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Machine learning applications for electricity market agent-based models: A systematic literature review
However, the electricity market is made up of many different variables and data inputs.
Optimizing a domestic battery and solar photovoltaic system with deep reinforcement learning
A lowering in the cost of batteries and solar PV systems has led to a high uptake of solar battery home systems.
The impact of online machine-learning methods on long-term investment decisions and generator utilization in electricity markets
Through the prediction of electricity demand profile over the next 24h, we can simulate the predictions made for a day-ahead market.
Exploring market power using deep reinforcement learning for intelligent bidding strategies
If any single generator company, or a collaborating group of generator companies, control more than ${\sim}$11$\%$ of generation capacity and bid strategically, prices begin to increase by ${\sim}$25$\%$.
Deep Reinforcement Learning in Electricity Generation Investment for the Minimization of Long-Term Carbon Emissions and Electricity Costs
A change from a high-carbon emitting electricity power system to one based on renewables would aid in the mitigation of climate change.
Optimizing carbon tax for decentralized electricity markets using an agent-based model
A way to achieve this is to decarbonize the electricity grid.
Optimising energy and overhead for large parameter space simulations
A Pareto frontier can be used to identify the sets of optimal parameters for which each is the `best' for a given combination of objectives -- thus allowing decisions to be made with full knowledge.
