A smart grid new energy consumption management system

Abstract

The application discloses a kind of new energy consumption management systems of smart grid, it is related to the technical field of smart grid, the system includes following component parts: two-way interaction mechanism construction, intelligent decision engine, collaborative optimization scheduling strategy and user participation and incentive mechanism and visual monitoring and operation management.The application has realized the rapid transmission and sharing of data by constructing the two-way interaction mechanism between microgrid and main grid, and through collaborative optimization scheduling strategy, can rapidly adjust scheduling strategy under emergency and extreme weather conditions, guarantee the continuity and stability of power supply, simultaneously, visual monitoring and operation management module provides intuitive system operation state display and intelligent operation management, improves operation efficiency and security, to significantly enhance the security and stability of power grid and the ability to respond to emergency situations.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of smart grids, in particular to a smart grid new energy consumption management system. BACKGROUND

[0002] With the transformation of global energy structure and the implementation of sustainable development strategy, the development and utilization of new energy are increasingly valued, and the smart grid, as the core of future power systems, has high automation, informatization and interactivity, which is of great significance for improving the consumption rate of new energy, ensuring the safe and stable operation of the power grid and promoting user participation in power market transactions.

[0003] The traditional grid collaborative optimization scheduling strategy lacks effective information exchange and sharing mechanism between microgrids and main grids, and it is difficult to achieve efficient collaboration, cannot achieve precise prediction, dynamic adjustment and real-time control, and cannot quickly adjust the scheduling strategy under emergency events and extreme weather conditions, therefore, it is particularly important to develop a smart grid new energy consumption management system. SUMMARY

[0004] The purpose of the application is to make up for the shortcomings of the prior art, and provide a smart grid new energy consumption management system, which realizes efficient consumption of new energy and safe and stable operation of the power grid by constructing a two-way interaction mechanism, an intelligent decision engine, a collaborative optimization scheduling strategy, a user participation and incentive mechanism and visual monitoring and operation and maintenance management.

[0005] The application provides the following technical solutions to solve the above technical problems: a smart grid new energy consumption management system, which comprises a two-way interaction mechanism construction, an intelligent decision engine, a collaborative optimization scheduling strategy, a user participation and incentive mechanism and visual monitoring and operation and maintenance management.

[0006] The two-way interaction mechanism construction: a two-way real-time communication interface between the microgrid and the main grid is established to realize fast transmission and sharing of data, through this mechanism, the system can monitor the power generation capacity of the microgrid and the state of the energy storage system and the load demand, and simultaneously obtain the operation state of the main grid, load prediction and scheduling instructions;

[0007] The intelligent decision engine: an intelligent decision engine is constructed through an algorithm model and artificial intelligence technology, which comprehensively considers new energy generation characteristics, power grid safety and stability constraints and economic cost factors according to real-time data, intelligently decides the grid-connected mode of the microgrid, formulates the optimal power transaction strategy and the charge and discharge plan of the energy storage system, and maximizes the consumption rate of new energy under the premise of ensuring the safe and stable operation of the power grid;

[0008] The synergistic optimization scheduling strategy: realizes the synergistic optimization scheduling between the microgrid and the main grid, through accurate prediction, dynamic adjustment and real-time control, in the event of emergencies and extreme weather conditions, the system can quickly adjust the scheduling strategy, guarantee the continuity and stability of power supply, and improve the resilience of the power grid;

[0009] The user participation and incentive mechanism: design user participation mechanism and incentive mechanism, encourage users to adjust power consumption behavior according to system guidance, participate in power market transactions, promote local consumption of new energy, and enhance user participation and satisfaction through economic incentives and information transparency means.

