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Research Highlights from Our Team

Reinforcement Learning for Electricity Network Operation

Author: Aidan O’Sullivan

March 2020

This paper presents the background material required for the Learning to Run Power Networks Challenge. The challenge is focused on using Reinforcement Learning to train an agent to manage the real-time operations of a power grid, balancing power flows and making interventions to maintain stability.

Cost and carbon reductions from industrial demand-side management: Study of potential savings at a cement plant

Author: Daniel Summerbell

March 2017

Demand-side management (DSM) has the potential to reduce electricity costs and the carbon emissions associated with electricity use for industrial consumers. It also has an important role to play in integrating variable forms of generation, such as wind and solar, into the grid. This will be a key part of any grid decarbonisation strategy. This paper describes a method that can be used to develop a new production schedule for a wide range of manufacturing facilities.

Potential reduction of carbon emissions by performance improvement: A cement industry case study

Author: Daniel Summerbell

June 2016

This paper analyses a case study of a plant in the UK, operating a pre-calciner type kiln commissioned in 1986. Production data was analysed to examine the day-to-day variation in the fuel-derived CO2 emissions, in order to estimate the potential for operational improvement. Optimising the factors affecting performance was predicted to reduce energy consumption by 8.5% and CO2 emissions by 19.5%.

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