IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v239y2025ics0960148124021578.html
   My bibliography  Save this article

Hourly electrical load estimates in a 100 % renewable scenario in Italy

Author

Listed:
  • Buzzi, Fulvio
  • Bischi, Aldo
  • Gabbrielli, Roberto
  • Desideri, Umberto

Abstract

The study of the impact of the zero-emissions scenarios of several countries on the electrical demand is relevant to analyze the feasibility of the sustainable energy transition. This paper presents a structured method to assess the effect of the 100 % renewable scenario on the hourly electrical load profile of a country taking Italy as reference case study. The hourly discretization is a fundamental approach to evaluate the contribution of the intermittent renewable sources during the day and the proposed methodology can be easily applied to several countries' scenarios. Numerous decarbonization scenarios consider the adoption of electricity in several sectors. Consequently, the primary energy reduction (40 % in the scenario considered) is usually accompanied by a relevant increase of the annual electricity demand (94%–124 %). In this paper, each sector's contribution is estimated separately, and the results show demand peaks above 100 GW and a baseload above 50 GW, which is more than double that of recent years. Electricity consumption is higher in the colder months and hydrogen and synthetic fuel production impacts significantly on the total electricity demand (26%–32 %). The results of this work will be used to verify the feasibility of net zero scenarios with hourly discretization in further research analysis.

Suggested Citation

  • Buzzi, Fulvio & Bischi, Aldo & Gabbrielli, Roberto & Desideri, Umberto, 2025. "Hourly electrical load estimates in a 100 % renewable scenario in Italy," Renewable Energy, Elsevier, vol. 239(C).
    • Handle: RePEc:eee:renene:v:239:y:2025:i:c:s0960148124021578
    DOI: 10.1016/j.renene.2024.122089
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148124021578
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2024.122089?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. He, Yaoyao & Cao, Chaojin & Wang, Shuo & Fu, Hong, 2022. "Nonparametric probabilistic load forecasting based on quantile combination in electrical power systems," Applied Energy, Elsevier, vol. 322(C).
    2. Wang, Xinlin & Yao, Zhihao & Papaefthymiou, Marios, 2023. "A real-time electrical load forecasting and unsupervised anomaly detection framework," Applied Energy, Elsevier, vol. 330(PA).
    3. Neumann, Fabian & Hagenmeyer, Veit & Brown, Tom, 2022. "Assessments of linear power flow and transmission loss approximations in coordinated capacity expansion problems," Applied Energy, Elsevier, vol. 314(C).
    4. Leiria, Daniel & Johra, Hicham & Marszal-Pomianowska, Anna & Pomianowski, Michal Zbigniew, 2023. "A methodology to estimate space heating and domestic hot water energy demand profile in residential buildings from low-resolution heat meter data," Energy, Elsevier, vol. 263(PB).
    5. Wang, Xinlin & Ahn, Sung-Hoon, 2020. "Real-time prediction and anomaly detection of electrical load in a residential community," Applied Energy, Elsevier, vol. 259(C).
    6. Zappa, William & Junginger, Martin & van den Broek, Machteld, 2019. "Is a 100% renewable European power system feasible by 2050?," Applied Energy, Elsevier, vol. 233, pages 1027-1050.
    7. Yu, Binbin & Li, Jianjing & Liu, Che & Sun, Bo, 2022. "A novel short-term electrical load forecasting framework with intelligent feature engineering," Applied Energy, Elsevier, vol. 327(C).
    Full references (including those not matched with items on IDEAS)

