Dynamic Interval Forecasting-Based Demand Response Scheduling for Hydrogen-Battery Microgrids Under Renewable Uncertainty
High penetration of wind and photovoltaic generation introduces significant uncertainty into microgrid operation, particularly when renewable resources are coordinated with hydrogen storage, battery storage, grid interaction, and flexible demand. This paper proposes a demand-response-assisted chance-constrained scheduling framework for a grid-connected wind/PV/hydrogen/battery microgrid. A dynamic interval forecasting model is first developed by integrating a hybrid iTransformer–LSTM–KAN architecture with Monte Carlo Dropout and an adaptive confidence-level mechanism, enabling time-varying uncertainty bounds for wind and PV generation. The resulting prediction intervals are embedded into a chance-constrained optimization model that jointly schedules renewable dispatch, battery charging/discharging, electrolyzer operation, fuel cell generation, grid power exchange, and flexible load shifting. The proposed framework is evaluated on a microgrid consisting of 2 MW wind generation, 3 MW PV generation, 2 MWh battery storage, a 1 MW electrolyzer, a 0.5 MW fuel cell, and 20% flexible load participation. Results show that the proposed strategy reduces total operating cost by 60.1%, renewable curtailment by 65.3%, and grid purchase energy from 18.39 MWh to 10.49 MWh compared with deterministic scheduling. These findings demonstrate that coupling dynamic renewable uncertainty with demand-side flexibility can enhance renewable accommodation, reduce grid dependence, and improve the economic operation of hydrogen battery microgrids.