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This chapter discusses trading in the day-ahead and intraday electricity markets, first in general and then from the perspectives of renewable power generators and demand response, respectively. This includes an overview of the relevant literature, a theoretical framework, and illustrative quantitative examples. The uncertainty of renewable electricity supply drives price volatility, especially in the intraday market, and leads to balancing cost for renewables, which can be optimized through forecasting and trading. The increased intraday price volatility can be exploited and moderated by demand response, which thereby helps to balance the uncertainty of renewables.
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 development of European power markets is highly influenced by integrated electricity and heat systems. Therefore, decarbonization policies for the electricity and heat sectors, as well as numerical models that are used to guide such policies, should consider cross-sectoral interdependencies. However, although many model-based policy assessments for the highly interconnected European electricity system exist, international studies that consider interactions with the heat sector are rare. In this contribution, we systematically study the potential benefits of integrated heat and power systems by conducting a model comparison experiment. Five large-scale market models covering electricity and heat supply were utilized to study the interactions between a rather simple coal replacement scenario and a more ambitious policy that supports decarbonization through power-to-heat. With a focus on flexibility provision, emissions reduction, and economic efficiency, although the models agree on the qualitative effects, there are considerable quantitative differences. For example, the estimated reductions in overall CO2 emissions range between 0.2 and 9.0 MtCO2/a for a coal replacement scenario and between 0.2 and 25.0 MtCO2/a for a power-to-heat scenario. Model differences can be attributed mainly to the level of detail of CHP modeling and the endogeneity of generation investments. Based on a detailed comparison of the modeling results, implications for modeling choices and political decisions are discussed.
Climate target achievement has a crucial influence on the modelling and the decision processes in the energy sector. It induced the development of several policy instruments to mitigate greenhouse gas emissions, including administrative and market-based mechanisms for phasing out coal-fired generation technologies. In order to analyse such instruments, electricity market and energy system models are widely used. However, results and corresponding recommendations largely depend on the formulation of the respective model. This motivates a systematic comparison of five large-scale electricity market models which are applied to European scenarios considering the period until 2030. An evolved diff-in-diff approach is proposed to analyse the effects of two coal phase-out strategies. This contribution expands on that of earlier studies and provides some more general takeaways for both modellers and decision-makers. For instance, the evolved diff-in-diff analysis shows the influence of the reference scenario when evaluating a policy instrument. Furthermore, the importance of technical aspects such as constraints for combined heat and power plants are discussed and implications regarding three dimensions (economic, environmental, and security of supply) are presented.
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.
Electricity is a peculiar economic good, the most important reason being that it needs to be supplied at the very moment of consumption. As a result, wholesale electricity prices fluctuate widely at hourly or sub-hourly time scales, regularly reaching multiples of their average, and even turn negative. This paper examines whether the demand for electricity responds to such price variations in the very short term. To solve the classical identification problem when estimating a demand curve, we use weather-driven wind energy generation as an instrument. Our robustness checks confirm that wind energy is indeed a strong and valid instrument. Using data from Germany, we estimate that a 1 €/MWh increase in the wholesale electricity price causes the aggregate electricity demand to decline by 67–80 MW or 0.12–0.14%, contradicting the conventional wisdom that electricity demand is highly price-inelastic. These estimates are statistically significant and robust across model specifications, estimators, and sensitivity analyses. At average price and demand, our estimates correspond to a price elasticity of demand of about –0.05. Comparing situations with high and low wind energy (5–95th percentile), we estimate that prices vary by 26 €/MWh, and the corresponding demand response to wholesale electricity prices is about 2 GW, or 2.6% of peak load. Our analysis suggests that the demand response in Germany can be attributed primarily to industrial consumers.
The “When2Heat” dataset comprises synthetic national time series for heat demand and heat pumps’ coefficient of performance (COP) in hourly resolution. Heat demands for space and water heating are computed by combining gas standard load profiles with spatial temperature reanalysis data and population geodata. With this update, we extend the dataset to 28 European countries and the period from 2008 to 2019, including new, state-of-the-art data sources. For the geographical extension, we propose a novel approach, shifting established German heat demand curves based on country-specific heating thresholds to account for regional differences in thermal insulation and user behavior. Using the example of Italy, we illustrate the effect of shifting heat demand curves. The dataset, scripts, and input parameters are publicly available under an open-source license on the Open Power System Data platform.
The development of European power markets is highly influenced by integrated electricity and heat systems. Therefore, decarbonization policies for the electricity and heat sectors, as well as numerical models that are used to guide such policies, should consider cross-sectoral interdependencies and need evaluation. Many model-based policy assessments evaluate potential benefits of combined heat and power. However, the extent of benefits, such as emissions reductions, found in existing studies is subject to considerable variations. While scenarios and model inputs may partly explain such variations, differences in results may also be related to the model formulation itself. Against this background, this study is the first to compare electricity market models in the context of potential benefits of integrated electricity and heat systems in decarbonization. Five large-scale market models covering electricity and heat supply were utilized to study the interactions between a rather simple coal replacement scenario and a more ambitious policy that supports decarbonization through power-to-heat. With a focus on flexibility provision, emissions reduction, and economic efficiency, although the models agree on the qualitative effects, there are considerable quantitative differences. For example, the estimated reductions in overall CO2 emissions range between 0.2 and 9.0 MtCO2/a for a coal replacement scenario and between 0.2 and 25.0 MtCO2/a for a power-to-heat scenario. Model differences can be attributed mainly to the level of detail of combined heat and power modeling and the endogeneity of generation investments. Based on a detailed comparison of the modeling results, implications for modeling choices and political decisions are discussed.