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

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Incentive Based Load Shedding Management in a Microgrid Using Combinatorial Auction with IoT Infrastructure.

Bizzat Hussain Zaidi¹, Ihsan Ullah², Musharraf Alam³

¹Department of Electrical Engineering, DHA Suffa University, Karachi 75500, Pakistan.

Sensors (Basel, Switzerland)

|April 3, 2021

Summary

This study introduces an incentive-based load shedding system using reverse combinatorial auctions for microgrids. It effectively manages consumer load curtailment during peak hours to enhance social welfare.

Keywords:

IoT combinatorial auction energy efficiency evolutionary algorithms load shedding microgrid smart grid

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Area of Science:

Electrical Engineering
Computer Science
Operations Research

Background:

Microgrids require efficient demand-side management strategies.
Incentive mechanisms can encourage consumer participation in load shedding.
Traditional load shedding methods may not fully optimize social welfare.

Purpose of the Study:

To propose a novel incentive-based load shedding management scheme for IoT-enabled microgrids.
To leverage reverse combinatorial auctions for optimizing load curtailment.
To enhance overall social welfare by incentivizing consumers.

Main Methods:

Developed an incentive-based load shedding scheme utilizing reverse combinatorial auction principles.
Employed Particle Swarm Optimization (PSO) and a hybrid Genetic Algorithm (GA) for winner determination.
Simulated the proposed scheme using MATLAB for performance and stability evaluation.

Main Results:

The proposed scheme effectively manages load shedding in microgrids with up to 50 users.
Combinatorial auctions proved to be a suitable mechanism for this application.
The PSO and GA algorithms successfully determined optimal bidding packages.

Conclusions:

Incentive-based load shedding using combinatorial auctions is a viable solution for microgrids.
The developed scheme enhances social welfare and consumer participation.
The simulation results validate the effectiveness and stability of the proposed management system.