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 critic...

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Main Authors: Yibo Jiang, Zhe Wang, Shiqi Bian, Siyang Liao, Huibin Lu
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Energies
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Online Access:https://www.mdpi.com/1996-1073/17/24/6396
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author Yibo Jiang
Zhe Wang
Shiqi Bian
Siyang Liao
Huibin Lu
author_facet Yibo Jiang
Zhe Wang
Shiqi Bian
Siyang Liao
Huibin Lu
author_sort Yibo Jiang
collection DOAJ
description 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.
format Article
id doaj-art-48e24fee83884b3d9789173762fce6e3
institution Kabale University
issn 1996-1073
language English
publishDate 2024-12-01
publisher MDPI AG
record_format Article
series Energies
spelling doaj-art-48e24fee83884b3d9789173762fce6e32024-12-27T14:23:42ZengMDPI AGEnergies1996-10732024-12-011724639610.3390/en17246396Source-Load Collaborative Optimization Method Considering Core Production Constraints of Electrolytic Aluminum LoadYibo Jiang0Zhe Wang1Shiqi Bian2Siyang Liao3Huibin Lu4State Grid (Suzhou) City & Energy Research Institute, Suzhou 215000, ChinaState Grid (Suzhou) City & Energy Research Institute, Suzhou 215000, ChinaSchool of Electrical and Automation, Wuhan University, Wuhan 430072, ChinaSchool of Electrical and Automation, Wuhan University, Wuhan 430072, ChinaState Grid (Suzhou) City & Energy Research Institute, Suzhou 215000, ChinaWith 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.https://www.mdpi.com/1996-1073/17/24/6396electrolytic aluminum loadproduction constraintelectrolytic bath temperaturecost modelingsource-load coordination
spellingShingle Yibo Jiang
Zhe Wang
Shiqi Bian
Siyang Liao
Huibin Lu
Source-Load Collaborative Optimization Method Considering Core Production Constraints of Electrolytic Aluminum Load
Energies
electrolytic aluminum load
production constraint
electrolytic bath temperature
cost modeling
source-load coordination
title Source-Load Collaborative Optimization Method Considering Core Production Constraints of Electrolytic Aluminum Load
title_full Source-Load Collaborative Optimization Method Considering Core Production Constraints of Electrolytic Aluminum Load
title_fullStr Source-Load Collaborative Optimization Method Considering Core Production Constraints of Electrolytic Aluminum Load
title_full_unstemmed Source-Load Collaborative Optimization Method Considering Core Production Constraints of Electrolytic Aluminum Load
title_short Source-Load Collaborative Optimization Method Considering Core Production Constraints of Electrolytic Aluminum Load
title_sort source load collaborative optimization method considering core production constraints of electrolytic aluminum load
topic electrolytic aluminum load
production constraint
electrolytic bath temperature
cost modeling
source-load coordination
url https://www.mdpi.com/1996-1073/17/24/6396
work_keys_str_mv AT yibojiang sourceloadcollaborativeoptimizationmethodconsideringcoreproductionconstraintsofelectrolyticaluminumload
AT zhewang sourceloadcollaborativeoptimizationmethodconsideringcoreproductionconstraintsofelectrolyticaluminumload
AT shiqibian sourceloadcollaborativeoptimizationmethodconsideringcoreproductionconstraintsofelectrolyticaluminumload
AT siyangliao sourceloadcollaborativeoptimizationmethodconsideringcoreproductionconstraintsofelectrolyticaluminumload
AT huibinlu sourceloadcollaborativeoptimizationmethodconsideringcoreproductionconstraintsofelectrolyticaluminumload