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Institute
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.
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.
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 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.
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.
This paper analyses the potential impact of the world's two main coal phase-out instruments: 1) direct regulatory intervention restricting the operation of existing coal-fired generation capacity and prohibiting investment in new capacity and 2) market-based CO2-pricing instruments that make emission-intensive coal-fired generation less competitive. We quantify these instruments' potential effects in the empirical setting of Germany, where both instruments are employed concurrently. However, this paper's approach and methodology can be applied in any jurisdiction or energy system worldwide.
Our paper provides quantitative results with a pan-European partial-equilibrium electricity system model. The model applies an innovative three-step approach. Step 1 solves an investment and dispatch problem with reduced technical and temporal complexity for European countries. Step 2 computes the dispatch problem at a bihourly resolution and step 3 solves the investment and dispatch problem for Germany with increased technical complexity.
Our results confirm that both a regulated phase-out as well as a price on CO2 emissions can reduce the amount of coal-fired generation in an electricity system. If CO2 prices remain at current levels, coal-fired power plants leave the market significantly before the regulated phase-out date, reducing demand for employees in the Lusatian lignite industry. Regions and federal governments should take this finding into account when planning and preparing for structural change.
Since energy system models require a large amount of technical and economic data, their quality significantly affects the reliability of the results. However, some publicly available data sets, such as the transmission system operators’ day-ahead load forecasts, are known to be biased and inaccurate, leading to lower energy system model performance. We propose a time series model that enhances the accuracy of transmission system operators’ load forecast data in real-time, using only the load forecast error’s history as input. We further present an energy system model developed specifically for price forecasts of the short-term day-ahead market. We demonstrate the effectiveness of the improved load data as input by applying it to this model, which shows a strong reduction in pricing errors, particularly during periods of high prices and tight markets. Our results highlight the potential of our method the enhance the accuracy of energy system models using improved input data.
Importing green chemical energy carriers is crucial for meeting European climate targets. However, estimating the costs of supplying these energy carriers to Europe remains challenging, leading to a wide range of reported supply-cost estimates. This study analyzes the estimated supply costs of green chemical energy carriers at the European border using a dataset of 1050 data points from 30 studies. The results reveal significant variations in supply costs, with a projected four-fold difference in 2030 and a five-fold difference in 2050 across all energy carriers. The main drivers of cost differences are varying production costs, particularly influenced by the weighted average costs of capital and capital expenditures of renewable energy sources, electrolyzers, and carrier-specific conversion processes. Transport costs also contribute to variations, mainly influenced by the choice of energy carrier and the weighted average costs of capital. To optimize cost-efficiency and sustainability in the chemical energy carrier sector, this paper recommends prioritizing transparency and sensitivity analyses of key input parameters, classifying energy carriers based on technological and economic status, and encouraging research and development to reduce production costs.
Investments in electricity transmission and generation capacity must be made despite significant uncertainty about the future developments. The sources of this uncertainty include, among others, the future levels and spatiotemporal distribution of electricity demand, fuel costs and future energy policy. In recent years, these uncertainties have increased due to the ongoing evolution of supply- and demand-side technologies and rapid policy changes designed to encourage a transition to low-carbon energy systems. Because transmission and generation investments have long lead times and are difficult to reverse, they are subject to a considerable – and arguably growing – amount of risk.
This paper is set up to answer the question: What do we know about future hydrogen import costs on the European border? Our work focuses on breaking down, structuring and analysing the body of literature on hydrogen supply chains. We provide a comprehensive analysis on (i) What ranges of costs exist in the literature for specific steps of hydrogen supply chains? (ii) Which assumptions show the highest uncertainty? (iii) Related, what assumption seems to have the most substantial effect on the resulting EUR/kgH2 import cost estimate? While we narrow our discussion focus on North Africa and Australia to Europe in 2050, we hope policymakers, research fellows and energy-sector stakeholders around the globe might be interested in our results.
