All Issue

2022 Vol.42, Issue 6 Preview Page

Research Article

30 December 2022. pp. 75-91
Abstract
References
1
Ministry of Trade, Industry and Energy (MOTIE), The 9th Basic Plan of Long-Term Electricity Supply and Demand (2020-2034), 2020.
2
Yang, D. and van der Meer, D., Post-processing in Solar Forecasting: Ten Overarching Thinking Tools, Renewable and Sustainable Energy Reviews, Vol. 140, 110735, 2021, https://doi.org/10.1016/j.rser.2021.110735 10.1016/j.rser.2021.110735
3
Lorenz, E. and Heinemann, D., Prediction of Solar Irradiance and Photovoltaic Power, Renewable Energy, Vol. 1, pp. 239-292, 2012, https://doi.orf/10.1016/B978-0-08-087872-0.00114-1 10.1016/B978-0-08-087872-0.00114-1
4
Mellit, A., Massi Pavan, A., Ogliari, E., Leva, S., and Lughi, V., Advanced Methods for Photovoltaic Output Power Forecasting: A Review, Applied Sciences, Vol. 10, No. 2, 487, 2020, https://doi.org/10.3390/app10020487 10.3390/app10020487
5
PVOutput, Data Services, 2022. https://pvoutput.org/index.jsp last accessed on the 9th August 2022.
6
Haghdadi, N., Copper, J., Bruce, A., and MacGill, I., A Method to Estimate the Location and Orientation of Distributed Photovoltaic Systems from Their Generation Output Data, Renewable Energy, Vol. 108, pp. 390-400, 2017, https://doi.org/10.1016/j.renene.2017.02.080 10.1016/j.renene.2017.02.080
7
Saint-Drenan, Y. M., Bofinger, S., Fritz, R., Vogt, S., Good, G. H., and Dobschinski, J., An Empirical Approach to Parameterizing Photovoltaic Plants for Power Forecasting and Simulation, Solar Energy, Vol. 120, pp. 479-493, 2015, https://doi.org/10.1016/j.solener.2015.07.024 10.1016/j.solener.2015.07.024
8
Ruelle, V. D., Jeppesen, M., and Brear, M., Rooftop PV Model Technical Report, Technical Report July, University of Melbourne, Melbourne, 2016, https://aemo.com.au/-/media/files/electricity/nem/planning_and_forecasting/demand-forecasts/nefr/2016/uom-rooftop-pv-model-technical-report.pdf
9
Meng, B., Loonen, R. C., and Hensen, J. L., Data-driven Inference of Unknown Tilt and Azimuth of Distributed PV Systems, Solar Energy, Vol. 211, pp. 418-432, 2020, https://doi.org/10.1016/j.solener.2020.09.077 10.1016/j.solener.2020.09.077
10
Wu, Y. K., Huang, C. L., Phan, Q. T., and Li, Y. Y., Completed Review of Various Solar Power Forecasting Techniques Considering Different Viewpoints, Energies, Vol. 15, No. 9, 3320, 2022, https://doi.org/10.3390/en15093320 10.3390/en15093320
11
Guzman Razo, D. E., Müller, B., Madsen, H., and Wittwer, C., A Genetic Algorithm Approach as a Self-learning and Optimization Tool for PV Power Simulation and Digital Twinning, Energies, Vol. 13, No. 24, 6712, 2020, https://doi.org/10.3390/en13246712 10.3390/en13246712
12
Danner, P. and de Meer, H., Location and Solar System Parameter Extraction from Power Measurement Time Series, Energy Informatics, Vol. 4, No. 3, pp. 1-20, 2021, https://doi.org/10.1186/s42162-021-00176-2 10.1186/s42162-021-00176-2
13
Killinger, S., Engerer, N., and Müller, B., QCPV: A Quality Control Algorithm for Distributed Photovoltaic Array Power Output, Solar Energy, Vol. 143, pp. 120-131, 2017, https://doi.org/10.1016/j.solener.2016.12.053 10.1016/j.solener.2016.12.053
14
Ministry of Trade, Industry and Energy (MOTIE), Solar linked ESS is used as a safe power supply and demand, 2020. https://www.motie.go.kr/motie/ne/presse/press2/bbs/bbsView.do?bbs_cd_n=81&bbs_seq_n=163192 last accessed on the 6th July 2022.
15
Kim, C. K., Kim, H.-G., Kang, Y.-H., and Yun, C.-Y., Analysis of Clear Sky Index Defined by Various Ways Using Solar Resource Map Based on Chollian Satellite Imagery, Journal of the Korean Solar Energy Society, Vol. 39, No. 3, pp.47-57, 2019, https://doi.org/10.7836/kses.2019.39.3.047 10.7836/kses.2019.39.3.047
16
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N., ERA5-Land: A State-of-the-Art Global Reanalysis Dataset for Land Applications, Earth Syst. Sci. Data, Vol. 13, pp. 4349-4383, 2021, https://doi.org/10.5194/essd-13-4349-2021 10.5194/essd-13-4349-2021
17
Mayer, M. J. and Gróf, G., Extensive Comparison of Physical Models for Photovoltaic Power Forecasting, Applied Energy, Vol. 283, 116239, 2021, https://doi.org/10.1016/j.apenergy.2020.116239 10.1016/j.apenergy.2020.116239
18
