Intelligent monitoring and control system based on composite energy

Abstract

The invention provides an intelligent monitoring and control system based on composite energy, and relates to the technical field of energy monitoring. The intelligent monitoring and control system comprises a multi-energy acquisition and conversion module, a data acquisition and sensing network and a central intelligent controller which integrates an energy scheduling module based on reinforcement learning and deep learning algorithms, a virtual energy mapping module, a fault prediction and diagnosis module and a user interaction and control interface. The utilization efficiency of renewable energy sources (including solar energy and wind energy) is improved, particularly when the renewable energy sources are sufficiently supplied, the solar energy and the wind energy are preferentially used, and meanwhile surplus electric power is stored in the energy storage unit. When renewable energy sources are insufficient, fossil energy power generation is used as supplement. Through the energy optimization configuration, the system can greatly reduce the use of fossil energy, so that the carbon emission is reduced, and the goal of energy conservation and emission reduction is helped to be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of energy monitoring, and in particular to an intelligent monitoring and control system based on composite energy. Background Art

[0002] According to a method and system for intelligent collaborative control of power grid operation monitoring and source-grid-load-storage disclosed in Chinese patent publication number "CN112054555A", it discloses the establishment of a power grid system operation monitoring platform, whose structure is mainly divided into a data interaction and acquisition module and an operation status monitoring module. The combination of the two enhances the readability and operability of system data. The data monitored by the platform is calculated and analyzed through a composite intelligent algorithm of fuzzy theory, quantum behavior particle swarm algorithm and consistency algorithm, and the best collaborative control strategy is intelligently matched to achieve dynamic adjustment of the system. The data in the platform can be comprehensively and intuitively displayed through a visual output terminal, and finally the optimal result of the composite intelligent collaborative control unit is calculated, including load curves, power plant output, marginal costs and other information, and it has the function of downloading and saving intelligent collaborative control solutions.

[0003] The above patent documents and prior art have the following technical problems when used:

[0004] Problem 1: In traditional energy management systems, power supply often faces problems such as unstable output of renewable energy and high dependence on fossil energy generation, leading to an imbalance in energy supply and demand.

[0005] Problem two: Traditional energy management often lacks flexibility in energy production and storage, resulting in the inability to effectively store excess renewable energy or the inability to quickly dispatch available energy when energy demand peaks. The above-mentioned patents and similar professions have poor and incomplete adjustment plans for the comprehensive control of composite energy. Summary of the invention

[0006] In view of the deficiencies of the prior art, the present invention provides an intelligent monitoring and control system based on composite energy, which solves the technical problem of unbalanced energy supply and demand in the prior art.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0008] Intelligent monitoring and control system based on composite energy, which includes solar energy, wind energy, fossil energy and energy storage system, including:

[0009] Multi-energy collection and conversion module, used to connect different energy types to the energy management network through a multi-mode interconnection interface;

[0010] Data collection and sensor networks collect energy data including voltage, current, temperature, power output and environmental parameters in real time, and process the data locally through edge computing devices;

[0011] The central intelligent controller is used to integrate the energy scheduling module, virtual energy mapping module and fault prediction and diagnosis module based on reinforcement learning and deep learning algorithms; the energy scheduling module based on reinforcement learning and deep learning algorithms is used to dynamically adjust the output ratio of multiple energy sources according to user needs, weather forecasts, energy costs and equipment status; the virtual energy mapping module builds a virtual model of the physical energy system based on digital twin technology and simulates the operating status of the energy system in real time; the fault prediction and diagnosis module predicts possible abnormalities of the equipment through machine learning models and generates maintenance suggestions;

[0012] The user interaction and control interface provides users with interactive functions including real-time energy monitoring, intelligent scheduling control, energy-saving suggestions and energy consumption analysis through the cloud platform or mobile devices.

[0013] The multi-energy collection and conversion module includes a solar photovoltaic system, a wind power generator set, a fossil fuel power generation equipment and an energy storage unit.

[0014] It also includes drone and robot operation and maintenance units, which are deployed for inspection and maintenance tasks of energy equipment. They use machine vision and automatic navigation technology to detect the operating status of equipment and generate repair or replacement recommendations.

[0015] It also includes a cross-industry energy linkage module, which applies the system to multiple industry scenarios, including industrial production, transportation energy integration, agricultural energy-saving systems, etc., to maximize resource utilization through energy cascade utilization and intelligent regulation.

