Two-Stage Charging Strategy for Plug-In Electric Vehicles at the Residential Transformer Level

Geng, Bo; Mills, James K.; Sun, Dong

doi:10.1109/tsg.2013.2246198

Cited by 82 publications

(34 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Optimized EV charging control based on cost minimization [27][28][29][30][31] and risk assessment [32] is developed to simultaneously realize cost minimization and respect network constraints. In addition, distribution market based EV charging coordination is developed in [33][34][35][36][37] to enable EV participation in multiple electricity markets.…”

Section: Recent Related Workmentioning

confidence: 99%

Multi-Time Scale Control of Demand Flexibility in Smart Distribution Networks

2017

View full text Add to dashboard Cite

This paper presents a multi-timescale control strategy to deploy electric vehicle (EV) demand flexibility for simultaneously providing power balancing, grid congestion management, and economic benefits to participating actors. First, an EV charging problem is investigated from consumer, aggregator, and distribution system operator's perspectives. A hierarchical control architecture (HCA) comprising scheduling, coordinative, and adaptive layers is then designed to realize their coordinative goal. This is realized by integrating multi-time scale controls that work from a day-ahead scheduling up to real-time adaptive control. The performance of the developed method is investigated with high EV penetration in a typical residential distribution grid. The simulation results demonstrate that HCA efficiently utilizes demand flexibility stemming from EVs to solve grid unbalancing and congestions with simultaneous maximization of economic benefits to the participating actors. This is ensured by enabling EV participation in day-ahead, balancing, and regulation markets. For the given network configuration and pricing structure, HCA ensures the EV owners to get paid up to five times the cost they were paying without control.

show abstract

Section: Recent Related Workmentioning

confidence: 99%

Multi-Time Scale Control of Demand Flexibility in Smart Distribution Networks

2017

View full text Add to dashboard Cite

show abstract

“…Electric vehicles (EVs) have been presented as a promising solution for reducing fossil fuel consumption and air pollution worldwide [1][2][3][4]. The market penetration of EVs has rapidly increased in populated cities in recent years.…”

Section: Introductionmentioning

confidence: 99%

Research on Power Demand Suppression Based on Charging Optimization and BESS Configuration for Fast-Charging Stations in Beijing

et al. 2018

View full text Add to dashboard Cite

Featured Application: The proposed strategy is aimed to solve the peak charging power demand issue of pure electric bus fast-charging station. It is effective for the charging power suppression of stations in the area with extremely limited distribution capacity.Abstract: In order to reduce the recharging time of electric vehicles, the charging power and voltage are becoming higher, which has led to a huge distribution capacity demand and load fluctuation, especially in pure electric buses (PEBs) with large onboard batteries. Based on one actual direct current (DC) fast-charging station, a two-step strategy for the suppression of the peak charging power was developed in this paper, which combined charging optimization and a battery energy storage system (BESS) configuration. A novel charging strategy was proposed, with the PEBs fast-charging during operating hours and normal charging at night, based on a new charging topology. Then, a charging sequence optimization model was established, according to the operation characteristics analysis of the DC fast-charging station. The particle swarm optimization (PSO) algorithm is applied to optimize the charging sequence, which is disordered at present. Linear programming is used to configure the battery energy storage system in order to further decrease the peak charging power and satisfy the distribution capacity constraint. The two-step strategy was simulated by the dataset from the real station. The results show that the distribution capacity demand, charging load fluctuation, electricity cost, and size of the BESS were significantly decreased.

show abstract

“…Generally, PEVs can be connected to grid using centralized or decentralized coordination approaches with the energy being transferred from grid to vehicle (G2V) [5][6][7][8][9][10][11][12][13][14][15] or from vehicle to grid (V2G) [2,[16][17][18][19]. Reference [9] presents an online PEV coordination algorithm based on maximum sensitivity selections to minimize cost of generating energy for vehicle charging, reduce grid losses and regulate network node voltages.…”

Section: Introductionmentioning

confidence: 99%

Combined Online and Delayed Coordinated Charging of Plug-In Electric Vehicles Considering Wind and Rooftop PV Generations

Masoum

Abu-Siada

Islam

2016

Technol Econ Smart Grids Sustain Energy

View full text Add to dashboard Cite

Emerging smart grid technology is creating unique opportunity for wide applications of renewable distributed generation resources, smart appliances and plugin electric vehicles (PEVs). This paper presents a combined online (real-time) and delayed (overnight and next day) PEV coordination scheme based on fuzzy reasoning and maximum sensitivity selections (MSS) considering wind and rooftop PV generations. The proposed combined online fuzzy-based and delayed MSS-based (OL-F/DL-MSS) algorithm aims at reducing energy generation cost by charging the electric vehicles during high renewable energy generation periods. To include vehicle owners' satisfaction, they are classified into three groups; high priority consumers requiring expensive PEV charging as soon as arriving home during early evening hours, low priority consumers requesting cheap overnight PEV charging and medium priority consumers requiring inexpensive PEV charging within 24 hours. PEV coordination is performed to minimize the cost of generating energy and regulate node voltages while utilizing the available renewable energy resources for vehicle battery charging. Detailed simulations for uncoordinated and combined online/overnight coordinated PEV charging are presented for a 449 node smart grid

show abstract

Two-Stage Charging Strategy for Plug-In Electric Vehicles at the Residential Transformer Level

Cited by 82 publications

References 33 publications

Multi-Time Scale Control of Demand Flexibility in Smart Distribution Networks

Multi-Time Scale Control of Demand Flexibility in Smart Distribution Networks

Research on Power Demand Suppression Based on Charging Optimization and BESS Configuration for Fast-Charging Stations in Beijing

Combined Online and Delayed Coordinated Charging of Plug-In Electric Vehicles Considering Wind and Rooftop PV Generations

Contact Info

Product

Resources

About