Skip to main content

Advertisement

SpringerLink
Log in

Enhanced leader particle swarm optimisation (ELPSO): a new algorithm for optimal scheduling of home appliances in demand response programs

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

Smart grids enable the residential consumers to have an active role in the management of their electricity consumption through home energy management (HEM) systems. HEM systems adjust the ON–OFF status and/or operation modes of home appliances under demand response programs, typically in a way that the electricity bill of the home is minimised and/or the peak load is minimised. This represents a constrained multi-objective optimisation problem with integer decision variables. The existing methodologies for optimal scheduling of home appliances have two drawbacks; most of them have not taken the consumers’ comfort into account and also powerful optimisation algorithms have not been used for solving this problem. In this paper, the problem of optimal scheduling of home appliances in HEM systems is formulated as a constrained, multi-objective optimisation problem with integer decision variables and a powerful variant of particle swarm optimisation, named as enhanced leader particle swarm optimisation (ELPSO) is proposed for solving this problem. Optimal scheduling of appliances is done for ten different scenarios that consider different demand response programs. The problem is solved for two different smart homes respectively with 10 and 11 appliances, both including electric vehicle as a big residential load. The results indicate the superiority of ELPSO over basic PSO, artificial bee colony, backtracking search algorithm, gravitational search algorithm and dragonfly algorithm. In the proposed multi-objective formulation, the effect of weight factor on optimal electricity bill of the home and optimal comfort of the consumers is meticulously investigated.

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

Similar content being viewed by others

References

  • Agnetis A, de Pascale G, Detti P, Vicino A (2013) Load scheduling for household energy consumption optimization. IEEE Trans Smart Grid 4:2364–2373

    Article  Google Scholar 

  • Arun S, Selvan M (2019) Smart residential energy management system for demand response in buildings with energy storage devices. Front Energy. https://doi.org/10.1007/s11708-018-0538-2

    Article  Google Scholar 

  • Alowaifeer M, Alamri A, Meliopoulos AS (2018) Reliability and cost impacts of home energy management systems. In: 2018 IEEE international conference on probabilistic methods applied to power systems (PMAPS), IEEE, pp 1–6

  • Asare-Bediako B, Kling W, Ribeiro P (2012) Home energy management systems: evolution, trends and frameworks. In: Universities power engineering conference (UPEC), 2012 47th international, IEEE, pp 1–5

  • Civicioglu P (2013) Backtracking search optimization algorithm for numerical optimization problems. Appl Math Comput 219:8121–8144

    MathSciNet  MATH  Google Scholar 

  • González-Briones A, De La Prieta F, Mohamad M, Omatu S, Corchado J (2018a) Multi-agent systems applications in energy optimization problems: a state-of-the-art review. Energies 11:1928

    Article  Google Scholar 

  • González-Briones A, Prieto J, De La Prieta F, Herrera-Viedma E, Corchado JM (2018b) Energy optimization using a case-based reasoning strategy. Sensors 18:865

    Article  Google Scholar 

  • Huang Y, Wang L, Guo W, Kang Q, Wu Q (2016) Chance constrained optimization in a home energy management system. IEEE Trans Smart Grid 9:252–260

    Article  Google Scholar 

  • Jordehi AR (2015) Enhanced leader PSO (ELPSO): a new PSO variant for solving global optimisation problems. Appl Soft Comput 26:401–417

    Article  Google Scholar 

  • Jordehi AR (2018) Enhanced leader particle swarm optimisation (ELPSO): an efficient algorithm for parameter estimation of photovoltaic (PV) cells and modules. Sol Energy 159:78–87

    Article  Google Scholar 

  • Jordehi AR (2019a) Optimisation of demand response in electric power systems, a review. Renew Sustain Energy Rev 103:308–319

    Article  Google Scholar 

  • Jordehi AR (2019b) Binary particle swarm optimisation with quadratic transfer function: A new binary optimisation algorithm for optimal scheduling of appliances in smart homes. Appl Soft Comput 78:465–480

    Article  Google Scholar 

  • Jordehi AR, Jasni J (2013) Parameter selection in particle swarm optimisation: a survey. J Exp Theor Artif Intell 25:527–542

