Energy Management of Industrial Energy Systems via Rolling Horizon and Hybrid Optimization: A Real-Plant Application in Germany.
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| Title: | Energy Management of Industrial Energy Systems via Rolling Horizon and Hybrid Optimization: A Real-Plant Application in Germany. |
|---|---|
| Authors: | Kyriakidis, Loukas1 (AUTHOR) rushit.kansara@dlr.de, Kansara, Rushit1 (AUTHOR), Roldán Serrano, Maria Isabel1 (AUTHOR) |
| Source: | Energies (19961073). Aug2025, Vol. 18 Issue 15, p3977. 29p. |
| Subjects: | Energy management, Interior-point methods, Mathematical programming, Surrogate-based optimization, Clean energy, Resource allocation, Forecasting methodology |
| Geographic Terms: | Germany |
| Abstract: | Industrial energy systems are increasingly required to reduce operating costs and CO2 emissions while integrating variable renewable energy sources. Managing these objectives under uncertainty requires advanced optimization strategies capable of delivering reliable and real-time decisions. To address these challenges, this study focuses on the short-term operational planning of an industrial energy supply system using the rolling horizon approach (RHA). The RHA offers an effective framework to handle uncertainties by repeatedly updating forecasts and re-optimizing over a moving time window, thereby enabling adaptive and responsive energy management. To solve the resulting nonlinear and constrained optimization problem at each RHA iteration, we propose a novel hybrid algorithm that combines Bayesian optimization (BO) with the Interior Point OPTimizer (IPOPT). While global deterministic and stochastic optimization methods are frequently used in practice, they often suffer from high computational costs and slow convergence, particularly when applied to large-scale, nonlinear problems with complex constraints. To overcome these limitations, we employ the BO–IPOPT, integrating the global search capabilities of BO with the efficient local convergence and constraint fulfillment of the IPOPT. Applied to a large-scale real-world case study of a food and cosmetic industry in Germany, the proposed BO–IPOPT method outperformed state-of-the-art solvers in both solution quality and robustness, achieving up to 97.25%-better objective function values at the same CPU time. Additionally, the influence of key parameters, such as forecast uncertainty, optimization horizon length, and computational effort per RHA iteration, was analyzed to assess their impact on system performance and decision quality. [ABSTRACT FROM AUTHOR] |
| Copyright of Energies (19961073) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 187312629 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Energy Management of Industrial Energy Systems via Rolling Horizon and Hybrid Optimization: A Real-Plant Application in Germany. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kyriakidis%2C+Loukas%22">Kyriakidis, Loukas</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> rushit.kansara@dlr.de</i><br /><searchLink fieldCode="AR" term="%22Kansara%2C+Rushit%22">Kansara, Rushit</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Roldán+Serrano%2C+Maria+Isabel%22">Roldán Serrano, Maria Isabel</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Aug2025, Vol. 18 Issue 15, p3977. 29p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Energy+management%22">Energy management</searchLink><br /><searchLink fieldCode="DE" term="%22Interior-point+methods%22">Interior-point methods</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+programming%22">Mathematical programming</searchLink><br /><searchLink fieldCode="DE" term="%22Surrogate-based+optimization%22">Surrogate-based optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Clean+energy%22">Clean energy</searchLink><br /><searchLink fieldCode="DE" term="%22Resource+allocation%22">Resource allocation</searchLink><br /><searchLink fieldCode="DE" term="%22Forecasting+methodology%22">Forecasting methodology</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Germany%22">Germany</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Industrial energy systems are increasingly required to reduce operating costs and CO2 emissions while integrating variable renewable energy sources. Managing these objectives under uncertainty requires advanced optimization strategies capable of delivering reliable and real-time decisions. To address these challenges, this study focuses on the short-term operational planning of an industrial energy supply system using the rolling horizon approach (RHA). The RHA offers an effective framework to handle uncertainties by repeatedly updating forecasts and re-optimizing over a moving time window, thereby enabling adaptive and responsive energy management. To solve the resulting nonlinear and constrained optimization problem at each RHA iteration, we propose a novel hybrid algorithm that combines Bayesian optimization (BO) with the Interior Point OPTimizer (IPOPT). While global deterministic and stochastic optimization methods are frequently used in practice, they often suffer from high computational costs and slow convergence, particularly when applied to large-scale, nonlinear problems with complex constraints. To overcome these limitations, we employ the BO–IPOPT, integrating the global search capabilities of BO with the efficient local convergence and constraint fulfillment of the IPOPT. Applied to a large-scale real-world case study of a food and cosmetic industry in Germany, the proposed BO–IPOPT method outperformed state-of-the-art solvers in both solution quality and robustness, achieving up to 97.25%-better objective function values at the same CPU time. Additionally, the influence of key parameters, such as forecast uncertainty, optimization horizon length, and computational effort per RHA iteration, was analyzed to assess their impact on system performance and decision quality. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Energies (19961073) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/en18153977 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 29 StartPage: 3977 Subjects: – SubjectFull: Energy management Type: general – SubjectFull: Interior-point methods Type: general – SubjectFull: Mathematical programming Type: general – SubjectFull: Surrogate-based optimization Type: general – SubjectFull: Clean energy Type: general – SubjectFull: Resource allocation Type: general – SubjectFull: Forecasting methodology Type: general – SubjectFull: Germany Type: general Titles: – TitleFull: Energy Management of Industrial Energy Systems via Rolling Horizon and Hybrid Optimization: A Real-Plant Application in Germany. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kyriakidis, Loukas – PersonEntity: Name: NameFull: Kansara, Rushit – PersonEntity: Name: NameFull: Roldán Serrano, Maria Isabel IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 19961073 Numbering: – Type: volume Value: 18 – Type: issue Value: 15 Titles: – TitleFull: Energies (19961073) Type: main |
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