storage configuration cycle, and obtain the energy storage configuration strategy for each energy storage unit in the current energy storage configuration cycle. Based on the city's predicted operational status dataset, the future energy storage configuration requirements for the future energy storage configuration cycle are obtained and prioritized. An optimal energy storage configuration is selected from the set of optimal energy storage configuration solutions, and is denoted as the total energy storage configuration strategy for each energy storage unit in the future energy storage configuration cycle. Obtain the subset of energy storage roles corresponding to each energy storage unit in the future energy storage configuration cycle. Based on the primary energy storage configuration strategy, further decompose the total energy storage configuration strategy for the future energy storage configuration cycle to obtain the secondary energy storage configuration strategy for each energy storage unit in the future energy storage configuration cycle. Based on the configuration conflict detection of primary and secondary energy storage configuration strategies of each energy storage unit, when there is a configuration conflict between the primary and secondary energy storage configuration strategies of the same energy storage unit, the configuration conflict is adjusted according to the priority of the current energy storage configuration requirements. When there is no configuration conflict between the primary energy storage configuration strategy and the secondary energy storage configuration strategy of the same energy storage unit, the configuration is smoothly adjusted according to the operational safety of the distributed energy storage system. The results of configuration conflict adjustments and configuration smoothing adjustments are summarized to generate a comprehensive energy storage configuration strategy.

[0085] In this embodiment, the current energy storage configuration requirement refers to the energy storage unit's energy storage configuration requirement within the current energy storage configuration cycle.

[0086] In this embodiment, the configuration target of the preset distributed energy storage configuration is, for example, the configuration target of the preset security dimension.

[0087] In this embodiment, the configuration target function refers to a function used to represent a preset configuration target, which is used to accurately represent the preset configuration target and facilitates data analysis.

[0088] In this embodiment, the optimization priority weight of the configuration target refers to the priority weight of the preset configuration target for optimization. For example, the priority weight of the security dimension configuration target a1 is b1, and the priority weight of the flexibility dimension configuration target a2 is b2. If b1 > b2, then the security dimension target a1 will be optimized first.

[0089] In this embodiment, the quantization of the configuration objective function is used to ensure the consistency of each configuration objective function.

[0090] In this embodiment, the energy storage unit constraint conditions refer to the constraints on the energy storage unit during operation, such as temperature constraints.

[0091] In this embodiment, the power grid constraint conditions refer to the constraints on the power grid during its operation.

[0092] In this embodiment, the energy storage configuration optimization model refers to a model used to optimize the energy storage configuration of a distributed energy storage system.

[0093] In this embodiment, corresponding data is obtained based on the energy storage role set and input into the energy storage configuration optimization model, wherein the corresponding data refers to data that is associated with the energy storage role set.

[0094] In this embodiment, the multi-objective optimization algorithm refers to the algorithm used to optimize the energy storage configuration in the energy storage configuration optimization model.

[0095] In this embodiment, an optimal energy storage configuration solution set refers to a set of optimal solutions obtained according to the energy storage configuration optimization model. Since the optimization objectives of different energy storage units are not the same, there are multiple optimal solutions.

[0096] In this embodiment, the total energy storage configuration strategy for the current energy storage configuration cycle refers to the optimal energy storage configuration strategy of the energy storage unit within the current energy storage configuration cycle, and refers to the overall energy storage configuration strategy of the distributed energy storage system within the current energy storage configuration cycle.

