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Minimization of Economic Losses in Photovoltaic System Cleaning Schedules Based on a Novel Methodological Framework for Performance Ratio Forecast and Cost Analysis

Author

Listed:
  • Fabian Zuñiga-Cortes

    (Electrical and Electronic Engineering Department, Universidad del Valle, Calle 13 # 100-00, Cali 760032, Colombia)

  • Juan D. Garcia-Racines

    (Electrical and Electronic Engineering Department, Universidad del Valle, Calle 13 # 100-00, Cali 760032, Colombia)

  • Eduardo Caicedo-Bravo

    (Electrical and Electronic Engineering Department, Universidad del Valle, Calle 13 # 100-00, Cali 760032, Colombia)

  • Hernan Moncada-Vega

    (Maintenance Planning Department of T&D, Celsia S.A., Calle 15 # 29B-30, Autopista Cali-Yumbo, Yumbo 760502, Colombia)

Abstract

The growing interest in deploying photovoltaic systems and achieving their benefits as sustainable energy supplier raises the need to seek reliable medium-term and long-term operations with optimal performance and efficient use of economic resources. Cleaning scheduling is one of the activities that can positively impact performance. This work proposes a methodological framework to define the optimal scheduling of the cleaning activities of photovoltaic systems. The framework integrates a forecast model of the performance ratio, including the environmental variables’ effect. In addition, an economic analysis involving the economic losses and maintenance costs of cleaning is used. This framework is applied to a case study of a photovoltaic system located in Yumbo, Colombia. Based on the historical data on irradiance, active energy, temperature, rainfall, and wind speed, the obtained forecast model of the photovoltaic system’s performance ratio in a 60-day horizon has a mean absolute percentage error lesser of than 11%. The next cleaning date is forecasted to be beyond the horizon in a 19-day range, which will decrease as time goes by. This framework was applied to historical data and compared to actual cleaning dates performed by the utility company. The results show a loss of USD 33.616 due to unnecessary, early, or late cleaning activities.

Suggested Citation

  • Fabian Zuñiga-Cortes & Juan D. Garcia-Racines & Eduardo Caicedo-Bravo & Hernan Moncada-Vega, 2023. "Minimization of Economic Losses in Photovoltaic System Cleaning Schedules Based on a Novel Methodological Framework for Performance Ratio Forecast and Cost Analysis," Energies, MDPI, vol. 16(16), pages 1-18, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:16:p:6091-:d:1221725
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    References listed on IDEAS

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    1. Sean J. Taylor & Benjamin Letham, 2018. "Forecasting at Scale," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 37-45, January.
    2. Ramez Abdallah & Adel Juaidi & Salameh Abdel-Fattah & Mahmoud Qadi & Montaser Shadid & Aiman Albatayneh & Hüseyin Çamur & Amos García-Cruz & Francisco Manzano-Agugliaro, 2022. "The Effects of Soiling and Frequency of Optimal Cleaning of PV Panels in Palestine," Energies, MDPI, vol. 15(12), pages 1-18, June.
    3. Marta Redondo & Carlos A. Platero & Antonio Moset & Fernando Rodríguez & Vicente Donate, 2023. "Soiling Modelling in Large Grid-Connected PV Plants for Cleaning Optimization," Energies, MDPI, vol. 16(2), pages 1-13, January.
    4. Ullah, Zia & Elkadeem, M.R. & Kotb, Kotb M. & Taha, Ibrahim B.M. & Wang, Shaorong, 2021. "Multi-criteria decision-making model for optimal planning of on/off grid hybrid solar, wind, hydro, biomass clean electricity supply," Renewable Energy, Elsevier, vol. 179(C), pages 885-910.
    5. Tossa, Alain K. & Soro, Y.M. & Thiaw, L. & Azoumah, Y. & Sicot, Lionel & Yamegueu, D. & Lishou, Claude & Coulibaly, Y. & Razongles, Guillaume, 2016. "Energy performance of different silicon photovoltaic technologies under hot and harsh climate," Energy, Elsevier, vol. 103(C), pages 261-270.
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    1. Janusz Teneta & Mirosław Janowski & Karolina Bender, 2023. "Analysis of the Deposition of Pollutants on the Surface of Photovoltaic Modules," Energies, MDPI, vol. 16(23), pages 1-14, November.

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