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<p>Growing global interest in environmental issues and the need to maintain energy sustainability have increased debates around the appropriate management of energy resources. For Brazil, continually adjusting the electrical system to meet contractual requirements is an extremely challenging problem. Likewise, the complexity of the UFRRJ university’s energy matrix and the difficulty of controlling additional energy demand are critically important. This study aims to investigate classical optimization methods that help reduce energy demand contracts in the public sector, using UFRRJ as a case study. The incorporation of quadratic programming, Lagrange multipliers, conjugate gradient method, gradient descent, penalty methods, Newton’s method, and genetic algorithms aims to improve the terms of demand contracts, promoting sustainability and cost reduction at the institution. Testing the methods and tools demonstrate their effectiveness in reducing energy costs and provides a basis for their implementation in real-world scenarios.</p>

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Application of classical optimization methods to reduce demand for contracted energy in the Brazilian public sector

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月27日

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  • Rodrigo Cabral de Freitas,
  • Ronaldo Malheiros Gregorio,
  • Marcelo Dib Cruz

摘要

Growing global interest in environmental issues and the need to maintain energy sustainability have increased debates around the appropriate management of energy resources. For Brazil, continually adjusting the electrical system to meet contractual requirements is an extremely challenging problem. Likewise, the complexity of the UFRRJ university’s energy matrix and the difficulty of controlling additional energy demand are critically important. This study aims to investigate classical optimization methods that help reduce energy demand contracts in the public sector, using UFRRJ as a case study. The incorporation of quadratic programming, Lagrange multipliers, conjugate gradient method, gradient descent, penalty methods, Newton’s method, and genetic algorithms aims to improve the terms of demand contracts, promoting sustainability and cost reduction at the institution. Testing the methods and tools demonstrate their effectiveness in reducing energy costs and provides a basis for their implementation in real-world scenarios.