A grid based multi-objective evolutionary algorithm for the optimization of power plants

There is an increasing need for optimization of energy conversion systems, in particular concerning energy consumption and efficiency to reduce their environmental impact. Usually, optimization is based on designers’ backgrounds, which are able to analyze system performances and modify appropriate operating parameters. However, if these changes aim to optimize simultaneously multiple conflicting objectives, the task becomes quite complex and the use of sophisticated tools is mandatory. This paper presents a multi-objective optimization method that permits solutions that simultaneously satisfy multiple conflicting objectives to be determined. The optimization process is carried out by using an evolutionary algorithm developed around an innovative technique that consists of partitioning the solution search space (i.e., a population of solutions) into parallel corridors. Within these corridors, “header” solutions are trapped to be then involved in a reproduction process of new populations by using genetic operators. The proposed methodology is coupled to specific power plant models that are used to optimize two different power plants: (i) a cogeneration thermal plant and (ii) an advanced steam power station. In both cases the proposed technique has shown to be very powerful, robust and reliable. Further, this methodology can be used as an effective tool to find the set of best solutions and thus providing a realistic support to the decision-making.