Bibliographic citations
Sarango, D., (2021). Modelamiento de series sintéticas de caudales mensuales en la planificación de la operación del sistema eléctrico interconectado del Perú [Universidad Nacional Agraria La Molina]. https://hdl.handle.net/20.500.12996/4737
Sarango, D., Modelamiento de series sintéticas de caudales mensuales en la planificación de la operación del sistema eléctrico interconectado del Perú []. : Universidad Nacional Agraria La Molina; 2021. https://hdl.handle.net/20.500.12996/4737
@phdthesis{sunedu/3016949,
title = "Modelamiento de series sintéticas de caudales mensuales en la planificación de la operación del sistema eléctrico interconectado del Perú",
author = "Sarango Julca, Douglas Donal",
publisher = "Universidad Nacional Agraria La Molina",
year = "2021"
}
The generation of synthetic monthly flows is a good option for planning studies of the generation of electric power of the National Interconnected Electrical System (SEIN). The best fit model for the generation of synthetic series is the Periodic Auto Regresive of Moving Averages - PARMA (1,1). For each point of interest, 300 synthetic series of 52- year long monthly flows (1965-2016) were generated, thus obtaining the expected monthly synthetic series, which was used in the Simulation Model of the Optimal Economic Operation of the Peruvian Electric System (PERSEO) for the planning of SEIN power generation during the 2016-2019 period. In this paper, the results obtained are presented for the Mantaro river basin as it is currently producing more than 30% of the energy generated in the SEIN, having found a difference of 1.70 US $ / Mwh in the value of the marginal cost of energy when comparing the use of synthetic series with the historical series of monthly flows, which implies a variation, in terms of revenue of US $ 33.79 million for the company ELECTROPERU SA concessionaire of this basin. It is concluded that the synthetic series of monthly flows has a significant impact on the calculation of the marginal costs of the SEIN, because the hydrological series currently used in the PERSEO model deliver higher marginal costs than when using synthetic series, thus demonstrating the advantage of using them in the planning of the SEIN operation.
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