Co-optimized bidding strategy of an integrated wind-thermal system in electricity day ahead and reserve market under uncertainties

Document Type : Power Article

Authors

1 management faculty , Allame Tabataba'e University, Tehran, Iran.

2 Industrial Management Dept, Faculty of Management and Accounting , Allameh Tabataba’i University, Tehran, Iran

3 Associate Professor , Department of Management and Accounting , Allameh Tabataba’i University, Tehran, Iran

Abstract

Nowadays renewable energy sources, such as wind and solar, whether independently or integrated with other resources, are considered in power system, specifically self-scheduling, bidding and offering strategy problems. However, the uncertain nature of these sources has turned out the greatest challenge for their owners, which makes the bidding and offering in the restructured electricity market more complicated because wind energy generation may cause penalty fees for its generation mismatches. Hence, the primary objective of this paper is to suggest a novel bidding strategy framework based on fuzzy random variable for a wind-thermal system in the electricity market for the first time. The uncertainties associated with day ahead energy, spinning reserve market prices and imbalance prices, are characterized by random fuzzy variables and the uncertainties associated with wind power outputs are modeled as a LR fuzzy numbers. The proposed self-scheduling model maximizes the expected profits while it controls the risk by providing different possibility and probability levels for decision makers.

A mathematical modeling approach is applied in this research by using a mixed-integer non-linear programming model which is implemented in Lingo software in a case study of thermal generation unit to investigate the efficiency of the proposed model. A sensitivity analysis is applied to validate the performance of the proposed model. Numerical results reveal that taking advantage of wind power generation alongside with thermal generation will substantially increase the profitability of the integrated generation company.

Keywords

Main Subjects

References

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Journal of Modeling in Engineering
Volume 21, Issue 75
December 2023
Pages 29-51

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History

  • Receive Date: 14 December 2022
  • Revise Date: 20 March 2023
  • Accept Date: 06 June 2023

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