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Related Experiment Video

Updated: Jun 23, 2026

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

Improved performance control on the Grid.

M E Tellier¹, G D Riley, T L Freeman

¹Centre for Novel Computing, School of Computer Science, University of Manchester, Manchester M13 9PL, UK.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences

|May 20, 2009

Summary

This summary is machine-generated.

This study optimizes the threshold value in the performance control system (PerCo) for improved efficiency. The optimal threshold enhances performance by 10% and closely matches linear programming schedules.

Related Experiment Videos

Last Updated: Jun 23, 2026

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

Area of Science:

Computer Science
Operations Research

Background:

A threshold-based performance control system (PerCo) was previously introduced by Mayes et al. in 2005.
An initial experimental evaluation of PerCo was presented, highlighting the need for further investigation into its parameters.

Purpose of the Study:

To investigate the significance of the threshold value within the PerCo system.
To establish a more rigorous theoretical basis for the threshold-based rescheduling heuristic.

Main Methods:

Utilized simulation to determine the optimal threshold value for specific application scenarios.
Compared the performance and execution time of the optimized PerCo system against established scheduling methods.

Main Results:

Identified an 'optimal' threshold value that yielded a 10% performance improvement for the studied application.
Demonstrated that the execution time of the optimal threshold-based schedule closely approximated that of a linear programming optimal schedule (within 0.5%).

Conclusions:

The threshold value plays a critical role in the effectiveness of the PerCo system.
Threshold-based rescheduling offers a computationally efficient approach with performance comparable to advanced optimization techniques.