Simultaneous network reconfiguration and capacitor allocations using a novel dingo optimization algorithm

Power loss and voltage magnitude fluctuations are two major issues in distribution networks that have drawn a lot of attention. Numerous strategies have been put forward to provide remedies to lessen the undesirable effects of these issues. Combining two of these approaches and dealing with them simultaneously to get more effective outcomes is essential. Therefore, this study hybridizes the network reconfiguration and capacitor allocation strategies using a novel dingo optimization algorithm (DOA) to solve the optimization problems. The optimization problems for simultaneous network reconfiguration and capacitor allocations were formulated and solved using a novel DOA. To demonstrate its effectiveness, DOA’s results were contrasted with those of the other optimization techniques. The methodology was validated on the IEEE 33-bus network and implemented in the MATLAB program. The results demonstrated that the best network reconfiguration was accomplished with switches 7, 11, 17, 27, and 34 open, and buses 8, 29, and 30 were the best places for capacitors with ideal sizes of 512, 714, and 495 kVAr, respectively. The voltage profile was significantly improved, and the power losses were significantly decreased. When compared to some of the different methods, DOA came out on top

Optimal Allocation of Photovoltaic Distributed Generations in Radial Distribution Networks

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