Abstract
Electric vehicles (EVs) have been proposed previously to be a form of flexible electrical load (including potentially vehicle to grid generation) that could be controlled to help support distribution networks. Considering each vehicle individually poses many challenges including significant smart grid control system computational effort and uncertainty. This study proposes an aggregation and control methodology for the grid to consider a number of EVs in a similar way to more established energy storage systems (ESS) allowing existing ESS control algorithms to be utilised. Central to the methodology is the knowledge that flexibility will only be realised if drivers are willing to use utility controlled charging posts and as such the drivers' requirements are prioritised; a minimum amount of energy is guaranteed to be within each vehicle at the time of departure.
Highlights
The UK has a target of reducing carbon emissions to 80% of 1990 levels by 2050 [1]
utility controlled charging (UCC) was implemented, in the ‘My Electric Avenue’ trial [17], where a binary on-off decision is taken to ensure that voltage and thermal limits are not exceeded on the feeders studied
To reduce the occurrence of this situation, when the remaining required aggregate power is charging power, it is averaged between only the Electric vehicles (EVs) that are presently below the state of charge (SOC) required at departure
Summary
The UK has a target of reducing carbon emissions to 80% of 1990 levels by 2050 [1]. EVs in 2013 had a UK new-car market share of 0.34% [3] and will only reduce carbon emissions with consumer adoption which relies on sufficient charging infrastructure and managing its impact on the electrical distribution network. If EVs are considered as inflexible loads, the network impacts as EV popularity grows have been studied by [4]. This paper takes the approach of establishing the flexibility that can be reliably called upon from EVs in aggregate, to form a virtual energy storage system (VESS), as an input to wider microgrid control systems
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