Next Article in Journal
Adjusting the Parameters of Metal Oxide Gapless Surge Arresters’ Equivalent Circuits Using the Harmony Search Method
Next Article in Special Issue
Extension of Operating Range in Pump-Turbines. Influence of Head and Load
Previous Article in Journal
Influence of the Periodicity of Sinusoidal Boundary Condition on the Unsteady Mixed Convection within a Square Enclosure Using an Ag–Water Nanofluid
Previous Article in Special Issue
Power Swing Generated in Francis Turbines by Part Load and Overload Instabilities
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle
Energies 2017, 10(12), 2165; https://doi.org/10.3390/en10122165

Hydro Power Reservoir Aggregation via Genetic Algorithms

Department of Electric Power Engineering, NTNU, 7491 Trondheim, Norway
*
Author to whom correspondence should be addressed.
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)
Full-Text   |   PDF [926 KB, uploaded 18 December 2017]   |  

Abstract

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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

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

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top