Skip to main navigation menu Skip to main content Skip to site footer

Stochastic Simulation to Determine the Present Net Value and Uncertainty Costin a Wind Power Plant

Abstract

This document presents a proposal for stochastic simulation to determine the investment uncertainties and the uncertainty cost of operation for a wind power plant. Taking as a study object a hypothetical wind plant in Cabo de la Vela, in La Guajira, Colombia, with the same characteristics as the current  Jepirachi plant of EPM, where the probability density function of the net present value of the project is found, and the probability of project profitability. Additionally, the uncertainty cost for the operation of the plant is determined depending on the different monthly scenarios of the primary energy (wind speed). This cost approximates a quadratic function that serves as a cost element in the problem of economic dispatch considering renewables. Finally, the results obtained and future developments are discussed

Keywords

Monte Carlo Simulations, Stochastic Process, Uncertainty Modeling, Wind Energy.

PDF (Español)

References

  • J. Hetzer, D. Yu and K. Bhattarai, An economic
  • dispatch model incorporating wind power, IEEE
  • Transactions on Energy Conversion, 23(2), 603- DOI: https://doi.org/10.1109/TEC.2008.918809
  • (2008)
  • J. Zhao, F. Wen, Z. Dong, Y. Xue and K. Wong,
  • Optimal dispatch of electric vehicles and wind
  • power using enhanced particle swarm optimization,
  • IEEE Transactions on Industrial Informatics,
  • (4), 889-899 (2012)
  • S. Surender, P. Bijwe and A. Abhyankar Realtime
  • economic dispatch considering renewable
  • power generation variability and uncertainty
  • over scheduling period, IEEE Systems Journal,
  • (4), 1440-1451 (2015)
  • J. Arévalo, F. Santos and S. Rivera, Uncertainty
  • Cost Functions for Solar Photovoltaic Generation,
  • Wind Energy Generation, and Plug-In Electric
  • Vehicles: Mathematical Expected Value and
  • Verification by Monte Carlo Simulation, International
  • Journal of Power and Energy Conversion,
  • in print.
  • Comisión de Regulación de Energía y Gas, Resolución
  • de 2015, por la cual se modifica la
  • metodología para determinar la energía firme
  • de plantas eólicas, 8 de mayo de 2015.
  • Unidad de Planeación Minero Energética, Registro
  • de Proyectos de Generación, Octubre de
  • Irina Khindanova, A Monte Carlo Model of a
  • Wind Power Generation Investment, Journal of
  • Applied Business and Economics vol. 15, 2013.
  • Roy Yates, David Goodman, Probability and
  • Stochastic Processes: A Friendly Introduction
  • for Electrical and Computer Engineers, Wiley,
  • M. Celeska, K. Najdenkoski, V. Stoilkov, A.
  • Buchkovska, Z. Kokolanski and V. Dimchev,
  • Estimation of Weibull parameters from wind
  • measurement data by comparison of statistical
  • methods, IEEE EUROCON 2015 - International
  • Conference on Computer as a Tool (EUROCON),
  • Salamanca, 2015, pp. 1-6.
  • R. Wang, W. Li and B. Bagen, Development
  • of Wind Speed Forecasting Model Based on the
  • Weibull Probability Distribution, 2011 International
  • Conference on Computer Distributed Control
  • and Intelligent Environmental Monitoring,
  • Changsha, 2011, pp. 2062-2065. DOI: https://doi.org/10.1055/s-0030-1259989
  • WindPower Program Wind Statistics
  • and the Weibull Distribution, disponible
  • en: http://www.wind-powerprogram.
  • com/wind_statistics.htm 2013.
  • Unidad de Planeación Minero Energética, Atlas
  • de Vientos y Energía Eólica, 2016.
  • Alvaro Pinilla, Luis Rodríguez, Rodrigo Trujillo,
  • Performance Evaluation of Jepirachi Wind
  • Park, Renewable Energy, 2008

Downloads

Download data is not yet available.

Similar Articles

You may also start an advanced similarity search for this article.