Dynamic distributed multi-objective mantis search algorithm based on Transformer hybrid strategy for novel power system dispatch

With massive renewable energy connected to the grid and increasing the complexity of power systems (PSs), traditional centralized PS models can not satisfy the needs of modern PSs. Consequently, a distributed novel power systems model (DNPSs) is proposed in this study aiming at efficient complementarity and optimal dispatch of multiple energy sources by coordinating the utilization of thermal, wind, photovoltaic, hydro, nuclear, biomass, and tidal energy. To address the dispatch problem in DNPSs, this study presents a distributed multi-objective mantis search algorithm (DMOMSA) which combines a distributed computing framework and a multi-objective mantis search algorithm. Although DMOMSA has significant optimal capabilities, the problem of premature convergence still exists. To overcome the problem of premature convergence of heuristic algorithms, this study presents a dynamic distributed multi-objective mantis search algorithm based on Transformer hybrid strategy (Transformer-DDMOMSA). The Transformer-DDDMOMSA strengthens the exploratory capacity of Transformer-DDMOMSA to avoid premature convergence by introducing a hybrid strategy consisting of the Transformer prediction strategy, Levy Flight, and Gaussian mutation. Simulation results indicate that Transformer-DDMOMSA is compared with non-dominated sorting moth flame optimization, multi-objective gray wolf optimizer, multi-objective mantis search algorithm, and multi-objective ant-lion optimizer: (1) in case I, generator costs are reduced by at least 2.36 %, 2.54 %, 2.30 %, and 4.20 %, respectively, while carbon dioxide emissions are reduced by at least 6.11 %, 9.92 %, 4.64 %, and 13.45 %, respectively; (2) in case II, generator costs are reduced by at least 2.38 %, 2.37 %, 1.92 %, and 2.70 %, respectively, while carbon dioxide emissions are reduced by at least 8.80 %, 12.01 %, 8.76 %, and 13.14 %, respectively; (3) the Transformer-DDMOMSA performs excellent in three evaluation metrics values of diversity, Euclidean distance and hypervolume.