Intelligent power grid new energy consumption management system

Abstract

The invention discloses an intelligent power grid new energy consumption management system, and relates to the technical field of intelligent power grids, and the system comprises the following components: bidirectional interaction mechanism construction, an intelligent decision engine, a collaborative optimization scheduling strategy and user participation and incentive mechanism, and visual monitoring and operation and maintenance management. According to the invention, a two-way interaction mechanism between the micro-grid and the main network is constructed, rapid transmission and sharing of data are realized, a scheduling strategy can be rapidly adjusted under emergency and extreme weather conditions through collaborative optimization of the scheduling strategy, continuity and stability of power supply are guaranteed, and meanwhile, the system is convenient to use. The visual monitoring and operation and maintenance management module provides visual system operation state display and intelligent operation and maintenance management, and improves the operation and maintenance efficiency and safety, thereby remarkably enhancing the safety and stability of the power grid and the capability of coping with emergencies.

Description

Technical Field

[0001] The present invention relates to the technical field of smart grids, and in particular to a smart grid new energy consumption management system. Background Art

[0002] With the transformation of the global energy structure and the implementation of the sustainable development strategy, the development and utilization of new energy sources have received increasing attention. As the core of the future power system, smart grids are highly automated, information-based and interactive, and are of great significance for improving the absorption rate of new energy, ensuring the safe and stable operation of power grids, and promoting users' participation in power market transactions.

[0003] The traditional grid coordinated optimization dispatching strategy and the coordinated dispatching between microgrids and main grids lack an effective information exchange and sharing mechanism, making it difficult to achieve efficient collaboration. It is also impossible to quickly adjust the dispatching strategy in emergencies and extreme weather conditions through accurate prediction, dynamic adjustment and real-time control. Therefore, it is particularly important to develop a smart grid new energy consumption management system. Summary of the invention

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

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a smart grid new energy consumption management system, which includes a two-way interactive mechanism construction, an intelligent decision-making engine, a collaborative optimization scheduling strategy and a user participation and incentive mechanism, as well as visual monitoring and operation and maintenance management;

[0006] The two-way interactive mechanism is constructed as follows: a two-way real-time communication interface is established 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, and obtain the operating status, load forecast and dispatch instructions of the main grid at the same time;

[0007] The intelligent decision-making engine: through the algorithm model and artificial intelligence technology, the intelligent decision-making engine is constructed. The engine comprehensively considers the characteristics of new energy power generation, the safety and stability constraints of the power grid, and the economic cost factors based on real-time data, and intelligently decides the grid connection mode of the microgrid, formulates the optimal power trading strategy and the charging and discharging plan of the energy storage system, and maximizes the absorption rate of new energy while ensuring the safe and stable operation of the power grid;

[0008] The collaborative optimization dispatching strategy: realizes the collaborative optimization dispatching between the microgrid and the main grid. Through accurate prediction, dynamic adjustment and real-time control, the system can quickly adjust the dispatching strategy in emergencies and extreme weather conditions to ensure the continuity and stability of power supply and improve the resilience of the power grid;

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

[0010] Furthermore, the two-way interactive mechanism construction determines the main objectives of the two-way interactive 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 interactive mechanism based on demand analysis, selects appropriate communication protocols according to system requirements, and develops two-way real-time communication interfaces on the microgrid and main grid sides, including but not limited to hardware interfaces and software interfaces, performs functional tests, performance tests, and security tests on the communication interfaces to ensure that data can be transmitted accurately and quickly, collects the power generation capacity, energy storage system status, and load demand data of the microgrid in real time through sensors and smart meter equipment, obtains the operating status, load forecast, and dispatch instruction information from the main grid dispatch center, and performs cleaning, verification, and compression preprocessing operations on the collected data;

[0011] The integrated real-time monitoring platform displays the real-time status data, charts and alarm information of the microgrid and the main grid, uses data analysis algorithms to process real-time data, identifies system operating status, predicts future trends, and discovers potential problems. Based on the analysis results, it provides optimization scheduling and load balancing suggestions to the control systems of the microgrid and the main grid. According to the scheduling instructions of the main grid and the real-time status of the microgrid, it generates the power generation, energy storage and load management strategies of the microgrid. Through the control interface, it sends control instructions to the power generation equipment, energy storage system and load equipment in the microgrid to achieve precise control. According to the control execution effect and system status changes, it timely adjusts the scheduling strategy and control instructions, formulates and implements strict data transmission security strategies, access control strategies and emergency response mechanisms, regularly inspects and maintains communication interfaces, data acquisition equipment, and monitoring platforms to ensure stable operation of the system, and continuously optimizes and upgrades the two-way interactive mechanism according to technological development and actual demand changes.

