A method and system for urban distributed energy storage scheduling based on big data

Abstract

The present invention provides a city distributed energy storage scheduling method and system based on big data, belonging to the field of smart grid technology. The method includes obtaining a city map, determining a first city feature based on the city map, and clarifying monitoring requirements based on the first city feature to form a monitoring layer; using the monitoring layer data to identify a second city feature based on the first city feature, thereby defining an energy storage main body framework, building a game mechanism, and obtaining an energy storage allocation plan; using the energy storage allocation plan to perform spatial adaptation, and perform supplementary positioning of fixed energy storage and dynamic route planning of mobile energy storage to obtain supplementary positioning results and planning results, generating constraint conditions based on the supplementary positioning results and planning results, setting an elastic response basis, performing performance verification, and generating a constraint scheduling plan based on the performance verification results to schedule the city distributed energy storage, break through the single scheduling strategy, enhance the collaborative capability of multiple energy storage units, and improve the overall system efficiency.

Description

Technical Field

[0001] The present invention relates to the field of smart grid technology, and in particular to a big data-based urban distributed energy storage scheduling method and system. Background Art

[0002] With the acceleration of urbanization and the increase in the penetration rate of renewable energy, urban energy systems face challenges such as intensified supply and demand fluctuations, insufficient grid resilience, and conflicts of interest among multiple energy storage entities. Current problems with distributed energy storage applications include a single scheduling strategy that mainly relies on preset charging and discharging modes, insufficient coordinated optimization of multiple energy storage units, and the need to improve the intelligence of emergency response mechanisms.

[0003] Therefore, the present invention provides a method and system for urban distributed energy storage scheduling based on big data. Summary of the Invention

[0004] The present invention provides a big data-based urban distributed energy storage scheduling method and system. This method builds an urban feature monitoring layer through multi-source data fusion, optimizes energy storage allocation based on a game mechanism, and realizes the coordinated layout of fixed / mobile energy storage in combination with spatial adaptation. Ultimately, it forms a flexible scheduling solution, realizes monitoring-decision-adaptation-response closed-loop management, improves the dynamic coordination capability of distributed energy storage, breaks through a single scheduling strategy, realizes multi-objective optimization, enhances the coordination capability of multiple energy storage units, and improves the overall system efficiency.

[0005] The present invention provides a method for urban distributed energy storage scheduling based on big data, comprising:

[0006] Step 1: Obtain a city map, determine a first city feature based on the city map, and define monitoring requirements based on the first city feature to form a monitoring layer;

[0007] Step 2: Acquire monitoring layer data, use the monitoring layer data to identify second city characteristics based on the first city characteristics, and then define the energy storage main structure, and establish a corresponding game mechanism based on the energy storage main structure to derive an energy storage allocation plan;

[0008] Step 3: Use the energy storage allocation plan to perform spatial adaptation. Based on the spatial adaptation results, perform supplementary positioning of fixed energy storage and obtain the supplementary positioning results. Simultaneously, perform dynamic route planning for mobile energy storage and obtain the planning results.

[0009] Step 4: Generate constraint conditions based on the supplementary positioning results and planning results, set an elastic response basis based on the constraint conditions, perform performance verification, and generate a constrained scheduling plan based on the performance verification results to dispatch the urban distributed energy storage.

[0010] The present invention provides a method for urban distributed energy storage scheduling based on big data, which obtains a city map, determines a first city feature based on the city map, and defines monitoring requirements based on the first city feature to form a monitoring layer, including:

[0011] Calculate the building density index of each area from the city map, mark special terrain features, and derive spatial structure characteristics. Based on the building density index and special terrain features, perform functional zoning of the city to derive functional zoning characteristics. Mark the radiation range of key infrastructure of the city's energy system based on the functional zoning results to derive infrastructure layout characteristics.

[0012] The first city characteristics are derived by integrating the spatial structure characteristics, functional zoning characteristics and infrastructure layout characteristics;

[0013] Based on the first city characteristics, corresponding monitoring requirements are determined from a monitoring-requirement mapping table to form a monitoring layer.

[0014] The present invention provides a method for urban distributed energy storage scheduling based on big data, which obtains monitoring layer data, uses the monitoring layer data to identify second city characteristics based on the first city characteristics, and then defines the energy storage main body framework, including:

[0015] Spatially superimposing the monitoring layer data with the first city characteristics to establish a spatiotemporal correlation matrix, and extracting the second city characteristics based on the spatiotemporal correlation matrix;

[0016] Deconstruct the secondary city's characteristics to derive dynamic, spatiotemporal, and environmental characteristics. These characteristics are then divided into different categories to produce a comprehensive analysis of the results. This analysis then determines the core attributes of the energy storage entity, the communication network architecture, and the dynamic strategy system.

[0017] The energy storage main body architecture is defined according to the core attributes of the energy storage main body, the communication network architecture and the dynamic strategy system.

[0018] The present invention provides a method for urban distributed energy storage scheduling based on big data. The method divides dynamic characteristics, spatiotemporal characteristics, and environmental characteristics, obtains division results, and comprehensively analyzes the division results to determine the core attributes of the energy storage entity, the communication network architecture, and the dynamic strategy system, including:

[0019] Performing a first division on the dynamic characteristics to determine load fluctuation characteristics and device state characteristics, determining the energy storage subject decision frequency based on the load fluctuation characteristics, determining the energy storage subject feasibility based on the device state characteristics, and determining the energy storage subject core attributes by combining the energy storage subject decision frequency and the energy storage subject feasibility;

[0020] Performing a second division on the spatiotemporal characteristics to determine regional coupling characteristics and network congestion characteristics, determining an energy storage subject interaction topology based on the regional coupling characteristics, determining an energy storage subject communication architecture based on the network congestion characteristics, and determining a communication network architecture based on the energy storage subject interaction topology and the energy storage subject communication architecture;

[0021] A third division is performed on the environmental characteristics to determine the temperature-load sensitivity and the rainfall-photovoltaic correlation. The decision space of the energy storage subject is determined based on the temperature-load sensitivity. The type migration of the energy storage subject is determined based on the rainfall-photovoltaic correlation. The dynamic strategy system is determined by comprehensively considering the decision space of the energy storage subject and the type migration of the energy storage subject.

[0022] The present invention provides a method for urban distributed energy storage scheduling based on big data, which establishes a corresponding game mechanism based on the energy storage main architecture to obtain an energy storage allocation plan, including:

[0023] Based on the energy storage subject framework, the game initialization configuration is performed, and participants are extracted from the energy storage subject structure according to the configuration results. The participants are then layered to obtain upper-level participants and lower-level participants.

[0024] Determine the upper-level participant strategy space based on the upper-level participants, and set corresponding upper-level game rules based on the upper-level participants and the corresponding upper-level participant strategy spaces. At the same time, determine the lower-level participant strategy space based on the lower-level participants, and set corresponding lower-level game rules based on the lower-level participants and the corresponding lower-level participant strategy spaces.

[0025] Performing cross-layer coupling processing on the upper-layer game rules and the lower-layer game rules, and establishing a game mechanism according to the coupling cross-layer coupling processing results;

[0026] According to the game mechanism, the equilibrium result of the upper-level game and the stable strategy set of the lower-level game are output, and then the energy storage allocation plan is derived based on the equilibrium result and the stable strategy set.

