Hydro Power Reservoir Aggregation via Genetic Algorithms
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 cre...
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MDPI AG
2018
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oai:localhost:DHQB_123456789-38062018-10-22T08:44:19Z Hydro Power Reservoir Aggregation via Genetic Algorithms Energies Markus Löschenbrand (Department of Electric Power Engineering, NTNU, 7491 Trondheim, Norway) Magnus Korpås (Department of Electric Power Engineering, NTNU, 7491 Trondheim, Norway) Technology 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. 2018-08-22T07:35:05Z 2018-08-22T07:35:05Z 1996 Other http://lrc.quangbinhuni.edu.vn:8181/dspace/handle/DHQB_123456789/3806 en_US MDPI AG |
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en_US |
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Technology |
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Technology Markus Löschenbrand (Department of Electric Power Engineering, NTNU, 7491 Trondheim, Norway) Magnus Korpås (Department of Electric Power Engineering, NTNU, 7491 Trondheim, Norway) Hydro Power Reservoir Aggregation via Genetic Algorithms |
| description |
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. |
| format |
Other |
| author |
Markus Löschenbrand (Department of Electric Power Engineering, NTNU, 7491 Trondheim, Norway) Magnus Korpås (Department of Electric Power Engineering, NTNU, 7491 Trondheim, Norway) |
| author_facet |
Markus Löschenbrand (Department of Electric Power Engineering, NTNU, 7491 Trondheim, Norway) Magnus Korpås (Department of Electric Power Engineering, NTNU, 7491 Trondheim, Norway) |
| author_sort |
Markus Löschenbrand (Department of Electric Power Engineering, NTNU, 7491 Trondheim, Norway) |
| title |
Hydro Power Reservoir Aggregation via Genetic Algorithms |
| title_short |
Hydro Power Reservoir Aggregation via Genetic Algorithms |
| title_full |
Hydro Power Reservoir Aggregation via Genetic Algorithms |
| title_fullStr |
Hydro Power Reservoir Aggregation via Genetic Algorithms |
| title_full_unstemmed |
Hydro Power Reservoir Aggregation via Genetic Algorithms |
| title_sort |
hydro power reservoir aggregation via genetic algorithms |
| publisher |
MDPI AG |
| publishDate |
2018 |
| url |
http://lrc.quangbinhuni.edu.vn:8181/dspace/handle/DHQB_123456789/3806 |
| _version_ |
1717292447026905088 |
| score |
9,463379 |