Application of long short-term memory networks in virtual power plant data centers

• Published in: 大数据
• Main Authors: Jun CHEN, Siheng NING
• Format: Article
• Language: Chinese
• Published: China InfoCom Media Group 2023-11-01
• Subjects: virtual power plant;data center;deep learning;long short-term memory network;container technology
• Online Access: http://www.j-bigdataresearch.com.cn/thesisDetails#10.11959/j.issn.2096-0271.2023077

The intermittent, random and uncontrollable power generation characteristics of renewable energy pose challenges for the full utilization of green energy.The high energy consumption feature of the virtual power plant data center makes it an efficient absorption and regulation strategy for the intermittent (non-dispatchable) power in renewable energy.This paper proposes a method to predict the ＆quot;source-load＆quot; dual-state of the virtual power plant using a long short-term memory network that incorporates time-embedded encoding.The results indicate that using the model presented in this paper can achieve proactive alerts for ＆quot;power shortages＆quot; at 15-minute intervals, creating ample buffer time windows for container suspension and backup.Combined with container technology, it realizes dynamic energy consumption management in data centers, thereby enhancing the robustness of the virtual power plant data center against power supply-demand imbalances.This technology is of great significance for stabilizing grid operations, accelerating the application of green clean energy, constructing a service pattern for the energy ecosystem and speeding up the digital transformation of the grid.