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Scenario Optimization of Complex Water Supply Systems for Energy Saving and Drought-Risk Management

  • Jacopo NapolitanoEmail author
  • Giovanni M. Sechi
Conference paper
  • 23 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11973)

Abstract

The management of complex water supply systems needs a close attention to economic aspects concerning high costs related to energy requirements in water transfers. Specifically, the optimization of activation schedules of water pumping plants is an important issue, especially managing emergency and costly water transfers under drought-risk. In such optimization context under uncertainty conditions, it is crucial to assure simultaneously energy savings and water shortage risk alleviating measures. The model formulation needs to highlight these requirements duality to guarantee an adequate water demand fulfillment respecting an energy saving policy. The proposed modeling approach has been developed using a two stages scenario optimization in order to consider a cost-risk balance, and to achieve simultaneously energy and operative costs minimization assuring an adequate water demand fulfillment for users. The optimization algorithm has been implemented using GAMS interfaced with CPLEX solvers. An application of the proposed optimization approach has been tested considering a water supply system located in a drought-prone area in North-West Sardinia (Italy). By applying this optimization procedure, a robust strategy in pumping activation was obtained for this real case water supply system.

Keywords

Scenario analysis Energy optimization Water management 

References

  1. 1.
    Ahuja, R., Magnanti, T., Orlin, J.: Network Flows: Theory, Algorithms, and Applications. Prentice Hall, Englewood Cliffs (1993)zbMATHGoogle Scholar
  2. 2.
    D’Ambrosio, C., Lodi, A., Wiese, S., Bragalli, C.: Mathematical programming techniques in water network optimization. Eur. J. Oper. Res. 243(3), 774–788 (2015)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Diestel, R.: Graph Theory, 3rd edn. Springer, New York (2005)zbMATHGoogle Scholar
  4. 4.
    GAMS: A user’s guide. GAMS Development Corporation. Washington DC, USA (2008)Google Scholar
  5. 5.
    Jensen, P., Barnes, J.: Network Flow Programming. Wiley, New York (1980)zbMATHGoogle Scholar
  6. 6.
    Kang, D., Lansey, K.: Multiperiod planning of water supply infrastructure based on scenario analysis. ASCE J. Water Resour. Plann. Manag. 140, 40–54 (2014)CrossRefGoogle Scholar
  7. 7.
    IBM: Cplex Optimization Studio (2017). http://www-03.ibm.com/software
  8. 8.
    Lerma, N., Paredes-Arquiola, J., Andreu, J., Solera, A., Sechi, G.M.: Assessment of evolutionary algorithms for optimal operating rules design in real water resource systems. Environ. Model Softw. 69, 425–436 (2015)CrossRefGoogle Scholar
  9. 9.
    Napolitano, J., Sechi, G.M., Zuddas, P.: Scenario optimization of pumping schedules in complex water supply system considering a cost-risk balancing approach. Water Resour. Manag. 30, 5231–5246 (2016)CrossRefGoogle Scholar
  10. 10.
    Nault, J., Papa, F.: Lifecycle assessment of a water distribution system pump. ASCE J. Water Resour. Plann. Manag. 141(12), A4015–004 (2015)Google Scholar
  11. 11.
    Pallottino, S., Sechi, G.M., Zuddas, P.: A DSS for water resource management under uncertainty by scenario analysis. Water Resour. Manag. 28(12), 3975–3987 (2014)CrossRefGoogle Scholar
  12. 12.
    Pasha, M.F.K., Lansey, K.: Strategies to develop warm solutions for real-time pump scheduling for water distribution systems. Environ. Model Softw. 20, 1031–1042 (2004)Google Scholar
  13. 13.
    RAS: Piano stralcio di bacino regionale per l’utilizzazione delle risorse idriche. Regione autonoma della Sardegna, Italy (2006)Google Scholar
  14. 14.
    Rockafellar, R.T., Wets, R.J.B.: Scenario and policy aggregation in optimization under uncertainty. Math. Oper. Res. 16, 119–147 (1991)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Sassu, E., Zucca, R., Sechi, G.M.: Calibration of regional flow-duration curves evaluating water resource withdrawal from diversion dam. In: Garrote, L., Tsakiris, G., Tsihrintzis, V.A., Vangelis, H., Tigkas, D. (eds.) Managing Water Resources for a Sustainable Future, Proceedings of the 11th World Congress of EWRA on Water Resource and Environment, Madrid, 25–29 June 2019 (2019)Google Scholar
  16. 16.
    Sechi, G.M., Gaivoronski, A.A., Napolitano, J.: Optimizing pumping activation in multi-reservoir water supply systems under uncertainty with stochastic quasi-gradient methods. Water Resour. Manag. 33(2), 1881–1895 (2019)CrossRefGoogle Scholar
  17. 17.
    Sechi, G.M., Sulis, A.: Water system management through a mixed optimization-simulation approach. ASCE J. Water Resour. Plann. Manag. 135, 160–170 (2009)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of CagliariCagliariItaly

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