Advertisement

Off-Line and On-Line Optimization Under Uncertainty: A Case Study on Energy Management

  • Allegra De Filippo
  • Michele Lombardi
  • Michela Milano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10848)

Abstract

Optimization problems under uncertainty arise in many application areas and their solution is very challenging. We propose here methods that merge off-line and on-line decision stages: we start with a two stage off-line approach coupled with an on-line heuristic. We improve this baseline in two directions: (1) by replacing the on-line heuristics with a simple anticipatory method; (2) by making the off-line component aware of the on-line heuristic. Our approach is grounded on a virtual power plant management system, where the load shifts can be planned off-line and the energy balance should be maintained on-line. The overall goal is to find the minimum cost energy flows at each point in time considering (partially shiftable) electric loads, renewable and non-renewable energy generators, and electric storages. We compare our models with an oracle operating under perfect information and we show that both our improved models achieve a high solution quality, while striking different trade-offs in terms of computation time and complexity of the off-line and on-line optimization techniques.

Keywords

Optimization Uncertainty Energy management 

References

  1. 1.
    Bai, H., Miao, S., Ran, X., Ye, C.: Optimal dispatch strategy of a virtual power plant containing battery switch stations in a unified electricity market. Energies 8(3), 2268–2289 (2015)CrossRefGoogle Scholar
  2. 2.
    Bent, R.W., Van Hentenryck, P.: Scenario-based planning for partially dynamic vehicle routing with stochastic customers. Oper. Res. 52(6), 977–987 (2004)CrossRefGoogle Scholar
  3. 3.
    Birge, J.R., Louveaux, F.: Introduction to Stochastic Programming. Series in Operations Research and Financial Engineering. Springer, New York (1997).  https://doi.org/10.1007/978-1-4614-0237-4CrossRefzbMATHGoogle Scholar
  4. 4.
    Bordin, C., Anuta, H.O., Crossland, A., Gutierrez, I.L., Dent, C.J., Vigo, D.: A linear programming approach for battery degradation analysis and optimization in offgrid power systems with solar energy integration. Renew. Energy 101, 417–430 (2017)CrossRefGoogle Scholar
  5. 5.
    Bracewell, R.N.: The Fourier Transform and its Applications, vol. 31999. McGraw-Hill, New York (1986)zbMATHGoogle Scholar
  6. 6.
    De Filippo, A., Lombardi, M., Milano, M., Borghetti, A.: Robust optimization for virtual power plants. In: Esposito, F., Basili, R., Ferilli, S., Lisi, F. (eds.) AI*IA 2017. LNCS, vol. 10640, pp. 17–30. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-70169-1_2CrossRefGoogle Scholar
  7. 7.
    Espinosa, A.N., Ochoa, L.N.: Dissemination document “low voltage networks models and low carbon technology profiles”. Technical report, University of Manchester, June 2015Google Scholar
  8. 8.
    Gamou, S., Yokoyama, R., Ito, K.: Optimal unit sizing of cogeneration systems in consideration of uncertain energy demands as continuous random variables. Energy Convers. Manag. 43(9), 1349–1361 (2002)CrossRefGoogle Scholar
  9. 9.
    Van Hentenryck, P., Bent, R.: Online Stochastic Combinatorial Optimization. The MIT Press, Cambridge (2009)zbMATHGoogle Scholar
  10. 10.
    Hodge, B.-M., Lew, D., Milligan, M., Holttinen, H., Sillanpää, S., Gómez-Lázaro, E., Scharff, R., Söder, L., Larsén, X.G., Giebel, G., et al.: Wind power forecasting error distributions: an international comparison. In: 11th Annual International Workshop on Large-Scale Integration of Wind Power into Power Systems as well as on Transmission Networks for Offshore Wind Power Plants Conference (2012)Google Scholar
