CHAPTER V
to improve the accuracy of wavelet-based prediction method.
Secondly, the errors of solar insolation forecasts sometimes are extreme large, which will cause the large under expectation or large over expectation of PV outputs. However, no relevant researches have focused on estimating the errors of forecasts yet. Therefore, this thesis also focuses on the error estimation of solar insolation forecasts. Given that some big errors concentrate in a certain interval of a variable, this thesis proposed the error estimation method, which can extract the signicant variables and areas to tell the big errors (positive and negative) and judge what types of error the forecast is likely to have, based on statistics method. The proposed error estimation method can provide the operators with more useful information, identifying the extreme situations which cause the large deviation of power, from the input variables already employed in the solar insolation prediction with high condence.
Thirdly, to keep the supply-demand balance, the generation schedule of controllable gen-erators is employed, which determines a ture-on and ture-o schedule and generation outputs of controllable generators according to the predicted power demand and predicted renewable generation outputs. However, we should consider the uncertainties of forecasts because of the high penetration of renewable power generation which causes signicant variations of net power demand. This thesis proposed a useful strategy, where the damage caused by possible supply-demand imbalance arising from the large errors of forecasts is evaluated by penalty or necessarily additional cost necessary to x it, to deal with the uncertainties of forecasts of renewable generation outputs. The proposed strategy can achieve the economically optimal generation schedule with the less total cost compared with the conventional method, which uses the spinning reserves to prevent the possible imbalance incurred by the uncertainties of forecasts.
Finally, the generation schedules sometimes are with high probabilities of imbalance for
shortage or excess, even though they are economically optimal. These schedules may be unacceptable to the power system operators because of the concern of the stability and reliability of power system. Therefore, the optional constraints, the maximum probability of imbalance for shortage and the maximum probability of imbalance for excess constraints, can be used to exclude some extreme situations where PIS or PIE are very large. The proposed constraints are optional and the decision relies on whether the operators desire a more stable system or not. Generation schedule with high adaptability to cope with the uncertainty of forecasts is available at the expense of the low economy of generation schedule.
Bibliography
[1] C. J. Campbell, Oil and gas liquids 2004 scenario, Association for the Study of Peak Oil&Gas. [Online]. Available: http://www.peakoil.net/uhdsg/Default.htm
[2] J. Conti, P. Holtberg, et al., International Energy Outlook 2011, U.S. Energy Infor-mation Administration, DOE/EIA-0484, Sept. 2011.
[3] World energy outlook 2012 - renewable energy outlook, International Energy Agency, 2012.
[4] BP Statistical Review of World Energy June 2013, BP p.l.c., 2013.
[5] D. Fyfe, World energy outlook 2010, International Energy Agency, Jan. 2011.
[6] Renewables 2011 global status report, REN21 Renewable Energy Policy Network for the 21st Century, pp. 17-18, 2011.
[7] BP energy outlook 2030, BP p.l.c., Jan. 2013.
[8] Y.P. Cai, et al., Community-scale renewable energy systems planning under uncertaintyAn interval chance-constrained programming approach, Renewable and Sustainable Energy Reviews, vol. 13, no. 4, pp. 721-735, 2009.
[9] Marty, We need more power, [Online]. Available:
http://www.solarwindapplications.com/we-need-more-power/
[10] H. Morais, et al., Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming, Renewable Energy, vol. 35, no. 1, pp. 151-156, 2010.
[11] J. Bebic, Power system planning: emerging practices suitable for evaluating the impact of high-penetration photovoltaics, National Renewable Energy Laborotory, NREL/SR-581-42297, Feb. 2008.
[12] M. A. Eltawil and Z. Zhao, Grid-connected photovoltaic power systems: Technical and potential problemsA review, Renewable and Sustainable Energy Reviews, vol. 14, issue 1, pp. 112-129, July 2009.
[13] M. Makhlouf, F. Messai, H. Benalla, Modeling and simulation of grid-connected photo-voltaic distributed generation system, Journal of Theoretical and Applied Information Technology, vol. 45, no. 2, pp. 378-386, Nov. 2012.
[14] S. Ali, N. Pearsall, and G. Putrus, Impact of high penetration level of grid-connected photovoltaic systems on the UK low voltage distribution network, International Con-ference on Renewable Energies and Power Quality, March 2012.
