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A Framework for Supporting Energy Transactions in Smart-Grid Environment

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IoT for Smart Grids

Part of the book series: Power Systems ((POWSYS))

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Abstract

With the increasing connection of Distributed Energy Resources (DER), traditional energy consumers are becoming prosumers, who can both consume and generate energy. This enables Peer-to-Peer (P2P) energy trading, where direct energy trading between small-scale DERs takes place. This chapter introduces a P2P platform based on market theory for supporting the energy trading in micro-grid environment. Rather than employing a centralized auction mechanism, the introduced solution follows a distributed approach, where auctions are initiated ad-hoc by energy producers. Experimental results based on real data validate the efficiency of proposed framework, as we achieve considerable energy savings.

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References

  1. Palensky, P., Dietrich, D.: Demand side management: demand response, intelligent energy systems, and smart loads. IEEE Trans. Ind. Inf. 7(3), 381–388 (2011). https://doi.org/10.1109/TII.2011.2158841

    Article  Google Scholar 

  2. Velik, R., Nicolay, P.: Grid-price-dependent energy management in microgrids using a modified simulated annealing triple-optimizer. Appl. Energy 130, 384–395 (2014)

    Article  Google Scholar 

  3. Dutta, G., Mitra, K.: A literature review on dynamic pricing of electricity. J. Oper. Res. Soc. 68(10), 1131–1145 (2017). https://doi.org/10.1057/s41274-016-0149-4

    Article  Google Scholar 

  4. Ren, H., Wu, Q., Gao, W., Zhou, W.: Optimal operation of a grid-connected hybrid pv/fuel cell/battery energy system for residential applications. Energy 113, 702–712 (2016). https://doi.org/10.1016/j.energy.2016.07.091, http://www.sciencedirect.com/science/article/pii/S0360544216310155

    Article  Google Scholar 

  5. Wan, C. et al.: Photovoltaic and solar power forecasting for smart grid energy management. CSEE J. Power Energy Syst. 1(4), 38–46 (2015). https://doi.org/10.17775/CSEEJPES.2015.00046

    Article  Google Scholar 

  6. Mohamed, F.A., Koivo, H.N.: Online management of microgrid with battery storage using multiobjective optimization. In: 2007 International Conference on Power Engineering, Energy and Electrical Drives, pp. 231–236 (2007). https://doi.org/10.1109/POWERENG.2007.4380118

  7. Clarke, J., Conner, S., Fujii, G., Geros, V., Jhannesson, G., Johnstone, C., Karatasou, S., Kim, J., Santamouris, M., Strachan, P.: The role of simulation in support of internet-based energy services. Energy Build. 36(8), 837–846 (2004)

    Article  Google Scholar 

  8. Magni, L., De Nicolao, G., Magnani, L., Scattolini, R.: A stabilizing model-based predictive control algorithm for nonlinear systems. Automatica 37(9), 1351–1362 (2001)

    Article  MathSciNet  Google Scholar 

  9. Mayne, D.Q., Rawlings, J.B., Rao, C.V., Scokaert, P.O.: Constrained model predictive control: stability and optimality. Automatica 36(6), 789–814 (2000)

    Article  MathSciNet  Google Scholar 

  10. Ramachandran B., et al.: An intelligent auction scheme for smart grid market using a hybrid immune algorithm. IEEE Trans. Ind. Electron. 58(10), 4603–4612 (2011). https://doi.org/10.1109/TIE.2010.2102319

    Article  Google Scholar 

  11. Wang, Y., Saad, W., Han, Z., Poor, H.V., Baar, T.: A game-theoretic approach to energy trading in the smart grid. IEEE Trans. Smart Grid 5(3), 1439–1450 (2014). https://doi.org/10.1109/TSG.2013.2284664

    Article  Google Scholar 

  12. Costa, L.M., Kariniotakis, G.: A stochastic dynamic programming model for optimal use of local energy resources in a market environment. In: 2007 IEEE Lausanne Power Tech, pp. 449–454 (2007). https://doi.org/10.1109/PCT.2007.4538359

  13. Khatib, T., Mohamed, A., Sopian, K.: Optimization of a pv/wind micro-grid for rural housing electrification using a hybrid iterative/genetic algorithm: Case study of kuala terengganu, malaysia. Energy Build. 47, 321–331 (2012). https://doi.org/10.1016/j.enbuild.2011.12.006. http://www.sciencedirect.com/science/article/pii/S0378778811006013

    Article  Google Scholar 

  14. Parisio, A., Rikos, E., Tzamalis, G., Glielmo, L.: Use of model predictive control for experimental microgrid optimization. Appli. Energy 115, 37–46 (2014). https://doi.org/10.1016/j.apenergy.2013.10.027, http://www.sciencedirect.com/science/article/pii/S0306261913008477

    Article  Google Scholar 

  15. Parisio, A., Rikos, E., Glielmo, L.: A model predictive control approach to microgrid operation optimization. IEEE Trans. Control Syst. Technol. 22(5), 1813–1827 (2014). https://doi.org/10.1109/TCST.2013.2295737