[0097] In this embodiment, corresponding data is obtained based on the energy storage role set and input into the energy storage configuration optimization model, and a set of optimal energy storage configuration solutions is obtained according to the multi-objective optimization algorithm. Where F represents a set of optimal energy storage configuration solutions; Di1 represents the ratio of the actual discharge depth to the rated discharge depth of the i1th energy storage unit; u0 represents the fitting parameter of the actual discharge depth and the number of charge-discharge cycles of the energy storage unit; u1 represents the fitting parameter of the rated discharge depth and the number of charge-discharge cycles of the energy storage unit; e represents the natural base; d(i1,j1) represents the charge-discharge amount of the i1th energy storage unit during the j1st charge-discharge cycle; M represents the rated total number of charge-discharge cycles of the energy storage unit; S(i1,j1) represents the real-time state of charge of the i1th energy storage unit during the j1st charge-discharge cycle; Ei1 represents the stored energy of the i1th energy storage unit. Let (i, y) represent the utilization rate configuration objective function; Vi1 represents the response speed of the i1th energy storage unit and the critical node (X, Y); (xi1, yj1) represents the location data of the i1th energy storage unit. This represents the distance between the i1th energy storage unit and the critical node; pi1 represents the total value of the volatility and error rate of the load curve and the energy storage curve; 1-pi1 represents the safety configuration objective function; a1 represents the optimization priority weight of the utilization configuration objective function; a2 represents the optimization priority weight of the flexibility configuration objective function; a3 represents the optimization priority weight of the safety configuration objective function; n1 represents the number of energy storage units; m1 represents the number of charge and discharge cycles of the energy storage unit in the current energy storage configuration cycle.

[0098] In this embodiment, the energy storage role subset corresponding to the current energy storage configuration cycle refers to the set of roles corresponding to a single energy storage unit in the current energy storage configuration cycle.

[0099] In this embodiment, a split refers to the splitting of the total energy storage configuration strategy for the current energy storage configuration cycle based on the subset of energy storage roles corresponding to the current energy storage configuration cycle, in order to clarify the energy storage configuration strategy of each energy storage unit.

[0100] In this embodiment, the primary energy storage configuration strategy refers to the specific energy storage configuration strategy of each energy storage unit within the current energy storage configuration cycle.

[0101] In this embodiment, the total energy storage configuration strategy of each energy storage unit in the future energy storage configuration cycle refers to the overall energy storage configuration strategy of each energy storage unit in the future energy storage configuration cycle.

[0102] In this embodiment, the subset of energy storage roles corresponding to the future energy storage configuration cycle refers to the set of roles corresponding to a single energy storage unit in the future energy storage configuration cycle.

[0103] In this embodiment, secondary splitting refers to splitting the total energy storage configuration strategy for future energy storage configuration cycles based on the primary energy storage configuration strategy. In other words, secondary splitting is quickly implemented based on the primary splitting to clarify the energy storage configuration strategy for each energy storage unit.

[0104] In this embodiment, the secondary energy storage configuration strategy refers to the specific energy storage configuration strategy of each energy storage unit during the future energy storage configuration cycle.

[0105] In this embodiment, configuration conflict detection refers to detecting whether there is a conflict between the primary energy storage configuration strategy and the secondary energy storage configuration strategy. For example, if the primary energy storage configuration strategy controls the energy storage unit to discharge completely, and the secondary energy storage configuration strategy controls the same energy storage unit to continue discharging, then it is determined that there is a configuration conflict between the primary energy storage configuration strategy and the secondary energy storage configuration strategy.

[0106] In this embodiment, configuration conflict adjustment refers to adjusting according to configuration conflict items to eliminate configuration conflict items. For example, when an energy storage unit is completely discharged in a primary energy storage configuration strategy, the same energy storage unit is assigned a charging-related energy storage configuration strategy in a secondary energy storage configuration strategy.

[0107] In this embodiment, configuration smoothing adjustment refers to the adjustment that ensures the operational safety of the distributed energy storage system to the maximum extent when switching the energy storage configuration strategy of the energy storage unit, that is, the configuration adjustment made to ensure the lifespan of the energy storage unit to the maximum extent, such as avoiding overcharging and over-discharging of the energy storage unit.

[0108] In this embodiment, the comprehensive energy storage configuration strategy refers to the overall energy storage configuration strategy that is finally optimized and determined by the energy storage unit, which is obtained by summarizing the configuration conflict adjustment results and the configuration smoothing adjustment results.

[0109] The beneficial effects of the above technical solution are as follows: by acquiring and summarizing the energy storage configuration strategies of each energy storage unit in the current energy storage configuration cycle and future energy storage configuration cycles, a comprehensive energy storage configuration strategy is generated, which effectively realizes and optimizes the configuration strategy of urban distributed energy storage.

