IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i7p3042-d1108497.html
   My bibliography  Save this article

Energy Planning of Renewable Energy Sources in an Italian Context: Energy Forecasting Analysis of Photovoltaic Systems in the Residential Sector

Author

Listed:
  • Domenico Palladino

    (DUEE Department, Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), Via Anguillarese 301, Santa Maria di Galeria, 00123 Rome, Italy)

  • Nicolandrea Calabrese

    (DUEE Department, Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), Via Anguillarese 301, Santa Maria di Galeria, 00123 Rome, Italy)

Abstract

Solar photovoltaic systems will play a key role in the country’s energy mix thanks to their ability to meet increasing energy needs while reducing greenhouse gas emissions. Despite the potential of solar photovoltaic energy, several criticalities remain, such as the intermittent nature and the need for significant land use for its implementation. In this regard, this work aimed at evaluating the photovoltaic potentiality in a national context by 2030 and 2050, considering only installations on the roof surfaces of existing buildings, i.e., without consuming additional land. This study has allowed the answering of three key points: (i) the roof surface could represent a valuable and alternative solution for new installations, since it could amount to around 450 km 2 , (ii) the national target cannot be reached by only using installations on existing buildings, although some regions could get close to the target by 2050, and (iii) long-term energy incentives should be implemented branching out to each national region, considering their photovoltaic potential. Finally, a regional potential index was also defined, capable of evaluating the photovoltaic potential in each region, helping policymakers to adopt the most suitable energy strategies.