    Article  Google Scholar 

  16. Chaouachi, A., Kamel, R.M., Andoulsi, R., Nagasaka, K.: Multiobjective intelligent energy management for a microgrid. IEEE Trans. Ind. Electron. 60(4), 1688–1699 (2013). https://doi.org/10.1109/TIE.2012.2188873

    Article  Google Scholar 

  17. Tutkun, N.: Minimization of operational cost for an off-grid renewable hybrid system to generate electricity in residential buildings through the svm and the bcga methods. Energy Build. 76, 470–475 (2014). https://doi.org/10.1016/j.enbuild.2014.03.003, http://www.sciencedirect.com/science/article/pii/S0378778814002138

    Article  Google Scholar 

  18. Kyriakarakos, G., Dounis, A.I., Arvanitis, K.G., Papadakis, G.: A fuzzy logic energy management system for polygeneration microgrids. Renew. Energy 41, 315–327 (2012). https://doi.org/10.1016/j.renene.2011.11.019. http://www.sciencedirect.com/science/article/pii/S0960148111006215

    Article  Google Scholar 

  19. Morales, J.M., Conejo, A.J., Prez-Ruiz, J.: Short-term trading for a wind power producer. IEEE Trans. Power Syst. 25(1), 554–564 (2010). https://doi.org/10.1109/TPWRS.2009.2036810

    Article  Google Scholar 

  20. Multi-dimensional procurement auctions for power reserves: Butler Wilson, R., Chao, H.p. Robust incentive-compatible scoring and settlement rules. 22, 161–83 (2002)

    Google Scholar 

  21. Liu, T. et al.: Energy management of cooperative microgrids with p2p energy sharing in distribution networks. In: 2015 IEEE International Conference on Smart Grid Communications, pp. 410–415 (2015). https://doi.org/10.1109/SmartGridComm.2015.7436335

  22. Roadmap 2050 project (2018). http://www.roadmap2050.eu/

  23. Energy transition: The global energiewende (2018). https://energytransition.org/

  24. Liu, N., Yu, X., Wang, C., Li, C., Ma, L., Lei, J.: Energy-sharing model with price-based demand response for microgrids of peer-to-peer prosumers. IEEE Trans. Power Syst. 32(5), 3569–3583 (2017). https://doi.org/10.1109/TPWRS.2017.2649558

    Article  Google Scholar 

  25. Motalleb, M., Ghorbani, R.: Non-cooperative game-theoretic model of demand response aggregator competition for selling stored energy in storage devices. Appl. Energy 202, 581–596 (2017). https://doi.org/10.1016/j.apenergy.2017.05.186. http://www.sciencedirect.com/science/article/pii/S0306261917307481

    Article  Google Scholar 

  26. Gregoratti, D., Matamoros, J.: Distributed energy trading: the multiple-microgrid case. IEEE Trans. Ind. Electron. 62(4), 2551–2559 (2015). https://doi.org/10.1109/TIE.2014.2352592

    Article  Google Scholar 

  27. Zhang, C., Wu, J., Zhou, Y., Cheng, M., Long, C.: Peer-to-peer energy trading in a microgrid. Appl. Energy 220, 1–12 (2018). https://doi.org/10.1016/j.apenergy.2018.03.010, http://www.sciencedirect.com/science/article/pii/S0306261918303398

    Article  Google Scholar 

  28. Alam, M.R., St-Hilaire, M., Kunz, T.: An optimal p2p energy trading model for smart homes in the smart grid. Energy Effic. 10(6), 1475–1493 (2017). https://doi.org/10.1007/s12053-017-9532-5

    Article  Google Scholar 

  29. Liu, T., Tan, X., Sun, B., Wu, Y., Guan, X., Tsang, D.H.K.: Energy management of cooperative microgrids with p2p energy sharing in distribution networks. In: 2015 IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 410–415 (2015). https://doi.org/10.1109/SmartGridComm.2015.7436335

  30. Danassis, P., Siozios, K., Korkas, C., Soudris, D., Kosmatopoulos, E.: A low-complexity control mechanism targeting smart thermostats. Energy Build. 139, 340–350 (2017). https://doi.org/10.1016/j.enbuild.2017.01.013, http://www.sciencedirect.com/science/article/pii/S0378778817300555

    Article  Google Scholar 

  31. Algorithmic Game Theory. Cambridge University Press, Cambridge (2007). https://doi.org/10.1017/CBO9780511800481

    Google Scholar 

  32. Australian Energy Market Operator: Public energy price data. https://www.aemo.com.au/Electricity/National-Electricity-Market-NEM. Accessed 04 May 2019

  33. Public weather data. https://energyplus.net/weather. Accessed 04 May 2019

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Correspondence to Kostas Siozios .

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Siozios, K. (2019). A Framework for Supporting Energy Transactions in Smart-Grid Environment. In: Siozios, K., Anagnostos, D., Soudris, D., Kosmatopoulos, E. (eds) IoT for Smart Grids. Power Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-03640-9_11

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  • DOI: https://doi.org/10.1007/978-3-030-03640-9_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03169-5

  • Online ISBN: 978-3-030-03640-9

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