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A Sustainable Energy Investment Planning Model Based on the Micro-Grid Concept Using Recent Metaheuristic Optimization Algorithms

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conference contribution
posted on 18.08.2021, 00:33 by Soheil MohseniSoheil Mohseni, Alan BrentAlan Brent, Daniel BurmesterDaniel Burmester
This paper develops an innovative sustainable energy investment planning framework to select the most economically viable option among a range of conceptual micro-grid (MG) projects proposed to be implemented in an area, as well as the sizes of the associated components. It is assumed that the technical feasibility of a project is verified in advance based on the estimation of the potentials of renewable energy sources. Since the considered optimal capacity planning problem is not amenable to exact methods of optimization due to its non-deterministic polynomial-time hard (NP-hard) nature, metaheuristic optimization algorithms (MHOAs) are used in the devised optimum sizing approach. We demonstrate the applicability and efficacy of the proposed modelling framework based on three representative MGs in different topologies. The efficiencies of four newly introduced MHOAs viz. the moth-flame optimization algorithm, the sine-cosine algorithm, the multi-verse optimizer, and the water evaporation optimization algorithm are examined in solving the considered problem, whilst the hybrid genetic algorithm-particle swarm optimization is chosen as the benchmark algorithm. The simulation results indicate that the MFOA yields the highest quality solution sets in solving the MG design problems, and provides the most accurate estimate of the life-cycle costs of the MGs.

History

Preferred citation

Mohseni, S., Brent, A. C. & Burmester, D. (2019, June). A Sustainable Energy Investment Planning Model Based on the Micro-Grid Concept Using Recent Metaheuristic Optimization Algorithms. In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings 2019 IEEE Congress on Evolutionary Computation (CEC) (00 pp. 219-226). IEEE. https://doi.org/10.1109/CEC.2019.8790007

Conference name

2019 IEEE Congress on Evolutionary Computation (CEC)

Conference start date

10/06/2019

Conference finish date

13/06/2019

Title of proceedings

2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings

Volume

00

Publication or Presentation Year

01/06/2019

Pagination

219-226

Publisher

IEEE

Publication status

Published