Energy source network load storage based energy coordination transmission method and system

Abstract

The application discloses an energy source network load storage-based energy source coordinated power transmission method and system, belongs to the technical field of source network load storage, and comprises the following steps: S1, using an intelligent prediction and scheduling module, combining a machine learning and deep learning algorithm, and predicting power generation on the power supply side; S2, deploying an efficient energy storage unit; S3, designing a multi-source hybrid power supply mode; S4, constructing an intelligent monitoring and management platform; S5, integrating distributed energy resources, connecting household photovoltaic and community wind power stations to the main network through micro-grid technology, and promoting the interaction between the large power grid and the distributed energy; and S6, applying virtual power plant technology.The application deploys a machine learning and deep learning algorithm, combines meteorological forecasts and seasonal change factors, accurately predicts power generation on the power supply side, and realizes dynamic scheduling according to load side demand prediction results, so that accurate prediction enables power production to be more closely matched with actual demand, and overproduction and energy waste are avoided.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of source network load storage, and in particular to an energy collaborative power transmission method and system based on source network load storage. BACKGROUND

[0002] The energy collaborative power transmission technology based on source network load storage is a comprehensive energy management and transmission technology that integrates four key elements: power source (source), power grid (network), load (load), and energy storage (storage). This technology aims to achieve efficient matching of energy production and consumption through intelligent and information-based means, optimize the operational efficiency of the energy system, improve the utilization rate of renewable energy, and enhance the flexibility and stability of the power system.

[0003] Because conventional power sources and new energy sources have different characteristics and volatility, it is difficult to seamlessly integrate them in the power supply system, so in the current energy collaborative power transmission technology, the specific regional power supply faces the complex problem of complementary and optimal configuration between conventional power sources and new energy sources, which ultimately leads to relatively weak stability of power supply and also limits the effective use of energy. SUMMARY

[0004] The purpose of the present application is to provide an energy collaborative power transmission method and system based on source network load storage to solve the problems raised in the background art.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical solution: an energy collaborative power transmission method based on source network load storage, the method comprising the following steps:

[0006] S1: Use an intelligent prediction and scheduling module to predict the power generation capacity on the power source side using machine learning and deep learning algorithms, and realize dynamic scheduling of power according to the load side demand prediction results;

[0007] S2: Deploy efficient energy storage units to store excess energy at the required location to smooth out the volatility of new energy and meet the demand during peak periods;

[0008] S3: Design a multi-source hybrid power supply mode to integrate various forms of renewable energy such as wind, solar, and hydro energy, and optimize the configuration ratio according to different energy characteristics and geographical conditions to ensure stable power supply;

[0009] S4: Build an intelligent monitoring and management platform to monitor the status of the entire energy network in real time and automatically adjust the operating parameters to optimize the energy transmission path and improve the efficiency of the power system;

[0010] S5: Integrate distributed energy resources by connecting household photovoltaic and community wind power stations to the main grid through microgrid technology to promote interaction between the main grid and distributed energy;

[0011] S6: Apply virtual power plant technology, integrate dispersed power generation equipment, energy storage systems and controllable loads into a whole through information technology and communication technology, participate in power market transactions as a single entity, enhance energy utilization and support capacity for the power grid.

[0012] Preferably, the method uses blockchain technology to build a decentralized energy trading platform, ensuring that transaction records are tamper-proof and transparent.

[0013] Preferably, in the method, an AI-driven adaptive control protection mechanism is developed to automatically adjust parameters based on real-time data to optimize performance and continuously improve decision-making processes through self-learning capabilities.

[0014] Preferably, based on S6, quantum computing is introduced to assist in solving large-scale optimization problems, and a special quantum algorithm is written to speed up the solution speed through the cooperation of classical computers and quantum computers, and to find the optimal power transmission scheme.

[0015] Preferably, flexible HVDC transmission technology is used to allow control of the direction and amount of power transmission, reducing energy loss, increasing system stability and controllability, and effectively addressing intermittent new energy output, especially suitable for long-distance large-capacity power transmission and offshore wind farm access to land power grid.

[0016] Preferably, environment-aware intelligent hardware devices are used, which can automatically adjust their working modes according to changes in the surrounding environment (temperature T, humidity H, light intensity L, etc.) to achieve energy-saving purposes, and the adjustment formula is as follows:

[0017] P adjusted =P base ×(1-α·T+β·H+γ·L)

[0018] Where P adjusted is the adjusted power output, P base is the basic power output, and α, β and γ are the influence coefficients of temperature, humidity and light intensity, respectively.

[0019] Preferably, a zero-carbon emission regional energy solution is developed for geographical areas that rely entirely on renewable energy and efficient energy storage units.

[0020] All energy sources and technology combinations within the region are considered to ensure stable power supply while reducing carbon footprint.

[0021] The preferred scheme is to design a self-healing smart grid architecture in S1, when detecting local faults, the power grid can quickly isolate the fault section, reconfigure the power transmission path, ensure normal work, and minimize the risk of power failure, the recovery time calculation formula is as follows:

[0022]

[0023] Where T recovery is the recovery time, D is the distance of the fault section, S is the processing speed, and C is a fixed constant representing additional time consumption.