[0016] It also includes a blockchain and energy trading platform to achieve the following functions:

[0017] Multi-node energy trading and allocation based on smart contracts ensures the transparency and immutability of transactions;

[0018] The user carbon credit management system generates carbon credits based on the user's energy-saving behavior and supports their use in the virtual economic system.

[0019] The multi-energy collection and conversion module also includes an energy exchange and storage optimization unit, which realizes dynamic conversion and sharing of energy forms through an energy balance algorithm, and supports layered storage and discharge strategies of multiple energy sources to adapt to transient changes in load demand and dynamic optimization of peak and valley electricity prices.

[0020] The data acquisition and sensing network further integrates environmental prediction sensing units, including wind speed sensors, solar radiation intensity sensors and air quality sensors, combined with the built-in meteorological data interface of the central intelligent controller, to enhance the prediction accuracy of future energy production and load demand.

[0021] The virtual energy mapping module further includes a scenario simulation and optimization unit, which can simulate the implementation effects of different energy configuration strategies in a digital twin environment, determine the optimal solution for energy scheduling through a multi-objective optimization algorithm, and automatically generate an implementable scheduling strategy.

[0022] The drone and robot operation and maintenance unit further integrates a high-precision fault detection and repair unit, which uses thermal imaging technology, ultrasonic detection technology and a vision-based deep learning model to accurately locate subtle cracks in energy equipment, component aging and power loss.

[0023] The blockchain and energy trading platform further supports the construction of a regional energy super network, allowing independent communities or industrial parks to achieve cross-regional allocation and sharing of energy through blockchain protocols, optimizing overall energy distribution efficiency, and supporting each node to obtain transaction records and transparent display of network status in real time.

[0024] The present invention provides an intelligent monitoring and control system based on composite energy, which has the following beneficial effects:

[0025] 1. The present invention improves the utilization efficiency of renewable energy (including solar energy and wind energy) through the collaborative work of multiple energy sources, especially when the supply of renewable energy is sufficient, solar energy and wind energy are used first, and the excess electricity is stored in the energy storage unit. When renewable energy is insufficient, fossil energy is used for power generation as a supplement. Through this energy optimization configuration, the system can significantly reduce the use of fossil energy, thereby reducing carbon emissions and helping to achieve energy conservation and emission reduction goals. In addition, the system supports a low-carbon priority mode. In this mode, the scheduling algorithm gives priority to energy sources with low carbon emissions, further reducing the carbon footprint of the community or industrial park.

[0026] 2. The present invention integrates solar energy, wind energy, fossil energy and energy storage units, and utilizes multi-energy collection and conversion modules to dynamically adjust the output ratio of various energy sources according to real-time data and load demand. The central intelligent controller uses reinforcement learning and deep learning algorithms to optimize energy scheduling based on multiple factors such as weather forecasts, energy costs, and equipment status. Especially during peak electricity price periods, intelligent scheduling gives priority to the use of stored electricity or renewable energy, thereby reducing dependence on external power grids and energy costs. In addition, the energy balance algorithm of the energy storage system supports storing electricity during low electricity price periods and discharging during high electricity price periods, effectively reducing energy consumption costs.

[0027] 3. The integration of the blockchain and energy trading platform of the present invention makes the energy trading process transparent and tamper-proof. Through smart contracts, the system can realize multi-node energy trading and allocation, ensuring fair transactions between energy suppliers and demanders. In particular, with the support of regional energy super networks, independent communities or industrial parks can achieve cross-regional energy allocation and sharing through blockchain protocols, thereby maximizing the utilization efficiency of energy resources. Each node can obtain transaction records and network status in real time, which increases the transparency and trust of the system and promotes energy interconnection and optimized allocation between different regions. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 is a load and energy output curve diagram of the present invention;

[0029] Figure 2 A comparison histogram of the present invention and the traditional solution;

[0030] Figure 3 A comparison chart of battery SOC variation trends of the present invention;

[0031] Figure 4 This is an energy efficiency comparison diagram of the present invention. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Specific embodiment one:

[0034] As shown in the figure, the intelligent monitoring and control system based on composite energy, including solar energy, wind energy, fossil energy and energy storage system, includes the following components:

[0035] Multi-energy collection and conversion module: including solar photovoltaic systems, wind power generators, fossil fuel power generation equipment and energy storage units. The multi-energy collection and conversion module connects different energy types to the energy management network through a multi-mode interconnection interface;

