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Demand Side Management Using Strawberry and Enhanced Differential Evolution Algorithms

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Advances in Network-Based Information Systems (NBiS 2017)

Abstract

Today, electricity is the most worthwhile resource which makes human life very easy. To overcome the gap among demand and supply of electricity, new techniques and methods are being explored. However, electricity demand is increasing constantly, which causes serious crisis. To tackle this problem, demand side management integrated with traditional grids through intercommunication between utility and customers. In this research work, we comparatively look over the two meta-heuristic algorithms: strawberry algorithm (SBA) and enhanced differential evolution (EDE) algorithm in terms of cost minimization, peak to average ratio reduction and maximizing user comfort. For electricity bill calculation, critical peak pricing (CPP) scheme is used. Simulation results show that both optimization techniques work significantly to achieve the desired objectives. SBA performs better then EDE in term of cost minimization while EDE performs better then SBA in terms of user comfort (UC) maximization, PAR reduction and energy consumption minimization.

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References

  1. Zhu, Z., Tang, J., Lambotharan, S., Chin, W., Fan, Z.: An integer linear programming based optimization for home demand-side management in smart grid. In: IEEE PES Innovat. Smart Grid Technol. (ISGT). Washington, DC 2012, pp. 1–5 (2012). doi:10.1109/ISGT.2012.6175785

  2. Ma, J., (Henry) Chen, H., Song, L., Li, Y.: IEEE Residential Load Scheduling in Smart Grid: A Cost Efficiency Perspective Digital Object Identifier. doi:10.1109/TSG.2015.2419818

  3. 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). doi:10.1109/TSG.2015.2435708

    Article  Google Scholar 

  4. Afar, S., Brotcorne, L., Marcotte, P., Savard, G.: Achieving an optimal trade-off between revenue and energy peak within a smart grid environment. Renew. Energy 91, 293–301 (2016). doi:10.1016/j.renene.2016.01.055. ISSN 0960–1481

    Article  Google Scholar 

  5. Khan, M.A., Javaid, N., Mahmood, A., Khan, Z.A., Alrajeh, N.: A generic demand-side management model for smart grid. doi:10.1002/er.3304

  6. Zhao, Z., Lee, W.C., Shin, Y., Song, K.-B.: An optimal power scheduling method for demand response in home energy management system digital object identifier 129, 452–470. doi:10.1109/TSG.2013.2251018

  7. Jalali, M.M., Kazemi, A.: Demand side management in a smart grid with multiple electricity suppliers. doi:10.1016/j.energy.2015.01.027

  8. 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). doi:10.1016/j.ijepes.2015.11.099. ISSN 0142–0615

    Article  Google Scholar 

  9. 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, 319 (2017)

    Article  Google Scholar 

  10. Rasheed, M.B., Javaid, N., Ahmad, A., Khan, Z.A., Qasim, U., Alrajeh, N.: An efficient power scheduling scheme for residential load management in smart homes. Appl. Sci. 5, 1134–1163 (2015). doi:10.3390/app5041134

    Article  Google Scholar 

  11. Mahmood, D., Javaid, N., Alrajeh, N., Khan, Z.A., Qasim, U., Ahmed, I., Ilahi, M.: Realistic scheduling mechanism for smart homes. MDPI Energies 9, 1–28 (2016)

    Google Scholar 

  12. 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 

  13. Merrikh-Bayat, F.: A numerical optimization algorithm inspired by the strawberry plant

    Google Scholar 

  14. Storn, R., Price, K.V.: Differential evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces. ICSI, TR-95-012, March 1995

    Google Scholar 

  15. Arafa, M., Sallam, E.A., Fahmy, M.M.: An enhanced differential evolution optimization algorithm. In: 2014 Fourth International Conference on Digital Information and Communication Technology and its Applications (DICTAP), pp. 216–225. IEEE, May 2014

    Google Scholar 

  16. Gul, M.S., Patidar, S.: Understanding the energy consumption and occupancy of a multi-purpose academic building. Energy Build. 87, 155–165 (2015)

    Article  Google Scholar 

  17. Logenthiran, T., Srinivasan, D., Shun, T.Z.: Demand side management in SG using heuristic optimization. IEEE Trans. Smart Grid 3(3), 1244–1252 (2012)

    Article  Google Scholar 

  18. Palensky, P., Dietrich, D.: Demand side management: demand response, intelligent energy systems, and smart loads. IEEE Trans. Ind. Inform. 7(3), 381–388 (2011)

    Article  Google Scholar 

  19. Lam, J.C., Li, D.H.: Electricity consumption characteristics in shopping malls in subtropical climates. Energy Convers. Manage. 44(9), 1391–1398 (2003)

    Article  Google Scholar 

  20. Gellings, C.W., Chamberlin, J.H.: Demand-side management, vols. 1–5. EPRI, Palo Alto, CA (1988)

    Google Scholar 

  21. Hashmi, M., Hnninen, S., Mki, K.: Survey of smart grid concepts, architectures, and technological demonstrations worldwide. In: 2011 IEEE PES Conference on Innovative Smart Grid Technologies (ISGT Latin America), pp. 1–7. IEEE, October 2011

    Google Scholar 

  22. Rahimi, F., Ipakchi, A.: Demand response as a market resource under the smart grid paradigm. IEEE Trans. Smart Grid 1(1), 82–88 (2010)

    Article  Google Scholar 

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

    Article  Google Scholar 

  24. Chiu, W.-Y., Sun, H., Poor, H.V.: Energy imbalance management using a robust pricing scheme. IEEE Trans. Smart Grid 4(2), 896–904 (2013)

    Article  Google Scholar 

  25. California Energy Commission, Energy Efficiency and Conservation: Trends and Policy Issues, May 2003. Report No. 100–03-008F

    Google Scholar 

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Correspondence to Nadeem Javaid .

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Sadiq, H.A., Khan, M.S., Ali, I., Ali, I., Pamir, Javaid, N. (2018). Demand Side Management Using Strawberry and Enhanced Differential Evolution Algorithms. In: Barolli, L., Enokido, T., Takizawa, M. (eds) Advances in Network-Based Information Systems. NBiS 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-319-65521-5_90

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  • DOI: https://doi.org/10.1007/978-3-319-65521-5_90

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65520-8

  • Online ISBN: 978-3-319-65521-5

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