[0024] The preferred scheme of the present application also creates an energy collaborative control module based on big data analysis, which integrates big data analysis, cloud computing, and Internet of Things technology, collects, processes, and analyzes data from various links of source, network, load, and storage, makes optimal decisions according to the analysis results, guides energy production and consumption, and ensures efficient operation of the system.

[0025] An energy collaborative power transmission system based on source, network, load and storage, for implementing the steps of the method, comprising:

[0026] Intelligent prediction and scheduling module: for predicting power generation on the power supply side and dynamically scheduling according to the demand on the load side;

[0027] Efficient energy storage unit: deployed at the required location, storing excess electrical energy and stabilizing new energy fluctuations;

[0028] Multi-source hybrid power supply module: integrates multiple renewable energy sources, optimizes the configuration ratio, and stabilizes power supply;

[0029] Intelligent monitoring and management platform: real-time monitoring of energy network status, automatic adjustment of operating parameters;

[0030] Virtual power plant: as a single entity participating in power market transactions;

[0031] Self-healing smart grid architecture: quickly responds to and repairs local faults to ensure stable operation of the power grid;

[0032] Energy transaction platform based on blockchain: ensures the security and transparency of transaction records.

[0033] Compared with the prior art, the technical effects and advantages of the present application are:

[0034] The energy collaborative transmission method and system based on source network load storage, (1) the present application can accurately predict the power generation capacity of the power supply side by deploying machine learning and deep learning algorithms, combining meteorological forecasts, seasonal changes and other factors, and realizing dynamic scheduling according to the demand prediction results of the load side. Accurate prediction enables power production to closely match actual demand, avoiding excessive production and energy waste. The ability to quickly respond to market changes improves the flexibility of the system, allowing power companies to adjust their power generation plans in a short time to adapt to sudden demand fluctuations or supply interruptions. The intelligent prediction and scheduling module not only improves the efficiency of power production and reduces resource waste, but also serves as an auxiliary decision-making tool to help plan future energy infrastructure construction. Accurate demand prediction can provide advance notice to the energy storage system to adjust the charging and discharging strategy, ensuring sufficient power reserves during peak periods, and also providing more accurate output plans for virtual power plants.

[0035] (2) The present application integrates dispersed power generation equipment, energy storage systems and controllable loads into a logical "virtual power plant" through the reinforcement of virtual power plants (VPP), so that it participates in power market transactions as a whole. VPP as a whole participates in market transactions, can obtain scale effect, improve bargaining power and income level. Provide frequency regulation, peak filling and other auxiliary services to enhance the flexibility and stability of the power grid, while also providing additional revenue sources for operators.

[0036] (3) The present application can maintain a certain power supply capacity of the power grid even in extreme weather conditions (such as hurricanes, earthquakes, etc.) through the function of efficient energy storage units and the function of stabilizing new energy fluctuations. When traditional power generation facilities are damaged, efficient energy storage units can provide emergency power support to maintain the operation of critical infrastructure such as hospitals and communication base stations. Energy storage devices can restore power supply in some areas in a short time to speed up post-disaster reconstruction. Efficient energy storage units not only stabilize the operation of the power grid, but also maximize economic benefits; in addition, efficient energy storage units can also serve as backup power sources to provide emergency power support in the event of main grid failure. Efficient energy storage units cooperate with multi-source hybrid power supply mode to dynamically adjust storage and release strategies according to the real-time output of different energy sources, further optimizing energy allocation.

[0037] (4) The present application can automatically adjust the working mode according to the changes in the surrounding environment through the design of environment-aware intelligent hardware devices, achieving the purpose of energy saving while improving the user experience. At the same time, these devices can accept user instructions through a remote configuration interface to further personalize settings and meet the needs of different scenarios.

[0038] (5) The self-healing intelligent power grid architecture designed by the application can quickly isolate the fault section and reconfigure the power transmission path when a local fault is detected, thereby minimizing the risk of power outage. At the same time, the machine learning algorithm predicts the fault trend and arranges maintenance in advance to reduce the probability of actual failure, thereby forming an intelligent power grid system combining self-repair and preventive maintenance. BRIEF DESCRIPTION OF DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the description of the embodiments or the prior art will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0040] Fig. 1 Flow chart of the energy collaborative power transmission method based on source network load storage of the present application;

[0041] Fig. 2 Flow chart of the energy collaborative power transmission system based on source network load storage of the present application;

[0042] Fig. 3 Overall flow chart of the energy collaborative power transmission system based on source network load storage of the present application. DETAILED DESCRIPTION

[0043] In the following description, a large number of specific details are given in order to provide a more thorough understanding of the present application. However, it is obvious to those skilled in the art that the present application can be implemented without one or more of these details. In other examples, some technical features known in the art are not described in order not to obscure the present application.

[0044] Unless the direction is defined separately, the directions such as up, down, left, right, front, back, inside and outside mentioned in this paper are based on the directions such as up, down, left, right, front, back, inside and outside in the drawings of the present application, which are explained here.

[0045] The present embodiment provides an energy collaborative power transmission method based on source network load storage as shown in Figs. 1-3 The method comprises the following steps:

[0046] S1: Use the intelligent prediction and scheduling module to combine machine learning and deep learning algorithms to predict the power generation capacity (traditional energy and renewable energy) on the power supply side, and realize dynamic scheduling of the power system according to the load demand prediction results;

[0047] S2: Deploy high-efficiency energy storage units such as lithium batteries, flow batteries, etc. in appropriate locations to store excess electricity and smooth out new energy fluctuations and meet peak demand;

[0048] S3: Design a multi-source hybrid power supply mode that integrates wind, solar, and hydro power, optimizes the proportion of different energy sources and geographical conditions, and ensures stable power supply;

[0049] S4: Build an intelligent monitoring and management platform to monitor the status of the entire energy network in real time and automatically adjust operating parameters, optimize energy transmission paths, and improve the efficiency of the power system;

[0050] S5: Integrate distributed energy resources and connect home photovoltaic and community wind power stations to the main grid through microgrid technology to promote interaction between the main grid and distributed energy;

[0051] S6: Apply virtual power plant (VPP) technology to integrate dispersed power generation equipment, energy storage systems, and controllable loads into a whole as a single entity to participate in power market transactions, enhance energy utilization, and support the power grid.

[0052] In this embodiment, the process of running the intelligent prediction and scheduling module in S1 includes training historical data using machine learning and deep learning algorithms such as long short-term memory networks (LSTM) and convolutional neural networks (CNN) to predict future power generation on the power supply side. Combining weather forecasts, seasonal changes, and other factors can improve prediction accuracy. For load demand prediction, user behavior analysis, time series analysis, and other methods are used to estimate power consumption in different time periods. Based on this information, power generation plans are dynamically adjusted to ensure supply and demand balance.

[0053] In this embodiment, multi-modal data analysis is introduced based on S1, integrating meteorological data, power grid operation status, user power consumption patterns, and other sources of data to build more accurate prediction models. This method not only improves prediction accuracy but also better handles complex and variable power demand. For example, in cities, intelligent prediction combined with satellite cloud maps, ground weather station data, and historical power consumption records can predict power demand peaks during summer heatwaves several days in advance. Based on this prediction, power companies can arrange power generation plans in advance to reduce temporary peak shaving pressure. At the same time, the system can dynamically adjust the prediction results according to real-time weather changes to ensure the stability of power supply.

[0054] In this embodiment, reinforcement learning is used to optimize the scheduling strategy, and the generation plan is adjusted in real time to adapt to the rapidly changing market demand, thereby better supporting the operation of the virtual power plant (VPP). This method can find the optimal scheduling scheme by simulating the decision-making process under different scenarios. For example, in a virtual power plant (VPP), a reinforcement learning algorithm is used to manage multiple distributed energy resources (DERs) such as home photovoltaic systems, small wind turbines, and energy storage devices. Through continuous trial and error and learning, the algorithm learns to maximize the economic benefits of DERs in different time periods. For example, during the day when the light is sufficient, prefer to use photovoltaic power generation, and store the excess power in the battery; while at night, switch to wind power or other forms of renewable energy, to ensure stable power supply throughout the day while reducing costs.

[0055] In this embodiment, the operation mode of the high-efficiency energy storage unit in S2 includes selecting energy storage technologies suitable for local environmental conditions, such as lithium batteries for fast response requirements due to their high energy density, and flow batteries for long-term energy storage. Energy storage devices are deployed at key nodes of the power grid, such as near substations or next to new energy power stations. When new energy power generation is in excess, store the excess power; when demand peaks arrive, release the stored energy to smooth fluctuations and ensure stable power supply.

[0056] In this embodiment, an intelligent energy storage management module is developed based on S2, which can automatically monitor the status of energy storage equipment and intelligently charge and discharge according to the demand of the power grid, ensuring efficient use of the energy storage system. This method can automatically determine the best charging and discharging time according to factors such as electricity price fluctuations and load conditions. Assuming that a commercial park has installed a large lithium battery energy storage module, the intelligent energy storage management module will automatically charge at night during the low electricity price period and release the stored energy to support park power consumption during the day during the high electricity price period. In addition, when the power grid load is low, the module system will instruct the energy storage device to charge; while in the peak period, it will release the stored energy to support the power grid and smooth fluctuations. This intelligent management can significantly reduce the park's electricity bill while providing auxiliary services to the power grid.

[0057] In this embodiment, the thermal management system is explored to optimize battery performance and extend service life, especially for the safety and stability of energy storage equipment under extreme weather conditions. Good thermal management helps maintain the optimal working temperature range of the battery, preventing performance degradation or safety problems caused by overheating. For energy storage projects located in tropical regions, it is crucial to use a liquid cooling system to control the temperature of the battery pack. The liquid cooling system removes heat by circulating coolant, keeping the working temperature of the battery module around 25°C. This not only improves the charging and discharging efficiency of the battery, but also extends its service life. In contrast, traditional air cooling systems may not effectively cope with high temperature environments and are prone to cause battery overheating damage.

