Markov chains estimation of the optimal periodicity for cleaning photovoltaic panels installed in the dehesa

RENEWABLE ENERGY

卷:179

Article

Sanchez-Barroso, Gonzalo¹  Gonzalez-Dominguez, Jaime¹  Garcia-Sanz-Calcedo, Justo¹  Sanz, Joaquin Garci² 

[1] Univ Extremadura, Engn Projects Area, Av Elvas S N, Badajoz 06071, Spain

[2] Innodesarrollo S L, Ind Pk el Nevero,Nevero Quince Ocho St 24, Badajoz 06006, Spain

关键词: Dust accumulation;    Cleaning photovoltaic systems;    Cleaning frequency;    Predictive maintenance;    Markov chains;   

DOI  :  10.1016/j.renene.2021.07.075

来源: Elsevier

【 摘 要 】

The European Dehesa has a very high potential for the production of clean energy due to the solar irradiation it receives. Its arid climate, however, means that airborne dust particles accumulate on the photovoltaic panels, with the resulting reduction in transmittance of the glass top-sheets. Cleaning the module surfaces involves an economic investment that, to be profitable, has to be offset by sufficient increased energy production. The objective of the present study was to determine the optimal periodicity for cleaning photovoltaic panels installed in the Dehesa, and thus subject to its specific climate. To this end, an experimental installation was set up, and three cleaning plans (monthly, quarterly, and semiannually) were tested against equivalent not-cleaned controls. The results showed monthly cleaning to increase a year's worth of energy generation by 11.15%. From weekly inspections and continuous monitoring of the panels' output power, a Markov-chains based mathematical model of the degradation of energy production was developed. The conclusion drawn from it was that the cleaning frequency should be monthly from July to October (with the optimal frequency being every three weeks), but that from November to June cleaning is unprofitable since it provides no significant improvement in output. Modelling the degradation of energy output constitutes a powerful tool with which to increase the bankability of photovoltaic plants. (c) 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

【 授权许可】

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