NDLI: An innovative probabilistic methodology for net transfer capacity evaluation

Content Provider	IEEE Xplore Digital Library
Author	Adinolfi, F. Massucco, S. Saviozzi, M. Silvestro, F. Ciapessoni, E. Cirio, D. Pitto, A.
Copyright Year	2015
Description	Author affiliation: DITEN, Univ. of Genova, Genoa, Italy (Adinolfi, F.; Massucco, S.; Saviozzi, M.; Silvestro, F.) \|\| RSE - Ricerca sul Sistema Energetico, Milan, Italy (Ciapessoni, E.; Cirio, D.; Pitto, A.)
Abstract	In the context of transmission system planning, research proposes methods to assess the effect of uncertainties of power system operating condition due to forecasting errors of intermittent generation and loads. In particular probabilistic power flow methods are illustrated to calculate the probability distributions of the voltages and the branch currents, starting from the distributions of power injections/absorptions. These uncertainties play a key role in the operational planning of power systems, as certain configurations of load and intermittent generation can cause security problems. This paper aims to propose a probabilistic methodology to assess Net Transfer Capacity (NTC) among network areas, which quantifies forecast error uncertainties by applying the Point Estimate Method (PEM) combined with Third Order Polynomial Normal (TPN) Transformation. This approach is compared with a conventional NTC assessment technique and has been validated against Monte-Carlo benchmark on an IEEE test system.
Starting Page	1
Ending Page	6
File Size	1007100
Page Count	6
File Format	PDF
e-ISBN	9781479976935
DOI	10.1109/PTC.2015.7232712
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-29
Publisher Place	Netherlands
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Probabilistic logic Wind forecasting Security Standards Uncertainty Load modeling Predictive models Point Estimate Method Net Transfer Capacity Total Transfer Capacity Probabilistic Security Assessment
Content Type	Text
Resource Type	Article

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