[0010] Further, the two-way interaction mechanism determines the main goal of the two-way interaction mechanism, collects the existing technical conditions, communication infrastructure and data interface type information of the microgrid and the main grid, designs the overall architecture of the two-way interaction mechanism based on demand analysis, selects the appropriate communication protocol according to the system requirements, and develops two-way real-time communication interfaces on the microgrid and the main grid side, including but not limited to hardware interfaces and software interfaces, and performs function test, performance test and security test on the communication interface to ensure that data can be accurately and quickly transmitted. Real-time collection of microgrid power generation capacity, energy storage system state, load demand data through sensors and smart meters, obtaining operation state, load forecast and dispatching instruction information from the main grid dispatching center, cleaning, verifying, compressing and preprocessing the collected data;

[0011] Integrating real-time monitoring platform, displaying real-time state data, charts and alarm information of microgrid and main grid, processing real-time data using data analysis algorithms, identifying system operation state, predicting future trends, and discovering potential problems, based on analysis results, providing optimization scheduling, load balancing suggestions to microgrid and main grid control system, generating power generation, energy storage and load management strategies for microgrid according to main grid dispatching instructions and microgrid real-time state, sending control instructions to microgrid power generation equipment, energy storage system and load equipment through control interface, realizing accurate control, adjusting scheduling strategy and control instructions in time according to control execution effect and system state change, formulating and implementing strict data transmission security policy, access control policy and emergency response mechanism, regularly inspecting and maintaining communication interface, data acquisition equipment and monitoring platform to ensure stable operation of the system, continuously optimizing and upgrading the two-way interaction mechanism according to technical development and actual demand changes.

[0012] Further, the intelligent decision engine determines the main target of the intelligent decision engine, including but not limited to maximizing new energy consumption rate, ensuring safe and stable operation of the power grid, optimizing economic cost, sorting out and determining the data source required by the decision engine, designing the system architecture of the intelligent decision engine, cleaning the collected raw data, removing noise, missing values and inconsistent data, extracting useful features for decision from the cleaned data, selecting algorithm model according to the decision target, training the selected model using historical data, adjusting model parameters to optimize its performance, intelligently judging whether the microgrid is in island operation or grid-connected operation state based on real-time data, formulating the optimal power trading strategy combined with market price, power grid dispatching instruction, new energy power generation prediction factors, formulating the charging and discharging plan of the energy storage system according to the current state of the energy storage system, new energy power generation prediction and load demand prediction, to balance the supply and demand relationship and optimize the economic cost, outputting the decision result to the corresponding control system for execution, real-time monitoring the decision execution process to ensure effective execution of the decision, evaluating the effect after the decision execution, feeding back and adjusting the decision engine according to the evaluation result, optimizing the algorithm model, adjusting the parameters and improving the decision strategy, continuously accumulating new data, periodically updating and retraining the model using new data, improving the accuracy and adaptability of the decision engine, paying attention to the technical development in the field of artificial intelligence and smart grid, introducing new technologies and algorithms to upgrade and iterate the decision engine in a timely manner.

[0013] Further, the collaborative optimization scheduling strategy clearly defines the boundary between the microgrid and the main grid, determines the range and interface of collaborative scheduling, designs and implements efficient and reliable communication link between the microgrid and the main grid, ensures real-time data transmission and fast response to instructions, defines the data exchange format and communication protocol between the microgrid and the main grid, ensures that both parties can accurately understand and execute the requests or instructions of the other party, collects real-time operation data from the microgrid and the main grid, including but not limited to power generation, load, voltage, frequency key parameters, cleans, filters and checks the data to ensure its accuracy and reliability, fuses the data of the microgrid and the main grid, forms a complete power grid operation view, uses historical data and weather information to accurately predict the power generation capacity of new energy, predicts future load based on historical load data and external factors;

[0014] According to the prediction results and the grid operation state, the influence and risk of the emergency or extreme weather on the power grid are evaluated, the target of the coordinated dispatching is determined, the optimization algorithm is selected according to the target, the safe and stable operation of the power grid, the equipment capacity limitation and the power transmission capacity limitation are taken as the constraint conditions of the optimization algorithm, the coordinated optimization dispatching strategy between the micro-grid and the main grid is formulated according to the algorithm output, the system operation state is monitored in real time, when the emergency or extreme weather condition is detected, the dynamic adjustment mechanism of the dispatching strategy is triggered, the operation state of the micro-grid and the main grid is adjusted in real time according to the adjusted dispatching strategy, the effect of the coordinated optimization dispatching is evaluated, the dispatching strategy is adjusted according to the evaluation results, the optimization algorithm model or the adjustment parameter setting is optimized, and new operation data and dispatching experience are accumulated, so as to provide data support for subsequent optimization.

