Towards multipartite adaptive binary-real quantum inspired evolutionary algorithm for scheduling wind-thermal units.

The scheduling of power generating units is also recognized as Unit Commitment (UC); is an important and complex constrained mixed-integer nonlinear optimization problem. Traditionally UC was solved for thermal Units only, however, with the widespread adoption of Renewable energy sources like wind farms, UC is now being solved for hybrid generating units. This makes UC even more challenging due to the inherent uncertainty of the velocity of the wind. Evolutionary Algorithms (EAs) are used for solving UC as the traditional techniques employed for solving UC, like Dynamic Programming, suffer from the 'curse of dimensionality. However, EAs are unconstrained optimization techniques, and that is why they require special methods for handling constraints. Operator design and constraint handling in an EA is largely dependent on the problem representation. This paper proposes a novel adaptive quantum-inspired evolutionary algorithm with binary and real representation and a novel Repair heuristic for constraint handling in solving UC with thermal and wind farms. Initial testing has been performed on a well-known UC problem with ten thermal units and wind farm for twenty-four hours varying load model. The results achieved are competing as compared to other well-known techniques. [ABSTRACT FROM AUTHOR]