Cultured differential computation algorithm for optimal contracted capacity of power consumer with self-owned generating units

Contracted capacity setting is a discrete and nonlinear optimization problem in consideration of expenditure on the electricity from the utility and cost of the self-owned generating units (SOGUs) at the same time. This paper proposes the cultured differential computation algorithm (CDCA) to solve this problem. The cultural algorithm (CA) used in the CDCA extracts and saves the domain knowledge or problem properties during the evolution process. The differential computation of the CDCA provides fast converging characteristics in searching the optimal solution through operations of mutation, crossover, and selection operations which are efficient and different from the existing generic algorithm (GA). To verify feasibility of the proposed method, the paper employs the real data obtained from a large optoelectronics factory in Taiwan. In comparison with the existing optimization methods, the proposed CDCA approach has superior results as revealed in the numerical results in terms of the computation time needed and the quality of solution obtained.