Artificial intelligence based system for mixed operation microgrids for non-interconnected areas

Abstract

The increasing penetration of renewable energy resources in isolated and weakly interconnected electrical systems has introduced significant operational challenges, particularly in ensuring supply continuity, minimizing power fluctuations, and improving energy efficiency under highly variable generation conditions. In this context, this doctoral thesis addresses the operational management—rather than the planning—of a small-scale microgrid corresponding to node 611, focusing on real-time decision-making strategies supported by predictive analytics. The proposed work presents the design, integration, and validation of an intelligent energy management system (EMS) enhanced by short-term photovoltaic generation forecasting based on lightweight machine learning and deep learning models. The forecasting framework evaluates multiple architectures, including multilayer perceptron (MLP), convolutional neural networks (CNN), long short-term memory networks (LSTM), and ensemblebased approaches, using standard performance metrics such as root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R²). The selected predictive model is subsequently embedded into the operational logic of the EMS to inform state transitions and power flow decisions. The EMS operates as a finite-state machine, where transitions are governed by real-time measurements, predicted renewable generation, battery state of charge, and load demand. This strategy aims to reduce operational perturbations perceived by end users, improve the utilization of renewable resources, and decrease dependency on the main grid during critical operating conditions. The system is implemented and evaluated through detailed simulations, considering realistic operating constraints and time scales consistent with microgrid control applications. Although Raspberry Pi platforms are referenced as representative lowcost controllers, the proposed approach is intentionally formulated to be applicable to a broader class of single-board computing (SBC) platforms suitable for embedded and edge-based energy management applications. Simulation results demonstrate that the integration of predictive models into the EMS contributes to smoother operational behavior, improved power balance, and enhanced resilience against renewable generation variability. The contributions of this thesis include: (i) a predictive-enhanced operational EMS tailored for small-scale microgrids, (ii) a comparative evaluation of lightweight forecasting models suitable for real-time deployment, and (iii) a reproducible framework that bridges data-driven prediction and controloriented energy management. The findings support the feasibility of incorporating artificial intelligence techniques into embedded microgrid controllers to improve operational performance without increasing system complexity or hardware requirements.