Multi-agent Deep Reinforcement Learning for Zero Energy Communities

TL;DR

This paper proposes a multi-agent deep reinforcement learning approach to optimize energy sharing in zero energy communities, enabling buildings to learn collaborative energy management strategies that improve overall community energy balance.

Contribution

It introduces a novel multi-agent DRL framework for community energy sharing, focusing on improving collective energy status rather than individual building efficiency.

Findings

Agents learn to collaborate over time.

Energy sharing policies approach optimal solutions.

Buildings without renewables prefer neighbor requests.

Abstract

Advances in renewable energy generation and introduction of the government targets to improve energy efficiency gave rise to a concept of a Zero Energy Building (ZEB). A ZEB is a building whose net energy usage over a year is zero, i.e., its energy use is not larger than its overall renewables generation. A collection of 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. Each building is represented by an intelligent agent that learns over time the appropriate behaviour to share energy. We have evaluated the proposed solution in a multi-agent simulation built using osBrain. Results indicate that with time agents 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.