The Distributed Generator types have different combinations of real and reactive power characteristics, which can affect the total power loss and the voltage support/control of the radial ...distribution networks (RDNs) in different ways. This paper investigates the impact of DG’s penetration level (PL) on the power loss and voltage profile of RDNs based on different DG types. The DG types are modeled depending on the real and reactive power they inject. The voltage profiles obtained under various circumstances were fairly compared using the voltage profile index (VPI), which assigns a single value to describe how well the voltages match the ideal voltage. Two novel effective power voltage stability indices were developed to select the most sensitive candidate buses for DG penetration. To assess the influence of the DG PL on the power loss and voltage profile, the sizes of the DG types were gradually raised on these candidate buses by 1% of the total load demand of the RDN. The method was applied to the IEEE 33-bus and 69-bus RDNs. A PL of 45–76% is achieved on the IEEE 33-bus and 48–55% penetration on the IEEE 69-bus without an increase in power loss. The VPI was improved with increasing PL of DG compared to the base case scenario.
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.
This research investigates the impact of integrating photovoltaic (PV) systems into power grids to address voltage instability and efficiency issues caused by load imbalances. This study employed the ...Newton-Raphson power flow solution algorithm to analyze the power flow problem, strategically placing PV units using a new voltage stability pointer (NVSP), and determining optimal PV unit sizes derived from the exact power loss formula. The study also assesses frequency stability post-PV integration utilizing the IEEE 14-bus test system as a reference on ETAP 19.0 and MATLAB R2018a. The NVSP analysis identified buses 9, 14, 13, 12, and 11 as suitable locations for PV integration. Optimal PV unit sizes for these buses were determined. After PV integration, there was a notable improvement in voltage profiles, with bus 14 experiencing a 3% voltage magnitude increase. Voltage magnitudes at other buses also fell within an acceptable range (1.007 to LI 10 p.u.), enhancing the overall network voltage profile. Moreover, active and reactive power losses significantly decreased, resulting in a 62.86% reduction in active power losses and a 67.40% reduction in reactive power losses, leading to improved network performance. However, some cases of frequency deviation, especially at PV buses, were observed. In conclusion, PV integration holds great potential for enhancing power grid performance by improving voltage profiles and reducing power losses.
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.
One of the most affordable methods for improving the performance of radial distribution networks is through the deployment of capacitors. However, the optimal allocation of capacitors is a serious ...issue that must be resolved. This paper proposed a novel dingo optimization algorithm (DOA) to tackle the problem of the allocation of capacitors. A single objective function, subjected to several equality and inequality constraints, was formulated and solved using the proposed DOA for capacitor allocation. The performance of the network was assessed using voltage profile, real power, and reactive power losses as the performance metrics. The results indicated a significant improvement in the performance of the network as real power as well as reactive power losses were greatly reduced by 34.44 and 35.20%, respectively, and the overall network voltage profile was enhanced. Thus, DOA proved effective in the allocation of capacitors in radial distribution networks.
The Static Synchronous Compensator (STATCOM) is a controller that regulates network characteristics to improve network power transfer capabilities. Nevertheless, in order to properly utilize it, it ...must be allocated optimally. The STATCOM's ideal location and capacity to handle some of the transmission system's difficulties is referred to as the "STATCOM allocation." As a result, this research uses the firefly algorithm (FA) to assign STATCOM to improve system voltage magnitudes and mitigate power losses. The proposed approach was implemented in a Matlab program and validated on an IEEE 14-bus network. According to the findings, the overall real and reactive power losses were reduced by 11.73 and 14.7%, respectively. The magnitudes of the voltages at buses 7 and 13 were reduced while remaining within the required voltage restrictions, resulting in a better network voltage profile. As a result, optimum STATCOM allocation increases the network's capacity and performance.
Flexible alternating current transmission system (FACTS) devices are becoming part of the modern network because of their importance in power network parameter control. However, the optimal device ...placement and parameter settings are crucial to achieving the set objectives. Therefore, this study investigates the most efficient metaheuristic optimization algorithm between particle swarm optimization (PSO) and the firefly algorithm (FA) in allocating a static synchronous compensator (STATCOM) controller for the multi-objectives of loss minimization and voltage violation mitigation. The efficient algorithm is the one whose applications in device allocation will result in effective system loss and bus voltage violation minimizations in the network. Meanwhile, the choice of a static synchronous compensator out of FACTS devices was as a result of its reactive power compensation capabilities, while PSO and FA were considered because of their computational efficiencies among other metaheuristic algorithms. The simulations were implemented on an IEEE 14-bus system utilizing the MATLAB package. Active power loss minimizations of 0.43 and 0.73 MW were achieved when STATCOM was optimized with PSO and FA, respectively. Therefore, the achievements in power losses and voltage deviation reductions in this case indicate some advantages of FA over PSO. Besides, the effectiveness of metaheuristic algorithms in FACTS device allocation has also been demonstrated in this study.
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