Institute of Energy Economics at the University of Cologne

BACKGROUND: The shift from solitary to social behavior is one of the major evolutionary transitions. Primitively eusocial bumblebees are uniquely placed to illuminate the evolution of highly eusocial insect societies. Bumblebees are also invaluable natural and agricultural pollinators, and there is widespread concern over recent population declines in some species. High-quality genomic data will inform key aspects of bumblebee biology, including susceptibility to implicated population viability threats. RESULTS: We report the high quality draft genome sequences of Bombus terrestris and Bombus impatiens, two ecologically dominant bumblebees and widely utilized study species. Comparing these new genomes to those of the highly eusocial honeybee Apis mellifera and other Hymenoptera, we identify deeply conserved similarities, as well as novelties key to the biology of these organisms. Some honeybee genome features thought to underpin advanced eusociality are also present in bumblebees, indicating an earlier evolution in the bee lineage. Xenobiotic detoxification and immune genes are similarly depauperate in bumblebees and honeybees, and multiple categories of genes linked to social organization, including development and behavior, show high conservation. Key differences identified include a bias in bumblebee chemoreception towards gustation from olfaction, and striking differences in microRNAs, potentially responsible for gene regulation underlying social and other traits. CONCLUSIONS: These two bumblebee genomes provide a foundation for post-genomic research on these key pollinators and insect societies. Overall, gene repertoires suggest that the route to advanced eusociality in bees was mediated by many small changes in many genes and processes, and not by notable expansion or depauperation.

<p>Car sharing services gain momentum as a potential alternative to various modes of transportation, including privately owned cars. This trend goes hand in hand with a renewed interest in the sharing economy, which has as essential premise that product ownership is of minor relevance. Using an online experiment, this study investigates if individual differences in psychological ownership influence the effects of well-known instrumental car attributes (price, parking convenience, and car type) on people's intentions to select a shared car. Results confirmed that instrumental attributes generally impact preferences for car sharing services, and that a low psychological ownership may lead to a higher preference for a shared car under specific circumstances. This suggests that not only instrumental car attributes, but also psychological disposition, specifically psychological ownership, of potential customers need to be taken into consideration when developing measures to stimulate car sharing services in society.</p>

A large share of integrated wind power causes technical and financial impacts on the operation of the existing electricity system due to the fluctuating behaviour and unpredictability of wind power. The presented stochastic bottom-up electricity market model optimises the unit commitment considering five kinds of markets and taking explicitly into account the stochastic behaviour of the wind power generation and of the prediction error. It can be used for the evaluation of varying electricity prices and system costs due to wind power integration and for the investigation of integration measures

A ship-mounted sodar was used to measure wind turbine wakes in an offshore wind farm in Denmark. The wake magnitude and vertical extent were determined by measuring the wind speed profile behind an operating turbine, then shutting down the turbine and measuring the freestream wind profile. These measurements were compared with meteorological measurements on two offshore and one coastal mast at the same site. The main purposes of the experiment were to evaluate the utility of sodar for determining wind speed profiles offshore and to provide the first offshore wake measurements with varying distance from a wind turbine. Over the course of a week, 36 experiments were conducted in total. After quality control of the data (mainly to exclude rain periods), 13 turbine-on, turbine-off pairs were analyzed to provide the velocity deficit at hub height as a function of the distance from the turbine. The results are presented in the context of wake measurements at other coastal locations. The velocity deficit is predicted with an empirical model derived from onshore measurements based on transport time dependent on surface roughness. The measurements are closer to those predicted using an onshore rather than an offshore roughness despite the relatively low turbulence experienced during the experiments.

Public transport operators (PTOs) increasingly face a challenging problem in switching from conventional diesel to more sustainable battery electric buses (BEBs). In this study, we optimize the opportunity fast-charging schedule of transit BEB networks in order to minimize the charging costs and the impact on the grid. Two mixed-integer linear programming (MILP) formulations that use different discretization approaches are developed and compared. Discrete-Time Optimization (DTO) resembles a time-expanded network that discretizes the time and decisions to equal discrete slots. Discrete-Event Optimization (DEO) discretizes the time and decisions into nonuniform slots based on arrival and departure events in the network. In addition to the DEO’s higher practicability, the comparative computational study carried out on the transit-bus network in the city of Rotterdam, Netherlands, shows that the DEO is superior to the DTO in terms of computational performance. To show the potential benefits of the optimal schedule, it is compared with two reference common-sense greedy strategies: First-in-First-Served and Lowest-Charge-Highest-Priority.

