Robust integer optimization and scheduling problems for large electricity consumers

Vujanic, Robin; Mariéthoz, Sébastien; Goulart, Paul J.; Morari, Manfred

doi:10.1109/acc.2012.6314921

Cited by 34 publications

(23 citation statements)

References 7 publications

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“…As demand for operating reserve occurs due to unexpected events, the reserve provider does not know in advance what amount and, above all, when reserve service has to be dispatched. Vujanic et al 34 address this issue with a robust optimization approach which has been applied to a cement plant that provides reserve by shifting load. Here, the uncertainty lies in the time of required reserve dispatch and is assumed to affect the start times of the scheduled tasks.…”

Section: Introductionmentioning

confidence: 99%

Air separation with cryogenic energy storage: Optimal scheduling considering electric energy and reserve markets

et al. 2015

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The concept of cryogenic energy storage (CES) is to store energy in the form of liquid gas and vaporize it when needed to drive a turbine. Although CES on an industrial scale is a relatively new approach, the technology is well-known and essentially part of any air separation unit (ASU) that utilizes cryogenic separation. In this work, we assess the operational benefits of adding CES to an existing air separation plant.We investigate three new potential opportunities: (1) increasing the plant's flexibility for load shifting, (2) storing purchased energy and selling it back to the market during higher-price periods, (3) creating additional revenue by providing operating reserve capacity. We develop a mixed-integer linear programming (MILP) scheduling model and apply a robust optimization approach to model the uncertainty in reserve demand.The proposed model is applied to an industrial case study, which shows significant potential economic benefits.

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Section: Introductionmentioning

confidence: 99%

Air separation with cryogenic energy storage: Optimal scheduling considering electric energy and reserve markets

et al. 2015

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“…2 or as in the model discussed in [36]. The major advantage in this case is that the computation of a robust solution involves an optimization program that is as hard as the original nominal problem in the sense that its dimension (number of variables and constraints) is the same.…”

Section: Remark 32mentioning

confidence: 96%

Robust Optimization of Schedules Affected by Uncertain Events

Vujanic

Goulart

Morari

2016

J Optim Theory Appl

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In this paper, we present a new method for finding robust solutions to mixed-integer linear programs subject to uncertain events. We present a new modeling framework for such events that result in uncertainty sets that depend parametrically on the decision taken. We also develop results that can be used to compute corresponding robust solutions. The usefulness of our proposed approach is illustrated by applying it in the context of a scheduling problem. For instance, we address uncertainty on the start times chosen for the tasks or on which unit they are to be executed. Delays and unit outages are possible causes for such events and can be very common in practice. Through our approach, we can accommodate them without altering the remainder of the schedule. We also allow for the inclusion of recourse on the continuous part of the problem, that is, we allow for the revision of some of the decisions once uncertainty is observed. This allows one to increase the performance of the robust solutions. The proposed scheme is also computationally favorable since the robust optimization problem to be solved remains a mixed-integer linear program, and the number of integer variables is not increased with respect to the nominal formulation. We finally apply the method to a concrete batch scheduling problem and discuss the effects of robustification in this case.Communicated by Christodoulos Floudas.

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“…Here control techniques rather than scheduling might be better suited. As investigated by Vujanic et al (2012), robust optimization might help creating flexible schedules to support the ancillary services of cement plants. Since energy availability and prices can be treated like any other resource in the scheduling models, many of the formulations in the literature use a discrete-time approach.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization of steel production scheduling with complex time-sensitive electricity cost

Hadera

Harjunkoski

Sand

et al. 2015

Computers & Chemical Engineering

128

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Energy-intensive industries can take advantage of process flexibility to reduce operating costs by optimal scheduling of production tasks. In this study we develop a MILP formulation to extend a continuous-time model with energy-awareness to optimize the daily production schedules and the electricity purchase including the load commitment problem. The sources of electricity that are considered are purchase on volatile day-ahead markets, time-of-use (TOU) contracts and base load contracts, as well as onsite generation. The possibility to sell electricity back to the grid is also included. The model is applied to the melt shop section of a stainless steel plant. Due to the large-scale nature of the combinatorial problem which prevents the solution of a monolithic model to nearoptimality, we propose a bi-level heuristic algorithm to tackle instances of industrial size. Case study scenarios show that the potential impact of high prices in the day-ahead markets of electricity can be mitigated by jointly optimizing the production schedule and the associated net electricity consumption cost.

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Robust integer optimization and scheduling problems for large electricity consumers

Cited by 34 publications

References 7 publications

Air separation with cryogenic energy storage: Optimal scheduling considering electric energy and reserve markets

Air separation with cryogenic energy storage: Optimal scheduling considering electric energy and reserve markets

Robust Optimization of Schedules Affected by Uncertain Events

Optimization of steel production scheduling with complex time-sensitive electricity cost

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