Integrating Future Smart Home Operation Platform With Demand Side Management via Deep Reinforcement Learning
Tan Li, Yuanzhang Xiao, Linqi Song
Abstract
Residential demand side management (DSM) is a promising technique in smart grids to improve the power system robustness and to reduce the energy cost. However, the ongoing paradigm shift of computation, such as mobile edge computing for smart home, poses a big challenge to residential DSM. Therefore, it is important to schedule the new smart home computing tasks and traditional DSM in a smart way. In this paper, we investigate an integrated home energy management system (HEMS) that participates in a DSM program and implements smart home computation tasks by offloading tasks with the help of a Smart Home Operation Platform (SHOP). The goal of HEMS is to maximize the user's expected total reward, defined as the reward from completing computing tasks minus the cost of energy consumption, execution delay, running the SHOP servers, and the penalty of violating the DSM requirements. We solve this task scheduling based DSM problem using a deep reinforcement learning method. The DSM program considered in this paper requires the household to reduce a certain amount of energy consumption within a specified time window, which, in stark contrast to the well-studied real-time pricing, results in a long-term temporal interdependence and thus a high-dimensional state space in our formulated problem. To address this challenge, we use the Deep Deterministic Policy Gradient (DDPG) method to characterize the high-dimensional state space and action space, which uses deep neural networks to estimate the state and to generate the action. Experimental results show that our proposed method achieves better performance gains over reasonable baselines.