# Modelling the transition to a low-carbon energy supply

arXiv: https://arxiv.org/abs/2111.00987 · HTML: https://mlsanity.com/paper/2111.00987/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 257 references that are in the map.
- First posted on arXiv 25 Sep 2021.

## Builds on (most cited first, 6 of 6)

- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [An Integrated Optimization + Learning Approach to Optimal Dynamic Pricing for the Retailer with Multi-type Customers in Smart Grids](https://mlsanity.com/paper/1612.05971/) (2016)
- [Long-term electricity market agent based model validation using genetic algorithm based optimization](https://mlsanity.com/paper/2005.10346/) (2020)
- [Optimising energy and overhead for large parameter space simulations](https://mlsanity.com/paper/1910.02516/) (2019)
- [Optimizing carbon tax for decentralized electricity markets using an agent-based model](https://mlsanity.com/paper/2006.01601/) (2020)
- [The impact of online machine-learning methods on long-term investment decisions and generator utilization in electricity markets](https://mlsanity.com/paper/2103.04327/) (2021)

## Abstract (arXiv)

A 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 can not 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, however, 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. 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. The objective of this thesis is to create a novel, open-source agent-based model to better understand the manner in which the whole electricity market reacts to different factors using state-of-the-art machine learning and artificial intelligence methods. In contrast to other works, this thesis looks at both the long-term and short-term impact that different behaviours have on the electricity market by using these state-of-the-art methods.