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April 12, 2026Open Access

Edge-Intelligent and Cyber-Resilient Coordination of Electric Vehicles and Distributed Energy Resources in Modern Distribution Grids

Key Points

This research aims to develop a framework for coordinating electric vehicles and distributed energy resources safely and resiliently within modern distribution grids.
Developed a hierarchical edge–cloud architecture for coordination of electric vehicles and distributed energy resources.
Integrated multi-agent system coordination and reinforcement learning within a safety compliance framework.
Implemented cybersecurity mechanisms to ensure stability in real-time operations.
Proposed architecture preserves safety while enabling adaptive learning through structured control.
Ensured compliance with IEEE 1547 standards, enhancing regulatory acceptance.
Demonstrated empirical validation using software-in-the-loop, hardware-in-the-loop, and power hardware-in-the-loop methods.

Abstract

The rapid electrification of transportation and proliferation of distributed energy resources (DERs) are transforming distribution grids into highly dynamic, data-intensive, and cyber-physical systems. While reinforcement learning (RL), multi-agent coordination, and edge computing offer powerful tools for adaptive control, their deployment in safety-critical utility environments raises concerns regarding stability, certification compatibility, cyber-resilience, and regulatory acceptance. This paper presents an architecture-centric framework for edge-intelligent and cyber-resilient coordination of electric vehicles (EVs) and DERs that reconciles adaptive learning with deterministic safety guarantees. The proposed hierarchical edge–cloud architecture integrates multi-agent system (MAS) coordination, constraint-invariant reinforcement learning, and embedded cybersecurity mechanisms within a structured control hierarchy. Learning-enabled edge agents operate exclusively within standards-compliant safety envelopes enforced through supervisory constraint projection, control barrier functions, and Lyapunov-consistent stability safeguards. Protection-critical functions remain deterministic and isolated from adaptive layers, preserving compatibility with IEEE 1547 and existing utility protection schemes. The framework further incorporates anomaly triggered policy freezing, fail-safe fallback modes, and communication-aware resilience mechanisms to prevent unsafe transient behavior in non-stationary, distributed environments. Unlike simulation-only learning approaches, the architecture embeds progressive validation through software-in-the-loop (SIL), hardware-in-the-loop (HIL), and power hardware-in-the-loop (PHIL) testing to empirically verify transient stability, constraint compliance, and cyber-resilience under realistic timing and disturbance conditions. Beyond technical performance, the paper situates edge intelligence within standards evolution, governance structures, workforce transformation, techno-economic assessment, and equitable deployment pathways. By framing adaptive control as a bounded, auditable augmentation layer rather than a disruptive replacement for certified infrastructure, the proposed architecture provides a pragmatic roadmap for evolutionary modernization of distribution systems.

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