Research article Special Issues

An automated model for rooftop PV systems assessment in ArcGIS using LIDAR

  • Received: 19 April 2015 Accepted: 13 August 2015 Published: 27 August 2015
  • As photovoltaic (PV) systems have become less expensive, building rooftops have come to be attractive for local power production. Identifying rooftops suitable for solar energy systems over large geographic areas is needed for cities to obtain more accurate assessments of production potential and likely patterns of development. This paper presents a new method for extracting roof segments and locating suitable areas for PV systems using Light Detection and Ranging (LIDAR) data and building footprints. Rooftop segments are created using seven slope (tilt), ve aspect (azimuth) classes and 6 different building types. Moreover, direct beam shading caused by nearby objects and the surrounding terrain is taken into account on a monthly basis. Finally, the method is implemented as an ArcGIS model in ModelBuilder and a tool is created. In order to show its validity, the method is applied to city of Philadelphia, PA, USA with the criteria of slope, aspect, shading and area used to locate suitable areas for PV system installation. The results show that 33.7% of the buildings footprints areas and 48.6% of the rooftop segments identi ed is suitable for PV systems. Overall, this study provides a replicable model using commercial software that is capable of extracting individual roof segments with more detailed criteria across an urban area.

    Citation: Mesude Bayrakci Boz, Kirby Calvert, Jeffrey R. S. Brownson. An automated model for rooftop PV systems assessment in ArcGIS using LIDAR[J]. AIMS Energy, 2015, 3(3): 401-420. doi: 10.3934/energy.2015.3.401

    Related Papers:

  • As photovoltaic (PV) systems have become less expensive, building rooftops have come to be attractive for local power production. Identifying rooftops suitable for solar energy systems over large geographic areas is needed for cities to obtain more accurate assessments of production potential and likely patterns of development. This paper presents a new method for extracting roof segments and locating suitable areas for PV systems using Light Detection and Ranging (LIDAR) data and building footprints. Rooftop segments are created using seven slope (tilt), ve aspect (azimuth) classes and 6 different building types. Moreover, direct beam shading caused by nearby objects and the surrounding terrain is taken into account on a monthly basis. Finally, the method is implemented as an ArcGIS model in ModelBuilder and a tool is created. In order to show its validity, the method is applied to city of Philadelphia, PA, USA with the criteria of slope, aspect, shading and area used to locate suitable areas for PV system installation. The results show that 33.7% of the buildings footprints areas and 48.6% of the rooftop segments identi ed is suitable for PV systems. Overall, this study provides a replicable model using commercial software that is capable of extracting individual roof segments with more detailed criteria across an urban area.


