DefinePK

Abstract

The transition toward next-generation smart grids is being driven by the integration of advanced metering infrastructure, distributed energy resources, and intelligent control systems that generate massive volumes of high-resolution data. Demand response (DR), a key functionality within this paradigm, plays a central role in balancing intermittent renewable generation, reducing peak loads, and improving overall energy efficiency. However, the reliance on fine-grained data streams and advanced artificial intelligence (AI) models for DR optimization has raised significant privacy, security, and trust concerns. Unauthorized access, inference attacks, model inversion, and cyber intrusions pose risks not only to individual consumers but also to the operational stability and resilience of the grid. Addressing these challenges requires novel frameworks that simultaneously enable data-driven optimization and preserve user privacy while ensuring cyber-resilience. This paper presents a comprehensive AI and big data–enabled architecture for privacy-preserving and cybersecure demand response in smart grids, with a focus on sustainable energy optimization. First, the study characterizes the threat landscape across the DR lifecycle, from data acquisition and transmission to forecasting, scheduling, and actuation. Specific vulnerabilities such as consumer load profile reconstruction, adversarial data poisoning, coordinated cyberattacks, and unauthorized appliance-level inference are analyzed in detail. Building on this threat assessment, the paper introduces a layered framework that integrates big data analytics with advanced AI techniques, while embedding privacy-enhancing technologies and robust security mechanisms. The proposed framework consists of four layers. The edge layer ensures secure data acquisition through lightweight cryptography and preprocessing to filter noise and anomalies before transmission. The learning layer employs federated learning, differential privacy, and secure aggregation protocols to train accurate and robust forecasting models without exposing raw data. The optimization layer implements privacy-preserving demand response scheduling using distributed algorithms, secure multi-party computation, and chance-constrained optimization. Finally, the monitoring layer integrates adversarially robust anomaly detection, intrusion detection, and real-time resilience assessment to counter advanced persistent threats and maintain grid stability. Experimental validation is carried out on real-world and synthetic datasets, including smart meter consumption records and benchmark DR scenarios. Evaluation metrics cover forecast accuracy (MAPE, RMSE), energy cost savings, peak-to-average load reduction, privacy leakage (ε, δ in differential privacy), attack success rates, and latency overheads. Results demonstrate that the integration of AI and big data analytics with privacy-preserving mechanisms retains up to 95% of baseline forecasting performance while substantially reducing adversarial vulnerabilities. Moreover, the proposed architecture achieves measurable improvements in energy cost efficiency and resilience under simulated cyberattack scenarios. The novelty of this research lies in combining privacy-preserving computation, AI-driven optimization, and big data analytics into a unified, next-generation framework that not only enhances demand response efficiency but also ensures trustworthiness, sustainability, and long-term resilience of smart grids. By aligning technical innovation with regulatory compliance and governance principles, this study provides actionable insights for researchers, grid operators, and policymakers aiming to build secure, privacy-aware, and sustainable energy ecosystems.