A Case Study on a Stochastic-Based Optimisation Approach towards the Integration of Photovoltaic Panels in Multi-Residential Social Housing

The socioeconomic reality and the energy retrofit potential of the social housing neighbourhoods in Portugal are stimulating challenges to be addressed by research to pursue suitable energy efficient strategies to be integrated into these buildings. Therefore, this study explored a stochastic-based...

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Autores principales: Rui Oliveira, Ricardo M.S.F. Almeida, António Figueiredo, Romeu Vicente
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Publicado: MDPI AG 2021
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spelling oai:doaj.org-article:de4e7c4b84e840f1bbf7a7b81307ef872021-11-25T17:27:17ZA Case Study on a Stochastic-Based Optimisation Approach towards the Integration of Photovoltaic Panels in Multi-Residential Social Housing10.3390/en142276151996-1073https://doaj.org/article/de4e7c4b84e840f1bbf7a7b81307ef872021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/22/7615https://doaj.org/toc/1996-1073The socioeconomic reality and the energy retrofit potential of the social housing neighbourhoods in Portugal are stimulating challenges to be addressed by research to pursue suitable energy efficient strategies to be integrated into these buildings. Therefore, this study explored a stochastic-based optimisation approach towards the integration of photovoltaic (PV) panels, considering different scenarios that combine the occupancy rate, the internal gains, the envelope refurbishment and the heating system efficiency. The optimisation approach has as its objective the minimisation of the life cycle cost of the photovoltaic system while using a limited space area on the rooftop for its installation. This study allowed concluding that the use of passive measures such as improving the thermal performance of the building envelope is essential to attain a lower optimal-sizing of a photovoltaic installation. The results reveal a decreasing trend in the PV optimal sizing, attaining a reduction up to 30% of the total number of PV panels installed on the sloped rooftop in several scenarios with 50% of occupancy rate. However, the impact can be greater when passive measures are coupled to more efficient heating systems, with higher COP, which result in a decrease up to 64% of the number of PV panels. Thus, the approach proposed is of paramount importance to aid in the decision-making process of design and sizing of photovoltaic installation, highlighting the practical application potential for social housing and a contribution for mitigation of the energy poverty of low-income families that live in these buildings.Rui OliveiraRicardo M.S.F. AlmeidaAntónio FigueiredoRomeu VicenteMDPI AGarticlesocial housingenergy demandenergy refurbishmentinternal gainsoccupation ratelife cycle costTechnologyTENEnergies, Vol 14, Iss 7615, p 7615 (2021)
institution DOAJ
collection DOAJ
language EN
topic social housing
energy demand
energy refurbishment
internal gains
occupation rate
life cycle cost
Technology
T
spellingShingle social housing
energy demand
energy refurbishment
internal gains
occupation rate
life cycle cost
Technology
T
Rui Oliveira
Ricardo M.S.F. Almeida
António Figueiredo
Romeu Vicente
A Case Study on a Stochastic-Based Optimisation Approach towards the Integration of Photovoltaic Panels in Multi-Residential Social Housing
description The socioeconomic reality and the energy retrofit potential of the social housing neighbourhoods in Portugal are stimulating challenges to be addressed by research to pursue suitable energy efficient strategies to be integrated into these buildings. Therefore, this study explored a stochastic-based optimisation approach towards the integration of photovoltaic (PV) panels, considering different scenarios that combine the occupancy rate, the internal gains, the envelope refurbishment and the heating system efficiency. The optimisation approach has as its objective the minimisation of the life cycle cost of the photovoltaic system while using a limited space area on the rooftop for its installation. This study allowed concluding that the use of passive measures such as improving the thermal performance of the building envelope is essential to attain a lower optimal-sizing of a photovoltaic installation. The results reveal a decreasing trend in the PV optimal sizing, attaining a reduction up to 30% of the total number of PV panels installed on the sloped rooftop in several scenarios with 50% of occupancy rate. However, the impact can be greater when passive measures are coupled to more efficient heating systems, with higher COP, which result in a decrease up to 64% of the number of PV panels. Thus, the approach proposed is of paramount importance to aid in the decision-making process of design and sizing of photovoltaic installation, highlighting the practical application potential for social housing and a contribution for mitigation of the energy poverty of low-income families that live in these buildings.
format article
author Rui Oliveira
Ricardo M.S.F. Almeida
António Figueiredo
Romeu Vicente
author_facet Rui Oliveira
Ricardo M.S.F. Almeida
António Figueiredo
Romeu Vicente
author_sort Rui Oliveira
title A Case Study on a Stochastic-Based Optimisation Approach towards the Integration of Photovoltaic Panels in Multi-Residential Social Housing
title_short A Case Study on a Stochastic-Based Optimisation Approach towards the Integration of Photovoltaic Panels in Multi-Residential Social Housing
title_full A Case Study on a Stochastic-Based Optimisation Approach towards the Integration of Photovoltaic Panels in Multi-Residential Social Housing
title_fullStr A Case Study on a Stochastic-Based Optimisation Approach towards the Integration of Photovoltaic Panels in Multi-Residential Social Housing
title_full_unstemmed A Case Study on a Stochastic-Based Optimisation Approach towards the Integration of Photovoltaic Panels in Multi-Residential Social Housing
title_sort case study on a stochastic-based optimisation approach towards the integration of photovoltaic panels in multi-residential social housing
publisher MDPI AG
publishDate 2021
url https://doaj.org/article/de4e7c4b84e840f1bbf7a7b81307ef87
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