Yazar "Shokouhandeh, Hassan" seçeneğine göre listele

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    Cost-Effective Optimization of Sizing and Charging Profiles for PHEV Parking Lots in Smart Microgrids Using Harmony Search Algorithm
    (Institute of Electrical and Electronics Engineers Inc., 2025) Kamarposhti, Mehrdad Ahmadi; Shokouhandeh, Hassan; Ahmed, Emad M.; Zaki, Zaki A.; Çolak, İlhami; Barhoumi, El Manaa; Eguchi, Kei
    This paper presents an optimization approach for managing the charging and discharging of electric vehicles (EVs) in parking lots using the Harmony Search (HS) and Differential Evolution (DE) algorithms. The study is conducted on a standard IEEE 33-bus grid considering three EV penetration levels: 11.3%, 35%, and 45%. The objective is to minimize operational costs while improving grid performance. Simulation results indicate that increasing EV penetration slightly raises overall expenses due to the higher cost of vehicle charging compared to the revenue from discharging. However, EV participation significantly reduces ohmic losses and improves the grid load profile. The proposed HS algorithm outperforms the DE algorithm by achieving lower microgrid costs and better convergence efficiency. Specifically, HS reduces energy losses by up to 40%, demonstrating its effectiveness in optimizing energy management for microgrids with high EV integration. © 2013 IEEE.
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    Optimizing energy management in microgrids with ant colony optimization: Enhancing reliability and cost efficiency for sustainable energy systems
    (Oxford University Press, 2024) Kamarposhti, Mehrdad Ahmadi; Shokouhandeh, Hassan; Lee, Yeonwoo; Kang, Sun-Kyoung; Çolak, İlhami; Barhoumi, El Manaa
    This paper investigates the application of ant colony optimization (ACO) for energy management in microgrids, incorporating distributed generation resources such as solar panels, fuel cells, wind turbines, battery storage, and microturbine. The study evaluates energy management in two scenarios, with the first utilizing all available resources and the second applying specific operational constraints to wind turbines and solar panels. Results demonstrate that ACO effectively reduces operational costs and enhances system reliability, contributing to sustainable energy management practices. Additionally, the study compares ACO with the imperialist competitive algorithm, highlighting ACO's superior performance in cost reduction and efficiency improvements. This research advances energy system optimization and supports the development of sustainable solutions in microgrids. © 2024 The Author(s). Published by Oxford University Press.