Climate change has become a central focus of political and societal concern, ranking as one of the most important global challenges of our time. To meet the objectives of the Paris Agreement, Germany primarily emphasizes the increasing use of renewable energies and an increase in energy efficiency. The German government's ambitious goal is to reach greenhouse gas neutrality by 2045.To achieve this ambitious goal of a climate-neutral building stock by 2045, further investments in energy-efficient refurbishments and renewable energy technologies (and especially renewable heat) are necessary. 

In this context, the main objective of this cumulative dissertation is to find empirical evidence for effects of energy efficiency improvements as well as of different heating technologies on real estate sales prices and rents. In addition, regarding renewable energy technologies beyond the aforementioned heating systems, different drivers for the expansion of rooftop photovoltaic (PV) systems are investigated.

Energy efficiency and renewable energy are key pillars of the energy transition that is high on the political agenda of governments in face of the climate crisis. Germany, however, is underperforming in its emissions reduction goals. There is still room for improvement, especially in the building sector – but this is often associated with high upfront investments. There is evidence that the market for energy efficiency in the German rental housing market is inefficient, resulting in underinvestment. To investigate these inefficiencies, this study estimates a hedonic pricing model combined with a total-cost-of-use perspective based on the observation of warm rents for a sample of 3,903,473 rental offers from 2014 to 2021. In a “perfect world”, the effect of the energy performance score given in energy performance certificates as an indicator of energy consumption or demand, respectively, is expected to be zero, as corresponding costs are already included in the warm rent. If the coefficient is significantly different from zero, it can be interpreted as measure for inefficiencies. The study further investigates whether disclosing heating costs in real estate advertisements could lead to a better appreciation of energy efficiency in the rental market and thus contribute to closing the information gap. Results show that the market for energy efficiency is indeed inefficient; however, the disclosure of full information can help to overcome these inefficiencies. These results lead to several important policy implications.

Exploiting regional heterogeneity in population dynamics across more than 10,000 municipalities in Germany, we provide robust empirical evidence that population aging depresses real estate prices and rents. Using millions of individual real estate listings and detailed demographic data at the municipality level, we estimate that average sales prices in 2020 would have been up to 12% higher if the population age distribution had been the same as in 2008. We show that population aging not only reduces prices but also increases the availability of real estate. In addition, we document substantial heterogeneity in price responses across dwelling type, property characteristics, and urban-rural status, suggesting that lower housing demand and life-cycle dissaving are driving our results. We predict that population aging will continue to exert downward pressure on house prices and exacerbate regional disparities in Germany.

On the market where prospective renters meet dwelling offers, competitive forces and rational behavior on both sides would imply that the monthly basic rent should reflect differences in expected monthly heating costs – other things being equal. We test this hypothesis by specifying a hedonic price model reflecting a total-cost-of-renting perspective. Drawing on 844,229 apartment listings for rent from 2014 to 2020 on a small spatial scale, we find a premium for more energy-efficient apartments; however, it is rather small. If the energy performance score decreases by 10 kWh/m2a, the monthly basic rent increases, on average, by roughly €0.01 per square meter living area. The expected energy cost savings thereby exceed the premium by a factor of three to seven. Rather, we find discounts of up to 9.2 % if apartments use heating technologies that are known to be inefficient. We explore various explanations for these outcomes, considering both landlord and renter behavior.

Distribution grids are more and more penetrated by decentralized, flexible units. For this reason, aspects of active system operation become increasingly relevant there. The management of many active units on the lower voltage network levels is a challenge that requires improved data communication and multi-energy-system operation management. In this work, we propose a common data layer for different generic technology models that can be used in a standardized manner to depict energy consumption, generation, and conversion. The technology models allow to compute system operation points or dispatch schedules for different stakeholders related to distribution grids e.g., system operators.

Market-based congestion management has been proposed as a more efficient means of coping with congestion issues in zonal market designs. However, strategic behaviour has been identified as a fundamental problem of market-based approaches. This paper focuses on strategic behaviour in the setting of a local flexibility market. In this type of market, trading occurs in parallel with the intraday market and flexibility is verified by reporting baselines through a verification platform. Under these conditions, market monitoring based on statistical tests is presented as a countermeasure. By implementing tests that are robust against autocorrelation and based on the illustrative example, it is shown that the identification of strategic behaviour is possible. In combination with appropriate regulatory sanctioning, strategic behaviour can become less attractive and, in the best case, be prevented leading to a reduction in congestion management costs. The simplified example presented in this paper can serve as a basis for more complex use cases where additional factors need to be considered.

