The rising concerns over global climate change and depleting fossil fuel reserves are two of the main reasons for the ongoing efforts towards the electrification of the transportation sector. While greenhouse gases (GHGs) emissions from other sectors are generally falling, emissions from the road transport have increased over the past few decades, with both full electric vehicles (FEVs) and plug-in hybrid electric vehicles (PHEVs) being recognized as potential alternatives to combat climate change and reduce GHG emissions. However, widespread integration of FEVs and PHEVs will substantially increase the load on the power system which will eventually affect the reliability of existing power systems. In this paper, a probabilistic model for integrating FEVs and PHEVs with existing power grids is proposed that incorporates important FEV and PHEV characteristics, such as battery capacity, charge depleting distance, and charging rates. In addition, user behavior is taken into account through time of recharging, arrival and departure times, and daily miles driven. Furthermore, different charging strategies, i.e., opportunistic charging and controlled charging with and without vehicle-to-grid (V2G) scheme have been considered to evaluate the impact of FEVs and PHEVs on the composite power system. IEEE-RTS-79 system is used to examine the proposed probabilistic technique considering different FEV and PHEV penetration levels as well as charging strategies. Simulation results show that even a relatively low penetration level of FEVs or PHEVs might have a significant impact on the system reliability unless a proper charging and/or discharging schemes are utilized.

This paper proposes two electric energy management systems (EMSs) in the context of a gridconnected residential neighbourhood with electric vehicles (EVs), battery storage, and solar photovoltaic (PV) generation. The EMSs were developed to minimize the cost of electricity whilst having no impact on routine individual energy needs and travel patterns. The EMSs were evaluated using common sets of real data with the aim to compare the effectiveness of a centralized EMS with decentralized EMS. The models also accounted for the battery capacity degradation and the associated costs. Simulation studies and numerical analyses were presented to validate the effectiveness of the proposed EMSs considering a high-density residential building in Sydney, Australia. The simulation results indicate that the centralized EMS is more effective compared to the decentralized EMS in terms of cost savings. It is also observed that the energy management strategies significantly reduce the energy drawn from the grid compared to un-optimized energy management schemes.

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Harmonic current estimation is the key aspect of Active Power Filter (APF) control algorithms to generate a reference current for harmonic compensation. This paper proposes a novel structure for harmonic current estimation scheme based on Trigonometric Orthogonal Principle (TOP) and Self Tuning Filter (STF). The key advantages of the proposed method are its simplicity, low computational burden and faster execution time in comparison to the conventional harmonic current estimation approaches. The TOP method provides a simple and fast approach to extract the reference current, while STF provides a simplified structure to generate the required synchronization signal that eliminates the need of a Phase Locked Loop (PLL) algorithm for synchronization. As a result, it exhibits less complexity in implementation and less consumption of microcontroller's resources; thus, the proposed method can be implemented using a low-cost microcontroller. It is shown in the paper that the proposed method provides 10 times gain in processing speed as compared to the conventional DQ method. The proposed approach is analyzed in detail, and its effectiveness and superior performance are verified using simulation and experimental results.

With a spike in popularity and sales, the electric vehicles (EVs) have revolutionized the transportation industry. As EV technology advances, the EVs are becoming more accessible and affordable. Therefore, a rapid proliferation of light-duty EVs have been noticed in the residential sector. Even though the increased charging demand of EVs is manageable in largescale, the low-voltage (LV) residential networks might not be capable of managing localized capacity issues of large scale EV integration. Dynamic electricity tariff coupled with demand response and smart charging management can provide grid assistance to some extent. However, uncoordinated charging, if clustered in a residential distribution feeder, can risk grid assets because of overloading and can even jeopardize the reliability of the network by violating voltage constraints. This paper proposes a coordinated residential EV management system for power grid support. Charging and discharging of residential EV batteries are coordinated and optimized to address grid overloading during peak demand periods and voltage constraint violations. The EV management for grid support is formulated as a mixedinteger programming based optimization problem to minimize the inconveniences of EV owner while providing grid assistance. The proposed methodology is evaluated via a case study based on a residential feeder in Sydney, Australia with actual load demand data. The simulation results indicate the efficacy of the proposed EV management method for mitigating grid overloading and maintaining desired bus voltages.