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Stochastic Decision-Making Optimization Model for Large Electricity Self-Producers Using Natural Gas in Industrial Processes: An Approach Considering a Regret Cost Function

Author

Listed:
  • Laís Domingues Leonel

    (Department of Energy Engineering and Electrical Automation, Polytechnique School University of São Paulo, São Paulo 05508-010, SP, Brazil)

  • Mateus Henrique Balan

    (Department of Energy Engineering and Electrical Automation, Polytechnique School University of São Paulo, São Paulo 05508-010, SP, Brazil)

  • Luiz Armando Steinle Camargo

    (Department of Energy Engineering and Electrical Automation, Polytechnique School University of São Paulo, São Paulo 05508-010, SP, Brazil)

  • Dorel Soares Ramos

    (Department of Energy Engineering and Electrical Automation, Polytechnique School University of São Paulo, São Paulo 05508-010, SP, Brazil)

  • Roberto Castro

    (MRTS Consultoria, São Paulo 05503-001, SP, Brazil)

  • Felipe Serachiani Clemente

    (Alcoa, São Paulo 04794-000, SP, Brazil)

Abstract

In the context of high energy costs and energy transition, the optimal use of energy resources for industrial consumption is of fundamental importance. This paper presents a decision-making structure for large consumers with flexibility to manage electricity or natural gas consumption to satisfy the demands of industrial processes. The proposed modelling energy system structure relates monthly medium and hourly short-term decisions to which these agents are subjected, represented by two connected optimization models. In the medium term, the decision occurs under uncertain conditions of energy and natural gas market prices, as well as hydropower generation (self-production). The monthly decision is represented by a risk-constrained optimization model. In the short term, hourly optimization considers the operational flexibility of energy and/or natural gas consumption, subject to the strategy defined in the medium term and mathematically connected by a regret cost function. The model application of a real case of a Brazilian aluminum producer indicates a measured energy cost reduction of USD 3.98 millions over a six-month analysis period.

Suggested Citation

  • Laís Domingues Leonel & Mateus Henrique Balan & Luiz Armando Steinle Camargo & Dorel Soares Ramos & Roberto Castro & Felipe Serachiani Clemente, 2024. "Stochastic Decision-Making Optimization Model for Large Electricity Self-Producers Using Natural Gas in Industrial Processes: An Approach Considering a Regret Cost Function," Energies, MDPI, vol. 17(21), pages 1-19, October.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:21:p:5389-:d:1509423
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    References listed on IDEAS

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    1. Shapiro, Alexander & Tekaya, Wajdi & da Costa, Joari Paulo & Soares, Murilo Pereira, 2013. "Risk neutral and risk averse Stochastic Dual Dynamic Programming method," European Journal of Operational Research, Elsevier, vol. 224(2), pages 375-391.
    2. Silva, Rodolfo Rodrigues Barrionuevo & Martins, André Christóvão Pio & Soler, Edilaine Martins & Baptista, Edméa Cássia & Balbo, Antonio Roberto & Nepomuceno, Leonardo, 2022. "Two-stage stochastic energy procurement model for a large consumer in hydrothermal systems," Energy Economics, Elsevier, vol. 107(C).
    3. Marlon Mesquita Lopes Cabreira & Felipe Leite Coelho da Silva & Josiane da Silva Cordeiro & Ronald Miguel Serrano Hernández & Paulo Canas Rodrigues & Javier Linkolk López-Gonzales, 2024. "A Hybrid Approach for Hierarchical Forecasting of Industrial Electricity Consumption in Brazil," Energies, MDPI, vol. 17(13), pages 1-15, June.
    4. Laís Domingues Leonel & Mateus Henrique Balan & Dorel Soares Ramos & Erik Eduardo Rego & Rodrigo Ferreira de Mello, 2021. "Financial Risk Control of Hydro Generation Systems through Market Intelligence and Stochastic Optimization," Energies, MDPI, vol. 14(19), pages 1-18, October.
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