Optimal Strategy for Comfort-Based Home Energy Management System Considering Impact of Battery Degradation Cost Model

Binghui Han, Younes Zahraoui, Marizan Mubin (), Saad Mekhilef, Mehdi Seyedmahmoudian and Alex Stojcevski
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Binghui Han: Power Electronics and Renewable Energy Research Laboratory (PEARL), Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
Younes Zahraoui: FinEst Centre for Smart Cities, Tallinn University of Technology, Ehitajate tee 5, 19086 Tallinn, Estonia
Marizan Mubin: Power Electronics and Renewable Energy Research Laboratory (PEARL), Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
Saad Mekhilef: Power Electronics and Renewable Energy Research Laboratory (PEARL), Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
Mehdi Seyedmahmoudian: School of Software and Electrical Engineering, Faculty of Science, Engineering and Technology, Swinburne University of Technology, Victoria, VIC 3122, Australia
Alex Stojcevski: School of Software and Electrical Engineering, Faculty of Science, Engineering and Technology, Swinburne University of Technology, Victoria, VIC 3122, Australia

Mathematics, 2023, vol. 11, issue 6, 1-26

Abstract: With the deployment of renewable energy generation, home energy storage systems (HESSs), and plug-in electric vehicles (PEVs), home energy management systems (HEMSs) are critical for end users to improve the increasingly complicated energy production and consumption in the home. However, few of the previous works study the impact of different models of battery degradation cost in the optimization strategy of a comfort-based HEMS framework. In this paper, a novel scheduling algorithm based on a mixed-integer programming (MIP) model is proposed for the HEMS. Total cost minimization, peak load shifting, and residents’ thermal comfort satisfaction are combined and considered in the optimal scheduling algorithm. The impact of battery degradation costs on the charging and discharging strategy of HESS and PEV is also compared and discussed in this case study. This case study shows that the proposed optimal algorithm of HEMS not only flattens the peak load and satisfies the thermal comfort of residents but also has better flexibility and economic advantages, reducing the electricity cost by 30.84% and total cost by 24.16%. The sensitivity analysis of the parameters for the charging and discharging strategy also guarantees the lowest cost and prolongs the service life of the battery.

Keywords: optimization algorithm; home energy management system; degradation cost; thermal comfort; peak load shifting (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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