| EPSRC Reference: |
EP/M507167/2 |
| Title: |
Intelligent SME Energy Management and Trading with Ancillary Services |
| Principal Investigator: |
Rogers, Professor AC |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Computer Science |
| Organisation: |
University of Oxford |
| Scheme: |
Technology Programme |
| Starts: |
01 October 2015 |
Ends: |
31 October 2016 |
Value (£): |
103,058
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| EPSRC Research Topic Classifications: |
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| EPSRC Industrial Sector Classifications: |
| Energy |
Information Technologies |
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| Related Grants: |
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| Panel History: |
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Summary on Grant Application Form |
The core vision of this project is the development and pilot of a low cost, robust platform for intelligent building energy
usage that, combined with a new peer-to-peer energy market system, will facilitate localised energy trading and enable
participation of small to medium enterprises in National Grid balancing services.
The academic researchers, will work with commercial lead KiwiPower to:
(i) Develop algorithms that learn the thermal performance of the commercial building in which the building management
system is installed, enabling optimal control of the building's heating and air conditioning system during demand response
periods (where, for example, air conditioning use will be optimally curtailed to reduce peak loads without adversely affecting
the comfort of the building's occupants).
(ii) Design, develop and evaluate and effective local energy market in which autonomous trading agents representing both
individual buildings and also standalone generation and storage facilities can interact to optimally balance energy demand
against local generation and storage capacity.
The new platform and market will enable SMEs to control and monitor their production and consumption assets, to
automatically manage supply and demand at a localised level, and respond to national balancing requirements and
financial incentives to shift demand.
This project is innovative as:
(i) The novel low cost intelligent energy system, incorporating learning algorithms, overcomes existing cost barriers for
SMEs, enabling them to benefit from the latest energy optimisation algorithms to reduce costs and improve efficiency.
(ii) It will prove the technical and commercial viability of localised peer-to-peer energy markets and the ability for SMEs to
be involved in national electricity balancing services.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.ox.ac.uk |