[15] S. Park, et al., Battery management for grid-connected PV systems with a battery, Proceedings of the 2012 ACM/IEEE International Symposium on Low Power Electronics and Design, ACM, pp. 115-120, July 2012.
[16] M. Bragard, N. Soltau, et al., The balance of renewable sources and user demands in grids: power electronics for modular battery energy storage systems, Power Electronics, IEEE Transactions on, vol. 25, no. 12, pp. 3049-3056, Dec. 2010.
[17] F. Katiraei, R. Iravani, et al., Microgrids management, Power and Energy Magazine, IEEE, vol. 6, no. 3, pp. 54-65, May-June 2008.
[18] E. M. Constantinescu, V. M. Zavala, et al., A Computational Framework for Uncer-tainty Quantication and Stochastic Optimization in Unit Commitment With Wind Power Generation, IEEE Transactions on Power Systems, vol. 26, no. 1, pp. 431-441, Feb. 2011.
[19] P. Surekha, N. Archana, DR. S. Sumathi, Unit commitment and economic load dispatch using self adaptive dierential evolution, WSEAS Transactions on Power Systems, vol. 7, issue 4, pp. 159-171, Oct. 2012.
[20] A. K. M. Zakzouk, A. R. M. Alamoud, and B. H. Khoshaim, Factors Aecting the Performance of a Photovoltaic Power System (PVPS), International Journal of Solar Energy, vol. 5, no. 2, pp. 67-81, 1987.
[21] Factors aecting output, [Online]. Available: http://www.plugintothesun.co.uk/solar-power/solar-technology/factors-aecting-output/
[22] B. Marion, M. Anderberg, et al., PVWATTS Version 2 − enhanced spatial resolution for calculating grid-connected PV performance, National Renewable Energy Laboratory, NREL/CP-560-30941, Oct. 2001.
[23] P. Zhang, H. Takano. J. Murata, Wavelet-based daily solar insolation prediction and its error estimation, International Conference on Electrical Engineering, Xiamen, July 2013. (in press)
[24] P. Zhang, H. Takano. J. Murata, et al., Optimal generation schedule of micro-grids considering the uncertainties of forecasts, Seventeenth International Conference on In-telligent System Applications to Power System, Tokyo, July 2013. (in press)
[25] C. Mustacchi, V. Cena, and M. Rocchi, Stochastic simulation of hourly global radiation sequences, Solar Energy, vol. 23, no. 1, pp. 47-51, 1979.
[26] U. Amato, A. Andretts, et al., Markov processes and Fourier analysis as a tool to describe and simulate daily solar irradiance, Solar Energy, vol. 37, no. 3, pp. 179-194, 1986.
[27] R. J. Aguiar, M. Collares-Perreira, and J. P. Conde, Simple procedure for generating sequences of daily radiation values using library of Markov transition matrices, Solar Energy, vol. 40, no. 3, pp. 269-279, 1988.
[28] H. Shuichi, M. Mamoru, and I. Toshikazu, Statistical time series models of solar ra-diation and outdoor temperature identication of seasonal models by Kalman lter, Energy and Buildings, vol. 15, pp. 373-383, 1990.
[29] S. Hokoi, M. Matsumoto, and M. Kagawa, Stochastic models of solar radiation and outdoor temperature, ASHRAE Trans., vol. 2, pp. 245-252, 1990.
[30] R. J. Aguiar, and M. T. Collares-Perreira, A.G: A time dependent autoregressive Gaus-sian model for generating synthetic hourly radiation, Solar Energy, vol. 49, no. 3, pp. 167-174, 1992.
[31] S. M. El Shazly, Estimation of hourly and daily global solar radiation at clear days using an approach based on modied version of Gaussian distribution, Advanced in Atmospheric Science, vol. 13, no. 3, pp. 349-358, 1996.
[32] L. L. Mora-Lopez and M. Sidrach-de-Cardona, Multiplicative ARMA models to gener-ate hourly series of global irradiation, Solar Energy, vol. 63, pp. 283-391, 1998.
[33] C. Craggsa, E. Conwaya and N. M. Pearsall, Stochastic modeling of solar irradiance on horizontal and vertical planes at a northerly location, Renewable Energy, vol. 18, pp. 445-463, 1999.
[34] Z. Sen and A. D. Sahin, Spatial interpolation and estimation of solar irradiation by cumulative semivariograms, Solar Energy, vol. 71, no. 1, pp. 11-21, 2001.