With energy generation becoming increasingly decentralized, the need for congestion management across grid voltage levels is also increasing. To enable fair sharing of congestion costs among grid operators, these costs must be allocated to congested grid elements. We propose using the Shapley value for this purpose. The Shapley value is a cooperative game theory concept that was developed to share a total surplus generated by a coalition of players between the players based on their marginal contributions to the coalition. We apply this concept to share the costs of congestion management between grid elements based on their contributions to overall congestion management costs. To reduce the computational complexity of the Shapley value, we introduce two novel simplification approaches and compare them to existing methods using a numerical example based on CIGRE benchmark grids. The first method exploits the fact that the characteristic function for the congestion costs is obtained from an optimal power flow computation (i.e., a constrained optimization problem). It utilizes knowledge about which constraints are non-binding in the optimization to derive the values of related coalitions without calculating them. The second method takes advantage of the fact that the congestion management cost-allocation game is monotone and derives the values of coalitions based on this property. Both methods are implemented and compared to sampling. Using the first method, we are able to reduce computational complexity to less than 20% of that of the original problem while maintaining exact results. Our second approach is not dependent on detailed knowledge of the underlying optimization problem and can reduce the computational time by almost half with exact results and much further when compromising precision. While the methods are presented through an application example, they can be applied to other games with similar properties.

 

The future transformation of the European electricity system will be strongly influenced by both an ongoing
integration of variable renewable energy sources (VREs) and an increased proliferation of electric vehicles (EVs).
This combination will cause considerable uncertainty, especially since EV diffusion may greatly vary regarding
both the spatio-temporal penetration pattern and the achievable flexibility level. Notably, power plant investment
in the long run and dispatch in the short run will be affected. Hence, this paper assesses the impact of EV
penetration on the integration of VREs and the costs of CO2 emission reduction as well as the necessary investments
in controllable plant capacities under the consideration of frequency reserve and backup capacity
requirements. Applying an extended European energy system model, we found that EVs with a high flexibility
level may contribute tremendously to improved VRE integration, alleviating the number of necessary VRE investments
to achieve emission-reduction goals. Simultaneously, overall system costs are reduced even though
necessary investments in controllable plants, ensuring the abovementioned system stability needs, significantly
increase. Policy makers should hence ensure sufficient incentives to both exploit the EVs’ potential and safeguard
corresponding investments in controllable plants, which need to remain attractive even though full-load hours
are decreasing.

The German government is aiming for a climate-neutral building stock before 2050 to meet the defined goals of the Climate Action Plan 2050. Increasing the building stock's energy efficiency is therefore a high priority, and investments by private homeowners will greatly influence this, as around 46.5% of German homes are owner-occupied. To identify the possible monetary benefits of investments in energy retrofits, we investigate whether energy efficiency is reflected in the property values of German single-family homes. Therefore, we examine potential heterogeneous effects across regions. With 422,242 individual observations on a 1 km2-grid level from 2014 to 2018, this study adds to the extant literature by 1) examining the energy efficiency effect on housing values for the entire country and specifically investigating regional disparities in this context, and 2) estimating an energy efficiency value-to-cost ratio to compare housing values' increase with initial investment costs and future energy cost savings. Applying hedonic analysis, we find a positive relationship between energy efficiency and asking prices. If energy efficiency increases by 100 kWh/m2a, prices increase by 6.9% on average. We also find evidence for regional disparities. The effects are significantly weaker in large cities than in other urban areas, whereas the impact in rural regions is much stronger. According to this, housing shortage and higher purchasing power per capita were identified as drivers for low energy efficiency premiums. Finally, there is evidence that about 98% of future energy cost savings are already reflected in a higher housing value under myopic expectations regarding future energy prices.
 

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9221982

Will be published after the CIRED 2020 in Berlin.

currently under review

working paper can be found here

WeatherAggReOpt

Projektvorstellung

Clustering Investment Choice

Das Paper kann wie folgt zitiert werden: “This article can already be cited using the year of online availability and the DOI as follows: Author(s), Article Title, Journal (Year), DOI.”

