Urban energy mutual aid management system based on multistage cloud energy storage

Abstract

The invention discloses an urban energy mutual aid management system based on multistage cloud energy storage, and relates to the technical field of heat supply system energy management. The multi-stage cloud energy storage platform is in communication connection with a data acquisition module, an energy storage resource analysis module, an energy demand prediction module, an energy storage resource management module, an energy scheduling optimization module, a heat supply system power grid state monitoring and early warning module and a user interaction response module, and all the modules are in electric signal connection; the data acquisition module is used for collecting data from different levels of energy storage resources. By analyzing the energy supply and demand conditions of all the regions of the city and matching supply and demand gaps, cross-region mutual aid of energy is achieved, energy waste is effectively reduced, the overall energy utilization efficiency is further improved, and the system can further promote achievement of energy conservation and emission reduction targets according to weather forecast and energy demand prediction and adjustment of an energy storage strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management of heating systems, and in particular to an urban energy mutual assistance management system based on multi-level cloud energy storage. Background Art

[0002] With the acceleration of urban development and industrialization, energy consumption continues to rise, posing severe challenges to energy supply, environment and economy. Cloud energy storage, as a shared energy storage technology based on the existing heating system power grid, enables users to use shared energy storage resources composed of centralized or distributed energy storage facilities anytime, anywhere and on demand, and pay service fees according to usage needs. Urban energy mutual management refers to the unified management and optimized scheduling of various energy sources in the city through advanced information technology and intelligent means to achieve efficient utilization and mutual sharing of energy, effectively alleviate the problem of tight urban energy supply, improve energy utilization efficiency, reduce energy costs, and promote green and low-carbon development of cities.

[0003] In the existing technology, due to the wide variety of urban energy storage resources, scattered locations and large fluctuations in load demand, inaccurate scheduling is prone to occur when scheduling and managing energy storage resources from different levels, which in turn leads to over-reliance on a certain level of energy storage resources, resulting in energy surplus or shortage in certain areas, and response time lags, affecting the real-time scheduling of the heating system power grid and the response to user demand. Therefore, there is an urgent need for an urban energy mutual assistance management system based on multi-level cloud energy storage to solve the existing problems. Summary of the invention

[0004] The purpose of the present invention is to provide a city energy mutual assistance management system based on multi-level cloud energy storage to solve the problems raised in the above-mentioned background technology.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: An urban energy mutual assistance management system based on multi-level cloud energy storage, comprising a multi-level cloud energy storage platform, wherein the multi-level cloud energy storage platform is communicatively connected with a data acquisition module, an energy storage resource analysis module, an energy demand prediction module, an energy storage resource management module, an energy dispatch optimization module, a heating system power grid status monitoring and early warning module, and a user interactive response module, wherein electrical signals are connected between the modules; The multi-level cloud energy storage platform establishes an energy storage resource management platform based on cloud computing, integrates energy storage units at different levels, supports data collection, real-time monitoring, data analysis and scheduling optimization of the energy storage system, realizes centralized monitoring and management of distributed energy storage resources, ensures efficient utilization of energy storage resources, and reduces scheduling deviations caused by information asymmetry and management lag; The data acquisition module is used to collect data from energy storage resources at different levels, including the status, power, location information of energy storage equipment, and data on urban load demand, providing comprehensive and accurate data support and providing a basis for energy dispatch and management of the system; The energy storage resource analysis module is used to analyze the collected data, clarify the energy demand and supply trends, improve the accuracy and availability of data, and provide a scientific basis for system decision-making; The energy demand forecasting module is used to forecast the load demand in different regions based on historical data, real-time monitoring and user demand forecasting, including load demand, energy type and demand, to improve the forecast accuracy of energy demand, provide forward-looking guidance for the energy dispatching of the system, improve the accuracy of the dispatching of the heating system power grid, and reduce the pressure caused by the peak-valley difference on the stability of the heating system power grid; The energy storage resource management module is used to manage energy storage resources at different levels, including the access, exit, and status monitoring of energy storage equipment, as well as the optimal configuration and scheduling of energy storage resources, to achieve effective integration and efficient utilization of energy storage resources and reduce energy waste and shortage; The energy dispatch optimization module is used to formulate an energy dispatch plan according to energy demand forecast and energy storage resource status, realize mutual assistance and complementarity between energy storage resources at different levels, optimize energy dispatch strategy, reduce over-reliance on energy storage resources at a certain level, and improve energy utilization efficiency; The heating system power grid state monitoring and early warning module is used to monitor the operating state of each node of the heating system power grid, including the state of the energy storage device, the load of the heating system power grid and the system response time data. When the state of the heating system power grid is abnormal, it provides an early warning response, automatically adjusts the energy storage resource scheduling strategy, and promptly discovers the failure or abnormal fluctuation of the heating system power grid, ensures the safe operation of the heating system power grid, improves the system response speed, and reduces the interruption or instability of energy supply caused by problems in the heating system power grid; The user interaction response module is used to provide a user interaction interface to facilitate users to query energy usage and energy storage resource status information, and receive user feedback and demand response, enhance the user friendliness and interactivity of the system, improve user satisfaction and participation, and promote efficient use of energy.

