###### Abstract  
Renewable generation has been addressed in several aspects but it still represents a new paradigm for the expansion of the electricity supply. This paper aims to propose a new model for the sizing and siting problem of distributed generation (DG), based on renewable sources and considering three main aspects: technical, from the distribution utility viewpoint, economical, from the DG owner's viewpoint, and environmental, from a sustainability perspective. A multi-objective Genetic Algorithm and the Maximin metric are implemented to obtain optimal Pareto sets; also, three decision criteria, considering the concept of preference, are applied to select a final solution from Pareto sets. Case-studies are carried out in medium voltage systems: the 69-bus distribution test system, known from literature, and a 918-bus Brazilian distribution system. Diversity of alternatives in the obtained Pareto sets testify algorithm effectiveness in searching for solutions to the distributed generation sizing and siting problem, in order to ensure power loss reductions, investment return, and environmental benefits. The proposed methodology contributes to the discussions and perspectives among electricity utilities, DG owners, society, and regulators regarding planning and decision making tools. multi-objective Genetic Algorithm; distributed generation; sizing and siting; maximin metric; Max-Min approximation; power losses; equivalent CO2 emissions 2021 22 June 2021 27  
diverse approaches, such as technical (from planning and operation viewpoint), economic, environmental, normative, and technological. Therefore, many aspects can be considered in DG planning strategies. For example, concerning DG sizing, it is difficult to define an efficient modularization of a DG power plant, because every strategy is highly dependent on the DG concept adopted, like the ones described in [2; 4; 5; 6; 7]. Another aspect corresponds to DG siting, which can be done by selecting a bus in a specific network or among different networks. Also, DG benefits (single objective of interest) or benefit-sets can be used as criteria to DG siting/sizing planning and the target of the benefit DisCo, DG owners or final customers. Finally, network technical constraints associated to the system operation, as well as local regulations, especially about whom is the decision-maker, can also be fundamental issues. Several works in the technical literature address studies regarding DG planning in distribution networks by different methods and with different objectives. Firstly, Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) have been prominent in DG studies. In ref. [8], a methodology based on fuzzy-GA to improve voltage stability margin and net income is presented. In ref. [9], a hybrid GA with PSO is applied to reduce energy losses and voltage variation and improve a voltage stability index. Similarly, in ref. [10] a hybrid GA with a technique for order preference by similarity to the ideal solution to reduce energy losses and improve voltage profile is proposed. In ref. [11], a multi-objective GA to reduce DisCo costs is presented, and finally, in ref. [12], a multi-objective PSO to increase DG owner net income and to reduce DisCo costs is also proposed. Other methods have also been applied to deal with the referred problem. In ref. [13], an artificial bee colony algorithm is used to minimize active energy losses. Moreover, ant colony optimization to minimize DG owner's cost and energy active losses is proposed in [14; 15], respectively. In ref. [16], the authors present an ant colony optimization to reduce DG owner cost, and a cuckoo search algorithm is applied to reduce energy losses in [17]. Combinations of analytical methods and evolutionary algorithms have also been proposed for DG planning. For example, the Branch-and-bound method is applied in [18] to reduce distribution expansion costs. Differential evolution is used in [19] to solve the DG planning problem in order to increase net income.