Methodical approach for analyzing the impact of a mass introduction of electric vehicles on the electricity networks in Europe

Helmschrott, Thomas; Gödde, Markus; Szczechowicz, Eva; Matrose, Claas; Schnettler, Armin

doi:10.1109/peci.2012.6184612

Cited by 5 publications

(4 citation statements)

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“…Figure 1 shows the results of the calculations for 200 iterations. A detailed analysis of the influence of iterations on the results can be found in [2]. Moreover, it shows the loading of one line of the grid at one instant of time: The load profiles of the smart meter data and the load profiles of the vehicles have been allocated to the busses at one instant of time.…”

Section: B Description Of the Probabilistic Resultsmentioning

confidence: 99%

“…With the "EV flexibility aggregation" strategy representing a charging strategy from the Advanced World, the number of overloaded secondary substations is also reduced: As it can be seen from the following figure, the "EV flexibility aggregation" strategy shifts the load from noon and evening to the early morning hours. The main results of all variations and an analysis are available in [2]. Loa d by EVs is reduced, if necessary (4) TOU ta ri ffs with real ti me adjustments (6) IV.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…A general description of the load flow calculation tool and the statistical interpretation of the results are given in this section followed by a description of the probabilistic approach. A detailed description of the grids and algorithms is presented in [2]. Grid data representing all different assets has been converted to MATPOWER® format, which has been used for flow calculations [3].…”

Section: Approach Of the Load Flow Calculationmentioning

confidence: 99%

“…o At home (1) o At home and at work (2) o Everywhere (3) o At home and at work, but at work outside the considered grid.…”

Section: A Analysed Parametersmentioning

confidence: 99%

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Approach and main results of the G4V project analyzing the impact of a mass introduction of electric vehicles on electricity networks in Europe

Gödde

Szczechowicz

Helmschrott

et al. 2012

2012 IEEE Transportation Electrification Conference and Expo (ITEC)

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The Grid 4 Vehicles project (G4V) has analyzed the effects of a mass integration of electric and plug-in hybrid electric vehicles (EV and PHEV) into some distribution grids in Europe. A consortium consisting of six European research institutions and six European utilities has developed a methodology to investigate the effects of EV and PHEV in distribution grids and has proposed different options for intelligent and "grid-friendly" EV charging. This paper introduces the algorithms used for grid calculation, describes intelligent charging methodologies and demonstrates the most significant results of these calculations.

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Section: B Description Of the Probabilistic Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

Section: Approach Of the Load Flow Calculationmentioning

confidence: 99%

“…o At home (1) o At home and at work (2) o Everywhere (3) o At home and at work, but at work outside the considered grid.…”

Section: A Analysed Parametersmentioning

confidence: 99%

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Approach and main results of the G4V project analyzing the impact of a mass introduction of electric vehicles on electricity networks in Europe

Gödde

Szczechowicz

Helmschrott

et al. 2012

2012 IEEE Transportation Electrification Conference and Expo (ITEC)

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Performance analysis of future PEA distribution networks under high penetration of PEV home charging using the Monte Carlo method

Klayklueng

Dechanupaprittha

2018

IEEJ Transactions Elec Engng

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This article presents the performance analysis of future Provincial Electricity Authority of Thailand (PEA) distribution networks under high penetration of plug‐in electric vehicle (PEV) home charging using the Monte Carlo method. Network performance indices considered in this study are the voltage profile, power losses, load factor, and voltage unbalance. The voltage profile and power losses are evaluated based on the PEA criteria. In addition, the voltage unbalance factors (%VUF) are evaluated according to the IEC 61000‐2‐4: 2000‐06 standards. A selected PEA distribution network based on the existing data is used for the simulation study with the DIgSILENT PowerFactory. The penetration of PEV home charging at each node and phase is assigned using the Monte Carlo method, which in particular is used to randomly generate patterns of PEV charging behaviors. The simulation study presents five cases; the average points of charge, max‐to‐min charge, min‐to‐max charge, central feeder charge, and random point of charge. All cases are compared with a base case (without PEVs). Charging scenarios at phases ‘a’, ‘b’, and ‘c’ are assumed having overlapped time duration. The PEV charging duration per phase is assumed 6 h. Simulation results indicate that PEV charging time, charging point, and penetration levels at each phase and node are significant factors impacting the network performance. Voltage profiles slightly drop in all cases, meanwhile the average voltage drop increases by 7% compared with the base case; case 3 has the maximum voltage drop, which is deeper than PEA criteria. The power losses increase in all cases. The average power losses have increased by 103.52%. The %VUF increases gradually with the length of the feeder. The maximum %VUF occurs at the end of the feeder for all cases and is higher than 2.0% of the IEC standard. On the other hand, PEV charging improves the load factor. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Impacts of Plug-in Electric Vehicles Integration in Distribution Networks Under Different Charging Strategies

Soares

Barbeiro

Gouveia

et al. 2014

Plug in Electric Vehicles in Smart Grids

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The uncertainties related to when and where Plug-in Electric Vehicles (PEVs) will charge in the future requires the development of stochastic based approaches to identify the corresponding load scenarios. Such tools can be used to enhance existing system operators planning techniques, allowing them to obtain additional knowledge on the impacts of a new type of load, so far unknown or negligible to the power systems, the PEVs battery charging. This chapter presents a tool developed to evaluate the steady state impacts of integrating PEVs in distribution networks. It incorporates several PEV models, allowing estimating their charging impacts in a given network, during a predefined period, when different charging strategies are adopted (non-controlled charging, multiple tariff policies and controlled charging). It uses a stochastic model to simulate PEVs movement in a geographic region and a Monte Carlo method to create different scenarios of PEVs charging. It allows calculating the maximum number of PEVs that can be safely integrated in a given network and the changes provoked by PEVs in the load diagrams, voltage profiles, lines loading and energy losses. Additionally, the tool can also be used to quantify the critical mass (percentage) of PEV owners that need to adhere to controlled charging schemes in order to enable the safe operation of distribution networks.

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Methodical approach for analyzing the impact of a mass introduction of electric vehicles on the electricity networks in Europe

Cited by 5 publications

References 1 publication

Approach and main results of the G4V project analyzing the impact of a mass introduction of electric vehicles on electricity networks in Europe

Approach and main results of the G4V project analyzing the impact of a mass introduction of electric vehicles on electricity networks in Europe

Performance analysis of future PEA distribution networks under high penetration of PEV home charging using the Monte Carlo method

Impacts of Plug-in Electric Vehicles Integration in Distribution Networks Under Different Charging Strategies

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