Jun 2026· arXiv.org· Vol abs/2606.27542· 0 citations· 37 references
EngineeringComputer Science
TL;DR
An experimental evaluation study of the Alternating Direction Method of Multipliers (ADMM) in a fully experimental platform that features commercial 5G connectivity and real-time control demonstrates the potential of adaptive, communication-aware control strategies for real-world Smart Grid (SG) deployments.
Abstract
In this paper, we present an experimental evaluation study of the Alternating Direction Method of Multipliers (ADMM), which is a widely used technique in the distributed optimization of power distribution networks. The focus of this study is on how real 5G communication performance affects ADMM in a fully experimental platform that features commercial 5G connectivity and real-time control. The ADMM-based Distributed Optimal Power Flow (DOPF) problem is solved using the IEEE 123-bus unbalanced distribution feeder subdivided into five areas, each managed by a local controller implemented on a Raspberry Pi. To mitigate the impact of the communication network variability, we propose a delay threshold-based mechanism that yields a 7.75% reduction in convergence time compared to a no-threshold baseline. We also devised a policy to dynamically update the threshold value based on communication and computation conditions, achieving a 26.42% reduction in the convergence time compared with the static optimal threshold. These results demonstrate the potential of adaptive, communication-aware control strategies for real-world Smart Grid (SG) deployments.
Active distribution networks (ADNs) constitute a vital component of modern power systems, integrating multiple distributed generation (DG) units. Although DG integration improves efficiency by decreasing power losses, its deployment is frequently limited by technical constraints and financial considerations. Traditionally, network reconfiguration has been applied to loss optimization in distribution grids; however, in radial networks, its effectiveness is restricted because of the limited flexibility of power flow paths. Consequently, the combined application of DG placement and reconfiguration offers a more effective strategy for reducing active losses in contemporary distribution systems. During such optimization, it is crucial to include uncertainties in load and renewable generation to ensure realistic and stable results. In this context, the present study introduces a robust optimization framework designed to maintain reliable performance despite variations in generation and consumption. The proposed method ensures that optimal DG allocation and network configuration remain stable even under moderate fluctuations in system conditions. To evaluate its performance, both robust and deterministic formulations were run for a 70-bus system, and the obtained outcomes were compared with those reported in a reference study. The outcomes prove that the proposed approach significantly reduces daily energy losses under normal and uncertain operating conditions compared with the reference method.
Meisam Mahdavi, Abdullah G. Alharbi, A. BaQais et al.· IEEE Canadian Journal of Ele...· 0 citations
The densification of wireless networks and growing real-time service demands have intensified the need for intelligent, energy-efficient resource allocation. Traditional static and centralized methods fall short in adapting to the dynamic and interference-prone nature of 5G and emerging 6G environments. This study proposes a decentralized reinforcement learning (RL)-based framework for joint power and spectrum allocation in ultra-dense wireless systems. Each base station acts as an autonomous agent, making real-time decisions based on local traffic and interference conditions. Simulated using a custom Python-based environment with 50 base stations and 500 users, the RL approach is benchmarked against static and optimization-based methods. Results show the RL model achieves up to 91% energy efficiency, 94% spectrum utilization, and only 5% QoS degradation, outperforming baseline models. This work demonstrates the viability of RL for distributed resource management and provides a reproducible simulation toolkit to support further research in AI-driven wireless communication systems.
Mugerwa Joseph, Ajaegbu Chigozirim· International Journal Of Eng...· 0 citations
The transition of 5G and beyond wireless networks toward intelligence-driven and autonomous operation has revitalized strong interest in Non-Orthogonal Multiple Access (NOMA) as an efficient multiple access framework. Power allocation critically governs NOMA performance, directly impacting throughput, user fairness, and SIC effectiveness. This survey presents a focused review of power allocation strategies in NOMA, with emphasis on the progression from static and optimization-based dynamic schemes to data-driven Artificial Intelligence (AI) and Machine Learning (ML) driven approaches. In contrast to conventional strategies that require instantaneous channel state information and iterative optimization, AI/ML techniques enable adaptive, scalable, and low-latency decision-making in highly dynamic and nonconvex environments. Recent advances in reinforcement learning and deep learning for NOMA power control are discussed, highlighting key challenges like imperfect CSI, inter-cluster interference, and distributed learning constraints. This survey provides a concise AI-centric analysis and identifies promising directions for a practical learning-driven NOMA power allocation framework for future wireless networks. A consolidated, critically comparative analysis of NOMA power allocation that bridges the gap between 5G practice and 6G imperatives is also presented in this survey.
