In traditional ecological scheduling, a single monthly or daily model will lead to the incomplete transmission of ecological information or increase the complexity of solving problems. Therefore, a multi-timescale nested model (MTNM) is proposed. Although the MTNM can express the daily flow process of environmental flow, the quadratic nested calculation method cannot obtain the optimal solution for the daily scheduling scheme. Targeting the problem that long and short-term objectives cannot obtain the optimal solution at the same time, this paper proposes a multi-timescale integrated model (MTIM) which considers the monthly, 10-day, and daily scale. The model is applied to the Liujiaxia reservoir. The scheduling results show that, compared with the MTNM, the MTIM can better meet the multi-objective demand. In a wet year, when both models can guarantee water supply and ecological demand, the MTIM increases electricity generation by 0.91%. In a dry year, electricity generation can still be increased by 4.35% without sacrificing the ecological and water supply benefits of the lower reaches. In different typical years, the MTIM can improve the contradictory relationship between multi-objective by improving the utilization efficiency of water. The results can improve the decision support for the operation process of other reservoirs with ecological needs.

Integrating photovoltaic power stations into large-capacity hydropower stations is an efficient and promising method for regulating large-scale photovoltaic power generation. However, constrained by the time step length, traditional long-term scheduling of hydro-PV hybrid systems does not adequately consider short-term operational performance indicators, resulting in sub-optimal scheduling plans that fail to coordinate the consumption of photovoltaic power and the utilization of water resources in the basin. To address this, this study established a long-term optimal scheduling model for hydro-PV hybrid systems. This model overcomes the limitation of the time step length in long-term scheduling by incorporating long-term power generation goals and short-term operation performance targets into the long-term optimal scheduling process based on scheduling auxiliary functions. In case studies, the optimised model balanced the long-term power-generation goals and short-term operational performance targets by redistributing energy across different periods. Compared to optimization models that did not consider short-term operation performance, in a typical normal year, the model effectively reduced the electricity curtailment volume (28.54%) and power shortage volume (10.91%) of the hybrid system while increasing on-grid electricity (0.03%). Similar improvements were observed in wet and dry years. These findings provide decision support for hydropower scheduling in the context of large-scale photovoltaic power integration.