A Unified Framework for Energy-Aware Microgrid Control Theory

Thesis Abstract

In the context of modern electrical power systems, microgrids present a promising pathway toward reliable, resilient, and sustainable energy delivery, yet their operation is constrained by volatile renewable generation, dynamic loads, and limited communication bandwidth, which together challenge real-time control and economic optimization. This study addresses these challenges by developing a unified framework for energy-aware microgrid control theory that integrates hierarchical decision-making, predictive uncertainty handling, and communication-aware optimization to achieve enhanced reliability, efficiency, and grid-foreign interactions. The aim is to formulate a cohesive theoretical model that reconciles grid-forming and grid-following paradigms, storage-enabled dispatch, and demand-side flexibility within a single analytic construct, enabling provable stability guarantees and practical implementability. Specific objectives include (i) deriving a multi-timescale control model that blends model predictive control, Lyapunov stability theory, and stochastic optimization to handle forecast errors and contingencies; (ii) embedding energy-aware constraints that capture storage degradation, battery state-of-health, and renewable intermittency within a unified objective function; (iii) proposing a communication-aware optimization mechanism that remains robust under partial observability and delayed information exchange; (iv) validating the framework against standard microgrid benchmarks and realistic case studies, and (v) delivering a set of design guidelines for controllers, sensors, and communication protocols. The methodology adopts a theory-driven, mixed-methods approach anchored in systems engineering and control theory. A formal mathematical model of a representative microgrid with distributed energy resources (DERs), energy storage systems, and flexible loads is established, drawing on Lyapunov stability theory, stochastic programming, and hierarchical model predictive control (MPC). The population comprises simulated, hardware-in-the-loop (HIL) tested, and real-world microgrid configurations, with a sample of three representative configurations (a) a campus microgrid with photovoltaic and lithium-ion storage, (b) a rural community microgrid with diesel backup and wind generation, and (c) an urban microgrid with active demand response and vehicle-to-grid capabilities. Data collection employs (i) high-resolution (1 s) generation and load profiles, (ii) battery degradation and efficiency characterizations, (iii) communication latency and packet loss statistics, and (iv) market pricing and ancillary service signals. Instruments include validated DER models, state-of-charge and degradation models, and calibrated communication simulators. Validity and reliability are ensured through cross-validation of models against benchmark datasets and HIL experiments, with sensitivity analyses to evaluate robustness under forecast errors and data dropouts. Analytical techniques encompass a suite of methods stochastic MPC to manage uncertainty in renewable production and loads, Lyapunov-based stability proofs to guarantee asymptotic stability under time-varying conditions, robust optimization to handle model mismatches, and regression analyses to quantify drivers of energy efficiency and reliability. Additionally, game-theoretic concepts are employed to model interactions between prosumers within the microgrid boundary, and information-theoretic metrics assess the impact of communication constraints on control performance. The framework integrates theory and algorithmic development to yield a modular control architecture with clear interfaces between forecasting, optimization, and low-level inverter/grid controllers. Expected findings indicate that the energy-aware unified framework achieves statistically significant improvements in renewable utilization, reduced operational costs, and enhanced resilience to faults and communication delays, compared with conventional MPC-based approaches. Specifically, simulations and HIL tests are anticipated to show a 12–18% reduction in energy curtailment, a 6–12% decrease in total operating cost, and a 20–30% improvement in voltage and frequency regulation margins under high-renewable penetration scenarios. The study contributes to knowledge by delivering a theoretically grounded yet practically implementable model that unifies controller synthesis, energy storage considerations, and communication constraints into a single, provably stable framework. It provides theoretical insights into the interaction between energy-aware optimization and information constraints and offers practical guidelines for integrating sensors, storage management strategies, and resilient communication protocols. The main conclusion posits that a unified energy-aware control theory can systematically improve microgrid performance across reliability, efficiency, and resilience objectives, with recommendations emphasizing modular controller design, robust state estimation, and adaptive communication strategies to sustain performance in dynamic environments. Future work recommended includes extending the framework to multi-microgrid coordination, exploring learning-augmented control under limited data, and refining degradation-aware storage models for long-term planning.