Power Flow Optimization with Graph Neural Networks

Power flow analysis is an important tool in power engineering for planning and operating power systems. The standard power flow problem consists of a set of non-linear equations, which are traditionally solved using numerical optimization techniques, such as the Newton-Raphson method. However, these methods can become computationally expensive for larger systems, and convergence to the global optimum is usually not guaranteed. In recent years, several methods using Graph Neural Networks (GNNs) have been proposed to speed up the computation of the power flow solutions, without making large sacrifices in terms of accuracy. This class of models can learn localized features that are independent from a global graph structure. Therefore, by representing power systems as graphs these methods can, in principle, generalize to systems of different size and topology. However, most of the current approaches have only been applied to systems with a fixed topology and none of them were trained simultaneously on systems of different topology. Hence, these models are not fully shown to generalize to widely different systems or even to small perturbations of a given system. In this thesis, several supervised GNN models are proposed to solve the power flow problem, using established GNN blocks from the literature. These GNNs are trained on a set of different tasks, where the goal is to study the generalizability to both perturbations and completely different systems, as well as comparing performance to standard Multi-Layered Perceptron (MLP) models. The experimental results show that the GNNs are comparatively successful at generalizing to widely different topologies seen during training, but do not manage to generalize to unseen topologies and are not able to outperform an MLP on slight perturbations of the same energy system. The study presented in this thesis allowed to draw important insights about the applicability of GNN as power flow solvers. In the conclusion, several possible ways for improving the GNN-based solvers are discussed

Total Variation Graph Neural Networks

Source at https://proceedings.mlr.press/v202/.Recently proposed Graph Neural Networks (GNNs) for vertex clustering are trained with an unsupervised minimum cut objective, approximated by a Spectral Clustering (SC) relaxation. However, the SC relaxation is loose and, while it offers a closed-form solution, it also yields overly smooth cluster assignments that poorly separate the vertices. In this paper, we propose a GNN model that computes cluster assignments by optimizing a tighter relaxation of the minimum cut based on graph total variation (GTV). The cluster assignments can be used directly to perform vertex clustering or to implement graph pooling in a graph classification framework. Our model consists of two core components: i) a message-passing layer that minimizes the ℓ1 distance in the features of adjacent vertices, which is key to achieving sharp transitions between clusters; ii) an unsupervised loss function that minimizes the GTV of the cluster assignments while ensuring balanced partitions. Experimental results show that our model outperforms other GNNs for vertex clustering and graph classification

Power Flow Balancing With Decentralized Graph Neural Networks

We propose an end-to-end framework based on a Graph Neural Network (GNN) to balance the power flows in energy grids. The balancing is framed as a supervised vertex regression task, where the GNN is trained to predict the current and power injections at each grid branch that yield a power flow balance. By representing the power grid as a line graph with branches as vertices, we can train a GNN that is accurate and robust to changes in topology. In addition, by using specialized GNN layers, we are able to build a very deep architecture that accounts for large neighborhoods on the graph, while implementing only localized operations. We perform three different experiments to evaluate: i) the benefits of using localized rather than global operations and the tendency of deep GNN models to oversmooth the quantities on the nodes; ii) the resilience to perturbations in the graph topology; and iii) the capability to train the model simultaneously on multiple grid topologies and the consequential improvement in generalization to new, unseen grids. The proposed framework is efficient and, compared to other solvers based on deep learning, is robust to perturbations not only to the physical quantities on the grid components, but also to the topology

Molecular Diversity of Hard Tick Species from Selected Areas of a Wildlife-Livestock Interface Ecosystem at Mikumi National Park, Morogoro Region, Tanzania

Ticks are one of the most important arthropod vectors and reservoirs as they harbor a wide variety of viruses, bacteria, fungi, protozoa, and nematodes, which can cause diseases in human and livestock. Due to their impact on human, livestock, and wild animal health, increased knowledge of ticks is needed. So far, the published data on the molecular diversity between hard ticks species collected in Tanzania is scarce. The objective of this study was to determine the genetic diversity between hard tick species collected in the wildlife-livestock interface ecosystem at Mikumi National Park, Tanzania using the mitochondrion 16S rRNA gene sequences. Adult ticks were collected from cattle (632 ticks), goats (187 ticks), and environment (28 ticks) in the wards which lie at the border of Mikumi National Park. Morphological identification of ticks was performed to genus level. To identify ticks to species level, molecular analysis based on mitochondrion 16S rRNA gene was performed. Ticks representing the two genera (Hyalomma and Rhipicephalus) were identified using morphological characters. Six species were confirmed based on mitochondrion 16S rRNA gene, including Rhipicephalus microplus, Rhipicephalus evertsi, Hyalomma rufipes, Hyalomma truncatum, Hyalomma marginatum, and Hhyalomma turanicum. The presence of different clusters of tick species reflects the possible biological diversity of the hard ticks present in the study region. Further studies are however required to quantify species of hard ticks present in the study region and the country in general over a larger scale

Diversity of viruses in hard ticks (Ixodidae) from select areas of a wildlife-livestock interface ecosystem at Mikumi National Park, Tanzania