[0012] Furthermore, the intelligent decision engine determines the main goals of the intelligent decision engine, including but not limited to maximizing the new energy absorption rate, ensuring the safe and stable operation of the power grid, and optimizing economic costs, sorting out and determining the data sources 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 features useful for decision-making from the cleaned data, selecting an algorithm model according to the decision goal, training the selected model using historical data, adjusting model parameters to optimize its performance, and intelligently judging whether the microgrid is in an island operation or grid-connected operation state based on real-time data, and formulating the optimal The power trading strategy is developed according to the current status of the energy storage system, the forecast of new energy power generation, and the forecast of load demand. The charging and discharging plan of the energy storage system is formulated to balance the supply and demand relationship and optimize the economic cost. The decision results are output to the corresponding control system for execution. The decision execution process is monitored in real time to ensure that the decision is effectively executed, and the effect after the decision execution is evaluated. According to the evaluation results, the decision engine is adjusted for feedback, the algorithm model is optimized, the parameters are adjusted, and the decision strategy is improved. New data is continuously accumulated, and the model is regularly updated and retrained with new data to improve the accuracy and adaptability of the decision engine. Focus on the technological development in the fields of artificial intelligence and smart grids, and timely introduce new technologies and new algorithms to upgrade and iterate the decision engine.

[0013] Furthermore, the collaborative optimization scheduling strategy defines the boundary between the microgrid and the main grid, determines the scope and interface of the collaborative scheduling, designs and implements an efficient and reliable communication link between the microgrid and the main grid, ensures real-time data transmission and rapid 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 each other's requests or instructions, collects real-time operation data from the microgrid and the main grid, including but not limited to key parameters such as power generation, load, voltage, and frequency, cleans, filters, and verifies the data to ensure the accuracy and reliability of the data, integrates the data of the microgrid and the main grid to form a complete grid operation view, uses historical data and meteorological information to accurately predict the power generation capacity of new energy, and predicts future loads based on historical load data and external factors;

[0014] According to the prediction results and the operating status of the power grid, the impact and risks of emergencies or extreme weather on the power grid are evaluated, the goals of coordinated dispatch are clarified, and the optimization algorithm is selected according to the goals. The safe and stable operation of the power grid, equipment capacity limitations, and transmission capacity limitations are used as constraints of the optimization algorithm. According to the algorithm output, a coordinated optimization dispatch strategy between the microgrid and the main grid is formulated, and the system operating status is monitored in real time. When an emergency or extreme weather conditions are detected, the dynamic adjustment mechanism of the dispatch strategy is triggered. According to the adjusted dispatch strategy, the operating status of the microgrid and the main grid is adjusted in real time through the control system. The effect of the coordinated optimization dispatch is evaluated, and the dispatch strategy is adjusted according to the evaluation results. Feedback adjustment, optimize the algorithm model or adjust parameter settings, continuously accumulate new operating data and dispatch experience, and provide data support for subsequent optimization.

[0015] Furthermore, the power generation capacity of new energy is accurately predicted, and future load is predicted based on historical load data and external factors. The prediction formula for the power generation capacity of new energy is: Among them, P new_energy (t) represents the predicted power generation of renewable energy at time t, P base (t) is the basic power generation value based on the historical average power generation at time t, W(t) is the comprehensive meteorological index at time t, which can comprehensively consider the meteorological factors of temperature, humidity, and wind speed. The weights under different meteorological conditions are determined by analyzing historical data, and the index is calculated. S(t) is the seasonal adjustment factor at time t. The efficiency of new energy power generation is different in different seasons. The adjustment coefficients for different seasons are determined by analyzing historical data. α and M are adjustment coefficients, which can accurately predict the power generation capacity of new energy.

