LCOE-Based Optimization for the Design of Small Run-of-River Hydropower Plants

Claude Boris Amougou, David Tsuanyo (), Davide Fioriti, Joseph Kenfack, Abdoul Aziz and Patrice Elé Abiama
Additional contact information
Claude Boris Amougou: National Centre for Development of Technologies, Ministry of Scientific Research and Innovation, Yaoundé P.O. Box 1457, Cameroon
David Tsuanyo: National Centre for Development of Technologies, Ministry of Scientific Research and Innovation, Yaoundé P.O. Box 1457, Cameroon
Davide Fioriti: Department of Energy, Systems, Territory and Construction Engineering, University of Pisa, Largo Lucio Lazzarino 1, 56122 Pisa, Italy
Joseph Kenfack: Laboratory of Small Hydropower and Hybrids Systems, National Advanced School of Engineering, University of Yaoundé I, Yaoundé P.O. Box 8390, Cameroon
Abdoul Aziz: National Centre for Development of Technologies, Ministry of Scientific Research and Innovation, Yaoundé P.O. Box 1457, Cameroon
Patrice Elé Abiama: National Centre for Development of Technologies, Ministry of Scientific Research and Innovation, Yaoundé P.O. Box 1457, Cameroon

Energies, 2022, vol. 15, issue 20, 1-35

Abstract: Run-of-river hydropower plants are a cost-efficient technology that produce a power output proportional to the instantaneous flow of water diverted from the exploited stream by exploiting several mechanical, hydraulic, and electric devices. However, as no storage is available, its design and operation is tailored according to the unpredictability of its power generation. Hence, the modelling of this type of power plants is a necessity for the promotion of its development. Accordingly, based on models from the literature, this study proposes a comprehensive methodology for optimally designed small run-of-river hydropower plants based on a levelized cost of energy (LCOE). The proposed methodology aims at facilitating a faster design for more cost-effective and energy-efficient small hydropower plants. Depending on the average daily flow rates and the gross head of a given site, the model proposed in this study calculates the diameter, thickness, and length of a penstock; it also suggests the optimal selection of a turbine, determines the admissible suction head of a turbine for its optimal implementation, and determines the optimal number of turbines, all in order to minimize the LCOE of the proposed project. The model is tested to design a small run-of-river hydropower plant with a capacity of 6.32 MW exploiting the river Nyong in Mbalmayo. The results confirm the profitability of the investment with an LCOE of around 0.05 USD/kWh, which is the lowest limit value of the LCOE range for small hydropower plants, as presented in the IPCC (Intergovernmental Panel on Climate Change) report, assuming a project lifespan of 50 years and a discount rate of 12.5%. These results also show that it may be worth to provide the energy sector with a small hydropower design tool with a graphical interface. In addition, it would be appropriate to use a similar method in an off-grid context where a hydropower plant, with or without storage, is combined with another source to meet the electrical needs of a given population.

Keywords: run-of-river; hydropower; optimal design; levelized cost of energy (LCOE); genetic algorithm; energy systems; sizing (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/20/7507/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/20/7507/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:20:p:7507-:d:940017

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().