Impacts of solar photovoltaic system on distribution network performance
With the growth in energy demand and the depletion of fossil fuels, renewable energy resources have been seen as one of the most promising ways to sustain the future energy needs. However, the integration of renewables into the existing distribution networks can cause potential network problems. The...
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With the growth in energy demand and the depletion of fossil fuels, renewable energy resources have been seen as one of the most promising ways to sustain the future energy needs. However, the integration of renewables into the existing distribution networks can cause potential network problems. The issue is particularly acute if the renewable energy is generated from solar photovoltaic (PV) system with high variability. In this regard, this thesis deals with the modelling of a typical Malaysian distribution network that aims to analyze the impact of PV integration at distribution networks level. More specifically, the number of tap change for On- Load Tap Changer (OLTC) transformers is evaluated under various weather conditions; PV penetration levels as well as PV installed locations. The weather conditions were further categorized using variability index. In this way, the impact of solar variability can be properly assessed. The correlations of network losses and PV penetration levels have also been comprehensively analyzed. It is also important to highlight that actual solar PV generation data of various time resolution were collected and used in this work. This maintains the actual intermittency nature of PV generation. Furthermore, case studies have been performed for both low and medium voltage networks. The results suggest that sudden voltage variation and reverse power flow are the main concern of PV integration on the distribution network. The presented study shows that network losses are at the minimum level with a 50% PV penetration level. In addition, the findings suggest that high solar variability day could increase the tap change operations as much as 274% in average as compared to the network without PV system. In addition, a year-round analysis further suggests that the total annual tap change may operate 164% more frequently in average than a network without PV system. |
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Lau, Cheiw Yun |
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Lau, Cheiw Yun |
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Impacts of solar photovoltaic system on distribution network performance |
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Impacts of solar photovoltaic system on distribution network performance |
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Impacts of solar photovoltaic system on distribution network performance |
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Impacts of solar photovoltaic system on distribution network performance |
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Impacts of solar photovoltaic system on distribution network performance |
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impacts of solar photovoltaic system on distribution network performance |
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my-utem-ep.206202022-06-08T12:28:43Z Impacts of solar photovoltaic system on distribution network performance 2017 Lau, Cheiw Yun T Technology (General) TK Electrical engineering. Electronics Nuclear engineering With the growth in energy demand and the depletion of fossil fuels, renewable energy resources have been seen as one of the most promising ways to sustain the future energy needs. However, the integration of renewables into the existing distribution networks can cause potential network problems. The issue is particularly acute if the renewable energy is generated from solar photovoltaic (PV) system with high variability. In this regard, this thesis deals with the modelling of a typical Malaysian distribution network that aims to analyze the impact of PV integration at distribution networks level. More specifically, the number of tap change for On- Load Tap Changer (OLTC) transformers is evaluated under various weather conditions; PV penetration levels as well as PV installed locations. The weather conditions were