A Decision-Support Framework for Contracted Demand and Tariff Management in Brazilian Group A Consumers

Large electricity consumers under Brazilian Group A tariffs face a cost–risk trade-off when defining contracted demand, since inadequate sizing leads either to payments for unused capacity or to penalties for exceeding regulatory limits. Despite this relevance, practical decision-support tools that convert tariff rules into reproducible contract optimization remain limited. This paper presents DSManager (version 0.0), a tool based on Python (version 3.14.4) developed to optimize tariff modality and contracted demand for Group A consumer units. The framework incorporates the mathematical formulation of Brazilian tariff structures, including green and blue time-of-use modalities, taxes, and excess-demand penalties, and evaluates two optimization strategies: the Maximum Recorded Demand method and a Grid Search procedure for direct minimization of the billing cost function. The tool was implemented with Pandas (version 3.0.2) and Streamlit (version 1.57.0) and validated using real billing data from two consumer units in the Equatorial Pará concession area. In the retrospective case, the green tariff combined with Grid Search produced projected savings of US$9554.20 relative to the unchanged contract. In the real implementation case, reducing contracted demand from 450 kW to 240 kW yielded an observed average saving of US$1944.31 per month. The results demonstrate the practical value of the proposed tool for tariff management and electricity cost reduction in large consumers.