A new optimisation framework based on Monte Carlo embedded hybrid variant mean–variance mapping considering uncertainties - Norhafidzah, Mohd Saad; Muhamad Zahim, Sujod; Mohd Ikhwan, Muhammad Ridzuan; Mohammad Fadhil, Abas; Mohd Shawal, Jadin; Mohd Fadzil, Abdul Kadir (Elsevier Inc., 2024)
This study proposes a new optimisation framework based on Monte Carlo embedded hybrid variant mean–variance mapping (MVMO-SH) optimisation​ for planning Photovoltaic Distributed Generation (PVDG) in the urban Radial Distribution Network (RDN). The Active Power Loss (APL) index was calculated considering the risk of uncertain photovoltaic generation and urban load distributions. The Monte Carlo Probability Density Function method was initially used to manage uncertainties. The Monte Carlo-embedded MVMO-SH was then used to optimise PVDG in the urban RDN. Simulations were run for several scenarios in three load cases based on 288 segments: residential, commercial, and industrial urban loads. The MVMO-SH had the lowest APL index compared to genetic algorithm and particle swarm optimisation when the probabilistic power flow with PVDG was optimised under uncertainty. The APL indexes with three PVDG installations in the 33-bus RDN for residential, commercial, and industrial urban load models were 0.4094, 0.4811, and 0.4655, respectively. In the 69-bus RDN, the APL indexes with three PVDG installations for residential, commercial, and industrial urban load models were 0.3403, 0.3570, and 0.3504, respectively. For all load models examined, there was a significant reduction in the APL index for the case of three PVDGs compared to the system without PVDG. The findings showed that uncertainty significantly impacted the optimal location and size of PVDG in the RDN.