A Synthesis Method to Generate Hourly Electricity Production Time-series of Wind Plants in Peru for Long-term Expansion Planning

Authors

  • Ruben Felix Facultad de Ingeniería Mecánica, Universidad Nacional de Ingeniería, Rímac, Lima, Peru https://orcid.org/0000-0002-6046-9551
  • Jaime E. Luyo Facultad de Ingeniería Mecánica, Universidad Nacional de Ingeniería, Rímac, Lima, Peru

Keywords:

Expansion planning, power systems, renewable energy, synthesis methods, wind energy

Abstract

Plenty of works have treated the system expansion planning problem in the presence of intermittent renewable energy resources like wind. However, most of those proposals have been approached from scenarios of plenty of data, which is not the rule in developing countries, where principal investment actors have recently switched their focus. In contrast to operation problems where existing literature can be successfully applied since it requires short-term historical time-series gathered from the same studied plants, proposals for planning problems are almost impossible to apply because of a lack of information and measurement about renewable resources in places where no renewable plants have been previously installed. In order to fill this information gap, this paper presents a novel methodology to synthesize wind production time-series on an hourly time scale, taking as inputs aggregate data such as monthly wind speed average values and Weibull annual parameters. The methodology comprises four steps, from data gathering to calculating electrical power produced by a wind farm. Three application tests are performed for different places in India, Chile, and Peru to validate the proposed methodology. The results show that the methodology successfully synthesizes time-series of output power, correctly achieves persistence characteristics, and slightly over or underestimates the produced wind energy, having a discrepancy of ±6.2% in the yearly total.

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Author Biographies

Ruben Felix, Facultad de Ingeniería Mecánica, Universidad Nacional de Ingeniería, Rímac, Lima, Peru

Received a B.Sc. in Mechanical Electrical Engineering in 2013 and an M.Sc. in Energetics in 2019 from the Universidad Nacional de Ingeniería (UNI), Peru. Specialized in Machine Learning by the Universidad Ricardo Palma (URP), Peru and in Solar Energy by the ational Institute of Solar Energy (NISE), India. He is currently a Ph.D. researcher at UNI with interest in renewable energies, energy planning and planning optimization.

Jaime E. Luyo, Facultad de Ingeniería Mecánica, Universidad Nacional de Ingeniería, Rímac, Lima, Peru

Holds a Ph.D. in Economics from the Universidad Nacional Mayor de San Marcos (UNMSM), Peru. Mechanical Electrical Engineer, graduated with "Unanimous Distinction" at Universidad Nacional de Ingeniería (UNI). Master in Electrical Engineering (Systems and Control) by the Rensselaer Polytechnic Institute, New York, USA. Former Director of the Doctoral Program in Energetics at UNI and former Vice Minister of Electricity of Peru.

References

IRENA, “Global energy transformation: A roadmap to 2050 (2019 edition),” tech. rep., IRENA, 2019.

IRENA, “Renewable Power Generation Costs in 2021,” tech. rep., 2022.

IEA, “World Energy Investment 2022,” tech. rep., 2022.

M. R. R. Tabar, M. Anvari, G. Lohmann, D. Heinemann, M. Wächter, P. Milan, E. Lorenz, and J. Peinke, “Kolmogorov spectrum of renewable wind and solar power fluctuations,” The European Physical Journal Special Topics, vol. 223, pp. 2637–2644, 10 2014.

M. Anvari, G. Lohmann, M. Wächter, P. Milan, E. Lorenz, D. Heinemann, M. R. R. Tabar, and J. Peinke, “Short term fluctuations of wind and solar power systems,” New Journal of Physics, vol. 18, p. 063027, 6 2016.

D. K. Critz, S. Busche, and S. Connors, “Power systems balancing with high penetration renewables: The potential of demand response in Hawaii,” Energy Conversion and Management, vol. 76, pp. 609–619, 12 2013.

COES, “Technical Procedure PR-01 - Short-Term Ooperation programming,” 2014.

COES, “Technical Procedure PR-37 - Medium-term operation programming,” 2016.

F. A. Wolak, “Long-Term Resource Adequacy in Wholesale Electricity Markets with Significant Intermittent Renewables,” tech. rep., National Bureau of Economic Research, Cambridge, MA, 7 2021.

A. Heydari, D. Astiaso Garcia, F. Keynia, F. Bisegna, and L. De Santoli, “A novel composite neural network based method for wind and solar power forecasting in microgrids,” Applied Energy, vol. 251, p. 113353, 10 2019.

Ã. B. Filik and T. Filik, “Wind Speed Prediction Using Artificial Neural Networks Based on Multiple Local Measurements in Eskisehir,” Energy Procedia, vol. 107, pp. 264–269, 2 2017.

