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Carbon pricing is widely considered a key policy instrument for achieving substantial climate change mitigation. However, implementation remains patchy and price levels vary significantly across countries and regions. In this article, we analyze the structural social, political, and economic conditions under which carbon prices have been implemented so far. We estimate a Tobit regression model to investigate variations in explicit carbon prices over 262 national and subnational jurisdictions. Our results highlight well-governed institutions and public attitudes as the most important conditions for carbon pricing and characterize fossil fuel consumption as a barrier to the implementation of carbon prices. The results suggest that governance and public attitude need to be integrated into political economy analysis. Policy makers should take regulatory capacities and public attitudes seriously when designing carbon pricing policies.
The hydrogen economy is currently experiencing a surge in attention, partly due to the possibility of absorbing wind and solar energy production peaks through electrolysis. A fundamental challenge with this approach is low utilization rates of various parts of the integrated electricity-hydrogen system. To assess the importance of capacity utilization, this paper introduces a novel stylized numerical energy system model incorporating the major elements of electricity and hydrogen generation, transmission and storage, including both "green" hydrogen from electrolysis and "blue" hydrogen from natural gas reforming with CO2 capture and storage (CCS). Balancing renewables with electrolysis results in low utilization of electrolyzers, hydrogen pipelines and storage infrastructure, or electricity transmission networks, depending on whether electrolyzers are co-located with wind farms or demand centers. Blue hydrogen scenarios face similar constraints. High renewable shares impose low utilization rates of CO2 capture, transport and storage infrastructure for conventional CCS, and of hydrogen transmission and storage infrastructure for a novel process (gas switching reforming) that enables flexible power and hydrogen production. In conclusion, both green and blue hydrogen can facilitate the integration of wind and solar energy, but the cost related to low capacity utilization erodes much of the expected economic benefit.
Frequency stability requires equalizing supply and demand for electricity at short time scales. Such electricity balancing is often understood as a sequential process in which random shocks, such as weather events, cause imbalances that system operators close by activating balancing reserves. By contrast, we study electricity balancing as a market where the equilibrium price (imbalance price) and quantity (system imbalance) are determined by supply and demand. System operators supply imbalance energy by activating reserves; market parties that, deliberately or not, deviate from schedules create a demand for imbalance energy. The incentives for deliberate strategic deviations emerge from wholesale market prices and the imbalance price. We empirically estimate the demand curve of imbalance energy, which describes how sensitive market parties are to imbalance prices. To overcome the classical endogeneity problem of price and quantity, we deploy instruments derived from a novel theoretical framework. Using data from Germany, we find a decline in the demand for imbalance energy by 2.2 MW for each increase in the imbalance price by EUR 1 per MWh. This significant price response is remarkable because the German regulator prohibits strategic deviations. We also estimate cross-market equilibriums between intraday and imbalance markets, finding that a shock to the imbalance price triggers a subsequent adjustment of the intraday price.
Wind and solar energy are often expected to fall victim to their own success: the higher their share in electricity production, the more their revenue on electricity markets (their “market value”) declines. While in conventional power systems, the market value may converge to zero, this study demonstrates that “green” hydrogen production, through adding electricity demand in low-price hours, can effectively and permanently halt the decline. With an analytical derivation, a Monte Carlo simulation, and a numerical electricity market model, I find that – due to flexible hydrogen production alone – market values across Europe likely converge above €19 ± 9 MWh-1 for solar energy and above €27 ± 8 MWh-1 for wind energy in 2050 (annual mean estimate ± standard deviation). This lower boundary is in the range of the projected levelized costs of renewables and has profound implications. Market-based renewables may hence be within reach. simulation, and a numerical electricity market model, I find that – due to flexible hydrogen production alone – market values across Europe likely converge above €19 ± 9 MWh-1 for solar energy and above €27 ± 8 MWh-1 for wind energy in 2050 (annual mean estimate ± standard deviation). This lower boundary is in the range of the projected levelized costs of renewables and has profound implications. Market-based renewables may hence be within reach.
The European electricity industry, the dominant sector of the world’s largest cap-and-trade scheme, is one of the most-studied examples of carbon pricing. In particular, numerical models are often used to study the uncertain future development of carbon prices and emissions. While parameter uncertainty is often addressed through sensitivity analyses, the potential uncertainty of the models themselves remains unclear from existing single-model studies. Here, we investigate such model-related uncertainty by running a structured model comparison experiment, in which we exposed five numerical power sector models to aligned input parameters—finding stark model differences. At a carbon price of 27 EUR/t in 2030, the models estimate that European power sector emissions will decrease by 36–57% when compared to 2016. Most of this variation can be explained by the extent to which models consider the market-driven decommissioning of coal- and lignite-fired power plants. Higher carbon prices of 57 and 87 EUR/t yield a stronger decrease in carbon emissions, by 45–75% and 52–80%, respectively. The lower end of these ranges can be attributed to the short-term fuel switch captured by dispatch-only models. The higher reductions correspond to models that additionally consider market-based investment in renewables. By further studying cross-model variation in the remaining emissions at high carbon prices, we identify the representation of combined heat and power as another crucial driver of differences across model results.