Yang, D., Alessandrini, S., Antonanzas, J., Antonanzas-Torres, F., Badescu, V., Beyer, H. G., Blaga, R., Boland, J., Bright, J.M., Coimbra, C. F. M., David, M., Frimane, Â., Gueymard, C. A., Hong, T., Kay, M. J., Killinger, S., Kleissl, J., Lauret, P., Lorenz, E., van der Meer, D., Paulescu, M., Perez, R., Perpiñán-Lamigueiro, O., Peters, I. M., Reikard, G., Renné, D., Saint-Drenan, Y.-M., Shuai, Y., Urraca, R., Verbois, H., Vignola, F., Voyant, C., and Zhang, J., Verification of Deterministic Solar Forecasts, Solar Energy, Vol. 210, pp. 20-37, 2020, https://doi.org/10.1016/j.solener.2020.04.019 10.1016/j.solener.2020.04.019
19
Starke, A. R., Lemos, L. F., Boland, J., Cardemil, J. M., and Colle, S., Resolution of the Cloud Enhancement Problem for One-minute Diffuse Radiation Prediction, Renewable Energy, Vol. 125, pp. 472-484, 2018, https://doi.org/10.1016/j.renene.2018.02.107 10.1016/j.renene.2018.02.107
20
Loutzenhiser, P. G., Manz, H., Felsmann, C., Strachan, P. A., Frank, T. H., and Maxwell, G. M., Empirical Validation of Models to Compute Solar Irradiance on Inclined Surfaces for Building Energy Simulation, Solar Energy, Vol. 81, No. 2, pp. 254-267, 2007, https://doi.org/10.1016/j.solener.2006.03.009 10.1016/j.solener.2006.03.009
21
Perez, R., Stewart, R., Seals, R., and Guertin, T., The Development and Verification of the Perez Diffuse Radiation Model (No. SAND88-7030), Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); State Univ. of New York, Albany (USA), Atmospheric Sciences Research Center, 1988. 10.2172/7024029
22
Schwingshackl, C., Petitta, M., Wagner, J. E., Belluardo, G., Moser, D., Wind Effect on PV Module Temperature: Analysis of Different Techniques for an Accurate Estimation, Energy Procedia, Vol. 40, pp. 77-86, 2013, https://doi.org/10.1016/j.egypro.2013.08.010 10.1016/j.egypro.2013.08.010
23
Freeman, J. M., DiOrio, N. A., Blair, N. J., Neises, T. W., Wagner, M. J., Gilman, P., and Janzou, S., System Advisor Model (SAM) General Description (version 2017.9. 5) (No. NREL/TP-6A20-70414), National Renewable Energy Lab. (NREL), Golden, CO (United States), 2018. 10.2172/1440404
24
Saltelli, A., Sensitivity Analysis for Importance Assessment, Risk Analysis, Vol. 22, No. 3, pp. 579-590, 2002, https://doi.org/10.1111/0272-4332.00040 10.1111/0272-4332.00040
25
Sobol, I. M., Global Sensitivity Indices for Nonlinear Mathematical Models and Their Monte Carlo Estimates, Mathematics and Computers in Simulation, Vol. 55, No. 1-3, pp. 271-280, 2001, https://doi.org/10.1016/S0378-4754(00)00270-6 10.1016/S0378-4754(00)00270-6
26
Saltelli, A., Annoni, P., Azzini, I., Campolongo, F., Ratto, M., and Tarantola, S., Variance based Sensitivity Analysis of Model Output, Design and Estimator for the Total Sensitivity Index, Computer Physics Communications, Vol. 181, No. 2, pp. 259-270, 2010, https://doi.org/10.1016/j.cpc.2009.09.018 10.1016/j.cpc.2009.09.018
27
Sun, V., Asanakham, A., Deethayat, T., and Kiatsiriroat, T., Evaluation of Nominal Operating Cell Temperature (NOCT) of Glazed Photovoltaic Thermal Module, Case Studies in Thermal Engineering, Vol. 28, 101361, 2021, https://doi.org/10.1016/j.csite.2021.101361 10.1016/j.csite.2021.101361
28
Snoek, J., Larochelle, H., and Adams, R. P., Practical Bayesian Optimization of Machine Learning Algorithms, Advances in Neural Information Processing Systems, 25, 2012.
29
Jacobson, M. Z. and Jadhav, V., World Estimates of PV Optimal Tilt Angles and Ratios of Sunlight Incident Upon Tilted and Tracked PV Panels Relative to Horizontal Panels, Solar Energy, Vol. 169, pp. 55-66, 2018, https://doi.org/10.1016/j.solener.2018.04.030 10.1016/j.solener.2018.04.030
30
Ineichen, P., Perez, R., and Seals, R., The Importance of Correct Albedo Determination for Adequately Modeling Energy Received by Tilted Surfaces, Solar Energy, Vol. 39, No. 4, pp. 301-305, 1987, https://doi.org/10.1016/S0038-092X(87)80016-6 10.1016/S0038-092X(87)80016-6
31
Gilman, A., Dobos, N., DiOrio, J., Freeman, S., Janzou, D., and Ryberg, SAM Photovoltaic Model Technical Reference Update, National Renewable Energy Laboratory, pp. 15-19, 2020.
32
PVSyst, Albedo Usual Coefficients, 2022. https://www.pvsyst.com/help/albedo.htm last accessed on the 24th July 2022.
33
Ministry of Environment, Environmental Geographic Information Service, Land Cover Map (1:25000), 2014. https://egis.me.go.kr/api/land.do last accessed on the 24th July 2022.
Information
  • Publisher :Korean Solar Energy Society
  • Publisher(Ko) :한국태양에너지학회
  • Journal Title :Journal of the Korean Solar Energy Society
  • Journal Title(Ko) :한국태양에너지학회 논문집
  • Volume : 42
  • No :6
  • Pages :75-91
  • Received Date : 2022-09-19
  • Revised Date : 2022-11-05
  • Accepted Date : 2022-11-07