A Decentralized Multi Agent ANN Based Control Technique for Operation Cost Optimization of Isolated Microgrid Systems | [Mehran University Research Journal of Engineering and Technology • 2021]

Author(s):

1. Asad Ullah: Department of Electrical Engineering, University of Engineering and Technology,Taxila,Pakistan

2. Salman Amin: Department of Electrical Engineering, University of Engineering and Technology,Taxila,Pakistan

3. Shaikh Saaqib Haroon: Department of Electrical Engineering, University of Engineering and Technology,Taxila,Pakistan

4. Intisar Ali Sajjad: Department of Electrical Engineering, University of Engineering and Technology,Taxila,Pakistan

5. Hammad Shaukat: Department of Electrical Engineering, University of Engineering and Technology,Taxila,Pakistan

Abstract:

With an ever increasing electrical load demand and the associated fuel price to generate it, researchers are compelled to find alternate, cheap and environment friendly ways for power generation to cater to this technoeconomic conundrum. Renewable Energy Sources (RES) are now being integrated in Distributed Generation (DG) based environment as a solution to this problem, hence resulting in Microgrids, with multiple sources and loads demarcating their footprint. Control and management of a diverse generation profile within a single microgrid is arduous and computationally intensive for a single centralized controller. This paper addresses this inherent problem and proposes a decentralized Multiagent based intelligent control technique to efficiently encompass the heterogeneous generation profile of Microgrids. Artificial Neural Network (ANN) based intelligent agents are deployed at the planning stage for each component of the Microgrid. These agents are responsible for maintaining individual local control parameters within the prescribed control margins, hence creating a multi-agent environment in a Microgrid structure. The proposed intelligent multi-agent control scheme is tested on a test system to furnish its merits over its traditionally employed counterparts. Simulation results show that ANN integration not only reduced the computational burden but also reduced the overall operating cost (around 10.2 %) by reducing the thermal generation.

Page(s): 512-530

DOI: DOI not available

Published: Journal: Mehran University Research Journal of Engineering and Technology, Volume: 40, Issue: 3, Year: 2021

Keywords:

ANN based Multi Agent Management , Renewables in Isolated Microgrid , Cost Optimization , Decentralized Microgrid Control and Management

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