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Optimization Model for the Long-Term Operation of an Interprovincial Hydropower Plant Incorporating Peak Shaving Demands

Authors :
Jian Wang
Cao Rui
Chuntian Cheng
Jianjian Shen
Source :
Energies; Volume 13; Issue 18; Pages: 4804, Energies, Vol 13, Iss 4804, p 4804 (2020)
Publication Year :
2020
Publisher :
Multidisciplinary Digital Publishing Institute, 2020.

Abstract

The increasing peak-to-valley load difference in China pose a challenge to long-distance and large-capacity hydropower transmission via high-voltage direct current (HVDC) lines. Considering the peak shaving demands of load centers, an optimization model that maximizes the expected power generation revenue is proposed here for the long-term operation of an interprovincial hydropower plant. A simulation-based method was utilized to explore the relationships between long-term power generation and short-term peak shaving revenue in the model. This method generated representative daily load scenarios via cluster analysis and approximated the real-time electricity price of each load profile with the time-of-use price strategy. A mixed-integer linear programming model with HVDC transmission constraints was then established to obtain moving average (MA) price curves that bridged two time-coupled operations. The MA price curves were finally incorporated into the long-term optimization model to determine monthly generation schedules, and the inflow uncertainty was addressed by discretized inflow scenarios. The proposed model was evaluated based on the operation of the Xiluodu hydropower system in China during the drawdown season. The results revealed a trade-off between long-term energy production and short-term peak shaving revenue, and they demonstrated the revenue potential of interprovincial hydropower transmission while meeting peak shaving demands. A comparison with other long-term optimization methods demonstrated the effectiveness and reliability of the proposed model in maximizing power generation revenue.

Details

Language :
English
ISSN :
19961073
Database :
OpenAIRE
Journal :
Energies; Volume 13; Issue 18; Pages: 4804
Accession number :
edsair.doi.dedup.....ab9521da1fd43b98591d9d456e286a9c
Full Text :
https://doi.org/10.3390/en13184804