Source-Load Collaborative Optimization Method Considering Core Production Constraints of Electrolytic Aluminum Load

With the deep implementation of the national “dual carbon” strategy, the development of a new power system dominated by renewable energy has accelerated significantly. Electrolytic aluminum load, as an important energy-intensive industrial resource, possesses response flexibility, providing a critical pathway for the efficient utilization of renewable energy. However, ensuring the safety of its production process during demand-side response remains a key challenge. This study systematically investigates the core production constraint of electrolytic aluminum load—electrolytic bath temperature—and its impacts on chemical reaction rates, current efficiency, and production equipment. A detailed coupling relationship between core production constraints and active power regulation is established. By quantifying the effects of temperature variation on the electrolytic aluminum production process, a demand-side response control cost model for electrolytic aluminum load is proposed. Additionally, a day-ahead scheduling model is developed with the objective of minimizing system operating costs while considering the participation of electrolytic aluminum load. Simulation results demonstrate that this method significantly reduces wind curtailment and load shedding while ensuring the safety of electrolytic aluminum production. It provides a novel approach for enhancing system economic efficiency, improving renewable energy utilization, and promoting the deep integration of power systems with industrial loads.

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Publication Details

Journal
Energies
Published
2024-12-19
DOI
https://doi.org/10.3390/en17246396
Citations
4
Primary Topic
Integrated Energy Systems Optimization
Type
article
Field-Weighted Citation Impact
0.83

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article

Source-Load Collaborative Optimization Method Considering Core Production Constraints of Electrolytic Aluminum Load

Siyang Liao, Zhe Wang, Yibo Jiang, Huining Lu et al.
4 citations
Energies
Integrated Energy Systems Optimization
0.83
article

Source-Load Collaborative Optimization Method Considering Core Production Constraints of Electrolytic Aluminum Load

Siyang Liao, Zhe Wang, Yibo Jiang, Huining Lu, Shiqi Bian
article en
4 citations

Abstract

With the deep implementation of the national “dual carbon” strategy, the development of a new power system dominated by renewable energy has accelerated significantly. Electrolytic aluminum load, as an important energy-intensive industrial resource, possesses response flexibility, providing a critical pathway for the efficient utilization of renewable energy. However, ensuring the safety of its production process during demand-side response remains a key challenge. This study systematically investigates the core production constraint of electrolytic aluminum load—electrolytic bath temperature—and its impacts on chemical reaction rates, current efficiency, and production equipment. A detailed coupling relationship between core production constraints and active power regulation is established. By quantifying the effects of temperature variation on the electrolytic aluminum production process, a demand-side response control cost model for electrolytic aluminum load is proposed. Additionally, a day-ahead scheduling model is developed with the objective of minimizing system operating costs while considering the participation of electrolytic aluminum load. Simulation results demonstrate that this method significantly reduces wind curtailment and load shedding while ensuring the safety of electrolytic aluminum production. It provides a novel approach for enhancing system economic efficiency, improving renewable energy utilization, and promoting the deep integration of power systems with industrial loads.

EnergiesVol. 17(24)
Wuhan University (CN), Energy Research Institute (CN), Suzhou Research Institute (CN)
National Natural Science Foundation of China, State Grid Corporation of China
Affordable and clean energy
Openalex Percentile: Top 26%
Integrated Energy Systems Optimization
0.83
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