[0015] Further, the generation capacity of the new energy is accurately predicted, the future load is predicted based on historical load data and external factors, and the new energy generation capacity prediction formula is: wherein, represents the predicted new energy generation power at time , is a basic generation power value at time based on the historical average generation power, is a meteorological comprehensive index at time , which can comprehensively consider temperature, humidity, wind speed meteorological factors, the weight under different meteorological conditions is determined through analysis of historical data, and the index is calculated, is a seasonal adjustment factor at time , the new energy generation efficiency is different in different seasons, the adjustment coefficient of different seasons is determined through analysis of historical data, and are adjustment coefficients, and the accurate prediction of the new energy generation capacity is realized.

[0016] The power load prediction formula is: wherein, represents the future load prediction value at time , is a historical load value at time based on historical load data, is an external economic activity index at time , which can consider the factors of GDP growth rate and industrial production index, the weight of different economic activity factors is determined through analysis of historical data, and the index is calculated, is a special event influence factor at time , the influence coefficient is determined through analysis of the load change during the special event in the historical data, and is the adjustment coefficient, and the accurate prediction of future load.

[0017] Further, the explicit coordinated scheduling target selects an optimization algorithm according to the target, takes the safe and stable operation of the power grid, the capacity limit of the equipment, and the transmission capacity limit as the constraint conditions of the optimization algorithm, formulates the coordinated optimization scheduling strategy between the microgrid and the main grid according to the algorithm output, and the formula of the explicit coordinated scheduling target is: , wherein: is the time range of scheduling is the time is the generation cost of the time , which is related to the output power of the generator and the fuel cost, is the power grid transmission loss cost of the time is other possible costs, such as equipment maintenance cost;

[0018] According to the algorithm output, the coordinated optimization scheduling strategy is formulated: the output of the optimization algorithm will be a series of values of control variables, including but not limited to the output power of the generator and the state of the switch. Based on these outputs, a specific scheduling strategy is formulated. The generator scheduling specifies the output power of each generator at each time point. The network reconfiguration adjusts the topology of the power grid according to the state of the switch to optimize the transmission efficiency and reduce the loss. The energy storage system scheduling also needs to formulate the charging and discharging strategy of the energy storage if the system contains energy storage equipment.

[0019] Further, the user participation and incentive mechanism understands the user's cognition of the power market and new energy, electricity habit and participation willingness through questionnaire survey and interview, studies the local power market structure, new energy generation capacity, power grid capacity and demand side management status, sets specific goals of improving user participation, promoting new energy consumption and optimizing power resource allocation, formulates a comprehensive strategy including economic incentive, information transparency and technical support in multiple dimensions combined with market demand and status, develops a smart meter and an energy management system, provides personalized electricity suggestions according to the user's electricity habit and the demand of the power grid, establishes a user-friendly power trading platform, allows users to directly participate in power buying and selling, encourages users to share new energy power through microgrid and energy storage in the community, improves the self-sufficiency ability, sets up a differentiated electricity price structure, encourages users to use electricity in the valley period, reduces the peak load pressure, gives electricity discount, point reward and direct subsidy to the users who actively participate in the power market transaction and use clean energy, and encourages users to invest in distributed energy systems.

[0020] Provide tax incentives, loan preferential policies support, through the APP, website channel, real-time display of power market supply and demand, price changes, new energy power generation information, for users to provide power analysis report, use media, community activities in a variety of channels, popularization of power market knowledge, new energy advantage and participation mode, hold online and offline training courses, teach users how to effectively use smart meters, participate in market transactions, manage home energy, gradually promote smart meters, energy management systems and power trading platform infrastructure, collect user power data, market transaction records, regularly evaluate mechanism effect, according to the monitoring results and user feedback, continuously adjust and optimize user participation mechanism and incentive mechanism, set up user feedback channel, collect and respond to user opinions and suggestions in time, based on user feedback and market demand changes, continuously iterate and upgrade system functions and incentive mechanism, ensure long-term effectiveness and user satisfaction.

