Bio-Inspired Optimisation Methods Applied to Low Carbon Power and Energy Problems: A Survey

Abstract

Bio-inspired optimisation methods have been widely applied to complex real-world problems, particularly in low-carbon power and energy systems, where optimisation tasks often involve high-dimensional, constrained and mixed-integer characteristics. Traditional approaches struggle with these challenges due to nonconvexity, nonlinearity and computational complexity. This paper provides a comprehensive review of bio-inspired optimisation techniques applied to key low-carbon energy problems, including economic load dispatch, unit commitment, optimal power flow, distributed generation planning, heat exchanger design, and parameter estimation for PEM fuel cells and solar cell models. By analysing the strengths and limitations of existing methods, we highlight their effectiveness in addressing computational efficiency, constraint handling and convergence behaviour. The paper also identifies research gaps and discusses future directions, providing a structured reference for algorithm developers and practitioners. This review aims to enhance the adoption and refinement of bio-inspired optimisation techniques for sustainable energy solutions.

Keywords

benchmarks / genetic algorithms / optimisation

Acknowledgements

This research is financially supported by the Shenzhen Science Fund for Excellent Young Scholars (Grant RCYX20221008093036022), the Special Support Plan for Outstanding Young Talents of Guangdong Province (Grant 2023TQ07L745), the Youth Innovation Promotion Association CAS (Grant 2021358), and the Shenzhen Science and Technology Research and Development Fund (Grant JCYJ20200109114839874). Figure 1 of this article was created by artist Yilin Yang from New York University Tisch School of the Arts, and we thank her contribution to this article.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

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