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Managing Energy in Smart Homes Using Binary Particle Swarm Optimization

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Complex, Intelligent, and Software Intensive Systems (CISIS 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 611))

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Abstract

The greenhouse gas emission is increasing around the globe. In order to reduce its emission factor, the concept of microgrid is introduced, which integrates renewable energy sources. The microgrid has a point of common coupling which helps to exchange power with utility during different times of a day to meet load demand. Based on all the system constraints, an energy management strategy is proposed in this research work, which helps to minimize the power consumption peak and operating cost of microgrid. For this purpose the appliances of each smart home in the residential area and distributed generator of microgrid are scheduled using binary particle swarm optimization to economically meet the consumer demand considering the desired objectives. For this purpose, proposed strategy is employed for the economic energy management of homes and microgrid. Significance of the proposed strategy is proved through performing simulations.

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References

  1. Tayab, U.B., Roslan, M.A.B., Hwai, L.J., Kashif, M.: A review of droop control techniques for microgrid. Renew. Sustain. Energy Rev. 76, 717–727 (2017)

    Article  Google Scholar 

  2. Global Greenhouse Gas Emissions Data? 6 April 2017. https://www.epa.gov/ghgemissions/global-greenhouse-gas-emissions-data

  3. Zhou, B., Li, W., Chan, K.W., Cao, Y., Kuang, Y., Liu, X., Wang, X.: Smart home energy management systems: concept, configurations, and scheduling strategies. Renew. Sustain. Energy Rev. 61, 30–40 (2016)

    Article  Google Scholar 

  4. Liu, H., Ji, Y., Zhuang, H., Hongbin, W.: Multi-objective dynamic economic dispatch of microgrid systems including vehicle-to-grid. Energies 8(5), 4476–4495 (2015)

    Article  Google Scholar 

  5. Zhang, J., Yihong, W., Guo, Y., Wang, B., Wang, H., Liu, H.: A hybrid harmony search algorithm with differential evolution for day-ahead scheduling problem of a microgrid with consideration of power flow constraints. Appl. Energy 183, 791–804 (2016)

    Article  Google Scholar 

  6. Rasheed, M.B., Javaid, N., Ahmad, A., Awais, M., Khan, Z.A., Qasim, U., Alrajeh, N.: Priority and delay constrained demand side management in real-time price environment with renewable energy source. Int. J. Energ. Res. 40(14), 2002–2021 (2016)

    Article  Google Scholar 

  7. Wang, L., Wang, Z., Yang, R.: Intelligent multiagent control system for energy and comfort management in smart and sustainable buildings. IEEE Trans. Smart Grid 3(2), 605–617 (2012)

    Article  Google Scholar 

  8. Motevasel, M., Seifi, A.R.: Expert energy management of a micro-grid considering wind energy uncertainty. Energ. Convers. Manage. 83, 58–72 (2014)

    Article  Google Scholar 

  9. Lin, W.-M., Chia-Sheng, T., Tsai, M.-T.: Energy management strategy for microgrids by using enhanced bee colony optimization. Energies 9(1), 5 (2015)

    Article  Google Scholar 

  10. Zhang, D., Evangelisti, S., Lettieri, P., Papageorgiou, L.G.: Economic and environmental scheduling of smart homes with microgrid: DER operation and electrical tasks. Energ. Convers. Manage. 110, 113–124 (2016)

    Article  Google Scholar 

  11. Valle, D.Y., Venayagamoorthy, G.K., Mohagheghi, S., Hernandez, J.-C., Harley, R.G.: Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Trans. Evol. Comput. 12(2), 171–195 (2008)

    Article  Google Scholar 

  12. Beheshti, Z., Shamsuddin, S.M., Hasan, S.: Memetic binary particle swarm optimization for discrete optimization problems. Inf. Sci. 299, 58–84 (2015)

    Article  Google Scholar 

  13. Chen, C., Duan, S., Cai, T., Liu, B., Gangwei, H.: Smart energy management system for optimal microgrid economic operation. IET Renew. Power Gener. 5(3), 258–267 (2011)

    Article  Google Scholar 

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Authors and Affiliations

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

    Samia Abid, Ayesha Zafar, Rabiya Khalid, Sakeena Javaid & Nadeem Javaid

  2. Cameron Library, University of Alberta, Edmonton, AB, T6G 2J8, Canada

    Umar Qasim

  3. Computer Information Science, Higher Colleges of Technology, Fujairah, 4114, United Arab Emirates

    Zahoor Ali Khan

Authors
  1. Samia Abid
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  2. Ayesha Zafar
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  3. Rabiya Khalid
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  4. Sakeena Javaid
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  5. Umar Qasim
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  6. Zahoor Ali Khan
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  7. Nadeem Javaid
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Corresponding author

Correspondence to Nadeem Javaid .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Politècnico di Torino, Istituto Superiore Mario Boella, Turin, Italy

    Olivier Terzo

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Abid, S. et al. (2018). Managing Energy in Smart Homes Using Binary Particle Swarm Optimization. In: Barolli, L., Terzo, O. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2017. Advances in Intelligent Systems and Computing, vol 611. Springer, Cham. https://doi.org/10.1007/978-3-319-61566-0_18

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  • DOI: https://doi.org/10.1007/978-3-319-61566-0_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61565-3

  • Online ISBN: 978-3-319-61566-0

  • eBook Packages: EngineeringEngineering (R0)

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