Impact of different time series aggregation methods on optimal energy system design

Abstract

Modelling renewable energy systems is a computationally-demanding task due to the high fluctuation of supply and demand time series. To reduce the scale of these, this paper discusses different methods for their aggregation into typical periods. Each aggregation method is applied to a different type of energy system model, making the methods fairly incomparable. To overcome this, the different aggregation methods are first extended so that they can be applied to all types of multidimensional time series and then compared by applying them to different energy system configurations and analyzing their impact on the cost optimal design. It was found that regardless of the method, time series aggregation allows for significantly reduced computational resources. Nevertheless, averaged values lead to underestimation of the real system cost in comparison to the use of representative periods from the original time series. The aggregation method itself, e.g. k means clustering, plays a minor role. More significant is the system considered: Energy systems utilizing centralized resources require fewer typical periods for a feasible system design in comparison to systems with a higher share of renewable feed-in. Furthermore, for energy systems based on seasonal storage, currently existing models integration of typical periods is not suitable.

Cited by in corpus (25)

• Synergies of sector coupling and transmission reinforcement in a cost-optimised, highly renewable European energy system
• Time series aggregation for energy system design: Modeling seasonal storage
• Cost optimal scenarios of a future highly renewable European electricity system: Exploring the influence of weather data, cost parameters and policy constraints
• The strong effect of network resolution on electricity system models with high shares of wind and solar
• Reviewing energy system modelling of decentralized energy autonomy
• Endogenous learning for green hydrogen in a sector-coupled energy model for Europe
• Global LCOEs of decentralized off-grid renewable energy systems
• Approximating Power Flow and Transmission Losses in Coordinated Capacity Expansion Problems
• Extreme events in time series aggregation: A case study for optimal residential energy supply systems
• The Sensitivity of Power System Expansion Models
• Importance subsampling: improving power system planning under climate-based uncertainty
• Bottom-up energy supply optimization of a national building stock
• Little to lose: the case for a robust European green hydrogen strategy
• Trading off regional and overall energy system design flexibility in the net-zero transition
• Adequacy of time-series reduction for renewable energy systems
• Evaluation of Temporal Complexity Reduction Techniques Applied to Storage Expansion Planning in Power System Models
• Inverse methods: How feasible are spatially low-resolved capacity expansion modelling results when disaggregated at high spatial resolution?
• Decarbonization of Existing Heating Networks through Optimal Producer Retrofit and Low-Temperature Operation
• A practical scenario generation method for electricity prices on day-ahead and intraday spot markets
• Heuristics for Transmission Expansion Planning in Low-Carbon Energy System Models
• Integrating quantum and classical computing for multi-energy system optimization using Benders decomposition
• Towards time series aggregation with exact error quantification for optimization of energy systems
• Temporal Resolution of Measurements and the Effects on Calibrating Building Energy Models
• Impact of Spatial and Technology Aggregation on Optimal Energy System Design
• Three stages in the co-transformation of the energy and mobility sector