Description:The embodiments of the present invention generally relate to smart grid technologies and, more specifically, to methods and systems for improving power system stability and efficiency through artificial intelligence (AI), advanced control mechanisms, real-time data analytics, and decentralized energy management. The invention integrates renewable energy sources, predictive fault detection, automated demand-response mechanisms, and cybersecurity frameworks to enhance grid resilience, optimize energy distribution, and ensure sustainable, efficient, and secure power management.
BACKGROUND OF THE INVENTION
The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

Traditional power grids rely on centralized generation and distribution, often resulting in inefficiencies, transmission losses, and reliability issues. The increasing demand for electricity, coupled with the integration of renewable energy sources such as solar and wind power, has further complicated grid management, leading to voltage fluctuations, power outages, and increased operational costs.

Conventional grid systems lack the ability to adapt dynamically to changes in energy demand and supply. Without real-time data analytics and automation, grid operators face significant challenges in balancing load distribution, detecting faults, and mitigating risks. This results in frequent power failures, economic losses, and consumer dissatisfaction.

With the growing penetration of renewable energy sources, grid stability has become a major concern. Unlike conventional fossil fuel-based generation, renewable energy sources are highly variable, making it difficult to maintain grid frequency and voltage stability. This variability requires smart forecasting, predictive analytics, and energy storage solutions to ensure a reliable power supply.

Cybersecurity threats have also become a major challenge for modern power systems. The increasing digitization of grid operations exposes the system to risks such as cyberattacks, data breaches, and unauthorized access. Without a secure framework for monitoring and preventing intrusions, power grids are vulnerable to operational disruptions and data manipulations.

The lack of decentralized energy trading mechanisms in conventional power grids leads to inefficient utilization of surplus energy. Consumers who generate excess energy from solar panels or other renewable sources have limited opportunities to trade or sell their surplus power to other users in a secure and transparent manner.

In addition, traditional grid systems do not support adaptive fault detection and automated recovery mechanisms. Power outages caused by equipment failures, natural disasters, or sudden load variations require manual intervention, resulting in delays in fault restoration and increased maintenance costs.

OBJECTIVE OF THE INVENTION

Some of the objects of the present disclosure, which at least one embodiment herein satisfies are listed herein below.

An objective of the present invention is to develop an intelligent smart grid system that enhances power system stability and efficiency through AI-driven decision-making, real-time monitoring, and predictive analytics. The system aims to address the limitations of conventional power grids by integrating advanced computational models, adaptive energy management, and decentralized trading mechanisms.

Another objective of the present invention is to enable real-time demand-response optimization by deploying machine learning algorithms that can predict load fluctuations and automatically adjust energy distribution. By doing so, the system can reduce energy wastage, minimize power losses, and ensure a stable power supply even during peak demand conditions.

Another objective of the present invention is to incorporate automated fault detection and self-healing capabilities in the grid. By utilizing sensor networks and AI-based diagnostics, the system can identify potential grid failures in advance, initiate corrective measures, and autonomously reroute power to avoid large-scale blackouts.

Another objective of the present invention is to improve renewable energy integration in the power grid. The system employs predictive analytics to assess energy generation patterns from renewable sources and optimize their utilization, ensuring a balanced mix of renewable and conventional energy to enhance sustainability.

Another objective of the present invention is to enhance grid cybersecurity by implementing AI-based anomaly detection and blockchain-enabled encryption mechanisms. This ensures secure energy transactions, prevents unauthorized access, and protects the grid from potential cyber threats, thereby increasing operational reliability.

Another objective of the present invention is to reduce grid maintenance costs by implementing predictive maintenance models that can anticipate equipment failures and schedule proactive maintenance activities. This approach minimizes unexpected breakdowns, reduces repair costs, and extends the lifespan of critical grid components.

SUMMARY OF THE INVENTION
This section is provided to introduce certain objects and aspects of the present disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.

In an aspect, the present invention provides a smart grid system that integrates artificial intelligence, real-time monitoring, and decentralized energy management to enhance power system stability and efficiency. The system includes an AI-powered control mechanism that continuously analyzes grid conditions, predicts load variations, detects potential faults, and optimizes energy distribution to ensure a reliable and cost-effective power supply.
The invention incorporates automated fault detection and self-healing mechanisms, renewable energy forecasting models, blockchain-based energy trading platforms, and AI-driven cybersecurity frameworks. These components work together to enhance grid resilience, reduce operational costs, support sustainable energy utilization, and create a more adaptive, secure, and intelligent power infrastructure.
BRIEF DESCRIPTION OF DRAWINGS
The accompanying drawings, which are incorporated herein, and constitute a part of this invention, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present invention. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that invention of such drawings includes the invention of electrical components, electronic components or circuitry commonly used to implement such components.

FIG. 1 illustrates an exemplary method for improving power grid efficiency, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION OF THE INVENTION

In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.

The ensuing description provides exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.

Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

The word “exemplary” and/or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and/or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising” as an open transition word without precluding any additional or other elements.

Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms “a”, “an”, and “the” are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items.

The present invention provides an intelligent smart grid system that enhances power system stability and efficiency by integrating artificial intelligence (AI), real-time data analytics, automated fault detection, and decentralized energy management. The system is designed to monitor, analyze, and control energy distribution in real-time, ensuring optimal power utilization, enhanced grid resilience, and reduced operational costs.

The invention discusses an AI-driven control mechanism that continuously evaluates power demand, supply fluctuations, voltage stability, and fault conditions. The control system incorporates machine learning algorithms trained on historical grid data to predict peak demand periods, detect anomalies, and automate energy balancing. This proactive approach minimizes voltage fluctuations, frequency instabilities, and transmission losses, leading to improved grid performance.