In this paper, we endogenously compute worst-case weather events in a transmission system expansion planning problem using the robust optimization approach. Mathematically, we formulate a three-level mixed-integer optimization problem, which we convert to a bi-level problem via the strong duality concept. We solve the problem using a constraint-and-column generation algorithm. We use cardinality-constrained uncertainty sets to model the effects of extreme weather realizations on supply from renewable generators.
To unlock the high potential of green hydrogen in reaching the ambitious 1.5 °C goal declared by the Paris Agreement, enormous (public) investments are needed. Social acceptance of these investments is required in order to implement hydrogen technologies as fast and efficiently as possible. This study investigates which benefits associated with green hydrogen foster its social acceptance. Using a between-subject design, we implement two different treatments. Both treatments have in common that the participants experience the transformation into a hydrogen economy in a virtual reality scenario. In "Info Security of Supply", we provide the participants information about the benefits of green hydrogen regarding the security of energy supply and climate protection. In Control, we inform them only about the benefits of climate protection. Subsequently, the participants decide about the financial support of a hydrogen project. Our preliminary results show a higher support if the focus is solely on the positive impact of green hydrogen for climate protection.
Offshore wind energy is rapidly expanding, facilitated largely through auctions run by governments. We provide a detailed quantified overview of utilised auction schemes, including geographical spread, volumes, results, and design specifications. Our comprehensive global dataset reveals heterogeneous designs. Although most auction designs provide some form of revenue stabilisation, their specific instrument choices vary and include feed-in tariffs, one-sided and two-sided contracts for difference, mandated power purchase agreements, and mandated renewable energy certificates.
We review the schemes used in all eight major offshore wind jurisdictions across Europe, Asia, and North America and evaluate bids in their jurisdictional context. We analyse cost competitiveness, likelihood of timely construction, occurrence of strategic bidding, and identify jurisdictional aspects that might have influenced auction results. We find that auctions are embedded within their respective regulatory and market design context, and are remarkably diverse, though with regional similarities. Auctions in each jurisdiction have evolved and tend to become more exposed to market price risks over time. Less mature markets are more prone to make use of lower-risk designs. Still, some form of revenue stabilisation is employed for all auctioned offshore wind energy farms analysed here, regardless of the specific policy choices. Our data confirm a coincidence of declining costs and growing diffusion of auction regimes.
In this paper, 180 papers on photovoltaic (PV) output forecasting were reviewed and a database of forecast errors was extracted for statistical analysis. The paper shows that among the forecast models, hybrid models are most likely to become the primary form of PV output forecasting in the future. The use of data processing techniques is positively correlated with the forecast quality, while the lengths of the forecast horizons and out-of-sample test sets have negative effects on the forecast accuracy. The paper also found that the use of data normalization, the wavelet transform, and the inclusion of clear sky index and numerical weather prediction variables are the most effective data processing techniques. Furthermore, the paper found some evidence of “cherry picking” in the reporting of errors and we recommend that the test sets be at least one year long to avoid any distortion in the performance of the models.
Two German offshore wind auctions in 2017 and 2018 saw more than 50% of winning capacity with “zero bids”. The nature of these surprisingly low bids is, however, not yet clear. In our paper, we discuss four hypotheses for possible causes for the auction results: (i) the bids are expected to be profitable due to market development and technological progress, (ii) bids can be perceived as “options to build” that can be realized if projects are profitable, (iii) bids are adjusted to secure grid access, and (iv) other long term reasons not primarily driven by the profitability of the winning bids. Our results suggest that there is evidence for all hypotheses to influence the decision making of auction bidders. In fact, we suggest to see the four hypothesis as cumulative value components, which reveal the true value of winning the auction at “zero cost” in aggregate.
This paper develops an integrated fundamental investment model which considers both the gas and electricity sector. Furthermore, we adopt the theory of stochastic programming with recourse in the combined model to account for uncertainty in the gas market. This approach enables us to analyze how uncertain gas demand in other sectors affects decisions to invest in electricity generation capacities. We find an overall decrease and a reallocation of investments in gas-fired power plants. We also quantify the expected costs of ignoring uncertainty.