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Data · dataset · 2022

LV-NVAT (Low voltage network violation analysis tool)

Listed in Teesside University Research Data Repository

The Low Voltage Network Analysis Tool (LV-NVAT) is a low voltage (230 V 1φ, 400 V 3φ) distribution network simulation framework used for predicting the steady state voltage and thermal constraints that evolving network demands are likely to cause on UK LV networks.

Description

It can take simple data time series (representing customer load demand) as input. It runs 1 simulated day at 1 min temporal resolution and simple building level spatial resolution.

It is the basic framework that I have used to build all of my power networks research simulations upon. The framework runs in MATLAB, and interfaces with the freely available distribution power systems simulation tool OpenDSS. The framework allows for monte-carlo simulation, from which the statistical likelihoods and severities of violations can be determined, and automatically calculates the effect of reinforcing the chosen network with higher capacity cabling.

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The basic framework is intended for use by engineers with some experience in energy systems and demand time series modelling, but with little to no experience in electrical power system simulation. Furthermore, an engineer with some coding experience and a basic understanding of node-link type network representation will be capable of extending the framework to include additional networks. The framework does not include the detailed storage placement and dispatch optimisation algorithms, or recoductoring optimisation algorithms that were designed for earlier works, but if this is of signficant interest, I will endevour to include it in future versions of this framework.

A detailed instruction manual is included in this dataset. This should enable the user to fully understand the purpose and operation of the framework, though if any queries or issues arise, please do not hesitate to contact me at my permanent email address - R.C.Johnson897@gmail.com.

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Catalogue records · 1

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