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Title:Optimization and control of wind energy systems for grid integration
Author(s):Deshmukh, Anand Pramod
Director of Research:Allison, James T
Doctoral Committee Chair(s):Allison, James T
Doctoral Committee Member(s):Alleyne, Andrew; Beck, Carolyn; Kim, Harrison
Department / Program:Industrial&Enterprise Sys Eng
Discipline:Systems & Entrepreneurial Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Wind energy
Model predictive control
Automatic generation control
Abstract:Wind energy is a rapidly expanding source of renewable energy, but wind resources are highly intermittent. This makes increasing the level of wind energy penetration in an overall energy portfolio challenging if power quality and grid frequency is to be maintained. In a conventional power system, grid frequency regulation is typically achieved by means of some form of active power control (APC) of power generation plants. Active power control of plant power output aims to maintain the power balance between generation and consumption. Wind turbines have historically not participated in the active power control and are therefore isolated from the grid using sophisticated power electronics, increasing the cost of wind energy. Interest in studying APC of wind turbines for grid frequency regulation has been revived recently. Most of the proposed approaches either focus on single turbine, or overlook the effect of APC strategies on actuator usage and mechanical loading of the system. However, wind energy based power generation plants have an array of wind turbines that interact with each other aerodynamically in a complicated manner. In this work we introduce a new distributed APC strategy in which a farm level controller optimally distributes the task of regulation to all the wind turbines in a farm accounting for dynamic wake effects introduced due to control actions of each of those wind turbines. An individual model predictive controller at each wind turbine then tracks the power references passed on by farm level controller, subject to mechanical loading constraints. The results from this approach are compared with the greedy approach when the individual wind turbines only optimize their own power production without consideration of downstream neighbors. We then extend the idea of this hierarchical control to co-optimization of wind farms and battery energy storage for regulation, and then for simultaneous wind farm layout and control design to exploit the synergy between layout design and wind farm control. We show that the regulation performance scores for the distributed APC approach are statistically significantly better than the greedy approach. We also show that co-optimization of wind farm and battery energy storage provides significantly superior performance over baseline, for a much smaller battery capacity, providing a potentially significant cost saving. Finally, we show that designing a layout with simultaneous consideration of control system design, allows us harness synergistic relationships between layout and control. Capitalizing on these synergy mechanisms enables increased annualized wind farm energy production.
Issue Date:2017-02-02
Type:Thesis
URI:http://hdl.handle.net/2142/97653
Rights Information:Copyright 2017 Anand Deshmukh
Date Available in IDEALS:2017-08-10
Date Deposited:2017-05


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