Suggested Citation

  • Domenico Palladino & Nicolandrea Calabrese, 2023. "Energy Planning of Renewable Energy Sources in an Italian Context: Energy Forecasting Analysis of Photovoltaic Systems in the Residential Sector," Energies, MDPI, vol. 16(7), pages 1-28, March.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:7:p:3042-:d:1108497
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/7/3042/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/7/3042/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Putri Nor Liyana Mohamad Radzi & Muhammad Naveed Akhter & Saad Mekhilef & Noraisyah Mohamed Shah, 2023. "Review on the Application of Photovoltaic Forecasting Using Machine Learning for Very Short- to Long-Term Forecasting," Sustainability, MDPI, vol. 15(4), pages 1-21, February.
    2. Hugo Bezerra Menezes Leite & Hamidreza Zareipour, 2023. "Six Days Ahead Forecasting of Energy Production of Small Behind-the-Meter Solar Sites," Energies, MDPI, vol. 16(3), pages 1-14, February.
    3. Hermoso, Virgilio & Bota, Gerard & Brotons, Lluis & Morán-Ordóñez, Alejandra, 2023. "Addressing the challenge of photovoltaic growth: Integrating multiple objectives towards sustainable green energy development," Land Use Policy, Elsevier, vol. 128(C).
    4. Jesús Polo & Nuria Martín-Chivelet & Miguel Alonso-Abella & Carlos Sanz-Saiz & José Cuenca & Marina de la Cruz, 2023. "Exploring the PV Power Forecasting at Building Façades Using Gradient Boosting Methods," Energies, MDPI, vol. 16(3), pages 1-12, February.
    5. L. Cabezón & L. G. B. Ruiz & D. Criado-Ramón & E. J. Gago & M. C. Pegalajar, 2022. "Photovoltaic Energy Production Forecasting through Machine Learning Methods: A Scottish Solar Farm Case Study," Energies, MDPI, vol. 15(22), pages 1-14, November.
    6. Yao, Wanxiang & Kong, Xiangru & Xu, Ai & Xu, Puyan & Wang, Yan & Gao, Weijun, 2023. "New models for the influence of rainwater on the performance of photovoltaic modules under different rainfall conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    7. Monica Borunda & Adrián Ramírez & Raul Garduno & Gerardo Ruíz & Sergio Hernandez & O. A. Jaramillo, 2022. "Photovoltaic Power Generation Forecasting for Regional Assessment Using Machine Learning," Energies, MDPI, vol. 15(23), pages 1-25, November.
    8. Yandi G. Landera & Oscar C. Zevallos & Rafael C. Neto & Jose F. da Costa Castro & Francisco A. S. Neves, 2023. "A Review of Grid Connection Requirements for Photovoltaic Power Plants," Energies, MDPI, vol. 16(5), pages 1-24, February.
    9. Gaetano Mannino & Giuseppe Marco Tina & Mario Cacciato & Leonardo Merlo & Alessio Vincenzo Cucuzza & Fabrizio Bizzarri & Andrea Canino, 2023. "Photovoltaic Module Degradation Forecast Models for Onshore and Offshore Floating Systems," Energies, MDPI, vol. 16(5), pages 1-18, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jacek Caban & Arkadiusz Małek & Branislav Šarkan, 2024. "Strategic Model for Charging a Fleet of Electric Vehicles with Energy from Renewable Energy Sources," Energies, MDPI, vol. 17(5), pages 1-17, March.
    2. Alessandro Bessi & Mariangela Guidolin & Piero Manfredi, 2024. "Diffusion of Solar PV Energy in Italy: Can Large-Scale PV Installations Trigger the Next Growth Phase?," Energies, MDPI, vol. 17(3), pages 1-23, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mateusz Sumorek & Adam Idzkowski, 2023. "Time Series Forecasting for Energy Production in Stand-Alone and Tracking Photovoltaic Systems Based on Historical Measurement Data," Energies, MDPI, vol. 16(17), pages 1-23, September.
    2. Fouzi Harrou & Ying Sun & Bilal Taghezouit & Abdelkader Dairi, 2023. "Artificial Intelligence Techniques for Solar Irradiance and PV Modeling and Forecasting," Energies, MDPI, vol. 16(18), pages 1-5, September.
    3. L. G. B. Ruiz & M. C. Pegalajar, 2023. "Advances in Energy Efficiency through Neural-Network-Based Models," Energies, MDPI, vol. 16(5), pages 1-3, February.
    4. Rania A. Ibrahim & Nahla E. Zakzouk, 2023. "Bi-Functional Non-Superconducting Saturated-Core Inductor for Single-Stage Grid-Tied PV Systems: Filter and Fault Current Limiter," Energies, MDPI, vol. 16(10), pages 1-24, May.
    5. Socrates Kaplanis & Eleni Kaplani & John K. Kaldellis, 2023. "PV Temperature Prediction Incorporating the Effect of Humidity and Cooling Due to Seawater Flow and Evaporation on Modules Simulating Floating PV Conditions," Energies, MDPI, vol. 16(12), pages 1-19, June.
    6. Tomasz Popławski & Sebastian Dudzik & Piotr Szeląg, 2023. "Forecasting of Energy Balance in Prosumer Micro-Installations Using Machine Learning Models," Energies, MDPI, vol. 16(18), pages 1-24, September.
    7. Chun-Che Huang & Wen-Yau Liang & Roger R. Gung & Pei-An Wang, 2023. "Rough-Set-Based Rule Induction with the Elimination of Outdated Big Data: Case of Renewable Energy Equipment Promotion," Sustainability, MDPI, vol. 15(20), pages 1-19, October.
    8. Grzegorz Dudek & Paweł Piotrowski & Dariusz Baczyński, 2023. "Intelligent Forecasting and Optimization in Electrical Power Systems: Advances in Models and Applications," Energies, MDPI, vol. 16(7), pages 1-11, March.
    9. Juan A. Tejero-Gómez & Ángel A. Bayod-Rújula, 2023. "Analysis of Photovoltaic Plants with Battery Energy Storage Systems (PV-BESS) for Monthly Constant Power Operation," Energies, MDPI, vol. 16(13), pages 1-22, June.
    10. Wen-Chang Tsai & Chia-Sheng Tu & Chih-Ming Hong & Whei-Min Lin, 2023. "A Review of State-of-the-Art and Short-Term Forecasting Models for Solar PV Power Generation," Energies, MDPI, vol. 16(14), pages 1-30, July.
    11. Wenxiao Chu & Maria Vicidomini & Francesco Calise & Neven Duić & Poul Alberg Østergaard & Qiuwang Wang & Maria da Graça Carvalho, 2023. "Review of Hot Topics in the Sustainable Development of Energy, Water, and Environment Systems Conference in 2022," Energies, MDPI, vol. 16(23), pages 1-20, December.
    12. Max Olinto Moreira & Betania Mafra Kaizer & Takaaki Ohishi & Benedito Donizeti Bonatto & Antonio Carlos Zambroni de Souza & Pedro Paulo Balestrassi, 2022. "Multivariate Strategy Using Artificial Neural Networks for Seasonal Photovoltaic Generation Forecasting," Energies, MDPI, vol. 16(1), pages 1-30, December.
    13. Hubert Kryszk & Krystyna Kurowska & Renata Marks-Bielska & Stanisław Bielski & Bartłomiej Eźlakowski, 2023. "Barriers and Prospects for the Development of Renewable Energy Sources in Poland during the Energy Crisis," Energies, MDPI, vol. 16(4), pages 1-17, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:7:p:3042-:d:1108497. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.