Abstract

A de-centralised load management technique exploiting the flexibility in the charging of Electric Vehicles (EVs) is presented. Two charging regimes are assumed. The Controlled Charging Regime (CCR) between 16:30 hours and 06:00 hours of the next day and the Uncontrolled Charging Regime (UCR) between 06:00 hours and 16:30 hours of the same day. During the CCR, the charging of EVs is coordinated and controlled by means of a wireless two-way communication link between EV Smart Charge Controllers (EVSCCs) at EV owners’ premises and the EV Load Controller (EVLC) at the local LV distribution substation. The EVLC sorts the EVs batteries in ascending order of their states of charge (SoC) and sends command signals for charging to as many EVs as the transformer could allow at that interval based on the condition of the transformer as analysed by the Distribution Transformer Monitor (DTM). A real and typical urban LV area distribution network in Great Britain (GB) is used as the case study. The technique is applied on the LV area when its transformer is carrying the future load demand of the area on a typical winter weekday in the year 2050. To achieve the load management, load demand of the LV area network is decomposed into Non-EV load and EV load. The load on the transformer is managed by varying the EV load in an optimisation objective function which maximises the capacity utilisation of the transformer subject to operational constraints and non-disruption of daily trips of EV owners. Results show that with the proposed load management technique, LV distribution networks could accommodate high uptake of EVs without compromising the useful normal life expectancy of distribution transformers before the need for capacity reinforcement.

Highlights

  • The uptake of low carbon technologies (LCTs), Electric Vehicles (EVs) and Heat Pumps (HPs), at low voltage (LV) distribution networks, in the quest of cutting down on greenhouse gas (GHG) emissions in the transportation and residential sectors, has the potential to cause general load increase and may lead to higher and longer peak load demand [1] [2]

  • A de-centralised load management technique exploiting the flexibility in the charging of EVs was presented

  • During the Controlled Charging Regime, it was assumed the charging of EVs is coordinated and controlled by means of a wireless two-way communication link between EV Smart Charge Controllers at EV owners’ premises and the EV Load Controller at the local LV substation

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Summary

Introduction

The uptake of low carbon technologies (LCTs), Electric Vehicles (EVs) and Heat Pumps (HPs), at low voltage (LV) distribution networks, in the quest of cutting down on greenhouse gas (GHG) emissions in the transportation and residential sectors, has the potential to cause general load increase and may lead to higher and longer peak load demand [1] [2]. A de-centralised load management technique, which proffers solution to the issue of distribution transformer overloading in LV area distribution networks hosting considerable number of EVs and HPs, is presented. The proposed de-centralised load management technique exploits the flexibility in the charging of EVs, by taking advantage of long periods of EVs parking, to coordinate and control their charging without disrupting normal daily trips of EV owners or violating operational limits of the network. In the proposed load management technique, two charging regimes are introduced: 1) Controlled Charging Regime (CCR)—This is between 16:30 hours and 06:00 hours of the day Within this period, the charging of EVs is coordinated and controlled by means of a wireless two-way communication link between EV Smart Charge Controllers (EVSCCs) at EV owners’ premises and the EV Load Controller (EVLC) at the local LV distribution substation. The proposed load management technique requires no travel plan information from EV owners It avoids bi-directional operation of EV batteries.

Daily Commuting Patterns and EVs Use
The Architecture of the De-Centralised Load Management Technique
The Design Formulation
Case Study
Implementation
Results and Discussion
Conclusions
Full Text
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