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Towards Heuristic Algorithms: GA, WDO, BPSO, and BFOA for Home Energy Management in Smart Grid

  • Mudassar Naseem
  • Samia Abid
  • Rabia Khalid
  • Ghulam Hafeez
  • Sardar Mahboob Hussain
  • Nadeem Javaid
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 2)

Abstract

In this paper, we analyse the scheduling of residential appliances to: 1) reduce cost, and 2) reduce Peak to Average Ratio (PAR) by smoothing load profile. We consider 10 different residential appliances which are categorized into three different groups: shiftable interruptible, shiftable uninterruptible and regular appliances to flexibly control the load. To schedule appliances, Home Energy Management (HEM) systems are designed by using four different heuristic algorithms: Bacterial Forging Optimization Algorithm (BFOA), Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO) and Wind Driven Optimization (WDO).

Keywords

Heuristic Algorithm Smart Grid Smart Home Demand Response Demand Side Management 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Deng, Ruilong, Zaiyue Yang, Mo-Yuen Chow, and Jiming Chen. “A survey on demand response in smart grids: Mathematical models and approaches.” IEEE Trans. Industrial Informatics 2015, 11, 570-582Google Scholar
  2. 2.
    Nguyen, Hung Khanh, Ju Bin Song, and Zhu Han. “Distributed demand side management with energy storage in smart grid.” IEEE Trans. Parallel and Distributed Systems 2015, 26, 3346-3357.Google Scholar
  3. 3.
    Bozchalui MC, Hashmi SA, Hassen H, Canizares CA, Bhattacharya K. “Optimal operation of residential energy hubs in smart grids.” IEEE Trans. Smart Grid 2012, 3, 1755-1766.Google Scholar
  4. 4.
    Roh, Hee-Tae, and Jang-Won Lee. “Residential demand response scheduling with multiclass appliances in the smart grid.” IEEE Trans. Smart Grid 2016, 7, 94-104.Google Scholar
  5. 5.
    Fernandes, Filipe, Tiago Sousa, Marco Silva, Hugo Morais, Zita Vale, and Pedro Faria. “Genetic algorithm methodology applied to intelligent house control.” Computational Intelligence Applications In Smart Grid (CIASG), 2011 IEEE Symposium on. IEEE, 2011.Google Scholar
  6. 6.
    Pedrasa, Michael Angelo A., Ted D. Spooner, and Iain F. MacGill. “Scheduling of demand side resources using binary particle swarm optimization” IEEE Trans. Power Systems 2009, 24, 1173-1181.Google Scholar
  7. 7.
    Das, Swagatam, Arijit Biswas, Sambarta Dasgupta, and Ajith Abraham “Bacterial foraging optimization algorithm: theoretical foundations, analysis, and applications.” Foundations of Computational Intelligence 2009, 3, 23-55.Google Scholar
  8. 8.
    Rasheed, Muhammad Babar, Nadeem Javaid, Ashfaq Ahmad, Zahoor Ali Khan, Umar Qasim, and Nabil Alrajeh. “An Efficient Power Scheduling Scheme for Residential Load Management in Smart Homes.” Applied Sciences 2015, 5, 1134-1163.Google Scholar
  9. 9.
    Maringer, Dietmar G. “Portfolio management with heuristic optimization.” Springer Science and Business Media 2006, 8Google Scholar
  10. 10.
    Shirazi, Elham, and Shahram Jadid. “Optimal residential appliance scheduling under dynamic pricing scheme via HEMDAS.” Energy and Buildings 2015, 93, 40-49.Google Scholar
  11. 11.
    Chrostopher O. Adika, LingfengWang “Smart charging and appliance scheduling approaches to demand side management” Electrical Power Systems 2014, 57, 232-240.Google Scholar
  12. 12.
    John S. Vardakas, Nizar Zorba, Christos V. Verikoukis “Power demand control scenarios for smart grid applications with finite number of appliance” Applied Energy 2016, 162, 83-98.Google Scholar
  13. 13.
    Sareen Althaher, Pierluigi Mancarella, Joseph Mutale “Automated Demand Response from Home Energy Management System Under Dynamic Pricing and Power and Comfort Constraints” IEEE Trans. Smart Grid 2015, 6, 1874 - 1883Google Scholar
  14. 14.
    Raj, Joshua Samuel, and S. Devi Priya. “Contribution of BFO in grid scheduling.” Computational Intelligence & Computing Research (ICCIC), IEEE International Conference on. IEEE, 2012.Google Scholar
  15. 15.
    Jun Li, Jianwu Dang, Feng Bu, Jiansheng Wang “Analysis and improvement of the bacterial foraging optimization algorithm.” Journal of Computing Science and Engineering 2014, 8, 1-10.Google Scholar
  16. 16.
    Oladeji, Olamide, and O. O. Olakanmi. “A genetic algorithm approach to energy consumption scheduling under demand response.” 6th International Conference on Adaptive Science and Technology (ICAST). IEEE, 2014.Google Scholar
  17. 17.
    Ten, Viktor, Zhandos Yessenbayev, Akmaral Shamshimova, and Albina Khakimova. “Optimized Small-Scaled Hybrid Energy Management of a Smart House Based on Genetic Algorithm” 14th International Conference on Machine Learning and Applications (ICMLA). IEEE, 2015.Google Scholar
  18. 18.
    Carpinelli, Guido, Shahab Khormali, Fabio Mottola, and Daniela Proto. “Optimal integration of distributed energy storage devices in smart grids.” IEEE Trans. smart grid 2013, 4, 985-995.Google Scholar
  19. 19.
    Del Valle Y, Venayagamoorthy GK, Mohagheghi S, Hernandez JC, Harley RG. “Particle swarm optimization: basic concepts, variants and applications in power systems.” IEEE Trans. evolutionary computation 2008, 12, 171-195.Google Scholar
  20. 20.
    Faria, Pedro, Joo Soares, Zita Vale, Hugo Morais, and Tiago Sousa. “Modified particle swarm optimization applied to integrated demand response and DG resources scheduling.” IEEE Trans. Smart Grid 2013, 4, 606-616.Google Scholar
  21. 21.
    Bayraktar, Zikri, Muge Komurcu, Jeremy A. Bossard, and Douglas H. Werner. “The wind driven optimization technique and its application in electromagnetics.” IEEE trans. antennas and propagation 2013, 61, 2745-2757.Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Mudassar Naseem
    • 1
  • Samia Abid
    • 1
  • Rabia Khalid
    • 1
  • Ghulam Hafeez
    • 1
  • Sardar Mahboob Hussain
    • 1
  • Nadeem Javaid
    • 1
  1. 1.COMSATS Institute of Information TechnologyIslamabadPakistan

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