[0027] The present invention provides a method for urban distributed energy storage scheduling based on big data. The method uses an energy storage allocation scheme to perform spatial adaptation, performs supplementary positioning of fixed energy storage based on the spatial adaptation results, and obtains supplementary positioning results. At the same time, dynamic route planning of mobile energy storage is performed to obtain planning results, including:

[0028] Converting the logical topological nodes in the energy storage allocation scheme into an actual geographic coordinate system, performing spatial adaptation based on the actual geographic coordinate system, classifying according to the spatial adaptation results, and performing collision analysis between fixed energy storage and mobile energy storage to obtain a first collision analysis result for the fixed energy storage and a second collision analysis result for the mobile energy storage;

[0029] Matching hard collision handling and soft collision handling corresponding to the first collision analysis result and the second collision analysis result from the collision handling table;

[0030] Performing a first supplementary positioning on the hard conflict processing corresponding to the first collision analysis result based on the actual geographic coordinate system, performing a second supplementary positioning on the soft conflict processing corresponding to the first collision analysis result, and combining the first supplementary positioning and the second supplementary positioning to obtain a supplementary positioning result;

[0031] Based on the actual geographic coordinate system, a first dynamic route planning is performed for the hard conflict processing corresponding to the second collision analysis result, and a second dynamic route planning is performed for the soft conflict processing corresponding to the second collision analysis result. The first dynamic route planning and the second dynamic route planning are combined to obtain a planning result.

[0032] The present invention provides a method for urban distributed energy storage scheduling based on big data. Constraints are generated based on supplementary positioning results and planning results. An elastic response basis is set based on the constraints, and performance verification is performed. A constraint scheduling scheme is generated based on the performance verification results to schedule urban distributed energy storage. The method includes:

[0033] Performing a first extraction on the supplementary positioning result, performing a second extraction on the planning result, and determining corresponding constraint conditions based on the first extraction and the second extraction;

[0034] Establishing a three-level response mechanism based on the constraints, and performing energy storage-strategy binding based on the three-level response mechanism to form a foundation for elastic response;

[0035] The elastic response basis is validated for effectiveness, key adjustment parameters are identified based on the effectiveness validation results, the elastic response basis is adjusted, and a hierarchical constraint scheduling plan is output to schedule urban distributed energy storage.

[0036] The present invention provides an urban distributed energy storage dispatching system based on big data, comprising:

[0037] Monitoring module: obtaining a city map, determining a first city feature based on the city map, and defining monitoring requirements based on the first city feature to form a monitoring layer;

[0038] Allocation module: obtains monitoring layer data, uses the monitoring layer data to identify the characteristics of the second city based on the characteristics of the first city, further defines the energy storage main structure, and establishes a corresponding game mechanism based on the energy storage main structure to derive an energy storage allocation plan;

[0039] Adaptation module: Uses the energy storage allocation plan to perform spatial adaptation, performs supplementary positioning of fixed energy storage based on the spatial adaptation results, and obtains supplementary positioning results. At the same time, it performs dynamic route planning for mobile energy storage and obtains planning results.

[0040] Verification module: Generates constraints based on the supplementary positioning results and planning results, sets an elastic response basis based on the constraints, performs performance verification, and generates a constrained scheduling plan based on the performance verification results to dispatch urban distributed energy storage.

[0041] Compared with the existing technology, the beneficial effects of this application are as follows: building a city feature monitoring layer through multi-source data fusion, optimizing energy storage distribution based on a game mechanism, combining spatial adaptation to achieve fixed / mobile energy storage coordinated layout, and finally forming a flexible scheduling plan to achieve monitoring-decision-adaptation-response closed-loop management, improve the dynamic coordination capability of distributed energy storage, break through the single scheduling strategy, achieve multi-objective optimization, enhance the coordination capability of multiple energy storage units, and improve the overall system efficiency.

[0042] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0043] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0045] Figure 1 This is a flow chart of a method for urban distributed energy storage scheduling based on big data provided by an embodiment of the present invention;

[0046] Figure 2 This is a schematic diagram of the structure of a big data-based urban distributed energy storage scheduling system provided by an embodiment of the present invention;

[0047] Figure 3 The technical architecture of the distributed energy storage system provided by the embodiment of the present invention. DETAILED DESCRIPTION

[0048] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention. Example 1:

[0049] The embodiment of the present invention provides a method for urban distributed energy storage scheduling based on big data, such as Figure 1 Shown, including:

[0050] Step 1: Obtain a city map, determine a first city feature based on the city map, and define monitoring requirements based on the first city feature to form a monitoring layer;

[0051] Step 2: Acquire monitoring layer data, use the monitoring layer data to identify second city characteristics based on the first city characteristics, and then define the energy storage main structure, and establish a corresponding game mechanism based on the energy storage main structure to derive an energy storage allocation plan;

[0052] Step 3: Use the energy storage allocation plan to perform spatial adaptation. Based on the spatial adaptation results, perform supplementary positioning of fixed energy storage and obtain the supplementary positioning results. Simultaneously, perform dynamic route planning for mobile energy storage and obtain the planning results.

[0053] Step 4: Generate constraint conditions based on the supplementary positioning results and planning results, set an elastic response basis based on the constraint conditions, perform performance verification, and generate a constrained scheduling plan based on the performance verification results to dispatch the urban distributed energy storage.

[0054] In this embodiment, the first city characteristic is a comprehensive representation of spatial structure + functional zoning + infrastructure, for example, City A: high-density R&D area + distributed photovoltaic storage network, and Area B: low-density eco-city + smart microgrid.

[0055] In this embodiment, the monitoring requirements are the energy parameters and frequencies that need to be monitored in different areas, for example, in commercial areas: real-time load (1s / time), voltage fluctuation, and in industrial areas: harmonic distortion rate (1min / time).

[0056] In this embodiment, the monitoring layer is a data collection system composed of sensors and communication networks. For example, the hardware includes smart meters (such as Huawei IoT meters) and environmental sensors, and the communication includes 5G+LoRa (such as a smart city energy IoT). The connection with the BMS / EMS is that the BMS three-level architecture (BMU / BCU / BAU) is responsible for energy storage unit management, while the monitoring layer provides external environmental data (such as temperature and load) to assist the BAU / EMS in optimizing scheduling strategies. For example, the monitoring layer detects a surge in load in a commercial area, the BAU / EMS adjusts the PCS discharge mode, and terrain data indicates that photovoltaic power generation is limited in mountainous areas, the BMS switches to energy storage power supply mode.

[0057] In this embodiment, the second city characteristics are dynamic, multi-dimensional urban operation laws extracted after fusing and analyzing the monitoring layer data (real-time load, equipment status, etc.) with the first city characteristics (static spatial structure, functional zoning, etc.) through a spatiotemporal correlation matrix. The core components include dynamic characteristics: energy behavior that changes over time (such as the load fluctuation curve of the commercial district), spatiotemporal characteristics: the coupling law of spatial distribution and temporal evolution (such as the electricity consumption hotspots during peak hours in the morning and evening along the subway line), and environmental characteristics: the impact of external factors on the energy system (such as the sensitivity of temperature to air-conditioning load).

[0058] In this embodiment, the first city feature is a static anatomical map of the city, which is generated based on geographic information and planning documents, and answers what is where. The second city feature is a dynamic electrocardiogram of the city, which is obtained through the spatiotemporal coupling analysis of real-time data and static features, and answers how it changes and interacts.

[0059] In this embodiment, the energy storage main architecture is an energy storage system design that integrates core attributes, communication architecture, and strategy. For example, the commercial area architecture has the following core attributes: high-power lithium battery energy storage (2MW), communication: 5G + edge computing (low latency), strategy: load following + demand response; the industrial area architecture has the following core attributes: long-life liquid flow battery (10MWh), communication: fiber-optic private network (high reliability), strategy: smoothing photovoltaic fluctuations + black start backup.

[0060] In this embodiment, the game mechanism is designed through a layered game mechanism, and the upper-layer strategy space and the lower-layer strategy space are constructed respectively, and a multi-level collaborative game model is formed after cross-layer coupling processing.

[0061] In this embodiment, the energy storage allocation plan is a resource scheduling plan generated based on the game results. For example, the commercial area plan: 2MW lithium battery energy storage + 3 mobile energy storage vehicles are deployed during the morning rush hour to achieve load peak shaving. The industrial area plan: liquid flow batteries are activated during the night shift for valley charging to smooth production load during the day.

[0062] In this embodiment, spatial adaptation involves mapping logical nodes to geographic coordinates and analyzing spatial feasibility. For example, the energy storage node in the commercial district is adapted to the vicinity of a substation in Lujiazui (avoiding underground pipe corridors).

[0063] In this embodiment, the supplementary positioning results are the final optimized site selection plan formed after hard and soft conflict processing for conflicts discovered during spatial adaptation of fixed energy storage. This plan includes: the revised geographic coordinates (latitude and longitude) of the energy storage station, the adjusted coverage radius and service range, and additional buffering measures (such as soundproof walls and safety distance calibration). For example, the original planned location of a certain energy storage power station (coordinate A) conflicts with an underground gas pipeline. After supplementary positioning, the following measures are implemented: hard processing: moving 150 meters east to coordinate B (to avoid the pipeline); soft processing: installing an explosion-proof wall at coordinate B (to reduce safety risks). The final result: coordinate B is confirmed as a legal and compliant site selection, and is marked as requiring regular safety inspections.

[0064] In this embodiment, the planning result is a spatiotemporal scheduling scheme formed after optimizing the dynamic route conflicts of the mobile energy storage. It includes: optimized path nodes (including GPS coordinate sequences), time window arrangements (arrival / departure times at each station), and a list of emergency backup routes. For example, the original planned route of the mobile energy storage vehicle passes through the morning rush hour congestion area. After planning, the following hard processing is implemented: detouring via the elevated road (avoiding the restricted area); soft processing is implemented: the delivery time is adjusted from 07:30 to 08:15 (avoiding the peak congestion). The final result: a schedule containing three alternative routes is generated, and it is marked that the route will automatically switch to route C in rainy days.

[0065] In this embodiment, the constraints are scheduling boundary conditions formed by integrating spatial and dynamic constraints. For example, the hard constraint is that the coordinates of the fixed energy storage station cannot be changed (regulatory requirements), and the soft constraint is that the mobile energy storage vehicle must be charged during the period of low electricity prices (economic requirements).

[0066] In this embodiment, the elastic response foundation is an executable framework that integrates constraints, response mechanisms, and binding strategies. For example, the elastic response rule base of a new district microgrid contains 12 preset scenario strategies.

[0067] In this embodiment, performance verification is to evaluate the response effect through simulation or actual measurement, for example, to verify whether the mobile energy storage vehicle can reach the critical load point within 30 minutes under simulated typhoon weather.

[0068] In this embodiment, the constrained scheduling scheme is a final scheduling instruction set with hierarchical output. For example, the first-level scheme is: during daily periods, charging and discharging according to the electricity price strategy; the third-level scheme is: in disaster mode, fixed energy storage operates at full power, and mobile energy storage vehicles are used to centrally provide protection for hospitals.

[0069] In this embodiment, a hierarchical stress test is conducted based on the elastic response basis, and a five-dimensional evaluation system is simultaneously established for evaluation. Test results and evaluation results are obtained, and the sensitivity of each parameter in the test results and evaluation results to the overall performance is quantified. The dominant key adjustment parameters are identified, and the elastic response basis is adjusted based on the key adjustment parameters. A three-level constraint scheduling plan is generated, namely, normal version, emergency version, and transition version, to schedule urban distributed energy storage.

[0070] The working principle and beneficial effects of the above technical solution are: building a city feature monitoring layer through multi-source data fusion, optimizing energy storage distribution based on a game mechanism, combining spatial adaptation to achieve a coordinated layout of fixed / mobile energy storage, and ultimately forming a flexible scheduling solution to achieve closed-loop management of monitoring-decision-adaptation-response, thereby improving the dynamic coordination capabilities of distributed energy storage, breaking through a single scheduling strategy, achieving multi-objective optimization, enhancing the coordination capabilities of multiple energy storage units, and improving overall system efficiency. Example 2:

[0071] An embodiment of the present invention provides a method for urban distributed energy storage scheduling based on big data, which obtains a city map, determines a first city characteristic based on the city map, and defines monitoring requirements based on the first city characteristic to form a monitoring layer, including:

[0072] Calculate the building density index of each area from the city map, mark special terrain features, and derive spatial structure characteristics. Based on the building density index and special terrain features, perform functional zoning of the city to derive functional zoning characteristics. Mark the radiation range of key infrastructure of the city's energy system based on the functional zoning results to derive infrastructure layout characteristics.

[0073] The first city characteristics are derived by integrating the spatial structure characteristics, functional zoning characteristics and infrastructure layout characteristics;

[0074] Based on the first city characteristics, corresponding monitoring requirements are determined from a monitoring-requirement mapping table to form a monitoring layer.

[0075] In this embodiment, the building density index is the ratio of the floor area of buildings per unit area, reflecting the intensity of regional development. For example, high-density areas (>0.6) are urban CBDs and commercial centers; medium-density areas (0.3-0.6) are ordinary residential areas; and low-density areas (<0.3) are suburbs and ecological protection areas.

[0076] In this embodiment, special terrain features are natural or artificial terrain elements that affect the layout of energy facilities. For example, rivers / lakes → need to consider cross-river cables or energy storage station flood control design, mountains / hills → affect the layout of photovoltaic power stations, and the location of energy storage stations needs to be optimized, and underground spaces (such as subways and tunnels) → require special power supply solutions (such as energy storage in a highway tunnel).

[0077] In this embodiment, the spatial structure feature is the urban spatial pattern formed by the comprehensive building density and terrain. For example, high density + flat terrain (such as the Bund) → suitable for centralized energy storage + microgrid, low density + complex terrain (such as mountainous areas) → suitable for distributed photovoltaic + mobile energy storage.

[0078] In this embodiment, the functional zoning characteristics are urban functional types (such as commercial, industrial, residential, and ecological) divided based on spatial characteristics. For example, commercial areas (such as financial streets) → high electricity demand and high-power energy storage are required; industrial areas (such as industrial parks) → stable loads and suitable for peak-valley arbitrage energy storage; ecological areas (such as wetlands) → restricted development and suitable for integrated photovoltaic storage.

[0079] In this embodiment, the process of functional zoning is to input building density, terrain data, population density, etc., use GIS tools (such as ArcGIS) to divide similar areas, and perform functional zoning in combination with urban planning.

[0080] In this embodiment, the key infrastructure of the urban energy system is the core facilities that support energy supply, such as fixed energy storage power stations, mobile energy storage vehicles, and substations / distribution stations.

