Transformer-LSTM-Attention Deep LearningPrediction and Multi-Time-ScaleError-Compensation Optimization forHydro-Wind-PV-Storage Clean Energy Bases UsingCascade-Hydropower Reserve

High wind and photovoltaic (PV) penetration propagates forecast errors across day-ahead, intraday and real-time dispatch and increases short-term regulation demand. This paper proposes a Transformer-LSTM-attention deep learning prediction and multi-time-scale error-compensation optimization strategy for a hydro–wind–PV–storage clean energy base. The prediction module generates load, wind, PV and runoff forecasts at 1 h, 15 min and 1 min resolutions by combining temporal feature extraction, sequence modeling and attention weighting. The dispatch model represents residual load, wind and PV errors as a net-load deviation and converts quantile-based, or source-wise conservative, error margins into day-ahead and intraday cascade-hydropower reserve requirements. In real-time operation, the configured hydropower reserve compensates minute-level deviations, with storage and thermal units providing supplementary regulation under ramping, operating and network constraints. A case study is conducted on a Southwest China cascade-basin hydro–wind–PV–storage base with four cascade hydropower stations and a modified 30-bus equivalent local-grid system. The day-ahead MAPE values are 5.79%, 0.79% and 0.52% for wind, PV and load. Compared with fixed reserve, the proposed strategy reduces day-ahead-to-intraday hydropower fluctuation errors from 8.42% to 4.81% and from 7.48% to 6.02%; intraday-to-real-time errors decrease from 8.27% to 6.84% and from 8.44% to 8.09%. The power-supply guarantee rate remains 100.00%, while the required storage power capacity decreases by 34.00% and 32.11%. These results show improved cross-stage dispatch consistency and lower short-term storage regulation demand under the tested high-renewable operating conditions.