Norhafidzah, Mohd Saad and Muhamad Zahim, Sujod and Mohd Ikhwan, Muhammad Ridzuan and Mohammad Fadhil, Abas and Mohd Shawal, Jadin and Mohd Fadzil, Abdul Kadir (2024) A new optimisation framework based on Monte Carlo embedded hybrid variant mean–variance mapping considering uncertainties. Decision Analytics Journal, 10 (100368). ISSN 2772-6622. (Published)
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Abstract
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.
| Item Type: | Article |
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| Uncontrolled Keywords: | Optimisation; Photovoltaic distributed generation; Power loss minimisation; Probabilistic power flow; Siting and sizing; Uncertainty energy management |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Faculty of Electrical and Electronic Engineering Technology |
| Depositing User: | Miss Amelia Binti Hasan |
| Date Deposited: | 19 Jan 2024 04:09 |
| Last Modified: | 19 Jan 2024 04:09 |
| URI: | http://umpir.ump.edu.my/id/eprint/40103 |
| Download Statistic: | View Download Statistics |
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