In this study, the concept of symmetry is introduced by finding the optimal state of a power system. An electric vehicle type load is present, where the supply stores’ electrical energy causes an imbalance in the system. The optimal conditions are related by adjusting the voltage of the bus location. The key variables are the load voltage deviation (LVD), the variation of the load and the power, and the sizing of the distributed photovoltaic (DPV), which are added to the system for power stability. Here, a method to optimize the fast-charging stations (FCSs) and DPV is presented using an optimization technique comparison. The system tests the distribution line according to the bus grouping in the IEEE 33 bus system. This research presents a hypothesis to solve the problem of the voltage level in the system using metaheuristic algorithms: the cuckoo search algorithm (CSA), genetic algorithm (GA), and simulated annealing algorithm (SAA) are used to determine the optimal position for DPV deployment in the grid with the FCSs. The LVD, computation time, and total power loss for each iteration are compared. The voltage dependence power flow is applied using the backward/forward sweep method (BFS). The LVD is applied to define the objective function of the optimization techniques. The simulation results show that the SAA showed the lowest mean computation time, followed by the GA and the CSA. A possible location of the DPV is bus no. 6 for FCSs with high penetration levels, and the best FCS locations can be found with the GA, with the best percentage of best hit counter on buses no. 2, 3, 13, 14, 28, 15, and 27. Therefore, FCSs can be managed and handled in optimal conditions, and this work supports future FCS expansion.

This paper presents a phase-shifted PWM-controlled DC to DC power converter with a synchronous double rectifier. The power switches can be directed to achieve the zero-voltage switching condition using the technology of the full-bridge power converter and increase the efficiency of the converter on the secondary side of the transformer. A current doubling rectifier with low current ripple and synchronized rectifier circuits are also adopted. The secondary side of the high-frequency transformer uses the synchronous double rectifier. The synchronous double rectifier circuit converts AC voltage to DC voltage to enable high-current applications with two filtering inductors to reduce losses and dissipate heat, resulting in an efficient system. The proposed method can control the input DC voltage of the phase-shifted power converter at 400 V and achieves the required DC voltage of 24 V for battery charging applications under the zero-voltage switching condition. The results of simulations and experiments show 86.7% efficiency at 40% load condition and 91.8% efficiency at full load condition.

Power flow analysis is essential for evaluating the steady-state operation of modern smart grids, particularly when distributed energy resources and vehicle-to-grid (V2G) systems are integrated into distribution networks. This study presents a hybrid quantum–classical framework that combines a Variational Quantum Linear Solver (VQLS) with the COBYLA optimizer for AC power flow analysis. The nonlinear power flow equations are linearized iteratively, and the resulting Jacobian-based linear system is approximated using a parameterized quantum circuit. The framework is evaluated using a modified IEEE 33-bus radial distribution system with an aggregated V2G unit connected at Bus 18. In the main optimization run, the VQLS cost decreases rapidly and approaches a stable nonzero value, while the resulting bus-voltage profile follows the overall trend of the Newton–Raphson reference solution. A separate 50-iteration convergence assessment also shows a substantial cost reduction, although the prescribed tolerance of 10⁻⁴ is not reached. The V2G scenario analysis indicates that active power losses during the evening peak are reduced as V2G participation increases, with the 50% smart-discharge case producing the lowest losses among the investigated conditions. In addition, a resource-scaling assessment for systems ranging from 14 to 300 buses shows different growth trends between the normalized classical computation index and the VQLS circuit-depth index. These results support the numerical feasibility of integrating a VQLS-based approximation into iterative power flow analysis. The study remains a simulation-based proof of concept and does not claim computational superiority or quantum speedup over established classical solvers.

Phase-asymmetric electric-vehicle (EV) charging can aggravate voltage unbalance in radial distribution feeders, while a smart-inverter battery energy storage system (BESS) can provide both energy shifting and fast reactive-power support. This study designs and evaluates a proximal policy optimization (PPO)-based controller for coordinated active- and reactive-power dispatch under asymmetric EV demand. The analysis uses a corrected phase-decoupled three-phase extension of the IEEE 33-bus feeder, with voltage unbalance quantified using the negative-to-positive sequence voltage ratio. Active-power-only (P-only) and coordinated P–Q controllers are evaluated using identical BESS hardware, operating limits, and terminal state-of-charge (SOC) requirements, enabling the incremental contribution of inverter reactive-power capability to be assessed under matched conditions. Across three independent training seeds, P–Q control reduces daily feeder loss from 3874.822 to 3809.751 kWh/day, worst voltage-unbalance factor (VUF) from 2.7879% to 2.5940%, and mean hourly maximum VUF from 1.5422% to 1.3628%, corresponding to reductions of 1.679%, 6.954%, and 11.632%, respectively, relative to the no-BESS case. The corresponding P-only reductions are 0.610%, 4.362%, and 8.427%, respectively. All reported learned-policy evaluations return the BESS to 50% terminal SOC, preventing the comparison from being biased by net battery depletion. Without retraining, the P–Q policies retain approximately 5.66–7.86% mean worst-VUF reduction under the tested load, photovoltaic, EV-demand, and combined-profile perturbations. The results demonstrate repeatable voltage-unbalance mitigation and show that coordinated P–Q operation provides an incremental benefit beyond active-power-only control under the investigated conditions. However, residual undervoltage under severe loading and the phase-decoupled feeder representation limit the scope of the conclusions.

This paper presents a simulation-based proof of concept for integrating an existing Variational Quantum Linear Solver (VQLS) with a Newton-type AC power flow procedure for smart grids with vehicle-to-grid (V2G) participation. The novelty of the work lies not in proposing a new VQLS algorithm but in its engineering integration with a reduced Jacobian-based power flow subproblem and V2G operating scenarios. The proposed framework linearizes the nonlinear AC power flow equations and applies a four-qubit Real-Amplitudes VQLS circuit with the COBYLA optimizer to approximate a selected 16 × 16 reduced Jacobian system. The complete voltage profile is then reconstructed through the hybrid quantum–classical procedure. The method is evaluated using a modified IEEE 33-bus radial distribution system with an aggregated V2G unit connected at Bus 18. The main optimization run shows a rapid reduction and subsequent stabilization of the VQLS cost, while the resulting bus-voltage profile follows the overall trend of the Newton–Raphson reference solution. A separate 50-iteration assessment also reduces the cost substantially but does not reach the reference tolerance of 10⁻⁴. The V2G scenario analysis shows that prescribed V2G active-power support can reduce active power losses under both normal and stressed operating conditions, with loss reductions of 26.26%, 27.85%, and 30.52% in the base peak-load, N-1 contingency support, and dynamic railway-peak support cases, respectively. A resource-scaling assessment using a normalized classical computation index and a VQLS circuit-depth index is included only to illustrate resource-growth trends, not to establish computational superiority. The results support the feasibility of the proposed integration under ideal statevector simulation, but no quantum speedup or advantage over established classical solvers is claimed.