Risk Averse Optimal Operation of Coastal Energy Hub Considering Seawater ‎Desalination and Energy Storage Systems

Document Type : Research paper

Authors

1 Department of Electrical, Biomedical, and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, ‎Qazvin, Iran

2 Faculty of Electrical Engineering, Shahid Beheshti University, Evin, Tehran, Iran

3 Department of Industrial Engineering, College of Technical and Engineering, Saveh Branch, Islamic Azad University ‎‎, Saveh, Iran‎

Abstract

An optimal day-ahead operation of a microgrid based on coastal energy hub is presented in this paper. The proposed CEH included wind turbine, photovoltaic unit, combined cooling, heat and power, and seawater desalination. The purpose of the optimization is minimization of the operational and environmental costs considering several technical limitations. The CEH includes an ice storage conditioner together with an energy storage system, i.e. thermal energy storage system. Particularly, the impacts of an innovative rechargeable and emerging ESS that is solar-powered compressed air energy storage is scrutinized, on the efficiency and operational and pollution costs of the CEH. It is clear that there is an intrinsic deviation between predicted and actual uncertainty variables in MG. This paper presents a bi-level stochastic optimal operation model based on risk averse strategy of information gap decision theory to overcome this information gap and to help Microgrid operator. To reduce the complexity of the proposed model, Karush-Kuhn-Tucker method is used for converting the bi-level problem into a single level. The Augmented Epsilon Constraint method is used to deals with multi objective optimization problem to harvest the maximum horizon of the uncertainties of the parameters. The proposed model implemented the Time of Use program as a price-based demand response program. Finally, the efficacy of the SPCAES for minimizing the operational cost and pollutions in the day-ahead operation is depicted by implementation of the presented model on the typical CEH.

Keywords

References

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Journal of Operation and Automation in Power Engineering
Volume 10, Issue 2
August 2022
Pages 90-104

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History

  • Receive Date: 26 April 2021
  • Revise Date: 26 May 2021
  • Accept Date: 29 May 2021

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