An inertial neurodynamic algorithm for collaborative time-varying energy management for energy internet containing distributed energy resources

• Published in: International journal of electrical power & energy systems Vol. 154; p. 109406
• Main Authors: Zhao, Gui, He, Xing, Li, Chaojie
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2023
• Subjects: Convergence rate; Energy internet; Energy management; Inertial neurodynamics; Time-varying; Convergence rate; Inertial neurodynamics; Energy internet; Time-varying; Energy management
• ISSN: 0142-0615
• DOI: 10.1016/j.ijepes.2023.109406

This paper investigates the energy management of an Energy Internet (EI) that integrates multiple energy networks. Based on the structure of EI and the fluctuation of load, an optimal energy management model considering part of the load as time-varying factor is proposed. Under the influence of load, the actual energy management problem (EMP) is formulated as a time-varying optimization problem subject to a set of global and local constraints. Its optimal solution varies continuously with time, which leads to difficulties in obtaining optimal solutions by conventional algorithms. Consequently, to obtain better tracking performance, this paper proposes a distributed inertial projection neurodynamics algorithm (DIPNA) based on the Nesterov accelerated gradient descent method. This algorithm tracks the optimal solution of the EMP with a fast convergence rate O1/t2, providing a referenceable active power for each energy unit in real time. Finally, the performance evaluation results demonstrated the effectiveness and fast convergence of the proposed algorithm. •Collaborative management of multiple energy sources throughout the energy internet system is achieved.•The model takes into account the fluctuation of load•Proposes a distributed reach dynamics algorithm with fast convergence characteristics.