Long-Term Electricity Procurement for Large Industrial Consumers under Uncertainty

Qi Zhang, Andreas M. Bremen, Ignacio E. Grossmann, Jose M. Pinto

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Due to the need for adjusting plant operations to time-varying electricity prices and changing product demand, managing the procurement of electricity in power-intensive businesses has become a major challenge. Large industrial electricity consumers often enter into long-term contracts with favorable rates. However, such power contracts require the consumers to commit themselves to the amount that they are going to purchase months in advance when future demand is not yet known with certainty. In this work, we simultaneously optimize long-term electricity procurement and production planning while considering uncertainty in product demand. We propose a multiscale multistage stochastic programming model in which a 1-year planning horizon is divided into seasons, with each season represented by two characteristic weeks; also, each season corresponds to a stage at which the demand for that season is revealed. The progressive hedging algorithm is applied to solve industrially relevant large-scale instances of the mixed-integer linear programming model. Moreover, two different sets of nonanticipativity constraints are proposed, which exhibit different computational behavior. We emphasize the use of the value of stochastic solution for multistage problems when evaluating the benefits of the stochastic optimization, which are demonstrated in an illustrative example as well as an industrial air separation case.

Original languageEnglish (US)
Pages (from-to)3333-3347
Number of pages15
JournalIndustrial and Engineering Chemistry Research
Volume57
Issue number9
DOIs
StatePublished - Mar 7 2018
Externally publishedYes

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