All Outputs (8)

The Role of Electric Vehicle Charging Technologies in the Decarbonisation of the Energy Grid (2022)
Journal Article
Waldron, J., Rodrigues, L., Gillott, M., Naylor, S., & Shipman, R. (2022). The Role of Electric Vehicle Charging Technologies in the Decarbonisation of the Energy Grid. Energies, 15(7), Article 2447. https://doi.org/10.3390/en15072447

Vehicle-to-grid (V2G) has been identified as a key technology to help reduce carbon emissions from the transport and energy sectors. However, the benefits of this technology are best achieved when multiple variables are considered in the process of c... Read More about The Role of Electric Vehicle Charging Technologies in the Decarbonisation of the Energy Grid.

Cost, context, or convenience? Exploring the social acceptance of demand response in the United Kingdom (2021)
Journal Article
Naghiyev, E., Shipman, R., Goulden, M., Gillott, M., & Spence, A. (2022). Cost, context, or convenience? Exploring the social acceptance of demand response in the United Kingdom. Energy Research and Social Science, 87, Article 102469. https://doi.org/10.1016/j.erss.2021.102469

The energy sector, and buildings in particular, are one of the main contributors to climate change. Demand-Side Management (DSM) has the potential to realise energy savings on the demand as well as the supply side. However, the domestic sector still... Read More about Cost, context, or convenience? Exploring the social acceptance of demand response in the United Kingdom.

Online machine learning of available capacity for vehicle-to-grid services during the coronavirus pandemic (2021)
Journal Article
Shipman, R., Roberts, R., Waldron, J., Rimmer, C., Rodrigues, L., & Gillott, M. (2021). Online machine learning of available capacity for vehicle-to-grid services during the coronavirus pandemic. Energies, 14(21), Article 7176. https://doi.org/10.3390/en14217176

Vehicle-to-grid services make use of the aggregated capacity available from a fleet of vehicles to participate in energy markets, help integrate renewable energy in the grid and balance energy use. In this paper, the critical components of such a ser... Read More about Online machine learning of available capacity for vehicle-to-grid services during the coronavirus pandemic.

We got the power: Predicting available capacity for vehicle-to-grid services using a deep recurrent neural network (2021)
Journal Article
Shipman, R., Roberts, R., Waldron, J., Naylor, S., Pinchin, J., Rodrigues, L., & Gillott, M. (2021). We got the power: Predicting available capacity for vehicle-to-grid services using a deep recurrent neural network. Energy, 221, Article 119813. https://doi.org/10.1016/j.energy.2021.119813

© 2021 Elsevier Ltd Vehicle-to-grid (V2G) services utilise a population of electric vehicle batteries to provide the aggregated capacity required to participate in power and energy markets. Such participation relies on the prediction of available cap... Read More about We got the power: Predicting available capacity for vehicle-to-grid services using a deep recurrent neural network.

User engagement in community energy schemes: A case study at the Trent Basin in Nottingham, UK (2020)
Journal Article
Rodrigues, L., Gillott, M., Waldron, J., Cameron, L., Tubelo, R., Shipman, R., …Bradshaw-Smith, C. (2020). User engagement in community energy schemes: A case study at the Trent Basin in Nottingham, UK. Sustainable Cities and Society, 61, Article 102187. https://doi.org/10.1016/j.scs.2020.102187

© 2020 Elsevier Ltd ‘Community Energy’ refers to people working together to reduce and manage energy use and increase and support local energy generation. It has the potential to support the infrastructural, social and cultural changes needed to redu... Read More about User engagement in community energy schemes: A case study at the Trent Basin in Nottingham, UK.

Where Will You Park? Predicting Vehicle Locations for Vehicle-to-Grid (2020)
Journal Article
Shipman, R., Waldron, J., Naylor, S., Pinchin, J., Rodrigues, L., & Gillott, M. (2020). Where Will You Park? Predicting Vehicle Locations for Vehicle-to-Grid. Energies, 13(8), https://doi.org/10.3390/en13081933

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Vehicle‐to‐... Read More about Where Will You Park? Predicting Vehicle Locations for Vehicle-to-Grid.

Learning capacity: predicting user decisions for vehicle-to-grid services (2019)
Journal Article
Shipman, R., Naylor, S., Pinchin, J., Gough, R., & Gillott, M. (2019). Learning capacity: predicting user decisions for vehicle-to-grid services. Energy Informatics, 2(1), Article 37. https://doi.org/10.1186/s42162-019-0102-2

The electric vehicles (EV) market is projected to continue its rapid growth, which will profoundly impact the demand on the electricity network requiring costly network reinforcements unless EV charging is properly managed. However, as well as import... Read More about Learning capacity: predicting user decisions for vehicle-to-grid services.

SCENe things: IoT-based monitoring of a community energy scheme (2019)
Journal Article
Shipman, R., & Gillott, M. (2019). SCENe things: IoT-based monitoring of a community energy scheme. Future Cities and Environment, 5(1), https://doi.org/10.5334/fce.64

This paper describes a technology platform for monitoring homes within a community energy scheme. A range of sensors was deployed to measure in-home environmental conditions, occupancy, electrical power, electrical energy, thermal energy, heating beh... Read More about SCENe things: IoT-based monitoring of a community energy scheme.