TY - JOUR
T1 - Wind power scenario generation and reduction in stochastic programming framework
AU - Sharma, Kailash Chand
AU - Jain, Prerna
AU - Bhakar, Rohit
PY - 2013/2/1
Y1 - 2013/2/1
N2 - Wind power trading in pool-based electricity markets is a decision-making problem and is generally modeled using a multi-stage stochastic programming approach because of the implicit uncertainty of wind input. In any stochastic programming approach, representation of random input process is a major issue. Due to uncertainty in wind availability, generated power by wind turbines is stochastic and is represented by possible values with corresponding probability of occurrence or scenarios. Accurate representation of uncertainty generally requires the consideration of large number of scenarios, thus necessitating the need for scenario-reduction techniques. This article presents simplified algorithms for wind power scenario generation and reduction. A time series based auto regressive moving average model is used for scenario generation, and probability distance based backward reduction is used for scenario reduction. The algorithms have been implemented for next-day scenario generation of wind farm located at Barnstable, Massachusetts, USA. The results prove the ability of the proposed algorithms in wind uncertainty modeling. These algorithms can successfully be utilized to generate optimal wind power bids for trading in electricity markets.
AB - Wind power trading in pool-based electricity markets is a decision-making problem and is generally modeled using a multi-stage stochastic programming approach because of the implicit uncertainty of wind input. In any stochastic programming approach, representation of random input process is a major issue. Due to uncertainty in wind availability, generated power by wind turbines is stochastic and is represented by possible values with corresponding probability of occurrence or scenarios. Accurate representation of uncertainty generally requires the consideration of large number of scenarios, thus necessitating the need for scenario-reduction techniques. This article presents simplified algorithms for wind power scenario generation and reduction. A time series based auto regressive moving average model is used for scenario generation, and probability distance based backward reduction is used for scenario reduction. The algorithms have been implemented for next-day scenario generation of wind farm located at Barnstable, Massachusetts, USA. The results prove the ability of the proposed algorithms in wind uncertainty modeling. These algorithms can successfully be utilized to generate optimal wind power bids for trading in electricity markets.
KW - scenario generation
KW - scenario reduction
KW - stochastic programming
KW - time series
KW - wind power uncertainty
UR - http://www.scopus.com/inward/record.url?scp=84872567436&partnerID=8YFLogxK
UR - http://dx.doi.org/10.1080/15325008.2012.742942
U2 - 10.1080/15325008.2012.742942
DO - 10.1080/15325008.2012.742942
M3 - Article
AN - SCOPUS:84872567436
SN - 1532-5008
VL - 41
SP - 271
EP - 285
JO - Electric Power Components and Systems
JF - Electric Power Components and Systems
IS - 3
ER -