Ezzeldin Ayman Ibrahim Ismail

A Reinforcement Learning-Assisted ADMM Framework for Peer-to-Peer Kilowatt and Negawatt Trading with Cloud Energy Storage

Rooftop solar lets households generate their own electricity, but the benefits are unevenly shared and home batteries are expensive. This thesis develops a system that lets neighbours trade electricity, and reductions in their energy use, directly with one another, while sharing a common pool of storage instead of buying individual batteries. To coordinate many homes at once, it uses artificial intelligence to run the calculations far faster while keeping each household's data private. On a model network, it lowers grid reliance, eases strain on power lines, and reduces bills. This makes local clean-energy sharing cheaper, fairer, and faster.