Conceptual Design of Wind Farms Through Novel Multi Objective Swarm Optimization Syracuse University Syracuse University SURFACE SURFACE Dissertations ALL SURFACE May 2015 Conceptual Design of Wind Fa[.]
Syracuse University SURFACE Dissertations - ALL SURFACE May 2015 Conceptual Design of Wind Farms Through Novel Multi-Objective Swarm Optimization Weiyang Tong Syracuse University Follow this and additional works at: https://surface.syr.edu/etd Part of the Engineering Commons Recommended Citation Tong, Weiyang, "Conceptual Design of Wind Farms Through Novel Multi-Objective Swarm Optimization" (2015) Dissertations - ALL 243 https://surface.syr.edu/etd/243 This Dissertation is brought to you for free and open access by the SURFACE at SURFACE It has been accepted for inclusion in Dissertations - ALL by an authorized administrator of SURFACE For more information, please contact surface@syr.edu ABSTRACT Wind is one of the major sources of clean and renewable energy, and global wind energy has been experiencing a steady annual growth rate of more than 20% over the past decade In the U.S energy market, although wind energy is one of the fastest increasing sources of electricity generation (by annual installed capacity addition), and is expected to play an important role in the future energy demographics of this country, it has also been plagued by project underperformance and concept-to-installation delays There are various factors affecting the quality of a wind energy project, and most of these factors are strongly coupled in their influence on the socio-economic, production, and environmental objectives of a wind energy project To develop wind farms that are profitable, reliable, and meet community acceptance, it is critical to accomplish balance between these objectives, and therefore a clean understanding of how different design and natural factors jointly impact these objectives is much needed In this research, a Multi-objective Wind Farm Design (MOWFD) methodology is developed, which analyzes and integrates the impact of various factors on the conceptual design of wind farms This methodology contributes three major advancements to the wind farm design paradigm: (I) provides a new understanding of the impact of key factors on the wind farm performance under the use of different wake models; (II) explores the crucial tradeoffs between energy production, cost of energy, and the quantitative role of land usage in wind farm layout optimization (WFLO); and (III) makes novel advancements on mixed-discrete particle swarm optimization algorithm through a multi-domain diversity preservation concept, to solve complex multi-objective optimization (MOO) problems A comprehensive sensitivity analysis of the wind farm power generation is performed to understand and compare the impact of land configuration, installed capacity decisions, incoming wind speed, and ambient turbulence on the performance of conventional array layouts and optimized wind farm layouts For array-like wind farms, the relative importance of each factor was found to vary significantly with the choice of wake models, i.e., appreciable differences in the sensitivity indices (of up to 70%) were observed across the different wake models In contrast, for optimized wind farm layouts, the choice of wake models was observed to have no significant impact on the sensitivity indices The MOWFD methodology is designed to explore the tradeoffs between the concerned performance objectives and simultaneously optimize the location of turbines, the type of turbines, and the land usage More importantly, it facilitates WFLO without prescribed conditions (e.g., fixed wind farm boundaries and number of turbines), thereby allowing a more flexible exploration of the feasible layout solutions than is possible with other existing WFLO methodologies In addition, a novel parameterization of the Pareto is performed to quantitatively explore how the best tradeoffs between energy production and land usage vary with the installed capacity decisions The key to the various complex MO-WFLOs performed here is the unique set of capabilities offered by the new Multi-Objective Mixed-Discrete Particle Swarm Optimization (MO-MDPSO) algorithm, developed, tested and extensively used in this dissertation The MO-MDPSO algorithm is capable of dealing with a plethora of problem complexities, namely: multiple highly nonlinear objectives, constraints, high design space dimensionality, and a mixture of continuous and discrete design variables Prior to applying MO-MDPSO to effectively solve complex WFLO