期刊
SENSORS
卷 20, 期 7, 页码 -出版社
MDPI
DOI: 10.3390/s20072157
关键词
home energy management; deep reinforcement learning; smart home appliance; energy storage system; electric vehicle
资金
- National Research Foundation of Korea (NRF) Grant through the Korea Government (MSIP) [2018R1C1B6000965]
- Competency Development Program for Industry Specialists of the Korean Ministry of Trade, Industry and Energy (MOTIE) [P0002397]
- National Research Foundation of Korea [2018R1C1B6000965] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
This paper presents a hierarchical deep reinforcement learning (DRL) method for the scheduling of energy consumptions of smart home appliances and distributed energy resources (DERs) including an energy storage system (ESS) and an electric vehicle (EV). Compared to Q-learning algorithms based on a discrete action space, the novelty of the proposed approach is that the energy consumptions of home appliances and DERs are scheduled in a continuous action space using an actor-critic-based DRL method. To this end, a two-level DRL framework is proposed where home appliances are scheduled at the first level according to the consumer's preferred appliance scheduling and comfort level, while the charging and discharging schedules of ESS and EV are calculated at the second level using the optimal solution from the first level along with the consumer environmental characteristics. A simulation study is performed in a single home with an air conditioner, a washing machine, a rooftop solar photovoltaic system, an ESS, and an EV under a time-of-use pricing. Numerical examples under different weather conditions, weekday/weekend, and driving patterns of the EV confirm the effectiveness of the proposed approach in terms of total cost of electricity, state of energy of the ESS and EV, and consumer preference.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据