Data analysis and robust modelling of the impact of renewable generation on long term security of supply and demand

IEEE Power and Energy Society General Meeting

Volumn 2015-September, Issue , 2015, Pages

  Troffaes, Matthias C M ^a   Williams, Edward ^a   Dent, Chris ^a  

^a DURHAM UNIVERSITY   (United Kingdom)

Author keywords

Analytical models; Biological system modeling; Correlation; Data models; Histograms; Sensitivity analysis; Wind power generation

Indexed keywords

ANALYTICAL MODELS; CORRELATION METHODS; DATA STRUCTURES; ECONOMICS; ELECTRIC POWER GENERATION; SENSITIVITY ANALYSIS; WIND POWER;

BIOLOGICAL SYSTEM MODELING; CONVENTIONAL GENERATION; EXPECTED ENERGY NOT SERVED; HISTOGRAMS; HISTORICAL OBSERVATION; RENEWABLE GENERATION; STATISTICAL TECHNIQUES; TIME-DEPENDENT MODELS;

BIOLOGICAL SYSTEMS;

EID: 84956854255     PISSN: 19449925     EISSN: 19449933     Source Type: Conference Proceeding    
DOI: 10.1109/PESGM.2015.7286070     Document Type: Conference Paper

References (12)

1. C. J. Dent and S. Zachary, "Further results on the probability theory of capacity value of additional generation," in International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), Durham, England, 2014.
2. R. Billinton, Y. Gao, and R. Karki, "Composite system adequacy assessment incorporating large-scale wind energy conversion systems considering wind speed correlation," IEEE Transactions on Power Systems, vol. 24, no. 3, pp. 1375-1382, Aug. 2009.
3. W. Wangdee and R. Billinton, "Considering load-carrying capability and wind speed correlation of WECS in generation adequacy assessment," IEEE Transactions on Energy Conversion, vol. 21, no. 3, pp. 734-741, Sep. 2006.
4. R. Karki, P. Hu, and R. Billinton, "A simplified wind power generation model for reliability evaluation," IEEE Transactions on Energy Conversion, vol. 21, no. 2, pp. 533-540, Jun. 2006.
5. P. Pinson, "Very-short-term probabilistic forecasting of wind power with generalized logitnormal distributions," Journal of the Royal Statistical Society: Series C (Applied Statistics), vol. 61, no. 4, pp. 555-576, 2012.
6. J. Tastu, P. Pinson, P.-J. Trombe, and H. Madsen, "Probabilistic forecasts of wind power generation accounting for geographically dispersed information," IEEE Transactions on Smart Grid, vol. 5, no. 1, pp. 480-489, Jan. 2014.
7. P. Walley, Statistical Reasoning with Imprecise Probabilities. London: Chapman and Hall, 1991.
8. M. C. M. Troffaes and G. de Cooman, Lower Previsions, ser. Wiley Series in Probability and Statistics. Wiley, 2014. [Online]. Available: http://eu. wiley. com/WileyCDA/WileyTitle/productCd-0470723777. html
9. M. Falk, F. Marohn, R. Michel, D. Hofmann, M. Macke, C. Spachmann, and S. Englert, A First Course on Time Series Analysis with SAS. epubli GmbH, Aug. 2012. [Online]. Available: http://statistik. mathematik. uni-wuerzburg. de/timeseries/
10. C. J. Geyer, "Practical Markov chain Monte Carlo," Statistical Science, vol. 7, no. 4, pp. 473-483, 1992. [Online]. Available: http://www. jstor. org/stable/2246094
11. A. B. Attya, Y. G. Hegazy, and M. A. Moustafa, "Random operation of conventional distributed generators based on generation techniques," in Canadian Conference on Electrical and Computer Engineering (CCECE 2008), May 2008, pp. 1203-1206.
12. C.-L. Hor, S. Watson, and S. Majithia, "Analyzing the impact of weather variables on monthly electricity demand," IEEE Transactions on Power Systems, vol. 20, no. 4, pp. 2078-2085, Nov. 2005.