Parallelized Bi-level optimization model with continuous search domain for selection of run-of-river hydropower projects

Hazem U. Abdelhady, Yehya E. Imam, Ziad Shawwash and Ashraf Ghanem

Renewable Energy, 2021, vol. 167, issue C, 116-131

Abstract: A comprehensive optimization model was developed to identify the optimal set of non-storage based small hydropower projects (SHP) along a given river reach. The model was based on a greedy genetic algorithm that maximizes net annual benefit. For each project, the model determines intake location, penstock diameter and length, and turbine number and capacity. The novel features in the model include: 1) bi-level optimization that produces robust results, 2) improved search for SHP intake locations, 3) inclusion of turbine number as an explicit optimization variable, and 4) nested optimization modules and parallelized execution. Model performance was assessed through application to the Mamquam River in Canada and the Guder River in Ethiopia. For the Mamquam River, the model identified seven feasible projects. The model provided suggestions for further improvement of one of the two existing SHPs on the river through shifting the intake 1.2 km upstream of the existing intake to provide a greater net benefit. No SHPs have been constructed on the Guder River yet; however, the model suggested 19 potential SHPs that could provide 62% higher annual net benefit compared to the results of a previous model recently applied to the same study area.

Keywords: Hydroelectric power; Run-of-River; Optimization; Intake location; Penstock design; Turbine selection (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148120317985
Full text for ScienceDirect subscribers only

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:eee:renene:v:167:y:2021:i:c:p:116-131

DOI: 10.1016/j.renene.2020.11.055

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().