Abstract
The Unit Commitment (UC) problem consists on the day-ahead scheduling of thermal generation units. The scheduling process is based on a forecast for the demand, which adds uncertainty to the decision of starting or shutting down units. With the increasing penetration of renewable energy sources, namely wind power, the level of uncertainty is such that deterministic UC approaches that rely uniquely on point forecasts are no longer appropriate. The UC approach reported in this paper considers a stochastic formulation and includes constraints for the technical limits of thermal generation units, like ramp-rates and minimum and maximum power output, and also for the power flow equations by integrating the DC model in the optimization process. The objective is to assess the ability of the stochastic UC approach to decrease the expected value of load shedding and wind power loss when compared to the deterministic UC approach. A case study based on IEEE-RTS 79 system, which has 24 buses and 32 thermal generation units, for two different penetrations of wind power and a 24-hour horizon is carried out. The computational performance of the methodology proposed is also discussed to show that considerable performance gains without compromising the robustness of the stochastic UC approach can be achieved.
Original language | English |
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Title of host publication | 2016 13th International Conference on the European Energy Market, EEM 2016 |
Publisher | IEEE Computer Society |
ISBN (Electronic) | 9781509012978 |
DOIs | |
Publication status | Published - 25 Jul 2016 |
Externally published | Yes |
Event | 13th International Conference on the European Energy Market, EEM 2016 - Porto, Portugal Duration: 6 Jun 2016 → 9 Jun 2016 |
Publication series
Name | International Conference on the European Energy Market, EEM |
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Volume | 2016-July |
ISSN (Print) | 2165-4077 |
ISSN (Electronic) | 2165-4093 |
Conference
Conference | 13th International Conference on the European Energy Market, EEM 2016 |
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Country/Territory | Portugal |
City | Porto |
Period | 6/06/16 → 9/06/16 |
Bibliographical note
Publisher Copyright:© 2016 IEEE.
Keywords
- Stochastic Unit Commitment
- Uncertainty Modeling
- Wind Power