Upside
Lead Research Organisation:
University of Manchester
Department Name: Mathematics
Abstract
Our research will have three parts. First we will perform state of the art statistical modelling of CO2 intensity in UK
electricity generation. This will provide powerful statistical representations of daily variation in the use of CO2 intensive
fuels (principally coal and gas), taking into account both seasonal and within-day fluctuations.
Second, we will develop a method to generate daily carbon intensity profile predictions (for both average level and
statistical variation).
Third, we will produce a control algorithm to generate UPS recharging schedules optimised for the lowest carbon intensity.
This will provide day-ahead schedules which aim to minimise the CO2 produced in recharging the UPS fleet, by taking into
account the abovementioned statistical models.
The above statistical models and generated control policies will also be published by Upside as part of their open source
approach, adding to the rich real-time UK grid data available to the public.
electricity generation. This will provide powerful statistical representations of daily variation in the use of CO2 intensive
fuels (principally coal and gas), taking into account both seasonal and within-day fluctuations.
Second, we will develop a method to generate daily carbon intensity profile predictions (for both average level and
statistical variation).
Third, we will produce a control algorithm to generate UPS recharging schedules optimised for the lowest carbon intensity.
This will provide day-ahead schedules which aim to minimise the CO2 produced in recharging the UPS fleet, by taking into
account the abovementioned statistical models.
The above statistical models and generated control policies will also be published by Upside as part of their open source
approach, adding to the rich real-time UK grid data available to the public.
Planned Impact
The main impacts from Upside's cloud service, once it has been developed using the algorithms described above and has
achieved significant commercial scale, will be
1. Reduction of UK CO2 emissions through the use of stored low-carbon electricity to provide fast response to National
Grid, displacing traditional carbon-intensive and inefficient fast response generation.
2. Reduction in the amount of installed generation required in the UK to provide operating reserve, as devices such as UPS
are dual-purposed to provide this service without the need for significant capital expenditure on dedicated generation
infrastructure. This will reduce pressure on electricity customers' bills.
achieved significant commercial scale, will be
1. Reduction of UK CO2 emissions through the use of stored low-carbon electricity to provide fast response to National
Grid, displacing traditional carbon-intensive and inefficient fast response generation.
2. Reduction in the amount of installed generation required in the UK to provide operating reserve, as devices such as UPS
are dual-purposed to provide this service without the need for significant capital expenditure on dedicated generation
infrastructure. This will reduce pressure on electricity customers' bills.
Organisations
| Description | We have developed a statistical model for the online prediction of the level of CO2 generated by UK electricity production. Further we have developed an accompanying online algorithm for CO2 prediction. When combined with an appropriate database of fuel consumption by type, the developed algorithm provides online predictions of CO2 emissions from electricity generation. This information may be used to schedule electricity demand in order to minimising the resulting CO2 emissions. |
| Exploitation Route | This EPSRC grant is linked to an Innovate UK grant led by Upside Ltd. Its outcomes will be used directly by Upside Ltd. Further, with the third party company Future Decisions Ltd we will explore the possibility of making our online CO2 predictions available freely for non-commercial use. |
| Sectors | Energy,Environment |
| Description | The online CO2 prediction algorithm developed in this research project has been coupled with an online database of both current and historic fuel use by type for UK electricity generation. This database work was undertaken by a third party company, Future Decisions Ltd. It allows the developed algorithms to be used for online CO2 prediction. |
| First Year Of Impact | 2016 |
| Sector | Energy |
| Impact Types | Economic |