A statistical modeling methodology for long-term wind generation and power ramp simulations in new generation locations

Research output: Contribution to journalJournal article – Annual report year: 2018Researchpeer-review

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A statistical modeling methodology for long-term wind generation and power ramp simulations in new generation locations. / Ekström, Jussi; Koivisto, Matti Juhani; Mellin, Ilkka; Millar, Robert John; Lehtonen, Matti.

In: Energies, Vol. 11, No. 9, 2442, 2018.

Research output: Contribution to journalJournal article – Annual report year: 2018Researchpeer-review

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Ekström, Jussi ; Koivisto, Matti Juhani ; Mellin, Ilkka ; Millar, Robert John ; Lehtonen, Matti. / A statistical modeling methodology for long-term wind generation and power ramp simulations in new generation locations. In: Energies. 2018 ; Vol. 11, No. 9.

Bibtex

@article{f0c4d53b06ba4937809c17205d5c2d40,
title = "A statistical modeling methodology for long-term wind generation and power ramp simulations in new generation locations",
abstract = "In future power systems, a large share of the energy will be generated with wind power plants (WPPs) and other renewable energy sources. With the increasing wind power penetration, the variability of the net generation in the system increases. Consequently, it is imperative to be able to assess and model the behavior of the WPP generation in detail. This paper presents an improved methodology for the detailed statistical modeling of wind power generation from multiple new WPPs without measurement data. A vector autoregressive based methodology, which can be applied to long-term Monte Carlo simulations of existing and new WPPs, is proposed. The proposed model improves the performance of the existing methodology and can more accurately analyze the temporal correlation structure of aggregated wind generation at the system level. This enables the model to assess the impact of new WPPs on the wind power ramp rates in a power system. To evaluate the performance of the proposed methodology, it is verified against hourly wind speed measurements from six locations in Finland and the aggregated wind power generation from Finland in 2015. Furthermore, a case study analyzing the impact of the geographical distribution of WPPs on wind power ramps is included.",
author = "Jussi Ekstr{\"o}m and Koivisto, {Matti Juhani} and Ilkka Mellin and Millar, {Robert John} and Matti Lehtonen",
year = "2018",
doi = "10.3390/en11092442",
language = "English",
volume = "11",
journal = "Energies",
issn = "1996-1073",
publisher = "M D P I AG",
number = "9",

}

RIS

TY - JOUR

T1 - A statistical modeling methodology for long-term wind generation and power ramp simulations in new generation locations

AU - Ekström, Jussi

AU - Koivisto, Matti Juhani

AU - Mellin, Ilkka

AU - Millar, Robert John

AU - Lehtonen, Matti

PY - 2018

Y1 - 2018

N2 - In future power systems, a large share of the energy will be generated with wind power plants (WPPs) and other renewable energy sources. With the increasing wind power penetration, the variability of the net generation in the system increases. Consequently, it is imperative to be able to assess and model the behavior of the WPP generation in detail. This paper presents an improved methodology for the detailed statistical modeling of wind power generation from multiple new WPPs without measurement data. A vector autoregressive based methodology, which can be applied to long-term Monte Carlo simulations of existing and new WPPs, is proposed. The proposed model improves the performance of the existing methodology and can more accurately analyze the temporal correlation structure of aggregated wind generation at the system level. This enables the model to assess the impact of new WPPs on the wind power ramp rates in a power system. To evaluate the performance of the proposed methodology, it is verified against hourly wind speed measurements from six locations in Finland and the aggregated wind power generation from Finland in 2015. Furthermore, a case study analyzing the impact of the geographical distribution of WPPs on wind power ramps is included.

AB - In future power systems, a large share of the energy will be generated with wind power plants (WPPs) and other renewable energy sources. With the increasing wind power penetration, the variability of the net generation in the system increases. Consequently, it is imperative to be able to assess and model the behavior of the WPP generation in detail. This paper presents an improved methodology for the detailed statistical modeling of wind power generation from multiple new WPPs without measurement data. A vector autoregressive based methodology, which can be applied to long-term Monte Carlo simulations of existing and new WPPs, is proposed. The proposed model improves the performance of the existing methodology and can more accurately analyze the temporal correlation structure of aggregated wind generation at the system level. This enables the model to assess the impact of new WPPs on the wind power ramp rates in a power system. To evaluate the performance of the proposed methodology, it is verified against hourly wind speed measurements from six locations in Finland and the aggregated wind power generation from Finland in 2015. Furthermore, a case study analyzing the impact of the geographical distribution of WPPs on wind power ramps is included.

U2 - 10.3390/en11092442

DO - 10.3390/en11092442

M3 - Journal article

VL - 11

JO - Energies

JF - Energies

SN - 1996-1073

IS - 9

M1 - 2442

ER -