[0110] Example 7: Based on Example 1, this invention provides a method for configuring urban distributed energy storage, which executes a comprehensive energy storage configuration strategy and provides real-time feedback of operational data, iteratively updating configuration targets, energy storage configuration optimization models, and functional roles, including: The integrated energy storage configuration strategy is decomposed into energy storage configuration control commands that can be executed by each energy storage unit and sent to the distributed energy storage system in the target city area to execute the integrated energy storage configuration strategy. The distributed energy storage system is monitored in real time to obtain the actual operating data of each energy storage unit and provide real-time feedback. The real-time feedback results are compared and analyzed with the preset configuration targets. Combined with the latest urban operation status dataset, the preset configuration targets, energy storage configuration optimization model and functional roles are iteratively updated.

[0111] In this embodiment, the energy storage configuration control command refers to the command obtained by decomposing the comprehensive energy storage configuration strategy and used to control the energy storage configuration of each energy storage unit.

[0112] In this embodiment, the actual operating data refers to the operating data generated by the distributed energy storage system executing the comprehensive energy storage configuration strategy.

[0113] In this embodiment, the preset configuration target, energy storage configuration optimization model, and functional roles are iteratively updated. For example, the priority weight of the preset configuration target is iteratively updated.

[0114] The beneficial effects of the above technical solutions are as follows: by implementing a comprehensive energy storage configuration strategy and providing real-time feedback on operational data, the configuration objectives, energy storage configuration optimization model, and functional roles are iteratively updated, ensuring the long-term adaptability of urban distributed energy storage configuration.

[0115] Example 8: Based on Example 1, the present invention provides a method for configuring distributed energy storage in cities, which further includes: The system simulates extreme operating conditions for the distributed energy storage system within a target city area. During the energy storage configuration cycle, it periodically simulates these extreme operating conditions to evaluate the matching degree between the energy storage function role set and the comprehensive energy storage configuration strategy. Based on the matching degree, it expands the energy storage function role set.

[0116] In this embodiment, the extreme operating conditions for energy storage configuration of the distributed energy storage system within the target city area are preset, such as the operating condition of a sudden failure of a certain energy storage unit.

[0117] In this embodiment, the matching degree between the energy storage functional role set and the comprehensive energy storage configuration strategy is evaluated. For example, a matching degree threshold is set. When the matching degree between the corresponding energy storage functional role set and the comprehensive energy storage configuration strategy is lower than the matching degree threshold during periodic simulations of extreme operating conditions, it indicates that the energy storage functional role set cannot adapt to extreme operating conditions. Then, the functional roles are expanded according to the matching degree.

[0118] The beneficial effects of the above technical solutions are: by conducting regular simulations of extreme operating conditions, the energy storage function set can be expanded, which is conducive to further optimizing the configuration of urban distributed energy storage.

[0119] Example 9: This invention provides an urban distributed energy storage configuration system, referencing... Figure 3 ,include: The data acquisition and processing module is used to collect multi-source data of distributed energy storage systems and operational data of the target city area in real time, and perform data fusion processing to obtain a city operation status dataset. The energy storage function role module is used to determine the energy storage configuration cycle based on the city operation status dataset and dynamically evaluate the grid demand characteristics. It defines the core functional roles and auxiliary functional roles of each energy storage unit in the target city area for the current energy storage configuration cycle and constructs a set of energy storage function roles. The integrated energy storage configuration module is used to preset the configuration target of distributed energy storage configuration, establish an energy storage configuration optimization model, input relevant data according to the energy storage role set, obtain the energy storage configuration strategy of each energy storage unit in the current energy storage configuration cycle and the future energy storage configuration cycle, and perform summary processing to generate an integrated energy storage configuration strategy. The energy storage configuration feedback module is used to execute comprehensive energy storage configuration strategies and provide real-time feedback on operational data, and to iteratively update configuration targets, energy storage configuration optimization models, and functional roles.

[0120] The beneficial effects of the above technical solution are as follows: By collecting multi-source data from distributed energy storage systems and operational data of the target city area in real time and performing data fusion processing, a city operation status dataset is obtained; the energy storage configuration cycle is determined and the grid demand characteristics are dynamically evaluated; the core functional roles and auxiliary functional roles of the energy storage unit in the current energy storage configuration cycle are defined, and a set of energy storage functional roles is constructed; configuration targets are preset, an energy storage configuration optimization model is established, corresponding data is input to obtain the energy storage configuration strategy of the energy storage unit, and the data is summarized and processed to generate a comprehensive energy storage configuration strategy; the comprehensive energy storage configuration strategy is executed and operational data is fed back in real time, and the configuration targets, energy storage configuration optimization model, and functional roles are iteratively updated; the configuration strategy of urban distributed energy storage is effectively optimized.

