DC Microgrid Cluster Control With Predefined Convergence Time
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional distributed control strategies for DC microgrid clusters lack explicit control over convergence time and transient behavior, leading to delayed stabilization and inaccurate tracking under dynamic load conditions and cyber uncertainties, and do not provide guaranteed performance within predefined time bounds.
Innovation Solution
A Prescribed Performance-based Predefined Time (PP-PDT) control strategy is implemented in a distributed hierarchical control system, ensuring convergence of voltage regulation and power-sharing errors within user-defined performance bounds and a tunable settling time, independent of initial conditions, through transformation-based control laws and layered cyber-physical coordination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional distributed control strategies are used, then system complexity is reduced and ease of operation is improved, but convergence time is unbounded and transient performance is poor
Solution Approach 1:
The control system is segmented into three hierarchical layers: primary control for fast local voltage regulation, secondary control for distributed power sharing coordination, and tertiary control for centralized economic optimization. This segmentation allows each layer to operate independently with specific functions, achieving bounded convergence time without overwhelming system complexity.
Solution Approach 2:
The control strategy incorporates preliminary action by establishing predefined performance bounds and convergence time limits before operation. The primary controller immediately begins voltage regulation upon disturbance detection, and the secondary controller pre-coordinates power sharing among DGUs before tertiary economic optimization begins, ensuring bounded convergence without excessive complexity.
2Measurement precision
If conventional distributed control strategies are used, then implementation simplicity is improved, but tracking accuracy under dynamic conditions deteriorates
Solution Approach 1:
The control system implements multi-layer feedback mechanisms: primary feedback for voltage regulation, secondary feedback for power sharing accuracy through distributed consensus algorithms, and tertiary feedback for economic optimization. This feedback structure ensures high tracking accuracy under dynamic load conditions while maintaining manageable algorithm complexity through modular architecture.
Solution Approach 2:
The control strategy employs dynamic adaptation where the secondary controller continuously adjusts power sharing references based on real-time DGU status and load conditions. The tertiary controller dynamically optimizes economic dispatch parameters, enabling high tracking accuracy without requiring overly complex static control algorithms.
3Speed
If conventional control strategies are used, then system simplicity is maintained, but transient response performance deteriorates
Solution Approach 1:
The control architecture is segmented into hierarchical layers with clearly defined time scales: primary control for immediate transient response (milliseconds), secondary control for intermediate power sharing adjustment (seconds), and tertiary control for slow economic optimization (minutes). This segmentation achieves fast transient response while keeping each layer's complexity manageable through functional decomposition.
Solution Approach 2:
The control system performs preliminary action by having the primary controller immediately respond to transients with fast voltage regulation, while the secondary controller is prepared to adjust power sharing references as soon as the primary controller detects significant disturbances. This preliminary preparation and rapid sequential action achieve fast transient response without requiring all control elements to be maximally complex.
Data Source
AI summary
A power generation system for operating a direct current (DC) microgrid (MG) cluster comprises a DC MG cluster including a plurality of DC MGs interconnected via tie-lines for transferring electric power based on bus voltage differences. Each DC MG includes a plurality of distributed generation units (DGUs) supplying local loads. The power generation system also includes a distributed hierarchical control system comprising a primary controller, a secondary controller, and a tertiary controller controls the operation of the DC MG cluster. A two-layered cyber network supports communication between DGUs and MGs via lower and upper layers, respectively, with pinning links enabling inter-layer communication. The tertiary controller minimizes total generation cost, the secondary controller ensures optimal power allocation, and the primary controller performs droop control. A user-defined control parameter sets a predefined upper limit for convergence time, enabling prescribed performance-based predefined time (PP-PDT) control across the DC MG cluster.