Research Assistant, Nanotechnology Research Center, SQU | January 2024- Current

Vision-Informed Multi-Modal Multi-Agent Reinforcement Learning for Active Distribution Network Scheduling:
• Proposed a multimodal multi-agent reinforcement learning-based scheduling framework for active distribution networks. The proposed framework integrates electrical grid measurements with soiling loss features extracted from solar panel images using a hybrid CNN–Vision Transformer (ViT) model, enabling agents controlling energy storage systems, flexible loads, solar plants, and static var compensation devices to learn cooperative and optimal operational policies.
• Demonstrated significant improvements in grid performance and sustainability. The framework achieved superior scheduling outcomes—reducing operational costs, minimizing network losses, enhancing grid flexibility, and lowering carbon emissions—outperforming conventional scheduling strategies. Moreover, the proposed framework is validated on the large-scale 2289-bus Nizwa distribution network in Oman.

Deep Learning-based Geospatial Mapping Framework for Large-Scale Electric Power Grids:
• Introduced a novel residual graph convolutional network with attention mechanism that enables precise geospatial prediction of energy infrastructure components (poles, service points, substations) and their spatial interconnections within large-scale distribution and transmission networks.
• Validated the framework on real-world regional grids (Oman and Nigeria), achieving high link-prediction accuracies (95.88% and 92.98%) and near-perfect regression performance (R² ≈ 0.99), demonstrating strong generalizability and robustness across diverse geospatial contexts.
Neural Architecture Search via Reinforcement Learning for Image-based Solar Panel Soiling Loss Prediction:
• Proposed a novel RL–based NAS framework that explores a discrete neural architecture search space for image-based regression of solar panel soiling loss.
• The project aims to effectively address the computational limitations and adaptability challenges of conventional NAS methods.
• The work is currently under review.
Robust Deep Learning Model for Spatiotemporal Forecasting of Renewable Energy Sources:
• Developed a robust CNN–BiLSTM model with a spatiotemporal attention mechanism that jointly captures spatial correlations and temporal dependencies in large-scale renewable energy data, enabling accurate multi-horizon forecasting without requiring site-specific model training or additional preprocessing steps.
• Enhanced resilience and performance through advanced optimization and robust learning by employing a correntropy-based loss function to mitigate the effects of data contamination (incompleteness, Gaussian noise, and outliers) and introducing a Partial Reinforcement Optimization strategy for efficient hyperparameter tuning—demonstrating superior accuracy over state-of-the-art forecasting methods on real-world photovoltaic (Arizona) and wind (Texas) datasets.
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Research Assistant, Sustainable Energy Research Center, SQU | September 2023- December 2023 (Full-time), January 2023- August 2023 (Part-time)

Virtual Inertia System Installation in the National Grid Network of Sultanate of Oman:
• Investigated the optimal implementation of Virtual Inertia Systems (VIS) in Oman's national grid to compensate for the lack of inertia from renewable energy sources.
• The project aimed to enhance frequency stability and mitigate voltage fluctuations in the grid due to increased renewable energy integration. The study assessed the techno-economic feasibility of VIS installations across the OETC 2027 grid model for both winter and summer scenarios, achieving an optimal balance between technical requirements and economic constraints.
Microgrid Resource Planning Framework Using Multi-Agent Deep Reinforcement Learning Approach:
• Developed a comprehensive framework utilizing Multi-agent Deep Deterministic Policy Gradient (MADDPG) to integrate renewable energy sources (RESs), battery energy storage systems (BESSs), and electric vehicle charging stations (EVCSs). This framework incorporates dynamic simulation environments and responds to stochastic variables like trip durations and sporadic nature of RESs.
• Aimed to optimize network power losses, total installation and operational costs, greenhouse gas emissions, and system voltage stability. The model also focuses on improving the state of charge (SOC) for EVs, using practical weather data and EV behavior across multiple grid networks. The approach outperformed contemporary techniques across various performance metrics.
Techno-economic and Environmental Assessment of Electric Vehicle Charging Stations in low-inertia Microgrids:
• Developed a novel Multi-objective Optimization framework to facilitate optimal allocation of photovoltaic (PV) systems, wind turbines (WT), battery energy storage system-based virtual synchronous generators (BESS-VSG), and electric vehicle charging stations (EVCS) in microgrids. This model uniquely addresses frequency instability through BESS-VSG, which simulates the inertia of traditional synchronous generators.
• This research aimed to optimize total net present cost (TNPC), levelized cost of electricity (LCOE), energy loss, frequency deviation, voltage stability, and carbon emissions. The approach was tested on real-world grid networks in Masirah Island, Oman, and Ankara, Turkey, showing significant reductions in carbon emissions and improvements in economic and technical metrics compared to existing methods.
Stochastic Multi-objective Architecture for Strategic Integration of Distributed Energy Resources in Microgrids:
• Developed a novel Multi-objective Architecture to optimize the allocation and sizing of renewable energy-based distributed generation (RDG) and the operational strategies of battery energy storage systems (BESS) in microgrids. This model evaluates uncertainty in load demands and renewable outputs to ensure robust system performance under varying conditions.
• The model aims to enhance voltage stability margins and reduce annual operational costs while ensuring substantial energy transfers during peak and off-peak periods. The model's effectiveness was demonstrated on practical distribution grids, surpassing other contemporary metaheuristics in terms of cost, energy loss, and voltage stability.
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Power Systems Engineer, DPDC Smart Grid Pilot Project, NKSoft KEMA Corporation, USA — Dhaka, Bangladesh | July 2022 – August 2023 (Full-time), January 2022 – June 2022 (Internship)

Technical Team Member:
• Actively contributed as a member of the technical team working on the "DPDC Smart Grid Pilot Project," a government-funded national initiative in Bangladesh aimed at modernizing the selected grid infrastructure in Dhaka city and implementing Advanced Distribution Management Systems (ADMS) for the DPDC networks.
GIS Database Integration with ADMS:
• Served as the engineer in charge of integrating Geographic Information System (GIS) databases with SCADA and ADMS systems to enhance spatial data management and operational efficiency.
• This involved mapping grid components and ensuring seamless data flow between systems.
Automatic Circuit Reclosers and Load Switches Allocation:
• Utilized GIS databases to allocate Automatic Circuit Reclosers (ACRs) and Automated Load Switches (ALS) within the distribution network, optimizing system reliability and fault management.
Feeder Topology Analysis:
• Conducted feeder topology analysis using ETAP and CYME software to assess network configurations, perform load flow studies, and support decision-making for grid improvements.
FLISR and Network Reconfiguration:
• Assessed Fault Location, Isolation, and Service Restoration (FLISR) techniques and optimal network reconfiguration strategies to enhance grid resilience and minimize downtime during outages.
SCADA Software Development:
• Played a key role in the development and enhancement of SCADA (Supervisory Control and Data Acquisition) software, focusing on real-time monitoring, control, and visualization of the smart grid infrastructure.