Clustering based analysis of residential duck curve mitigation through solar pre-cooling: A case study of Australian housing stock

Shayan Naderi, Simon Heslop, Dong Chen, Scott Watts, Iain MacGill, Gloria Pignatta and Alistair Sproul

Renewable Energy, 2023, vol. 216, issue C

Abstract: A clustering based analysis of solar pre-cooling for around 450 Australian homes equipped with rooftop Photovoltaics (PV) and Air Conditioning (AC) is presented. By simulating the thermal performance of nine typical Australian building types for each household, a virtual housing stock is created. These modeled building types include 2-, 6-, and 8-star homes, and for each star rating, three different construction weights (i.e., light, medium, and heavy). The analysis finds that there are four clusters of AC excluded net demand profiles that usefully characterize the household dataset, out of which three clusters cover around 97% of households. Homes that fall within these three clusters offer ‘flattening’ demand profile improvement, which is mainly achieved by minimum demand mitigation, with a maximum of up to 4 kW across the households. Analyzing the limiting factors shows that thermal comfort requirements of households do not particularly impact on solar pre-cooling which is, instead, mainly limited by the house's AC size and typical surplus PV generation. Finally, the cost savings resulting from implementing solar pre-cooling during the three months of summer can be as high as around A$185 with a zero Feed-in Tariff (FiT) and typical Australian residential prices.

Keywords: Thermal mass; Air conditioning; Energy storage; Thermal comfort; Machine learning; Data mining (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:216:y:2023:i:c:s0960148123009783

DOI: 10.1016/j.renene.2023.119064

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