[0081] In this embodiment, the radiation range is the spatial range of effective service provided by the infrastructure, for example, an energy storage station covers a radius of 2 km, and a photovoltaic power station directly affects the stability of the surrounding 1 km power grid.

[0082] In this embodiment, the infrastructure layout feature is the spatial distribution pattern of key facilities, such as a grid pattern, or a point pattern with mobile supplements.

[0083] In this embodiment, the monitoring-demand mapping table is a correspondence table between city characteristics and monitoring parameters. For example, if the city characteristic is a high-density commercial area, the monitoring parameters are real-time load and voltage sag, and the corresponding frequency is 1s / time. If the city characteristic is an ecological area, the monitoring parameters are ambient temperature and humidity and photovoltaic output, and the corresponding frequency is 1h / time.

[0084] The working principle and beneficial effects of the above technical solution are: dividing urban functional areas by building density and terrain characteristics, constructing a spatial-functional-energy multi-dimensional feature system based on the radiation range of infrastructure, generating a precise monitoring layer based on feature mapping, realizing spatial demand identification and dynamic monitoring network deployment of urban energy systems, and providing a spatial decision-making benchmark for subsequent energy storage optimization. Example 3:

[0085] An embodiment of the present invention provides a method for urban distributed energy storage scheduling based on big data, which obtains monitoring layer data, uses the monitoring layer data to identify second city characteristics based on first city characteristics, and then defines an energy storage main body architecture, including:

[0086] Spatially superimposing the monitoring layer data with the first city characteristics to establish a spatiotemporal correlation matrix, and extracting the second city characteristics based on the spatiotemporal correlation matrix;

[0087] Deconstruct the secondary city's characteristics to derive dynamic, spatiotemporal, and environmental characteristics. These characteristics are then divided into different categories to produce a comprehensive analysis of the results. This analysis then determines the core attributes of the energy storage entity, the communication network architecture, and the dynamic strategy system.

[0088] The energy storage main body architecture is defined according to the core attributes of the energy storage main body, the communication network architecture and the dynamic strategy system.

[0089] In this embodiment, the spatiotemporal correlation matrix is a multidimensional data matrix constructed by spatiotemporally integrating monitoring layer data (such as electricity load and temperature) with primary city characteristics (such as building density and functional zoning), reflecting the dynamic correlation of the urban energy system. For example, the commercial area matrix: high building density + peak load, the industrial area matrix: stable load + high temperature sensitivity (such as the summer electricity consumption curve of a steel plant).

[0090] In this embodiment, dynamic characteristics are energy parameters that change over time, such as load fluctuations (such as morning and evening peak hours in commercial areas) and changes in energy storage SOC; spatiotemporal characteristics are energy patterns of spatial distribution and temporal evolution, such as the east-west difference in photovoltaic output (such as the morning peak hour in the east) and the change in the optimal path of mobile energy storage vehicles over time; environmental characteristics are the impact of the external environment on the energy system, such as the impact of temperature on air conditioning load (such as +5°C in summer → load +20%) and the suppression of photovoltaic output by rainfall.

[0091] In this embodiment, the division result is to cluster or classify the three types of features to form an operational strategy unit. For example, dynamic characteristics → divide high-frequency regulation type energy storage (such as responding to sudden load changes) and low-frequency buffer type energy storage (such as peak-valley arbitrage); spatiotemporal characteristics → identify high-value scheduling areas (such as the need for mobile energy storage coverage along the subway line).

[0092] In this embodiment, the comprehensive analysis combines the division results to generate decision rules, for example, high temperature + high load area → prioritize the use of fixed energy storage and pre-deploy mobile energy storage vehicles.

[0093] In this embodiment, the core attributes of the energy storage entity are the inherent characteristics of the energy storage unit, which determine its scheduling priority. For example, power / capacity: 2MW / 4MWh energy storage power station vs. 0.5MW / 1MWh mobile energy storage vehicle, response speed: lithium battery (milliseconds) vs. flow battery (seconds), life decay: number of cycles (for example, capacity decays to 80% after 6000 cycles).

[0094] In this embodiment, the communication network architecture is a data transmission system between the energy storage unit and the management system. For example, layered communication: local CAN bus (BMU-BCU, such as within the BMS), wide area 5G (BAU-EMS, such as remote control of the dispatch center), protocols: MQTT (cloud data synchronization), IEC 61850 (grid interaction).

[0095] In this embodiment, the dynamic strategy system is a set of scheduling rules adjusted based on real-time data. For example, the normal strategy is peak-valley arbitrage (charging during valley hours and discharging during peak hours), and the emergency strategy is that when the mains power is interrupted, the mobile energy storage vehicle automatically switches to the backup power mode.

[0096] The working principle and beneficial effects of the above technical solution are: integrating urban characteristics with real-time monitoring data through the spatiotemporal correlation matrix, intelligently extracting dynamic operation characteristics, and building an intelligent decision-making system that includes energy storage entity attributes, communication architecture and dynamic strategies to achieve precise coordinated scheduling of distributed energy storage. Example 4:

[0097] The present invention provides a method for urban distributed energy storage scheduling based on big data. The method divides dynamic characteristics, spatiotemporal characteristics, and environmental characteristics, obtains division results, and comprehensively analyzes the division results to determine the core attributes of the energy storage entity, the communication network architecture, and the dynamic strategy system, including:

[0098] Performing a first division on the dynamic characteristics to determine load fluctuation characteristics and device state characteristics, determining the energy storage subject decision frequency based on the load fluctuation characteristics, determining the energy storage subject feasibility based on the device state characteristics, and determining the energy storage subject core attributes by combining the energy storage subject decision frequency and the energy storage subject feasibility;

[0099] Performing a second division on the spatiotemporal characteristics to determine regional coupling characteristics and network congestion characteristics, determining an energy storage subject interaction topology based on the regional coupling characteristics, determining an energy storage subject communication architecture based on the network congestion characteristics, and determining a communication network architecture based on the energy storage subject interaction topology and the energy storage subject communication architecture;

[0100] A third division is performed on the environmental characteristics to determine the temperature-load sensitivity and the rainfall-photovoltaic correlation. The decision space of the energy storage subject is determined based on the temperature-load sensitivity. The type migration of the energy storage subject is determined based on the rainfall-photovoltaic correlation. The dynamic strategy system is determined by comprehensively considering the decision space of the energy storage subject and the type migration of the energy storage subject.

[0101] In this embodiment, the first division divides the system operating parameters into two types of key indicators: load fluctuation characteristics and equipment status characteristics by analyzing dynamic characteristics.

[0102] In this embodiment, the load fluctuation characteristics are the patterns of changes in electricity load over time (peak-to-valley difference, mutation frequency, etc.). For example, in commercial areas, the load suddenly rises at 9 a.m., and in industrial areas, the load is stable (such as a steel plant with 24-hour continuous production).

[0103] In this embodiment, the device status feature is the health of the energy storage device (SOC, SOH, temperature, etc.). For example, for a lithium battery pack, SOC = 80%, SOH = 90% (good health); for a faulty battery, abnormal temperature (e.g., a single cell > 50°C triggers an alarm).

[0104] In this embodiment, the decision frequency of the energy storage entity is the dispatch instruction update frequency determined according to load fluctuations. For example, high-frequency decision-making: second-level response (such as responding to rapid fluctuations in photovoltaic cloud clusters), low-frequency decision-making: hour-level adjustment (such as peak-valley arbitrage).

