TY - GEN
T1 - Optimal scheduling of uncertain wind energy and demand response in unit commitment using binary grey Wolf optimizer (BGWO)
AU - Srikanth Reddy, K.
AU - Panwar, Lokesh Kumar
AU - Panigrahi, B. K.
AU - Kumar, Rajesh
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/4/7
Y1 - 2017/4/7
N2 - The uncertain wind energy handling is a vital aspect of modern power system operational planning with considerable wind penetration. The handling of same is a techno-economic constrained procedure considering its effect on energy and reserve scheduling of whole generation mix. This paper presents a dynamic penalty cost based methodology to solve power system resource scheduling problem with uncertain wind energy, thermal units and responsive loads using binary grey wolf optimizer (BGWO). The proposed methodology considers the effect of uncertain wind energy in terms of total rescheduling cost, total energy balancing cost and total reserve cost. The unit commitment procedure is solved using BGWO and the economic dispatch of committed thermal units alongside the wind energy, responsive load scheduling is solved using Lambda iteration technique. In addition, two different levels of wind uncertainty level are considered to examine the variation techno-economic aspects of proposed methodology. The simulation results are presented and discussed with respect to various performance attributes and the same demonstrate the superior performance of proposed dynamic penalty cost models over existing static cost models.
AB - The uncertain wind energy handling is a vital aspect of modern power system operational planning with considerable wind penetration. The handling of same is a techno-economic constrained procedure considering its effect on energy and reserve scheduling of whole generation mix. This paper presents a dynamic penalty cost based methodology to solve power system resource scheduling problem with uncertain wind energy, thermal units and responsive loads using binary grey wolf optimizer (BGWO). The proposed methodology considers the effect of uncertain wind energy in terms of total rescheduling cost, total energy balancing cost and total reserve cost. The unit commitment procedure is solved using BGWO and the economic dispatch of committed thermal units alongside the wind energy, responsive load scheduling is solved using Lambda iteration technique. In addition, two different levels of wind uncertainty level are considered to examine the variation techno-economic aspects of proposed methodology. The simulation results are presented and discussed with respect to various performance attributes and the same demonstrate the superior performance of proposed dynamic penalty cost models over existing static cost models.
KW - Binary grey Wolf optimizer (BGWO)
KW - Demand response
KW - Dynamic penalty cost models (DPCM)
KW - Static mean adjustment cost model (SMACM)
KW - Unit Commitment
KW - Wind energy uncertainty
UR - http://www.scopus.com/inward/record.url?scp=85018323451&partnerID=8YFLogxK
U2 - 10.1109/UPCON.2016.7894677
DO - 10.1109/UPCON.2016.7894677
M3 - Conference contribution
AN - SCOPUS:85018323451
T3 - 2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering, UPCON 2016
SP - 344
EP - 349
BT - 2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering, UPCON 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering, UPCON 2016
Y2 - 9 December 2016 through 11 December 2016
ER -