MultiObjective Optimal Sizing of Wind PV HES using Genetic Algorithm

  • John Nduli Kilyungi Pan African University Institute for Basic Sciences Technology and Innovation (PAUSTI), Kenya
  • Stanley Kamau Jomo Kenyatta University of Agriculture & Technology, Kenya
  • Evan Murimi Jomo Kenyatta University of Agriculture & Technology, Kenya


Electricity is an important resource, required for continued development and improvement of the world. However, access to electricity is a problem within various regions of the world, for example Africa. Renewable Energy Systems (RES) provide a viable source of electricity for regions that are not connected to the grid. However, sizing of the components required to convert these renewable sources to energy is still an active area of research. In this paper, the correlation between the power produced by the Renewable Energy System and the cost of the system is used to determine the components to choose. This correlation is materialized as the Excess Power Supply Probability, which is combined with the Low Power Supply Probability of a system to form a multiobjective cost function. The novel approach is then compared with another multiobjective cost function used in research, and is found to perform better in optimizing for cost, while also providing better flexibility to the designer.
Dec 4, 2019
How to Cite
KILYUNGI, John Nduli; KAMAU, Stanley; MURIMI, Evan. MultiObjective Optimal Sizing of Wind PV HES using Genetic Algorithm. JOURNAL OF SUSTAINABLE RESEARCH IN ENGINEERING, [S.l.], v. 5, n. 2, p. 41-49, dec. 2019. ISSN 2409-1243. Available at: <>. Date accessed: 31 mar. 2020.

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