Distribution Systems Expansion Planning Considering the Exploitation of Distributed Generation by a Multi-objective Genetic Algorithm

This paper presents an algorithm dedicated to the static expansion planning of distribution systems using Genetic Algorithms (GA), where conventional expansion alternatives are considered together with the exploitation of Distributed Generation (DG). The problem is formulated as a multiobjective optimization problem, where the objective function to be minimized is composed by the annualized costs relative to the energy losses, the expected customer interruption cost (ECOST) and the investments in the system, together with the voltage drop in the network. The expansion alternatives considered are the installation of DG units based on different energy sources, the capacity enlargement of cables, the network reconfiguration and the installation of new switching devices. In the GA evolutionary process, for each feasible (radial) configuration, a non-linear power flow problem is solved and the ECOST is calculated. The fitness value of each individual is obtained by a weighted sum of its objectives function values and a penalization strategy is applied if any of the constraints are violated. A system composed of 33 nodes was used to validate the algorithm and demonstrate its applicability to large systems.