Urban distributed energy storage scheduling method and system based on big data

Abstract

The invention provides an urban distributed energy storage scheduling method and system based on big data, and belongs to the technical field of smart power grids, and the method comprises the steps: obtaining an urban map, determining a first urban feature based on the urban map, determining a monitoring demand according to the first urban feature, and forming a monitoring layer; using monitoring layer data to identify a second city feature based on the first city feature, further defining an energy storage main body framework, constructing a vertical game mechanism, and obtaining an energy storage distribution scheme; performing space adaptation by using an energy storage distribution scheme, performing supplementary positioning of fixed energy storage and dynamic route planning of mobile energy storage to obtain a supplementary positioning result and a planning result, generating constraint conditions according to the supplementary positioning result and the planning result, setting an elastic response basis, and performing efficiency verification; and a constraint scheduling scheme is generated according to an efficiency verification result, urban distributed energy storage is scheduled, a single scheduling strategy is broken through, the cooperative capability of multiple energy storage units is enhanced, and the overall system efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart grids, and particularly to a method and system for urban distributed energy storage scheduling based on big data. Background Art

[0002] With the acceleration of the urbanization process and the increase in the penetration rate of renewable energy, the urban energy system faces challenges such as intensified supply-demand fluctuations, insufficient grid resilience, and interest conflicts among multiple energy storage entities. The existing problems in the current distributed energy storage applications include a single scheduling strategy, mainly relying on preset charge-discharge modes, insufficient coordination optimization of multiple energy storage units, and the need to improve the intelligence level of the emergency response mechanism.

[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 method and system for urban distributed energy storage scheduling based on big data provided by the present invention construct an urban feature monitoring layer through multi-source data fusion, optimize energy storage allocation based on a game mechanism, realize the collaborative layout of fixed / mobile energy storage in combination with spatial adaptation, and finally form an elastic scheduling plan to achieve closed-loop management of monitoring - decision - adaptation - response, improve the dynamic coordination ability of distributed energy storage, break through the single scheduling strategy, achieve multi-objective optimization, enhance the collaborative ability of multiple energy storage units, and improve the overall system efficiency.

[0005] The present invention provides a method for urban distributed energy storage scheduling based on big data, including: Step 1: Obtain a city map, determine the first urban feature based on the city map, and clarify the monitoring requirements according to the first urban feature to form a monitoring layer; Step 2: Obtain the monitoring layer data, use the monitoring layer data to identify the second urban feature based on the first urban feature, then define the energy storage entity framework, and establish a corresponding game mechanism based on the energy storage entity framework to obtain an energy storage allocation plan; Step 3: Perform spatial adaptation using the energy storage allocation plan, supplement the positioning of fixed energy storage according to the spatial adaptation result to obtain a supplementary positioning result, and at the same time, perform dynamic route planning for mobile energy storage to obtain a planning result; Step 4: Generate constraint conditions according to the supplementary positioning result and the planning result, set the elastic response basis based on the constraint conditions, perform effectiveness verification, and generate a constraint scheduling plan according to the effectiveness verification result to schedule the urban distributed energy storage.

[0006] The present invention provides a method for urban distributed energy storage scheduling based on big data. Obtain a city map, determine the first urban feature based on the city map, and clarify the monitoring requirements according to the first urban feature to form a monitoring layer, including: Calculate the building density index of each area from the city map, mark the special terrain features, obtain the spatial structure features, perform functional zoning on the city based on the building density index and the special terrain features, obtain the functional zoning features, and mark the radiation range of the key infrastructure of the urban energy system according to the functional zoning results to obtain the infrastructure layout features; Integrate the spatial structure features, functional zoning features, and infrastructure layout features to obtain the first urban feature; Based on the first urban feature, determine the corresponding monitoring requirements from the monitoring-demand mapping table to form a monitoring layer.

[0007] The present invention provides a method for urban distributed energy storage scheduling based on big data. Obtain the data of the monitoring layer, and use the data of the monitoring layer to identify the second urban feature based on the first urban feature, and then define the energy storage main framework, including: Perform spatial superposition of the monitoring layer data and the first urban feature to establish a spatio-temporal correlation matrix, and extract the second urban feature based on the spatio-temporal correlation matrix; Decompose the second urban feature to obtain dynamic features, spatio-temporal features, and environmental features. Divide the dynamic features, spatio-temporal features, and environmental features respectively to obtain the division results, and comprehensively analyze the division results to further determine the core attributes of the energy storage main body, the communication network framework, and the dynamic strategy system; Define the energy storage main framework according to the core attributes of the energy storage main body, the communication network framework, and the dynamic strategy system.

[0008] The present invention provides a method for urban distributed energy storage scheduling based on big data. Divide the dynamic features, spatio-temporal features, and environmental features respectively to obtain the division results, and comprehensively analyze the division results to further determine the core attributes of the energy storage main body, the communication network framework, and the dynamic strategy system, including: Perform a first division on the dynamic features to determine the load fluctuation feature and the equipment state feature. Determine the decision-making frequency of the energy storage main body according to the load fluctuation feature, and determine the feasibility of the energy storage main body according to the equipment state feature. Comprehensively consider the decision-making frequency of the energy storage main body and the feasibility of the energy storage main body to determine the core attributes of the energy storage main body; Perform a second division on the spatio-temporal features to determine the regional coupling feature and the network congestion feature. Determine the interaction topology of the energy storage main body according to the regional coupling feature, and determine the communication framework of the energy storage main body according to the network congestion feature. Comprehensively consider the interaction topology of the energy storage main body and the communication framework of the energy storage main body to determine the communication network framework; Perform a third division on the environmental features to determine the temperature-load sensitivity and the rainfall-photovoltaic correlation. Determine the decision-making space of the energy storage main body according to the temperature-load sensitivity, and determine the type migration of the energy storage main body according to the rainfall-photovoltaic correlation. Comprehensively consider the decision-making space of the energy storage main body and the type migration of the energy storage main body to determine the dynamic strategy system.

[0009] The present invention provides a method for scheduling urban distributed energy storage based on big data. A corresponding game mechanism is established based on the energy storage main framework to obtain an energy storage allocation scheme, including: Perform game initialization configuration based on the energy storage main framework. Extract participants from the energy storage main framework according to the configuration results, and stratify the participants to obtain upper-layer participants and lower-layer participants; Determine the upper-layer participant strategy space according to the upper-layer participants, and set corresponding upper-layer game rules according to the upper-layer participants and the corresponding upper-layer participant strategy space. At the same time, determine the lower-layer participant strategy space according to the lower-layer participants, and set corresponding lower-layer game rules according to the lower-layer participants and the corresponding lower-layer participant strategy space; Perform cross-layer coupling processing on the upper-layer game rules and the lower-layer game rules, and establish a game mechanism according to the coupling cross-layer coupling processing results; Output the equilibrium result of the upper-layer game and the stable strategy set of the lower-layer game according to the game mechanism, and then obtain the energy storage allocation scheme according to the equilibrium result and the stable strategy set.

