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The analysis of Photovoltaic Power using Terrain Data based on LiDAR Surveying and Weather Data Measurement System

LiDAR 측량 기반의 지형자료와 기상 데이터 관측시스템을 이용한 태양광 발전량 분석

  • Received : 2019.01.08
  • Accepted : 2019.06.18
  • Published : 2019.06.30

Abstract

In this study, we conducted a study to predict the photovoltaic power by constructing the sensor based meteorological data observation system and the accurate terrain data obtained by using LiDAR surveying. The average sunshine hours in 2018 is 4.53 hours and the photovoltaic power is 2,305 MWh. In order to analyze the effect of photovoltaic power on the installation angle of solar modules, we installed module installation angle at $10^{\circ}$ intervals. As a result, the generation time was 4.24 hours at the module arrangement angle of $30^{\circ}$, and the daily power generation and the monthly power generation were the highest, 3.37 MWh and 102.47 MWh, respectively. Therefore, when the module arrangement angle is set to $30^{\circ}$, the generation efficiency is increased by about 4.8% compared with the module angle of $50^{\circ}$. As a result of analyzing the influence of the seasonal photovoltaic power by the installation angle of the solar module, it was found that the photovoltaic power was high in the range of $40^{\circ}{\sim}50^{\circ}$, where the module angle was large from November to February when the weather was cold. From March to October, it was found that the photovoltaic power amount is $10^{\circ}{\sim}30^{\circ}$ with small module angle.

본 연구에서는 LiDAR 측량을 활용하여 취득한 정밀 지형자료와 센서 기반의 기상데이터 관측시스템을 구축하여 태양광 발전량을 예측하는 연구를 수행하였다. 2018년 평균 일조시간은 4.53 시간으로 나타났으며, 태양광 발전량은 2,305 MWh으로 분석되었다. 그리고 태양광 모듈의 설치각도에 따른 태양광 발전량의 영향을 분석하고자 모듈 설치각도를 $10^{\circ}$ 간격으로 배치한 결과, 모듈 배치 각도 $30^{\circ}$에서 발전시간은 4.24 시간으로 나타났으며, 일 발전량과 월 발전량이 각각 3.37 MWh와 102.47 MWh로 가장 높게 평가되었다. 따라서 모듈 배치 각도를 $30^{\circ}$로 설계시 모듈 각도 $50^{\circ}$에 비해 발전효율이 약 4.8% 상승하는 것을 알 수 있었다. 또한 태양광 모듈의 설치각도에 따른 계절별 태양광 발전량의 영향을 분석한 결과, 날씨가 차가운 11월~2월까지는 모듈 각도가 큰 $40^{\circ}{\sim}50^{\circ}$가 태양광 발전량이 높게 나타났으며 날씨가 따뜻한 3월~10월까지는 모듈 각도가 작은 $10^{\circ}{\sim}30^{\circ}$가 태양광 발전량이 높게 나타남을 알 수 있었다.

Keywords

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Figure 1. Study site

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Figure 2. LiDAR equipment

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Figure 3. 3D topographic modeling data

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Figure 4. PV module arrangement using Drop function

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Figure 5. Condition setting for PV module arrangement

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Figure 6. Loss ratio according to PV module azimuth angle

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Figure 7. Real-time weather data observation system

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Figure 8. Prediction result of PV power

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Figure 9. Analysis of monthly PV power to PV module angle

Table 1. Specification of LiDAR VZ-400

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Table 2. Prediction result of PV power

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Table 3. Analysis of PV power to PV module angle

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Table 4. Analysis of Photovoltaic power to Phtovoltaic module angle

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References

  1. Kim HY. 2010. A Study on the Improvement of the Accuracy of Photovoltaic Facility Location Using the Geostatistical Analysis. Journal of the Korean Association of Geographic Information Studies. 13(2):146-156. https://doi.org/10.11108/kagis.2010.13.2.146
  2. No ST. 2014. Comparison of Measured and Predicted Photovoltaic Electricity Generation and Input Options of Various Softwares. The International Journal of The Korea Institute of Ecological Architecture and Environment. 14(6):87-92.
  3. Doh JH, Kim DS, Koo HD. 2014. High Utilization of Photovoltaic Power System in Rural Green Village Location Analysis and Evaluation using GIS. Journal of the Korean Society of Rural Planning. 20(1):51-62. https://doi.org/10.7851/ksrp.2014.20.1.051
  4. Park JI, Park MH, Lim RT. 2010. A Study on the New Renewable Energy Suitable Position Using GIS, Proceeding of the Korean Cadastre Information Association. 129-141.
  5. Sim JB, Lee JD, Lee SS, Lee KE. 2010. The Better Position of Powerstation Desided by Solar through GIS Analysis. Proceeding of the Korean Society of Civil Engineering. 1126-1129.
  6. Lee KH, Kim WJ. 2016. Forecasting of 24 hour ahead Photovoltaic Power Output using Support Vector Regression. Journal of Korean Institute of Information Technology. 14(3): 175-183.
  7. Lee GS, Lee JJ. 2018a. Analysis of Solar Plant Site based on Airborne LiDAR data. Journal of the Korean Cadastre Information Association. 20(1):37-47. https://doi.org/10.46416/JKCIA.2018.04.20.1.37
  8. Lee GS, Lee JJ. 2018b. The Analysis of Solar Radiation to Solar Plant Area based on UAV Geospatial Information System. Journal of Cadastre & Land InformatiX. 48(1):5-14. https://doi.org/10.22640/LXSIRI.2018.48.1.5
  9. Lee KR, Lee WH. 2015. Solar Power Plant Location Analysis Using GIS and Analytic Hierarchy Process. Journal of the Korean Association of Geographic Information Studies. 18(4):1-13. https://doi.org/10.11108/kagis.2015.18.4.001
  10. Lee DJ, Lee JP, Lee CS, Lim JY, Ji PS. 2015. Development of PV Power Prediction Algorighm using Adaptive Neuro-Fuzzy Model. The Transaction of the Korean Institute of Electrical Engineers. 64(4):246-250. https://doi.org/10.5370/KIEEP.2015.64.4.246
  11. Lee CS, Ji PS. 2015. Development of Daily PV Power Forecasting Models using ELM. The Transaction of the Korean Institute of Electrical Engineers. 64(3):164-168. https://doi.org/10.5370/KIEEP.2015.64.3.164
  12. Escobar RA, Cortes C, Pino A, Salgado M, Pereira EB, Martins FR, Boland J, and Cardemil JM. 2015. Estimating the potential for solar energy utilization in Chile by satellite derived data and ground station measurements. Solar Energy. 121:139-151. https://doi.org/10.1016/j.solener.2015.08.034
  13. Huang Shengli, Paul M. Rich, Robert Crabtree, Christopher Potter, and Pinde Fu. 2008. Modeling Near-Surface Air Temperature from Solar Radiation and Lapse Rate: Application over Complex Terrain in Yellowstone National Park, USA. Physical Geography. 29(2):158-178. https://doi.org/10.2747/0272-3646.29.2.158

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