• Energy Research
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The weekly release of the U.S. inventory level by the DOE-EIA is known as the market mover in the U.S. oil futures market. We uncover suspicious trading patterns in the WTI futures markets in days when the inventory level is released that are higher than market forecasts: there are significantly more orders initiated by buyers in the two hours preceding the official release of the inventory level, with a drop in the average price of -0.25% ahead of the news release. This finding is consistent with informed trading. We also provide evidence of an asymmetric response of the oil price to oil-inventory news, and highlight an over-reaction that is partly compensated in the hours following the announcement.

International audience This chapter is about the recent development of wind energy in three European countries, France, Germany and Portugal, investigated through landscape issues. Its objective is to provide information about the role of institutions and landscape practices in the management of energy transition.‘Energy landscapes’ are understood here as landscapes with a low carbon content which physically and socially include the presence of new energy infrastructures (Nadaï and Van der Horst, 2010; Selman, 2010; Pasqualetti, 2000). Landscape is envisaged as a cultural and social process whose transformations we will try to monitor using a dual analysis, on one hand, of the institutions which support organisation and perception, and on the other, of the practices with which the spaces are experienced, managed and practised, especially when they are reorganised around new energy infrastructures.After having definedour method, we will examine the deployment of wind energy and its articulation to landscape issues in each of the three countries. We will conclude by comparing the practices and institutional issues linked to the emergence of these new energy landscapes. The comparison of the results from each country will allow us to discuss the role of landscape in the deployment of wind energy and to conclude in general on its importance in managing the energy transition.

The functional trait framework provides a powerful corpus of integrated concepts and theories to assess how environmental factors influence ecosystem functioning through community assembly. While common in plant ecology, this approach is under-used in microbial ecology. After an introduction of this framework in the context of microbial ecology and enzymology, we propose an approach 1) to elucidate new links between soil microbial community composition and microbial traits; and 2) to disentangle mechanisms underlying “total” potential enzyme activity in soil (sum of 7 hydrolase potential activities). We address these objectives using a terrestrial grassland ecosystem model experiment with intact soil monoliths from three European countries (Switzerland, France and Portugal) and two management types (Conventional-intensive and Ecological-intensive), subjected to 4 rain regimes (Dry, Wet, Intermittent and Normal) under controlled conditions in a common climate chamber. We found tight associations between proxies of microbial ecoenzymatic community-weighted mean traits (enzymatic stoichiometry and biomass-specific activity) and community composition, bringing new information on resource acquisition strategy associated with fungi, Gram positive and Gram negative bacteria. We demonstrate that microbial biomass explained most of the total enzyme activity before altered rain regimes, whereas adjustments in biomass-specific activity (enzyme activity per unit of microbial biomass) explained most variation under altered rain regime scenarios. Furthermore, structural equation models revealed that the variation of community composition was the main driver of the variation in biomass-specific enzyme activity prior to rain perturbation, whereas physiological acclimation or evolutionary adaptation became an important driver only under altered rain regimes. This study presents a promising trait-based approach to investigate soil microbial community response to environmental changes and potential consequences for ecosystem functioning. We argue that the functional trait framework should be further implemented in microbial ecology to guide experimental and analytical design.

Decarbonisation of energy systems requires deep structural change. The purpose of this research was to analyse the rates of change taking place in the energy systems of each Member State of the European Union (EU), and the EU in aggregate, in the light of the EU's climate change mitigation objectives. Trends on indicators such as sectoral activity levels and composition, energy intensity, and carbon intensity of energy were compared with decadal benchmarks derived from deep decarbonisation scenarios. The methodology applied provides a useful and informative approach to tracking decarbonisation of energy systems. The results show that while the EU has made significant progress in decarbonising its energy system. On a number of indicators assessed the results show that a significant acceleration from historical levels is required in order to reach the rates of change seen on the future benchmarks for deep decarbonisation. The methodology applied provides an example of how the research community and international organisations could complement the transparency mechanism developed by the Paris Agreement on climate change, to improve understanding of progress toward low-carbon energy systems.

We present ASGriDS, an asynchronous Smart Grid simulation framework. ASGriDS is multi-domain, it simultaneously models the power network along with its physical loads/generators, controllers, and communication infrastructure. ASGriDS provides a unified workflow in a pythonic environment, to describe, run and control complex SmartGrid deployment scenarios. ASGriDS is an event-driven simulator that can run in either real-time or accelerated real-time. As it is modular and its components interact asynchronously, it can run either locally on a distributed infrastructure, also in hardware-in-the-loop setups, and on top of emulated/physical communication links. In this paper, we present the design of our simulator and we demonstrate its use with a generation control problem on a low voltage network. We use ASGriDS to deploy a realtime controller based on optimal power flow, on top of TCP and UDP based communication network, under various packet loss conditions.

Virtual primary frequency regulation (VPFR) is a coordinated demand and generation response, which is proposed to counteract post-disturbance frequency fluctuations and deviations. But meanwhile, it also influences oscillation stability. VPFR’s continuity, slope and response time are three characteristics that have large influences on oscillation stability. Due to uncertainties of these characteristics, VPFR’s influences are nondeterministic and random. In order to analyze them, first, VPFR is modeled and combined with existing models of power systems for eigenvalue analysis. The repeated LHS method is developed to more efficiently sample uncertainties. Eigenvalue analysis is repeatedly made for each sampling to estimate mean values of participation factors and distributions of damping ratios for critical modes. The former indicate where VPFR increases system damping, where not. The latter show the influences of regulation parameters on damping ratios for VPFR at a selected location. Utilizing this statistical information, system operators can select VPFR’s locations and parameters through price signals to improve system damping. Two test cases on a 16-generator, 68-bus system show that VPFR implemented in this way can improve oscillation stability as well as frequency stability. Finally, VPFR’s performance is further investigated with motor-type loads.