Distributed neurodynamic algorithms for collaborative energy management in energy internet considering time-varying factors

Abstract

This paper investigates the energy management problem of the energy Internet under time-varying conditions . In the context of coupled multi-energy networks, the energy Internet is considered to be composed of multiple energy bodies and requires collaborative planning of multiple energy networks. A model for distributed energy management with a non-smooth cost function and line congestion constraints is proposed, with the goal of reducing overall operating costs and improving customer benefits while considering load as a time-varying factor. Then, a neurodynamic time-varying algorithm for addressing the energy management problem executed in a fully distributed manner is proposed. On the one hand, the predictive effect of the differential feedback term is exploited and embedded in the implementation of the proposed algorithm, thus speeding up the convergence. On the other hand, the algorithm is executed in a distributed manner, and only limited information is exchanged among the agents to complete the optimal operation locally, thus reducing the communication burden and ensuring privacy and robustness. Finally, theoretical proofs guarantee the stability of the proposed algorithm, and simulation experiments illustrate the effectiveness and robustness of the proposed algorithm. • In this paper, a model for EI in a time-varying load scenario is proposed for optimal energy allocation among EBs integrating renewable energy, flexible loads, and multi-energy coupling, while trading energy. • A neurodynamic algorithm implemented in a fully distributed manner that enjoys hardware implementation and parallel computation is proposed in this paper. • The algorithm can effectively tackle EMPs with non-smooth objective functions and constraints under time-varying conditions.