An improved multi-objective generalized differential evolution (I-GDE3) approach to solve optimal reactive power dispatch (ORPD) with multiple and competing objectives is proposed in this article. The objective functions are minimization of real power loss and bus voltage profile improvement. For maintaining good diversity, the concepts of simulated binary crossover (SBX) based recombination and dynamic crowding distance (DCD), are implemented in the GDE3 algorithm. I-GDE3 obtains the Pareto-solution set for ORPD that is impervious to load drifts and perturbations. The performance of the proposed approach is tested in standard IEEE 118-bus and IEEE 300-bus test systems and the result demonstrates the capability of the I-GDE3 algorithm in generating diverse and well distributed Pareto-optimal solutions that are less sensitive to various loading conditions along with load perturbations. The performance of I-GDE3 is compared with respect to multi-objective performance measures namely span, hyper-volume and C-measure. The results show the effectiveness of I-GDE3 and confirm its potential to solve the multi-objective RPD problem.

Power generation is challenged to meet energy demand during peak hours. As a result of limited non-renewable energy resources, power utilities heavily rely on fossil fuels. Therefore, scientists and researchers are looking for some distributed generators to provide additional power during peak hours. During such period, load demand is solved using solar power. As a consequence, grid-connected solar Photovoltaic (PV) systems are catching the attention owing to their ability to significantly reduce the use of fossil fuels. Under Partial Shading Condition (PSC), this paper utilizes Luo Converter along with Cascaded Artificial Neural Network (ANN), which is a Machine Learning Based Maximum Power Point Tracking (ML-MPPT) approach for tracking optimal power from PV system. The gained DC supply is converted into AC voltage using VSI attached to the system. In addition, PI controller engaged controls the voltage at grid side and results in effective grid synchronization. Furthermore, MATLAB Simulink analysis is carried out and the outcomes reveal the effectiveness of proposed system with 98% efficiency under different PV circumstances.

The main objective of implementing charging stations is to ensure the good charging to the Electric Vehicles by using a solar PV array which is interconnected to the battery energy storage systems. The charging station regulates the supply voltage and frequency without the use of a mechanical speed governor. It also assures that energy gained from grid or by the DG set will have the unity power factor (UPF) when the load is nonlinear. Besides this, the Point of Common Coupling (PCC) voltage is synchronised with the grid/generator voltage in order to provide continuous charging. In order to increase the optimal efficiency of the charging stations, the charging stations will perform the active/reactive power transfer from the vehicle to grid, vehicle to house and vehicle to vehicle (V2V) power transfer. The operational experiment of the charging station is simulated and verified by using MATLAB/SIMULINK.

In this article, optimum sizing and modeling of a stand-alone dc microgrid (DCMG) system for domestic applications with hybrid storage system is been proposed. The hybrid storage system consists of a lithium-ion battery (LIB) and supercapacitor (SC). The DCMG is designed to meet the load requirements of a house located in Aruppukottai, in the southern region of Tamil Nadu, India. The daily energy demand is estimated as 7.77 kWh with a peak load of 1.19 kW. This DCMG is designed and simulated by using Homer Pro software. The optimum sizing of solar photovoltaics (PVs), wind turbine (WT), LIB, and SC is evaluated based on cost of energy (CoE) and net present cost (NPC), and all feasible configurations are discussed. The feasible configurations include PV+LIB, PV+WT+LIB, WT+LB, PV+WT+SC, PV+SC, and WT+SC and are compared in terms of CoE and NPC. The results show that the PV+LIB architecture is the optimum configuration, with a CoE of ${\$}$ 0.2/kWh and an NPC of $7,334.