Skip to main content

Advertisement

SpringerLink
Log in

Scheduling of smart home appliances for optimal energy management in smart grid using Harris-hawks optimization algorithm

  • Research Article
  • Published:
Optimization and Engineering Aims and scope Submit manuscript

Abstract

With arrival of advanced technologies, automated appliances in residential sector are still in unlimited growth. Therefore, the design of new management schemes becomes necessary to be achieved for the electricity demand in an effort to ensure safety of domestic installations. To this end, the Demand Side Management (DSM) is one of suggested solution which played a significant role in micro-grid and Smart Grid systems. DSM program allows end-users to communicate with the grid operator so they can contribute in making decisions and assist the utilities to reduce the peak power demand through peak periods. This can be done by managing loads in a smart way, while keeping up customer loyalty. Nowadays, several DSM programs are proposed in the literature, almost all of them are focused on the domestic sector energy management system. In this original work, four heuristics optimization algorithms are proposed for energy scheduling in smart home, which are: bat algorithm, grey wolf optimizer, moth flam optimization, algorithm, and Harris hawks optimization (HHO) algorithm. The proposed model used in this experiment is based on two different electricity pricing schemes: Critical-Peak-Price and Real-Time-Price. In addition, two operational time intervals (60 min and 12 min) were considered to evaluate the consumer’s demand and behavior of the suggested scheme. Simulation results show that the suggested model schedules the appliances in an optimal way, resulting in electricity-cost and peaks reductions without compromising users’ comfort. Hence, results confirm the superiority of HHO algorithm in comparison with other optimization techniques.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Abbreviations

\(L\left( t \right)\) :

Energy-consumption of all appliances at time slot t

\(E_{a,\,\,t}^{price}\) :

Electricity-price at any time interval t

\(E_{\text{interru}}\) :

Energy consumed by interruptible appliances

\(E_{shiftable}\) :

Energy consumed by non-interruptible appliances

\(E_{fixed}\) :

Energy consumed by fixed appliances

\(\varphi_{app}\) :

Power rating of () appliance

t :

Time-slot

\(IN\) :

Group of interruptible appliances

\(X\left( t \right)\) :

Status of appliances OFF\ON

\(X_{fixed}^{\text{app}}\) :

State of fixed appliances OFF\ON

\(X_{Shiftable}^{\text{app}}\) :

State of shiftable appliances OFF\ON

\(X_{in}^{\text{app}}\) :

State of interruptible-appliances OFF\ON

\(\varphi_{in}^{t}\) :

Power-rating of interruptible-appliances

\(\varphi_{Shiftable}^{t}\) :

Power-rating of shiftable appliances

\(\varphi_{fixed}^{t}\) :

Power-rating of fixed appliances

\(L_{total}^{Sched}\) :

Total-load scheduled per 24-h

\(L_{total}^{Unsched}\) :

Total-load unscheduled during 24-h

\(C_{total}^{Sched}\) :

Total-cost scheduled per 24-h

\(C_{total}^{USched}\) :

Total-cost unscheduled during 24-h

\(t_{\alpha }\) :

Start time of appliance

\(t_{\beta }\) :

End time of appliance

PAR:

Peak average ratio

EC:

Electricity cost

LoT:

Length of operation time

RTP:

Real time pricing

CPP:

Critical peak pricing

ToU:

Time of use

SG:

Smart grid

DSM:

Demand side management

RES:

Renewable energy sources

HHO:

Harris hawks optimization

GWO:

Grey wolf optimizer

MFO:

Moth flam optimizer

BA:

Bat algorithm

PSO:

Particle-swarm-optimization

GA:

Genetic-algorithm

DE:

Differential-evolution

SSA:

Salp swarm algorithm

References

  • Abushnaf J, Rassau A (2018) An efficient scheme for residential load scheduling integrated with demand side programs and small—scale distributed renewable energy generation and storage. Int Trans Electr Energ Syst. https://doi.org/10.1002/etep.2720

    Article  Google Scholar 

  • Asfaw Takuro Sato, Kammen Daniel M, Duan Bin, Macuha Martin, Zhou Zhenyu, Jun Wu, Muhammad Tariq SA (2015) Smart grid standards: specifications, requirements, and technologies, 1st edn. Wiley, Chennai

    Google Scholar 

  • Asghar A, Mirjalili S, Faris H, Aljarah I (2019) Harris hawks optimization: algorithm and applications. Fut Gen Comput Syst 97:849–872. https://doi.org/10.1016/j.future.2019.02.028

    Article  Google Scholar 

  • Bradac Z, Kaczmarczyk V, Fiedler P, Republic C (2015) Optimal scheduling of domestic appliances via MILP. Energies. https://doi.org/10.3390/en8010217

    Article  Google Scholar 

  • Cavalcante L, Alexandre S, Aoki R et al (2018) Customer targeting optimization system for price—based demand response programs. Int Trans Electr Energ Syst. https://doi.org/10.1002/etep.2709

    Article  Google Scholar 

  • Chen Y, Liu RP, Wang C et al (2012) Consumer operational comfort level based power demand management in the smart grid. 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe). Berlin. IEEE, Berlin, pp 1–6

    Google Scholar 

  • Faiz Z, Bilal T, Awais M, Gull S (2018) Demand side management using chicken swarm optimization. In: International conference on intelligent networking and collaborative systems. Springer, Toronto, Canada, pp 155–165

  • Hashmi M, Hänninen S, Mäki K (2011) Survey of smart grid concepts, architectures, and technological demonstrations worldwide. IEEE PES Conference on innovative smart grid technologies Latin America (ISGT LA). IEEE, Medellin, pp 1–7

    Google Scholar 

  • Heidari AA, Mirjalili S, Faris H et al (2019) Harris Hawks optimization: algorithm and applications. Fut Gen Comput Syst. https://doi.org/10.1016/j.future.2019.02.028

    Article  Google Scholar 

  • Hydro, Waterloo North. https://www.wnhydro.com/en/your-home/time-of-use-rates.asp. Last visited: 06 June 2019

  • Jin Z, Kang C, Kai L (2010) Demand side management in China. IEEE PES Gen Meeting. IEEE, Providence, pp 4–7

    Google Scholar 

  • Khalid A, Javaid N, Mateen A (2016) Demand side management using hybrid bacterial foraging and genetic algorithm optimization techniques. 10th International Conference on Complex, Intelligent, and Software Intensive Systems Demand. IEEE, Fukuoka, pp 494–502

    Google Scholar 

  • Khalid A, Khan ZA, Javaid N (2019) Game theory based electric price tariff and salp swarm algorithm for demand side management. Fifth HCT Information Technology Trends (ITT). IEEE, Dubai, pp 1–5

    Google Scholar 

  • Khan S, Khan ZA, Javaid N, Shuja SM (2019a) Energy efficient scheduling of smart home. Advances in intelligent systems and computing. Springer, Cham, pp 67–79

    Google Scholar 

  • Khan ZA, Khalid A, Javaid N et al (2019b) Exploiting nature-inspired-based artificial intelligence techniques for coordinated day-ahead scheduling to efficiently manage energy in smart grid. IEEE Access 7:140102–140125. https://doi.org/10.1109/ACCESS.2019.2942813

    Article  Google Scholar 

  • Mahmood ANJ, NAK SR (2016) An optimized approach for home appliances scheduling in smart grid. 19th International Multi-Topic Conference (INMIC). IEEE, Islamabad, pp 1–5

    Google Scholar 

  • Mirjalili S (2015) Knowledge-based systems moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228–249. https://doi.org/10.1016/j.knosys.2015.07.006

    Article  Google Scholar 

  • Mirjalili S, Mirjalili SM, Lewis A (2014) Grey Wolf optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007

    Article  Google Scholar 

  • Recep C, Altas IH (2016) Scheduling of domestic shiftable loads via Cuckoo search optimization algorithm. 4th International Istanbul Smart Grid Congress and Fair (ICSG). IEEE, Istanbul, pp 16–19

    Google Scholar 

  • Rekha CBD, Vijayakumar V (2017) Genetic algorithm based demand side management for smart grid. Wirel Pers Commun 93:481–502. https://doi.org/10.1007/s11277-017-3959-z

    Article  Google Scholar 

  • Sethi BK, Mukherjee D, Singh D et al (2020) Smart home energy management system under false data injection attack. Int Trans Electr Energy Syst. https://doi.org/10.1002/2050-7038.12411

    Article  Google Scholar 

  • Shirazi E, Jadid S (2015) Optimal residential appliance scheduling under dynamic pricing scheme via HEMDAS. Energy Build 93:40–49. https://doi.org/10.1016/j.enbuild.2015.01.061

    Article  Google Scholar 

  • Shuja SM, Javaid N, Khan S, Khan ZA (2019a) Efficient scheduling of smart home appliances for energy management by cost and PAR optimization algorithm in smart grid efficient scheduling of smart home appliances for energy management by cost and PAR optimization algorithm in smart grid. In: Workshops of the international conference on advanced information networking and applications, pp 398–411

  • Shuja SM, Javaid N, Qasim U, Butt AA (2019b) Towards efficient scheduling of smart appliances for energy management by candidate solution updation algorithm in smart grid. International conference on advanced information networking and applications. Springer, Berlin Heidelberg, pp 67–81

    Google Scholar 

  • Ullah I, Javaid N, Khan ZA (2015) An incentive-based optimal energy consumption scheduling algorithm for residential users. Procedia - Procedia Comput Sci 52:851–857. https://doi.org/10.1016/j.procs.2015.05.142

    Article  Google Scholar 

  • Wu Z, Tazvinga H, Xia X (2015) Demand side management of photovoltaic-battery hybrid system. Appl Energy 148:294–304. https://doi.org/10.1016/j.apenergy.2015.03.109

    Article  Google Scholar 

  • Vos BA, Officer CT Effective Business Models for Demand Response under the Smart Grid Paradigm By Arthur Vos, Chief Technology Officer and VP Comverge. 4244

  • Yang X (2010) A new metaheuristic bat-inspired algorithm. Studies in computational intelligence. Springer, Berlin Heidelberg, pp 65–74

    Google Scholar 

Download references

Acknowledgements

Many thanks to the Electrical Engineering Department, Faculty of Sciences and Applied Sciences at Bouira University for financing this work. This work was conducted fully in Department of Electrical Engineering at University of Jaen; Spain.

Funding

This work was supported in part by the Exceptional National Program of Algeria PNE and the Key Program of Fundamental Research of Electrical Engineering Department at Jaen University, Spain 2020.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, University of Farhat Abbas, Sétif 1, Algeria

    Souhil Mouassa & Tarek Bouktir

  2. Department of Electrical Engineering, University of Jaén, 23700 EPS Linares, Jaén, Spain

    Souhil Mouassa & Francisco Jurado

Authors
  1. Souhil Mouassa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tarek Bouktir
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Francisco Jurado
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Souhil Mouassa.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mouassa, S., Bouktir, T. & Jurado, F. Scheduling of smart home appliances for optimal energy management in smart grid using Harris-hawks optimization algorithm. Optim Eng 22, 1625–1652 (2021). https://doi.org/10.1007/s11081-020-09572-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11081-020-09572-1

Keywords

Search

Navigation