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Optimal generator rescheduling with distributed slack bus model for congestion management using improved teaching learning based optimization algorithm

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Abstract

This article proposes a new strategy for Congestion Management (CM) through Generation Rescheduling (GR) with Distributed Slack Bus (DSB) model using Improved Teaching Learning Based Optimization (ITLBO) algorithm. Most of the previous research mainly focuses on rescheduling the existing generators for CM and least concerned about the consequence of this rescheduling on the slack bus. As a result, the slack bus is supposed to carry the entire residual effect of all other generators which leads to increased Congestion Management Cost (CMC). This work proposes a distributed slack bus model for reducing the excess burden on the slack bus and a two-fold contribution is made to develop the proposed technique. First, is the selection of the participating generator for CM by means of Incremental Generator Sensitivity Factor (IGSF), which adopts the generator sensitivity as well as the bidding cost. Next, CM is formulated as an optimization problem with the objective function involving congestion management cost and solved using ITLBO algorithm, which incorporates the self-motivated learning concept with the basic TLBO operators. For evaluation, the standard IEEE 30-bus and IEEE 118-bus systems are used and the obtained results are compared with the other CM systems reported through literature.

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Abbreviations

n :

number of buses in the system

n g :

number of generator buses in the system

n l :

number of transmission lines in the system

P ij :

real power flow through line k connected between buses i and j

Δ:

refers to change in a variable

R u g :

incremental bidding cost of generator at bus g in $/MW

R d g :

decremental bidding cost of generator at bus g in $/MW

ΔP u Gg :

incremental real power to be increased on generator at bus g for congestion alleviation

ΔP d Gg :

incremental real power to be decreased on generator at bus g for congestion alleviation

P 0 Gg :

value of real power generation at generator bus g resulted in market clearing procedure

P min Gg :

minimum limit on real power of generator bus g

P max Gg :

maximum limit on real power of generator at bus g

F k :

apparent power flow in MVA through transmission line k connected between buses i and j

F max k :

maximum limit of apparent power flow through transmission line k connected between buses i and j

F 0 k :

apparent power through transmission line k resulted in market clearing procedure

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Acknowledgements

This research work was funded by World Bank Group under Robert S McNamara Fellowship Grant.

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Authors and Affiliations

  1. Department of EEE, Sri Shakthi Institute of Engineering and Technology, Coimbatore, India

    S T SUGANTHI

  2. Department of EEE, Kalasalingam University, Krishnan koil, India

    D DEVARAJ

  3. Department of EEE, Anna University, Chennai, India

    S HOSIMIN THILAGAR

  4. Faculty of Engineering, Multimedia University, Cyberjaya, Malaysia

    K RAMAR

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  1. S T SUGANTHI
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  2. D DEVARAJ
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Correspondence to S T SUGANTHI.

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SUGANTHI, S.T., DEVARAJ, D., THILAGAR, S.H. et al. Optimal generator rescheduling with distributed slack bus model for congestion management using improved teaching learning based optimization algorithm. Sādhanā 43, 181 (2018). https://doi.org/10.1007/s12046-018-0941-8

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