[0021] Further, the visual monitoring and operation and maintenance management determines the key parameters of the microgrid and the main grid that need to be monitored, plans the layout, function modules and specific functions of the visual monitoring interface and the intelligent operation and maintenance management system, designs a data collection scheme, integrates data into a central database or data warehouse, uses a graphical design tool to design an intuitive and easy-to-use visual monitoring interface, the interface should support multi-view switching to allow users to observe the system status from different angles, develop a remote monitoring module to allow operation and maintenance personnel to monitor the status of system equipment in real time through a mobile application, use analysis techniques to develop fault diagnosis algorithms to automatically identify and diagnose equipment failures, set thresholds to automatically trigger warning notifications when system parameters exceed normal ranges or equipment has potential failures, including but not limited to SMS, email, and APP push methods, integrate the data collection system, visual monitoring interface, and intelligent operation and maintenance management system components to ensure smooth data transmission and function coordination;

[0022] Perform comprehensive system testing to ensure system stability and reliability and meet requirements, train operation and maintenance personnel on system use, deploy the system to an actual environment for trial operation and debugging to ensure that the system can operate normally and meet actual requirements, establish an operation and maintenance team responsible for daily system monitoring, data backup, and fault handling, continuously optimize system functions and performance based on user feedback and system operation, improve user experience and system stability, strengthen system security measures, regularly perform security vulnerability scanning and repair work to ensure system data security and privacy.

[0023] Compared with the prior art, the intelligent park electric energy monitoring and management system has the following beneficial effects:

[0024] I. The system establishes a two-way interaction mechanism between the microgrid and the main grid, realizes the rapid transmission and sharing of data, and through the collaborative optimization scheduling strategy, can quickly adjust the scheduling strategy under emergency and extreme weather conditions, guarantee the continuity and stability of power supply, at the same time, the visual monitoring and operation and maintenance module provides intuitive system operation state display and intelligent operation and maintenance management, improves the operation and maintenance efficiency and safety, thereby significantly enhances the safety and stability of the power grid and the ability to respond to emergencies.

[0025] II. The intelligent decision engine built by the algorithm model and artificial intelligence technology can analyze the characteristics of new energy power generation, power grid safety and stability constraints and economic cost factors in real time, and formulate the optimal power trading strategy and the charging and discharging plan of the energy storage system, so as to maximize the consumption rate of new energy. This mechanism effectively overcomes the disadvantages of traditional methods lacking intelligent decision-making ability, and significantly improves the utilization efficiency of new energy. BRIEF DESCRIPTION OF DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of these drawings.

[0027] Figure 1 An operation flow chart of an intelligent power grid new energy consumption management system. DETAILED DESCRIPTION

[0028] The technical solutions in the embodiments of the present application will be described clearly and completely below. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0029] Embodiment one

[0030] Integrate various sensors and monitoring equipment to build a comprehensive and multi-level power grid monitoring system, real-time monitor key equipment and environmental parameters of power grid, including the running state of generator, the load condition of transmission line, the remaining capacity of energy storage system and external environmental changes, through big data analysis technology, real-time analysis and early warning of monitoring data, timely discovery of abnormal conditions and prediction of potential risks.

[0031] Based on historical data and expert experience, classify and grade various emergencies, clearly define response measures and dispatch strategies for different levels, develop detailed emergency response plans, including emergency command processes, resource allocation schemes, personnel division of labor and responsibilities, regularly conduct drills and evaluations on emergency response plans to ensure their effectiveness and operability.