[0121] Example 10: This invention provides an urban distributed energy storage configuration platform, referencing... Figure 4 ,include: Distributed energy storage systems are used to store and visualize energy within a target urban area. A distributed energy storage configuration system is used to configure and visualize the energy storage of a distributed energy storage system.

[0122] The beneficial effects of the above technical solutions are as follows: by visualizing the distributed energy storage system and the distributed energy storage configuration system, it is beneficial to observe the distributed energy storage status and distributed energy storage configuration status of the target urban area in real time.

[0123] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for configuring distributed energy storage in urban areas, characterized in that, include: The system collects multi-source data from distributed energy storage systems within the target city area in real time, as well as operational data from the target city area, and performs data fusion processing to obtain a city operational status dataset. The multi-source data includes: energy storage unit data, sub-control unit data, main control unit data, central control unit data, and other unit data. The operational data includes: power grid operational data, environmental data, geographic data, and social correlation data. Other units include cloud platforms, wireless communication gateways, smart air conditioners, PCS energy storage converters, touch screen display units, and metering meters. Based on the city operation status dataset, the energy storage configuration cycle is determined and the grid demand characteristics are dynamically evaluated. The core functional roles and auxiliary functional roles of each energy storage unit in the target city area are defined for the current energy storage configuration cycle, and a set of energy storage functional roles is constructed. The system presets configuration targets for distributed energy storage, establishes an energy storage configuration optimization model, inputs relevant data based on the energy storage function role set, obtains and aggregates the energy storage configuration strategies of each energy storage unit in the current and future energy storage configuration cycles, and generates a comprehensive energy storage configuration strategy, which specifically includes: Obtain the current energy storage configuration requirements for the current energy storage configuration cycle, preset the configuration target of distributed energy storage configuration, set the quantitative configuration objective function and the configuration target optimization priority weight, and establish an energy storage configuration optimization model by combining the energy storage unit constraints and grid constraints. According to the set of energy storage function roles, relevant data is obtained and input into the energy storage configuration optimization model. A set of optimal energy storage configuration solutions is obtained according to the multi-objective optimization algorithm. The relevant data includes: the current urban operation status dataset, the predicted urban operation status dataset, and aggregated data of the energy storage units corresponding to the set of energy storage function roles. Quantify the priority of the current energy storage configuration demand and sort the priorities. Select an optimal energy storage configuration from the set of optimal energy storage configuration solutions based on the current energy storage configuration demand corresponding to the highest priority. This is denoted as the total energy storage configuration strategy of the energy storage unit in the current energy storage configuration cycle. Obtain the subset of energy storage roles corresponding to each energy storage unit in the current energy storage configuration cycle, split the total energy storage configuration strategy for the current energy storage configuration cycle, and obtain the energy storage configuration strategy for each energy storage unit in the current energy storage configuration cycle. Based on the city's predicted operational status dataset, the future energy storage configuration requirements for the future energy storage configuration cycle are obtained and prioritized. An optimal energy storage configuration is selected from the set of optimal energy storage configuration solutions, and is denoted as the total energy storage configuration strategy for each energy storage unit in the future energy storage configuration cycle. Obtain the subset of energy storage roles corresponding to each energy storage unit in the future energy storage configuration cycle. Based on the primary energy storage configuration strategy, further decompose the total energy storage configuration strategy for the future energy storage configuration cycle to obtain the secondary energy storage configuration strategy for each energy storage unit in the future energy storage configuration cycle. Based on the configuration conflict detection of primary and secondary energy storage configuration strategies of each energy storage unit, when there is a configuration conflict between the primary and secondary energy storage configuration strategies of the same energy storage unit, the configuration conflict is adjusted according to the priority of the current energy storage configuration requirements. When there is no configuration conflict between the primary energy storage configuration strategy and the secondary energy storage configuration strategy of the same energy storage unit, the configuration is smoothly adjusted according to the operational safety of the distributed energy storage system. Summarize the results of configuration conflict adjustment and configuration smoothing adjustment to generate a comprehensive energy storage configuration strategy; Implement integrated energy storage configuration strategies and provide real-time feedback on operational data, iteratively updating configuration targets, energy storage configuration optimization models, and functional roles.

