Multi-Voltage Level Active Distribution Network with Large Share of Weather-Dependent Generation

<div>Utility-scale and small scale wind and solar power installations along with electric vehicle charging stations, and other active sources of energy are increasing at the medium and lower voltage levels in the distribution grid. This situation required a better understanding of the impact of high penetration of weather-dependent renewable energy sources on the operating conditions of the distribution network at both medium and low voltage levels. Despite the need, a multi-voltage level distribution network model, based on real network data and weather-dependent renewable generation data, has not been presented for distribution grid studies. This paper presents a comprehensive multi-voltage level active distribution network model based on real network data along with load and generation time-series for about a year. The network topology is modelled based on geographical data for various rural, semi-urban, and urban locations. The distribution network is embodied with a large share of renewable generation sources, with generation time-series simulated from meteorological data. The network is also flexible to incorporate other assets such as electric vehicle charging stations, storage, etc. Thus, the presented active distribution network model can be used to study, optimize, and control the effects of weather dependent generation and other network assets in the distribution grid.</div>

Multi-Voltage Level Active Distribution Network with Large Share of Weather-Dependent Generation

<div>Utility-scale and small scale wind and solar power installations along with electric vehicle charging stations, and other active sources of energy are increasing at the medium and lower voltage levels in the distribution grid. This situation required a better understanding of the impact of high penetration of weather-dependent renewable energy sources on the operating conditions of the distribution network at both medium and low voltage levels. Despite the need, a multi-voltage level distribution network model, based on real network data and weather-dependent renewable generation data, has not been presented for distribution grid studies. This paper presents a comprehensive multi-voltage level active distribution network model based on real network data along with load and generation time-series for about a year. The network topology is modelled based on geographical data for various rural, semi-urban, and urban locations. The distribution network is embodied with a large share of renewable generation sources, with generation time-series simulated from meteorological data. The network is also flexible to incorporate other assets such as electric vehicle charging stations, storage, etc. Thus, the presented active distribution network model can be used to study, optimize, and control the effects of weather dependent generation and other network assets in the distribution grid.</div>

Hydra: a method for strain-minimizing hyperbolic embedding of network- and distance-based data

We introduce hydra (hyperbolic distance recovery and approximation), a new method for embedding network- or distance-based data into hyperbolic space. We show mathematically that hydra satisfies a certain optimality guarantee: it minimizes the ‘hyperbolic strain’ between original and embedded data points. Moreover, it is able to recover points exactly, when they are contained in a low-dimensional hyperbolic subspace of the feature space. Testing on real network data we show that the embedding quality of hydra is competitive with existing hyperbolic embedding methods, but achieved at substantially shorter computation time. An extended method, termed hydra+, typically outperforms existing methods in both computation time and embedding quality.

Classes of preferential attachment and triangle preferential attachment models with power-law spectra

Preferential attachment (PA) models are a common class of graph models which have been used to explain why power-law distributions appear in the degree sequences of real network data. Among other properties of real-world networks, they commonly have non-trivial clustering coefficients due to an abundance of triangles as well as power laws in the eigenvalue spectra. Although there are triangle PA models and eigenvalue power laws in specific PA constructions, there are no results that existing constructions have both. In this article, we present a specific Triangle Generalized Preferential Attachment Model that, by construction, has non-trivial clustering. We further prove that this model has a power law in both the degree distribution and eigenvalue spectra.

A Group Testing-Based Distributed Network Traffic Monitoring Approach for Smart Grid

In this paper, we address the problem of real-time network traffic monitoring in the communication network of smart grid. And we propose an effective distributed network traffic monitoring approach. In our algorithm, instead of measuring all the origin-destination pairs, we just need to measure partial origin-destination pairs that flows our communication network. From the measured origin-destination pairs, we can obtain all the origin-destination pairs via our recovery algorithm. Finally, we validate the properties of our method by real network data.

Three-Dimensional Adjustment of Terrestrial Geodetic Networks

This paper shows how terrestrial geodetic networks can be rigorously adjusted in three dimensions. It summarizes investigations about the types of field data and how many of each type are necessary for the adjustment, the role of coordinate systems used, the mathematical models and which methods of least squares adjustment are desirable, and the attainable accuracies of adjusted parameters for chosen precisions of field observations. The trends for optimum design and minimum field data requirements for successful terrestrial three-dimensional adjustment are indicated. The conclusions relied on extensive use of statistical tests. Both simulated and real network data were used.