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Demand Response Optimization Model to Energy and Power Expenses Analysis and Contract Revision

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  • Filipe Marangoni

    (Universidade Tecnológica Federal do Paraná (UTFPR), Medianeira 85884-000, Paraná, Brazil
    Graduate Program in Electrical and Computer Engineering (CPGEI), Universidade Tecnológica Federal do Paraná (UTFPR), Curitiba 80230-901, Paraná, Brazil)

  • Leandro Magatão

    (Graduate Program in Electrical and Computer Engineering (CPGEI), Universidade Tecnológica Federal do Paraná (UTFPR), Curitiba 80230-901, Paraná, Brazil)

  • Lúcia Valéria Ramos de Arruda

    (Graduate Program in Electrical and Computer Engineering (CPGEI), Universidade Tecnológica Federal do Paraná (UTFPR), Curitiba 80230-901, Paraná, Brazil)

Abstract

This paper proposes a mathematical model based on mixed integer linear programming (MILP). This model aids the decision-making process in local generation use and demand response application to power demand contract adequacy by Brazilian consumers/prosumers. Electric energy billing in Brazil has some specificities which make it difficult to consider the choice of the tariff modality, the determination of the optimal contracted demand value, and demand response actions. In order to bridge this gap, the model considers local generation connected to the grid (distributed generation) and establishes an optimized solution indicating power energy contract aspects and the potential reduction in expenses for the next billing period (12 months). Different alternative sources already available or of interest to the consumer can be considered. The proposed mathematical model configures an optimization tool for the feasibility analysis of local generation use and, concomitantly, (i) checking the tariff modality, (ii) revising the demand contract, and (iii) suggesting demand response actions. The presented result shows a significant reduction in the energy and power expenses, which confirms the usefulness of this proposal. In the end, the optimized answers promote benefits for both, the consumer/prosumer and the electric utility.

Suggested Citation

  • Filipe Marangoni & Leandro Magatão & Lúcia Valéria Ramos de Arruda, 2020. "Demand Response Optimization Model to Energy and Power Expenses Analysis and Contract Revision," Energies, MDPI, vol. 13(11), pages 1-23, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:11:p:2803-:d:366015
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    References listed on IDEAS

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    1. Coimbra-Araújo, Carlos H. & Mariane, Leidiane & Júnior, Cicero Bley & Frigo, Elisandro Pires & Frigo, Michelle Sato & Araújo, Izabela Regina Costa & Alves, Helton José, 2014. "Brazilian case study for biogas energy: Production of electric power, heat and automotive energy in condominiums of agroenergy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 826-839.
    2. Rodrigo Martins & Holger C. Hesse & Johanna Jungbauer & Thomas Vorbuchner & Petr Musilek, 2018. "Optimal Component Sizing for Peak Shaving in Battery Energy Storage System for Industrial Applications," Energies, MDPI, vol. 11(8), pages 1-22, August.
    3. Pepermans, G. & Driesen, J. & Haeseldonckx, D. & Belmans, R. & D'haeseleer, W., 2005. "Distributed generation: definition, benefits and issues," Energy Policy, Elsevier, vol. 33(6), pages 787-798, April.
    4. Pedro Faria & João Spínola & Zita Vale, 2018. "Distributed Energy Resources Scheduling and Aggregation in the Context of Demand Response Programs," Energies, MDPI, vol. 11(8), pages 1-17, July.
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    Cited by:

    1. Ovidiu Ivanov & Samiran Chattopadhyay & Soumya Banerjee & Bogdan-Constantin Neagu & Gheorghe Grigoras & Mihai Gavrilas, 2020. "A Novel Algorithm with Multiple Consumer Demand Response Priorities in Residential Unbalanced LV Electricity Distribution Networks," Mathematics, MDPI, vol. 8(8), pages 1-24, July.
    2. Fernando V. Cerna & Mahdi Pourakbari-Kasmaei & Luizalba S. S. Pinheiro & Ehsan Naderi & Matti Lehtonen & Javier Contreras, 2021. "Intelligent Energy Management in a Prosumer Community Considering the Load Factor Enhancement," Energies, MDPI, vol. 14(12), pages 1-24, June.

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