Skip to main content
SpringerLink
Log in
Book cover

International Conference on Intelligent Networking and Collaborative Systems

INCoS 2017: Advances in Intelligent Networking and Collaborative Systems pp 505–514Cite as

Pigeon Inspired Optimization and Enhanced Differential Evolution in Smart Grid Using Critical Peak Pricing

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 8))

Abstract

In this paper, we have evaluated the performance of heuristic algorithms; Enhanced Differential Evolutionary (EDE) and Pigeon Inspired Optimization(PIO) for Demand Side Management (DSM). Moreover, Critical Peak Pricing (CPP) is used as a price traffic. The main purpose of this paper is to reduce Peak to Average Ratio (PAR) and electricity cost by scheduling appliances according to categories and constraints. Simulation results demonstrate that PIO outperforms in terms of user comfort.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Bobmann, T., Staffell, I.: The shape of future electricity demand: exploring load curves in 2050s Germany and Britain. Energy 90(2), 1317–1333 (2015)

    Google Scholar 

  2. Bouvier, S.: Deploying a smarter grid through cable solutions and services. Accessed 18 May 2017

    Google Scholar 

  3. Yan, Y., Qian, Y., Sharif, H., Tipper, D.: A survey on smart grid communication infrastructures: motivations, requirements and challenges. IEEE Commun. Surv. Tutor. 15, 5–20 (2013)

    Article  Google Scholar 

  4. Ma, R., Chen, H.H., Huang, Y.R., Meng, W.: Smart grid communication: its challenges and opportunities. IEEE Trans. Smart Grid 4, 36–46 (2013)

    Article  Google Scholar 

  5. Ipakchi, A., Albuyeh, F.: Grid of the future. IEEE Power Energy 7, 52–62 (2009)

    Article  Google Scholar 

  6. Nguyen, H.K., Song, J.B., Han, Z.: Distributed demand side management with energy storage in smart grid. IEEE Trans. Parallel Distrib. Syst. 26, 3346–3357 (2015)

    Article  Google Scholar 

  7. Goran, S.: Demand side management: benefits and challenges. Energy Policy 36(12), 4419–4426 (2008)

    Article  Google Scholar 

  8. Yao, E., Samadi, P., Wong, V.W.S., Schober, R.: Residential demand side management under high penetration of rooftop photovoltaic units. IEEE Trans. Smart Grid 7(3), 1597–1608 (2016)

    Article  Google Scholar 

  9. Chiu, T.C., Shih, Y.Y., Pang, A.C., Pai, C.W.: Optimized day-ahead pricing with renewable energy demand-side management for smart grids. IEEE Internet Things J. PP(99), 1–10 (2016)

    Google Scholar 

  10. Jagruti, T., Basab, C.: Demand side management in developing nations: a mitigating tool for energy imbalance and peak load management. Energy 114(1), 895–912 (2016)

    Google Scholar 

  11. Mohsenian-Rad, A.H., Wong, V.W.S., Jatskevich, J., Schober, R., Leon-Garcia, A.: Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid. IEEE Trans. Smart Grid 1(3), 320–331 (2010)

    Article  Google Scholar 

  12. Thillainathan, L., Dipti, S., Zong, S.T.: Demand side management in smart grid using heuristic optimization. IEEE Trans. Smart Grid 3(3), 1244–1252 (2012)

    Article  Google Scholar 

  13. Logenthiran, T., et al.: Multi-Agent System (MAS) for short-term generation scheduling of a microgrid. In: 2010 IEEE International Conference on Sustainable Energy Technologies (ICSET). IEEE (2010)

    Google Scholar 

  14. Ghazvini, M.A.F., Faria, P., Ramos, S., Morais, H., Vale, Z.: Incentive-based demand response programs designed by asset-light retail electricity providers for the day-ahead market. Energy 82, 786–799 (2015)

    Article  Google Scholar 

  15. U.S. Department of Energy: Benefits of demand response in electricity markets and recommendations for achieving them (2006)

    Google Scholar 

  16. Boisvert, R., Cappers, P., Neenan, B.: The benefits of customer participation in wholesale electricity markets. Electricity 15(3), 44–51 (2002)

    Google Scholar 

  17. Hong, S.H., Yu, M., Huang, X.: A real-time demand response algorithm for heterogeneous devices in buildings and homes. Energy 80, 123–132 (2015)

    Article  Google Scholar 

  18. Gao, D., Sun, Y., Lu, Y.: A robust demand response control of commercial buildings for smart grid under load prediction uncertainty. Energy 93(1), 275–283 (2015)

    Article  Google Scholar 

  19. Zhu, Z., Tang, J., Lambotharan, S., Chin, W.H., Fan, Z.: An integer linear programming based optimization for home demand-side management in smart grid. In: 2012 IEEE PES Innovative Smart Grid Technologies (ISGT), pp. 1–5. IEEE (2012)

    Google Scholar 

  20. Samadi, P., Wong, V.W.S., Schober, R.: Load scheduling and power trading in systems with high penetration of renewable energy resources. IEEE Trans. Smart Grid 7(4), 1802–1812 (2016)

    Article  Google Scholar 

  21. Zhao, Z., Lee, W.C., Shin, Y., Song, K.B.: An optimal power scheduling method for demand response in home energy management system. IEEE Trans. Smart Grid 4(3), 1391–1400 (2013)

    Article  Google Scholar 

  22. Javaid, N., Javaid, S., Abdul, W., Ahmed, I., Almogren, A., Alamri, A., Niaz, I.A.: A hybrid genetic wind driven heuristic optimization algorithm for demand side management in smart grid. Energies 10(3), 319 (2017)

    Article  Google Scholar 

  23. Ma, K., Yao, T., Yang, J., Guan, X.: Residential power scheduling for demand response in smart grid. Int. J. Electr. Power Energy Syst. 78, 320–325 (2016)

    Article  Google Scholar 

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

    Article  Google Scholar 

  25. Srinivasan, D., Rajgarhia, S., Radha Krishnan, B.M., Sharma, A., Khincha, H.P.: Game-theory based dynamic pricing strategies for demand side management in smart grids. Energy 126, 132–143 (2016). Elsevier

    Article  Google Scholar 

  26. Mahmood, D., Javaid, N., Alrajeh, N., Khan, Z.A., Qasim, U., Ahmed, I., Ilahi, M.: Realistic scheduling mechanism for smart homes. Energies 9(3), 202 (2016)

    Article  Google Scholar 

  27. Ahmad, A., Javaid, N., Alrajeh, N., Khan, Z.A., Qasim, U.: Khan, A.: A modified feature selection and artificial neural network-based day-ahead load forecasting model for a smart grid. Appl. Sci. 5(4), 1756–1772 (2016)

    Article  Google Scholar 

  28. Rasheed, M.B., Javaid, N., Awais, M., Khan, Z.A., Qasim, U., Alrajeh, N., Iqbal, Z., Javaid, Q.: Real time information based energy management using customer preferences and dynamic pricing in smart homes. Energies 9(7), 542 (2016)

    Article  Google Scholar 

  29. Khan, M.A., Javaid, N., Mahmood, A., Khan, Z.A., Alrajeh, N.: A generic demand-side management model for smart grid. Int. J. Energy Res. 39(7), 954–964 (2015)

    Article  Google Scholar 

  30. Karimi-Nasab, M., Modarres, M., Seyedhoseini, S.M.: A self-adaptive PSO for joint lot sizing and job shop scheduling with compressible process times. Appl. Soft Comput. 28, 137–147 (2015)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. COMSATS Institute of Information Technology, Islamabad, 44000, Pakistan

    Zunaira Amjad, Saadia Batool, Hafsa Arshad, Komal Parvez, Mashab Farooqi & Nadeem Javaid

Authors
  1. Zunaira Amjad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Saadia Batool
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hafsa Arshad
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Komal Parvez
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mashab Farooqi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nadeem Javaid
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nadeem Javaid .

Editor information

Editors and Affiliations

  1. Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Department of Computer Science, Ryerson University, Toronto, Ontario, Canada

    Isaac Woungang

  3. School of Business, UNSW Australia, Canberra, Aust Capital Terr, Australia

    Omar Khadeer Hussain

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Amjad, Z., Batool, S., Arshad, H., Parvez, K., Farooqi, M., Javaid, N. (2018). Pigeon Inspired Optimization and Enhanced Differential Evolution in Smart Grid Using Critical Peak Pricing. In: Barolli, L., Woungang, I., Hussain, O. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-319-65636-6_45

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-65636-6_45

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65635-9

  • Online ISBN: 978-3-319-65636-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation