The process of deregulation that has involved electricity markets has introduced several new interesting research topics in power system area. This thesis addresses one of the fascinating issues among them: the study of distributed generation both renewable and conventional integration in distribution networks. From the distribution network operator (DNO)'s point of view, it is interesting to develop a comprehensive methodology which considers various distributed generation technologies as an option for supplying the demand. In this thesis, the planning problem has been multi-objectively modeled. This will help the planner in decision making while knowing the trade-offs between the objective functions. for finding the Pareto optimal front of the problem a hybrid Genetic-Immune algorithm is proposed. The fuzzy satisfying method is used to find the final solution. Various objectives like cost, active losses, emission and the technical constraint satisfaction have been taken into account. The decision variables are the distribution network reinforcement strategies and also the investment decisions regarding DG units, in case where DNO can invest in DG units too. Another aspect which makes the proposed models more flexible, is considering the uncertainties of the input parameters. The uncertainties of input data have been treated in three different ways namely, probabilistic, possibilistic and finally mixed possibilistic-probabilistic methods. In this thesis, two types of models have been developed: centralized and unbundled DG planning model. In both models, the DNO is responsible to provide a reliable and efficient network for his costumers in its territory. In centrally controlled planning context, the DNO is authorized to make investment in DG units. In this model, the optimal size, number of DG units, location, DG technology and timing of investment in both DG units and network components are determined. The developed model will not only be useful in the centrally controlled planning context but also is applicable to other power markets that need to assess, monitor and guide the decisions of DG developers. In unbundled DG planning model, the DNO is not authorized to make investment decisions in DG options. The decision variables of DNO are limited to feeder/substation expansion/reinforcement, capacitor placement, network reconfiguration and smart grid technologies.
multi objective distributed generation planning in a flexible environment