Grid Power Optimization Based on Adapting Load Forecasting and Weather Forecasting for System Which Involves Wind Power Systems

ABSTRACT

This paper describes the performance, generated power flow distribution and redistribution for each power plant on the grid based on adapting load and weather forecasting data. Both load forecasting and weather forecasting are used for collecting predicting data which are required for optimizing the performance of the grid. The stability of each power systems on the grid highly affected by load varying, and with the presence of the wind power systems on the grid, the grid will be more exposed to lowering its performance and increase the instability to other power systems on the gird. This is because of the intermittence behavior of the generated power from wind turbines as they depend on the wind speed which is varying all the time. However, with a good prediction of the wind speed, a close to the actual power of the wind can be determined. Furthermore, with knowing the load characteristics in advance, the new load curve can be determined after being subtracted from the wind power. Thus, with having the knowledge of the new load curve, and data that collected from SACADA system of the status of all power plants, the power optimization, load distribution and redistribution of the power flows between power plants can be successfully achieved. That is, the improvement of performance, more reliable, and more stable power grid.

This paper describes the performance, generated power flow distribution and redistribution for each power plant on the grid based on adapting load and weather forecasting data. Both load forecasting and weather forecasting are used for collecting predicting data which are required for optimizing the performance of the grid. The stability of each power systems on the grid highly affected by load varying, and with the presence of the wind power systems on the grid, the grid will be more exposed to lowering its performance and increase the instability to other power systems on the gird. This is because of the intermittence behavior of the generated power from wind turbines as they depend on the wind speed which is varying all the time. However, with a good prediction of the wind speed, a close to the actual power of the wind can be determined. Furthermore, with knowing the load characteristics in advance, the new load curve can be determined after being subtracted from the wind power. Thus, with having the knowledge of the new load curve, and data that collected from SACADA system of the status of all power plants, the power optimization, load distribution and redistribution of the power flows between power plants can be successfully achieved. That is, the improvement of performance, more reliable, and more stable power grid.

KEYWORDS

Wind Power Systems; Grid; Power Plants; Wind Forecasting; Load Forecasting; Power Optimization

Wind Power Systems; Grid; Power Plants; Wind Forecasting; Load Forecasting; Power Optimization

Cite this paper

F. Aula and S. Lee, "Grid Power Optimization Based on Adapting Load Forecasting and Weather Forecasting for System Which Involves Wind Power Systems,"*Smart Grid and Renewable Energy*, Vol. 3 No. 2, 2012, pp. 112-118. doi: 10.4236/sgre.2012.32016.

F. Aula and S. Lee, "Grid Power Optimization Based on Adapting Load Forecasting and Weather Forecasting for System Which Involves Wind Power Systems,"

References

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[2] Department of Energy of USA, “20% Wind Energy by 2030,” Energy Efficiency and Renewable Energy, 2008.

[3] M. Espinoza, J. Suykens, R. Belmans, and B. De Moor, “Electric Load Forecasting Using Kernel-Based Modeling,” IEEE Control Systems, Vol. 27, No. 5, 2007, pp. 4357.

[4] H. L. Willis, “Spatial Electric Load Forecasting,” Marcel Dekker, New York, 1996.

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[6] P. Qingle and Z. Min, “Very Short-Term Load Forecasting Based on Neural Network and Rough Set,” International Conference on Intelligent Computation Technology and Automation, Vol. 3, Changsha, 11-12 May 2010, pp. 1132-1135.

[7] A. G. Bakirtzis, V. Petridis, S J. Kiartzis, M. C. Alexiadis, and A. H. Maissis, “A Neural Network Short-Term Load Forecasting Model for the Greek Power System,” IEEE Transactions on Power Systems, Vol. 11, No. 2, 1996, pp. 858-863. doi:10.1109/59.496166

[8] D. Papalexopoulos, S. Hao and T. M. Peng, “An Implementation of a Neural Network Based Load Forecasting Model for the EMS,” IEEE Transactions on Power Systems, Vol. 9, No. 4, 1994, pp. 1956-1962. doi:10.1109/59.331456

[9] R. Afkhami-Rohani, T. L. Lu, A. Abaye, M. Davis and D. J. Maratukulam, “ANNSTLF-A Neural Network-Based Electric Load Forecasting System,” IEEE Transactions on Neural Networks, Vol. 8, No. 4, 1997, pp. 835-846. doi:10.1109/72.595881

[10] H. Chen, C. A. Canizares and A. Singh, “ANN Based ShortTerm Load Forecasting in Electricity Markets,” Proceedings of the IEEE Power Engineering Society Transmission and Distribution Conference, Vol. 2, Columbus, 28 Jaunary-1 February 2001, pp. 411-415.

[11] Z. Baharudin, M. F. Jamaluddin and N. Saad, “Fuzzy Logic Technique for Short Term Load Forecasting,” BICET, Darussalam, August 2005.

[12] Z. Baharudin, N. Saad and R. Ibrahim, “A Fuzzy Logic Technique for Short Term Load forecasting,” 2nd ICAIET, Kuala Lumpur, 3-5 August 2004, pp. 63-67.

[13] PJM Interconnection LLC. www.pjm.com/

[14] J. Charney, R. Fjortoft and J. von Neumann, “Numerical Integration of the Barotropic Vorticity Equation,” Tellus, Vol. 2, No. 4, 1950, pp. 237-254. doi:10.1111/j.2153-3490.1950.tb00336.x

[15] C. Monteiro, et al., “Wind Power Forecasting: State-ofthe-Art 2009,” Institute for Systems and Computer Engineering of Porto (INESC Porto) and Decision and Information Sciences Division, Argonne National Laboratory, Argonne, 2009.

