Process analysis-based industrial production modelling with uncertainty: A linear fractional programming for joint optimization of total caron emissions and emission intensity

Haotian Li, Jianxue Wang, Zelong Lu, Yao Zhang, Guo Hou and Lin Xue

Applied Energy, 2025, vol. 382, issue C, No S0306261924025881

Abstract: Energy-intensive industries are characterized by high energy consumption and carbon emissions during production, necessitating joint control over energy and carbon. Existing research on industrial optimization typically focuses on minimizing total carbon emissions, often overlooking the average carbon emission intensity of final products, and rarely address the mitigation of industrial carbon emissions from a coordinated perspective of sources, loads and storage. As a promising approach to facilitate energy conservation and decarbonization through the coordination of these resources, the integrated source-grid-load-storage system has been paid increasing attention. This paper addresses the day-ahead scheduling problem of the industrial system with source-grid-load-storage resources. Firstly, the generalized industrial production process is modelled by using a State-Task Network (STN) representation, quantifying energy consumption, carbon emissions and economic performance in a factory. Subsequently, a day-ahead scheduling model of a factory is proposed to minimize the average cost during a production cycle, including production costs and carbon tax. This model integrates the minimization of the average carbon emission intensity of final products in the objective function and constraints total carbon emissions over the production cycle, achieving joint optimization of total carbon emissions and emission intensity. The proposed model is a mixed integer linear fractional programming (MILFP) model, which can be converted to a mixed integer linear programming (MILP) model using the Charnes-Cooper transformation, enabling efficient resolution by commercial solver. Additionally, a chance-constrained information gap decision (CC-IGD) theory is adopted to cope with endogenous and exogenous stochastic factors in the real production process. Finally, case studies of electrolytic aluminum production verify the efficiency of the proposed method, demonstrating reductions in both average cost and average carbon emission intensity in industrial production, along with better risk adaptability.

Keywords: Energy-intensive industries; Day-ahead scheduling; Carbon emission intensity; Mixed integer linear fractional programming; Chance-constrained information gap decision theory (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2024.125204

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