Optimal Control Strategy of NG Piston Engine as a DG Unit Obtained by an Utilization of Artificial Neural Network

Jaroslaw Milewski, Lukasz Szablowski, Jerzy Kuta, Wojciech Bujalski

Abstract

The paper presents a control strategy concept of a piston engine fueled by Natural Gas as a DG unit obtained by using an Artificial Neural Network. The control strategy is based on several factors and directs the operation of the unit in the context of changes occurring in the market, while taking into account the operating characteristics of the unit. The control strategy is defined by an objective function: for example, work at maximum profit, maximum service life, etc. The results of simulations of the piston engine as a DG unit at chosen loads are presented. Daily changes in the prices of fuel and electricity are factored into the simulations.

References

  1. Adam, N. B., Elahee, M., and Dauhoo, M. (2011). Forecasting of peak electricity demand in mauritius using the non-homogeneous gompertz diffusion process. Energy, 36:6763-6769.
  2. Al-Sulaiman, F., Dincer, I., and Hamdullahpur, F. (2010). Energy analysis of a trigeneration plant based on solid oxide fuel cell and organic rankine cycle. International Journal of Hydrogen Energy, 35(10):5104- 5113.
  3. Amjady, N. and Keynia, F. (2009). Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm. Energy, 34:46-57.
  4. Azadeh, A., Ghaderi, S., and Sohrabkhani, S. (2008). A simulated-based neural network algorithm for forecasting electrical energy consumption in iran. Energy Policy, 36:2637-2644.
  5. Azadeh, A., Ghaderi, S., Tarverdian, S., and Saberi, M. (2007). Integration of artificial neural networks and genetic algorithm to predict electrical energy consumption. Applied Mathematics and Computation, 186:1731-1741.
  6. Beccali, M., Cellura, M., Brano, V. L., and Marvuglia, A. (2004). Forecasting daily urban electric load profiles using artificial neural networks. Energy Conversion and Management, 45:2879-2900.
  7. Budzianowski, W. (2011). Opportunities for bioenergy in poland: Biogas and solid biomass fuelled power plants. Rynek Energii, 94(3):138-146. cited By (since 1996) 1.
  8. Cai, Y., zhou Wang, J., Tang, Y., and chen Yang, Y. (2011). An efficient approach for electric load forecasting using distributed art (adaptive resonance theory) & hsartmap (hyper-spherical artmap network) neural network. Energy, 36:1340-1350.
  9. Chaichana, K., Patcharavorachot, Y., Chutichai, B., Saebea, D., Assabumrungrat, S., and Arpornwichanop, A. (2012). Neural network hybrid model of a direct internal reforming solid oxide fuel cell. International Journal of Hydrogen Energy, 37(3):2498-2508.
  10. Corria, M. E., Cobas, V. M., and Lora, E. S. (2006). Perspectives of stirling engines use for distributed generation in brazil. Energy Policy, 34:3402-3408.
  11. Demuth, H., Beale, M., and Hagan, M. Neural network toolboxTM 6 user's guide matlab R . Technical report.
  12. Foresee, F. and Hagan, M. (1997). Gauss-newton approximation to bayesian regularization. In Proceedings of the 1997 International Joint Conference on Neural Networks.
  13. Hajimolana, S., Hussain, M., Daud, W., Soroush, M., and Shamiri, A. (2011). Mathematical modeling of solid oxide fuel cells: A review. Renewable and Sustainable Energy Reviews, 15(4):1893-1917. cited By (since 1996) 0.
  14. Jagaduri, R. T. and Radman, G. (2007). Modeling and control of distributed generation systems including pem fuel cell and gas turbine. Electric Power Systems Research, 77:83-92.
  15. Kavaklioglu, K., Ceylan, H., Ozturk, H. K., and Canyurt, O. E. (2009). Modeling and prediction of turkey's electricity consumption using artificial neural networks. Energy Conversion and Management, 50:2719-2727.
  16. Kupecki, J. and Badyda, K. (2011). SOFC-based microCHP system as an example of efficient power generation unit. Archives of Thermodynamics, 32(3):33-43.
  17. Lanzini, A., Santarelli, M., and Orsello, G. (2010). Residential solid oxide fuel cell generator fuelled by ethanol: Cell, stack and system modelling with a preliminary experiment. Fuel Cells, 10(4):654-675.
  18. Maine, T. and Chapman, P. (2007). Prices and output from distributed photovoltaic generation in south australia. Energy Policy, 35:461-466.
  19. Milewski, J. and Lewandowski, J. (2009). Solid oxide fuel cell fuelled by biogases. Archives of Thermodynamics, 30(4):3-12.
  20. Milewski, J., Miller, A., and Salacinski, J. (2005). The conception of high temperature fuel cell exhaust gas heat utilization. Prace Naukowe Politechniki Warszawskiej z. Mechanika, 211.
  21. Ocnasu, D., Gombert, C., Bacha, S., Roye, D., Blache, F., and Mekhtoub, S. (2008). Real-time hybrid facility for the study of distributed power generation systems. Revue des Energies Renouvelables, 11(3):343-356.
  22. Paatero, J., Sevon, T., Lehtolainen, A., and Lund, P. (2002). Distributed power system topology and control studies by numerical simulation. In Second International Symposium on Distributed Generation: Power System and Market Aspects.
  23. Rezaei, N. and Haghifam, M.-R. (2008). Protection scheme for a distribution system with distributed generation using neural networks. Electrical Power and Energy Systems, 30:235-241.
  24. Rodriguez, P., Timbus, A., Teodorescu, R., Liserre, M., and Blaabjerg, F. (2007). Flexible active power control of distributed power generation systems during grid faults. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 54(5).
  25. Tarroja, B., Mueller, F., Maclay, J., and Brouwer, J. (2008). Parametric thermodynamic analysis of a solid oxide fuel cell gas turbine system design space. In Proceedings of the ASME Turbo Expo, volume 2, pages 829- 841.
  26. Wang, J., Kang, L., Chang, L., Cao, B., and Xu, D. (2004). Energy complementary control of a distributed power generation system based on renewable energy. IEEE.
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Paper Citation


in Harvard Style

Milewski J., Szablowski L., Kuta J. and Bujalski W. (2012). Optimal Control Strategy of NG Piston Engine as a DG Unit Obtained by an Utilization of Artificial Neural Network . In Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-8565-21-1, pages 171-176. DOI: 10.5220/0004029401710176


in Bibtex Style

@conference{icinco12,
author={Jaroslaw Milewski and Lukasz Szablowski and Jerzy Kuta and Wojciech Bujalski},
title={Optimal Control Strategy of NG Piston Engine as a DG Unit Obtained by an Utilization of Artificial Neural Network},
booktitle={Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2012},
pages={171-176},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004029401710176},
isbn={978-989-8565-21-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Optimal Control Strategy of NG Piston Engine as a DG Unit Obtained by an Utilization of Artificial Neural Network
SN - 978-989-8565-21-1
AU - Milewski J.
AU - Szablowski L.
AU - Kuta J.
AU - Bujalski W.
PY - 2012
SP - 171
EP - 176
DO - 10.5220/0004029401710176