problems, this new algorithm was tested on a large and diverse suite of popular benchmark problems; the convergence and Pareto cov- erage offered by this algorithm was found to be competitive with some of the most popular MOO algorithms (e.g., GAs) The unique potential of the MO-MDPSO algorithm is further established through application to the following complex practical engineering problems: (I) a disc brake design problem, (II) a multi-objective wind farm layout optimization problem, simultaneously optimizing the location of turbines, the selection of turbine types, and the site orientation, and (III) simultaneously minimizing land usage and maximizing capacity factors under varying land plot availability CONCEPTUAL DESIGN OF WIND FARMS THROUGH NOVEL MULTI-OBJECTIVE SWARM OPTIMIZATION By Weiyang Tong B.E., Beihang University, 2005 M.S., Beihang University, 2009 M.S., Syracuse University, 2011 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Mechanical Engineering in the Graduate School of Syracuse University Syracuse University Syracuse, New York May, 2015 c Copyright 2015 by Weiyang Tong All Rights Reserved DEDICATION I dedicate this thesis to my maternal grandmother, Yushan Wang, who raised me, loved me, and always believed in me I also dedicate this thesis to my paternal grandmother, Anqi Yang, who was a great woman of endless patience, eternal kindness, and boundless love vi ACKNOWLEDGMENT I would like to express my deepest appreciation towards my advisor, Prof Achille Messac, for his immense help throughout my doctoral studies Prof Messac provided me with invaluable advice and technical supervision that formed the foundation of the research presented in this dissertation and in the several well regarded journal articles that I have authored/co-authored thereof He also inculcated in me a spirit of professionalism that greatly contributed to my professional growth I am thankful for his devotion to my future Without his guidance, and persistent support, this dissertation would not have been possible I would also like also to thank my co-advisor, Dr Souma Chowdhury Souma has been a tremendous mentor, an excellent colleague, and a great friend I am thankful for the superb example he set as an outstanding scholar and former student of Prof Messac’s The enthusiasm, inspiration, and sharp insights he has on research will always remain an excellent source of motivation for me in my future career I would like to thank my doctoral committee members, Prof Utpal Roy, Prof John Dannenhoffer, Prof Jeongmin Ahn, Prof Benjamin Akih-Kumgeh, and my committee chair Prof Can Isik, for their valuable advice and comments, as well as their willingness to serve on my committee Special thanks to Prof Roy and Prof Dannenhoffer, who have been supportive in many ways within the MAE department in Syracuse University I wish to extend my warmest thanks to my former and present colleagues, Dr Jie Zhang, Dr Junqiang Zhang, Samuel Notaro, and my dear friend Ali Mehmani, who have helped me in many different ways at the Multidisciplinary Design and Optimization Laboratory I greatly appreciate their friendship, and their contributions to my research and this vii dissertation I am also grateful to my closest friends, Xiaomeng Li, Ang Gao, Jia Li, Xu Meng, Zi Wang, Bensong Yu, and Zhen Liu, who cheered me up even in the worst of times and made my life (far away from home) fun Special thanks to “Fly Empire” and “Starkville Soccer Group”, you gave me a lot of happiness and helped through the tough times Sponsorship of this work by the National Science Foundation awards CMMI-1100948, and CMMI-1437746 is also gratefully acknowledged These acknowledgements would not be complete without thanking the wonderful staff at Syracuse University, including Kathleen Datthyn-Madigan, Kimberly Drumm-Underwood, Kristin Shapiro, Linda Manzano, and Deborah Brown at the MAE department, and Cathy Mentor at the Sluztker Center, for all their efforts Finally, my deepest thanks go to my family; I would like to express my sincere gratitude to my parents, Lian Yu and Jun Tong, whose love and encouragement have always been my greatest strength; and I am also grateful to my uncle Fu Tong and my aunt Xiulian Zheng, who have always been supportive of all my academic endeavors viii CONTENTS DEDICATION vi ACKNOWLEDGMENT vii LIST OF TABLES xiii LIST OF FIGURES xiv LIST OF ACRONYMS xvi I Technical Preliminaries xviii Research Motivation and Objective 1.1 1.2 1.3 1.4 Overview of Wind Farm Development 1.1.1 Economic Aspect 1.1.2 Engineering Aspect 1.1.3 Environmental Aspect