FG Energiewirtschaft
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Institute
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.
Operators seek to achieve economic objectives when dispatching assets in electricity markets subject to asset’s technical as well as market constraints. To ensure optimal operation in energy markets, the parametric uncertainty of key market drivers such as wholesale and balancing services prices should be investigated. The research problem to be covered in the paper deals with the revenue maximization of a price-taker battery storage power plant participating in competitive wholesale electricity and balancing markets. The paper uses a multi-stage optimization approach to model uncertainty of a battery energy storage system participating in the Spanish Market. The study reveals that a significant benefit of using stochastic solution (over 5 percent) is obtained when the role of battery energy storage system is analyzed for 2020.
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.
Our paper assesses the economic viability of green hydrogen exports from the middle east and north Africa to the EU in the long-term (for the year 2050). For this purpose, we simulate a techno-economic hydrogen supply chain based on generation from renewable energy sources. The chain of production, conversion, and transportation costs, in combination with potentials, yields a merit order of hydrogen supply. The paper focuses on one low-cost option for imports from the middle east and north Africa region. In addition, it provides a classification of additional options. We find that due to proximity, retrofitting and potentially even installing new pipelines from Morocco, Algeria and Tunisia could provide nearly half the expected European demand of 60 million tons p.a. by 2050. Based on our assumptions, the costs would be around 2 €/kgH 2 .
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 quantify the net external effects of conventional and renewable electricity generators by analyzing housing prices in their vicinity. Using a Differences-in-Differences approach, we find that (1) wind turbines reduce prices significantly, (2) solar fields have no significant impact, and (3) conventional plants over 1 km away show positive net effects. We set out to explain this result by disentangling the positive local external effects of energy generation, which we measure in terms of local purchasing power and tax revenues. Our results show that the commissioning of conventional power stations results in a significant increase in both purchasing power and business tax income in the vicinity. We thus conclude that significant financial participation of the local public in the development of renewable energy projects, especially wind turbines, could be key to increasing their acceptance and accelerating their expansion.
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.