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Journal of cleaner production v.184, 2018년, pp.82 - 91   SCIE
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A method of strategic evaluation of energy performance of Building Integrated Photovoltaic in the urban context

Costanzo, V.    (School of the Built Environment, University of Reading, RG6 6AW, Reading, UK   ); Yao, R.    (School of the Built Environment, University of Reading, RG6 6AW, Reading, UK   ); Essah, E.    (School of the Built Environment, University of Reading, RG6 6AW, Reading, UK   ); Shao, L.    (School of the Built Environment, University of Reading, RG6 6AW, Reading, UK   ); Shahrestani, M.    (School of the Built Environment, University of Reading, RG6 6AW, Reading, UK   ); Oliveira, A.C.    (Centre for Renewable Energy Research, University of Porto, 4200-465, Porto, Portugal   ); Araz, M.    (Department of Energy Systems Engineering, Faculty of Engineering, Yasar University, 35100, Izmir, Turkey   ); Hepbasli, A.    (Department of Energy Systems Engineering, Faculty of Engineering, Yasar University, 35100, Izmir, Turkey   ); Biyik, E.    (Department of Energy Systems Engineering, Faculty of Engineering, Yasar University, 35100, Izmir, Turkey  );
  • 초록  

    Abstract This paper presents an integrated bottom-up approach aimed at helping those dealing with strategical analysis of installation of Building Integrated Photo Voltaic (BIPV) to estimate the electricity production potential along with the energy needs of urban buildings at the district scale. On the demand side, hourly energy profiles are generated using dynamic building simulation taking into account actual urban morphologies. On the supply side, electricity generated from the system is predicted considering both the direct and indirect components of solar radiation as well as local climate variables. Python-based Algorithm editor Grasshopper is used to interlink four types of modelling and simulation tools as 1) generation of 3-D model, 2) solar radiation analysis, 3) formatting weather files (TMY data set) and 4) dynamic energy demand. The method has been demonstrated for a cluster of 20 buildings located in the Yasar University in Izmir (Turkey), for which it is found the BIPV system could achieve an annual renewable share of 23%, in line with the Renewable Energy Directive target of 20%. Quantitatively-compared demand and supply information at hourly time step shows that only some energy needs can be met by BIPV, so there is a need for an appropriate matching strategy to better exploit the renewable energy potential. Highlights A bottom-up approach to estimate BIPV electricity yield in urban buildings. Python-based algorithm editor Grasshopper is used to interconnect simulation tools. Comparison of electricity supply and demand informs about matching issues. The approach has been tested for a real case study in Yasar University, Turkey. BIPV system could achieve an annual renewable share of 23% under BAU scenario. Graphical abstract [DISPLAY OMISSION]


  • 주제어

    Solar potential .   Urban modelling .   BIPV .   Energy matching .   Energy supply and demand.  

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