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

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## Abstract

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.

## Keywords

Smart grid Demand side management Heuristic techniques## List of symbols

- \(\rho\)
Power rating

- \(\varsigma _{a,t}\)
Electricity price at time interval t

- \(E_{in}\)
Power consumption of interruptible appliances

- \(\rho _{in}\)
Power rating of interruptible appliances

*t*Time slot

*IN*Set of interruptible appliances

- \(sv_{in}\)
ON/OFF status of interruptible appliances

*NI*Set of non-interruptible appliances

- \(E_{ni}\)
Power consumption of non-interruptible appliances

- \(\rho _{ni}\)
Power rating of non-interruptible appliances

- \(sv_{ni}\)
ON/OFF status of non-interruptible appliances

*B*Set of base appliances

- \(E_{b}\)
Power consumption of base appliances

- \(\rho _{b}\)
Power rating of base appliances

- \(sv_{b}\)
ON/OFF status of base appliances

*L*(*t*)Power consumption of all appliances at time interval t

- \(L_{total}^{sch}\)
Per day total scheduled load

- \(L_{total}^{uns}\)
Per day total unscheduled load

- \(C_{total}^{sch}\)
Per day total scheduled cost

- \(C_{total}^{uns}\)
Per day total unscheduled cost

- \(t_{\alpha }\)
Start time of an appliance

- \(t_{\beta }\)
End time of an appliance

- F
Scaling factor

- NP
Population size

## Abbreviations

- SG
Smart grid

- SM
Smart meter

- DSM
Demand side management

- DR
Demand response

- RES
Renewable energy sources

- PAR
Peak to average ratio

- TOU
Time of use

- IBR
Inclined block rate

- CPP
Critical peak pricing

- DAP
Day ahead pricing

- RTP
Real time pricing

- HSA
Harmony search algorithm

- DE
Differential evolution

- EDE
Enhanced differential evolution

- GA
Genetic algorithm

- CR
Cross over rate

- HEM
Home energy management

- EMC
Energy management controller

- HAN
Home area network

- HMCR
Harmony memory consideration rate

- bw
Bandwidth

- PSO
Particle swarm optimization

- MILP
Mixed integer linear programming

- PA
Pitch adjustment rate

- MKP
Multiple knapsack problem

## Notes

### Funding

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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