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Hybrid Time-Scale Optimal Scheduling Considering Multi-Energy Complementary Characteristic
- Source :
- IEEE Access, Vol 9, Pp 94087-94098 (2021)
- Publication Year :
- 2021
- Publisher :
- IEEE, 2021.
-
Abstract
- Evaluating the potential utilization of hybrid energy systems and determining the multi-scale optimal operation strategy is critical to power system planning in the context of energy structure adjustment, especially for large-scale hybrid energy systems. Considering the long-term and short-term complementary characteristics, this paper puts forward a coordinated optimization framework for the integrated energy system in the world’s largest multi-energy complementary base on Yellow River’s upper reaches. The main procedures are as follows: 1) cross-correlation method is introduced for individually analyzing the long- and short-term complementary characteristics of wind power, photovoltaic, and hydropower in this multi-energy complementary base; 2) a double-layer model combining the long-term optimal operation model and short-term optimal operation model for determining the proportion of multiple energy and optimizing the maximum peak-shaving ability; 3) Large-Scale System Decomposition-Coordination Method is applied for solving the proposed double-layer operation model. The results show that wind power 23%, photovoltaic 35%, hydropower 42% can keep the most stable generation in the long-term complementary operation. This proportion results can improve the system peak regulation capacity with 50.8% (sunny day’s morning peak) and 24.2% (rainy day’s morning peak) in the optimal short-term operation.
- Subjects :
- optimal operation strategy
Mathematical optimization
Wind power
General Computer Science
Scale (ratio)
Computer science
business.industry
Photovoltaic system
General Engineering
Context (language use)
multi-energy complementary characteristics
Renewable energy
TK1-9971
Electric power system
large-scale energy
General Materials Science
Electrical engineering. Electronics. Nuclear engineering
Hybrid time-scale
business
Energy (signal processing)
Hydropower
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 9
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....e8512cae75cbd32ad6f2d23373e05f82