A model predictive control approach to the problem of wind power smoothing with controlled battery storage

M. Khalid; A. V. Savkin

doi:10.1016/j.renene.2009.11.030

A model predictive control approach to the problem of wind power smoothing with controlled battery storage

M. Khalid^*
, A. V. Savkin

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

218 Scopus citations

Abstract

The aim of this study is to design a controller, based on model predictive control (MPC), to smooth the wind power output, which is generated from a wind farm, and subject to a variety of constraints on the system model. In order to employ the model predictive controller, we propose a wind power prediction system, which is used by the controller within its predictive optimization. The proposed controller is capable of smoothing wind power by utilizing inputs from our prediction system, and optimizes the maximum ramp rate requirement and also the state of the charge of the battery under practical constraints. The proposed prediction model is capable of predicting the wind power several steps ahead which is used in the optimization part of the controller. We illustrate the effectiveness of the proposed controller with a simulation example, employing real wind farm data under a variety of hard constraints.

Original language	English
Pages (from-to)	1520-1526
Number of pages	7
Journal	Renewable Energy
Volume	35
Issue number	7
DOIs	https://doi.org/10.1016/j.renene.2009.11.030
State	Published - Jul 2010
Externally published	Yes

Bibliographical note

Funding Information:
This work was supported by the Australian Research Council. We would like to thank Hugh Outhred, Iain MacGill, Merlinde Kay, and Nicholas Cutler at the Centre of Energy and Environmental Markets (CEEM), UNSW for their assistance in getting the required data. We are also grateful to Roaring 40s for their cooperation in terms of providing the required time series data to carry out this research.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Energy storage
Model predictive control
Wind power prediction

ASJC Scopus subject areas

Renewable Energy, Sustainability and the Environment

Access to Document

10.1016/j.renene.2009.11.030

Cite this

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title = "A model predictive control approach to the problem of wind power smoothing with controlled battery storage",

abstract = "The aim of this study is to design a controller, based on model predictive control (MPC), to smooth the wind power output, which is generated from a wind farm, and subject to a variety of constraints on the system model. In order to employ the model predictive controller, we propose a wind power prediction system, which is used by the controller within its predictive optimization. The proposed controller is capable of smoothing wind power by utilizing inputs from our prediction system, and optimizes the maximum ramp rate requirement and also the state of the charge of the battery under practical constraints. The proposed prediction model is capable of predicting the wind power several steps ahead which is used in the optimization part of the controller. We illustrate the effectiveness of the proposed controller with a simulation example, employing real wind farm data under a variety of hard constraints.",

keywords = "Energy storage, Model predictive control, Wind power prediction",

author = "M. Khalid and Savkin, \{A. V.\}",

note = "Funding Information: This work was supported by the Australian Research Council. We would like to thank Hugh Outhred, Iain MacGill, Merlinde Kay, and Nicholas Cutler at the Centre of Energy and Environmental Markets (CEEM), UNSW for their assistance in getting the required data. We are also grateful to Roaring 40s for their cooperation in terms of providing the required time series data to carry out this research.",

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AU - Savkin, A. V.

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AB - The aim of this study is to design a controller, based on model predictive control (MPC), to smooth the wind power output, which is generated from a wind farm, and subject to a variety of constraints on the system model. In order to employ the model predictive controller, we propose a wind power prediction system, which is used by the controller within its predictive optimization. The proposed controller is capable of smoothing wind power by utilizing inputs from our prediction system, and optimizes the maximum ramp rate requirement and also the state of the charge of the battery under practical constraints. The proposed prediction model is capable of predicting the wind power several steps ahead which is used in the optimization part of the controller. We illustrate the effectiveness of the proposed controller with a simulation example, employing real wind farm data under a variety of hard constraints.

KW - Energy storage

KW - Model predictive control

KW - Wind power prediction

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