A highly efficient control framework for centralized residential charging coordination of large electric vehicle populations

Yi, Zonggen; Scoffield, Don; Smart, John; Meintz, Andrew; Murai, Jun; Mohanpurkar, Manish; Medam, Anudeep

doi:10.1016/j.ijepes.2019.105661

Cited by 69 publications

(31 citation statements)

References 35 publications

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“…With regard to EV charging, however, two simple charging strategies, uncoordinated and off-peak charging, are considered only. A similar approach is pursued by the main focus on smoothing the peaks of the load of a multitude of EVs [16,17]. An interesting approach to estimate the reduction of emissions by smart charging of EVs is reported in [18], in which a centrally managed charging strategy is compared to a distributed charging strategy with a focus on the California electric grid.…”

Section: Related Work On Smart Charging Of Electric Vehiclesmentioning

confidence: 99%

Discussing the Actual Impact of Optimizing Cost and GHG Emission Minimal Charging of Electric Vehicles in Distributed Energy Systems

Schulz

Hufendiek

2021

Energies

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Electric vehicles represent a promising opportunity to achieve greenhouse gas (GHG) reduction targets in the transport sector. Integrating them comprehensively into the energy system requires smart control strategies for the charging processes. In this paper we concentrate on charging processes at the end users home. From the perspective of an end user, optimizing of charging electric vehicles might strive for different targets: cost minimization of power purchase for the individual household or—as proposed more often recently—minimization of GHG emissions. These targets are sometimes competing and cannot generally be achieved at the same time as the results show. In this paper, we present approaches of considering these targets by controlling charging processes at the end users home. We investigate the influence of differently designed optimizing charging strategies for this purpose, considering the electrical purchase cost as well as the GHG emissions and compare them with the conventional uncontrolled charging strategy using the example of a representative household of a single family. Therefore, we assumed a detailed trip profile of such a household equipped with a local generation and storage system at the same time. We implemented the mentioned strategies and compare the results concerning effects on annual GHG emissions and annual energy purchase costs of the household. Regarding GHG emissions we apply a recently proposed approach by other authors based on hourly emission factors. We discuss the effectivity of this approach and derive, that there is hardly no real impact on actual GHG emissions in the overall system. As incorporating this GHG target into the objective function increases cost, we appraise such theoretical GHG target therefore counterproductive. In conclusion, we would thus like to appeal for dynamic electricity prices for decentralised energy systems, leading at the same time to cost efficient charging of electric vehicles unfolding clear incentives for end users, which is GHG friendly at the end.

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Section: Related Work On Smart Charging Of Electric Vehiclesmentioning

confidence: 99%

Discussing the Actual Impact of Optimizing Cost and GHG Emission Minimal Charging of Electric Vehicles in Distributed Energy Systems

Schulz

Hufendiek

2021

Energies

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“…Other than that, the better coordination between generation and EV charging also produce optimal power flow in the distribution system [8]. Also, research at [9] validated dynamic charging based on a two-stage hierarchical decision-making process which able to manage thousands of CS in realtime. Jian L et al [11] proposed a valley filling strategy scheme to coordinate CS operation.…”

Section: Introductionmentioning

confidence: 99%

“…There are two common approaches for EV charging coordination which are decentralised and centralised [7][8][9][10][11][12][13]. Decentralised approaches usually do not require robust communication infrastructure for data exchange between customer and aggregator compared to the centralised approach.…”

Section: Introductionmentioning

confidence: 99%

Enhancing power loss by optimal coordinated extensive CS operation during off-peak load at the distribution system

et al. 2021

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Minimise dependency of energy from depleted non-renewable had pushed the usage of electric vehicle (EV). However, the presence of charging station (CS) may cause another impact such as higher power loss, especially involving uncoordinated CS. The impact becomes vital when the numbers of CS to charge the EV increased dramatically. From research, CS at residential usually operated during off-peak load. Furthermore, the variation of the charging pattern that difficult to perceive had added severe condition. Thus, the exploration of the mitigation method is necessary to avoid the stress at the existing distribution network. This paper suggests a coordinated method based on the power loss forecast throughout the charging time. The method will prioritise the buses based on power loss impact on the network, which later to determine the suitable numbers of CS operation. The approach considers customer satisfaction to charge the EV at a specific duration fully. Thus, to present the effectiveness of the approach, the analysis conducted using a suitable distribution system with residential block. The results show a positive outcome in enhancing distribution power loss without interrupt customer satisfaction. The method is suitable to deal with many CS that operates simultaneously during off-peak load.

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“…It has to be kept in mind that such an LM or restrictions on charging power can also be implemented top-down from the control station across the charging points to guarantee distribution grid performance. On the other hand, Yan et al [21] found that traffic jam and weather forecasting can enhance the operation of LM.…”

Section: Introductionmentioning

confidence: 99%

Efficient Load Management for BEV Charging Infrastructure in Multi-Apartment Buildings

2020

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Interest in and demand for battery electric vehicles (BEVs) is growing strongly due to the increasing awareness of climate change and specific decarbonization goals. One of the largest challenges remains the provision of large-scale, efficient charging infrastructure in multi-apartment buildings. Successful load management (LM) for BEV charging directly influences the technical requirements and the economic and environmental aspects of charging infrastructure and can prevent costly distribution grid expansion. The main objective of this paper is to evaluate potential LM approaches in multi-apartment buildings to avoid an increase in existing electricity demand peaks with BEV diffusion. Using our model parameters, off-peak charging achieved a 40% reduction in the building’s demand peak at 100% BEV diffusion compared to uncontrolled charging and reduced the correlation between BEV charging and the national share of thermal power generation. The most efficient charging capacity in the private network was achieved at 0.44 kW/BEV. A verification of the model results with the demonstration phase of the “Urcharge” project supports our overall findings. Our results outline the advantages of LM across a large-scale BEV charging network to control the impact on the electricity system along with the diffusion of e-mobility.

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A highly efficient control framework for centralized residential charging coordination of large electric vehicle populations

Cited by 69 publications

References 35 publications

Discussing the Actual Impact of Optimizing Cost and GHG Emission Minimal Charging of Electric Vehicles in Distributed Energy Systems

Discussing the Actual Impact of Optimizing Cost and GHG Emission Minimal Charging of Electric Vehicles in Distributed Energy Systems

Enhancing power loss by optimal coordinated extensive CS operation during off-peak load at the distribution system

Efficient Load Management for BEV Charging Infrastructure in Multi-Apartment Buildings

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