Skip to main content

Advertisement

Log in

An Optimized Methodical Energy Management System for Residential Consumers Considering Price-Driven Demand Response Using Satin Bowerbird Optimization

  • Original Article
  • Published:
Journal of Electrical Engineering & Technology Aims and scope Submit manuscript

Abstract

Home energy management system (HEMS) is a section of demand response (DR), that plays an imperative role in the residential areas towards appliance management for the enhancement of energy efficiency and grid stability. In this article, a methodical home energy management system (Methodical-HEMS) was proposed based upon K-means, a machine learning algorithm and satin bowerbird optimization (SBO) algorithm to optimize the scheduling of appliances within a 24-h period. The K-means algorithm is used for defining the discrete comfort window (DCW) for schedulable appliance, while SBO algorithm is used for defining the suitable time slots for the schedulable appliance to operate within the DCW. Methodical-HEMS is considered for a single home with the day ahead time of use pricing, to minimize the overall electricity bill (EB) and to satisfy the consumer’s comfort. The performance of Methodical-HEMS is evaluated with other heuristic algorithms, including a particle swarm optimization algorithm, grey wolf optimization algorithm, artificial bee colony algorithm and genetic algorithm. The simulation outcomes demonstrate that, the SBO based HEMS algorithm effectually reduces the overall EB from ₹ 29.14/day to ₹ 22.84/day, minimizes the peak-to-average ratio by 10.28% and remains uncompromising on the consumer’s comfort.

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

Access this article

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

Instant access to the full article PDF.

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

Similar content being viewed by others

References

  1. Albadi MH, El-Saadany EF (2008) A summary of demand response in electricity markets. Electr Power Syst Res 78(11):1989–1996

    Article  Google Scholar 

  2. Awais M, Javaid N, Shaheen N, Iqbal Z, Rehman G, Muhammad K, Ahmad I (2015) An efficient genetic algorithm based demand side management scheme for smart grid. In: 18th international conference on network-based information systems, pp 351–356

  3. Bishop C (2006) Pattern recognition and machine learning. Springer, Berlin

    MATH  Google Scholar 

  4. Chellamani GK, Venkatesh Chandramani P (2019) Demand response management system with discrete time window using supervised learning algorithm. Cogn Syst Res 57:131–138. https://doi.org/10.1016/j.cogsys.2018.10.030

    Article  Google Scholar 

  5. Chen C, Kishore S, Snyder LV (2011) An innovative RTP-based residential power scheduling scheme for smart grids. In: Proceedings IEEE ICASSP, Prague, Czech Republic, pp 5956–5959

  6. Karaboga D, Akay B (2009) A comparative study of Artificial Bee Colony algorithm. Appl Math Comput 214(1):108–132

    MathSciNet  MATH  Google Scholar 

  7. Fei H, Li Q, Sun D (2017) A survey of recent research on optimization models and algorithms for operations management from the process view. Sci Program. https://doi.org/10.1155/2017/7219656

    Article  Google Scholar 

  8. Gan G, Ma C, Wu J (2007) Data clustering: theory, algorithms, and applications. SIAM, Society for Industrial and Applied Mathematics, Philadelphia. American Statistical Association, Alexandria

  9. Herter K, Wayland S (2010) Residential response to critical-peak pricing of electricity: California evidence. Energy 35(4):1561–1567

    Article  Google Scholar 

  10. Hussain HM, Javaid N, Iqbal S, Hasan QU, Aurangzeb K, Alhussein M (2018) An efficient demand side management system with a new optimized home energy management controller in smart grid. Energies 11(1):190

    Article  Google Scholar 

  11. Jung YG, Kang MS, Heo J (2014) Clustering performance comparison using k-means and expectation maximization algorithms. Biotechnol Biotechnol Equip 28(1):S44–S48

    Article  Google Scholar 

  12. Kennedy J, Eberhart R (1995) Particle swarm optimization. Proc IEEE Int Conf Neural Netw 6:1942–1948

    Article  Google Scholar 

  13. Khan SS, Ahmad A (2004) Cluster centre initialization algorithm for K-means clustering. Pattern Recogn Lett 25(11):1293–1302

    Article  Google Scholar 

  14. Liang Y, He L, Cao X, Shen Z (2013) Stochastic control for smart grid users with flexible demand. IEEE Trans Smart Grid 4(4):2296–2308

    Article  Google Scholar 

  15. Ma J, Chen HH, Song L, Li Y (2016) Residential load scheduling in smart grid: a cost efficiency perspective. IEEE Trans Smart Grid 7(2):771–784

    Google Scholar 

  16. Mahmood A, Javaid N, Khan MA, Razzaq S (2015) An overview of load management techniques in smart grid. Int J Energy Res 39(11):1437–1450

    Article  Google Scholar 

  17. Mahmood D, Javaid N, Alrajeh N, Khan ZA, Qasim U, Ahmed I, Ilahi M (2016) Realistic scheduling mechanism for smart homes. Energies 9:202. https://doi.org/10.3390/en9030202

    Article  Google Scholar 

  18. Mahmoudi N, Saha TK, Eghbal M (2014) Modelling demand response aggregator behavior in wind power offering strategies. Appl Energy 133:347–355

    Article  Google Scholar 

  19. Man KF, Tang KS, Kwong S (1996) Genetic algorithms: concepts and applications [in engineering design]. IEEE Trans Ind Electron 43(5):519–534

    Article  Google Scholar 

  20. Moosavi SHS, Bardsiri VK (2017) Satin bowerbird optimizer: a new optimization algorithm to optimize ANFIS for software development effort estimation. Eng Appl Artif Intell 60:1–15

    Article  Google Scholar 

  21. Negreiros M, Palhano A (2006) The capacitated centred clustering problem. Comput Oper Res 33(6):1639–1663

    Article  Google Scholar 

  22. Ozturk Y, Senthilkumar D, Kumar S, Lee G (2013) An intelligent home energy management system to improve demand response. IEEE Trans Smart Grid 4(2):694–701

    Article  Google Scholar 

  23. Rahim S, Javaid N, Ahmad A, Khan SA, Khan ZA, Alrajeh N, Qasim U (2016) Exploiting heuristic algorithms to efficiently utilize energy management controllers with renewable energy sources. Energy Build 129:452–470

    Article  Google Scholar 

  24. Rasheed MB, Javaid N, Awais M, Khan ZA, Qasim U, Alrajeh N, Iqbal Z, Javaid Q (2016) Real time information-based energy management using customer preferences and dynamic pricing in smart homes. Energies 9:542. https://doi.org/10.3390/en9070542

    Article  Google Scholar 

  25. Reddy SS (2017) Optimizing energy and demand response programs using multi-objective optimization. Electr Eng 99:397–406. https://doi.org/10.1007/s00202-016-0438-6

    Article  Google Scholar 

  26. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  27. Soares A, Gomes A, Henggeler Antunes C, Cardoso H (2013) Domestic load scheduling using genetic algorithms. In: Esparcia-Alcazar AI (ed) Applications of evolutionary computation, vol 7835, pp 142–151

    Chapter  Google Scholar 

  28. Souza Dutra MD, Anjos MF, Le Digabel S (2019) A realistic energy optimization model for smart-home appliances. Int J Energy Res 43(8):3237–3262

    Article  Google Scholar 

  29. Suri S (1989) Computing geodesic furthest neighbors in simple polygons. J Comput Syst Sci 39(2):220–235

    Article  MathSciNet  Google Scholar 

  30. Tsui KM, Chan SC (2012) Demand response optimization for smart home scheduling under real-time pricing. IEEE Trans Smart Grid 3(4):1812–1821

    Article  Google Scholar 

  31. Zhao B, Zhang X, Li P, Wang K, Xue M, Wang C (2014) Optimal sizing, operating strategy and operational experience of a stand-alone microgrid on Dongfushan Island. Appl Energy 113:1656–1666

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ganesh Kumar Chellamani.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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

Chellamani, G.K., Chandramani, P.V. An Optimized Methodical Energy Management System for Residential Consumers Considering Price-Driven Demand Response Using Satin Bowerbird Optimization. J. Electr. Eng. Technol. 15, 955–967 (2020). https://doi.org/10.1007/s42835-019-00338-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42835-019-00338-z

Keywords

Navigation