Optimal operation and planning of hybrid AC/DC power systems using multi-objective grasshopper optimization algorithm

Bakir H., Guvenc U., KAHRAMAN H. T.

NEURAL COMPUTING & APPLICATIONS, vol.34, no.24, pp.22531-22563, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 34 Issue: 24
  • Publication Date: 2022
  • Doi Number: 10.1007/s00521-022-07670-y
  • Journal Name: NEURAL COMPUTING & APPLICATIONS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, Index Islamicus, INSPEC, zbMATH
  • Page Numbers: pp.22531-22563
  • Keywords: Multi-objective optimal power flow, Renewable energy sources, Multi-terminal high-voltage direct current system, Multi-objective grasshopper optimization algorithm, INCORPORATING STOCHASTIC WIND, FLOW SOLUTION, LOSSES, EMISSION, COST
  • Karadeniz Technical University Affiliated: Yes

Abstract

Optimal power flow (OPF) in a hybrid alternating current and multi-terminal high-voltage direct current (AC-MTHVDC) grid is currently one of the most popular optimization problems in modern power systems. The critical necessity of addressing global warming and reducing generation costs is encouraging the integration of eco-friendly renewable energy sources (RESs) into the OPF problem. In this direction, the present research has centred on the formulation and solution of the multi-objective (MO) AC-MTHVDC-OPF problem incorporating RESs such as wind, solar, small-hydro, and tidal power. The available power of RESs is calculated by means of the Weibull, lognormal, and Gumbel probability density functions. The proposed MO-OPF optimizes the double and triple configurations of various objective functions, including total cost, the total cost with the valve-point effect, the total cost with emission and carbon tax, voltage deviation, and power loss. Multi-objective grasshopper optimization algorithm (MOGOA) is applied to find non-dominated Pareto-optimal solutions of the non-convex, nonlinear and high-dimensional MO/AC-MTHVDC-OPF problem. The obtained results are compared with the results of MSSA, MODA, MOALO, and MO_Ring_PSO_SCD algorithms. The comparison of results gives that MOGOA outperforms competitive optimizers with respect to the quality of Pareto-optimal solutions and their distribution.