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Research Article

Game theory and hybrid genetic algorithm for energy management and real-time pricing in smart grid: the Tunisian case

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Pages 816-826 | Received 20 Apr 2020, Accepted 11 Jul 2020, Published online: 31 Jul 2020

References

  • Alagoz, B. B., and A. Kaygusuz. 2016. Dynamic energy pricing by closed-loop fractional-order PI control system and energy balancing in smart grid energy markets. Transactions of the Institute of Measurement and Control 38 (5):565–78. doi:10.1177/0142331215579949.
  • Alagoz, B. B., A. Kaygusuz, M. Akcin, and S. Alagoz. 2013. A closed-loop energy price controlling method for real-time energy balancing in a smart grid energy market. Energy 59:95–104. doi:10.1016/j.energy.2013.06.074.
  • Ali, A., D. Raisz, and K. Mahmoud. 2018. Optimal scheduling of electric vehicles considering uncertain RES generation using interval optimization. Electrical Engineering 100 (3):1675–87. doi:10.1007/s00202-017-0644-x.
  • Alqunun, K., T. Guesmi, and A. Farah. 2020. Load shedding optimization for economic operation cost in a microgrid. Electrical Engineering 1–13. doi:10.1007/s00202-019-00909-3.
  • Alsalloum, H., L. Merghem-Boulahia, and R. Rahim. 2020. Hierarchical system model for the energy management in the smart grid: A game theoretic approach. Sustainable Energy, Grids and Networks 21:100329. doi:10.1016/j.segan.2020.100329.
  • ANME. 2012. Rapport. Accessed December 26, 2019http://www.anme.nat.tn/fileadmin/user1/doc/DEP/Rapport_final__PST.pdf.
  • ANME. 2017. Rapport. Accessed December 30, 2019. https://www.itu.int/en/ITU-D/Regional-Presence/ArabStates/Documents/events/2017/ICTCC/Presentations/Session8/ANME_ICT%20%20CC_Tunisia%20_July%202017.pdf.
  • Aranizadeh, A., A. Zaboli, O. A. Gashteroodkhani, and B. Vahidi. 2019. Wind turbine and ultra-capacitor harvested energy increasing in microgrid using wind speed forecasting. Engineering Science and Technology, an International Journal 22 (5):1161–67. doi:10.1016/j.jestch.2019.08.006.
  • Bahceci, S., A. Dogan, T. Yalcinoz, and F. Daldaban. 2017. Energy storage system location selection for smart grid applications on distribution networks. Electrical Engineering 99 (1):357–66. doi:10.1007/S00202-016-0416-Z.
  • Belgana, A., B. P. Rimal, and M. Maier. 2014. Multi-objective pricing game among interconnected smart microgrids. IEEE PES General Meeting| Conference & Exposition, USA, 1–5 doi:10.1109/PESGM.2014.6938942.
  • Dkhili, N., J. Eynard, S. Thil, and S. Grieu. 2019. A survey of modelling and smart management tools for power grids with prolific distributed generation. Sustainable Energy, Grids and Networks. doi:10.1016/j.segan.2019.100284.
  • Dorahaki, S., M. Rashidinejad, M. Mollahassani-pour, and A. Bakhshai. 2019. An efficient hybrid structure to solve economic-environmental energy scheduling integrated with demand side management programs. Electrical Engineering 101 (4):1249–60. doi:10.1007/s00202-019-00866-x.
  • Elkhorchani, H., and K. Grayaa. 2016. Novel home energy management system using wireless communication technologies for carbon emission reduction within a smart grid. Journal of Cleaner Production 135:950–62. doi:10.1016/j.jclepro.2016.06.179.
  • GIZ. 2019. Rapport. Accessed December 25, 2019. http://www.tunisieindustrie.gov.tn/projets-ENR.php.
  • Hakimi, S. M., A. Hasankhani, M. Shafie-khah, and J. P. Catalão. 2020. Demand response method for smart microgrids considering high renewable energies penetration. Sustainable Energy, Grids and Networks 21:100325. doi:10.1016/j.segan.2020.100325.