[0006] A further improvement of the technical solution of the present invention is that: in the data acquisition module, the process of collecting data of energy storage resources at different levels includes: Clarify the objectives and requirements to be collected, and determine the type and scope of data to be collected; Identify and access different types of energy storage devices (battery energy storage, flywheel energy storage, supercapacitors, etc.), capture data through sensors and communication interfaces equipped with energy storage devices, and then read real-time data from sensors and extract historical data from databases; Convert the data format of the collected data to conform to unified standards for subsequent processing, clean and verify the data to ensure data integrity and accuracy, and synchronize the cleaned data to align all data in chronological order; The data is transmitted to the multi-level cloud energy storage platform, a data warehouse is established using a relational database, and the collected data is stored in the data warehouse. An index and query mechanism are set up to ensure that the data can be stored for a long time and queried quickly. Data is backed up regularly and a distributed backup mechanism is used to prevent data loss.

[0007] A further improvement of the technical solution of the present invention is that: the data types include energy storage device status, power, location information and load data, the data range includes different levels of energy storage resources and urban load requirements, the device status of the energy storage device includes whether the energy storage device is operating normally, whether a fault occurs and the working mode of the device, the power information includes the current remaining power of the energy storage device, the charging and discharging rate and the battery health status, the location information is the geographical location of the energy storage device, the load data is the power demand data of the city or region, and the energy storage resource level covers multiple levels, including a single energy storage device, an energy storage group, a regional energy storage system and a city-level energy storage network.

[0008] A further improvement of the technical solution of the present invention is that in the energy storage resource analysis module, the process of clarifying energy demand and supply trends includes: Extract data from different energy storage devices from the data warehouse, including device status, power, charge and discharge rate, and temperature, and extract urban load demand data, including historical load records, real-time load data, and load peaks. Integrate data from different energy storage devices and urban load demand to ensure data integrity and consistency, form a unified data view, and standardize data to make data from different sources comparable and analyzable. Conduct trend analysis on historical load demand data, analyze the change pattern of load over time, calculate the load growth rate, use historical load data, combine weather and holiday factors, and use time series analysis to forecast load and clarify the trend of energy demand changes; Calculate the health index and operating efficiency index based on the status data of the energy storage equipment, including the temperature, voltage and current of the equipment, and evaluate the health status and operating efficiency of the energy storage equipment. Combined with the power and charge and discharge rate data of the energy storage equipment, evaluate the charge and discharge capabilities of various energy storage equipment, including calculating the maximum output power and continuous discharge time indicators of the equipment; Compare and analyze the load forecast results with the supply capacity of the energy storage equipment to determine whether the supply can meet the demand.

[0009] A further improvement of the technical solution of the present invention is that the process of determining whether the supply can meet the demand includes: Determine the type and specifications of energy storage equipment required based on load forecast results and electricity demand; Compare the load forecast results with the supply capacity of the energy storage equipment to analyze whether the supply can meet the demand and assess whether additional energy storage equipment is needed as redundancy or backup to deal with emergencies or load fluctuations. If the supply cannot meet the demand, formulate adjustment plans, including adding suppliers, increasing production capacity, optimizing energy storage equipment configuration, etc. Monitor load changes in real time and regularly analyze the use effect of energy storage equipment, including energy storage efficiency and equipment life, so as to adjust and optimize the energy storage equipment according to actual conditions to ensure its long-term stable operation and meet electricity demand.

[0010] A further improvement of the technical solution of the present invention is that: in the energy demand prediction module, the process of predicting the load demand of different regions includes: Obtain historical data on load demand, energy type, demand, weather conditions, and holidays over the past period from the data warehouse, collect current load demand, energy usage, and equipment status data in real time through sensors and smart meter devices, and collect user energy demand and preference information through questionnaires; Extract load demand forecasting features, including time, weather, holidays, and user behavior, based on the correlation between historical data and real-time monitoring data; According to the data characteristics and forecasting requirements, a forecasting model based on the time series analysis model is constructed, the selected model is trained using historical data, model parameters are adjusted to optimize forecasting performance, and the forecasting performance of the model is evaluated through cross-validation methods to ensure that the model has sufficient accuracy and generalization ability; The real-time monitoring data is input into the trained model to perform real-time load demand forecasting. The model outputs the load demand forecast results for different areas, including load demand, energy type and demand information.

