M
Mahdi Sedghi
Researcher at K.N.Toosi University of Technology
Publications - 24
Citations - 1140
Mahdi Sedghi is an academic researcher from K.N.Toosi University of Technology. The author has contributed to research in topics: Distributed generation & Optimization problem. The author has an hindex of 13, co-authored 22 publications receiving 915 citations. Previous affiliations of Mahdi Sedghi include Iran University of Science and Technology.
Papers
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Optimal Storage Planning in Active Distribution Network Considering Uncertainty of Wind Power Distributed Generation
TL;DR: In this article, the optimal planning of batteries in the distribution grid is presented, which determines the location, capacity and power rating of batteries while minimizing the cost objective function subject to technical constraints.
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Distribution network expansion considering distributed generation and storage units using modified PSO algorithm
TL;DR: In this article, a modified PSO algorithm is applied to solve the complex optimization problem of multistage distribution expansion planning, and the proposed strategies improve the distribution network from both economical and reliability points of view compared with other methods.
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Plug-in electric vehicle batteries degradation modeling for smart grid studies: Review, assessment and conceptual framework
TL;DR: In this paper, a conceptual framework for battery degradation modeling is proposed that can be easily used in smart grid studies, without necessarily requiring a detailed understanding of fundamental electrochemical processes, and the proposed framework considers not only the battery degradation, but also that of other related components in a smart grid.
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Cost-Benefit Analysis of V2G Implementation in Distribution Networks Considering PEVs Battery Degradation
Ali Ahmadian,Mahdi Sedghi,Behnam Mohammadi-Ivatloo,Ali Elkamel,Masoud Aliakbar Golkar,Michael Fowler +5 more
TL;DR: A stochastic methodology for smart charging of PEVs is presented and it is shown that the smart charging is economical in all conditions and also it reduces the battery degradation cost in comparison with uncoordinated charging.
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Assessment of optimization algorithms capability in distribution network planning: Review, comparison and modification techniques
TL;DR: The hybrid Tabu search/genetic algorithm (TS/GA) and the improved particle swarm optimization (PSO) algorithm proposed in this paper are the best choices for optimal distribution network planning.