ALEX enables community-level coordination of distributed energy resources through rational agents, improving grid stability and efficiency.
ALEX enables community-level coordination of distributed energy resources through rational agents, improving grid stability and efficiency.
Large populations of energy consumers strategically adjust bids in day-ahead markets to maximize welfare, leading to congestion management challenges.
The author presents a new market simulator for the Brazilian power system, highlighting the effectiveness of contracts in preventing market power abuse and the impact of different contracting levels on spot prices and revenues.
The author employs deep reinforcement learning to optimize bidding strategies for energy storage systems in multiple electricity markets, enhancing revenue potential and grid stability.
The author develops competitive algorithms for energy trading in volatile markets, incorporating machine-learned predictions to achieve a Pareto-optimal trade-off between consistency and robustness.