Privacy-aware multi-agent deep reinforcement learning for ethical coordinated control in smart regional grids

With the widespread integration of distributed energy resources (DERs), regional power systems are increasingly reliant on coordinated control among transmission, distribution, and microgrid layers. Traditional control strategies face significant challenges in managing the heterogeneity, volatility, and dynamic load responses inherent in such systems. To address these issues, this paper proposes a privacy-aware and ethically aligned deep reinforcement learning (DRL) framework for optimizing multi-agent coordinated control across hierarchical grid components. The proposed approach constructs a multi-layered state–action space encompassing transmission, distribution, and microgrid subsystems. A multi-agent DRL mechanism is integrated to achieve multi-objective optimization, including load shedding mitigation, overload prevention, reverse power flow control, and voltage stability. Importantly, our design incorporates privacy-preserving training protocols and explainable decision-making modules to ensure transparency, accountability, and secure deployment in critical infrastructure settings. Extensive simulations across diverse operational scenarios demonstrate the strategy’s superior responsiveness, improved renewable energy utilization, and robustness under uncertain conditions. The framework not only enhances operational efficiency but also aligns with emerging global demands for secure, transparent, and ethically governed AI deployment in smart grids. This study provides a novel pathway toward intelligent, privacy-preserving, and responsible regional grid control. • Proposes a novel privacy-aware multi-agent deep reinforcement learning (MADRL) framework for regional smart grid coordination. • Incorporates differential privacy mechanisms to protect sensitive data during distributed control optimization. • Introduces explainable policy interpretation to enhance transparency and ethical accountability in decision-making. • Demonstrates improved response time, renewable integration, and robustness across IEEE 33-bus and 69-bus test systems. • Addresses compliance with AI ethics and global data privacy regulations (e.g., GDPR, AI Act).