IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v184y2022icp948-959.html
   My bibliography  Save this article

The influence of extreme dust events in the current and future 100% renewable power scenarios in Tenerife

Author

Listed:
  • Cañadillas-Ramallo, David
  • Moutaoikil, Asmae
  • Shephard, Les E.
  • Guerrero-Lemus, Ricardo

Abstract

In this paper, the influence of dust and extreme dust events in the renewable energy generation in the island of Tenerife has been studied. Two different models have been proposed: one model to estimate the influence of atmospheric Particulate Matter (PM) in the Photovoltaic (PV) generation, and a second model that captures the influence of a set of weather and temporal variables, including PM, in the renewable generation of the island. Additionally, the hypothetic situation of extreme dust events occurring in a future 100% renewable electric system in the island of Tenerife is analyzed. Results show the interconnections between PM2.5, PM10 and wind resources on the present and future power output in the Island, and how the future storage capabilities need to take into account these events for a necessary increase in resilience related to climate change and related extreme weather events.

Suggested Citation

  • Cañadillas-Ramallo, David & Moutaoikil, Asmae & Shephard, Les E. & Guerrero-Lemus, Ricardo, 2022. "The influence of extreme dust events in the current and future 100% renewable power scenarios in Tenerife," Renewable Energy, Elsevier, vol. 184(C), pages 948-959.
  • Handle: RePEc:eee:renene:v:184:y:2022:i:c:p:948-959
    DOI: 10.1016/j.renene.2021.12.013
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S096014812101733X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2021.12.013?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. Bailek, Nadjem & Bouchouicha, Kada & Hassan, Muhammed A. & Slimani, Abdeldjalil & Jamil, Basharat, 2020. "Implicit regression-based correlations to predict the back temperature of PV modules in the arid region of south Algeria," Renewable Energy, Elsevier, vol. 156(C), pages 57-67.
    2. Amato T. Evan & Cyrille Flamant & Marco Gaetani & Françoise Guichard, 2016. "The past, present and future of African dust," Nature, Nature, vol. 531(7595), pages 493-495, March.
    3. Jufri, Fauzan Hanif & Widiputra, Victor & Jung, Jaesung, 2019. "State-of-the-art review on power grid resilience to extreme weather events: Definitions, frameworks, quantitative assessment methodologies, and enhancement strategies," Applied Energy, Elsevier, vol. 239(C), pages 1049-1065.
    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. Henning Meschede & Paul Bertheau & Siavash Khalili & Christian Breyer, 2022. "A review of 100% renewable energy scenarios on islands," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 11(6), November.

