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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.
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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

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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

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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)

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