|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|84703||2018||13 صفحه PDF||سفارش دهید||8802 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : International Journal of Electrical Power & Energy Systems, Volume 98, June 2018, Pages 23-35
One of the big challenging issues for the operators of smart home is optimal scheduling of these homes within various uncertainties that can lead to increase or decrease of the operation cost of smart home. In this paper, information gap decision theory (IGDT) is proposed for robust scheduling of apartment smart building in the presence of price uncertainty. IGDT approach doesnât depend on the size of model. So, the operators of apartment smart building which are known as small scale loads can use IGDT to make more informed decisions against the price uncertainty. IGDT method contains two functions i.e. robustness function and opportunity functions. Robustness function is used to model the negative impacts of market price uncertainty while the opportunity function is used to model positive effects of market price uncertainty. By comparing the obtained results from robustness function of IGDT, it can be found that by taking risk-averse strategy and analyzing one of the obtained strategies, operation cost of apartment smart building is increased 26.18% while robustness of apartment smart building against increase of market price is increased up to 51.87% which means that the apartment smart building has become robust against increase of market price. On the other hand, according to the obtained results from opportunity function of IGDT, by taking risk-seeking strategy and analyzing one of the obtained strategies, due to 56.92% reduction of market price, the operation cost of smart home is reduced 3â¯Â£ which is 26.18% of total operation cost of apartment smart building. In fact, these strategies obtained from robustness and opportunity functions help home energy management system to take appropriate decisions to handle various possible outcomes of uncertainty. The proposed IGDT-based sample model is solved using General Algebraic Modeling System (GAMS).