The Energy Journal

Abstract: Price incentives for reducing fossil fuel related carbon emissions are an important component of effective and efficient climate policy. Current incentives stem from a mixture of energy taxes and carbon pricing (incentivizing less emissions) and diverse support measures for fossil fuels (incentivizing more emissions). We develop a net carbon price indicator that complements existing subsidy and carbon pricing indicators. It can be calculated on different aggregation levels and compared across countries. We calculate the different components and our aggregate indicator for the year 2018 and for eight countries including the worlds' six largest emitters. Our analysis reveals large differences in net carbon prices across countries and across sectors within countries. We argue that the sectoral differences can inform about adequate national policy reforms while the aggregate national indicator can be useful for international negotiations about comparable national efforts.

Abstract: Offshore grids, with multiple interacting transmission and generation units connecting to the shores of several countries, are expected to have an important role in the cost-effective energy transition. Such massive new infrastructure expanding into a new physical space will require new offshore energy market designs. Decisions on these designs today will influence the overall value potential of offshore grids in the future. This paper investigates different possible market configurations and their impacts on operational costs and required congestion management, as well as prices and emissions. We use advanced integrated energy system optimisation, applied to a study case on the North Sea region towards 2050. Our analysis confirms the well-known concept of nodal pricing as the most preferable market configuration. Nodal pricing minimises costs (0.2-1.6 b€/year lower) and CO2 emissions (0.6-5.6 Mton/year lower) with respect to alternative market designs investigated. The performance of the different market designs is highly influenced by the overall architecture of the offshore grid, and the rest of the energy system. E.g., flexibility options help reducing the spread between the designs. But the results are robust: nodal pricing in offshore grids emerges as the preferable market configuration for a cost-effective energy transition to carbon neutrality.

Abstract: This paper studies the forecast accuracy and explainability of a battery of day-ahead (Henry Hub and Title Transfer Facility (TTF)) natural gas price and volatility models. The results demonstrate the dominance of non-linear, non-parametric models with deep structure relative to various competing model specifications. By employing the explainable artificial intelligence (XAI) approach, we document that the price of natural gas is formed strategically based on crude oil and electricity prices. While the conditional volatility of natural gas returns is driven by long-memory dynamics and crude oil volatility, the informativeness of the electricity predictor has improved over the most recent volatile time period. Although we reveal that predictive non-linear relationships are inherently complex and time-varying, our findings in general support the notion that natural gas, crude oil and electricity are interconnected. Focusing on the periods when markets experienced sharp structural breaks and extreme volatility (e.g., the COVID-19 pandemic and the Russia-Ukraine conflict), we show that deep learning models provide better adaptability and lead to significantly more accurate forecast performance.