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Advancing Microgrid Resilience Through Vehicle-to-Grid Integration: A Review of Current Trends and Future Directions Akash, Khairul Bashar; Ahmed, Shishir; Emon, Md Afrad Hasan; Shifat, Sk Md Raihan
Control Systems and Optimization Letters Vol 3, No 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.220

Abstract

The integration of vehicle-to-grid (V2G) technology within microgrids is emerging as a transformative solution to enhance the resilience, reliability, and efficiency of modern energy systems. With electric vehicle (EV) adoption accelerating worldwide, V2G allows EVs to function not only as transportation assets but also as mobile, bidirectional energy storage resources capable of strengthening grid flexibility. By enabling EVs to discharge electricity back into the grid, V2G systems contribute to critical grid services such as peak shaving, load leveling, frequency regulation, and emergency backup during power disruptions. For microgrids, which are decentralized energy systems designed to operate either independently or in coordination with the main grid, the integration of V2G significantly improves stability, demand management, and the capacity to supply critical loads under adverse conditions. This dual functionality positions V2G as an important enabler of resilient, community-based energy networks. Nevertheless, widespread deployment faces challenges, including concerns over battery degradation, the absence of standardized interoperability, high infrastructure and implementation costs, and regulatory and market uncertainties. Furthermore, the full potential of V2G relies heavily on advancements in communication protocols, optimization-based energy management strategies, and intelligent control algorithms that can balance user preferences with system needs. This review examines the present state of V2G integration in microgrids, outlining its advantages, barriers, and future research directions, while emphasizing the importance of supportive regulations, large-scale pilot projects, and continued technological innovation in enabling the transition toward decentralized, decarbonized, and digitized energy systems
Performance Analysis and Visual Evaluation of A Deep Learning-Based Wildfire Detection System Shifat, Sk Md Raihan; Joarder, Humayra Atia; Hasan, Md Najmul
Control Systems and Optimization Letters Vol 4, No 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i1.239

Abstract

Wildfires represent a critical threat to ecosystems, human safety, and economic stability, emphasizing the necessity for rapid and reliable detection mechanisms. Traditional approaches such as satellite monitoring and manual surveillance are often hindered by latency, limited spatial resolution, and environmental constraints, thereby underscoring the value of automated and intelligent solutions. This study presents DeepFire, a real-time wildfire detection framework developed using the YOLOv8 architecture. Data preprocessing involved normalization, removal of irrelevant objects, and extensive data augmentation to enhance generalization and mitigate potential overfitting. The dataset encompassed diverse environmental conditions, including varying smoke intensities, vegetation densities, and viewing perspectives. Experimental evaluation demonstrated outstanding performance, achieving a mean Average Precision (mAP@0.5) of 0.995, precision and recall values of 0.995, and an F1-score of 1.00 at the optimal confidence threshold for detection. The mAP@0.5 metric was selected for its suitability in assessing localization accuracy under real-time constraints, whereas mAP@0.5:0.95 is discussed in the main text for comprehensive benchmarking. Qualitative assessments further verified the model’s robustness in accurately classifying a wide range of fire and non-fire scenarios. Future research will focus on enhancing dataset diversity, improving deployment efficiency, and validating system performance under real-world conditions.