further categorized using variability index. In this way, the impact of solar variability can be properly assessed. The correlations of network losses and PV penetration levels have also been comprehensively analyzed. It is also important to highlight that actual solar PV generation data of various time resolution were collected and used in this work. This maintains the actual intermittency nature of PV generation. Furthermore, case studies have been performed for both low and medium voltage networks. The results suggest that sudden voltage variation and reverse power flow are the main concern of PV integration on the distribution network. The presented study shows that network losses are at the minimum level with a 50% PV penetration level. In addition, the findings suggest that high solar variability day could increase the tap change operations as much as 274% in average as compared to the network without PV system. In addition, a year-round analysis further suggests that the total annual tap change may operate 164% more frequently in average than a network without PV system. 2017 Thesis http://eprints.utem.edu.my/id/eprint/20620/ http://eprints.utem.edu.my/id/eprint/20620/1/Impacts%20Of%20Solar%20Photovoltaic%20System%20On%20Distribution%20Network%20Performance.pdf text en public http://eprints.utem.edu.my/id/eprint/20620/2/Impacts%20of%20solar%20photovoltaic%20system%20on%20distribution%20network%20performance.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=106112&query_desc=kw%2Cwrdl%3A%20Impacts%20Of%20Solar%20Photovoltaic%20System%20On%20Distribution%20Network%20Performance mphil masters Universiti Teknikal Malaysia Melaka Faculty Of Electrical Engineering Gan, Chin Kim 1. Alam, M.J.E., Muttaqi, K.M. & Sutanto, D., 2014. A Novel Approach for Ramp-Rate Control of Solar PV Using Energy Storage to Mitigate Output Fluctuations Caused by Cloud Passing.IEEE Transactions on Energy Conversion, 29(2), pp.507–518. 2. Alam, M.J.E., Muttaqi, K.M. & Sutanto, D., 2013. A SAX-based advanced computational tool for assessment of clustered rooftop solar PV impacts on LV and MV networks in smart grid. IEEE Transactions on Smart Grid, 4(1), pp.577–585. 3. Au, M.T., et al., 2008. A Simplified Approach in Estimating Technical Losses in Distribution Network Based on Load Profile and Feeder Characteristics. 2nd IEEE International Conference on Power and Energy (PECon 08), pp. 1661-1665. 4. Au, M.T., Anthony, T. & Mohamad, M., 2009. Strategies in technical loss reduction and it's impact on harmonic performance of distribution network. IEEE Bucharest Power Tech Conference, pp. 1-6. 5. Beckwith Electric, 1998. Basic considerations for the application of LTC transformerss and associated controls. In Tap Changer Controls, Application Note #17, pp. 1–8. 6. Beckwith Electric, 2001. Inverse Time Delay on LTC / Regulator Controls. In Tap Changer Controls Application Note # 29, pp. 1–4. 7. Bellia, H., Youcef, R. & Fatima, M., 2014. A Detailed Modeling of Photovoltaic Module using MATLAB. NRIAG Journal of Astronomy and Geophysics, 3(1), pp.53–61. 8. Bondre, K. & Nambiar, H., 2011. India has Commissioned 186MW Solar Projects. REUTERS. Available at: http://uk.reuters.com/article/2011/12/13/india-solar-idUKI8E7MB02C20111213 [Accessed on 10 October 2014]. 9. Bosch, J.L., Zheng, Y. & Kleissl, J., 2013. Deriving cloud velocity from an array of solar radiation measurements. Solar Energy, 87, pp.196–203. 10. Busrah, A.M., Au, M.T. & Mohamad, M., 2011. End user load profile analysis for distribution system planning. In 21st International Conference on Electricity Distribution. CIRED, pp. 1–4. 11. Caamano-Martin, E. et al., 2008. Interaction Between Photovoltaic Distributed Generation and Electricity Networks. Progress in photovoltaics: Research and applications, 16(August), pp.629–643. 12. Chenvidhya, D. et al., 2003. A Thai national demonstration project on PV grid-interactive systems: Power quality observation. In 3rd World Conference on Photovoltaic Energy Conversion. pp. 2152–2154. 13. Chicco, G., Schlabbach, J. & Spertino, F., 2009. Experimental assessment of the waveform distortion in grid-connected photovoltaic installations. Solar Energy, 83, pp.1026–1039. 14. Chokmaviroj, S., Wattanapong, R. & Suchart, Y., 2006. Performance of a 500 kWP grid connected photovoltaic system at Mae Hong Son Province, Thailand. Renewable Energy, 31,pp.19–28. 