[0011] A further improvement of the technical solution of the present invention is that: in the energy scheduling optimization module, the process of formulating the energy scheduling plan includes: Based on the energy demand forecast results and the current status of energy storage resources, supply and demand balance analysis is conducted to determine the supply and demand gap of various energy sources in different time periods, and to evaluate the mutual assistance and complementary potential between energy storage resources at different levels, including the complementarity between different types of energy storage equipment and the mutual assistance between energy storage equipment of the same type but with different capacities; According to the supply and demand balance analysis and the analysis results of the mutual assistance and complementarity of energy storage resources, the corresponding energy dispatch strategies are matched, including economic dispatch, demand response dispatch and safety dispatch; Combine real-time data with historical data to generate a specific energy scheduling plan, and evaluate the generated energy scheduling plan to optimize the energy scheduling plan.

[0012] A further improvement of the technical solution of the present invention is that the process of evaluating and generating the energy scheduling solution includes: Analyze the optimal configuration and scheduling of energy storage resources, conduct economic benefit evaluation and environmental impact evaluation on the generated scheduling plan, and optimize and adjust the plan based on the evaluation results to improve energy utilization efficiency, reduce costs and reduce environmental impact; Convert the optimized dispatch plan into specific dispatch instructions and send them to relevant energy production and consumption units and energy storage equipment management areas; In the process of executing the scheduling plan, energy production and consumption as well as the operating status of energy storage equipment are monitored in real time so that the scheduling plan can be dynamically adjusted and optimized according to actual conditions. The scheduling plan can be dynamically adjusted based on real-time monitoring results and new energy demand forecast results.

[0013] A further improvement of the technical solution of the present invention is that in the heating system power grid state monitoring and early warning module, the monitoring process of the operating state of each node of the heating system power grid includes: Collect the operation data of each node of the heating system power grid in real time, including data of each link of substation, transmission line, heating system power network and energy storage equipment, and analyze the operation status of each node of the heating system power grid, extract the energy storage equipment status, heating system power grid load and system response time parameters, analyze the change trend and mutual relationship of each parameter, so as to understand the overall operation status of the heating system power grid; According to the operation specifications and safety standards of the heating system power grid, set the abnormal thresholds of the parameters of the energy storage device status, heating system power grid load and system response time, compare the real-time collected data with the abnormal thresholds to detect whether the data deviates from the expected value, and combine the real-time data of each parameter with the abnormal threshold to calculate the early warning index to determine whether to trigger the early warning mechanism; When the heating system grid status is abnormal, the heating system grid status monitoring and early warning module will take early warning response measures, sound an alarm and display early warning information, and send the early warning information to relevant personnel so that timely response measures can be taken; According to the early warning information, the scheduling strategy of energy storage resources is adjusted. When the load of the heating system grid is too high, the discharge rate of the energy storage equipment is increased to supplement the power of the heating system grid. When the power of the energy storage equipment is insufficient, the charging strategy is adjusted to fully charge it. Locate the heating system power grid fault, determine the location, type and impact range of the fault, and formulate a fault recovery plan based on the fault location results, dispatch backup power or increase or decrease load to restore the stable operation of the heating system power grid, repair or replace faulty equipment to eliminate potential fault hazards.

[0014] A further improvement of the technical solution of the present invention is that the process of formulating the fault recovery plan includes: Based on the early warning information, combined with historical data and the fault case library, the fault type and location are preliminarily determined. Combined with the on-site survey results and the power grid monitoring system data, a comprehensive analysis is conducted to determine the specific location, type and impact range of the fault, draw a fault diagram, and identify the fault point, affected area and potential risks; According to the fault location results, formulate a fault recovery plan, including dispatching backup power supplies and increasing or decreasing loads, and analyze the feasibility and safety of the plan to ensure that no secondary damage is caused to the heating system during the recovery process; Based on the developed fault recovery plan, dispatch instructions are issued, backup power is connected and load is adjusted, and the recovery process is monitored to ensure that all operations proceed smoothly according to the plan. Then, the fault is analyzed in depth to find out the root cause of the fault, including equipment aging, design defects and improper operation, and targeted preventive measures are formulated based on the results of the fault cause analysis.

[0015] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art: 1. The present invention provides an urban energy mutual assistance management system based on multi-level cloud energy storage. By analyzing the energy supply and demand conditions in various areas of the city and matching the supply and demand gap, cross-regional energy mutual assistance is achieved, which not only effectively reduces energy waste, but also improves the overall energy utilization efficiency. The system can also adjust the energy storage strategy according to weather forecasts and energy demand forecasts, further promoting the realization of energy conservation and emission reduction goals.

[0016] 2. The present invention provides an urban energy mutual assistance management system based on multi-level cloud energy storage, which ensures the stability and reliability of urban energy supply through the energy storage allocation mechanism. The built-in heating system power grid status monitoring and early warning module can monitor the heating system power grid status, and immediately activate the plan once an abnormality is found, thereby enhancing the resilience of the urban energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0018] Figure 1 It is a module diagram of the present invention.

[0019] Figure 2 Flowchart illustrating energy demand and supply trends for the present invention.

