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Security-constrained optimal power flow with wind and thermal power generators using fuzzy adaptive artificial physics optimization algorithm

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Abstract

In this paper, a new fuzzy adaptive artificial physics optimization (FAAPO) algorithm is used to solve security-constrained optimal power flow (SCOPF) problem with wind and thermal power generators. The stochastic nature of wind speed is modeled as a Weibull probability density function. The production cost is modeled with the overestimation and underestimation of available wind energy and included in the conventional SCOPF. Wind generation cost model comprises two components, viz. reserve capacity cost for wind power surplus and penalty cost for wind power shortage. The selection of optimal gravitational constant (G) is a tedious process in conventional artificial physics optimization (APO) method. To overcome this limitation, the gravitational constant (G) is fuzzified in this work. Therefore, based upon the requirement, the gravitational constant changes adaptively. Hence, production cost is reduced, settles at optimum point and takes less number of iterations. The proposed algorithm is tested on IEEE 30-bus system and Indian 75-bus practical system, including wind power in both the test systems. It is observed that FAAPO can outperform BAT algorithm and APO algorithm. Hence, the proposed algorithm can be used for integration of wind power with thermal power generators.

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Authors and Affiliations

  1. Department of Electrical Engineering, National Institute of Technology Warangal, Warangal, India

    Kiran Teeparthi & D. M. Vinod Kumar

Authors
  1. Kiran Teeparthi
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  2. D. M. Vinod Kumar
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Correspondence to Kiran Teeparthi.

Appendix

Appendix

See Tables 11 and 12.

Table 11 IEEE 30-bus system: generator cost coefficients and their limits
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Table 12 Indian 75-bus system: generator cost coefficients and their limits
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Teeparthi, K., Vinod Kumar, D.M. Security-constrained optimal power flow with wind and thermal power generators using fuzzy adaptive artificial physics optimization algorithm. Neural Comput & Applic 29, 855–871 (2018). https://doi.org/10.1007/s00521-016-2476-4

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