15. Chow, C.W. et al., 2011. Intra-hour forecasting with a total sky imager at the UC.pdf. Solar Energy, 85, pp.1–28. 16. Cohen, M.A. & Callaway, D.S., 2016. Effects of distributed PV generation on California’s distribution system, Part 1: Engineering simulations. Solar Energy, 128(April), pp.126–138. 17. Curtright, A.E. & Apt, J., 2008. The character of power output from utility-scale photovoltaic systems. Progress in Photovoltaics: Research and Applications, 16, pp.241–247. 18. Ding, K. et al., 2012. A MATLAB-simulink-based PV module model and its application under conditions of nonuniform irradiance. IEEE Transactions on Energy Conversion, 27(4), pp.864– 872. 19. Dugan, R.C. & Mcdermott, T.E., 2011. An Open Source Platform for Collaborating on Smart Grid Research. In IEEE Conferences Publication. pp. 1–7. 20. Ebad, M. & Grady, W.M., 2016. An approach for assessing high-penetration PV impact on distribution feeders. Electric Power Systems Research, 133, pp.347–354. Elkind, E.N. et al., 2013. Report: Renewable Energy Beyond 2020: Next steps for California. UCLA Environmental Law Center. 21. ElNozahy, M.S. & Salama, M.M. a., 2013. Technical impacts of grid-connected photovoltaic systems on electrical networks—A review. Journal of Renewable and Sustainable Energy, 5(3), p.032702. 22. Eltawil, M. a. & Zhao, Z., 2010. Grid-connected photovoltaic power systems: Technical and potential problems—A review. In Renewable and Sustainable Energy Reviews, 14(1), pp.112– 129. 23. Enslin, J.H.R., 2010. Network impacts of high penetration of photovoltaic solar power systems. In IEEE PES General Meeting. pp. 1–5. 24. Glover, J.D., Sarma, M.S. & Overbye, T., 2012. Power Systems Analysis and Design, Available at: http://www.amazon.com/dp/0534548849. [Accessed on 27 March 2015]. Grigoleit, T., Rothacher, T. & Hildebrandt, M., 2014. The Photovoltaic Market in Germany. Germany Trade & Invest, pp.3–14. 25. Hahn, C.J. & Warren, S.G., 2007. A gridded climatology of clouds over land (1971-96) and ocean (1954-97) from surface observations worldwide. Oak Ridge National Laboratory, Carbon Dioxide Information Analysis Center, p.55. 26. Hamill, T.M. & Nehrkorn, T., 1993. A Short-Term Cloud Forecast Scheme Using Cross Correlations. Weather and Forecasting, 8(4), pp.401–411. 137 27. Hartung, M., Baerthlein, E.-M. & Panosyan, A., 2014. Comparative study of tap changer control algorithms for distribution networks with high penetration of renewables. In International Conference on Electricity Distribution (CIRED). pp. 11–12. 28. Hoff, T.E. & Perez, R., 2012. Modeling PV fleet output variability. Solar Energy, 86(8), pp.2177–2189. 29. Ibraheem, W.E., 2014. Impact of Photovoltaic Systems Integration on Electricity Distribution Networks. Dissertation: Universiti Teknikal Malaysia Melaka (UTeM). Iglesias, J.G. et al., 2002. Power quality in European electricity supply networks 2nd Edition, Brussels: Union of the ELectricity Industry: Brussels (Belgium). Available at: <http://www.eurelectric.org> [Accessed on 3 October 2015]. 30. Ineichen, P. & Perez, R., 2002. A New Airmass Independent Formulation for the Linke Turbidity Coefficient. Solar Energy, 73(3), pp.151–157. 31. Inman, R.H., Pedro, H.T.C. & Coimbra, C.F.M., 2013. Solar forecasting methods for renewable energy integration. Progress in Energy and Combustion Science, 39(6), pp.535–576. 32. Jewell, W. & Ramakumar, R., 1987. The effects of moving clouds on electric utilities with dispersed photovoltaic generation. IEEE Transaction on Energy Conversion, EC-2(4), pp.570– 576. 33. Kern, E.C., Gulachenski, E.M. & Kern, G.A., 1989. Cloud effects on distributed photovoltaic generation: Slow transients at the Gardner, Massachusetts photovoltaic experiment. IEEE Transaction on Energy Conversion, 4(2), pp.184–190. 34. Kersting, W.H., 2012. Distribution System Line Models. In Distribution System Modeling and Analysis. CRC Press, pp. 125–144. Available at: <http://dx.doi.org/10.1201/9781420041736.ch6> [Accessed on 12 July 2015]. 35. KeTTHA, 2011. Handbook on the Malaysian Feed-in Tariff for the Promotion of Renewable Energy. Kementerian Tenaga, Teknologi Hijau Dan Air, Malaysia. Kleissl, J. et al., 2012. Aggregate Solar Variability. In IEEE Power and Energy Society General Meeting. pp. 12–14. 