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Affine arithmetic-based methodology for energy hub operation-scheduling in the presence of data uncertainty

Affine arithmetic-based methodology for energy hub operation-scheduling in the presence of data uncertainty

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In this study, the role of self-validated computing for solving the energy hub-scheduling problem in the presence of multiple and heterogeneous sources of data uncertainties is explored and a new solution paradigm based on affine arithmetic is conceptualised. The benefits deriving from the application of this methodology are analysed in details, and several numerical results are presented and discussed.

References

    1. 1)
    2. 2)
    3. 3)
    4. 4)
    5. 5)
      • 5. Ghasemi, A., Hojjat, M., Javidi, M.H.: ‘Introducing a new framework for management of future distribution networks using potentials of energy hubs’. Proc. of 2012 Second Iranian Conf. on Smart Grids (ICSG), Teheran, Iran, 2012.
    6. 6)
      • 6. Schulze, M., Friedrich, L., Gautschi, M.: ‘Modeling and optimization of renewables: applying the energy hub approach’. Proc. of IEEE Int. Conf. on Sustainable Energy Technologies, 2008, ICSET, 2008, pp. 8388.
    7. 7)
      • 7. Fabrizio, E., Filippi, M., Torbino, M.: ‘Operational optimization of actual energy systems by means of the energy hub theory’. Proc. of Building Simulation 2011, 14–16 November 2011, Sydney, Australia, pp. 27932798.
    8. 8)
      • 8. Parisio, A., del Vecchio, C., Velotto, G.: ‘Robust optimization of operations in energy hub’. Proc. of the 2011 50th IEEE Conf. on Decision and Control and European Control Conf. (CDC-ECC), Orlando, FL, 12–15 December 2011, pp. 49434948.
    9. 9)
    10. 10)
      • 10. Boonchuay, C., Tomsovic, K., Li, F., Ongsakul, W.: ‘Robust optimization-based dc optimal power flow for managing wind generation uncertainty’. AIP Conf. Proc., 2012, vol. 1499, no. 1, pp. 3135.
    11. 11)
    12. 12)
      • 12. Alvarado, F.L., Hu, Y., Adapa, R.: ‘Uncertainty in power system modeling and computation’. Proc. IEEE Int. Conf. Systems, Man and Cybernetics, October 1992, vol. 1, pp. 754760.
    13. 13)
      • 13. de Rocquigny, E., Devictor, N., Tarantola, S.: ‘Uncertainty in industrial practice: a guide to quantitative uncertainty management’ (Wiley, New Jersey, 2008).
    14. 14)
      • 14. Barboza, L.V., Dimuro, G.P., Reiser, R.H.S.: ‘Towards interval analysis of the load uncertainty in power electric systems’. Proc. Eighth Int. Conf. Probabilistic Methods Applied to Power Systems, Ames, IA, Iowa State University, September 2004.
    15. 15)
      • 15. Bontempi, G.: ‘Simulating continuous dynamical systems with uncertainty: the probability and the possibility approaches. Fuzzy partial differential equations and relational equations’, in Nikravesh, M., Zadeh, L.A. (Eds.): ‘Series studies in fuzziness and soft computing’ (Physica-Verlag, Springer, 2003).
    16. 16)
      • 16. Shafer, G.: ‘A mathematical theory of evidence’ (Princeton University Press, Princeton, 1976), vol. 1.
    17. 17)
    18. 18)
    19. 19)
      • 19. Moore, R.: ‘Methods and applications of interval analysis’, SIAM, 1979, 2.
    20. 20)
      • 20. Barboza, L.V., Dimuro, G.P., Reiser, R.H.: ‘Towards interval analysis of the load uncertainty in power electric systems’. Proc. of the Int. Conf. on Probabilistic Methods Applied to Power Systems, 2004, pp. 538544.
    21. 21)
      • 21. Neher, M.: ‘From interval analysis to Taylor models – an overview’. Proc. of the Int. Association for Mathematics and Computers in Simulation, 2005.
    22. 22)
      • 22. Moore, B.R.: ‘Methods and applications of interval analysis’. SIAM Studies in Applied Mathematics, SIAM, Philadelphia, PA, 1975.
    23. 23)
      • 23. de Figueiredo, L.H., Stolfi, J.: ‘Self-validated numerical methods and applications’. Proc. Brazilian Mathematics Colloquium Monograph, IMPA, Rio de Janeiro, Brazil, 1997.
    24. 24)
    25. 25)
    26. 26)
      • 26. Wan, Y.H., Parsons, B.K.: ‘Factors relevant to utility integration of intermittent renewable technologies’. National Renewable Energy Laboratory, 1993.
    27. 27)
      • 27. Soman, S.S., Zareipour, H., Malik, O., Mandal, P.: ‘A review of wind power and wind speed forecasting methods with different time horizons’. Proc. of North American Power Symp. (NAPS), Arlington, TX, 2010.
    28. 28)
      • 28. Ninin, J., Messine, F.: ‘A mixed affine reformulation method for global optimization’. Toulouse Global Optimization (TOGO 2010), Toulouse, September 2010, pp. 101104.
    29. 29)
    30. 30)
      • 30. Teoh, C.-C., Sheble, G.B.: ‘Applying spot rate forecast technique in estimating energy price uncertainties’. Proc. of Int. Conf. on Probabilistic Methods Applied to Power Systems, PMAPS, Stockholm, Sweden, 2006.
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