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Arya Abdollahi

Publications and source records attributed to Arya Abdollahi.

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Community-Centered Resilience Enhancement of Urban Power and Gas Networks via Microgrid Partitioning, Mobile Energy Storage, and Data-Driven Risk Assessment

Urban energy systems face increasing challenges due to high penetration of renewable energy sources, extreme weather events, and other high-impact, low-probability disruptions. This project proposes a community-centered, open-access framework to enhance the resilience and reliability of urban power and gas networks by integrating microgrid partitioning, mobile energy storage deployment, and data-driven risk assessment. The approach involves converting passive distribution networks into active, self-healing microgrids using distributed energy resources and remotely controlled switches to enable flexible reconfiguration during normal and emergency operations. To address uncertainties from intermittent renewable generation and variable load, an adjustable interval optimization method combined with a column and constraint generation algorithm is developed, providing robust planning solutions without requiring probabilistic information. Additionally, a real-time online risk assessment tool is proposed, leveraging 25 multi-dimensional indices including load, grid status, resilient resources, emergency response, and meteorological factors to support operational decision-making during extreme events. The framework also optimizes the long-term sizing and allocation of mobile energy storage units while incorporating urban traffic data for effective routing during emergencies. Finally, a novel time-dependent resilience and reliability index is introduced to quantify system performance under diverse operating conditions. The proposed methodology aims to enable resilient, efficient, and adaptable urban energy networks capable of withstanding high-impact disruptions while maximizing operational and economic benefits.

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Research on Sectionalizing Switches Placement Problem of Distribution System Automation Based on Multi-Objective Optimization Analysis

Achieving high distribution-reliability levels and concurrently minimizing operating costs can be considered as the main issues in distribution system optimization. Determination of the optimal number and location of automation devices in the distribution system network is an essential issue from the reliability and economical points of view. To address these issues, this paper develops a multi-objective model, wherein the primary objective, optimal automation devices placement is implemented aiming at minimizing the operating costs, while in the second objective the reliability indices improvement is taken into account. So, modified non dominated sorting genetic algorithm, is developed and presented to solve this multi-objective mixed-integer non-linear programming problem. The feasibility of the proposed algorithm examined by application to two distribution feeders of the Tabriz distribution network containing the third feeder of the Azar substation with a distributed generation unit and first and third feeders of ElGoli substation which form a double feed feeder.

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