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A semi-Markov model of the variability of power generation from renewable sources

Autorzy
Treść / Zawartość
Identyfikatory
Warianty tytułu
Konferencja
Conference of the Polish Society of Operation and Systems Research BOS(12; 17–19.09.2012; Warsaw, Poland)
Języki publikacji
EN
Abstrakty
EN
The paper presents a new approach to modeling the variability of power generation from a renewable source such as wind or flowing water. The force of the power generating agent is assumed to change according to the semi-Markov process with finite state space. For the purpose of its construction, the range of possible values expressing the agent’s force is divided into a finite number of subintervals. It is natural to assume that the time during which the agent’s force remains within one such interval, and the probabilities of transitions to neighboring intervals depend to some extent on the agent’s earlier behavior. The model’s accuracy is determined by the number of subintervals used and the assumed degree to which the agent’s force depends on its history. This degree is expressed by the number of the most recently entered subintervals relevant to predicting the agent’s future behavior. According to the presupposed accuracy level, an appropriately complex state-space and a diagram of the inter-state transitions for the modeled process have been constructed. Subsequently, it is demonstrated how certain parameters of this process, related to forecasting power generation, can be calculated by means of the calculus of the Laplace transforms.
Rocznik
Strony
81--90
Opis fizyczny
Bibliogr. 9 poz., rys.
Twórcy
  • Systems Research Institute of the Polish Academy of Sciences, ul. Newelska 6, 01-447 Warsaw, Poland
Bibliografia
  • [1] AI B., YANG H., SHEN H., LIAO X., Computer-aided design of PV/wind hybrid system, Renewable Energy, 2003, 28, 1492–1512.
  • [2] ALBADI M.H., EL-SAADANY E.F., Overview of wind power intermittency impacts on power systems, Electric Power Systems Research, 2010, 80, 627–632.
  • [3] CARTA J.A., RAMIREZ P., VELAZQUEZ S., A review of wind speed probability distributions used in wind energy analysis, Renewable and Sustainable Energy Reviews, 2009, 13, 933–955.
  • [4] JAFARIAN M., RANJBAR A.M., Fuzzy modeling techniques and artificial neural networks to estimate annual energy output of a wind turbine, Renewable Energy, 2010, 35, 2008–2014.
  • [5] KARKI R., PO H., BILLINTON R., A simplified wind power generation model for reliability evaluation, IEEE Transactions on Energy Conversion, 2006, 21, 533–540.
  • [6] KULKARNI M.A., PATIL S., RAMA G.V., SEN P.N., Wind speed prediction using statistical regression and neural network, Journal of Earth System Science, 2008, 117, 457–463.
  • [7] LIN Z., ZHANG D., GAO L., KONG Z., Using an adaptive self-tuning approach to forecast power loads, Neurocomputing, 2008, 71, 559–563.
  • [8] LIU H., TIAN H.-Q., CHEN C., LI Y.-F., A Hybrid Statistical Method to Predict Wind Speed and Wind Power, Renewable Energy, 2010, 35, 1857–1861.
  • [9] XIAO Y.Q., LI Q.S., LI Z.N., CHOW Y.W., LI G.Q., Probability distributions of extreme wind speed and its occurrence interval, Engineering Structures, 2006, 28, 1173–1181.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b844e60f-1969-455c-9a60-4f19130e8307
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