Multi-agent Deep Reinforcement Learning for Zero Energy Communities

A Zero Energy Building (ZEB) has its net energy usage over a period of one year as zero, i.e., its energy use is not larger than its overall renewables generation. A collection of such ZEBs forms a Zero Energy Community (ZEC). This paper addresses the problem of energy sharing in such a community. This is different from previously addressed energy sharing between buildings as our focus is on the improvement of community energy status, while traditionally research focused on reducing losses due to transmission and storage, or achieving economic gains. We model this problem in a multi-agent environment and propose a Deep Reinforcement Learning (DRL) based solution. Results indicate that with time buildings learn to collaborate and learn a policy comparable to the optimal policy, which in turn improves the ZEC's energy status. Buildings with no renewables preferred to request energy from their neighbours rather than from the supply grid.