Prediction-based manufacturing center self-adaptive demand side energy optimization in cyber physical systems

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• 作者：Xinyao Sun (1)
  Xue Wang (1)
  Jiangwei Wu (1)
  Youda Liu (1)
  
• 关键词：cyber physical systems ; manufacturing center ; self ; adaptive ; demand side management ; particle swarm optimization
• 刊名：Chinese Journal of Mechanical Engineering
• 出版年：2014
• 出版时间：May 2014
• 年：2014
• 卷：27
• 期：3
• 页码：488-495
• 全文大小：
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• 作者单位：Xinyao Sun (1)
  Xue Wang (1)
  Jiangwei Wu (1)
  Youda Liu (1)
  
  1. State Key Laboratory of Precision Measurement Technology and Instrument, Tsinghua University, Beijing, 100084, China
  
• ISSN：2192-8258

文摘

Cyber physical systems(CPS) recently emerge as a new technology which can provide promising approaches to demand side management(DSM), an important capability in industrial power systems. Meanwhile, the manufacturing center is a typical industrial power subsystem with dozens of high energy consumption devices which have complex physical dynamics. DSM, integrated with CPS, is an effective methodology for solving energy optimization problems in manufacturing center. This paper presents a prediction-based manufacturing center self-adaptive energy optimization method for demand side management in cyber physical systems. To gain prior knowledge of DSM operating results, a sparse Bayesian learning based componential forecasting method is introduced to predict 24-hour electric load levels for specific industrial areas in China. From this data, a pricing strategy is designed based on short-term load forecasting results. To minimize total energy costs while guaranteeing manufacturing center service quality, an adaptive demand side energy optimization algorithm is presented. The proposed scheme is tested in a machining center energy optimization experiment. An AMI sensing system is then used to measure the demand side energy consumption of the manufacturing center. Based on the data collected from the sensing system, the load prediction-based energy optimization scheme is implemented. By employing both the PSO and the CPSO method, the problem of DSM in the manufacturing center is solved. The results of the experiment show the self-adaptive CPSO energy optimization method enhances optimization by 5% compared with the traditional PSO optimization method.