    加载中
    [1] World Data Bank, Urban population, 2015. Available from: http://data.worldbank.org/indicator/SP.URB.TOTL.IN.ZS.
    [2] Nguyen HT, Pearce JM (2012) Incorporating shading losses in solar photovoltaic potential assessment at the municipal scale. Solar Energy 86: 1245-1260. doi: 10.1016/j.solener.2012.01.017
    [3] Wiginton L, Nguyen H, Pearce JM (2010) Quantifying rooftop solar photovoltaic potential for regional renewable energy policy. Computers, Environment and Urban Systems 34: 345-357. doi: 10.1016/j.compenvurbsys.2010.01.001
    [4] Suna D, Polo AL, Haas R. Demand side Value of PV. Vienna University of Technology: Institute of Power Systems and Energy Economics Group, Report of IEE project; 2006. 55p.
    [5] Voivontas D, Assimacopoulos D, Mourelatos A. et al. (1998) Evaluation of renewable energy potential using a GIS decision support system. Renewable energy 13: 333-344. doi: 10.1016/S0960-1481(98)00006-8
    [6] Izquierdo S, Rodrigues M, Fueyo N. (2008) A method for estimating the geographical distribution of the available roof surface area for large-scale photovoltaic energy-potential evaluations. Solar Energy 82: 929-939. doi: 10.1016/j.solener.2008.03.007
    [7] Kabir MH, Endlicher W, Jägermeyr J. (2010) Calculation of bright roof-tops for solar pv applications in dhaka megacity, bangladesh. Renewable Energy 35: 1760-1764. doi: 10.1016/j.renene.2009.11.016
    [8] Charabi Y, Gastli A. (2011) PV site suitability analysis using gis-based spatial fuzzy multicriteria evaluation. Renewable Energy 36: 2554-2561. doi: 10.1016/j.renene.2010.10.037
    [9] Chaudhari M, Frantzis L, Hoff TE. PV grid connected market potential under a cost breakthrough scenario. Navigant Consulting, Inc. Retrieved on September; 2004. p 93. Report No.: 2004-117373.
    [10] Paidipati J, Frantzis L, Sawyer H, at al. Rooftop photovoltaics market penetration scenarios. Golden (CO): National Renewable Energy Laboratory; 2008 November. p 92. Report No.: NREL/SR-581-42306.
    [11] Suzuki S, Ito M, Kurokawa K. An analysis of pv resource in residential areas by means of aerial photo images. in Proceedings of the 22nd European Photovoltaic Solar Energy Conference, 2007 September 3-7; Milan, Italy. (2007), p.3571-3575.
    [12] Gennusa ML, Lascari G, Rizzo G, et al. (2011) A model for predicting the potential diffusion of solar energy systems in complex urban environments. Energy Policy, 39: 5335-5343. doi: 10.1016/j.enpol.2011.05.031
    [13] Compagnon R. (2004) Solar and daylight availability in the urban fabric. Energy and Buildings 36: 321-328. doi: 10.1016/j.enbuild.2004.01.009
    [14] Anders S, Bialek T. Technical potential for rooftop photovoltaics in the San Diego region. in Solar 2006: Renewable Energy - Key to Climate Recovery, 2006 July 9-13; Denver (CO). American Solar Energy Society (ASES) Curran Associates, Inc. (Mar 2007). p.8-13.
    [15] Nguyen HT, Pearce JM. (2013) Automated quantification of solar photovoltaic potential in cities. International Review for Spatial Planning and Sustainable Development 1: 49-60. doi: 10.14246/irspsd.1.1_49
    [16] Denholm P, Margolis R. Supply curves for rooftop solar PV-generated electricity for the United States. Golden(CO): National Renewable Energy Laboratory; 2008 November. p 19. Report No.: NREL/TP-6A0-44073 .
    [17] Freitas S, Catita C, Redweik P, et al. (2015) Modelling solar potential in the urban environment: State-of-the-art review. Renewable and Sustainable Energy Reviews 41: 915-931. doi: 10.1016/j.rser.2014.08.060
    [18] Angelis-Dimakis A, Biberacher M, Dominguez J, et al. (2011) Methods and tools to evaluate the availability of renewable energy sources. Renewable and Sustainable Energy Reviews 15: 1182-1200. doi: 10.1016/j.rser.2010.09.049
    [19] Melius J, Margolis R. and Ong S. Estimating Rooftop Suitability for PV: A Review of Methods, Patents, and Validation Techniques. Golden(CO): National Renewable Energy Laboratory; 2013 December. p 30. Report No.: NREL/TP-6A20-60593.
    [20] Lukac N, Žlaus D, Seme S, et al. (2013) Rating of roofs surfaces regarding their solar potential and suitability for pv systems, based on lidar data. Applied Energy 102: 803-812. doi: 10.1016/j.apenergy.2012.08.042
    [21] Lukac N. and Zalik B. (2013) Gpu-based roofs' solar potential estimation using lidar data. Computers & Geosciences, 52: 34-41.
    [22] Huang Y, Yu B, Hu Z, et al. Locating suitable roofs for utilization of solar energy in downtown area using airborne lidar data and object-based method: A case study of the lujiazui region, Shanghai. in 2012 Second International Workshop on Earth Observation and Remote Sensing Applications, 2012 June 8-11; Shanghai, China. IEEE, (2012). p. 322-326.
    [23] Jacques DA, Gooding J, Giesekam JJ, et al. (2014) Methodology for the assessment of (PV) capacity over a city region using low-resolution lidar data and application to the city of leeds (UK). Applied Energy 124: 28-34. doi: 10.1016/j.apenergy.2014.02.076
    [24] Gooding J, Crook R, Tomlin AS. (2015) Modelling of roof geometries from low-resolution lidar data for city-scale solar energy applications using a neighbouring buildings method. Applied Energy 148: 93-104. doi: 10.1016/j.apenergy.2015.03.013
