IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v155y2020icp1139-1146.html
   My bibliography  Save this article

Complementary relationship between small-hydropower and increasing penetration of solar photovoltaics: Evidence from CAISO

Author

Listed:
  • Shan, Rui
  • Sasthav, Colin
  • Wang, Xianxun
  • Lima, Luana M.M.

Abstract

To achieve the 100% green electricity goal, we need to understand the relationship between resources in the market and identify the flexible clean resources (i.e., hydropower) to integrate power from wind and photovoltaic (PV). This paper reveals a complementary relationship between small hydropower plants and solar PVs in the California Independent System Operator (CAISO) based on the system-wide hourly generation data from 2013 to 2017. When the solar PV increases its portion in the generation mix by 1%, small hydro will increase its portion by 0.01–0.06%. Such response is obvious in the net demand peak hours, both morning and evening. The low operation cost, flexibility, and dispatchability of small hydro in CAISO explain this complementarity. Due to its benefit in emission and low Levelized Cost of Electricity (LCOE), it is suggested to consider more small hydro projects to accommodate additional PV capacity for the 100% green electricity goal. Our estimation indicates that the current feasible potential of small hydro is sufficient, if the relation stays the same over years. If developers can mitigate the environmental impact, more technical potential will become feasible. Thus, small hydro could integrate more solar PV and reduce the demand for natural gas plants and batteries.