    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. Wang, Xinlin & Wang, Hao & Li, Shengping & Jin, Haizhen, 2024. "A reinforcement learning-based online learning strategy for real-time short-term load forecasting," Energy, Elsevier, vol. 305(C).
    2. Rosendal, M. & Janin, J. & Heggarty, T. & Pisinger, D. & Bramstoft, R. & Münster, M., 2025. "The benefits and challenges of soft-linking investment and operational energy system models," Applied Energy, Elsevier, vol. 385(C).
    3. Saima Akhtar & Sulman Shahzad & Asad Zaheer & Hafiz Sami Ullah & Heybet Kilic & Radomir Gono & Michał Jasiński & Zbigniew Leonowicz, 2023. "Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead," Energies, MDPI, vol. 16(10), pages 1-29, May.
    4. Cheng, Yi & Azizipanah-Abarghooee, Rasoul & Azizi, Sadegh & Ding, Lei & Terzija, Vladimir, 2020. "Smart frequency control in low inertia energy systems based on frequency response techniques: A review," Applied Energy, Elsevier, vol. 279(C).
    5. Yang, Weijia & Sparrow, Sarah N. & Wallom, David C.H., 2024. "A comparative climate-resilient energy design: Wildfire Resilient Load Forecasting Model using multi-factor deep learning methods," Applied Energy, Elsevier, vol. 368(C).
    6. Alassi, Abdulrahman & Bañales, Santiago & Ellabban, Omar & Adam, Grain & MacIver, Callum, 2019. "HVDC Transmission: Technology Review, Market Trends and Future Outlook," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 530-554.
    7. Deng, Xu & Lv, Tao & Meng, Xiangyun & Li, Cong & Hou, Xiaoran & Xu, Jie & Wang, Yinhao & Liu, Feng, 2024. "Assessing the carbon emission reduction effect of flexibility option for integrating variable renewable energy," Energy Economics, Elsevier, vol. 132(C).
    8. Wu, Chunying & Sun, Lingfang & Piao, Heng & Yao, Lijia, 2024. "Adaptive fuzzy finite time integral sliding mode control of the coordinated system for 350 MW supercritical once-through boiler unit to enhance flexibility," Energy, Elsevier, vol. 302(C).
    9. Melliger, Marc, 2023. "Quantifying technology skewness in European multi-technology auctions and the effect of design elements and other driving factors," Energy Policy, Elsevier, vol. 175(C).
    10. Gong, Ying & Shan, Xiaobiao & Luo, Xiaowei & Pan, Jia & Xie, Tao & Yang, Zhengbao, 2019. "Direction-adaptive energy harvesting with a guide wing under flow-induced oscillations," Energy, Elsevier, vol. 187(C).
    11. Gustavo G. Koch & Caio R. D. Osório & Ricardo C. L. F. Oliveira & Vinícius F. Montagner, 2023. "Robust Control Based on Observed States Designed by Means of Linear Matrix Inequalities for Grid-Connected Converters," Energies, MDPI, vol. 16(4), pages 1-24, February.
    12. Lee, Yoonjae & Ha, Byeongmin & Hwangbo, Soonho, 2022. "Generative model-based hybrid forecasting model for renewable electricity supply using long short-term memory networks: A case study of South Korea's energy transition policy," Renewable Energy, Elsevier, vol. 200(C), pages 69-87.
    13. Wu, Han & Liang, Yan & Heng, Jiani, 2023. "Pulse-diagnosis-inspired multi-feature extraction deep network for short-term electricity load forecasting," Applied Energy, Elsevier, vol. 339(C).
    14. Millinger, M. & Reichenberg, L. & Hedenus, F. & Berndes, G. & Zeyen, E. & Brown, T., 2022. "Are biofuel mandates cost-effective? - An analysis of transport fuels and biomass usage to achieve emissions targets in the European energy system," Applied Energy, Elsevier, vol. 326(C).
    15. Gharibpour, Hassan & Aminifar, Farrokh & Rahmati, Iman & Keshavarz, Arezou, 2021. "Dual variable decomposition to discriminate the cost imposed by inflexible units in electricity markets," Applied Energy, Elsevier, vol. 287(C).
    16. Newbery, David, 2021. "National Energy and Climate Plans for the island of Ireland: wind curtailment, interconnectors and storage," Energy Policy, Elsevier, vol. 158(C).
    17. Ma, Kai & Nie, Xuefeng & Yang, Jie & Zha, Linlin & Li, Guoqiang & Li, Haibin, 2025. "A power load forecasting method in port based on VMD-ICSS-hybrid neural network," Applied Energy, Elsevier, vol. 377(PB).
    18. Mohammad Mehdi Sharifi Nevisi & Mehrdad Shoeibi & Francisco Hernando-Gallego & Diego Martín & Sarvenaz Sadat Khatami, 2025. "An Evolutionary Deep Reinforcement Learning-Based Framework for Efficient Anomaly Detection in Smart Power Distribution Grids," Energies, MDPI, vol. 18(10), pages 1-35, May.
    19. Wang, Xinlin & Yao, Zhihao & Papaefthymiou, Marios, 2023. "A real-time electrical load forecasting and unsupervised anomaly detection framework," Applied Energy, Elsevier, vol. 330(PA).
    20. Li, Wei & Xu, Shengguan & Wang, Qiuwang & Wang, Xiaoyuan & Wang, Bohong & Zeng, Min, 2025. "Adsorption thermochemical battery-based heat transformer for low-grade energy upgrading," Renewable Energy, Elsevier, vol. 242(C).

    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:eee:renene:v:239:y:2025:i:c:s0960148124021578. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    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.