CEM is formally defined, distinguish it from rule-based and optimization-based paradigms through structured comparison, and articulate its core architectural layers; one of the most operationally demanding EI node environments in modern infrastructure.
Abstract
Modern energy management systems, even within advanced energy internet (EI) infrastructures, remain fundamentally reactive, optimization-bound, and incapable of reasoning about context, intent, or uncertainty. While the EI paradigm has established a powerful cyber-physical architecture for interconnecting distributed energy resources via software-defined packetized networks, the question of how such systems should think, adapt, and govern energy decisions intelligently remains an open challenge. This paper introduces cognitive energy management (CEM); a new conceptual framework that addresses this gap by redefining how energy systems perceive, reason, learn, and act within complex operational environments. Grounded in the EI cyber-physical foundation, CEM extends beyond conventional optimization by embedding goal-directed reasoning and continuous adaptation into the energy management loop, positioning itself as the cognitive governance layer of EI-based infrastructures. We formally define CEM, distinguish it from rule-based and optimization-based paradigms through structured comparison, and articulate its core architectural layers. To demonstrate the framework's practical value, we develop a toy problem grounded in smart port energy management; one of the most operationally demanding EI node environments in modern infrastructure. Specifically, we model a predictive vessel turnaround scenario in which a CEM-enabled system plans energy procurement, storage pre-charging, and load scheduling across a six-hour operational horizon. The demonstration illustrates how CEM moves the EI beyond feasibility-seeking toward intelligent, anticipatory energy governance.
Advanced Metering Infrastructure (AMI) is a key enabler of digital and intelligent power systems, particularly in microgrid environments. However, existing research often addresses AMI from fragmented perspectives, limiting a comprehensive understanding of its role within integrated and data-driven energy systems. This paper presents a structured analysis of AMI based on a bibliometric and thematic review of recent literature, identifying the main research trends, technological drivers, and emerging directions in the field. The results reveal a transition of AMI toward a data-centric platform that supports real-time monitoring, bidirectional energy management, and intelligent decision-making. Key domains include cybersecurity, data analytics, communication systems, and distributed energy integration, while emerging technologies such as artificial intelligence and the Internet of Energy play a critical role in future developments. Finally, the paper outlines key challenges and provides strategic recommendations to support the effective deployment of AMI in microgrids, contributing to the development of resilient and sustainable energy systems.
Juan Camilo Riaño-Rueda, Melisa de Jesús Barrera-Durango, N. Muñoz-Galeano et al.· Electricity· 0 citations
The increasing integration of renewable energy resources, distributed energy systems, and intelligent sensing technologies has accelerated the transformation of conventional power grids into interconnected cyber–physical smart energy ecosystems. In this context, the convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) has emerged as a key enabler for intelligent monitoring, adaptive energy management, resilient grid operation, and sustainable energy coordination. Although numerous studies have investigated AI, IoT, blockchain, and cybersecurity technologies individually, many existing reviews focus on isolated domains without adequately addressing the interactions between intelligent operational control, communication infrastructures, decentralized coordination, sustainability, and cyber resilience. Accordingly, this paper presents a comprehensive system-level review of AI–IoT-enabled smart energy ecosystems, focusing on smart grids, microgrids, intelligent energy management, blockchain-enabled decentralized coordination, carbon emissions monitoring, and cyber-resilient energy infrastructures. Unlike existing surveys that primarily emphasize individual technologies or algorithmic performance, this work highlights the cross-layer integration and architectural interdependencies between AI-driven operational intelligence, IoT-enabled monitoring, secure communication frameworks, and sustainability-oriented energy management. The paper also discusses key challenges related to interoperability, scalability, cybersecurity, communication latency, and distributed coordination, in addition to future research directions toward resilient, autonomous, and sustainable intelligent energy ecosystems.
Maen Takruri, Mohammad Rabih, Lucas Mouhannad Dbeiss et al.· Engineer· 0 citations
The growing deployment of distributed renewable generation, storage, electric vehicles, heat pumps, smart buildings, and controllable demand is expanding the flexibility available to local energy systems. Energy communities provide the governance context for collective participation and value creation, while community virtual power plants provide the operational mechanism for aggregating distributed resources and connecting them to local optimisation, flexibility markets, grid services, and resilience functions. This PRISMA-ScR-informed framework development study synthesises the literature from Scopus, Web of Science, and IEEE Xplore to examine how artificial intelligence supports community VPP operation, where agentic AI adds capabilities beyond established optimisation, reinforcement learning, and multi-agent systems, and which design requirements follow governed orchestration. The synthesis shows that current evidence is strongest for component-level forecasting, scheduling, bidding, adaptive control, distributed coordination, and digital-twin validation, whereas integrated agentic orchestration remains an emerging direction. Classical multi-agent systems already provide decentralised representation, communication, negotiation, and coordinated control; the additional role proposed for agentic AI is therefore narrower and concerns context-aware multi-step workflow orchestration, governed tool use, exception handling, grounded explanation, and bounded delegation across existing analytical and control services. The study introduces operational flexibility intelligence as the capability to transform potential distributed flexibility into deployable, authorised, market-, grid-, resilience-, and community-compatible action. It further develops a conceptual layered reference architecture in which agentic orchestration operates through governed tools and interfaces rather than bypassing validated resource controllers. Fairness, comfort, privacy, cybersecurity, resilience, auditability, and human-in-command authority are treated as cross-cutting operational constraints. The architecture defines a design and validation agenda rather than an empirically validated implementation.
The literature on Internet of Things (IoT)-enabled autonomous lighting and HVAC optimisation in smart homes is critically reviewed, tracing the evolution of home energy management from manual and rule-based control to sensor-driven, edge-capable architectures.
Livingstone Aduku, Ikenna Calistus Dialoke, Abubakar Surajo Imam· International Journal of Eng...· 0 citations
The increasing penetration of distributed energy resources, variable renewable generation, battery energy storage systems, electric vehicles, and power-electronic interfaces is changing the way modern smart grids and microgrids are operated, protected, and controlled. This systematic literature review follows the PRISMA 2020 framework and examines 87 original research papers, complemented by a supplementary synthesis of 18 contextual studies that provide bibliometric, historical, and conceptual perspectives on the evolution of AI in smart grids. The primary studies are organized into six thematic clusters: energy management and forecasting; cybersecurity and intrusion detection; renewable energy integration and microgrid management; fault detection, diagnosis, and grid stability; explainable and trustworthy artificial intelligence; and emerging technologies, including digital twins, blockchain, the Internet of Things, edge computing, and federated learning. The review shows that deep learning, reinforcement learning, and ensemble machine learning are increasingly used for load forecasting, demand response, converter-interfaced renewable integration, intrusion detection, and operational optimization. However, the literature remains uneven. Fault detection, converter-aware protection, and real-time stability assessment receive considerably less attention than energy management and cybersecurity, despite their importance for inverter-based resources, grid-forming converters, electric-vehicle charging systems, and battery interfacing. Four critical gaps are identified: limited cross-grid generalizability, weak validation under realistic converter and protection constraints, insufficient adversarial robustness of AI-enabled defense systems, and limited explainability in real-time safety-critical applications. The paper provides a structured taxonomy, identifies deployment barriers, and proposes research directions for trustworthy AI in power-electronic-rich smart grids and microgrids.
Reham Alsbua, M. Al-Soeidat, Ahmad A. Salah et al.· Energies· 0 citations
A comprehensive review of RL-based control applications across RES-integrated energy domains, including power grids, microgrids, and building energy systems, identifies emerging trends and highlights dominant design patterns across power grid, microgrid, and building-level applications.
P. Michailidis, F. Minelli, H. Coban et al.· Infrastructures· 0 citations
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