[35] C. Zhu, The calculation and distribution of diuse radiation in China, Acta Energiae Solaris Sinica, vol. 5, no. 3, pp. 242-249, 1984.
[36] M.S. Audi and M.A. Alsaad, Simple hourly global solar radiation prediction models, Renewable Energy, vol. 1, no. 3/4, pp. 473-478, 1991.
[37] G. Su, Q. Liu, F. Deng and X. Xin, The hourly global clear sky radiation model based on the least squares support vector machines, Journal of Beijing Normal University (Natural Science), vol. 43, no. 3, pp. 274-278, 2007.
[38] Y. Jiang, Computation of monthly mean daily global solar radiation in China using articial neural networks and comparison with other empirical models, Energy, vol. 34, issue. 9, pp. 1276-1283, 2009.
[39] C. Ertekin, and O. Yaldiz. Comparison of some existing models for estimating global solar radiation for Antalya (Turkey), Energy Conversion and Management, vol. 41, no. 4, pp. 311-330, 2000.
[40] M. T. Y. Tadros, Uses of sunshine duration to estimate the global solar radiation over eight meteorological stations in Egypt, Renewable Energy, vol. 21, issue. 2, pp. 231-246, 2000.
[41] J. Almorox, and C. Hontoria, Global solar radiation estimation using sunshine duration in Spain, Energy Conversion and Management, vol. 45, no. 9, pp. 1529-1535, 2004.
[42] H. O. Menges, C. Ertekin, and M. H. Sonmete. Evaluation of global solar radiation mod-els for Konya, Turkey, Energy Conversion and Management, vol. 47, no. 18, pp. 3149-3173, 2006.
[43] K. Bakirci Correlations for estimation of daily global solar radiation with hours of bright sunshine in Turkey, Energy, vol. 34, no. 4, pp. 485-501, 2009.
[44] A. Mellit, Articial intelligence technique for modelling and forecasting of solar radia-tion data: a review, Articial Intelligence and Soft Computing, vol. 1, no. 1, pp. 52-76, 2008.
[45] F. O. Hocaoglu, Stochastic approach for daily solar radiation modeling, Solar Energy, vol. 85, no. 2, pp. 278-287, 2011.
[46] J. Tovar-Pescador, Modelling the statistical properties of solar radiation and proposal of a technique based on Boltzmann statistics, Badescu, V. (Ed.), In Modeling Solar Radiation at the Earth's Surface, Springer, Berlin, pp. 55.91, 2008.
[47] A. Zeroual, M. Ankrim, and A. J. Wilkinson, Stochastic modeling of daily global solar radiation measured in Marrakesh, Morocco, Renewable Energy, vol. 6, pp. 787-793, 1995.
[48] S. Sa, A. Zeroual, and M. M. Hassani, Prediction of global daily solar radiation using higher order statistics, Renewable Energy, vol. 27, pp. 647-666, 2002.
[49] V. Badescu and M. Paulescu, Autocorrelation properties of the sunshine number and sunshine stability number, Meteorology and Atmospheric Physics, vol. 112, no. 3, pp. 139-154, 2011.
[50] R. St. Boata and P. Gravila, Functional fuzzy approach for forecasting daily global solar irradiation, Atmospheric Research, vol. 112, pp. 79-88, 2012.
[51] C. Paoli, C. Voyant, et al., Forecasting of preprocessed daily solar radiation time series using neural networks, Solar Energy, vol. 84, no. 12, pp. 2146-2160, 2010.
[52] A. Sfetsos and A.H. Coonick, Univeriate and multivariate forecasting of hourly solar radiation with articial intelligence techniques, Solar Energy, vol. 68, no. 2, pp. 169-178, 2000.
[53] J. Cao and X. Lin, Application of the diagonal recurrent wavelet neural network to solar irradiation forecast assisted with fuzzy technique, Engineering Applications of Articial Intelligence, vol. 21, no. 8, pp. 1255-1263, 2008.
[54] A. Mellit, M. Benghanem and S.A. Kalogirou, An adaptive wavelet-network model for forecasting daily total solar-radiation, Applied Energy, vol. 83, no. 7, pp. 705-722, 2006.
[55] A. Mellit and A.M. Pavan, A 24-h forecast of solar irradiance using articial neural net-work: Application for performance prediction of a grid-connected PV plant at Trieste, Italy, Solar Energy, vol. 84, no. 5, pp. 807-821, 2010.
