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Photovoltaic (PV) system installation has encouraged to be further expedited to minimize climate change and thus, rooftop solar PV systems have been sparkled in every corner of the world. However, due to technological constraints linked to voltage and currents, the PV hosting capacity has been substantially constrained. Therefore, this paper proposes a competent approach to maximize PV hosting capacity in a low voltage distribution network based on voltage control and dynamic line rating of the cables. Coordinated voltage control is applied with an on-load tap changing transformer, and reactive power compensation and active power curtailment of PV inverters. A case study with probabilistic and deterministic assessments is carried out on a real Sri Lankan network to show how the PV hosting capacity is constrained. The findings revealed the capability of integrated voltage control schemes and dynamic line rating in maximizing hosting capacity. The study is expanded by incorporating the PV rephasing approach in conjunction with the aforementioned control techniques, and the effectiveness of PV-rephasing is clearly demonstrated. When compared to voltage control and conductor static rating, the combined rephasing, voltage control, and DLR yielded a 60% increase in PV hosting capacity.

The modern photovoltaic (PV) inverters are embedded with smart control capabilities such as Volt/Var and Volt/Watt functions to mitigate overvoltage issues. The Volt/Var control has gained a significant attention in regulating grid voltage through reactive power compensation. However, the reactive power capability of a PV inverter is limited during peak irradiance and could be improved by curtailing the active power generation and by oversizing the PV inverter. This paper analyzes the performance of Volt/Var function of smart PV inverters in mitigating overvoltage issues due to high PV integration and thus increasing the hosting capacity of low voltage distribution networks (LVDNs). The study is conducted on a real Malaysian LVDN considering two different Volt/Var set points under different PV penetration levels. Results demonstrate that the oversized smart PV inverter could enhance the Volt/Var functionality by increasing its reactive power capability than a typical smart PV inverter. Further it reveals that adaptation of sensitive Volt/Var set points with shorter deadbands increase the PV hosting capacity of LVDNs.

Electric vehicles (EVs) are economically and environmentally friendly compared to gasoline engine vehicles. In EV technology, bi-directionality of dc-dc converters have significantly enlarged the scope of EV applications with the use of regenerative braking and reverse charging technique in present days. Driving a synchronous machine (SM) connected to a three-port dc link is the aim of this work. A battery and a supercapacitor are connected via two bi-directional dc-dc converters (BDC), which are triggered by Pulse Width Modulation (PWM) technique. The converters are controlled in bi-directional in order to facilitate a high figure in efficiency with accurate levels of outputs. The proposed two converter systems are well explained using PSCAD™/EMTDC™ and MATLAB/Simulink™ simulation results.

Minimization of real power loss and improvement of voltage authenticity of the network are amongst the key issues confronting power systems owing to the heavy demand development problem, contingency of transmission and distribution lines and the financial costs. The distributed generators (DG) has become one of the strongest mitigating strategies for the network power loss and to optimize voltage reliability over integration of capacitor banks and network reconfiguration. This paper introduces an approach for the optimizing the placement and sizes of different types of DGs in radial distribution systems using a fine-tuned particle swarm optimization (PSO). The suggested approach is evaluated on IEEE 33, IEEE 69 and a real network in Malaysian Context. Simulation results demonstrate the productiveness of active and reactive power injection into the electric power system and the comparison depicts that the suggested fine-tuned PSO methodology could accomplish a significant reduction in network power loss than the other research works.

In today’s world, distributed generation (DG) is an outstanding solution to tackle the challenges in power grids such as the power loss of the system that is intensified by the exponential increase in demand for electricity. Numerous optimization algorithms have been used by several researchers to establish the optimal placement and sizing of DGs to alleviate this power loss of the system. However, in terms of the reduction of active power loss, the performance of these algorithms is weaker. Furthermore, the premature convergence, the precision of the output, and the complexity are a few major drawbacks of these optimization techniques. Thus, this paper proposes the multileader particle swarm optimization (MLPSO) for the determination of the optimal locations and sizes of DGs with the objective of active power loss minimization while surmounting the drawbacks in previous algorithms. A comprehensive performance analysis is carried out utilizing the suggested approach on the standard IEEE 33 bus system and a real radial bus system in the Malaysian context. The findings reveal a 67.40% and an 80.32% reduction of losses in the two systems by integrating three DGs with a unity power factor, respectively. The comparison of the results with other optimization techniques demonstrated the effectiveness of the proposed MLPSO algorithm in optimal placement and sizing of DGs.

With the technological advancements, distributed generation (DG) has become a common method of overwhelming the issues like power losses and voltage drops which accompanies with the leaf of the feeders of radial distribution networks. Many researchers have used several optimization techniques and tools which could be used to locate and size the DG units in the system. Particle swarm optimization (PSO) is one of the famous optimization techniques. However, the premature convergence is identified as a fundamental adverse effect of this optimization technique. Therefore, the optimization problem can direct the objective function to a local minimum. This paper presents a variant of PSO techniques, “comprehensive learning particle swarm optimization (CLPSO)” to determine the optimal placement and sizing of the DGs, which uses a novel learning strategy whereby all other particles’ historical best information and learning probability value are used to update a particle’s velocity. The CLPSO particles learn from one exampler for few iterations, instead of learing from global and personal best values in every iteration in PSO and this technique retains the swarm's variability to avoid premature convergence. A detailed analysis was conducted for the IEEE 33 bus system. The comparison results have revealed a higher convergence and an accuracy than the PSO.

In today’s world, Distributed Generation (DG) has become an outstanding solution to cater to power system challenges caused due to the exponential growth of load demand. Many researchers have used various optimization techniques for the optimal planning of location and the size of the DGs. However, premature convergence, precision of the output and complexity are few major drawbacks of these optimization techniques. In this paper, Multi-Leader Particle Swarm Optimization (MLPSO) is utilized to determine the optimal locations and sizes of DGs with the intention of active power loss minimization. Thus, the primary drawback of premature convergence in existing optimization techniques is suppressed. A comprehensive performance analysis is carried out on IEEE 33 bus system. The findings reveal a 67.40% reduction of loss by integrating three DGs with unity power factor. The comparison of the results with other optimization techniques has demonstrated the effectiveness of MLPSO Algorithm.