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Multi-objective chance-constrained transmission congestion management through optimal allocation of energy storage systems and TCSC devices

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Abstract

In this paper, a multi-objective mixed-integer nonlinear programming model for transmission congestion management through optimal placement and sizing of thyristor-controlled series capacitor (TCSC) devices and electrical energy storages (EESs) is presented. This problem is modeled as a two-objective optimization problem, where objective functions are maximizing social welfare and minimizing flow-gate marginal price index. The proposed model is implemented on 30-bus and 118-bus transmission systems and includes thermal units and wind farms. In order to model the uncertainties of load demand and wind speed, the chance-constrained method has been utilized. Also, in order to solve the proposed model, a modified gray wolf optimizer algorithm has been introduced, where the results demonstrate that the proposed algorithm compared to the other three algorithms not only reduced the computational time, but also achieved more optimal results. In addition, the results illustrate that the optimal placement of TCSCs and EESs has led to a 57.97% reduction in congestion surplus. The results also demonstrate that the implementation of demand response program by increasing the flexibility of the system leads to a smoother local marginal price curve in the network and thus reduces the congestion surplus by 8.71%.

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Authors and Affiliations

  1. Department of Electrical Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran

    Rahman Ansaripour, Hassan Barati & Ahmad Ghasemi

  2. Department of Electrical and Computer Engineering, Jundi-Shapur University of Technology, Dezful, Iran

    Ahmad Ghasemi

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  1. Rahman Ansaripour
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  2. Hassan Barati
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  3. Ahmad Ghasemi
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Correspondence to Hassan Barati.

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Ansaripour, R., Barati, H. & Ghasemi, A. Multi-objective chance-constrained transmission congestion management through optimal allocation of energy storage systems and TCSC devices. Electr Eng 104, 4049–4069 (2022). https://doi.org/10.1007/s00202-022-01599-0

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