[0058] In this embodiment, the operation mode of the multi-source hybrid power supply mode in S3 includes planning the locations of wind farms, photovoltaic power stations, hydropower stations, etc. according to geographical environment and resource distribution, and connecting them to the power grid using advanced power electronic converters. Real-time monitoring of the output of each energy source is achieved using a power management system, which dynamically adjusts the proportion of each type of energy based on factors such as real-time electricity prices and weather conditions, ensuring optimal efficiency of system operation.

[0059] In this embodiment, an energy priority scheduling mechanism is implemented based on S3, which maximizes economic benefits while meeting environmental protection goals based on the availability and cost of different energy sources. This mechanism can dynamically adjust the proportion of each type of energy based on factors such as real-time market prices and weather conditions. For example, in a comprehensive energy demonstration project, the system integrates wind power, solar power, and natural gas power. When the wind is strong, wind power is preferred; when the sunlight is sufficient, photovoltaic power is switched to; and in the case of insufficient wind and light, natural gas power is started as a supplement. In addition, the system also considers changes in market electricity prices and selects to increase the proportion of renewable energy when the price is low, thereby maximizing economic benefits.

[0060] In this embodiment, a complementary energy model is constructed to analyze the interaction between different energy forms and develop the best energy combination strategy to enhance the flexibility and stability of the system. The complementary energy model can help identify the synergies between different energies and optimize the overall system operation. A structure that combines the advantages of wind power and solar power is designed, with solar power supplementing wind power when it is insufficient, and vice versa, ensuring continuous and stable power supply. For example, in an island microgrid project, wind power and solar power complement each other to provide power for island residents. Even if it encounters consecutive rainy days or windless weather, the system can rely on another energy form to maintain basic power supply and ensure the normal operation of residents' lives.

[0061] In this embodiment, the operation mode of the intelligent monitoring and management platform in S4 includes establishing a central control center integrated with a SCADA (Supervisory Control and Data Acquisition) system, which is responsible for collecting real-time data from various subsystems. Through data analysis software, these data are processed to identify potential problems and automatically send alerts or instructions to the corresponding devices for self-adjustment. In addition, a visual interface should be included for operators to intuitively understand the status of the entire system.

[0062] In this embodiment, an anomaly detection algorithm is introduced based on S4 to monitor the health status of the system in real time, providing early warning of potential failures and reducing downtime. The anomaly detection algorithm can establish a baseline of normal operation mode by analyzing a large amount of historical data, and immediately issue an alert if it deviates from the baseline.

[0063] In this embodiment, digital twin technology is applied to create a virtual mapping of the power grid, simulate the operation effect under different scenarios, assist decision-making process, and improve management efficiency. Digital twin technology can test new dispatching strategies or equipment upgrade schemes without interfering with the actual operation of the power grid, and evaluate their impact on the power grid.

[0064] In this embodiment, the operation mode of distributed energy resource integration in S5 includes encouraging residents to install household photovoltaic panels or small wind turbines and connecting them to the main grid through microgrid technology. The microgrid is equipped with necessary components such as inverters and transformers to ensure that distributed energy can be safely and reliably connected to the main grid. At the same time, a reasonable compensation mechanism is designed to encourage users to participate and promote the effective use of renewable energy.

[0065] In this embodiment, based on S5, a community-level energy sharing mechanism is promoted, and a P2P trading platform is used to directly exchange excess electricity between neighbors, forming a closer energy community. This can promote the effective use of local energy and reduce the loss caused by long-distance power transmission. For example, in a community, all households have installed household photovoltaic panels and implemented electricity sharing through a P2P trading platform within the community. When a household produces more electricity than it needs, it can sell it to other households that need it through the platform. This model not only increases residents' income but also promotes energy recycling within the community. In addition, the community has established a dedicated energy management center responsible for coordinating electricity transactions, managing and maintaining public facilities, and ensuring the smooth operation of the entire system.

[0066] In this embodiment, a flexible policy framework is designed to provide subsidies and support for distributed energy users, encouraging more people to participate in green energy production. By introducing preferential policies and providing technical support, individuals and enterprises are encouraged to invest in the construction of distributed energy facilities.

[0067] In this embodiment, the operation mode of virtual power plant (VPP) technology in S6 includes developing a comprehensive energy management module that can integrate scattered power generation equipment, energy storage systems, and controllable loads to form a logical "virtual power plant". Through information technology and communication technology, centralized management and optimal scheduling of these resources are achieved, enabling them to participate in power market transactions as a whole and provide auxiliary services such as frequency regulation and peak filling.

