This work package will forecast and intelligently manage demand side utility grid behaviour, leading to a significant reduction in peak loads, primary energy consumption and improved grid operation. A new characterisation methodology, enabling communication between each demand side instance (e.g. building, wastewater treatment plant) or aggregated entity (e.g. campus of buildings) and inter-connected utility networks will be developed, conveying quantitative data, including gas, water, electricity, heating and cooling metrics, energy footprint and CO2 emissions (DOE, 2013). New metrics, including building capacitance, temperature ramping rates, thermal lag and energy savings from harvested rainwater (Warren, 2014), will be defined, integrating end use entities with utility grids (Mancarella, 2013), enabling cohesive integration between commercial, residential (Li and Hong 2013 ; Gans et. al, 2013) and industrial demand side domains (O’Donnell, 2013). Frequency of data transfer is balanced against system needs, with statistical methods used to represent system behaviour. An analytical and modelling infrastructure will be created, leveraging new metrics, to communicate the demand side state in real time. Forecast models will optimise performance at the overall system level, enabling flexibility through virtual storage and demand response (Mahmoudi et. al., 2014). New ancillary services should reduce management workload associated with multiple demand side participants. Multi-utility consolidation can surpass current demand response and load shifting solutions that primarily focus on mitigating peak electrical loads.
|Conference||A framework to assess the interoperability of commercial buildings at a district scale
2018; Building Simulation and Optimization BSO 2018, United Kingdom (excluding Northern Ireland); Shamsi, M.H., Ali, U., Alshehri, F. and O'Donnell, J.
|Conference||GIS-Based Residential Building Energy Modeling at the District Scale
2018; Building Simulation and Optimization BSO 2018, United Kingdom (excluding Northern Ireland); Ali, U., Shamsi, M.H., Hoare, C. and O'Donnell, J.
|Journal||Quantitative evaluation of deep retrofitted social housing using metered gas data
2018; Energy and Buildings; Beagon, P., Boland, F. and O'Donnell, J.
|Conference||Operational characterisation of neighbourhood heat energy after large-scale building retrofit
2018; The 9th International Cold Climate Conference, Sweden; Beagon, P., Boland, L. and O'Donnell, J.
|Journal||Identifying Stakeholders and Key Performance Indicators for District and Building Energy Performance Analysis
2017; Energy and Buildings; Yehong, L., O'Donnell, J., Garcia-Castro, R. and Vega-Sanchez, S.
|Conference||A generalization approach for reduced order modelling of commercial buildings
2017; CISBAT 2017 - International Conference Future Buildings & Districts Energy Efficiency from Nano to Urban Scale, Switzerland; Shamsi, M.H., O'Grady, W., Ali, U. and O'Donnell, J.
|Conference||Control strategies for building energy systems to unlock demand side flexibility - A review
2017; IBPSA Building Simulation 2017, United States of America; Claus, J., Finck, C., Vogler-Finck, P. and Beagon, P.
|Conference||Next Generation Building and District Metrics to Enable Energy Systems Integration
2016; CLIMA 2016: 12th REHVA World Congress, Denmark; Beagon, P., Warren, J., Finn, D. and O'Donnell, J.