[0036] Multi-energy collection and conversion module:

[0037]

[0038] P total :Total output power, used to meet load demand; by calculating the power output of each energy module, determine the current energy supply and demand balance; Total renewable energy output power, including the real-time output of all solar photovoltaic arrays and wind turbines; P renewable,i : Real-time power output of the i-th renewable energy source; P storage : Energy storage system power output or input (positive value indicates discharge, negative value indicates charge); P load : Current load demand power;

[0039] The multi-energy collection and conversion module includes a solar photovoltaic system: including solar panels, MPPT controllers and inverters, which are responsible for converting solar energy into direct current, and then outputting alternating current through the inverter. Wind turbine generator sets: including wind turbines, generators and rectifiers, which convert wind energy into electrical energy and output it stably. Fossil energy power generation equipment: including gas or diesel generator sets, mainly used as auxiliary energy. Energy storage system: lithium battery: provides fast response short-term energy replenishment. Thermal energy storage device: stores excess energy and releases it when demand is low. Each energy module operates in real time according to environmental conditions. The photovoltaic panel tracks the maximum power point through the MPPT controller and outputs electrical energy. The wind turbine generator automatically adjusts the output according to the wind speed and speed. The energy storage system charges or discharges to balance supply and demand according to the instructions of the central intelligent controller. The multi-mode interconnection interface transmits the collected real-time power, voltage and equipment status data to the data acquisition and sensing network.

[0040] Energy collection and conversion: Each energy module (solar energy, wind energy, fossil energy) converts energy into electrical energy through a collection device, and the energy storage module assists in regulating energy; Data collection and transmission: Sensors and edge computing devices monitor and process energy module operation data in real time; Intelligent optimization and scheduling: The central intelligent controller executes the scheduling algorithm based on the collected data, optimizes energy distribution, monitors faults and generates operation instructions; Execution and feedback: Each module adjusts its operation according to the instructions and feeds back the status to the control center in real time; Drone and robot operation and maintenance: Regularly inspect equipment to automatically discover and repair faults; Energy trading and sharing: The blockchain platform records energy trading, points and distribution information;

[0041] Data collection and sensor network: real-time collection of energy data including voltage, current, temperature, power output and environmental parameters, and local processing of data through edge computing devices;

[0042] The data smoothing formula includes the following:

[0043] D filtered (t) = α·D raw (t)+(1-α)·D filtered (t-1)

[0044] D fiktered (t): smoothed data output at time t; D raw(t): original data at time t; α: smoothing factor, which controls the degree of data smoothing, usually with a value of 0.1≤α≤0.5;

[0045] The anomaly detection formula includes the following:

[0046] S anomaly =∥X current -X PCA recon ∥

[0047] S anomaly : Anomaly score, indicating the degree to which the data deviates from the normal pattern; X current : The data point currently collected, X PCA_recon : Normal data points reconstructed based on principal component analysis (PCA).

[0048] Data acquisition and sensor network Electrical sensors: monitor electrical parameters such as voltage, current, and power. Environmental sensors: including temperature and humidity sensors, anemometers, and solar radiation sensors. Edge computing devices: Embedded processors (ARM architecture): responsible for preliminary data processing. Industrial gateways: realize data upload and protocol conversion (support MQTT, Modbus). Communication modules: Ethernet module: used for stable data transmission. Wireless module (Wi-Fi / G): suitable for dynamic scenarios. Sensors collect operating data (voltage, current, power, etc.) of each energy module in real time. The edge device runs a data smoothing algorithm (including weighted moving average method) to denoise the data. The data is transmitted to the central intelligent controller, and the system sorts the data according to the timestamp.

[0049] Central intelligent controller: Integrates the following parts:

[0050] An energy scheduling module based on reinforcement learning and deep learning algorithms, which is used to dynamically adjust the output ratio of multiple energy sources according to user demand, weather forecast, energy cost and equipment status;

[0051] The virtual energy mapping module builds a virtual model of the physical energy system based on digital twin technology and simulates the operating status of the energy system in real time;

[0052] Fault prediction and diagnosis module, which uses machine learning models to predict possible equipment anomalies and generate maintenance recommendations;

[0053] User interaction and control interface: Provide users with interactive functions including real-time energy monitoring, intelligent scheduling control, energy-saving suggestions and energy consumption analysis through cloud platforms or mobile devices, and support

[0054] Support users to choose different energy optimization modes, including "low-carbon priority mode" and "cost priority mode".

