MINIMUM COST GENERATION UNIT EXPANSION PLANNING USING REAL CODED IMPROVED GENETIC ALGORITHM

摘要

This paper presents a development of Real Coded Improved Genetic Algorithm (RCIGA) and its application to a minimum cost generation unit expansion planning (GUEP) problem. GUEP is a highly constrained non linear system, so it can be solved by any one of the optimization techniques called genetic algorithm. RCIGA is a global optimizer and it provides faster convergence speed and the search space is increased. In this method, the GUEP solution is vectors of real values. RCIGA is used to calculate the combination of units to obtain minimum cost function and meet out the forecasted demand. The RCIGA approach is applied to the test system of five candidate units and fifteen existing units with 7 period of planning.