WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
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Article Citation - WoS: 4Citation - Scopus: 5A Smart Building Energy Management Incorporating Clustering-Based Tariffs in the Presence of Domestic Solar Energy, Battery, and Electric Vehicle(Pergamon-elsevier Science Ltd, 2024) Mohammadpourfard, Mousa; Mohammadi-ivatloo, Behnam; Mohammadpourfard, Mousa; 03.06. Department of Energy Systems Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologySmart buildings play a crucial role in optimizing energy management within the power network. As end-users of the power network, they have the ability to not only reduce economic costs for householders but also modify the technical indices of the power network. To promote efficient device management in smart homes (SH), demand response programs are recommended for consumers. This research investigates the application of clusteringbased electricity pricing strategy aimed at effectively managing the energy devices of a residential smart home. The utilized method categorizes the electricity tariff into five rates according to the clustering of the realtime pricing program. Ward's clustering method is utilized to cluster and determine new electricity tariffs. The primary goal of the energy management program is to minimize the building's energy cost, which is accomplished through the utilization of the multi-verse optimizer. The smart home consists of essential and manageable appliances, a photovoltaic panel (PV), a sodium-sulfur (NaS) battery, and an electric vehicle (EV). The initial parameters of the PV and EV are modeled stochastically by their probability distribution functions and calculated using the Latin hypercube sampling algorithm. The smart building's performance is assessed by taking into account various demand response programs. The numerical results present that the application of the clusteringbased management method has resulted in a significant reduction of 23-43 % in the electricity cost of smart homes. Additionally, the smart home exhibits a more linear consumption pattern when considering the electricity tariffs based on the clustering approach.Article Citation - WoS: 19Citation - Scopus: 20Multi-Objective Optimization of a Novel Supercritical Co2 Cycle-Based Combined Cycle for Solar Power Tower Plants Integrated With Sofc and Lng Cold Energy and Regasification(Wiley, 2022) Taheri, Muhammad Hadi; Gökçen Akkurt, Gülden; Mohammadpourfard, Mousa; Aminfar, Habib; Gökçen Akkurt, Gülden; 03.06. Department of Energy Systems Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyThis study presents a new system for solar power, which is generated through a solar power tower with a molten salt cycle. To increase the consumption of energy losses, besides the closed supercritical carbon dioxide (sCO2) Brayton cycle, a liquid natural gas (LNG) open-cycle was used as a heat sink alongside a cascade organic Rankine cycle with the capability of working at low temperatures. LNG is implemented for a solid oxide fuel cell input, after cooling down the power generation systems and power generation. Besides the economic and thermodynamic analysis, destruction of exergy has been controlled and parametric studies are performed to investigate the influence of relative factors on the performance of the system. To optimize the system, a genetics algorithm has been employed by considering two reciprocal objective functions of the total cost rate and the exergy efficiency. The results of multi-objective optimization show that the optimized point has a total product cost rate of $115.3/h and an exergy efficiency of 71%. Furthermore, exergy analysis shows that the molten salt heat exchangers and the LNG heat exchangers have the maximum rates of irreversibility and must be taken into consideration as a major priority for optimization.