Suggested Citation

  • Shan, Rui & Sasthav, Colin & Wang, Xianxun & Lima, Luana M.M., 2020. "Complementary relationship between small-hydropower and increasing penetration of solar photovoltaics: Evidence from CAISO," Renewable Energy, Elsevier, vol. 155(C), pages 1139-1146.
  • Handle: RePEc:eee:renene:v:155:y:2020:i:c:p:1139-1146
    DOI: 10.1016/j.renene.2020.04.008
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S096014812030536X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2020.04.008?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. Voyant, Cyril & Notton, Gilles & Kalogirou, Soteris & Nivet, Marie-Laure & Paoli, Christophe & Motte, Fabrice & Fouilloy, Alexis, 2017. "Machine learning methods for solar radiation forecasting: A review," Renewable Energy, Elsevier, vol. 105(C), pages 569-582.
    2. Steve Cicala, 2022. "Imperfect Markets versus Imperfect Regulation in US Electricity Generation," American Economic Review, American Economic Association, vol. 112(2), pages 409-441, February.
    3. Li, Song & Goel, Lalit & Wang, Peng, 2016. "An ensemble approach for short-term load forecasting by extreme learning machine," Applied Energy, Elsevier, vol. 170(C), pages 22-29.
    4. Jager, Henriëtte I. & Efroymson, Rebecca A. & Opperman, Jeff J. & Kelly, Michael R., 2015. "Spatial design principles for sustainable hydropower development in river basins," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 808-816.
    5. Xianxun Wang & Lihua Chen & Qijuan Chen & Yadong Mei & Hao Wang, 2018. "Model and Analysis of Integrating Wind and PV Power in Remote and Core Areas with Small Hydropower and Pumped Hydropower Storage," Energies, MDPI, vol. 11(12), pages 1-24, December.
    6. Harrison Fell & Daniel T. Kaffine, 2018. "The Fall of Coal: Joint Impacts of Fuel Prices and Renewables on Generation and Emissions," American Economic Journal: Economic Policy, American Economic Association, vol. 10(2), pages 90-116, May.
    7. James Bushnell & Kevin Novan, 2018. "Setting with the Sun: The Impacts of Renewable Energy on Wholesale Power Markets," NBER Working Papers 24980, National Bureau of Economic Research, Inc.
    8. Abbasi, Tasneem & Abbasi, S.A., 2011. "Small hydro and the environmental implications of its extensive utilization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(4), pages 2134-2143, May.
    9. Boehlert, Brent & Strzepek, Kenneth M. & Gebretsadik, Yohannes & Swanson, Richard & McCluskey, Alyssa & Neumann, James E. & McFarland, James & Martinich, Jeremy, 2016. "Climate change impacts and greenhouse gas mitigation effects on U.S. hydropower generation," Applied Energy, Elsevier, vol. 183(C), pages 1511-1519.
    10. Denholm, Paul & Hand, Maureen, 2011. "Grid flexibility and storage required to achieve very high penetration of variable renewable electricity," Energy Policy, Elsevier, vol. 39(3), pages 1817-1830, March.
    11. Peng, Lu & Liu, Shan & Liu, Rui & Wang, Lin, 2018. "Effective long short-term memory with differential evolution algorithm for electricity price prediction," Energy, Elsevier, vol. 162(C), pages 1301-1314.
    12. McCarthy, Ryan & Yang, Christopher & Ogden, Joan M., 2008. "California Baseline Energy Demands to 2050 for Advanced Energy Pathways," Institute of Transportation Studies, Working Paper Series qt3f37c9sx, Institute of Transportation Studies, UC Davis.
    13. Yang, Zhang & Ce, Li & Lian, Li, 2017. "Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods," Applied Energy, Elsevier, vol. 190(C), pages 291-305.
    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. Jager, Henriette I. & Griffiths, Natalie A. & Hansen, Carly H. & King, Anthony W. & Matson, Paul G. & Singh, Debjani & Pilla, Rachel M., 2022. "Getting lost tracking the carbon footprint of hydropower," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    2. Kenfack, Joseph & Nzotcha, Urbain & Voufo, Joseph & Ngohe-Ekam, Paul Salomon & Nsangou, Jean Calvin & Bignom, Blaise, 2021. "Cameroon's hydropower potential and development under the vision of Central Africa power pool (CAPP): A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    3. Sasthav, Colin & Oladosu, Gbadebo, 2022. "Environmental design of low-head run-of-river hydropower in the United States: A review of facility design models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    4. Hansen, Carly & Musa, Mirko & Sasthav, Colin & DeNeale, Scott, 2021. "Hydropower development potential at non-powered dams: Data needs and research gaps," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    5. Kandi, Ali & Meirelles, Gustavo & Brentan, Bruno, 2022. "Employing demand prediction in pump as turbine plant design regarding energy recovery enhancement," Renewable Energy, Elsevier, vol. 187(C), pages 223-236.
    6. Huang, Xiaoxun & Hayashi, Kiichiro & Fujii, Minoru & Villa, Ferdinando & Yamazaki, Yuri & Okazawa, Hiromu, 2023. "Identification of potential locations for small hydropower plant based on resources time footprint: A case study in Dan River Basin, China," Renewable Energy, Elsevier, vol. 205(C), pages 293-304.

