A Hybrid Approach for Power Flow Management in Smart Grid Connected System

Abstract

In this manuscript, a hybrid approach for power flow management (PFM) in smart grid (SG) connected hybrid renewable energy sources (HRES) is proposed. The HRES contains photovoltaic (PV), wind turbine (WT), and battery. Owing to the resource application, the intermediate power generations have unpredictable and variable, resulting power fluctuation occurred in HRES. Moreover, the intelligent controller is proposed for stabilizing the power fluctuations. The proposed method is the joint execution of Random Forest Algorithm (RFA) and Manta Ray Foraging algorithm (MRFA) called Random Forest Manta Ray Foraging algorithm (RFMRFA). The major objective of the proposed method is “optimal operation of RES for diminishing the cost of electricity generation by hourly day-ahead including real-time scheduling”. Here, the load requirements are predicted with the help of Random Forest Algorithm, also Manta Ray Foraging algorithm is to create the optimal control signals depending on power variation amid the source and load side. The proposed method is activated in matrix laboratory/Simulink site. Here, the statistical analysis of proposed with existing methods under all cases is examined. The simulation outcomes demonstrate that the RFMRFA method has minimal computational time than the exiting methods under various trails. Without losing the obtainable energy, the RFMRFA model is cost-effective power generation of SG and efficacious application of RES. Furthermore, the statistical analyses like mean, median, and standard deviation are analyzed. Here, the computation time of the RFMRFA with existing methods is examined.

KEYWORDS:

• Hourly day-ahead
• Load demand
• Optimum operation
• Power flow management
• Power generation
• Renewable energy source
• Real-time scheduling
• Smart grid system

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.

Notes on contributors

R. Sumathi

R Sumathi received the BE degree in electrical and electronics engineering from the Madras University, India, in 1998, the ME degree in process control and instrumentation from the Annamalai University, India, in 2004 and PhD in the area of control theory from the Anna University Chennai, India, in 2015. She is currently working as associate professor in the Department of Electrical and Electronics Engineering at Sri Krishna College of Engineering and Technology, Coimbatore, India. She has 15 years of teaching and research experience. Her research interests include modelling and identification of control systems, learning control algorithms for robot manipulators and real-time implementation of control systems.

P. Umasankar

P Umasankar received the BE degree in electronics and communication engineering from University of Madras, India, in 1992 and ME degree in power electronics and drives from Anna University, Chennai, India, in 2007. He received his PhD degree in the area of power electronic converters from Anna University, Chennai, India, in 2016. He is currently working as professor in the Department of Electrical and Electronics Engineering, Mahendra Engineering College, Namakkal, India. He has 12 years of teaching and 11 years of industrial experience. His research interest includes neural networks, fuzzy logic, intelligent techniques, optimization, power electronics and realtime implementation of power electronic systems. Email: [email protected]