A multi-objective optimization framework for building-integrated PV envelope design balancing energy and cost

建筑集成光伏 建筑围护结构 光伏系统 可靠性工程 计算机科学 概念设计 工程设计过程 外观 启发式 工程类 机械工程 土木工程 物理 气象学 电气工程 操作系统 热的
作者
Tharushi Imalka Samarasinghalage,W.M. Pabasara Upalakshi Wijeratne,Rebecca Yang,Ron Wakefield
出处
期刊:Journal of Cleaner Production [Elsevier BV]
卷期号:342: 130930-130930 被引量:21
标识
DOI:10.1016/j.jclepro.2022.130930
摘要

The design of building-integrated photovoltaic (BIPV) envelopes involves a large set of envelope-related parameters, PV-related parameters and conflicting performance criteria. Therefore, optimization of BIPV design is crucial and it has become a complex process. Existing BIPV design optimization frameworks lack BIPV product and application type-related variables, and most studies cannot handle large sets of design variables and automatically generate a set of alternative optimal designs. Therefore, an optimization framework has become a major requirement in BIPV envelope design. This study introduces a multi-objective optimization (MOO) framework to optimize life cycle energy (LCE) and life cycle cost (LCC) simultaneously, at the conceptual BIPV envelope design stage, considering different BIPV application types for the selection of appropriate BIPV products and designs. A set of envelope design features, as well as PV-related features such as tilt angle, window-to-wall ratio (WWR), PV placement and PV product type, are included as design variables in the framework. The novelty of this study is that it generates a set of the best BIPV design alternatives based on multiple objectives, PV products and building features. Alternative designs include the best building surface features and the best BIPV product for a given performance criterion. The framework is demonstrated using canopy, roof sheet and cladding BIPV applications and the results incorporate different design solutions for each case study. The results show that MOO is operational for early BIPV design decisions based on technical considerations of energy and cost. Mid-design stage decisions can be guided by these simulated results but are not sufficient to define design decisions. Client subjectivity, taste and preference may heavily impact the feasibility of applying the optimization results, because in some cases, energy optimization is not the overarching goal of the client.
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