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Research Papers: Design Automation

Optimization of a Wind-Integrated Microgrid System With Equipment Sizing and Dispatch Strategy Under Resource Uncertainty

[+] Author and Article Information
Tzu-Chieh Hung

Department of Mechanical Engineering,
National Taiwan University,
Taipei 10617, Taiwan

Kuei-Yuan Chan

Department of Mechanical Engineering,
National Taiwan University,
Taipei 10617, Taiwan
e-mail: chanky@ntu.edu.tw

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received May 22, 2014; final manuscript received January 7, 2015; published online February 16, 2015. Assoc. Editor: Harrison M. Kim.

J. Mech. Des 137(4), 041403 (Apr 01, 2015) (10 pages) Paper No: MD-14-1296; doi: 10.1115/1.4029584 History: Received May 22, 2014; Revised January 07, 2015; Online February 16, 2015

The global quest for energy sustainability has motivated the development of efficiently transforming various renewable natural resources, such as wind, into energy. This transformation requires long-term planning, and we are interested in how to make systematic decisions when the dependency on the existing power plant decreases, toward eventual microgrid systems. The present study investigates the upgrading of an existing power system into one with a wind-integrated microgrid. The standard approach applies wind resource assessment to determine suitable wind farm locations with high energy potential and then develops specific dispatch strategies to meet the power demand for the wind-integrated system with low cost, high reliability, and low impact on the environment. However, the uncertainties in wind resource result in fluctuating power generation. The installation of additional energy storage devices is thus needed in the dispatch strategy to ensure a stable power supply. The present work proposes a design procedure for obtaining the optimal rated power of the wind farm and the size of storage devices considering wind resource assessment and dispatch strategy under uncertainty. Two wind models are developed from real-world wind data and apply in the proposed optimization framework. Based on comparisons of system reliability between the optimal results and real operating states, an appropriate wind model can be chosen to represent the wind characteristics of a particular region. Results show that the wind model in the optimization framework should consider the uncertainties of wind resource to maintain high system reliability. The proposed method provides a gradual planning of a power system and leads the existing power system toward energy sustainability.

Copyright © 2015 by ASME
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Figures

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Fig. 1

Flowchart of proposed approach for component sizing and dispatch strategy

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Fig. 6

Actual implementation of optimal wind turbine sizing without uncertainty. (a) System state and (b) grid storage state.

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Fig. 2

Power spectral density analysis of wind data and residual. (a) Historical wind data and (b) residual.

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Fig. 3

Wind data and modeled trend

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Fig. 4

Wind speed model without uncertainty

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Fig. 5

Results of optimal wind turbine sizing without uncertainty. (a) System state and (b) grid storage state.

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Fig. 7

Results of optimal grid storage sizing without uncertainty. (a) System state and (b) grid storage state.

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Fig. 13

Results of optimal wind turbine sizing under uncertainty. (a) System state and (b) grid storage state.

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Fig. 14

Actual implementation of optimal wind turbine sizing under uncertainty. (a) System state and (b) grid storage state.

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Fig. 15

Results of optimal grid storage sizing under uncertainty. (a) System state and (b) grid storage state.

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Fig. 16

Actual implementation of optimal grid storage sizing under uncertainty. (a) System state and (b) grid storage state.

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Fig. 8

Actual implementation of optimal grid storage sizing without uncertainty. (a) System state and (b) grid storage state.

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Fig. 9

Nondominated solutions of wind turbine and grid storage sizing without uncertainty

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Fig. 10

Statistical analysis results of wind speed. (a) Probability plot and (b) histogram fitting.

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Fig. 11

Logarithm of wind speed model under uncertainty. (a) Trend and (b) histogram of residual.

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Fig. 12

Wind speed model under uncertainty with 95% confidence interval

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