[0016] Power load forecasting formula: L(t) = L h istory (t)×(1+γ×F(t))×(1+δ×E(t)), where L(t) represents the future load forecast value at time t, L h istory (t) is the historical load value at time t based on historical load data. F(t) is the external economic activity index at time t. The GDP growth rate and industrial production index factors can be considered. The weights of different economic activity factors are determined by analyzing historical data and the index is calculated. E(t) is the special event impact factor at time t. Its impact coefficient is determined by analyzing the load changes during special events in historical data. γ and δ are adjustment coefficients for accurate prediction of future loads.

[0017] Furthermore, the goal of the collaborative dispatch is clarified, and the optimization algorithm is selected according to the goal. The safe and stable operation of the power grid, the equipment capacity limitation, and the transmission capacity limitation are used as the constraints of the optimization algorithm. According to the output of the algorithm, a collaborative optimization dispatch strategy between the microgrid and the main grid is formulated; the goal formula of the collaborative dispatch is clarified as: MinimizeJ=∑ t∈T (C gen (t)+C loss (t)+C other (t)), where: T is the scheduling time range, C gen (t) is the power generation cost at time t, which is related to the output power of the generator and the fuel cost. loss (t) is the grid transmission loss cost at time t, C other (t) is other possible costs, equipment maintenance costs;

[0018] Develop collaborative optimization scheduling strategies based on algorithm outputs: The output of the optimization algorithm will be a series of control variable values, including but not limited to the output power of the generator and the state of the switch. Based on these outputs, develop specific scheduling strategies: Generator scheduling: Specify the output power of each generator at each time point. Network reconstruction: 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 includes energy storage equipment, it is also necessary to develop a charging and discharging strategy for energy storage.

[0019] Furthermore, the user participation and incentive mechanism uses questionnaires and interviews to understand users' understanding of the electricity market, new energy, electricity usage habits and willingness to participate, studies the local electricity market structure, new energy power generation capacity, grid capacity and demand-side management status, sets specific goals for improving user participation, promoting new energy consumption and optimizing power resource allocation, and formulates a comprehensive strategy including economic incentives, information transparency and technical support in multiple dimensions in combination with market demand and status quo, develops smart meters and energy management systems, provides personalized electricity usage suggestions based on users' electricity usage habits and grid demand, establishes a user-friendly electricity trading platform, allows users to directly participate in electricity trading, encourages users in the community to share new energy electricity through microgrids and energy storage, improves self-sufficiency, sets a differentiated electricity price structure, encourages users to use electricity during off-peak hours, reduces peak load pressure, and provides electricity discounts, point rewards and direct subsidies to users who actively participate in electricity market transactions and use clean energy, to encourage users to invest in distributed energy systems;

[0020] Provide tax exemptions and loan preferential policy support, display the power market supply and demand situation, electricity price changes, and new energy power generation information in real time through APP and website channels, provide users with electricity consumption analysis reports, use media and community activities to popularize power market knowledge, new energy advantages and participation methods, hold online and offline training courses, teach users how to effectively use smart meters, participate in market transactions, and manage home energy, gradually promote smart meters, energy management systems and power trading platform infrastructure, collect user electricity consumption data and market transaction records, regularly evaluate the effectiveness of the mechanism, and continuously adjust and optimize user participation mechanisms and incentive mechanisms based on monitoring results and user feedback, set up user feedback channels, promptly collect and respond to user opinions and suggestions, and based on user feedback and market demand changes, continuously iterate and upgrade system functions and incentive mechanisms to ensure long-term effectiveness and user satisfaction.

[0021] Furthermore, the visual monitoring and operation and maintenance management determines the key parameters of the microgrid and the main grid that need to be monitored, and functional planning: planning the layout of the visual monitoring interface, functional modules and specific functions of the intelligent operation and maintenance management system, designing data collection schemes, integrating data into a central database or data warehouse, and using graphical design tools to design an intuitive and easy-to-use visual monitoring interface. The interface should support multi-view switching so that users can observe the system status from different angles, develop a remote monitoring module, and allow operation and maintenance personnel to monitor the status of system equipment in real time through mobile applications. Using analysis technology, develop fault diagnosis algorithms to automatically identify and diagnose equipment failures, set thresholds, and automatically trigger early warning notifications when system parameters exceed the normal range or equipment has potential failures, including but not limited to SMS, email, and APP push methods, integrate the various components of the data collection system, visual monitoring interface, and intelligent operation and maintenance management system to ensure smooth data transmission and collaborative function.

