4.7 Article

Modeling soiling losses for rooftop PV systems in suburban areas with nearby forest in Madrid

期刊

RENEWABLE ENERGY
卷 178, 期 -, 页码 420-428

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.renene.2021.06.085

关键词

PV modeling; Soiling losses; PV performance; Aerosols

资金

  1. Ministerio de Economia y Competitividad (MINECO) [ENE2017-469 83790-C3-1, ENE2017-469 83790-C3-2, ENE2017-469 83790-C3-3]
  2. European Regional Development Fund
  3. Solar Energy Research Center SERC-Chile [ANID/FONDAP/15110019]
  4. Chilean Economic Development Agency (CORFO) [17PTECES-75830]
  5. GORE-Antofagasta, Chile, Project FIC-R Antofagasta 2017 [30488824-0]

向作者/读者索取更多资源

This study focused on the characterization of soiling losses for rooftop PV systems in a suburban forest area in Madrid, with the evaluation and analysis of two available soiling models. The results will contribute to future research on the potential impact of pollution on the growth and penetration of distributed PV systems in European cities.
Particle deposition on the surface of modules in PV systems produces energy output losses with an impact that highly depends on the meteorological and climatic conditions. This work presents the characterization of soiling losses for a suburban forest area in Madrid focused on rooftop PV systems. The soiling loss measured in the testing system can reach around 6%/day for a tilt angle of 8 degrees during summer. Models assessment is also presented and analyzed here using two available soiling models from the well-known pvlib package. The use of the models is not straightforward and some assumptions and recommendations are also presented in this work to produce the best predictions. The applicability of physical models to suburban areas, particularly in large cities in Europe, is remarked by the availability of air quality monitoring ground stations. These results will enhance future studies on the potential impact of soiling in European cities that will help to the distributed PV systems growth and penetration. (C) 2021 Elsevier Ltd. All rights reserved.

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