Spatial decomposition methods for modelling multi-resolution European electricity systems with high shares of renewables

Abstract

Energy system modelling involves a technology-rich portfolio in high temporal and spatial resolution to properly estimate the impact of renewable energy variability and the role of storage technologies. Consequently, such models comprises tens of millions of variables and constraints, and demand vast computational resources for optimization. Thus, electricity networks are often coarsened to lower computational costs. However, this coarsening has a significant effect on the accuracy of the results. Another common approach is modelling only single regions and optimizing the cost of these isolated electricity networks. However, isolated electricity networks do not consider the power flows between countries, hence they have a large variance in electricity demand from renewable sources. These variances are especially undesirable in large networks and can only be smoothened by using an inter-regional network. The objective of this study is to investigate heuristic methods for spatial decomposition to improve results from isolated country models, while accounting for the benefits of international cooperation from the larger European electricity network. The study utilizes the existing high correlation between power flows from low- and high-resolution models to develop heuristics, and tests these methods by running simulations on isolated countries with disaggregated resources from low to high resolution and then optimizing these networks for minimal cost. The renewable electricity generated from the optimized isolated networks are compared to the benchmark European electricity network. Our study clearly indicates that spatial decomposition has a positive impact on the modelling results. Compared to an isolated model, the costs of the system stay within a ±8% range, while maintaining an accuracy in electricity output upto 5% of the European electricity model.