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access icon free Impact of scaled fitness functions on a floating-point genetic algorithm to optimise the operation of standalone microgrids

  • Author(s): Munira Batool 1 ; Farhad Shahnia 2 ; Syed M. Islam 3
    • View affiliations
  • Source: Volume 13, Issue 8, 10 June 2019, p. 1280 – 1290
    DOI:  10.1049/iet-rpg.2018.5519 , Print ISSN 1752-1416, Online ISSN 1752-1424
© The Institution of Engineering and Technology
Received 02/07/2018, Accepted 12/02/2019, Revised 01/12/2018, Published 19/02/2019

Standalone hybrid remote area power systems, also known as microgrids (MGs), can provide reasonably priced electricity in geographically isolated and the edge of grid locations for their operators. To achieve the reliable operation of MGs, whilst consuming minimal fossil fuels and maximising the penetration of renewables, the voltage and frequency should be maintained within acceptable limits. This can be accomplished by solving an optimisation problem. Floating-point genetic algorithm (FP-GA) is a heuristic technique that has a proven track record of effectively identifying the optimal solutions. However, in addition to needing appropriate operators, the solver needs a fitness function to yield the most optimal control variables. In this study, a suitable fitness function is formulated, by including the operational, interruption and technical costs, which are then solved with an FP-GA, with different combinations of operators. The developed fitness function and the considered operators are tested for the non-linear optimisation problem of a 38-bus MG. Detailed discussions are provided on the impact, which different operators have upon the outcomes of the fitness function.

Inspec keywords: costing; power generation economics; power markets; distributed power generation; floating point arithmetic; nonlinear programming; power generation reliability; genetic algorithms

Other keywords: fossil fuels; electricity pricing; scaled fitness functions; standalone microgrids; floating-point genetic algorithm; MG; FP-GA; standalone hybrid remote area power systems; optimal control variables; nonlinear optimisation problem

Subjects: Optimisation techniques; Reliability; Power system management, operation and economics; Distributed power generation

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