A Framework for Adaptive Power Management in Smart Microgrids

Thesis Abstract

The increasing integration of renewable energy sources and advanced energy storage systems into electrical grids necessitates efficient and adaptive power management solutions, particularly within smart microgrids that aim to optimize energy utilization and enhance system resilience. Despite growing deployment, existing power management strategies often lack the dynamic adaptability required to respond effectively to fluctuating generation and demand patterns, thereby limiting microgrid reliability and economic performance. This research aims to develop a comprehensive framework for adaptive power management in smart microgrids, focusing on optimizing operational efficiency, stability, and sustainability through a dynamic control architecture. The specific objectives include (1) analyzing current power management approaches in microgrids and identifying their limitations; (2) designing a novel adaptive control framework that incorporates real-time data analytics, predictive algorithms, and load forecasting; and (3) evaluating the performance of the proposed framework through simulation and practical validation. The study employs a mixed-method research design, integrating quantitative and qualitative data collection techniques. The quantitative component involves the collection of operational data from three interconnected microgrids within a metropolitan area over a period of 12 months, encompassing data on power flows, generation output, demand profiles, and storage levels from a sample size of 50 smart microgrid facilities. Data will be collected using SCADA (Supervisory Control and Data Acquisition) systems and IoT-enabled sensors to ensure high-resolution, real-time measurements. Complementary qualitative interviews will be conducted with 20 microgrid operators and engineers to gather insights into current management practices and operational challenges, analyzed through thematic analysis. The analytical framework adopts a combination of regression analysis, to establish relationships between demand patterns and generation variability; machine learning algorithms, specifically Random Forest and Long Short-Term Memory (LSTM) networks, for load and generation forecasting; and multi-objective optimization techniques, such as Pareto efficiency, to balance trade-offs between cost, reliability, and environmental impact. The framework will be implemented in a validated simulation environment using MATLAB/Simulink and OpenDSS, with subsequent practical validation conducted in a pilot microgrid testbed. Validation metrics will include system stability indices, economic cost-benefit analysis, and environmental impact assessments. It is anticipated that the findings will demonstrate the superiority of the proposed adaptive framework in reducing peak load stresses by approximately 25%, lowering operational costs by up to 15%, and improving renewable energy utilization efficiencies by 20% compared to conventional management strategies. The study is expected to reveal that real-time data analytics and predictive intelligence significantly enhance microgrid responsiveness and resilience. Key theoretical contributions include integrating elements of control theory, systems engineering, and behavioral economics to inform adaptive management strategies, and extending existing models such as the Hierarchical Control Model and the Distributed Energy Resource Management (DERM) framework with adaptive, data-driven modules. This research contributes to the existing body of knowledge by providing a systematic, scalable framework for dynamic power management adaptable to varying microgrid configurations and operational contexts. The principal conclusion underscores the importance of incorporating real-time analytics, predictive algorithms, and multi-objective optimization in microgrid control systems. Based on these findings, recommendations include adopting the proposed framework in future microgrid projects, developing standardized protocols for real-time data utilization, and exploring the integration of emerging technologies such as blockchain for secure energy exchanges. Future research avenues suggested include extending the framework to multi-microgrid coordination, exploring advanced machine learning techniques for anomaly detection, and addressing regulatory challenges related to real-time adaptive energy management.