EPSRC Centre for Doctoral Training Cloud Computing for Big Data


Alexander Kell

PhD title

Modelling the transition to a low-carbon electricity mix

My research will look at how the transition to a low-carbon electricity supply is crucial to limit the impacts of climate change. Reducing carbon emissions could help prevent the world from reaching a tipping point, where runaway emissions are likely. Runaway emissions could lead to extremes in weather conditions around the world - especially in problematic regions unable to cope with these conditions.

However, the movement to a low-carbon energy supply cannot happen instantaneously due to the existing fossil-fuel infrastructure and the requirement to maintain a reliable energy supply. Therefore, a low-carbon transition is required. Though, the decisions various stakeholders should make over the coming decades to reduce these carbon emissions are not obvious. This is due to many long-term uncertainties, such as electricity, fuel and generation costs, human behaviour and the size of electricity demand. In addition, the electricity generators invested in by generation companies are controlled by many heterogeneous actors in many markets around the world. These markets are known as decentralised electricity markets. Decentralised electricity markets stand in contrast to centralised control markets, where a central actor, such as a government, invest and control the market. A well-choreographed low-carbon transition is, therefore, required between all of the heterogenous actors in the system, as opposed to changing the behaviour of a single, centralised actor.

To account for these long-term uncertainties in decentralised electricity markets, energy modelling can be used to aid stakeholders to better understand the energy system. This allows for decisions to be made with more information. Energy models enable a quantitative analysis of how an electricity system may develop over the long term, and often use scenario analysis to investigate different decisions stakeholders could make. Simulations are powerful tools which can be used to generate insight, and are based upon the complexity of these models. Simulations are computer programs which have been designed to mimic a real-life system, to allow users to gain a better understanding of said system.

In my thesis, a novel agent-based simulation model, ElecSim, is created and used. ElecSim adopts an agent-based approach to simulation where each generation company within the system is modelled with its behaviour. This allows for fine-grained control and modelling of these generation companies. Thus allowing ElecSim to be used to investigate the following significant challenges in moving towards a low-carbon future:


1. Predictions must be made to predict electricity demand at various time intervals in the future. We modelled the impact of poor predictions on generator investments and utilisation over the long-term.

2. Devising a carbon tax can be challenging due to multiple competing objectives, and the inability for an iterative learning approach. In this work, we used ElecSim to model multiple different carbon tax policies using a genetic algorithm.

3. Many decentralised electricity markets have become oligopolies, where a few generation companies own a majority of the electricity supply. In this thesis, we used reinforcement learning and ElecSim to find ways to ensure healthy competition.

This requires a number of core challenges to be addressed to ensure ElecSim is fit for purpose. These are:

1. Development of the ElecSim model, where the replication of the pertinent features of the electricity market was required. For example, generation company investment behaviour, electricity market design and temporal granularity.

2. The complexity of a model increases with the replication of increasing market features. Therefore, optimisation of the code was required to maintain computational tractability, to allow for multiple scenario runs.

3. Once the model has been developed, its long-term behaviour must be verified to ensure accuracy. In this work, cross-validation was used to validate ElecSim.

4. To ensure that the salient parameters are found, a sensitivity analysis was run. In addition, various example scenarios were generated to show the behaviour of the model.

5. Predicting short-term electricity demand is a core challenge for electricity markets. This is so that electricity supply can be matched with demand. In this work, various methodologies were used to predict demand 30 minutes and a day ahead.


Matthew Forshaw & Stephen McGough


Segmenting Residential Smart Meter Data for Short-Term Load Forecasting - Kell, A. Forshaw, M. McGough, S. - e-Energy '18 Proceedings of the Ninth International Conference on Future Energy Systems - 2018

ElecSim: Stochastic Open-Source Agent-Based Model to Inform Policy for Long-Term Electricity Planning - Kell, A. Forshaw, M. McGough, S. - e-Energy '19 Proceedings of the Tenth International Conference on Future Energy Systems - 2019

Optimising energy and overhead for large parameter space simulations - Kell, A. Forshaw, M. McGough, S. - IGSC 2019 - The Tenth International Green and Sustainable Computing Conference - 2019

Modelling Carbon Tax in the UK Electricity Market using an Agent-Based Model - Kell, A. Forshaw, M. McGough, S. - e-Energy '19 Proceedings of the Tenth International Conference on Future Energy Systems - 2019