PV power conversion and short-term forecasting in a tropical,densely-built environment in Singapore |
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| Affiliation: | 1. Solar Energy Research Institute of Singapore, National University of Singapore, 7 Engineering Drive 1, Singapore 117574, Singapore;2. Universidade Federal de Santa Catarina, Campus Trindade, Caixa Postal 476, Florianópolis, SC 88040-900, Brazil;3. Universidade Federal de Minas Gerais, Av. Antônio Carlos, 6627, Belo Horizonte, MG 31270-901, Brazil;4. Universidade Federal de São Paulo, Campus Baixada Santista, Av. Almirante Saldanha da Gama 89, Santos, SP 11030-400, Brazil;5. Instituto Nacional de Pesquisas Espaciais, Brazilian Institute for Space Research, Caixa Postal 515, São José dos Campos, SP 12245-970, Brazil;1. Centre for Energy Sciences, Department of Mechanical Engineering, Faculty of Engineering, 50603 Kuala Lumpur University of Malaya, Malaysia;2. Mechanical Engineering Department, Collage of Engineering, King Saud University, 11421 Riyadh, Saudi Arabia;3. Dept. of Mechanical Engineering, Dhaka University of Engineering and Technology, Gazipur, 1700, Bangladesh;1. Huadian Electric Power Research Institute, Hangzhou, Zhejiang 310030, China;2. Department of Energy Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China;3. Key Laboratory of Thermo-Fluid Science and Engineering, Ministry of Education, School of Energy and Power Engineering, Xi''an Jiaotong University, Xi''an 710049, China;1. Ocean Engineering and Technology Research Center, Iranian National Institute for Oceanography and Atmospheric Science, Tehran, Iran;2. Griffith School of Engineering, Gold Coast Campus, Griffith University, QLD, 4222, Australia;1. Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117585, Singapore;2. Water Desalination & Reuse (WDR) Center King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia |
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| Abstract: | With the substantial growth of solar photovoltaic installations worldwide, forecasting irradiance becomes a critical step in providing a reliable integration of solar electricity into electric power grids. In Singapore, the number of PV installation has increased with a growth rate of 70% over the past 6 years. Within the next decade, solar power could represent up to 20% of the instant power generation. Challenges for PV grid integration in Singapore arise from the high variability in cloud movements and irradiance patterns due to the tropical climate. For a thorough analysis and modeling of the impact of an increasing share of variable PV power on the electric power system, it is indispensable (i) to have an accurate conversion model from irradiance to solar power generation, and (ii) to carry out irradiance forecasting on various time scales. In this work, we demonstrate how common assumptions and simplifications in PV power conversion methods negatively affect the output estimates of PV systems power in a tropical and densely-built environment such as in Singapore. In the second part, we propose and test a novel hybrid model for short-term irradiance forecasting for short-term intervals. The hybrid model outperforms the persistence forecast and other common statistical methods. |
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| Keywords: | PV power conversion Solar irradiance forecasting Short-term prediction PV systems Tropical regions |
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