Smart energy management for optimal economic operation in grid-connected hybrid power system

ABSTRACT

This manuscript presents a hybrid method for smart energy management (EM) in grid connected microgrid (MG) system. The grid connected micro grid system consists of photovoltaic (PV), wind turbine (WT), micro turbine (MT), and battery. The proposed method is the combined execution of Radial Basis Function Neural Network (RBFNN) and Squirrel Search Algorithm (SSA), hence it is called RBFNN-SSA method. Here, the necessary load demand of grid-connected MG system is constantly monitored by AI strategy. SSA has developed the perfect combination of MG considering the forecast load demand. The major intention of the RBFNN-SSA method is fuel cost involvement, grid power hourly power variation, operation with maintenance cost of grid connected micro grid system. The constraints are the accessibility of renewable energy sources (RES), power requirement and state of charge (SoC) of storage elements. Batteries are used as an energy source, to stabilize and allow the renewable power system units for maintaining constant output power. The proposed method is activated in MATLAB/Simulink working site. Then, the efficiency is assessed with existing methods such as improved artificial bee colony (IABC), bacterial foraging optimizer and artificial neural network (BFOANN), ant lion optimizer (ALO), grasshopper optimization algorithm with particle swarm optimization including artificial neural network (GOAPSNN). The root mean square error (RMSE), mean absolute percentage error (MAPE), and mean bias error (MBE) of proposed and existing methods under 50 and 100 count of trails are also analyzed. The proposed technique achieves the RMSE is 9.3, MAPE is 4.2, and MBE is 2 for 50 number of trails. For 100 count of trails, the proposed method achieves the RMSE is 13.5, MAPE is 3.9 and MBE is 5.7. The mean, median and standard deviation of RBFNN-SSA method achieves 0.9681, 0.9062 and 0.1099. The elapsed time of RBFNN-SSA method attains 30.15 s.

KEYWORDS:

• Energy Management
• power flow
• grid connected MG system
• micro turbine
• squirrel search algorithm

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Notes on contributors

Kallol Roy

Mr. Kallol Roy received B.E degree in Electrical Engineering from Burdwan University, Burdwan, West Bengal in 2002. He received M.E degree in Power Engineering from Jadavpur University, Kolkata, West Bengal in 2006. He is currently Assistant Professor in Electrical Engineering Department, University Institute of Technology, Burdwan University, West Bengal. He is currently working toward the Ph.D. degree at the Department of Power Engineering, Jadavpur University, Kolkata, West Bengal, India. His research interests include Micro Grid.

Kamal Krishna Mandal

Dr. Kamal Krishna Mandal received B.E from Jadavpur University, Kolkata, West Bengal in 1986. He received M.E from Allahabad University, Allahabad, Uttar Pradesh in 1998. He has received Ph.D. degree also. He is currently Professor in Power Engineering Department, Jadavpur University, Kolkata, West Bengal, India. His research interests include soft computing techniques, Power system optimization.

Atis Chandra Mandal

Dr. Atis Chandra Mandal received M.Sc in Physics from Burdwan University, Burdwan, West Bengal . He received Ph.D. degree from Jadavpur University, Kolkata, West Bengal, research done at Saha Institute of Nuclear Physics, Kolkata. He is currently Professor in Physics Department, Burdwan University, Burdwan, West Bengal, India. His research interests include soft computing techniques.