Realistic Home Energy Management System Considering the Life Cycle of Photovoltaic and Energy Storage Systems

Zaid A. Al Muala, Mohammad A. Bany Issa, Daniel Sansó-Rubert Pascual and Pastora M. Bello Bugallo ()
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Zaid A. Al Muala: TECH-NASE Research Group, Department of Chemical Engineering, Universidade de Santiago de Compostela, 15705 Santiago de Compostela, Spain
Mohammad A. Bany Issa: TECH-NASE Research Group, Department of Chemical Engineering, Universidade de Santiago de Compostela, 15705 Santiago de Compostela, Spain
Daniel Sansó-Rubert Pascual: Department of Legal and Political Sciences, Universidad Europea, 28670 Madrid, Spain
Pastora M. Bello Bugallo: TECH-NASE Research Group, Department of Chemical Engineering, Universidade de Santiago de Compostela, 15705 Santiago de Compostela, Spain

Sustainability, 2023, vol. 15, issue 14, 1-19

Abstract: Home Energy Management Systems (HEMSs) have become necessary due to energy security and climate change concerns. Scheduling the operating time of household appliances is one of the most effective strategies used by HEMSs to reduce electricity costs, with several studies proposing optimization strategies for scheduling home appliances to reduce the grid energy usage cost. This work considers energy usage costs from Renewable Energy Sources (RESs) and Energy Storage Systems (ESSs) in the appliance-scheduling strategy and energy flow management. The objectives are reducing the real electricity cost while maintaining a longer battery lifespan, reducing battery charging/discharging losses, and using PV power efficiently. To achieve this, we developed a pricing model of battery energy usage, in addition to modeling the PV energy usage cost based on the Levelized Cost of Energy (LCOE) for PV systems. PV-battery energy usage cost models were introduced into the optimization problem solved using the Augmented Grey Wolf Optimization (AGWO) and Particle Swarm Optimization (PSO) algorithms in MATLAB. We developed an efficient energy flow management algorithm. We collected real data from a home in Vigo, Spain, and simulated four scenarios. The results show that the proposed system using AGWO and PSO reduced the real cost by 25.87% and 25.98%, respectively. Compared with an existing energy-usage-pricing model, the AGWO reduced the energy losses by 40.429% and extended the battery lifespan by 68.282%. Similarly, the PSO reduced the energy losses by 45.540% and extended the battery lifespan by 84.56%. Moreover, the proposed system reached the breakeven point of the system in a shorter time.

Keywords: augmented grey wolf optimization; scheduling appliances; smart grid; battery lifecycle; cost reduction; energy management; particle swarm optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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