Too much or not enough? Planning electric vehicle charging infrastructure: A review of modeling options

Metais, Marc-Olivier; Jouini, Oualid; Pérez, Yannick; Berrada, Jaâfar; Suomalainen, Emilia

doi:10.1016/j.rser.2021.111719

Cited by 175 publications

(67 citation statements)

References 66 publications

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“…c s vt ≤ 0 for all t except t = τ (1) v and t = τ (3) v , (21b) and no disconnection outside the final parking time interval…”

Section: B Drivers Flexibility Scenariosmentioning

confidence: 99%

“…Recharging EVs requires to develop an extended and pervasive charging infrastructure. Reference [5] estimates that, between 2019-2025, more than 2 billion Dollars will be necessary to improve the public and residential charging infrastructure across major U.S. metropolitan areas, whereas, in France, 2 billion Euros will be required to achieve the target of 7 million deployed public and private charger by 2030 [3], [6]. In addition to these investments, others will be necessary to adapt the electrical grid infrastructure, in particular distribution grids.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Planning of Single-Port and Multi-Port Charging Stations for Electric Vehicles in Medium Voltage Distribution Networks

Mukherjee¹,

Sossan²

2021

Preprint

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This paper describes a method based on mixedinteger linear programming to cost-optimally locate and size chargers for electric vehicles (EVs) in distribution grids as a function of the driving demand. The problem accounts for the notion of single-port chargers (SPCs), where a charger can interface one EV maximum, and multi-port chargers (MPCs), where the same charger can interface multiple EVs. The advantage of MPCs is twofold. First, multiple ports allow arbitraging the charging among multiple vehicles without requiring the drivers to plug and unplug EVs. Second, the charger's power electronics is not sized for the total number of charging ports, enabling cost savings when the grid constraints are bottleneck of the problem. The proposed method can account for different charger typologies, such as slow and fast chargers, and model the drivers' flexibility of plugging and unplugging their EVs. Simulation results from a synthetic case study show that implementing MPCs is beneficial over both SPCs and drivers' flexibility in terms of total investments required for the charging infrastructure.

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“…c s vt ≤ 0 for all t except t = τ (1) v and t = τ (3) v , (21b) and no disconnection outside the final parking time interval…”

Section: B Drivers Flexibility Scenariosmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Planning of Single-Port and Multi-Port Charging Stations for Electric Vehicles in Medium Voltage Distribution Networks

Mukherjee¹,

Sossan²

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…A graph representation of a street network is frequently used for the allocation of charging stations as it allows to reflect high spatial resolution in modeling and, simultaneously, the simplification of complex networks while not suffering from information loss [50,51]. Edges typically represent connections between nodes, and nodes represent junctions or ends of edges.…”

Section: Graph-based Charging Infrastructure Allocation Optimizationmentioning

confidence: 99%

“…Edges typically represent connections between nodes, and nodes represent junctions or ends of edges. Metais et al [50] differentiated between node-based and flowbased approaches. During node-based approaches, charging demand is assigned to nodes based on population density, and the goal is to allocate charging infrastructure in such a way that as much demand as possible is covered at the nodes.…”

Section: Graph-based Charging Infrastructure Allocation Optimizationmentioning

confidence: 99%

“…This approach requires origin-destination data describing the traffic load between all nodes in the network, which is not always available [52]. Metais et al [50] concluded in their review on graph-based allocation approaches that overall, both approaches, node-based and flow-based, offer benefits, while node-based approaches offer input data simplicity and the ability to regard the charging capacity of charging stations, flow-based approaches succeed in reflecting the nature of traffic flow between nodes illustrating origin or destinations in the network. Furthermore, the authors stated that the combination of both approaches may yield the most robust charging station allocation model.…”

Section: Graph-based Charging Infrastructure Allocation Optimizationmentioning

confidence: 99%

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Minimum-Cost Fast-Charging Infrastructure Planning for Electric Vehicles along the Austrian High-Level Road Network

2022

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Given the ongoing transformation of the transport sector toward electrification, expansion of the current charging infrastructure is essential to meet future charging demands. The lack of fast-charging infrastructure along highways and motorways is a particular obstacle for long-distance travel with battery electric vehicles (BEVs). In this context, we propose a charging infrastructure allocation model that allocates and sizes fast-charging stations along high-level road networks while minimizing the costs for infrastructure investment. The modeling framework is applied to the Austrian highway and motorway network, and the needed expansion of the current fast-charging infrastructure in place is modeled under different future scenarios for 2030. Within these, the share of BEVs in the car fleet, developments in BEV technology and road traffic load changing in the face of future modal shift effects are altered. In particular, we analyze the change in the requirements for fast-charging infrastructure in response to enhanced driving range and growing BEV fleets. The results indicate that improvements in the driving range of BEVs will have limited impact and hardly affect future costs of the expansion of the fast-charging infrastructure. On the contrary, the improvements in the charging power of BEVs have the potential to reduce future infrastructure costs.

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Operation and Control of EV Infrastructure for Microgrid

Viswanathan,

Gunapriya,

Mathankumar

et al. 2024

Microgrids for Commercial Systems

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Too much or not enough? Planning electric vehicle charging infrastructure: A review of modeling options

Cited by 175 publications

References 66 publications

Optimal Planning of Single-Port and Multi-Port Charging Stations for Electric Vehicles in Medium Voltage Distribution Networks

Optimal Planning of Single-Port and Multi-Port Charging Stations for Electric Vehicles in Medium Voltage Distribution Networks

Minimum-Cost Fast-Charging Infrastructure Planning for Electric Vehicles along the Austrian High-Level Road Network

Operation and Control of EV Infrastructure for Microgrid

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