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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| Tác giả chính: | , |
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| Định dạng: | Other |
| Ngôn ngữ: | en_US |
| Năm xuất bản: |
MDPI AG
2018
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| Chủ đề: | |
| Truy cập Trực tuyến: | http://lrc.quangbinhuni.edu.vn:8181/dspace/handle/DHQB_123456789/3806 |
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| Tóm tắt: | 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. |
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