Hybrid meta-heuristic optimization based home energy management system in smart grid

  • Zahoor Ali KhanEmail author
  • Ayesha Zafar
  • Sakeena Javaid
  • Sheraz Aslam
  • Muhammad Hassan Rahim
  • Nadeem Javaid
Original Research


The emergence of the smart grid has empowered the consumers to manage the home energy in an efficient and effective manner. In this regard, home energy management (HEM) is a challenging task that requires efficient scheduling of smart appliances to optimize energy consumption. In this paper, we proposed a meta-heuristic based HEM system (HEMS) by incorporating the enhanced differential evolution (EDE) and harmony search algorithm (HSA). Moreover, to optimize the energy consumption, a hybridization based on HSA and EDE operators is performed. Further, multiple knapsacks are used to ensure that the load demand for electricity consumers does not exceed a threshold during peak hours. To achieve multiple objectives at the same time, hybridization proved to be effective in terms of electricity cost and peak to average ratio (PAR) reduction. The performance of the proposed technique; harmony EDE (HEDE) is evaluated via extensive simulations in MATLAB. The simulations are performed for a residential complex of multiple homes with a variety of smart appliances. The simulation results show that EDE performs better in terms of cost reduction as compared to HSA. Whereas, in terms of PAR, HSA is proved to be more efficient as compared to EDE. However, the proposed scheme outperforms the existing meta-heuristic techniques (HSA and EDE) in terms of cost and PAR.


Smart grid Demand side management Heuristic techniques 

List of symbols


Power rating

\(\varsigma _{a,t}\)

Electricity price at time interval t


Power consumption of interruptible appliances

\(\rho _{in}\)

Power rating of interruptible appliances


Time slot


Set of interruptible appliances


ON/OFF status of interruptible appliances


Set of non-interruptible appliances


Power consumption of non-interruptible appliances

\(\rho _{ni}\)

Power rating of non-interruptible appliances


ON/OFF status of non-interruptible appliances


Set of base appliances


Power consumption of base appliances

\(\rho _{b}\)

Power rating of base appliances


ON/OFF status of base appliances


Power consumption of all appliances at time interval t


Per day total scheduled load


Per day total unscheduled load


Per day total scheduled cost


Per day total unscheduled cost

\(t_{\alpha }\)

Start time of an appliance

\(t_{\beta }\)

End time of an appliance


Scaling factor


Population size



Smart grid


Smart meter


Demand side management


Demand response


Renewable energy sources


Peak to average ratio


Time of use


Inclined block rate


Critical peak pricing


Day ahead pricing


Real time pricing


Harmony search algorithm


Differential evolution


Enhanced differential evolution


Genetic algorithm


Cross over rate


Home energy management


Energy management controller


Home area network


Harmony memory consideration rate




Particle swarm optimization


Mixed integer linear programming


Pitch adjustment rate


Multiple knapsack problem



This project was full financially supported by the King Saud University, through the Vice Deanship of Research Chairs.

Compliance with ethical standards

Conflict of interest

The authors declare no conflicts of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.CIS, Higher Colleges of TechnologyFujairahUnited Arab Emirates
  2. 2.COMSATS University IslamabadIslamabadPakistan

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