Technological advancement, environmental concerns, and social factors have made plug-in electric vehicles (PEVs) popular and attractive vehicles. Such a trend has caused major impacts to electrical distribution systems in terms of efficiency, stability, and reliability. Moreover, excessive power loss, severe voltage deviation, transformer overload, and system blackouts will happen if PEV charging activities are not coordinated well. This paper presents an optimal charging coordination method for a random arrival of PEVs in a residential distribution network with minimum power loss and voltage deviation. The method also incorporates capacitor switching and on-load tap changer adjustment for further improvement of the voltage profile. The meta-heuristic methods, binary particle swarm optimization (BPSO) and binary grey wolf optimization (BGWO), are employed in this paper. The proposed method considers a time-of-use (ToU) electricity tariff such that PEV users will get more benefits. The random PEV arrival is considered based on the driving pattern of four different regions. To demonstrate the effectiveness of the proposed method, comprehensive analysis is conducted using a modified of IEEE 31 bus system with three different PEV penetrations. The results indicate a promising outcome in terms of cost and the distribution system stress minimization.

Optimal Network Reconfiguration (NR) is a well-accepted approach to minimize power loss and enhance voltage profile in the Electrical Distribution Networks (EDN). Since the NR problem contains huge combinational search space, most researchers consider the meta-heuristic techniques to attain NR solution. However, these meta-heuristic techniques do not guarantee to obtain the optimal solution besides they require large processing time to converge. This is mainly due to (1) random initialization and updating of population and (2) the continuous verification of population during the search process. With the aim of reducing the computational time and improving the consistency in obtaining the optimal solution as well as minimizing power loss and enhancing the voltage profile of the EDN, this work proposes a new method based on two-stage optimizations. The proposed method introduces an approach to simplify the network into simplified network graph. Then, this approach is utilized for guided initializations and generations of the population and for the proper population's codification. The proposed method is implemented using the firefly algorithm and verified on 33-bus and 118-bus test systems. The results show the ability of the proposed method to obtain the optimal solution within fast computational time and with superior consistency compared to the conventional methods.

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The circularly polarized (CP) microstrip antennas, both of singly- and doubly-fed types, possess inherent limitation in gain, impedance and axial-ratio bandwidths. These limitations are caused mainly by the natural resonance of the patch antenna which has a high unloaded Q-factor and the frequency-dependent excitation of two degenerative modes (TM01 and TM10) when using a single feed. Many applications which require circular polarization, large bandwidth, and good performance, especially in the field of wireless communication, are still difficult to be designed by using antenna software. Some consideration to take will include the application target and design specification, the materials to be used, and the method to choose (formula, numerical analysis, etc). This paper explains and analyzes the singly-fed microstrip antenna with circular polarization and large bandwidth. This singly-fed type of microstrip antenna provides certain advantage of requiring no external circular polarizer, e.g. the 900 hybrid, as it only needs to apply some perturbation or modification to a patch radiator with a standard geometry. The design of CP and large-bandwidth microstrip antenna is done gradually, by firstly truncating one tip, then truncating the whole three tips, and finally modifying it into a pentagonal patch structure and adding an air-gap to obtain larger bandwidths of impedance, gain and axial ratio. The last one antenna structure results in a novelty because it is a rare design of antenna which includes all types of bandwidth (impedance, gain, and axial ratio) being simultaneously larger than the origin antenna. The resulted characteristic performance of the 1-tip (one-tip) antenna shows respectively 1.9% of impedance bandwidth, 3.1% of gain bandwidth, and 0.45% of axial-ratio bandwidth. For the 3-tip (three-tip) step, the resulted bandwidths of respectively impedance, gain, and axial ratio are 1.7%, 3.3% and 0.5%. The pentagonal structure resulted in the bandwith values of 15.67%, 52.16% and 4.11% respectively for impedance, gain, and axial ratio.

<span>The increasing demand of electricity and number of distributed generations connected to power system greatly influence the level of power service reliability. This paper aims at improving the reliability in an electric power distribution system by optimizing the number and location of sectionalizers using the Ant Colony Optimization (ACO) and Simulated Annealing (SA) methods. Comparison of these two methods has been based on the reliability indices commonly used in distribution system: SAIFI, SAIDI, and CAIDI. A case study has been taken and simulated at a feeder of Pujon, a place in East Java province of Indonesia, to which some distributed generators were connected. Using the existing reliability indices condition as base reference, the addition of two distributed plants, which were micro hydro and wind turbine plants, has proven to lower the indices as much as 0.78% for SAIFI, 0.79% for SAIDI, and 2.32% for CAIDI. The optimal relocation of the existing 16 sectionalizers in the network proved to decrease further the reliability indices as much as 43.96% for SAIFI, 45.52% for SAIDI, and 2.8% for CAIDI, which means bringing to much better reliability condition. The implementation of the SA method on the considered data in general resulted in better reliability indices than using the ACO method.</span>