[0022] Conduct comprehensive system testing to ensure that the system is stable, reliable and meets requirements; provide system usage training to operation and maintenance personnel; deploy the system to the actual environment; conduct trial runs and debugging to ensure that the system can operate normally and meet actual requirements; establish an operation and maintenance team to be responsible for daily system monitoring, data backup, and troubleshooting; continuously optimize system functions and performance based on user feedback and system operation status, improve user experience and system stability, strengthen system security protection measures, regularly scan and repair security vulnerabilities, and ensure the security and privacy of system data.

[0023] Compared with the existing technology, a smart park power monitoring and management system has the following beneficial effects:

[0024] 1. The system has established a two-way interactive mechanism between the microgrid and the main grid, realized the rapid transmission and sharing of data, and through the collaborative optimization of the dispatching strategy, can quickly adjust the dispatching strategy in emergencies and extreme weather conditions to ensure the continuity and stability of power supply. At the same time, the visual monitoring and operation and maintenance management module provides an intuitive display of the system operation status and intelligent operation and maintenance management, which improves the efficiency and safety of operation and maintenance, thereby significantly enhancing the safety and stability of the power grid and its ability to respond to emergencies.

[0025] 2. The system uses algorithm models and artificial intelligence technology to build an intelligent decision-making engine that can analyze the characteristics of renewable energy power generation, grid safety and stability constraints, and economic cost factors in real time, formulate optimal electricity trading strategies and charging and discharging plans for energy storage systems, thereby maximizing the absorption rate of renewable energy. This mechanism effectively overcomes the shortcomings of traditional methods that lack intelligent decision-making capabilities and significantly improves the utilization efficiency of renewable energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 An operation flow chart of a smart grid new energy consumption management system. DETAILED DESCRIPTION

[0028] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. 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.

[0029] Embodiment 1

[0030] Integrate a variety of sensors and monitoring equipment to build a comprehensive, multi-level power grid monitoring system to monitor key power grid equipment and environmental parameters in real time, including the operating status of generators, the load of transmission lines, the remaining capacity of the energy storage system, and changes in the external environment. Through big data analysis technology, the monitoring data can be analyzed and warned in real time to detect abnormal situations in a timely manner and predict potential risks.

[0031] Based on historical data and expert experience, various types of emergencies are classified and graded, response measures and dispatch strategies at different levels are clarified, and detailed emergency response plans are formulated, including emergency command processes, resource allocation plans, personnel division of labor and responsibilities. Emergency response plans are regularly rehearsed and evaluated to ensure their effectiveness and operability.

[0032] Clarify the boundary between microgrid and main grid, determine the scope and interface of coordinated dispatch, design and implement efficient and reliable communication links between microgrid and main grid, ensure real-time data transmission and rapid response to instructions, define the data exchange format and communication protocol between microgrid and main grid, ensure that both parties can accurately understand and execute each other's requests or instructions, collect real-time operation data from microgrid and main grid respectively, including but not limited to key parameters of power generation, load, voltage and frequency, clean, filter and verify the data to ensure the accuracy and reliability of the data, integrate the data of microgrid and main grid to form a complete grid operation view, use historical data and meteorological information to accurately predict the power generation capacity of new energy, predict future load based on historical load data and external factors, and according to the prediction results and grid operation status, accurately predict the power generation capacity of new energy, and predict future load based on historical load data and external factors. The prediction formula of new energy power generation capacity is: Among them, P new_energy (t) represents the predicted power generation of renewable energy at time t, P base (t) is the basic power generation value based on the historical average power generation at time t, W(t) is the comprehensive meteorological index at time t, which can comprehensively consider the meteorological factors of temperature, humidity, and wind speed. The weights under different meteorological conditions are determined by analyzing historical data, and the index is calculated. S(t) is the seasonal adjustment factor at time t. The efficiency of new energy power generation is different in different seasons. The adjustment coefficients for different seasons are determined by analyzing historical data. α and M are adjustment coefficients, which can accurately predict the power generation capacity of new energy.

