A HEN-PPO strategy for home energy management system with reduce EV anxieties

Abstract

This article proposes a novel method for scheduling residential building loads, including thermal and EV (Electric Vehicle) loads, to maximize demand response potential in smart home environment. In this approach, a load scheduling solution based on deep reinforcement learning (DRL) has been proposed. A policy search method based on Home Energy Management with Noise-Adaptive Proximal Policy Optimization (HEN-PPO) has been used to train the network. HEN-PPO can mitigate the influence of noise on temperature and solar radiation sensor data. The modification of the reward function for the system has been taken into account to address the distinct human behavior of randomly distributed cases of EV anxiety about range and time in conjunction with the optimal utilization of photovoltaic (PV) generation. This approach effectively manages both discrete and continuous activities of electrical loads at the same time, categorized as shiftable and regulatable, throughout the entire building model. Additionally, to replicate human behavior, real switching data from different household appliances has been used to determine the parameters for the probability distribution of residential load usage. Dynamic and unpredictable factors such as user behavior, ambient conditions, and real-time electricity pricing challenge scheduling residential building loads which are efficiently managed in this approach. To validate the effectiveness of the proposed method, simulation tests were carried out using real-time data distribution of household loads and surrounding temperature. The simulation results demonstrate promising energy savings, achieving 24%–29% reductions with the proposed approach.

Keywords

• Home appliances
• Home automation
• Energy management
• Load management
• Home network
• Schedules