The aim of this study is to propose a set of indicators and metrics to evaluate and measure the efficiency and intelligence on microgrid electric substations. It was used a methodology to stablish indicators for evaluating the energy efficiency combined with two decision support methods: AHP (Analytic Hierarchy Process) for definition the importance of criteria selected, and TOPSIS (Technique for Order Preference by Similarity to Ideal of Solution) to classify the most efficiency and intelligent substation. As a result, it is expected to obtain the improvement of efficiency of power substations of a microgrid and follow the process of continuous improvement of their intelligence. Geller (2004) emphasizes the importance to adopt energy efficiency measures, through the use of alternative energy resources. The author states that the numbness in the current energy sources will result in irreparable harm to the environment, generating major climate change .It is necessary consider an innovative energy management model capable for meeting the challenges to meet the new standards of consumption, combining environmental, social and economic aspects . Smart Grids (SG) are able to incorporate information and communication technology in all aspects of generating electricity, allowing the entrance of new suppliers in the energy network, generating a better management of distribution and consumption, optimizing efficiency, minimizing costs, and diminishing environmental impacts. Currently, the SG is in development stage, with large spaces for research and development of enhancements and applications. Countries like USA, Japan and other European Community are planning to implement this novel grid till mid-2020 to 2030 Rivera (2013). In Brazil, this trend follows a different reality, due to economic crisis and regulatory aspects. In this country, most of smart grid projects are in development research and they are in slower progress status when compared to developed countries Rivera (2013). The implementation of a smart grid, with various alternatives of energy sources, provides better management and optimal use of each alternative. So, the necessity of managing the power distribution network more efficiently as possible increases Caneppele (2011). Microgrid is a concept widely disseminated in the literature. It can be defined as an intelligent electrical network applied in a smaller scale. As example we can cite: large commercial centers, a large industries; a large condominiums, a neighborhoods, etc. The aim of this article is to propose a set of indicators and metrics to evaluate and measure the efficiency and intelligence on microgrid electric substations. The methodology proposed will support mathematically the decision maker in the energy management of a huge television studio, which can be considered a microgrid. The research methodology followed a procedural structure analysis based on literature review focused on central themes of research: (i) substations (ii) energy efficiency (iii) microgrids (iv) multiple criteria decision methods (MCDM), trying to select the best methods to be considered in the context of energy efficiency of substations in microgrids. The model used in this work considered a hybrid multi-criteria decision-making (MCDM) method, putting together two methods widely used in literature: AHP (Analytic Hierarchy Process), and (TOPSIS Technique for Order Preference by Similarity to Ideal of Solution). AHP method was proposed by Saaty (2000). It is a multi-criteria support tool for decision making, which reveals the wide number of applications in current literature. Through peer comparisons, it is possible to perform the trials because the priorities calculated by the method will capture both the subjective measures, and the depth the particular criterion (or alternative) n relation to another. The second method used will be the TOPSIS. The technique is used to examine the performance of options by similarity to ideal solution. It was introduced by Hwang and Yoon Hwang (1981), in 1981, since then, it has been applied by researchers from different areas. With this technique, the best alternative would be the one closest to the ideal solution (positive ideal solution - PIS), and more distant from the unideal solution (negative ideal solution - NIS). The positive solution is made up of all the best attainable values for each criterion. However, the ideal solution, shows the worst values attainable for each decision criterion . Using the two techniques AHP and TOPSIS was developed a model of selection and classification of indicators and metrics to evaluate the efficiency of the substations on a microgrid attachments. During the construction of this model were made interviews and meetings with some managers and specialists in the area, especially engineers, to capture their perceptions on the issues. A case of study focusing on the application of the data measured in the field, to validate the proposed model. The research in question is related to a specific issue of a microgrid, however, the method developed can cover other microgrids, aside from smart grids. Summarizing, the model covers six main steps: (i) analysis of the objectives and expected results of an efficient and intelligent power grid; (ii) the definition of the types of indicators; (iii) the definition of criteria for selection and classification of indicators; (iv) the proposition of indicators; (v) application of AHP method for weighting of criteria on the selection and classification of Network Intelligence and proposed Efficiency indicators; and (vi) use of TOPSIS method to the final ranking of the proposed indicators and related monitoring and evaluation metrics of a microgrid attachments. After the research, the main results are displayed: (i) a model of selection and ranking indicators and metrics to evaluate the efficiency and intelligence on microgrid substations was created; and (ii) a consistent set of indicators and metrics for monitoring and evaluation of the efficiency of a microgrid electric substations was developed. Using the modelling previously described via a multi-criteria decision-making (MCDM), four electric substations were evaluated in a microgrid (a huge television studio in Brazil). It was observed that the substation that obtained the best position in the final ranking is that one which has the best value for all criteria used in the model, and considered by all engineers and specialists the more efficient and intelligent. It is important to mention this research reached the proposed objective. And, a set of indicators and metrics to evaluate and measure the efficiency and intelligence on microgrid electric substations was proposed. In the case studied presented in this study, the network is not yet fully intelligent, it is not possible to have the measurement of all parameters of efficiency and intelligence needed for a better evaluation. As the network becomes more intelligent, new parameters can be measured and added to the project (being incorporated to the model), being necessary to review the indicator weights used. Conclusively, the project is not static it needs to be revisited as new parameters are added to the microgrid.