[0068] In this embodiment, the interaction ability of VPP with the market is strengthened based on S6, allowing the virtual power plant to adjust output in real time according to market prices and increase income sources. By participating in power market transactions, additional income is obtained, and auxiliary services are provided for the power grid. For example, the distributed energy resources of multiple enterprises are integrated into a virtual power plant. When the electricity market price rises, the virtual power plant quickly increases power generation and sells to the market to obtain higher income; and during the price trough period, it reduces output to save costs. In addition, the virtual power plant can also provide frequency regulation, peak filling and other auxiliary services for the power grid, further increasing income sources. For example, in a power grid frequency fluctuation event, the virtual power plant quickly responds to provide the necessary frequency regulation service and receives recognition and rewards from the power grid operator.

[0069] In this embodiment, a collaboration network between VPPs is established, and multiple virtual power plants can work together to cope with large-scale power demand and improve the flexibility of the overall power grid. Without affecting their independence, regional power supply and demand imbalance problems are solved.

[0070] In this embodiment, the method uses blockchain technology to build a decentralized energy trading platform, ensuring that transaction records are tamper-proof and transparent. The platform supports direct peer-to-peer (P2P) energy transactions between users, reducing the cost of intermediaries and promoting localized energy production and consumption. Specifically, the platform uses smart contracts to automate the transaction process, ensuring the security and immediacy of each transaction; at the same time, it uses encryption algorithms to protect user privacy and data security, preventing malicious attacks and information leaks. In addition, the blockchain platform can also integrate a reputation evaluation system to encourage users to comply with market rules and maintain a good trading order.

[0071] In this embodiment, a smart contract template library is introduced, allowing users to customize transaction rules according to their own needs and enhance the flexibility of the platform. A variety of preset contract templates are provided, and users only need to select the appropriate template and fill in the necessary parameters to complete complex transaction settings. On the blockchain energy trading platform, the development team has prepared a series of smart contract templates, such as fixed price contracts, floating price contracts, long-term power supply agreements, etc. Users can choose the contract type that suits them and adjust the specific terms according to the actual situation, such as transaction amount, delivery date, etc. This flexible design allows users to quickly reach a transaction, reducing the complex negotiation process. In addition, the platform also provides contract editing tools to help users create custom smart contracts to meet special needs.

[0072] In this embodiment, a cross-chain interoperability protocol is developed to enable seamless transfer of assets across different blockchain networks, expanding the scope of transactions. The compatibility problem between different blockchains is solved, and the free circulation of assets is realized.

[0073] In this embodiment, an AI-driven adaptive control protection mechanism is developed in the method, which can automatically adjust parameters based on real-time data to optimize performance and has self-learning ability to continuously improve the decision-making process. Combined with edge computing, this mechanism can handle a large amount of real-time data locally, quickly respond, and reduce the data transmission burden of the cloud. The AI-controlled module can continuously collect environmental variables, load changes, power quality indicators, and other multi-dimensional information, analyze them, and dynamically adjust the balance between power generation, energy storage, and load to ensure efficient and reliable operation of the power system. In addition, it can predict potential failures and take preventive measures in advance to improve system reliability and security.

[0074] In this embodiment, transfer learning technology is applied to enable the AI model to quickly adapt to different regions or different types of power systems, reducing deployment cycles. Useful knowledge can be extracted from existing models and applied to new environments to speed up training and improve accuracy.

[0075] In this embodiment, natural language processing (NLP) is integrated to enable the system to receive instructions through voice or text input, improving user experience. NLP technology makes human-computer interaction more natural and smooth, reducing user operation difficulty.

[0076] In this embodiment, based on S6, quantum computing is introduced to assist in solving large-scale optimization problems such as optimal power flow distribution. Quantum algorithms are written to speed up the solution speed through the cooperation of classical computers and quantum computers, and the optimal power transmission scheme is found. Quantum computers have powerful computing capabilities beyond classical computers, especially good at handling problems involving complex constraints and high-dimensional spaces. Through quantum algorithms, the solution speed can be significantly accelerated, providing more accurate results to better support energy system planning and operation management. For example, when dealing with large-scale power networks containing a large number of nodes, quantum computing can help find the optimal power transmission scheme to minimize energy loss and operating costs while maintaining system stability and reliability.

[0077] In this embodiment, quantum computing service providers are cooperated with to gain access to quantum computers. Special quantum algorithms are written for specific large-scale optimization problems such as optimal power flow distribution. Through the cooperation of classical computers and quantum computers, the solution speed is accelerated, and the optimal power transmission scheme is found. This helps to minimize energy loss and operating costs while maintaining system stability and reliability.

[0078] In this embodiment, a quantum heuristic algorithm is constructed, which can benefit from the concept of quantum computing even without complete quantum hardware, improving the performance of traditional algorithms. The algorithm draws on the basic principles of quantum mechanics, such as superposition and entanglement, and applies them to classical computers, significantly improving the efficiency of solving problems.

[0079] In this embodiment, we study how to combine quantum computing results with existing power system simulation tools to provide more accurate support for grid planning. In this way, we can take advantage of the powerful computing power of quantum computing and use mature simulation tools for detailed verification and optimization.

