Application of Three-Phase Power Flow Analysis to the Nigerian Distribution Networks

Single-phase power flow analysis is used to study most distribution networks in Nigeria. The use of single-phase-power flow analysis assumes that the network is balanced and that the conductor phases act identically. However, Nigerian distribution networks are highly imbalanced because of untransposed lines, irregularly distributed loads in conductor phases, mismatched conductor sizes, and spacing. Consequently, single-phase modeling of the networks fails to reflect actual network behavior, resulting in an incorrect power flow solution. This research presents the three-phase modeling of radial distribution networks for a three-phase-power flow study of Nigerian distribution networks. Olusanya's 54-bus and Ajinde's 62-bus distribution networks in Nigeria were evaluated, both of which were very imbalanced. Without making any assumptions about the network components, these two distribution networks were properly modeled. Each network's three-phase power flow study was carried out in the MATLAB environment. The power flow solutions for each network demonstrated unevenness in the voltage profile for each network phase, as well as inequality in the real and reactive power losses in each phase, indicating that the deployed three-phase-power flow analysis properly mirrored the underlying network characteristics. Therefore, applying three-phase power flow analysis to distribution networks is critical for proper assessment of distribution network performance

A Novel Classification of the 330 kV Nigerian Power Network Using a New Voltage Stability Pointer

The incessant power outages that characterize the Nigerian power network (NGP), as in all developing countries, are not limited to the shortage of fuel for power generation. However, differential power shortages between the generated power and the load demand are alarming. In this study, we propose a new voltage stability pointer (NVSP) based on a reduced one-line power network to act as a classifier. The NVSP was trained with a support vector machine (SVM) using a medium Gaussian kernel classification toolbox (mGkCT) in the MATLAB environment. This classification is based on the power network susceptibility to voltage instability. NGP 28-bus 330 kV data were extracted and modeled in the MATLAB environment and tested with the NVSP-mGkCT classifier. The NVSP-mGkCT was able to classify the lines viz. stable and unstable lines for the base and contingency cases. Similarly, the linear load dynamics and non-linear load dynamics were evaluated on the basis of critical buses using the NVSP. The aim of this work was to help the Transmission Company of Nigeria (TCN) and the National Control Centre (NCC) to be pre-emptive with respect to possible voltage collapse due to voltage instability. The simulation results show that NVSP was able to flag vulnerable lines in the NGP

Integration of Solar Photovoltaic Distributed Generators in Distribution Networks Based on Site’s Condition

The significance of Distributed Generators (DGs) in the technical and economic operations of electric power distribution systems cannot be overemphasized in recent times. This is essential as a result of the incessant increase in electrical energy demand, which is becoming considerably difficult to meet with the conventional means of energy supply. Thus, DGs offer better alternatives for providing a quality supply of energy near the site of consumption. This type of energy supply is cleaner and cheaper most of the time due to the lessened transmission losses, which consequently reduced the cost of operation at the transmission and distribution levels of the power system. In this work, an approach for placement and sizing of solar PV DGs into radial distribution networks (RDN) based on the solar PV capacity factor of the site was analyzed using particle swarm optimization. The aim of this study is to analyze the effect of the approach on the real and reactive power losses within the network as well as the bus voltage profile. Constraints on credible system operation parameters, which includes bus voltage limits, power balance, and power flow limits, are considered in the formulation of the optimization problem. In order to verify the viability of the deployed approach, steady-state performance analyses were executed on IEEE 33-bus RDN; and the results obtained were compared with the results from other approaches reported in the literature

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