Capacity configuration of hydropower-PV complementary station that is robust to the inter-annual variability in streamflow and PV energy
Conventional methods for sizing variable renewable energy (VRE) capacity integrated into hydropower are fed with several typical weather days or a single weather year dataset, which reduce computational complexity while neglecting the inter-annual variability of VRE and streamflow and yielding potentially unreliable results. This study proposes a novel tractable long-term model at hourly resolution to investigate the potential of hydro–VRE complementary operation for load following, reliable electricity supplying and to size the VRE capacity integrated into the hydropower stations . This methodology was applied to an actual hydropower-photovoltaic (PV) complementary station in China. We found that (1) PV capacity integrated into the hydropower is highly sensitive to the inter-annual variability of streamflow and PV energy; the PV capacity sized based on single-year-simulation ranges from 446 MW to 725 MW. (2) The PV deployment capacity based on 12-year-simulation is 510 MW. Compared with the PV deployment capacities based on single-year-simulation, it enables better hydropower-PV complementary load following performances and an average increase of 2.06 × 10 5 MWh in the annual electricity supply.
Authors
- Yifei Zheng (ORCID: https://orcid.org/0000-0001-9257-8464)
- Chuntian Cheng (ORCID: https://orcid.org/0000-0002-4693-6264)
- Juntao Zhang (ORCID: https://orcid.org/0009-0001-0827-7181)
- Hui Cao (ORCID: https://orcid.org/0000-0002-8373-1081)
- Yuqi Yang (ORCID: https://orcid.org/0009-0008-7077-7559)
Institutions
- Dalian University of Technology (CN)
Publication Details
- Journal
- Renewable Energy
- Published
- 2025-04-14
- DOI
- https://doi.org/10.1016/j.renene.2025.123163
- Citations
- 4
- Primary Topic
- Integrated Energy Systems Optimization
- Type
- article
- Field-Weighted Citation Impact
- 2.92
Funders
- National Natural Science Foundation of China
- National Postdoctoral Program for Innovative Talents