[0080] In this embodiment, we use flexible high-voltage direct current (HVDC-Flexible) technology to allow more flexible control of power transmission direction and quantity, reducing energy loss, increasing system stability and controllability, and effectively dealing with intermittent new energy output. It is particularly suitable for long-distance high-capacity power transmission and offshore wind farm access to land power grids. The HVDC-Flexible system is equipped with advanced converter technology and control systems, which can respond to changes in power demand within milliseconds, quickly adjust voltage and frequency, and ensure the safe and stable operation of the power grid. In addition, it has strong anti-interference ability and self-recovery function, and can maintain normal working state even in extreme weather conditions.

[0081] In this embodiment, we develop a modular multilevel converter (MMC) to improve the conversion efficiency and reliability of the HVDC system. The modular multilevel converter (MMC) is an advanced power electronic converter with high efficiency, low harmonic distortion, and other characteristics, widely used in high-voltage direct current transmission fields.

[0082] In this embodiment, we design a hybrid operation mode for HVDC and AC power grids, fully utilizing the advantages of both transmission methods to optimize power transmission paths. The hybrid operation mode can use the most suitable technology at different stages to improve overall transmission efficiency.

[0083] In this embodiment, we use environment-aware intelligent hardware devices that have built-in sensors that can automatically adjust their working mode according to changes in the surrounding environment (temperature T, humidity H, light intensity L, etc.) to achieve energy-saving purposes. The adjustment formula is as follows:

[0084] P adjusted =P base ×(1-α·T+β·H+γ·L)

[0085] where P adjusted is the adjusted power output, P baseis the base power output, and a, b, and g are the influence coefficients of temperature, humidity, and light intensity, respectively. This formula represents the specific impact of environmental factors on the device's power output: when the temperature rises, the power output will decrease accordingly (negative correlation); increasing humidity or light intensity may lead to an increase in power output (positive correlation). Coefficients a, b, and g can be calibrated according to specific application scenarios to ensure optimal energy-saving effects. The design of environment-aware intelligent hardware devices allows automatic adjustment of working modes according to changes in the surrounding environment, achieving energy-saving purposes while improving user experience. At the same time, these devices can accept user instructions through a remote configuration interface, further personalizing settings to meet the needs of different scenarios.

[0086] In this embodiment, an adaptive learning algorithm is introduced, allowing the device to automatically optimize parameter settings based on long-term usage habits, further improving energy-saving effects. The adaptive learning algorithm can identify user usage patterns by accumulating a large amount of running data and adjust the device's working parameters accordingly. For example, a smart air conditioner can automatically adjust the temperature setting value according to the user's daily routine, ensuring comfort and maximizing energy saving. For example, the air conditioning system will record the user's daily on-off time and set temperature, and predict future usage based on these data. If it is found that the user usually adjusts the temperature to a lower level when sleeping at night, the air conditioner will lower the temperature in the late afternoon to ensure that the room reaches the ideal temperature before sleeping. In addition, the system can also adjust the strategy according to seasonal changes, such as appropriately increasing the upper limit of temperature in winter and vice versa in summer, always maintaining the most comfortable indoor environment.

[0087] In this embodiment, a remote configuration interface is provided, allowing users to adjust the behavior of the device through a mobile application or web interface, increasing the convenience of use. The remote configuration interface provides an intuitive operation interface, allowing users to manage smart devices at home anytime, anywhere. Users can remotely control the lighting system at home through the mobile application, set the timing switch or brightness adjustment, and manage daily life conveniently. For example, before going on a trip, users can turn off all unnecessary electrical appliances through the application and set the smart socket to turn on the air purifier at a specific time to ensure fresh air when they return home. In addition, the application also supports voice assistant integration, allowing users to easily control devices through voice commands, such as "turn on the living room light" or "dim the bedroom light." To ensure information security, the application uses multiple encryption technologies and identity verification mechanisms to prevent unauthorized access.

[0088] This embodiment develops a zero-carbon emission regional energy solution for a specific geographical area, relying entirely on renewable energy and efficient energy storage units. It comprehensively considers all energy sources and technology combinations within the region to ensure a stable power supply under any circumstances, while minimizing the carbon footprint. The solution includes, but is not limited to, the rational layout of renewable energy sources such as solar, wind, hydro, and biomass energy, as well as the construction of corresponding energy storage facilities and the application of smart grid technologies, to achieve efficient energy conversion and storage, meet the region's electricity demand, and promote green and low-carbon development.

[0089] In this embodiment, regional energy policies are formulated to encourage businesses and individuals to invest in renewable energy projects, creating a virtuous cycle. Governments and relevant agencies can stimulate the enthusiasm of all sectors of society by introducing preferential policies and providing technical support. Public education activities are conducted to increase residents' awareness and support for the concept of zero carbon emissions, promoting participation from the whole society. Various forms of publicity activities are held to popularize knowledge of low-carbon living, guiding the public to change consumption habits and choose more environmentally friendly lifestyles.

