@phdthesis{Riepin2022, author = {Riepin, Iegor}, title = {Modeling challenges of modern energy markets : studies on uncertainty, complexity, and constant change}, doi = {10.26127/BTUOpen-5976}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-59762}, school = {BTU Cottbus - Senftenberg}, year = {2022}, abstract = {This dissertation is a compilation of four self-contained research articles that focus on selected subjects in the field of energy economics. The first article focuses on the competitiveness of offshore wind in mature markets. In this work, we harmonise auction results based on the auction design features. We show that offshore wind power generation can be considered commercially competitive in mature markets without subsidy. Furthermore, once auction results are harmonised, we observe similar expected revenue streams of wind farms across countries. This finding means that different auction designs can fairly reflect the actual costs of developing wind farms and thus translate cost reductions into lower bids. The second article explores the impacts of uncertainty in integrated electricity and gas system optimization models. We address the trade-off that the energy research community faces on a daily basis, i.e., whether to neglect uncertainty when constructing an energy system model and accept a suboptimal solution or to incorporate uncertainty and increase model complexity. Our research aims to bring a systematic understanding of which parametric uncertainties most substantially affect long-term planning decisions in energy system models. In the third article, we focus on seasonal flexibility in the European natural gas market. We develop a market optimization model to simulate the operation of the gas market over a long period. This allows us to explore structural trends in market development, which are driven by changing supply and demand fundamentals. Our work contributes to the methodological question of how to measure the contributions of different flexibility options. Finally, the fourth article investigates the value of Projects of Common Interest—gas infrastructure projects supported by EU public funds—in maintaining gas system resilience amid cold-winter demand spikes and supply shortages. For this purpose, we develop the first application of adaptive robust optimization to gas infrastructure expansion planning. The model endogenously identifies the unfortunate realizations of unknown parameters and suggests the optimal investments strategies to address them. We find that (i) robust solutions point to consistent preferences for specific infrastructure projects, (ii) the real-world construction efforts have been focused on the most promising projects, and (iii) most projects are unlikely to be realized without financial support.}, subject = {Energy modelling; Energy systems; Stochastic programming; Uncertainty; Energiemodellierung; Energiesysteme; Stochastische Programmierung; Unsicherheit; Energieversorgung; Energiemarkt; Stochastisches Modell}, language = {en} } @phdthesis{BatzLineiro2025, author = {Batz Li{\~n}eiro, Taimyra}, title = {Insights into renewable energy support instruments and costs : evidence from German programs}, doi = {10.26127/BTUOpen-7156}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-71567}, school = {BTU Cottbus - Senftenberg}, year = {2025}, abstract = {This dissertation compiles three research articles that evaluate renewable energy support instruments and estimate the costs of deploying renewable sources at the national level. The research is based on the German experience; however, the findings are relevant to other markets and technologies. The first article examines the German photovoltaic (PV) auction program. Since the main objectives of renewable auctions are project realization and competitive prices, proper evaluation requires estimating realization rates in addition to early indicators such as auctioned capacity and resulting prices. Realization rates were estimated using an innovative approach that merged diverse public databases into a detailed, project-specific dataset. The analysis also considers the impact of developer characteristics on market behaviour and project outcomes, testing hypotheses on the influence of size and experience on bidding strategies and success. Results confirm that the German PV program was effective, with projects largely completed. While auctions helped discover competitive prices, subsidy reductions were mainly driven by falling technology costs. The second article applies the same data-merging algorithm to the German onshore wind auction program. Unlike PV auctions, which emphasized primary objectives, the wind design also sought