Multi Objective Optimization of Lifecycle Cost, Unmet Load, and Renewable Energy Capacity for an Expansion of Existing Standalone Internal Combustion Generator (ICG) Systems

Abstract

This paper presents, for the first time, the application of the strength Pareto evolutionary algorithm to the multi-objective design of isolated hybrid systems, minimising both the total cost throughout the useful life of the installation and the unmet load. For this task, a multi-objective evolutionary algorithm (MOEA) and a genetic algorithm (GA) have been used in order to find the best combinations of components for the hybrid system and control strategy. Also, a novel control strategy has been developed and it will be expounded in this article. As an example of application, a PV–wind–diesel system has been designed, obtaining a set of possible solutions (Pareto set) from which the designer can choose those which he/she prefers considering the costs and unmet load of each. The results obtained demonstrate the practical utility of the design method used.