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Ebrahim Mortaz

Researcher at Pace University

Publications -  12
Citations -  270

Ebrahim Mortaz is an academic researcher from Pace University. The author has contributed to research in topics: Renewable energy & Microgrid. The author has an hindex of 7, co-authored 12 publications receiving 174 citations. Previous affiliations of Ebrahim Mortaz include Amirkabir University of Technology & Auburn University.

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Microgrid energy scheduling using storage from electric vehicles

TL;DR: In this article, an optimization model for the energy management that considers the energy and storage provided by the EVs is proposed to minimize the expected total operation costs including generation, day-ahead market, battery wear, and real-time balancing costs for the next 24 hours.
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Evaluating the impact of renewable generation on transmission expansion planning

TL;DR: This paper studies how renewable generation, wind and solar in particular, can impact the transmission expansion planning (TEP), and uses the Binary Particle Swarm Optimization to solve the TEP problem.
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Optimizing the size of a V2G parking deck in a microgrid

TL;DR: A mathematical model is proposed that aims to determine the optimal number of V2G stations in a microgrid parking deck by minimizing the total cost of investment and operation and shows that investing in the V1G technology can reduce the long-term cost of electricity supply for the microgrid.
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An optimization model for siting and sizing of vehicle-to-grid facilities in a microgrid

TL;DR: The simulation results show that investing in the V2G technology can considerably improve the long-term economics of the microgrid, but the obtained profit and the payback period can vary from one plan to another.
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Imbalance accuracy metric for model selection in multi-class imbalance classification problems

TL;DR: This paper introduces a new metric, imbalance accuracy metric (IAM), that can be used as a solo measure for model evaluation and selection aiming to eliminate the need for group metric computation and simplify the model selection.