Modeling of electric vehicle loads for power flow analysis based on PSAT

Kongjeen, Yuttana; Bhumkittipich, Krischonme

doi:10.1109/ecticon.2016.7561430

Cited by 31 publications

(13 citation statements)

References 8 publications

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“…In general, the LVD value must be minimal, which shows that the system still has a good level of voltage. Therefore, the change in load on each bus must be appropriate, as described in Equation (13), [36,37].…”

Section: Load Voltage Deviation (Lvd)mentioning

confidence: 99%

Impact of Plug-in Electric Vehicles Integrated into Power Distribution System Based on Voltage-Dependent Power Flow Analysis

Kongjeen

Bhumkittipich

2018

Energies

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This paper proposes the impact of plug-in electric vehicles (PEVs) integrated into a power distribution system based on voltage-dependent control. The gasolinegate situation has many people turning to electric vehicles as a more environmentally friendly option, especially in smart community areas. The advantage of PEVs is modern vehicles that can use several types of fuel cells and batteries as energy sources. The proposed PEVs model was developed as a static load model in power distribution systems under balanced load conditions. The power flow analysis was determined by using certain parameters of the proposed electrical network. The main research objective was to determine the voltage magnitude profiles, the load voltage deviation, and total power losses of the electrical power system by using the new proposed methodology. Furthermore, it investigated the effects of the constant power load, the constant current load, the constant impedance load, and the plug-in electric vehicles load model. The IEEE 33 bus system was selected as the test system. The proposed methodology assigned the balanced load types in a steady state condition and used the new methodology to solve the power flow problem. The simulation results showed that increasing the plug-in electric vehicles load had an impact on the grids when compared with the other four load types. The lowest increased value for the plug-in electric vehicles load had an effect on the load voltage deviation (0.062), the total active power loss (120 kW) and the total reactive power loss (80 kVar), respectively. Therefore, this study verified that the load of PEVs can affect the electrical power system according to the time charging and charger position. Therefore, future work could examine the difference caused when PEVs are attached to the electrical power system by means of the conventional or complex load type.

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Section: Load Voltage Deviation (Lvd)mentioning

confidence: 99%

Impact of Plug-in Electric Vehicles Integrated into Power Distribution System Based on Voltage-Dependent Power Flow Analysis

Kongjeen

Bhumkittipich

2018

Energies

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“…It shows that the system still has a good level of voltage. Therefore, the change in load on each bus must be appropriate, as described in (13), [33][34].…”

Section: Load Voltage Deviation (Lvd)mentioning

confidence: 99%

Impact of Plug<strong>-</strong>in Electric Vehicles Integrated into Power Distribution System based on Voltage Dependent Power Flow

Kongjeen¹,

Bhumkittipich²

2018

Preprint

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This paper proposes the impact of plug-in electric vehicles integrated into power distribution system based on voltage dependent control. The plug-in electric vehicles was modeled as the static load model in power distribution systems under balanced load condition. The power flow analysis is determined by using the basic parameters of the electrical network. The main point of this study are compare with voltage magnitude profiles, load voltage deviation, and total power losses of the electrical power system. There are investigating the affected from constant power load, constant current load, constant impedance load and plug-in electric vehicles load, respectively. The IEEE 33 bus test system is used to test the proposed method by assigning each load type to a balanced load in steady state and applied the solving methodology based on the bus injection to branch injection matric, branch current to bus voltage matrix, and current injection matrix to solve the power flow problem. The simulation results showed that the plug-in electric vehicles load had the lowest impact compared to other loads. The lowest plug-in values for the electric vehicle loads were 0.062, 119.67 kW and 79.31 kVar for the load voltage deviation, total active power loss and total reactive power loss, respectively. Therefore, this study can be verified that the plug-in electric vehicles load were affected to the lowest of the electrical power system in condition to same sizing and position. So that, in condition to the plug-in electric vehicles load added into the electrical power system with the conventional load type or complex load type could be considered that the affected from the plug-in electric vehicles load in next study.

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“…Nowadays, the reliability assessment of a power distribution network (PDN) must incorporate the effects of the unavoidable fluctuation of load consumption on the node voltages. Typical examples are represented by the pervasive spread of distributed generators (DGs), for which the injected power depends on (1) weather conditions; (2) the impact of hubs for charging electrical vehicles, located in different points of the network; and (3) the unpredictable user behavior, in a scenario in which both users and buildings play an active role in the continuous monitoring of the fluid electricity fees, and modify their power consumption accordingly [1][2][3][4]. Therefore, the inherent statistical nature of the problem makes a deterministic interpretation unsuitable, and demands for stochastic methodologies for the uncertainty quantification (UQ) [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Compressed Machine Learning Models for the Uncertainty Quantification of Power Distribution Networks

et al. 2020

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Today’s spread of power distribution networks, with the installation of a significant number of renewable generators that depend on environmental conditions and on users’ consumption profiles, requires sophisticated models for monitoring the power flow, regulating the electricity market, and assessing the reliability of power grids. Such models cannot avoid taking into account the variability that is inherent to the electrical system and users’ behavior. In this paper, we present a solution for the generation of a compressed surrogate model of the electrical state of a realistic power network that is subject to a large number (on the order of a few hundreds) of uncertain parameters representing the power injected by distributed renewable sources or absorbed by users with different consumption profiles. Specifically, principal component analysis is combined with two state-of-the-art surrogate modeling strategies for uncertainty quantification, namely, the least-squares support vector machine, which is a nonparametric regression belonging to the class of machine learning methods, and the widely adopted polynomial chaos expansion. Such methods allow providing compact and efficient surrogate models capable of predicting the statistical behavior of all nodal voltages within the network as functions of its stochastic parameters. The IEEE 8500-node test feeder benchmark with 450 and 900 uncertain parameters is considered as a validation example in this study. The feasibility and strength of the proposed method are verified through a systematic assessment of its performance in terms of accuracy, efficiency, and convergence, based on reference simulations obtained via classical Monte Carlo analysis.

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Modeling of electric vehicle loads for power flow analysis based on PSAT

Cited by 31 publications

References 8 publications

Impact of Plug-in Electric Vehicles Integrated into Power Distribution System Based on Voltage-Dependent Power Flow Analysis

Impact of Plug-in Electric Vehicles Integrated into Power Distribution System Based on Voltage-Dependent Power Flow Analysis

Impact of Plug<strong>-</strong>in Electric Vehicles Integrated into Power Distribution System based on Voltage Dependent Power Flow

Compressed Machine Learning Models for the Uncertainty Quantification of Power Distribution Networks

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