Next Article in Journal
Influence of Blending Model n-Butanol Alcoholysis Derived Advanced Biofuel Blends with Diesel on the Regulated Emissions from a Diesel Hybrid Vehicle
Next Article in Special Issue
Solar Photovoltaic Model Parameter Identification with Improved Metaheuristic Algorithm Based on Balanced Search Strategies
Previous Article in Journal
A Hybrid Energy Storage System and the Contribution to Energy Production Costs and Affordable Backup in the Event of a Supply Interruption—Technical and Financial Analysis
Previous Article in Special Issue
Smart Grid Systems: Addressing Privacy Threats, Security Vulnerabilities, and Demand–Supply Balance (A Review)
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Energies
Volume 19
Issue 2
10.3390/en19020307
energies-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

An Adaptive Multi-Layer Heuristic Framework for Real-Time Energy Optimization in Smart Grids

by
Atef Gharbi
1,*,
Mohamed Ayari
2,
Nasser Albalawi
3,
Ahmad Alshammari
3,
Nadhir Ben Halima
4 and
Zeineb Klai
3
1
Department of Information Systems, Faculty of Computing and Information Technology, Northern Border University, Rafha 91911, Saudi Arabia
2
Department of Information Technology, Faculty of Computing and Information Technology, Northern Border University, Rafha 91911, Saudi Arabia
3
Department of Computer Sciences, Faculty of Computing and Information Technology, Northern Border University, Rafha 91911, Saudi Arabia
4
Department of Information Technology, Community College of Qatar, Doha P.O. Box 7344, Qatar
*
Author to whom correspondence should be addressed.
Energies 2026, 19(2), 307; https://doi.org/10.3390/en19020307
Submission received: 11 November 2025 / Revised: 3 January 2026 / Accepted: 5 January 2026 / Published: 7 January 2026
(This article belongs to the Special Issue Smart Grid and Energy Storage)
Browse Figures
Versions Notes

Abstract

Smart grids face significant challenges in coordinating demand-side management (DSM), dynamic pricing, data aggregation, and network feasibility in real time. To address this, we propose H-EMOS-Lite, an adaptive, multi-layer heuristic framework that integrates these components into a unified, real-time optimization loop. Evaluated on fully reproducible generated demand, price, and grid datasets based on realistic residential energy systems, H-EMOS-Lite achieves a 2.1% reduction in peak load and completes a full 24 h (96-interval) optimization for 100 households in under 0.25 s, demonstrating its suitability for near-real-time residential energy systems. The framework outperforms three baselines—Independent DSM, Sequential Optimization, and Particle Swarm Optimization (PSO)—by effectively balancing energy cost, peak load reduction, and temporal smoothness of the aggregate load profile, while avoiding abrupt, unsynchronized load shifts that induce secondary peaks—common in uncoordinated approaches. By embedding physical feasibility and cross-layer feedback directly into the optimization loop, H-EMOS-Lite enables scalable, interpretable, and deployable coordination for smart distribution systems.
Keywords: demand-side management; multi-layer co-optimization; heuristic optimization; dynamic pricing; data aggregator placement; bi-level feedback demand-side management; multi-layer co-optimization; heuristic optimization; dynamic pricing; data aggregator placement; bi-level feedback

Share and Cite

MDPI and ACS Style

Gharbi, A.; Ayari, M.; Albalawi, N.; Alshammari, A.; Ben Halima, N.; Klai, Z. An Adaptive Multi-Layer Heuristic Framework for Real-Time Energy Optimization in Smart Grids. Energies 2026, 19, 307. https://doi.org/10.3390/en19020307

AMA Style

Gharbi A, Ayari M, Albalawi N, Alshammari A, Ben Halima N, Klai Z. An Adaptive Multi-Layer Heuristic Framework for Real-Time Energy Optimization in Smart Grids. Energies. 2026; 19(2):307. https://doi.org/10.3390/en19020307

Chicago/Turabian Style

Gharbi, Atef, Mohamed Ayari, Nasser Albalawi, Ahmad Alshammari, Nadhir Ben Halima, and Zeineb Klai. 2026. "An Adaptive Multi-Layer Heuristic Framework for Real-Time Energy Optimization in Smart Grids" Energies 19, no. 2: 307. https://doi.org/10.3390/en19020307

APA Style

Gharbi, A., Ayari, M., Albalawi, N., Alshammari, A., Ben Halima, N., & Klai, Z. (2026). An Adaptive Multi-Layer Heuristic Framework for Real-Time Energy Optimization in Smart Grids. Energies, 19(2), 307. https://doi.org/10.3390/en19020307

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop