Title :
Scenario reduction for stochastic unit commitment with wind penetration
Author :
Yonghan Feng ; Ryan, Sarah M.
Author_Institution :
Ind. & Manuf. Syst. Eng., Iowa State Univ., Ames, IA, USA
Abstract :
Uncertainties in the day-ahead forecasts for load and wind energy availability are considered in a reliability unit commitment problem. A two-stage stochastic program is formulated to minimize total expected cost, where commitments of thermal units are viewed as first-stage decisions and dispatch is relegated to the second stage. Scenario paths of hourly loads are generated according to a weather forecast-based load model. Wind energy scenarios are obtained by identifying analogue historical days. Net load scenarios are then created by crossing scenarios from each set and subtracting wind energy from load. A new heuristic scenario reduction method termed forward selection in recourse clusters (FSRC) is customized to alleviate the computational burden. Results of applying FSRC are compared with those of a classical scenario reduction method, fast forward selection (FFS) by evaluating the expected dispatch costs when the commitment decisions derived from each subset of scenarios are applied to the whole scenario set. In an instance down-sampled from data of an Independent System Operator in the U.S., the expected dispatch costs for both scenario reduction methods are similar, but FSRC improves reliability.
Keywords :
cost reduction; load forecasting; power generation dispatch; power generation reliability; power generation scheduling; stochastic programming; thermal power stations; weather forecasting; wind power plants; FFS; FSRC; day-ahead forecasts; expected dispatch costs; fast forward selection; first-stage decisions; forward selection in recourse clusters; heuristic scenario reduction method; independent system operator; net load scenarios; reliability unit commitment problem; scenario paths; stochastic unit commitment; thermal units; total expected cost minimization; two-stage stochastic program; weather forecast-based load model; wind energy availability; wind energy scenarios; wind penetration; Generators; Programming; Reliability; Sensitivity; Stochastic processes; Wind energy; Scenario reduction; Stochastic programming; Unit commitment;
Conference_Titel :
PES General Meeting | Conference & Exposition, 2014 IEEE
Conference_Location :
National Harbor, MD
DOI :
10.1109/PESGM.2014.6939138