This article provides a new methodology to compute a reduced but accurate network representation in a Transmission Expansion Planning (TEP) context. Considering this reduced network should lead to the same investment decisions as if the whole original network were considered. A set of relevant lines to be preserved is defined based on a proxy of the TEP solution. An optimal network partition, resulting from solving the multicut problem, is computed in such a way that the two ends of each of these relevant lines are allocated to two different areas. An iterative Kron reduction is then applied to each area to eliminate most of the buses that are not connected to any inter-area line. This two-step process results in a compact but representative reduced network. Our algorithm has been implemented in General Algebraic Modelling Software (GAMS) and Matrix Laboratory (MatLab) and has been tested on the standard IEEE 118 bus system and a case study based on the European power system. The method produces very promising results and, in the considered case studies, leads to the same, or equally efficient, investment decisions and essentially the same total costs as when considering the whole original network.

Methods for downstream river flow prediction can be categorized into physics-based and empirical approaches. Although based on well-studied physical relationships, physics-based models rely on numerous hydrologic variables characteristic of the specific river system that can be costly to acquire. Moreover, simulation is often computationally intensive. Conversely, empirical models require less information about the system being modeled and can capture a system’s interactions based on a smaller set of observed data. This article introduces two empirical methods to predict downstream hydraulic variables based on observed stream data: a linear programming (LP) model, and a convolutional neural network (CNN). We apply both empirical models within the Colorado River system to a site located on the Green River, downstream of the Yampa River confluence and Flaming Gorge Dam, and compare it to the physics-based model Streamflow Synthesis and Reservoir Regulation (SSARR) currently used by federal agencies. Results show that both proposed models significantly outperform the SSARR model. Moreover, the CNN model outperforms the LP model for hourly predictions whereas both perform similarly for daily predictions. Although less accurate than the CNN model at finer temporal resolution, the LP model is ideal for linear water scheduling tools.