• Hanis Farhah Jamahori Centre of Electrical Energy Systems, Institute of Future Energy, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia
  • Md. Pauzi Abdullah Centre of Electrical Energy Systems, Institute of Future Energy, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia
  • Abid Ali Interdisciplinary Institute of Technological Innovation (3IT), Université de Sherbrooke, 3000 Boulevard Université, Sherbrooke, J1K OA5 Québec, Canada



Photovoltaic (PV), solar irradiance, time-varying load data, active and reactive power loss, voltage deviation


Most distributed renewable energy generation (DREG) planning studies are performed using a constant load model and a dispatchable generation unit. However, the renewable generation unit and load demand vary in real life, and the generation size at the peak demand varies accordingly with loading levels. Such considerations may lead to the erroneous conclusion: the power loss reduction and bus voltage improvement may not be optimal. Consequently, the generation unit must be adequately integrated to offer optimal capacity in the distribution system while considering non-constant load demand as a part of DREG planning. Therefore, the impact of integrating photovoltaic (PV) considering historical solar weather data and varying load demand for five different voltage-dependent load models is proposed in this study. Particle swarm optimization (PSO) is employed to find the optimal location and size of PV with the objective to minimize power losses in the distribution system using IEEE 33-bus and IEEE 69-bus test systems. The findings are evaluated based on the comparative analysis of power losses reduction, PV penetration level, power loss index, and voltage deviation index. Findings revealed that the proposed model is effective in determining the optimal location and size of PV with a significant reduction of power losses that varies between 13.84% to 32.71% in 33-bus, and between 18.56% to 43.80% in 69-bus. In addition, the improvement in minimum bus voltage and other performance indices are also significant.


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