In this paper, a distributed neurodynamic approach is proposed for constrained convex optimization. The objective function is a sum of local convex subproblems, whereas the constraints of these subproblems are coupled. Each local objective function is minimized individually with the proposed neurodynamic optimization approach. Through information exchange between connected neighbors only, all nodes can reach consensus on the Lagrange multipliers of all global equality and inequality constraints, and the decision variables converge to the global optimum in a distributed manner. Simulation results of two power system cases are discussed to substantiate the effectiveness and characteristics of the proposed approach.

In this article, a discrete-time distributed optimization algorithm is proposed for solving the economic dispatch (ED) problem with some groups of generator units to communicate over a connected graph, which is independent of the power system. The ED problem is converted to a distributed optimization problem with an objective of the sum of individual convex functions and constraints of local generators. Based on the optimal conditions, a class of distributed algorithms is designed to find the solution to the ED problem. The distributed algorithm can be realized as a multiagent system with a connected graph, whose convergence can be proved using the dynamic analysis method. Moreover, experiments with simulations are presented to demonstrate the performance of the proposed algorithm.

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.