A novel approach for wind speed variability estimation for smart energy system operation
A novel approach for wind speed variability estimation for smart energy system operation
- Author(s): S. Sengupta 1 ; S. Sengupta 1 ; H. Saha 1
- DOI: 10.1049/icp.2021.1089
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- Author(s): S. Sengupta 1 ; S. Sengupta 1 ; H. Saha 1
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View affiliations
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Affiliations:
1:
Centre of Excellence for Green Energy and Sensor Systems , IIEST Shibpur , India
Source:
Michael Faraday IET International Summit 2020 (MFIIS 2020),
2021
p.
106 – 114
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Affiliations:
1:
Centre of Excellence for Green Energy and Sensor Systems , IIEST Shibpur , India
- Conference: Michael Faraday IET International Summit 2020 (MFIIS 2020)
- DOI: 10.1049/icp.2021.1089
- ISBN: 978-1-83953-523-9
- Location: Online Conference
- Conference date: 03-04 October 2020
- Format: PDF
Due to the randomness of the sources, renewable integrated smart energy systems need to be modeled in a stochastic framework. For this, variability patterns of wind speed are generated by existing methods which involve large historical data handling. In this paper, day before data based synthetic wind speed patterns are generated using Discrete-Time Stochastic Petri Net (DTSPN) method for estimation of the variability. Once the possible scenarios are generated, wind power generation can be pre-estimated enabling a smart and cost effective energy system scheduling of operation. The novelty of this method is 1) it does not need any predefined statistical parameters of past data 2) it does not require assumption or analysis of the probability distribution function (pdf) that may be followed by historical data and 3) day-ahead wind speed pattern can be generated with the day before data only. Generated and actual wind speed patterns are compared to demonstrate a good match. Results of three existing methods are compared with those of the proposed method in terms of data requirement, accuracy and execution time, confirming the efficiency of this newly applied method. All the results are validated with actual data available from local infrastructure. To check the thorough applicability of the proposed method throughout the year, analysis has been carried out for different quarters of a year and demonstrative results are presented in the text.
Inspec keywords: stochastic processes; power generation scheduling; wind power; wind power plants; statistical distributions; discrete time systems; Petri nets; smart power grids; power engineering computing; data handling
Subjects: Other topics in statistics; Data handling techniques; Combinatorial mathematics; Combinatorial mathematics; Wind power plants; Other topics in statistics; Power engineering computing