[0010] The present invention provides a method for scheduling urban distributed energy storage based on big data. Use the energy storage allocation scheme for spatial adaptation, perform supplementary positioning of fixed energy storage according to the spatial adaptation results to obtain supplementary positioning results, and at the same time, perform dynamic route planning of mobile energy storage to obtain planning results, including: Convert the logical topology nodes in the energy storage allocation scheme into the actual geographic coordinate system, perform spatial adaptation based on the actual geographic coordinate system, classify according to the spatial adaptation results, perform collision analysis on fixed energy storage and mobile energy storage to obtain the first collision analysis result of fixed energy storage and the second collision analysis result of mobile energy storage; Match the corresponding hard conflict handling and soft conflict handling of the first collision analysis result and the second collision analysis result from the collision - handling table; Perform the first supplementary positioning on the hard conflict handling corresponding to the first collision analysis result based on the actual geographic coordinate system, perform the second supplementary positioning on the soft conflict handling corresponding to the first collision analysis result, and synthesize the first supplementary positioning and the second supplementary positioning to obtain the supplementary positioning result; Perform the first dynamic route planning on the hard conflict handling corresponding to the second collision analysis result based on the actual geographic coordinate system, perform the second dynamic route planning on the soft conflict handling corresponding to the second collision analysis result, and synthesize the first dynamic route planning and the second dynamic route planning to obtain the planning result.

[0011] The present invention provides a method for scheduling urban distributed energy storage based on big data. Constraint conditions are generated according to supplementary positioning results and planning results. An elastic response basis is set based on the constraint conditions, effectiveness verification is carried out, and a constrained scheduling scheme is generated according to the effectiveness verification results to schedule urban distributed energy storage, including: Perform a first extraction on the supplementary positioning results, perform a second extraction on the planning results, and determine corresponding constraint conditions based on the first extraction and the second extraction; Establish a three-level response mechanism according to the constraint conditions, and perform energy storage-strategy binding according to the three-level response mechanism, thereby forming an elastic response basis; Perform effectiveness verification on the elastic response basis, identify key adjustment parameters according to the effectiveness verification results, adjust the elastic response basis, and output a hierarchical constrained scheduling scheme to schedule urban distributed energy storage.

[0012] The present invention provides a system for scheduling urban distributed energy storage based on big data, including: Monitoring module: Obtain a city map, determine the first city feature based on the city map, and clarify the monitoring requirements according to the first city feature to form a monitoring layer; Allocation module: Obtain the monitoring layer data, use the monitoring layer data to identify the second city feature based on the first city feature, thereby define the energy storage main framework, and establish a corresponding game mechanism based on the energy storage main framework to obtain an energy storage allocation scheme; Adapter module: Perform spatial adaptation using the energy storage allocation scheme, perform supplementary positioning of fixed energy storage according to the spatial adaptation results to obtain supplementary positioning results, and at the same time, perform dynamic route planning of mobile energy storage to obtain planning results; Verification module: Generate constraint conditions according to the supplementary positioning results and the planning results, set an elastic response basis based on the constraint conditions, perform effectiveness verification, and generate a constrained scheduling scheme according to the effectiveness verification results to schedule urban distributed energy storage.

[0013] Compared with the prior art, the beneficial effects of the present application are as follows: By constructing a city feature monitoring layer through multi-source data fusion, optimizing energy storage allocation based on a game mechanism, combining spatial adaptation to achieve coordinated layout of fixed / mobile energy storage, and finally forming an elastic scheduling scheme, realizing closed-loop management of monitoring-decision-making-adaptation-response, improving the dynamic coordination ability of distributed energy storage, breaking through a single scheduling strategy, achieving multi-objective optimization, enhancing the collaborative ability of multiple energy storage units, and improving the overall system efficiency.

[0014] Other features and advantages of the present invention will be described in the subsequent specification, and, in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained by the structures specifically pointed out in the written specification and the drawings.

[0015] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Description of the Drawings

[0016] 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, but do not constitute a limitation to the present invention. In the accompanying drawings: Figure 1 is a schematic flowchart of a method for urban distributed energy storage scheduling based on big data provided by an embodiment of the present invention; Figure 2 is a schematic structural diagram of a system for urban distributed energy storage scheduling based on big data provided by an embodiment of the present invention; Figure 3 is the technical architecture of the distributed energy storage system provided by an embodiment of the present invention. Detailed Embodiments

[0017] The following describes the preferred embodiments of the present invention 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. Embodiment 1:

[0018] An embodiment of the present invention provides a method for urban distributed energy storage scheduling based on big data, as Figure 1 shown, including: Step 1: Obtain a city map, determine the first city feature based on the city map, and clarify the monitoring requirements according to the first city feature to form a monitoring layer; Step 2: Obtain the monitoring layer data, use the monitoring layer data to identify the second city feature based on the first city feature, and then define the energy storage main framework, and establish a corresponding game mechanism based on the energy storage main framework to obtain an energy storage allocation plan; Step 3: Perform spatial adaptation using the energy storage allocation plan, perform supplementary positioning of fixed energy storage according to the spatial adaptation result to obtain a supplementary positioning result, and at the same time, perform dynamic route planning of mobile energy storage to obtain a planning result; Step 4: Generate constraint conditions according to the supplementary positioning result and the planning result, set an elastic response basis based on the constraint conditions, perform effectiveness verification, and generate a constraint scheduling plan according to the effectiveness verification result to schedule the urban distributed energy storage.

[0019] In this embodiment, the first city feature is a comprehensive representation of the spatial structure + functional zoning + infrastructure. For example, City A: high-density R & D area + distributed optical storage network, Area B: low-density ecological city + smart microgrid.

[0020] In this embodiment, the monitoring requirements are the energy parameters and frequencies to be monitored in different regions. For example, in the commercial area: real-time load (once every 1 second), voltage fluctuation; in the industrial area: harmonic distortion rate (once every 1 minute).

[0021] In this embodiment, the monitoring layer is a data acquisition system composed of sensors and communication networks. For example, in terms of hardware: smart meters (such as Huawei IoT meters), environmental sensors; in terms of communication: 5G + LoRa (such as in a certain smart city energy Internet of Things). The association with BMS / EMS is that the BMS three-level architecture (BMU / BCU / BAU) is responsible for the management of energy storage units, while the monitoring layer provides external environmental data (such as temperature, load) to assist BAU / EMS in optimizing the scheduling strategy. For example, when the monitoring layer detects a sharp increase in the load in a commercial area → BAU / EMS adjusts the PCS discharge mode; when the terrain data indicates limited photovoltaic power generation in mountainous areas → BMS switches to the energy storage power supply mode.

[0022] In this embodiment, the second urban feature is the dynamic and multi-dimensional urban operation rules extracted through the fusion analysis of the monitoring layer data (real-time load, equipment status, etc.) and the first urban feature (static spatial structure, functional zoning, etc.) using a spatio-temporal correlation matrix. The core components include dynamic features: energy behaviors that change over time (such as the load fluctuation curve in the commercial area), spatio-temporal features: the coupling rules of spatial distribution and temporal evolution (such as the electricity consumption hotspots during morning and evening rush hours along the subway line), and environmental features: the impact of external factors on the energy system (such as the sensitivity of temperature to air-conditioning load).

