A Semi-Markov Model of the Variability of Power Generation from Renewable Sources

• Autorzy: Jacek Malinowski
• 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. (original abstract)

Słowa kluczowe

EN

Energy production; Renewable energy sources; Mathematical modeling

PL

Produkcja energii; Odnawialne źródła energii; Modelowanie matematyczne

• Czasopismo: Operations Research and Decisions
• Rocznik: 2013
• Tom: 23
• Numer: nr 2
• Strony: 81--90

Twórcy

autor

Jacek Malinowski

• Polish Academy of Sciences

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.

Identyfikatory

DOI

10.5277/ord130207