C. Fu, G.-Q. Li, K.-P. Lin, and H.-J. Zhang, “Short-Term Wind Power Prediction Based on Improved Chicken Algorithm Optimization Support Vector Machine,” Sustainability, vol. 11, p. 512, 1 2019.

T. Ouyang, X. Zha, L. Qin, Y. He, and Z. Tang, “Prediction of wind power ramp events based on residual correction,” Renewable Energy, vol. 136, pp. 781–792, 6 2019.

H. C. Bylling, S. Pineda, and T. K. Boomsma, “The impact of short-term variability and uncertainty on long-term power planning,” Annals of Operations Research, vol. 284, no. 1, pp. 199–223, 2020.

Y. Feng, Scenario generation and reduction for long-term and short-term power system generation planning under uncertainties. PhD thesis, Iowa State University, Ames, 2014.

S. Tabrizian, “Technological innovation to achieve sustainable development - Renewable energy technologies diffusion in developing countries,” Sustainable Development, vol. 27, pp. 537–544, 5 2019

S. Sen and S. Ganguly, “Opportunities, barriers and issues with renewable energy development - A discussion,” Renewable and Sustainable Energy Reviews, vol. 69, pp. 1170–1181, 3 2017.

F. C. Kaminsky, R. H. Kirchhoff, C. Y. Syu, and J. F. Manwell, “A Comparison of Alternative Approaches for the Synthetic Generation of a Wind Speed Time Series,” Journal of Solar Energy Engineering, vol. 113, no. 4, p. 280, 1991.

H. G. Beyer and K. Nottebaum, “Synthesis of long-term hourly wind speed time series on the basis of European wind atlas data,” Solar Energy, vol. 54, pp. 351–355, 5 1995.

R. S. R. Gorla, M. K. Pallikonda, and G. Walunj, “Use of Rayleigh Distribution Method for Assessment of Wind Energy Output in Cleveland-Ohio,” Renewable Energy Research and Applications, vol. 1, no. 1, pp. 11–18, 2020.

A. D. Sahin and Z. Sen, “First-order Markov chain approach to wind speed modelling,” Journal of Wind Engineering and Industrial Aerodynamics, 2001.

A. Shamshad, M. A. Bawadi, W. M. Wan Hussin, T. A. Majid, and S. A. Sanusi, “First and second order Markov chain models for synthetic generation of wind speed time series,” Energy, 2005.

F. O. Hocaoglu, Ã. N. Gerek, and M. Kurban, “The Effect of Markov Chain State Size for Synthetic Wind Speed Generation,” Probabilistic Methods Applied to Power Systems, 2008. PMAPS’08. Proceedings of the 10th International Conference, 2008.

K. Brokish and J. Kirtley, “Pitfalls of modeling wind power using Markov chains,” in 2009 IEEE/PES Power Systems Conference and Exposition, PSCE 2009, 2009.

A. N. Legesse, A. K. Saha, and R. P. Carpanen, “Generating wind speed time series for time domain simulation of wind turbines,” in 5th Southern African Universities Power Engineering Conference, (Stellenbosch), 2017.

N. B. Negra, O. Holmstrøm, B. Bak-Jensen, and P. Sørensen, “Model of a synthetic wind speed time series generator,” Wind Energy, 2008.

R. Turner, X. Zheng, N. Gordon, M. Uddstrom, G. Pearson, R. de Vos, and S. Moore, “Creating Synthetic Wind Speed Time Series for 15 New Zealand Wind Farms,” Journal of Applied Meteorology and Climatology, vol. 50, pp. 2394–2409, 12 2011.

J. Chen and C. Rabiti, “Synthetic wind speed scenarios generation for probabilistic analysis of hybrid energy systems,” Energy, vol. 120, pp. 507–517, 2 2017.

S. Rose and J. Apt, “Generating wind time series as a hybrid of measured and simulated data,” Wind Energy, vol. 15, pp. 699–715, 7 2012.

S. Hagspiel, A. Papaemannouil, M. Schmid, and G. Andersson, “Copula-based modeling of stochastic wind power in Europe and implications for the Swiss power grid,” Applied Energy, 2012.

C. Sarmiento, C. Valencia, and R. Akhavan-Tabatabaei, “Copula autoregressive methodology for the simulation of wind speed and direction time series,” Journal of Wind Engineering and Industrial Aerodynamics, 2018.

R. Carapellucci and L. Giordano, “A methodology for the synthetic generation of hourly wind speed time series based on some known aggregate input data,” Applied Energy, 2013.