2. The method for configuring urban distributed energy storage according to claim 1, characterized in that, Real-time collection and fusion processing of multi-source data from distributed energy storage systems within the target city area, as well as operational data of the target city area, yields a city operational status dataset, including: Set a target city area and collect multi-source data from distributed energy storage systems within the target city area in real time; Real-time collection of operational data within the target city area; Based on time synchronization processing and spatial mapping processing, multi-source data and operational data are fused to obtain a city operational status dataset.

3. The method for configuring urban distributed energy storage according to claim 1, characterized in that, Based on the urban operation status dataset, the energy storage configuration cycle is determined and the grid demand characteristics are dynamically assessed, including: Data is selected from the urban operation status dataset to obtain load data and energy storage data from the power grid operation data and perform data processing, including data identification processing, data correction processing and data standardization processing. Extract load data features from the processed load data and plot the load curve; Plot the energy storage curve based on the processed energy storage data; Obtain the volatility and error rate of the load curve and energy storage curve for the current period, identify the energy storage configuration data of the power grid for the current period, and determine the current energy storage configuration cycle according to the preset cycle rule base; Dynamic assessments are conducted based on the energy storage curves and load curves within the current energy storage configuration cycle to obtain grid demand characteristics. These dynamic assessments include dynamic security assessment, dynamic flexibility assessment, and dynamic utilization rate assessment.

4. The method for configuring urban distributed energy storage according to claim 3, characterized in that, Also includes: When the current energy storage configuration period is determined, energy storage configuration is performed according to the current energy storage configuration period, and the energy storage configuration period is re-determined at the beginning of the next energy storage configuration period; Obtain the grid demand characteristics of the previous energy storage configuration cycle, compare and analyze them with the grid demand characteristics of the current energy storage configuration cycle, and optimize the preset energy storage configuration for the current energy storage configuration cycle.

5. A method for configuring urban distributed energy storage according to claim 4, characterized in that, Define the core functional roles and auxiliary functional roles of each energy storage unit within the target city area for the current energy storage configuration cycle, and construct a set of energy storage functional roles, including: Based on the grid security requirements characteristics within the current energy storage configuration cycle, key nodes of the distributed energy storage system within the current energy storage configuration cycle are defined. The distance and response speed between each energy storage unit and key nodes within the target city area are obtained. Combined with the real-time state of charge, energy storage health, and energy storage configuration constraints of each energy storage unit, the core functional role and auxiliary functional role of each energy storage unit in the current energy storage configuration cycle are defined through a preset role allocation strategy. Each energy storage unit is categorized and aggregated according to its core functional role, and aggregated data is obtained to construct a set of energy storage functional roles within the current energy storage configuration cycle.

6. The method for configuring urban distributed energy storage according to claim 1, characterized in that, Implement integrated energy storage configuration strategies and provide real-time feedback on operational data; iteratively update configuration targets, energy storage configuration optimization models, and functional roles, including: The integrated energy storage configuration strategy is decomposed into energy storage configuration control commands that can be executed by each energy storage unit and sent to the distributed energy storage system in the target city area to execute the integrated energy storage configuration strategy. The distributed energy storage system is monitored in real time to obtain the actual operating data of each energy storage unit and provide real-time feedback. The real-time feedback results are compared and analyzed with the preset configuration targets. Combined with the latest urban operation status dataset, the preset configuration targets, energy storage configuration optimization model and functional roles are iteratively updated.

7. The method for configuring urban distributed energy storage according to claim 1, characterized in that, Also includes: The system simulates extreme operating conditions for the distributed energy storage system within a target city area. During the energy storage configuration cycle, it periodically simulates these extreme operating conditions to evaluate the matching degree between the energy storage function role set and the comprehensive energy storage configuration strategy. Based on the matching degree, it expands the energy storage function role set.