Blue hydrogen and industrial base products: The future of fossil fuel exporters in a net-zero world
(2022)
Is there a place for today’s fossil fuel exporters in a low-carbon future? This study explores trade channels between energy exporters and importers using a novel electricity-hydrogen-steel energy systems model calibrated to Norway, a major natural gas producer, and Germany, a major energy consumer. Under tight emission constraints, Norway can supply Germany with electricity, (blue) hydrogen, or natural gas with re-import of captured CO2. Alternatively, it can use hydrogen to produce steel through direct reduction and supply it to the world market, an export route not available to other energy carriers due to high transport costs. Although results show that natural gas imports with CO2 capture in Germany is the least-cost solution, avoiding local CO2 handling via imports of blue hydrogen (direct or embodied in steel) involves only moderately higher costs. A robust hydrogen demand would allow Norway to profitably export all its natural gas production as blue hydrogen. However, diversification into local steel production, as one example of easy-to-export industrial base products, offers an effective hedge against the possibility of lower European blue hydrogen demand. Thus, it is recommended that hydrocarbon exporters like Norway consider a strategic energy export transition to a diversified mix of blue hydrogen and climate-neutral industrial base products.
In the context of 100% renewable electricity systems, prolonged periods with persistently scarce supply from wind and solar resources have received increasing academic and political attention. This article explores how such scarcity periods relate to energy storage requirements. To this end, we contrast results from a time series analysis with those from a system cost optimization model, based on a German 100% renewable case study using 35 years of hourly time series data. While our time series analysis supports previous findings that periods with persistently scarce supply last no longer than two weeks, we find that the maximum energy deficit occurs over a much longer period of nine weeks. This is because multiple scarce periods can closely follow each other. When considering storage losses and charging limitations, the period defining storage requirements extends over as much as 12 weeks. For this longer period, the cost-optimal storage capacity is about three times larger compared to the energy deficit of the scarcest two weeks. Adding other sources of flexibility for the example of bioenergy, the duration of period that defines storage requirements lengthens to more than one year. When optimizing system costs based on single years rather than a multi-year time series, we find substantial inter-annual variation in storage requirements with the most extreme year needing more than twice as much storage as the average year. We conclude that focusing on short-duration extreme events or single years can lead to an underestimation of storage requirements and costs of a 100 % renewable system.
The European Commission has proposed a carbon border adjustment mechanism (CBAM) that would apply the carbon price prevailing in the EU emissions trading system to import-related emissions. We conducted a survey to study perceptions of an EU CBAM among German key stakeholders from industry, civil society, and research in July 2021. We find that substantial support for CBAM exists as well as the expectation that the mechanism will eventually be introduced. We identified divergent views on key design options among stakeholder groups. Stakeholders from industry generally favour the continuation of free allocation of emissions allowances, rebates for exporters from the EU, coverage of only scope 1 emissions, and use of revenues for domestic spending. Stakeholders from civil society prefer phasing out free allocation, coverage only of imports, an emissions scope including all indirect emissions, exempting low-income countries and countries that do implement non-price-based climate policies, and the use of revenues to finance green transformation in low-income countries. Respondents from research would generally rather see free allocation being phased out, emissions coverage of scope 1 and 2, exemptions for low-income countries and countries that do implement non-price-based policies with comparable effects in relevant sectors and a transfer of revenues to support clean technologies in low-income countries and green technologies in the EU. Our survey design allows us to identify three cross-stakeholder group clusters, one containing stakeholders who are comparably more hesitant towards CBAM, a second one with respondents most in favour of introducing CBAM, as well as a 'middle ground' cluster which contains views that are often in between the other two. We also compare the survey responses to the design of the Commission's CBAM proposal to identify the most likely points of political disagreement.
The Role of Weather Predictions in Electricity Price Forecasting Beyond the Day-Ahead Horizon
(2022)
Forecasts of meteorology-driven factors, such as intermittent renewable generation, are commonly included in electricity price forecasting models. We show that meteorological forecasts can be used directly to improve price forecasts multiple days in advance. We introduce an autoregressive multivariate linear model with exogenous variables and LASSO for variable selection and regularization. We used variants of this model to forecast German wholesale prices up to ten days in advance and evaluate the benefit of adding meteorological forecasts, namely wind speed and direction, solar irradiation, cloud cover, and temperature forecasts of selected locations across Europe. The resulting regression coefficients are analyzed with regard to their spatial as well as temporal distribution and are put in context with underlying power market fundamentals. Wind speed in northern Germany emerges as a particularly strong explanatory variable. The benefit of adding meteorological forecasts strongest when autoregressive effects are weak, yet the accuracy of the meteorological forecasts is sufficient for the model to identify patterns. Forecasts produced 2-4 days in advance exhibit an improvement in RMSE by 10-20%. Furthermore, the forecasting horizon is shown to impact the choice of the regularization penalty that tends to increase at longer forecasting horizons.
Numerical optimization models are used to develop scenarios of the future energy system. Usually, they optimize the energy mix subject to engineering costs such as equipment and fuel. For onshore wind energy, some of these models use cost-potential curves that indicate how much electricity can be generated at what cost. These curves are upward sloping mainly because windy sites are occupied first and further expanding wind energy means deploying less favorable resources. Meanwhile, real-world wind energy expansion is curbed by local resistance, regulatory constraints, and legal challenges. This presumably reflects the perceived adverse effect that onshore wind energy has on the local human population, as well as other negative external effects. These disamenity costs are at the core of this paper. We provide a comprehensive and consistent set of cost-potential curves of wind energy for all European countries that include disamenity costs, and which can be used in energy system modeling. We combine existing valuation of disamenity costs from the literature that describe the costs as a function of the distance between turbine and households with gridded population data, granular geospatial data of wind speeds, and additional land-use constraints to calculate such curves. We find that disamenity costs are not a game changer: for most countries and assumptions, the marginal levelized cost of onshore wind energy increase by 0.2–12.5 €/MWh.