TY - JOUR
T1 - Novel Power Smoothing and Generation Scheduling Strategies for a Hybrid Wind and Marine Current Turbine System
AU - Anwar, Muhammad Bashar
AU - El Moursi, Mohamed Shawky
AU - Xiao, Weidong
N1 - Publisher Copyright:
© 1969-2012 IEEE.
PY - 2017/3
Y1 - 2017/3
N2 - Grid integration of wind energy is a major challenge as grid codes require electricity generation units to schedule their generation ahead of the trading period and to limit the power fluctuations. This paper proposes novel strategies for mitigating the effects of wind intermittency by developing hybrid off-shore wind and marine current turbines. Unlike the random nature of wind, marine currents have slow cyclic variations and are highly predictable. The proposed methods involve optimal sizing strategy for the hybrid system and rely on the resource predictions. Prediction Intervals for wind speed forecasts using Bootstrapped Artificial Neural Networks have been developed and validated. Marine current speeds have been mathematically modeled using the Harmonic Analysis Method. Subsequently, novel power fluctuation mitigation and generation scheduling strategies using minimum energy storage have been designed based on the UK electricity market regulations and the predicted wind and current speeds. The results demonstrate the effectiveness of the proposed methods which ensure successful mitigation of power fluctuations, reliable dispatch scheduling of renewable generation, and significant cost saving potential.
AB - Grid integration of wind energy is a major challenge as grid codes require electricity generation units to schedule their generation ahead of the trading period and to limit the power fluctuations. This paper proposes novel strategies for mitigating the effects of wind intermittency by developing hybrid off-shore wind and marine current turbines. Unlike the random nature of wind, marine currents have slow cyclic variations and are highly predictable. The proposed methods involve optimal sizing strategy for the hybrid system and rely on the resource predictions. Prediction Intervals for wind speed forecasts using Bootstrapped Artificial Neural Networks have been developed and validated. Marine current speeds have been mathematically modeled using the Harmonic Analysis Method. Subsequently, novel power fluctuation mitigation and generation scheduling strategies using minimum energy storage have been designed based on the UK electricity market regulations and the predicted wind and current speeds. The results demonstrate the effectiveness of the proposed methods which ensure successful mitigation of power fluctuations, reliable dispatch scheduling of renewable generation, and significant cost saving potential.
KW - Generation scheduling
KW - marine current energy
KW - power fluctuation mitigation
KW - prediction algorithms
KW - wind energy systems
UR - http://www.scopus.com/inward/record.url?scp=85013895181&partnerID=8YFLogxK
U2 - 10.1109/TPWRS.2016.2591723
DO - 10.1109/TPWRS.2016.2591723
M3 - Article
AN - SCOPUS:85013895181
SN - 0885-8950
VL - 32
SP - 1315
EP - 1326
JO - IEEE Transactions on Power Systems
JF - IEEE Transactions on Power Systems
IS - 2
M1 - 7513423
ER -