[0105] In this embodiment, the feasibility of the energy storage entity is the dispatchability of the energy storage unit (0-100%) based on the device status assessment. For example, a feasibility of 90% means that healthy battery packs can operate at full power, and a feasibility of 50% means that aging battery packs need to operate at a reduced capacity.

[0106] In this embodiment, the core attribute of the energy storage entity is the role of the energy storage unit determined by the comprehensive decision-making frequency and feasibility, for example, the fast response type: high decision-making frequency + high feasibility (such as lithium battery energy storage), the buffer backup type: low decision-making frequency + medium feasibility (such as lead-acid battery).

[0107] In this embodiment, the regional coupling feature is the correlation between energy demands of different regions (such as load synchronization of adjacent regions). For example, strong coupling means that the loads of subway stations and commercial areas increase synchronously, while weak coupling means that the loads of industrial areas and residential areas increase in opposite directions (such as night shift factories and residential electricity consumption).

[0108] In this embodiment, the network congestion feature is the grid transmission bottleneck (such as line capacity limitation, node voltage exceeding the limit), for example, the congested area is the old urban distribution network, and the unobstructed area is the newly built development zone.

[0109] In this embodiment, the energy storage entity interaction topology is the collaborative relationship between energy storage units (master-slave, peer-to-peer, hierarchical, etc.). For example, the master-slave topology is: the central energy storage station commands the mobile energy storage vehicle, and the peer-to-peer topology is: multiple microgrids are interconnected.

[0110] In this embodiment, the communication architecture of the energy storage entity is the communication network design (latency, bandwidth, protocol, etc.), low latency: 5G private network, for example, high reliability: optical fiber + LoRa dual redundancy.

[0111] In this embodiment, the communication network architecture is an integration solution of interactive topology and communication technology, for example, a layered architecture: BAU (master control) - BCU (regional) - BMU (local) three-level CAN bus, edge computing: local decision-making + cloud collaboration.

[0112] In this embodiment, the temperature-load sensitivity is the degree of impact of temperature changes on the load (such as the proportion of air conditioning load). For example, high sensitivity: commercial complex (every temperature increase of 1°C → load +5%), low sensitivity: data center (constant temperature operation, stable load).

[0113] In this embodiment, the rainfall-PV correlation refers to the degree to which rainfall suppresses PV output. For example, a strong correlation is observed in coastal areas, and a weak correlation is observed in arid areas.

[0114] In this embodiment, the decision space of the energy storage entity is the dispatchable range under environmental constraints (such as temperature-limited charging and discharging power). For example, high-temperature power restriction: lithium batteries need to be derated by 50% above 40°C; rainy day strategy: switch to energy storage power supply when photovoltaic power outages occur.

[0115] In this embodiment, the energy storage entity type migration is to adjust the energy storage type according to environmental changes (such as switching between fixed / mobile energy storage roles). For example, on sunny days: fixed energy storage prioritizes photovoltaic consumption; on rainy days: mobile energy storage vehicles support critical loads (such as tunnel lighting emergencies).

[0116] In this embodiment, the dynamic strategy system is a scheduling rule library that comprehensively responds to environmental conditions. For example, the normal strategy is to charge and discharge according to the power configuration strategy when the temperature is 25°C; the emergency strategy is to impose power restrictions when the temperature is >35°C, giving priority to ensuring power supply to hospitals.

[0117] The working principle and beneficial effects of the above technical solution are: through a three-level intelligent division mechanism, key parameters are extracted from dynamic, spatiotemporal and environmental characteristics respectively, the core attributes, communication architecture and dynamic strategy system of the energy storage entity are constructed, the adaptive optimization scheduling of the distributed energy storage system is realized, the accurate analysis and integration of multi-dimensional characteristics are achieved, and the energy consumption ratio is improved. Example 5:

[0118] An embodiment of the present invention provides a method for urban distributed energy storage scheduling based on big data, which establishes a corresponding game mechanism based on the energy storage main body architecture to obtain an energy storage allocation plan, including:

[0119] Based on the energy storage subject framework, the game initialization configuration is performed, and participants are extracted from the energy storage subject structure according to the configuration results. The participants are then layered to obtain upper-level participants and lower-level participants.

[0120] Determine the upper-level participant strategy space based on the upper-level participants, and set corresponding upper-level game rules based on the upper-level participants and the corresponding upper-level participant strategy spaces. At the same time, determine the lower-level participant strategy space based on the lower-level participants, and set corresponding lower-level game rules based on the lower-level participants and the corresponding lower-level participant strategy spaces.

[0121] Performing cross-layer coupling processing on the upper-layer game rules and the lower-layer game rules, and establishing a game mechanism according to the coupling cross-layer coupling processing results;

[0122] According to the game mechanism, the equilibrium result of the upper-level game and the stable strategy set of the lower-level game are output, and then the energy storage allocation plan is derived based on the equilibrium result and the stable strategy set.

[0123] In this embodiment, the game initialization configuration sets the initial conditions of the game based on the energy storage entity architecture (core attributes, communication architecture, and strategy system), including the division of participants and the definition of the strategy space. For example, in a certain urban microgrid, resources such as a 2MW lithium battery energy storage station, five mobile energy storage vehicles, and a photovoltaic power station are incorporated into the game system, and their respective decision parameters (such as charging and discharging costs and response speed) are initialized.

[0124] In this embodiment, the upper-level participants are high-level decision-makers responsible for macro-resource allocation, such as the regional energy dispatch center (such as a city's power grid company) and the microgrid master control system (EMS / BAU integrated platform); the lower-level participants are energy storage units or local controllers that perform specific operations, such as fixed energy storage power stations (such as 2MW / 4MWh lithium battery systems) and mobile energy storage vehicles (such as 0.5MW / 1MWh emergency power supplies).

[0125] In this embodiment, the upper-level participant strategy space corresponding to the upper-level participants corresponds to the global power configuration strategy, energy storage unit scheduling priority, and network congestion management. The upper-level game rules are aimed at grid stability, constraining the charging and discharging behavior of the lower-level participants, and setting a penalty mechanism (such as overload fines); the lower-level participant strategy space corresponding to the lower-level participants is charging and discharging power selection, response delay adjustment, and the lower-level game rules corresponding to the local optimization strategy (such as peak-valley arbitrage) are to maximize their own benefits under the upper-level rules and avoid violating safety constraints (such as SOC exceeding the limit).

[0126] In this embodiment, the objective function corresponding to the upper-level game rules is , Indicates the charging and discharging power corresponding to the upper-level participants; represents the load demand vector of time period t; G represents the grid topology correlation matrix; γ represents the strategy smoothness weight coefficient; T represents the total number of dispatch periods; represents the charging and discharging power of the lower-level participant in time period t; represents the charging and discharging power of the upper participant in time period t; represents the L2 norm; the boundary condition of the objective function corresponding to the upper-level game rule is , where A, b represents the linearized power grid safety boundary; It represents the feasible domain of the lower-level strategy corresponding to the strategy space of the lower-level participants, and finally derives an executable equilibrium strategy based on the upper-level game objective function.