    Article  Google Scholar 

  • Jordehi AR, Jasni J, Abd Wahab N, Kadir MZ, Javadi MS (2015) Enhanced leader PSO (ELPSO): a new algorithm for allocating distributed TCSC’s in power systems. Int J Electr Power Energy Syst 64:771–784

    Article  Google Scholar 

  • Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optim 39:459–471

    Article  MathSciNet  Google Scholar 

  • Khemakhem S, Rekik M, Krichen L (2019) Double layer home energy supervision strategies based on demand response and plug-in electric vehicle control for flattening power load curves in a smart grid. Energy 167:312–324

    Article  Google Scholar 

  • Ma K, Hu S, Yang J, Xu X, Guan X (2018) Appliances scheduling via cooperative multi-swarm PSO under day-ahead prices and photovoltaic generation. Appl Soft Comput 62:504–513

    Article  Google Scholar 

  • Mehrjerdi H, Bornapour M, Hemmati R, Ghiasi SMS (2019) Unified energy management and load control in building equipped with wind-solar-battery incorporating electric and hydrogen vehicles under both connected to the grid and islanding modes. Energy 168:919–930

    Article  Google Scholar 

  • Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput & Applic 27:1053–1073

    Article  Google Scholar 

  • Mohsenian-Rad A-H, Leon-Garcia A (2010) Optimal residential load control with price prediction in real-time electricity pricing environments. IEEE Trans Smart Grid 1:120–133

    Article  Google Scholar 

  • Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179:2232–2248

    Article  Google Scholar 

  • Setlhaolo D, Xia X (2015) Optimal scheduling of household appliances with a battery storage system and coordination. Energy Build 94:61–70

    Article  Google Scholar 

  • Setlhaolo D, Xia X, Zhang J (2014) Optimal scheduling of household appliances for demand response. Electr Power Syst Res 116:24–28

    Article  Google Scholar 

  • Shakeri M, Shayestegan M, Reza SS, Yahya I, Bais B, Akhtaruzzaman M, Sopian K, Amin N (2018) Implementation of a novel home energy management system (HEMS) architecture with solar photovoltaic system as supplementary source. Renew Energy 125:108–120

    Article  Google Scholar 

  • Sharifi AH, Maghouli P (2018) Energy management of smart homes equipped with energy storage systems considering the PAR index based on real-time pricing. Sustain Cities Soc 45:579–587

    Article  Google Scholar 

  • Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: The 1998 IEEE international conference on evolutionary computation proceedings, 1998. IEEE world congress on computational intelligence, IEEE, pp 69–73

  • Shokri Gazafroudi A, Soares J, Fotouhi Ghazvini MA, Pinto T, Vale Z, Corchado JM (2019) Stochastic interval-based optimal offering model for residential energy management systems by household owners. Int J Electr Power Energy Syst 105:201–219

    Article  Google Scholar 

  • Widén J (2014) Improved photovoltaic self-consumption with appliance scheduling in 200 single-family buildings. Appl Energy 126:199–212

    Article  Google Scholar 

  • Yao E, Samadi P, Wong VW, Schober R (2016) Residential demand side management under high penetration of rooftop photovoltaic units. IEEE Trans Smart Grid 7:1597–1608

    Article  Google Scholar 

  • Zhang D, Shah N, Papageorgiou LG (2013) Efficient energy consumption and operation management in a smart building with microgrid. Energy Convers Manag 74:209–222

    Article  Google Scholar 

  • Zhu J, Lin Y, Lei W, Liu Y, Tao M (2019) Optimal household appliances scheduling of multiple smart homes using an improved cooperative algorithm. Energy 171:944–955

    Article  Google Scholar 

Download references

Acknowledgements

The author declares that there is no potential conflict of interest for this paper.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Lashtenesha-Zibakenar Branch, Islamic Azad University, Lashtenesha, Iran

    Ahmad Rezaee Jordehi

Authors
  1. Ahmad Rezaee Jordehi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ahmad Rezaee Jordehi.

Additional information

Publisher’s Note

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

The Matlab source codes may be requested by Email to the corresponding author.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rezaee Jordehi, A. Enhanced leader particle swarm optimisation (ELPSO): a new algorithm for optimal scheduling of home appliances in demand response programs. Artif Intell Rev 53, 2043–2073 (2020). https://doi.org/10.1007/s10462-019-09726-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10462-019-09726-3

Keywords

Search

Navigation