Construction of Smart Grid Load Forecast Model by Edge Computing

Xudong Pang, Xiangchen Lu, Hao Ding and Josep M. Guerrero
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Xudong Pang: Electrical Engineering Department, Yanshan University, Qinhuangdao 066000, China
Xiangchen Lu: School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
Hao Ding: Electrical Engineering Department, Yanshan University, Qinhuangdao 066000, China
Josep M. Guerrero: Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark

Energies, 2022, vol. 15, issue 9, 1-16

Abstract: This research aims to minimize the unnecessary resource consumption by intelligent Power Grid Systems (PGSs). Edge Computing (EC) technology is used to forecast PGS load and optimize the PGS load forecasting model. Following a literature review of EC and Internet of Things (IoT)-native edge devices, an intelligent PGS-oriented Resource Management Scheme (RMS) and PGS load forecasting model are proposed based on task offloading. Simultaneously, an online delay-aware power Resource Allocation Algorithm (RAA) is developed for EC architecture. Finally, comparing three algorithms corroborate that the system overhead decreases significantly with the model iteration. From the 40th iteration, the system overhead stabilizes. Moreover, given no more than 50 users, the average user delay of the proposed delay-aware power RAA is less than 13 s. The average delay of the proposed algorithm is better than that of the other two algorithms. This research contributes to optimizing intelligent PGS in smart cities and improving power transmission efficiency.

Keywords: edge computing; intelligent Power Grid System (PGS); PGS load; resource management (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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