An Improved Binary Grey Wolf Optimizer (IBGWO) for Unit Commitment Problem in Thermal Generation

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.