KaspaFog: uma abordagem na névoa para o gerenciamento de fontes e cargas de eletricidade de uma Microgrid com foco na redução energética

• Eric B. Barros UFBA
• Wesley O. Souza UFBA
• Matheus T. M. Barbosa UFBA
• Bruno G. Batista UNIFEI
• Bruno T. Kuehne UNIFEI
• Dionisio Leite UFMS
• Maycon L. M. Peixoto UFBA

DOI: https://doi.org/10.5753/sbrc.2021.16730

Resumo

Em Microgrids a produção de energia é realizada combinando fontes renováveis e não renováveis de geração de energia. Desse modo, é fundamental o controle da geração não renovável para evitar o desperdício. Esse tipo de problema tem sido investigado por várias pesquisas, que empregam variações do ajuste do controlador Proporcional-Integral-Derivativo (PID) para evitar perdas de energia. Entretanto, nenhum dos trabalhos empregou uma estratégia para reduzir o tempo de equilíbrio entre os geradores de energia. Nesse contexto, este trabalho apresenta o KaspaFog, uma abordagem que emprega uma estratégia de predição de dados utilizando o modelo SARIMA e uma rede neural com aprendizado por reforço para ajustar o controle da geração de energia. O KaspaFog é uma infraestrutura na névoa apoiada pela nuvem, devido à necessidade de processamento e tempos de respostas rápidos. Com o uso do KaspaFog, foi alcançada uma redução de 18% na produção de energia não renovável em comparação ao ajuste Ziegler-Nichols.

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