Journal Article

Optimal renewable distributed generation planning in radial distribution systems: a probabilistic and multi-objective approach with enhanced Young’s double-slit experiment optimizer

Kamel, Salah, Bouaouda, Anas, Hashim, Fatma A., El Mariami, Faissal, Tarraq, Ali

2024

Overview

Due to the stochastic nature of renewable energy sources, demand fluctuations, and the complexity of distribution systems, addressing the Optimal Renewable Distributed Generation Planning (ORDGP) problem in radial distribution systems (RDS) practically requires a combination of probabilistic and multi-objective approaches. Therefore, this study’s primary objective is to meticulously integrate both technical and economic aspects by simultaneously allocating photovoltaic and wind turbine generators within the standard IEEE 69-bus RDS, considering voltage-dependent and time-varying mixed loads. Moving closer to real-world scenarios, the complexity of ORDGP is heightened by considering uncertainties in solar and wind power generation, achieved through a new probabilistic model to evaluate the expected energy output from these sources. Additionally, this study targets six objectives for the first time, including reducing energy losses, enhancing voltage stability, refining load balancing, ensuring reliable supply, and maximizing total savings over a five-year period. To effectively address the ORDGP problem, an enhancement is introduced in the global search capacity of the recent Young’s double-slit experiment (YDSE) optimizer, resulting in the modified YDSE (mYDSE) algorithm. The robustness of the mYDSE optimizer is validated through nonparametric tests, including the Freidman mean rank test and Wilcoxon sum test. Encouragingly, this study reveals promising results, including an 85.66% reduction in total energy losses and notable improvements in technical system metrics. Additionally, the proposed method suggests substantial economic savings, estimated at up to 12.77 million US dollars. Remarkably, the mYDSE optimizer emerges as a leader, surpassing recent methods and demonstrating its ability to maximize economic benefits while enhancing technical system performance. The proposed approach promises to pave the way for balanced and realistic solutions, with profound implications for the sustainable evolution of power systems.

• Publisher: Springer London,Springer Nature B.V
• Subject: Algorithms / Alternative energy sources / Artificial Intelligence / Complexity / Computational Biology/Bioinformatics / Computational Science and Engineering / Computer Science / Data Mining and Knowledge Discovery / Distributed generation / Energy distribution / Image Processing and Computer Vision / Multiple objective analysis / Optimization / Original Article / Probabilistic models / Probability and Statistics in Computer Science / Probability theory / Radial distribution / Rank tests / Renewable energy sources / Time dependence / Turbogenerators / Voltage stability / Wind power generation / Wind turbines