[0090] In this embodiment, a self-healing smart grid architecture is designed in S1. When a local fault is detected, the grid can quickly isolate the faulty section, reconfigure the power transmission path, ensure normal operation, and minimize the risk of power outages. The recovery time calculation formula is as follows:

[0091]

[0092] Where T recovery D is the recovery time, S is the distance to the faulty section, C is a fixed constant representing additional time consumption, and D / S is the distance to the faulty section. This formula is used to estimate the time required from the occurrence of a fault to the restoration of normal power supply. D / S represents the time for fault location and isolation, while C includes additional factors such as communication delays and switching operations. This architecture, through a high-speed communication network and automated control system, enables real-time monitoring and rapid response to the power grid status, ensuring that even in the event of a fault, normal power supply can be quickly restored, protecting users' power experience. The self-healing smart grid architecture design allows the grid to quickly isolate the faulty section and reconfigure the power transmission path when a local fault is detected, minimizing the risk of power outages. Simultaneously, machine learning algorithms predict fault trends and schedule maintenance in advance, reducing the probability of actual faults, thus forming a smart grid system that combines self-repair and preventative maintenance.

[0093] In this embodiment, the principle of redundancy design is introduced, and backup equipment is set at key nodes to ensure that the system can maintain basic functions in any case. Redundant design can improve the reliability and disaster resistance of the system, and ensure the continuous operation of important facilities. For example: in the data center, the power distribution adopts the principle of redundant design, and sets up double-loop power supply and multiple UPS (uninterruptible power supply) units. When the main power supply fails, the standby power supply takes over immediately to ensure the normal operation of servers and other key equipment. In addition, the system is equipped with diesel generators as the ultimate backup to provide continuous power supply in extreme cases. This multi-level redundant design ensures that the data center will not be affected by power outages under any circumstances.

[0094] In this embodiment, machine learning algorithms are applied to predict failure trends and take preventive measures in advance to reduce the probability of actual failures. By analyzing historical failure data, future problem points can be predicted, and preventive measures can be taken in advance. For example: in a power plant, technicians analyzed ten years of equipment operation data, including temperature, pressure, vibration, and other parameters, using machine learning algorithms. Through long-term learning, the algorithm can identify signs of aging in some equipment and predict the time of future failure. Based on these predictions, technicians developed detailed maintenance plans and replaced aging components in advance to avoid sudden failures. In addition, the algorithm can automatically generate detailed diagnostic reports to help engineers quickly locate the cause of the failure and shorten the repair cycle. To ensure the accuracy of the algorithm, technicians regularly update the data set and retrain the algorithm to keep it up to date.

[0095] In this embodiment, the method also creates an energy collaborative control module based on big data analysis, which integrates big data analysis, cloud computing, and Internet of Things technology to collect, process, and analyze data from various links of the source, network, load, and storage. Based on the analysis results, it makes optimal decisions to guide energy production and consumption and ensure efficient operation of the system. The system can identify long-term trends and short-term fluctuations, predict future energy demand, optimize energy distribution strategies, and continuously improve its performance through feedback mechanisms. In addition, it supports cross-department collaboration to help energy suppliers, grid operators, and consumers establish closer cooperation to promote the sustainable development of the energy industry.

[0096] In this embodiment, a data lake is constructed to store and manage data from different sources, breaking down information silos and promoting data sharing. As a large-scale data storage solution, the data lake can accommodate structured and unstructured data, supporting a variety of analysis tools and technologies. The development of predictive maintenance technology can foresee the aging and failure risk of equipment through data analysis, arrange maintenance in a timely manner, reduce operating costs, and can detect potential problems in advance to avoid unexpected downtime, improve the availability and reliability of equipment.

[0097] An energy collaborative transmission system based on source network load storage is used to implement the steps of the method, comprising:

[0098] Intelligent prediction and scheduling module: used for predicting power generation on the power supply side and dynamically scheduling according to the demand on the load side;

[0099] Efficient energy storage unit: deployed in the required position, storing excess electrical energy and stabilizing new energy fluctuations;

[0100] Multi-source hybrid power supply module: integrates multiple renewable energy sources, optimizes the configuration ratio to ensure stable power supply;

[0101] Intelligent monitoring and management platform: real-time monitoring of energy network state, automatic adjustment of operating parameters, optimization of energy transmission path;

[0102] Virtual power plant (VPP): as a single entity participating in power market transactions, improving energy utilization and supporting grid capabilities;

[0103] Self-healing smart grid architecture: quickly responds to and repairs local faults to ensure stable operation of the power grid;

[0104] Energy transaction platform based on blockchain: ensures the security and transparency of transaction records, and promotes direct transactions between users;

[0105] Environmentally aware intelligent hardware device: automatically adjusts the working mode according to environmental changes to achieve energy-saving effect;

[0106] Zero-carbon emission regional energy solution: develops a solution that relies entirely on renewable energy and efficient energy storage units for specific regions, ensuring stable power supply while reducing carbon emissions.

