Abstract
To address gaps in security, resilience, and scalability in smart grid electricity trading, this study proposes Cloud-RESilient, a secure cloud-based architecture for local energy market and peer-to-peer trading platforms. The design features a hybrid edge-central cloud topology, where edge nodes support low-latency local trading and the central cloud enables scalable cross-cluster coordination. A multi-layer security framework integrates end-to-end encryption, AI-driven intrusion detection, and homomorphic encryption to protect data and enable privacy-preserving computation. A data-driven renewable energy source adaptation module based on the LSTM-ARIMA model is incorporated to manage generation variability. Validation is conducted in a simulated urban smart grid environment comprising five microgrid clusters and 10,000 prosumers, using real-world datasets including two years of renewable generation and over 10,000 cyber threat patterns. Evaluation covers three areas: security through penetration testing and privacy audits, resilience through fault tolerance and response to renewable fluctuations, and performance through scalability and latency testing. Results show that Cloud-RESilient achieves zero data leakage, 99.98% platform uptime, 92% accuracy in one-hour renewable forecasts, and 120 ms order processing time under full load. These outcomes confirm the effectiveness of the proposed methodology in delivering secure, resilient, and scalable electricity trading solutions for smart grids.
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