Conference Paper:

16th International Conference on the European Energy Market (EEM), Ljubljana 2019

There is an ongoing debate on the introduction of capacity markets in most European countries while a few of them have already established capacity markets. Since the implementation of independent national capacity markets is not in line with the target of a pan-European internal electricity market we investigate the impacts of uncoordinated capacity markets compared with coordinated capacity markets. A probabilistic approach for the determination of capacity requirements is proposed and a European electricity market model (E2M2s) is applied for evaluation. The model simultaneously optimizes investments and dispatch of power plants. Besides the impact on generation investments, market prices and system costs we analyze effects on production and security of supply. While coordinated capacity markets reveal high potentials for cross-border synergies and cost savings, uncoordinated and unilateral implementations can lead to inefficiencies, in particular free riding effects and endanger security of supply due to adverse allocation of generation capacity.

Den Volltext finden Sie hier.

http://www.et-energie-online.de/AktuellesHeft/Inhalt.aspx

Weitere Details finden Sie hier.

We present a stochastic modelling approach to describe the dynamics of hourly electricity prices. The suggested methodology is a stepwise combination of several mathematical operations to adequately characterize the distribution of electricity spot prices. The basic idea is to analyze day-ahead prices as panel of 24 cross-sectional hours and to identify principal components of hourly prices to account for the cross correlation between hours. Moreover, non-normality of residuals is addressed by performing a normal quantile transformation and specifying appropriate stochastic processes for time series before fit. We highlight the importance of adequate distributional forecasts and present a framework to evaluate the distribution forecast accuracy. The application for German electricity prices 2015 reveal that: (i) An autoregressive specification of the stochastic component delivers the best distribution but not always the best point forecasting results. (ii) Only a complete evaluation of point, interval and density forecast, including formal statistical tests, can ensure a correct model choice.

Mit dem weiteren Ausbau fluktuierender erneuerbarer Energien (EE) steigen in Zukunft die Anforderungen an die Aufrechterhaltung der Systembilanz. Aufgrund der Variabilität und Unvorhersehbarkeit der EE-Einspeisung wird sich der Regelleistungsbedarf in Zukunft erhöhen. Um dem entgegenzuwirken, sind adäquate Reservebemessungsverfahren von großer Bedeutung. Während in der Praxis in der Regel einfache deterministische oder probabilistische Modelle angewendet werden, entwickeln wir in diesem Beitrag ein verbessertes dynamisches Verfahren zur Reservebemessung unter Verwendung nichtparametrischer Verteilungen zur Beschreibung der Prognosefehler. Ein weiterer Mehrwert des vorgestellten Ansatzes besteht in der Berücksichtigung zeitabhängiger Wahrscheinlichkeitsverteilungen für Kraftwerksausfälle und einer anschließenden Faltung von konditionalen Wahrscheinlichkeitsverteilungen für Last-, Wind- und Solarprognosefehler. Das dynamische Verfahren wird mit einem statischen Ansatz zur Reservebemessung mit unkonditionalen Wahrscheinlichkeitsverteilungen verglichen. Basierend auf historischen Daten quantifizieren wir den Regelleistungsbedarf und analysieren die ökonomischen Auswirkungen dynamischer Reservebedarfe mit Hilfe einer Fallstudie für Deutschland und die Betrachtungsjahre 2013 und 2030.

Traditionell basiert das Engpassmanagement in Deutschland auf bilateralen Verträgen. Jedoch setzt eine Vergütung basierend auf bilateralen Verträgen nur begrenzt Anreize. So haben die Kraftwerksbetreiber keine Anreize, ihre Grenzkosten offenzulegen. Zudem ist die Teilnahme am Redispatch nicht verpflichtend, wodurch ein mögliches hohes Potential nicht genutzt wird. Aus diesen Gründen ist es wichtig, das Engpassmanagement in Deutschland neu zu gestaltet. In diesem Artikel werden daher verschiedene Redispatch-Vergütungsmodelle, wie sie in der Literatur diskutiert werden (Inderst, Wambach (2007), Hakvoort et al. (2009)), miteinander verglichen und hinsichtlich ihrer Anreize für Produzenten, Übertragungsnetzbetreiber und Konsumenten bewertet. Des Weiteren wird ein nachträglich zonales Modell als eine kurzfristige, effiziente Engpassmanagementmethode entwickelt. Der Fokus dieser Analyse liegt auf Deutschland als eines der größten Strommärkte in Europa.

With ever increasing shares of intermittent renewables also the need for flexible supply and demand resources as well as storages is increasing. In particular it is expected that decentralized storages and demand response can contribute to the integration of higher shares of renewables into the electricity system. This raises also a challenge related to market design which is often overlooked. The issue is how an existing small‐scale storage would optimally bid into a voluntary power exchange like those established in Europe.

