Measurements of network impact from electric vehicles during slow and fast charging

Seljeseth, Helge; Taxt, Henning; Solvang, Tarjei

doi:10.1049/cp.2013.1197

Cited by 13 publications

(10 citation statements)

References 1 publication

(1 reference statement)

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“…During charging, a BMS will vary power depending on the SOC (Seljeseth et al, 2013;Kapoor, 2012). Typically, there is a constant current (CC) and a constant voltage (CV) phase, pictured conceptually in Fig.…”

Section: Battery Charging and State Of Chargementioning

confidence: 99%

Charging Electric Vehicles in the Smart Grid

Develder

Strobbe

Craemer³

2016

Smart Grids From a Global Perspective

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High level challenges that motivate the evolution towards smart grids include (i) the anticipated electrification of transportation, including electrical vehicles (EVs), and (ii) the increasing penetration of distributed renewable energy sources (DRES). This chapter will discuss how the extra grid load stemming from the EVs can be handled, including the context of reduced control over power generation in light of DRES adoption (especially solar and wind power). After a basic introduction to common EV charging technology, we give two illustrative examples of controlling EV charging: avoiding peaks, and balancing against renewable generation. We then qualitatively present possible demand response (DR) strategies to realize such control. Finally, we highlight the need for, and underlying principles of, (smart grid) simulation tools, e.g., to study the effectiveness of such DR mechanisms.

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Section: Battery Charging and State Of Chargementioning

confidence: 99%

Charging Electric Vehicles in the Smart Grid

Develder

Strobbe

Craemer³

2016

Smart Grids From a Global Perspective

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“…When fast charging occurs, it is assumed that the battery will be fast charged up to 80% SOC. The fast charging power profile FC ( ), as illustrated in Figure 3, is based upon measurements that are discussed in [55].…”

Section: Ev Charging Loadmentioning

confidence: 99%

MV and LV Residential Grid Impact of Combined Slow and Fast Charging of Electric Vehicles

et al. 2015

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This article investigates the combined low voltage (LV) and medium voltage (MV) residential grid impact for slow and fast electric vehicle (EV) charging, for an increasing local penetration rate and for different residential slow charging strategies. A realistic case study for a Flemish urban distribution grid is used, for which three residential slow charging strategies are modeled: uncoordinated charging, residential off-peak charging, and EV-based peak shaving. For each slow charging strategy, the EV hosting capacity is determined, with and without the possibility of fast charging, while keeping the grid within its operating limits. The results show that the distribution grid impact is much less sensitive to the presence of fast charging compared to the slow charging strategy. EV-based peak shaving results in the lowest grid impact, allowing for the highest EV hosting capacity. Residential off-peak charging has the highest grid impact, due the load synchronization effect that occurs, resulting in the lowest EV hosting capacity. Therefore, the EV users should be incentivized to charge their EVs in a more grid-friendly OPEN ACCESSEnergies 2015, 8 1761 manner when the local EV penetration rate becomes significant, as this increases the EV hosting capacity much more than the presence of fast charging decreases it.

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“…• Harmonics: Caused by the power electronics inside converters such as found inside vehicle chargers or photovoltaic (PV) inverters. Harmonics will not be looked into here, but the use of power electronics such as found inside PEV chargers can create problematic harmonics [20].…”

Section: Grid Congestion Metricsmentioning

confidence: 99%

“…20 EN 50160 voltage magnitude stats for the multiaggregator balancing scenarios; a V < 0.9 pu, b V < 0.85 pu and c VUF > 2 %…”

mentioning

confidence: 99%

Integration of Distribution Grid Constraints in an Event-Driven Control Strategy for Plug-in Electric Vehicles in a Multi-Aggregator Setting

Craemer

Vandael

Claessens

2014

Plug in Electric Vehicles in Smart Grids

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In literature, several mechanisms are proposed to prevent Plug-in Electric Vehicles (PEVs) from overloading the distribution grid [1]. However, it is unclear how such technical mechanisms influence the market level control strategies of a PEV aggregator. Moreover, the presence of multiple aggregators in the same distribution grid further complicates the problem. Often, grid congestion management mechanisms are proposed to solve the potential interference between the technical and market objectives. Such methods come at the expense of additional complexity and costs, which is not beneficial for the large scale application of demand response. In our work, we investigate this problem by combining a simple low level voltage droop controller with an event driven control strategy for the coordination of charging PEVs. The approach is evaluated in different distribution grid settings, using two different market objectives for the aggregator.

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Measurements of network impact from electric vehicles during slow and fast charging

Cited by 13 publications

References 1 publication

Charging Electric Vehicles in the Smart Grid

Charging Electric Vehicles in the Smart Grid

MV and LV Residential Grid Impact of Combined Slow and Fast Charging of Electric Vehicles

Integration of Distribution Grid Constraints in an Event-Driven Control Strategy for Plug-in Electric Vehicles in a Multi-Aggregator Setting

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