Recent advances on privacy-preserving distributed optimisation algorithms and their applications in smart grids

The rapid digitalisation of power systems and the proliferation of distributed energy resources have spurred the adoption of distributed optimisation in smart grids. However, frequent information exchange among agents raises significant privacy concerns. This paper provides a comprehensive review of recent advances in privacy-preserving distributed optimisation, with a particular focus on smart grid energy management. Three main categories, including encryption-based methods, differential privacy, and correlated randomness, are systematically discussed in terms of their theoretical foundations, algorithmic developments, and implementation features. Key challenges and future research directions are also highlighted, including improving robustness under non-ideal communication conditions, achieving adaptive tradeoffs between privacy and accuracy, and integrating data-driven intelligence into privacy-preserving mechanisms. This review offers an in-depth understanding of the current research landscape and valuable insights for developing secure and efficient distributed optimisation algorithms in smart grids.