The Study and Application of Power Plant Planning based on Evolutionary Programming

An improved multi-group evolutionary programming is proposed and applied to power plant planning. The minimum total cost capacity expansion scheme is established to meet the nonlinear constraints of planning and operating. In this paper the planning objective costs take into account unnerved energy costs besides investment, operation and environment pollution costs. A novel bi-group evolutionary programming based on communication (BCEP) algorithm is proposed. In this algorithm, evolution of Cauchy operator and Gauss operator are parallel performed with different mutation strategies, and then the group can explore the solution space separately and search the local part in detail synchronously. Information is exchanged when sub-groups are reorganized. The result of experiment shows that the algorithm is effective. Then it is applied to program 10-year power plant planning in a certain province.