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This work studies the power quality impacts of electric transportation charging including electric buses (EBs) and electric vehicles (EVs) on distribution systems. The stochastic harmonic models for EBs and EVs and the stochastic models for their usage scenarios are built utilising the measured charging data. Based on these models, a power quality analysis method considering the charging uncertainties of EBs and EVs is proposed in this study and then their impacts on distribution systems are investigated. The Monte Carlo simulation is integrated into the proposed method to study the under voltage, overcurrent, voltage unbalance, transformer overload, harmonic current, voltage etc. caused by the stochastic charging of EBs and EVs. An actual distribution system acquired from Taiwan Power Company is taken as an example to validate the practicability of the proposed method. The test results of the proposed stochastic model are compared with those of the conservative model. [ABSTRACT FROM AUTHOR]

This study measures the long‐term electric energy data of an electric vehicle (EV) charging station, and uses these field measurements to analyse and construct the load behaviour model of an EV charging station. According to the statistics of field measurements, the state of charge of EV battery pack at the start of charging and the number of EVs starting charging in each time interval can be modelled using Poisson distribution, Roulette wheel selection and uniform distribution. The load behaviour of an EV charging station can be modelled and analysed accordingly. To validate the accuracy of the proposed load behaviour model, a portion of field measurements is reserved for load behaviour model verification. The proposed load behaviour model can also be used to analyse the impacts of EV charging stations on distribution systems. Test results show that the load behaviour model proposed in this study has higher accuracy and the impacts of EV charging stations on distribution systems can be analysed more effectively and precisely. [ABSTRACT FROM AUTHOR]

Vulnerability mitigation is one of the most important issues for power system planning and operation. Recent research reveals that energy storage systems (ESSs) have a great potential to be used to mitigate power system vulnerability. However, their benefits greatly depend on the locations and capacities of ESSs installed. Therefore this study aims to find the optimal locations and capacities of ESSs for power system vulnerability mitigation. A vulnerability assessment is proposed in this study to calculate the impact factors for power systems based on generator and line outages. A bus impact severity (BIS) analysis is then designed and used to find the vulnerable buses in the power system. Formulations of optimal locations and capacities for ESS placement are derived and solved by a genetic algorithm‐based method. Since better locations for ESS placement can be found, the search space for ESS placement can be reduced effectively. Accordingly, the computational time for finding the optimal solution can be saved and the possibility to obtain a global optimum solution can be improved. Test results show that the proposed method can be used to find the optimal locations and capacities for ESS placement and mitigate system vulnerability effectively. [ABSTRACT FROM AUTHOR]