Optimal charging strategies of electric vehicles in the UK power market

Acha, Salvador; Green, Tim; Shah, Nilay

doi:10.1109/isgt.2011.5759128

Cited by 55 publications

(35 citation statements)

References 18 publications

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“…In this work, it is assumed that EVs will not provide grid balancing. The driving and charging profile used to model hourly EV demands is based on a modified DSM profile given in [22], and depicted in Figure 8 The electricity system is considered as a single node, as such transmission and distribution losses are accounted for by a simple increase of 7%, regardless of local generation levels but assuming a small increased efficiency of the electrical network by 2050. These losses are applied to the total electrical demands.…”

Section: Demand Sidementioning

confidence: 99%

“…The solar thermal water heating demands are modelled first, such that heat pumps follow a reduced net heating demand. Electric vehicle (EV) charging profiles [22] are utilised to derive the scenario specific EV hourly demands. Finally the resistive heating demands are calculated utilising current Economy 7 water and space heating profiles.…”

Section: Demand Side Overviewmentioning

confidence: 99%

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The implications of heat electrification on national electrical supply-demand balance under published 2050 energy scenarios

Quiggin

Buswell

2016

Energy

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Citation: QUIGGIN, D. and BUSWELL, R.A., 2016 AbstractPublished UK 2050 energy scenarios specify a range of decarbonised supply side technologies combined with electrification of transportation and heating. These scenarios are designed to meet CO 2 reduction targets whilst maintaining reliability of supply. Current models of the UK energy system either make significant assumptions about the role of demand side management or do not carry out the analysis at sufficient resolution and hence determining the impact of heat electrification on the reliability of supply of the scenarios is not possible. This paper presents a new model that estimates national supply and demand, hour-by-hour. Calculations are based on 11 years of weather data which allows a probabilistic assessment of deficit frequency throughout the day. It is found that achieving demand reduction targets are far more important than meeting electrification targets and that significant adoption of CHP is most likely to deliver a viable energy future for the UK.

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Section: Demand Sidementioning

confidence: 99%

Section: Demand Side Overviewmentioning

confidence: 99%

The implications of heat electrification on national electrical supply-demand balance under published 2050 energy scenarios

Quiggin

Buswell

2016

Energy

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“…The three scenarios consider CO 2 emission variations during the day based on how the fuel mix is composed. This data serves to incentivise when EVs are charged once a cost to carbon is allocated, similar to the work presented in [12]. In all scenarios the same number of EVs is considered and the static as well as mobile demand is identical, however price signals are the only variables that change in each scenario.…”

Section: ) Case Study Scenariosmentioning

confidence: 84%

Modelling spatial and temporal agent travel patterns for optimal charging of electric vehicles in low carbon networks

Acha

Dam

Shah

2012

2012 IEEE Power and Energy Society General Meeting

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Abstract--The ability to determine optimal charging profiles of electric vehicles (EVs) is paramount in developing an efficient and reliable smart-grid. However, so far the level of analysis proposed to address this issue lacks combined spatial and temporal elements, thus making mobility a key challenge to address for a proper representation of this problem. This paper details the principles applied to represent optimal charging of EVs by employing an agent-based model that simulates the travelling patterns of vehicles on a road network. The output data is used as a reliable forecast so an optimal power flow model can devise optimal charging scenarios of EVs in a local electrical network. The effectiveness of the model is illustrated by presenting a multi-day case study in an urban area. Results show a high level of detail and variability in EV charging when a present-day carbon fuel mix is compared to one with lower carbon intensity.Index Terms-Agent based transport modelling, coordination of distributed energy resources, distribution network operation, electric vehicle charging, local smart-grids, optimal power flow.

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“…Ideal LV network smart-charging strategy solutions will be casespecific, but nevertheless generic studies show that EVs furthest away to substations will create the most losses; thus it is particularly here where DNOs need to enhance their monitoring and control capabilities [11]. Likewise, a coordinated charging approach reduces power losses and improves the load factor of the network -this can be done by aiming to reduce thermal loading of the cables [10] and/or by obeying flexible tariffs that closely reflect the spot prices of energy [12]. Nevertheless, it is important to note that smart charging is not without costs [13].…”

Section: Modelling the Impact Of Electric Vehicles On The Electricitymentioning

confidence: 99%

Spatial and temporal electric vehicle demand forecasting in central London

Acha¹,

Dam²,

Shah³

2013

22nd International Conference and Exhibition on Electricity Distribution (CIRED 2013)

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Optimal charging strategies of electric vehicles in the UK power market

Cited by 55 publications

References 18 publications

The implications of heat electrification on national electrical supply-demand balance under published 2050 energy scenarios

The implications of heat electrification on national electrical supply-demand balance under published 2050 energy scenarios

Modelling spatial and temporal agent travel patterns for optimal charging of electric vehicles in low carbon networks

Spatial and temporal electric vehicle demand forecasting in central London

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