Optimal Allocation of Photovoltaic Distributed Generations in Radial Distribution Networks

Samson Oladayo Ayanlade (), Funso Kehinde Ariyo, Abdulrasaq Jimoh, Kayode Timothy Akindeji, Adeleye Oluwaseye Adetunji, Emmanuel Idowu Ogunwole and Dolapo Eniola Owolabi
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Samson Oladayo Ayanlade: Department of Electrical and Electronic Engineering, Faculty of Engineering and Technology, Lead City University, Ibadan 200255, Nigeria
Funso Kehinde Ariyo: Department of Electronic and Electrical Engineering, Faculty of Technology, Obafemi Awolowo University, Ile-Ife 220101, Nigeria
Abdulrasaq Jimoh: Department of Electronic and Electrical Engineering, Faculty of Technology, Obafemi Awolowo University, Ile-Ife 220101, Nigeria
Kayode Timothy Akindeji: Smart Grid Research Center, Department of Electrical Power Engineering, Faculty of Engineering and the Built Environment, Durban University of Technology, Durban 4000, South Africa
Adeleye Oluwaseye Adetunji: Department of Electrical and Electronic Engineering, Faculty of Engineering and Environmental Sciences, Osun State University, Osogbo 210001, Nigeria
Emmanuel Idowu Ogunwole: Department of Electrical, Electronic and Computer Engineering, Cape Peninsula University of Technology, Cape Town 7535, South Africa
Dolapo Eniola Owolabi: Department of Electronic and Electrical Engineering, Faculty of Engineering and Technology, Ladoke Akintola University of Technology, Ogbomoso 210214, Nigeria

Sustainability, 2023, vol. 15, issue 18, 1-26

Abstract: Photovoltaic distributed generation (PVDG) is a noteworthy form of distributed energy generation that boasts a multitude of advantages. It not only produces absolutely no greenhouse gas emissions but also demands minimal maintenance. Consequently, PVDG has found widespread applications within distribution networks (DNs), particularly in the realm of improving network efficiency. In this research study, the dingo optimization algorithm (DOA) played a pivotal role in optimizing PVDGs with the primary aim of enhancing the performance of DNs. The crux of this optimization effort revolved around formulating an objective function that represented the cumulative active power losses that occurred across all branches of the network. The DOA was then effectively used to evaluate the most suitable capacities and positions for the PVDG units. To address the power flow challenges inherent to DNs, this study used the Newton–Raphson power flow method. To gauge the effectiveness of DOA in allocating PVDG units, it was rigorously compared to other metaheuristic optimization algorithms previously documented in the literature. The entire methodology was implemented using MATLAB and validated using the IEEE 33-bus DN. The performance of the network was scrutinized under normal, light, and heavy loading conditions. Subsequently, the approach was also applied to a practical Ajinde 62-bus DN. The research findings yielded crucial insights. For the IEEE 33-bus DN, it was determined that the optimal locations for PVDG units were buses 13, 25, and 33, with recommended capacities of 833, 532, and 866 kW, respectively. Similarly, in the context of the Ajinde 62-bus network, buses 17, 27, and 33 were identified as the prime locations for PVDGs, each with optimal sizes of 757, 150, and 1097 kW, respectively. Remarkably, the introduction of PVDGs led to substantial enhancements in network performance. For instance, in the IEEE 33-bus DN, the smallest voltage magnitude increased to 0.966 p.u. under normal loads, 0.9971 p.u. under light loads, and 0.96004 p.u. under heavy loads. These improvements translated into a significant reduction in active power losses—61.21% under normal conditions, 17.84% under light loads, and 33.31% under heavy loads. Similarly, in the case of the Ajinde 62-bus DN, the smallest voltage magnitude reached 0.9787 p.u., accompanied by an impressive 71.05% reduction in active power losses. In conclusion, the DOA exhibited remarkable efficacy in the strategic allocation of PVDGs, leading to substantial enhancements in DN performance across diverse loading conditions.

Keywords: active power loss; distributed generation; voltage profile; reactive power loss; PVDG; dingo optimization algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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