Skip to main content

Part of the book series: AIRO Springer Series ((AIROSS,volume 4))

  • 357 Accesses

Abstract

This Chapter ranges through a wide variety of production and demand management problems related to energy commodities systems. The Oil and Gas production enhancement is discussed via oil wells optimal placement as well as water and gas optimal injection, namely waterflooding and gas lift. Further is analyzed the optimal schedule and design of commodities generation in energy hubs and combined cooling, heat and power systems, reaching finally the chemical processes where the specification of the products is taken into account, with mixing tanks, pools, and blending points. Overall, each of these problems is qualitatively discussed identifying the typical objective functions, variables and constraints generalizing its structure, the problem typology is identified as well as the most common methods to solve it.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. F.A. Aliev, M.K. Ilyiasov, M.A. Dzhamelbekov, Modelling of operation of the gaslift borehole cavity. Technical Report 4, Dokl. NANA (2008)

    Google Scholar 

  2. F.A. Aliev, N.A. Ismailov, N.S. Mukhtarova, Algorithm to determine the optimal solution of a boundary control problem. Autom. Remote. Control. 76(4), 627–633 (2015)

    Article  MathSciNet  Google Scholar 

  3. M. Alipour, B. Mohammadi-Ivatloo, K. Zare, Stochastic risk-constrained short-term scheduling of industrial cogeneration systems in the presence of demand response programs. Appl. Energy 136(Supplement C), 393–404 (2014)

    Google Scholar 

  4. R. Baltean-Lugojan, R. Misener, Piecewise parametric structure in the pooling problem: from sparse strongly-polynomial solutions to NP-hardness. J. Glob. Optim. 71, 655–690 (2018)

    Article  MathSciNet  Google Scholar 

  5. N. Boland, T. Kalinowski, F. Rigterink, A polynomially solvable case of the pooling problem. J. Glob. Optim. 67(3), 621–630 (2017)

    Article  MathSciNet  Google Scholar 

  6. G. Cardoso, M. Stadler, A. Siddiqui, C. Marnay, N. DeForest, A. Barbosa-Póvoa, P. Ferrão, Microgrid reliability modeling and battery scheduling using stochastic linear programming. Electr. Power Syst. Res. 103(Supplement C), 61–69 (2013)

    Google Scholar 

  7. S.S. Dey, A. Gupte, Analysis of MILP techniques for the pooling problem. Oper. Res. 63(2), 412–427 (2015)

    Article  MathSciNet  Google Scholar 

  8. S.S. Dey, B. Kocuk, A. Santana, A study of rank-one sets with linear side constraints and application to the pooling problem. Preprint. arXiv:1902.00739 (2019)

    Google Scholar 

  9. R.M. Fonseca, O. Leeuwenburgh, P.M.J. Van den Hof, J.D. Jansen, Ensemble-based hierarchical multi-objective production optimization of smart wells. Comput. Geosci. 18(3), 449–461 (2014)

    Article  MathSciNet  Google Scholar 

  10. D. Haugland, The computational complexity of the pooling problem. J. Global Optim. 64(2), 199–215 (2016)

    Article  MathSciNet  Google Scholar 

  11. D. Haugland, E.M.T. Hendrix, Pooling problems with polynomial-time algorithms. J. Optim. Theory Appl. 170(2), 591–615 (2016)

    Article  MathSciNet  Google Scholar 

  12. T.B. Jönsthövel, M.B. van Gijzen, S. MacLachlan, C. Vuik, A. Scarpas, Comparison of the deflated preconditioned conjugate gradient method and algebraic multigrid for composite materials. Comput. Mech. 50(3), 321–333 (2012)

    Article  MathSciNet  Google Scholar 

  13. A. Marandi, E. de Klerk, J. Dahl, Solving sparse polynomial optimization problems with chordal structure using the sparse bounded-degree sum-of-squares hierarchy. Discret. Appl. Math. 275, 95–110 (2020)

    Article  MathSciNet  Google Scholar 

  14. A.H. Mirzadzhanzade, I.M. Ametov, A.M. Khasaev, Technology and machinery of oil extraction. Technical report, All-Union Scientific-Research Institute at the Ministerium of the Petroleum IndustryMoscow (1986)

    Google Scholar 

  15. R. Misener, C.A. Floudas, GloMIQO: global mixed-integer quadratic optimizer. J. Global Optim. 57(1), 3–50 (2013)

    Article  MathSciNet  Google Scholar 

  16. R. Misener, J.P. Thompson, C.A. Floudas, APOGEE: global optimization of standard, generalized, and extended pooling problems via linear and logarithmic partitioning schemes. Comput. Chem. Eng. 35(5), 876–892 (2011)

    Article  Google Scholar 

  17. R. Misener, C.A. Floudas, Antigone: algorithms for continuous/integer global optimization of nonlinear equations. J. Global Optim. 59(2), 503–526 (2014)

    Article  MathSciNet  Google Scholar 

  18. L. Moretti, E. Martelli, G. Manzolini, An efficient robust optimization model for the unit commitment and dispatch of multi-energy systems and microgrids. Appl. Energy 261(C) (2019). https://doi.org/10.1016/j.apenergy.2019.113859

  19. R. Nabben, C. Vuik, A comparison of deflation and coarse grid correction applied to porous media flow. SIAM J. Numer. Anal. 42(4), 1631–1647 (2004)

    Article  MathSciNet  Google Scholar 

  20. C. Vuik, A. Segal, J.A. Meijerink, An efficient preconditioned CG method for the solution of a class of layered problems with extreme contrasts in the coefficients. J. Comput. Phys. 152(1), 385–403 (1999)

    Article  Google Scholar 

  21. M. Zugno, J.M. Morales, H. Madsen, Commitment and dispatch of heat and power units via affinely adjustable robust optimization. Comput. Oper. Res. 75(C), 191–201 (2016)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A. Bischi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

D’Ambrosio, C. et al. (2021). Production and Demand Management. In: Hadjidimitriou, N.S., Frangioni, A., Koch, T., Lodi, A. (eds) Mathematical Optimization for Efficient and Robust Energy Networks. AIRO Springer Series, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-030-57442-0_5

Download citation

Publish with us

Policies and ethics