Conceptual Design of Wind Farms 10 1.2.1 Wind Farm Development Process 10 1.2.2 Role of Land Resource 12 Multi-Objective Mixed-Discrete Optimization Problems 14 1.3.1 Swarm-based Algorithms 15 Research Goals and Impact 16 1.4.1 Research Motivation 16 1.4.2 Research Objectives 1.4.2.1 Analyzing the Sensitivity of Wind Farm Power Output to Key Factors 1.4.2.2 Multi-Objective Wind Farm Design Framework 1.4.2.3 Land Use Related Considerations 1.4.2.4 Multi-Objective Mixed-Discrete Particle Swarm Optimization 17 Research Impact 19 Dissertation Outline 22 1.4.3 1.5 ix 17 17 18 18 158 [74] M A Lackner and C N Elkinton, “An analytical framework for offshore wind farm layout optimization,” Wind Engineering, vol 31, no 1, pp 17–31, January 2007 [75] S Chowdhury, J Zhang, A Messac, and L Castillo, “Unrestricted wind farm layout optimization (UWFLO): Investigating key factors influencing the maximum power generation,” Renewable Energy, vol 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in Proceedings of 2014 AIAA Science and Technology Forum and Exposition, National Harbor, MA, USA, January 13-17 2014 [157] NDSU, “North Dakota agricultural weather network,” ndawn.ndsu.nodak.edu/, 2009, last accessed Apr 2015 [158] L Chen and E MacDonald, “A system-level cost-of-energy wind farm layout optimization with landowner modeling,” Energy Conversion and Management, vol 77, pp 484–494, January 2014 WEIYANG TONG EDUCATION SYRACUSE UNIVERSITY, Syracuse, NY, USA Sep 2011 – May 2015 PhD in Mechanical & Aerospace Engineering Jan 2010 – Aug 2011 MS in Mechanical & Aerospace Engineering BEIHANG UNIVERSITY (BUAA), Beijing, China Sep 2006 – Jan 2009 MS in Aerospace Propulsion Theory & Engineering Sep 2001 – Jul 2005 BE in Aerospace Power Engineering PROFESSIONAL EXPERIENCE September 2013 – April 2015 Research Associate I, Department of Aerospace Engineering Mississippi State University, Mississippi State, MS September 2011 – August 2013 Teaching Assistant, Department of Mechanical & Aerospace Engineering Syracuse University, Syracuse, NY September 2006 – January 2009 Research Assistant, Department of Aerospace Engineering Beihang University, Beijing, China TEACHING EXPERIENCE January 2015 – May 2015 Engineering Design Optimization Instructor: Achille Messac January 2013 – May 2013 Senior Design Course (Mechanical Engineering) Instructor: Frederick J Carranti September 2012 – December 2012 Advanced Practical Design Optimization Instructor: Achille Messac January 2012 – May 2012 Senior Design Course (Aerospace Engineering) Instructor: John F Dannenhoffer, III September 2011 – December 2011 Introduction to Practical Design Optimization Instructor: Achille Messac JOURNAL ARTICLES [1] Tong, W., Chowdhury, S., and Messac, A., Sensitivity of Wind Farm Output to Wind Conditions, Land Configuration, and Installed Capacity, Under Different Wake Models Journal of Mechanical Design, doi:10.1115/1.4029892 (In Press) [2] Tong, W., Chowdhury, S., and Messac, A., A Multi-objective Mixed-discrete Particle Swarm Optimization Algorithm with Multi-domain Diversity Preservation Structural and Multidisciplinary Optimization (Accepted) [3] Mehmani, A., Chowdhury, S., Tong, W., and Messac, A., Adaptive Switching of Variable-Fidelity Models in Population-based Optimization Algorithm Engineering and Applied Sciences Optimization (In Press) [4] Victor Maldonado, Chowdhury, S., Messac, A., and Weiyang Tong A New Modular Product Platform Planning Approach to Design Macro-scale Reconfigurable Unmanned Aerial Vehicles (UAVs) Journal of Aircraft (Accepted) [5] Chowdhury, S., Zhang, Jie, Tong, W., and Messac, A., Modeling the Influence of Land-Shape on the Energy Production Potential of a Wind Farm Site Journal of Energy Resources Technology, 136(1): 011203 (10 pages), 2013 [6] Chowdhury, S., Tong, W., Messac, A., and Zhang, Jie A Mixed-Discrete Particle Swarm Optimization Algorithm with Explicit Diversity-Preservation Structure and Multidisciplinary Optimization, 47(3): pp 367-388, 2013 PEER-REVIEWED CONFERENCE PAPERS [1] Tong, W., Chowdhury, S., and Messac, A., “Multi-Domain Diversity Preservation to Mitigate Particle Stagnation and Enable Better Pareto Converge in Mixed-Discrete Particle Swarm Optimization”, Proceedings of the AIAA Aviation and Aeronautics Forum and Exposition, Dallas, Texas, USA, June 22-26, 2015 [2] Chowdhury, S., Tong, W., Mehmani, A., and Messac, A., “Visualizing Model Uncertainties in MultiObjective Wind Farm Layout Optimization”, Proceedings of the 11th World Congress on Structural and Multidisciplinary Optimization, Sydney, Australia, June 7-12, 