Forecast-Informed Day-Ahead TS-DS Scheduling with Bidirectional Power Exchange

Abstract

This paper presents a forecast-informed day-ahead scheduling framework for coordinated transmission-distribution system (TS-DS) operation with solar photovoltaic (PV) and wind generation. The proposed framework establishes an end-to-end forecast-to-scheduling workflow in which weather-based forecasts are converted into hourly plant-level renewable availability profiles and directly integrated into a nonlinear AC optimal power flow (AC-OPF) model. Python-based solar and wind forecasting, automated Excel-based data exchange, and GAMS-based optimization are integrated to coordinate thermal generation, renewable dispatch and curtailment, load service, voltage regulation, network power flows, and bidirectional active/reactive power exchange over a 24-hour horizon. The framework is evaluated on a coupled 24-bus transmission and IEEE 33-bus radial distribution system containing transmission-connected wind and PV resources together with distribution-side PV generation. Results show that transmission and distribution demands are served with negligible flexible-load shedding while all bus-voltage magnitudes remain within the prescribed 0.95-1.05 p.u. limits. During the midday low-load and high-PV period, the optimized interface active power reverses direction, demonstrating distribution-to-transmission export of surplus PV generation. These results demonstrate the capability of forecast-informed coordinated scheduling to capture both conventional import operation and renewable-driven reverse power flow while maintaining network feasibility.