[0023] In this embodiment, the first urban feature is the static anatomical map of the city, generated based on geographical information and planning documents, answering what is where. The second urban feature is the dynamic electrocardiogram of the city, obtained through the spatio-temporal coupling analysis of real-time data and static features, answering how it changes and interacts.

[0024] In this embodiment, the main framework of the energy storage is the energy storage system design scheme that integrates core attributes, communication architecture, and strategies. For example, in the commercial area framework: core attribute: high-power lithium-ion energy storage (2MW), communication: 5G + edge computing (low latency), strategy: load following + demand response; in the industrial area framework: core attribute: long-life flow battery (10MWh), communication: fiber-optic private network (high reliability), strategy: smoothing photovoltaic fluctuations + black start standby.

[0025] In this embodiment, the game mechanism is designed through a hierarchical game mechanism, respectively constructing the upper-layer strategy space and the lower-layer strategy space, and forming a multi-level collaborative game model after cross-layer coupling processing.

[0026] In this embodiment, the energy storage distribution plan is a resource scheduling plan generated based on the game result. For example, in the business district plan: 2MW lithium battery energy storage and 3 mobile energy storage vehicles are called during the morning peak to achieve load peak shaving. In the industrial park plan: a flow battery is enabled during the night shift for valley charging to smooth the production load during the day.

[0027] In this embodiment, spatial adaptation is to map logical nodes to geographical coordinates and analyze spatial feasibility. For example, adapt the energy storage nodes in the business district to near a certain substation in Lujiazui (avoiding the underground pipe gallery).

[0028] In this embodiment, the supplementary positioning result is the final optimized site selection plan formed after hard and soft conflict handling for the conflict problems found in the spatial adaptation of fixed energy storage, including: the corrected geographical coordinates (latitude and longitude) of the energy storage station, the adjusted coverage radius and service range, and additional buffer measures (such as sound insulation walls and safety distance calibration). For example, the original planned location (coordinate A) of a certain energy storage power station conflicts with the underground gas pipeline. After supplementary positioning: Hard handling: Translate 150 meters eastward to coordinate B (avoiding the pipeline). Soft handling: Install an explosion-proof wall at coordinate B (reducing safety risks). Final result: Confirm that coordinate B is a legal and compliant site selection and mark that regular safety inspections are required.

[0029] In this embodiment, the planning result is a time-space scheduling plan formed after optimizing the dynamic route conflicts of mobile energy storage, including: optimized path nodes (including GPS coordinate sequences), time window arrangements (arrival / leave times at each site), and a list of emergency backup routes. For example, the original planned path of the mobile energy storage vehicle passes through the morning peak congestion area. After planning: Hard handling: Detour through the elevated road (avoiding the restricted area). Soft handling: Adjust the delivery time from 07:30 to 08:15 (avoiding the congestion peak). Final result: Generate a scheduling table containing 3 alternative routes and mark that it will automatically switch to route C on rainy days.

[0030] In this embodiment, the constraint conditions are the scheduling boundary conditions formed by integrating spatial and dynamic limitations. For example, hard constraints: The coordinates of the fixed energy storage station cannot be changed (required by regulations). Soft constraints: The mobile energy storage vehicle needs to charge during the low electricity price period (economic requirement).

[0031] In this embodiment, the elastic response basis is an executable framework that integrates constraints, response mechanisms, and binding strategies. For example, the elastic response rule library of a microgrid in a new area contains 12 preset scenario strategies.

[0032] In this embodiment, the effectiveness verification is to evaluate the response effect through simulation or actual measurement. For example, simulate a typhoon weather to verify whether the mobile energy storage vehicle can reach the key load points within 30 minutes.

[0033] In this embodiment, the constraint scheduling scheme is the final scheduling instruction set output in a hierarchical manner. For example, the first-level scheme: during daily periods, charge and discharge according to the electricity price strategy; the third-level scheme: in the disaster mode, the fixed energy storage operates at full power, and the mobile energy storage vehicles centrally guarantee the hospital.

[0034] In this embodiment, hierarchical stress tests are carried out based on the elastic response basis, a five-dimensional evaluation system is synchronously established for evaluation, the test results and evaluation results are obtained, the sensitivity impacts of each parameter in the test results and evaluation results on the overall efficiency are quantified, the dominant key adjustment parameters are identified, and the elastic response basis is adjusted based on the key adjustment parameters to generate three-level constraint scheduling schemes of the normal version, emergency version, and transition version, and the urban distributed energy storage is scheduled.

[0035] The working principle and beneficial effects of the above technical solution are as follows: a city feature monitoring layer is constructed through multi-source data fusion, the energy storage allocation is optimized based on the game mechanism, the fixed / mobile energy storage collaborative layout is realized in combination with spatial adaptation, and finally an elastic scheduling scheme is formed to achieve closed-loop management of monitoring - decision - adaptation - response, improve the dynamic collaborative ability of distributed energy storage, break through the single scheduling strategy, achieve multi-objective optimization, enhance the collaborative ability of multiple energy storage units, and improve the overall system efficiency. Embodiment 2:

[0036] An embodiment of the present invention provides a method for scheduling urban distributed energy storage based on big data. A city map is obtained, the first city feature is determined based on the city map, and the monitoring requirements are clarified according to the first city feature to form a monitoring layer, including: Calculate the building density index of each area from the city map, mark the special terrain features, obtain the spatial structure features, conduct functional zoning of the city based on the building density index and special terrain features, obtain the functional zoning features, and mark the radiation range of the key infrastructure of the urban energy system according to the functional zoning results to obtain the infrastructure layout features; The first city feature is obtained by integrating the spatial structure features, functional zoning features, and infrastructure layout features; Based on the first city feature, the corresponding monitoring requirements are determined from the monitoring - demand mapping table to form a monitoring layer.

[0037] In this embodiment, the building density index is the proportion of the floor area of buildings per unit area, reflecting the regional development intensity. For example, high-density areas (>0.6): urban CBDs, commercial centers; medium-density areas (0.3 - 0.6): ordinary residential areas; low-density areas (<0.3): suburbs, ecological protection areas.

[0038] In this embodiment, the special topographic features are natural or artificial topographic elements that affect the layout of energy facilities. For example, rivers / lakes → flood control design for cross-river cables or energy storage stations needs to be considered; mountains / hills → affect the layout of photovoltaic power plants, and the location of energy storage sites needs to be optimized; underground spaces (such as subways, tunnels) → require special power supply solutions (such as energy storage in a certain highway tunnel).

[0039] In this embodiment, the spatial structure features are the urban spatial patterns formed by the comprehensive building density and topography. 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 photovoltaics + mobile energy storage.

[0040] In this embodiment, the functional zoning features are urban functional types (such as commercial, industrial, residential, ecological) divided based on spatial features. For example, commercial areas (such as Financial Street) → high electricity demand, high-power energy storage is required; industrial areas (such as industrial parks) → stable load, suitable for peak-valley arbitrage energy storage; ecological areas (such as wetlands) → restricted development, suitable for integrated photovoltaics and energy storage.

