Flexibility Driven Generation Maintenance Scheduling in the Presence of Demand Response Resources to Attenuate Wind Output Variability Considering Gas Demand Uncertainty

Document Type : Original Article

Authors

1 Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

2 Electrical Engineering Department, Shahid Bahonar University of Kerman, Kerman, Iran.

3 Department of Electrical Engineering, Shahid Bahonar University of Kerman

10.22109/jemt.2023.344730.1389

Abstract

Generation maintenance scheduling (GMS) is one of the most important and influential programs on short-term scheduling. On the other hand, the variability nature of distributed renewable resources is led to the need for a power system to provide flexibility. In order to achieve a flexible operation, it is essential to develop a flexible GMS framework. For this purpose, it has used the flexibility index of the system in order to evaluate the flexibility of the power system. In flexibility studies, modeling and predicting the variability of renewable resources is important. Gas-fired power plants are one of the most important suppliers of flexibility in the supply-side. Therefore, the reliable operation of electricity grids depend on the natural gas availability . Furthermore, gas demand is subject to various uncertainties, especially in cold seasons, which will have significant effects on power system. In this paper, the uncertainties of wind and gas load is considered through forecasting by ARIMA method in Python. In this paper, natural gas and electricity demand responses are implemented as flexibility provisions from demand-side resources. It is worth noting that the objectives of increasing flexibility, leveling the energy index of reliability and reducing emission and costs have been considered as the objectives of optimizing GMS . The proposed framework is implemented on a modified IEEE 24 bus. According to the results, the system flexibility has been improved without increasing costs. The flexibility index in proposed model has improved by about 19.11%, due to the use of DRRs.

Keywords

Main Subjects

References

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Journal of Energy Management and Technology
Volume 7, Issue 3
September 2023
Pages 142-152

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History

  • Receive Date: 31 May 2022
  • Revise Date: 12 October 2022
  • Accept Date: 15 March 2023

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