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Capacity credits of wind and solar generation: The Spanish case

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  • Tapetado, Pablo
  • Usaola, Julio

Abstract

This paper analyses the capacity credits (CCs) of renewable photovoltaic (PV), concentrated solar power (CSP) and wind technologies in the Spanish power system. This system has steadily increased the share of renewables, reaching a penetration level of over 30%. The predictions made by ENTSO-e suggest that this level will increase to 50% by 2030. Therefore, different scenarios are studied in this paper to investigate the evolution of renewable integration and assess the corresponding contributions to reliability. The assessment is performed using a sequential Monte Carlo (SMC) method considering the seasonality of renewable generation and the uncertainties related to renewable sources, failure issues and the maintenance of thermal-based units. The baseline for SMC is provided by historical annual time series of irradiance and wind power data from the Spanish system. In the solar case, these time series are transformed into power time series with models of CSP and PV generation. The former includes different thermal storage strategies. For wind generation, a moving block bootstrap (MBB) technique is used to generate new wind power time series. The CC is assessed based on the equivalent firm capacity (EFC) using standard reliability metrics, namely, the loss of load expectation (LOLE). The results highlight the low contribution of renewables to power system adequacy when the Spanish power system has a high share of renewable generation. In addition, the results are compared with those of similar studies.

Suggested Citation

  • Tapetado, Pablo & Usaola, Julio, 2019. "Capacity credits of wind and solar generation: The Spanish case," Renewable Energy, Elsevier, vol. 143(C), pages 164-175.
    • Handle: RePEc:eee:renene:v:143:y:2019:i:c:p:164-175
    DOI: 10.1016/j.renene.2019.04.139
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    References listed on IDEAS

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    1. Simoglou, Christos K. & Bakirtzis, Emmanouil A. & Biskas, Pandelis N. & Bakirtzis, Anastasios G., 2018. "Probabilistic evaluation of the long-term power system resource adequacy: The Greek case," Energy Policy, Elsevier, vol. 117(C), pages 295-306.
    2. Dominguez, R. & Baringo, L. & Conejo, A.J., 2012. "Optimal offering strategy for a concentrating solar power plant," Applied Energy, Elsevier, vol. 98(C), pages 316-325.
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    Cited by:

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    2. Chen, Jiahao & Sun, Bing & Li, Yunfei & Jing, Ruipeng & Zeng, Yuan & Li, Minghao, 2022. "Credible capacity calculation method of distributed generation based on equal power supply reliability criterion," Renewable Energy, Elsevier, vol. 201(P1), pages 534-547.
    3. Wen, Lei & Song, Qianqian, 2023. "ELCC-based capacity value estimation of combined wind - storage system using IPSO algorithm," Energy, Elsevier, vol. 263(PB).
    4. Loisel, Rodica & Lemiale, Lionel & Mima, Silvana & Bidaud, Adrien, 2022. "Strategies for short-term intermittency in long-term prospective scenarios in the French power system," Energy Policy, Elsevier, vol. 169(C).
    5. Rodica Loisel & Lionel Lemiale & Silvana Mima & Adrien Bidaud, 2022. "Strategies for short-term intermittency in long-term prospective scenarios in the French power system," Post-Print hal-04568072, HAL.
    6. Xu, Tingting & Gao, Weijun & Qian, Fanyue & Li, Yanxue, 2022. "The implementation limitation of variable renewable energies and its impacts on the public power grid," Energy, Elsevier, vol. 239(PA).
    7. Li, Yanxue & Zhang, Xiaoyi & Gao, Weijun & Ruan, Yingjun, 2020. "Capacity credit and market value analysis of photovoltaic integration considering grid flexibility requirements," Renewable Energy, Elsevier, vol. 159(C), pages 908-919.
    8. Wang, Renshun & Wang, Shilong & Geng, Guangchao & Jiang, Quanyuan, 2024. "Multi-time-scale capacity credit assessment of renewable and energy storage considering complex operational time series," Applied Energy, Elsevier, vol. 355(C).
    9. Chen, Tao & Pipattanasomporn, Manisa & Rahman, Imran & Jing, Zejia & Rahman, Saifur, 2020. "MATPLAN: A probability-based planning tool for cost-effective grid integration of renewable energy," Renewable Energy, Elsevier, vol. 156(C), pages 1089-1099.

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