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Optimal Location of Small Hydropower Dams: Balancing Renewable Energy Gains and River Connectivity Impacts

O'Hanley, J.R., Ioannidou, C. (2017) Optimal Location of Small Hydropower Dams: Balancing Renewable Energy Gains and River Connectivity Impacts. In: 21st Conference of the International Federation of Operational Research Societies, 17-21 Jul 2017, Québec City, Canada. (Unpublished) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://ifors.org/ifors-2017/

Abstract

We address the problem of locating small hydropower dams in an environmentally friendly manner. We propose the use of a multi-objective optimization model to maximize total hydropower production, while limiting negative impacts on river connectivity. Critically, we consider the so called “backwater effects” that dams have on power generation at nearby upstream sites via changes in water surface profiles. We further account for the likelihood that migratory fish and other aquatic species can successfully pass hydropower dams and other artificial/natural barriers and how this is influenced by backwater effects. Although naturally represented in nonlinear form, we manage through a series of linearization steps to formulate a mixed integer linear programing model. We illustrate the utility of our proposed framework using a case study from England and Wales. Interestingly, we show that for England and Wales, a region heavily impacted by a large number of existing river barriers, that installation of small hydropower dams fitted with even moderately effective fish passes can, in fact, create a win-win situation that results in increased hydropower and improved river connectivity.

Item Type: Conference or workshop item (Lecture)
Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
H Social Sciences > HA Statistics > HA33 Management Science
Q Science > QH Natural history > QH75 Conservation (Biology)
S Agriculture > SH Aquaculture. Fisheries. Angling
Divisions: Faculties > Social Sciences > Kent Business School > Management Science
Faculties > Social Sciences > Kent Business School > Centre for Logistics and Heuristic Organisation (CLHO)
Depositing User: Jesse O'Hanley
Date Deposited: 19 Jul 2019 10:23 UTC
Last Modified: 22 Jul 2019 11:41 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/75461 (The current URI for this page, for reference purposes)
O'Hanley, J.R.: https://orcid.org/0000-0003-3522-8585
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