Abstract
This paper presents a novel modeling technique for modeling wind based renewable energy sources considering their intermittent nature. This algorithm utilizes historical data of wind speeds and take into consideration wind power characteristics by determining the most closely cumulative distribution function. Monte Carlo Simulation is used for determining the most likelihood wind power at each hour at each season. Furthermore, the same modeling algorithm is applied for modeling system demand. The outcomes from the proposed technique and another probabilistic model are compared for showing the validity of the introduced technique. The introduced methodology is implemented using MATLAB program, Finally, the outcomes show that the introduced novel strategy maintains an appropriate result.
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Abdelaziz, A.Y., Othman, M.M., Ezzat, M., Mahmoud, A.M., Kanwar, N. (2018). A Probabilistic Modeling Strategy for Wind Power and System Demand. In: Zelinka, I., Senkerik, R., Panda, G., Lekshmi Kanthan, P. (eds) Soft Computing Systems. ICSCS 2018. Communications in Computer and Information Science, vol 837. Springer, Singapore. https://doi.org/10.1007/978-981-13-1936-5_64
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DOI: https://doi.org/10.1007/978-981-13-1936-5_64
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