An Exploratory Simulation-Based Study on Energy Savings with Smart Grids in Three Brazilian Municipalities
Abstract
This study investigates a solution to optimize energy distribution in smart grids by integrating renewable sources and promoting energy efficiency in alignment with the United Nations Sustainable Development Goals (SDGs). As renewable energy sources such as solar energy become more widely adopted, the efficient integration of these intermittent sources brings challenges to traditional electrical grids, which may fail to meet demands without adaptive solutions. Using the DEVS formalism, applied through the MS4Me tool, scenarios were simulated in three Brazilian cities with varying distribution of solar panels. The results indicate that solar energy production varies across regions, affecting energy savings and carbon emission reductions. The analysis highlighted the economic and environmental benefits of integrating solar panels, especially in regions with high solar irradiation. This investigation proposes strategies for energy management in urban areas, underscoring the essential role of software modeling and scenario simulation for smart grids.
Keywords:
smart grids, solar energy, renewable sources, simulation, energy efficiency
References
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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.
Published
2024-12-05
How to Cite
DA SILVA, Ana Clara A. G.; SARAIVA, Filipe Castro; DE OLIVEIRA, Igor Faria; TEIXEIRA JUNIOR, Gilmar; G. NETO, Valdemar V..
An Exploratory Simulation-Based Study on Energy Savings with Smart Grids in Three Brazilian Municipalities. In: REGIONAL SCHOOL ON INFORMATICS OF 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.
