TY - GEN
T1 - An Improved Binary Grey Wolf Optimizer (IBGWO) for Unit Commitment Problem in Thermal Generation
AU - Srikanth Reddy, K.
AU - Saad Al-Sumaiti, Ameena
AU - Gupta, Vishu
AU - Kumar, Rajesh
AU - Saxena, Akash
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - Generation scheduling and unit commitment procedures in a power system constitute a key operational planning feature of any power system. The complex, constrained unit commitment problem presents a computational challenge, and improving the solution quality can make a substantial impact in the long run. This paper presents an improved binary grey wolf optimization (IBGWO) to solve the unit commitment problem in power system operational planning. The IBGWO enhances the balance between exploration and exploitation properties of the grey wolf search. This would improve both the local as well as the global search properties of the classical grey wolf algorithm. In order to execute and implement the improved grey wolf optimization to unit commitment with binary decision variables, binary transformation of the real valued variant is employed. The IBGWO is tested using test system with different sizes ranging from 10 thermal units to 100 thermal units. The solution quality indices along with convergence characteristics are presented and compared to the existing approaches. The same demonstrates the improved solution quality in the form of reduced operational cost.
AB - Generation scheduling and unit commitment procedures in a power system constitute a key operational planning feature of any power system. The complex, constrained unit commitment problem presents a computational challenge, and improving the solution quality can make a substantial impact in the long run. This paper presents an improved binary grey wolf optimization (IBGWO) to solve the unit commitment problem in power system operational planning. The IBGWO enhances the balance between exploration and exploitation properties of the grey wolf search. This would improve both the local as well as the global search properties of the classical grey wolf algorithm. In order to execute and implement the improved grey wolf optimization to unit commitment with binary decision variables, binary transformation of the real valued variant is employed. The IBGWO is tested using test system with different sizes ranging from 10 thermal units to 100 thermal units. The solution quality indices along with convergence characteristics are presented and compared to the existing approaches. The same demonstrates the improved solution quality in the form of reduced operational cost.
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
UR - http://www.scopus.com/inward/record.url?scp=85084284646&partnerID=8YFLogxK
U2 - 10.1109/ICPS48983.2019.9067624
DO - 10.1109/ICPS48983.2019.9067624
M3 - Conference contribution
AN - SCOPUS:85084284646
T3 - 2019 8th International Conference on Power Systems: Transition towards Sustainable, Smart and Flexible Grids, ICPS 2019
BT - 2019 8th International Conference on Power Systems
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Conference on Power Systems, ICPS 2019
Y2 - 20 December 2019 through 22 December 2019
ER -