Yazar "Kamalasadan, S." seçeneğine göre listele

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    Optimal Energy Management of Power Grid with Electric Vehicles and Flexible Loads
    (Institute of Electrical and Electronics Engineers Inc., 2023) Aygun, A.I.; Kamalasadan, S.
    This work introduces a convex optimization for the management of demand response and home appliances using electric vehicles during a power outage. The algorithm manages the power distribution based on vehicle availability and ensures complete demand during a power outage or supply shortage. In addition, the paper demonstrates an algorithm that reduces peak demand by controlling home devices without violating consumer comfort. Various household appliances, such as air conditioner (AC), water heater (WH), clothes dryer (CD), and dish washer (DW) are developed to achieve more precise results. This algorithm's primary objective is not only to minimize or distribute power consumption but also to move it to a better pricing period based on the flexibility rate. The results demonstrate that with the proposed control architecture the total load are more balanced (more than 40%) when compared to without the control and the usable temperature can be controlled more effectively. © 2023 IEEE.
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    An Optimal Hybrid Management of Electric Vehicle Fleet Charging and Load Scheduling in Active Electric Distribution System
    (Institute of Electrical and Electronics Engineers Inc., 2024) Aygun, A.I.; Hasan, M.S.; Joshi, A.; Kamalasadan, S.
    This paper introduces a novel scheduling framework designed to manage the charging of electric vehicles (EVs) in a way that considers its effects on the power grid. Leveraging the Alternating Direction Method of Multipliers (ADMM), the methodology offers a significant advantage by enabling decentralized sub-problems, allowing for efficient and rapid solutions. The methodology developed as an algorithmic framework incorporates various scheduling approaches for EV charging, including demand management techniques like valley filling and peak shaving, along with real-time pricing (RTP) considerations. These strategies aim to modify individual electricity consumption patterns to reduce peak demand, ultimately enhancing energy efficiency and ensuring the stability of the power system. The results of the study highlight the crucial role of distributed optimization in improving both demand management strategies and cost objectives. The results indicated that the proposed method shows significant improvement in overall energy efficiency when compared to the state-of-the-art centralized convex optimization framework. © 1972-2012 IEEE.