2 research outputs found
Continuous Multiagent Control using Collective Behavior Entropy for Large-Scale Home Energy Management
With the increasing popularity of electric vehicles, distributed energy
generation and storage facilities in smart grid systems, an efficient
Demand-Side Management (DSM) is urgent for energy savings and peak loads
reduction. Traditional DSM works focusing on optimizing the energy activities
for a single household can not scale up to large-scale home energy management
problems. Multi-agent Deep Reinforcement Learning (MA-DRL) shows a potential
way to solve the problem of scalability, where modern homes interact together
to reduce energy consumers consumption while striking a balance between energy
cost and peak loads reduction. However, it is difficult to solve such an
environment with the non-stationarity, and existing MA-DRL approaches cannot
effectively give incentives for expected group behavior. In this paper, we
propose a collective MA-DRL algorithm with continuous action space to provide
fine-grained control on a large scale microgrid. To mitigate the
non-stationarity of the microgrid environment, a novel predictive model is
proposed to measure the collective market behavior. Besides, a collective
behavior entropy is introduced to reduce the high peak loads incurred by the
collective behaviors of all householders in the smart grid. Empirical results
show that our approach significantly outperforms the state-of-the-art methods
regarding power cost reduction and daily peak loads optimization