[0127] In this embodiment, the objective function corresponding to the lower-level game rules is ,in, represents the charging and discharging power corresponding to the i-th lower-level participant; represents the reference power sent down from the upper layer of the i-th lower-layer participant; Indicates the battery health weight; Represents the SOC health weight matrix; Represents the SOC state vector; the boundary conditions of the objective function corresponding to the underlying game rules ,in, represents the charging and discharging efficiency corresponding to the i-th lower-level participant; represents the energy storage rated capacity corresponding to the i-th lower-level participant; represents the state of charge of the i-th lower-level participant in time period t; represents the scheduling time step; represents the SOC safety lower limit corresponding to the i-th lower-level participant; represents the SOC safety upper limit corresponding to the i-th lower-level participant; represents the charging and discharging power of the i-th lower-level participant in time period t; represents the maximum charge and discharge power corresponding to the i-th lower-level participant; Represents the state of charge of the i-th lower-level participant in time period t+1.

[0128] In this embodiment, the cross-layer coupling processing and game mechanism coordinates the conflicts between the upper and lower layer strategies and establishes a unified decision-making framework. For example, dynamic power configuration feedback: the upper layer publishes time-sharing power configuration according to the power grid status, and the lower layer energy storage unit adjusts the charging and discharging plan accordingly; spare capacity reservation: the upper layer requires the lower layer participants to maintain 20% spare capacity during specific periods to cope with sudden loads.

[0129] In this embodiment, the equilibrium result and stable strategy set are stable states in which neither party can unilaterally increase its benefits after the game converges. For example, the equilibrium result is: the upper-level dispatching center and the lower-level energy storage power station reach an agreement - the discharge power configuration during peak hours is increased by 15%, and the energy storage unit responds with 90% feasibility. The stable strategy set is: mobile energy storage vehicles are fixedly deployed to hospitals on rainy days and dynamically support commercial areas on sunny days.

[0130] The working principle and beneficial effects of the above technical solution are: through the design of a layered game mechanism, the upper-level strategy space and the lower-level strategy space are constructed separately, and after cross-layer coupling processing, a multi-level collaborative game model is formed, and finally the equilibrium solution and stable strategy are output to achieve the optimal allocation of energy storage resources and enhance the overall efficiency of the system. Example 6:

[0131] The embodiment of the present invention provides a method for urban distributed energy storage scheduling based on big data. The method uses an energy storage allocation plan to perform spatial adaptation, performs supplementary positioning of fixed energy storage based on the spatial adaptation results, and obtains supplementary positioning results. Simultaneously, the method performs dynamic route planning for mobile energy storage and obtains planning results, including:

[0132] Converting the logical topological nodes in the energy storage allocation scheme into an actual geographic coordinate system, performing spatial adaptation based on the actual geographic coordinate system, classifying according to the spatial adaptation results, and performing collision analysis between fixed energy storage and mobile energy storage to obtain a first collision analysis result for the fixed energy storage and a second collision analysis result for the mobile energy storage;

[0133] Matching hard collision handling and soft collision handling corresponding to the first collision analysis result and the second collision analysis result from the collision handling table;

[0134] Performing a first supplementary positioning on the hard conflict processing corresponding to the first collision analysis result based on the actual geographic coordinate system, performing a second supplementary positioning on the soft conflict processing corresponding to the first collision analysis result, and combining the first supplementary positioning and the second supplementary positioning to obtain a supplementary positioning result;

[0135] Based on the actual geographic coordinate system, a first dynamic route planning is performed for the hard conflict processing corresponding to the second collision analysis result, and a second dynamic route planning is performed for the soft conflict processing corresponding to the second collision analysis result. The first dynamic route planning and the second dynamic route planning are combined to obtain a planning result.

[0136] In this embodiment, the logical topology node is an abstract network connection point that represents the functional location of energy storage in the power system (such as node A → node B), for example, a virtual node in a microgrid model (such as a photovoltaic energy storage interface node); the actual geographic coordinate system is a physical location based on longitude and latitude or city coordinates.

[0137] In this embodiment, collision analysis detects conflicts between energy storage layout and geography / facility. The first collision analysis (stationary) detects rigid conflicts between the site selection of fixed energy storage and geography / regulations. For example, the planned location of a certain energy storage power station overlaps with an underground gas pipeline, resulting in a conflict. The second collision analysis (mobile) diagnoses conflicts between mobile energy storage and time-varying factors such as traffic and the environment during dynamic operation. For example, a mobile energy storage vehicle's route passes over a height-restricted bridge, resulting in a conflict.

[0138] In this embodiment, hard conflicts are non-negotiable safety / regulatory conflicts, and the corresponding solution is to relocate the energy storage station if the safety distance between the energy storage station and the chemical plant is insufficient. Soft conflicts are optimizable efficiency / cost conflicts, and the corresponding solution is to adjust the time period or route if the energy storage vehicle route is congested.

[0139] In this embodiment, the first supplementary positioning is a mandatory position correction taken for hard conflicts (such as regulatory prohibitions and safety risks) of fixed energy storage; the second supplementary positioning is an optimization adjustment measure taken for soft conflicts (non-rigid constraint problems) found in spatial adaptation of fixed energy storage.

[0140] In this embodiment, the first dynamic route planning is a guaranteed safe path generated for hard conflicts of mobile energy storage (such as height restrictions and no-driving zones); the second dynamic route planning is an economical and efficient path optimized for soft conflicts of mobile energy storage (such as congestion and electricity price fluctuations).

[0141] The working principle and beneficial effects of the above technical solution are: through geographic coordinate conversion and collision detection analysis, intelligent identification of fixed / mobile energy storage conflict types, combined with conflict handling strategies for precise spatial adaptation, and ultimately generating optimized energy storage layout plans and dynamic route planning, achieving precise spatial configuration of energy storage resources and forming an intelligent spatial adaptation mechanism. Example 7:

[0142] An embodiment of the present invention provides a method for dispatching urban distributed energy storage based on big data. The method generates constraint conditions based on supplementary positioning results and planning results, sets an elastic response basis based on the constraint conditions, performs performance verification, and generates a constraint dispatching scheme based on the performance verification results to dispatch urban distributed energy storage. The method includes:

[0143] Performing a first extraction on the supplementary positioning result, performing a second extraction on the planning result, and determining corresponding constraint conditions based on the first extraction and the second extraction;

[0144] Establishing a three-level response mechanism based on the constraints, and performing energy storage-strategy binding based on the three-level response mechanism to form a foundation for elastic response;

[0145] The elastic response basis is validated for effectiveness, key adjustment parameters are identified based on the effectiveness validation results, the elastic response basis is adjusted, and a hierarchical constraint scheduling plan is output to schedule urban distributed energy storage.

[0146] In this embodiment, the first extraction is to extract key parameters (such as coordinates, capacity, and radiation range) from the corrected position of the fixed energy storage. For example, a certain energy storage power station is finally located at the coordinates (121.5°E, 31.2°N), has a capacity of 2MW, and a coverage radius of 1.5km. → It is extracted as "position constraint: ±50m, power constraint: ≤1.5MW".

[0147] In this embodiment, the second extraction step is to extract dynamic parameters (such as path nodes, time windows, and dispatchable power) from the mobile energy storage route planning. For example, the mobile energy storage vehicle needs to pass through points A, B, and C between 07:00 and 09:00, stopping at each station for 15 minutes. The extracted parameters are "time constraint: ±5 minutes, path tolerance: 200m."

