Energy system optimization models used for capacity expansion and dispatch planning are established tools for decision-making support in both energy industry and energy politics. The ever-increasing complexity of the systems under consideration leads to an increase in mathematical problem size of the models. This implies limitations of today’s common solution approaches especially with regard to required computing times. To tackle this challenge many model-based speed-up approaches exist which, however, are typically only demonstrated on small generic test cases. In addition, in applied energy systems analysis the effects of such approaches are often not well understood. The novelty of this study is the systematic evaluation of several model reduction and heuristic decomposition techniques for a large applied energy system model using real data and particularly focusing on reachable speed-up. The applied model is typically used for examining German energy scenarios and allows expansion of storage and electricity transmission capacities. We find that initial computing times of more than two days can be reduced up to a factor of ten while having acceptable loss of accuracy. Moreover, we explain what we mean by “effectiveness of model reduction” which limits the possible speed-up with shared memory computers used in this study.

Today's energy system models calculate power flows between simplified nodes representing transmission and distribution grid of a region or a countryso called copper plates. Such nodes are often restricted to a few tens thus the grid is not well represented or totally neglected in the whole energy system analysis due to limited computational performance using such models. Here we introduce our new methodology of node-internal grid calculation representing the electricity grid in cost values based on strong correlations between peak load, grid cost and feed-in share of wind and photovoltaic capacity. We validate in our case study this approach using a 491 node model for Germany. This examination area is modelled as enclosed energy system to calculate the grid in a 100% renewable energy system in 2050 enabling maximum grid expansion. Our grid model facilitates grid expansion cost and reduces computational effort. The quantification of the German electricity grid show that the grid makes up to 12% of total system cost equivalent up to 12 billion € per year.

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Current linear energy system models (ESM) acquiring to provide sufficient detail and reliability frequently bring along problems of both high intricacy and increasing scale. Unfortunately, the size and complexity of these problems often prove to be intractable even for commercial state-of-the-art linear programming solvers. This article describes an interdisciplinary approach to exploit the intrinsic structure of these large-scale linear problems to be able to solve them on massively parallel high-performance computers. A key aspect are extensions to the parallel interior-point solver PIPS-IPM originally developed for stochastic optimization problems. Furthermore, a newly developed GAMS interface to the solver as well as some GAMS language extensions to model block-structured problems will be described.

The accelerated urbanization and industrialization in China is leading to major challenges due to rising energy demand and emissions. Cities in particular play an important role in the decision-making and implementation processes for the energy transition. However, they often have only limited local energy potential and are heavily dependent on supply regions. We therefore assess how a predominantly renewable power supply can be implemented based on the availability of local or imported renewable resources. We present a case study in which an advanced energy system model is parametrized and applied to address questions which are relevant to the transformation of the energy system in China. The model is capable of simultaneously optimizing investment decisions and hourly power balances of a scenario year, taking into account different storage technologies, regional power exchange and policy constraints such as carbon cap, carbon price and renewable portfolio standards. The study takes the Beijing-Tianjin-Hebei metropolitan region with Inner Mongolia as a supply region—considered as exemplary regions characterized by heterogeneous infrastructures, resources and consumption—as its model. Starting from a context-related normative energy scenario, we analyze a possible future electricity system under various assumptions using the Renewable Energy Mix (REMix) energy system model developed at the DLR (German Aerospace Center). Depending on the estimated potentials of renewable energies, technology costs and the projected electricity demand, the metropolitan region is mainly supplied with imported wind and solar power. A sensitivity analysis considers installed capacities, annual generation, CO2 emissions and costs. The results indicate that the assumption of storage costs is of great importance for the future total costs of an electricity system. Variations in other parameters led to different generation portfolios with similar system costs. Our results provide insights into future regional infrastructure needs, and underline the importance of regional coordination and governance for the energy transition in China.