Daily operation optimisation of hybrid stand-alone system by model predictive control considering ageing model

  1. Dufo-López, R. 1
  2. Fernández-Jiménez, L.A. 2
  3. Ramírez-Rosado, I.J. 1
  4. Artal-Sevil, J.S. 1
  5. Domínguez-Navarro, J.A. 1
  6. Bernal-Agustín, J.L. 1
  1. 1 Universidad de Zaragoza
    info

    Universidad de Zaragoza

    Zaragoza, España

    ROR https://ror.org/012a91z28

  2. 2 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

Revista:
Energy Conversion and Management

ISSN: 0196-8904

Año de publicación: 2017

Volumen: 134

Páginas: 167-177

Tipo: Artículo

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DOI: 10.1016/J.ENCONMAN.2016.12.036 SCOPUS: 2-s2.0-85007271204 WoS: WOS:000393002100014 GOOGLE SCHOLAR

Otras publicaciones en: Energy Conversion and Management

Repositorio institucional: lockAcceso abierto Editor

Objetivos de desarrollo sostenible

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2014/00062/001

Resumen

This article presents a method for optimising the daily operation (minimising the total operating cost) of a hybrid photovoltaic-wind-diesel-battery system using model predictive control. The model uses actual weather forecasts of hourly values of wind speed, irradiation, temperature and load. Five control variables are optimised, and thus their optimal set points values determine the optimal control strategy for each day. This involves the use of an accurate model for estimating the degradation of the batteries by considering the capacity loss due to corrosion and degradation. The model considers the extra costs of maintaining and replacing the diesel generator due to running out of its optimal conditions. The optimisation is carried out by means of genetic algorithms. An example of application compares the total operating cost obtained using the optimal control strategy for each day with the cost of using the optimal control strategy found for the whole year, obtaining savings of up to 7.8%. Also the comparison with the cost of using the “load following” control strategy is analysed, obtaining savings of up to 37.7%. © 2016 Elsevier Ltd