Abstract
The uncertainty associated with the increasingly wind power penetration in power systems must be considered when performing the traditional day-ahead scheduling of conventional thermal units. This uncertainty can be represented through a set of representative wind power scenarios that take into account the time-dependency between forecasting errors. To create robust Unit Commitment (UC) schedules, it is widely seen that all possible wind power scenarios must be used. However, using all realizations of wind power might be a poor approach and important savings in computational effort can be achieved if only the most representative subset is used. In this paper, the new hybrid metaheuristic DEEPSO and clustering techniques are used in the traditional stochastic formulation of the UC problem to investigate the robustness of the UC schedules with increasing number of wind power scenarios. For this purpose, expected values for operational costs, wind spill, and load curtailment for the UC solutions are compared for a didactic 10 generator test system. The obtained results shown that it is possible to reduce the computation burden of the stochastic UC by using a small set of representative wind power scenarios previously selected from a high number of scenarios covering the entire probability distribution function of the forecasting uncertainty.
Original language | English |
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Title of host publication | 2015 IEEE Eindhoven PowerTech, PowerTech 2015 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781479976935 |
DOIs | |
Publication status | Published - 31 Aug 2015 |
Externally published | Yes |
Event | IEEE Eindhoven PowerTech, PowerTech 2015 - Eindhoven, Netherlands Duration: 29 Jun 2015 → 2 Jul 2015 |
Publication series
Name | 2015 IEEE Eindhoven PowerTech, PowerTech 2015 |
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Conference
Conference | IEEE Eindhoven PowerTech, PowerTech 2015 |
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Country/Territory | Netherlands |
City | Eindhoven |
Period | 29/06/15 → 2/07/15 |
Bibliographical note
Publisher Copyright:© 2015 IEEE.
Keywords
- Clustering Techniques
- Metaheuristic Optimization
- Unit Commitment
- Wind Power Uncertainty