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A novel chaotic differential evolution hybridized with quadratic programming for short-term hydrothermal coordination

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Abstract

In this paper, a viable global optimizer based on chaotic differential evolution is hybridized with sequential quadratic programming, an efficient local search technique to exploit short-term hydrothermal coordination (STHTC) involved for power generation and its efficient management. A multi-objective optimization framework is established for minimizing the total cost of thermal generators with valve point loading effects satisfying power balance constraint as well as generator operating and hydrodischarge limits, respectively. The proposed model is implemented on various systems comprising hydrogenerating units as well as different thermal units. The results are compared with state-of-the-art heuristic techniques recently employed on STHTC problems, while the reliability, stability and effectiveness of the proposed framework are validated through the comprehensive analysis of Monte Carlo simulations.

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Abbreviations

x THi , y THi , z THi , u THi , e THi :

Cost coefficients for thermal power generation

P TH it :

Generated output power in time t of thermal unit i

\( P_{{{\text{TH}}i}}^{\hbox{min} } ,P_{{{\text{TH}}i}}^{\hbox{max} } \) :

Minimum and maximum thermal generation limits for unit i

P d t :

Power demand at time t

P l t :

Losses due to power transmission at time t

N TH, N h :

Total number of thermal and hydroelectric units

DR i , UR i :

Down and up ramp rate limits of thermal unit i

Q hjt , V hjt :

Water discharge rate and storage volume of jth reservoir at time

P h jt :

Generated power from jth hydroelectric unit at time t

C 1 j , C 2 j , C 3 j , C 4 j , C 5 j , C 6 j :

Power generation coefficients of jth hydroelectric unit

\( P_{{{\text{h}}j}}^{\hbox{min} } ,P_{{{\text{h}}j}}^{\hbox{max} } \) :

Lower and upper limits of jth hydroelectric unit

\( Q_{{{\text{h}}j}}^{\hbox{min} } ,Q_{{{\text{h}}j}}^{\hbox{max} } \) :

Minimum and maximum water discharge rate of jth reservoir

t, T :

Time index and scheduling period

Pop:

Randomly generated population of candidate solutions

P size :

Population size

STHTCsize :

Problem size

Β :

Weight factor

Crate:

Crossover rate

ζ:

Chaotic variable

f val :

Cost of the fitness evaluation function

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Acknowledgement

Authors acknowledge funding provided by Higher Education Commission, Pakistan, via Grant No. [213-59038-2EG2-092(50023576)].

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Authors and Affiliations

  1. Department of Electrical Engineering, Comsats Institute of Information Technology, Wah, Pakistan

    F. A. Chaudhry, M. Amin, M. Iqbal & R. D. Khan

  2. HITEC University, Taxila, Pakistan

    J. A. Khan

Authors
  1. F. A. Chaudhry
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  2. M. Amin
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  3. M. Iqbal
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  4. R. D. Khan
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Correspondence to F. A. Chaudhry.

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Chaudhry, F.A., Amin, M., Iqbal, M. et al. A novel chaotic differential evolution hybridized with quadratic programming for short-term hydrothermal coordination. Neural Comput & Applic 30, 3533–3544 (2018). https://doi.org/10.1007/s00521-017-2940-9

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