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Antar Kumar Biswas

Publications and source records attributed to Antar Kumar Biswas.

5 recordsLinked to original sources

Census Tract-Level Power Outage Prediction and Sensitivity Analysis During Extreme Events

This paper develops a two-stage hurdle model for predicting power outage occurrence and severity at the census-tract level. The proposed framework is then used to assess the sensitivity of power outage to socioeconomic, demographic, and environmental factors during extreme events. Five heterogeneous data streams are integrated at the census tract level: 15-minute customer outage data, OpenMeteo hourly weather records, American Community Survey (ACS) socioeconomic indicators, Centers for Disease Control (CDC) social vulnerability indices (SVI), and Geographic Information System (GIS) derived vegetation coverage. The proposed framework is validated using a high-resolution power outage dataset covering 290 census tracts in the Detroit area over a period exceeding 14 months, with a temporal resolution of 15 minutes.

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A Review of Community-Centric Power System Resilience: Strategies, Data-Driven Methods, and Techno-Legal Perspectives

This paper presents a comprehensive review of community-centric power system resilience, emphasizing the integration of community-level resilience considerations and techno-legal governance frameworks with engineering-based resilience enhancement strategies and data-driven approaches to address extreme events. Recent large-scale outages have demonstrated that power disruptions can cascade beyond electrical infrastructure and disproportionately affect vulnerable communities, critical services, and interconnected urban systems, highlighting the need for resilience approaches that integrate technical, social, and regulatory dimensions. Within this community-centric perspective, the review first summarizes state-of-the-art strategies for enhancing power system resilience, including network hardening, resource allocation, optimal scheduling, and system reconfiguration techniques, while highlighting the growing role of artificial intelligence (AI) and data-driven analytics in supporting resilience planning and operational decision-making. It then examines the interdependencies between power system resilience and community resilience, addressing socioeconomic and behavioral dimensions, cross-infrastructure interconnections, and the emerging role of resilience hubs. The paper further examines the techno-legal frameworks governing resilient energy systems by comparing the regulatory landscapes of the European Union (EU) and the United States, highlighting key similarities and distinctions that shape resilience planning and implementation. By analyzing state-of-the-art engineering-based, AI-driven, and techno-legal methods for assessing and mitigating the impacts of high-impact, low-probability (HILP) events, the review identifies critical research gaps and outlines promising directions for future investigation.

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Predictive Modeling of Power Outages during Extreme Events: Integrating Weather and Socio-Economic Factors

This paper presents a novel learning based framework for predicting power outages caused by extreme events. The proposed approach targets low-probability high-consequence outage scenarios and leverages a comprehensive set of features derived from publicly available data sources. We integrate EAGLE-I outage records from 2014 to 2024 with weather, socioeconomic, infrastructure, and seasonal event data. Incorporating social and demographic indicators reveals patterns of community vulnerability and improves understanding of outage risk during extreme conditions. Four machine learning models are evaluated, including Random Forest (RF), Graph Neural Network (GNN), Adaptive Boosting (AdaBoost), and Long Short-Term Memory (LSTM). Experimental validation is performed on a large-scale dataset covering counties in the lower peninsula of Michigan. Among all models tested, the LSTM network achieves higher accuracy.

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Secure and Decentralized Peer-to-Peer Energy Transactions using Blockchain Technology

This paper presents an optimal peer-to-peer (P2P) energy transaction mechanism leveraging decentralized blockchain technology to enable a secure and scalable retail electricity market for the increasing penetration of distributed energy resources (DERs). A decentralized bidding strategy is proposed to maximize individual profits while collectively enhancing social welfare. The market design and transaction processes are simulated using the Ethereum testnet, demonstrating the blockchain network's capability to ensure secure, transparent, and sustainable P2P energy trading among DER participants.

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Decentralized P2P Trading based on Blockchain for Retail Electricity Markets

This paper introduces peer to peer (P2P) trading mechanisms based on decentralized Blockchain to facilitate retail electricity market for ever-increasing distributed energy resources (DERs). The Blockchain network supports fast and secure retail trading among DERs and facilitates a sustainable local P2P trading platform. In this decentralized Blockchain architecture no single entity or organization has control over the entire system rather all users collectively maintain control. The effectiveness of the proposed automated market design and optimization is simulated using different use case scenarios in an open source Blockchain Simulator and MATLAB. The results show the efficacy of the trading mechanism in achieving demand response through strategies such as peak load shaving, load shifting, and integration of DERs.

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