NSGA-based multiobjective optimisation for modular strategical planning of electric distribution systems

Abstract

Strategic planning requires the optimisation of many objects. The paper aims at the solution of this multiobjective optimisation problem using a nondominated sorting genetic algorithm (NSGA)-based approach. Distribution systems design using modular strategical planning indeed requires the minimisation/maximisation of many objects depending on mixed-integer variables. In the paper the concept of modularity has been used in the formulation of the strategical planning of electrical distribution systems. In this way, innovative and more rational design solutions have been identified. Each module represents the topology and behaviour of each part of the distribution system. The entire system has been represented by the combination of one or more modules. Each combination is characterised by a set of functions expressing the installation cost per year and per squared km, the operational cost, the maximum unavailability, the voltage dips and short interruptions and the voltage unbalances due to single-phase loads feed. In this way, the set of parameter values analytically describing the entire system can be identified, these being the set of optimisation variables.