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.
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References
F.A. Aliev, M.K. Ilyiasov, M.A. Dzhamelbekov, Modelling of operation of the gaslift borehole cavity. Technical Report 4, Dokl. NANA (2008)
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)
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)
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)
N. Boland, T. Kalinowski, F. Rigterink, A polynomially solvable case of the pooling problem. J. Glob. Optim. 67(3), 621–630 (2017)
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)
S.S. Dey, A. Gupte, Analysis of MILP techniques for the pooling problem. Oper. Res. 63(2), 412–427 (2015)
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)
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)
D. Haugland, The computational complexity of the pooling problem. J. Global Optim. 64(2), 199–215 (2016)
D. Haugland, E.M.T. Hendrix, Pooling problems with polynomial-time algorithms. J. Optim. Theory Appl. 170(2), 591–615 (2016)
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)
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)
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)
R. Misener, C.A. Floudas, GloMIQO: global mixed-integer quadratic optimizer. J. Global Optim. 57(1), 3–50 (2013)
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)
R. Misener, C.A. Floudas, Antigone: algorithms for continuous/integer global optimization of nonlinear equations. J. Global Optim. 59(2), 503–526 (2014)
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
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)
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)
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)
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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
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