Modelo de diagnóstico para projetos de micro e minigeração distribuída de energia fotovoltaica

Abstract

Photovoltaic energy has a promising market throughout the world, and Brazil is still at the beginning of its use. One of the options to increase the participation of this energy in the Brazilian energy matrix is with distributed micro and mini-generation (MMGD). To promote MMGD growth in photovoltaic energy, a number of economic, political, environmental and social factors must be present, mainly because the adoption of an innovation is a complex process. Consequently, there is uncertainty about the success that investors can have with the implementation of photovoltaic systems. So the decision about investing in photovoltaic energy must be made on the basis of objective and measurable factors. Given this context, the aim of the study is to propose a diagnostic model for the implementation of distributed micro and mini-generation projects of photovoltaic energy. The diagnosis was developed through a Performance Measurement System (SMD) based on the Key Performance Indicators (KPI) concept, built from Critical Success Factors (FCS) and grouped into six Fundamental Viewpoints (PVF): Economic, Environmental, Market, Political, Social and Technological. The modeling was structured in order to calculate the impact that each PVF has on the PVF set by the Analytic Hierarchy Process (AHP) weighting method. The research was applied with 19 specialists, researchers in photovoltaic energy and 32 investors in photovoltaic energy. As results, the proposed mathematical formulation was able to weigh the indicators and measure the success of the projects. Of the projects diagnosed, 15 reached a Success Index 76%, judged as "Full Success" projects and 17 were judged as "Potential Success" projects. It was found that with the promotion of some KPI, the "Potential Success" projects could go beyond the "Full Success" level, through the reflection promoted by the diagnosis. In addition, a computational tool was built, with off-line availability, so that future investors can evaluate their projects. It concluded that the diagnosis was able to evaluate the MMGD projects and to judge them properly. The main contributions of this work are the identification of success factors and the measurement methodology developed for the diagnostic model. It can serve to generate new diagnostic models in other subjects and to apply this diagnosis in future MMGD projects, providing more precise decisions at the elaboration of photovoltaic projects.