A Distributed Energy Resource Coordination Framework for Microgrid Resilience

Thesis Abstract

The increasing penetration of distributed energy resources (DERs) and the rising frequency of grid disturbances necessitate robust coordination mechanisms to enhance microgrid resilience against outages, volatility, and cascading failures. This study addresses the gap in integrated frameworks that simultaneously optimize DER dispatch, storage control, network reconfiguration, and communication-induced uncertainties to sustain critical loads during islanded operation and post-disturbance recovery. The aim is to develop a distributed energy resource coordination framework that achieves resilience by coordinating heterogeneous DERs, storage, and controllable loads through a multi-agent optimization architecture under uncertainty, with provable stability and convergence guarantees. Specific objectives are (i) to formulate a distributed optimization model that minimizes expected unsatisfied critical load and energy losses while respecting network constraints and DER dynamics; (ii) to incorporate uncertainty through stochastic and robust optimization for renewable generation, load variability, and communication delays; (iii) to design a microgrid-wide coordination protocol enabling scalable, privacy-preserving information exchange among Distributed Energy Resource Operators (DER-Os) and a central resilience supervisor; (iv) to validate the framework via simulation on a detailed 33-bus distribution test system with 12 DERs, 4 storage units, and 6 critical-load nodes; and (v) to evaluate performance under various disturbance scenarios, including line faults, forecast errors, and cyber-physical attacks. The methodology combines a mixed-methods approach with quantitative modeling and qualitative sensitivity analysis. The population comprises microgrid configurations and operator agents drawn from three operational archetypes photovoltaic-plus-storage-diesel, wind-storage, and demand-response-enabled microgrids. A sample of 20 distinct microgrid cases is constructed by varying network topology, DER mix, and load profiles to assess framework generality. Data collection relies on synthetic but physically consistent time-series generated from validated solar irradiance and wind speed models, historical load traces, and representative outage sequences. Instruments include a distributed optimization engine implemented as an iterative alternating direction method of multipliers (ADMM) with privacy-preserving encryption for inter-agent communication, a stochastic scenario generator for renewable and load uncertainty, and a resilience metric suite comprising Energy Availability, Critical Load Sacrificed, and Time-to-Recovery indices. Validity and reliability are established through cross-validation against a high-fidelity detailed simulink-based microgrid model and replication across three independent simulation environments. Data analysis employs (i) quantitative performance evaluation using multivariate regression to relate resilience metrics to DER mix, storage capacity, and communication latency; (ii) convergence and stability analysis of the distributed optimization using Lyapunov functions and eigenvalue bounds; (iii) sensitivity analyses for parameter uncertainty; and (iv) scenario-based hypothesis testing with ANOVA to detect significant differences in resilience outcomes across archetypes. The study anticipates findings that demonstrate improved resilience scores under the proposed framework relative to centralized and heuristic approaches, with reduced unmet critical load, diminished energy losses, faster recovery times, and robust operation under communication delays and cyber-physical threats. Expected contributions include (a) the theoretical development of a provably convergent distributed coordination framework tailored for microgrid resilience, integrating ADMM-based optimization with robust and stochastic elements; (b) a practical protocol for cross-operator coordination that preserves data privacy while ensuring secure resilience decision-making; (c) a comprehensive understanding of DER mix and storage sizing requirements necessary to sustain critical loads during islanded operation and fault contingencies; and (d) a validated methodology and benchmark dataset enabling reproducible assessment of distributed DER coordination strategies under uncertainty. The study concludes that a distributed coordination framework, when designed with appropriate robustness margins and secure communication protocols, can substantially enhance microgrid resilience without resorting to centralized control, and it recommends standardized resilience metrics, scalable deployment guidelines, and governance structures for multi-operator microgrids to facilitate wide adoption.