High Efficient AI-Based Smart Grid Energy Management And Control System

A smart or intelligent grid is basically an advanced electric power system of tomorrow that integrates the state-of-the-art power electronics, computers, information, communication, and cyber technologies   The broad objectives of SG can be summarized as follows  ? optimum resource

Project Title

High Efficient AI-Based Smart Grid Energy Management And Control System

Project Area of Specialization

Electrical/Electronic Engineering

Project Summary

A smart or intelligent grid is basically an advanced electric power system of tomorrow that integrates the state-of-the-art power electronics, computers, information, communication, and cyber technologies
  The broad objectives of SG can be summarized as follows
 • optimum resource utilization
 • higher energy efficiency
 • higher system reliability
• higher system security
 • economical electricity distribution to consumers.

Artificial intelligence (AI) techniques, such as expert systems (ESs), fuzzy logic (FL), and artificial neural networks (ANNs or NNWs) and deep learning have brought an advancing frontier in power electronics and power engineering. 
These techniques provide powerful tools for design, simulation, control, estimation, fault diagnostics, and fault-tolerant control in modern smart grid (SG) and renewable energy systems (RESs). 
The AI technology and deep learning have gone through fast evolution during last several decades, and their applications have increased rapidly in modern industrial systems. 

Project Objectives

Our main focus is to model a hybrid smart-grid energy management system with renewable energy sources and energy storage systems such as a battery. The proposed system is controlled with the help of an energy management system based on an AI approach with deep learning. The performance of the proposed EMS is presented for a clear day and cloudy day conditions.

Project Implementation Method

The generation, transmission, and distribution of electrical energy have entered into technological change and reforms globally. Renewable energy-based technologies particularly solar photovoltaic and wind energy conversion systems are invigorated due to their abundant availability and have the potential to provide an eco-friendly and sustainable solution for future power requirements. To accommodate the fluctuating nature of these resources, the operation of power generating systems should be efficient and responsive, thus the concept of the smart grid is playing a key role in such transitions
The control, monitoring, and protection of an SG are extremely complex, particularly if the grid is large. The whole control system can be centralized (with local override), with the integration of latest control, computer, information, communication, and cyber technologies.
Wireless communications between smart appliances and central systems should be secured to protect against interception or manipulation. Customer portals: Attackers can use social engineering techniques to access customer accounts and change customer settings.

Benefits of the Project

Although traditional numerical methods for smart grid dispatch problems can sometimes fail to converge, deep learning methods will always generate a solution

There fore, using deep learning will highly efficient the smart grid, make it more reliable and increases it demand in market. reduces energy losses Although traditional numerical methods for smart grid dispatch problems can sometimes fail to converge, deep learning methods will always generate a solution thus, helps in power dispatching.

Technical Details of Final Deliverable

The recent development of eco-friendly technologies, promoting sustainable development, such as RE resources and hybrid MG can find increased acceptance socially and economically. The optimal energy management strategy of such a system requires consideration of design state, geophysical conditions, load demand management, and parametric constraints. Moreover, the burden on the grid is reduced by 42.56% and 32.91% for clear and cloudy days respectively. This approach takes benefit from the price gaps between high and low-price hours of electricity from the utility grid. Also, transmission losses are reduced as energy is utilized from the RE sources and BESS has recharged accordingly.

 Therefore, using deep learning will highly efficient the smart grid, make it more reliable, and increases its demand in the market. reduces energy losses Although traditional numerical methods for smart grid dispatch problems can sometimes fail to converge, deep learning methods will always generate a solution thus, helps in power dispatching.

Final Deliverable of the Project

HW/SW integrated system

Core Industry

Energy

Other Industries

Core Technology

Artificial Intelligence(AI)

Other Technologies

Sustainable Development Goals

Affordable and Clean Energy

Required Resources

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