Shortening Quasi-Static Time-Series Simulations for Cost-Benefit Analysis of Low Voltage Network Operation with Photovoltaic Feed-In
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Date
2015-01-14
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Abstract
Executing quasi-static time-series simulations is
time consuming, especially when yearly simulations are required,
for example, for cost-benefit analyses of grid operation strategies.
Often only aggregated simulations outputs are relevant to grid
planners for assessing grid operation costs. Among them are total
network losses and power exchange through MV/LV substation
transformers. In this context it can be beneficial to explore
alternatives to running quasi-static time-series simulations with
complete input data that can produce the results of interest with
high accuracy but in less time. This paper explores two methods
for shortening quasi-static time-series simulations through reducing
the amount of input data and thus the required number of
power flow calculations; one is based on downsampling and the
other on vector quantization. The results show that execution time
reductions and sufficiently accurate results can be obtained with
both methods, but vector quantization requires considerably less
data to produce the same level of accuracy as downsampling. In
particular, when the simulations consider voltage control or when
more than one simulation with the same input data is required,
vector quantization delivers a far superior trade-off between data
reduction, time savings, and accuracy. However, the method does
not reproduce peak values in the results accurately. This makes
it less precise, for example, for detecting voltage violations.
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Keywords
power flow calculation, PV generation, vector quantization, quasi-static time-series