[0055] The energy dispatch formula includes the following:

[0056] R=-(Energy_Cost+λ·CO2_Emission)

[0057] R: reward function value, used to optimize the scheduling strategy; Energy_Cost: current energy usage cost; CO2_Emission: carbon emissions; λ: trade-off coefficient, balancing economic costs and environmental protection goals.

[0058] The virtual energy mapping formula includes the following:

[0059] E virtual =f(E real ,C env ,C op )

[0060] E virtual :Energy status in virtual environment; E real : Real-time data of physical equipment; C env : Environmental conditions (temperature, humidity, etc.); C op : Operation parameters (scheduling mode).

[0061] The central intelligent controller includes: High-performance industrial servers: used to run complex algorithms (including reinforcement learning, deep learning). GPU / TPU: accelerate the training and reasoning of deep learning models. Database: used to store historical data and model results. Receive collected data and divide it into two parts: real-time scheduling data. Historical data archive for prediction and model optimization.

[0062] Scheduling Optimization:

[0063] Call the reinforcement learning module to evaluate the reward function of the current energy allocation strategy and adjust the output ratio of solar energy, wind energy and energy storage according to the results.

[0064] Virtual energy mapping: Use digital twin technology to build a virtual model of the energy system and simulate the effects of different scheduling strategies.

[0065] Fault prediction: Analyze time series data through LSTM model, identify abnormal trends and generate maintenance recommendations

[0066] The intelligent monitoring and control system further includes drone and robot operation and maintenance units: deployed for inspection and maintenance tasks of energy equipment, detecting the operating status of equipment through machine vision and automatic navigation technology, and generating repair or replacement recommendations.

[0067] Path optimization formula for drone and robot operation and maintenance units:

[0068]

[0069] Cost(P): path cost; d(P i ,P i+1 ): The distance between inspection points.

[0070] The UAV and robot operation and maintenance unit includes UAV: ​​Thermal imaging camera: detects hot spots of energy equipment. GPS positioning module: ensures accurate inspection path. Robot: LiDAR: used for positioning and environmental modeling. Modular maintenance tools: different maintenance tool heads can be replaced. Control center: equipped with real-time monitoring and task allocation system. The control center assigns UAVs and robots to perform tasks according to the inspection plan. UAVs collect images and thermal imaging data of equipment and automatically identify hot spots or cracks. Ground robots repair faults under control instructions, including replacing wind turbine blades and repairing photovoltaic modules.

[0071] The intelligent monitoring and control system further includes a cross-industry energy linkage module: the system is applied to multiple industry scenarios, including industrial production, transportation energy integration, agricultural energy-saving systems, etc., to maximize resource utilization through energy cascade utilization and intelligent regulation.

[0072] The intelligent monitoring and control system further includes a blockchain and energy trading platform: used to achieve the following functions:

[0073] Multi-node energy trading and allocation based on smart contracts ensures the transparency and immutability of transactions;

[0074] The user carbon credit management system generates carbon credits based on the user's energy-saving behavior and supports their use in the virtual economic system.

[0075] The smart contract formula of blockchain and energy trading platform includes the following:

[0076] Energy transferred =min(Demand receiver ,Supply provider )

[0077] Energy transferred : The amount of energy actually traded; Demand receiver :Energy demand of the receiver; Supply provider :Energy supply of the provider.

[0078] Blockchain and energy trading platform distributed ledger nodes: each regional energy management center runs a blockchain node; smart contract processor: executes energy trading smart contracts; communication module: supports high-speed data synchronization; energy suppliers and demanders submit transaction requests through smart contracts; blockchain platform matches supply and demand and records transactions; the system generates carbon credits and synchronizes them to user accounts.

[0079] The multi-energy collection and conversion module includes an energy exchange and storage optimization unit, which realizes the dynamic conversion and sharing of energy forms through the energy balance algorithm, and supports the layered storage and discharge strategies of multiple energy sources to adapt to the transient changes in load demand and the dynamic optimization of peak and valley electricity prices.

[0080] The core of the multi-energy collection and conversion module is to achieve efficient energy conversion and management between solar energy, wind energy, fossil energy and energy storage systems. To this end, we designed an energy exchange and storage optimization unit, whose core goal is to optimize the overall energy efficiency by dynamically adjusting energy output and storage methods. In this module, the energy balance algorithm is responsible for allocating energy among various energy sources and dynamically selecting the optimal energy combination based on load demand and system status. This process takes into account multiple factors, including the current energy production capacity, the charging and discharging status of the energy storage system, real-time load demand, and energy price fluctuations.

[0081] Dynamic energy conversion and sharing: Through the charging and discharging functions of the energy storage unit, fossil energy is used to generate electricity when renewable energy is insufficient. At the same time, excess energy is stored preferentially when renewable energy is sufficient.

[0082] Layered storage and discharge strategy: The energy storage system supports different levels of energy storage (including high-efficiency storage and conventional storage). When a rapid response to load changes is required, high-efficiency storage units are mobilized first to avoid frequent charging and discharging of conventional energy storage units.

[0083] Peak-valley electricity price optimization: When electricity prices are low, the charging efficiency of the energy storage unit is increased, and when electricity prices are high, the energy storage system is used first to supply power, thereby optimizing the overall electricity cost.

[0084] This module implements energy scheduling through the following formula:

[0085] P total (t) = P solar (t)+P wind (t)+P fossil (t)+P battery (t)

[0086] in:

[0087] P total (t) is the total output power at time t; P solar (t) is the solar output, P wind (t) is the wind energy output; P fossil (t) is the fossil energy output; P battery (t) is the output of the energy storage system.

[0088] For the energy storage part, the discharge power calculation formula includes the following:

[0089] P battery (t) = min(P battery,max ,SOC(t)·C battery )

[0090] Among them, P battery,max is the maximum discharge power, SOC(t) is the current state of charge of the battery, C battery is the battery capacity.

[0091] The data acquisition and sensing network further integrates environmental prediction sensing units, including wind speed sensors, solar radiation intensity sensors and air quality sensors, combined with the built-in meteorological data interface of the central intelligent controller to enhance the prediction accuracy of future energy production and load demand.

[0092] Data acquisition and sensor network In order to accurately predict future energy production and load demand, the data acquisition and sensor network not only relies on basic electrical parameters (including voltage, current, and power), but also enhances the prediction accuracy of future energy production and demand by integrating environmental prediction sensor units. In this system, multiple environmental sensors are integrated, including: Wind speed sensor: used to collect wind speed data in real time to predict wind power generation potential. Solar radiation intensity sensor: real-time monitoring of solar radiation intensity for adjusting solar power generation prediction. Air quality sensor: monitors changes in surrounding air quality as a reference for system environmental impact. Meteorological data interface: This interface obtains detailed data on future weather, including wind speed, solar radiation, temperature, etc. by connecting to external meteorological forecast services. The data acquisition and sensor system will perform preliminary analysis and processing on the collected data through edge computing devices, reducing the burden of cloud data transmission. By combining environmental data with historical load demand data and using time series analysis methods for prediction, future energy demand can be more accurately assessed and energy scheduling strategies can be optimized.

[0093] The virtual energy mapping module further includes a scenario simulation and optimization unit, which can simulate the implementation effects of different energy configuration strategies in a digital twin environment, determine the optimal solution for energy scheduling through a multi-objective optimization algorithm, and automatically generate an implementable scheduling strategy.

[0094] Virtual Energy Mapping and Scenario Simulation Module The virtual energy mapping module uses digital twin technology to build a virtual model of the physical energy system, simulates the operating status of the entire system in real time, and helps the decision-making system make the best energy scheduling decision. By digitally modeling all components and parameters of the actual energy system, the performance of each component can be monitored and evaluated in real time. The scenario simulation and optimization unit in this module provides a variety of energy configuration solutions based on simulation results and multi-objective optimization algorithms (including genetic algorithms, particle swarm optimization algorithms, etc.). The optimization process not only considers the maximization of energy production, but also considers factors such as energy cost, system load balance, and environmental impact to ensure that the scheduling strategy can achieve the best under different operating conditions. The objective function is to minimize the comprehensive energy cost:

[0095]

[0096] C solar (t),C wind (t), C battery (t) and C fossil (t) are the costs of solar energy, wind energy, energy storage system and fossil energy at time t; T is the optimization time period. In addition, the multi-objective optimization algorithm can optimize multiple objectives at the same time (minimizing cost, minimizing carbon emissions, maximizing load response, etc.), and generate multiple scheduling strategies through different weight combinations for the intelligent controller to choose.

[0097] The drone and robot operation and maintenance unit further integrates a high-precision fault detection and repair unit. Through thermal imaging technology, ultrasonic detection technology and vision-based deep learning models, it can accurately locate subtle cracks in energy equipment, component aging and power loss, and can be equipped with optional modular maintenance tools to achieve rapid on-site repairs.

[0098] The drone and robot operation and maintenance unit is responsible for the inspection and maintenance of energy equipment. By combining machine vision, thermal imaging technology, ultrasonic detection and deep learning models, it can efficiently and accurately identify and locate equipment faults. Specific functions include the following: Thermal imaging technology: used to detect equipment overheating, potential circuit shorts or component damage. Ultrasonic detection: used to detect cracks, aging, corrosion and other problems on the surface or inside of the equipment. Visual recognition: analyze the appearance of the equipment based on the deep learning model to identify tiny cracks, corrosion or other damage. Modular repair tools: Based on the test results, the system will guide the operation and maintenance robot to use special repair tools for rapid on-site repair. Drones or robots inspect equipment according to preset paths. Use thermal imaging and ultrasonic detection technology to obtain the real-time status of the equipment. Deep learning models analyze the equipment status to determine whether there is a potential fault. If the fault is identified, the system automatically generates a repair plan and repairs it through modular repair tools.

[0099] The blockchain and energy trading platform further supports the construction of a regional energy super network, allowing independent communities or industrial parks to achieve cross-regional allocation and sharing of energy through blockchain protocols, optimizing overall energy distribution efficiency, while supporting each node to obtain transaction records in real time and transparently display network status.

[0100] The blockchain and energy trading platform are used to achieve transparent trading, allocation and sharing of energy. The platform ensures the fairness and transparency of energy trading through the smart contract mechanism, and ensures the immutability of data through decentralized storage. Specific functions include: Multi-node energy trading: Transaction settlement is carried out through smart contracts, and direct transactions can be carried out between nodes to ensure the free flow of energy. Carbon credit management system: Carbon credits are automatically generated according to the user's energy-saving behavior, and users can use the credits for energy trading, discounts or other rewards. Regional energy super network construction: Allow multiple independent communities or industrial parks to achieve cross-regional energy allocation and sharing through blockchain protocols, thereby maximizing the use of energy resources in each region. Transparent display: All transaction records, energy status, network status and other information are transparently displayed through blockchain technology to enhance the trust of the system. Users conduct energy transactions through the platform and select the required energy type and quantity. Smart contracts automatically execute transactions to ensure the transparency and immutability of transactions. After the transaction is completed, the carbon credit system automatically generates carbon credits based on the user's energy-saving behavior and supports credit redemption or trading. The platform provides real-time transaction records and energy distribution for each node to view, ensuring the openness and transparency of the entire system. Specific embodiment 2:

[0102] As shown in the figure, based on the dynamic load demand, the performance of the intelligent scheduling scheme and the traditional scheduling scheme (fossil energy priority) of the composite energy system is compared, and the system advantages are evaluated from the following indicators: energy cost, carbon emissions, system energy efficiency, load satisfaction rate, and priority use of renewable energy. Through the reinforcement learning algorithm, energy storage and fossil energy are dynamically scheduled. While meeting the dynamic load demand, the cost, emission and performance of the two schemes are compared.

[0103] Further practical applications include the following:

[0104] Application 1: Intelligent energy management system for industrial parks:

[0105] The industrial park consists of many factories, and the energy consumption in the park is relatively large, especially the power demand fluctuates greatly during the peak production period. In order to optimize energy use and reduce costs, the park decided to adopt an intelligent energy management system, which combines solar energy, wind energy, fossil energy and energy storage systems to improve the utilization rate of renewable energy, reduce the proportion of reliance on fossil energy, and optimize energy costs through intelligent scheduling.

[0106] First, the park installed a solar photovoltaic system and a wind turbine to provide renewable energy, and configured a natural gas generator as a backup power source to ensure that peak demand is met when renewable energy is insufficient. An energy storage system is also deployed in the park to store excess solar and wind power during the day to avoid energy waste, and release the stored power during peak demand to reduce dependence on the external power grid.

[0107] In the operation of the system, the central intelligent controller uses reinforcement learning algorithms to dynamically adjust energy output based on real-time data of various energy sources and park load requirements. The controller can perform intelligent scheduling based on weather changes, equipment status, and electricity market prices. For example, it increases the charging capacity of the energy storage system when electricity prices are low, and gives priority to using the power of the energy storage system to supply power when electricity prices are high. Through digital twin technology, the system can also simulate the operating status of the energy system in real time, evaluate the effects of different scheduling strategies, and generate the optimal energy allocation plan. In addition, environmental data such as wind speed, solar radiation, and air quality are collected in real time through sensors to help the system accurately predict the production potential of renewable energy and further optimize the scheduling strategy.

[0108] To ensure the stable operation of the system, the park has also deployed drone and robot operation and maintenance systems. Drones regularly inspect the wind turbines and solar photovoltaic arrays in the park, and detect equipment anomalies, including overheating, cracks or aging, through thermal imaging and visual recognition technology. Robots are responsible for on-site fault repair and can perform modular repairs when problems are detected, improving maintenance efficiency and reducing equipment downtime.

[0109] In addition, enterprises in the park can realize energy transactions through the blockchain platform, sell their own excess renewable energy to other enterprises, or purchase energy from other enterprises. The smart contract mechanism ensures the transparency and immutability of transactions, and improves the efficiency of energy flow between enterprises in the park. At the same time, the carbon credit system generates points based on the energy-saving performance of each enterprise, encouraging enterprises to actively participate in energy conservation and low-carbon emissions.

[0110] After the implementation of this system, the utilization rate of renewable energy in the park has been significantly improved, and energy costs have been greatly reduced, especially during peak electricity price periods. Through intelligent scheduling, the energy storage system operates efficiently, balances energy supply and demand, and avoids excessive reliance on fossil energy. The drone and robot operation and maintenance system ensures the efficient operation of equipment, reduces downtime, and improves the production efficiency of the park. At the same time, the energy trading and sharing platform promotes energy exchange between enterprises in the park and optimizes the overall energy distribution efficiency. In the end, the park not only reduced energy consumption, but also achieved a significant reduction in carbon emissions.

[0111] Application 2: Community Smart Energy Management System:

[0112] Urban residential communities, which originally relied on traditional power grids for energy, have decided to adjust their energy structure and build a green community based on an intelligent energy management system in the face of rising electricity prices and environmental protection pressures. The goal is to reduce electricity bills and carbon emissions by maximizing the use of renewable energy and improving the intelligence of energy scheduling.

[0113] To achieve this goal, solar photovoltaic panels and micro wind turbines are installed in the community as the main source of renewable energy. In addition, an energy storage system is also configured to store excess electricity during the day for emergency use. The power load in the community fluctuates greatly, especially during peak hours. The core task of the intelligent energy management system is to balance and optimize the scheduling in this fluctuation.

[0114] Through data collection and sensor networks, the system monitors the electricity usage of each household in real time, and obtains real-time information such as solar radiation intensity and wind speed through environmental sensors, combined with weather forecast data, to predict future energy production and load demand. The central intelligent controller dynamically adjusts the output of various types of energy based on these data through reinforcement learning and deep learning algorithms. For example, when electricity prices are low, the system will give priority to charging through the energy storage system, and when electricity prices are high, it will give priority to using stored electricity to reduce residents' electricity bills. At the same time, the system also uses digital twin technology to create a virtual model of the community energy system, simulate the effects of different energy scheduling strategies in real time, and provide the best energy distribution plan.

[0115] Residents in the community can participate in energy trading through the blockchain platform, selling their own excess solar power or purchasing other residents' surplus power. Each resident's energy-saving behavior will receive reward points through the carbon credit management system, and the points can be exchanged for energy, discounts or other rewards on the platform. The smart contract of the blockchain platform ensures the transparency and immutability of all transactions, ensuring the fairness and security of transactions.

[0116] To ensure the long-term stable operation of the system, the community has also deployed drones and robots to conduct equipment inspection and maintenance. Drones regularly inspect solar panels and wind turbines, using thermal imaging and visual recognition technology to quickly identify potential faults or damage. Robots can perform on-site repairs to ensure the efficient operation of equipment and handle any faults in a timely manner to avoid downtime affecting energy supply.

[0117] Through the implementation of this intelligent energy management system, the proportion of renewable energy used in the community has been greatly increased, and electricity bills have been greatly reduced, especially during peak hours, the cost of electricity consumption has been effectively controlled. Through intelligent scheduling, the operation of the energy storage system is more efficient, and the community can store electricity when electricity demand is low and use the stored electricity reasonably when demand is high. The drone and robot inspection system ensures the stable operation of the equipment and reduces the cost of maintenance and overhaul. At the same time, the deployment of the blockchain trading platform makes energy transactions more transparent, promotes energy sharing among residents in the community, and further improves the overall energy utilization efficiency. In the end, while achieving green energy-saving goals, the community also reduces carbon emissions and becomes a more sustainable living environment.

[0118] It should be noted that, in this article, relational terms such as including first and second, etc. 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 these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "including a reference structure" do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0119] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent monitoring and control system based on composite energy, wherein the composite energy includes solar energy, wind energy, fossil energy and energy storage system, characterized in that: include: Multi-energy collection and conversion module, used to connect different energy types to the energy management network through a multi-mode interconnection interface; Data collection and sensor networks collect energy data including voltage, current, temperature, power output and environmental parameters in real time, and process the data locally through edge computing devices; The central intelligent controller is used to integrate the energy scheduling module, virtual energy mapping module and fault prediction and diagnosis module based on reinforcement learning and deep learning algorithms; the energy scheduling module based on reinforcement learning and deep learning algorithms is used to dynamically adjust the output ratio of multiple energy sources according to user needs, weather forecasts, energy costs and equipment status; the virtual energy mapping module builds a virtual model of the physical energy system based on digital twin technology and simulates the operating status of the energy system in real time; the fault prediction and diagnosis module predicts possible abnormalities of the equipment through machine learning models and generates maintenance suggestions; The user interaction and control interface provides users with interactive functions including real-time energy monitoring, intelligent scheduling control, energy-saving suggestions and energy consumption analysis through the cloud platform or mobile devices.

2. The intelligent monitoring and control system based on composite energy according to claim 1 is characterized in that: The multi-energy collection and conversion module includes a solar photovoltaic system, a wind power generator set, a fossil fuel power generation equipment and an energy storage unit.

3. The intelligent monitoring and control system based on composite energy according to claim 1 is characterized in that: It also includes drone and robot operation and maintenance units, which are deployed for inspection and maintenance tasks of energy equipment. They use machine vision and automatic navigation technology to detect the operating status of equipment and generate repair or replacement recommendations.

4. The intelligent monitoring and control system based on composite energy according to claim 1 is characterized in that: It also includes a cross-industry energy linkage module, which applies the system to multiple industry scenarios, including industrial production, transportation energy integration, agricultural energy-saving systems, etc., to maximize resource utilization through energy cascade utilization and intelligent regulation.

5. The intelligent monitoring and control system based on composite energy according to claim 1 is characterized in that: It also includes a blockchain and energy trading platform to achieve the following functions: Multi-node energy trading and allocation based on smart contracts ensures the transparency and immutability of transactions; The user carbon credit management system generates carbon credits based on the user's energy-saving behavior and supports their use in the virtual economic system.

6. The intelligent monitoring and control system based on composite energy according to claim 1 is characterized in that: The multi-energy collection and conversion module also includes an energy exchange and storage optimization unit, which realizes dynamic conversion and sharing of energy forms through an energy balance algorithm, and supports layered storage and discharge strategies of multiple energy sources to adapt to transient changes in load demand and dynamic optimization of peak and valley electricity prices.

7. The intelligent monitoring and control system based on composite energy according to claim 1 is characterized in that: The data acquisition and sensing network further integrates environmental prediction sensing units, including wind speed sensors, solar radiation intensity sensors and air quality sensors, combined with the built-in meteorological data interface of the central intelligent controller, to enhance the prediction accuracy of future energy production and load demand.

8. The intelligent monitoring and control system based on composite energy according to claim 1 is characterized in that: The virtual energy mapping module further includes a scenario simulation and optimization unit, which can simulate the implementation effects of different energy configuration strategies in a digital twin environment, determine the optimal solution for energy scheduling through a multi-objective optimization algorithm, and automatically generate an implementable scheduling strategy.

9. The intelligent monitoring and control system based on composite energy according to claim 3 is characterized in that: The drone and robot operation and maintenance unit further integrates a high-precision fault detection and repair unit, which uses thermal imaging technology, ultrasonic detection technology and a vision-based deep learning model to accurately locate subtle cracks in energy equipment, component aging and power loss.

10. The intelligent monitoring and control system based on composite energy according to claim 5, characterized in that: The blockchain and energy trading platform further supports the construction of a regional energy super network, allowing independent communities or industrial parks to achieve cross-regional allocation and sharing of energy through blockchain protocols, optimizing overall energy distribution efficiency, and supporting each node to obtain transaction records and transparent display of network status in real time.

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