The electricity distribution system is constantly evolving, with the dissemination of
distributed energy resources, smart grids and the inclusion of competitive markets, in which the
use of energy efficiency as a relevant resource for sector planning must be ensured through
public policy. Brazil has for decades had a program and policy in this area, however, the results
of its program for the insertion of energy efficiency actions with the financial resources of the
electricity utilities, have less impact than similar programs in other countries, and there is the
imminent need to meet the emission reduction targets proposed by the country at the 21st
Conference of the Parties of the United Nations in December 2015 in Paris. In this context, the
present thesis proposes the development of a non-parametric model for the determination of
performance indicators and classification of energy efficiency projects. It also contributes to
the evaluation of the Energy Efficiency Program, through a methodology to analyze the
projects, classify and quantify the variables, resulting in recommendations for the improvement
of the program. A database of projects submitted to ANEEL by the utilities of the electricity
distribution sector from 2008 to 2016 was used. The model was developed using Data
Envelopment Analysis (DEA), a non-parametric method that uses linear programming to
calculate the most efficient units in a given set of decision-making units. The results show that
the best performance was achieved by projects in the industrial and cogeneration categories,
however, these constitute only 4.24 % of the projects presented and 5.28 % of the total
investments in the last eight years, indicating the need to review energy efficiency regulatory
strategies. As a solution to improve the selection of the best performing projects, it is suggested
assigning weights to the categories to optimize the project results, by incentives to the projects
with the best performance, without excluding the participation of all others, simply by providing
energy savings goals and demand reduction at peak hours compatible with the best performing
categories.
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