To comply with sustainability goals, many companies buy green energy to serve their energy demand. This is typically done by engaging in bilateral power purchase agreements (PPA) with renewable energy producers (REP). A PPA can be flexibly structured, but the core principle is that a buyer (company) agrees to buy future energy production of a seller (REP) at an agreed-upon fixed price. PPAs are financially attractive for sellers, providing price certainty, unlike trading in electricity markets. However, PPAs can bring quantity uncertainty for buyers due to the uncertainty of future green energy delivery. This uncertainty in the long-term endangers sustainability targets, and in the short-term complicates reliable and cost-efficient demand matching. Thus, multiple strategies have been used in PPAs to encourage sellers to provide accurate and good-faith predictions of their short-term and longer-term future production. Yet, it has been shown that REPs can have incentives to misreport predicted values. This has discouraged some companies from engaging in PPAs. In this paper, we first investigate how PPA structure and pricing can incentivize REPs to provide more reliable predictions. This shifts the risk of production uncertainty to REPs, increasing the chance that REPs adopt batteries. We further study how having batteries for REPs affects their own revenue as well as the reliability of their energy predictions for buyers. We use analytical and simulation approaches to propose a decision tree for a win-win PPA structure, which improves reliability for buyers while maintaining profitability for REPs.

Congestion management schemes have taken a prominent place in current electricity market design discussions. In this paper, the implications of establishing zonal pricing in Europe are analyzed with regard to potential zonal delimitations and associated effects on total system costs. Thereby, a nodal model sets the benchmark for efficiency and provides high-resolution input data for a cluster analysis based on Ward's minimum variance method. The proposed zonal configurations are tested for sensitivity to the number of zones and structural changes in the electricity market. Furthermore, dispatch and redispatch costs are computed to assess the costs of electricity generation and transmission. The results highlight that suitable bidding zones are not bound to national borders and that losses in static efficiency resulting from the aggregation of nodes into zones are relatively small.

The seaborne steam coal market has changed in recent years; demand has grown fast, important players have emerged, and since 2007 prices have increased significantly and remained relatively high. In this paper, we analyze steam coal market equilibria in the years 2006 and 2008 by testing for two possible market structure scenarios: perfect competition and an oligopoly setup with major exporters competing in quantities. The assumed oligopoly scenario cannot explain market equilibria for any year. While we find that the competitive model simulates market equilibria well in 2006, the competitive model is yet not able to reproduce real market outcomes in 2008. The analysis shows that not all available supply capacity was utilized in 2008. We conclude that either unknown capacity bottlenecks or more sophisticated non-competitive strategies were the cause for the high prices in 2008.

Renewable energies are meant to cover a large share of the future electricity demand. However, the availability of wind and solar power depends on local weather conditions. In this article, we analyze the impact of the stochastic availability of wind and solar energy on the cost-minimal power plant mix and the related total system costs. To determine optimal conventional, renewable and storage capacities for different shares of renewables, we apply a stochastic investment and dispatch optimization model to the European electricity market. The model considers stochastic feed-in structures and full load hours of wind and solar technologies and different correlations between regions and technologies. Key findings include a lower value of fluctuating renewables and higher system costs compared to deterministic investment and dispatch models. Furthermore, the value of solar technologies is—relative to wind turbines—underestimated when neglecting negative correlations between wind speeds and solar radiation. Keywords: Stochastic programming, Electricity, Renewable energy

In many net-zero energy scenarios, electrolytic hydrogen is a key component to decarbonize hard-to-abate sectors and to provide flexibility to the power sector. In current energy systems that are not yet fully decarbonized, however, the hydrogen ramp-up raises the concern of increasing power sector emissions. To avoid such additional emissions, recent EU regulation defines requirements for electrolytic hydrogen to qualify as green along three dimensions: the additionality, the proximity, and the simultaneity of renewable electricity generation. Focusing on the temporal dimension, this article investigates the effects of a strict hourly simultaneity requirement, full temporal flexibility, as well as simultaneity exemptions in the current EU regulation. We develop a model of a renewables-hydrogen project, consisting of individual wind turbines, solar panels, hydrogen electrolysis, and hydrogen storage. As a novelty, the model optimizes not only dispatch but also investment decisions, and we expose it to different regulatory conditions. We show that a flexible definition of green hydrogen does not necessarily increase power sector emissions. By contrast, requiring hourly simultaneity implies that rational investors build much larger wind turbines, hydrogen electrolyzers, and hydrogen storage than needed—meaning additional costs and embedded carbon, underutilized assets, and a potential slow-down of green hydrogen deployment. These adverse effects can only partially be mitigated by including solar panels and by the EU simultaneity exceptions. We argue that current energy transition trends further lower the risk of increasing power sector emissions under a flexible definition of green hydrogen and recommend this as the way forward for a sustainable hydrogen policy.

