Finite State Machine-Based Load Scheduling Algorithm for Smart Home Energy Management

Abstract

The problem of scheduling household appliances with the availability of renewable energy is the biggest challenge in the smart home energy management system. The components such as renewable energy resources, household appliances, utility grid, storage batteries are pooled into a nonlinear, time-varying, indefinite, and dynamic structure that is impossible to control and refine. For this, real-time pricing is applied in most nations to withstand the burden on the grid. This requires attention to utilize renewable energy effectively. In this paper, a load scheduling method to schedule the loads based on the availability of solar energy and customer preferences is presented. First, the availability of solar energy is forecasted ahead one day using Regression Analysis. Second, the finite state machine approach-based load scheduling algorithm is implemented and tested using MATLAB Simulink and Lab VIEW. LabVIEW-based GUI is developed to visualize the MATLAB schedule for loads. The problem is divided into several states with the availability of solar power, and if solar power is unavailable, grid power is utilized. The loads preferred by the consumers are scheduled in alignment with the production of solar power with the finite state machine scheduling algorithm. Also, the loads considered are able to consume instantaneous energy with the instantaneous production of energy, thereby reducing CO₂ emission by not consuming power from the grid. Finally, the loads are scheduled accordingly, and it is concluded that coordination can be established between energy providers, and the system proposed can flatten out the load profile.

KEYWORDS:

• Energy management
• Finite state machine
• Load scheduling
• Scheduling algorithm
• Simulink model
• Solar irradiance

ACKNOWLEDGEMENTS

The authors would like to thank AICTE for funding our research work in providing all facilities required for the essential components used in the smart home energy management system.

DISCLOSURE STATEMENT

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

Additional information

Notes on contributors

M. L. Merlin Sajini

M L Merlin Sajini received the BE degree in electrical and electronics engineering from CSI Institute of Technology, affiliated to Anna University, Chennai and the ME degree in power electronics and drives from Alagappa Chettiar College of Technology, affiliated to Anna University, Tiruchirappalli, India. She is working as an assistant professor in the Department of Electrical and Electronics Engineering, Coimbatore Institute of Technology and pursuing the PhD degree in integrated renewable energy resources.

S. Suja

S Suja received a BE degree from Government College of Technology, Coimbatore, ME and PhD from PSG College of Technology, Coimbatore. She is working as an associate professor in the Department of Electrical and Electronics Engineering, Coimbatore Institute of Technology, affiliated to Anna University, Chennai. She has teaching experience of 30 years, and publications include many international and national journals, international and national conferences. Her research interest includes power system, wavelets, embedded system applications, renewable energy and soft computing techniques. Email: [email protected]

S. Merlin Gilbert Raj

S Merlin Gilbert Raj received the BE degree in electronics and communication engineering from CSI Institute of Technology, affiliated to Anna University Chennai India, the ME degree in communication systems from Mepco Schlenk Engineering College, affiliated to Anna University, Chennai India. He is working as an assistant professor in the Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Deemed to be University. Email: [email protected]

S. Kowsalyadevi

S Kowsalyadevi received a BE degree from Coimbatore Institute of Technology, Coimbatore, ME from Coimbatore Institute of Technology, Coimbatore. Currently, she working with Soliton Technologies for the past 3 years, worked in FPGA RTL - design, simulation and timing analysis. Also, she worked with Mu Sigma Business Solutions for 3 years in the data analytics domain, have good knowledge of linear regression and is familiar with different modeling techniques. Email: [email protected]

Charisma Maria

Charisma Maria received the BTech degree in electrical and electronics engineering from SNMIMT, MG University, Kerala. MTech in power systems from College of Engineering Trivandrum, APJ Abdul Kalam Technological University, Kerala. She is now pursuing PhD in the area of load scheduling in smart grid environment at Coimbatore Institute of Technology, Coimbatore. Email: [email protected]