Eficiência energética e microgeração distribuída: modelo para estimativa do potencial impacto financeiro em unidades consumidoras industriais de uso não intensivo de energia

Abstract

The industrial sector is the largest consumer of energy in the world. In this sector, companies classified as non-energy intensive have untapped energy efficiency potential, such as small and medium-sized enterprises (SMEs). It is possible to evidence that the academic literature acknowledges the existence of the so-called energy efficiency gap, which is related to the non-implementation of energy management and energy efficiency measures, despite their cost-benefit ratio. Research to study this untapped potential presents a technological solution and does not address the risks and uncertainties that may influence the adoption of a technological solution. This study aimed to propose a model for evaluating the potential financial impact of energy efficiency and microgeneration actions distributed in non-energy-intensive industrial consumer units. The Dynamic Systems Modeling was used to model the behavior of two medium-sized Brazilian companies. The scenarios considered as uncertainties the variations in the macro energy panorama, changing parameters with Gross Domestic Product (GDP), Tariff Flag (BTar), Energy Development Cost (CDE), National Wide Consumer Price Index (IPCA), Periodic Tariff Review (RTP) that result in the Annual Tariff Review (RTA). Variables specifically related to the environment for the insertion of MGD complemented the scenario variations, representing the different combinations of the Energy Compensation System (SCE), Tax Incentives and subsidies. The estimated financial results of each scenario are presented and analyzed considering the premises of the computational model. The model developed allowed to evaluate complementary measures to the current financial metrics in an uncertain environment, identifying the most robust alternatives. From the model it was possible to identify decisions that would lead to overinvestment and highlight the variables that most influence. The model fills theoretical gaps identified in the literature related to the decision process about energy efficiency investments.