Towards ‘Smarter’ Systems: Key Cyber-Physical Performance-Cost Tradeoffs in Smart Electric Vehicle Charging with Distributed Generation

The growing penetration of electric vehicles (EV) into the market is driving sharper spikes in consumer power demand. Meanwhile, growing renewable distributed generation (DG) is driving sharper spikes in localised power supply. This leads to growing temporally unsynchronised spikes in generation and consumption, which manifest as localised over- or undervoltage and disrupt grid service quality. Smart Grid solutions can respond to voltage conditions by curtailing charging EVs or available DG through a network of cyber-enabled sensors and actuators. How to optimise efficiency, ensure stable operation, deliver required performance outputs and minimally overhaul existing hardware remains an open research topic. This thesis models key performance-cost tradeoffs relating to Smart EV Charging with DG, including architectural design challenges in the underpinning Information and Communications Technology (ICT). Crucial deployment optimisation balancing various Key Performance Indicators (KPI) is achieved. The contributions are as follows: • Two Smart EV Charging schemes are designed for secondary voltage control in the distribution network. One is optimised for the network operator, the other for consumers/generators. This is used to evaluate resulting performance implications via targeted case study. • To support these schemes, a multi-tier hierarchical distributed ICT architecture is designed that alleviates computation and traffic load from the central controller and achieves user fairness in the network. In this way it is scalable and adaptable to a wide range of network sizes. • Both schemes are modelled under practical latency constraints to derive interlocking effects on various KPIs. Multiple latency-mitigation strategies are designed in each case. • KPIs, including voltage control, peak shaving, user inconvenience, renewable energy input, CO2 emissions and EV & DG capacity are evaluated statistically under 172 days of power readings. This is used to establish key performancecost tradeoffs relevant to multiple invested bodies in the power grid. • Finally, the ICT architecture is modelled for growing network sizes. Quality-of- Service (QoS) provision is studied for various multi-tier hierarchical topologies under increasing number of end devices to gauge performance-cost tradeoffs related to demand-response latency and network deployment