Design allocation of multistate series-parallel systems for power systems planning: A multiple objective evolutionary approach

Abstract

This paper presents an extension and application of a recent developed multiple objective evolutionary algorithm to solve design allocation problems commonly found in the power systems area. The evolutionary algorithm introduced is called MOMS-GA, a multiobjective genetic algorithm developed to solve multistate design allocation problems. MOMS-GA works under the assumption that both the system and its components can experience more than two possible states of performance. MOMS-GA uses the universal moment generating function (UMGF) approach to evaluate the different reliability indices of the system. Therefore, system availability is represented by a multistate availability function which extends the traditional binary state availability. Three different design allocation problems commonly found in power systems planning are solved to show the performance of the algorithm. The multiobjective formulation considered in the first two examples corresponds to the maximization of system availability, minimization of system investment cost, and maximization of expected system capacity. In the third example the multiobjective formulation seeks to maximize system availability, minimize system investment cost, and minimize expected unsupplied demand.