Lekshmi Nair M, Neelakantan Pc· International Journal of Com...· 0 citations
Distribution network reconfiguration plays an important role in maintaining system performance during fault conditions while minimizing operational losses. This paper presents an Adaptive Modified Firefly Algorithm (AMFA) for determining optimal switching configurations in a distribution network under several feeder fault scenarios. The proposed method evaluates a total of 64 possible switching combinations and demonstrates fast convergence, reaching optimal or near-optimal solutions within only $1-3$ iterations. The results show a significant reduction in power losses from an initial condition of 0.5378 MW to as low as 0.0394 MW, while ensuring that all 14 general loads remain supplied. In addition to solution quality, the computational performance of the method is highly efficient, requiring only 5-7 seconds to obtain the optimal configuration. This is considerably faster than conventional manual operation, which typically takes 10-15 minutes and may not guarantee the minimum loss condition. The findings indicate that the proposed AMFA approach is capable of improving both the speed and accuracy of decision-making in distribution system reconfiguration, making it a practical solution for real-time applications under fault conditions.
Prasetio Hamiseno, Ardyono Priyadi, Muhammad Rivai et al.· International Seminar on Int...· 0 citations
With the high penetration of distributed generation, the operational state of active distribution networks exhibits strong uncertainty and fast time-varying characteristics. Power quality issues such as line losses and harmonics have become major factors affecting distribution network optimization. To address this, this paper proposes a joint active and reactive power optimization strategy for active distribution networks based on dynamic community partition. First, causal correlation weights among network nodes are extracted, and the Louvain algorithm is employed for initial community partition. Furthermore, driven by physical constraints, online adaptive updating of community boundaries is achieved. Second, within each community, a multi-objective optimization model that minimizes active power loss and voltage deviation is established, and harmonic constraints are incorporated to actively suppress harmonic pollution while reducing losses and regulating voltage. To solve this problem, a hybrid Tabu Search-Adaptive Particle Swarm Optimization is proposed and embedded into the Alternating Direction Method of Multipliers framework, enabling consistent coordination of boundary variables among communities and distributed parallel solving. Simulation results demonstrate that the proposed strategy effectively reduces system losses and voltage deviations, validating its effectiveness.
The safe and secure operation of power system networks remains a significant challenge due to the ever-increasing demand for electrical energy. In deregulated environments, there is a strong emphasis on the optimal and efficient utilization of existing resources. This work aims to address line congestion by optimally re-dispatching generation resources and proactively managing demand through advanced demand response (DR) programs. An elasticity based, multi-period load model is employed to enhance the realism and effectiveness of DR strategies. The novelty of the proposed work is the holistic approach that simultaneously addresses economic, environmental, and technical objectives, incorporating realistic DR behavior and the advanced modified elephant herding optimization (MEHO) technique. This work proposes a MEHO algorithm for multi-objective congestion management with coordinated generation and DR programs, with comparative analysis against MPSO on both IEEE 30-bus and IEEE 118-bus systems. The MEHO algorithm generates seven unique Pareto-optimal solutions that represent various trade-offs between the conflicting objectives, demonstrating the implementation's remarkable performance on the IEEE 30-bus and IEEE 118-bus test system. MEHO achieves 3.5 to 6.2% better cost solutions, 2.3 to 5.2% lower emissions, and 60 to 62.5% better congestion indices across both test systems.
Jayesh G. Priolkar, G. Kunkolienkar· International Journal of Ele...· 0 citations