    [25] Voegtle T, Steinle E, Tovari D.(2005) Airborne laserscanning data for determination of suitable areas for photovoltaics. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 36: 215-220.
    [26] Jochem A, Höfle B, Rutzinger M, et al. (2009) Automatic roof plane detection and analysis in airborne lidar point clouds for solar potential assessment. Sensors 9: 5241-5262. doi: 10.3390/s90705241
    [27] Santos T, Gomes N, Brito M, et al. Solar potential analysis in Lisbon using lidar data. in Proc. in 31st EARSeL symposium and 35th general assembly Prague, 2011 May 30 -June 2; Prague, Czech Republic. European Association of Remote Sensing Laboratories (EARSeL), (2013). p.13-20.
    [28] Levinson R, Akbari H, Pomerantz M, et al. (2009) Solar access of residential rooftops in four California cities. Solar Energy 83: 2120-2135. doi: 10.1016/j.solener.2009.07.016
    [29] Kassner R, Koppe W, Schüttenberg T, et al. Analysis of the solar potential of roofs by using offcial lidar data. in Proceedings of the International Society for Photogrammetry, Remote Sensing and Spatial Information Sciences, (ISPRS Congress), 2008 July 3-11; Beijing, China. XXIst ISPRS Congress Technical Commission IV, (2008). p. 399-404.
    [30] Nguyen HT, Pearce JM, Harrap R, et al. (2012) The application of lidar to assessment of rooftop solar photovoltaic deployment potential in a municipal district unit. Sensors 12: 4534-4558. doi: 10.3390/s120404534
    [31] Li Z, Zhang Z, Davey K. (2015) Estimating geographical pv potential using lidar data for buildings in downtown san francisco, Transactions in GIS Forthcoming 2015.
    [32] Gooding J, Edwards H, Giesekam J, et al. (2013) Solar city indicator: A methodology to predict city level pv installed capacity by combining physical capacity and socio-economic factors. Solar Energy 95: 325-335. doi: 10.1016/j.solener.2013.06.027
    [33] Jo JH, Rose Z, Cross J, et al. (2015) Application of airborne lidar data and geographic information systems (GIS) to develop a distributed generation system for the town of normal, IL. AIMS Energy 3: 173-183. doi: 10.3934/energy.2015.2.173
    [34] Kodysh JB, Omitaomu OA, Bhaduri BL, et al. (2013) Methodology for estimating solar potential on multiple building rooftops for photovoltaic systems. Sustainable Cities and Society 8: 31-41. doi: 10.1016/j.scs.2013.01.002
    [35] Freeh R, Wu S, Mayor's Offce of Sustainability, Greenworks Philadelphia Progress Report. Mayor's Offce of Sustainability, City of Philadelphia; 2015. p 58.
    [36] Solar energy, solar city partnership, 2015. Available from: http://www.phila.gov/green/solar.html.
    [37] Pennsylvania spatial data access, Data Access Wizard, 2015. Available from: http://www.pasda.psu.edu/uci/SearchPage.aspx.
    [38] ArcGIS Resources, Creating raster dems and dsms from large lidar point collections, 2014. Available from: http://resources.arcgis.com/en/help/main/10.2/index.html#//015w0000004q000000.
    [39] Brooks B, Understanding the Cal Fire Solar Photovoltaic Installation Guideline. Brooks Engineering; 2011 March. p 27.
    [40] Chistensen CB, Barker GM. Effects of tilt and azimuth on annual incident solar radiation for united states locations. in Proceedings of Solar Forum 2001: Solar Energy: The Power to Choose, 2001 April 21-25; Washington D.C, USA.
    [41] ArcGIS Resources, How slope works, 2014. Available from: http://resources.arcgis.com/en/help/main/10.2/index.html#/How_Slope_works/009z000000vz000000/.
    [42] Alexander C, Smith-Voysey S, Jarvis C, et al. (2009) Integrating building footprints and lidar elevation data to classify roof structures and visualise buildings. Computers, Environment and Urban Systems 33: 285-292. doi: 10.1016/j.compenvurbsys.2009.01.009
    [43] California Energy Commission, Solar Ready. 2013 Residential Compliance Manual; 2014. p 14.
    [44] Fu P, Rich PM. (2002) A geometric solar radiation model with applications in agriculture and forestry. Computers and Electronics in Agriculture 37: 25-35. doi: 10.1016/S0168-1699(02)00115-1
    [45] Fu P, Rich P. The solar analyst 1.0 manual. Helios Environmental Modeling Institute, LLC; 2000. p 49.
    [46] Feldman D, Barbose G, Margolis R. Photovoltaic (PV) pricing trends: historical, recent, and near-term projections. Sun Shot Department of Energy; 2012 November. p 23. Report No: DOE/GO-102012-3839.
    [47] Goodrich A, James T, Woodhouse M. Residential, commercial, and utilityscale photovoltaic (PV) system prices in the United States: current drivers and cost-reduction opportunities. Golden(CO): National Renewable Energy Laboratory; 2012 February. p 64. Report No: NREL/TP-6A20-53347.
    [48] Brooks W, Dunlop J. Photovoltaic (PV) installer resource guide. NABCEP, 2012 March. p 162.
    [49] Guide book for solar photovoltaic projects in Philadelphia. Mayor's Offce of Sustainability City of Philadelphia, Resources, 2011 March. p 87.
    [50] Hofierka J, Kanuk J. (2009) Assessment of photovoltaic potential in urban areas using open-source solar radiation tools. Renewable Energy 34: 2206-2214. doi: 10.1016/j.renene.2009.02.021
  • Reader Comments
  • © 2015 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(8561) PDF downloads(1791) Cited by(31)

Article outline

Figures and Tables

Figures(10)  /  Tables(5)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog