Hybrid management system for renewable energies using IoT and AI to ensure grid stability

Abstract

Hybrid management system for renewable energies (100) using IoT and AI to ensure grid stability, including: a variety of renewable energy sources, including at least one of the following: solar, wind or biomass plants; one or more IoT-enabled sensors and controllers configured to collect real-time data regarding energy generation, consumption, storage capacity, and network parameters; a communication module for transmitting the recorded data to a central processing unit; an artificial intelligence module (AI module) that is operationally coupled with the central processing unit, wherein the AI ​​module is configured to: forecasts the generation and demand for renewable energy based on historical and real-time data; the energy distribution between consumption, storage and grid supply is optimized; and performs a dynamic load distribution to prevent network instability; a control unit configured to perform adaptive control of renewable energy sources and storage units based on the outputs of the AI ​​module; the system (100) provides real-time monitoring, intelligent decision-making and predictive maintenance to ensure efficient use of renewable energy and improved reliability of the power grid.

Description

[0001] The present invention relates to the field of renewable energy systems and intelligent energy management. More specifically, it is a hybrid management system for renewable energies that integrates the Internet of Things (IoT) and artificial intelligence (AI) to optimize energy generation, storage, and distribution, with a focus on improving grid stability and reliability.

[0002] The globally increasing demand for clean and sustainable energy has accelerated the integration of renewable energy sources such as solar, wind, and biomass into modern power grids. However, the intermittent and unpredictable nature of renewable energy generation poses a significant challenge to maintaining grid stability, reliability, and efficiency. Conventional grid systems are primarily designed for centralized power plants and often lack the necessary flexibility to handle variable energy feed-ins and fluctuating demand. Existing renewable energy management solutions rely heavily on manual control or basic automation, which is insufficient for real-time resource optimization. Furthermore, limitations in forecasting, load balancing, and fault detection frequently lead to inefficiencies, energy losses, and even grid instability.While IoT-based monitoring systems now exist, they typically only serve for data collection without intelligent decision-making. Similarly, AI models have been independently investigated for energy forecasting or optimization, but rarely seamlessly integrated into IoT-enabled infrastructure for dynamic grid management. Therefore, there is a need for a hybrid renewable energy management system that intelligently combines IoT-based real-time monitoring with AI-driven analysis and decision-making. Such a system can improve grid stability by forecasting demand and generation, optimizing energy storage, facilitating load balancing, and ensuring the efficient use of hybrid renewable resources.

[0003] To solve this problem, the present invention offers a hybrid management system for renewable energies that uses IoT and AI for grid stability.

[0004] The system enables real-time monitoring and intelligent control of hybrid renewable energy sources through the integration of IoT and AI, thus ensuring optimal resource utilization.

[0005] The system improves grid stability by forecasting fluctuations between supply and demand and dynamically balancing loads to reduce the risk of power outages and fluctuations.

[0006] The system improves energy efficiency through intelligent planning of storage and distribution, minimizing losses and reducing dependence on conventional energy sources.

[0007] The system enables scalability and flexibility, allowing the seamless integration of multiple renewable energy sources such as solar, wind and biomass into both microgrids and large power grids.

[0008] The system reduces downtime, lowers operating costs and extends the lifespan of renewable energy infrastructure.

[0009] The system supports the sustainable energy transition by enabling reliable, automated and intelligent management of clean energy resources, thereby contributing to the reduction of CO2 emissions.

[0010] In one embodiment, the present invention provides a hybrid management system for renewable energies that uses IoT and AI for grid stability.

[0011] The present invention discloses a hybrid renewable energy management system that integrates Internet of Things (IoT) and artificial intelligence (AI) technologies to ensure grid stability and the efficient use of renewable resources. The system comprises a network of IoT-enabled sensors and devices for real-time data acquisition from multiple renewable energy sources, storage units, and grid components. The collected data is processed using AI-based models to forecast energy generation and demand, optimize energy distribution, and enable intelligent load balancing. The system dynamically controls hybrid energy sources and storage units, thereby reducing problems associated with fluctuations, preventing grid instability, and improving overall energy efficiency.Furthermore, the invention enables predictive maintenance, fault detection and adaptive scalability, making it suitable for both micronetworks and large-scale network applications.

[0012] The invention is explained again below with reference to the figure. It shows: Fig. : a representation of the block diagram of a hybrid management system for renewable energies that uses IoT and AI for grid stability.

[0013] Fig.Figure 1 shows a block diagram of a hybrid renewable energy management system (100) using IoT and AI to ensure grid stability. The present invention relates to a hybrid renewable energy management system (100) designed to integrate multiple renewable energy sources, such as solar panels, wind turbines, and biomass plants, into conventional power grids. The system (100) includes IoT-enabled sensors and controllers for continuously monitoring parameters such as energy generation, consumption patterns, storage capacity, weather conditions, and grid load. The collected data is transmitted in real time via secure communication protocols to a central processing unit. An AI-driven decision module processes the collected data to perform forecasts, optimizations, and anomaly detection.Forecasting models predict both renewable energy generation and user demand by analyzing historical and real-time data. Optimization algorithms then determine the most efficient distribution of energy between consumption, storage, and grid supply. The AI ​​module also facilitates dynamic load balancing and ensures that fluctuations in renewable energy generation do not affect grid stability. Furthermore, predictive fault detection and maintenance alerts are generated to minimize downtime and extend the lifespan of the equipment. The system (100) is also equipped with adaptive control mechanisms that can automatically adjust the contribution of renewable energy sources, activate or deactivate storage units, and manage grid interaction based on current demand and supply conditions.It also supports scalability and interoperability, enabling seamless integration into existing infrastructure and expansion to accommodate additional renewable energy sources or storage units. By combining IoT-enabled real-time monitoring with AI-based intelligent control, the invention ensures efficient use of renewable energies, improved grid reliability, and a sustainable approach to energy management. Reference symbol list 100 System

Claims

[1] Hybrid management system for renewable energy (100) using IoT and AI to ensure grid stability, comprising: a variety of renewable energy sources, including at least one of the following: solar, wind or biomass plants; one or more IoT-enabled sensors and controllers configured to collect real-time data regarding energy generation, consumption, storage capacity and grid parameters; a communication module for transmitting the recorded data to a central processing unit; an artificial intelligence module (AI module) that is operationally coupled with the central processing unit, wherein the AI ​​module is configured to: forecasts the generation and demand for renewable energy based on historical and real-time data; the energy distribution between consumption, storage and grid supply is optimized; and performs a dynamic load distribution to prevent network instability; a control unit configured to perform adaptive control of renewable energy sources and storage units based on the outputs of the AI ​​module; the system (100) provides real-time monitoring, intelligent decision-making and predictive maintenance to ensure efficient use of renewable energy and improved reliability of the power grid. [2] System (100) according to claim 1, wherein the IoT-enabled sensors are further configured to monitor environmental parameters such as temperature, humidity, wind speed and solar radiation to improve forecast accuracy. [3] System (100) according to claim 1, wherein the AI ​​module uses machine learning algorithms selected from regression models, neural networks or enhanced learning for prediction and optimization. [4] System (100) according to claim 1, wherein the control unit is configured to activate or deactivate energy storage units based on the predicted demand and supply conditions in order to minimize energy losses. [5] System (100) according to claim 1, wherein the system (100) further comprises a predictive maintenance module configured to detect anomalies, faults or performance reductions in renewable energy components and to generate maintenance alerts. [6] System (100) according to claim 1, wherein the communication module supports secure wireless protocols such as Wi-Fi, LoRa, ZigBee or 5G to enable reliable transmission of real-time data. [7] System (100) according to claim 1, wherein the AI ​​module is further configured to prioritize the use of renewable energy over conventional energy sources in order to reduce CO2 emissions. [8] System (100) according to claim 1, wherein the system (100) provides a user interface which can be accessed via a mobile or web application to enable real-time monitoring, visualization and control by operators.

Images