Elastic Grid Command Architecture for Outage-Resilient Control
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Solution Overview
Problem
Power distribution systems face challenges in synchronizing generators with the grid, leading to potential overloading and damage, and traditional command and control architectures are inadequate in handling system failures and disruptions, necessitating more resilient monitoring and control systems.
Innovation Solution
The development of resilient decision systems that include a distribution node network with a command node and candidate nodes, which form localized multi-partner enclaves to manage and adjust electrical grid operations based on historical and operational data, using voting algorithms and edge analytics to detect anomalies and implement adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional hierarchical command and control architectures are used, then system structure is simple and easy to manage, but the system reliability deteriorates under failures and disruptions
Solution Approach 1:
The system divides the electrical grid into multiple localized enclaves or zones, each with its own decision-making capabilities. Candidate nodes are distributed throughout the network, and during normal operation, a command node coordinates centrally. During failures, these segmented zones can operate autonomously, maintaining reliability while preserving manageable structure through modular organization.
Solution Approach 2:
The system dynamically transitions between hierarchical and decentralized modes based on operational conditions. During normal operation, the hierarchical structure provides simple management. During failures or disruptions, the system dynamically reconfigures to allow candidate nodes to become command nodes within their localized enclaves, maintaining reliability without permanent structural complexity.
2Adaptability or versatility
If centralized command node control is used, then communication pathways are simple and easy to manage, but the system adaptability deteriorates during failures and disruptions
Solution Approach 1:
Communication pathways dynamically reconfigure based on system state. During normal operation, simple centralized pathways connect the command node to candidate nodes. During failures, the system adaptively establishes alternative communication routes within localized enclaves, allowing candidate nodes to assume command functions and maintain system adaptability without permanent pathway complexity.
Solution Approach 2:
Candidate nodes possess embedded decision models and can autonomously make decisions within their localized enclaves when the command node is unavailable. This self-service capability enables the system to adapt to failures without complex external coordination, as each node can independently assess situations and execute appropriate actions.
3Reliability
If localized multi-partner enclaves are formed during exigency, then system resilience improves, but decision-making complexity increases
Solution Approach 1:
The system segments the grid into localized enclaves during failures, with each enclave containing a subset of candidate nodes that form a multi-partner decision-making structure. This segmentation contains complexity within manageable local groups rather than requiring system-wide complex coordination, improving resilience while limiting decision-making complexity to localized scopes.
Solution Approach 2:
The decision models at candidate nodes continuously receive feedback from operational data, historical baselines, and real-time system state. This feedback mechanism enables automated, data-driven decision-making within enclaves, reducing the perceived complexity by replacing human judgment with algorithmic processes that systematically evaluate multiple factors and execute decisions.
4Measurement precision
If comprehensive historical and operational data analysis is performed, then measurement precision improves, but processing time increases
Solution Approach 1:
The system pre-processes historical operational data to establish baseline models and patterns during normal conditions. When disturbances occur, the decision models compare real-time data against these pre-established baselines, enabling rapid detection and characterization of anomalies without requiring comprehensive real-time analysis of all historical data, thus maintaining precision while reducing processing time.
Data Source
AI summary
Disclosed are systems and methods for utilizing a unique elastic command and control architecture to incorporate certain resiliency qualities in power grid management and outage mitigation.