Open AccessStochastic PV model for power system planning applications
Author(s) -
AlSumaiti Ameena Saad,
Ahmed Mohammed Hassan,
Rivera Sergio,
El Moursi Mohammed Shawky,
Salama Mohamed M.A.,
Alsumaiti Tareefa
Publication year - 2019
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/iet-rpg.2019.0345
Subject(s) - photovoltaic system , reliability engineering , renewable energy , computer science , electric power system , grid , benchmark (surveying) , grid connected photovoltaic power system , probabilistic logic , monte carlo method , electricity generation , power (physics) , maximum power point tracking , engineering , electrical engineering , statistics , physics , geometry , mathematics , geodesy , quantum mechanics , inverter , voltage , artificial intelligence , geography
Planning photovoltaic (PV) power systems integration into the grid necessitates accurate modelling of renewable power generation. Global solar irradiance, weather temperature and PV power losses due to overheating specifically in hot regimes are major factors contributing to PV power generation uncertainty. This study targets demonstrating the effectiveness of deploying advanced five parameter probabilistic distribution ‘Wakeby’ for modelling PV uncertain power generation, measured as a function of such factors, in power system planning applications. The impact of different approaches for incorporating weather temperature on PV energy estimation is studied. Wakeby‐Monte Carlo Simulation for PV power data training with an emphasis on MCS stopping criteria for such advanced distribution is presented. The model is tested and verified in 31‐bus distribution system to demonstrate its effectiveness over other literature uncertainty modelling approaches when planning integration of PV systems' integration into the grid to minimise the grid losses cost. Real PV power measurements are utilised as benchmark verifying the accuracy and suitability of the presented uncertainty modelling approach. Simulation results demonstrate a small error of $4.7 in the expected annual cost of grid losses when deploying Wakeby model compared to the benchmark case and that error can vary significantly when deploying other PV models.