Abstract-In this paper, the overvoltage problems that might arise from the integration of photovoltaic panels into low-voltage distribution networks is addressed. A distributed scheme is proposed that adjusts the reactive and active power output of inverters to prevent or alleviate such problems. The proposed scheme is model-free and makes use of limited communication between the controllers, in the form of a distress signal, only during emergency conditions. It prioritizes the use of reactive power, while active power curtailment is performed only as a last resort. The behavior of the scheme is studied using dynamic simulations on a single low-voltage feeder and on a larger network composed of 14 low-voltage feeders. Its performance is compared to a centralized scheme based on the solution of an Optimal Power Flow problem, whose objective function is to minimize the active power curtailment. The proposed scheme successfully mitigates overvoltage situations due to high photovoltaic penetration and performs almost as well as the Optimal Power Flow based solution with significantly less information and communication requirements.Index Terms-low-voltage photovoltaic systems, active distribution network management, voltage control

Abstract-When a smart meter, be it single-phase or threephase, is connected to a three-phase network, the phase(s) to which it is connected is (are) initially not known. This means that each of its measurements is not uniquely associated with a phase of the distribution network. This phase information is important because it can be used by Distribution System Operators to take actions in order to have a network that is more balanced.In this work, the correlation between the voltage measurements of the smart meters is used to identify the phases. To do so, the constrained k-means clustering method is first introduced as a reference, as it has been previously used for phase identification. A novel, automatic and effective method is then proposed to overcome the main drawback of the constrained k-means clustering, and improve the quality of the clustering. Indeed, it takes into account the underlying structure of the low-voltage distribution networks beneath the voltage measurements without a priori knowledge on the topology of the network. Both methods are analysed with real measurements from a distribution network in Belgium. The proposed algorithm shows superior performance in different settings, e.g. when the ratio of single-phase over threephase meters in the network is high, when the period over which the voltages are averaged is longer than one minute, etc.

Abstract:The emergence of electric vehicles (EVs), combined with the rise of renewable energy production capacities, will strongly impact the way electricity is produced, distributed and consumed in the very near future. This position paper focuses on the problem of optimizing charging strategies for a fleet of EVs in the context where a significant amount of electricity is generated by (distributed) renewable energy. It exposes how a mobile application may offer an efficient solution for addressing this problem. This app can play two main roles. Firstly, it would incite and help people to play a more active role in the energy sector by allowing photovoltaic (PV) panel owners to sell their electrical production directly to consumers, here the EVs' agents. Secondly, it would help distribution system operators (DSOs) or transmission system operators (TSOs) to modulate more efficiently the load by allowing them to influence EV charging behaviour in real time. Finally, the present paper advocates for the introduction of a two-sided market-type model between EV drivers and electricity producers.

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This paper highlights the importance of the knowledge of the phase identification for the different measurement points inside a low-voltage distribution network. Besides considering existing solutions, we propose a novel method for identifying the phases of the measurement devices, based exclusively on voltage measurement correlation. It relies on graph theory and the notion of maximum spanning tree. It has been tested on a real Belgian LV network, first with simulated unbalanced voltage for which it managed to correctly identify the phases of all measurement points, second, on preliminary data from a real measurement campaign for which it shows encouraging results.

Le chapitre 3, central dans cet ouvrage, propose une étude des effets des sons sur la faune marine et sur les seuils d'impacts. Ces impacts acoustiques, parfois importants, peuvent s'exprimer tout au long du cycle de vie des organismes marins. Ils peuvent induire des réponses biologiques aux différents niveaux d'intégration du vivant, tant sur l'individu (réponses physiologiques et comportementales), que sur les populations et les espèces, ainsi que sur les interactions qui les lient au sein d'assemblage d'espèces (interactions interspécifiques). Ces réponses peuvent varier en fonction du stade de développement (ontogénie), de l'âge ou du sexe, du contexte, mais également en fonction de la distance à la source et des caractéristiques de la source sonore. Dans ce chapitre, en plus des études sur les mammifères marins et sur la faune benthique, nous avons fait le choix de

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As more and more photovoltaic units are being installed, some LV networks have already attained their maximum hosting capacity, i.e. the maximum amount of distributed energy resources that they can accommodate during regular operations without suffering problems, such as overvoltages.As an alternative to network reinforcement, active network management (ANM) can, to a certain extent, increase their hosting capacity by controlling the power flows. In the framework of ANM, a distributed control scheme was previously presented. It makes use of a distress signal sent by each participating unit, when its terminal voltage is higher than 1.1 p.u. All units then proceed to absorb the maximum reactive power available. If the problem is not resolved, the units proceed to active power curtailment. This paper extends this control scheme to the case of unbalanced three-phase four-wire distribution networks with single-and/or three-phase inverters. The control scheme works by first partitioning the inverters into four groups, three for the single-phase inverters (one for each phase), and one for the three-phase converters. Each group then independently applies a distributed algorithm similar to the one previously presented. Their performance are then compared to those of two reference schemes, an on-off algorithm that models the default behaviour of PV inverters when there is an overvoltage, and the other one based on an unbalanced OPF. Its resulting total curtailed energy always lies between the two, with the on-off algorithm presenting the poorest performance, and the proposed algorithm losing its edge when the network is strongly unbalanced.