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.