| Title |
Regional deep energy Q-Net-based energy scheduling for industrial energy management system |
| ID_Doc |
40934 |
| Authors |
Gayathri, N; Krishnakumar, C |
| Title |
Regional deep energy Q-Net-based energy scheduling for industrial energy management system |
| Year |
2024 |
| Published |
Electrical Engineering, 106, 1 |
| DOI |
10.1007/s00202-023-02037-5 |
| Abstract |
Intelligent load scheduling for the smart city is essential due to the growth of industrialization, which directly consumes energy from the grid. The energy management system in industrialization is enhanced using the Internet of things in the same domain. The general need in a smart city is to maintain the energy utilization and cost that the industrial Internet of things could achieve for industry application. Researchers have also found a solution to this problem, but every time the generator cannot sell energy, the industry purchases the same. There is a chance for the industry to supply its energy to other industries or the grid. By keeping this constraint, an intelligent approach to load scheduling based on optimized Q-Net is proposed in this paper, comprising the supplies as renewable energy, battery, and industrial energy. Also, the reactive power flow of smart cities during scheduling is mitigated by classifying the energy available in the bus network. The proposed method scheduled demand power with 99% accuracy with the lowest cost of 13.7041$/KW for peak hr. scheduling with a demand of 6000KW/hr. |
| Author Keywords |
Industrial Internet of things; Q-learning; Optimization; Smart city; Prosumer and consumer; Green energy management |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:001072268800001 |
| WoS Category |
Engineering, Electrical & Electronic |
| Research Area |
Engineering |
| PDF |
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