[0020] Figure 3 It is a flow chart for monitoring the operating status of each node in the heating system power grid of the present invention. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Embodiment 1, as Figure 1 , Figure 2 As shown, the present invention provides an urban energy mutual assistance management system based on multi-level cloud energy storage, including a multi-level cloud energy storage platform, and the multi-level cloud energy storage platform is communicatively connected with a data acquisition module, an energy storage resource analysis module, an energy demand prediction module, an energy storage resource management module, an energy scheduling optimization module, a heating system power grid status monitoring and early warning module and a user interactive response module, wherein electrical signals are connected between each module.

[0022] Multi-level cloud energy storage platform, establishes a cloud computing-based energy storage resource management platform, integrates energy storage units at different levels, supports data collection, real-time monitoring, data analysis and scheduling optimization of the energy storage system, realizes centralized monitoring and management of distributed energy storage resources, ensures efficient use of energy storage resources, and reduces scheduling deviations caused by information asymmetry and management lags.

[0023] The data acquisition module is used to collect data from energy storage resources at different levels, including the status, power, location information of energy storage equipment, and data on urban load demand, to provide comprehensive and accurate data support, to provide a basis for the energy dispatch and management of the system, to clarify the objectives and requirements to be collected, and to determine the data type and data range to be collected. The data type includes the status, power and location information of energy storage equipment, and the data range includes energy storage resources at different levels and urban load demand. The equipment status of energy storage equipment includes whether the energy storage equipment is operating normally, whether a fault occurs, and the working mode of the equipment (charging, discharging, standby, etc.). The power information includes the current remaining power of the energy storage equipment, the charging and discharging rate, and the battery health status. The location information is the geographical location of the energy storage equipment for dispatch and resource management. The load data is the power demand data of the city or region. The energy storage resource level covers multiple levels, including Single energy storage devices, energy storage groups, regional energy storage systems and city-level energy storage networks can identify and access different types of energy storage devices (battery energy storage, flywheel energy storage, supercapacitors, etc.), capture data through sensors and communication interfaces equipped with energy storage devices, and then read real-time data from sensors, extract historical data from databases, convert the data format of collected data to make it conform to unified standards, facilitate subsequent processing, and perform data cleaning and verification to ensure data integrity and accuracy. Time synchronization is performed on the cleaned data to align all data correctly in chronological order, and the data is transmitted to a multi-level cloud energy storage platform. A relational database is used to establish a data warehouse, and the collected data is stored in the data warehouse. An index and query mechanism are set up to ensure that data can be stored for a long time and queried quickly. Data is backed up regularly, and a distributed backup mechanism is used to prevent data loss.

[0024] The energy storage resource analysis module is used to analyze the collected data, clarify the energy demand and supply trends, improve the accuracy and availability of data, and provide a scientific basis for the system's decision-making. It extracts data from different energy storage devices from the data warehouse, including device status, power, charge and discharge rate, and temperature, and extracts urban load demand data, including historical load records, real-time load data, and load peaks. It integrates data from different energy storage devices and urban load demands to ensure data integrity and consistency, form a unified data view, and standardize data to make data from different sources comparable and analyzable. It performs trend analysis on historical load demand data and analyzes the load change pattern over time to calculate the load growth rate. It uses historical load data, combined with weather and holiday factors, to use time series analysis to predict loads, clarify energy demand change trends, and calculates health indexes and operating efficiency indexes based on the status data of energy storage devices, including device temperature, voltage, and current, to evaluate energy storage devices. The health status and operating efficiency of energy storage equipment are evaluated in combination with the power and charge and discharge rate data of energy storage equipment, including the calculation of the maximum output power and continuous discharge time indicators of the equipment. The load forecast results are compared and analyzed with the supply capacity of the energy storage equipment to determine whether the supply can meet the demand. According to the load forecast results and power demand, the required energy storage equipment type (lithium-ion battery, sodium-sulfur battery, etc.) and specifications (energy storage capacity, charge and discharge rate, etc.) are determined. The load forecast results are compared with the supply capacity of the energy storage equipment to analyze whether the supply can meet the demand. It is evaluated whether additional energy storage equipment is needed as redundancy or backup to deal with emergencies or load fluctuations. If the supply cannot meet the demand, an adjustment plan is formulated, including adding suppliers, increasing production capacity, optimizing the configuration of energy storage equipment, etc. The load changes are monitored in real time, and the use effect of the energy storage equipment, including energy storage efficiency and equipment life, is regularly analyzed to adjust and optimize the energy storage equipment according to the actual situation to ensure its long-term stable operation and meet the power demand.

