A Distributed Real-Time Short-Term Solar Irradiation Forecasting Network for Photovoltaic Systems
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UNLV Theses, Dissertations, Professional Papers, and Capstones 12-15-2018 A Distributed Real-Time Short-Term Solar Irradiation Forecasting Network for Photovoltaic Systems Michael Adelbert Gacusan Follow this and additional works at: https://digitalscholarship.unlv.edu/thesesdissertations Part of the Computer Engineering Commons, and the Electrical and Computer Engineering Commons Repository Citation Gacusan, Michael Adelbert, "A Distributed Real-Time Short-Term Solar Irradiation Forecasting Network for Photovoltaic Systems" (2018). UNLV Theses, Dissertations, Professional Papers, and Capstones. 3489. http://dx.doi.org/10.34917/14279612 This Thesis is protected by copyright and/or related rights. It has been brought to you by Digital Scholarship@UNLV with permission from the rights-holder(s). You are free to use this Thesis in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/ or on the work itself. This Thesis has been accepted for inclusion in UNLV Theses, Dissertations, Professional Papers, and Capstones by an authorized administrator of Digital Scholarship@UNLV. For more information, please contact [email protected]. A DISTRIBUTED REAL-TIME SHORT-TERM SOLAR IRRADIATION FORECASTING NETWORK FOR PHOTOVOLTAIC SYSTEMS By Michael Adelbert G. Gacusan Bachelor of Science in Engineering - Electrical Engineering University of Nevada, Las Vegas 2013 A thesis submitted in partial fulfillment of the requirements for the Master of Science in Engineering – Electrical Engineering Department of Electrical and Computer Engineering Howard R. Hughes College of Engineering The Graduate College University of Nevada, Las Vegas December 2018 Copyright by Michael Adelbert G. Gacusan, 2018 All Rights Reserved Thesis Approval The Graduate College The University of Nevada, Las Vegas November 13, 2018 This thesis prepared by Michael Adelbert G. Gacusan entitled A Distributed Real-Time Short-Term Solar Irradiation Forecasting Network for Photovoltaic Systems is approved in partial fulfillment of the requirements for the degree of Master of Science in Engineering – Electrical Engineering Department of Electrical and Computer Engineering Venkatesan Muthukumar, Ph.D. Kathryn Hausbeck Korgan, Ph.D. Examination Committee Chair Graduate College Interim Dean Emma Regentova, Ph.D. Examination Committee Member Yahia Baghzouz, Ph.D. Examination Committee Member Robert Boehm, Ph.D. Graduate College Faculty Representative ii Abstract Solar irradiation forecasting is essential for PV connected electrical grids to maintain reliability, stability, and effective matching of real-time demand to power distribution. This research paper develops and evaluates proposed forecasting methods using wireless sensor networks. Each node of the network is capable of monitoring illuminance data and communicate it through RF and/or WiFi. The nodes are calibrated with respect to irradiance data from an industry-standard pyranometer. Power consumption of each node type is also collected at different operating states. The proposed sensor network can estimate a cloud motion vector or a cloud shadow’s speed and direction from the data collected. By processing the collected data further, a forecasted solar irradiance ramp-down time-of-arrival is possible. The results are evaluated for both artificial and on-site cloud shadows. iii Acknowledgements They say, "if you want to go fast, go alone; if you want to go far, go together." Clearly, I did not go fast as it took me five years to get this far. But thanks to my family, friends, mentors, colleagues, and my significant other, I have reached where I am today. You guys know who you are. You guys are awesome! iv Ad Majorem Dei Gloriam v Table of Contents Abstract ......................................................................................................................................... iii Acknowledgements ...................................................................................................................... iv Table of Contents ......................................................................................................................... vi List of Tables .............................................................................................................................. viii List of Figures ............................................................................................................................... ix List of Equations .......................................................................................................................... xi Chapter 1: Introduction ............................................................................................................... 1 Chapter 2: Literature Review ...................................................................................................... 5 2.1 Solar Energy Variability ..................................................................................................... 5 2.2 Current Solar Forecasting Models .................................................................................... 8 2.2.1 Statistical and Numerical Models ............................................................................... 8 2.2.2 Satellite and Ground-based Sky Image Processing ................................................... 9 2.2.3 Sensor Network ........................................................................................................... 10 2.3 Summarization of Literature Review .............................................................................. 11 Chapter 3: Hardware Architecture and Software Framework .............................................. 15 3.1 Hardware Architecture..................................................................................................... 15 3.1.1 BeagleBone Black Host System ................................................................................. 16 3.1.2 ESP8266 Node ............................................................................................................. 20 3.1.3 Triple LoRa System .................................................................................................... 23 3.1.4 Quad Illuminance Comparison System .................................................................... 25 3.1.5 Cloud Motion Vector System ..................................................................................... 28 3.2 Software Framework ........................................................................................................ 31 3.2.1 Cloud Shadow Direction Estimation......................................................................... 31 3.2.2 Cloud Shadow Speed Estimation .............................................................................. 35 3.2.3 Forecasting .................................................................................................................. 38 Chapter 4: Calibration ............................................................................................................... 42 vi Chapter 5: Data Collection ........................................................................................................ 51 5.1 Synthetic Cloud Shadow Data .......................................................................................... 51 5.2 Actual Cloud Shadow Data .............................................................................................. 53 5.3 PV System Power Generation Data ................................................................................. 55 5.4 Power Consumption Data ................................................................................................. 56 Chapter 6: Data Analaysis and Results .................................................................................... 58 6.1 Data Analysis ..................................................................................................................... 58 6.2 Results ................................................................................................................................ 62 Chapter 7: Conclusion ................................................................................................................ 64 Appendix A: Cloud Motion Vector Angle Estimation............................................................. 65 Appendix B: Cloud Motion Vector Speed Estimation ............................................................ 67 Appendix C: Synthetic Cloud Shadow Experimental Outputs .............................................. 69 Appendix D: Actual Cloud Shadow Experimental Outputs ................................................... 89 Appendix E: Time-of-Arrival Estimation Sample ................................................................ 122 Appendix F: UNLV Microgrid Model Sample Outputs ........................................................ 123 Bibliography .............................................................................................................................. 124 Curriculum Vitae ...................................................................................................................... 128 vii List of Tables Table 3.1: BeagleBone Black to Sensors Pinout .......................................................................... 17 Table 3.2: NodeMCU