Assessing the Wind Energy Potential in Bangladesh Enabling Wind Energy Development with Data Products Mark Jacobson, Caroline Draxl, Tony Jimenez, and Barbara O’Neill National Renewable Energy Laboratory Taj Capozzola Harness Energy Jared A. Lee, Francois Vandenberghe, and Sue Ellen Haupt National Center for Atmospheric Research Technical Report NREL/TP-5000-71077 September 2018 A product of the USAID-NREL Partnership Contract No. IAG-17-2050 NOTICE This work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the United States Agency for International Development (USAID) under Contract No. IAG-17-2050. The views expressed in this publication do not necessarily represent the views of the DOE or the U.S. Government including This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. U.S. Department of Energy (DOE) reports produced after 1991 and a growing number of pre-1991 documents are available free via www.OSTI.gov. NREL prints on paper that contains recycled content. Cover photo: Meteorological tower installed at Rajshahi, Bangladesh. Photo from Harness Energy Acknowledgments We would like to extend a special thank you to the USAID Bangladesh Mission—Sher Khan, Shayan Shafi, Karl Wurster, and Kerry Reeves—for invaluable guidance and support and to the project team from the Government of Bangladesh—Bazlur Rahman, Md. Atiqur Rahman, and team—for consistent assistance with travel logistics, intergovernmental communications, and landowner relations. Without their devotion to the project’s success, we could not have completed this work. We would also like to thank Jennifer Leisch of USAID Washington for her guidance and support throughout this project. Special thanks to Harness Energy LLC, which installed all nine of the meteorological stations across Bangladesh. Enduring unique installation challenges with the environment and effectively communicating with government officials, local leaders, landowners, and subcontractors ensured successful implementation of the measurement equipment. Thanks to Ben Schloesser, Wade Grewe, and Will Everhart for their work. We also thank Sarfarez Ahmed for assistance with numerous in-country challenges. We extend our gratitude to the National Center for Atmospheric Research (NCAR) team—in particular Wanli Wu—for their major contribution to the modeling part of the project. Their expertise, work, and willingness to help access NCAR’s computing resources were invaluable for the successful completion of this report. We would like to acknowledge the use of computational resources at NCAR-Wyoming Supercomputing Center, provided by the National Science Foundation and the State of Wyoming and supported by NCAR’s Computational and Information Systems Laboratory. The boundary conditions for the modeling were provided by the Data Support Section of the Computational and Information Systems Laboratory at NCAR. NCAR is supported by grants from the National Science Foundation. We also thank the following people: Billy Roberts from the National Renewable Energy Laboratory (NREL) for creating the wind resource maps; Ted Kwasnik, Paul Edwards, and Michael Rossol from NREL for help with data analysis and the Renewable Energy Data Explorer; and Heidi Tinnesand from NREL for her support with data analyses. i List of Acronyms ADP Asia Development Bank AGL above ground level CFSR Climate Forecast System Reanalysis ECMWF European Centre for Medium-Range Weather Forecasts ERA-Interim European Re-Analysis Interim FDDA Four-Dimensional Data Assimilation GFS Global Forecast System GIS geographic information system GOB Government of Bangladesh GOB-PD Government of Bangladesh, Power Division IEC International Electric Commission MAE mean absolute error ME mean error or bias MERRA Modern Era Reanalysis for Research and Applications MET meteorological MYJ Mellor-Yamada-Janjic NCAR National Center for Atmospheric Research NCEP National Centers for Environmental Prediction NREL National Renewable Energy Laboratory NWP numerical weather prediction PBL planetary boundary layer QC quality control QF quality factor RE Data Explorer Renewable Energy Data Explorer RFP request for proposal RMSE root-mean-square error SODAR sonic detection and ranging SOM self-organizing map USAID United States Agency for International Development UTC Coordinated Universal Time WMO World Meteorological Organization WRF Weather Research and Forecasting YSU Yonsei University ii Executive Summary USAID Bangladesh and the U.S. Department of Energy’s National Renewable Energy Laboratory partnered with the Government of Bangladesh to develop a national wind resource assessment. The assessment used sophisticated resource modeling that was validated by a ground measurement campaign. Results from the project included a long-term, correlated wind data set; validated high-resolution wind resource maps; and publicly available data accessible through the RE Data Explorer (https://www.re- explorer.org/). The Renewable Energy (RE) Data Explorer allows users to access and download the Bangladesh wind resource assessment data and related geographic information system (GIS) data sets and perform customized technical potential analyses. Figure ES-1 is an example wind resource map for Bangladesh that can be created with the RE Data Explorer. Figure ES-1. Wind resource map of Bangladesh and measurement locations This project improved upon existing global data sets by using best-in-class modeling techniques and analysis. Global models are good first steps in predicting wind resources in various parts of the world, but they do not generate the accuracy needed to reduce project risk and stimulate renewable investment. This project improved the quality of modeled wind resource data for Bangladesh. In addition to the modeling effort, the project team used a multi-year, local data-collection campaign to validate the model and further improve the accuracy of the data sets. The measurement campaign consisted of nine meteorological sites representing all geographical regions of the country. Seven meteorological towers and one remote- iii sensing, sonic detection and ranging unit (deployed at two sites) collected the data. Site-selection criteria focused on geographic diversity and proximity to existing transmission lines. Figure ES-1 shows measurement locations. The measurement campaign spanned June 2014 through December 2017. The results of the Bangladesh wind resource assessment will help Bangladesh overcome significant energy challenges. Its energy sector suffers from power shortages, increasing demand, decreasing domestic natural gas reserves, and inadequate transmission infrastructure. As part of a comprehensive plan to overcome these challenges, Bangladesh has committed that 10% of the total generation capacity will be renewable energy by 2021 (Power Division 2016). High-quality renewable energy resource data and other GIS data, such as those developed in this assessment, are important if Bangladesh wishes to reach its 10% renewable energy capacity target. These data support informed decision making, ranging from policy and investment decisions to reliable power sector planning. Specifically, the Bangladesh wind resource assessment will help reduce technical risk and encourage private-sector interest in the nascent wind market in Bangladesh. In addition, the Government of Bangladesh may use the results of the wind resource assessment to develop well-designed policies that could encourage investment in wind energy. This report provides a comprehensive description of the Bangladesh wind resource assessment, including details on the modeling approach and methods, instrumentation, data quality-control techniques, and resulting data sets. iv Table of Contents 1 Introduction .......................................................................................................................................... 1 1.1 Wind Resource Assessment Scope ................................................................................................ 1 1.2 Using Data Products to Expand Market Opportunities for Wind .................................................. 3 2 Approach ............................................................................................................................................... 5 3 Siting ...................................................................................................................................................... 9 3.1 Measurement Site Selection .......................................................................................................... 9 3.1.1 Step 1. Desktop Analysis ................................................................................................. 9 3.1.2 Step 2. Micrositing ......................................................................................................... 11 3.1.3 Step 3. Land Lease ......................................................................................................... 12 3.2 Landowner/Community Relationships ........................................................................................ 12 4 Instrumentation .................................................................................................................................. 14 4.1 Tower Evaluation and Selection .................................................................................................