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Hydro Power Reservoir Aggregation via Genetic Algorithms

Department of Electric Power Engineering, NTNU, 7491 Trondheim, Norway
Author to whom correspondence should be addressed.
Energies 2017, 10(12), 2165;
Received: 10 November 2017 / Revised: 14 December 2017 / Accepted: 15 December 2017 / Published: 18 December 2017
(This article belongs to the Special Issue Hydropower 2017)
Electrical power systems with a high share of hydro power in their generation portfolio tend to display distinct behavior. Low generation cost and the possibility of peak shaving create a high amount of flexibility. However, stochastic influences such as precipitation and external market effects create uncertainty and thus establish a wide range of potential outcomes. Therefore, optimal generation scheduling is a key factor to successful operation of hydro power dominated systems. This paper aims to bridge the gap between scheduling on large-scale (e.g., national) and small scale (e.g., a single river basin) levels, by applying a multi-objective master/sub-problem framework supported by genetic algorithms. A real-life case study from southern Norway is used to assess the validity of the method and give a proof of concept. The introduced method can be applied to efficiently integrate complex stochastic sub-models into Virtual Power Plants and thus reduce the computational complexity of large-scale models whilst minimizing the loss of information. View Full-Text
Keywords: hydro power; reservoir aggregation; scheduling; evolutionary algorithm; genetic algorithm hydro power; reservoir aggregation; scheduling; evolutionary algorithm; genetic algorithm
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MDPI and ACS Style

Löschenbrand, M.; Korpås, M. Hydro Power Reservoir Aggregation via Genetic Algorithms. Energies 2017, 10, 2165.

AMA Style

Löschenbrand M, Korpås M. Hydro Power Reservoir Aggregation via Genetic Algorithms. Energies. 2017; 10(12):2165.

Chicago/Turabian Style

Löschenbrand, Markus, and Magnus Korpås. 2017. "Hydro Power Reservoir Aggregation via Genetic Algorithms" Energies 10, no. 12: 2165.

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