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Fast Charging of an Electric Bus Fleet and Its Impact on the Power Quality Based on On-Site Measurements. ENERGIES 2022. [DOI: 10.3390/en15155555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The subject of this study was a distribution substation that feeds 14 fast DC chargers (80 kW) located at the bus depot in Lublin, Poland. The voltage variations were determined to be within the PN-EN 50160 standard limit values (±10% Un). There were several events registered when 4th, 6th, 8th, and 10th voltage harmonics were above the PN-EN 50160 limit during the charging of the electric buses. The obtained maximum 10 min average values of the total voltage harmonic distortion (THD) were 3.36%, 2.27%, and 2.89% for the first, second, and third phase, respectively, i.e., below the limit value of 8% required by PN-EN 50160. Due to the exceedance of the 6th voltage harmonic, the PN-EN 50160 requirements were not met.
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Prediction of Charging Demand of Electric City Buses of Helsinki, Finland by Random Forest. ENERGIES 2022. [DOI: 10.3390/en15103679] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Climate change, global warming, pollution, and energy crisis are the major growing concerns of this era, which have initiated the electrification of transport. The electrification of roadway transport has the potential to drastically reduce pollution and the growing demand for energy and to increase the load demand of the power grid, thereby giving a rise to technological and commercial challenges. Thus, charging load prediction is a crucial and demanding issue for maintaining the security and stability of power systems. During recent years, random forest has gained a lot of popularity as a powerful machine learning technique for classification as well as regression analysis. This work develops a random forest (RF)-based approach for predicting charging demand. The proposed method is validated for the prediction of public e-bus charging demand in the city of Helsinki, Finland. The simulation results demonstrate the effectiveness of our scheme.
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Technology Development and Spatial Diffusion of Auxiliary Power Sources in Trolleybuses in European Countries. ENERGIES 2021. [DOI: 10.3390/en14113040] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Trolleybus transport is one of the classic means of public transport in cities. Its popularity varied in the past and was largely related to the fuel market situation. As fuel prices fell, electricity-powered transport lost popularity. The situation was similar during fuel crises. Trolleybuses gained in popularity then. Nowadays, the development of alternative power sources (APS) technology makes trolleybus transport partially independent of the overhead contact system, which is its great advantage. It is thus possible to develop trolleybus connections in areas where there is no justification for building overhead wiring infrastructure. The article analyses the development of on-board APS and their spatial diffusion in trolleybus systems in Europe. The main result of the research procedure indicates that the development of battery technologies, which could accelerate the closure of trolleybus transport due to the strong competition of electric buses not requiring an overhead contact line, allows for the dynamic development of this branch of transport. The situation in 71 trolleybus systems in Central and Western Europe which had any experience in the use of APS in 2011–2021 was examined. As a result of the analysis, the dynamics of APS diffusion were determined, in particular, a significant increase in the number of trolleybus systems using on-board batteries from 7 in 2011 to 44 in 2021.
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