[0033] Power load forecasting formula: L(t) = L history (t)×(1+O×F(t))×(1+δ×E(t)), where L(t) represents the future load forecast value at time t, L history(t) is the historical load value at time t based on historical load data. F(t) is the external economic activity index at time t. The GDP growth rate and industrial production index can be considered. The weights of different economic activity factors are determined by analyzing historical data, and the index is calculated. E(t) is the special event impact factor at time t. Its impact coefficient is determined by analyzing the load changes during special events in historical data. O and δ are adjustment coefficients. Accurately predict future loads. Adjust seasonal patterns based on historical data. Evaluate the impact and risk of emergencies or extreme weather on the power grid. Clarify the goal of coordinated dispatch. Select the optimization algorithm based on the goal. Take the safe and stable operation of the power grid, equipment capacity limitations, and transmission capacity limitations as constraints of the optimization algorithm. According to the output of the algorithm, formulate a coordinated optimization dispatch strategy between the microgrid and the main grid. MinimizeJ=∑ t∈T (C gen (t)+C loss (t)+C other (t)), where: T is the scheduling time range, C gen (t) is the power generation cost at time t, which is related to the output power of the generator and the fuel cost. loss (t) is the grid transmission loss cost at time t, C other (t) is other possible costs, such as equipment maintenance costs. The system operating status is monitored in real time. When an emergency or extreme weather conditions are detected, the dynamic adjustment mechanism of the dispatch strategy is triggered. According to the adjusted dispatch strategy, the operating status of the microgrid and the main grid is adjusted in real time through the control system. The effect of the collaborative optimization dispatch is evaluated, and the dispatch strategy is adjusted based on the evaluation results. The algorithm model is optimized or the parameter settings are adjusted. New operating data and dispatch experience are continuously accumulated to provide data support for subsequent optimization.

[0034] Optimization algorithms are used to rationally allocate limited resources to ensure priority power supply to critical loads in emergencies. Optimal resource allocation plans are developed by comprehensively considering multiple factors, including grid security, economic costs, and environmental benefits. Through real-time data transmission and sharing, coordinated scheduling and optimal configuration of resources between microgrids and main grids are achieved.

[0035] Conduct a comprehensive assessment of the emergency response process, summarize lessons learned, identify existing problems and deficiencies, and based on the assessment results, provide feedback and adjustments to emergency response plans and scheduling strategies, continuously improve and optimize system performance, establish a comprehensive user feedback mechanism, promptly collect and respond to user opinions and suggestions, and improve user satisfaction and the long-term effectiveness of the system.

[0036] Embodiment 2

[0037] Establish unified communication interface standards to ensure seamless connection between microgrids and main grids and between different devices; implement interface testing and debugging to ensure the accuracy and real-time nature of data transmission; develop efficient data acquisition and processing systems 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 analysis technology to conduct in-depth data mining and identify potential risks of supply and demand imbalance.

[0038] Historical data is used to train the algorithm model of the intelligent decision-making engine to improve prediction accuracy and decision-making efficiency. An online learning mechanism is introduced to enable the algorithm to adaptively adjust and optimize to cope with the ever-changing power grid environment. Based on real-time data and prediction results, the intelligent decision-making engine automatically formulates grid connection modes, power trading strategies, and energy storage system charging and discharging plans, and implements automated control strategies to ensure rapid execution of decisions and accurate feedback.

[0039] Establish a close coordination mechanism between the microgrid and the main grid, MinimizeJ=∑ t∈T (C gen (t)+C loss (t)+C other (t)), where: T is the scheduling time range, C gen (t) is the power generation cost at time t, which is related to the output power of the generator and the fuel cost. loss (t) is the grid transmission loss cost at time t, C other (t) are other possible costs, such as equipment maintenance costs, information sharing and joint dispatch, the introduction of market mechanisms, the promotion of optimal allocation and efficient use of power resources, the formulation of detailed emergency response plans and dispatch strategies for emergencies and extreme weather conditions, and the ensuring of the effectiveness and operability of the plans through simulation exercises and actual combat tests.

