Coopetition and coordination mechanisms between energy and peak-shaving benefits in giant cascade hydropower systems: From mechanism identification to operational application

Giant cascade hydropower station systems (GCHSS) function as basin-scale regulating infrastructures and coordinate multiple utilization objectives, including power generation, at the medium-term scale. Meanwhile, with large-scale wind and photovoltaic (PV) integration, hydropower objectives are shifting from prioritizing energy benefits to balancing energy and peak-shaving benefits. However, the medium-term trade-off and synergy mechanisms between these benefits, along with the peak-shaving performance of the GCHSS subject to basin-wide comprehensive utilization requirements, remain insufficiently clarified. To address this gap, this study proposes a research framework. First, hydropower output is classified by its role in peak shaving. Second, peak-shaving evaluation indicators are established to characterize peak-shaving effectiveness and capacity, together with net load shape indicators to describe peak-shaving demand characteristics. Then, a peak-shaving optimization model is formulated from a power-system perspective to identify critical transitions between energy and peak-shaving benefits. Finally, a bi-level peak-shaving operation optimization model is developed from a cascade-operation perspective. It coordinates medium- and short-term operations and improves peak-shaving benefits while meeting basin-wide comprehensive utilization requirements. A case study of the GCHSS in the upper Yellow River shows that: (1) As hydropower energy increases, the relationship between energy benefits and peak-shaving benefits evolves from coordination to competition. (2) A larger maximum regulation capacity, or net load curves characterized by higher mean levels, lower volatility, steeper gradients, and wider peaks, leads to a broader synergistic range between the two benefits. (3) The optimal generation levels for balancing the two benefits in spring, summer, autumn, and winter are 60%, 56%, 63%, and 66% of the corresponding theoretical maximum generation, respectively. (4) the GCHSS reduces the peak–valley difference, tracking pressure, and volatility of the residual load by 18.64%, 20.44%, and 14.34%, respectively, with stronger peak-shaving performance from summer to early autumn and weaker performance in mid- to late winter.