A Probabilistic Modeling Based on Monte Carlo Simulation of Wind Powered EV Charging Stations for Steady-States Security Analysis

Kim, Sunoh; Hur, Jin

doi:10.3390/en13205260

Cited by 27 publications

(7 citation statements)

References 23 publications

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“…To date, few studies have examined the implication of the increase in EV numbers on the grid system [85][86][87][88][89][90][91]. A study conducted by Papadopoulos et al [92] examined how charging EVs may affect the grid power demand profile in the UK by 2030.…”

Section: Impact Of Evs Uptake On Power Grid Capacitymentioning

confidence: 99%

Electric Vehicles: V2G for Rapid, Safe, and Green EV Penetration

2022

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Low carbon and renewable energy sources (RESs) are fast becoming a key sustainable instrument in meeting the global growth of electricity demand while curbing carbon emissions. For example, the gradual displacement of fossil-fuelled vehicles with electrically driven counterparts will inevitably increase both the power grid baseload and peak demand. In many developed countries, the electrification process of the transport sector has already started in tandem with the installation of multi-GW renewable energy capacity, particularly wind and solar, huge investment in power storage technology, and end-user energy demand management. The expansion of the Electric Vehicle (EV) market presents a new opportunity to create a cleaner and transformative new energy carrier. For instance, a managed EV battery charging and discharging profile in conjunction with the national grid, known as the Vehicle-to-Grid system (V2G), is projected to be an important mechanism in reducing the impact of renewable energy intermittency. This paper presents an extensive literature review of the current status of EVs and allied interface technology with the power grid. The main findings and statistical details are drawn from up-to-date publications highlighting the latest technological advancements, limitations, and potential future market development. The authors believe that electric vehicle technology will bring huge technological innovation to the energy market where the vehicle will serve both as a means of transport and a dynamic energy vector interfacing with the grid (V2G), buildings (V2B), and others (V2X).

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Section: Impact Of Evs Uptake On Power Grid Capacitymentioning

confidence: 99%

Electric Vehicles: V2G for Rapid, Safe, and Green EV Penetration

2022

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“…Then, a bottom-up approach was designed to forecast the aggregated EV charging model. Gaussian mixture distribution was used in [29] to determine the EV charging demand pattern based-on actual data, which was collected from various charging stations in South Korea. The researcher used the Monte Carlo simulation to generate the aggregated EV charging demand.…”

Section: Introductionmentioning

confidence: 99%

Electric Vehicle Charging Model in the Urban Residential Sector

et al. 2022

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Electric vehicles (EVs) have become increasingly popular because they are highly efficient and sustainable. However, EVs have intensive electric loads. Their penetrations into the power system pose significant challenges to the operation and control of the power distribution system, such as a voltage drop or transformer overloading. Therefore, grid operators need to prepare for high-level EV penetration into the power system. This study proposes data-driven, parameterized, individual, and aggregated EV charging models to predict EV charging loads in the urban residential sector. Actual EV charging profiles in Saskatchewan, Canada, were analyzed to understand the characteristics of EV charging. A location-based algorithm was developed to identify residential EV charging from raw data. The residential EV charging data were then used to tune the EV charging model parameters, including battery capacity, charging power level, start charging time, daily EV charging energy, and the initial state of charge (SOC). These parameters were modeled by random variables using statistic methods, such as the Burr distribution, the uniform distribution, and the inverse transformation methods. The Monte Carlo method was used for EV charging aggregation. The simulation results show that the proposed models are valid, accurate, and robust. The EV charging models can predict the EV charging loads in various future scenarios, such as different EV numbers, initial SOC, charging levels, and EV types (e.g., electric trucks). The EV charging models can be embedded into load flow studies to evaluate the impact of EV penetration on the power distribution systems, e.g., sustained under voltage, line loss, and transformer overloading. Although the proposed EV charging models are based on Saskatchewan’s situation, the model parameters can be tuned using other actual data so that the proposed model can be widely applied in different cities or countries.

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“…Electric vehicles have significant advantages in alleviating global energy shortages and environmental pollution [1][2][3]. At the same time, distributed power generation, which is mainly composed of photovoltaic and wind power, develops rapidly [4]. However, the access of large-scale electric vehicles and the intermittence of renewable energy will aggravate the peak-valley difference of power grid load, making the operation of power grid more difficult to control [5,6].…”

Section: Introductionmentioning

confidence: 99%

Situation Awareness of Electric Vehicle Charging Load Based on Random Forest Algorithm

Wang

et al. 2022

International Transactions on Electrical Energy Systems

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Due to the important characteristics of energy saving and carbon reduction, electric vehicles have attracted worldwide attention. It can be predicted that the power grid will be faced with the access problem of large-scale electric vehicles. In order to master the user behavior characteristics of electric vehicle load, it is necessary to establish the model based on electric vehicle charging behavior. In this paper, combined with the electric vehicle charging demand and the situational awareness results of the dispatchable resources in the station area, the characteristic indicators of the electric vehicle load are quantitatively analyzed. Situational prediction of electric vehicle load based on random forest algorithm is proposed, and the sample set is divided and trained. A simulation example is used to verify the effectiveness of the method provided in load forecasting.

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A Probabilistic Modeling Based on Monte Carlo Simulation of Wind Powered EV Charging Stations for Steady-States Security Analysis

Cited by 27 publications

References 23 publications

Electric Vehicles: V2G for Rapid, Safe, and Green EV Penetration

Electric Vehicles: V2G for Rapid, Safe, and Green EV Penetration

Electric Vehicle Charging Model in the Urban Residential Sector

Situation Awareness of Electric Vehicle Charging Load Based on Random Forest Algorithm

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