Smart Grid
Load data from three actual datasets. The first dataset is from Homestead on the southeast coast of Florida and covers electricity load data from January 1, 2018 to June 22, 2020 and corresponding weather forecast data. The second dataset includes energy consumption data from smart meters installed in London homes. We selected data from 5,567 London households spanning the period from November 24, 2011 to February 27, 2014 participating in the Low Carbon London project led by UK Power Networks.
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It proposes a lossless and stateless compression method for IEC 61850 Sampled Values flows. A set of bitmaps is introduced to indicate the size in bytes of the sampled currents and voltages, and to flag the presence or absence of the Quality field. The method has been evaluated for different profiles, and it can provide bandwidth savings between 30 and 52%, with a very low computational cost as a counterpart. It has been implemented and tested in two different hardware platforms, with traffic generated by a Merging Unit.
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163 Views
The Kirk circle is a simple and effective method for representing power graphs and visualizing their topology. In general, nodes (buses) in an electrical network are numbered with neighboring nodes assigned consecutive or closely proximal numbers. This allows for sequential mapping of these nodes in increasing order of their numerical labels to evenly spread points on a Kirk circle. In the Kirk circle, the edge connections (branches) between nodes are indicated by straight lines (chords) between the appropriate points on the circle.
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185 Views
ER-SPG is a Matlab code for producing synthetic power graphs using well-known Erdos-Renyi Random Model. It scales power graphs and achieves connectivity in each scale by different approach, and accordingly connected graphs with average degree between 2 to 5 (normally between 2.3 to 3.1) can be produced by ER_SPG with the structures similar to power graphs. It also reorders the graph vertices to obtain consecutive numbering similar to power graphs. This algorithm is also provides locations of zero injection buses (ZIBs) as operational data of power graphs.
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1046 Views
The Integrated Energy Management and Forecasting Dataset is a comprehensive data collection specifically designed for advanced algorithmic modeling in energy management. It combines two distinct yet complementary datasets - the Energy Forecasting Data and the Energy Grid Status Data - each tailored for different but related purposes in the energy sector.
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3119 Views
This dataset proposes a new method of modelling dynamic loads based on instantaneous p-q theory, to be employed in large power system networks in a digital real time environment. In order to decrease the computational burden associated to the dynamic load modelling, a p-q- theory-based approach for load modelling is proposed in this dataset. This approach is based on the well-known p-q- instantaneous theory developed for power electronics converters, and it consts only of linear controllers and of a minimal usage of control loops, reducing the required computational power.
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344 Views
Dataset1
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315 Views
Conversion loss modeling plays a crucial role in hybrid AC/DC microgrid (MG) energy management (EM). However, accurate calculation of the conversion losses is often very costly. Additionally, existing surrogate models typically rely on fixed-voltage DC buses, leading to excessive voltage magnitudes. To overcome these limitations, we propose surrogate models based on piecewise linear neural networks (NNs) that estimate conversion losses using converter power and variable-voltage DC buses.
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295 Views
This dataset has EV charging data from 2019 to the present day. SFU's Burnaby campus currently has two different types of Electric Vehicle Charging Stations on campus. There is no additional charge to use the station; however, the Permit or Daily Rate required in each lot remains in effect for the EV Reserved stalls.
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265 Views