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Abstract
Using a high-resolution planning model of the Great Britain power system and 25years of simulated wind and PV generation data, this study compares different methods to reduce time resolution of energy models to increase their computational tractability: downsampling, clustering, and heuristics. By comparing model results in terms of costs and installed capacities across different methods, this study shows that the best method depends heavily on input data and the setup of model constraints. This implies that there is no one-size-fits-all approach to the problem of time step reduction, but heuristic approaches appear promising. In addition, the 25years of time series demonstrate considerable inter-year variability in wind and PV power output. This further complicates the problem of time detail in energy models as it suggests long time series are necessary. Model results with high shares of PV and wind generation using a single or few years of data are likely unreliable. Better modeling and planning methods are required to determine robust scenarios with high shares of variable renewables. The methods are implemented in the freely available open-source modeling framework Calliope.
Highlights
Energy system models were first developed in the 1970s by the International Energy Agency (IEA) and the International Institute for Applied Systems Analysis (IIASA) in the aftermath of the international oil crisis
The results show that different methods, including downsampling, heuristic selection of time steps, and clustering, lead to substantially different model results, in particular when modeling high shares of variable renewable generation
The ‘‘90% renewable generation” scenario substantially increased the difference in results between different amounts of time steps and time resolution reduction methods
Summary
With the displacement of traditional power generation by variable renewables expected to increase, energy modelers have been moving downwards in this table from lower to higher resolutions. Approaches such as average availabilities for technologies, which were sufficient when modeling baseload or completely dispatchable generators such as coal or nuclear power, have been replaced with more explicit treatment of time. Assuming a model with a single year of 8760 hourly time steps, 20 technologies (such as wind generation or electric heating), 20 locations and 5 time-dependent constraints (such as maximum power generation per location, storage charging, and discharging), more than 17 million total constraints would result Reducing such a model’s size by one or two orders of magnitude by reducing the number of time steps brings with it a concomitant reduction in computational complexity, and in required CPU time and memory requirements to solve it
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