secondary goals such as actor diversity and regional balance. In addition to realization rates, the study evaluates these goals by examining community energy companies (CECs) and the effect of including a grid expansion area (GEA). Findings show the initial scheme failed to promote wind deployment at competitive prices, while design elements intended to achieve secondary objectives neither met their goals nor prevented larger actors from gaming the system. The third article investigates the broader economic implications of Germany's support mechanisms, focusing on net subsidies under the Renewable Energy Sources Act. It quantifies the aggregated costs of all RES units installed between 2000 and 2021 and projects future payments. The analysis reveals that despite high historical costs, current and future support is declining due to reduced levelized costs of electricity (LCOEs) and higher wholesale prices. The study highlights the central role of wind and solar in cost-effective deployment and recommends policies to enhance the efficiency and effectiveness of future renewable investments.}, subject = {Renewable energy; Policy; Auction; Germany; F{\"o}rderpolitik; Erneuerbare Energie; Ausschreibungen; Deutschland; Deutschland; Erneuerbare Energien; {\"O}ffentliche F{\"o}rderung; {\"O}ffentliche Ausschreibung}, language = {en} } @phdthesis{Moebius2025, author = {M{\"o}bius, Thomas}, title = {Modelling uncertainty and risk preferences in European electricity markets, and the prediction of wholesale electricity prices}, doi = {10.26127/BTUOpen-7138}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-71388}, school = {BTU Cottbus - Senftenberg}, year = {2025}, abstract = {This dissertation is a compilation of five self-contained research articles that explore two key topics in energy economics. The first two articles focus on modelling uncertainties in energy systems, whereas the remaining three articles deal with electricity price forecasting. The first article develops an integrated European electricity and natural gas market model to examine the effects of uncertainty on both markets. The model is formulated as a stochastic optimisation problem and takes into account five key uncertainty factors: gas and electricity demand, installed renewable energy capacity, and fuel and CO2 prices. The result is the value of incorporating uncertainty as part of an energy system model. The second article examines the interactions between flexibility and investment risk in European electricity markets, focusing on the investment strategies of risk-averse decision-makers, taking into account various flexibility options. To this end, a stochastic investment model for the expansion of transmission and generation capacities in the European electricity system is developed, which takes into account the risk preferences of decision-makers through the implementation of conditional value at risk. The third article evaluates the impact of data pre-processing on the performance of energy system models. Motivated by the bias and inaccuracy of the day-ahead load forecasts from transmission system operators, this article first develops a time series model that improves the accuracy of load forecasts. The improved data is then implemented in an energy system model with the aim of testing the influence of data quality on the model results. The fourth article develops a hybrid model that combines a techno-economic energy system model with statistical models for electricity price forecasting, leveraging their respective advantages. The techno-economic energy system model provides an in-depth understanding of market mechanisms, while the stochastic models takes into account non-technical factors such as the expectations and speculative behaviour of market participants by interpreting prices. The fifth article combines an energy system model with various econometric models for electricity price forecasting in the day-ahead market. To this end, a price estimator derived from the energy system model is integrated as an exogenous variable into statistical and machine learning approaches. The results show that integrating this market clearing price into econometric models significantly improves forecast accuracy.