LEAST-COST GENERATION EXPANSION PLANNING USING AN IMPERIALIST COMPETITIVE ALGORITHM

This paper presents using Imperialist Competitive Algorithm (ICA) and its application to a least-cost Generation expansion planning (GEP) problem. Least-cost GEP problem isconcerned with a highly constrained nonlinear dynamic optimization problem that can only be fully solved by complete enumeration, a process which is computationally impossible in a real-world GEP problem. In this paper, Imperialist Competitive algorithmincorporating a stochastic technique and random initial population scheme is developed to provide a faster search mechanism. The main advantage of the ICA approach is that the curse of dimensionality and a local optimal trap inherent in mathematical programming methods can be simultaneously overcome. The ICA approach is applied to two test systems, one with 15 existing power plants, 5 types of candidate plants anda 14-year planning period, and the other, a practical long-term system with a 24-year planning period.

Keywords:

Generation Expansion Planning , Genetic Algorithm , Global Optimization , Improved Genetic Algorithm