Liana Cipcigan

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An agent-based control system that coordinates the battery charging of electric vehicles in distribution networks is presented. The objective of the control system is to charge the electric vehicles at times of low electricity prices within distribution network technical constraints. Search techniques and neural networks are used for the decision making of(More)
This paper presents a new method to mitigate inrush currents caused by transformer energization. The method uses a grounding resistor connected at the transformer neutral and delayed energization of each phase of the transformer. The proposed method was tested by PSCAD/EMTDC simulation and laboratory experiments. Both simulation and experimental result show(More)
A model-based approach is described to forecast triad periods for commercial buildings, using a multi-staged analysis that takes a number of different data sources into account, with each stage adding more accuracy to the model. In the first stage, a stochastic model is developed to calculate the probability of having a “triad” on a daily and half-hourly(More)
Many efforts are recently being dedicated to developing models that seek to provide insights into the techno-economic benefits of battery storage coupled to photovoltaic (PV) generation system. However, not all models consider the operation of the PV – battery storage system with a feed-in tariff (FiT) incentive, different electricity rates and battery(More)
Accurate information regarding the uncertainty of short-term forecast for aggregate wind power is a key to efficient and cost effective integration of wind farms into power systems. This paper presents a methodology for producing wind power forecast scenarios. Using historical wind power time series data and the Kernel Density Estimator (KDE), probabilistic(More)
With an increasing interest in Electric Vehicles (EVs), it is essential to understand how EV charging could impact demand on the Electricity Grid. Existing approaches used to achieve this make use of a centralised data collection mechanism - which often is agnostic of demand variation in a given geographical area. We present an in-transit data processing(More)
With an increase in the number of monitoring sensors deployed on physical infrastructures, there is a corresponding increase in data volumes that need to be processed. Data measured or collected by sensors is typically processed at destination or "in-transit" (i.e. from data capture to delivery to a user). When such data are processed in-transit over a(More)
Recent advances in the type and variety of sensing technologies have led to an extraordinary growth in the volume of data being produced, and led to a number of streaming applications that make use of this data. Sensors typically monitor environmental or physical phenomenon at pre-defined time intervals or triggered by user defined events. Understanding how(More)