Multi-source coordinated low-carbon optimal dispatching for interconnected power systems considering carbon capture

The overall electricity consumption of electrolytic aluminum and ferroalloy loads is significant, and some of these loads have dispatch potential that can be used to locally absorb wind power while reducing dependence on conventional thermal power. To characterize the uncertainty of wind power, a fu...

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Veröffentlicht in:Energy Informatics Jg. 7; H. 1; S. 61 - 18
Hauptverfasser: Sun, Jiawen, Hua, Dong, Song, Xinfu, Liao, Mengke, Li, Zhongzhen, Jing, Shibo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 01.12.2024
Springer Nature B.V
SpringerOpen
Schlagworte:
Algorithms
Aluminum
Carbon
Carbon content
Carbon sequestration
Computer Science
Confidence intervals
Electric power distribution
Electrical loads
Electricity consumption
Emissions
Error correction
Ferroalloys
Forecasting
Fuzzy sets
Information Systems and Communication Service
Integer programming
Linear programming
Low-carbon optimal scheduling
Mixed integer
Operating costs
Optimization
Power dispatch
Probability distribution
Reduction (electrolytic)
Robustness optimization
Schedules
Scheduling
Source⁃load coordination
Statistical analysis
Thermal power
Uncertainty
Unit commitment
Wasserstein distance
Wind power
Wind power consumption
Uncertainty
Unit combination
Robustness optimization
Wasserstein distance
Wind power consumption
Source⁃load coordination
Low-carbon optimal scheduling
ISSN:2520-8942, 2520-8942
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Zusammenfassung:The overall electricity consumption of electrolytic aluminum and ferroalloy loads is significant, and some of these loads have dispatch potential that can be used to locally absorb wind power while reducing dependence on conventional thermal power. To characterize the uncertainty of wind power, a fuzzy set of wind power forecasting error probability distribution based on the Wasserstein distance was first established, and the approximate radius of the fuzzy set was corrected under extreme scenarios. By introducing joint chance constraints, the inequalities of uncertain variables were established at the lowest confidence level to improve the reliability of the model. Next, a two-stage distributed robust optimal scheduling model for source-load coordination was developed. In the first stage, wind power forecasting information was fully utilized to schedule the electrolytic aluminum load and optimize unit commitment. In the second stage, the uncertainty of wind power output was considered to schedule the ferroalloy load and optimize unit output. The model was approximately transformed into a mixed-integer linear programming problem and solved using a sequential algorithm. The IEEE 24-bus system was used for case validation. The validation results show that the model can effectively improve wind power absorption capacity, reduce overall operating costs, and achieve a balance between low carbon emissions and robustness.
Bibliographie:ObjectType-Article-1
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ISSN:2520-8942
2520-8942
DOI:10.1186/s42162-024-00367-7