Electrification of the transportation sector can play a vital role in reshaping smart cities. With an increasing number of electric vehicles (EVs) on the road, deployment of well-planned and efficient charging infrastructure is highly desirable. Unlike level 1 and level 2 charging stations, level 3 chargers are super-fast in charging EVs. However, their installation at every possible site is not techno-economically justifiable because level 3 chargers may cause violation of critical system parameters due to their high power consumption. In this paper, we demonstrate an optimized combination of all three types of EV chargers for efficiently managing the EV load while minimizing installation cost, losses, and distribution transformer loading. Effects of photovoltaic (PV) generation are also incorporated in the analysis. Due to the uncertain nature of vehicle users, EV load is modeled as a stochastic process. Particle swarm optimization (PSO) is used to solve the constrained nonlinear stochastic problem. MATLAB and OpenDSS are used to simulate the model. The proposed idea is validated on the real distribution system of the National University of Sciences and Technology (NUST) Pakistan. Results show that an optimized combination of chargers placed at judicious locations can greatly reduce cost from $3.55 million to $1.99 million, daily losses from 787kWh to 286kWh and distribution transformer congestion from 58% to 22% when compared to scenario of optimized placement of level 3 chargers for 20% penetration level in commercial feeders. In residential feeder, these statistics are improved from $2.52 to $0.81 million, from 2167kWh to 398kWh and from 106% to 14%, respectively. It is also realized that the integration of PV improves voltage profile and reduces the negative impact of EV load. Our optimization model can work for commercial areas such as offices, university campuses, and industries as well as residential colonies. INDEX TERMS Charging stations placement, distribution system, electric vehicles (EVs), optimization. NOMENCLATURE SETS N Set of buses in the system T Set of time periods M Set of line sections O Set of types of chargers E Set of electric vehicles INDICES i Index of bus number t Index of time period j Index of line section l Index of level of charging station e Index of electric vehicle The associate editor coordinating the review of this manuscript and approving it for publication was Zhiyi Li. PARAMETERS P j,loss Power loss of jth line section C Charging power of a charger SOC init Initial state of charge of a battery c Cost of a charger c p,l Per unit electrical energy cost S j,max Maximum transfer capacity of line section j η ch Charging efficiency of EV d max Maximum range when EV is fully charged VARIABLES n Number of charging station V i,t Voltage magnitude of bus i at time interval t S j,t Power flow through line section j at time interval t dist trav,e Travelled distance by electric vehicle e

Flexible AC Transmission Systems (FACTS) play an important role in minimizing power losses and voltage deviations while increasing the real power transfer capacity of transmission lines. The extent to which these devices can provide benefits to the transmission network depend on their optimal location and sizing. However, finding appropriate locations and sizes of these devices in an electrical network is difficult since it is a nonlinear problem. This paper proposes a technique for the optimal placement and sizing of FACTS, namely the Thyristor-Controlled Series Compensators (TCSCs), Shunt VARs Compensators (SVCs), and Unified Power Flows Controllers (UPFCs). To find the optimal locations of these devices in a network, weak buses and lines are determined by constructing PV curves of load buses, and through the line stability index. Then, the whale optimization algorithm (WOA) is employed not only to find an ideal ratings for these devices but also the optimal coordination of SVC, TCSC, and UPFC with the reactive power sources already present in the network (tap settings of transformers and reactive power from generators). The objective here is the minimization of the operating cost of the system that consists of active power losses and FACTS devices cost. The proposed method is applied to the IEEE 14 and 30 bus systems. The presented technique is also compared with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The findings showed that total system operating costs and transmission line losses were considerably reduced by WOA as compared to existing metaheuristic optimization techniques.

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Silicone rubber (SiR) is extensively used in outdoor insulation and other applications. However, like other polymers, SiR also degrades and lessens its performance in the exposition of environmental stresses. Nanofillers improve the aging behavior of polymeric insulating materials. To investigate the effect of nanosilica on the aging behavior of SiR, we fabricated nanocomposites of SiR with 2.5 wt.% nanosilica (SNC-2.5) and 5 wt.% nanosilica (SNC-5) by mechanical mixing and ultrasonication method. The prepared samples were subjected to uniform ultraviolet (UV) radiation, heat, humidity, salt fog, and acid rain along with neat SiR in cyclic manner at 2.5 kV for 5000 h in a specially fabricated weathering chamber. Neat SiR and both nanocomposites showed gradual decrease in transparency. Random loss and recovery in hydrophobicity and increase and decrease in leakage current (LC) were recorded for all samples, in which SNC-5 showed the best hydrophobic behavior and least LC. For SNC-5, Fourier transform infrared (FTIR) results showed negligible reduction in absorption peaks at important wave-numbers and increase and intactness were observed at absorption peaks related to the hydrophobic methyl group. Scanning electron microscopy (SEM) results also concurred with FTIR, LC measurements, and hydrophobicity analysis.