    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. Yu, Min Gyung & Pavlak, Gregory S., 2023. "Risk-aware sizing and transactive control of building portfolios with thermal energy storage," Applied Energy, Elsevier, vol. 332(C).
    2. Yao He & Yongchun Yang & Meimei Wang & Xudong Zhang, 2022. "Resilience Analysis of Container Port Shipping Network Structure: The Case of China," Sustainability, MDPI, vol. 14(15), pages 1-17, August.
    3. Sang-Guk Yum & Kiyoung Son & Seunghyun Son & Ji-Myong Kim, 2020. "Identifying Risk Indicators for Natural Hazard-Related Power Outages as a Component of Risk Assessment: An Analysis Using Power Outage Data from Hurricane Irma," Sustainability, MDPI, vol. 12(18), pages 1-15, September.
    4. Popović, Željko N. & KovaÄ ki, Neven V. & Popović, Dragan S., 2020. "Resilient distribution network planning under the severe windstorms using a risk-based approach," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    5. Chen, Lei & Jiang, Yuqi & Zheng, Shencong & Deng, Xinyi & Chen, Hongkun & Islam, Md. Rabiul, 2023. "A two-layer optimal configuration approach of energy storage systems for resilience enhancement of active distribution networks," Applied Energy, Elsevier, vol. 350(C).
    6. Thomas M. Missimer & James H. MacDonald & Seneshaw Tsegaye & Serge Thomas & Christopher M. Teaf & Douglas Covert & Zoie R. Kassis, 2024. "Natural Background and the Anthropogenic Enrichment of Mercury in the Southern Florida Environment: A Review with a Discussion on Public Health," IJERPH, MDPI, vol. 21(1), pages 1-44, January.
    7. Khalilullah Mayar & David G. Carmichael & Xuesong Shen, 2022. "Resilience and Systems—A Review," Sustainability, MDPI, vol. 14(14), pages 1-22, July.
    8. Sara McElroy & Anna Dimitrova & Amato Evan & Tarik Benmarhnia, 2022. "Saharan Dust and Childhood Respiratory Symptoms in Benin," IJERPH, MDPI, vol. 19(8), pages 1-11, April.
    9. Liping Huang & Zhaoxiong Huang & Chun Sing Lai & Guangya Yang & Zhuoli Zhao & Ning Tong & Xiaomei Wu & Loi Lei Lai, 2021. "Augmented Power Dispatch for Resilient Operation through Controllable Series Compensation and N-1-1 Contingency Assessment," Energies, MDPI, vol. 14(16), pages 1-24, August.
    10. Raphaël Rousseau-Rizzi & Kerry Emanuel, 2022. "Natural and anthropogenic contributions to the hurricane drought of the 1970s–1980s," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    11. Younesi, Abdollah & Shayeghi, Hossein & Safari, Amin & Siano, Pierluigi, 2020. "Assessing the resilience of multi microgrid based widespread power systems against natural disasters using Monte Carlo Simulation," Energy, Elsevier, vol. 207(C).
    12. Banghua Xie & Changfan Li & Zili Wu & Weiming Chen, 2021. "Topological Modeling Research on the Functional Vulnerability of Power Grid under Extreme Weather," Energies, MDPI, vol. 14(16), pages 1-27, August.
    13. Zhang, Wangxin & Han, Qiang & Shang, Wen-Long & Xu, Chengshun, 2024. "Seismic resilience assessment of interdependent urban transportation-electric power system under uncertainty," Transportation Research Part A: Policy and Practice, Elsevier, vol. 183(C).
    14. Emenike, Scholastica N. & Falcone, Gioia, 2020. "A review on energy supply chain resilience through optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    15. Hou, Hui & Tang, Junyi & Zhang, Zhiwei & Wang, Zhuo & Wei, Ruizeng & Wang, Lei & He, Huan & Wu, Xixiu, 2023. "Resilience enhancement of distribution network under typhoon disaster based on two-stage stochastic programming," Applied Energy, Elsevier, vol. 338(C).
    16. Rocchetta, Roberto, 2022. "Enhancing the resilience of critical infrastructures: Statistical analysis of power grid spectral clustering and post-contingency vulnerability metrics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    17. Tang, Liangyu & Han, Yang & Zalhaf, Amr S. & Zhou, Siyu & Yang, Ping & Wang, Congling & Huang, Tao, 2024. "Resilience enhancement of active distribution networks under extreme disaster scenarios: A comprehensive overview of fault location strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    18. Wu, Raphael & Sansavini, Giovanni, 2021. "Energy trilemma in active distribution network design: Balancing affordability, sustainability and security in optimization-based decision-making," Applied Energy, Elsevier, vol. 304(C).
    19. Hassan, Muhammed A. & Bailek, Nadjem & Bouchouicha, Kada & Nwokolo, Samuel Chukwujindu, 2021. "Ultra-short-term exogenous forecasting of photovoltaic power production using genetically optimized non-linear auto-regressive recurrent neural networks," Renewable Energy, Elsevier, vol. 171(C), pages 191-209.
    20. Dong, Xiao-Jian & Shen, Jia-Ni & He, Guo-Xin & Ma, Zi-Feng & He, Yi-Jun, 2021. "A general radial basis function neural network assisted hybrid modeling method for photovoltaic cell operating temperature prediction," Energy, Elsevier, vol. 234(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:184:y:2022:i:c:p:948-959. 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.