}, subject = {Energy system modelling; Risk aversion; Uncertainty; Electricity price forecasting; Energiesystemmodellierung; Unsicherheit in Energiesystemen; Risikoaversion; Strompreisprognosen; Energiewirtschaft; Elektrisches Energiesystem; Modellierung; Unsicherheit; Risikoverhalten; Prognose; Strompreis}, language = {en} } @phdthesis{Engelhorn2022, author = {Engelhorn, Thorsten}, title = {On the value of the wind's infeed and the costs of capacity build-up : a bottom-up modelling for the German market}, doi = {10.26127/BTUOpen-6122}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-61221}, school = {BTU Cottbus - Senftenberg}, year = {2022}, abstract = {In this dissertation, I present three research articles on the economics of wind energy in Germany. The first two chapters primarily assess onshore wind energy's empirical value in the wholesale market. The third chapter discusses the efficiency of the German wind support scheme. In the first chapter, a database on German onshore wind turbines installed was created by using a general method to fill incomplete stock data. By using the database and highly granular wind speed data, hourly infeed and annual market values of around 25,700 wind turbines in Germany from 2005 to 2015 were calculated, and its drivers - infeed-price-correlation and standard deviation of infeed - analysed. Further, the spread in market values on turbine level was shown, regional differences quantified and the effect of turbine age on market values discussed. Turbines in central Germany had, on average, lower market values than turbines in the north, south or west of Germany. Modern turbines reached higher market values than older turbines. The second chapter on wind turbines' market values covers the period until 2019. This analysis confirmed that market values were highly diverse on turbine level. Further, it explained why the market value of a single turbine matters in the market premium model. In this context, the magnitude of the outperformance of technologically advanced turbines was assessed. Last, the effect of inter-annual weather variability on wholesale electricity prices and market values was quantified, using 19 different years of winds speeds and infeed data. In the third chapter, it was analysed whether the German wind support scheme incentivised a cost-minimal capacity build-up. Two cost-minimal benchmark scenarios were developed and compared to the historical capacity build-up between 1995 and 2015. The costs of the benchmark scenarios were significantly lower than those of the historical build-up. The benchmark cost reduction largely stemmed from greater efficiency in terms of using better wind sites. It was further shown that a spatially concentrated capacity-build up, as induced in the benchmarks, is beneficial from a consumer's point of view.}, subject = {Onshore wind power; Direct marketing; Market value; Cost minimisation; Efficiency; Windenergie an Land; Direktvermarktung; Marktwert; Kostenminimierung; Effizienz; Windenergie; Kostenoptimierung; Marktwert}, language = {en} } @phdthesis{Engelbrecht2017, author = {Engelbrecht, Daniel}, title = {Untersuchung der Potentiale und Anforderungen an ein Wirkleistungsmanagement im Verteilnetz mit hohem Anteil von dezentralen Erzeugungsanlagen}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-43479}, school = {BTU Cottbus - Senftenberg}, year = {2017}, abstract = {In der Dissertation werden neue Potentiale und Anforderungen sowie neue Verfahren und Methoden zur Bereitstellung von Systemdienstleistungen erarbeitet. Der Fokus wird dabei auf das Wirkleistungsmanagement im Verteilnetz gelegt. Bei einer zunehmenden Durchdringung von Erneuerbaren Energien steigt der gegenseitige Einfluss mit den technischen Systemen im Netzbetrieb und bestehenden sowie zuk{\"u}nftigen M{\"a}rkten. In der Arbeit werden organisatorische und technische L{\"o}sungen entwickelt, welche eine sichere Bereitstellung von Regelleistung sowie eine Unterst{\"u}tzung durch den Verteilnetzbetreiber f{\"u}r ein systemdienliches Engpassmanagement des {\"U}bertragungsnetzbetreibers erm{\"o}glichen. Anhand eines 110-kV-Modellnetzes werden die technischen L{\"o}sungen aufgezeigt und bewertet. Dabei zeigt sich, dass eine erweiterte Betriebsplanung im Verteilnetz notwendig wird. M{\"o}gliche Konflikte k{\"o}nnen im Vorfeld durch eine Netzzustandsbewertung und Informations{\"u}bermittlung gel{\"o}st werden. Weiterhin k{\"o}nnen Anforderungen des {\"U}NB zur Unterst{\"u}tzung zum Redispatch im Rahmen einer Vortagesplanung integriert werden. Zur Realisierung der Prozesse und somit Erf{\"u}llung der neuen Anforderungen sind zum Teil technische {\"A}nde-rungen in den Systemen der Netzf{\"u}hrung sowie organisatorische und rechtliche Anpassungen in der Handhabung des Engpassmanagements notwendig. Hierzu werden in der vorliegenden Arbeit konkrete Empfehlungen ausgesprochen.}, subject = {Wirkleistungsmanagement; Verteilnetz; Erneuerbare Energien; Regelleistung; Redispatch; Active power management; Distribution system; Control power; Redispatch; Renewable energy; Lastverteilung ; Erneuerbare Energien; Wirkleistung; Verteilungsnetz}, language = {de} }