[0041] In this embodiment, the process of functional zoning is to input building density, topographic data, population density, etc., use GIS tools (such as ArcGIS) to divide similar areas, and conduct functional zoning in combination with urban planning.

[0042] In this embodiment, the key infrastructure of the urban energy system is the core facilities that support energy supply. For example, fixed energy storage power stations, mobile energy storage vehicles, and substations / distribution stations.

[0043] In this embodiment, the radiation range is the spatial range effectively served by the infrastructure. For example, for energy storage stations: the coverage radius is 2 km; for photovoltaic power plants: directly affect the stability of the surrounding 1 km power grid.

[0044] In this embodiment, the infrastructure layout features are the spatial distribution patterns of key facilities. For example, grid-like, or dot-like + mobile supplement.

[0045] In this embodiment, the monitoring-demand mapping table is a correspondence table between urban features and monitoring parameters. For example, if the urban feature is a high-density commercial area, the monitoring parameters are real-time load and voltage sag, and the corresponding frequency is 1 time per second; if the urban feature is an ecological area, the monitoring parameters are environmental temperature and humidity, and photovoltaic output, and the corresponding frequency is 1 time per hour.

[0046] The working principle and beneficial effects of the above technical solution are: divide urban functional areas through building density and topographic features, construct a spatial-functional-energy multi-dimensional feature system in combination with the radiation range of infrastructure, generate a precise monitoring layer based on feature mapping, realize the spatial demand identification and dynamic monitoring network deployment of the urban energy system, and provide a spatial decision-making benchmark for subsequent energy storage optimization. Embodiment 3:

[0047] An embodiment of the present invention provides a method for scheduling urban distributed energy storage based on big data. Monitor layer data is obtained, and the second urban feature is identified based on the first urban feature using the monitor layer data, and then the energy storage main framework is defined, including: A spatio-temporal correlation matrix is established by spatially overlaying the monitor layer data with the first urban feature, and the second urban feature is extracted based on the spatio-temporal correlation matrix; The second urban feature is disassembled to obtain dynamic features, spatio-temporal features, and environmental features. The dynamic features, spatio-temporal features, and environmental features are respectively divided to obtain division results, and the division results are comprehensively analyzed to determine the core attributes of the energy storage main body, the communication network framework, and the dynamic strategy system; The energy storage main framework is defined according to the core attributes of the energy storage main body, the communication network framework, and the dynamic strategy system.

[0048] In this embodiment, the spatio-temporal correlation matrix is a multi-dimensional data matrix constructed by spatio-temporally fusing the monitor layer data (such as electricity load, temperature) with the first urban feature (such as building density, functional zoning), which reflects the dynamic correlation of the urban energy system. For example, the commercial area matrix: high building density + peak load, and the industrial area matrix: stable load + high temperature sensitivity (such as the summer electricity consumption curve of a steel plant).

[0049] In this embodiment, the dynamic feature is an energy parameter that changes over time. For example, load fluctuations (such as morning and evening peaks in the commercial area), and changes in the energy storage SOC; the spatio-temporal feature is an energy law of spatial distribution + temporal evolution. For example, the east-west difference in photovoltaic power output (such as the early peak in the east), and the optimal path of the mobile energy storage vehicle changing over time; the environmental feature is the impact of the external environment on the energy system. For example, the impact of temperature on the air conditioning load (such as +5°C in summer → load +20%), and the suppression of rainfall on the photovoltaic output.

[0050] In this embodiment, the division result is to cluster or classify the three types of features to form an operable strategy unit. For example, dynamic features → divide into high-frequency regulated energy storage (such as coping with load mutations) and low-frequency buffered energy storage (such as peak-valley arbitrage); spatio-temporal features → identify high-value scheduling areas (such as areas along the subway line that require mobile energy storage coverage).

[0051] In this embodiment, the comprehensive analysis is to combine the division results to generate decision rules. For example, high temperature + high load area → preferentially call fixed energy storage and pre-deploy mobile energy storage vehicles.

[0052] In this embodiment, the core attribute of the energy storage entity is the inherent characteristic of the energy storage unit, which determines its scheduling priority. For example, power / capacity: a 2MW / 4MWh energy storage power station vs. a 0.5MW / 1MWh mobile energy storage vehicle, response speed: lithium battery (millisecond level) vs. flow battery (second level), life attenuation: number of cycles (e.g., capacity decays to 80% after 6000 cycles).

[0053] In this embodiment, the communication network architecture is the data transmission system between the energy storage unit and the management system. For example, hierarchical communication: local CAN bus (BMU - BCU, such as inside the BMS), wide - area 5G (BAU - EMS, such as remote control by the dispatching center), protocols: MQTT (cloud data synchronization), IEC 61850 (grid interaction).

[0054] In this embodiment, the dynamic policy system is a set of scheduling rules adjusted based on real - time data. For example, normal policy: peak - valley arbitrage (charging during valley hours and discharging during peak hours), emergency policy: when the mains power is interrupted, the mobile energy storage vehicle automatically switches to the backup power mode.

[0055] The working principle and beneficial effects of the above - mentioned technical solution are as follows: By fusing urban characteristics and real - time monitoring data through a spatio - temporal correlation matrix, dynamically operating characteristics are intelligently extracted, and an intelligent decision - making system including the attributes of the energy storage entity, communication architecture, and dynamic policies is constructed to achieve precise coordinated scheduling of distributed energy storage. Embodiment 4:

[0056] The embodiment of the present invention provides a method for scheduling urban distributed energy storage based on big data. The dynamic characteristics, spatio - temporal characteristics, and environmental characteristics are respectively divided to obtain division results, and the division results are comprehensively analyzed. Furthermore, the core attributes of the energy storage entity, the communication network architecture, and the dynamic policy system are determined, including: Perform a first division on the dynamic characteristics to determine the load fluctuation characteristics and equipment status characteristics. Determine the decision frequency of the energy storage entity according to the load fluctuation characteristics, determine the feasibility of the energy storage entity according to the equipment status characteristics, and comprehensively determine the core attributes of the energy storage entity based on the decision frequency and feasibility of the energy storage entity; Perform a second division on the spatio - temporal characteristics to determine the regional coupling characteristics and network congestion characteristics. Determine the interaction topology of the energy storage entity according to the regional coupling characteristics, determine the communication architecture of the energy storage entity according to the network congestion characteristics, and comprehensively determine the communication network architecture based on the interaction topology and communication architecture of the energy storage entity; Perform a third division on the environmental characteristics to determine the temperature - load sensitivity and rainfall - photovoltaic correlation. Determine the decision space of the energy storage entity according to the temperature - load sensitivity, determine the type migration of the energy storage entity according to the rainfall - photovoltaic correlation, and comprehensively determine the dynamic policy system based on the decision space and type migration of the energy storage entity.

[0057] In this embodiment, through the analysis of dynamic features, the system operation parameters are divided into two key indicators: load fluctuation features and equipment status features.

[0058] In this embodiment, the load fluctuation features are the laws of electricity load changing over time (such as peak-valley difference, mutation frequency, etc.). For example, in the commercial area: the load surges at 9 am; in the industrial area: the load is stable (such as a steel plant operating continuously for 24 hours).

