Equilibrium-inspired multiagent optimizer with extreme transfer learning for decentralized optimal carbon-energy combined-flow of large-scale power systems
A shared responsibility of carbon emission is introduced in decentralized OCECF. An equilibrium-inspired multiagent optimizer is proposed for decentralized OCECF. The Nash game can ensure a self-organizing optimal operation of each agent. The convergence rate can be dramatically accelerated by extreme transfer learning. The carbon emission and power loss of power network can be significantly reduced.