8. A city-wide distributed energy storage configuration system, characterized in that, include: The data acquisition and processing module is used to collect multi-source data from distributed energy storage systems within the target city area in real time, as well as the target city area's operational data, and perform data fusion processing to obtain a city operational status dataset. The multi-source data includes: energy storage unit data, sub-control unit data, main control unit data, central control unit data, and other unit data. The operational data includes: power grid operational data, environmental data, geographic data, and socially related data. Other units include a cloud platform, wireless communication gateway, smart air conditioner, PCS energy storage converter, touch screen display unit, and metering meter. The energy storage function role module is used to determine the energy storage configuration cycle based on the city operation status dataset and dynamically evaluate the grid demand characteristics. It defines the core functional roles and auxiliary functional roles of each energy storage unit in the target city area for the current energy storage configuration cycle and constructs a set of energy storage function roles. The integrated energy storage configuration module is used to preset the configuration targets for distributed energy storage configuration, establish an energy storage configuration optimization model, input relevant data according to the energy storage function role set, obtain the energy storage configuration strategies of each energy storage unit in the current energy storage configuration cycle and future energy storage configuration cycles, and perform summary processing to generate an integrated energy storage configuration strategy, which specifically includes: Obtain the current energy storage configuration requirements for the current energy storage configuration cycle, preset the configuration target of distributed energy storage configuration, set the quantitative configuration objective function and the configuration target optimization priority weight, and establish an energy storage configuration optimization model by combining the energy storage unit constraints and grid constraints. According to the set of energy storage function roles, relevant data is obtained and input into the energy storage configuration optimization model. A set of optimal energy storage configuration solutions is obtained according to the multi-objective optimization algorithm. The relevant data includes: the current urban operation status dataset, the predicted urban operation status dataset, and aggregated data of the energy storage units corresponding to the set of energy storage function roles. Quantify the priority of the current energy storage configuration demand and sort the priorities. Select an optimal energy storage configuration from the set of optimal energy storage configuration solutions based on the current energy storage configuration demand corresponding to the highest priority. This is denoted as the total energy storage configuration strategy of the energy storage unit in the current energy storage configuration cycle. Obtain the subset of energy storage roles corresponding to each energy storage unit in the current energy storage configuration cycle, split the total energy storage configuration strategy for the current energy storage configuration cycle, and obtain the energy storage configuration strategy for each energy storage unit in the current energy storage configuration cycle. Based on the city's predicted operational status dataset, the future energy storage configuration requirements for the future energy storage configuration cycle are obtained and prioritized. An optimal energy storage configuration is selected from the set of optimal energy storage configuration solutions, and is denoted as the total energy storage configuration strategy for each energy storage unit in the future energy storage configuration cycle. Obtain the subset of energy storage roles corresponding to each energy storage unit in the future energy storage configuration cycle. Based on the primary energy storage configuration strategy, further decompose the total energy storage configuration strategy for the future energy storage configuration cycle to obtain the secondary energy storage configuration strategy for each energy storage unit in the future energy storage configuration cycle. Based on the configuration conflict detection of primary and secondary energy storage configuration strategies of each energy storage unit, when there is a configuration conflict between the primary and secondary energy storage configuration strategies of the same energy storage unit, the configuration conflict is adjusted according to the priority of the current energy storage configuration requirements. When there is no configuration conflict between the primary energy storage configuration strategy and the secondary energy storage configuration strategy of the same energy storage unit, the configuration is smoothly adjusted according to the operational safety of the distributed energy storage system. Summarize the results of configuration conflict adjustment and configuration smoothing adjustment to generate a comprehensive energy storage configuration strategy; The energy storage configuration feedback module is used to execute comprehensive energy storage configuration strategies and provide real-time feedback on operational data, and to iteratively update configuration targets, energy storage configuration optimization models, and functional roles.

9. A city-wide distributed energy storage configuration platform, characterized in that, The urban distributed energy storage configuration platform includes the distributed energy storage configuration system as described in claim 8 and the distributed energy storage system: Distributed energy storage systems are used to store and visualize energy within a target urban area. A distributed energy storage configuration system is used to configure and visualize the energy storage of a distributed energy storage system.

Images