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Parametric Urban-Scale Analysis of Space Cooling Energy Needs and Potential Photovoltaic Integration in Residential Districts in South-West Europe

Author

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  • Andrea Zambito

    (Institute for Renewable Energy, European Academy of Bolzano (EURAC Research), Viale Druso 1, 39100 Bolzano, Italy
    Faculty of Science and Technology, Free University of Bozen-Bolzano, Piazza Università, 1, 39100 Bolzano, Italy)

  • Giovanni Pernigotto

    (Faculty of Science and Technology, Free University of Bozen-Bolzano, Piazza Università, 1, 39100 Bolzano, Italy)

  • Simon Pezzutto

    (Institute for Renewable Energy, European Academy of Bolzano (EURAC Research), Viale Druso 1, 39100 Bolzano, Italy)

  • Andrea Gasparella

    (Faculty of Science and Technology, Free University of Bozen-Bolzano, Piazza Università, 1, 39100 Bolzano, Italy)

Abstract

The energy needs for space cooling are becoming a significant share of the energy balance of different Member States of the European Union, in particular the Mediterranean countries. This trend has been observed and monitored by the European Union, which has started a number of initiatives to promote the reduction in the energy demand for space cooling and have it satisfied by renewable energy sources, such as photovoltaic electrical energy. Nevertheless, even if the potential of those solutions has been widely investigated at the single-building level, this scale of analysis seems not fully adequate to support the definition of the energy policies addressed towards the renovation of the current cities into smart ones, with a large share of their energy demand satisfied with renewable energy. In this framework, this research aims to investigate the topic of building energy performance for space cooling services by adopting an urban-scale approach. In detail, a parametric simulation plan was run with CitySim in order to assess the impact of different quantities, i.e., climate conditions, districts’ and buildings’ geometry features, and the thermal quality of the building envelope, on the overall cooling energy need for districts and the specific building energy performance. Furthermore, the advantages of the integration of photovoltaic systems to supply power to the cooling system were analyzed, identifying the district configurations with the highest potential. For instance, in Athens, the share of space cooling demand satisfied by PV in high-rise nZEB configurations ranges between 64% (Building Density = 0.25) and 87% (Building Density = 1), while in the low-rise nZEB configurations it ranges between 81% (Building Density = 0.25) and 75% (Building Density = 1).

Suggested Citation

  • Andrea Zambito & Giovanni Pernigotto & Simon Pezzutto & Andrea Gasparella, 2022. "Parametric Urban-Scale Analysis of Space Cooling Energy Needs and Potential Photovoltaic Integration in Residential Districts in South-West Europe," Sustainability, MDPI, vol. 14(11), pages 1-19, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:11:p:6521-:d:824897
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    References listed on IDEAS

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    1. George Stamatellos & Olympia Zogou & Anastassios Stamatelos, 2021. "Energy Performance Optimization of a House with Grid-Connected Rooftop PV Installation and Air Source Heat Pump," Energies, MDPI, vol. 14(3), pages 1-23, January.
    2. Ki Uhn Ahn & Deuk Woo Kim & Young Jin Kim & Seong Hwan Yoon & Cheol Soo Park, 2016. "Issues to Be Solved for Energy Simulation of An Existing Office Building," Sustainability, MDPI, vol. 8(4), pages 1-12, April.
    3. Kuczyński, T. & Staszczuk, A., 2020. "Experimental study of the influence of thermal mass on thermal comfort and cooling energy demand in residential buildings," Energy, Elsevier, vol. 195(C).
    4. Baetens, R. & De Coninck, R. & Van Roy, J. & Verbruggen, B. & Driesen, J. & Helsen, L. & Saelens, D., 2012. "Assessing electrical bottlenecks at feeder level for residential net zero-energy buildings by integrated system simulation," Applied Energy, Elsevier, vol. 96(C), pages 74-83.
    5. Best, Robert E. & Flager, Forest & Lepech, Michael D., 2015. "Modeling and optimization of building mix and energy supply technology for urban districts," Applied Energy, Elsevier, vol. 159(C), pages 161-177.
    6. Simon Pezzutto & Matteo De Felice & Reza Fazeli & Lukas Kranzl & Stefano Zambotti, 2017. "Status Quo of the Air-Conditioning Market in Europe: Assessment of the Building Stock," Energies, MDPI, vol. 10(9), pages 1-17, August.
    7. Swan, Lukas G. & Ugursal, V. Ismet, 2009. "Modeling of end-use energy consumption in the residential sector: A review of modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1819-1835, October.
    8. Chen, Yixing & Hong, Tianzhen & Piette, Mary Ann, 2017. "Automatic generation and simulation of urban building energy models based on city datasets for city-scale building retrofit analysis," Applied Energy, Elsevier, vol. 205(C), pages 323-335.
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