[0059] In this embodiment, the equipment status features are the health status of energy storage devices (such as SOC, SOH, temperature, etc.). For example, for a lithium battery pack: SOC = 80%, SOH = 90% (good health status); for a faulty battery: abnormal temperature (such as when the temperature of a single cell > 50°C triggers an alarm).

[0060] In this embodiment, the decision-making frequency of the energy storage entity is the update frequency of the scheduling instruction determined according to the load fluctuation. For example, for high-frequency decision-making: second-level response (such as coping with the rapid fluctuation of photovoltaic cloud clusters); for low-frequency decision-making: hourly adjustment (such as peak-valley arbitrage).

[0061] In this embodiment, the feasibility of the energy storage entity is the schedulable capacity of the energy storage unit (0 - 100%) evaluated based on the equipment status. For example, with a feasibility of 90%: a healthy battery pack can operate at full power; with a feasibility of 50%: an aging battery pack needs to operate at a derated power.

[0062] In this embodiment, the core attribute of the energy storage entity is the role of the energy storage unit determined by comprehensively considering the decision-making frequency and feasibility. For example, for the fast response type: high decision-making frequency + high feasibility (such as lithium-ion energy storage); for the buffer backup type: low decision-making frequency + medium feasibility (such as lead-acid batteries).

[0063] In this embodiment, the regional coupling feature is the correlation of energy demands in different regions (such as the load synchronization of adjacent regions). For example, for strong coupling: the loads of the subway station and the commercial area increase synchronously; for weak coupling: the loads of the industrial area and the residential area are opposite (such as night shift factories and residential electricity consumption).

[0064] In this embodiment, the network congestion feature is the bottleneck of power grid transmission (such as line capacity limitation, node voltage over-limit). For example, for the congested area: the distribution network in the old urban area; for the unobstructed area: the newly developed area.

[0065] In this embodiment, the interaction topology of the energy storage entity is the collaborative relationship between energy storage units (master-slave, peer-to-peer, hierarchical, etc.). For example, for the master-slave topology: the central energy storage station commands mobile energy storage vehicles; for the peer-to-peer topology: multiple microgrids are interconnected.

[0066] In this embodiment, the communication architecture of the energy storage entity is the communication network design (delay, bandwidth, protocol, etc.). For low delay: 5G private network; for high reliability: fiber + LoRa dual redundancy, for example.

[0067] In this embodiment, the communication network architecture is an integrated solution of interactive topology and communication technology. For example, in the hierarchical architecture: a three-level CAN bus of BAU (total control) - BCU (area) - BMU (local), and edge computing: local decision-making + cloud collaboration.

[0068] In this embodiment, the temperature-load sensitivity is the degree of influence of temperature change on the load (such as the proportion of air-conditioning load). For example, high sensitivity: commercial complex (when the temperature rises by 1°C → load + 5%), low sensitivity: data center (constant temperature operation, stable load).

[0069] In this embodiment, the rainfall-photovoltaic correlation is the degree of inhibition of rainfall on photovoltaic power output. For example, strong correlation: coastal areas, weak correlation: arid areas.

[0070] In this embodiment, the decision-making space of the energy storage entity is the schedulable range under environmental constraints (such as temperature limiting the charge and discharge power). For example, power cut-off in high temperature: lithium batteries need to derate by 50% when the temperature is above 40°C, and rainy-day strategy: switch to energy storage power supply when photovoltaic power is cut off.

[0071] In this embodiment, the migration of the energy storage entity type is to adjust the energy storage type according to environmental changes (such as the role switch between fixed / mobile energy storage). For example, on sunny days: fixed energy storage preferentially absorbs photovoltaic power, and on rainy days: mobile energy storage vehicles support critical loads (such as tunnel lighting emergency).

[0072] In this embodiment, the dynamic policy system is a scheduling rule library that comprehensively responds to the environment. For example, normal policy: charge and discharge according to the power configuration policy when the temperature is 25°C, and emergency policy: forcibly limit power when the temperature > 35°C to give priority to ensuring the power supply of hospitals.

[0073] The working principle and beneficial effects of the above technical solutions are as follows: Through a three-level intelligent division mechanism, key parameters are extracted from dynamic, spatio-temporal, and environmental characteristics respectively, and the core attributes, communication architecture, and dynamic policy system of the energy storage entity are constructed to achieve the adaptive optimal scheduling of the distributed energy storage system, realize the accurate analysis and fusion of multi-dimensional characteristics, and improve the energy consumption ratio. Embodiment 5:

[0074] The embodiment of the present invention provides a method for scheduling urban distributed energy storage based on big data. A corresponding game mechanism is established based on the energy storage entity architecture to obtain an energy storage allocation scheme, including: Perform game initialization configuration based on the energy storage entity architecture, extract participants from the energy storage entity construction according to the configuration results, and stratify the participants to obtain upper-layer participants and lower-layer participants; Determine the upper-layer participant strategy space according to the upper-layer participants, and set the corresponding upper-layer game rules according to the upper-layer participants and the corresponding upper-layer participant strategy space. At the same time, determine the lower-layer participant strategy space according to the lower-layer participants, and set the corresponding lower-layer game rules according to the lower-layer participants and the corresponding lower-layer participant strategy space; Perform cross-layer coupling processing on the upper-layer game rules and the lower-layer game rules, and establish a game mechanism according to the coupling cross-layer coupling processing results; Output the equilibrium result of the upper-layer game and the stable strategy set of the lower-layer game according to the game mechanism, and then obtain the energy storage allocation plan according to the equilibrium result and the stable strategy set.

[0075] In this embodiment, the game initialization configuration sets the initial conditions of the game based on the energy storage main framework (core attributes, communication framework, strategy system), including participant division, strategy space definition, etc. For example, in a certain urban microgrid, resources such as a 2MW lithium battery energy storage station, 5 mobile energy storage vehicles, and a photovoltaic power station are incorporated into the game system, and their respective decision parameters (such as charge and discharge costs, response speeds, etc.) are initialized.

[0076] In this embodiment, the upper-layer participants are high-level decision-makers responsible for macro resource allocation, such as regional energy dispatch centers (such as the power grid company of a certain city), and microgrid master control systems (EMS / BAU integrated platforms); the lower-layer 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).

[0077] In this embodiment, the upper-layer participant strategy space corresponding to the upper-layer participants corresponds to the global power allocation strategy, energy storage unit scheduling priority, network congestion management. The upper-layer game rules aim at grid stability, restrict the charge and discharge behaviors of the lower-layer participants, and set a penalty mechanism (such as overload fines); the lower-layer participant strategy space corresponding to the lower-layer participants is the selection of charge and discharge power, response delay adjustment, and local optimization strategies (such as peak-valley arbitrage). The corresponding lower-layer game rules are to maximize their own benefits under the superior rules and avoid violating safety constraints (such as SOC over-limit).

