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Paper RSE 15004467

An Innovative Probabilistic Methodology for Net Transfer Capacity Evaluation

Paper

Powertech 2015 Towards Future Power Systems and Emerging Technologies 29-2, Giugno/Luglio-2015.

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D. Cirio (RSE SpA) , A. Pitto (RSE SpA), E. Ciapessoni (RSE SpA) , F. Adinolfi (Università degli Studi di Genova), S. Massucco (Università degli Studi di Genova), M. Saviozzi (Università degli Studi di Genova), F. Silvestro (Università degli Studi di Genova)

GRID RESILIENCE 2015 - Security and vulnerabilities of the electrical system

The work here illustrated is intended to frame the problem of assessing the Net Transfer Capacity (NTC) among network areas accounting for the impact of the above mentioned forecast uncertainties. This paper aims to propose a probabilistic methodology to assess NTC, which adopts a power flow technique based on Point Estimate Method (PEM) combined with Third Order Polynomial Normal (TPNT) Transformation.

In the context of electric transmission system planning research proposes methods for the assessment of the effect of uncertainties (due to forecasting errors of intermittent generation and loads) on power system operating condition. 

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.

The work here illustrated is intended to frame the problem of assessing the Net Transfer Capacity (NTC) among network areas accounting for the impact of the above mentioned forecast uncertainties. This paper aims to propose a probabilistic methodology to assess NTC, which adopts a power flow technique based on Point Estimate Method (PEM) combined with Third Order Polynomial Normal (TPNT) Transformation.

his approach is compared with a conventional NTC assessment technique and has been validated against Monte-Carlo benchmark on an IEEE test system.

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