Estimating hydro state of charge profiles for large-scale nodal and zonal pricing models using a heuristic approach

The power system is undergoing major restructuring in order to decarbonize the electricity generation. Therefore, it is becoming an increasingly important task to explore alternative market designs for the European electricity market design as nodal pricing. In order to do so, it is necessary to solve large-scale optimal power flow (OPF) problems, which are computationally intensive and need to be sequentialized. In this context, myopic foresight and the seasonality of hydro storages becomes a modeling issue that needs careful addressing. In order to address these challenges, in this work, a two stage heuristic is proposed to obtain hydro state of charge profiles for large-scale nodal and zonal models. Initial profiles are obtained from a spatially aggregated zonal model and are used as guidance for a sequentialized soft-constrained nodal modal with high spatial resolution. The resulting state of charge profiles are assessed against hydro modeling methods proposed in literature and perform well also under scenarios of high congestions because of the short-term flexibility of the proposed method. On a long timescale, following the zonal input profiles leads to good results also for the nodal model. The obtained state of charge profiles are used to perform a redispatching case study to demonstrate the applicability to comparative nodal vs. zonal studies. Computational results show that the nodal model leads to 2.5% lower system costs than the zonal model when redispatching is considered.