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.

A method of strategic evaluation of energy performance of Building Integrated Photovoltaic in the urban context

Costanzo V.
Primo
;
2018-01-01

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.
2018
BIPV; Energy matching; Energy supply and demand; Solar potential; Urban modelling
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/412613
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