A Scenario Generation Method for Wind Power Ramp Events Forecasting

Jie Zhang; Brian Hodge; Mingjian Cui; Deping Ke; Yuanzhang Sun; Di Gan

doi:10.1109/PESGM.2015.7285818

A Scenario Generation Method for Wind Power Ramp Events Forecasting

Jie Zhang, Brian Hodge, Mingjian Cui, Deping Ke, Yuanzhang Sun, Di Gan

Power Systems Engineering

Wuhan University

Research output: Contribution to conference › Paper › peer-review

7 Scopus Citations

Abstract

Wind power ramp events (WPREs) have received increasing attention in recent years due to their significant impact on the reliability of power grid operations. In this paper, a novel WPRE forecasting method is proposed which is able to estimate the probability distributions of three important properties of the WPREs. To do so, a neural network (NN) is first proposed to model the wind power generation (WPG) as a stochastic process so that a number of scenarios of the future WPG can be generated (or predicted). Each possible scenario of the future WPG generated in this manner contains the ramping information, and the distributions of the designated WPRE properties can be stochastically derived based on the possible scenarios. Actual data from a wind power plant in the Bonneville Power Administration (BPA) was selected for testing the proposed ramp forecasting method. Results showed that the proposed method effectively forecasted the probability of ramp events.

Original language	American English
Number of pages	5
DOIs	https://doi.org/10.1109/PESGM.2015.7285818 https://doi.org/10.1109/PESGM.2015.7285818
State	Published - 2015
Event	IEEE Power and Energy Society General Meeting, PESGM 2015 - Denver, United States Duration: 26 Jul 2015 → 30 Jul 2015

Conference

Conference	IEEE Power and Energy Society General Meeting, PESGM 2015
Country/Territory	United States
City	Denver
Period	26/07/15 → 30/07/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

NREL Publication Number

NREL/CP-5D00-63878

Keywords

Genetic algorithm (GA)
neural networks
ramp forecasting
stochastic process model
stochastic scenario generation
wind power ramp events

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Cite this

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title = "A Scenario Generation Method for Wind Power Ramp Events Forecasting",

abstract = "Wind power ramp events (WPREs) have received increasing attention in recent years due to their significant impact on the reliability of power grid operations. In this paper, a novel WPRE forecasting method is proposed which is able to estimate the probability distributions of three important properties of the WPREs. To do so, a neural network (NN) is first proposed to model the wind power generation (WPG) as a stochastic process so that a number of scenarios of the future WPG can be generated (or predicted). Each possible scenario of the future WPG generated in this manner contains the ramping information, and the distributions of the designated WPRE properties can be stochastically derived based on the possible scenarios. Actual data from a wind power plant in the Bonneville Power Administration (BPA) was selected for testing the proposed ramp forecasting method. Results showed that the proposed method effectively forecasted the probability of ramp events.",

keywords = "Genetic algorithm (GA), neural networks, ramp forecasting, stochastic process model, stochastic scenario generation, wind power ramp events",

author = "Jie Zhang and Brian Hodge and Mingjian Cui and Deping Ke and Yuanzhang Sun and Di Gan",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE Power and Energy Society General Meeting, PESGM 2015 ; Conference date: 26-07-2015 Through 30-07-2015",

year = "2015",

doi = "10.1109/PESGM.2015.7285818",

language = "American English",

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TY - CONF

T1 - A Scenario Generation Method for Wind Power Ramp Events Forecasting

AU - Zhang, Jie

AU - Hodge, Brian

AU - Cui, Mingjian

AU - Ke, Deping

AU - Sun, Yuanzhang

AU - Gan, Di

PY - 2015

Y1 - 2015

N2 - Wind power ramp events (WPREs) have received increasing attention in recent years due to their significant impact on the reliability of power grid operations. In this paper, a novel WPRE forecasting method is proposed which is able to estimate the probability distributions of three important properties of the WPREs. To do so, a neural network (NN) is first proposed to model the wind power generation (WPG) as a stochastic process so that a number of scenarios of the future WPG can be generated (or predicted). Each possible scenario of the future WPG generated in this manner contains the ramping information, and the distributions of the designated WPRE properties can be stochastically derived based on the possible scenarios. Actual data from a wind power plant in the Bonneville Power Administration (BPA) was selected for testing the proposed ramp forecasting method. Results showed that the proposed method effectively forecasted the probability of ramp events.

AB - Wind power ramp events (WPREs) have received increasing attention in recent years due to their significant impact on the reliability of power grid operations. In this paper, a novel WPRE forecasting method is proposed which is able to estimate the probability distributions of three important properties of the WPREs. To do so, a neural network (NN) is first proposed to model the wind power generation (WPG) as a stochastic process so that a number of scenarios of the future WPG can be generated (or predicted). Each possible scenario of the future WPG generated in this manner contains the ramping information, and the distributions of the designated WPRE properties can be stochastically derived based on the possible scenarios. Actual data from a wind power plant in the Bonneville Power Administration (BPA) was selected for testing the proposed ramp forecasting method. Results showed that the proposed method effectively forecasted the probability of ramp events.

KW - Genetic algorithm (GA)

KW - neural networks

KW - ramp forecasting

KW - stochastic process model

KW - stochastic scenario generation

KW - wind power ramp events

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DO - 10.1109/PESGM.2015.7285818

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T2 - IEEE Power and Energy Society General Meeting, PESGM 2015

Y2 - 26 July 2015 through 30 July 2015

ER -

A Scenario Generation Method for Wind Power Ramp Events Forecasting

Abstract

Conference

Bibliographical note

NREL Publication Number

Keywords

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