PhD & Masters Students

Fabiano Pallonetto

Demand response algorithms for Home Area Networks (HAN) utilising forecasted meteorological data and utility demand-supply profiles

Biography: Fabiano received a bachelor and master degree in Computer Science from University of Pisa. During his academic studies abroad he started to be interested in the energy field. His Master’s thesis title was “Energy efficiency optimization through home automation”. From 2007 to 2011, Fabiano worked as chief executive for Delta Energie. During the last years the company grew in the market of renewable energy installation focusing its business in solar panels and home automation.

Fabiano is funded by the programme for Research in Third-Level Institutions (PRTLI) Cycle 5 and co-funded under the European Regional Development Fund (ERDF).

Project: Electricity is different from other commodities. It can't be stored economically, and the supply of and demand for electricity must be balanced in real time. Mismatches in supply and demand can threaten grid integrity within seconds. Grid conditions can change from day to day or hour to hour especially with the introduction of Renewable Energy Sources in the electricity generation fuel mix. Demand levels also can change rapidly and unexpectedly. Increasing grid capacity to maintain reserve margins sufficient for demand is possible, but is not a good solution because the electric system is highly capital-intensive, and both generation and transmission system investments have long lead times. Whereas the cost of electric power varies on a short time scale, customers generally face retail electricity rates that are fixed for months or years at a time, representing the average costs of electricity production. This disconnect between costs of short-term marginal electricity production and fixed retail rates paid by consumers leads to an inefficient use of resources. By contrast, Demand Response (DR) generally induces demand shedding, shifting or limiting during times when the electric grid is near its capacity, or when electric wholesale prices are high. Under conditions of tight electricity supply, DR can significantly reduce peak price and, in general, electricity price volatility. Given that building energy demand accounts for up to a third of primary energy consumption globally, buildings will therefore be central to a scaling up of demand response to levels that significantly reduce costs, mitigate environmental impacts, increase reliability and balance intermittent resources of an electric grid that is changing quickly.


Local and Remote Estimations Using Fitted Polynomials in Distribution Systems

Conor Murphy, Andrew Keane,
Journal Paper IEEE Transactions on Power Systems published on 18/10/2016

Determining Optimal Smart Inverter Settings for Distributed Photovoltaic Systems

Alison O'Connell, Huijuan Li, Matthew Rylander, Jeff Smith,
Conference Paper Winnipeg, Canada published on 02/09/2015

A Self-Learning Energy Management System for a Smart-Grid-Ready Residential Building

Fabiano Pallonetto, Yerlan Turekeshov, Eleni Mangina and Donal Finn,
Conference Paper Athens, Greece, 24-26 August 2015 published on 23/08/2015

Using Energy Storage to Manage High Net Load Variability at Sub-Hourly Time-Scales

Ciara O'Dwyer, Damian Flynn,
Journal Paper IEEE TRANSACTIONS ON POWER SYSTEMS published on 01/07/2015

Exploring the Demand Response Potential of a Smart-Grid Ready House Using Building Simulation Software

Fabiano Pallonetto, Simos Oxizidis, Donal Finn,
Ottawa, Canada, 7-10 May 2014 published on 07/05/2014

High Resolution Space Time Data: Methodology for Residential Building Simulation Modelling

Olivier Neu, Simeon Oxizidis, Damian Flynn, Fabiano Pallonetto, and Donal Finn,
Conference Paper Chambéry, France, 25th Aug. 2013 published on 28/08/2013

Demand Response Optimisation of All-Electric Residential Buildings in a Dynamic Grid Environment: Irish Case Study

Fabiano Pallonetto, Simos Oxizidis, Roisin Duignan, Olivier Neu, Donal Finn,
Conference Paper Chambéry, 25-28 August 2013 published on 25/08/2013

A restful API to control a energy plus smart grid-ready residential building

Fabiano Pallonetto, Eleni Mangina, Donal Finn,
November 5-6, Memphis, USA published on