M
Muhammad Fahad Zia
Researcher at National University of Computer and Emerging Sciences
Publications - 32
Citations - 1279
Muhammad Fahad Zia is an academic researcher from National University of Computer and Emerging Sciences. The author has contributed to research in topics: Renewable energy & Microgrid. The author has an hindex of 6, co-authored 23 publications receiving 582 citations. Previous affiliations of Muhammad Fahad Zia include King Fahd University of Petroleum and Minerals & University of Management and Technology.
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Journal ArticleDOI
Microgrids energy management systems: A critical review on methods, solutions, and prospects
TL;DR: A comparative and critical analysis on decision making strategies and their solution methods for microgrid energy management systems are presented and various uncertainty quantification methods are summarized.
Journal ArticleDOI
Microgrid Transactive Energy: Review, Architectures, Distributed Ledger Technologies, and Market Analysis
Muhammad Fahad Zia,Mohamed Benbouzid,Elhoussin Elbouchikhi,S. M. Muyeen,Kuaanan Techato,Josep M. Guerrero +5 more
TL;DR: The proposed architecture and analytical review of distributed ledger technologies and local energy markets pave the way for advanced research and industrialization of transactive energy systems.
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Optimal operational planning of scalable DC microgrid with demand response, islanding, and battery degradation cost considerations
TL;DR: A practical degradation cost model for a Li-ion battery is developed to optimize battery scheduling and achieve its realistic operational cost and would aid in DC microgrids adoption planning that would expectedly replace traditional AC grids in the future.
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Energy Management System for an Islanded Microgrid With Convex Relaxation
TL;DR: Numerical simulations are carried out to prove the effectiveness of the proposed strategy in reducing the operating and emission costs of the islanded MG and it is shown that the developed convex energy management system formulation has an optimality gap of less than 1% with reduced computational cost.
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Learning-Based Methods for Cyber Attacks Detection in IoT Systems: A Survey on Methods, Analysis, and Future Prospects
TL;DR: Both machine and deep learning methods are presented and analyzed in relation to the detection of cyber attacks in IoT systems and the difficulties faced by the IoT devices or systems after the occurrence of an attack are faced.