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Loading Margin Estimation in Malaysia Power System with PV Generator using Statistical Models: Artificial Neural Networks (ANN)
◎NORHAFIZ SALIM・辻 隆男・大山 力(横浜国立大学)
This paper presents the significant of using ANN model to estimate the Loading Margin (LM) enhancement in Malaysia grid system while incurred with Photovoltaic Generator. Estimation of power variations [1] becoming substantial with huge amount of free renewable energy resources i.e. wind and solar recently. It is projected in near future that solar energy will be treated as a main generating plant which would cover up almost 70% of total generation’s capacity. To imagine that multiple PV generators are scattered over the grid, an explicit from of coordination is definitely needed among them by the Transmission System Operator (TSO). Definitely this would offer great opportunity for TSO to reliably operate the network during intermittency of renewable energy scenarios whereby a sort of visible observation of online reactive power management is inevitably available