Muhammed Sait Aydin

The growing share of Electric Vehicles (EVs) in the personal automobile market is expected to accelerate in the years to come. With increased demand at a household level, technical problems such as transformer and feeder overloading are likely to emerge. Therefore, this highlights the need for a comprehensive impact analysis framework for EVs to overcome the challenges ahead. A smoother transition, exploiting scalable performance indicators, to Low Voltage (LV) networks with this new form of demand could be achieved as imminent problems can be computed in a realistic manner. To this end, the impact analysis framework is proposed and the corresponding performance indicators are formulated to be used by researchers and/or Distribution Network Operators (DNOs) for different purposes. Under different scenarios, the impacts of EVs on the real underground unbalanced three-phase network are comprehensively explored considering household voltage profiles, transformer-loading, utilization of feeders, and daily total energy losses. For the summer and winter seasons, three cases covering all possible circumstances are investigated: without EVs, with EVs, and a worst-case scenario where all EVs connect at the same time. From the study, it can be deduced that the impact of EVs on the network and household voltage could reach unacceptable levels, and diversifying the connection times of EVs is vital to coping with potential problems posed by residential-level participation in EVs.

The increasing Photovoltaic (PV) penetration in residential Low Voltage (LV) networks is likely to result in a voltage rise problem. One of the potential solutions to deal with this problem is to adopt a distribution transformer fitted with an On-Load Tap Changer (OLTC). The control of the OLTC in response to local measurements reduces the need for expensive communication channels and remote measuring devices. However, this requires developing an advanced decision-making algorithm to estimate the existence of voltage issues and define the best set point of the OLTC. This paper presents a decentralized data-driven control approach to operate the OLTC using local measurements at a distribution transformer (i.e., active power and voltage at the secondary side of the transformer). To do so, Monte Carlo simulations are utilized offline to produce a comprehensive dataset of power flows throughout the distribution transformer and customers’ voltages for different PV penetrations. By the application of the curve-fitting technique to the resulting dataset, models to estimate the maximum and the minimum customers’ voltages are defined and embedded into the control logic to manage the OLTC in real time. The application of the approach to a real UK LV feeder shows its effectiveness in improving PV hosting capacity without the need for remote monitoring elements.