Machine learning-based optimal distributed generation and electric vehicle load management

Author:

Gujjarlapudi Ch Sekhar1,Sarkar Dipu2,Gunturi Sravan Kumar3,Odyuo Yanrenthung1

Affiliation:

1. Research scholar, Department of Electrical and Electronics Engineering, National Institute of Technology, Nagaland, India

2. Associate Professor, Department of Electrical and Electronics Engineering, National Institute of Technology, Nagaland, India (corresponding author: )

3. Assistant Professor, Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, Telangana, India

Abstract

The load profile of radial distribution networks (RDNs) is significantly impacted when plug-in electric vehicles (PEVs) are connected to them in large numbers. The disturbances in the load profile may lead to increased power losses in distribution lines and deterioration of the network voltage profile. The provision of distributed generation at strategic locations in the distribution network can help to compensate for the impact on the electrical network due to PEV loads. This paper proposes the use of machine learning (ML)-based models for determining the optimal location of distributed generators (DGs) in an RDN. The proposed method considered time-varying load in addition to PEV load. The suggested method determines optimal placement of DGs based on the power loss reduction index and voltage deviation index reduction index. Four distinct types of ML models were used in the proposed approach using synthesised data on the Institute of Electrical and Electronics Engineers' 33-bus RDN. The performance of the ML models was evaluated with respect to mean squared error and mean absolute percentage error and, for the ML models considered, the random forest ML model gave the best performance.

Publisher

Thomas Telford Ltd.

Subject

General Energy,Renewable Energy, Sustainability and the Environment

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