FG Energiewirtschaft
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This paper focuses on seasonal demand swings in the European natural gas market. We quantify and compare the role of different flexibility options (domestic production, gas storage, and pipeline and LNG imports) to assess European demand fluctuations in monthly resolution. We contribute to the existing literature on seasonal flexibility by addressing the problem with a mathematical gas market optimization model. Our paper provides valuable empirical insights into the decline of gas production in northwestern Europe. Furthermore, we focus on how specific flexibility features differ between pipeline supplies and LNG supplies and between gas imports and storage dispatch. In terms of methodology, we construct a bottom-up market optimization model and publish the complete source code (which is uncommon for gas market models). Furthermore, we propose a new metric—the scaled coefficient of variation—to quantify the importance of supply sources for seasonal flexibility provision.
Solar and wind power generation forecasts using elastic net in time-varying forecast combinations
(2022)
Precise renewable energy feed-in forecasts are essential for an effective and efficient integration of renewables into energy systems, and research contributions that help to reduce the uncertainty related to renewables are in high demand. This importance will increase in the future, as renewable energies are the world’s fastest growing electricity generation capacities. Forecast combinations have been empirically proven to outperform individual forecasting models in many disciplines. Our work uses an elastic net method, with cross-validation and rolling window estimation, in the context of renewable energy forecasts. Namely, the forecast combinations are obtained using regional data from Germany for both solar photovoltaic and wind feed-in during the period 2010–2018, with quarter-hourly frequency. The dynamic elastic net estimation, preceded by dynamic data pre-processing, improves forecasting accuracy for both photovoltaic and wind power feed-in forecasts. Moreover, our forecasting framework outperforms benchmarks such as simple average and individual forecasts. Our forecasting framework can be applied widely to estimate renewable power in other countries, systems, or individual power plants.
Research into renewable energy market values is a popular field in energy economics. However, most contributions abstract from market values being highly individual and mostly study (nationwide) averages, usually based on a single or a “normal” wind year, if specifying wind conditions at all, and a limited set of technologies. However, market values of renewable energy resources are not monolithic but highly diverse. In this article, to shed light on this diversity, we illustrate the historical development of onshore wind’s market value in Germany, from 2001 to 2019, for the fleet and all operating wind energy converters. We use highly granular wind speed data and a comprehensive database of wind capacities. Our results show the downward trend, the distributions, and the variance of market values. In this context, we explain why the performance of a single wind energy converter (compared to the fleet’s performance) matters in the market premium model. Hereby, we also assess the magnitude of the outperformance of technologically advanced wind turbines as compared to less advanced turbines. In the second part of our research, we analyse the effect of the inter-annual weather variability on wholesale electricity prices, and market values. Our analysis is based on 19 different years of wind speeds, corresponding offshore and solar infeed, and an electricity market model to generate weather-congruent wholesale electricity prices.
The best wind locations are nowadays often occupied by old, less efficient and relatively small wind turbines. Many of them will soon reach the end of their operating lifetime, or lose financial support. Therefore, repowering comes to the fore. However, social acceptance and land use restrictions have been under constant change since the initial expansions, which makes less area available for new turbines, even on existing sites. For the example of Germany, this study assesses the repowering potential for onshore wind energy in high detail, on the basis of regionally differentiated land eligibility criteria. The results show that under the given regional criteria, repowering will decrease both operating capacity and annual energy yield by roughly 40% compared to the status quo. This is because around half of the wind turbines are currently located in restricted areas, given newly enacted exclusion criteria. Sensitivity analyses on the exclusion criteria show that the minimum distance to discontinuous urban fabric is the most sensitive criterion in determining the number of turbines that can be repowered. As regulations on this can vary substantially across different regions, the location-specific methodology chosen here can assess the repowering potential more realistically than existing approaches.
The merit order effect describes the lowering of the average wholesale electricity price due to increased capacity of renewable energies. This effect has been observed in many places. While it is beneficial for consumers, it has triggered concerns about the economic viability of legacy plants. Is this merit order effect a permanent feature of high shares of renewable energy, or merely a transitory phase? This article develops a simple theoretical model to shed light on this question. It introduces intermittent renewable energies into a conventional mix of base load and peak load and investigates the effect when base and peak load remain unchanged in the short-term, and when they fully adjust in the long-term. We find that the merit order effect is a temporary phenomenon that is caused by the slow pace of capacity adjustments. We also find that the introduction of renewables has a mild beneficial long-term effect when base load is provided monopolistically, as renewables compete with base load to a limited extent. Consequently, concerns about the adverse consequences for conventional power plants resulting from renewable capacity are only partially justified. While in the short-term the generation portfolio suffers from “stranded assets”, market forces will gradually reduce overcapacity. We also confirm that renewable expansion can be complemented by energy-only markets to incentivize efficient investment in conventional capacity.
Why is Germany’s energy transition so expensive? Quantifying costs from wind energy decentralisation
(2021)
This paper discusses the efficiency of promotion schemes for renewable energy sources using the example of onshore wind energy in Germany. We analyse whether the scheme incentivised a cost-minimal capacity build-up by developing a model to derive two cost-minimal benchmark scenarios, which are then compared to the historical capacity build-up between 1995 and 2015. The costs of the two cost-minimising benchmark scenarios are significantly lower than those of the historical build-up. The benchmark cost reduction largely stems from greater efficiency—fewer turbines are being constructed overall but they are being placed at better wind sites so the annual production of wind energy remains unchanged. Hence, aggregated turbine land use is also significantly down in these scenarios. Furthermore, we compare costs for consumers, as protecting consumers from price discrimination of producers is sometimes used to justify higher payments for low-wind sites. However, our results show that the efficiency gain from building at high wind sites outweighs the distributional effect, even from a consumer’s perspective.
The interdependence of electricity and natural gas markets is becoming a major topic in energy research. Integrated energy models are used to assist decision-making for businesses and policymakers addressing challenges of energy transition and climate change. The analysis of complex energy systems requires large-scale models, which are based on extensive databases, intertemporal dynamics and a multitude of decision variables. Integrating such energy system models results in increased system complexity. This complexity poses a challenge for energy modellers to address multiple uncertainties that affect both markets. Stochastic optimisation approaches enable an adequate consideration of uncertainties in investment and operation planning; however, stochastic modelling of integrated large-scale energy systems further scales the level of complexity. In this paper, we combine integrated and stochastic optimisation problems and parametrise our model for European electricity and gas markets. We analyse and compare the impact of uncertain input parameters, such as gas and electricity demand, renewable energy capacities and fuel and CO2 prices, on the quality of the solution obtained in the integrated optimisation problem. Our results quantify the value of encoding uncertainty as a part of a model. While the methodological contribution should be of interest for energy modellers, our findings are relevant for industry experts and stakeholders with an empirical interest in the European energy system.
Auctions have become the primary instrument for promoting renewable energy around the world. However, the data published on such auctions are typically limited to aggregated information (e.g., total awarded capacity, average payments). These data constraints hinder the evaluation of realisation rates and other relevant auction dynamics. In this study, we present an algorithm to overcome these data limitations in German renewable energy auction programme by combining publicly available information from four different databases. We apply it to the German solar auction programme and evaluate auctions using quantitative methods. We calculate realisation rates and—using correlation and regression analysis—explore the impact of PV module prices, competition, and project and developer characteristics on project realisation and bid values. Our results confirm that the German auctions were effective. We also found that project realisation took, on average, 1.5 years (with 28% of projects finished late and incurring a financial penalty), nearly half of projects changed location before completion (again, incurring a financial penalty) and small and inexperienced developers could successfully participate in auctions.