A NOVEL PROBABILISTIC GENERATION MODEL FOR GRID CONNECTED PV BASED DISTRIBUTED GENERATION

  • Muhammad Faisal Nadeem Khan University of Engineering and Technology Taxila
Keywords: Probabilistic, modeling, clustering, solar irradiance, beta parameters

Abstract

The feasibility of renewable energy resources such as solar energy is marked by uncertainties that make it an unpredictable mean of power generation. To guarantee an uninterrupted power supply, solar irradiance modelling can be taken as a useful step towards meeting the operational challenges of electric power grid. This paper proposes a dynamic Probabilistic generation model to estimate and generate the time-coupled solar irradiance patterns. Initially, clustering of yearly solar irradiance measurements is performed to obtain a meaningful grouping of similar days. One-hour time step is considered to construct a time-coupled probabilistic model of solar irradiance data based on a Beta distribution. The parameters of beta distribution are found by considering the variations of irradiance patterns at two successive time steps. The probabilistic model is then used to generate number of aggregate solar irradiance generation scenarios. The effectiveness of proposed scenario generation approach is evaluated through Average Mean Absolute Percentage Error (AMAPE) and comparison with the probabilistic model already available in the literature.

References

Ela, E., Diakov, V., Ibanez, E., & Heaney, M. 2013. Impacts of variability and uncertainty in solar photovoltaic generation at multiple timescales (No. NREL/TP-5500-58274). National Renewable Energy Laboratory (NREL), Golden, CO.

Renewables, R. (2012). Global status report. Renewable Energy Policy Network for the 21st Century.

Achleitner, S., Kamthe, A., Liu, T., & Cerpa, A. E. 2014, April. SIPS: Solar irradiance prediction system. In Proceedings of the 13th international symposium on Information processing in sensor networks (pp. 225-236). IEEE Press.

Teke, A., Yıldırım, H. B., & Çelik, Ö. 2015. Evaluation and performance comparison of different models for the estimation of solar radiation. Renewable and Sustainable Energy Reviews, 50, 1097-1107.

Angstrom, A. 1924. Solar and terrestrial radiation. Report to the international commission for solar research on actinometric investigations of solar and atmospheric radiation. Quarterly Journal of the Royal Meteorological Society, 50(210), 121-126.

Khatib, T., Mohamed, A., & Sopian, K. 2012. A review of solar energy modeling techniques. Renewable and Sustainable Energy Reviews, 16(5), 2864-2869.

Yorukoglu, M., & Celik, A. N. 2006. A critical review on the estimation of daily global solar radiation from sunshine duration. Energy Conversion and Management, 47(15), 2441-2450.

Besharat, F., Dehghan, A. A., & Faghih, A. R. 2013. Empirical models for estimating global solar radiation: A review and case study. Renewable and Sustainable Energy Reviews, 21, 798-821.

Aguiar, R., & Collares-Pereira, M. 1992. Statistical properties of hourly global radiation. Solar Energy, 48(3), 157-167.

Gueymard, C. 2000. Prediction and performance assessment of mean hourly global radiation. Solar Energy, 68(3), 285-303.

Kaplanis, S. N. 2006. New methodologies to estimate the hourly global solar radiation; Comparisons with existing models. Renewable Energy, 31(6), 781-790.

Duzen, H., & Aydin, H. 2012. Sunshine-based estimation of global solar radiation on horizontal surface at Lake Van region (Turkey). Energy Conversion and Management, 58, 35-46.

Teke, A., & Yıldırım, H. B. 2014. Estimating the monthly global solar radiation for Eastern Mediterranean Region. Energy conversion and management, 87, 628-635.

Elminir, H. K., Azzam, Y. A., & Younes, F. I. 2007. Prediction of hourly and daily diffuse fraction using neural network, as compared to linear regression models. Energy, 32(8), 1513-1523.

Rahimikhoob, A. 2010. Estimating global solar radiation using artificial neural network and air temperature data in a semi-arid environment. Renewable Energy, 35(9), 2131-2135.

Korachagaon, I. 2012. Estimating Global Solar Radiation Potential for Brazil by Iranna-Bapat's Regression Models. International journal of Emerging Technology and Advanced Engineering, 2(2).

Hung, D. Q., Mithulananthan, N., & Lee, K. Y. 2014. Determining PV penetration for distribution systems with time-varying load models. IEEE Transactions on Power Systems, 29(6), 3048-3057.

Hagan, K. E., Oyebanjo, O. O., Masaud, T. M., & Challoo, R. 2016, February. A probabilistic forecasting model for accurate estimation of PV solar and wind power generation. In Power and Energy Conference at Illinois (PECI), 2016 IEEE (pp. 1-5). IEEE.

Khatod, D. K., Pant, V., & Sharma, J. 2013. Evolutionary programming based optimal placement of renewable distributed generators. IEEE Transactions on Power systems, 28(2), 683-695.

Teng, J. H., Luan, S. W., Lee, D. J., & Huang, Y. Q. 2013. Optimal charging/discharging scheduling of battery storage systems for distribution systems interconnected with sizeable PV generation systems. IEEE Transactions on Power Systems, 28(2), 1425-1433.

Salameh, Z. M., Borowy, B. S., & Amin, A. R. 1995. Photovoltaic module-site matching based on the capacity factors. IEEE transactions on Energy conversion, 10(2), 326-332.

Sajjad, I. A., Chicco, G., & Napoli, R. 2015. Probabilistic generation of time-coupled aggregate residential demand patterns. IET Generation, Transmission & Distribution, 9(9), 789-797.

Jiménez-Pérez, P. F., & Mora-López, L. 2016. Modeling and forecasting hourly global solar radiation using clustering and classification techniques. Solar Energy, 135, 682-691.

Nassar, M. E., & Salama, M. A. 2015, May. A novel probabilistic load model and probabilistic power flow. In Electrical and Computer Engineering (CCECE), 2015 IEEE 28th Canadian Conference on (pp. 881-886). IEEE.

Dougherty, R. L., Edelman, A. S., & Hyman, J. M. 1989. Nonnegativity-, monotonicity-, or convexity-preserving cubic and quintic Hermite interpolation. Mathematics of Computation, 52(186), 471-494.

Wu, L., & Shahidehpour, M. 2010. A hybrid model for day-ahead price forecasting. IEEE Transactions on Power Systems, 25(3), 1519-1530.

Conejo, A. J., Plazas, M. A., Espinola, R., & Molina, A. B. 2005. Day-ahead electricity price forecasting using the wavelet transform and ARIMA models. IEEE transactions on power systems, 20(2), 1035-1042.

Teng, J. H., Luan, S. W., Lee, D. J., & Huang, Y. Q. 2013. Optimal charging/discharging scheduling of battery storage systems for distribution systems interconnected with sizeable PV generation systems. IEEE Transactions on Power Systems, 28(2), 1425-1433.

Published
2020-03-05
Section
Electrical Engineering (2)