This paper presents a novel framework for privacy-preserving, fair, and efficient peer-to-peer (P2P) energy trading within smart grids. The proposed approach introduces an auction-based dynamic pricing mechanism combined with a proportional energy allocation policy to ensure balanced benefits for both buyers and sellers. The pricing model adapts in real-time based on supply-demand ratios to optimize costs and revenues while maintaining fairness. To safeguard sensitive user data, a blockchain-based decentralized transaction system (DTS) is developed using Ethereum smart contracts, MetaMask, and Ganache. This system enables secure energy transactions without compromising privacy. The model is validated with real and simulated datasets, demonstrating significant reductions in buyer energy bills by 12.7% compared to existing methods. The proposed solution effectively balances economic optimization with data privacy, making it a robust framework for future decentralized energy markets.

This paper presents an in-depth review of cybersecurity challenges and advanced solutions in modern power-generation systems, with particular emphasis on smart grids. It examines vulnerabilities in devices such as smart meters (SMs), Phasor Measurement Units (PMUs), and Remote Terminal Units (RTUs) to cyberattacks, including False Data Injection Attacks (FDIAs), Denial of Service (DoS), and Replay Attacks (RAs). The study evaluates cutting-edge detection and mitigation techniques, such as Cluster Partition, Fuzzy Broad Learning System (CP-BLS), multimodal deep learning, and autoencoder models, achieving detection accuracies of (up to 99.99%) for FDIA identification. It explores critical aspects of power generation, including resource assessment, environmental and climatic factors, policy and regulatory frameworks, grid and storage integration, and geopolitical and social dimensions. The paper also addresses the transmission and distribution (T&D) system, emphasizing the role of smart-grid technologies and advanced energy-routing strategies that leverage Artificial Neural Networks (ANNs), Generative Adversarial Networks (GANs), and game-theoretic approaches to optimize energy flows and enhance grid stability. Future research directions include high-resolution forecasting, adaptive optimization, and the integration of quantum–AI methods to improve scalability, reliability, and resilience.

Massive utilization of IoT-enabled consumer electronic devices such as fans, cooling and heating equipment, etc., controlled through the energy management controller interconnected with the smart meters, are highly susceptible to cyber-attacks. Any cyber-attack on the smart meter may lead to changes in the operating cycle or leakage of information from these consumer electronic devices. Protecting these consumer electronic devices from vulnerabilities necessitates an effective solution to detect vulnerabilities in the smart meter. For this purpose, in the presented study, a deep learning-based model by using a combination of TinyBERT and a convolutional neural network is proposed to detect any vulnerability that may lead to a possible cyber-attack, i.e., ransomware. Simulation results of the proposed study evaluated on a real-life dataset demonstrate that the proposed model effectively detects the ransomware attack with an accuracy of 95.40% as compared to the other state-of-the-art methods.

Decentralized microgrids, driven by IoT devices and renewable energy in smart homes, face challenges in secure energy routing due to collusion attacks and scalability issues. Existing protocols like Ad-hoc Distance Vector Security Extension (AODV-SEC) and blockchain-based solutions suffer from slow authentication (3.1ms latency) and static paths, contributing to 45% of microgrid incidents. The Secure Routing Energy Mechanism (SREM) addresses these with Field-Programmable Gate Array (FPGA) accelerated authentication (<1ms latency), dynamic path optimization via hourly gij updates, and collusion-resistant checks (98% detection accuracy). Simulations show SREM achieves 10