Hierarchical Control Strategies for Spatiotemporally Varying Systems with Application to Airborne Wind Energy

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Bafandeh, A. (2018). Hierarchical Control Strategies for Spatiotemporally Varying Systems with Application to Airborne Wind Energy. Unc Charlotte Electronic Theses And Dissertations.
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Abstract

Wind energy represents a leading renewable resource for the production of clean and sustainable electricity for on- and off-grid networks. Nevertheless, tower and foundation costs, which typically represent about 30 percent of the total installation cost of existing wind turbines, limit the operating altitude (hub height) of conventional turbines to no more than 220m. Consequently, conventional systems are not able to utilize significantly stronger winds that are present at higher altitudes. Airborne wind energy (AWE) systems eliminate both the tower and foundation by using tethers and a lifting body to reach higher altitudes where stronger wind exists. In the target installation sites, it is desirable to maximize the percentage of total energy generated from the wind, recognizing that the AWE system will need to be supplemented with conventional sources. This leads to two critical control challenges: (i) Optimizing the operating altitude of an AWE system to maximize the energy generated from the wind and (ii) developing a supervisory controller for an integrated AWE-battery-generator system, recognizing that the optimal control of the overall system requires strategic coordination of the three elements. This dissertation describes and validates, using real wind data, a statistical modeling and hierarchical control approach to addressing the aforementioned challenges. Specifically, physics-driven models are used to characterize the AWE system itself, whereas statistical models are used to characterize the stochastically varying wind profile and electricity demand. A hallmark of the proposed modeling approach lies in the development surrogate regret metrics from these statistical models (termed the energy deficit metric for a stand-alone AWE system and generator excess metric for the integrated system), which provide estimates of the difference between the optimal output of the system and the existing output of the system. Ultimately, these surrogate regret metrics are used to manage a balance between exploration and exploitation of the spatiotemporally varying environment, through several novel candidate hierarchical control structures. Each of the hierarchical control structures fuses coarse, global control at an upper level with fine, local control at a lower level, using a combination of model predictive control and extremum seeking tools.

Details
Author

Bafandeh, Alireza

Title

Hierarchical Control Strategies for Spatiotemporally Varying Systems with Application to Airborne Wind Energy

Physical Description

1 online resource (132 pages) : PDF

Date

2018

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

Wind energy represents a leading renewable resource for the production of clean and sustainable electricity for on- and off-grid networks. Nevertheless, tower and foundation costs, which typically represent about 30 percent of the total installation cost of existing wind turbines, limit the operating altitude (hub height) of conventional turbines to no more than 220m. Consequently, conventional systems are not able to utilize significantly stronger winds that are present at higher altitudes. Airborne wind energy (AWE) systems eliminate both the tower and foundation by using tethers and a lifting body to reach higher altitudes where stronger wind exists. In the target installation sites, it is desirable to maximize the percentage of total energy generated from the wind, recognizing that the AWE system will need to be supplemented with conventional sources. This leads to two critical control challenges: (i) Optimizing the operating altitude of an AWE system to maximize the energy generated from the wind and (ii) developing a supervisory controller for an integrated AWE-battery-generator system, recognizing that the optimal control of the overall system requires strategic coordination of the three elements. This dissertation describes and validates, using real wind data, a statistical modeling and hierarchical control approach to addressing the aforementioned challenges. Specifically, physics-driven models are used to characterize the AWE system itself, whereas statistical models are used to characterize the stochastically varying wind profile and electricity demand. A hallmark of the proposed modeling approach lies in the development surrogate regret metrics from these statistical models (termed the energy deficit metric for a stand-alone AWE system and generator excess metric for the integrated system), which provide estimates of the difference between the optimal output of the system and the existing output of the system. Ultimately, these surrogate regret metrics are used to manage a balance between exploration and exploitation of the spatiotemporally varying environment, through several novel candidate hierarchical control structures. Each of the hierarchical control structures fuses coarse, global control at an upper level with fine, local control at a lower level, using a combination of model predictive control and extremum seeking tools.

Genre

doctoral dissertations

Subjects--Topics

Mechanical engineering
Electrical engineering

Degree

Ph.D.

Keywords

Airborne Wind Energy
Model Predictive Control
Nonlinear Control
Optimal Control
Renewable Energy Systems
Spatiotemporal Optimization

Subject Area

Mechanical Engineering

Advisor(s)

Vermillion, Christopher

Committee Members

Kelly, Scott
Keanini, Russell
Hong, Tao
Kakad, Yogendra

Degree Note

Thesis (Ph.D.)--University of North Carolina at Charlotte, 2018.

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Rights Holder Information

Copyright is held by the author unless otherwise indicated.

Identifier

Bafandeh_uncc_0694D_11665

Permalink

http://hdl.handle.net/20.500.13093/etd:107