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.

Achieving the goals of distribution systems operation often involves taking vital decisions with adequate consideration for several but often contradictory technical and economic criteria. Hence, this paper presents a modified analytical approach for optimal location and sizing of solar PV-based DG units into radial distribution network (RDN) considering strategic combination of important power system planning criteria. The considered criteria are total planning cost, active power loss and voltage stability, under credible distribution network operation constraints. The optimal DG placement approach is derived from the modification of the analytical approach for DG placement using line-loss sensitivity factor and the multiobjective constriction factor-based particle swarm optimization is adopted for optimal sizing. The effectiveness of the proposed procedure is tested on the IEEE 33-bus system modeled using Matlab considering three scenarios. The results are compared with existing reports presented in the literature and the results obtained from the proposed approach shows credible improvement in the RDN steady-state operation performance for line-loss reduction, voltage profile improvement and voltage stability improvement.

Integrating distributed generators (DGs) into electrical power networks remains a significant area of research which has several technical and economic benefits for optimum performance, especially at the distribution level. This study presents a model of power distribution networks with renewable distributed generation (DG) units. It adapted Loss Sensitivity Factor (LSF) and Constriction Coefficient Particle Swarm Optimization (CCPSO) technique to ascertain the optimal placement and sizing of the DG units. The numerical analysis is executed on the Imalefalafia 32-bus network which is a real distribution network in Nigeria and the commonly known standard IEEE 33-bus network. The result shows that the scenario where cost minimization with loss reduction and voltage stability improvement concurrently performs better than other scenarios for different numbers of allocated DGs. With 3 DGs incorporation, the real and reactive power loss reduction of 62.46% and 62.32%, respectively, were achieved in scenario three compared to 14.06% and 14.46% in scenario two and 61.48% and 61.46% in scenario one for IEEE 33-bus RDN while for Imalefalafia 32-bus, the real and reactive power loss were reduced by 71.07% and 71.13% were achieved in scenario three compared to 43.95% and 43.99% in scenario two and 55.87% and 55.92% in scenario one. It was also observed that the voltage at the worst performing bus improves significantly better for scenario 3 for all numbers of DGs compared to the other scenarios.