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Optimal deterministic and robust selection of electricity contracts

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 [1, 2, 5, 11, 13, 14]. In particular, the algorithm in [2] 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 [2] when applied to consumption data of various types of clients. A robust version of ECSP based on a Seasonal and Trend decomposition approach
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https://hal.uca.fr/hal-03257033
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Submitted on : Thursday, June 10, 2021 - 4:09:38 PM
Last modification on : Tuesday, July 13, 2021 - 3:27:39 AM
Long-term archiving on: : Saturday, September 11, 2021 - 7:21:42 PM

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David Wu, Viet Hung Nguyen, Michel Minoux, Hai Tran. Optimal deterministic and robust selection of electricity contracts. Journal of Global Optimization, Springer Verlag, 2021, ⟨10.1007/s10898-021-01032-z⟩. ⟨hal-03257033⟩

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