Considering the Impacts of Metal Depletion on the European Electricity System

The transformation of the European electricity system could generate unintended environment-related trade-offs, e.g., between greenhouse gas emissions and metal depletion. The question thus emerges, how to shape policy packages considering climate change, but without neglecting other environmental and resource-related impacts. In this context, this study analyzes the impacts of different settings of potential policy targets using a multi-criteria analysis in the frame of a coupled energy system and life cycle assessment model. The focus is on the interrelationship between climate change and metal depletion in the future European decarbonized electricity system in 2050, also taking into account total system expenditures of transforming the energy system. The study shows, firstly, that highly ambitious climate policy targets will not allow for any specific resource policy targets. Secondly, smoothing the trade-off is only possible to the extent of one of the policy targets, whereas, thirdly, the potential of recycling as a techno-economic option is limited.</p

Dekarbonisierung des Energiesystems durch verstärkten Einsatz erneuerbaren Stroms im Wärme-, Verkehrs- und Industriesektor bei gleichzeitigen Stilllegungen von Kraftwerken – Auswirkungen auf die Versorgungssicherheit in Süddeutschland

Der Umbau der Energieversorgung zu einem von erneuerbaren Energien dominierten Energiesystem stellt die Versorgungssicherheit im Stromsektor vor neuen Herausforderungen. Zur Analyse der Versorgungssicherheit wird daher ein Modellkonzept entwickelt und angewendet, das sowohl die Untersuchung der Entwicklung der Stromnachfrage als auch die der optimalen Erzeugungstechnologien unter Berücksichtigung technischer, ökonomischer und klimapolitischer Restriktionen gestattet

Speeding up Energy System Models - a Best Practice Guide

Background Energy system models (ESM) are widely used in research and industry to analyze todays and future energy systems and potential pathways for the European energy transition. Current studies address future policy design, analysis of technology pathways and of future energy systems. To address these questions and support the transformation of today’s energy systems, ESM have to increase in complexity to provide valuable quantitative insights for policy makers and industry. Especially when dealing with uncertainty and in integrating large shares of renewable energies, ESM require a detailed implementation of the underlying electricity system. The increased complexity of the models makes the application of ESM more and more difficult, as the models are limited by the available computational power of today’s decentralized workstations. Severe simplifications of the models are common strategies to solve problems in a reasonable amount of time – naturally significantly influencing the validity of results and reliability of the models in general. Solutions for Energy-System Modelling Within BEAM-ME a consortium of researchers from different research fields (system analysis, mathematics, operations research and informatics) develop new strategies to increase the computational performance of energy system models and to transform energy system models for usage on high performance computing clusters. Within the project, an ESM will be applied on two of Germany’s fastest supercomputers. To further demonstrate the general application of named techniques on ESM, a model experiment is implemented as part of the project. Within this experiment up to six energy system models will jointly develop, implement and benchmark speed-up methods. Finally, continually collecting all experiences from the project and the experiment, identified efficient strategies will be documented and general standards for increasing computational performance and for applying ESM to high performance computing will be documented in a best-practice guide

Integrating vehicle-to-grid technology into energy system models: novel methods and their impact on greenhouse gas emissions

The electrification of the transport sector plays a key role in the global energy transition and it is of great necessity to assess emissions induced by electric vehicles in the long term for effective policy-making. Typical life cycle assessment may not consider the impact of electric vehicle integration in future electricity systems adequately, or the time-dependent characteristics of electricity generation mix and EV charging patterns. The solution requires modeling methods to integrate electric vehicle into energy system models, especially with vehicle-to-grid option. However, relevant methods have not been evaluated, yet. This integration is mathematically ambitious especially for huge and heterogeneous fleets of electric vehicles and brings energy system models to their computational limits. So far, current studies have proposed several aggregation methods for the load from electric vehicle charging, which simplify the original problem but may provoke bias. In our contribution, we propose a novel method of integrating vehicle-to-grid compliant electric vehicles into energy system models and demonstrate its feasibility by comparing it with two recent others from the literature. Taking the performance of the individual modeling method as the benchmark, we improve one of the two methods from the literature with updated parameters and additional constraints. We apply all three aggregation methods in a simple energy system model for comparing and analyzing their performances from multiple aspects, that is, solution accuracy, computational complexity, parameter requirement, and their impact on greenhouse gas emissions. Finally, we discuss the reasons behind the differences and give recommendations for further research

Multivariate time series imputation for energy data using neural networks

Multivariate time series with missing values are common in a wide range of applications, including energy data. Existing imputation methods often fail to focus on the temporal dynamics and the cross-dimensional correlation simultaneously. In this paper we propose a two-step method based on an attention model to impute missing values in multivariate energy time series. First, the underlying distribution of the missing values in the data is learned. This information is then further used to train an attention based imputation model. By learning the distribution prior to the imputation process, the model can respond flexibly to the specific characteristics of the underlying data. The developed model is applied to European energy data, obtained from the European Network of Transmission System Operators for Electricity. Using different evaluation metrics and benchmarks, the conducted experiments show that the proposed model is preferable to the benchmarks and is able to accurately impute missing values

Greenhouse gas emissions of electric vehicles in Europe considering different charging strategies

The growing market share of electric vehicles (EV) has increased the interest in charging strategies and their effects on the electricity system as well as their climatic soundness. However, the benefits of different charging strategies including Vehicle-to-Grid (V2G) on a large regional scale, e.g. in Europe, have not been analyzed sufficiently. This study examines the impact of different charging strategies on greenhouse gas (GHG) emissions from electricity generation and EV batteries in Europe in 2050. To consider indirect emissions and potentially additional battery degradation due to V2G, a model coupling concept is applied to link Life Cycle Assessment (LCA) with the electricity system model, PERSEUS-EU. Overall, EV could reduce the GHG emissions by 36% by simply replacing conventional cars. Controlled unidirectional charging and V2G add another 4 or 11 percentage points on the European level. However, for these gains an efficient implementation of V2G is required