Title An Evolutionary Computational Approach for Designing Micro Hydro Power Plants
Authors TAPIA CÓRDOBA, ALEJANDRO, GUTIÉRREZ REINA, DANIEL, MILLÁN GATA, PABLO, MILLÁN GATA, PABLO, TAPIA CÓRDOBA, ALEJANDRO, GUTIÉRREZ REINA, DANIEL
External publication No
Means Energies
Scope Article
Nature Científica
JCR Quartile 3
SJR Quartile 1
JCR Impact 2.70200
Area International
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062672972&doi=10.3390%2fen12050878&partnerID=40&md5=6284337638e8765e31d0f3086165a305
Publication date 01/03/2019
ISI 000462646700111
Scopus Id 2-s2.0-85062672972
DOI 10.3390/en12050878
Abstract Micro Hydro Power Plants (MHPP) constitute an effective, environmentally-friendly solution to deal with energy poverty in rural isolated areas, being the most extended renewable technology in this field. Nevertheless, the context of poverty and lack of qualified manpower usually lead to a poor usage of the resources, due to the use of thumb rules and user experience to design the layout of the plants, which conditions the performance. For this reason, the development of robust and efficient optimization strategies are particularly relevant in this field. This paper proposes a Genetic Algorithm (GA) to address the problem of finding the optimal layout for an MHPP based on real scenario data, obtained by means of a set of experimental topographic measurements. With this end in view, a model of the plant is first developed, in terms of which the optimization problem is formulated with the constraints of minimal generated power and maximum use of flow, together with the practical feasibility of the layout to the measured terrain. The problem is formulated in both single-objective (minimization of the cost) and multi-objective (minimization of the cost and maximization of the generated power) modes, the Pareto dominance being studied in this last case. The algorithm is first applied to an example scenario to illustrate its performance and compared with a reference Branch and Bound Algorithm (BBA) linear approach, reaching reductions of more than 70% in the cost of the MHPP. Finally, it is also applied to a real set of geographical data to validate its robustness against irregular, poorly sampled domains.
Keywords MHPP; hydro-power; penstock; optimization; GA; simulated annealing; evolutionary computation
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