[0148] In this embodiment, the three-level response mechanism is as follows: Level 1 response is to execute routine scheduling according to preset strategies in the absence of abnormal conditions. For example, energy storage in commercial areas is charged during low electricity price periods (2:00-5:00 a.m.) and discharged during peak periods (18:00-21:00) to reduce grid load pressure; Level 2 response is to activate backup capacity and dynamically adjust resources when local abnormalities are detected (such as sudden load increases or voltage fluctuations). For example, if the load in an industrial area suddenly increases by 10%, the dispatch center will instruct nearby mobile energy storage vehicles to provide support, while fixed energy storage stations will increase their discharge power to the upper limit; Level 3 response is to prioritize the power supply to critical facilities in the face of major failures (such as power outages and disasters), sacrificing some economic efficiency. For example, if a typhoon causes a substation failure, non-essential loads will be immediately cut off, and all energy storage resources will be concentrated to power hospitals and emergency command centers.

[0149] In this embodiment, energy storage-strategy binding is to assign a specific response strategy to each energy storage unit. For example, "energy storage station A" is bound to "secondary response strategy": when a local voltage drop is detected, the discharge power is automatically increased by 10%.

[0150] In this embodiment, the key adjustment parameters are variables that significantly affect performance, such as energy storage response delay time (target: optimized from 5 minutes to 2 minutes), SOC safety threshold (adjusted from 20%-90% to 25%-85% to extend life).

[0151] In this embodiment, the distributed energy storage system, such as Figure 3As shown, distributed energy storage participates as a flexible resource, including the extraction of supplementary positioning results, the optimized coordinates of fixed energy storage stations (such as battery cell groups) → converted into power output range constraints (such as units 1-3 covering the eastern area load); planning results extraction, the path of mobile energy storage vehicles (which need to communicate through PCS and gateways) → generation of time-space availability constraints (such as unit 4 can be dispatched from 09:00 to 11:00); three-level response execution, level 1: BAU instructs BCU to perform normal charging and discharging through the CAN bus, level 3: the cloud platform directly overrides PCS to switch to black start mode, among which 232 is used for data terminal communication to realize data exchange with the background system (such as receiving time synchronization information), and 485 is multi-device long-distance communication BAU / EMS PCS energy storage converter: controls PCS operating mode (such as charge and discharge strategy), BAU / EMS Touch screen display unit: upload battery data and receive user setting instructions, BAU / EMS Metering: Reads meter data (such as power statistics); The touch screen display unit functions as a human-machine interface (HMI): Users can set charge and discharge strategies and view real-time data (such as SOC, voltage, temperature, etc.) through the touch screen; Data transfer: Sends user commands to the BAU / EMS master control unit via 485 communication; Connection method: Connects to the BAU / EMS via RS-485; "Battery unit 1" to "Battery unit 6" refer to six battery packs; Intelligent air conditioning function, Ambient temperature control: Adjusts the battery compartment temperature to ensure that the battery operates within the optimal range (lithium iron phosphate batteries are sensitive to temperature); Linkage control: May receive commands through the BAU / EMS (such as starting the air conditioner at high temperatures).

[0152] The working principle and beneficial effects of the above technical solution are: by extracting the constraints of supplementary positioning (fixed energy storage) and dynamic planning (mobile energy storage), a three-level resilience mechanism including prevention, response, and recovery is constructed, energy storage resources and strategies are dynamically bound, key parameters are optimized after performance verification, and finally a hierarchical scheduling plan is generated to achieve dynamic and coordinated management of urban energy storage. Example 8:

[0153] The embodiment of the present invention provides a city distributed energy storage dispatching system based on big data, such as Figure 2 Shown, including:

[0154] Monitoring module: obtaining a city map, determining a first city feature based on the city map, and defining monitoring requirements based on the first city feature to form a monitoring layer;

[0155] Allocation module: obtains monitoring layer data, uses the monitoring layer data to identify the characteristics of the second city based on the characteristics of the first city, further defines the energy storage main structure, and establishes a corresponding game mechanism based on the energy storage main structure to derive an energy storage allocation plan;

[0156] Adaptation module: Uses the energy storage allocation plan to perform spatial adaptation, performs supplementary positioning of fixed energy storage based on the spatial adaptation results, and obtains supplementary positioning results. At the same time, it performs dynamic route planning for mobile energy storage and obtains planning results.

[0157] Verification module: Generates constraints based on the supplementary positioning results and planning results, sets an elastic response basis based on the constraints, performs performance verification, and generates a constrained scheduling plan based on the performance verification results to dispatch urban distributed energy storage.

[0158] The working principle and beneficial effects of the above technical solution are: building a city feature monitoring layer through multi-source data fusion, optimizing energy storage distribution based on a game mechanism, combining spatial adaptation to achieve a coordinated layout of fixed / mobile energy storage, and ultimately forming a flexible scheduling solution to achieve closed-loop management of monitoring-decision-adaptation-response, thereby improving the dynamic coordination capabilities of distributed energy storage, breaking through a single scheduling strategy, achieving multi-objective optimization, enhancing the coordination capabilities of multiple energy storage units, and improving overall system efficiency.

[0159] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for urban distributed energy storage scheduling based on big data, characterized in that: include: Step 1: Obtain a city map, determine a first city feature based on the city map, and define monitoring requirements based on the first city feature to form a monitoring layer; Step 2: Acquire monitoring layer data, use the monitoring layer data to identify second city characteristics based on the first city characteristics, and then define an energy storage main structure. Based on the energy storage main structure, establish a corresponding game mechanism to derive an energy storage allocation plan. Step 3: Use the energy storage allocation plan to perform spatial adaptation. Based on the spatial adaptation results, perform supplementary positioning of fixed energy storage and obtain the supplementary positioning results. Simultaneously, perform dynamic route planning for mobile energy storage and obtain the planning results. Step 4: Generate constraints based on the supplementary positioning results and planning results, set an elastic response basis based on the constraints, perform performance verification, and generate a constrained scheduling plan based on the performance verification results to dispatch the city's distributed energy storage; In step 1, a city map is obtained, a first city feature is determined based on the city map, and monitoring requirements are determined based on the first city feature to form a monitoring layer, including: Calculate the building density index of each area from the city map, mark special terrain features, and derive spatial structure characteristics. Based on the building density index and special terrain features, perform functional zoning of the city to derive functional zoning characteristics. Mark the radiation range of key infrastructure of the city's energy system based on the functional zoning results to derive infrastructure layout characteristics. The first city characteristics are derived by integrating the spatial structure characteristics, functional zoning characteristics and infrastructure layout characteristics; Determining corresponding monitoring requirements from a monitoring-requirement mapping table based on the first city characteristics to form a monitoring layer; In step 2, monitoring layer data is obtained, and the monitoring layer data is used to identify second city characteristics based on the first city characteristics, thereby defining the energy storage main structure, including: Spatially superimposing the monitoring layer data with the first city characteristics to establish a spatiotemporal correlation matrix, and extracting the second city characteristics based on the spatiotemporal correlation matrix; Deconstruct the secondary city's characteristics to derive dynamic, spatiotemporal, and environmental characteristics. These characteristics are then divided into their respective categories to produce a comprehensive analysis of the results, ultimately determining a dynamic strategy system for the core attributes of the energy storage entity and the communication network architecture. Defining the energy storage main body architecture according to the core attributes of the energy storage main body and the dynamic strategy system of the communication network architecture; The second city characteristics are the urban operation laws extracted after fusing and analyzing the monitoring layer data with the first city characteristics through the spatiotemporal correlation matrix. The second city characteristics include dynamic characteristics, spatiotemporal characteristics, and environmental characteristics. The dynamic characteristics are the energy behaviors that change over time, the spatiotemporal characteristics are the coupling laws of spatial distribution and temporal evolution, and the environmental characteristics are the impact of external factors on the energy system.

