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Cites:

Information Journal Paper

Title

Optimal Bidding Strategy of a GenCo Owning CAES in Energy and Reserve Markets using Stochastic Programming

Pages

  2-13

Abstract

 With the electricity industry restructuring along with the increasing expansion of renewable energy sources, the uncertainties in the system have increased. This has led to increased attention to electricity producers’ bidding methods. A new method has been proposed in this paper for the profit-based self-scheduling of a generation company (Gen Co. ) comprising a compressed air energy storage (CAES) along with wind and thermal power plants. Stochastic Optimization has been used in the proposed algorithm due to the uncertainties of parameters like energy and spinning reserve prices, the amount of called-on reserve, the imbalance cost and wind power generation, . The Gen Co’ s optimal bids for energy and spinning reserve markets are obtained. The proposed method is initially applied to a test system from another paper; comparing its results with the results of the previous method demonstrates the capability of the proposed method in the scheduling of CAES units. Finally, the data of a real power system are used to present the complementary results of the proposed algorithm.

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  • Cite

    APA: Copy

    AKBARI, EBRAHIM, HOOSHMAND, RAHMAT ALLAH, Gholipour, Mehdi, & PARASTEGARI, MOEIN. (2020). Optimal Bidding Strategy of a GenCo Owning CAES in Energy and Reserve Markets using Stochastic Programming. JOURNAL OF ENERGY MANAGEMENT, 10(2 ), 2-13. SID. https://sid.ir/paper/378760/en

    Vancouver: Copy

    AKBARI EBRAHIM, HOOSHMAND RAHMAT ALLAH, Gholipour Mehdi, PARASTEGARI MOEIN. Optimal Bidding Strategy of a GenCo Owning CAES in Energy and Reserve Markets using Stochastic Programming. JOURNAL OF ENERGY MANAGEMENT[Internet]. 2020;10(2 ):2-13. Available from: https://sid.ir/paper/378760/en

    IEEE: Copy

    EBRAHIM AKBARI, RAHMAT ALLAH HOOSHMAND, Mehdi Gholipour, and MOEIN PARASTEGARI, “Optimal Bidding Strategy of a GenCo Owning CAES in Energy and Reserve Markets using Stochastic Programming,” JOURNAL OF ENERGY MANAGEMENT, vol. 10, no. 2 , pp. 2–13, 2020, [Online]. Available: https://sid.ir/paper/378760/en

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