[56] J. Cao and X. Lin, Study of hourly and daily solar irradiation forecast using diagonal recurrent wavelet neural networks, Energy Conversion & Management, vol 49, no. 6, pp. 1396-1406, 2008.
[57] M. Benghanem, A. Mellit, and S. N. Alamri, ANN-based modelling and estimation of daily global solar radiation data: A case study, Energy Conversion & Management, vol 50, pp. 1644-1655, 2009.
[58] A. Mellit, et al,. A simplied model for generating sequences of global solar radiation data for isolated sites: Using articial neural network and a library of Markov transition matrices approach, Solar Energy, vol 79, pp. 469-482, 2005.
[59] J. Cao and S. Cao, Study of forecasting solar irradiance using neural networks with preprocessing sample data by wavelet analysis, Energy, vol. 31, no. 15, pp. 3435-3445, 2006.
[60] S. Cao and J. Cao, Forecast of solar irradiance using recurrent neural network combined with wavelet analysis, Applied Thermal Engineering, vol. 25, no. 2, pp. 161-172, 2005.
[61] I. Daubechies, Ten lectures on wavelets, 1st ed., SIAM, 1992.
[62] S. Mallat, A wavelet tour of signal processing, 2nd ed., Academic Press, 1999.
[63] I.T. Jollie, Principal component analysis, 2nd ed., Springer-Verlag, 2010.
[64] A. Ralston, E.D. Reilly and D. Hemmendinger, Encyclopedia of computer science, 4th ed., Grove's Dictionaries, 2000.
[65] Ministry of the Environment, Government of Japan. [Online]. Available:
http://www.env.go.jp/press/le_view.php?serial=11912&hou_id=10025
[66] HEPCO Sustainability Report 2008, [Online]. Available:
http://www.hepco.co.jp/english/environment/pdf/report2008.pdf
[67] S. Lu, A. J. Brothers, C. A. McKinstry, S. Jin, Low probability tail event analysis and mitigation in the BPA control area, Pacic Northwest National Laboratory, 2010.
[68] G. T. Heydt, V. Vittal, S. Malhara, et al., Characterization and impact of extreme forecast errors on power systems, Electric Power Components and Systems, vol. 39, no. 15, pp. 1685-1700, 2010.
[69] D. Heinemann, L. Elke, and G. Marco, Forecasting of solar radiation, Solar energy resource management for electricity generation from local level to global scale, Nova Science Publishers, New York, 2006.
[70] A. Mills, et al., Understanding variability and uncertainty of photovoltaics for integra-tion with the electric power system, 2010.
[71] J. Han and M. Kamber, Data mining: concepts and techniques, Academic Press, 2001.
[72] P. Zhang, H. Takano, J. Murata, Daily solar radiation prediction based on wavelet analysis, SICE Annual Conference (SICE), 2011 Proceedings of, pp. 712-717, 2011.
[73] F. Nomiyama, J. Asai, et al., A study on global solar radiation forecasting models using meteorological data and their application to wide area forecast, Power System Technology (POWERCON), 2012 IEEE International Conference on, pp. 1-6, 2012.
[74] P. A. Ruiz, C. R. Philbrick, K. W. Cheung, and P. W. Sauer, Uncertainty management in the unit commitment problem, IEEE Trans. on Power Systems, vol. 24, no. 2, pp. 642-651, May 2009.
[75] H. Takano, J. Murata, P. Zhang, et al., A determination method for the optimal op-eration of controllable generators in a micro grid the copes with unstable outputs of renewable energy generation, The Papers of Joint Technical Meeting on Power Engi-neering and Power Systems EngiEngi-neering, IEE Japan, PE-12-088 PSE-12-104, Aug. 2012.
[76] A. Bhardwaj, N. S. Tung and V. Kamboj, Unit commitment in power system: a review, International Journal of Electrical and Power Engineering, vol. 6, no. 1, pp. 51-57, 2012.
[77] N. S. Tung, G. Kaur, G. Kaur and A. Bhardwaj, Optimization techniques in unit commitment a review, International Journal of Engineering Science and Technology, vol. 4, no. 4, pp. 1623-1627, Apr. 2012.
