Hierarchical Control and Optimization for Microgrids: Balancing Multiple Time Scales with Event-Triggered Mechanisms

Authors: ke  zeng
Venue: IEEE_org_ICIST_2024_Conference
Published: 2024-08-21 07:31:04

Abstract: Microgrids are dynamic systems that require a coordinated approach to managing multiple time scales across their control layers. This paper proposes a hierarchical control architecture that combines real-time optimization with event-triggered mechanisms to enhance operational efficiency and stability. The architecture is designed to handle both fast transient dynamics and slower, long-term energy management decisions. Simulation results demonstrate how this approach outperforms traditional control strategies in both stability and resource optimization.

Main paper: 
 introduction:: 
 The growing interest in sustainable energy has accelerated the deployment of microgrids, small-scale power systems capable of operating autonomously or in conjunction with larger grids. A microgrid's complexity arises from its reliance on distributed energy resources (DERs) such as solar panels, wind turbines, and energy storage systems, which are inherently variable and uncertain. Traditional control strategies often struggle to address the dynamic and multi-layered nature of microgrid operations, especially when considering the wide range of time scales at which decisions need to be made-from real-time adjustments to long-term optimization.To overcome these challenges, there has been a shift toward more sophisticated control strategies that incorporate multi-time scale frameworks and event-triggered control mechanisms. A multi-time scale approach allows the coordination of control actions across various time horizons, ensuring that both fast dynamics and slower system-level decisions are managed efficiently. Simultaneously, event-triggered control reduces unnecessary computations and control actions by only updating control signals when certain conditions or thresholds are met. This paper presents an integrated control strategy for microgrids that combines multi-time scale optimization with event-triggered mechanisms. The goal is to achieve a robust, adaptive control system that enhances both the stability and efficiency of microgrid operations under varying conditions. The proposed approach is validated through detailed simulations that demonstrate its superiority over conventional methods in terms of both performance and computational efficiency. 
 
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