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Wind power bidding strategy in a short-term power market based on particle swarm optimization | |
Author | Nitipong Thipwiang |
Call Number | AIT Thesis no.ET-10-10 |
Subject(s) | Wind power--Forecasting |
Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Energy |
Publisher | Asian Institute of Technology |
Abstract | Wind power is economically competitive with other conventional power production because it can operate without fuel cost and greenhouse gases emission. However, wind energy is uncertain in nature since the wind power production is unpredictable at a given time. In competitive electricity market, wind farm owners are subjected to penalties in balancing market due to wind power forecast errors, and resulting in low revenue from wind power bidding. Moreover, the best prediction of wind power forecast neglecting imbalance cost will not lead to the highest revenue. This thesis proposes an optimal wind power bidding strategy using particle swarm optimization considering positive and negative imbalance costs. Test results on 100 MW wind power generation indicate that the proposed strategy based on particle swarm optimization is an effective tool gaining a higher revenue than the strategy based on wind power forecasting strategy only. |
Year | 2010 |
Type | Thesis |
School | School of Environment, Resources, and Development (SERD) |
Department | Department of Energy and Climate Change (Former title: Department of Energy, Environment, and Climate Change (DEECC)) |
Academic Program/FoS | Energy Technology (ET) |
Chairperson(s) | Weerakorn Ongsakul; |
Examination Committee(s) | Marpaung, Charles O.P.;Singh, Jai Govind; |
Scholarship Donor(s) | RTG Fellowship; |
Degree | Thesis (M.Eng.) - Asian Institute of Technology, 2010 |