Global Energy System Modelling linked to spatial data with focus on renewable energy resources – a case study

Abstract

The paper presents an approach that bridges the gap between the consideration of spatial correlations in future energy systems and common energy system modelling approaches with a focus on forecasting the entire energy system. Therefore the energy system model TASES (Time And Space resolved Energy Simulation) has been developed in order to tackle best all relevant geographical correlations in energy systems. Especially renewable energy sources are often location dependent and highly intermittent The model is a snap shot model focusing on one year, including seasonal and day/night variations among the region of interest. It outlines the optimal energy system setup in terms of locations for PV, wind turbines or biomass power plants also with respect on an optimal transmission grid as part of the entire system. Remote sensing data are used to derive spatial indicators which are utilized as geographic discrete parameters in the TASES model. Scope of the outlined model framework is the analysis especially of the impact triggered by spatially varying system parameters on the entire energy system. This is particularly relevant with considering renewable energy resources. That gives the opportunity to study spatial infrastructure setups of the energy system with respect to single locations. A first case study linked to a MESSAGE long-term model run is elaborated and discussed.