Optimizing the Layout of Run-of-River Powerplants Using Cubic Hermite Splines and Genetic Algorithms

Despite the clear advantages of mini hydropower technology to provide energy access in remote areas of developing countries, the lack of resources and technical training in these contexts usually lead to suboptimal installations that do not exploit the full potential of the environment. To address t...

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Bibliographic Details
Published inApplied sciences Vol. 12; no. 16; p. 8133
Main Authors Córdoba, Alejandro Tapia, Gata, Pablo Millán, Reina, Daniel Gutiérrez
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.08.2022
Subjects
Algorithms
Approximation
Costs
Design optimization
Developing countries
Efficiency
Electricity distribution
Energy
Energy utilization
Environmental impact
genetic algorithm
Genetic algorithms
Hermite splines
Hydroelectric plants
Hydroelectric power
Integer programming
Interpolation
layout optimization
LDCs
Linear programming
micro-hyropower plant
Optimization
Penstocks
Plant layout
Power plants
Terrain
Turbines
Variables
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Summary:Despite the clear advantages of mini hydropower technology to provide energy access in remote areas of developing countries, the lack of resources and technical training in these contexts usually lead to suboptimal installations that do not exploit the full potential of the environment. To address this drawback, the present work proposes a novel method to optimize the design of mini-hydropower plants with a robust and efficient formulation. The approach does not involve typical 2D simplifications of the terrain penstock layout. On the contrary, the problem is formulated considering arbitrary three-dimensional terrain profiles and realistic penstock layouts taking into account the bending effect. To this end, the plant layout is modeled on a continuous basis through the cubic Hermite interpolation of a set of key points, and the optimization problem is addressed using a genetic algorithm with tailored generation, mutation and crossover operators, especially designed to improve both the exploration and intensification. The approach is successfully applied to a real-case scenario with real topographic data, demonstrating its capability of providing optimal solutions while dealing with arbitrary terrain topography. Finally, a comparison with a previous discrete approach demonstrated that this algorithm can lead to a noticeable cost reduction for the problem studied.
ISSN:2076-3417
2076-3417
DOI:10.3390/app12168133