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Peak Shaving Model for Coordinated Hydro-Wind-Solar System Serving Local and Multiple Receiving Power Grids via HVDC Transmission Lines

Authors :
Benxi Liu
Jay R. Lund
Shengli Liao
Xiaoyu Jin
Lingjun Liu
Chuntian Cheng
Source :
IEEE Access, Vol 8, Pp 60689-60703 (2020)
Publication Year :
2020
Publisher :
IEEE, 2020.

Abstract

To meet the rapid growth of electricity demand and reduce carbon intensity, China is developing renewable energies rapidly including hydropower, wind and solar power. Due to the geographical mismatch of energy sources and demands in China, many long-distance and large-scale UHVDC and HVDC transmission projects have been built to transmit electric power from the western renewable bases to eastern coastal load centers. Some provincial power sources serve both local demands and deliver power to multiple regional power grids via HVDC transmission lines. As large capacity HVDC power transmission projects have great impacts on receiving-end power grids. Thus, the local exporting power grid should consider both local demands and energy importing area demands. A mixed-integer linear programming day-ahead peak shaving model to minimize the peak-valley difference in residual load after renewable generation of multiple power grids is developed. The model uses chance constraints to compensate for forecast errors of wind and solar power with hydropower, and introduces maximum daily power regulation times and stair-like power curve constraints of HVDC tie lines to avoid frequent HVDC power change and ensure power grid safety. The case studies in Yunnan province, which has large scale hydro, wind and solar power sources and delivers power to multiple regional power grids via HVDC transmission lines, shows the proposed model can shave peaks from multiple power grids effectively, hydropower can compensate for wind and solar forecast error and obtain satisfying results for multiple power grids, and that HVDC constraints can avoid their frequent power change and ensure the power grid safety.

Details

Language :
English
ISSN :
21693536
Volume :
8
Database :
Directory of Open Access Journals
Journal :
IEEE Access
Publication Type :
Academic Journal
Accession number :
edsdoj.35c97fc91d4ca39e92a5b2d1ef1f5e
Document Type :
article
Full Text :
https://doi.org/10.1109/ACCESS.2020.2979050