Enhancing run-of-river hydropower capacity assessment through integrated time series flow regime modeling and...

Area of Science:

• Environmental science and engineering
• Renewable energy systems
• Hydrology and water resource management

Background:

• Run-of-river hydropower (RoR HP) offers a sustainable energy alternative with a lower ecological impact than reservoir systems.
• Seasonal flow variability in some regions challenges consistent energy generation and operational efficiency in RoR HP.
• Conventional assessments often overlook flow regime continuity, leading to potential overestimation of hydropower potential.

Purpose of the Study:

• To introduce a novel framework integrating continuous wavelet transform (CWT) and flow duration curve (FDC) analyses for evaluating RoR HP potential.
• To quantify functional flow rates (FFRs) while accounting for critical flow regime continuity, often missed in traditional methods.
• To compare continuous exceedance probability (CEP) with discrete exceedance probability (DEP) for more accurate hydropower assessments.

Main Methods:

• Applied CWT with a Morlet wavelet basis to a 22-year hourly discharge dataset from a subtropical watershed.
• Decomposed flow time series data to identify dominant temporal scales using the global wavelet power spectrum.
• Derived FFR thresholds via CEP, incorporating temporal flow continuity, and compared results with traditional DEP-based FDCs.

Main Results:

• Identified primary (1-year) and secondary (3.5-year) hydrological cycles influencing hydropower feasibility.
• Found that conventional DEP-based FDCs systematically overestimate usable hydropower potential due to neglecting temporal continuity.
• CEP consistently yielded lower, more realistic energy and capacity-factor estimates than DEP across evaluated continuity thresholds (3-24 hours).

Conclusions:

• The CEP framework provides a robust tool for assessing RoR HP potential, mitigating financial risk, and enhancing climate resilience.
• Prioritizing temporal continuity leads to more accurate capacity factor estimations, enabling optimized energy output and reduced operational uncertainties.
• The proposed methodology enhances the viability of RoR HP systems as low-impact renewable energy solutions, supporting environmental management goals.