Distributed Neurodynamic Optimization for Energy Internet Management

A neurodynamics-based algorithm is developed in this paper for solving multicoupled distributed optimization problems. In this formulation, each agent solves a local optimization problem with regard to its own cost function. The objective function is a sum of local convex subproblems, which are not necessarily strict convex and smooth. With communication between neighbors only, decision variables lie in a distributed manner and succeed to converge to the global optimum. The proposed method is suitable for solving large-scale problems in energy Internet management thanks to neural networks' capability of real-time computation. To verify the algorithm, a power-heat-gas multienergy system model is constructed. The simulation results on a 5-energy-hub multienergy system are demonstrated to show the efficacy of the algorithm.