arXiv subjects

Sifat Chowdhury

Publications and source records attributed to Sifat Chowdhury.

4 recordsLinked to original sources

arXiv2026

Resilient Grid Hardening against Multiple Hazards: An Adaptive Two-Stage Stochastic Optimization Approach

The growing prevalence of extreme weather events driven by climate change poses significant challenges to power system resilience. Infrastructure damage and prolonged power outages highlight the urgent need for effective grid-hardening strategies. While some measures provide long-term protection against specific hazards, they can become counterproductive under conflicting threats. In this work, we develop an adaptive two-stage stochastic optimization framework to support dynamic decision-making for hardening critical grid components under multiple hazard exposures. Unlike traditional approaches, our model adapts to evolving climate conditions, enabling more resilient investment strategies. Furthermore, we integrate long-term (undergrounding) and short-term (vegetation management) hardening actions to jointly minimize total system costs. Extensive simulation results validate the effectiveness of the proposed framework in reducing outage and repair costs while enhancing the adaptability and robustness of grid infrastructure planning.

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arXiv2024

Two-Stage Stochastic Optimal Power Flow for Microgrids With Uncertain Wildfire Effects

Large-scale power outages caused by extreme weather events are one of the major factors weakening grid resilience. In order to prevent the critical infrastructure from cascading failure, power lines are often proactively de-energized under the threat of a progressing wildfire. In this context, the potential of microgrid (MG) functioning in islanded mode can be exploited to enhance the resiliency of the power grid. However, there are numerous uncertainties originating from these types of events and an accurate modeling of the MG is required to harness its full potential. In this paper, we consider the uncertainty in line outages depending on fire propagation and reduced solar power generation due to the particulate matter in wildfire smoke. We formulate a two-stage stochastic MG optimal power flow problem by utilizing a second-order cone relaxation of the DistFlow model. Leveraging an effective approximation of the resistive heat gain, we separate the complicating constraints of dynamic line rating from the resulting optimization problem. Extensive simulation results corroborate the merits of our proposed framework, which is tested on a modified IEEE 22-bus system.

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arXiv2022

Co-optimization of Battery Routing and Load Restoration for Microgrids with Mobile Energy Storage Systems

Mobile energy storage systems (MESS) offer great operational flexibility to enhance the resiliency of distribution systems in an emergency condition. The optimal placement and sizing of those units are pivotal for quickly restoring the curtailed loads. In this paper, we propose a model for load restoration in a microgrid while concurrently optimizing the MESS routes required for the same. The model is formulated as a mixed integer second order cone program by considering the state of charge and evolution of the lower and upper bounds of battery capacities. Simulation results tested on the IEEE 123- bus benchmark system demonstrate the efficacy of the proposed model.

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arXiv2021

Mitigating Greenhouse Gas Emissions Through Generative Adversarial Networks Based Wildfire Prediction

Over the past decade, the number of wildfire has increased significantly around the world, especially in the State of California. The high-level concentration of greenhouse gas (GHG) emitted by wildfires aggravates global warming that further increases the risk of more fires. Therefore, an accurate prediction of wildfire occurrence greatly helps in preventing large-scale and long-lasting wildfires and reducing the consequent GHG emissions. Various methods have been explored for wildfire risk prediction. However, the complex correlations among a lot of natural and human factors and wildfire ignition make the prediction task very challenging. In this paper, we develop a deep learning based data augmentation approach for wildfire risk prediction. We build a dataset consisting of diverse features responsible for fire ignition and utilize a conditional tabular generative adversarial network to explore the underlying patterns between the target value of risk levels and all involved features. For fair and comprehensive comparisons, we compare our proposed scheme with five other baseline methods where the former outperformed most of them. To corroborate the robustness, we have also tested the performance of our method with another dataset that also resulted in better efficiency. By adopting the proposed method, we can take preventive strategies of wildfire mitigation to reduce global GHG emissions.

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