Large-scale integrated hydrological modelling for hydroelectric generation in the Italian Alpine Region

Storage hydropower systems are pillars in clean energy production, especially in mountainous regions and higher altitudes where water availability is abundant. With the increasing penetration of variable renewable energy sources such as wind and solar, which are highly weather-dependent and often misaligned with energy demand, the role of hydropower reservoirs, which conversely can act as water batteries, is becoming more significant.

Modelling these systems within hydrological frameworks provides reliable tools for testing energy and water management policies. However, the missing knowledge of characteristics and regulation rules hampers the implementation of accurate human system modules into the modelling frameworks, especially over larger spatial domains.

As a case study, we select the Italian Alpine Region (IAR), characterised by a complex mountainous topography and significantly altered by the presence of human systems. This area covers Italy's entire northern mountain chain and houses over 300 large hydropower systems (i.e., with installed power above 3MW), representing up to 75% of all installed hydropower systems in Italy.

In this context, the study aims to simulate the hydroelectric system over IAR by introducing a new comprehensive inventory of hydropower-related infrastructures tailored to model the interaction between natural and human systems. In addition, we introduced a dynamic operating rule for the storage reservoirs, allowing us to simulate both storage and pumped-storage hydropower systems.

This modeling framework rule has been implemented in HYPERstreamHS, a hydrological model capable of simultaneously simulating hydropower production and assessing river network flow alteration. Our results show that the modelling framework, using accurate and detailed representations of hydropower systems and their operations within a hydrological model, not only robustly simulates hydroelectric systems behavior in IAR but also improves streamflow simulation.