The integration of Electric Vehicles (EVs) into low-voltage (LV) residential distribution networks inevitably increases the overall demand, especially peak demand, which may cause thermal or voltage issues. In this paper, a 400V practical residential distribution network is modelled and used to quantify these impacts due to the growing penetration of EVs. Residential load profiles in 1-minute resolution and EV charging profiles with recorded State of Charge (SOC) are randomly and statistically created. Then, a simple charging management algorithm with locally made decision is suggested at EV users' charging points. Results prove that this approach can mitigate the negative impacts of EV charging on network assets. Moreover, it can reduce EV users' electricity cost for charging based on existing UK electricity price scheme 'Economy 7,' without compromising EV usage or substantial network infrastructure reinforcement or installation of extensive monitor, control and communication system. The simulation models and analysis are implemented in MATLAB/OpenDSS as an LV distribution network simulation platform.
|Title of host publication||IEEE PES APPEEC 2016 - 2016 IEEE PES Asia Pacific Power and Energy Engineering Conference|
|Number of pages||5|
|Publication status||Published - 9 Dec 2016|
|Event||2016 IEEE PES Asia Pacific Power and Energy Engineering Conference, APPEEC 2016 - Xi'an, China|
Duration: 25 Oct 2016 → 28 Oct 2016
|Conference||2016 IEEE PES Asia Pacific Power and Energy Engineering Conference, APPEEC 2016|
|Period||25/10/16 → 28/10/16|
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- charging management
- electric vehicles
- high resolution load profile
- low-voltage distribution networks
- multi-objective optimization