Energy Management of Industrial Energy Systems via Rolling Horizon and Hybrid Optimization: A Real-Plant Application in Germany

Loukas Kyriakidis (), Rushit Kansara and Maria Isabel Roldán Serrano ()
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Loukas Kyriakidis: German Aerospace Center, Institute of Low-Carbon Industrial Processes, Simulation and Virtual Design Department, Walther-Pauer-Straße 5, 03046 Cottbus, Germany
Rushit Kansara: German Aerospace Center, Institute of Low-Carbon Industrial Processes, Simulation and Virtual Design Department, Walther-Pauer-Straße 5, 03046 Cottbus, Germany
Maria Isabel Roldán Serrano: German Aerospace Center, Institute of Low-Carbon Industrial Processes, Simulation and Virtual Design Department, Walther-Pauer-Straße 5, 03046 Cottbus, Germany

Energies, 2025, vol. 18, issue 15, 1-29

Abstract: Industrial energy systems are increasingly required to reduce operating costs and CO 2 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.

Keywords: food and cosmetic industry; energy management; rolling horizon approach; BO–IPOPT (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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