2015 [3] Mehmani, A., Tong, W., Chowdhury, S., and Messac, A., “A Visually-Informed Decision-Making Platform for Wind Farm Layout Optimization”, Proceedings of the 11th World Congress on Structural and Multidisciplinary Optimization, Sydney, Australia, June 7-12, 2015 [4] Tong, W., Chowdhury, S., and Messac, A., “Multi-Objective WindFarm Optimization Simultaneously Optimizing COE and Land Footprint of Wind Farms under Different Land Plot Availability”, Proceedings of the AIAA 2015 Science and Technology Forum and Exposition, Paper No AIAA20151802, Kissimmee, Florida, USA, January 5-9, 2015 [5] Tong, W., Chowdhury, S., and Messac, A., “A New Multi-Objective Mixed-Discrete Particle Swarm Optimization Algorithm”, Proceedings of the ASME 2014 International Design Engineering Technical Conference (IDETC) & Computers and Information in Engineering Conference (CIE), Paper No DETC2014-35572, Buffalo, NY, USA, August 17-20, 2014 [6] Tong, W., Chowdhury, S., and Messac, A., “A Consolidated Visualization of Wind Farm Energy Production Potential and Optimal Land Shapes under Different Land Area and Nameplate Capacity Decisions”, Proceedings of the AIAA 2014 Science and Technology Forum and Exposition, Paper No AIAA2014-0998, National Harbor, Maryland, USA, August 13-17, 2014 [7] Chowdhury, S., Mehmani, A., Tong, W., and Messac, A., “A Visually-Informed Decision-Making Platform for Model-based Design of Wind Farms”, AIAA Aviation and Aeronautics Forum and Exposition, Paper No AIAA2014-2727, Atlanta, Georgia, June 16-20, 2014 [8] Tong, W., Chowdhury, S., Mehmani, A., Zhang, Jie, and Messac, A., “Sensitivity of Array-like and Optimized Wind Farm Output to Key Factors and Choice of Wake Models”, Proceedings of the ASME 2013 International Design Engineering Technical Conference (IDETC) & Computers and Information in Engineering Conference (CIE), Paper No DETC2013-13196, Portland, OR, USA, August 4-7, 2013 [9] Chowdhury, S., Victor Maldonado, Tong, W., and Messac, A., “Comprehensive Product Platform Planning (CP3) for a Modular Family of Unmanned Aerial Vehicles”, Proceedings of the ASME 2013 International Design Engineering Technical Conference (IDETC) & Computers and Information in Engineering Conference (CIE), Paper No DETC2013-13181, Portland, OR, USA, August 4-7, 2013 [10] Tong, W., Chowdhury, S., Mehmani, A., and Messac, A., “Multi-Objective Wind Farm Design: Exploring the Trade-off between Capacity Factor and Land Use”, Proceedings of the 10th World Congress on Structural and Multidisciplinary Optimization, Paper No 5590, Orlando, FL, USA, May 19-24, 2013 [11] Chowdhury, S., Victor Maldonado, Tong, W., and Messac, A., “Macro-scale Reconfigurable Unmanned Aerial Vehicles for Civilian Offshore Applications”, Proceedings of the 10th World Congress on Structural and Multidisciplinary Optimization, Orlando, Paper No., 5597, FL, USA, May 19-24, 2013 [12] Mehmani, A., Chowdhury, S., Zhang, Jie, Tong, W., and Messac, A., “Model Selection based on Regional Error Estimation of Surrogates”, Proceedings of the 10th World Congress on Structural and Multidisciplinary Optimization, Orlando, Paper No., 5447, FL, USA, May 19-24, 2013 [13] Mehmani, A., Chowdhury, S., Zhang, Jie, Tong, W., and Messac, A., “Quantifying Regional Error in Surrogates by Modeling its Relationship with Sample Density”, 54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, Paper No AIAA 2013-1751, Boston, Massachusetts, April 8-11, 2013 [14] Tong, W., Chowdhury, S., Zhang, Jie, and Messac, A., “Impact of Different Wake Models on the Estimation of Wind Farm Power Generation”, Proceedings of the 12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Paper No AIAA2012-5430, Indianapolis, IN, USA, September 17-19, 2012 [15] Zhang, Junqiang, Chowdhury, S., Zhang, Jie, Tong, W., and Messac, A., “Optimal Preventive Maintenance Time Windows for Offshore Wind Farms Subject to Wake Losses”, Proceedings of the 12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Indianapolis, IN, US, September 17-19, 2012 ... 1.2.1 Conceptual Design of Wind Farms Wind Farm Development Process Conceptual design is the very first phase of wind farm development, where a utility-scale wind farm may consist of hundreds of. .. Objectives in Wind Farm Design CHAPTER FOUR Identifying Key Natural & Design Factors Part II: Conceptual Design of Wind Farms CHAPTER FIVE Multi-Objective Wind Farm Design Framework CHAPTER SIX Multi-Objective. .. in Wind Farm Power Estimation in Wind Farm Design in the Multi-Objective Optimization Solver A Novel Approach to the Conceptual Design of Wind Farms 46 Primary