[0107] In this embodiment, reference is made to Fig. 2As can be seen, (1) the multi-source hybrid power supply system is the energy inlet of the entire system, integrating power from different energy sources (such as solar energy, wind energy, and traditional power grids) and transmitting it to subsequent modules. (2) The intelligent prediction and scheduling module receives real-time data from the multi-source hybrid power supply system, analyzes it in combination with weather forecasts, historical data, and the like, predicts energy supply and demand in the future period, and then formulates an optimal energy scheduling plan. (3) The high-efficiency energy storage unit stores excess power or releases stored energy when needed according to the instructions of the intelligent prediction and scheduling module to balance supply and demand differences. (4) The distributed resource integration coordinates decentralized energy production points (such as household solar panels) to enable them to participate in overall energy management and improve resource utilization efficiency. (5) The virtual power plant (VPP) integrates multiple distributed energy resources to form a virtual power plant that can be centrally managed and scheduled, optimizing regional energy distribution. (6) The intelligent monitoring and management platform monitors and manages the running state of the entire system in real time to ensure that all components work efficiently according to the predetermined strategy, while providing fault warnings and maintenance recommendations.

[0108] In this embodiment, reference is made to Fig. 3 As can be seen, the input, processing, and output step flow structure of each individual module of the multi-source hybrid power supply system, intelligent prediction and scheduling module, high-efficiency energy storage unit, distributed resource integration, virtual power plant (VPP), and intelligent monitoring and management platform are finally summarized into a total system.

[0109] It should be noted that in this paper, relationship terms such as one and two are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the term "includes", "contains" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device. Without more limitations. The statement "includes a limited element" does not exclude the presence of additional identical elements in the process, method, article or device that includes the element.

[0110] Although embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A source network load storage-based energy collaborative transmission method, characterized in that, The method comprises the following steps: S1: Use an intelligent prediction and scheduling module to predict power generation on the power supply side using machine learning and deep learning algorithms, and implement dynamic scheduling of power based on load side demand prediction results; S2: Deploy high-efficiency energy storage units to store excess energy at the required location to smooth out new energy volatility and meet peak demand; S3: Design a multi-source hybrid power supply mode that integrates wind, solar, and hydro power, optimizes the proportion of different energy sources and geographical conditions, and ensures stable power supply; S4: Build an intelligent monitoring and management platform to monitor the status of the entire energy network in real time and automatically adjust operating parameters, optimize energy transmission paths, and improve the efficiency of the power system; S5: Integrate distributed energy resources by connecting home photovoltaic and community wind power stations to the main grid through microgrid technology to promote interaction between the main grid and distributed energy; S6: Apply virtual power plant technology to integrate dispersed power generation equipment, energy storage systems, and controllable loads into a single entity to participate in power market transactions, enhancing energy utilization and support for the grid; In the method, an AI-driven adaptive control protection mechanism is developed to automatically adjust parameters based on real-time data to optimize performance and continuously improve decision-making processes through self-learning; Based on S6, quantum computing is introduced to assist in solving large-scale optimization problems, and a quantum algorithm is developed to accelerate solution speed through collaboration between classical computers and quantum computers to find the optimal power transmission solution; Flexible HVDC technology is used to control the direction and amount of power transmission; Environmentally aware intelligent hardware devices are used to automatically adjust their operating modes based on changes in the surrounding environment to achieve energy-saving purposes, with the adjustment formula as follows: ， wherein, is the adjusted power output, is the base power output, and are the influence coefficients of temperature, humidity and light intensity, respectively; A zero-carbon emission regional energy solution that relies entirely on renewable energy and high-efficiency energy storage units is developed for geographical regions; All energy sources and technology combinations within the region are considered to ensure stable power supply while reducing carbon footprint; In S1, a self-healing smart grid architecture is designed to quickly isolate fault sections, reconfigure power transmission paths, and ensure normal operation to minimize the risk of power outages, with the recovery time calculation formula as follows: ， wherein is the recovery time, is the distance of the fault section, is the processing speed, is a fixed constant representing additional time consumption.

2. The method of claim 1, wherein the method is a source-grid-load-storage based energy coordinated transmission method. The method uses blockchain technology to build a decentralized energy trading platform to ensure tamper-proof and transparent transaction records.

3. The method of claim 2, wherein the method is a source-grid-load-storage based energy co-transmission method. The method also creates an energy coordination control module based on big data analysis, which integrates big data analysis, cloud computing, and IoT technology to collect, process, and analyze data from various aspects of the source, grid, load, and storage, make optimal decisions based on analysis results, guide energy production and consumption, and ensure efficient system operation.

4. A source-net-load-storage based energy co-transmission system, characterized in that, Steps for implementing the method of any one of claims 1-3, comprising: Intelligent prediction and scheduling module: for predicting power generation on the power supply side and dynamically scheduling based on load side demand; High-efficiency energy storage units: deployed at the required location to store excess energy and smooth out new energy fluctuations; Multi-source hybrid power supply module: integrates multiple renewable energy sources and optimizes the configuration ratio for stable power supply; Intelligent monitoring and management platform: real-time monitoring of energy network status, automatic adjustment of operating parameters; Virtual power plant: as a single entity participating in power market transactions; Self-healing smart grid architecture: quickly responds to and repairs local faults to ensure stable operation of the power grid; Blockchain-based energy trading platform: ensures the security and transparency of transaction records.

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