[0032] Define the boundaries of the microgrid and the main grid, determine the scope and interface of collaborative scheduling, design and implement efficient and reliable communication links between the microgrid and the main grid to ensure real-time data transmission and rapid response to instructions, define data exchange formats and communication protocols between the microgrid and the main grid to ensure accurate understanding and execution of each other's requests or instructions, collect real-time operation data from the microgrid and the main grid, including but not limited to power generation, load, voltage, frequency key parameters, clean, filter and verify the data to ensure its accuracy and reliability, fuse the data of the microgrid and the main grid to form a complete power grid operation view, use historical data and weather information to accurately predict the power generation capacity of new energy, predict future load based on historical load data and external factors, and based on the prediction results and power grid operation state, the power generation capacity of new energy is accurately predicted, the future load is predicted based on historical load data and external factors, and the new energy power generation capacity prediction formula is: wherein, represents the new energy prediction power at time , is the base power value at time based on historical average power generation, is the weather comprehensive index at time , which can consider temperature, humidity, wind speed weather factors, and the weight under different weather conditions is determined through analysis of historical data, is the seasonal adjustment factor at time , the efficiency of new energy power generation is different in different seasons, and the adjustment coefficient of different seasons is determined through analysis of historical data, and are adjustment coefficients for accurate prediction of new energy power generation capacity;

[0033] Power load prediction formula: wherein, represents the future load prediction value at time , is the historical load value at time based on historical load data, is the external economic activity index at time , which can consider GDP growth rate, industrial production index factors, and the weight of different economic activity factors is determined through analysis of historical data, and the index is calculated, It is time The impact factors of special events are determined by analyzing load changes during special events in historical data to establish their impact coefficients. and It is an adjustment coefficient for accurate prediction of future load, seasonal pattern adjustments calculated based on historical data, assessing the impact and risks of sudden events or extreme weather on the power grid, clarifying the objectives of coordinated dispatch, selecting optimization algorithms based on the objectives, and using the safe and stable operation of the power grid, equipment capacity limitations, and transmission capacity limitations as constraints for the optimization algorithm. Based on the algorithm output, a coordinated optimization dispatch strategy between the microgrid and the main grid is formulated. ,in: The time range of scheduling It is time The cost of generating electricity is related to the generator's output power and fuel costs. It is time The cost of power grid transmission losses, Other possible costs include equipment maintenance costs, real-time monitoring of system operation status, triggering a dynamic adjustment mechanism for scheduling strategies when sudden events or extreme weather conditions are detected, adjusting the operation status of microgrids and main grids in real time through the control system according to the adjusted scheduling strategy, evaluating the effect of collaborative optimization scheduling, adjusting the scheduling strategy based on the evaluation results, optimizing the algorithm model or adjusting parameter settings, and continuously accumulating new operation data and scheduling experience to provide data support for subsequent optimization.

[0034] By using optimization algorithms to rationally allocate limited resources, priority power supply to critical loads is ensured during emergencies. Taking into account factors such as grid security, economic costs, and environmental benefits, the optimal resource allocation scheme is formulated. Through real-time data transmission and sharing, the coordinated scheduling and optimized allocation of resources between the microgrid and the main grid are realized.

[0035] Conduct a comprehensive assessment of the handling process of emergencies, summarize lessons learned, identify existing problems and shortcomings, and adjust emergency response plans and dispatch strategies based on the assessment results. Continuously improve and optimize system performance, establish a sound user feedback mechanism, collect and respond to user opinions and suggestions in a timely manner, and improve user satisfaction and the long-term effectiveness of the system.

[0036] Example 2

[0037] Establish unified communication interface standards to ensure seamless connection between microgrids and the main grid, as well as between different devices. Implement interface testing and debugging to ensure the accuracy and real-time performance of data transmission. Develop an efficient data acquisition and processing system to monitor and collect key data on power generation capacity, energy storage status, load demand, and grid operation status in real time. Apply big data analytics to deeply mine the data and identify potential supply and demand imbalance risks.

[0038] By training the algorithm model of the intelligent decision engine with historical data, the prediction accuracy and decision-making efficiency are improved. An online learning mechanism is introduced, enabling the algorithm to adaptively adjust and optimize in order to cope with the ever-changing power grid environment. Based on real-time data and prediction results, the intelligent decision engine automatically formulates grid connection mode, power trading strategy and energy storage system charging and discharging plan, and implements automated control strategy to ensure rapid execution and accurate feedback of decisions.

