Scheduling optimization and risk analysis for energy-intensive industries under uncertain electricity market to facilitate financial planning

被引:5
作者
Gangwar, Sachin [1 ]
Fernandez, David
Pozo, Carlos [3 ]
Folgado, Ruben [2 ]
Jimenez, Laureano
Boer, Dieter [1 ]
机构
[1] Univ Rovira I Virgili, Dept Engn Mecan, Av Paisos Catalans 26, Tarragona 43007, Spain
[2] Messer Iber Gases SAU, Km 3-8, Vilaseca 43480, Tarragona, Spain
[3] Univ Rovira I Virgili, Dept Engn Quim, Av Paisos Catalans 26, Tarragona 43007, Spain
关键词
Uncertainty; Optimization; Scheduling; Spot market forecast; Risk analysis; energy -intensive industry; OF-THE-ART; FRAMEWORK; CHALLENGES; MANAGEMENT; OPERATION; SYSTEMS; MODEL;
D O I
10.1016/j.compchemeng.2023.108234
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The planning of energy-intensive processes is intrinsically uncertain due to their dependence on the volatile energy market, with scheduling having a vast impact on the final production cost of these plants. Traditional stochastic methods are mathematically very complex, which translates into a significant computational effort that might prevent a timely response to varying electricity prices. To encounter this uncertainty, we develop a reliable hybrid simulation-optimization approach for optimizing the production plant scheduling, combining scenario analysis with risk analysis. The proposed methodology is demonstrated with real data from a cryogenic air separation plant in Tarragona (Spain). This approach also informs decision-makers about risk or expected shortfall associated with the implied scenario. The generic methodology used here can be easily adapted to schedule facilities in other energy-intensive sectors such as cement, metallurgy or pulp and paper.
引用
收藏
页数:17
相关论文
共 60 条
[41]   Moving horizon closed-loop production scheduling using dynamic process models [J].
Pattison, Richard C. ;
Touretzky, Cara R. ;
Harjunkoski, Iiro ;
Baldea, Michael .
AICHE JOURNAL, 2017, 63 (02) :639-651
[42]   Mean-conditional value at risk model for the stochastic project scheduling problem [J].
Rezaei, Fatemeh ;
Najafi, Amir Abbas ;
Ramezanian, Reza .
COMPUTERS & INDUSTRIAL ENGINEERING, 2020, 142
[43]   Conditional value-at-risk for general loss distributions [J].
Rockafellar, RT ;
Uryasev, S .
JOURNAL OF BANKING & FINANCE, 2002, 26 (07) :1443-1471
[44]   Optimization under uncertainty: state-of-the-art and opportunities [J].
Sahinidis, NV .
COMPUTERS & CHEMICAL ENGINEERING, 2004, 28 (6-7) :971-983
[45]  
Sarykalin S., 2008, State-of-the-Art Decision-Making Tools in the Information-Intensive Age, INFORMS TutORials in Operations Research, P270, DOI DOI 10.1287/EDUC.1080.0052
[46]  
Scharfhausen F.M., 2009, ELECT TARIFF STRUCTU
[47]   A review of air separation technologies and their integration with energy conversion processes [J].
Smith, AR ;
Klosek, J .
FUEL PROCESSING TECHNOLOGY, 2001, 70 (02) :115-134
[48]   Integrated Multiscale Design, Market Participation, and Replacement Strategies for Battery Energy Storage Systems [J].
Sorourifar, Farshud ;
Zavala, Victor M. ;
Dowling, Alexander W. .
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, 2020, 11 (01) :84-92
[49]  
Todd D, 2009, Providing Reliability Services through Demand Response: A Preliminary Evaluation of the Demand Response Capabilities of Alcoa Inc
[50]   Optimal demand response scheduling of an industrial air separation unit using data-driven dynamic models [J].
Tsay, Calvin ;
Kumar, Ankur ;
Flores-Cerrillo, Jesus ;
Baldea, Michael .
COMPUTERS & CHEMICAL ENGINEERING, 2019, 126 :22-34