Microgrid Parameter Coordination for Disaster Resilience
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Solution Overview
Problem
Current methods for enhancing the resilience of power distribution systems against disasters, particularly extreme weather events, are inadequate due to their focus on quasi-steady state operations and lack of consideration for dynamic behavior at finer time scales, leading to instability and failure in microgrid systems during natural disasters.
Innovation Solution
A holistic framework for coordinating network and control parameters of interconnected microgrids, involving the adjustment of series resistances, reactances, and phase shifts in tie-lines, as well as control parameters like tracking time constants and droop gains, to achieve asymptotical stability through Monte Carlo simulations, Linear Matrix Inequalities, and Sum of Squares methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional operation strategies focusing on quasi-steady state are used, then device complexity is reduced, but system stability deteriorates during dynamic disaster events
Solution Approach 1:
The patent applies dynamics by transitioning from static quasi-steady state analysis to dynamic time-domain simulation that captures transient behavior during disaster events. The framework models microgrid systems with time-varying parameters and uses dynamic simulation to assess stability under evolving disaster conditions, allowing the system to adapt operation strategies in real-time rather than relying on fixed pre-disaster plans.
Solution Approach 2:
The framework systematically varies key system parameters including tie-line capacity, generator capacity, load demand, and controller settings across multiple simulation scenarios. By changing these parameters dynamically during disaster event simulation, the framework identifies optimal parameter configurations that maintain stability while accounting for the evolving system state during disasters.
2Stability of the object's composition
If detailed dynamic analysis at finer time scales is performed, then system stability improves, but loss of time increases due to computational complexity
Solution Approach 1:
The framework performs preliminary dynamic simulations during the planning phase to pre-identify critical stability thresholds and optimal operation strategies before disasters occur. By conducting detailed time-domain simulations in advance under various hypothetical disaster scenarios, the system prepares pre-computed stability margins and control parameters that can be quickly deployed during actual events without requiring real-time complex computation.
Solution Approach 2:
The framework focuses computational resources on analyzing only the most critical subsystems and parameters that dominate system stability behavior during disasters. Rather than performing exhaustive dynamic analysis of every component, the framework identifies key tie-lines, generators, and loads that have disproportionate impact on overall stability and concentrates detailed simulation efforts on these critical elements, achieving sufficient accuracy with reduced computation time.
3Reliability
If comprehensive parameter coordination is implemented, then reliability improves against disasters, but device complexity increases
Solution Approach 1:
The framework segments the complex parameter coordination problem into distinct modules: disaster scenario definition, dynamic simulation engine, stability assessment criteria, and optimization algorithms. Each module handles specific aspects of the analysis independently, allowing for targeted refinement and validation of individual components while maintaining overall system coherence. This modular structure manages complexity by breaking down the comprehensive parameter coordination task into manageable segments.
Solution Approach 2:
The framework develops a universal dynamic simulation and assessment platform that can evaluate multiple disaster types (hurricanes, earthquakes, wildfires), various microgrid configurations, and different operation strategies within a single integrated system. By creating multi-functional tools that handle diverse scenarios and system types, the framework achieves comprehensive reliability assessment without proportionally increasing operational complexity for end users.
Data Source
AI summary
Systems and methods for coordinating network and control parameters of a power distribution system (PDS) with interconnected microgrids in response to a subset of interconnected microgrids entering island-mode due to a predicted future disaster, generating samples of network and control parameter combinations, determining optimal adjustments of network and control parameters with respect to the disaster condition, determining optimal set of network and control parameters to be reinforced or adjusted, activating the parameter adjustments and reinforcements on the determined tie lines and PCCs of the microgrids.