[0039] Establish a close collaboration mechanism between the microgrid and the main network. ,in: The time range of scheduling It is time The cost of generating electricity is related to the generator's output power and fuel costs. It is time The cost of power grid transmission losses, Other possible costs include equipment maintenance costs, information sharing and joint dispatch, the introduction of market mechanisms, the promotion of optimal allocation and efficient utilization of power resources, and the development of detailed emergency response plans and dispatch strategies for emergencies and extreme weather conditions. These plans are then tested through simulations and real-world operations to ensure their effectiveness and operability.

[0040] Conduct activities to popularize new energy knowledge and educate users on energy conservation and emission reduction, raise users' environmental awareness and participation, provide personalized electricity use guidance and services to help users optimize their electricity use behavior, design diversified economic incentives to encourage users to adjust their peak electricity use periods, and establish an information transparency platform to allow users to understand the power supply and demand situation and their own electricity use in real time, thereby enhancing their sense of participation.

[0041] Design an intuitive and easy-to-use visual monitoring interface to display the power grid operation status, dispatching plan and execution effect. Introduce 3D modeling and virtual reality technology to enhance the immersiveness and interactivity of monitoring. Introduce an intelligent operation and maintenance management system to realize remote monitoring of equipment status and fault diagnosis. Utilize IoT and big data technology to predict equipment failures in advance and arrange preventive maintenance.

[0042] It will be apparent to those skilled in the art that the application is not limited to the details of the above-exemplified embodiments and that the present application can be implemented in other particular forms without departing from the spirit or essential characteristics of the present application. The embodiments should therefore be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the above description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. No reference signs in the claims should be considered as limiting the scope of the claims with respect to the figures of the patent document.

