Advertisement

Chinese Journal of Mechanical Engineering

, Volume 27, Issue 3, pp 488–495 | Cite as

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

  • Xinyao Sun
  • Xue WangEmail author
  • Jiangwei Wu
  • Youda Liu
Article
  • 177 Downloads

Abstract

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.

Keywords

cyber physical systems manufacturing center self-adaptive demand side management particle swarm optimization 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    ILIC M D, LE X, KHAN U A, et al. Modeling of future cyber-physical energy systems for distributed sensing and control[J]. IEEE Transactions on Systems, Man and Cybernetics-Part A: Systems and Humans, 2010, 40(4): 825–838.CrossRefGoogle Scholar
  2. [2]
    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
  3. [3]
    LOGENTHIRAN T, SRINIVASAN D, TAN Zongshun. Demand side management in smart grid using heuristic optimization[J]. IEEE Transactions on Smart Grid, 2012, 3(3): 1244–1252.CrossRefGoogle Scholar
  4. [4]
    SUN Dongye, LIN Xinyou, QIN Datong, et al. Power-balancing instantaneous optimization energy management for a novel series-parallel hybrid electric bus[J]. Chinese Journal of Mechanical Engineering, 2012, 25(6): 1161–1170.CrossRefGoogle Scholar
  5. [5]
    TONG Liang, YAN Ping, LIU Fei. Monitoring computer numerical control machining progress based on information fusion[J]. Chinese Journal of Mechanical Engineering, 2011, 24(6): 1074–1082.CrossRefGoogle Scholar
  6. [6]
    HE Yan, LIU Fei. Methods for integrating energy consumption and environmental impact considerations into the production operation of machining processes[J]. Chinese Journal of Mechanical Engineering, 2010, 23(4): 428–435.CrossRefGoogle Scholar
  7. [7]
    XU Ming, JIN Bo, YU Yaxin, et al. Using artificial neural networks for energy regulation based variable-speed electrohydraulic drive[J]. Chinese Journal of Mechanical Engineering, 2010, 23(3): 327–335.CrossRefGoogle Scholar
  8. [8]
    KAH-HOE N, SHEBLE G B. Direct load control: a profit-based load management using linear programming[J]. IEEE Transactions on Power Systems, 1998, 13(2): 688–694.CrossRefGoogle Scholar
  9. [9]
    HSU Y Y, SU C C. Dispatch of direct load control using dynamic programming[J]. IEEE Transactions on Power Systems, 1991, 6(3): 1056–1061.CrossRefGoogle Scholar
  10. [10]
    WU Qiuwei, WANG Peng, GOEL L. Direct load control(DLC) considering nodal interrupted energy assessment rate(NIEAR) in restructured power systems[J]. IEEE Transactions on Power Systems, 2010, 25(3): 1449–1456.CrossRefGoogle Scholar
  11. [11]
    BHATTACHARYYA K, CROW M L. A fuzzy logic based approach to direct load control[J]. IEEE Transactions on Power Systems, 1996, 11(2): 708–714.CrossRefGoogle Scholar
  12. [12]
    VAN DEN BERGH F, ENGELBRECHT A P. A cooperative approach to particle swarm optimization[J]. IEEE Transactions on Evolutionary Computation, 2004, 8(3): 225–239.CrossRefGoogle Scholar
  13. [13]
    WANG Xue, WANG Sheng, MA Junjie. An improved co-evolutionary particle swarm optimization for wireless sensor networks with dynamic deployment[J]. Sensors, 2007, 7(3): 354–370.CrossRefGoogle Scholar
  14. [14]
    HUANG C M, HUANG C J, WANG M L. A particle swarm optimization to identifying the ARMAX model for short-term load forecasting[J]. IEEE Transactions on Power Systems, 2005, 20(2): 1126–1133.CrossRefGoogle Scholar
  15. [15]
    LIN C J, CHEN C H, LIN C T. A hybrid of cooperative particle swarm optimization and cultural algorithm for neural fuzzy networks and its prediction applications[J]. IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews, 2009, 39(1): 55–68.CrossRefGoogle Scholar
  16. [16]
    ZHANG Yun, ZHOU Quan, SUN Caixin, et al. RBF neural network and ANFIS-based short-term load forecasting approach in real-time price environment[J]. IEEE Transactions on Power Systems, 2008, 23(3): 853–858.CrossRefGoogle Scholar
  17. [17]
    MATTEO D F, YAO Xin. Short-term load forecasting with neural network ensembles: a comparative study[J]. IEEE Computational Intelligence Magazine, 2011, 6(3): 47–56.CrossRefGoogle Scholar
  18. [18]
    CHEN Ying, LUH P B, CHE G, et al. Short-term load forecasting: similar day-based wavelet neural networks[J]. IEEE Transactions on Power Systems, 2010, 25(1): 322–330.CrossRefGoogle Scholar
  19. [19]
    KHOSRAVI A, NAHAVANDI S, CREIGHTON D, et al. Interval type-2 fuzzy logic systems for load forecasting: a comparative study[J]. IEEE Transactions on Power Systems, 2012, 27(3): 1274–1282.CrossRefGoogle Scholar
  20. [20]
    TIPPING M E. Sparse bayesian learning and the relevance vector machine[J]. Journal of Machine Learning Research, 2001 (1): 211–244.Google Scholar
  21. [21]
    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
  22. [22]
    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
  23. [23]
    WANG Xue, WANG Sheng. Hierarchical deployment optimization for wireless sensor networks[J]. IEEE Transactions on Mobile Computing, 2011, 10(7): 1028–1041CrossRefGoogle Scholar

Copyright information

© Chinese Mechanical Engineering Society and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.State Key Laboratory of Precision Measurement Technology and InstrumentTsinghua UniversityBeijingChina

Personalised recommendations