Distributed optimal wind farm control for fatigue load minimization: A consensus approach

Baros, Stefanos; Annaswamy, Anuradha M.

doi:10.1016/j.ijepes.2019.04.003

Cited by 20 publications

(10 citation statements)

References 22 publications

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“…This can be leveraged to minimize overall turbine structural loads while providing power output according to a schedule for the full farm. Recent research in this area can be found in Vali et al (2019b), Baros and Annaswamy (2019), Galinos et al (2020), and Stock et al (2020. In order to successfully apply these concepts, further research and possible test campaigns are needed to validate wind farm structural load models in relation to the effect that axial-induction-based wind farm control concepts have on the structural loads on specific components of the downstream turbines.…”

Section: Axial-induction-based Controlmentioning

confidence: 99%

Wind farm flow control: prospects and challenges

Bottasso²,

et al. 2022

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Abstract. Wind farm control has been a topic of research for more than two decades. It has been identified as a core component of grand challenges in wind energy science to support accelerated wind energy deployment and to transition to a clean and sustainable energy system for the 21st century. The prospect of collective control of wind turbines in an array, to increase energy extraction, reduce structural loads, improve the balance of systems, reduce operation and maintenance costs, etc. has inspired many researchers over the years to propose innovative ideas and solutions. However, practical demonstration and commercialization of some of the more advanced concepts has been limited by a wide range of challenges, which include the complex physics of turbulent flows in wind farms and the atmosphere, uncertainties related to predicting structural load and failure statistics, and the highly multi-disciplinary nature of the overall design optimization problem, among others. In the current work, we aim at providing a comprehensive overview of the state of the art and outstanding challenges, thus identifying the key research areas that could further enable commercial uptake and success of wind farm control solutions. To this end, we have structured the discussion on challenges and opportunities into four main areas: (1) insight in control flow physics, (2) algorithms and AI, (3) validation and industry implementation, and (4) integrating control with system design (co-design).

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Section: Axial-induction-based Controlmentioning

confidence: 99%

Wind farm flow control: prospects and challenges

Bottasso²,

et al. 2022

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“…The control approach is tested with [191,192] Receding horizon controller to follow power reference and reduce changes in thrust coefficient MPC to minimize tracking power, electrical power losses, variation in power reference and maximise available power P re f Jensen-Park model SimWindFarm Siniscalchi-Minna et al [203,204] Partitioning of wind farm to minimise wake effects; dispatch of power reference on partition; local MPC to follow power reference and maximise available power SimWindFarm and resulted in a 35% average reduction of fatigue damage in the wind turbine towers compared to the case where each turbine maintains its nominal power reference. In [209], a distributed approach to solve the Fatigue-load minimization optimal control problem is proposed. It follows the power reference while minimizing the fatigue loads in the wind farm.…”

Section: Linear Quadratic Regulatormentioning

confidence: 99%

“…The control approach is tested with SimWindFarm and resulted in a 35% average reduction of fatigue damage in the wind turbine towers compared to the case where each turbine maintains its nominal power reference. In [209], a distributed approach to solve the Fatigue‐load minimization optimal control problem is proposed. It follows the power reference while minimizing the fatigue loads in the wind farm.…”

Section: Provision Of Grid Servicesmentioning

confidence: 99%

Wind farm control ‐ Part I: A review on control system concepts and structures

Andersson

Anaya‐Lara

Tande

et al. 2021

IET Renewable Power Gen

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Wind farm control design is a recently new area of research that has rapidly become a key enabler for the development of large wind farm projects and their safe and efficient connection to the power grid. A comprehensive review of the intense research conducted in this area over the last 10 years is presented. Part I reviews control system concepts and structures and classifies them depending on their main objective (i.e. to maximise power production or to provide grid services. The work and key findings in each paper are discussed in detail with particular emphasis on the turbine side. Additionally, the review contributes to the existing reviews on the area by providing an elegant classification between model testing and control approaches. Areas where significant work is still needed are also discussed. In Part II, a thorough review on aerodynamic wind farm models for control design purposes is provided.

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“…Over the recent years though, the increasing penetration of renewable energy resources (RERs) and fast-varying demand response resources has resulted in significant reduction of the inertia of these systems and rendered stability a more critical issue. It is now imperative to develop advanced control methodologies that can enable wind power plants [8], [10], [13] and other RERs [5] to provide ancillary services in order to enhance power grid stability. At the same time, the electricity markets have to be restructured so at to promote optimal operation of power grids with renewables [15], [18], [21].…”

Section: Introductionmentioning

confidence: 99%

A Framework for Distributed and Compositional Stability Analysis of Power Grids

Baros,

Bernstein,

Hatziargyriou

2020

Preprint

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Operating modern power grids with stability guarantees is admittedly imperative. Classic stability methods are not well-suited for these dynamic systems as they involve centralized gathering of information and computation of the system's eigenvalues, processes which are oftentimes not privacy-preserving and computationally burdensome. System operators (SOs) would nowadays have to be able to quickly and efficiently assess smallsignal stability as the power grid operating conditions change more dynamically while also respect the privacy of the distributed energy resources (DERs). Motivated by all these, in this paper we introduce a framework that comprises a computationally efficient, privacy preserving, distributed and compositional stability assessment method. Our proposed method first calls for representative agents at various buses to exchange information with their neighbors and design their local controls in order to meet some local stability conditions. Following that, the agents are required to notify the SO whether their local conditions are satisfied or not. In case the agents cannot verify their local conditions they can augment their local controls using a global control input. The SO can then warrant stability of the interconnected power grid by assembling the local stability guarantees, established by the agents, in a compositional manner. We analytically derive the local stability conditions and prove that when they are collectively satisfied stability of the interconnected system ensues. We illustrate the effectiveness of our proposed DSA method via a numerical example centered around a three-bus power grid.

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Distributed optimal wind farm control for fatigue load minimization: A consensus approach

Cited by 20 publications

References 22 publications

Wind farm flow control: prospects and challenges

Wind farm flow control: prospects and challenges

Wind farm control ‐ Part I: A review on control system concepts and structures

A Framework for Distributed and Compositional Stability Analysis of Power Grids

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