Many of the recent emerging infectious diseases have occurred due to the transmission of the viruses that have wildlife reservoirs. Arthropods, such as ticks, are known to be important vectors for spreading viruses and other pathogens from wildlife to domestic animals and humans. In the present study, we explored the diversity of viruses in hard ticks (Ixodidae) from select areas of a wildlife-livestock interface ecosystem at Mikumi National Park, Tanzania using a metagenomic approach. cDNA and DNA were amplified with random amplification and Illumina high-throughput sequencing was performed. The high-throughput sequenced data was imported to the CLC genomic workbench and trimmed based on quality (Q = 20) and length (≥ 50). The trimmed reads were assembled and annotated through Blastx using Diamond against the National Center for Biotechnology Information non-redundant database and its viral database. The MEGAN Community was used to analyze and to compare the taxonomy of the viral community. The obtained contigs and singletons were further subjected to alignment and mapping against reference sequences. The viral sequences identified were classified into bacteria, vertebrates, and invertebrates, plants, and protozoans viruses. Sequences related to known viral families; Retroviridae, Flaviviridae, Rhabdoviridae, Chuviridae, Orthomyxoviridae, Phenuiviridae, Totiviridae, Rhabdoviridae, Parvoviridae, Caulimoviridae, Mimiviridae and several Phages were reported. This result indicates that there are many viruses present in the study region, which we are not aware of and do not know the role they have or if they have the potential to spread to other species and cause diseases. Therefore, further studies are required to delineate the viral community present in the region over a large scale

The pure PV-EV energy system – A conceptual study of a nationwide energy system based solely on photovoltaics and electric vehicles

The objective of this conceptual study is to reveal the substantial potential and synergy of solar energy and electric vehicles (EVs) working together. This potential is demonstrated by studying the feasibility of a nationwide energy system solely reliant on solar energy and EVs. Photovoltaic (PV) solar energy is already an important energy source globally, but due to its intermittency it requires energy storage to balance between times of high and low production. At the same time, a global drive is underway in the transport sector: the change from internal combustion engines to EVs. Cars are in fact stationary 95% of the time, and when the vehicle is connected to the grid, the EV battery can regulate the intermittent PV source using vehicle-to-grid (V2G) technology. This paper presents a conceptual study of a pure PV-EV based energy system, with Spain as a case study. Provided that Spain’s entire fleet of 29.4 million road going vehicles is switched to EVs, the study shows that 3.45 billion m2 of PV (73 m2 per capita) could give Spain a completely self-reliant energy system. The theoretical study is based on a combination of measured values, simulations, and assumptions. The conclusion of the analysis is undoubtedly extraordinary, namely that an entire country like Spain can power its complete energy system solely on PV, using EVs as the only energy storage resource

Cellular homeostasis of Escherichia coli probed by super-resolution microscopy

In elke cel zijn er miljoenen macromoleculen, waaronder DNA, RNA en eiwitten, die allemaal in een klein volume moeten verblijven. Stel je voor, dat de cel een keuken is in een groot restaurant. Hier moeten de manager, koks, keukenpersoneel en obers hand in hand werken om de restaurantgasten gauw van eten te voorzien. Daarvoor is het belangrijk om voldoende personeel in dienst te hebben. Met onderbezetting zouden de gasten erg lang moeten wachten op hun voedsel. Omdat de ruimte in elke keuken begrenst is, kunnen maar een beperkt aantal medewerkers worden ingehuurd, zonder dat ze elkaar bij het uitvoeren van hun opdrachten hinderen. In een cel verlopen processen vergelijkbaar met de werkzaamheden in zo’n keuken. Het aantal moleculen moet worden aangepast aan de celgrootte. Op microscopische schaal zitten de moleculen in een cel erg dicht bij elkaar. Dit fenomeen noemt men macromoleculaire crowding. De mate van crowding kan de effectiviteit van enzymreacties en andere processen beïnvloeden. Het is zeer waarschijnlijk, dat de cel – net zo als de keukenmanager – de eigen ‘volheid’ nauwkeurig regelt. In dit proefschrift wordt uitgelegd welke mechanismen cellen hebben om de crowding te regelen. Met behulp van fluorescentiemicroscopie is de ‘volheid’ van de cel onder verschillende groei-omstandigheden bepaald. Verder heb ik onderzoek gedaan aan zogenoemde mechanosensitieve kanalen. Dit zijn membraaneiwitten die belangrijk zijn in het reguleren van de concentratie aan osmotisch-actieve stoffen en daarmee het volume van de cel reguleren. Mijn onderzoek ondersteunt in het algemeen de hypothese van een strakke regulering van macromoleculaire crowding

On the mobility, membrane location and functionality of mechanosensitive channels in Escherichia coli

We thank Frans Bianchi and Franz Ho for assistance with molecular cloning, Tim Rasmussen for providing the pTRC-MscK plasmid, Andrew Robinson for providing the pBAD-mEos3.2 plasmid, Matthias Heinemann for assistance with the flow cytometry measurements, Paul Schavemaker for performing Smoldyn simulations and Michiel Punter for programming ImageJ plugins for PALM reconstructions and single-particle tracking. We thank Ian Booth for critical reading of the manuscript, and Christoffer Åberg and Matteo Gabba for valuable discussions. The authors would like to thank David Dryden and Marcel Reuter for performing preliminary experiments from which this work has been built. The work was funded by the EU FP7 ITN-network program NICHE.Peer reviewedPublisher PD