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Distributed Generation (DG) is a promising solution to many powersystem problems such as voltage regulation, power loss, etc. This articlepresents a new methodology using Fuzzy and Artificial Immune System(AIS) for the placement of Distributed Generators (DGs) in a radial dis-tribution system to reduce the real power losses and to improve the volt-age profile. A two-stage methodology is used for the optimal DG place-ment. In the first stage, the Fuzzy Set approach is used to find the optimalDG locations and in the second stage, Clonal Selection algorithm of AISis used to size the DGs corresponding to maximum loss reduction. Thisalgorithm is a new, population based, optimization method inspired bythe cloning principle of the human body immune system. The advantageof this algorithm is the population size is dynamic and it is determinedby the fitness values of the population. The proposed method is testedon standard IEEE-33 based bus test system. Net, the results are com-pared with different approaches available in the literature. The proposedmethod outperforms the other methods in terms of the quality of solu-tion and computational efficiency.

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Electricity, today, has not only become a necessity but also a tool for determining the economic standing and growth of a nation. The exponential growth in demand over the past two decades and the widening gap between demand and supply is a growing concern. So as to reduce this gap, in addition to adding new generating units, automation technology is being employed for reducing the T&D losses and therefore the increasing necessity of fast and efficient algorithms. This paper presents a two stage approach: first, Fuzzy Logic is used to find optimal capacitor locations and then Bat Algorithm is used to find optimal capacitor sizes in order to minimize losses. The proposed method is tested on 15-bus and 34-bus test systems and the results are presented.