Optimizing microgrid design and operation: A decision-making framework for residential distributed energy systems in Brazil

Abstract

This paper explores the optimization of microgrid design and operation for residential distributed energy systems in Brazil, addressing the growing demand for sustainable energy in the context of climate change. A decision-making framework based on Mixed-Integer Nonlinear Programming (MINLP) is proposed to integrate distributed energy resources (DERs) such as solar, wind, and biogas. Key challenges include managing the variability of renewable resources and complying with local regulations, while also addressing gaps in literature, particularly the impact of time-dependent efficiency profiles on energy sharing within microgrids. By employing innovative analyses and clustering techniques, the research optimizes microgrid configurations, accounting for seasonal demand fluctuations and the influence of incentive policies on system feasibility. The findings reveal that incorporating a time-dependent efficiency model can reduce total costs by 45 %. This reduction underscores the importance of accurate efficiency predictions, as the model captures variations in energy generation and utilization efficiency over time, improving system optimization. Additionally, the findings reveal that a well-structured optimization model can meet 100 % of electricity and hot water demands across all scenarios, with customized incentives playing a crucial role in reducing costs and promoting sustainability.