Abstract
Growing penetration of Electric Vehicles (EV) and Distributed Generation (DG) is driving sharper peaks in demand and supply, which, if poorly managed, manifest as over- or undervoltage and disrupt grid service quality. Smart charging schemes reschedule EV charging load according to factors such as grid stability, price signals, etc. It remains unclear how to do this while meeting the diverging needs and expectations of multiple concerned participants. This paper proposes two smart charging schemes for secondary voltage control in the distribution network and analyses performance-cost tradeoffs relating to key players in the Smart Grid. To support these schemes, a distributed communications architecture is designed that jointly minimises traffic burden, computation load and investment in Information and Communications Technology (ICT) hardware. Scheme I (Smart Curtailment), curtails load and DG for peak shaving. Scheme II (Smart Correction) optimises cost-efficiency for subscribing users by maximising power transfer during off-peak hours or when renewable energy is high. Performance of both schemes is consolidated statistically under almost 6 months of practical input profiles. Dramatic improvements in EV & DG capacity are demonstrated and key performance-cost tradeoffs relating to Voltage Control, Peak Shaving, User Inconvenience, CO2 Emissions and ICT Deployment Cost are identified.
Highlights
The United Kingdom (UK) government plans for all cars sold to be purely electric by 2030 [1]
This paper explicitly models key performance‐cost tradeoffs relating to diverse expectations of all concerned participants
Expected load under 0% and with 40% Electric Vehicles (EV) is applied for zero Distributed Generation (DG), and voltage deviation at each bus derived using
Summary
The United Kingdom (UK) government plans for all cars sold to be purely electric by 2030 [1]. In this case, when smart charging demand reacts to the cheaper energy prices it can lead to a very high peak load in the network In this case, the operator desires peak shaving, while consumers/generators desire peak charging. The contributions of this paper are as follows: Two smart charging schemes are designed relevant to divergent design objectives of operator and consumer/ generator Both achieve secondary voltage control in the distribution network and simultaneous increase in EV & DG capacity. Practical operational latency constraints are analysed and modelled, and multiple latency‐mitigation strategies are identified for each smart charging scheme Performance of both schemes is consolidated statistically for 172 days of 1s wind power input. This demonstrates the huge potential for smart charging to reduce carbon emissions alongside the peak load
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