Authors: Sajjad M Vatanchi, Juha Kiviluoma, Björn Klöve & Epari Ritesh Patro
Publication type: A1
Hydropower is a key source of flexibility in power systems as the shares of wind and solar rise. Yet, short-term scheduling models often treat hydrology and energy systems separately or with oversimplified linkages. This paper presents a PRISMA-based systematic review of quantitative short-term hydropower regulation studies that explicitly couple water and power-system models. A structured search in Scopus and Web of Science for 2000-2025 identified 106 studies meeting predefined inclusion criteria on operational time horizons, model coupling, and optimization detail. We classify the literature by coupling structure (uncoupled, sequential, iterative, full), temporal decision structure, uncertainty and risk treatment, hydrological representation, and solution method, and synthesize patterns by system scale and geography. The reviewed models have evolved from deterministic formulations with prescribed inflows and simplified market representations toward more detailed plant-level constraints, ensemble-informed inflow modelling, and stochastic/robust optimization, increasingly supported by machine-learning-based forecasting and decision support. However, coupling remains predominantly one-way; uncoupled, sequential designs dominate, while iterative and fully integrated water-power feedback are rare; rigorous validation against operational practice is also limited by data access and the prevalence of stylized test systems. Future work should prioritize scalable coupled formulations at realistic spatial and scenario resolution, hybrid NWP-hydrology-ML forecasting that remains reliable under non-stationarity, and explainable, auditable decision support. Progress will be accelerated by shared benchmark systems and datasets, stronger co-design with operators, and digital-twin-enabled validation that links state estimation, forecasting, and rolling-horizon scheduling to continuous performance monitoring under real institutional and environmental constraints.