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All Journal International Journal of Applied Power Engineering (IJAPE) Indonesian Journal of Electrical Engineering and Computer Science
Wan Azlan Wan Zainal Abidin
Universiti Malaysia Sarawak
Electrical & Electronics Engineering

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Priority-based market clearing model for off-grid P2P energy trading Wan Azlan Wan Zainal Abidin; Alan Ling Sieng Yew
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 1: January 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i1.pp24-37

Abstract

Standalone solar PV power system is being used as an option for electrification in remote areas around the world providing basic electricity needs. However, the approach suffers from power mismatch and energy efficiency issues. This paper proposes market-clearing model peer-to-peer energy trading (P2PET) based on multiple standalone solar power system design specification in rural Sarawak, Malaysia. The proposed system combined multiple standalone solar PV system within the community through P2PET trading concept. P2PET creates the platform for energy transaction between each system and even support business such as a workshop to operate high-power electrical appliances. As energy generation is constrained in an off-grid system, the proposed market-clearing model prioritizes the energy trading between the seller and business buyer who bring more benefit to the community. Subsequently, participants in energy trading have a selection of strategies to maximize personal benefits such as profit earning or energy sufficiency. Simulation studies are applied to verify the performance of the proposed model which increases energy efficiency, improves the local economy, and maximizes the community’s welfare from electrification.
Explainable AI for harmonic fingerprinting and voltage sag diagnosis in decentralized power grids: trends, challenges and future directions Mohd Hatta Jopri; Tole Sutikno; Yacine Djeghader; Mohd Riduan Mohd Shariff; Wan Azlan Wan Zainal Abidin
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i3.pp1484-1498

Abstract

The evolution of decentralized power grids has increased the complexity of power-quality monitoring, particularly harmonic fingerprinting and voltage sag diagnosis. Artificial intelligence improves disturbance detection and classification, yet black-box models limit transparency, engineering validation, and operator trust. This review synthesizes 108 selected studies on explainable artificial intelligence (XAI) for power-system diagnostics, focusing on SHapley Additive exPlanations (SHAP), local interpretable model-agnostic explanations (LIME), attention-based interpretability, visual analytics, and physics-informed learning. The review integrates harmonic fingerprinting with voltage sag diagnosis through their shared requirements for source attribution, temporal interpretation, physical consistency, and operator-oriented explanation. Four major deployment gaps are identified: data quality, computational latency, physical grounding, and trustworthiness. Future priorities include real-time embedded XAI, physics-informed neural networks, federated learning, standardized trustworthiness metrics, and adaptive model lifecycle management. The findings indicate that reliable autonomous diagnosis requires explainability to be integrated with predictive performance, electrical-system physics, computational efficiency, and field validation. This integration provides a stronger foundation for transparent, resilient, and trustworthy diagnostic systems in decentralized power grids.
Co-Authors Alan Ling Sieng Yew Mohd Hatta Jopri Mohd Riduan Mohd Shariff Tole Sutikno Yacine Djeghader