[0025] Furthermore, the expression of energy demand change trend is:

[0026] In the formula, is the energy demand at time t, As the benchmark energy demand, select the average demand in a certain period of time. is the weather impact coefficient at time t, which quantifies the impact of weather conditions on energy demand. is the holiday impact coefficient at time t, which is quantified based on the impact of holidays on energy demand. Energy demand may increase or decrease during holidays. To adjust the weight parameter of the impact of relative changes in energy demand on trends, is the time variable, It indicates the ratio of the current energy demand to the benchmark energy demand, reflecting the relative change in energy demand. To adjust the weight parameters of the impact of weather and holiday factors on trends, is the attenuation coefficient of the exponential function, which is used to reflect the smoothness of energy demand changes over time. Used to smooth the changing trend of energy demand, making the trend smoother and more in line with the actual situation. The value range is between 0 and 1. When t increases, due to The attenuation effect of It will gradually approach a stable value (but not necessarily 1), reflecting the long-term trend of energy demand over time.

[0027] Furthermore, the expression of the health index is:

[0028] In the formula, is the health index, is the standard deviation of the device temperature, and is the maximum and minimum allowable temperature of the device. is the standard deviation of the voltage data, is the rated voltage of the device, is the standard deviation of the current data, is the rated current of the device, The value range is between 0 and 1. The closer it is to 1, the better the health of the device.

[0029] The expression of the operating efficiency index is:

[0030] In the formula, is the operating efficiency index, is the actual output power of the device, is the input power of the device, is the coefficient of the effect of equipment temperature on efficiency, is the current device temperature, The optimal temperature for the highest equipment efficiency, is the maximum allowable temperature of the device, The value range is between 0 and 1. The closer it is to 1, the higher the equipment operating efficiency.

[0031] The expression of the maximum output power of the device is:

[0032] In the formula, is the maximum output power, is the rated voltage of the device, is the maximum current of the device under specific conditions, is the coefficient of the effect of device temperature on the internal resistance of the battery, is the current device temperature, is the reference device temperature.

[0033] The expression of continuous discharge time is:

[0034] In the formula, is the continuous discharge time, is the total power of the device, is the discharge current of the device, is the coefficient of the effect of discharge rate on the continuous discharge time, is the rated current of the device, is the exponent affected by the discharge rate, , which means that when the discharge rate increases, the continuous discharge time decreases faster.

[0035] The energy demand forecasting module is used to predict the load demand in different areas based on historical data, real-time monitoring and user demand forecasting, including load demand, energy type and demand, to improve the forecast accuracy of energy demand, provide forward-looking guidance for the system's energy scheduling, improve the accuracy of the heating system power grid scheduling, and reduce the pressure caused by peak-valley differences on the stability of the heating system power grid.

[0036] The energy storage resource management module is used to manage energy storage resources at different levels, including the access, exit, and status monitoring of energy storage equipment, as well as the optimal configuration and scheduling of energy storage resources, to achieve effective integration and efficient utilization of energy storage resources and reduce energy waste and shortages.

[0037] The energy scheduling optimization module is used to formulate energy scheduling plans based on energy demand forecasts and energy storage resource status, achieve mutual assistance and complementarity between energy storage resources at different levels, optimize energy scheduling strategies, reduce over-reliance on energy storage resources at a certain level, and improve energy utilization efficiency.

[0038] The heating system power grid status monitoring and early warning module is used to monitor the operating status of each node of the heating system power grid, including the status of energy storage equipment, the load status of the heating system power grid and the system response time data. When the heating system power grid status is abnormal, it provides early warning response, automatically adjusts the energy storage resource scheduling strategy, and promptly detects heating system power grid failures or abnormal fluctuations, ensuring the safe operation of the heating system power grid, improving the system response speed, and reducing energy supply interruptions or instability caused by heating system power grid problems.

[0039] The user interaction response module is used to provide a user interaction interface to facilitate users to query energy usage and energy storage resource status information, and receive user feedback and demand response, enhance the user-friendliness and interactivity of the system, improve user satisfaction and participation, and promote efficient use of energy.

[0040] Embodiment 2, as Figure 3 As shown, based on Example 1, the present invention provides a technical solution: Preferably, in the energy demand prediction module, the process of predicting the load demand of different regions includes: Obtain historical data related to load demand, energy type, demand, weather conditions and holidays in the past period from the data warehouse; collect current load demand, energy usage and equipment status data in real time through sensors and smart meter equipment; collect user energy demand and preference information through questionnaires; extract load demand forecasting features, including time, weather, holidays and user behavior, based on the correlation between historical data and real-time monitoring data; construct a forecasting model based on the time series analysis model according to data characteristics and forecasting needs; train the selected model with historical data, adjust model parameters to optimize forecasting performance, and evaluate the forecasting performance of the model through cross-validation method to ensure that the model has sufficient accuracy and generalization ability; input real-time monitoring data into the trained model to perform real-time load demand forecasting; the model outputs load demand forecasting results for different regions, including load demand, energy type and demand information.