  11. 11.
    Jurković, K., Pandšić, H., Kuzle, I.: Review on unit commitment under uncertainty approaches. In: 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pP. 1093–1097. IEEE (2015)Google Scholar
  12. 12.
    Kall, P., Wallace, S.W.: Stochastic Programming. Springer, Heidelberg (1994). ISBN 9780471951087zbMATHGoogle Scholar
  13. 13.
    Kaut, M., Wallace, S.W.: Evaluation of scenario-generation methods for stochastic programming. Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, Institut für Mathematik (2003)Google Scholar
  14. 14.
    Laporte, G., Louveaux, F.V.: The integer l-shaped method for stochastic integer programs with complete recourse. Oper. Res. Lett. 13(3), 133–142 (1993)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Mercier, L., Van Hentenryck, P.: Amsaa: a multistep anticipatory algorithm for online stochastic combinatorial optimization. In: Perron, L., Trick, M.A. (eds.) CPAIOR 2008. LNCS, vol. 5015, pp. 173–187. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-68155-7_15CrossRefzbMATHGoogle Scholar
  16. 16.
    Morales, J.M., Conejo, A.J., Madsen, H., Pinson, P., Zugno, M.: Integrating Renewables in Electricity Markets: Operational Problems, vol. 205. Springer, Boston (2013).  https://doi.org/10.1007/978-1-4614-9411-9CrossRefGoogle Scholar
  17. 17.
    Powell, W.B.: A unified framework for optimization under uncertainty. In: Optimization Challenges in Complex, Networked and Risky Systems, pp. 45–83. INFORMS (2016).  https://doi.org/10.1287/educ.2016.0149CrossRefGoogle Scholar
  18. 18.
    Palma-Behnke, R., Benavides, C., Aranda, E., Llanos, J., Sez, D.: Energy management system for a renewable based microgrid with a demand side management mechanism. In: 2011 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG), pp. 1–8, April 2011Google Scholar
  19. 19.
    Reddy, S.S., Sandeep, V., Jung, C.-M.: Review of stochastic optimization methods for smart grid. Front. Energy 11(2), 197–209 (2017)CrossRefGoogle Scholar
  20. 20.
    Kaplanis, S., Kaplani, E.: A model to predict expected mean and stochastic hourly global solar radiation i(h; nj) values. Renew. Energy 32(8), 1414–1425 (2007)CrossRefGoogle Scholar
  21. 21.
    Sahinidis, N.V.: Optimization under uncertainty: state-of-the-art and opportunities. Comput. Chem. Eng. 28(6), 971–983 (2004). FOCAPO 2003 Special issueCrossRefGoogle Scholar
  22. 22.
    Shapiro, A.: Sample average approximation. In: Gass, S.I., Fu, M.C. (eds.) Encyclopedia of Operations Research and Management Science, pp. 1350–1355. Springer, Boston (2013).  https://doi.org/10.1007/978-1-4419-1153-7CrossRefGoogle Scholar
  23. 23.
    Shapiro, A., Philpott, A.: A tutorial on stochastic programming. Manuscript (2007). www2.isye.gatech.edu/~ashapiro/publications.html
  24. 24.
    Wallace, S.W., Fleten, S.-E.: Stochastic programming models in energy. In: Stochastic Programming. Handbooks in Operations Research and Management Science, vol. 10, pp. 637–677. Elsevier (2003)Google Scholar
  25. 25.
    Winston, W.L., Goldberg, J.B.: Operations Research: Applications and Algorithms, vol. 3. Thomson Brooks/Cole, Belmont (2004)Google Scholar
  26. 26.
    Zhou, Z., Zhang, J., Liu, P., Li, Z., Georgiadis, M.C., Pistikopoulos, E.N.: A two-stage stochastic programming model for the optimal design of distributed energy systems. Appl. Energy 103, 135–144 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Allegra De Filippo
    • 1
  • Michele Lombardi
    • 1
  • Michela Milano
    • 1
  1. 1.DISIUniversity of BolognaBolognaItaly

Personalised recommendations