[0078] In this embodiment, the objective function corresponding to the upper-layer game rules is , represents the charge and discharge power corresponding to the upper-layer participants; represents the load demand vector at time period t; G represents the grid topology incidence matrix; γ represents the strategy smoothness weight coefficient; T represents the total number of scheduling time periods; represents the charge and discharge power of the lower-layer participants at time period t; Denote the charging and discharging power of the upper-layer participants at time period t; Denote the L2 norm; the boundary condition of the objective function corresponding to the upper-layer game rule is , where A and b denote the linearized power grid security boundary; Denote the feasible region of the lower-layer strategy corresponding to the lower-layer participant strategy space, and finally derive an executable equilibrium strategy according to the upper-layer game objective function.

[0079] In this embodiment, the objective function corresponding to the lower-layer game rule is , where Denote the charging and discharging power corresponding to the i-th lower-layer participant; Denote the reference power sent from the upper layer to the i-th lower-layer participant; Denote the battery health weight; Denote the SOC health degree weight matrix; Denote the SOC state vector; the boundary condition of the objective function corresponding to the lower-layer game rule , where Denote the charging and discharging efficiency corresponding to the i-th lower-layer participant; Denote the rated energy storage capacity corresponding to the i-th lower-layer participant; Denote the state of charge of the i-th lower-layer participant at time period t; Denote the scheduling time step; Denote the lower SOC safety limit corresponding to the i-th lower-layer participant; Denote the upper SOC safety limit corresponding to the i-th lower-layer participant; Denote the charging and discharging power of the i-th lower-layer participant at time period t; Denote the maximum charging and discharging power corresponding to the i-th lower-layer participant; Denote the state of charge of the i-th lower-layer participant at time period t + 1.

[0080] In this embodiment, the cross-layer coupling processing and game mechanism are to coordinate the strategy conflicts between the upper and lower layers and establish a unified decision-making framework. For example, dynamic power allocation feedback: the upper layer issues time-sharing power allocation according to the grid state, and the lower-layer energy storage unit adjusts the charging and discharging plan accordingly. Reserve capacity reservation: the upper layer requires the lower-layer participants to maintain 20% reserve capacity during specific time periods to cope with sudden loads.

[0081] In this embodiment, the equilibrium result and the stable strategy set are stable states where no party can unilaterally improve its benefits after the game converges. For example, the equilibrium result is that the upper-level dispatching center and the lower-level energy storage power station reach an agreement - the power configuration for discharging during peak hours is increased by 15%, and the energy storage unit responds with a feasibility of 90%. The stable strategy set is that the mobile energy storage vehicle is fixedly deployed to the hospital on rainy days and dynamically supports the commercial area on sunny days.

[0082] The working principle and beneficial effects of the above technical solution are as follows: Through the design of a hierarchical game mechanism, the upper-level strategy space and the lower-level strategy space are respectively constructed. After cross-layer coupling processing, a multi-level collaborative game model is formed, and finally, the equilibrium solution and the stable strategy are output to realize the optimal allocation of energy storage resources and enhance the overall efficiency of the system. Embodiment 6:

[0083] The embodiment of the present invention provides a method for dispatching urban distributed energy storage based on big data. It uses an energy storage allocation scheme for spatial adaptation, and based on the spatial adaptation result, it conducts supplementary positioning of fixed energy storage, obtains the supplementary positioning result. At the same time, it conducts dynamic route planning for mobile energy storage and obtains the planning result, including: Convert the logical topology nodes in the energy storage allocation scheme into the actual geographical coordinate system, conduct spatial adaptation based on the actual geographical coordinate system, classify according to the spatial adaptation result, and perform collision analysis on fixed energy storage and mobile energy storage to obtain the first collision analysis result of fixed energy storage and the second collision analysis result of mobile energy storage; Match the corresponding hard conflict handling and soft conflict handling of the first collision analysis result and the second collision analysis result from the collision - handling table; Conduct the first supplementary positioning for the hard conflict handling corresponding to the first collision analysis result based on the actual geographical coordinate system, conduct the second supplementary positioning for the soft conflict handling corresponding to the first collision analysis result, and synthesize the first supplementary positioning and the second supplementary positioning to obtain the supplementary positioning result; Conduct the first dynamic route planning for the hard conflict handling corresponding to the second collision analysis result based on the actual geographical coordinate system, conduct the second dynamic route planning for the soft conflict handling corresponding to the second collision analysis result, and synthesize the first dynamic route planning and the second dynamic route planning to obtain the planning result.

[0084] In this embodiment, the logical topology node is an abstract network connection point, representing the functional position of the energy storage in the power system (such as node A → node B). For example, the virtual node in the microgrid model (such as the photovoltaic energy storage interface node); the actual geographical coordinate system is the physical position based on longitude and latitude or urban coordinates.

[0085] In this embodiment, the collision analysis is to detect the conflicts between the energy storage layout and the geography / facilities. The first collision analysis (fixed type) represents the conclusion of the rigid conflict detection between the fixed energy storage site selection and the geography / regulations. For example, if the planned location of a certain energy storage power station overlaps with the underground gas pipeline → conflict; the second collision analysis (mobile type) represents the conflict diagnosis between the mobile energy storage and the time-varying factors such as traffic / environment during dynamic operation. For example, if the route of the mobile energy storage vehicle passes through a bridge with a height limit → conflict.

[0086] In this embodiment, the hard conflict is an uncompromisable safety / regulation conflict, and the corresponding handling method is that the safety distance between the energy storage station and the chemical plant is insufficient → relocation; the soft conflict is an optimizable efficiency / cost conflict, and the corresponding handling method is that the route of the energy storage vehicle is congested → adjust the time period or route.

[0087] In this embodiment, the first supplementary positioning is a mandatory position correction for the hard conflicts (such as regulations prohibition, safety risks) of the fixed energy storage; the second supplementary positioning is an optimization adjustment measure for the soft conflicts (non-rigid constraint problems) found in the spatial adaptation of the fixed energy storage.

[0088] In this embodiment, the first dynamic route planning is a guaranteed safety path generated for the hard conflicts (such as height limit, no-entry area) of the mobile energy storage; the second dynamic route planning is an economically efficient path optimized for the soft conflicts (such as congestion, electricity price fluctuation) of the mobile energy storage.

[0089] The working principle and beneficial effects of the above technical solution are: through the conversion of geographical coordinates and collision detection analysis, the conflict types of fixed / mobile energy storage are intelligently identified, and precise spatial adaptation is carried out in combination with the conflict handling strategy. Finally, an optimized energy storage layout plan and dynamic route planning are generated to achieve the precise spatial allocation of energy storage resources and form an intelligent spatial adaptation mechanism. Embodiment 7:

[0090] The embodiment of the present invention provides a method for scheduling urban distributed energy storage based on big data. Constraint conditions are generated according to the supplementary positioning result and the planning result, an elastic response basis is set based on the constraint conditions, performance verification is carried out, and a constraint scheduling plan is generated according to the performance verification result to schedule the urban distributed energy storage, including: Perform the first extraction on the supplementary positioning result, perform the second extraction on the planning result, and determine the corresponding constraint conditions based on the first extraction and the second extraction; Establish a three-level response mechanism according to the constraint conditions, and perform energy storage-strategy binding according to the three-level response mechanism, thereby forming an elastic response basis; Perform performance verification on the elastic response basis, identify key adjustment parameters according to the performance verification result, adjust the elastic response basis, and output a hierarchical constraint scheduling plan to schedule the urban distributed energy storage.

[0091] In this embodiment, the first extraction is to extract key parameters (such as coordinates, capacity, 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), with a capacity of 2 MW and a coverage radius of 1.5 km → extracted as "Position constraint: ±50 m, Power constraint: ≤1.5 MW".

[0092] In this embodiment, the second extraction is to extract dynamic parameters (such as path nodes, time windows, schedulable power) from the route planning of mobile energy storage. For example, the mobile energy storage vehicle needs to pass through points A, B, and C between 07:00 - 09:00 and stay at each station for 15 minutes → extracted as "Time constraint: ±5 minutes, Path tolerance: 200 m".

[0093] In this embodiment, the three - level response mechanism: The first - level response is to execute the conventional scheduling according to the preset strategy in the case of no abnormality. For example, the energy storage in the commercial area charges during the low - electricity - price period (2:00 - 5:00 am) and discharges during the high - electricity - price period (18:00 - 21:00) to reduce the load pressure on the power grid; The second - level response is to start the standby capacity and dynamically adjust resources when detecting local abnormalities (such as sudden load increase, voltage fluctuation). For example, when the load in an industrial area suddenly increases by 10%, the dispatching center instructs the nearby mobile energy storage vehicle to go for support, and at the same time, the fixed energy storage station increases the discharge power to the upper limit; The third - level response is to prioritize ensuring the power supply of key facilities in the face of major faults (such as power outages, disasters), sacrificing some economic efficiency. For example, when a typhoon causes a substation failure, immediately cut off the non - essential loads and concentrate all energy storage resources to supply power to hospitals and emergency command centers.

[0094] In this embodiment, energy storage - strategy binding is to assign specific response strategies to each energy storage unit. For example, bind "Energy storage station A" with the "second - level response strategy": when detecting a local voltage drop, automatically increase the discharge power by 10%.

[0095] In this embodiment, the key adjustment parameters are variables that significantly affect the efficiency. For example, the energy storage response delay time (goal: optimized from 5 minutes to 2 minutes), the SOC safety threshold (adjusted from 20% - 90% to 25% - 85% to extend the life).

[0096] In this embodiment, the energy storage system of distributed energy storage, such as Figure 3As shown in the figure, distributed energy storage participates as an elastic resource, including supplementary positioning result extraction, and the optimized coordinates of fixed energy storage stations (such as battery unit groups) are converted into power output range constraints (for example, units 1 - 3 cover the load in the eastern area); planning result extraction, the path of mobile energy storage vehicles (which need to communicate through PCS and gateways) generates time - space availability constraints (for example, unit 4 is schedulable from 09:00 to 11:00); three - level response execution, level 1: BAU executes normal charge - discharge through CAN - bus instructions to BCU, level 3: the cloud platform directly overrides PCS to switch to the black - start mode. Among them, 232 is used for data terminal communication to realize data exchange with the background system (such as receiving time - synchronization information), and 485 is for long - distance communication among multiple devices BAU / EMS PCS energy storage converter: Controls the working mode of PCS (such as charge - discharge strategy), BAU / EMS Touch - screen display unit: Uploads battery data and receives user - set instructions, BAU / EMS Metering electricity meter: Reads electricity meter data (such as electricity quantity statistics); the function of the touch - screen display unit is human - machine interaction (HMI): Users can set charge - discharge strategies through the touch - screen and view real - time data (such as SOC, voltage, temperature, etc.). Data transfer: Sends user instructions to the BAU / EMS master control unit through 485 communication. Connection method: Connects to BAU / EMS through RS - 485. "Battery unit 1" to "Battery unit 6" refer to six battery packs; the function of the intelligent air conditioner, environmental temperature control: Regulates the temperature of the battery compartment to ensure that the battery works within the best range (lithium iron phosphate batteries are sensitive to temperature). Linkage control: May receive instructions through BAU / EMS (such as starting the air conditioner at high temperature).

[0097] The working principle and beneficial effects of the above - mentioned technical solution are: By extracting the constraint conditions of supplementary positioning (fixed energy storage) and dynamic planning (mobile energy storage), a three - level elastic mechanism including prevention, response, and recovery is constructed, the energy storage resources and strategies are dynamically bound, the key parameters are optimized after effectiveness verification, and finally a hierarchical scheduling scheme is generated to realize the dynamic collaborative management of urban energy storage. Example 8:

[0098] An embodiment of the present invention provides a city distributed energy storage scheduling system based on big data, as Figure 2 shown, including: Monitoring module: Obtains the city map, determines the first city feature based on the city map, and clarifies the monitoring requirements according to the first city feature to form a monitoring layer; Allocation module: Obtains the monitoring layer data, uses the monitoring layer data to identify the second city feature based on the first city feature, then defines the energy storage main framework, and establishes a corresponding game mechanism based on the energy storage main framework to obtain an energy storage allocation scheme; Adaption module: Perform spatial adaptation using the energy storage distribution scheme, supplement and locate the fixed energy storage according to the spatial adaptation result to obtain the supplementary positioning result. At the same time, perform dynamic route planning for the mobile energy storage to obtain the planning result; Verification module: Generate constraint conditions based on the supplementary positioning result and the planning result, set the elastic response basis based on the constraint conditions, perform efficiency verification, and generate a constraint scheduling scheme according to the efficiency verification result to schedule the urban distributed energy storage.

[0099] The working principle and beneficial effects of the above technical solution are as follows: Construct a urban feature monitoring layer through multi-source data fusion, optimize energy storage distribution based on the game mechanism, realize the coordinated layout of fixed / mobile energy storage in combination with spatial adaptation, and finally form an elastic scheduling scheme to achieve closed-loop management of monitoring - decision - adaptation - response, improve the dynamic coordination ability of distributed energy storage, break through the single scheduling strategy, achieve multi-objective optimization, enhance the coordination ability of multiple energy storage units, and improve the overall system efficiency.

[0100] 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 them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for urban distributed energy storage scheduling based on big data, characterized in that, Including: Step 1: Obtain a city map, determine the first city feature based on the city map, and clarify the monitoring requirements according to the first city feature to form a monitoring layer; Step 2: Obtain the monitoring layer data, use the monitoring layer data to identify the second city feature based on the first city feature, then define the energy storage main framework, and establish a corresponding game mechanism based on the energy storage main framework to obtain the energy storage allocation plan; Step 3: Perform spatial adaptation using the energy storage allocation plan, perform supplementary positioning of fixed energy storage according to the spatial adaptation result to obtain the supplementary positioning result, and at the same time, perform dynamic route planning of mobile energy storage to obtain the planning result; Step 4: Generate constraint conditions according to the supplementary positioning result and the planning result, set the elastic response basis based on the constraint conditions, perform effectiveness verification, and generate a constraint scheduling plan according to the effectiveness verification result to schedule the urban distributed energy storage.