[0041] Furthermore, the expression for load demand prediction is:

[0042] In the formula, is the load demand forecast for energy type e in region r at the sth time unit, is the number of factors affecting load demand forecast, is the weight of the i-th influencing factor, is the load demand impact function of the i-th influencing factor on the energy type e in the s-th time unit and region r. It is a function fitted based on historical data and real-time monitoring data. is the time decay factor, is the attenuation coefficient, Indicates the time related to the current time s and the i-th influencing factor The time difference (absolute value) of the load demand for energy type e in region r (at a historical time point) is used to reflect the impact of time on load demand forecasting. As time goes by, the impact of historical data on current forecasting gradually weakens. is the load demand adjustment coefficient of energy type e in region r, which is used to fine-tune the prediction results according to regional characteristics and energy type.

[0043] In the energy scheduling optimization module, the energy scheduling plan formulation process includes: According to the energy demand forecast results and the current status of energy storage resources, supply and demand balance analysis is carried out to determine the supply and demand gaps of various types of energy in different time periods, and the mutual assistance and complementary potential between energy storage resources at different levels are evaluated, including the complementarity between different types of energy storage equipment and the mutual assistance between energy storage equipment of the same type but different capacities. Through the mutual assistance and complementary mechanism, it is ensured that energy storage resources at different levels cooperate and support each other. When fast-response energy storage resources are over-utilized, long-term energy storage resources can be dispatched to supplement them to prevent equipment overload or loss. When the long-term energy storage resources are in a high charging state, long-term energy storage resources can be used to alleviate short-term fluctuations and reduce dependence on fast-response equipment. According to the supply and demand balance analysis and the mutual assistance and complementary analysis of energy storage resources, the corresponding energy dispatching strategies are matched, including economic dispatch (with cost minimization as the goal), demand response dispatch (adjusting energy supply according to user needs), and energy storage resource dispatching. The system combines real-time data and historical data to generate specific energy dispatch plans, and evaluates the generated energy dispatch plans to optimize the energy dispatch plans, analyze the optimal configuration and dispatch of energy storage resources, conduct economic benefit evaluation and environmental impact evaluation on the generated dispatch plans, and optimize and adjust the plans according to the evaluation results to improve energy utilization efficiency, reduce costs and reduce the impact on the environment. The optimized dispatch plans are converted into specific dispatch instructions and issued to relevant energy production and consumption units and energy storage equipment management areas. In the process of executing the dispatch plans, the energy production and consumption conditions and the operating status of the energy storage equipment are monitored in real time, so as to dynamically adjust and optimize the dispatch plans according to the actual conditions, and dynamically adjust the dispatch plans according to the real-time monitoring results and the new energy demand forecast results.

[0044] In the heating system power grid status monitoring and early warning module, the monitoring process of the operating status of each node in the heating system power grid includes: Collect the operation data of each node of the heating system power grid in real time, including data of each link of substation, transmission line, heating system power network and energy storage equipment, analyze the operation status of each node of the heating system power grid, extract the energy storage equipment status, heating system power grid load and system response time parameters, analyze the change trend and mutual relationship of each parameter, so as to understand the overall operation status of the heating system power grid, set the abnormal threshold of each parameter of energy storage equipment status, heating system power grid load and system response time according to the operation specifications and safety standards of the heating system power grid, and compare the real-time collected data with the abnormal The system compares the data with the normal threshold to detect whether the data deviates from the expected value, and calculates the early warning index in combination with the real-time data and abnormal threshold of each parameter to determine whether the early warning mechanism is triggered. When the heating system power grid state is abnormal, the heating system power grid state monitoring and early warning module takes early warning response measures, sounds an alarm and displays early warning information, and sends the early warning information to relevant personnel so that timely response measures can be taken. According to the early warning information, the scheduling strategy of energy storage resources is adjusted. When the heating system power grid load is too high, the discharge rate of the energy storage equipment is increased to supplement the power of the heating system power grid. When the power of the energy storage equipment is insufficient, the early warning is adjusted. The charging strategy uses full power to quickly locate the fault of the heating system power grid, determine the location, type and impact range of the fault, and formulate a fault recovery plan based on the fault location results, dispatch backup power or increase or decrease load to restore the stable operation of the heating system power grid, repair or replace the faulty equipment to eliminate the hidden dangers of the fault, and preliminarily judge the type and location of the fault based on the early warning information, combined with historical data and the fault case library, and conduct a comprehensive analysis based on the on-site investigation results and the power grid monitoring system data to determine the specific location, type and impact range of the fault, draw a fault diagram, clarify the fault point, affected area and potential risks, formulate a fault recovery plan based on the fault location results, including dispatching backup power and increasing or decreasing load measures, analyze the feasibility and safety of the plan, and ensure that no secondary damage is caused to the heating system during the recovery process, based on the formulated fault recovery plan, issue a dispatch instruction, connect to the backup power supply and adjust the load, and monitor the recovery process to ensure that all operations are carried out smoothly according to the plan, and then conduct an in-depth analysis of the fault to find out the root cause of the fault, including equipment aging, design defects and improper operation, and formulate targeted preventive measures based on the results of the fault cause analysis.

[0045] Furthermore, the expression of the early warning index is:

[0046] In the formula, is the early warning index, is the total number of real-time data points, is the real-time data point of the j-th energy storage device status, is the abnormal threshold of the energy storage device status, is the real-time data point of the grid load of the jth heating system, is the abnormal threshold of the heating system grid load condition, is the real-time data point of the j-th system response time, is the abnormal threshold of system response time, Close to 0, it means the system is in normal condition and no warning is needed. When the warning threshold of 0.8 is reached or exceeded, the warning mechanism is triggered, an alarm is issued and corresponding response measures are taken.

[0047] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An urban energy mutual assistance management system based on multi-level cloud energy storage, including a multi-level cloud energy storage platform, characterized in that: The multi-level cloud energy storage platform is communicatively connected with a data acquisition module, an energy storage resource analysis module, an energy demand prediction module, an energy storage resource management module, an energy scheduling optimization module, a heating system grid status monitoring and early warning module, and a user interactive response module, wherein electrical signals are connected between the modules; The data acquisition module is used to collect data from energy storage resources at different levels; The energy storage resource analysis module is used to analyze the collected data and clarify the energy demand and supply trends; The energy demand forecasting module is used to forecast the load demand in different areas, including load demand, energy type and demand, based on historical data, real-time monitoring and user demand forecasting; The energy storage resource management module is used to manage energy storage resources at different levels; The energy dispatch optimization module is used to formulate an energy dispatch plan based on energy demand forecast and energy storage resource status to achieve mutual assistance and complementarity between energy storage resources at different levels; The heating system power grid status monitoring and early warning module is used to monitor the operating status of each node of the heating system power grid, and when the heating system power grid status is abnormal, provide an early warning response and automatically adjust the energy storage resource scheduling strategy; The user interaction response module is used to provide a user interaction interface to facilitate users to query energy usage and energy storage resource status information.

2. The urban energy mutual assistance management system based on multi-level cloud energy storage according to claim 1 is characterized in that: In the data acquisition module, the process of collecting data of energy storage resources at different levels includes: Clarify the objectives and requirements to be collected, and determine the type and scope of data to be collected; Identify and access different types of energy storage devices, capture data through sensors and communication interfaces equipped on the energy storage devices, and then read real-time data from sensors and extract historical data from the database; Convert the data format of the collected data, clean and verify the data, and synchronize the cleaned data to ensure that all data are correctly aligned in chronological order; The data is transmitted to the multi-level cloud energy storage platform, a data warehouse is established using a relational database, and the collected data is stored in the data warehouse. An index and query mechanism is set up, and data is backed up regularly.

3. The urban energy mutual assistance management system based on multi-level cloud energy storage according to claim 2 is characterized in that: The data types include the device status, power information, location information and load data of the energy storage device. The data range includes energy storage resources at different levels and urban load requirements. The device status of the energy storage device includes whether the energy storage device is operating normally, whether a fault occurs and the working mode of the device. The power information includes the current remaining power of the energy storage device, the charging and discharging rate and the battery health status. The location information is the geographical location of the energy storage device. The load data is the power demand data of the city or region. The energy storage resources cover multiple levels, including single energy storage devices, energy storage groups, regional energy storage systems and city-level energy storage networks.

4. The urban energy mutual assistance management system based on multi-level cloud energy storage according to claim 3 is characterized in that: In the energy storage resource analysis module, the process of clarifying energy demand and supply trends includes: Extract data from different energy storage devices from the data warehouse, including device status, power, charge and discharge rate, and temperature, and extract urban load demand data, including historical load records, real-time load data, and load peaks. Integrate data from different energy storage devices and urban load demand to form a unified data view. Conduct trend analysis on historical load demand data, analyze the change pattern of load over time, calculate the load growth rate, use historical load data, combine weather and holiday factors, and use time series analysis to forecast load and clarify the trend of energy demand changes; Calculate the health index and operating efficiency index based on the status data of the energy storage equipment, including the temperature, voltage and current of the equipment, and evaluate the health status and operating efficiency of the energy storage equipment. Combined with the power and charge and discharge rate data of the energy storage equipment, evaluate the charge and discharge capabilities of various energy storage equipment, including calculating the maximum output power and continuous discharge time indicators of the equipment; Compare and analyze the load forecast results with the supply capacity of the energy storage equipment to determine whether the supply can meet the demand.

5. The urban energy mutual assistance management system based on multi-level cloud energy storage according to claim 4 is characterized in that: The process of determining whether supply can meet demand includes: Determine the type and specifications of energy storage equipment required based on load forecast results and electricity demand; Compare the load forecast results with the supply capacity of the energy storage equipment to analyze whether the supply can meet the demand and evaluate whether additional energy storage equipment is needed as redundancy or backup to deal with emergencies or load fluctuations; Monitor load changes in real time and regularly analyze the use effect of energy storage equipment, including energy storage efficiency and equipment life, so as to adjust and optimize the energy storage equipment according to actual conditions.