36. Lau, C.Y., Gan, C.K., Baharin, K.A., et al., 2015. A review on the impacts of passing-clouds on distribution network connected with solar photovoltaic system. International Review of Electrical Engineering (I.R.E.E.), 10(3), pp.449–457. 37. Lau, C.Y. et al., 2016. Impact of Solar Photovoltaic System on Transformers Tap Changer in Low Voltage Distribution Networks. Energy Procedia, In Press. 38. Lau, C.Y., Gan, C.K., Tie, C.H., et al., 2015. Passing-cloud effects of solar photovoltaic system on distribution network voltages. Applied Mechanics and Materials, 785, pp.551–555. 39. Lave, M. & Kleissl, J., 2013. Cloud speed impact on solar variability scaling - Application to the wavelet variability model. Solar Energy, 91, pp.11–21. 40. Lave, M., Reno, M.J. & Broderick, R.J., 2015. Characterizing local high-frequency solar variability and its impact to distribution studies. Solar Energy, 118(2015), pp.327–337. 41. Li, D.H.W., Cheung, G.H.W. & Lam, J.C., 2005. Analysis of the operational performance and efficiency characteristic for photovoltaic system in Hong Kong. Energy Conversion and Management, 46, pp.1107–1118. 42. Lim, Y.S. & Tang, J.H., 2014. Experimental study on flicker emissions by photovoltaic systems on highly cloudy region: A case study in Malaysia. Renewable Energy, 64, pp.61–70.. 43. Lopez, M.L., Palancar, G.G. & Toselli, B.M., 2009. Effects of different types of clouds on surface UV-B and total solar irradiance at southern mid-latitudes: CMF determinations at Cordoba, Argentina. Atmospheric Environment, 43(19), pp.3130–3136. 44. Macedo, W.N. & Zilles, R., 2012. Operational results of grid-connected photovoltaic system with different inverter’s sizing factors (ISF). Progress in Photovoltaics: Research and Applications, 20(December 2006), pp.6–11. 45. Marty, C. & Philipona, R., 2000. The Clear-Sky Index to Separate Clear-Sky from Cloudy-Sky Situations in Climate Research. Geophysical Research Letters, 27(17), pp.2649–2652. 46. Mather, B.A., 2012. Quasi-static time-series test feeder for PV integration analysis on distribution systems. In IEEE Power and Energy Society General Meeting. pp. 1–8. Mcgranaghan, M. et al., 2008. Renewable Systems Interconnection Study: Advanced Grid Planning and Operations, Sandia National Laboratoris, California. 47. Méndez Quezada, V.H., Rivier Abbad, J. & Gómez San Román, T., 2006. Assessment of energy distribution losses for increasing penetration of distributed generation. IEEE Transactions on Power Systems, 21(2), pp.533–540. 48. N, R.A.A.N. & Bhana, T.O.R., 2013. Distribution System Analysis Tools for Studying High Penetration of PV with Grid Support Features. Arizona State University. 49. Nambiar, A.J., Kiprakis, A.E. & Wallace, A.R., 2010. Quantification of voltage fluctuations caused by a wave farm connected to weak, rural electricity networks. In ICHQP 2010 - 14th International Conference on Harmonics and Quality of Power. 50. Nguyen, A. et al., 2016. High PV penetration impacts on five local distribution networks using high resolution solar resource assessment with sky imager and quasi-steady state distribution system simulations. Solar Energy, 132, pp.221–235. 51. Nguyen, D.D. & Lehman, B., 2006. Modeling and Simulation of Solar PV Arrays under Changing Illumination Conditions. In IEEE COMPEL Workshop. pp. 295–299. 52. Omran, W.A., Kazerani, M. & Salama, M.M.A., 2009. A Study of the Impacts of Power Fluctuations Generated from Large PV Systems. In IEEE PES/IAS Conference on Sustainable Alternative Energy (SAE). pp. 1–6. 53. Papaioannou, I.T. et al., 2008. Harmonic impact of small photovoltaic systems connected to the LV distribution network. In 2008 5th International Conference on the European Electricity Market, EEM. 54. Passey, R. et al., 2011. The potential impacts of grid-connected distributed generation and how to address them: A review of technical and non-technical factors. Energy Policy, 39(10), pp.6280–6290. 55. Patsalides, M. et al., 2007. The effect of solar irradiance on the power quality behaviour of grid connected photovoltaic systems. In Proc. Conf. Renewable Energy and Power Quality. pp. 28– 35. 56. Pearsall, N.M., 2011. PV research and development in Europe-A view from the Technology Plattform. In 37th IEEE Photovoltaic Specialists Conference. pp. 200–205. 