Prediction-based manufacturing center self-adaptive demand side energy optimization in cyber physical systems
- 177 Downloads
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.
Keywordscyber physical systems manufacturing center self-adaptive demand side management particle swarm optimization
Unable to display preview. Download preview PDF.
- MACANA C A, QUIJANO N, MOJICA-NAVA E. A survey on cyber physical energy systems and their applications on smart grids[C]//IEEE PES Conference on Innovative Smart Grid Technologies, Medellin, Colombia, October 19–21, 2011: 1–7.Google Scholar
- TIPPING M E. Sparse bayesian learning and the relevance vector machine[J]. Journal of Machine Learning Research, 2001 (1): 211–244.Google Scholar
- HUANG N E, SHEN Zheng, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society of London-Series A: Mathematical, Physical and Engineering Sciences, 1998, 454(1971): 903–995.CrossRefzbMATHMathSciNetGoogle Scholar
- SAMADI P, MOHSENIAN R A H, SCHOBER R, et al. Optimal real-time pricing algorithm based on utility maximization for smart grid[C]//2010 1st IEEE International Conference on Smart Grid Communications, Gaithersburg, USA, October 4–6, 2010: 415–420.Google Scholar