[0040] Carry out new energy knowledge popularization and energy conservation and emission reduction education activities, improve users' environmental awareness and participation, provide personalized electricity usage guidance and services, help users optimize electricity usage behavior, design diversified economic incentives, encourage users to adjust peak electricity usage periods, and establish an information transparency platform to allow users to understand the electricity supply and demand situation and their own electricity usage in real time, and enhance their sense of participation.

[0041] Design an intuitive and easy-to-use visual monitoring interface to display the power grid operation status, dispatch plan and execution effect, introduce 3D modeling and virtual reality technology to enhance the immersion and interactivity of monitoring, introduce an intelligent operation and maintenance management system to realize remote monitoring and fault diagnosis of equipment status, and use the Internet of Things 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 invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A smart grid new energy consumption management system, characterized in that: The system includes the construction of a two-way interactive mechanism, an intelligent decision-making engine, collaborative optimization scheduling strategies, user participation and incentive mechanisms, and visual monitoring and operation and maintenance management; The two-way interactive mechanism is constructed as follows: a two-way real-time communication interface is established 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, and obtain the operating status, load forecast and dispatch instructions of the main grid at the same time; The intelligent decision-making engine: through the algorithm model and artificial intelligence technology, the intelligent decision-making engine is constructed. The engine comprehensively considers the characteristics of new energy power generation, the safety and stability constraints of the power grid, and the economic cost factors based on real-time data, and intelligently decides the grid connection mode of the microgrid, formulates the optimal power trading strategy and the charging and discharging plan of the energy storage system, and maximizes the absorption rate of new energy while ensuring the safe and stable operation of the power grid; The collaborative optimization dispatching strategy: realizes the collaborative optimization dispatching between the microgrid and the main grid. Through accurate prediction, dynamic adjustment and real-time control, the system can quickly adjust the dispatching strategy in emergencies and extreme weather conditions to ensure the continuity and stability of power supply and improve the resilience of the power grid; User participation and incentive mechanism: Design user participation and incentive mechanism to encourage users to adjust their electricity consumption behavior according to system guidance, participate in electricity market transactions, promote local consumption of new energy, and enhance user participation and satisfaction by providing economic incentives and information transparency; The visual monitoring and operation and maintenance management provides an intuitive visual monitoring interface to display the real-time operation status, scheduling plan and execution effect of the microgrid and the main grid. At the same time, an intelligent operation and maintenance management system is established to remotely monitor, diagnose faults and issue early warnings on system equipment to improve operation and maintenance efficiency and safety.

2. According to claim 1, a smart grid new energy consumption management system is characterized in that: The two-way interactive mechanism construction determines the main objectives of the two-way interactive 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 interactive mechanism based on demand analysis, selects a suitable communication protocol according to system requirements, and develops two-way real-time communication interfaces on the microgrid and the main grid, including but not limited to hardware interfaces and software interfaces, performs functional tests, performance tests, and safety tests on the communication interfaces, collects the power generation capacity, energy storage system status, and load demand data of the microgrid in real time through sensors and smart meter equipment, obtains the operating status, load forecast, and dispatch instruction information from the main grid dispatch center, and performs cleaning, verification, and compression preprocessing operations on the collected data; The integrated real-time monitoring platform displays the real-time status data, charts and alarm information of the microgrid and the main grid, uses data analysis algorithms to process real-time data, identifies system operating status, predicts future trends, and discovers potential problems. Based on the analysis results, it provides optimization scheduling and load balancing suggestions to the control systems of the microgrid and the main grid. According to the scheduling instructions of the main grid and the real-time status of the microgrid, it generates the power generation, energy storage and load management strategies of the microgrid. Through the control interface, it sends control instructions to the power generation equipment, energy storage system and load equipment in the microgrid to achieve precise control. According to the control execution effect and system status changes, it timely adjusts the scheduling strategy and control instructions, formulates and implements strict data transmission security strategies, access control strategies and emergency response mechanisms, regularly inspects and maintains communication interfaces, data acquisition equipment, and monitoring platforms, and continuously optimizes and upgrades the two-way interactive mechanism according to technological development and actual demand changes.