A significant share of the world's undiscovered oil and natural gas resources are assumed to lie under the seabed of the Arctic Ocean. Up until now, the exploitation of the resources especially under the European Arctic has largely been prevented by the challenges posed by sea ice coverage, harsh weather conditions, darkness, remoteness of the fields, and lack of infrastructure. Gradual warming has, however, improved the accessibility of the Arctic Ocean. We show for the most resource-abundant European Arctic Seas whether and how a climate induced reduction in sea ice might impact future accessibility of offshore natural gas and crude oil resources. Based on this analysis we show for a number of illustrative but representative locations which technology options exist based on a cost-minimization assessment. We find that under current hydrocarbon prices, oil and gas from the European offshore Arctic is not competitive on world markets.

This paper summarizes the findings of a study undertaken by the European Academies Science Advisory Council to evaluate the development challenges of concentrating solar power (CSP) and its consequent potential to contribute to low carbon electricity systems in Europe, the Middle East and North Africa (the MENA region) to 2050. The study reviewed the current status and prospective developments of the four main CSP technology families, and identified prospective technical developments, quantifying anticipated efficiency improvements and cost reductions. Similarly, developments in thermal energy storage were evaluated, and the role and value of CSP storage in electricity systems were examined. A key conclusion was that as the share of intermittent renewables in an electricity system increases, so does the value of thermal energy storage in CSP plants. Looking ahead, the study concludes that CSP should be cost competitive with fossil-fired power generation at some point in the 2020’s provided that commercial deployment continues at an increasing rate, and through support mechanisms that incentivise technology development. Incentive schemes should reflect the real value of electricity to the system, and should ensure sufficient transparency of cost data that learning rates can be monitored. Key factors which will determine CSP’s contribution in Europe and the MENA region over the period to 2050 are generating costs, physical constraints on construction of new plants and transmission, and considerations of security of supply. The study makes recommendations to European and MENA region policy makers on how the associated issues should be addressed.

Road network and trajectory representation learning are essential for traffic systems since the learned representation can be directly used in various downstream tasks (e.g., traffic speed inference, travel time estimation). However, most existing methods only contrast within the same scale, i.e., treating road network and trajectory separately, which ignores valuable inter-relations. In this paper, we aim to propose a unified framework that jointly learns the road network and trajectory representations end-to-end. We design domain-specific augmentations for road-road contrast and trajectory-trajectory contrast separately, i.e., road segment with its contextual neighbors and trajectory with its detour replaced and dropped alternatives, respectively. On top of that, we further introduce the road-trajectory cross-scale contrast to bridge the two scales by maximizing the total mutual information. Unlike the existing cross-scale contrastive learning methods on graphs that only contrast a graph and its belonging nodes, the contrast between road segment and trajectory is elaborately tailored via novel positive sampling and adaptive weighting strategies. We conduct prudent experiments based on two real-world datasets with four downstream tasks, demonstrating improved performance and effectiveness.

Energy conversion in the machines and information processors of the capital stock drives the growth of modern economies. This is exemplified for Germany, Japan, and the USA during the second half of the 20th century: econometric analyses reveal that the output elasticity, i.e. the economic weight, of energy is much larger than energyʼs share in total factor cost, while for labor just the opposite is true. This is at variance with mainstream economic theory according to which an economy should operate in the neoclassical equilibrium, where output elasticities equal factor cost shares. The standard derivation of the neoclassical equilibrium from the maximization of profit or of time-integrated utility disregards technological constraints. We show that the inclusion of these constraints in our nonlinear-optimization calculus results in equilibrium conditions, where generalized shadow prices destroy the equality of output elasticities and cost shares. Consequently, at the prices of capital, labor, and energy we have known so far, industrial economies have evolved far from the neoclassical equilibrium. This is illustrated by the example of the German industrial sector evolving on the mountain of factor costs before and during the first and the second oil price explosion. It indicates the influence of the 'virtually binding' technological constraints on entrepreneurial decisions, and the existence of 'soft constraints' as well. Implications for employment and future economic growth are discussed.

Abstract In 2007, Germany changed network access regulation in the natural gas sector and introduced a so-called entry-exit system. The spot market effects of the reregulation remain to be examined. We use cointegration analysis and a state space model with time-varying coefficients to study the development of natural gas spot prices in the two major trading hubs in Germany and the interlinked spot market in the Netherlands. To analyse information efficiency in more detail, the state space model is extended to an error correction model. Overall, our results suggest a reasonable degree of price convergence between the corresponding hubs. Market efficiency in terms of information processing has increased considerably among Germany and the Netherlands.

