In the classical electricity grid power demand is nearly instantaneously matched by power supply. In this paradigm, the changes in power demand in a low voltage distribution grid are essentially nothing but a disturbance that is compensated for by control at the generators. The disadvantage of this methodology is that it necessarily leads to a transmission and distribution network that must cater for peak demand. So-called smart meters and smart grid technologies provide an opportunity to change this paradigm by using demand side energy storage to moderate instantaneous power demand so as to facilitate the supply-demand match within network limitations. A receding horizon model predictive control method can be used to implement this idea. In this paradigm demand is matched with supply, such that the required customer energy needs are met but power demand is moderated, while ensuring that power flow in the grid is maintained within the safe operating region, and in particular peak demand is limited. This enables a much higher utilisation of the available grid infrastructure, as it reduces the peak-to-base demand ratio as compared to the classical control methodology of power supply following power demand. This paper investigates this approach for matching energy demand to generation in the last mile of the power grid while maintaining all network constraints through a number of case studies involving the charging of electric vehicles in a typical suburban low voltage distribution network in Melbourne, Australia.

As new technologies such as Photovoltaics (PVs), Embedded Generators (EGs) and Electric Vehicles (EVs) penetrate Low Voltage (LV) distribution networks, the need to understand the constraints and real time system state of LV networks becomes essential for smart grid development. This paper demonstrates that, given household Smart Meter demand data combined with 66kV/22kV zone substation SCADA voltage data, it is possible to model LV networks on a per-phase basis to a high degree of accuracy in near real time. Our model is based on a real suburb in urban northern Melbourne consisting of 113 customers, and is populated with real conductor properties, route lengths and customer phase allocations to create a true unbalanced three-phase, four-wire model. Time stepped load flow simulations of the model are compared with data loggers installed on the LV network for validation, and on average simulated results differ from real measurements by less than 0.5% for phase voltages and less than 10% for phase currents. Such accuracy allows for evaluation of PV, EV and EG impact mitigation strategies and planning in reference to the Electricity Distribution Code with a strong degree of confidence. This LV model is unique as it has been validated and is the only bottom-up load model that utilizes near real time Smart Meter and SCADA inputs to analyse impacts on distribution assets on a per phase, unbalanced basis.

The expected rise of electric vehicles will lead to significant additional demand on low voltage (LV) distribution systems. Uncontrolled charging could lead to problems such as thermal overload of transformers and lines, voltage deviation, harmonics, and phase unbalance. We propose two electric vehicle charging algorithms, one centralized and one distributed, and compare their performance in simulations that use real vehicle data, on a model based on a real LV network in northern Melbourne, Australia. Our experiments confirm that the locations of the vehicles in the network are an important factor in predicting adverse effects. Furthermore, our coordinated charging solutions allow penetrations of electric vehicles approximately 3-6 times higher than is possible using uncoordinated charging, in our network.