TY  - BOOK
Y1  - 2018///
SN  - 9781522531432; 1522529934; 9781522529934
PB  - IGI Global
UR  - 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
A1  - Ali, A.
A1  - Nor, N.M.
A1  - Ibrahim, T.
A1  - Romlie, M.F.
A1  - Bingi, K.
EP  - 463
AV  - none
N1  - cited By 1
N2  - 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.
KW  - Advanced Analytics; Big data; Data Analytics; Digital storage; Energy dissipation; Genetic algorithms; Integer programming; Solar power generation
KW  -  Computational tools; Mixed integer optimization; Objective functions; Optimization problems; Solar irradiation; Solar photovoltaics; Total energy loss; Voltage dependent load models
KW  -  Data handling
ID  - scholars10577
TI  - Big data storage for the modeling of historical time series solar irradiations
SP  - 433
ER  -