    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. Csereklyei, Zsuzsanna & Qu, Songze & Ancev, Tihomir, 2019. "The effect of wind and solar power generation on wholesale electricity prices in Australia," Energy Policy, Elsevier, vol. 131(C), pages 358-369.
    2. Denholm, Paul & Brinkman, Greg & Mai, Trieu, 2018. "How low can you go? The importance of quantifying minimum generation levels for renewable integration," Energy Policy, Elsevier, vol. 115(C), pages 249-257.
    3. Cocco Mariani, Viviana & Hennings Och, Stephan & dos Santos Coelho, Leandro & Domingues, Eric, 2019. "Pressure prediction of a spark ignition single cylinder engine using optimized extreme learning machine models," Applied Energy, Elsevier, vol. 249(C), pages 204-221.
    4. Yang, Haolin & Schell, Kristen R., 2022. "GHTnet: Tri-Branch deep learning network for real-time electricity price forecasting," Energy, Elsevier, vol. 238(PC).
    5. Zheng, Jianqin & Zhang, Haoran & Dai, Yuanhao & Wang, Bohong & Zheng, Taicheng & Liao, Qi & Liang, Yongtu & Zhang, Fengwei & Song, Xuan, 2020. "Time series prediction for output of multi-region solar power plants," Applied Energy, Elsevier, vol. 257(C).
    6. Ehsani, Behdad & Pineau, Pierre-Olivier & Charlin, Laurent, 2024. "Price forecasting in the Ontario electricity market via TriConvGRU hybrid model: Univariate vs. multivariate frameworks," Applied Energy, Elsevier, vol. 359(C).
    7. Ghimire, Sujan & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho, 2024. "Two-step deep learning framework with error compensation technique for short-term, half-hourly electricity price forecasting," Applied Energy, Elsevier, vol. 353(PA).
    8. Kuriqi, Alban & Pinheiro, António N. & Sordo-Ward, Alvaro & Garrote, Luis, 2019. "Flow regime aspects in determining environmental flows and maximising energy production at run-of-river hydropower plants," Applied Energy, Elsevier, vol. 256(C).
    9. Chan-Uk Yeom & Keun-Chang Kwak, 2017. "Short-Term Electricity-Load Forecasting Using a TSK-Based Extreme Learning Machine with Knowledge Representation," Energies, MDPI, vol. 10(10), pages 1-18, October.
    10. Zendehboudi, Sohrab & Rezaei, Nima & Lohi, Ali, 2018. "Applications of hybrid models in chemical, petroleum, and energy systems: A systematic review," Applied Energy, Elsevier, vol. 228(C), pages 2539-2566.
    11. John J. García Rendón & Alex F. Pérez-Libreros, 2019. "El precio spot de la electricidad y la inclusión de energía renovable no convencional: evidencia para Colombia," Documentos de Trabajo de Valor Público 17393, Universidad EAFIT.
    12. Jasiński, Tomasz, 2020. "Use of new variables based on air temperature for forecasting day-ahead spot electricity prices using deep neural networks: A new approach," Energy, Elsevier, vol. 213(C).
    13. Javier L'opez Prol & Wolf-Peter Schill, 2020. "The Economics of Variable Renewables and Electricity Storage," Papers 2012.15371, arXiv.org.
    14. Chang, Zihan & Zhang, Yang & Chen, Wenbo, 2019. "Electricity price prediction based on hybrid model of adam optimized LSTM neural network and wavelet transform," Energy, Elsevier, vol. 187(C).
    15. Díaz, Guzmán & Coto, José & Gómez-Aleixandre, Javier, 2019. "Prediction and explanation of the formation of the Spanish day-ahead electricity price through machine learning regression," Applied Energy, Elsevier, vol. 239(C), pages 610-625.
    16. Lu, Xin & Qiu, Jing & Lei, Gang & Zhu, Jianguo, 2022. "Scenarios modelling for forecasting day-ahead electricity prices: Case studies in Australia," Applied Energy, Elsevier, vol. 308(C).
    17. Glenk, Gunther & Reichelstein, Stefan, 2021. "Intermittent versus dispatchable power sources: An integrated competitive assessment," ZEW Discussion Papers 21-065, ZEW - Leibniz Centre for European Economic Research.
    18. Meng, Anbo & Wang, Peng & Zhai, Guangsong & Zeng, Cong & Chen, Shun & Yang, Xiaoyi & Yin, Hao, 2022. "Electricity price forecasting with high penetration of renewable energy using attention-based LSTM network trained by crisscross optimization," Energy, Elsevier, vol. 254(PA).
    19. Concettini, Silvia & Creti, Anna & Gualdi, Stanislao, 2022. "Assessing the regional redistributive effect of renewable power production through a spot market algorithm simulator: The case of Italy," Energy Economics, Elsevier, vol. 114(C).
    20. Qiao, Weibiao & Yang, Zhe, 2020. "Forecast the electricity price of U.S. using a wavelet transform-based hybrid model," Energy, Elsevier, vol. 193(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:155:y:2020:i:c:p:1139-1146. 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.