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• W3110364084 startingPage "107191" @default.
• W3110364084 abstract "This paper addresses the problem faced by large electricity consumers to simultaneously determine the optimal day-ahead electricity procurement and the optimal energy-aware production schedule. The inherent uncertainty of the problem, due to the bidding process in the day-ahead market, is dealt with by means of the stochastic programming modeling framework. In particular, a two-stage problem is formulated with the aim of establishing the optimal bidding strategy and the optimal production schedule hedging against price uncertainty. The optimal integrated solution is defined to minimize the overall cost and to control the risk of high cost scenarios due to uncertain price peaks. The stochastic model is solved with a scenario-decomposition approach. Extensive numerical experiments have been carried out to assess the performance of the proposed decision approach. The results collected when considering an industrial relevant case-study show the superiority of the proposed methodology in comparison with a deterministic approach." @default.
• W3110364084 created "2020-12-07" @default.
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• W3110364084 date "2021-02-01" @default.
• W3110364084 modified "2023-10-14" @default.
• W3110364084 title "Stochastic short-term integrated electricity procurement and production scheduling for a large consumer" @default.
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• W3110364084 doi "https://doi.org/10.1016/j.compchemeng.2020.107191" @default.
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