57. Pelland, S., Galanis, G. & Kallos, G., 2011. Solar and photovoltaic forecasting through postprocessing of the Global Environmental Multiscale numerical weather prediction model. Progress in photovoltaics: Research and applications, 21, pp.284–296. 58. Perez, R. et al., 2010. Validation of short and medium term operational solar radiation forecasts 142 in the US. Solar Energy, 84, pp.2161–2172. 59. Quiroz, J.E., Reno, M.J. & Broderick, R.J., 2013. Time series simulation of voltage regulation device control modes. In Conference Record of the IEEE Photovoltaic Specialists Conference. pp. 1700–1705. 60. Rafi, F.H.M., Hossain, M.J. & Lu, J., 2016. Hierarchical controls selection based on PV penetrations for voltage rise mitigation in a LV distribution network. International Journal of Electrical Power & Energy Systems, 81, pp.123–139. 61. Rahman, R.A. et al., 2010. Performance analysis of a grid-connected PV system at Malaysian Energy Centre, Malaysia. 2010 4th International Power Engineering and Optimization Conference (PEOCO), pp.480–483. 62. Ramachandran, V. & Solanki, S.K., 2011. Modeling of utility distribution feeder in OpenDSS and steady state impact analysis of distrivuted generation. West Virginia University. 63. Ramon-Marin, M. et al., 2014. Active power estimation of photovoltaic generators for distribution network planning based on correlation models. Energy, 64, pp.758–770. 64. Randofo, 2014. Predicting the weather with clouds. Instructables. Available at: http://www.instructables.com/id/Predicting-Weather-with-Clouds/?ALLSTEPS [Accessed on 2 March 2014]. 65. Ray, P.K., Mohanty, S.R. & Kishor, N., 2013. Classification of Power Quality Disturbances Due to Environmental Characteristics in Distributed Generation System. IEEE Transactions on Sustainable energy, 4(2), pp.302–313. 66. Reno, M.J., Ellis, A. & Quiroz, J., 2013. Modeling Distribution System Impacts of Solar Variabiliy and Interconnection Location. In World Renewable Energy Forum Conference. Sandia National Lavoratories, pp. 1–7. 67. S., V., Bindu, G.R. & Iyer, S.R., 2014. Estimation of Power Losses in Photovoltaic Array Configurations under Moving Cloud Conditions. 2014 Fourth International Conference on Advances in Computing and Communications, (3), pp.366–369. 68. Salameh, Z.M. & Giraud, F., 1999. Analysis of the effects of a passing cloud on a gridinteractive photovoltaic system with battery storage using neural networks. Ieee Transaction, 14(4), pp.1572–1577. 69. Sangpanich, U. et al., 2004. Study of Moving Cloud Effects on the Power Quality of GridConnected Photovoltaic System Compared with Simulation of PV Power Using a DC Generator. In Technical Digest of the 14th International Photovoltaic Science and Engineering Conference PVSEC-14. pp. 2–3. 70. Shindell, D., 1999. Solar Cycle Variability, Ozone, and Climate. Science, 284(5412), pp.305– 308. Available at: http://www.sciencemag.org/cgi/doi/10.1126/science.284.5412.305. 71. Singh, J., 2014. Solar Resource Assessment:Making Sense of Data, Kipp & Zonen: Meteorology and Climatology. Available at: http://www.kippzonen.com/News/537/Solar-ResourceAssessment-Making-Sense-of-Data#.WAyV1_l97IU [Accessed on 21 October 2016]. 72. Small, F., 2011. Integrating PV on Distribution. In Carnegie Mellon Conference on the Electricity Industry. 73. Smith, J.W., Dugan, R. & Sunderman, W., 2011. Distribution modeling and analysis of high penetration PV. In 2011 IEEE Power and Energy Society General Meeting. pp. 1–7. 74. Stein, J., Hansen, C. & Reno, M., 2012. The variability index: A new and novel metric for quantifying irradiance and PV output variability. In World Renewable Energy Forum Conference. pp. 1–7. Sandia Laboratory Center, California. Suruhanjaya Tenaga, 2010. The Malaysian Grid Code, pp.424 75. Tam, A., 2013. Feed-in Tariff and Renewable Energy Fund, Kuala Lumpur: Parliament of Malaysia. Available at: http://www.parlimen.gov.my/images/webuser/artikel/ ro/amy/FiT and RE Fund.pdf. 76. Tamura, J., Nakamura, H., et al., 2003. A new method of calculating in-plane irradiation by oneminute local solar irradiance. In 3rd World Conference on Photovoltaic Energy Conversion. pp. 2265–2268. 77. Tamura, J., Kurokawa, K. & Otani, K., 2003. The measurement and estimation of in-plane irradiation. In pp. 2–5. The Economic Planning Unit, 2010. 