Enhancing Smart Grids with Internet of Energy: Deep Reinforcement Learning and Convolutional Neural Network

TL;DR

This paper introduces a comprehensive approach to optimizing smart grid energy management using deep reinforcement learning, CNNs, and security frameworks to enhance efficiency, security, and sustainability in the Internet of Energy context.

Contribution

It develops novel DRL and CNN-based algorithms for scheduling, routing, and security in IoE-enabled smart grids, addressing dynamic energy management challenges.

Findings

Improved energy efficiency and reduced operational costs.

Effective detection of FDI attacks and electricity theft.

Enhanced security and reliability of smart grid systems.

Abstract

The increasing demand for electricity, coupled with the rise in greenhouse gas emissions, necessitates the integration of Renewable Energy Sources (RESs) into power grids. However, the fluctuating nature of RESs introduces new challenges in energy management. The Internet of Energy (IoE) framework provides a solution by enabling real-time monitoring, dynamic scheduling, and enhanced energy routing. This paper proposes a comprehensive approach to optimizing energy management in smart grids using Deep Reinforcement Learning (DRL) and Convolutional Neural Networks (CNN). The research focuses on three main objectives: optimizing operation scheduling, improving energy routing, and enhancing cyber-physical security. A DRL-based scheduling algorithm is developed to manage energy components effectively, while an optimized energy routing algorithm ensures efficient electricity flow. Additionally, a security framework utilizing Long Short-Term Memory (LSTM) and CNN is proposed to detect False Data Injection (FDI) attacks and electricity theft. The proposed methods aim to improve energy efficiency, reduce costs, and ensure the security of IoE-enabled power systems. This research bridges existing gaps by addressing the dynamic and complex nature of modern energy networks. The integration of these advanced technologies promises significant advancements in the reliability and efficiency of smart grids. Ultimately, this work contributes to the development of a sustainable and secure energy future.