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Optimal deterministic and robust selection of electricity contracts

Author

Listed:
  • David Wu

    (Sorbonne Université)

  • Viet Hung Nguyen

    (Université Clermont Auvergne)

  • Michel Minoux

    (Sorbonne Université)

  • Hai Tran

    (Energisme SAS)

Abstract

We address the Electricity Contract Selection Problem (ECSP), of finding best parameters of an electricity contract for a client based on his/her past records of electricity consumption over a fixed time period. The objective is to optimize the electricity bill composed by some fixed cost, the cost of the subscription of the electricity contract and penalties due to overpowering when consumption exceeds subscribed power. The ECSP can be formulated as a convex separable optimization problem subject to total order constraints. Due to this special structure, ECSP is a special case of two well known classes of convex separable optimization problems, namely the minimum network flow under convex separable cost and minimizing convex separable functions under chain constraints. Both classes are well treated in the litterature and can be solved in polynomial time (Ahuja and Orlin in Oper Res 49(5):784–789, 2001; Ahuja et al. in Manag Sci 49(7):950–964, 2003; Best et al. in SIAM J Optim 10:658–672, 2000; Karzanov and McCormick in SIAM J Comput 26(4):1245–1275, 1997; Minoux in Eur J Oper Res 18(3):377–387, 1984; Minoux, in Gallo and Sandi (eds) Netflow at pisa, mathematical programming studies, Springer, Berlin, 1986). In particular, the algorithm in Ahuja and Orlin (2001) achieves the best theoretical time complexity assuming that computing the objective function value at one specific point can be done in constant time. However, when we work on a big amount of historic data as in ECSP, the time required for evaluating the objective function cannot be assumed to be O(1) anymore. In this paper, we propose a new algorithm for ECSP which is specially designed to reduce the computational effort over large scale historical data. We present numerical results showing that our algorithm outperforms the algorithm in Ahuja and Orlin (2001) when applied to consumption data of various types of clients. A robust version of ECSP based on a Seasonal and Trend decomposition approach for modelling consumption uncertainty is also investigated. The resulting worst-case cost minimization problem is shown to be efficiently solvable using the same algorithm as for deterministic ECSP.

Suggested Citation

  • David Wu & Viet Hung Nguyen & Michel Minoux & Hai Tran, 2022. "Optimal deterministic and robust selection of electricity contracts," Journal of Global Optimization, Springer, vol. 82(4), pages 993-1013, April.
    • Handle: RePEc:spr:jglopt:v:82:y:2022:i:4:d:10.1007_s10898-021-01032-z
    DOI: 10.1007/s10898-021-01032-z
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    References listed on IDEAS

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    1. Dimitris Bertsimas & Melvyn Sim, 2004. "The Price of Robustness," Operations Research, INFORMS, vol. 52(1), pages 35-53, February.
    2. Kavousian, Amir & Rajagopal, Ram & Fischer, Martin, 2013. "Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior," Energy, Elsevier, vol. 55(C), pages 184-194.
    3. Ravindra K. Ahuja & Dorit S. Hochbaum & James B. Orlin, 2003. "Solving the Convex Cost Integer Dual Network Flow Problem," Management Science, INFORMS, vol. 49(7), pages 950-964, July.
    4. Fatih Issi & Orhan Kaplan, 2018. "The Determination of Load Profiles and Power Consumptions of Home Appliances," Energies, MDPI, vol. 11(3), pages 1-18, March.
    5. Minoux, M., 1984. "A polynomial algorithm for minimum quadratic cost flow problems," European Journal of Operational Research, Elsevier, vol. 18(3), pages 377-387, December.
    6. Ravindra K. Ahuja & James B. Orlin, 2001. "A Fast Scaling Algorithm for Minimizing Separable Convex Functions Subject to Chain Constraints," Operations Research, INFORMS, vol. 49(5), pages 784-789, October.
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