Unit commitment with wind power generation: integrating wind forecast uncertainty and stochastic programming.

Constantinescu, E. M. ; Zavala, V. M. ; Rocklin, M. ; Lee, S. ; Anitescu, M. (Mathematics and Computer Science) ; (Univ. of Chicago) ; (New York Univ.)

关键词: ARCHITECTURE;    FORECASTING;    IMPLEMENTATION;    POWER SYSTEMS;    PROGRAMMING;    SAMPLING;    SENSITIVITY ANALYSIS;    VELOCITY;    WEATHER;    WIND POWER;   

DOI  :  10.2172/1009334
RP-ID  :  ANL/MCS-TM-309
PID  :  OSTI ID: 1009334
Others  :  TRN: US201107%%745

美国|英语

来源: SciTech Connect

【 摘 要 】

We present a computational framework for integrating the state-of-the-art Weather Research and Forecasting (WRF) model in stochastic unit commitment/energy dispatch formulations that account for wind power uncertainty. We first enhance the WRF model with adjoint sensitivity analysis capabilities and a sampling technique implemented in a distributed-memory parallel computing architecture. We use these capabilities through an ensemble approach to model the uncertainty of the forecast errors. The wind power realizations are exploited through a closed-loop stochastic unit commitment/energy dispatch formulation. We discuss computational issues arising in the implementation of the framework. In addition, we validate the framework using real wind speed data obtained from a set of meteorological stations. We also build a simulated power system to demonstrate the developments.