We consider the problem of modeling the dependence among many time series. We build high‐dimensional time‐varying copula models by combining pair‐copula constructions with stochastic autoregressive copula and generalized autoregressive score models to capture dependence that changes over time. We show how the estimation of this highly complex model can be broken down into the estimation of a sequence of bivariate models, which can be achieved by using the method of maximum likelihood. Further, by restricting the conditional dependence parameter on higher cascades of the pair copula construction to be constant, we can greatly reduce the number of parameters to be estimated without losing much flexibility. Applications to five MSCI stock market indices and to a large dataset of daily stock returns of all constituents of the Dax 30 illustrate the usefulness of the proposed model class in‐sample and for density forecasting. Copyright © 2016 John Wiley & Sons, Ltd.

Energy management plays a crucial role in providing necessary system flexibility to deal with the ongoing integration of volatile and intermittent energy sources. Demand Response (DR) programs enhance demand flexibility by communicating energy market price volatility to the end-consumer. In such environments, home energy management systems assist the use of flexible end-appliances, based upon the individual consumer’s personal preferences and beliefs. However, with the latter heterogeneously distributed, not all dynamic pricing schemes are equally adequate for the individual needs of households. We conduct one of the first large scale natural experiments, with multiple dynamic pricing schemes for end consumers, allowing us to analyze different demand behavior in relation with household attributes. We apply a spectral relaxation clustering approach to show distinct groups of households within the two most used dynamic pricing schemes: Time-Of-Use and Real-Time Pricing. The results indicate that a more effective design of smart home energy management systems can lead to a better fit between customer and electricity tariff in order to reduce costs, enhance predictability and stability of load and allow for more optimal use of demand flexibility by such systems.

BACKGROUND: Cluster immunotherapy represents an interesting alternative to conventional up-dosing schedules because it allows achieving the maintenance dose within a shorter time interval. In this study, the efficacy and safety of cluster immunotherapy with a high polymerized allergen extract of a grass/rye pollen mixture have been evaluated in a randomized, double-blind, placebo-controlled, multicenter study. METHODS: In total, 121 patients with allergic rhinoconjunctivitis due to grass pollen were randomized 1 : 1 to verum or placebo group. A short cluster up-dosing schedule of only 1 week was applied to achieve the maintenance dose which was administered monthly during the study period of 1 year. Total combined symptom and medication score (TCS) was defined as primary outcome parameter. Secondary outcome parameters were individual symptom and medication scores, 'well days,' global improvement as well as immunological effects and nasal allergen challenge. The safety profile was evaluated based on the European academy of allergy and clinical immunology grading system. RESULTS: Significant reduction in the verum compared to the placebo group (intention-to-treat, population, verum: n = 55; placebo: n = 47) was found regarding TCS (P = 0.005), rhinoconjunctivitis total symptom score (RTSS, P = 0.006), and total rescue medication score (TRMS, P = 0.002). Additionally, secondary outcomes such as 'well days,' nasal challenge results, and increase of specific IgG4 were in favor of the active treatment. All systemic adverse reactions (0.8% of all injections in the verum group) were of mild intensity. No severe reactions related to the study medication were observed. CONCLUSION: Cluster immunotherapy with high polymerized grass pollen extracts resulted in significant clinical efficacy and has been shown to be a safe treatment for grass pollen-allergic patients.

Because of the decrease in domestic production in Europe, additional natural gas volumes will be required. In addition to Nord Stream, the major import pipeline projects, Nabucco and South Stream, have been announced to provide further gas supplies to Europe. This raises the question concerning whether and how these projects contribute to the European Union’s focus on security of supply. Applying the natural gas infrastructure model TIGER, this paper investigates the impact of these pipeline projects on southeastern Europe’s gas supply. Gas flows and marginal cost prices are evaluated in general and considering the possibility of supply disruptions via Ukraine for the year 2020. The model results show a positive impact of these pipelines on security of supply despite few consumer cut-offs that result from intra-European bottlenecks. South Stream is only highly utilized in case of a Ukraine crisis, supporting the idea that its main purpose is to bypass Ukraine.

Since the late 1990s, the European higher education system has had to face deep structural changes. With the public authorities seeking to create an environment of quasi-markets in the higher education sector, the increased competition induced by recent reforms has pushed all publicly financed higher education institutions (HEIs) to use their resources more efficiently. HEIs increasingly now aim at differentiating themselves from their competitors in terms of the range of outputs they produce. Assuming that different market positioning strategies will have different effects on the performance of HEIs, this article explores the existence of economies of scale and scope in the German higher education sector. Using an input-oriented distance function approach, we estimate the economies of scale and scope and the technical efficiency for 154 German HEIs from 2001 through 2007. Our results suggest that comprehensive universities should indeed orientate their activities to the concept of a full-university that combines teaching and research activities across a broad range of subjects. In contrast, praxis-oriented small and medium-sized universities of applied sciences should specialize in the teaching and research activities they conduct. (JEL codes: L25, I23, D24)