Quadratic Modelling of Uncertainty Costs for Renewable Generation and its Application on Economic Dispatch
Abstract
This article presents a method for the inclusion in the economic dispatch of renewable energy generation plants. It is used uncertainty costs, which are inherent to the stochastic nature of their primary energy source. In order to reach this objective, it is taken several investigations that have been carried out the modeling of this cost of uncertainty and from these, to achieve a quadratic approximation that adapts to the classical cost functions used in the economic dispatch models.Visitas al artículo
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J. Hetzer, D. Yu and K. Bhattarai, emph{An economic dispatch model incorporating wind power}, IEEE Transactions on Energy Conversion, 23(2), 603-611 (2008)
J. Zhao, F. Wen, Z. Dong, Y. Xue and K. Wong, emph{Optimal Dispatch of Electric Vehicles and Wind Power Using Enhanced Particle Swarm Optimization},IEEE Transactions on Industrial Informatics, 8(4), 889-899 (2012)
C. Mend'ez, Direcci'on: Sergio Rivera, emph{Modelaci'on cuadr'atica de los costos de incertidumbre para generaci'on renevobles E'olica y Solar y su aplicaci'on en el Despacho Econ'omico}, Tesis Pregrado Universidad Nacional de Colombia, 2017.
S. Surender, P. Bijwe and A. Abhyankar, emph{Real-time economic dispatch considering renewable power generation variability and uncertainty,} IEEE Systems Journal, 9(4), 1440-1451 (2015)
T. P. Chang, emph{Investigation on frequency distribution of global radiation using different probability density functions,}Int.J. Appl. Sci. Eng, vol. 8, no. 2, 99?107, (2010).
J. Ar'evalo, F. Santos and S. Rivera, emph{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,
http://www.inderscience.com/info/ingeneral/forthcoming.php?jcode=ijpec
R. Montanari, emph{Criteria for the economic planning of a low power hydroelectric plant,}Renewable Energy, vol. 28(13), pp. 2129-2145, (2003)
P. Cabus, emph{River flow prediction through rainfall?runoff modelling with a probability-distributed model (PDM) in Flanders, Belgium,}Agricultural Water Management, vol. 95, no. 7, pp. 859-868, (2008)
N. Mujere, emph{Flood Frequency Analysis Using the Gumbel Distribution,}International Journal on Computer Science and Engineering (IJCSE), vol. 3, no. 7, pp. 2774-2778, (2011)
FSM Sanchez, SJP Sichac'a, SRR Rodriguez, emph{Formulaci'on de funciones de Costo de Incertidumbre en Peque~nas Centrales Hidroel'ectricas dentro de una Microgrid,}Ingenier'ias USBmed 8 (1), 29-36, (2017)
N. Thomopoulos, emph{Essentials of Monte Carlo simulations. Statistical methods for
building simulation models, Illinois Institute of Technology,}ISBNN 97978--1-4614-60-4614-60211-3, Springer Science,
Springer Science, (2013)
N. Zhang, P. K. Behera and C. Williams,emph{Solar radiation prediction based on particle swarm optimization and evolutionary algorithm using recurrent neural networks,}The IEEE International Systems Conference (SysCon), 15-18 April, 2013, doi:10.1109/SysCon.2013.6549894 (2013)
J. Meng, G. Li and Y. Du,emph{Economic dispatch for power systems with wind and solar energy integration considering reserve risk,}Power and Energy Engineering Conference (APPEEC), 8-11 Dec., 2013, doi:10.1109/APPEEC.2013.6837207 (2013)
T. Valencia, S Rivera,emph{Simulacion Estocastica para Determinar el Valor Presente Neto y el Costo de Incertidumbre en una Planta Eolica},(2018)
R. D. Zimmerman, C. E. Murillo-S'anchez, and R. J. Thomas,emph{MATPOWER: Steady-State Operations, Planning and Analysis Tools for Power Systems Research and Education,}Power Systems, IEEE Transactions on, vol. 26, no. 1, pp. 12-19, Feb. (2011). (Digital Object Identifier: 10.1109/TPWRS.2010.2051168)
