| Title |
IoT driven framework based efficient green energy management in smart cities using multi-objective distributed dispatching algorithm |
| ID_Doc |
44033 |
| Authors |
Zhang, XY; Wang, DL; Zhang, YM; Manokaran, KB; Antony, AB |
| Title |
IoT driven framework based efficient green energy management in smart cities using multi-objective distributed dispatching algorithm |
| Year |
2021 |
| Published |
|
| DOI |
10.1016/j.eiar.2021.106567 |
| Abstract |
Nowadays, the concept of smart cities evolves from conceptual models to various stages of development. The smart services of cities like smart buildings, smart hospitals, smart traffic, smart factories, and smart transportation are vital to energizing resilient, reliable, seamless energy and electrical flow. One of these smart services' critical challenges is anticipated to run without disruptions using smart energy and smart electrical grids. Hence, in this research, the Internet of Things (IoT) role in green energy resources incorporation into smart electrical grids has been introduced using the Multi-Objective Distributed Dispatching algorithm (MODDA). Efficient energy management includes a tradeoff between the cost linked with utility function and energy consumption. The utility function can be the living comfort of the consumers or the utility firm's gross income. The interchanges between utility and energy consumption must, therefore, be recognized. The algorithm is intended to transmit the renewable green energy available to the battery, thermal, and load storing while preserving a thermal and battery storage limit. The experimental results at the lab-scale show the proposed dispatching algorithm minimizing the system cost and energy consumption. It is shown that substantial energy waste can be reduced by equipping a smart building with the proposed IoT system for effective green energy management. |
| Author Keywords |
Internet of things; Smart city; Green energy; Dispatching algorithm |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Social Science Citation Index (SSCI) |
| EID |
WOS:000624971700002 |
| WoS Category |
Environmental Studies |
| Research Area |
Environmental Sciences & Ecology |
| PDF |
|