2. The urban distributed energy storage scheduling method based on big data according to claim 1 is characterized in that: The dynamic characteristics, spatiotemporal characteristics, and environmental characteristics are divided separately to obtain the division results. The division results are comprehensively analyzed to determine the core attributes of the energy storage entity and the dynamic strategy system of the communication network architecture, including: Performing a first division on the dynamic characteristics to determine load fluctuation characteristics and device state characteristics, determining the energy storage subject decision frequency based on the load fluctuation characteristics, determining the energy storage subject feasibility based on the device state characteristics, and determining the energy storage subject core attributes by combining the energy storage subject decision frequency and the energy storage subject feasibility; Performing a second division on the spatiotemporal characteristics to determine regional coupling characteristics and network congestion characteristics, determining an energy storage subject interaction topology based on the regional coupling characteristics, determining an energy storage subject communication architecture based on the network congestion characteristics, and determining a communication network architecture based on the energy storage subject interaction topology and the energy storage subject communication architecture; A third division is performed on the environmental characteristics to determine the temperature-load sensitivity and the rainfall-photovoltaic correlation. The decision space of the energy storage subject is determined based on the temperature-load sensitivity. The type migration of the energy storage subject is determined based on the rainfall-photovoltaic correlation. The dynamic strategy system is determined by comprehensively considering the decision space of the energy storage subject and the type migration of the energy storage subject.

3. The urban distributed energy storage scheduling method based on big data according to claim 1 is characterized in that: Based on the energy storage main body structure, a corresponding game mechanism is established to obtain an energy storage allocation plan, including: Based on the energy storage subject framework, the game initialization configuration is performed, and participants are extracted from the energy storage subject structure according to the configuration results. The participants are then layered to obtain upper-level participants and lower-level participants. Determine the upper-level participant strategy space based on the upper-level participants, and set corresponding upper-level game rules based on the upper-level participants and the corresponding upper-level participant strategy spaces. At the same time, determine the lower-level participant strategy space based on the lower-level participants, and set corresponding lower-level game rules based on the lower-level participants and the corresponding lower-level participant strategy spaces. Performing cross-layer coupling processing on the upper-layer game rules and the lower-layer game rules, and establishing a game mechanism according to the coupling cross-layer coupling processing results; According to the game mechanism, the equilibrium result of the upper-level game and the stable strategy set of the lower-level game are output, and then the energy storage allocation plan is derived based on the equilibrium result and the stable strategy set.

4. The urban distributed energy storage scheduling method based on big data according to claim 1 is characterized in that: Use the energy storage allocation plan to perform spatial adaptation, and perform supplementary positioning of fixed energy storage based on the spatial adaptation results to obtain supplementary positioning results. At the same time, perform dynamic route planning for mobile energy storage and obtain planning results, including: Converting the logical topological nodes in the energy storage allocation scheme into an actual geographic coordinate system, performing spatial adaptation based on the actual geographic coordinate system, classifying according to the spatial adaptation results, and performing collision analysis between fixed energy storage and mobile energy storage to obtain a first collision analysis result for the fixed energy storage and a second collision analysis result for the mobile energy storage; Matching hard collision handling and soft collision handling corresponding to the first collision analysis result and the second collision analysis result from the collision handling table; Performing a first supplementary positioning on the hard conflict processing corresponding to the first collision analysis result based on the actual geographic coordinate system, performing a second supplementary positioning on the soft conflict processing corresponding to the first collision analysis result, and combining the first supplementary positioning and the second supplementary positioning to obtain a supplementary positioning result; Based on the actual geographic coordinate system, a first dynamic route planning is performed for the hard conflict processing corresponding to the second collision analysis result, and a second dynamic route planning is performed for the soft conflict processing corresponding to the second collision analysis result. The first dynamic route planning and the second dynamic route planning are combined to obtain a planning result.

5. The urban distributed energy storage scheduling method based on big data according to claim 1 is characterized in that: Generate constraints based on the supplementary positioning results and planning results, set an elastic response basis based on the constraints, perform performance verification, and generate a constrained scheduling plan based on the performance verification results to schedule urban distributed energy storage, including: Performing a first extraction on the supplementary positioning result, performing a second extraction on the planning result, and determining corresponding constraint conditions based on the first extraction and the second extraction; Establishing a three-level response mechanism based on the constraints, and performing energy storage-strategy binding based on the three-level response mechanism to form a foundation for elastic response; The elastic response basis is validated for effectiveness, key adjustment parameters are identified based on the effectiveness validation results, the elastic response basis is adjusted, and a hierarchical constraint scheduling plan is output to schedule urban distributed energy storage.

6. A method for urban distributed energy storage scheduling based on big data, characterized in that: include: Monitoring module: obtaining a city map, determining a first city feature based on the city map, and defining monitoring requirements based on the first city feature to form a monitoring layer; Allocation module: obtains monitoring layer data, uses the monitoring layer data to identify the characteristics of the second city based on the characteristics of the first city, further defines the energy storage main structure, and establishes a corresponding game mechanism based on the energy storage main structure to derive an energy storage allocation plan; Adaptation module: Uses the energy storage allocation plan to perform spatial adaptation, performs supplementary positioning of fixed energy storage based on the spatial adaptation results, and obtains supplementary positioning results. At the same time, it performs dynamic route planning for mobile energy storage and obtains planning results. Verification module: Generates constraints based on the supplementary positioning results and planning results, sets an elastic response basis based on the constraints, performs performance verification, and generates a constrained scheduling plan based on the performance verification results to dispatch the city's distributed energy storage; Monitoring module, including: Calculate the building density index of each area from the city map, mark special terrain features, and derive spatial structure characteristics. Based on the building density index and special terrain features, perform functional zoning of the city to derive functional zoning characteristics. Mark the radiation range of key infrastructure of the city's energy system based on the functional zoning results to derive infrastructure layout characteristics. The first city characteristics are derived by integrating the spatial structure characteristics, functional zoning characteristics and infrastructure layout characteristics; Determining corresponding monitoring requirements from a monitoring-requirement mapping table based on the first city characteristics to form a monitoring layer; Distribution module, including: Spatially superimposing the monitoring layer data with the first city characteristics to establish a spatiotemporal correlation matrix, and extracting the second city characteristics based on the spatiotemporal correlation matrix; Deconstruct the secondary city's characteristics to derive dynamic, spatiotemporal, and environmental characteristics. These characteristics are then divided into their respective categories to produce a comprehensive analysis of the results, ultimately determining a dynamic strategy system for the core attributes of the energy storage entity and the communication network architecture. Defining the energy storage main body architecture according to the core attributes of the energy storage main body and the dynamic strategy system of the communication network architecture; The second city characteristics are the urban operation laws extracted after fusing and analyzing the monitoring layer data with the first city characteristics through the spatiotemporal correlation matrix. The second city characteristics include dynamic characteristics, spatiotemporal characteristics, and environmental characteristics. The dynamic characteristics are the energy behaviors that change over time, the spatiotemporal characteristics are the coupling laws of spatial distribution and temporal evolution, and the environmental characteristics are the impact of external factors on the energy system.

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