Big data storage for the modeling of historical time series solar irradiations
This chapter proposes Big Data Analytics for the sizing and locating of solar photovoltaic farms to reduce the total energy loss in distribution networks. The Big Data Analytics, which uses the advance statistical and computational tools for the handling of large data sets, has been adopted for mode...
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| Main Authors: | , , , , |
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| Format: | Book |
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IGI Global
2018
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045729433&doi=10.4018%2f978-1-5225-3142-5.ch016&partnerID=40&md5=c1e3e005185b7b0efada02cfbde16986 http://eprints.utp.edu.my/21858/ |
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| Summary: | This chapter proposes Big Data Analytics for the sizing and locating of solar photovoltaic farms to reduce the total energy loss in distribution networks. The Big Data Analytics, which uses the advance statistical and computational tools for the handling of large data sets, has been adopted for modeling the 15 years of solar weather data. Total Power Loss Index (TPLI) is formulated as the main objective function for the optimization problem and meanwhile bus voltage deviations and penetrations of the PV farms are calculated. To solve the optimization problem, this study adopts the Mixed Integer Optimization using Genetic Algorithm (MIOGA) technique. By considering different time varying voltage dependent load models, the proposed algorithm is applied on IEEE 33 bus and IEEE 69 bus test distribution networks and optimum results are acquired. From the results, it is revealed that compared to single PV farm, the integration of two PV farms reduced more energy loss and reduced the total size of PV farms. Big Data Analytics is found very effective for the storing, handling, processing and the visualizing of the weather Big Data. © 2018, IGI Global. |
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