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The Optimal Tariff Definition Problem for a Prosumers’ Aggregation

  • Antonio Violi
  • Patrizia Beraldi
  • Massimiliano Ferrara
  • Gianluca Carrozzino
  • Maria Elena Bruni
Chapter
Part of the AIRO Springer Series book series (AIROSS, volume 1)

Abstract

This paper deals with the problem faced by an aggregator in defining the optimal tariff structure for a group of prosumers aggregated within a coalition. The random nature of the main parameters involved in the decision process is explicitly accounted for by adopting the stochastic programming framework and, in particular, the paradigm of integrated chance constraints. Numerical experiments carried out on a realistic test case shows the efficacy of the proposed approach in providing more profitable rates for both consumers and producers with respect to the standard market alternatives.

Keywords

Microgrid Tariff definition Chance constraints 

Notes

Acknowledgements

This work has been partially supported by Italian Minister of Economic Development, Bando HORIZON 2020 PON I&C 2014–2020, with the grant for research project F/050159/01-03/X32 “Power Cloud: Tecnologie e Algoritmi nell’ambito dell’attuale quadro regolatorio del mercato elettrico verso un new deal per i consumatori e i piccoli produttori di energia da fonti rinnovabili”.

References

  1. 1.
    Burger, S., Chaves-Ávila, J.P., Batlle, C., Pérez-Arriag, I.J.: A review of the value of aggregators in electricity systems. Renew. Sustain. Energy Rev. 77, 395–405 (2017)CrossRefGoogle Scholar
  2. 2.
    Min, D., Ryu, J., Choi, D.G.: A long-term capacity expansion planning model for an electric power system integrating large-size renewable energy technologies. Comput. Oper. Res. (2017) (in press)Google Scholar
  3. 3.
    Beraldi, P., Violi, A., Carrozzino, G., Bruni, M.E.: The optimal energy procurement: a stochatic programming approach. In: Springer Proceedings in Mathematics and Statistics, International Conference on Optimization and Decision Science ODS2017, vol. 217, pp. 357–365 (2017)Google Scholar
  4. 4.
    Beraldi, P., Violi, A., Bruni, M.E., Carrozzino, G.: A probabilistically constrained approach for the energy procurement problem. Energies 10(12), Article no. 2179 (2017)CrossRefGoogle Scholar
  5. 5.
    Beraldi, P., Violi, A., Carrozzino, G., Bruni, M.E.: A stochastic programming approach for the optimal management of aggregated distributed energy resources. Comput. Oper. Res. (2018) (in press)Google Scholar
  6. 6.
    Carrión, M., Conejo, A.J., Arroyo, J.M.: Forward contracting and selling price determination for a retailer. IEEE Trans. Power Syst. 22(4), 2105–2114 (2007)CrossRefGoogle Scholar
  7. 7.
    Triki, C., Violi, A.: Dynamic pricing of electricity in retail markets. 4OR 7(1), 21–36 (2009)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Nojavan, S., Qesmati, H., Zare, K., Seyyedi, H.: Large consumer electricity acquisition considering time-of-use rates demand response programs. Arab. J. Sci. Eng. 39(12), 8913–8923 (2014)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Fotouhi Ghazvini, M.A., Soares, J., Morais, H., Castro, R., Vale, Z.: Dynamic pricing for demand response considering market price uncertainty. Energies 10, 1245 (2017)CrossRefGoogle Scholar
  10. 10.
    Fridgen, G., Kahlen, M., Ketter, W., Rieger, A., Thimmel, M.: An empirical analysis of electricity tariffs for residential microgrids. Appl. Energ. 210, 800–814 (2018)CrossRefGoogle Scholar
  11. 11.
    Beraldi, P., Violi, A., Carrozzino, G., Bruni, M.E.: The optimal electric energy procurement problem under reliability constraints. Energy Procedia 136, 283–289 (2017)CrossRefGoogle Scholar
  12. 12.
    Ruszczyski, A., Shapiro, A.: Stochastic programming. In: Handbook in Operations Research and Management Science, vol. 672. Elsevier Science, Amsterdam (2003)Google Scholar
  13. 13.
    Rockafellar, R., Uryasev, S.: Optimization of conditional value-at-risk. J. Risk 2, 21–41 (2000)CrossRefGoogle Scholar
  14. 14.
    Beraldi, P., Conforti, D., Triki, C., Violi, A.: Constrained auction clearing in the Italian electricity market. 4 OR 2(1), 35–51 (2004)Google Scholar
  15. 14.
    Menniti, D., Scordino, N., Sorrentino, N., Violi, A.: Short-term forecasting of day-ahead electricity market price. In: 2010 7th International Conference on the European Energy Market (2010)Google Scholar
  16. 15.
    Beraldi, P., De Simone, F., Violi, A.: Generating scenario trees: a parallel integrated simulation optimization approach. J. Comput. Appl. Math. 233(9), 2322–2331 (2010)MathSciNetCrossRefGoogle Scholar
  17. 16.
    Beraldi, P., Bruni, M.E.: A clustering approach for scenario tree reduction: An application to a stochastic programming portfolio optimization problem. TOP 22, 1–16 (2013)MathSciNetzbMATHGoogle Scholar
  18. 17.
    Beraldi, P., Violi, A., Ferrara, M., Carrozzino, G., Bruni, M.E.: A stochastic programming approach for microgrid tariff definition. Tech. Rep. TESEO Lab. 7, (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Antonio Violi
    • 1
    • 2
  • Patrizia Beraldi
    • 1
  • Massimiliano Ferrara
    • 3
    • 4
  • Gianluca Carrozzino
    • 1
  • Maria Elena Bruni
    • 1
  1. 1.DIMEGUniversity of CalabriaRende (CS)Italy
  2. 2.INNOVA s.r.l.Rome (RM)Italy
  3. 3.Decision LabDIGIEC, Mediterranean University of Reggio CalabriaReggio CalabriaItaly
  4. 4.ICRIOSBocconi UniversityMilanItaly

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