6. The urban energy mutual assistance management system based on multi-level cloud energy storage according to claim 5 is characterized in that: In the energy demand forecasting module, the process of forecasting load demand in different regions includes: Obtain historical data on load demand, energy type, demand, weather conditions, and holidays over the past period from the data warehouse, collect current load demand, energy usage, and equipment status data in real time through sensors and smart meter devices, and collect user energy demand and preference information through questionnaires; Extract load demand forecasting features, including time, weather, holidays, and user behavior, based on the correlation between historical data and real-time monitoring data; According to the data characteristics and forecasting requirements, a forecasting model based on the time series analysis model is constructed, the selected model is trained using historical data, model parameters are adjusted to optimize forecasting performance, and the forecasting performance of the model is evaluated through cross-validation methods; The real-time monitoring data is input into the trained model to perform real-time load demand forecasting. The model outputs the load demand forecast results for different areas, including load demand, energy type and demand information.

7. The urban energy mutual assistance management system based on multi-level cloud energy storage according to claim 6 is characterized in that: In the energy scheduling optimization module, the energy scheduling plan formulation process includes: Based on the energy demand forecast results and the current status of energy storage resources, supply and demand balance analysis is conducted to determine the supply and demand gap of various energy sources in different time periods, and to evaluate the mutual assistance and complementary potential between energy storage resources at different levels, including the complementarity between different types of energy storage equipment and the mutual assistance between energy storage equipment of the same type but with different capacities; According to the supply and demand balance analysis and the analysis results of the mutual assistance and complementarity of energy storage resources, the corresponding energy dispatch strategies are matched, including economic dispatch, demand response dispatch and safety dispatch; Combine real-time data with historical data to generate a specific energy scheduling plan, and evaluate the generated energy scheduling plan to optimize the energy scheduling plan.

8. The urban energy mutual assistance management system based on multi-level cloud energy storage according to claim 7 is characterized in that: The process of evaluating and generating the energy scheduling plan includes: Analyze the optimal configuration and scheduling of energy storage resources, conduct economic benefit evaluation and environmental impact evaluation on the generated scheduling plan, and optimize and adjust the plan based on the evaluation results; Convert the optimized dispatch plan into specific dispatch instructions and send them to relevant energy production and consumption units and energy storage equipment management areas; In the process of executing the scheduling plan, energy production and consumption as well as the operating status of energy storage equipment are monitored in real time, and the scheduling plan is dynamically adjusted based on the real-time monitoring results and new energy demand forecast results.

9. The urban energy mutual assistance management system based on multi-level cloud energy storage according to claim 8 is characterized in that: In the heating system power grid state monitoring and early warning module, the monitoring process of the operating state of each node of the heating system power grid includes: Collect the operation data of each node of the heating system power grid in real time, including data of each link of substation, transmission line, heating system power network and energy storage equipment, and analyze the operation status of each node of the heating system power grid, extract the energy storage equipment status, heating system power grid load and system response time parameters; According to the operation specifications and safety standards of the heating system power grid, set the abnormal thresholds of the parameters of the energy storage device status, heating system power grid load and system response time, compare the real-time collected data with the abnormal thresholds to detect whether the data deviates from the expected value, and combine the real-time data of each parameter with the abnormal threshold to calculate the early warning index to determine whether to trigger the early warning mechanism; When the heating system grid status is abnormal, the heating system grid status monitoring and early warning module takes early warning response measures, sounds an alarm and displays early warning information, and sends the early warning information to relevant personnel; According to the early warning information, the scheduling strategy of energy storage resources is adjusted. When the load of the heating system grid is too high, the discharge rate of the energy storage equipment is increased to supplement the power of the heating system grid. When the power of the energy storage equipment is insufficient, the charging strategy is adjusted to fully charge it. Locate the heating system power grid fault, determine the location, type and impact range of the fault, and formulate a fault recovery plan based on the fault location results, dispatch backup power or increase or decrease load to restore the stable operation of the heating system power grid, and repair or replace faulty equipment.

10. The urban energy mutual assistance management system based on multi-level cloud energy storage according to claim 9 is characterized in that: The process of formulating the fault recovery plan includes: Based on the early warning information, combined with historical data and the fault case library, the fault type and location are preliminarily determined. Combined with the on-site survey results and the power grid monitoring system data, a comprehensive analysis is conducted to determine the specific location, type and impact range of the fault, draw a fault diagram, and identify the fault point, affected area and potential risks; According to the fault location results, formulate a fault recovery plan, including measures to dispatch backup power supplies and increase or decrease loads, and analyze the feasibility and safety of the plan; Based on the developed fault recovery plan, dispatch instructions are issued, backup power is connected and load is adjusted, the recovery process is monitored, and then the fault is analyzed in depth to find out the root cause of the fault, including equipment aging, design defects and improper operation, and targeted preventive measures are formulated based on the results of the fault cause analysis.

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