12 citations · 19 across the 9 of their papers we have counts for
4 papers · 1 filter
Exploring market power using deep reinforcement learning for intelligent bidding strategies
Alexander J. M. Kell, Matthew Forshaw, A. Stephen McGough
Decentralized electricity markets are often dominated by a small set of generator companies who control the majority of the capacity. In this paper, we explore the effect of the to…
Deep Reinforcement Learning in Electricity Generation Investment for the Minimization of Long-Term Carbon Emissions and Electricity Costs
Alexander J. M. Kell, Pablo Salas, Jean-Francois Mercure +2
A change from a high-carbon emitting electricity power system to one based on renewables would aid in the mitigation of climate change. Decarbonization of the electricity grid woul…
Optimizing carbon tax for decentralized electricity markets using an agent-based model
Alexander J. M. Kell, A. Stephen McGough, Matthew Forshaw
Averting the effects of anthropogenic climate change requires a transition from fossil fuels to low-carbon technology. A way to achieve this is to decarbonize the electricity grid.…
Long-term electricity market agent based model validation using genetic algorithm based optimization
Alexander J. M. Kell, Matthew Forshaw, A. Stephen McGough
Electricity market modelling is often used by governments, industry and agencies to explore the development of scenarios over differing timeframes. For example, how would the reduc…