FG Energiewirtschaft
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Institute
The future development of CO2emissions in the German electricity sector by 2030 is unclear: On the one hand, the amendment to the Climate Protection Act (from August 2021) aims to tighten emission reduction targets in order to promote the path to a decarbonised energy system. On the other hand, the complex interplay between the main instruments for reducing carbon emissions, fuel prices and the current energy shortage as a result of Russia's attack on Ukraine is creating pressure for an increase in emissions.
In our study, we examine whether Germany is on track to achieve its climate protection targets for 2030 and which measures can increase the likelihood. To do this, we use an energy system model parameterized to reflect the situation in the energy market in 2021, i.e. before Russia's attack on Ukraine. In addition to the quantitative side, we provide a qualitative analysis of the energy market in the light of recent events. We stress the importance of introducing a carbon price floor that can be dynamically changed in response to the development of other market forces and policies. This instrument would institutionalize a more plausible path to decarbonization and provide reassurance to investors.
Residents' acceptance of bioenergy is a prerequisite for its fast and efficient development but is ambiguously discussed in practice and research. We interpret a causal impact of bioenergy plant commissioning on housing prices in their vicinity as the average net external effects for residents. We utilize bioenergy plant construction data in Germany between 2007 and 2022 as a quasi-experiment with naturally occurring control groups in their vicinity and apply recently improved difference-in-difference estimation procedures to analyze instantaneous and heterogeneous treatment effects. We find a significant and negative impact on housing prices if plants use gaseous biofuel, generate electricity on-site, and are medium-sized. The magnitude of the effect is comparable to those for solar fields. In contrast, we find no impact for small and large bioenergy plants and those that use solid or liquid biofuels.
We study the effect of an employer’s communication concerning energy-efficient behavior on a firm’s employees’ behavioral responses. The management aims to enhance energy-efficient behavior performance by irregularly sending online messages to remote employees. We break down each message, analyzing the positive or negative emotions conveyed, collective or individual orientation, and ease of language. Comparing employees’ daily energy efficiency after receiving a message to their daily performance without a previous message, we find that messages generally result in an immediate increase in efficiency. Messages conveying negative emotions and those making a distinction between the manager and the employees have a smaller effect than messages conveying positive emotions and emphasizing a collective orientation that includes both the management and employees. Additionally, shorter messages are more likely to induce improved efficiency. Sending messages significantly impacts driver performance for up to six days after the message, resulting in economically relevant cost reductions.
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.
Auctions are a highly demanded policy instrument for the promotion of renewable energy sources. Their flexible structure makes them adaptable to country-specific conditions and needs. However, their success depends greatly on how those needs are operationalised in the design elements. Disaggregating data from the German onshore wind auction programme into individual projects, we evaluated the contribution of auctions to the achievement of their primary (deployment at competitive prices) and secondary (diversity) objectives and have highlighted design elements that affect the policy's success or failure. We have shown that, in the German case, the auction scheme is unable to promote wind deployment at competitive prices, and that the design elements used to promote the secondary objectives not only fall short at achieving their intended goals, but create incentives for large actors to game the system.
We conduct the first comprehensive meta-analysis of deterministic solar forecasting based on skill score, screening 1,447 papers from Google Scholar and reviewing the full texts of 320 papers for data extraction. A database of 4,687 points was built and analyzed with multivariate adaptive regression spline modelling, partial dependence plots, and linear regression. The marginal impacts on skill score of ten factors were quantified. The analysis shows the non-linearity and complex interaction between variables in the database. Forecast horizon has a central impact and dominates other factors' impacts. Therefore, the analysis of solar forecasts should be done separately for each horizon. Climate zone variables have statistically significant correlation with skill score. Regarding inputs, historical data and spatial temporal information are highly helpful. For intra-day, sky and satellite images show the most importance. For day-ahead, numerical weather predictions and locally measured meteorological data are very efficient. All forecast models were compared. Ensemble-hybrid models achieve the most accurate forecasts for all horizons. Hybrid models show superiority for intra-hour while image-based methods are the most efficient for intra-day forecasts. More training data can enhance skill score. However, over-fitting is observed when there is too much training data (longer than 2000 days). There has been a substantial improvement in solar forecast accuracy, especially in recent years. More improvement is observed for intra-hour and intra-day than day-ahead forecasts. By controlling for the key differences between forecasts, including location variables, our findings can be applied globally.