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Optimal mapping of hybrid renewable energy systems for locations using multi-criteria decision-making algorithm

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  • Diemuodeke, E.O.
  • Addo, A.
  • Oko, C.O.C.
  • Mulugetta, Y.
  • Ojapah, M.M.

Abstract

This paper presents the optimal mapping of hybrid energy systems, which are based on wind and PV, with the consideration of energy storage and backup diesel generator, for households in six locations in the South-South geopolitical (SS) zone of Nigeria: Benin-city, Warri, Yenagoa, Port Harcourt, Uyo and Calabar. The optima hybrid energy systems are able to meet 7.23 kWh/day household's electrical energy demand. The hybrid energy system for each of the locations was optimally obtained based on HOMER software computation and TOPSIS multi-criteria decision-making algorithm that considers technical, economic, environmental, and sociocultural criteria. Wind energy potential was conducted for the six locations using the Weibull distribution function; the wind speed ranges between 3.21 and 4.19 m/s at 10 m anemological height. The wind speeds and the wind characteristics were extrapolated for 30 m and 50 m hub heights. The solar resource potentials across the six locations are also presented – ranges between 4.21 and 4.71 kWh/m2/day. The best hybrid system for the locations in Benin-city, Yenagoa and Port Harcourt is the Diesel generator-PV-Wind-Battery system; whereas the best hybrid system for the locations in Warri, Uyo and Calabar is the PV-Wind-Battery system. The hybrid systems in Benin-city, Yenagoa and Port Harcourt emit CO2, only 8.47%, 15.02% and 14.09% of the business as usual (the diesel generator). The payback time ranges between 3.7 and 5.4 years, using 0.893 US$/kWh cost of energy obtained for the business as usual. The cost of energy of the hybrid systems ranges between 0.459 and 0.562 US$/kWh, which compares well with available data in the public domain. The design parameters of the optima hybrid energy systems are also presented. The methodology presented here will serve as a design tool for renewable energy professionals.

Suggested Citation

  • Diemuodeke, E.O. & Addo, A. & Oko, C.O.C. & Mulugetta, Y. & Ojapah, M.M., 2019. "Optimal mapping of hybrid renewable energy systems for locations using multi-criteria decision-making algorithm," Renewable Energy, Elsevier, vol. 134(C), pages 461-477.
    • Handle: RePEc:eee:renene:v:134:y:2019:i:c:p:461-477
    DOI: 10.1016/j.renene.2018.11.055
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