Enhanced Energy Management System in Smart Homes Considering Economic, Technical, and Environmental Aspects: A Novel Modification-Based Grey Wolf Optimizer

Moslem Dehghani, Seyyed Mohammad Bornapour () and Ehsan Sheybani ()
Additional contact information
Moslem Dehghani: Electrical Engineering Department, Faculty of Engineering, Yasouj University, Yasouj 7493475918, Iran
Seyyed Mohammad Bornapour: Electrical Engineering Department, Faculty of Engineering, Yasouj University, Yasouj 7493475918, Iran
Ehsan Sheybani: School of Information Systems and Management, Muma College of Business, University of South Florida, Tampa, FL 33620, USA

Energies, 2025, vol. 18, issue 5, 1-30

Abstract: Increasingly, renewable energy resources, energy storage systems (ESSs), and demand response programs (DRPs) are being discussed due to environmental concerns and smart grid developments. An innovative home appliance scheduling scheme is presented in this paper, which incorporates a local energy grid with wind turbines (WTs), photovoltaic (PV), and ESS, which is connected to an upstream grid, to schedule household appliances while considering various constraints and DRP. Firstly, the household appliances are specified as non-shiftable and shiftable (interruptible, and uninterruptible) loads, respectively. Secondly, an enhanced mathematical formulation is presented for smart home energy management which considers the real-time price of upstream grids, the price of WT, and PV, and also the sold energy from the smart home to the microgrid. Three objective functions are considered in the proposed energy management: electricity bill, peak-to-average ratio (PAR), and pollution emissions. To solve the optimization problem, a novel modification-based grey wolf optimizer (GWO) is proposed. When the wolves hunt prey, other wild animals try to steal the prey or some part of the prey, hence they should protect the prey; therefore, this modification mimics the battle between the grey wolves and other wild animals for the hunted prey. This modification improves the performance of the GWO in finding the best solution. Simulations are examined and compared under different conditions to explore the effectiveness and efficiency of the suggested scheme for simultaneously optimizing all three objective functions. Also, both GWO and improved GWO (IGWO) are compared under different scenarios, which shows that IGWO improvement has better performance and is more robust. It has been seen in the results that the suggested framework can significantly diminish the energy costs, PAR, and emissions simultaneously.

Keywords: smart home; appliances scheduling; renewable energy resources; improved grey wolf optimizer; energy storage system (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/5/1071/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/5/1071/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:5:p:1071-:d:1597356

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().