Um Estudo Exploratório baseado em Simulação sobre Economia de Energia com Smart Grids em Três Municípios Brasileiros
Resumo
Este estudo investiga uma solução para otimizar a distribuição de energia em smart grids por meio da integração de fontes renováveis e da promoção da eficiência energética, alinhando-se aos Objetivos de Desenvolvimento Sustentável (ODS) das Nações Unidas. Com a crescente adoção de fontes renováveis, como a energia solar, a integração eficiente dessas fontes intermitentes apresenta desafios para as redes elétricas tradicionais, que podem falhar em atender às demandas sem soluções adaptativas. Utilizando o formalismo DEVS, aplicado através da ferramenta MS4Me, cenários foram simulados em três cidades brasileiras com diferentes distribuições de painéis solares. Os resultados indicam que a produção de energia solar varia entre as regiões, afetando a economia de energia e a redução das emissões de carbono. A análise destacou os benefícios econômicos e ambientais da integração de painéis solares, especialmente em regiões com alta irradiação solar. Esta investigação propõe estratégias para a gestão de energia em áreas urbanas, ressaltando o papel essencial da modelagem de software e simulação de cenários para smart grids.
Palavras-chave:
smart grids, energia solar, fontes renováveis, simulação, eficiência energética
Referências
Amin, S. M. and Wollenberg, B. F. (2005). Toward a smart grid: power delivery for the 21st century. IEEE Power and Energy Magazine, 3(5):34–41.
Ananthavijayan, R., Karthikeyan Shanmugam, P., Padmanaban, S., Holm-Nielsen, J. B., Blaabjerg, F., and Fedak, V. (2019). Software architectures for smart grid system—a bibliographical survey. Energies, 12(6):1183.
Atawi, I. E., Al-Shetwi, A. Q., Magableh, A. M., and Albalawi, O. H. (2022). Recent advances in hybrid energy storage system integrated renewable power generation: Configuration, control, applications, and future directions. Batteries, 9(1):29.
Cárdenas, R., Arroba, P., Risco-Martín, J. L., and Moya, J. M. (2023). Modeling and simulation of smart grid-aware edge computing federations. Cluster Computing, 26(1):719–743.
Clark, Helen e Wu, H. (2016). The sustainable development goals: 17 goals to transform our world. Furthering the work of the United Nations, pages 36–54.
de França, B. B. N. and Travassos, G. H. (2015). Experimentation with dynamic simulation models in software engineering: planning and reporting guidelines. Empirical Software Engineering, 20(3):477–507.
Dechamps, P. (2023). The iea world energy outlook 2022–a brief analysis and implications. European Energy & Climate Journal, 11(3):100–103.
El-Hawary, M. E. (2014). The smart grid—state-of-the-art and future trends. Electric Power Components and Systems, 42(3-4):239–250.
Fang, X., Misra, S., Xue, G., and Yang, D. (2012). Smart grid—the new and improved power grid: A survey. IEEE Communications Surveys & Tutorials, 14(4):944–980.
Gharavi, H. and Ghafurian, R. (2011). Smart grid: The electric energy system of the future. Proceedings of the IEEE, 99(6):917–921.
Graciano Neto, V. V., Barros Paes, C. E., Garcés, L., Guessi, M., Manzano, W., Oquendo, F., and Nakagawa, E. Y. (2017). Stimuli-sos: a model-based approach to derive stimuli generators for simulations of systems-of-systems software architectures. JBCS, 23:1–22.
Güngör, V. C., Lu, B., and Hancke, G. P. (2010). Opportunities and challenges of wireless sensor networks in smart grid. IEEE Transactions on Industrial Electronics, 57(10):3557–3564.
Li, J., Herdem, M. S., Nathwani, J., and Wen, J. Z. (2023). Methods and applications for artificial intelligence, big data, internet of things, and blockchain in smart energy management. Energy and AI, 11:100208.
Manzano, W. A. E. (2023). Simulation-based optimization of system-of-systems software architectures. Master’s thesis, Universidade de São Paulo.
Marinescu, C. (2022). Progress in the development and implementation of residential ev charging stations based on renewable energy sources. Energies, 16(1):179.
Massaoudi, M., Abu-Rub, H., Refaat, S. S., Chihi, I., and Oueslati, F. S. (2021). Accurate smart-grid stability forecasting based on deep learning: Point and interval estimation method. In 2021 IEEE Kansas Power and Energy Conference (KPEC), pages 1–6. IEEE.
Molderink, A. e. a. (2010). Management and control of domestic smart grid technology. IEEE Transactions on Smart Grid, 1(2):109–119.
Neto, V. V. G. (2018). A simulation-driven model-based approach for designing softwareintensive systems-of-systems architectures. PhD thesis, Université de Bretagne Sud; Universidade de São Paulo (Brésil).
Neto, V. V. G., Teles, R. M., Ivamoto, M., Mello, L. H., and de Carvalho, C. L. (2010). Um sistema de apoio à decisão baseado em agentes para tratamento de ocorrências no setor elétrico. Revista de Informática Teórica e Aplicada, 17(2):139–153.
Qamruzzaman, M. (2022). Nexus between foreign direct investments renewable energy consumption: What is the role of government debt. International Journal of Multidisciplinary Research and Growth Evaluation, 3(3):514–522.