3. According to claim 1, a smart grid new energy consumption management system is characterized in that: The intelligent decision engine determines the main goals of the intelligent decision engine, including but not limited to maximizing the new energy absorption rate, ensuring the safe and stable operation of the power grid, and optimizing economic costs. It sorts out and determines the data sources required by the decision engine, designs the system architecture of the intelligent decision engine, cleans the collected raw data, removes noise, missing values ​​and inconsistent data, extracts features useful for decision-making from the cleaned data, selects algorithm models according to decision goals, trains the selected model using historical data, adjusts model parameters to optimize its performance, and intelligently determines whether the microgrid is in an island operation or grid-connected operation state based on real-time data. Combined with market prices, power grid dispatch instructions, and new energy power generation prediction factors, formulates Determine the optimal electricity trading strategy, formulate a charging and discharging plan for the energy storage system based on the current status of the energy storage system, new energy power generation forecasts, and load demand forecasts to balance supply and demand and optimize economic costs, output the decision results to the corresponding control system for execution, monitor the decision execution process in real time, evaluate the effect after decision execution, and make feedback adjustments to the decision engine based on the evaluation results, optimize the algorithm model, adjust parameters, and improve the decision strategy. Continuously accumulate new data, use new data to regularly update and retrain the model, improve the accuracy and adaptability of the decision engine, pay attention to technological developments in the fields of artificial intelligence and smart grids, and promptly introduce new technologies and new algorithms to upgrade and iterate the decision engine.

4. According to claim 1, a smart grid new energy consumption management system is characterized in that: The collaborative optimization scheduling strategy defines the boundary 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 operation data from the microgrid and the main grid, including but not limited to key parameters such as power generation, load, voltage, and frequency, cleans, filters, and verifies the data, integrates the data of the microgrid and the main grid, forms a complete grid operation view, uses historical data and meteorological information to accurately predict the power generation capacity of new energy, and predicts future loads based on historical load data and external factors; According to the prediction results and the operating status of the power grid, the impact and risks of emergencies or extreme weather on the power grid are evaluated, the goals of coordinated dispatch are clarified, and the optimization algorithm is selected according to the goals. The safe and stable operation of the power grid, equipment capacity limitations, and transmission capacity limitations are used as constraints of the optimization algorithm. According to the algorithm output, a coordinated optimization dispatch strategy between the microgrid and the main grid is formulated, and the system operating status is monitored in real time. When an emergency or extreme weather conditions are detected, the dynamic adjustment mechanism of the dispatch strategy is triggered. According to the adjusted dispatch strategy, the operating status of the microgrid and the main grid is adjusted in real time through the control system. The effect of the coordinated optimization dispatch is evaluated, and the dispatch strategy is adjusted according to the evaluation results. Feedback adjustment, optimize the algorithm model or adjust parameter settings, continuously accumulate new operating data and dispatch experience, and provide data support for subsequent optimization.

5. According to claim 4, a smart grid new energy consumption management system is characterized in that: The above-mentioned accurate prediction of the power generation capacity of new energy is based on the historical load data and external factors to predict the future load. The prediction formula of the power generation capacity of new energy is: Among them, P new_energy (t) represents the predicted power generation of renewable energy at time t, P base (t) is the basic power generation value based on the historical average power generation at time t, W(t) is the comprehensive meteorological index at time t, which can comprehensively consider the meteorological factors of temperature, humidity, and wind speed. The weights under different meteorological conditions are determined by analyzing historical data, and the index is calculated. S(t) is the seasonal adjustment factor at time t. The efficiency of new energy power generation is different in different seasons. The adjustment coefficients for different seasons are determined by analyzing historical data. α and M are adjustment coefficients, which can accurately predict the power generation capacity of new energy. Power load forecasting formula: L(t) = L history (t)×(1+O×F(t))×(1+δ×E(t)) where L(t) represents the future load forecast value at time t, L history (t) is the historical load value at time t based on historical load data. F(t) is the external economic activity index at time t. The GDP growth rate and industrial production index factors can be considered. The weights of different economic activity factors are determined by analyzing historical data and the index is calculated. E(t) is the special event impact factor at time t. Its impact coefficient is determined by analyzing the load changes during special events in historical data. O and δ are adjustment coefficients to accurately predict future loads.