[78] D. A. Halamay, T. K. A. Brekken, A. Simmons and S. McArthur, Reserve requirement impacts of large-scale integration of wind, solar, and ocean wave power generation, IEEE Transactions on Sustainable Energy, vol. 2, no. 3, pp. 321-328, 2011.
[79] A. Mills, M. Ahlstrom, M. Brower, et al., Understanding variability and uncertainty of photovoltaics for integration with the electric power system, Ernest Orlando Lawrence Berkeley National Laboratory, Technical Report LBNL-2855E, Dec. 2009.
[80] R. Masiello, K. Vu, L. Deng, et al., Research Evaluation of Wind Generation, Solar Generation, and Storage Impact on the California Grid, Prepared For: California En-ergy Commission, Public Interest EnEn-ergy Research Program, Prepared By: KEMA, Inc., PIER Final Project Report CEC-500-2010-010, June, 2010.
[81] D. Lew, M. Milligan, G. Jordan, et al., How do wind and solar power aect grid opera-tions: The western wind and solar integration study, In Proc. of The 8th International Workshop on Large Scale Integration of Wind Power and on Transmission Networks for Oshore Wind Farms, Bremen, Germany, Oct. 14-15, 2009
[82] Western Wind and Solar Integration Study, Prepared
for NREL by GE Energy, May 2010. [Online]. Available:
http://www.nrel.gov/wind/systemsintegration/pdfs/2010/wwsis_nal_report.pdf [83] J. Lee, S. K. Joo and K. Lee, Enhancement of operation eciency of grid interconnected
photovoltaic systems, Thin Solid Films, vol. 518, no. 22, pp. 6564-6566, 2010.
[84] Y. V. Makarov, V. Yuri, et al., Incorporating wind generation and load forecast uncer-tainties into power grid operations, Pacic Northwest National Laboratory, 2010.
[85] A. Tuohy, E. Denny, P. Melborn, R. Barth, M. O'Malley, Operating the Irish power system with increased levels of wind power, Proc. IEEE Power and Energy Society General Meeting, pp. 1-4, 2008.
[86] K. Rohrig and B. Lange, Application of wind power prediction tools for power system operations, Power and Energy Society General Meeting, pp. 1-5, July 2006.
[87] R. Doherty, E. Denny and M. O'Malley, System operation with a signicant wind power penetration, Power and Energy Society General Meeting, pp. 1002-1007, July 2004.
[88] A. da Silva, W. Sales, L. da Fonseca Manso and R. Billinton, Longterm probabilistic evaluation of operating reserve requirements with renewable sources, IEEE Transac-tions on Power Systems, vol. 25, no. 1, pp. 106-116, 2010.
[89] D. A. Halamay and T. K. A. Brekken, Monte Carlo analysis of the impacts of high renewable power penetration, In Proc. of 2011 IEEE Energy Conversion Congress and Exposition (ECCE), Phoenix, Arizona, Sep. 17-22, 2011.
[90] Y. V. Makarov, P. V. Etingov, N. A. Samaan, et al., Improving performance of power systems with large-scale variable generation additions, Power and Energy Society Gen-eral Meeting, 2012 IEEE, IEEE, pp. 1-8, 2012.
[91] Y. V. Makarov, S. Lu, N. Samaan, et al., Integration of uncertainty information into power system operations, In Power and Energy Society General Meeting, 2011 IEEE, IEEE, pp. 1-13, 2011.
[92] S. Takayama, R. Hara, et al., Scheduled operation of PV power station considering solar radiation forecast error, IEEJ Transactions on Power and Energy, vol. 131, no. 3, pp. 304-312, 2011.
[93] K. Y. Huang, H. T. Yang, and C. L. Huang, A new thermal unit commitment approach using constraint logic programming, IEEE Trans. on Power Systems, vol. 13, no. 3, pp. 936-945, Aug. 1998.
[94] H. Takano, J. Murata, et al., A study on improvement of tabu search-based determi-nation method for distribution network conguration, Journal of Interdetermi-national Con-ference on Electrical Engineering, 2013. (in press)
[95] A. Hertz, E, Taillard, and D. d. Werra, A tutorial on tabu search, [Online]. Available:
http://red.cs.nott.ac.uk/ãjp/courses/g5baim/les/IntroTS.pdf
[96] A. R. Famideh-Vojdani and F. D. Galiana, Economic dispatch with generation con-straints, IEEE Trans. on Automatic Control, vol. 25, pp. 213-217, 1980.