Conference Agenda

Strategies for decarbonising large-scale energy systems have a decisive impact on future energy costs and must, therefore, be thoroughly evaluated. Energy systems optimisation models (ESOMs) are frequently employed in the analysis and planning of energy systems. However, their scope and detail in terms of space, time, technologies, and economic sector can vary widely. Due to computational limitations, they can either have a high-resolution scope or a broader less detailed scope in the spatial and temporal dimensions. Stochastic optimisation is a method that offers the possibility to include uncertainties for time-varying inputs within different scenarios, which could represent, for example, different weather or demand profiles. When it comes to demand-side flexibility in the energy system, battery electric vehicles (BEVs) represent a source of uncertainty when considering long-term future energy scenarios. Against this background, this work analyses the influence that different charging infrastructure deployment scenarios and varying BEVs charging strategies (uncontrolled, load shifting and/or vehicle-to-grid) may have on the system design and operation that results from an ESOM. The results highlight these effects in the case of BEVs for the case study of the German energy system in 2050, by including uncertainties through stochastic optimisation.

This paper analyzes how climate risks are priced on financial markets. We show that climate tipping thresholds, disagreement about climate risks, and preferences that price in long-run risks are crucial to an understanding of the impact of climate change on asset prices. Our model simultaneously explains several findings that have been established in the empirical literature on climate finance. That is to say, (i) news about climate change can be hedged in financial markets, (ii) the share of green investors has significantly increased over the past decade, (iii) investors require a positive, although small, climate risk premium for holding “brown” assets, and (iv) “green” stocks outperformed “brown” stocks in the period 2011–2021. Furthermore, the model can explain why investments in mitigating climate change have been small in the past. Finally, the model predicts a strong, non-linear increase in the marginal gain from carbon-reducing investments as well as in the carbon premium if global temperatures continue to rise.

Defossilizing energy systems to mitigate climate change is a complex task. Energy system models can support decision-makers in this endeavor. However, despite efforts to mitigate climate change, Earth's temperature will rise, and changes in the climatic system will influence energy systems. Furthermore, the future climate's development is highly uncertain due to atmospheric complexity and uncertainties in the development of human greenhouse gas emissions.

In this study, we examine the influence of climate change on the European energy system, with a particular focus on residential heating. As temperatures rise, residential heating and cooling demands will change. To address this, we combine a model of the European power system with a building model. The building model utilizes climate projection temperatures as input data, reflecting the impact of climate change on residential heating. By integrating this with the power system model, we can analyze how the heating sector affects the power sector's development.

To assess uncertainty arising from climate projections, we compare different methods. We employ a novel time series clustering algorithm that selects representative days which reduce the amount of unmet demand compared to conventional clustering approaches. We then compare these results to classical robust optimization implemented with a column and constraint generation algorithm.

The model results will illustrate a European heating and power system robust to various climate developments, thereby supporting the energy transition in both sectors.