6. According to claim 4, a smart grid new energy consumption management system is characterized in that: The objectives of the collaborative dispatch are clarified, and an optimization algorithm is selected according to the objectives. The safe and stable operation of the power grid, equipment capacity limitations, and transmission capacity limitations are used as constraints of the optimization algorithm. Based on the output of the algorithm, a collaborative optimization dispatch strategy between the microgrid and the main grid is formulated; The target formula for collaborative scheduling is: MinimizeJ = ∑t ∈T (C gen (t)+C loss (t)+C other (t)), where: T is the scheduling time range, C gen (t) is the power generation cost at time t, which is related to the output power of the generator and the fuel cost. loss (t) is the grid transmission loss cost at time t, C other (t) is other possible costs, equipment maintenance costs; Develop collaborative optimization scheduling strategies based on algorithm outputs: The output of the optimization algorithm will be a series of control variable values, including but not limited to the output power of the generator and the state of the switch. Based on these outputs, develop specific scheduling strategies: Generator scheduling: Specify the output power of each generator at each time point. Network reconstruction: 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 includes energy storage equipment, it is also necessary to develop a charging and discharging strategy for energy storage.

7. The smart grid new energy consumption management system according to claim 1, characterized in that: The user participation and incentive mechanism uses questionnaires and interviews to understand users' understanding of the electricity market, new energy, electricity usage habits and willingness to participate, studies the local electricity market structure, new energy power generation capacity, grid capacity and demand-side management status, sets specific goals for improving user participation, promoting new energy consumption and optimizing power resource allocation, and formulates a comprehensive strategy with multiple dimensions including economic incentives, information transparency and technical support in combination with market demand and status quo, develops smart meters and energy management systems, provides personalized electricity usage suggestions based on users' electricity usage habits and grid demand, establishes a user-friendly electricity trading platform, allows users to directly participate in electricity trading, encourages users in the community to share new energy electricity through microgrids and energy storage, improves self-sufficiency, sets a differentiated electricity price structure, encourages users to use electricity during off-peak hours, reduces peak load pressure, and provides electricity fee discounts, point rewards and direct subsidies to users who actively participate in electricity market transactions and use clean energy, to encourage users to invest in distributed energy systems; Provide tax exemptions and loan preferential policy support, display the power market supply and demand situation, electricity price changes, and new energy power generation information in real time through APP and website channels, provide users with electricity consumption analysis reports, use media and community activities to popularize power market knowledge, new energy advantages and participation methods, hold online and offline training courses, teach users how to effectively use smart meters, participate in market transactions, and manage home energy, gradually promote smart meters, energy management systems and power trading platform infrastructure, collect user electricity consumption data and market transaction records, regularly evaluate the effectiveness of the mechanism, and continuously adjust and optimize user participation mechanisms and incentive mechanisms based on monitoring results and user feedback, set up user feedback channels, promptly collect and respond to user opinions and suggestions, and continuously iterate and upgrade system functions and incentive mechanisms based on user feedback and market demand changes.

8. The smart grid new energy consumption management system according to claim 1, characterized in that: The visual monitoring and operation and maintenance management determines the key parameters of the microgrid and the main grid that need to be monitored. Functional planning: planning the layout of the visual monitoring interface, functional modules and specific functions of the intelligent operation and maintenance management system, designing data collection schemes, integrating data into a central database or data warehouse, and using graphical design tools to design an intuitive and easy-to-use visual monitoring interface. The interface should support multi-view switching so that users can 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 mobile applications. Use analytical technology to develop fault diagnosis algorithms to automatically identify and diagnose equipment failures, set thresholds, and automatically trigger early warning notifications when system parameters exceed the normal range or potential equipment failures occur, including but not limited to SMS, email, and APP push methods, and integrate the various components of the data collection system, visual monitoring interface, and intelligent operation and maintenance management system; Conduct comprehensive system testing, train operation and maintenance personnel on system use, deploy the system to the actual environment, conduct trial runs and debugging, establish an operation and maintenance team, be responsible for daily system monitoring, data backup, and troubleshooting, and continuously optimize system functions and performance based on user feedback and system operation status, improve user experience and system stability, strengthen system security protection measures, and regularly scan and repair security vulnerabilities.

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