Application of Evolutionary and Hybrid Algorithms to Optimize Investments Strategies in Large Power Plants

This article is an extension of the work presented earlier, which compared and analyzed the economics of alternative maintenance plans. The proposed model combines genetic algorithms with Monte Carlo simulation to arrive at the most economic investment timing. The approach described earlier was characterized by a very long computing time making it difficult to use. This paper addresses several issues related to the computational efficiency and introduces several innovative solutions that not only improve the accuracy of the analysis but also provide new opportunities in selecting optimal investment scenarios. The methodology based on stochastic-deterministic optimization algorithms and activation genes is described and illustrated by a numerical example involving analysis of the optimal number of new investments and their most economic timing for a refurbishment of a large steam generating unit.