[16] M. Lange and U. Focken, “Physical Approach to ShortTerm Wind Power Prediction,” Springer-Verlag, Berlin, Heidelberg, 2006.

[17] A. Kusiak, H.-Y. Zheng and Z. Song, “Wind Farm Power Prediction: A Data-Mining Approach,” Wind Energy, Vol. 12, No. 3, 2009, pp. 275-293. doi:10.1002/we.295

[18] L. Freris and D. Infield, “Renewable Energy in Power Systems,” John Wily and Sons, Hoboken, 2008.

[19] Earth Networks, WeatherBug, 2011. http://weather.weatherbug.com

[20] AccuWeather, Inc., 2011. http://www.accuweather.com/

[21] Weather Underground, 2011, http://www.wunderground.com

[22] F. T. Aula and S. C. Lee, “Weather Adaptive Renewable Energy Based Self Correctional Dynamic Power System for 2020 and Beyond,” 21st International Conference on Systems Engineering, Las Vegas, 16-18 August 2011, pp. 19-21.

[1] S. Kaplan, “Power Plants: Characteristics and Costs,” Federation and American Scientists, CRS Report or Congress, 2008.

[2] Department of Energy of USA, “20% Wind Energy by 2030,” Energy Efficiency and Renewable Energy, 2008.

[3] M. Espinoza, J. Suykens, R. Belmans, and B. De Moor, “Electric Load Forecasting Using Kernel-Based Modeling,” IEEE Control Systems, Vol. 27, No. 5, 2007, pp. 4357.

[4] H. L. Willis, “Spatial Electric Load Forecasting,” Marcel Dekker, New York, 1996.

[5] G. E. P. Box and G. M. Jenkins, “Time Series Analysis: Forecasting and Control,” Prentice Hall, Saddle River, 1970.

[6] P. Qingle and Z. Min, “Very Short-Term Load Forecasting Based on Neural Network and Rough Set,” International Conference on Intelligent Computation Technology and Automation, Vol. 3, Changsha, 11-12 May 2010, pp. 1132-1135.

[7] A. G. Bakirtzis, V. Petridis, S J. Kiartzis, M. C. Alexiadis, and A. H. Maissis, “A Neural Network Short-Term Load Forecasting Model for the Greek Power System,” IEEE Transactions on Power Systems, Vol. 11, No. 2, 1996, pp. 858-863. doi:10.1109/59.496166

[8] D. Papalexopoulos, S. Hao and T. M. Peng, “An Implementation of a Neural Network Based Load Forecasting Model for the EMS,” IEEE Transactions on Power Systems, Vol. 9, No. 4, 1994, pp. 1956-1962. doi:10.1109/59.331456

[9] R. Afkhami-Rohani, T. L. Lu, A. Abaye, M. Davis and D. J. Maratukulam, “ANNSTLF-A Neural Network-Based Electric Load Forecasting System,” IEEE Transactions on Neural Networks, Vol. 8, No. 4, 1997, pp. 835-846. doi:10.1109/72.595881

[10] H. Chen, C. A. Canizares and A. Singh, “ANN Based ShortTerm Load Forecasting in Electricity Markets,” Proceedings of the IEEE Power Engineering Society Transmission and Distribution Conference, Vol. 2, Columbus, 28 Jaunary-1 February 2001, pp. 411-415.

[11] Z. Baharudin, M. F. Jamaluddin and N. Saad, “Fuzzy Logic Technique for Short Term Load Forecasting,” BICET, Darussalam, August 2005.

[12] Z. Baharudin, N. Saad and R. Ibrahim, “A Fuzzy Logic Technique for Short Term Load forecasting,” 2nd ICAIET, Kuala Lumpur, 3-5 August 2004, pp. 63-67.

[13] PJM Interconnection LLC. www.pjm.com/

[14] J. Charney, R. Fjortoft and J. von Neumann, “Numerical Integration of the Barotropic Vorticity Equation,” Tellus, Vol. 2, No. 4, 1950, pp. 237-254. doi:10.1111/j.2153-3490.1950.tb00336.x

[15] C. Monteiro, et al., “Wind Power Forecasting: State-ofthe-Art 2009,” Institute for Systems and Computer Engineering of Porto (INESC Porto) and Decision and Information Sciences Division, Argonne National Laboratory, Argonne, 2009.

[16] M. Lange and U. Focken, “Physical Approach to ShortTerm Wind Power Prediction,” Springer-Verlag, Berlin, Heidelberg, 2006.

[17] A. Kusiak, H.-Y. Zheng and Z. Song, “Wind Farm Power Prediction: A Data-Mining Approach,” Wind Energy, Vol. 12, No. 3, 2009, pp. 275-293. doi:10.1002/we.295

[18] L. Freris and D. Infield, “Renewable Energy in Power Systems,” John Wily and Sons, Hoboken, 2008.

[19] Earth Networks, WeatherBug, 2011. http://weather.weatherbug.com

[20] AccuWeather, Inc., 2011. http://www.accuweather.com/

[21] Weather Underground, 2011, http://www.wunderground.com

[22] F. T. Aula and S. C. Lee, “Weather Adaptive Renewable Energy Based Self Correctional Dynamic Power System for 2020 and Beyond,” 21st International Conference on Systems Engineering, Las Vegas, 16-18 August 2011, pp. 19-21.