FG Energiewirtschaft
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- Auction (1)
- CO2 price (1)
- Coal phase-out (1)
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- Data pre-processing (1)
- Day-ahead electricity prices (1)
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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.
This article details our experience developing and teaching an “Energy Systems Modeling” course, which sought to introduce graduate-level students to operations research, energy economics, and system modeling using the General Algebraic Modeling System (GAMS). In this paper, we focus on (i) the mathematical problems discussed in the course, (ii) the energy-related empirical interpretations of these mathematical problems, and (iii) the best teaching practices (i.e., our experiences regarding how to make the content interesting and accessible for students).
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.
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.
The accurate prediction of short-term electricity prices is vital for effective trading strategies, power plant scheduling, profit maximisation and efficient system operation. However, uncertainties in supply and demand make such predictions challenging. We propose a hybrid model that combines a techno-economic energy system model with stochastic models to address this challenge. The techno-economic model in our hybrid approach provides a deep understanding of the market. It captures the underlying factors and their impacts on electricity prices, which is impossible with statistical models alone. The statistical models incorporate non-techno-economic aspects, such as the expectations and speculative behaviour of market participants, through the interpretation of prices. The hybrid model generates both conventional point predictions and probabilistic forecasts, providing a comprehensive understanding of the market landscape. Probabilistic forecasts are particularly valuable because they account for market uncertainty, facilitating informed decision-making and risk management. Our model delivers state-of-the-art results, helping market participants to make informed decisions and operate their systems more efficiently.
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.
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.