Smart appliances’ run schedule and electric vehicles charging can be managed over a smart grid enabled home area network (HAN) to reduce electricity demand at critical times and add more plug-in electric vehicles to the grid, which eventually lower customers’ energy bills and reduce greenhouse gas emissions. Short range radio-based wireless communication technologies commonly adopted in a HAN are vulnerable to cyber attacks due to their wide interception range. In this work, a low-cost solution is proposed for securing the low-volume data exchange of sensitive tasks (e.g., key management and mutual authentication). Our approach utilizes the emerging concept of retro-reflector based visible light communication (Retro-VLC), where smart appliances, IoT sensors and other electric devices perform the sensitive data exchange with the HAN gateway via the secure Retro-VLC channel. To conduct the feasibility study, a multi-pixel Retro-VLC link is prototyped to enable quadrature amplitude modulation. The bit error rate of Retro-VLC is studied analytically, numerically and experimentally. A heterogeneous Retro-VLC + WLAN connection is implemented by socket programming. In addition, the working range, sniffing range, and key exchange latency are measured. The results validate the applicability of the Retro-VLC based solution.
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A Deep Reinforcement Learning Based Approach for Home Energy Management System
Home energy management system (HEMS) enables residents to actively participate in demand response (DR) programs. It can autonomously optimize the electricity usage of home appliances to reduce the electricity cost based on time-varying electricity prices. However, due to the existence of randomness in the pricing process of the utility and resident's activities, developing an efficient HEMS is challenging. To address this issue, we propose a novel home energy management method for optimal scheduling of different kinds of home appliances based on deep reinforcement learning (DRL). Specifically, we formulate the home energy management problem as an MDP considering the randomness of real-time electricity prices and resident's activities. A DRL approach based on proximal policy optimization (PPO) is developed to determine the optimal DR scheduling strategy. The proposed approach does not need any information on the appliances' models and distribution knowledge of the randomness. Simulation results verify the effectiveness of our proposed approach.
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- Award ID(s):
- 1917275
- NSF-PAR ID:
- 10158893
- Date Published:
- Journal Name:
- IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT 2020)
- Page Range / eLocation ID:
- 1 to 5
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ISGT45199.2020.9087647