A. Naimo, “A Novel Approach to Generate Synthetic Wind Data,” Procedia - Social and Behavioral Sciences, 2014.

D. D. Ambrosio, J. Schoukens, T. D. Troyer, M. Zivanovic, and M. C. Runacres, “Synthetic wind speed generation for the simulation of realistic diurnal cycles,” Journal of Physics: Conference Series, vol. 1618, p. 062019, 9 2020.

MEM Peru, “Peruvian Wind Atlas,” 2016.

Numerical Technologies, “Ntrand,” 2022.

NASA, “POWER,” 2022.

NREL, “RE Explorer,” 2022.

C. G. Justus, W. R. Hargraves, A. Mikhail, and D. Graber, “Methods for Estimating Wind Speed Frequency Distributions,” Journal of Applied Meteorology, vol. 17, pp. 350–353, 3 1978.

K. S. P. Kumar and S. Gaddada, “Statistical scrutiny of Weibull parameters for wind energy potential appraisal in the area of northern Ethiopia,” Renewables: Wind, Water, and Solar, vol. 2, p. 14, 12 2015.

N. Aghbalou, A. Charki, S. Elazzouzi, and K. Reklaoui, “A probabilistic assessment approach for wind turbine-site matching,” International Journal of Electrical Power & Energy Systems, vol. 103, pp. 497–510, 12 2018.

R Core Team, “R: A Language and Environment for Statistical Computing,” 2017.

W. N. Venables and B. D. Ripley, Modern Applied Statistics with S. New York: Springer, fourth ed., 2002.

A. Colmenar-Santos, S. Campíez-Romero, L. Enríquez-Garcia, and C. Pérez-Molina, “Simplified Analysis of the Electric Power Losses for On-Shore Wind Farms Considering Weibull Distribution Parameters,” Energies, vol. 7, pp. 6856–6885, 10 2014.

IEC, “IEC 61400-12-1:2017 Wind energy generation systems - Power performance measurements of electricity producing wind turbines,” tech. rep., 2017.

J. F. Manwell, J. G. McGowan, and A. L. Rogers, Wind Energy Explained: Theory, Design and Application, Second Edition. 2010.

D. Spera and T. Richards, “Modified Power Law Equations for Vertical Wind Profiles,” Wind Characteristics and Wind Energy Siting Conference, 1979.

HOMER, “HOMER Pro User Manual,” 2022.

M. Lydia, S. S. Kumar, A. I. Selvakumar, and G. E. Prem Kumar, “A comprehensive review on wind turbine power curve modeling techniques,” Renewable and Sustainable Energy Reviews, vol. 30, pp. 452–460, 2 2014.

E. Diaz-Dorado, C. Carrillo, J. Cidras, and E. Albo, “Estimation of energy losses in a Wind Park,” in 2007 9th International Conference on Electrical Power Quality and Utilisation, pp. 1–6, IEEE, 10 2007.

P. K. Steimer and O. Apeldoorn, “Medium voltage power conversion technology for efficient windpark power collection grids,” in The 2nd International Symposium on Power Electronics for Distributed Generation Systems, pp. 12–18, IEEE, 6 2010.

N. Inaba, R. Takahashi, J. Tamura, M. Kimura, A. Komura, and K. Takeda, “A consideration on loss characteristics and annual capacity factor of offshore wind farm,” in 2012 XXth International Conference on Electrical Machines, pp. 2022–2027, IEEE, 9 2012.

A. Madariaga, C. Martinez de Ilarduya, S. Ceballos, I. Martinez de Alegria, and J. Martin, “Electrical losses in multi-MW wind energy conversion systems,” Renewable Energy and Power Quality Journal, pp. 322–327, 4 2012.

G. S. Böhme, E. A. Fadigas, A. L. Gimenes, and C. E. Tassinari, “Wake effect measurement in complex terrain - A case study in Brazilian wind farms,” Energy, vol. 161, pp. 277–283, 10 2018.

EWEA, “Wind Farm Energy Loss Factors,” tech. rep., European Wind Energy Association, Brussels, Belgium, 2019.

Siemens, “Siemens Wind Turbine SWT-2.3-108,” tech. rep., Erlangen, Germany, 2011.

ME Chile, “Wind and Solar resource measurement campaign,” 2019.

A. P. Grantham, P. J. Pudney, and J. W. Boland, “Generating synthetic sequences of global horizontal irradiation,” Solar Energy, vol. 162, no. November 2017, pp. 500–509, 2018.

COES, “Technical Specification of Power Plants,” 2022.

Published

2023-04-18

How to Cite

Felix, R., & Luyo, J. E. (2023). A Synthesis Method to Generate Hourly Electricity Production Time-series of Wind Plants in Peru for Long-term Expansion Planning. IEEE Latin America Transactions, 21(5), 662–670. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/7410

Issue

Section

Electric Energy