Claims

1. A smart grid renewable energy consumption management system, characterized in that, The system includes a two-way interactive mechanism, an intelligent decision engine, a collaborative optimization scheduling strategy, a user participation and incentive mechanism, and visual monitoring and operation and maintenance management. The two-way interactive mechanism is constructed by establishing a two-way real-time communication interface between the microgrid and the main grid to achieve rapid data transmission and sharing. Through this mechanism, the system can monitor the power generation capacity of the microgrid, the status of the energy storage system, and the load demand in real time, while obtaining the operating status, load forecast, and scheduling instructions of the main grid. The intelligent decision engine: Through algorithm models and artificial intelligence technology, an intelligent decision engine is constructed. Based on real-time data, the engine comprehensively considers the characteristics of new energy power generation, grid security and stability constraints, and economic cost factors to intelligently decide the grid connection mode of the microgrid, formulate the optimal power trading strategy and energy storage system charging and discharging plan, and maximize the absorption rate of new energy while ensuring the safe and stable operation of the grid. The aforementioned collaborative optimization scheduling strategy enables collaborative optimization scheduling between the microgrid and the main grid. Through accurate prediction, dynamic adjustment, and real-time control, the system can quickly adjust the scheduling strategy under sudden events and extreme weather conditions to ensure the continuity and stability of power supply and improve the resilience of the power grid. The user participation and incentive mechanism: Design a user participation and incentive mechanism to encourage users to adjust their electricity consumption behavior according to the system guidance, participate in electricity market transactions, promote the local consumption of new energy, and enhance user participation and satisfaction by providing economic incentives and information transparency. The visualized monitoring and operation and maintenance management provides an intuitive visualized monitoring interface to display the real-time operating status, scheduling plan and execution effect of the microgrid and the main network. At the same time, it establishes an intelligent operation and maintenance management system to remotely monitor system equipment, diagnose and warn of faults, and improve operation and maintenance efficiency and security. The proposed collaborative optimization scheduling strategy clearly defines the boundaries between the microgrid and the main grid, determines the scope and interface of collaborative scheduling, designs and implements an efficient and reliable communication link between the microgrid and the main grid, defines the data exchange format and communication protocol between the microgrid and the main grid, collects real-time operating data from both the microgrid and the main grid, including power generation, load, voltage, and frequency, cleans, filters, and verifies the data, and fuses the data from the microgrid and the main grid to form a complete grid operation view. Using historical data and meteorological information, it accurately predicts the power generation capacity of new energy sources, and predicts future load based on historical load data and external factors. Based on the prediction results and the grid operating status, it assesses the impact of sudden events or extreme weather on the grid. The system identifies the impact and risks, clarifies the objectives of coordinated dispatch, selects optimization algorithms based on these objectives, and uses the safe and stable operation of the power grid, equipment capacity limitations, and transmission capacity limitations as constraints for the optimization algorithms. Based on the algorithm output, a coordinated optimization dispatch strategy between the microgrid and the main grid is formulated. The system's operating status is monitored in real time. When a sudden event or extreme weather condition is detected, a dynamic adjustment mechanism for the dispatch strategy is triggered. Based on the adjusted dispatch strategy, the operating status of the microgrid and the main grid is adjusted in real time through the control system. The effectiveness of the coordinated optimization dispatch is evaluated, and the dispatch strategy is adjusted based on the evaluation results. The algorithm model is optimized or parameter settings are adjusted, continuously accumulating new operating data and dispatch experience to provide data support for subsequent optimization. The aforementioned method for accurately predicting the power generation capacity of new energy sources involves forecasting future loads based on historical load data and external factors. The formula for predicting the power generation capacity of new energy sources is as follows: ,in, Indicates time Predicted power generation capacity of new energy sources Based on historical average power generation over time The basic power generation value, It is time The comprehensive meteorological index takes into account temperature, humidity, and wind speed. It is calculated by determining the weights of different meteorological conditions through analysis of historical data. It is time The seasonal adjustment factor is determined by analyzing historical data to identify the different seasonal adjustment coefficients for renewable energy generation efficiency, as these efficiency rates vary across seasons. and This is an adjustment factor for accurate prediction of new energy power generation capacity; the power load forecasting formula is: ,in, Indicates time The future load forecast, Based on historical load data in time Historical load values, It is time The external economic activity index, taking into account factors such as GDP growth rate and industrial production index, is calculated by determining the weights of different economic activity factors through analysis of historical data. It is time The impact factors of special events are determined by analyzing load changes during special events in historical data to establish their impact coefficients. and It is an adjustment factor, used for accurate prediction of future load.

2. The smart grid renewable energy consumption management system according to claim 1, characterized in that, The two-way interaction mechanism is constructed by defining its main objectives, collecting information on the existing technical conditions, communication infrastructure, and data interface types of the microgrid and the main grid, designing the overall architecture of the two-way interaction mechanism based on demand analysis, selecting a suitable communication protocol according to system requirements, developing two-way real-time communication interfaces on both the microgrid and main grid sides, including hardware and software interfaces, and conducting functional, performance, and security tests on the communication interfaces. Data on the microgrid's power generation capacity, energy storage system status, and load demand are collected in real time through sensors and smart meters, and operating status, load forecasts, and dispatch instructions are obtained from the main grid dispatch center. The collected data is then cleaned, verified, and compressed for preprocessing. An integrated real-time monitoring platform displays real-time status data, charts, and alarm information for both the microgrid and the main grid. Data analysis algorithms process this real-time data to identify system operating status, predict future trends, and uncover potential problems. Based on the analysis results, optimized scheduling and load balancing suggestions are provided to the control systems of both the microgrid and the main grid. Power generation, energy storage, and load management strategies for the microgrid are generated based on the main grid's scheduling instructions and the microgrid's real-time status. Control commands are sent to power generation equipment, energy storage systems, and load equipment within the microgrid via control interfaces to achieve precise control. Scheduling strategies and control commands are adjusted promptly based on control execution effects and system status changes. Strict data transmission security policies, access control policies, and emergency response mechanisms are formulated and implemented. Communication interfaces, data acquisition equipment, and the monitoring platform are regularly inspected and maintained. The two-way interaction mechanism is continuously optimized and upgraded based on technological advancements and changing practical needs.