The invention also includes automated fault detection and self-healing mechanisms that utilize sensor-based monitoring and AI-driven diagnostics. When a potential fault is detected, the system analyzes the severity, isolates the affected area, and reroutes power to maintain uninterrupted supply. The self-healing capability reduces downtime, maintenance costs, and the risk of large-scale outages, ensuring enhanced power grid reliability.

Another crucial aspect of the invention is renewable energy integration and predictive optimization. The system continuously monitors energy generation from solar, wind, and other renewable sources, predicting fluctuations based on weather conditions, historical trends, and real-time sensor inputs. An optimization algorithm determines the best mix of renewable and conventional energy to ensure a stable and sustainable power supply. Additionally, intelligent storage management ensures excess energy is stored and deployed efficiently when needed.

To enhance grid security, the invention incorporates AI-based cybersecurity protocols and blockchain technology. AI-driven anomaly detection algorithms continuously monitor grid operations for potential cyber threats, while blockchain encryption ensures secure peer-to-peer (P2P) energy transactions. The decentralized architecture prevents unauthorized access, data manipulation, and cyberattacks, ensuring a highly secure and tamper-proof smart grid system.

Furthermore, the invention supports decentralized energy trading by enabling consumers and producers to trade surplus energy securely using blockchain-based smart contracts. The P2P trading mechanism ensures transparent, efficient, and real-time transactions, allowing users to buy, sell, or share electricity dynamically based on demand and supply conditions. This feature promotes consumer empowerment, energy efficiency, and sustainability in power management.
In one embodiment, the invention implements an AI-powered adaptive load balancing system that continuously monitors and optimizes energy distribution across the grid. The system employs machine learning algorithms trained on historical consumption data, real-time sensor readings, and predictive analytics to forecast demand variations.

During peak load conditions, the AI algorithm redistributes energy from low-priority zones to high-priority zones, ensuring voltage stability and efficient power utilization. The system also interacts with renewable energy sources, intelligently prioritizing solar or wind power when available, thereby reducing reliance on fossil fuels. This embodiment improves grid stability, reduces operational costs, and enhances energy efficiency.

The load balancing system integrates automated demand response mechanisms that notify consumers of real-time pricing changes, encouraging energy-efficient consumption behaviors. Smart meters installed at consumer premises communicate dynamically with the grid, enabling users to adjust energy consumption based on real-time pricing.

In another embodiment, the invention employs a self-healing grid architecture that detects, isolates, and mitigates faults automatically. The system integrates high-speed sensors, AI-based diagnostics, and real-time monitoring units to continuously scan for anomalies in power lines, transformers, and distribution networks.

When a fault is detected, the AI-driven system analyzes the fault location, assesses severity, and initiates a self-healing protocol. The affected section of the grid is isolated, and power is automatically rerouted through alternative paths to maintain supply continuity. Additionally, an automated alert system notifies grid operators of the issue, providing real-time diagnostic reports and recommended corrective actions.

This embodiment reduces downtime, maintenance costs, and large-scale power disruptions, ensuring a highly resilient and self-sustaining smart grid infrastructure. The self-healing capability enhances grid reliability, minimizes manual interventions, and prevents cascading failures.

A third embodiment of the invention introduces a blockchain-based energy trading platform that enables peer-to-peer (P2P) transactions among consumers, prosumers (producer-consumers), and utilities. The system allows users to sell excess energy generated from rooftop solar panels, wind farms, or other distributed energy resources (DERs) directly to other consumers.

The blockchain-based smart contract system ensures secure, automated, and transparent energy transactions without the need for intermediaries. The platform records real-time energy production, consumption, and pricing data in an immutable ledger, ensuring trust and reliability in energy exchanges.

Users can set dynamic pricing models based on demand-supply conditions, allowing for efficient utilization of renewable energy and reducing dependency on conventional power plants. Additionally, the decentralized trading system helps utilities optimize grid load distribution, prevent energy wastage, and support a more sustainable and cost-effective power market.

While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter to be implemented merely as illustrative of the invention and not as limitation.
, Claims:1. A smart grid system for enhancing power system stability and efficiency, comprising:
A real-time monitoring unit for collecting data on power consumption, voltage, and frequency;
An AI-driven control system that processes real-time data and optimizes energy distribution;
A predictive analytics model configured to forecast demand fluctuations and adjust load balancing;
An automated fault detection mechanism that identifies and mitigates system failures in real time;
A decentralized energy management module utilizing blockchain technology for secure peer-to-peer energy trading.

2. A method for improving power grid efficiency, comprising:
Collecting real-time energy consumption data using IoT-enabled sensors;
Processing the collected data using an AI-based optimization model;
Predicting grid stability conditions based on historical fault data;
Implementing an automated recovery mechanism in case of power disturbances;
Enabling secure and transparent energy transactions via a blockchain network.

3. The system of claim 1, wherein the AI-driven control system comprises a machine learning model trained to recognize patterns in power demand and supply fluctuations.

4. The system of claim 1, wherein the automated fault detection mechanism utilizes a self-healing protocol to reroute power in case of grid disturbances.

5. The method of claim 2, wherein the AI-based optimization model dynamically adjusts power flow between renewable energy sources and conventional power plants.

6. The system of claim 1, wherein the blockchain-based energy management module ensures traceability and security in decentralized energy trading.

7. The method of claim 2, wherein the cybersecurity framework uses AI-based anomaly detection to identify and mitigate cyber threats to the smart grid.

8. The smart grid system of claim 1, wherein the system employs edge computing-based real-time analytics to process localized energy data at distributed nodes, reducing latency and enhancing decision-making speed for grid stability and efficiency.