It is shown that the optimal bidding strategy is based on a simple evaluation of the expected spread. If the expected spread implies a profitable operation of the storage, (almost) unconditional bids are submitted to the power exchange. Otherwise no bids are submitted. This bidding choice is shown to be suboptimal in a welfare perspective and the introduction of specific spread products is proposed to overcome this flaw in the current market design.

Economists concentrate usually on equilibria. For the case of the electricity market the long term equilibrium is given by the peak-load pricing model. In contrast control theory has focuses on system dynamics, that is how systems are affected by disturbances and shocks and how such situations may be controlled. Therefore rather deviations from system equilibria and ways to counteract those effects are analysed. Concerning uncertain impacts as input factor price changes and load pattern variations the dynamic perspective of control theory might provide a suitable tool for the analysis of electricity markets. Therefore in this paper the peak-load pricing model is translated to a control theoretic problem so that the impact of disturbances and the dynamics of investment activities can be examined.

Im Rahmen dieses Papers wird das mögliche Design regionaler Märkte betrachtet. Ne-ben den erforderlichen Kommunikations- und Informationssystemen stellt dies einen wesentlichen Aspekt der Umsetzung dar. Neben einer Analyse der unterschiedlichen Handelsmechanismen und Auktionsverfahren bestehender Märkte werden insbesonde-re die spezifischen Anforderungen an einen regionalen Markt untersucht. Dabei spielen auch Fragestellungen wie die freiwillige oder mandatorischen Handelsteilnahme, das Engpassmanagement, technologische Charakteristika der Erzeugungsanlagen der Marktteilnehmer und die Ankopplung an übergeordnete Märkte eine Rolle. Aus diesen Überlegungen wird abgeleitet, dass insbesondere ein Modell mit mehreren Auktionen pro Tag, bei dem auch komplexe Gebote möglich sind, für einen regionalen Marktplatz mit vielfältigen unterschiedlichen Erzeugern und Nachfragern geeignet erscheint. Ent-scheidend ist jedoch auch die Frage der Kopplung mit den übergeordneten nationalen bzw. internationalen Märkten.

The stylized model presented in this paper extends the approach developed by Fischer and Newell (2008) by analysing the optimal policy design in a context with more than one externality while taking explicitly into account uncertainty surrounding future emission damage costs. Well-designed support mechanisms for renewables are found to play a major role, notably as a means for compensating for technology spillovers, yet also for reducing the investors’ risks. However, the design of these support mechanisms needs to be target-aimed and well-focused. Besides uncertainty on the state of the world concerning actual marginal emission damage, we consider the technological progress through R&D as well as learning-by-doing. A portfolio of three policy instruments is then needed to cope with the existing externalities and optimal instrument choice is shown to be dependent on risk aversion of society as a whole as well as of entrepreneurs. To illus-trate the role of uncertainty for the practical choice of policy instruments, an empirical application is considered. Under some plausible parameter settings, direct subsidies to production are found to be of lower importance than very substantial R&D supports.

Strategic behavior by gas traders is likely to affect future gas prices. Within this article a computational game theoretic model is presented which allows assessing market power on the natural gas market and its influence on the electricity market. This model uses typical time segments to represent both seasonal load fluctuations on the natural gas market and hourly load fluctuations on the electricity market. An application is presented covering 40 regions and simultaneously optimizing dispatch and utilization of transmission lines on the power market as well as supply, transmission and storage on the natural gas market. The model is used to evaluate the influence of trader market power in the natural gas market on the electricity market. We compute price changes, sales volume and power plant utilization for three different market power specifications.

As a consequence of liberalization, electric utilities have developed different asset management strategies with the aim of reducing total maintenance costs by prolonging replacement cycles. A cost-minimizing network operator has to optimize the trade-off between costs for early replacement and quality penalties he faces in the case of outages (due to a resulting higher number of failures). The basic purpose of this paper is to identify the optimal decisions for a network operator under output-based quality regulation and to determine the main drivers for his decisions. In order to derive general insights, an analytical framework is chosen for this paper. This work builds on the foundations of optimal maintenance and replacement strategies as developed across Operations Research, Production Economics and Engineering Production Theory. As main results we are able to analytically derive optimal replacement strategies for network components depending on the characteristics of their failure distributions linked to the equipment’s cost, respective replacement cost, and quality penalties.