3. The smart grid renewable energy consumption management system according to claim 1, characterized in that, The intelligent decision engine defines its main objectives, including maximizing renewable energy absorption, ensuring the safe and stable operation of the power grid, and optimizing economic costs. It identifies and determines the data sources required for the decision engine, designs its system architecture, cleans the collected raw data to remove noise, missing values, and inconsistent data, extracts useful features from the cleaned data, selects an algorithm model based on the decision objectives, trains the selected model using historical data, adjusts model parameters to optimize its performance, and intelligently determines whether the microgrid is operating in islanded or grid-connected mode based on real-time data. Combining market prices, grid dispatch instructions, and renewable energy generation forecasts, it formulates the most effective decision-making strategy. The optimal power trading strategy formulates charging and discharging plans for the energy storage system based on its current status, new energy power generation forecasts, and load demand forecasts. This aims to balance supply and demand, optimize economic costs, and output the decision results to the corresponding control system for execution. The decision execution process is monitored in real time, and the effects of the decision execution are evaluated. Based on the evaluation results, the decision engine is adjusted to optimize the algorithm model, adjust parameters, and improve the decision strategy. New data is continuously accumulated, and the model is regularly updated and retrained using new data to improve the accuracy and adaptability of the decision engine. Attention is paid to the technological development in the fields of artificial intelligence and smart grids, and new technologies and algorithms are introduced in a timely manner to upgrade and iterate the decision engine.

4. The smart grid renewable energy consumption management system according to claim 1, characterized in that, The objectives of the coordinated scheduling are clearly defined. Based on these objectives, an optimization algorithm is selected, and the safe and stable operation of the power grid, equipment capacity limitations, and transmission capacity limitations are used as constraints for the optimization algorithm. Based on the algorithm output, a coordinated optimization scheduling strategy between the microgrid and the main grid is formulated. The objective formula for coordinated scheduling is defined as follows: ,in: The time range of scheduling It is time The cost of generating electricity is related to the generator's output power and fuel costs. It is time The cost of power grid transmission losses, It is the cost of equipment maintenance; Based on the algorithm output, a collaborative optimization scheduling strategy is formulated: The output of the optimization algorithm will be the values ​​of a series of control variables, including the output power of generators and the state of switches. Based on these outputs, specific scheduling strategies are formulated. Generator scheduling: Specify the output power of each generator at each time point. Network reconfiguration: Adjust the topology of the power grid according to the switch state to optimize transmission efficiency and reduce losses. Energy storage system scheduling: If the system contains energy storage devices, it is also necessary to formulate energy storage charging and discharging strategies.

5. The smart grid renewable energy consumption management system according to claim 1, characterized in that, The visualization monitoring and operation and maintenance management identifies the key parameters of the microgrid and main network that need to be monitored. Functional planning includes: planning the layout and functional modules of the visualization monitoring interface and the specific functions of the intelligent operation and maintenance management system; designing a data acquisition scheme; integrating data into a central database or data warehouse; designing an intuitive and easy-to-use visualization monitoring interface using graphical design tools, with the interface supporting multi-view switching to allow users to observe the system status from different angles; developing a remote monitoring module to allow operation and maintenance personnel to monitor the status of system equipment in real time through mobile applications; developing fault diagnosis algorithms using analytical techniques to automatically identify and diagnose equipment faults; setting thresholds to automatically trigger early warning notifications when system parameters exceed the normal range or equipment has potential faults, including SMS, email, and APP push notifications; and integrating the various components of the data acquisition system, visualization monitoring interface, and intelligent operation and maintenance management system. Conduct comprehensive system testing, train maintenance personnel on system usage, deploy the system to a real environment for trial operation and debugging, establish an maintenance team responsible for daily system monitoring, data backup, and troubleshooting, continuously optimize system functions and performance based on user feedback and system operation, improve user experience and system stability, strengthen system security protection measures, and regularly scan and fix security vulnerabilities.

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