Energy
F. Hasanlu; A. Fallah-Sabet; A. Fereidunian
Abstract
The electric energy demand has been increasing, following digitalization and development of urbanization, which has led to functional enhancement of home energy management system (HEMS) and its subsystems. A great amount of the produced electricity is used for household loads, whereas self-sufficient ...
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The electric energy demand has been increasing, following digitalization and development of urbanization, which has led to functional enhancement of home energy management system (HEMS) and its subsystems. A great amount of the produced electricity is used for household loads, whereas self-sufficient smart homes can supply all or a large portion of their electricity consumption by using renewable energy resources. In this study, an MILP model is formulated for energy scheduling on a 24-hour time horizon, to achieve the optimal performance of each home appliance for minimizing the smart home energy bill. The studied smart home can exchange electrical energy with the upstream network. A sensitivity analysis has been performed to show the impact of the changes in scheduling and energy prices on the electricity energy bill. The impact of the presence of renewable resources and electrical storage is studied on the electricity energy bill and the electrical energy sales profit of the house in different scenarios. Numerical results show that using the proposed model in the self-sufficient smart home reduces the amount of power purchased from the grid by 45%, transfers energy to the grid at some hours, and the energy bill is reduced by 65%.
Energy
F. Akhlaghinezhad; H. Bagheri Sabzevar
Abstract
Considering the global energy crisis and the need to reduce energy consumption while providing thermal comfort to occupants, building performance prediction using building simulation programs requires higher accuracy of output data. Therefore, it seems necessary to study the impact of occupant behavior, ...
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Considering the global energy crisis and the need to reduce energy consumption while providing thermal comfort to occupants, building performance prediction using building simulation programs requires higher accuracy of output data. Therefore, it seems necessary to study the impact of occupant behavior, which is the main source of uncertainty in residential buildings. The traditional courtyard houses, which are recognized as a successful passive house model, respond to different climatic conditions. Therefore, this research focuses on this building type to analyze occupant window opening control scenarios and determine which control works better. For this purpose, several probabilistic controls and their effects on the adaptive thermal comfort of occupants in zones around a central courtyard were compared in the three cities of Yazd, Bandar Abbas, and Tabriz. Energy Plus was used as a simulation program for the application of Grasshopper's energy management system (EMS) along with the Ladybug and Honeybee environmental plugins. The results show that the window control algorithms can increase the adaptive thermal comfort of occupants by 25.7%, 32.2%, and 20.3% in each of the climates of Yazd, Bandar Abbas, and Tabriz cities, respectively. Indoor and outdoor temperature were the most significant variables for opening windows in the warm and cold seasons, respectively.
