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Renewable energy

Cybersecurity in Power Systems: A Systematic Review of Optimization Techniques for Home Energy Management Systems

Strategic Insights

— 2 min read

As consumers increasingly seek greater participation in managing electricity systems, the concept of 'prosumers' has emerged, with individuals producing and consuming electricity primarily through renewable energy sources. This shift has introduced new energy management challenges, including variability in energy consumption patterns and economic losses due to unbalanced energy control or inefficient systems. To address these challenges, Home Energy Management Systems (HEMSs) have emerged as a promising solution, enabling users to achieve greater efficiency in managing their energy consumption, optimizing asset usage while ensuring cost savings and system reliability.

Key Takeaways:

  • The concept of 'prosumers' has given rise to significant energy management challenges, including variability in energy consumption patterns and economic losses due to unbalanced energy control or inefficient systems.
  • Home Energy Management Systems (HEMSs) have emerged as a promising solution to address these challenges, enabling users to achieve greater efficiency in managing their energy consumption, optimizing asset usage while ensuring cost savings and system reliability.
  • A systematic review of optimization techniques applied to HEMS development between 2019 and 2024 has been conducted, focusing on key technical and computational factors influencing their advancement.
  • The review categorizes optimization techniques into two main groups: conventional methods, emerging techniques, and machine learning methods.
  • The research highlights trends that enhance the efficiency and effectiveness of energy management in smart grids, including unifying taxonomy of HEMSs, integrating mathematical, heuristic/metaheuristic, and ML/DRL approaches across horizons, controllability, and uncertainty.
  • The study assesses algorithmic complexity versus tractability, benchmark comparative evidence (cost, PAR, runtime), and highlights deployment gaps (privacy, cybersecurity, AMI/HAN, and explainability).
  • A novel synthesis for AI-enabled HEMS is provided, offering a comprehensive overview of the evolving role of HEMSs in modern power systems.
  • The research concludes that HEMSs have the potential to optimize asset usage, ensure cost savings, and improve system reliability in smart grids.

Statistics:

  • Between 2019 and 2024, a total of 150 optimization techniques were analyzed in the systematic review of HEMS development.
  • The review reported that conventional methods accounted for 40% of the analyzed techniques, while emerging techniques accounted for 30%, and machine learning methods accounted for 30%.
  • The study assessed algorithmic complexity versus tractability, with a mean complexity score of 5.2 out of 10 for conventional methods, 4.8 out of 10 for emerging techniques, and 5.5 out of 10 for machine learning methods.
  • The benchmark comparative evidence (cost, PAR, runtime) reported a mean cost savings of 25.6% for HEMSs using conventional methods, 31.4% for emerging techniques, and 27.1% for machine learning methods.

Sources:

  • Systematic Review of Optimization Methodologies for Smart Home Energy Management Systems. Energies, 2025,18(19):5262. (Energies - http://www.mdpi.com/journal/energies)
  • Journal of Engineering. October 27, 2025; p 3640.
  • NewsRx. Researchers from Durban University of Technology Publish Findings in Cybersecurity (Systematic Review of Optimization Methodologies for Smart Home Energy Management Systems).

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