Jul 2026· 2026 6th International Conference on Inventive Computation and Information Technologies (ICICIT)· pp. 293-300· 0 citations· 20 references
Abstract
The distribution of renewable energy resources and Edge-IoT infrastructures have brought new challenges in intelligent smart microgrid management, such as dynamic-energy-demand changes, carbon-heavy energy-scheduling, communication overhead, and battery degradation. The deployment of renewable energy resources, distributed battery storage systems, and Edge-IoT infrastructures has created a number of challenges in the management of smart microgrids, such as dynamic changes in energy demand, carbon-heavy energy-scheduling, communication overhead, and battery degradation. To tackle these challenges, this paper introduces a personalized Federated Deep Reinforcement Learning (GridMind-FDRL) framework for carbon-aware and battery-safe decentralized smart microgrid optimization. The proposed framework combines federated learning, deep reinforcement learning, edge intelligence, carbon-aware energy scheduling and battery-aware adaptive optimization with a centralized framework for energy management. Unlike traditional centralized optimization methods, GridMind-FDRL allows for collaborative learning among distributed microgrid nodes while maintaining privacy and enabling low latency real-time optimization in dynamic Edge-IoT environments. The framework was tested with different operating conditions of intermittent renewables, varying load levels, and battery stress conditions. The results of the experiment showed that the energy efficiency could be 93.86%, carbon reduction 24.36%, battery health preservation 90.42%, communication efficiency 88.08%, and decision latency reduction 34.21%. The acquired results support the scalability, sustainability, and smart energy optimization ability of the suggested framework for the next-generation decentralized smart energy ecosystems.
The residential market has seen the rapid take-up of Internet of Things (IoT) devices, connected IIoT smart systems due to it. This situation gives rise to new issues and challenges for the energy management implemented in real time. The traditional centralized energy management mechanisms face great challenges like excessive latency, confidentiality vulnerability, scalability issues across heterogeneous smart home networks. The paper proposes a Federated Deep Reinforcement Learning (D-RLR) framework based on DQN agents and federated learning method. The framework allows for the decentralized training of DQN models in smart home nodes with the restriction that raw energy consumption data remain local. Each agent carries out the processing of grid price signal, usage data of appliances, observed amount and availability of local renewable energy to develop optimal load scheduling policy. The overall demand response policy is obtained by periodic federated aggregation of local models, helping to reduce communication latency. Experiments conducted on a simulated IIoT smart home environment, constructed using appliance consumption profiles derived from publicly available residential energy datasets and realistic solar irradiance-based renewable generation profiles, demonstrate that the proposed framework reduces peak energy consumption by 23% and improves demand response efficiency by 18% relative to centralized DRL baselines. Communication overhead generates roughly 65% lesser and user privacy is preserved throughout by design. The mechanism scales across nodes and supports real-time scheduling decisions under dynamically changing grid conditions. Therefore, it can be deployed practically for next-generation residential energy management.
Sundaram P, M. Jayaprakash· 2026 4th International Confe...· 0 citations
A detailed survey on the latest trends in AI based renewable energy integration, smart grid, EV, battery management systems, and real-time transportation data analytics is presented and important concerns regarding the edge system’s security, scale-ability, connectivity, privacy, and the direction of future research on enabling smart transportation were discussed.
Muthukumar Paramasivan, Manikandan Sivasubramanian, K. Alagar et al.· Proceedings of the Instituti...· 0 citations
A constrained, risk-sensitive multi-agent reinforcement learning framework is presented for joint task offloading and EH scheduling in battery-less 6G industrial networks, yielding a Pareto-non-dominated, statistically validated policy.
G. Marzoog, Ammar Kazm, Mustafa K. Ati· Sulaimani Journal for Engine...· 0 citations
A federated reinforcement learning framework for adaptive load balancing in the edge-fog-cloud continuum that optimizes energy efficiency and supports diverse quality of service requirements and uses a distributed experience replay buffer to reduce trial-and-error in reinforcement learning.
Si Liu, Midhun Chakkaravarthy· Future Technology· 0 citations
The proposed framework is validated by conducting simulation-based experiments on the benchmark datasets and synthetic autonomous workloads, where the novelty lies in the design of the system-level federated learning architecture, instead of the datasets themselves.
Jyotsnarani Tripathy, D. Rajalakshmi, A. N. Ramya Shree et al.· SN Computer Science· 0 citations
An AI-driven framework integrates hybrid energy harvesting mechanisms with Deep Reinforcement Learning (DRL) to optimize energy efficiency in IoT systems and achieves up to 300% improvement in network lifetime under low-energy harvesting conditions.
Elkhatim Abuelysar Elmobarak Mohammed Ali· Islamic University Journal o...· 0 citations
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