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SVM Methods for Optimal Management of a Virtual Power Plant

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Neural Nets and Surroundings

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 19))

Abstract

The current electrical grid is undergoing a deep renovation that poses new problems in terms of technologies, communication and control. The increasing level of penetration of renewable energy is leading towards the concept of distributed energy production, and it is expected that Virtual Power Plants (VPPs) will play an important role in the future smart grid. The stochastic nature of the power flows in the VPP, caused by the fluctuating availability of renewables, by the users’ demand and by the energy market price, complicates the task of power balancing for the VPP. This paper proposes the use of simple learning mechanisms to support power scheduling decisions and to improve a correct supply of the connected loads.

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References

  1. Lombardi, P., Powalko, M., Rudion, K.: Optimal operation of a virtual power. In: IEEE Power & Energy Society General Meeting (PES 2009) (2009)

    Google Scholar 

  2. El Bakari, K., Kling, W.L.: Virtual Power Plants: an answer to increasing distributed generation. In: Innovative Smart Grid Technologies Conference, Europe (2010)

    Google Scholar 

  3. European Commission, Community Research: New ERA for electricity in Europe. Distributed Generation: Key issues, challenges and proposed solutions, Brussels (2003)

    Google Scholar 

  4. Aloini, D., Crisostomi, E., Raugi, M., Rizzo, R.: Optimal Power Scheduling in a Virtual Power Plant. In: Innovative Smart Grid Technologies Conference, Europe (2011)

    Google Scholar 

  5. Mohsenian-Rad, A.-H., Leon-Garcia, A.: Optimal residential load control with price prediction in real-time electricity pricing environments. IEEE Transactions on Smart Grid 1(2) (2010)

    Google Scholar 

  6. Crisostomi, E., Tucci, M., Raugi, M.: Methods for Energy Price Prediction in the Smart Grid. In: Innovative Smart Grid Technologies Conference, Europe (2012)

    Google Scholar 

  7. Cartea, Á., Figueroa, M.G.: Pricing in Electricity Markets: A Mean Reverting Jump Diffusion Model with Seasonality. Applied Mathematical Finance 12(4), 313–335 (2005)

    Article  MATH  Google Scholar 

  8. GME (Gestore Mercati Energetici): Daily and Monthly Electricity Prices and Volumes (2012), http://www.mercatoelettrico.org/En

  9. Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)

    MATH  Google Scholar 

  10. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (2011)

    Google Scholar 

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Correspondence to Emanuele Crisostomi .

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Crisostomi, E., Tucci, M., Raugi, M. (2013). SVM Methods for Optimal Management of a Virtual Power Plant. In: Apolloni, B., Bassis, S., Esposito, A., Morabito, F. (eds) Neural Nets and Surroundings. Smart Innovation, Systems and Technologies, vol 19. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35467-0_27

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  • DOI: https://doi.org/10.1007/978-3-642-35467-0_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35466-3

  • Online ISBN: 978-3-642-35467-0

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