Vereno, D., Khodaei, A., Neureiter, C., and Lehnhoff, S. (2023). Quantum–classical co-simulation for smart grids: A proof-of-concept study on feasibility and obstacles. Energy Informatics, 6(Suppl 1):25.
Wang, R. (2023). Enhancing energy efficiency with smart grid technology: a fusion of tcn, bigru, and attention mechanism. Frontiers in Energy Research, 11:1283026.
Ananthavijayan, R., Karthikeyan Shanmugam, P., Padmanaban, S., Holm-Nielsen, J. B., Blaabjerg, F., and Fedak, V. (2019). Software architectures for smart grid system—a bibliographical survey. Energies, 12(6):1183.
Atawi, I. E., Al-Shetwi, A. Q., Magableh, A. M., and Albalawi, O. H. (2022). Recent advances in hybrid energy storage system integrated renewable power generation: Configuration, control, applications, and future directions. Batteries, 9(1):29.
Cárdenas, R., Arroba, P., Risco-Martín, J. L., and Moya, J. M. (2023). Modeling and simulation of smart grid-aware edge computing federations. Cluster Computing, 26(1):719–743.
Clark, Helen e Wu, H. (2016). The sustainable development goals: 17 goals to transform our world. Furthering the work of the United Nations, pages 36–54.
de França, B. B. N. and Travassos, G. H. (2015). Experimentation with dynamic simulation models in software engineering: planning and reporting guidelines. Empirical Software Engineering, 20(3):477–507.
Dechamps, P. (2023). The iea world energy outlook 2022–a brief analysis and implications. European Energy & Climate Journal, 11(3):100–103.
El-Hawary, M. E. (2014). The smart grid—state-of-the-art and future trends. Electric Power Components and Systems, 42(3-4):239–250.
Fang, X., Misra, S., Xue, G., and Yang, D. (2012). Smart grid—the new and improved power grid: A survey. IEEE Communications Surveys & Tutorials, 14(4):944–980.
Gharavi, H. and Ghafurian, R. (2011). Smart grid: The electric energy system of the future. Proceedings of the IEEE, 99(6):917–921.
Graciano Neto, V. V., Barros Paes, C. E., Garcés, L., Guessi, M., Manzano, W., Oquendo, F., and Nakagawa, E. Y. (2017). Stimuli-sos: a model-based approach to derive stimuli generators for simulations of systems-of-systems software architectures. JBCS, 23:1–22.
Güngör, V. C., Lu, B., and Hancke, G. P. (2010). Opportunities and challenges of wireless sensor networks in smart grid. IEEE Transactions on Industrial Electronics, 57(10):3557–3564.
Li, J., Herdem, M. S., Nathwani, J., and Wen, J. Z. (2023). Methods and applications for artificial intelligence, big data, internet of things, and blockchain in smart energy management. Energy and AI, 11:100208.
Manzano, W. A. E. (2023). Simulation-based optimization of system-of-systems software architectures. Master’s thesis, Universidade de São Paulo.
Marinescu, C. (2022). Progress in the development and implementation of residential ev charging stations based on renewable energy sources. Energies, 16(1):179.
Massaoudi, M., Abu-Rub, H., Refaat, S. S., Chihi, I., and Oueslati, F. S. (2021). Accurate smart-grid stability forecasting based on deep learning: Point and interval estimation method. In 2021 IEEE Kansas Power and Energy Conference (KPEC), pages 1–6. IEEE.
Molderink, A. e. a. (2010). Management and control of domestic smart grid technology. IEEE Transactions on Smart Grid, 1(2):109–119.
Neto, V. V. G. (2018). A simulation-driven model-based approach for designing softwareintensive systems-of-systems architectures. PhD thesis, Université de Bretagne Sud; Universidade de São Paulo (Brésil).
Neto, V. V. G., Teles, R. M., Ivamoto, M., Mello, L. H., and de Carvalho, C. L. (2010). Um sistema de apoio à decisão baseado em agentes para tratamento de ocorrências no setor elétrico. Revista de Informática Teórica e Aplicada, 17(2):139–153.
Qamruzzaman, M. (2022). Nexus between foreign direct investments renewable energy consumption: What is the role of government debt. International Journal of Multidisciplinary Research and Growth Evaluation, 3(3):514–522.
Vereno, D., Khodaei, A., Neureiter, C., and Lehnhoff, S. (2023). Quantum–classical co-simulation for smart grids: A proof-of-concept study on feasibility and obstacles. Energy Informatics, 6(Suppl 1):25.
Wang, R. (2023). Enhancing energy efficiency with smart grid technology: a fusion of tcn, bigru, and attention mechanism. Frontiers in Energy Research, 11:1283026.
Publicado
05/12/2024
Como Citar
DA SILVA, Ana Clara A. G.; SARAIVA, Filipe Castro; DE OLIVEIRA, Igor Faria; TEIXEIRA JUNIOR, Gilmar; G. NETO, Valdemar V..
Um Estudo Exploratório baseado em Simulação sobre Economia de Energia com Smart Grids em Três Municípios Brasileiros. In: ESCOLA REGIONAL DE INFORMÁTICA DE GOIÁS (ERI-GO), 12. , 2024, Ceres/GO.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 1-10.
DOI: https://doi.org/10.5753/erigo.2024.4844.