10th Malaysia Plan (Chapter 6: Building and Environment that Enhances Quality of Life). Prime Minister’s Depatrment, pp.246–310. Available at: file:///F:/References Paper/Malaysian 10th Plan.pdf. 78. Thomson, M. & Infield, D.G., 2007. Impact of widespread photovoltaics generation on distribution systems. IET Renewable. Power Generation, 1(1), pp.33–40. 79. Tie, C.H. & Gan, C.K., 2013. Impact of Grid-Connected Residential PV Systems on the Malaysia Low Voltage Distribution Network. In 2013 IEEE 7th International Power Engineering and Optimizatinon Conference (PEOCO2013). Langkawi, Malaysia, pp. 670–675. TNB, 2011. Electricity Supply Application Handbook, Tenaga National Berhad, Malaysia. TNB, 2013. TNB Technical Guidebook on Grid-interconnection of Photovoltaic Power Generation System to LV and MV Networks, Available at: <http://seda.gov.my/> [Accessed on 26 April 2016]. 80. Tonkoski, R., Turcotte, D. & El-Fouly, T.H.M., 2012. Impact of high PV penetration on voltage profiles in residential neighborhoods. IEEE Transactions on Sustainable Energy, 3(3), pp.518– 527. 81. Traube, J.W., 2013. Predicting the Effects of Short-Term Photovoltaic Variability on Power System Frequency for Systems with Integrated Energy Storage. Thesis: University of Colorado, United States. 82. Trueblood, C. et al., 2013. PV Measures Up for Fleet Duty. IEEE Power & Energy Magazine, (March/April), pp.33–44. United Arab Emirates, 2010. Detailed technical specification for 33/11kV 20/28MVA power transformers, NER & RTCC panels. Handbook Federal Electricity and Water Authority. Urbanetz, J., Braun, P., Ruther, R., 2012. Power quality analysis of grid-connected solar photovoltaic generators in Brazil. Energy Conversion and Management, 64, pp. 8-14. 83. Viawan, F., 2008. Voltage Control and Voltage Stability of Power Distribution Systems in the Presence of Distributed Generation. Thesis: Chalmers University of Technology, Sweden. Walling, R.A. & Clark, K., 2010. Grid support functions implemented in utility-scale PV systems. In 2010 IEEE PES Transmission and Distribution Conference and Exposition: Smart Solutions for a Changing World. pp. 1–5. 84. Widén, J. et al., 2010. Impacts of distributed photovoltaics on network voltages: Stochastic simulations of three Swedish low-voltage distribution grids. Electric Power Systems Research, 80(12), pp.1562–1571. 85. Wong, F. & Wills, K., 2011. China doubles solar power target to 10GW by 2015. REUTERS. Available at: http://www.reuters.com/article/2011/05/06/china-solaridUSL3E7G554620110506 [Accessed on 20 November 2014]. 86. Wong, J. et al., 2014. Grid-connected photovoltaic system in Malaysia: A review on voltage issues. Renewable and Sustainable Energy Reviews, 29, pp.535–545. 87. Wong, J. et al., 2011. Optimal utilisation of small-scale embedded generators in a developing country – A case study in Malaysia, Renewable and Sustainable Energy Reviews, 30, pp.12-25 88. World Meteorological Organization (WMO), 2014. Classification of cloud sturctures. WMO. Available at: https://www.wmo.int/pages/index_en.html [Accessed on 2 March 2015]. 89. Yan, R. et al., 2013. Analysis of yearlong performance of differently tilted photovoltaic systems in Brisbane, Australia. Energy Conversion and Management, 74, pp.102–108. 90. Yan, R., Marais, B. & Saha, T.K., 2014. Impacts of residential photovoltaic power fluctuation on on-load tap changer operation and a solution using DSTATCOM. Electric Power Systems Research, 111, pp.185–193. 91. Yan, R. & Saha, T.K., 2011. Investigation of Voltage Variations in Unbalanced Distribution Systems due to High Photovoltaic Penetrations. In IEEE Conferences Publication. Australia: IEEE Conferences Publication, pp. 1–8. 92. Yan, R. & Saha, T.K., 2012. Voltage Variation Sensitivity Analysis for Unbalanced Distribution Networks Due to Photovoltaic Power Fluctuations. IEEE Transactions on Power Systems, 27(2), pp.1078–1089. 93. Yan, W., Braun, M. & Appen, J. Von, 2011. Operation strategies in distribution systems with high level PV Penetration. In Solar world, pp. 2–6. Available at: https://www.bayernwerk.de/pages/eby_de/Innovationen/Smart_Grid/VOe_Projektbeschreibun g/Operation_Strategies_in_Distribution_Systems_with_High_Level_PV_Penetration_SolarW orldConference_201109.pdf. 94. Yang, D., Jirutitijaroen, P. & Walsh, W.M., 2012. Hourly solar irradiance time series forecasting using cloud cover index. Solar Energy, 86(12), pp.3531–3543 |