  • Kotb, S. A., A. Sadat, and A. R. Adly. 2019. A proposed optimization scheme for the Egyptian electrical network generation mix based on cost reduction. Electrical Engineering 1–8. doi:10.1007/s00202-019-00883-w.
  • Kumar, N. M., M. R. Kumar, P. R. Rejoice, and M. Mathew. 2017. Performance analysis of 100 kWp grid connected Si-poly photovoltaic system using PVsyst simulation tool. Energy Procedia 117:180–89. doi:10.1016/j.egypro.2017.05.121.
  • Lokeshgupta, B., and S. Sivasubramani. 2019. Cooperative game theory approach for multi-objective home energy management with renewable energy integration. IET Smart Grid 2 (1):34–41. doi:10.1049/iet-stg.2018.0094.
  • Maddouri, M., A. Debbiche, H. Elkhorchani, and K. Grayaa. 2018. Game theory and hybrid genetic algorithm for energy management and real time pricing in smart grid. International Conference on IEEE Electrical Sciences and Technologies in Maghreb (CISTEM), Algeria, 1–6 doi:10.1109/CISTEM.2018.8613383.
  • Reddy, S. S. 2017. Optimal power flow with renewable energy resources including storage. Electrical Engineering 99 (2):685–95. doi:10.1007/s00202-016-0402-5.
  • Saad, W., Z. Han, and H. V. Poor. 2011. Coalitional game theory for cooperative micro-grid distribution networks. International conference on IEEE communications workshops (ICC), 1–5 doi:10.1109/iccw.2011.5963577.
  • Saad, W., Z. Han, H. V. Poor, and T. Basar. 2012. Game-theoretic methods for the smart grid: An overview of microgrid systems, demand-side management, and smart grid communications. IEEE Signal Processing Magazine, Kyoto, Japan 29 (5):86–105. https://www.researchgate.net/publication/258655739.
  • Samadi, P., A. H. Mohsenian-Rad, R. Schober, V. W. Wong, and J. Jatskevich. 2010. Optimal real-time pricing algorithm based on utility maximization for smart grid. 2010 First IEEE International Conference on Smart Grid Communications, IEEE, USA, 415–20 doi:10.1109/SMARTGRID.2010.5622077.
  • Sharma, T., and P. Balachandra. 2019. Model based approach for planning dynamic integration of renewable energy in a transitioning electricity system. International Journal of Electrical Power & Energy Systems 105:642–59. doi:10.1016/j.ijepes.2018.09.007.
  • STEG. 2018. Rapport. Accessed December 11, 2019. https://www.steg.com.tn/fr/institutionnel/publication/rapport_act2018/Rapport_Annuel_steg_2018_fr.pdf.
  • UNDP. 2014. Rapport. Accessed December 15, 2019. https://www.undp.org/content/dam/undp/library/Environment%20and%20Energy/Climate%20Strategies/DREI%20Tunisia%20Report_Summary_French_24Mar15.pdf.
  • Vakili, R., S. Afsharnia, and S. Golshannavaz. 2018. Interconnected microgrids: Optimal energy scheduling based on a game‐theoretic approach. International Transactions on Electrical Energy Systems 28 (10):e2603. doi:10.1002/etep.2603.
  • Worighi, I., A. Maach, A. Hafid, O. Hegazy, and J. Van Mierlo. 2019. Integrating renewable energy in smart grid system: Architecture, virtualization and analysis. Sustainable Energy, Grids and Networks 18:100226. doi:10.1016/j.segan.2019.100226.
  • Zhang, J., and Y. Zheng. 2020. The flexibility pathways for integrating renewable energy into China’s coal dominated power system: The case of Beijing-Tianjin-Hebei region. Journal of Cleaner Production 245:118925. doi:10.1016/j.jclepro.2019.118925.
  • Zhao, T., F. H. Choo, L. Zhang, Y. Gu, and P. Wang. 2017. Game theory based distributed energy trading for microgrids parks. Asian Conference on IEEE Energy, Power and Transportation Electrification (ACEPT)(2017), Singapore, 1–7 doi:10.1109/ACEPT.2017.8168563.

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