Sanchai Dechanupaprittha

Impacts of Low Voltage PEVs Single Phase Charging on Electrical Distribution Network

This paper presents impacts of low voltage PEV single phase charging on electrical distribution network. Simulation model and analysis tools under DigSILENT Power Factory program were used in this research. There are 5 case studies under this analysis, where each case operates at different charging time durations between 16.00 and 24.00. Assessment of the impact of PEV charging is performed based on the PEA standards. The simulation results show that all charging case studies increase line loading in the distribution network over the limit by 80%. The voltage at the upstream point of the feeder slightly dropped while at the downstream point dropped 4 times lower. Average line losses for all case studies increase approximately 13.36% compared to the base case study.

Load shedding scheme based on voltage instability index using synchrophasor data

This paper presents load shedding scheme based on voltage instability index to prevent voltage collapse. For assessing effective of method, the scheme requires reliable voltage instability indicator which can evaluate the dynamic behaviors in system voltage stability for deciding the necessary control action. Hence, synchrophasor data using Phasor Measurement Units (PMUs) are used in calculation process of the proposed voltage instability index. This paper provides strategy to disconnect sufficient amount of load by successive steps of specific value. The proposed scheme is investigated and examined on two-area smart grid test system using DIgSILENT Powerfactory software. The simulation results show the performance of proposed load shedding scheme dealing with voltage instability as well as voltage collapse.

Synchrophasor-based adaptive controller design considering individual charging for interconnected smart grids stabilization

This paper demonstrates how to design an adaptable controller to charge plug-in electric vehicles (PEVs), considering an individual vehicle charging, in order to stabilize the interconnected smart grids embedded with the renewable energy supply, and alleviate the swing effect to the operation of controllable distributed generator in the system. To achieve these objectives, the Particle Swarm Optimization is adopted to explore the optimal parameters of the Proportional Integral (PI) controller. Moreover, this paper also shows how to utilize the Synchrophasor-based dynamic monitoring system for assessing stability considering renewable energy in the smart grids system. The potential for the practical utilization of the adaptive controller designed is evaluated and discussed at the end of the paper.

Synchrophasor based early-warning voltage instability index

This paper presents an early-warning voltage instability index based on synchrophasor data. The proposed voltage instability index observes the rate of V-Q sensitivity for assessing voltage instability and impending voltage collapse. The proposed index evaluates system voltage dynamics and indicates voltage instability with early-warning capability. This paper also shows a practical implementation of the proposed voltage instability index using measured synchrophasor data to assess situations of voltage instability and voltage collapse. The proposed index is investigated and examined by simulation study of a two-area smart grid test system using DIgSILENT Powerfactory software. The simulation results reveal performance and effectiveness of the simple-but-effective early-warning voltage instability indicator.

Impact analysis of fast charging to voltage profile in PEA distribution system by Monte Carlo simulation

This paper presents a method to obtain electric power consumption of plug-in electric vehicle (PEV) fast charging station by the Monte Carlo simulation. The state of charge (SoC) of battery, charging time and arrival time to charging station are considered. After obtaining a charging profile, a voltage profile is analyzed by DIgSIENT PowerFactory. Subsequently, an impact analysis of PEV fast charging stations to voltage profile of the Provincial Electricity Authority (PEA) distribution system is carried out. The simulation results show that charging station affects changes of voltage profile and voltage drop in the distribution system. In addition, the presented method is applicable, even if the rated charging power of charging station is changed, an approach presented in this research can also be applied to analyze the impact.

Optimal Phasor Measurement Unit Placement for Monitoring of PEA Bowin Power

This paper presents an optimal method of Phasor Measurement Unit (PMU) placement in Bowin area distribution grid of the Provincial Electricity Authority (PEA) using the Improved Greedy Method (IGM) in order to achieve a minimum number of PMUs for complete observability of all bus voltages. The proposed method introduces the criteria of bus selection for PMU placement, regarding a number of branches connected to the selected bus as well as the length of those branches. Subsequently, the mismatches of the observed voltages resulted from the proposed method are compared with those resulted from the Depth First Search (DFS) using the Power System Analysis Toolbox (PSAT) and the original Greedy method (GDY) to reveal the effectiveness of the proposed placement methods. For the DFS and GDY methods, a bus with the highest number of connected branches is initially selected as the first PMU bus. However, if there is more than one bus, the first PMU bus will be randomly selected from those qualified. On the other hand, the PMU bus firstly selected by the IGM must have the highest number of connected branches as well as the minimum total distance or total impedance of all observable buses. The findings resulted from the IGM are the optimal PMU placement with the total distance of the connected branches less than the other methods, especially for, large-scale networks. According to the comparison results among the DFS, GDY and IGM, the mean absolute error (MAE) of the angle and the magnitude voltage of all buses resulted from the IGM is the lowest for a wide range of load conditions, while the MAE resulted from the DFS is the highest.