2. The method for urban distributed energy storage scheduling based on big data according to claim 1, wherein Obtain a city map, determine the first city feature based on the city map, and clarify the monitoring requirements according to the first city feature to form a monitoring layer, including: Calculate the building density index of each area from the city map, mark the special terrain features to obtain the spatial structure features, perform functional zoning of the city based on the building density index and the special terrain features to obtain the functional zoning features, and mark the radiation range of the key infrastructure of the urban energy system according to the functional zoning result to obtain the infrastructure layout features; Comprehensively obtain the first city feature based on the spatial structure features, functional zoning features and infrastructure layout features; Determine the corresponding monitoring requirements from the monitoring-demand mapping table based on the first city feature to form a monitoring layer.

3. A method for urban distributed energy storage scheduling based on big data according to claim 1, characterized in that, Obtain the monitoring layer data, use the monitoring layer data to identify the second city feature based on the first city feature, and then define the energy storage main framework, including: Establish a spatio-temporal correlation matrix by spatially overlaying the monitoring layer data and the first city feature, and extract the second city feature based on the spatio-temporal correlation matrix; Decompose the second city feature to obtain the dynamic feature, spatio-temporal feature and environmental feature, respectively divide the dynamic feature, spatio-temporal feature and environmental feature to obtain the division result, and comprehensively analyze the division result to further determine the core attributes of the energy storage main body, the communication network framework and the dynamic strategy system; Define the energy storage main framework according to the core attributes of the energy storage main body, the communication network framework and the dynamic strategy system.

4. The method for dispatching urban distributed energy storage based on big data according to claim 3, wherein Respectively divide the dynamic feature, spatio-temporal feature and environmental feature to obtain the division result, and comprehensively analyze the division result to further determine the core attributes of the energy storage main body, the communication network framework and the dynamic strategy system, including: Perform the first division on the dynamic feature to determine the load fluctuation feature and the equipment state feature, determine the decision-making frequency of the energy storage main body according to the load fluctuation feature, determine the feasibility of the energy storage main body according to the equipment state feature, and comprehensively determine the core attributes of the energy storage main body based on the decision-making frequency of the energy storage main body and the feasibility of the energy storage main body; Perform a second division on the spatio-temporal characteristics to determine the regional coupling characteristics and network congestion characteristics. Determine the interaction topology of energy storage entities based on the regional coupling characteristics, determine the communication architecture of energy storage entities based on the network congestion characteristics, and comprehensively determine the communication network architecture based on the interaction topology of energy storage entities and the communication architecture of energy storage entities; Perform a third division on the environmental characteristics to determine the temperature-load sensitivity and rainfall-PV correlation. Determine the decision-making space of energy storage entities based on the temperature-load sensitivity, determine the type migration of energy storage entities based on the rainfall-PV correlation, and comprehensively determine the dynamic strategy system based on the decision-making space of energy storage entities and the type migration of energy storage entities.

5. A method for urban distributed energy storage scheduling based on big data according to claim 1, characterized in that, Establish a corresponding game mechanism based on the energy storage entity architecture to obtain an energy storage allocation plan, including: Perform game initialization configuration based on the energy storage entity architecture, extract participants from the energy storage entity construction according to the configuration results, and stratify the participants to obtain upper-layer participants and lower-layer participants; Determine the strategy space of the upper-layer participants according to the upper-layer participants, and set corresponding upper-layer game rules according to the upper-layer participants and the corresponding upper-layer participant strategy space. At the same time, determine the strategy space of the lower-layer participants according to the lower-layer participants, and set corresponding lower-layer game rules according to the lower-layer participants and the corresponding lower-layer participant strategy space; Perform cross-layer coupling processing on the upper-layer game rules and the lower-layer game rules, and establish a game mechanism according to the coupling cross-layer coupling processing results; Output the equilibrium result of the upper-layer game and the stable strategy set of the lower-layer game according to the game mechanism, and then obtain the energy storage allocation plan according to the equilibrium result and the stable strategy set.

6. The method for dispatching urban distributed energy storage based on big data according to claim 1, characterized in that Use the energy storage allocation plan for spatial adaptation, perform supplementary positioning of fixed energy storage according to the spatial adaptation result to obtain the supplementary positioning result, and at the same time, perform dynamic route planning of mobile energy storage to obtain the planning result, including: Convert the logical topology nodes in the energy storage allocation plan into the actual geographic coordinate system, perform spatial adaptation based on the actual geographic coordinate system, classify according to the spatial adaptation result, perform collision analysis on fixed energy storage and mobile energy storage to obtain the first collision analysis result of fixed energy storage and the second collision analysis result of mobile energy storage; Match the first collision analysis result and the second collision analysis result in the collision-processing table with the corresponding hard conflict handling and soft conflict handling; Perform the first supplementary positioning on the hard conflict handling corresponding to the first collision analysis result based on the actual geographic coordinate system, perform the second supplementary positioning on the soft conflict handling corresponding to the first collision analysis result, and comprehensively obtain the supplementary positioning result based on the first supplementary positioning and the second supplementary positioning; Perform the first dynamic route planning on the hard conflict handling corresponding to the second collision analysis result based on the actual geographic coordinate system, perform the second dynamic route planning on the soft conflict handling corresponding to the second collision analysis result, and comprehensively obtain the planning result based on the first dynamic route planning and the second dynamic route planning.

7. A method for urban distributed energy storage scheduling based on big data according to claim 1, characterized in that Generate constraint conditions according to the supplementary positioning result and the planning result, set the elastic response basis based on the constraint conditions, perform effectiveness verification, and generate a constraint scheduling plan according to the effectiveness verification result to schedule the urban distributed energy storage, including: Perform the first extraction on the supplementary positioning result, perform the second extraction on the planning result, and determine the corresponding constraint conditions based on the first extraction and the second extraction; Establish a three-level response mechanism according to the constraint conditions, and perform energy storage-strategy binding according to the three-level response mechanism, thereby forming a basis for elastic response; Verify the effectiveness of the elastic response basis, identify key adjustment parameters according to the effectiveness verification result, adjust the elastic response basis, and output a hierarchical constraint scheduling plan to schedule the urban distributed energy storage.

8. A method for urban distributed energy storage scheduling based on big data, characterized in that, Including: Monitoring module: Obtain the urban map, determine the first urban feature based on the urban map, and clarify the monitoring requirements according to the first urban feature to form a monitoring layer; Allocation module: Obtain the monitoring layer data, use the monitoring layer data to identify the second urban feature based on the first urban feature, thereby define the energy storage main framework, and establish a corresponding game mechanism based on the energy storage main framework to obtain the energy storage allocation plan; Adaptation module: Use the energy storage allocation plan for spatial adaptation, perform supplementary positioning of the fixed energy storage according to the spatial adaptation result to obtain the supplementary positioning result, and at the same time, perform dynamic route planning of the mobile energy storage to obtain the planning result; Verification module: Generate constraint conditions according to the supplementary positioning result and the planning result, set the elastic response basis based on the constraint conditions, perform effectiveness verification, and generate a constraint scheduling plan according to the effectiveness verification result to schedule the urban distributed energy storage.

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