Export file:


  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text


  • Citation Only
  • Citation and Abstract

Application of Airborne LiDAR Data and Geographic Information Systems (GIS) to Develop a Distributed Generation System for the Town of Normal, IL

1 Department of Technology, Illinois State University, Campus Box 5100, Normal, IL 61790-5100 USA;
2 Department of Geography-Geology, Illinois State University, Normal, IL, USA

Special Issues: Remote sensing and Geoinformation Technology to Explore and Predict Renewable Energy Potential

Distributed generation allows a variety of small, modular power-generating technologies to be combined with load management and energy storage systems to improve the quality and reliability of our electricity supply. As part of the US Environmental Protection Agency's effort to reduce CO2 emissions from existing power plants by 30% by 2030, distributed generation through solar photovoltaic systems provides a viable option for mitigating the negative impacts of centralized fossil fuel plants. This study conducted a detailed analysis to identify the rooftops in a town in Central Illinois that are suitable for distributed generation solar photovoltaic systems with airborn LiDAR data and to quantify their energy generation potential with an energy performance model. By utilizing the available roof space of the 9,718 buildings in the case study area, a total of 39.27 MW solar photovoltaic systems can provide electrical generation of 53,061 MWh annually. The unique methodology utilized for this assessment of a town's solar potential provides an effective way to invest in a more sustainable energy future and ensure economic stability.
  Article Metrics

Keywords photovoltaics; distributed generation; renewable energy; energy modeling; sustainability

Citation: Jin H. Jo, Zachary Rose, Jamie Cross, Evan Daebel, Andrew Verderber, John C. Kostelnick. Application of Airborne LiDAR Data and Geographic Information Systems (GIS) to Develop a Distributed Generation System for the Town of Normal, IL. AIMS Energy, 2015, 3(2): 173-183. doi: 10.3934/energy.2015.2.173


  • 1. US EPA (2014) Fact sheet: Clean Power Plan Overview. US EPA Carbon Pollution Standards. Available from: http://www2.epa.gov/carbon-pollution-standards/fact-sheet-clean-power-plan-overview.
  • 2. EIA (2013) Distributed Generation System Characteristics and Costs in the Buildings Sector. U.S. Energy Information Administration. Available from: http://www.eia.gov/analysis/studies/distribgen/system/pdf/full.pdf.
  • 3. Walla T, Widén J, Johansson J, et al. (2013) Determining and Increasing the Hosting Capacity for Photovoltaics in Swedish Distribution Grids, 27th European Photovoltaic Solar Energy Conference and Exhibition, 9.
  • 4. Jo JH, Otanicar T (2011) A hierarchical methodology for the mesoscale assessment of building integrated roof top solar energy systems. Renew Energ 36: 2992-3000.    
  • 5. Leitelt, L (2010) Developing a solar energy potential map for Chapel Hill, NC. Master's Project Report, University of North Carolina Chapel Hill. Available from: https://cdr.lib.unc.edu/indexablecontent?id=uuid:6e5c0eac-e631-4741-b038-d7e9c3e4da41&ds=DATA_FILE.
  • 6. Sauter R, Watson J (2007) Strategies for the deployment of micro-generation: Implications for social acceptance. Energ Policy 35: 2770-2779.
  • 7. NREL (2012) System Advisor Model Software Description. National Renewable Energy Laboratory. Available from: https://www.nrel.gov/analysis/sam/.
  • 8. NREL (2009) Solar Photovoltaic Cell/Module Manufacturing Activities 2008. National Renewable Energy Laboratory. Available from: http://www.eia.doe.gov/cneaf/solar.renewables/page/solarreport/table3_8.html.


This article has been cited by

  • 1. Mesude Bayrakci Boz, Kirby Calvert, Jeffrey R. S. Brownson, An automated model for rooftop PV systems assessment in ArcGIS using LIDAR, AIMS Energy, 2015, 3, 3, 401, 10.3934/energy.2015.3.401
  • 2. Yan Huang, Zuoqi Chen, Bin Wu, Liang Chen, Weiqing Mao, Feng Zhao, Jianping Wu, Junhan Wu, Bailang Yu, Estimating Roof Solar Energy Potential in the Downtown Area Using a GPU-Accelerated Solar Radiation Model and Airborne LiDAR Data, Remote Sensing, 2015, 7, 12, 17212, 10.3390/rs71215877
  • 3. Jin H. Jo, Jamie Cross, Zachary Rose, Evan Daebel, Andrew Verderber, David G. Loomis, Financing options and economic impact: distributed generation using solar photovoltaic systems in Normal, Illinois, AIMS Energy, 2016, 4, 3, 504, 10.3934/energy.2016.3.504

Reader Comments

your name: *   your email: *  

Copyright Info: 2015, Jin H. Jo, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Copyright © AIMS Press All Rights Reserved