دانلود مقاله ISI انگلیسی شماره 54934
ترجمه فارسی عنوان مقاله

بهینه سازی طراحی سیستم انرژی های تجدیدپذیر چندمعیاره برای ساختمان انرژی صفر خالص تحت عدم قطعیت

عنوان انگلیسی
A multi-criterion renewable energy system design optimization for net zero energy buildings under uncertainties
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
54934 2016 12 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Energy, Volume 94, 1 January 2016, Pages 654–665

ترجمه کلمات کلیدی
شبکه ساختمان انرژی صفر؛ طراحی سیستم های تجدیدپذیر؛ عدم اطمینان؛ تعادل انرژی سالانه؛ استرس شبکه؛ سرمایه گذاری اولیه
کلمات کلیدی انگلیسی
Net zero energy building; Renewable system design; Uncertainties; Annual energy balance; Grid stress; Initial investment
پیش نمایش مقاله
پیش نمایش مقاله  بهینه سازی طراحی سیستم انرژی های تجدیدپذیر چندمعیاره برای ساختمان انرژی صفر خالص تحت عدم قطعیت

چکیده انگلیسی

Net zero energy buildings (NZEBs) are promising to mitigate the increasing energy and environmental problems. For NZEBs, annual energy balance between renewable energy generation and building energy consumption is an essential and fundamental requirement. Conventional RES (renewable energy system) design methods for NZEBs have not systematically considered uncertainties associated with building energy generation and consumption. As a result, either the annual energy balance cannot be achieved or the initial investment of RES is unnecessarily large. Meanwhile, the uncertainties also have significant impacts on NZEB power mismatch which can cause severe grid stress. In order to overcome the above challenges, this study proposes a multi-criterion RES design optimization method for NZEBs under uncertainties. Under the uncertainties, Monte Carlo simulations have been employed to estimate the annual energy balance and the grid stress caused by power mismatch. Three criteria, namely the annual energy balance reliability, the grid stress and the initial investment, are used to evaluate the overall RES design performance based on user-defined weighted factors. A case study has demonstrated the effectiveness of the proposed method in optimizing the size of RES under uncertainties.