A Reinforcement-Learning-Based Secure Demand Response Scheme for Smart Grid System
| Autor: | Aparna Kumari, Sudeep Tanwar |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Computer Networks and Communications
Energy management Computer science Distributed computing Data security Energy consumption Computer Science Applications Demand response Smart grid Hardware and Architecture Signal Processing Scalability Reinforcement learning Markov decision process Information Systems |
| Zdroj: | IEEE Internet of Things Journal. 9:2180-2191 |
| ISSN: | 2372-2541 |
| DOI: | 10.1109/jiot.2021.3090305 |
| Popis: | Smart Grid (SG) systems necessitate secure Demand Response Management (DRM) schemes for real-time decisions making to increase the effectiveness and stability of SG systems along with data security. Motivated from the aforementioned discussion, in this paper, we propose Q-SDRM, a secure DRM scheme for Home Energy Management (HEM) using Reinforcement Learning (RL) and Ethereum Blockchain (EBC) to facilitate energy consumption reduction and decrease energy costs. In cooperation with RL, Q-learning is adopted to make optimal price decisions using Markov Decision Process (MDP) to reduce energy consumption, which benefits both consumers and utility providers. Then, Q-SDRM uses Ethereum Smart-Contract (ESC) to deal with data security issues and incorporate with off-chain storage InterPlanetary File System (IPFS) that handles data storage costs issue. Experimental results reveal the effectiveness of the proposed Q-SDRM scheme, which significantly reduces energy consumption and energy cost. The proposed scheme also provides secure access to energy data in real-time compared with state-of-the-art approaches regarding different evaluation metrics such as scalability, overall energy cost, and data storage cost. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|